Podcasts about Braeburn

Welcome to the KSL Greenhouse show! Join hosts Maria Shilaos and Taun Beddes as they talk about all things plants, tackle your toughest gardening questions, and offer tips that can help you maintain a beautiful yard. Listen on Saturdays from 8am to 11am at 102.7 FM, 1160 AM, kslnewsradio.com, or on the KSL NewsRadio app. Follow us on Facebook and Instagram at @kslgreenhouse. Happy planting! #KSLGreenhouse    10:05  Feature: When to Plant Vegetables Along the Wasatch Front  10:20  What’s the best way to get rid of fairy rings in my lawn, and when? When can I prune my mature Braeburn apple tree? How do I prevent the leaves on my plum and pluot trees from curling this year? Would Thuja Green Giants be good as privacy trees?  10:35  When’s a good time to remove the straw from my strawberry plants? How do I prune my peach trees? What are some recommendations for filling my garden boxes? Is it time to plant goji? When do I prune my apricot tree? Should fruit trees be pruned every year? What can I do about the hundreds of ants in my strawberry pots?  10:50  What can I do if my grass is extremely bumpy? Do I need to worry about the roots of my Oklahoma redbud tree causing a problem on my sidewalk? When and how should I prune my cherry tree, and when should I spray it? What’s causing the leaves of my elm tree to fall early and not turn green? 

Anyone looking for a weekend getaway from Seattle, or a small-town add-on to a trip to the Pacific Northwest, Whidbey Island is a great choice. Learn all about where to stay and things to do on Whidbey Island and hear about a fun time to visit during Mystery Weekend in February! Episode Highlights: Whidbey Island is one of the northern islands off of Seattle At the end of February each year the town of Langley hosts a Mystery Weekend You can get there via the Whidbey-Clinton ferry route or you can drive and take the bridge Learn more about Mystery Weekend: Kim's article on 5 Things to Know About Langley Mystery Weekend: https://stuffedsuitcase.com/langley-murder-mystery-weekend/ Langley Mystery Weekend: https://whidbeycamanoislands.com/event/langley-mystery-weekend/2025-02-22/ If you live locally, the winter is a great time to visit Whidbey Island because there aren't any crowds and the prices are much lower. Where to stay on Whidbey Island: Comforts of Whidbey - winery bed and breakfast Saratoga Inn - cute historic home turned into a hotel in downtown Langley The island is very dog-friendly with many hotels and restaurants that welcome dogs For the Langley Mystery Weekend you go to the tourism office and purchase a booklet and entry form to guess who did the crime. It offers discounts/coupons for local businesses and encourages you to explore and find clues at different locations. You then put all the clues together to guess and solve the crime. There are also volunteers around town who are in character as part of the story. A local resident writes the mystery every year and it changes each year so you can go every year for a different experience. You need a car to get around Whidbey Island. There is a long highway down the middle of the island and it is easy to navigate. Whidbey Island has an impressive food scene. A few to highlight include: Saltwater - offers pub-style seafood restaurant in Langley Savory - small-plates style restaurant that is perfect for sharing Langley Kitchen - good for breakfast Braeburn - good for lunch and breakfast Things to do in Whidbey Island: Take a hike or get outdoors Earth Sanctuary - $7.00 entrance fee, a space to be in nature, there is also a labyrinth Hammons Family Preserve - great walking trails State Park Beach with kite surfing Shopping Wine, cider and mead tasting rooms What to pack: a raincoat and waterproof shoes Where to take a photo: Hammons Preserve with a view of the water

Welcome to Pete's Pub Quiz, where we dive into a mix of pop culture and historical trivia! This episode features questions that will challenge your knowledge on a wide range of topics. Do you know the title of the Travis hit "Why Does It Always ____ on Me?" or in which US state Fort Knox is located? Test your trivia skills with these questions and more! Get ready for a fun-filled episode packed with intriguing questions and fascinating facts. Don't forget to subscribe for access to 20 bonus round questions each week!

Eli Shanks' of Punta de Fierro Fine Cider In this episode meet Eli Shanks, a passionate cider maker sharing his journey from urban Massachusetts to the picturesque landscapes of Chile, where he co-founded Punta de Fierro Cider. Eli developed an early interest in food systems and agriculture with influences from attending The Farm School, working at The Food Project and running an orchard in Concord, Massachusetts. Eli Shanks with bottle of Punta de Fierro The History of Cider in Chile Chile has a rich tradition in fermenting apples into cider. Historically, cider production was a significant part of the local economy. Families were taxed based on their cider tank space, and cider apples were a staple in many homes. Chilean cider, known as "Chicha", is a farmhouse product with various local recipes, often fermented naturally with residual sugar. Organizations and Collaborations Eli has co-founded the Chilean Cider Collective (COSIGI), which aims to preserve and promote Chile's unique cider heritage. COSIGI works closely with the Department of Agriculture and other local organizations to support cider makers and enhance cider production quality. Mother Pudu with baby Chilean Ciders Tasted in this Episode Punta de Fierro - is both the name of the cidery and the cider itself. This particular cider tasted during the recording was made in 2022. It was bottle #61 out of 771 bottles in total. All the apples came from one single orchard that Eli and his business partner Carlos Flores produce cider on.   TenCai Sidra - this cidery is owned by Rene Galindo. We tried two ciders during the recording. The first cider called The Truth. This cider was co-fermented with an indigenous berry called Maqui. They are smaller than blueberries and lend a purplish hue to the cider.   The second cider -called Futura was made by first grinding the apples and allowing them to macerate for approximately 24hours. The apple blend is Reineta and Braeburn. All the ciders were feremented dry and are delicious! Contact Info for Punta de Fierro Website: https://puntadefierro.com/ Contact Info for TenCai Sidra Website: https://tencaisidra.cl/ Mentions in this Cider Chat Cider Chat info flyer - scroll down this page and download and post flyer! Tag Cider Chat and use hashtag #xpromotecider   ciderGoingUP Campaign page - find a list of business supporting Cider Chat!

Little apple pies with malted crumble and yogurt: COOK TIME: 27 MINUTES PREP TIME: 20 MINUTES SERVES: 6-8 6 Braeburn apples 1 tsp vanilla paste 3 tbsp honey 8 x 10cm puff pastry discs 1 tbsp malted barley syrup 100 gm unsalted butter 100 gm wholemeal flour 50 gm brown sugar 100 ml greek yogurt Pre-heat a oven to 180*c Mix together the apple, honey and vanilla. Toss until well coated. Peel the apples, cut into 1/4 and remove the core and cut in half again, giving you 8 wedges per apple. Arrange around 5 pieces of apple together in a tight circle in a grease-proof paper lined oven tray. Place a piece of the puff pastry over the top. Mold the puff down and around the apple tightly. Refrigerate for 15 minutes. Make up the crumble, combine the butter, wholemeal flour, sugar and malt barley in a food processor and blend until smooth. Pour out onto an oven tray and spread out. Bake for 10 minutes before stirring and continuing to cook for another 5 minutes or until golden. Remove and allow to cool. Now bake the apple pies into the oven into the oven for 12 minutes or until the pie case is golden. To serve, turn the apple pie upside down onto place so the apple is facing upwards and sprinkle with the crumble mix. Finally serve with the yogurt. LISTEN ABOVESee omnystudio.com/listener for privacy information.

In this episode, we dive into the world of Ireland's Applegreen with Fiona Matthews, the managing director of ROI, to explore the spectacular food offer and the new Braeburn coffee concept. From their coffee to their strategic partnerships with renowned food brands like Marks and Spencer's, Fiona shares insights into the development of their offerings and their commitment to delivering exceptional customer experiences. With special guest: Fiona Matthews, Managing Director of ROI, Applegreen Hosted by: Dan Munford and Carolyn Schnare

Well hot diggity! We got ourselves a roarin' good story today. Everythin' you could possibly need in a story is here: Murder, death, storytelling, mystery, Braeburn, guns, an investigator, no cops. It's really a perfect tale. You can read "The Assassination of Braeburn by the Coward Dirty Rich" by DawnFade here: https://www.fimfiction.net/story/33089/the-assassination-of-braeburn-by-the-coward-dirty-rich

An infected tooth can be among your most uncomfortable experiences - but a root canal treatment can help. If you're in need of such care near Braeburn, call Houston's Dentist 101 at +1-713-773-1300 or click https://txdentist101.com/ today! Dentist 101 of Houston City: Houston Address: 9180 Bellaire Blvd Website: https://www.txdentist101.com/ Phone: +1-713-773-1300 Email: txdentist101bellaire@gmail.com

Michael Daingerfield is a Canadian actor. He's best known for voicing Ace Ventura in the Ace Ventura animated series, Gintoki Sakata in the Canadian dub of Gintama, Wheeljack in Transformers Armada, Inferno/Roadblock in Transformers Energon, and Braeburn in My Little Pony: Friendship is Magic. In this episode Michael and I discuss his career, Ace Ventura, Gintama, Transformers, the dubbing process, Star Wars, his toughest role, and My Little Pony. Michael's website and social media: https://www.michaeldaingerfield.com/ https://twitter.com/MDaingerfield https://www.instagram.com/michaeldaingerfield/

Today, Mitch and Isi discuss all things fruit; the horrors of hairy fruits and mushy apples, Mitch's preference for a cold and crunchy banana, watermelon life-hacks, surviving on coconuts, the versatilities of apples and question; what the hell a lemon posset is and if Halle Berry is actually a fruit? Interactive Transcript Support Easy English and get interactive transcripts and bonus content for all our episodes: easyenglish.fm/membership Transcript Mitch: [0:00] 12345678. Isi: [0:05] 12645678 What? 1264567. Mitch: [0:12] Easy English! Intro Mitch: 0:34 (Hello!) Hiya, welcome to the new episode of the Easy English Podcast. That is so formal. Isi: [0:39] I don't like to look at you while we record it. I have to laugh. Mitch: [0:44] We're so far away again. Isi: [0:46] Hello, down there, in the hallway. Mitch: [0:49] Yeah, it feels like we're in a hallway. Isi: [0:51] I will just directly say it. Mitch, We had The Big Veg Show (The Veg Cast. ) The Veg Cast. I hope people enjoyed it because I said it already, what comes this week. And it's The Big, Big Fruit Show. Mitch: [1:05] The Fruit Show, The Veg Cast and The Fruit Show. Isi: [1:07] Yeah, we couldn't do it both last time, so we need to talk about fruits. Mitch: [1:11] It wouldn't have been fair, though, to have thrown fruits on the ends of veg, because fruits don't... shouldn't be disrespected like that. Isi: [1:17] But fruits have a better life. Most of them are very sugary, so people usually like them more than veg. I would say. Mitch: [1:25] But we're savoury people. No, that sounds like... (We are savoury people.) That's actually a compliment. Because you can be a very unsavoury. Isi: [1:33] Will you make us a drink? Because I wanna ask our listeners for something, in between. Mitch: [1:37] Okay, feels like you're booting me out of the room, to say something private. Isi: [1:41] No, I just want a drink. Mitch: [1:42] If you'd like to listen to this podcast, without Mitch, give us a thumbs up. Isi: [1:46] What I wanted to ask, today is a little bit of a favour. You might know that, in podcast apps, where you listen to us. Um, there are several of podcast apps. It does help, if you give us a review of our podcast, on some apps you can leave a comment about our podcast. And this interaction, if you give like, a response to our podcast, will help others to find our podcast. So, if you could just today, if you like our podcast, take a second out of your day and see in your app where you can leave us a review, a comment, a rating That will be fantastic. It's weird to ask for things, but I think it would be really, really, really nice if you could help us with this. Anyway, and also, if you have questions for our podcast or for us, write us an email to podcast@english.video or on easyenglish.fm. You can also leave us an audio message. We have a section called 'Unhelpful Advice' and we are still waiting for your problems and issues to solve. Okay, now Mitch is back and we can go on with fruits. (Is margarita a fruit?) Topic of the Week Isi: [3:09] I have a few questions first, and then I would guide you through the world of fruits. Um, what is... (Come with me.) What is your favourite fruit? Mitch: [3:14] Off the top of my head, I'm thinking strawberries, but it probably isn't. But strawberries are just like, a solid fruit. Isi: [3:21] So I wanted to say peach, I really like a really good peach, but peach can be really shit as well. Mitch: [3:32] I know what yours is and it's my like, curveball, because when you... when you think of fruits, you think of sweetness. But I think, actually, if we were to really go into it, what fruit we eat the most, especially you, It would be a sour fruit. Isi: [3:48] Lemon. Yeah, lemon is probably my favourite fruit because I eat it most. Mitch: [3:53] It's my favourite pudding. Anything with lemon? Isi: [3:54] I love citrus fruits. Anyway, I love lime, love oranges... favourite pudding. Mitch: [3:59] Yeah. Anything with a lemon on it. (Lemon cake.) Lemon drizzle, for shizzle, ma nizzle, Lemon cheesecake. Isi: [4:05] Lemon posset. (Lemon posset.) Posset. Posset. Such a thing I've learned in England. Um, with watching 'Come Dine With Me'. Everybody does a lemon posset. It sounds so posh. I don't even know really what it is. It's a lemon cream or something. A lemon posset And they're always like; "for dessert, I have a lemon posset". And then you hear the other people talking in the off later in the car, and they're like; "a lemon posset, everybody's doing a lemon lemon posset and hers was not particularly good". Mitch: [4:38] I don't know what it is either. We should make one, just to sound fancy. Isi: [4:42] Lemon posset. Mitch: [4:43] Last night we had a lemon posset. Wasn't it just absolutely delightful, lemon posset. Isi: [4:47] I'll look it up now. Mitch: [4:48] I'm always very disappointed by nectarines. Isi: [4:53] Yeah! (Yeah.) Good nectarines are good. Mitch: [4:55] Yeah, but that's the... that's my I've never had a fully ripe one. I think ever. Isi: [5:01] I just looked up my least favourite fruit, and it's not in my list. So, we we have to do the list together. Um, a gooseberry Mitch: [5:10] You don't like gooseberries? Isi: [5:11] No, they're hairy. They're a weird mix of sweet and sour. And you know what they are... mushy. Mitch: [5:18] Er... mushy. Isi: [5:20] Don't like mushy foods at all. Mushy apples; urgh! Mushy bananas; urgh! Mitch: [5:26] Yeah. Oh, yeah! That That's my pet peeve. I love bananas, but they have to be kind of, not quite ripe. Isi: [5:37] No, yours are the least ripe I've ever seen. Mitch: [5:40] And in the fridge. Cold and crunchy. And probably my least favourite fruit is like a warm, mushy banana. Urgh! Urgh! Oh, I feel sick. Yours is gooseberry, because they're a bit hairy. Isi: [5:57] Yeah, gooseberry and my favourite. I don't know if my favourite would be lemon, but it has to be, because that's what I eat most. Mitch: [6:02] Uh, when you say a hairy fruit is a bit gross, isn't it? Like, have you ever eaten a kiwi? And you've forgotten to take off a little bit of the skin? And you're like, Ugh, what is that? And it's a bit of a hairy skin. Isi: [6:11] Actually, I recently learned that a lot of people eat it with the skin. You can eat the skin. You just eat it like that. Mitch: [6:16] That's disgusting. Isi: [6:17] OK, my favourites are strawberry, peach, mango, lemon. Mitch: [6:21] Yeah. Oh, I have one as well. Sorry. Do we have time for this last one? (No, we do.) I really want to use it more, but I don't know how to use it. And maybe, if anyone has a good recipe or a good way to like, cook it or prepare it. I really, really like rhubarb. Isi: [6:38] I love rhubarb. (I love the taste of rhubarb.) Rhubarb season is at the same time as strawberry. Mitch: [6:44] Oh, really? (I think so.) But I don't really know how to do it, but maybe someone who's listening can send us either a voice message to easyenglish.fm or write to us at podcast@easyenglish.video. Isi: [6:57] Yeah. Um... how do you? Yeah, how do you eat rhubarb in England? I've only seen it in cakes in... in Germany, I can just say we cook it, with a hell lot of sugar. (Where? In the oven or in a pan?) in a in a pot. (In a pot?) Yeah, you cook it and it kind of gets like this soupy, slimy mass. Sounds disgusting. It's quite good. And you can eat it with strawberries or with like, a vanilla sauce or something like this. Let's go now, through the berries. Strawberry, we already talked about. (Good berry.) Blueberry. Mitch: [7:28] I really like blueberries. Isi: [7:32] You like it more than me. We eat it basically every day. I still eat them. They're nice. Mitch: [7:36] Blueberry muffin. Isi: [7:38] Yeah, but you know what I don't like? And you often do it. Blueberry smoothies. Mitch: [7:43] Oh, I love the blueberry smoothy. Isi: [7:44] Too much blueberry. Then it is overbearing, isn't it? I like blueberries, I like them... I actually like both parts of them. Some are like, really big and not so sour, but really like, fresh. And then there're the little ones, that are super sour, both are good. Mitch: [7:58] Blueberries are... is a not safe for work fruit because, the skin always manages to sort of, somehow wrap itself around your teeth. Isi: [8:05] Mm, Yeah. And what is very English and maybe you can say how it's used here, is blackcurrant. Mitch: [8:15] Just someone saying blackcurrant makes you think of being like three years old with a glass of blackcurrant squash. I'm sure many other kids from the who grew up in the nineties, might think of that. Isi: [8:25] Which are the ones that we often see on our walks. Just recently, we saw a lot of them. They look like raspberries, but black. Mitch: [8:32] Oh, isn't that a gooseberry (No.) Blackberry? Yeah. Must be. Isi: [8:36] Like you don't know what a gooseberry is. Google Gooseberry now, so that you understand my. Mitch: [8:42] Goose... berry. They're not hairy. Isi: [8:47] They are hairy. Mitch: [8:49] Yeah? In this, they're not. Wait, it looks a bit like a grape. Which ones are hairy, though? Hairy fruits. Google is suggesting; "Are you thinking of Halle Berry?" Isi: [9:09] We stop with the berries, I'm not educated enough on berries. So citrus fruits, love citrus fruits. Mitch: [9:13] Yeah, absolutely. I have an issue, though. That I've never figured out, is that I don't know the difference between an orange, a tangerine and a clementine. I couldn't tell you what was what, or are they all types of oranges? Are clementines also oranges? And... is that what it is? Isi: [9:32] Clementines are the ones that you eat around like... (But is it an orange?) in winter and around Christmas and you peel them, right? That's clementines. Well yeah, I guess they're part of an orange. Then you have. Do you know kumquats? Mitch: [9:43] Yeah. Is that an orange? (Yeah. Blood oranges.) Oh, nice in a cocktail. Isi: [9:49] Valencia oranges. Best for juicing. Tangerines, juice for sweeter take on orange juice. Okay. Mitch: [9:56] Really, Tangerine? Isi: [9:58] Navel. Navel oranges, most common variety. And Seville/Seville Oranges. Perfect for marmalades. There you go. But these are the... that was the ultimate guide to winter oranges and tangerines. So there must be others as well. Mitch: [10:14] Right. Oranges is like the franchise. And then inside the franchise, there's different types. (Businesses of oranges.) Isi: [10:24] Ok, lime; amazing. (Love limes.) Ah, lime on... in drinks, on food. Basically, you can... you can put a bit of lime juice on nearly every food and it's good. Mitch: [10:35] Yeah. Really. Isi: [10:36] Melons. What's your favourite melon? Mitch: [10:41] Oh, I only know water and just like the yellow... what are the yellow melons called? (It says your honey dew.) Honey melon? Isi: [10:49] I like most, honey. (Really?) And then watermelon. Mitch: [10:51] More than... really. Isi: [10:54] Yeah, because I... I came to terms with watermelon, because you like it a lot. And we often have it in summer. And it's nice. It has to be good. We learnt how they have to look, but cannot explain it now, because I already forgot. Mitch: [11:06] Life hack. Not what you expect. It's the opposite of what you're expecting. Isi: [11:10] Yeah. Look it up. Google it. (The less round) How should the watermelon look? Mitch: [11:12] The less circular, the better, right? I think it was. Isi: [11:16] I think, yeah. And it should even be a bit yellow and weird. Mitch: [11:18] Yeah, circle and green is just not good. It has to be sort of like oblong and a bit brown and a bit yellow, I think. Isi: [11:25] Well, look it up yourself, please. I hope you don't have guarantees on that. So watermelon is nice. I like watermelon a lot, in a combination with, like, um, savoury, um, like feta, for example. Mitch: [11:36] Oh, yeah. Good shout Isi: [11:37] Um, feta cheese, watermelon, some balsamic... (Glaze.) glaze. And, um, some mint leafs. So, that's really good. Mitch: [11:50] I love the glaze. We should get that on Asda. Isi: [11:53] I'm getting hungry again. We always do this before food. Um, and but honey is also good. Also good with cheese. (Honey's not fruit!) Uh, honey melon, sorry. That also works very well. People that eat meat often eat it with, uh, in Germany, at least with ham. (Really?) That works very well, yeah. Mitch: [12:12] Oh yeah, we have ham and pineapple. Isi: [12:14] See. Stone fruits, Mitch. Cherries. Mitch: [12:19] I like cherries. (Like, or love?) Just like, 'cos you... It's a lot of. Is that when you're eating, there's a lot of this noise, like this. Not for say, for work, either. Just like the... blueberry. Isi: [12:41] Yeah, I'm not a big fan of cherries. I have to say I eat them, but I don't buy them, ever. Mitch: [12:47] I don't know what you do with it. They're selfish veg... like, fruits right? They don't really go with anything else, do they? What have you ever had a cherry with? Isi: [12:54] Yeah. And also like, cherry juice or so. It's too intense. Um, OK, we go in the world of tropical fruits. Bananas, we already talked about. (Yeah!) Coconuts, we had coconut yoghurt today. Mitch: [13:05] Coconut milk, I like. Coconut milk in any Asian dish. Isi: [13:11] Yeah, coconut milk is good. Do you like coconut meat or flesh? Or how do you call that? Mitch: [13:18] Doesn't it give you diarrhoea? (No! you've never eaten coconut?) I played a survival game once on the PlayStation. And if you... If you eat too many, you have diarrhoea for two days. Isi: [13:28] Oh dear, Oh! You know, Amarula is from the marula fruit. Mitch: [13:34] Oh, I love Amarula. Isi: [13:36] And I think the fruit is eaten by elephants. And that's why the big elephant is on it. Mitch: [13:40] Ah, that makes sense. Amarula fruit. Isi: [13:44] What do we forget? Oh, well, we forgot the big, I think the, the fruits of both our nations, probably. (Go on.) What is the... the fruit, that exactly now you get. Mitch: [14:00] Potatoes aren't fruit. The fruit of our nation? Both our nations? Isi: [14:08] Apples. Mitch: [14:09] Oh yeah, how did I not think about that. Isi: [14:12] Apples are eaten all day, every day. Apple juice, apple sauce. Apple sauce is a very English thing. Oh no, actually very German, too. With Reibekuchen. Mitch: [14:19] I tell you what is a very English thing with apples. (Apple mint sauce.) Cider. Isi: [14:26] Cider. Yeah, you see, it is a fruit of your nation. Mitch: [14:28] Have you ever had a proper cider? Isi: [14:32] Uh, I have... I have had cider... (Not Strongbow.) recently, at at our friends in London. I had cider. Mitch: [14:38] Did you? Oh, yeah, you did. Isi: [14:39] Yeah, a tiny glass, a cute little, tiny glass to try it. But it was too sweet for my liking. Mitch: [14:45] Oh God. Doesn't it make you realise that western... northwestern fruits are so boring, in comparison? Do you know what I mean? Do you think there are Mexican people saying; "Oh, do you know what I really love? Apples." Isi: [14:58] Maybe. Yeah, for sure. (No.) Yes. Mitch: [14:59] No. Not when you've got limes. I'm jealous. Let's go live in Mexico and just drink margaritas and mojitos all day. (Maybe we should do that. You know.) Caipirinhas. Isi: [15:10] We had apples today in our big yoghurt, with different fruits. Then it's OK. Um, the apples that I had were really small apples and like, red and green. And they were like, I only like apples when they are sour and hard. No mushy, no sweet, no nothing. Mitch: [15:25] Oh, really? Uh, we never talked about this. How have we never spoken about our favourite type of apple. Isi: [15:32] I know. I like Blackburn. (Blackburn?) Braeburn. Sorry. (Blackburn!) Blackburn is a place here. Bra. Braeburn, Braeburn, Braeburn. Mitch: [15:42] And what's your least favourite? Oh, there's actually way more than I ever heard. Isi: [15:45] I don't know what the mushy ones are called. Mitch: [15:48] I hate a pink lady. Isi: [15:50] Aren't they not mushy. Mitch: [15:52] They can get pretty mushy. That and a jazz. (Mashy, or mushy?) Mushy. That and a jazz apple. I like a Granny Smith. Isi: [16:01] Are those the green ones. (The green hard sour, more sour ones. ) Mm. Yeah, that's better. I also don't really like, uh, apple juice. Apple sauce, yes. Apple sauce was a good Reibekuchen. Which is like a... basically like a... hash browns. It's a bit like a big hash brown, isn't it? With apples. Mitch: [16:16] Yeah, that's right. Deep fried eggy, soaked, potato. (Grated potato.) Grated potato with egg and... Isi: [16:24] Made into like a dough with egg and... Mitch: [16:24] Did you know there's so many... one, two... there's Granny Smith, Fuji, Pink Lady, Honey Crisp, Envy, Gala, Pazazz, Jazz, Red Delicious, Braeburn, Cameo, Holston, Golden Delicious, Lady Alice, Hidden Rose Ambrosia... there's so many apples. Isi: [16:44] Oh, yeah. Jazz apple. I just see it here. Mitch: [16:45] 25 types of apples. Incredible. Isi: [16:48] Probably even more. Mitch: [16:49] Can I tell you one you've not mentioned yet, which I really like. I love plantain. Isi: [16:55] Ooh, I love plantain, too. Is that a fruit or a veg? Mitch: [16:58] Isn't it just a savoury banana? Isi: [17:01] Yeah, it is, but, uh, it's not the same as a... it's not... it's not the same as a banana. Mitch: [17:05] Mm. In, uh, England, because of Jamaican, uh, connections. Empiric connections, I might... might add. uh, it's quite often you can find plantain. And specifically, one thing I love. I'm not in ages. Plantain crisps. Salted plantain crisps. Isi: [17:22] Hm. So good. I love plantain. Absolutely love it. Plantain, you can also have sweet, by the way, if you wait long enough, you can also bake them. Mitch: [17:31] Oh right, maybe that's what I should get instead of bananas. Isi: [17:35] Hm... you cannot have them in your yoghurt. Um, do you... do you, uh, know a pomelo? I don't know if it if this is in English the same. It's written the same as I would say it in German. It's pomelo. (You know it?) Yeah. ( What is that?) Pomelo. Um, Google it. Mitch: [17:54] Po... pomelo, pomelo? Isi: [17:55] I mean, yeah, it looks a bit like a melon from outside. It is more like an orange. (Oh, yeah, it does.) Or like a grapefruit. Look from inside. It looks more. Mitch: [18:03] It has segments as well. Isi: [18:04] It has segments like oranges or grapefruits, and it is very dry. You can really break off the segments, sometimes. It's not that all the juice... like, it's not messy. Um, I like it, it's super, super healthy. I think. Mitch: [18:19] It has anti-aging properties. (You see!) Fights cancer. Isi: [18:22] Better get to know about it. Yeah. No, it's really healthy. It's really good. I mean, this list is long. I could now just, go up and down with it. Sweet Dakota rose watermelon. Mitch: [18:35] People gonna ask; what... what did you do on your Friday night? Isi: [18:38] Tawa tawa, tawa tawa. I don't know. Uh, what do we do? Mitch: [18:43] You'll never guess what. We had a wild night. (What is a Thornberry?) We spoke about fruit. Isi: [18:44] I've heard of a thornberry. I think we have to stop The Big Fruit Cast now. Mitch: [18:54] Fruit Show? Isi: [18:54] Um, OK, we have to stop this now. The fruits are taking over my mind. Um, it was nice to talk to you about fruits. Mitch: [19:04] Yeah, I feel like I know you better now that I know that you like a Granny Smith. Isi: [19:07] I... I don't even know a Granny Smith. (Oh, you said you like the green ones.) Ah so, yeah. Ah so. Mitch: [19:09] Ah so. Sour fruits, are the best kind of fruits. Isi: [19:16] Sour foods in general, yeah. Yeah, everything has to be sour, not bananas, though. Mitch: [19:21] Cheers to that, on your margarita. Isi: [19:24] And, um yeah, hope you like fruits. It's healthy. Eat them. Five a day. Bye. (And I hope all your dreams come true.) Te-ra! (Te-ra!)

(S8 E21) Do you know how many varieties of apples exist?! It's probably way more than you think. Rick and Kate are here to help you break down this delicious and ubiquitous fruit, and share their favorite tips. Where do apples come from? What can you do with them? Which varieties are best suited for what application? Sweet, savory, or both? Listen and get inspired. The What I Ate segment also includes an inspiring left-over dinner and a lentil "cacciatore."  . . . . . . You Won't Believe What I Ate Last Night is the ongoing conversation by Kate DeVore and Rick Fiori about their endeavor to be and stay healthy in a really tasty world with kindness and compassion towards themselves and others. Perfect if you are interested in: food, eating, diet, weight loss, weight management, health, fitness, compassion, kindness, meditation, mindfulness, humor, comedy, friendship, weight gain, foodie, podcasts, healthy eating.

Good morning from Pharma and Biotech Daily: the podcast that gives you only what's important to hear in the Pharma and Biotech world. Today, we'll be discussing some key news in the industry.## Addressing the Opioid CrisisThe biopharma industry is currently facing the challenge of addressing the opioid crisis, which is a complex and devastating issue. Researchers at the University of Chicago have made progress in finding a new pathway for pain relief without the risk of addiction and overdose. They have targeted the acetylcholine receptor in the brain instead of the dopamine receptor triggered by opioids, leading to extended periods of analgesic effects in mice, even in those with opioid tolerance. However, it may take time before these discoveries lead to the development of a new painkiller. In the meantime, there is an urgent need for opioid addiction disorder drugs like Brixadi from Braeburn, which offers long-lasting addiction treatment. It's crucial to work with the community to find the right recovery plan for each patient.## Drug Costs and the Opioid CrisisThe blame game between pharmacy benefit manager lobby PCMA and drugmaker lobby PhRMA continues over drug costs in the prescription supply chain. As the opioid crisis continues to rage, the launch of Brixadi provides patients with a new tool to overcome addiction.## The Life Sciences Generative AI SummitThe Life Sciences Generative AI Summit is a virtual event that aims to explore the use of generative AI in the field of life sciences. The summit will feature interactive discussions on how generative AI can accelerate product development and improve health outcomes. Dr. Bertalan Mesko, the Medical Futurist and Director of the Medical Futurist Institute, will be the keynote speaker at the event. The summit agenda includes sessions on various topics related to generative AI, including real-world examples of adoption and operational workflows for implementing generative AI solutions. Attendees will have the opportunity to take advantage of a 50% discount on Dr. Mesko's course on artificial intelligence in medicine and healthcare. The event will take place on September 28th in a virtual format, with on-demand viewing available for those unable to attend the live broadcast.## Mercalis: Support for Life Sciences CompaniesMercalis, formerly known as TrialCard, is a company that provides support to life sciences companies in the commercialization process. With 23 years of experience and a range of capabilities, Mercalis aims to help both life science companies and patients achieve their goals in the complex healthcare marketplace. The company offers end-to-end support, including strategic consulting, business initiatives, and patient support services. They also provide late-stage clinical trial supply management, post-marketing healthcare provider engagement services, and commercial data and insights. Mercalis is positioned as a comprehensive solution for life sciences companies, providing support throughout the commercialization process and leveraging its expertise in the healthcare marketplace.## Reliable Preservation with PHCBIThe importance of reliable preservation of samples and biologics in laboratories and scientific research is discussed. Failures and variances in temperature control can lead to the destruction of years of hard work. PHCBI brand high-performance refrigerators and ultra-low temperature and cryogenic freezers are highlighted as solutions that provide uniform protection. The advances in ultra-low temperature freezer technology are mentioned, emphasizing energy efficiency without compromising performance. Vaccine storage options, including undercounter refrigerators and freezers, as well as combination units with sliding and hinged doors, are also mentioned. These options are designed to safely store vaccines and biologics. Biomedical storage options are discussed next, with purpose-built models suitable for storing enzymes, reagents, and ot

On this weeks podcast @jennyandmaireadnow talk about clearing out the original family home, all the stuff we accumulate as we go through life and inspiration from the idea of Swedish Death cleaning. Jenny finds out she's been singing the wrong song lyrics forever and Mairead talks about her inspiring Dad. We finally get the steam cleaner review and we hear from Niamh and her snoring husband. Thanks to our sponsor Braeburn coffee at Applegreen

Wenn ihr keine Lust mehr habt verzweifelt am Apfelregal im Supermarkt zu stehen, dann haben Moritz und Till hier was für euch: Der große rote Apfeltest. (Die grünen Äpfel waren leider aus.) Sie haben vier der gängigsten Äpfel und einen Sonderapfel auf Aussehen, Geschmack, Süße und Konsistenz getestet.

In this episode, Sarah interviews Dr. Joshua Cohen, Chief Medical Officer at Braeburn, who talks about the stigma associated with opioid use disorder (OUD), and how our treatment of this condition is changing.Dr. Cohen talks about the recent FDA approval of Brixadi, the first long-acting buprenorphine treatment for opioid use disorder that has both weekly and monthly dosing options, and explains the technology behind its extended-release formula.Read the full article here:Brixadi Is a New Long-Acting Buprenorphine Treatment Against OUD For more life science and medical device content, visit the Xtalks Vitals homepage.Follow Us on Social MediaTwitter: @Xtalks Instagram: @Xtalks Facebook: https://www.facebook.com/Xtalks.Webinars/ LinkedIn: https://www.linkedin.com/company/xtalks-webconferences YouTube: https://www.youtube.com/c/XtalksWebinars/featuredSocial Post:

Upside down apple and honey pies with crispy crumble topping  Cook time: 25 minutes  Prep time: 30 minutes  Serves: 6  6 Braeburn apples   1 tsp vanilla paste   3 tbsp liquid honey   A pinch of salt  4 x 15cm puff pastry discs   100 gms unsalted butter   100 gms wholemeal flour   50 gms brown sugar   Greek yogurt  Peel the apples,then cut around the core and then into large wedges. Mix together in a bowl with the honey, vanilla and salt. Toss until coated.   Place 4 pieces of apple together in a small pile on a piece of grease proof paper and then lay a disc of puff pastry over the top. Press down around the edges to seal.   Refrigerate for 15 minutes while you make the crumble.   For the crumble, pre- heat a oven to 180*c. Combine the butter, wholemeal flour and sugars in a food processor and blend until smooth. Place onto a oven tray with piece of greaseproof and spread out.   Bake for 10 minutes before stirring and continuing to cook for another 10 minutes or until golden. Allow to cool.   Place the apple pies into the oven into the oven for 12 minutes. Serve by turning the apple pie upside down onto place so the apple is facing upwards and sprinkle with the crumble mix.   Finally serve with some yogurt as a refreshing side.  LISTEN ABOVESee omnystudio.com/listener for privacy information.

In this episode of the "The Top Line," Fierce Pharma's Fraiser Kansteiner and Eric Sagonowsky discuss the 20 most influential people in biopharma. We also cover a new topical gene therapy from Krystal Biotech, the FDA approval of Brixadi to treat patients with opioid use disorder, plus more headlines.  To learn more about the topics in this episode:  The most influential people in biopharma in 2023 FDA clears Braeburn's long-acting Brixadi to treat opioid use disorder Krystal's Vyjuvek becomes first topical gene therapy with FDA nod to treat rare skin disease Novo Nordisk pauses Wegovy marketing projects to dampen demand It's time for Fierce Biotech's 2023 Fierce 15—nominations are NOW OPEN "The Top Line" is produced by senior podcast producer Teresa Carey. The stories are by all our “Fierce” journalists. Like and subscribe wherever you listen to your podcasts.See omnystudio.com/listener for privacy information.

Tripp has worked on some of the best golf courses in the country, such as Atlanta Athletic Club (Riverside), Oak Hills, Oak Tree National, and Armonk's Whippoorwill Club. We hear from Tripp about his journey from winning a National Championship as a player at the University of Oklahoma to his rise a top course architect in the country.

Hello hello! Welcome back to All Plotted Out - An MLP:FiM podcast! This week sees the return of a pair of show titans, continues this season's fervent Spike revivification policy, and also experiments with being totally, absolutely fine. HOO dares displace Braeburn? HOO had the best debut script for the show? HOO is Jennifer Skelly? Now, while I don't you think you're ready for Jen Skelly (because my poddy's too Owloysius for you), may I interest you in more enthusiastic filler content? email: allplottedout@outlook.com FB: @allplottedout Backing music in the 'Top 25' segment - Welcome To The Party by Har-You Percussion Group, licensed under a Attribution-Noncommercial-No Derivative Works 3.0 United States License. http://freemusicarchive.org/music/Har-You_Percussion_Group/Sounds_Of_The_Ghetto_Youth/

The podcast where Steve has a great idea on site names and waffles on without a conclusion.Where Marijn says, ”Sorry, keep going I will interrupt you later”   and then  “ Hold on and let me continue”  and it is decided Marijn can interrupt as much as he likes but Steve has to do as he is told…Our Sponsor Braeburn Whisky for this episode gets a great mention https://braeburnwhisky.com/ The boys start on the Roadmap workshop that defines the order of products that will be released and feeds back to the team what order they will process the project.  Roadmap Strategy is required and agreed by the business… then the boys get stuck on questions around what the minimum settings are…  and Steve has a Daaa moment when he works out Marijn is right…Governance workshop takes frontstage and arguably the most important as so much is decided within this and getting then documented means you know so much about the project alreadyContent workshops are discussed that incorporate so much decision making…  The Elephant in the room ‘ Content Types' hits the discussion with a few erm and aaass when Steve rounds out what is needed to get started and define the baselineThe content workshop identified so much about the future of the project, migration, Change and Adoption Taxonomy strategy and some search aspects of the project…Portals are mentioned and Wendy wants to get involved… she has something to say… and then Steve puts Marijn on the spot with portals.  It is agreed an Intranet is a portal and opens up a bunch of things to decide like audiences, Viva Connect etc. all associated with publishing content…Migration cannot be forgotten so they do list the things that is required to ensure migration takes place… then some workshop around servicing the M365 environment with the helpdesk etc.After tasting Isobel Gowdie a Linkwood 14 year old cask strength whisky from Cask88 https://cask88.com/witches/ Steve finishes with a few thoughts on the Vision workshops and suggests that Marijn is right but only if we accept there are more than one Vision workshops required to make this work and get it right…. 

This is the link to the Brainstorm Infographic used in the podcast, and at the end of the page is the link to buy a bottle of the great whisky we taste in this podcast, sponsored by Braeburn Whisky.BrainStorm - 33 Ways To Unlock More Software Value.Steve and Marijn took this sheet and pulled numbers from a bag to select random values to apply to the Values sheet provided by Brainstorm Inc. Use Polls: Marijn immediately thinks Dancing Poles and Steve reminds every one of their brilliant ideas to create a Gentlemen's Club together mode image. Microsoft have just released a new update to make it easier for polls to be included in meetings.Build Activities: Steve Takes this as an opportunity to consider gamification and the various performance scores available from Microsoft.  They are all activity-based actions that generate indications that the tools are being used and actions are being taken byExplain Why the Change: Reference is made to the SouthCoast Summit where Steve and Marijn will be running a workshop at this event on Saturday 15th OctoberBring in an Expert:  Bring in an Expert or call a friend was referenced and Steve has an opportunity to make his favourite Experts quote (listen to check it out)   These people bring in their experience and ability to lead but Steve turns this around a little by saying for a M365 Project The Business are the experts!Be available for Help: Being available is possibly the best way to add value to the business.  Steve asks Marijn how he ensures people have access to the resources that deliver success.  A single point of contact is crucial for value which can be an email address or a SharePoint site or regular AMA sessions.Learn what your users need:  Understand the User stories from a deep dive into the business getting to the coal face and describing what the users want to have delivered that will deliver value.  Marijn described an approach used previous with a Mnemonic based on a Spy, in Dutch.Finally, Marijn points out that the value is what the user NEEDS not what IT thinks they want!Digitize Training:  Marijn talks about learning pathways and Steve points out that the same courses are available in Viva Learning, so they are available in SharePoint and in MS Teams… Steve shares a few nuggets from Viva Learning and Marijn gets distracted by a ringing phone and cuts off the girlfriend… but I am a witness we are recording.Gather Feedback:  It does not matter what the feedback is, just continue to get feedback.  Steve suggests that you should kick off as early as possible with a poll before your awareness campaign kicks off to confirmPersonalise your Training:   This about sing the Audience features to suggest training across the business.  Think about the matrix of Role v Skills so that Managers do not need to know how to create a page, but it would be good for them to understand how audiences work.  However, both need to understand pages and how they work.Custom videos or page instructions for the ‘feel' of the company so that they have customise training branded for the company that feels a little special.Our Sponsor Braeburn Whisky is described and of course Steve and Marijn have invested with Braeburn with 3 barrels between them already to do some cool things with

Starting in 1858 in Victoria, British Columbia, amid the Canadian Gold Rush, Oppy has always been a champion of innovation and opportunity. Here is the evidence. Oppy established Vancouver's first produce warehouse in 1887. Their founder, David Oppenheimer, was mayor from 1888 to 1891 and was credited with developing the city's infrastructure. They imported the first mandarin oranges from Japan in 1891, the Granny Smith in 1956, and from 1973 to 1988, they brought us Royal Galas, Braeburn, and Fuji apples. “Expect the world from us.” To some, those might seem daunting words to live up to, but to Kori Martin, at Oppy, they are words she strives to live up to every day. Oppy's goal is to add value to the needs of its partnerships with growers, retailers, and logistics. They do this with careful attention paid to developing their internal company culture.

Jessie Gladish was born and raised in Whitehorse, Yukon, Canada.  She considers herself lucky to have parents who took her and her sister camping, hiking, skiing, and taught them that being outside is possible in any weather and in the dark. After high school she moved to British Columbia to attempt post-secondary school and ended up working and traveling more than going to classes. She has since worked hard and earned a diploma in Adventure Guiding in 2012, and in 2021 finished a science degree in earth and environmental science with a focus on geology. Jessie has been running off and on since 2006. Jesse has now completed the Moab 240 twice; the Montane Yukon Arctic Ultra 300-mile race; winter ultras; desert ultras; 430 miles on skis; 300-mile Iditarod Trail; 120 mile fat bike race; 233 miles in the Yukon Ultra on her bike and many other races. Jessie currently lives a life of adventure with her husband in Salt Lake City, Utah.   Jessie is not your typical adventurer; she is whole other level.  There is a quiet unassuming confidence about her that comes through.  On this episode you may just get lost in her story telling like we did.  We talked a lot about her experiences taking on the Montane Yukon Arctic Ultra.  We discuss how her childhood impacted her life of adventure.  We also talk about the mental toughness it takes to accomplish such hard goals.  There are also some good wildlife encounter stories on this episode.  We are really hoping Jessie writes a book.  We will be the first to buy it!  I know you will enjoy this one and find a lot of inspiration from Jessie. Here is one of Jessie's race reports!  Enjoy! 2015 Montane Yukon Arctic Ultra Race Report By: Jessie Thomson-Gladish February 23rd, 2015: Over the past two weeks, I trudged at a speed of 3.5-4.5 km/hr, pulling a 65lb pulk loaded with all my winter survival and camping essentials, food and water.  This steady pace for 12 and a half days propelled me from Whitehorse to Dawson City on the Yukon Quest sled dog trail. The MYAU is a single-stage, multi-day race with four distances: a traditional 26 mile marathon, 100 miles, 300 miles, and the 430 mile. Participants choose one of 3 modes of transport: on foot, on cross-country skis, or on a fat tire snow bike. Each one has advantages and disadvantages, depending on the temperature, snow fall, terrain and mechanical issues. Why? I chose to attempt the 430 mile, on foot. Everyone wants to know why. Why do the race at all? Why on foot? Why not try the 100 mile first before jumping into the big distance? I wanted to try the YAU because it took me home to the Yukon, it followed the iconic Yukon Quest sled dog trail (a big part of Yukon gold rush history), it offered solitude, and it offered a major personal challenge which I felt I could achieve deep down but the potential for anything to go wrong was there – any mistake could lead to having to scratch from the race. Why on foot, well, I felt it was the simplest mode. Shoes are simple. Skis can break, waxing can be difficult, ski boots can be cold and hard to warm up in; mountain bikes can break down and are expensive to buy. I felt the benefit of coasting down hills on skis or a bike didn't quite outweigh the idea of walking the trail, although now having completed the distance I would like to try it on skis one year. The two guys from Sweden on their skis seemed to fly by me every day, after having sufficient rest at each checkpoint. I would travel later every night, they would be sleeping when I arrived, and sleeping when I left at 4 or 5am, only to fly by me again later in the morning or afternoon. Why the 430 mile? Well, I didn't want to arrive at 100 miles, or 300 miles, and feel good and wish I could keep going but have to stop. I figured if I had to scratch at any point I would be happy with the distance I did make, but I wanted that Dawson City destination in my head, just in case I could put one foot in front of the other for the whole way. 1- Start Day I feel like I could write pages and pages about the race, so I will! There are so many elements to it. The temperature was my biggest concern. We started at -30C in Shipyards Park and the first night at Rivendale Farms Checkpoint 1 on the Takhini River was reported down to -48C. Very cold night. Many people were not prepared for the low temps and when they attempted to camp/bivvy that first night found they were too cold to sleep and too tired to walk. I'm not sure how many racers scratched that first night, it seemed like half the field. The next day was cold too, around -30C all day. I managed to spend the night in my tent, however, I couldn't pack it up in the morning – I was too cold. I wondered if I was cut out for this and could hardly imagine another 12 days like the first one. Instead of stuffing my tent I just laid it in my sled to deal with it later when I had more body heat. I had never experienced packing up in this kind of cold before, even with growing up in the Yukon. Most normal humans do not go out in these temps and if they do it's for a short time with a cozy wood stove blazing for their return home. I spent a long time on this first night in my tent, about 10 hours, assessing my abilities and desire to go on. At 5am I was finally moving again, waiting for daylight and some feeling of safety and comfort from the sun so I could mentally recover from the reality of the extreme cold. 2 - Day Two The next checkpoint would be Dog Grave Lake, which was a long 33 mile (53 km) day. I wore my down jacket with fur-lined hood all day without breaking a sweat. Constantly trying to keep my hands and feet warm and monitoring for frostbite, keeping my face as covered as possible. Luckily it was a beautiful clear day, which makes the cold more bearable. Mountains to the south, snow crystals shining. The man I was walking with that day, Helmut, stopped to take photos more often than I hoped as it slowed us down quite a bit. Eventually, I left him behind as I pushed on to Dog Grave Lake CP, only to find it way farther than I had expected (or it just felt like that). Traveling in the dark (dark by 630pm at this point), alone, through winding low-land alder and willow growth, then up up up a huge climb seemed endless and unfair, until finally reaching the remote CP around 1030pm. The small wall tent was packed with sleeping bodies, and I found out from the volunteers most of them were scratching and waiting for a snowmobile ride out the next day. There was no room for me to sleep in the wall tent, so I set up my sleeping bag on some straw dog beds left over from the mushers who passed through days earlier and slept fairly well in the -41C night. I didn't set up my tent and instead just slept in my bag with my dads old army bag liner over top – much easier than dealing with tent poles. 3 - Day Three I woke early and left by 530am, walking by the half-moon light and enjoyed myself, knowing the sun would come up in a few hours and Braeburn CP was my next stop, though not for many miles (35 miles) and hours. Braeburn was the first chance to sleep inside, dry my stuff out, eat a massive burger and let it sink in that I'd traveled 100 miles up to that point. This was the finish line for many, but not even a quarter of the way to Dawson for the 430 mile race! 4 - Day Four From Braeburn to Ken Lake that fourth day was a beautiful one, although the longest day, at 45 miles, 74.5 km, it was a long haul. Chains of lakes with winding trail through the forests between. A flat day. I enjoyed catching up with Julie Pritchard, who had left Braeburn not long before me. We traveled together in silence and then chatted during our snack breaks. Before the sun set Oliver caught up with us, a 35 year old English doctor, and I ended up leaving the two of them behind to pick up my pace to Ken Lake CP. This was a long night for me, the lakes went on and on, and seemed to go uphill in the darkness. The forests between weren't as much fun as they were in the daylight and the CP seemed to be farther away than I'd hoped (a recurring phenomenon throughout the race..that last 10 km before each CP was unbelievably long). I'd left Braeburn at 5am and arrived at Ken Lake by 11pm. Ken Lake checkpoint is at a small fishing & hunting cabin with a wall tent set up for athletes to have a meal in. There is no indoor sleeping. I quickly set up my sleeping bag (no tent again), using my pulk to sleep against so I didn't roll down the sloped ground, changed my shoes and put my glorious down booties on. The small wall tent was warm, and I could dry my shoes and a few things out. I wolfed down the moose chili and a couple buns provided by the CP then hit the bag. 5 - Day Five I ended up sleeping in until 630am, far later than I wanted! I bolted up, packed up quickly in the cold and filled my thermoses with hot water from the hard-working volunteers and got started on the trail. I was headed for Carmacks, a long 35 miles away. More lakes to start with, and then the trail wound through a beautiful burned forest, and along the edge of the Yukon River. It felt good to see the Yukon River again. I caught up with Oliver and Tim and traveled with them most of the day. We were all tired and ended up snacking, breaking a lot, and walking painfully slow. We were close to Carmacks around 830/9pm, but still 4 km out when the snowmobile guys, Glenn and Ross, showed up and informed us we were cutting it close for arriving in Carmacks in time to make the 4.5 day cut-off time. We had no idea! We all thought it was the next morning. This kicked us into a gear I didn't know I had in me, and we literally ran 4 km to Carmacks, pulks flying behind us up small hills, down, and along the river all the way towards the lights of the tiny village. It was not fun, but once we made it in time had a good laugh about how close we were to being pulled out of the race for what would have been a silly mistake. Carmacks was a great place to be. The recreation center graciously gave us space inside, even for our pulks. So, it was a nice treat to dry everything out, reorganize the pulk, leave some gear behind that was too heavy and not being used, pick up the food drop bag and resupply the snacks. I ended up staying up until midnight as everything takes so long to do. I was able to talk on the phone and even check some emails. It was at this point I was realizing just how many friends and family were following my progress (via SPOTtracker online). I was overwhelmed by the support and love I felt, and it gave me extra energy and motivation. 6 - Day Six Carmacks to McCabe Creek, 38 miles..another great day, a solitary one, I saw almost no one. The Swedish guys passed me, and we exchanged a few words and the usual smiles and then they were flying away on their skis. The snowmobiles came by once, the comforting fatherly face of Glenn always brightened up my day or night. But other than that, I had a solo day all the way. The sunny, shimmery, winter wonderland day turned into a dark tunnel at night, as usual. This was the worst night of the race for me mentally and physically. It felt endless..endless trail in endless dark. The trail seemed to wind in circles in the forest and at one point I thought I saw a red glow of fire in the distance, but it must've been imagined because it took another couple hours, a mental breakdown, and acceptance of reality, before I finally stumbled back onto the river and across it to the CP. It was 10pm. McCabe Creek. Finally. I slept on the floor beside other racers in the shed provided by a local Yukoner's home. It was hot in the shed, but to let my body rest after such a long day on my feet was such a relief. I ate vegetables which tasted unbelievable. Rice and fish with the veggies then chicken, and then bread and peanut butter with something sweet for dessert. My body felt broken after this many days on my feet and very little rest – joints screamed, and my bones ached as I lay on the floor in my sleeping bag. It really felt like all the stress and fear of the cold had cumulated in my body and were now being released. It was also the turning point in the race for pain. I felt like if I woke up and was still in this much pain I'd have to quit, but what happened instead was I woke up feeling better than I had since the start. My body figured out what we were doing and suddenly felt stronger day by day from then on, instead of breaking down. 7 - Day Seven I left early, again. I was walking by 4 or 430am. I'd discovered my prime rest time was between 11pm and 4am, using some darkness to rest but getting away early enough to wait hours for the sunrise and maximize my daylight travel. The Swedish guys were still sleeping, of course, I would see them later on for sure. Today was a 6 mile long powerline walk near the highway towards Minto, then through low lying willow & alder land, along some lakes then eventually finding Pelly Crossing, 28 miles away, on the bank of the Pelly River. A shorter mileage day – but not a piece of cake by any means. I encountered overflow during the low laying land and had to put my snowshoes on to spread out my weight, use my poles to prod for harder ice sections that might not break through, and hope that my pulk didn't tip over into the puddle of water. I made it through high and dry, but the thought of getting wet feet in this cold environment got my heart racing. Pelly Crossing arrival in the daylight! That was my goal for the day, it felt great to roll in at 5pm, finally I had gotten somewhere at a ‘decent' hour. Glenn took me over to the store to buy apples and new snack food, which was all I was thinking about all day! In the rec center I sorted and dried my gear, repacked my sled, visited with volunteers and racers (Oliver and Tim were there, both having scratched due to recurring injuries..back pain and shin splints). I also made a phone call to my Dad and stepmom Denise, who'd been quite anxious and worried up to this point on how I was doing. They were relieved to hear my voice and that I sounded confident and happy, and I think starting to realize I may just make it to Dawson if I kept doing what I was doing. My Dad said if I kept going he would be in Dawson for the finish, and this unexpected news made me so happy; knowing he'd be at the end consumed much of my thoughts for the next 6 days on the trail. After my phone calls and organizing I wolfed down bison stew and went to sleep amongst the other snoring bodies. 8 - Day Eight 3am wake up..bison stew for breakfast..then I was off on the Pelly River for 16 km which was absolutely beautiful in the starry morning and eventual sunrise. The rest of the day was on a road into Pelly Farms (33-mile day) on the longest, most beautiful driveway I've ever seen. I was near tears a few times because of the beauty. It was a special day and I travelled alone again all day – I hadn't been on pace with anyone really at all yet and had spent more time than I ever had on my own in the wilderness. Arriving at the farm at 530pm as the sun was setting felt like a great end to the day. It got even better though once I realized I had arrived to heaven on earth. Pelly Farm is at the end of the Pelly River, just before it hits the Yukon River near Fort Selkirk. Dale and his wife run the farm, they have cows, chickens, pigs, and some beautiful collie dogs running around. Their house is tiny and cluttered, full of life with a real Yukon character; it was warm and inviting. Their generosity knew no bounds – we invaded their home, slept in their bunk beds, dried out gear, drank coffee and tea and used their tiny bathroom. The dinner they provided was a bread loaf pan of lasagna. Probably 2lbs of food. Apparently, it was a mix of bear and beef meat, and man did it taste good. I ate every ounce of it, plus a kit kat bar, and various chocolates and cookies and muffins kicking around. I slept like a log even with Jorn snoring on the bottom bunk, but only for a few hours. Julie and I woke up at 230am, ate pancakes and amazing farm fresh eggs, packed up and were on the move by 4am. Julie had shown up at the farm the night before, much to my surprise. She'd fallen behind before McCabe Creek due to getting sick and losing a full day of travel time. It was a hard decision, but she decided to scratch from the race. She had been taken to Pelly Crossing, and after some rest and a chat with the RD she decided to take a snowmobile ride to Pelly Farm to catch up with me to see if I wanted to finish the race together. She would be an unofficial racer without a finish ranking, but I think this just shows her true spirit – Julie was there for the trail and experience, not a medal or status. I was more than happy to spend the next few days, the most remote days of the race, together. We'd become a team. 9 - Day Nine Pelly Farms to Scroggie Creek CP is 65 miles. This meant we'd be camping out overnight somewhere in between the checkpoints. With really great information from Dale at the farm, we traveled about 50 km or so through the gorgeous burned forests and overflow sections, then up a 6 km hill climb and found a place to set up a bivvy beside the trail. We melted some water for our thermoses for the next day, ate a quick freeze-dried meal, and went right to sleep. We meant to wake up early, like 3am, but ended up sleeping in as I didn't hear my watch alarm buried in my sleeping bag. We slept til 645, and I bolted awake and we quickly packed up and were moving by 730. This meant we were later into Scroggie Creek than we wanted to be, but I suppose we needed the sleep too. We followed a valley all the way, so much of it was flat. A nice “7.5km to go” message was written in the snow by Mark Hines, keeper of Scroggie Creek CP this year, and a 3-time MYAU 430 finisher and professional ultra-athlete. It was so great to meet him, as I'd read his book last summer – a couple times – in preparation for the race and Julie is a good friend of his. We ate dinner and visited in the small cabin. This place is remote and Mark was here for the whole duration of the Yukon Quest and the MYAU (checkpoint manager for the dogs/mushers who started a day before us, plus our race..he was there for about two weeks straight). The only way in is by snowmobile and it's a long ride out either to Dawson or back to Pelly Farm. The dinner was chicken stew for me, and Mark made Julie a curry dish to make up for the last time he made it for her. I guess he mistook the cayenne for paprika and make it far too hot to the point of being inedible! Julie said this curry was just perfect. 10 - Day Ten We left Scroggie at 4am. 99 miles to Dawson City from here. 99 miles!! I'd been dreaming of the moment I could say that, especially since I'd made up a song called “99 Miles to Dawson” in preparation for this moment. We were on the Stewart River for a short time, then eventually wound through the forest and into mining territory. We passed cats and bulldozers, haul trucks and sluicers. Great white mounds of snow-covered tailing piles as well. That day we had the Black Hills/Eureka Dome climb ahead of us. It was a switchback road that took us from about 400m elevation up to almost 1200m. 2.5 hrs later we were sweaty on the top due to warm temps and spent the next few hours gently rolling along the ridge top, with a few surprisingly big hills to climb still. Also up here were many large wolf tracks. If I'd been alone my imagination may have wandered more to terrible scenarios that were unlikely to really happen, but in the company of another we were glad to find the tracks as evidence of animals moving about around us. Before we descended from the hills, we decided to set up a bivvy to get a couple hours sleep. It was already 930pm and Indian Creek CP was still a few hours away. 11 - Day Eleven We slept until 3am then quietly awoke and packed up our tents to continue on our way. By this point in the race, actually ever since Carmacks, the temperature had risen, it was now much more comfortable traveling. The nights were lows of -12C ish, and daytime highs were even up to -2C. It felt warm. Indian Creek CP was reached just as daylight was breaking. We had Gerard's amazing coffee, a pot of ichiban noodles, and a nice visit with his rather chubby rotweiller named Celise. Diane (medic) and Yann (photographer) were also hanging out there at the wall tent, so Julie and I had a tough time getting on our way! Coffee and socializing, plus some chocolate treats were enough to keep us there for a couple hours. But we had walking to do. And so, we continued. Our next big obstacle was King Soloman's Dome, another hefty climb up to 1100m after losing a bunch of elevation the day before. So up we went, starting the climb that night around 7pm. We made it to the first switchback and decided to sleep for a couple hours before the final push to Dawson up and over the Dome, and all the way ‘downhill' to Dawson on the other side. The night sky was great, bright stars, crisp night, maybe -15C or so, a slight breeze made it feel colder but we were protected by trees. We had boiled water and eaten our freeze-dried meal of choice by 11pm, crawled into our sleeping bags, and apparently, I was snoring within a minute of laying down. The northern lights were the last thing we saw before sleep, they were just coming out to dance as we slept. 12 - Day Twelve At 2am we packed up. The sky was clouded over, no stars, and a layer of fog to travel through in the middle of the night made our headlamp light difficult to see through. The physical summit of the Dome was anticlimactic, as we still had some uphill grinds to do along the mountaintop, but we did take a photo for Jorn, who had scratched before Scroggie Creek and gave us treats to continue on with, and said we “had to make it to Dawson, for him, and for everyone”. He gave us gummie bear packages and we took our photo holding onto the bright packages in the darkness. I then ate all them at once. We didn't have daylight until we were well off the Dome and onto the downhill road descent on Bonanza Creek Road. We ran a little bit, maybe a 6-7 km/hr jog, when we could. Two more sections of overflow to cross as well, just when we thought we'd put it behind us! The snowmobile guides caught up with us at some point, Gary said we were doing great and to just keep going. We knew Bernhard was ahead of us, and Shawn behind. With Dawson in our sights we passed Claim 33, a splash of color, finally after the black and white past couple days, and then past Dredge #4. Joanne and Lucy came out to meet us with hot chocolate. Music was playing from their vehicle to pump us up for the final 13 km. But it ain't over til it's over, and in true MYAU form the last 10 km was tough! It was mid-afternoon and we knew we'd arrive in daylight, but it made it no easier or faster. We still plugged along, wondering when the hell we'd see the city and that finish line. We had a visit with a local man and his Pomeranian fluff ball, then had to skirt past a barking black dog guarding the street. Finally we could see the bridge over the Klondike River and the path which would lead us into town. Walking the riverfront trail into Dawson City felt like coming home, kids playing and sledding, people going about their daily business, probably wondering what we were doing, or not caring at all. I could see the visitors center, which was the finish line and a small crowd of people gathered. My Dad and Denise were standing there just before the finish, relieved to see me looking fine after all that way and all the worrying. Finish line hugs and photos and congrats were a mix of emotions – I was so happy to be done, but also a little sad it was over. Post-Race Thoughts There are so many moments that happen in almost 700km of walking. Ups and downs, daylight, nighttime, sunrise, sunset, worrying, wondering, being amazed at scenery, eating and drinking, resting and walking, sleeping minimally, reorganizing, packing/unpacking. Things happen slow, but now that it's over it feels surreal and fast. 12.5 days of walking. I had thought I'd have some kind of great epiphany, some life revelations, some ingenious moment. Instead, I spent hours worrying about battery life, headlamp quality, how much water I had, my dwindling snack bag, sore hips, then sore heels, then a sore quad muscle, cold hands, layer on layer off, gloves on gloves off. Too hot too cold. Where is the checkpoint, how far have I gone, how fast am I going, how many hours can I sleep tonight? My mind was consumed by the present, which really is the beauty of survival at its simplest. Eat, sleep, water, shelter, keep moving. I loved it all, and even the moments I was alone in the dark and cold I felt in control and ready for anything. March 15, 2015 It's been 5 weeks since the start of the MYAU. Recovery has been easier than I thought it would be, but what isn't easy is realizing it's all over. The past year of thinking about the ultra, preparing for it, organizing my gear, buying more and more, training with my pulk and having it take up more mental space than I imagined it would has left a void I wasn't ready for. I want to be back on the trail where life is simple. Move forward, eat, sleep. I miss the sound of my footsteps and the scrape of my pulk on the snow, and the pull of my harness on my hips. I miss the volunteers and the racers, the animal tracks, the snow. I even miss my small headlamp beam in the dark. I plan to do the YAU again in 2017 and that seems too far away. My official result: 4th place out of 5 finishers on foot for the 430 mile. (19 people at the start line) 1st female in, and the only “official” female finisher of 2015. 2nd woman ever to finish, 1st Canadian woman to complete the 430. Official time: 293 hours 25 minutes (12.5 days) Thanks for taking an interest in my write up, and I hope it inspires you to challenge yourself in whatever way you want to. Links: http://yannbb.com/ (professional photographer, also on Instagram @_y_a_n_n_b_b_  ) http://www.arcticultra.de/en/event/results/results-2015 (MYAU website and results)

Seventeen year old Sam Bice lived with his family--and delivered the newspaper--in the Braeburn addition in 1972. He talks about what happened the night of the flood, the heavy death toll among his neighbors, and the odds things he found in the days following catastrophe.Sam Bice was born in Chamberlain, South Dakota and moved to Rapid City in 1960 where he has resided since. He joined the family business where he was a water well and foundation contractor until his retirement.Recorded April 2022, Interviewer: Adrian Ludens

In this episode, Dr. Andrew Cutler interviews Dr. Stephen Stahl on best practices for switching antipsychotics. Optional CME/CE Credits and Certificate Instructions: After listening to the podcast, to take the optional posttest and receive CME/CE credit, click: https://nei.global/POD22-Switch Learning Objectives: After completing this educational activity, you should be better able to: Understand the relevance of receptor binding properties and pharmacokinetic profiles when switching antipsychotics Follow evidence-based guidelines when switching antipsychotics Accreditation: In support of improving patient care, Neuroscience Education Institute (NEI) is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.  NEI designates this enduring material for a maximum of 1.0 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.  A posttest score of 70% or higher is required to receive CME/CE credit. The content in this activity pertains to pharmacology and is worth 1.0 continuing education hour of pharmacotherapeutics.  Credit Types. The following are being offered for this activity: Nurse Practitioner (ANCC): contact hours Pharmacy (ACPE): knowledge-based contact hours   Physician (ACCME): AMA PRA Category 1 Credits ™ Physician Assistant (AAPA): Category 1 CME credits Psychology (APA): CE credits Social Work (ASWB-ACE): ACE CE credits Non-Physician Member of the Healthcare Team: Certificate of Participation stating the program is designated for AMA PRA Category 1 Credits ™ Peer Review: The content was peer-reviewed by an MD specializing in psychiatry to ensure the scientific accuracy and medical relevance of information presented and its independence from bias. NEI takes responsibility for the content, quality, and scientific integrity of this CME/CE activity. Disclosures: All individuals in a position to influence or control content are required to disclose all relevant financial relationships, which were then mitigated prior to the activity being presented. Interviewer Andrew J. Cutler, MD Clinical Associate Professor, Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY Chief Medical Officer, Neuroscience Education Institute, Carlsbad, CA Consultant/Advisor: AbbVie, Acadia, AiCure, Alfasigma, Alkermes, Allergan, Atentiv, Cognitive Research, Intra-Cellular, Ironshore, Janssen, Lundbeck, Neurocrine, Noven, Otsuka, Sage, Sunovion, Supernus, Takeda, Teva Speakers Bureau: AbbVie, Acadia, Alkermes, Allergan, Intra-Cellular, Ironshore, Janssen, Lundbeck, Neurocrine, Noven, Otsuka, Sunovion, Supernus, Takeda, Teva, Tris Interviewee Stephen M. Stahl, MD, PhD, DSc (Hon.)  Clinical Professor, Department of Psychiatry and Neuroscience, University of California, Riverside School of Medicine, Riverside, CA Adjunct Professor, Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA Honorary Visiting Senior Fellow, University of Cambridge, Cambridge, UK Director of Psychopharmacology Services, California Department of State Hospitals, Sacramento, CA Grant/Research: Acadia, Avanir, Braeburn, Intra-Cellular, Ironshore, Lilly, Neurocrine, Otsuka, Shire, Sunovion Consultant/Advisor: AbbVie, Acadia, Alkermes, Allergan, Arbor, Axovant, Axsome, Celgene, ClearView, Concert, EMD Serono, Eisai, Ferring, Impel, Intra-Cellular, Ironshore, Janssen, Karuna, Lilly, Lundbeck, Merck, Otsuka, Pfizer, Relmada, Sage, Servier, Shire, Sunovion, Takeda, Taliaz, Teva, Tonix, Tris, Vifor Speakers Bureau: Acadia, Lundbeck, Otsuka, Perrigo, Servier, Sunovion, Takeda, Teva, Vertex Board Member: Genomind Pre-Interview Author Sabrina K. Bradbury-Segal, PhD Medical Writer, Neuroscience Education Institute, Carlsbad, CA No financial relationships to disclose. The Planning Committee and Peer Reviewer have no financial relationships to disclose. Disclosure of Off-Label Use: This educational activity may include discussion of unlabeled and/or investigational uses of agents that are not currently labeled for such use by the FDA. Please consult the product prescribing information for full disclosure of labeled uses. Cultural Linguistic Competencies and Implicit Bias: A variety of resources addressing cultural and linguistic competencies and implicit bias can be found in this downloadable handout. Support: This activity is supported by an unrestricted educational grant from Intra-Cellular Therapies. Released: January 26, 2022          CE credit expires: January 26, 2025          

Did you hear? Betty bought some bitter apples, and we've found that an orange can be quite annoying. We are still celebrating some fall feelings - this week, we talk about Apple Cider! Do you know which apple is best for making homemade apple cider? We also learn how to win votes and intoxicate people. Hang around to hear us take a special Halloween quiz! Plus, our first ever listener competition?    Send us your questions or just say hey:  webrewgood@gmail.com @webrewgood on Facebook, Instagram, and TikTok   RECIPE OF THE DAY Homemade Crock-pot Apple Cider! 6 quart slow cooker (reduce the amounts for a smaller cooker) 8-10 medium size sweet apples (I like McIntosh, Empire, Golden Delicious, Pink Lady, and Braeburn, do NOT use Red Delicious apples) 1 orange 3 cinnamon sticks 1/2 TBSP whole cloves 2 tsp whole allspice 2 inch knob of fresh ginger (optional) sweetener of choice, up to 1 cup (granulated sugar, brown sugar, honey, maple syrup, or combo)   INSTRUCTIONS Chop apples and orange into large chunks keeping the peels on. Put apples, orange, cinnamon, cloves, allspice, ginger in the slow cooker. Fill the slow cooker with enough water to cover all of the apples. It should fill it pretty much to the brim of the pot. Turn it on low and put the lid on. Allow to brew for 8-12 hours. (12 hours is best and even longer would be fine too!) Take a potato masher and mash all of the apples up. Strain the cider through a fine mesh sieve or through cheesecloth for a more clear cider. Add sweetener of choice to taste for your sweetness level. Store left over cider in the refrigerator for up to 5 days.   Links and Citations: https://www.smithsonianmag.com/smart-news/ancient-origins-apple-cider-180960662/ https://www.seriouseats.com/chris-lehault-5118607 https://www.britannica.com/story/was-johnny-appleseed-a-real-person http://www.nationalapplemuseum.com/appleciderandmore.html https://www.triviagenius.com/brief-history-of-american-apple-cider/X1uyim15UQAH9iQi https://www.mentalfloss.com/article/59048/11-ways-hard-cider-shaped-american-history https://www.buzzfeed.com/stitchcupcake46/get-in-the-spooky-spirit-and-well-tell-you-which-2akdob8fmx

In der Schweiz gibt es über 1000 verschiedene Apfelsorten. Zu den beliebtesten Sorten gehören Gala, Golden Delicious und der Braeburn. Alle diese Sorten kommen ursprünglich aus dem Ausland. Unter den beliebtesten zehn Sorten ist mit der Milwa- Diwa nur ein einziger Apfel, der in der Schweiz gezüchtet wurde. Die Milwa-Diwa ist eine Kreuzung aus Maigold und Elstar. Gezüchtet hat diese Sorte die Forschungsanstalt Acroscope in Wädenswil. Der Apfel schmeckt säuerlich und hat ein festes Fruchtfleisch. Er eignet sich neben dem Frischkonsum auch fürs Backen und Kochen. Zwischen 13 und 16 Kilo Äpfel Im Durchschnitt isst die Schweizer Bevölkerung 15 Kilo Äpfel pro Jahr und Kopf. Damit gilt der Apfel als beliebtestes Obst in der Schweiz und ist eine ideale Zwischenverpflegung. Äpfel enthalten viele Vitamine, Mineralstoffe, Spurenelemente und Fruchtsäure. Weil Äpfel nachreifen, sollte man sie kühl lagern, da der Reifeprozess durch die Kälte verlangsamt wird. Apfelkanton Thurgau Rund 80 Prozent der Schweizer Äpfel kommen aus der Ostschweiz mit dem Kanton Thurgau, aus dem Wallis oder der Waadt. Weitere grössere Apfelanbaugebiete gibt es im Kanton Bern oder der Zentralschweiz. Mit einer Anbaufläche von knapp vier Tausend Hektaren, ist der Apfel die grösste Obstkultur der Schweiz. Zu den Obstbäumen am Bürgerstock Das Dorf Kehrsiten, ein Ortsteil von Stansstad im Kanton Nidwalden, zählt rund 300 Einwohner und mehr als 400 Hochstammobstbäume. Das Dorf liegt am Vierwaldstättersee direkt unterhalb dem Bürgerstock. Eine enge Strasse von Stansstad, die nur mit Bewilligung befahren werden darf, führt am See entlang nach Kehrsiten. Von hier aus kann man auf einem Bergweg in anderthalb Stunden zum Bürgerstock wandern. Oder man fährt mit der Bahn. Besonders empfehlenswert ist der Felsenweg am Bürgerstock. Nach Kehrsiten kann man auch mit dem Kursschiff.

“[The branding] is a callout to help the consumer remember the apple when they go to the store and pick it up”  Vince Lopes (25:51 - 25:58)   While there are a hefty amount of different apple varieties available on the market right now, many are being taken out due to lack of consumer preference. This gives companies like T&G the opportunity to create new variations on an old favorite. At the forefront of the Envy and Jazz apples, T&G is looking to make them stand out above the rest.    If you've been an avid listener to The Produce Moms, this won't be the first time you've heard managed varieties in produce. You'll also know the time and effort that goes into bringing them to the market from planting the first tree, to hitting grocery shelves. The Envy apple, which is a cross between a Gala and a Braeburn apple, hit the market in 2008 but took nearly a decade to come to fruition. Other variations can even take longer.    Envy,  and its sister the Jazz apple, both hold up well and last longer in transit and on retail shelves. Envy, itself, has certain characteristics that allow consumers to cut them up even up to an hour before eating without worrying about browning. While these features are thanks to natural cross-pollination, they wouldn't have been possible if not for the evolution of the apple category.   Senior Vice President of Sales and Marketing at T&G Global North America Vince Lopes states that we're starting to see less and less of older varieties becaused new managed ones are becoming more and more versatile. For example, their Jazz apple has a balance between sweet and tart, while the Envy has a specific aroma and texture you can sink into with every bite. As such, both have an expansive versatility in usage whether it's in baking, cooking, or simply eating them raw.   These managed varieties are booming in popularity with over 60 trying to get on grocery shelves. In reality, any given retailer only holds about six. When making a specific branded variety, companies like T&G have to give consumers something to remember so that when they see it at the store, they buy it.    “We're trying to develop programs that are appealing to shoppers.” Cecilia Flores Paez (23:59 - 24:02)   Vice President of Sales and Operations at T&G Global North America Chris Willett states that what's unique about their branded varieties is that they have complete control from the fields to the retailer. Unlike Golden or Red Delicious apples that can be grown anywhere, by anyone, T&G have certain standards with their growers.    “We can manage volume and measure supply versus demand and grow accordingly.” Chris Willett (17:40 - 17:44)   T&G has also been working on advanced technologies when it comes to picking and packing. Some of their newer systems can see into fruits in the packaging line to point out imperfections. Others include drones that can fly over orchards for crop management and most recently they began using a machine that blows leaves off of trees therefore exposing the fruit to more sunlight for better color.    Head of Marketing for T&G Global North America, Cecilia Flores Paez is proud that T&G has gained notoriety with their Envy and Jazz apples. as of late. With a campaign partnership with Hallmark Channel, T&G recently won a Produce Marketing Excellence award for delivering a breakthrough campaign that made a difference during COVID-19 during the 2020 holidays. During the campaign, they worked on helping families celebrate the holidays with unique, new and delicious recipes that were named for Hallmark Channel holiday films. The Envy apple was also recently named “Best in Produce” by Kitchn - a food and lifestyle media outlet that debates for weeks to  select and name the best grocery items to try in America right now.    Being grown in both the United States and New Zealand, T&G is able to provide the Envy apple year round at most national and regional retailers. Jazz, on the other hand, is an heirloom variety and can be harder to find, but worth it when you do. How to get involved Join The Produce Moms Group on Facebook and continue the discussion every week!  Reach out to us - we'd love to hear more about where you are in life and business! Find out more here.    If you liked this episode, be sure to subscribe and leave a quick review on iTunes. It would mean the world to hear your feedback and we'd love for you to help us spread the word!

Der Apfel ist die beliebteste Schweizer Frucht. Schweizerinnen und Schweizer essen mehr als 16 Kilo Äpfel pro Kopf und Jahr – ob roh, als Saft oder getrocknet. Äpfel sind gesund: Sie enthalten viele Vitamine, Mineralstoffe und Spurenelemente. Für unterwegs ist ein Apfel die ideale Zwischenmahlzeit. Der Star unter den Äpfeln ist der Gala. Von dieser Sorte ernten die Bauern jährlich über 30000 Tonnen. Auf dem zweiten Platz folgt der Golden Delicious, der drittbeliebteste ist der Braeburn. Die Hauptsaison für Äpfel dauert in der Schweiz von August bis November. Ab Dezember werden die Früchte als Lagerware verkauft. In der Schweiz gibt es über 1000 Apfelsorten. In der Sendung «Treffpunkt» steht der Apfel im Mittelpunkt.

Patients dealing with damaged, cracked, or chipped teeth in Westchase, Houston can now get specialist dental crown treatment for their condition. Dentist 101 (713-773-1300) serves patients in Memorial, Alief, Bellaire, Chinatown, Sharpstown, and Braeburn. Go to https://txdentist101.com (https://txdentist101.com) to find more details.

Bryan Martin: Read Your Standard and Dream BIS Line upBryan Martin, retired professional handler, former AKC executive field representative and newly minted Hound group judge joins host Laura Reeves for part two of his amazing stories and advice for exhibitors. “My family would have dinner (and) during dessert, the book of standards came on the table. We had weekly discussions. ‘OK let's talk about heads tonight.’ We'd go round the table and everyone would say something and ‘OK who do you think has a good head? Who do you think has a bad head? Do you have a picture? Go get a picture.’ One night we’d talk about shoulders and why the shoulder needs to be a wrap-around, why the shoulder needs to be well laid back, why the shoulder blade and the upper arm have to be the same length, to make it a proper working hound, how the back end has to match the front end… How the pieces fit. It's form and function, which has stuck with me forever, and that's my basis is form and function judging. “It's amazing how many people in AKC, UKC … have no understanding how to read a standard. How to interpret a standard and how to go to somebody and talk to somebody about the standard. The how’s why’s and wherefores of studying standards. At the International show where we had to write a critique, we had to know the standards. What I ended up doing was showing people what the standard says about their dog. And they say ‘oh, I didn't know that’ and it's a whole educational program that is missed.” In the “Dream Best In Show Lineup” game, Martin’s choices were: Sporting – English Springer Spaniel, Ch Salilyn's AristocratHound – Afghan Hound, Ch Triumph Of GrandeurWorking – Doberman Pinscher, Ch. Brunswig's CryptoniteTerrier – Scottish Terrier, Ch. Braeburn's Close EncounterToy – Japanese Chin, GCh. Pem We-Syng Lucky MINon-Sporting – Dalmatian, Ch. Spotlight’s SpectacularHerding – German Shepherd Dog, GCh. Altana's MystiqueFinally, Best in Show in this mythical lineup?? Shannon, the Scottish Terrier, shown by George Ward. Listen above to hear all of this and more.   Support this podcast

On today's episode of All in the Industry®, Shari Bayer's guest is Brian Bistrong, Corporate Executive Chef of The Connell Company’s The Park, and Culinary Program Director of The Park’s hospitality group, Table & Banter. With years of rich, diverse experience working side-by-side with renowned chefs such as Wolfgang Puck, David Bouley, and Gray Kunz, Brian offers a wealth of knowledge and passion at New Jersey’s premier catering and events destination.  Brian's restaurant experience also includes his role as Executive Chef at The Harrison in Tribeca, and Chef/Owner of Braeburn in the West Village; plus, Chef de Cuisine at Bottega in Napa, CA; Executive Chef of Research and Development for Wolfgang Puck’s Test Kitchen, and Dean & DeLuca as the gourmet market’s Corporate Executive Chef. Today's show also features Shari's PR tip to protect and preserve your reputation; Speed Round; Industry News Discussion on NYC restaurants opening in Miami; and Solo Dining experience at Chef/Owner David Kinch's Manresa at Intersect by Lexus in the Meatpacking District, NYC. REMINDER: We have new All in the Industry® merch available, including All in the Industry hats & totes, and H.O.S.T. notebooks & pens. Through May 31, 2021, 100% of the proceeds from our hat sales (less shipping/handling) will be donated to the Independent Restaurant Coalition (IRC) to help with their continued efforts to #saverestaurants. Go to allintheindustry.com/merch to get your AITI swag today! #allintheindustry #allindustry #sharibayer #chef #restaurant #culinary #hospitality #theconnellcompany #brianbistrong #podcast #foodradio #hrn #nycImage courtesy of Brian Bistrong.Listen at Heritage Radio Network; subscribe/rate/review our show at iTunes, Stitcher or Spotify. Follow us @allindustry. Thanks for being a part of All in the Industry®. Heritage Radio Network is a listener supported nonprofit podcast network. Support All in the Industry by becoming a member!All in the Industry is Powered by Simplecast.  

Having completely forgotten they were going to do something to Nestler Croke this week, the team head off to investigate the Braeburn company, in the hope of discovering the source of the strange glowing spiders that keep appearing. Join Brilmara - Laura Kate Dale, Dee - Nick, and Dusty - Becky as they cause some chaos. The Dungeon Mistress is Jane Aerith Magnet.

Die Liebe zu Büchern geht bei uns durch den Magen: Diesmal gibt es Tarte Tatin - ein Apfelkuchen, der in Büchern immer wieder auftaucht und sofort ein Frankreich-Feeling heraufbeschwört. Genau die richtige Einstimmung für das Gespräch mit Martin Walker, dem Autor der Bruno-Krimis. Er lebt im Perigord - und mit ihm haben wir fast ein bisschen mehr über Essen als über Literatur geredet. Reichlich Bücher werden trotzdem serviert: Abgesehen vom aktuellen Bestseller hat Jan deutsche Literatur dabei, während Katharina (mal wieder) bei einem amerikanischen Roman gelandet ist. Die All-Time-Favorites kommen von zwei Hörerinnen (danke dafür!) und im Quiz gibt es Smalltalk-Futter für die nächste Party. Die Bücher dieser Folge: Lucinda Riley: Die Sonnenschwester / aus dem Englischen von Sonja Hauser, Sibylle Schmidt, Ursula Wulfekamp (Goldmann) Elke Erb: Gedichtverdacht (Urs Engeler) Iris Hanika: Echos Kammern (Droschl) Adrienne Brodeur: Wild Game / aus dem Englischen von Nicole Seifert (Droemer Knaur) Martin Walker: Connaisseur (Diogenes) Chad Harbach: Die Kunst des Feldspiels / aus dem Englischen von Stephan Kleiner und Johann Christoph Maas (Dumont) Arundhati Roy: Der Gott der kleinen Dinge / aus dem Englischen von Annette Grube (Fischer TB) Das Rezept für Tarte Tatin - Für die Füllung 1 kg Äpfel (Braeburn oder Elstar) - Für das Karamell 200 g Zucker und 50 g Butter - Für den Boden 1 Ei, 250 g Mehl, 125 g Butter, 50 g Zucker Es gibt extra Tarte-Tatin-Formen, aber es geht auch mit einer gewöhnlichen Springform. Zuerst die Äpfel schälen und in Spalten schneiden. Etwa 15 Minuten dünsten. Die Zutaten für den Teig verkneten und ca. 1 cm dick (in der Größe der Form) ausrollen. Für das Karamell im Kochtopf ein Drittel des Zuckers bei schwacher bis mittlerer Hitze schmelzen. Dann das nächste Drittel Zucker dazugeben und den Topf schütteln, sodass sich der Zucker verteilt. Wenn er fast vollständig zu Karamell verschmolzen ist, das letzte Drittel dazugeben. Vorsichtig rühren, die Butter dazugeben und weiter rühren, bis das Karamell cremig ist.  In die Form füllen, die Äpfel darauf verteilen und den ausgerollten Boden darüber legen. Die Ränder eindrücken und den Teig mit der Gabel einstechen. Etwa 30 Minuten bei 200 Grad im vorgeheizten Ofen backen. Nach dem Backen auf einen Teller stürzen. Wahrscheinlich läuft das Karamell ein bisschen an den Rändern runter - das gehört so!

Find us at: iTunes Spotify Patreon YouTube Angel Andrews, the creator of To Have and To Hold and voice actor extraordinaire, is with us to talk about an adorable pony sports episode! Applejack’s getting trained up to take down her loudmouth cousin Braeburn at Ponyville’s hottest sport, buckball. But when Dash and AJ start looking for a unicorn, they suddenly realize Pinkie Pie and Fluttershy are absolute buckball stars in the making. And if you think having the two most competitive ponies trainthe the two least competitive ponies won’t cause some hijinks, you clearly haven’t seen this show. Can Pinkie and Fluttershy overcome the pressure and find the love of the game to beat Braeburn? We discuss “Buckball Season” this week on Macintosh & Maud! Check out our merchandise at our new Teepublic store! We've got lots of fun designs to choose from and more on the way! Macintosh & Maud have a Patreon! If you contribute $2.00 or more you can listen to our reviews of Equestria Girls: Rainbow Rocks and Equestria Girls: Friendship Games, along with other great content! You can email us with feedback at macintoshandmaud@gmail.com, or you can connect with us on Instagram, Twitter and Facebook. Intro and outro music is "Madgapuff March (rmx) by JS Bach" by Lee Rosevere. Licensed under a CCO 1.0 Universal License. For the song and information about the artist, visit the song page at the Free Music Archive.

Aaaaand we're back! The gang takes a lovely stroll through the town of Braeburn and ends up at another locally owned business. They decide they want to be helpful too!   Thanks for tunng in, we hope your hearing hasn't suffered terribly. Ivana, @arcanevice, is the intoxicated train conductor, trying terribly to stay on the rails. Mollie, @MollieMM9, is just trying to enjoy her dang beer. John, @absurdistKolbold, is sighing heavily with disappointment and Forrest, @walk6070, is a hunk of burnin' love. Kaleigh, @kleeeee is cronching on some protein. Don't forget to rate us on iTunes if you like us and check out our patreon to make us do weird things.

Wil had a fun conversation with Lisa Carvey, owner of The Braeburn in Langley, WA on Whidby Island on the Puget Sound. They spoke for an hour and covered lots of actionable and philosophical ground. A fun conversation. Tons of respect for Lisa and her crew! Enjoy... .

Join Caroline Wilson and Corrie Perkin for Episode 92 ‘It’s More Than A Chicken Breast’. This week we discuss sexism and racism in various realms of public life, from Niki Savva’s insights into the hot mess of Canberra during the last leadership spill and Donald Trump’s inappropriate crack at four new female congresswomen to the progress made by Tanya Hosch at the AFL and the Adam Goodes doco The Final Quarter which hits our TV screens this week. Corrie’s ‘Crush of the Week’ is Megan Rapinoe – the co-captain of the US women’s soccer team. In BSF – Corrie’s been reading Rule Brittania by Daphne du Maurier, Caro reviews Ian Darlin’s compelling documentary The Final Quarter (which airs on Network 10 on Thursday July 18that 7.30pm) and Corrie bakes up an Ottolenghi Apple Cake (see recipe below). BSF is sponsored by VitalSmarts. VitalSmarts and their Crucial Conversations training can transform the culture of your business or help you confront difficult situations with confidence. Make sure you jump onto VitalSmarts website www.vitalsmarts.com.au/dstm - they've got a special offer for our podcast listeners - a free copy of their E Book Emotional Inequality - Soloutions for Women in the Workplace.  Corrie’s Grumpy about the chaff in her cutlery drawer and in ‘6 Quick Questions’ we talk Judi Dench, a month of extraordinary sport, the beauty of rainy days and Bunnings v Ikea v Bed, Bath and Table. Caro’s GLT is the super affordable range of jeans at Kmart. Spiced Apple Cake (from Yotam Ottolenghi's Simple)  130g unsalted butter 150g caster sugar 3 large eggs, beaten lightly 2 teaspoons vanilla extract 300g plain flour 2 teaspoons baking powder pinch of salt 200g soured cream Apple Topping 4 medium-large eating apples (e.g. Cox, Braeburn, Gala), weighing about 600g 70g demerara sugar 1 rounded teaspoon mixed spice Preheat the oven to 180C/Fan 160/Gas 4. Grease and line a 23cm cake tin. Put the butter and sugar in a bowl and beat until light and fluffy. Add the eggs and vanilla a little at a time (plus a little of the flour if it looks as if it’s curdling). Sift the flour, baking powder and salt together. Fold the flour mixture in a bit at time, alternating with spoonfuls of the soured cream. Spoon the batter into the prepared cake tin.    Peel, core and slice the apples into wedges (about 1.5cm thick). Put in a bowl. Mix the demerara sugar with the mixed spice and add to the apple. Toss gently to coat the pieces of apple. Gently lay the apple on top of the batter in the tin. It looked like far too much but I went with the recipe and, of course, it was absolutely perfect! Bake for about an hour - but may need longer.  For videos and pics make sure you follow us on Instagram @DontShootPod. Like our Facebook page and hit 'Sign Up' to receive weekly updates HERE.  Email the show via feedback@dontshootpod.com.au Follow us on Twitter via @dontshootpod 'Don't Shoot The Messenger' is produced, engineered and edited by Jane Nield for Crocmedia.

Elstar, Jonagold, Braeburn oder Golden Delicious - Äpfel sind echtes Power-Obst. Die erfolgreichste Hobby-Köchin Deutschlands, Katrin Bunner aus Speyer und Radio Regenbogen Moderator Patrick Gruben widmen ihnen hier eine eigene kleine Radioshow. Zurecht. Denn Äpfel sind beliebt, gesund und eigentlich "gut zu haben". Wie Sie sie am besten im Kühlschrank lagern - nur ein Fakt in diesem Podcast.

If you’re staying up to watch a certain royal wedding, or a game of rugby for that matter, these make the perfect snack for supper! Makes 44 large eggs + one extra4 pork sausages1 tsp fennel seeds¼ tsp black pepperSplash of milk¼ cup flour1 cup panko crumbsVegetable oil to shallow fry1 cup mayonnaise, I use Best Foods2 medium-sized red apples (Braeburn or Rose)1 tsp mustard powder, or use wholegrain mustard1 tsp fresh dill feathersSalt to season Preheat oven to 180 C.Place four eggs in saucepan of cold water and bring to the boil for 8 minutes to hardboil. Drain and run under cold water to cool enough to gently peel.Remove the casing from each of the sausages, add fennel seeds and pepper and mix. Divide into 4 portions, rolling each into circle. Pat into flat disc.Place the eggs in the centre of the sausage meat and gather up to enclose the eggs and shape into neat balls. Chill for 15 minutes.In a bowl beat the additional egg with a splash of milk. Set out a plate with flour and a shallow bowl with panko crumbs.Roll each egg in the flour, then beaten egg (drain excess) then coat well with breadcrumbs.Heat the oil in a small frying pan or saucpepan on a medium heat and fry the eggs, turning frequently until the crumbs are golden. Transfer to a baking tray and bake for 15 minutes until crispy and to finish cooking.Serve warm eggs, halved or quarterd, with mayonnaise for dipping.MayonnaiseChop one apple roughly (no need to peel or core). Place in a saucepan and with a little water. Add mustard, cover and gently simmer until the apple puffs and is tender.  Drain (reserve juice for your breakfast muesli).In a food processor blitz mayonnaise and gradually add apple. Processes until well combined and shiny. Stir through dill and season to taste. 

Katharine Beaumont: @katharinecodes Show Notes: In this episode, we hit the topic of machine learning from a 101 perspective: what it is, why it is important for us to know about it, and what it can be used for. Transcript: CHARLES: Hello everybody and welcome to The Frontside Podcast, Episode 94. My name is Charles Lowell, a developer here at The Frontside and your podcast host-in-training. Today I'm going to be flying it alone, but that's okay because we have a fantastic guest who's going to talk about a subject that I've been dying to learn about. But you know, given the number of things in the world, I haven't had a chance to get around it. But with us today is Katharine Beaumont who is a machine learning consultant. And she's going to talk to us, not surprisingly, about machine learning. So welcome, Katharine. KATHARINE: Hello. Thank you very much for having me. CHARLES: No, no, it's our pleasure. So, I guess my first question is, because I'm very much approaching this from first principles here, is what is machine learning as a discipline and how does it fit into the greater picture of technology? KATHARINE: Okay. Well, if you think about artificial intelligence which is one of those slightly undefinable fields because it encompasses so much, so it encompasses elements of robotics, linguistics, math, probability, philosophy, it has six main elements. So, a really basic definition of machine learning is getting, and this comes from Arthur Samuel in 1959, it's about getting computers to learn without being explicitly programmed. And that's hugely paraphrasing. But machine learning is an element that sits under the wider discipline of artificial intelligence. Artificial intelligence is one of those tricky to define fields because people have different opinions about what it is. And obviously philosophers can't agree what intelligence is, which makes it slightly complicated. But artificial intelligence as a broad brush is a discipline that borrows from philosophy, math, probability, statistics, linguistics, robotics, and spawned subfields like natural language processing, knowledge representation, automated reasoning, computer vision robotics, and machine learning. Machine learning is the, in a sense, the mathematical component of artificial intelligence in that from a basic point of view, even though you're looking at it from the perspective of computer science, you're utilizing algorithms that a lot of mathematicians will say, “Look, we've been doing this for years. And you've just stolen that from us,” that try and find patterns in data. And that pattern could be as basic as mapping, say, the square footage of a house to the price that it will sell at and making a prediction based on that for future examples, or it could be looking for patterns in images. CHARLES: Okay. You mentioned something that I love to do. I love stealing ideas from other disciplines. It feels great. KATHARINE: Who doesn't? CHARLES: Yeah. It's like free stuff. And the best part of ideas is the person who had it still has it after you've lifted it off of them. KATHARINE: Yeah. You just have to reference and then it's not plagiarizing. CHARLES: Yeah. So, how did you actually get into this? KATHARINE: Well, a few years ago, I was desperately bored in my job. CHARLES: So, what was that job that you were working on that was so desperately boring? You don't have to name a company. KATHARINE: Oh, I won't name the company but I will – I have to make a confession now which links back to something that we were saying off recording earlier, which was that it was doing web development. So, I'm sorry. And that's not to say that web development is boring at all. It's just that I wasn't particularly engaged, which is not a reflection on web development. CHARLES: No, no, no. I actually came – I was doing, before I got into web development, I was actually doing backend stuff for years. That was all I did. KATHARINE: Yeah, me too. I would have described myself as a server-side Java developer who then cross-trained into Ruby. And I thought I'd be doing exciting backend things in Ruby. But unfortunately, it was more, “We'd like you to move this component from this part of the page to this part of the page.” And I didn't really connect with that. And I started to wonder if I even should be a developer. CHARLES: Wow. KATHARINE: Larger forces than myself were at work to try and push me into management or analysis. And as happens, I think, after a few years. So, I started doing, in my spare time, looking at a website (and I'm sure you've heard of it) Coursera. CHARLES: Yeah. KATHARINE: So, this is the birthplace of the massive online, I can't remember what the second O is, MOOCs. Massive Online something learning. Maybe a Q in there. I'm not sure what. Do you know what the acronym is? CHARLES: I actually don't know. KATHARINE: Well, MOOCs anyway. Massive online learning courses. And there was one offered Andrew Ng from Stanford on machine learning. So, I took that and I just loved it. I really enjoyed it. And I really connected with the programming. I really enjoyed the programming. It was very fulfilling. So, it grew from that, really. And now, I've decided to go back to university. So, I'm a mature postgraduate student and I'm just currently weighing up my PhD options. So, whether to sacrifice four years for the greater good and the pursuit of knowledge or go back into an employment. So, we'll see. We'll see. And I'm quite enjoying not being employed, I have to admit. Or being employed on a freelance basis. It's wonderful. CHARLES: Right, right, right. Now, a couple of things stuck me when you were talking about – so obviously, you're studying a lot of the mathematics behind it. And you said that machine learning involves a lot of the – it's the mathematical component of artificial intelligence. But what strikes me is learning, to me, implies a lot of statefulness where you're accumulating state. Whereas my experience with mathematics is usually you're solving equations. You start from some set of facts and whether it's a dataset or some other thing, and you derive, boom, boom, boom, boom, boom, you get your answer. Whereas with learning, at least when I think about school learning, like spelling or, I don't know, paleontology or something, you're accumulating facts over a very long time. And the inferences that you make are not necessarily – they're drawn from all fo the sources that you got over all that period of time rather than some one set of facts that then you make this logical argument and poof, presto, you've got your answer. How does that square? I guess it's just a little bit off from my experience with mathematics. KATHARINE: So, I am being a bit reductionist. So probably, one way to explain it is that essentially, behind a lot of the machine learning algorithms, you're inputting numbers. And that might be the percentage of red, green, blue in a pixel for example. Or it might be the diameter of a wheel, for example, if you're looking at a component of a car. Or it might be a binary configuration if you want to input the configuration of a control panel, for example, and you're looking for anomalies. And you're running these numbers through an algorithm. And what you're getting out is either a continuous value, if you're looking at a problem with continuous data like house prices, or you're getting a probabilistic output like 60% certain these pixels together make a cat, for example. So, I am simplifying by saying it's math because what you're really doing is looking for patterns in data but a way to get a computer to understand it is to somehow input it as numbers, essentially, and to get numbers out of it. CHARLES: Oh. KATHARINE: Yeah, it's more algorithms, really. And I shouldn't have said that it was essentially math because I'm sure I'm going to get shouted at on the internet. CHARLES: Well, I certainly don't want to get you in trouble. But maybe that's a point that we should shy away a little bit, the high theoretical stuff, and bring it back. If I'm excited, not even if I'm excited, why should I be excited about it? I've heard that it's a hot topic. I've heard that a lot of people are excited about it. Is there a way that I as someone who has no specialization in this might actually be able to bring some of these techniques to bear on the problems that I'm working on? Perhaps even without understanding them first, like understanding how it works. What are some problems that I might be able to attack with these techniques? KATHARINE: Yeah, absolutely. So actually, and one of the things about machine learning that I should say is don't think, “Oh, it's not for me. I'm rubbish at math. I don't understand these concepts. I'm not willing to get my head around an algorithm,” because there are so many pre-configured APIs available from big companies like Google and Amazon and Microsoft and IBM, and I'm sure many, many more. And I'm not paid by any of them, I should say. So, you don't need to understand the inner workings of an algorithm to use it. So, one example is speech-to-text. So, if you imagine that you're working on a website and you want to make it accessible, maybe you could have a component to your navigation bar that allows users to record their voice and say, “I want to navigate to the shopping cart,” for example. And machine learning would be behind that processing. So maybe, behind it you'd have an API, I've used a few of them before just to play it, where you make a call to, say, IBM service and it returns you the text. And in your program you match on keywords like shopping cart and then change the menu bar for them. So, that's one really simple way you could do it. Another more complicated way to do it is to implement something like a recommender system. So, say you have a website where you offer customers products of some description. And the most famous example of this is Amazon and Netflix. Amazon, the shopping site, rather than now the big, big corporation. And you see what other customers like you bought, or Netflix, what you might enjoy. And that's based on taking your information, comparing your viewing habits to other people's viewing habits, and then drawing some kind of correlation between the programs you watch and trying to find programs that other people have watched that you haven't, that you might enjoy. That's more complicated, to be honest. CHARLES: But that is an example. Machine learning is what underlies all that. KATHARINE: Absolutely. And at the heart of some recommender systems, the mathematics behind it is finding a way to quantify people's preferences and measuring distances between them. But you don't need to understand that to understand the basics of how a recommender system works. CHARLES: Okay. And so, how does a recommender system work? KATHARINE: So, imagine me, yourself, and Mandy each read four books. And we rate them. But I read four books, you read three of the same books, and one different one, and Mandy reads three of the same books as me and one different one, for example. So, we've got a little gap but we're not really sure what the other person will think. And we know the genres of the books. And you can compare the genres and the ratings. So, you might rate sci-fi 6 out of 10 and romance 7 out of 10. And I might rate sci-fi and romance in equal ways. So then you might say, okay, there's a similarity between our preferences. So for this book, that Charles read, Katharine might like it. CHARLES: That makes so much sense. KATHARINE: Yeah. And maybe Mandy only likes romance, only rates it 0.3 for example. So we think, “Okay, well Mandy might not be able to recommend a book to Katharine and Charles.” CHARLES: Right, I see. Implicit in this though is there's this step of the actual learning, I guess. Or the actual teaching. How do you actually teach? Again, and this is kind of me trying to wrap my head around the concept, is I've got these set of facts and I'm inferring and I'm pattern-matching and I'm trying to draw conclusions with some certainty from this set of data. But is there this distinct actual teaching phase where you have to actually teach the computer and then it takes new facts and gives stuff? How does that work? How does it incorporate the different – I guess what I'm saying is, are there distinct phases? Or is it… KATHARINE: Yes, and it's not the same for every algorithm. So, I'm going to try and give you two examples of training. So, there's something called online learning where as a new example comes in, for example it might be – I'll just explain what a classifier is. So, this is a brief diversion. So, a classifier is a machine learning process where you're trying to put information in and you're trying to get discrete information out. So, discrete meaning like it's a cat or it's a dog or a weasel or a minion or something like that. Or, it's cancer or it's not cancer. Whereas continuous output might be the price of a car. So, in a classifier you're trying to work out what type something is. So, a really good example is there's a Google project where they've clustered artworks. So, they've taken lots of different artwork and their algorithms, which I won't explain now, have determined, “This is a ballet dancer. So, we're going to group all of these ballet dancer paintings together. This is a landscape, so we're going to group all the landscapes together.” So, online learning, you might get a bit if information in, like a picture, and you will classify it and you add it to your existing information. Whereas another type of learning is you take all of the information you have, you train the algorithm, and then you make predictions. So, you either make predictions as you go along with online learning, or you do all of the work upfront. So, one algorithm – have you heard of decision trees? CHARLES: No, I haven't. KATHARINE: So, do you ever read those rubbish teen magazines where you have a flowchart and it starts at the top like, “Do you like cats? Yes or no?” CHARLES: Oh right, yeah. KATHARINE: “Do you likes dogs? Yes or no,” and it tells you what kind of a person you are or what make up you should wear or something like that. CHARLES: Right, right, right, yeah. KATHARINE: Yeah, so decision trees are kind of like that. One example is you might get information about, it's a famous toy dataset, information about passengers on the Titanic about gender and age. And we all know the techniques on the Titanic, women and children first. I've lost my use of normal English. I'm sorry. CHARLES: Right. Maybe like a trope? KATHARINE: Yeah. So, women and children first. So, you put this data in a decision tree. And what happens is at the beginning you have all of this data. So, person A is a male. They're in their 50s. This is the type of ticket they had. And this is their income. I don't think income is one of them, but just as an example. And then you have person B, person C. So, you might have a hundred people. And the decision tree algorithm goes, “Okay, if I just looked at one of these features like gender, would that differentiate the people the most?” So, it already has the answer as to whether or not they survived or did not survive. And it's looking for the one feature that gives it the most information. And then it will split on that feature. So, you go form your thing at the top and the first question might be, “Were they male or female?” And then the decision tree will split down a level. And then your algorithm will go, “Alright, what's the next feature?” Maybe the next feature is, “Were they under the age of 30?” for example. And it works down. And you end up with this sort of flowchart. And once it's trained, you then get a new example you have, person X. and you basically just work your way through the decision tree to make the prediction of whether they survived or did not survive. CHARLES: I see. And so, do you do it with some sort of certainty? Because there's going to be variation, right? You're going to have some people who are a poor fellow in his 70s who survived. Like, it's not certain that he went down but there's some probability at the end? KATHARINE: Yes. Because you're using it to make a prediction, there is always a probabilistic element. And it depends on the decision tree algorithm. So, there are some decision tree algorithms that really don't work with contradictory data, for example. CHARLES: I see. KATHARINE: There's an element of picking your algorithm. If you're approaching a machine learning problem, you have three elements. The first one is choosing your feature and your algorithm. Then you have evaluating it, so you need a way of saying how good or bad the algorithm's doing on your data, how accurate is it for example. And then you have optimizing it, which is the dark art of machine learning. CHARLES: Right. That's the wrap across the knuckles. It's coming up with wrong numbers. KATHARINE: Yeah. That's – oh no, this took two weeks to run and I need it to take 20 seconds. Or, this is only 60% accurate and I need it to be more accurate. And it's easy, just as an example I'm just working on a course that I'm giving in a few weeks' time. And I just took a day to set up a website called Kaggle, K-A-G-G-L-E, for wine quality. So, it's got acidity, citric acid, residual sugar, chlorides. They're the features. They're the components that you're going to put in. And it has a quality score. So, what you're trying to do is you're trying to find a relationship between the features and the quality. And it's very easy to get it to work. So, I now have it working. But I have it working with a really low accuracy. So, it took maybe five minutes to get it to work and it's going to take me about half an hour to make it more accurate, and that's the optimization element. CHARLES: I see. How stable are these processes? Is it finicky and fragile so that if you get new types of features it just throws things way off? Or are there ways you could control for that? KATHARINE: Well, that's a particular type of problem and that all links in with the evaluation and optimization. And that's to do with something called overfitting. So, decision trees is an algorithm notorious for overfitting, depending on the data, that is. So, overfitting is when you get the algorithm to perform really well on the training data. And then you feed it in a new example and it might grossly misclassify it because it hasn't learned to generalize beyond the examples that you've given it. CHARLES: I see. Okay. So, it's just too concrete. It hasn't recognized deep patterns. It's only recognized something superficial. KATHARINE: Yes. So typically, if you have a finite amount of data, you only train your algorithm on a certain percentage. And then you test it on the rest. CHARLES: I see. KATHARINE: So, you hold back some data. But back to our conversation earlier about when does the training happen? Another example is something called K-nearest neighbors. You could just imagine that means three nearest neighbors, for example. So, we're trying to find who we're most similar to in a room. So, it's a room full of people. And all the people are standing next to already similar people, for example. So, you might have a room where marketing's in one corner and the software developers are in another corner and the project managers are in another corner. And you go it and you're looking for the three people who are the most similar to you and you're going to go and stand in that group, for example. So, in that type of machine learning, the training is happening as each new sample comes in, rather than upfront. And there are drawbacks to both methods and there are positives to both methods. And really, in a horrible, unsexy way, it's to do with the data. And that's normally where most people because it's the most boring part when you're talking about machine learning. CHARLES: Is the data? KATHARINE: Yeah. It's this backlash from data scientists, from the golden age of data scientists where it was the hottest job on the internet to now everyone cringing going, “Oh, I really don't want to deal with my data.” But with any machine learning problem, you can't just go, “Okay. Here you go, Charles. Here's a dataset. Learn something from it.” You need context. You need to understand it. You need to have an idea of what you're looking for. So, you're getting the machine to learn but you're using it as a tool to complement your knowledge, really. And you're feeding in your knowledge to it. And part of that are the decisions that you make on the algorithms to use. CHARLES: Okay. KATHARINE: And what you're looking for. CHARLES: So, here's something that I'm wondering is related, and again I have no idea – for some reason I always associate when people talk about neural nets as being related to teaching a computer something. Is that part of the discipline of machine learning? Or no? KATHARINE: I would say it is. But it has its own cool and trendy title of Deep Learning. But it's very much powerful machine learning. So, let's go back to this. This is a classic example of machine learning. It's probably the first example you'll come across if you do any course. House prices. So, imagine that you have a piece of paper and you're going to draw one line at one side and that is the price of a house, and you're going to draw a line at the bottom which is the size in square feet, and you're going to plot examples that you have. And you might find that there's a linear correlation between the two. So, you'll draw a line and that line of best fit is the human equivalent of doing linear regression on a computer, for example, where you're just trying to find a linear correlation between two things. And a lot of the principles in linear regression, which is a very simple learning algorithm, are found in some examples of neural networks, so some basic examples of neural networks. But instead of having one input, the size in square feet, you might have 20. And you might be repeating that process of trying to find the line of best fit with different combinations of features in different places again and again. And it scales up in complexity very quickly. But it's very similar basic principles. I'm hesitating to say ‘very similar' because they're notoriously more complicated. CHARLES: Yeah. I guess I'm not really divining what exactly, what makes it – why is it called a neural net? What makes it special? It sounds like if I'm just comparing the regressions of house prices, I'm comparing those datasets over and over again, how is that different from just a loop? What are you getting out of it? KATHARINE: Historically, neural networks come from a very simplified idea about how the brain works. So, in the early 20th century people had performed autopsies and divined the inner workings of kidneys and hearts and livers. And the brain was still a bit of a mystery. And then 2 men jointly won the Nobel Prize for Physiology, and I'm going to pronounce these names wrong, so I'm sorry. I think it's Santiago Ramón y Cajal is one and Camillo Golgi is another. And they completely disagreed about the brain but they used a staining technique from Golgi to look, using silver nitrate, at the cells in the brain. And the idea of the neuron doctrine was borne out of that, that the simplest unit to look at the brain at in order to understand it is the level of the neuron, this cell in the brain. And from there, several – well, everybody was a polyglot really, back then, so I don't want to say computer scientists. So, you had people like Frank Rosenblatt with perceptrons, Marvin Minsky and so many other people looking at a very simple idea which is that a neuron either fires or doesn't. And then you're linking boolean algebra to this cell. So, you're saying it either fires or it doesn't. And from that principle, people started drawing similarities between neurons and a basic function machine. And when I say function machine, I mean imagine when you were in elementary school and you're learning how adding up works. You might have a box with a plus on it and your teacher says, “I want you to put a three in a box and a four in a box and I want you to add them together. And what do you get out?” And obviously the answer is seven. And you can think about that little box with a plus as a function machine. So, now you could think of a little mathematical function machine where you put in some inputs and there something happens in the box. And then you'll either get a one or a zero out of it. CHARLES: And so, that's like your neuron, is the little box? KATHARINE: Yes. CHARLES: Okay. KATHARINE: Yes. So since then, the neuron doctrine is pretty much contested. There are several other elements of the brain that compose thinking and the circuitry. So, any cognitive scientist listening to this will say, “That's really not how the brain works.” You have to say it with a lot of disclaimers. But the whole idea of neural networks was borne out of this idea of thinking of a neuron as like a function machine. CHARLES: Right. And also, it doesn't actually discount the usefulness of neural networks. There are a lot of things where people didn't find what they set out to find but what they found was useful. KATHARINE: Absolutely. Yeah, and they are incredibly powerful, especially with multilayer networks which are deep networks or deep learning. CHARLES: Okay. So, I didn't want derail you from your explanation. So, you've got these little function boxes and those are the kind of neurons inside the neural network? KATHARINE: Yes. So, what happens is you might have a layer of 10 of them and you might have another layer after that of another 10. And each of them are connected in a simple neural network. And then you might have an output layer of five because you're looking to classify, I don't know, an apple into five different types of apple, for example. CHARLES: Now, when you're talking about a later, you're talking about, I've got the outputs of one layer of the network are the inputs to the next layer. KATHARINE: Yes. And in different neural network architectures they'll be connected differently. But in a simple neural network, you can assume that every neuron is connected to every neuron in the next layer. So, if you have two 10-layer, two layers each with 10 neurons in, the top neuron in one layer is going to have 10 connections going out of it into the next layer. CHARLES: Oh really? Wow, that's interesting. KATHARINE: Yes. And each connection has its own sort of configuration. CHARLES: So, you're like cross-wiring all the – okay. Wow, that's kind of… KATHARINE: And yeah, that's where the complexity comes in. CHARLES: Yeah. I was thinking it was like a simple exponential fan-out. But it's even more complicated, the number of combinations you can get. KATHARINE: Yes. But in theory, it's very simple because each neuron is like this function machine with inputs coming in and something going out. It just might go out to several different locations. But it scales up in complexity very quickly. So say we're classifying apples. So, we have five different attributes of an apple like its color, its texture, its weight, acidity. I don't know how else you would measure an apple. How shiny it is, for example. And we're putting all of that information in. And each one of those things that I've just listed is a feature. So, we might have an input per feature and we'll link them all up to the neurons in a layer and we'll move that information onto the next layer. And the really important thing is what's happening on those connections between them. Because on the connections you have weights, which is a way of changing the input from one neuron to another. So, the weight might be like 0.5 for example. So, it squashes the input. Or it might magnify it. And then you get the output. Now, once you have the output you can compare the output with what you know the right answer is. And then you have this idea of an error. So, you might be like, “You got this so wrong. It's not a Braeburn apple at all. It's actually a Granny Smith apple. And what you do is with each example that you train in, you use your information about the error to train the network. Because what you're trying to do is get the error to be as small as possible. And one of the techniques of that is called back propagation. And it's notoriously difficult to understand because it involves partial derivatives and a large element of calculus. But essentially, what you're doing is comparing the right answer with the answer that the network gave it and asking it to go back and change it, change those connection weights. CHARLES: And so, do you make them fluctuate at random or is there some – is there a method to the madness of changing the weights? KATHARINE: There is a method to the madness and it's called back propagation. And the reason I linked linear regression in early, so our really simple map of house size in square feet, and the price, is because it uses a similar technique called gradient descent which is an algorithm for looking at the error and changing the weight, those numbers on the connections, to try and get it to a minimal point. So, if we go back to our house price problem, I just want you to imagine in your mind that we've got this one axis going up which is the price, and one going across which is the size in square feet. And you've got line drawn, a diagonal line. If you just imagine now in your mind moving that line down till it's completely flat at the bottom, and then moving it up so it's vertical, so it's aligned with either axis in every point in between, what you could do is you could take the error on each of those lines. So, if we imagine we have these two axes, we have the price of a house on one side and we have the size in square feet in another. And we're going to draw a line from the top axis and we're going to sweep it down and draw a line at each point as it goes down until it's aligned with the bottom axis. And at each point we draw that line, we measure the error. So, what we'd do for that is all of the little points, all of the example data, we'd measure the difference between them and the line and we're going to, say, add them all up. So, what you'd end up with is you'd end up with a graph mapping the error against the gradient at all fo those different points. And it would look like a bowl. You'd have a lowest point. You'd have a point for some gradient where the error is the lowest. CHARLES: Right, yeah. Okay. I'm seeing it. I think I'm seeing it. So, you want to take that error function and you want to, what is it? Now this is – boy, I'm going back to high school math. You would take the derivative and find out where the tangent is and that's your min point? That's the root of the equation and that's the point where your error is lowest? KATHARINE: Yeah, exactly. Or there's an algorithm called gradient descent that does it automatically by taking little steps. So, it looks at the tangent of the gradient at a point and says, “If I move the gradient down is the error going to decrease. And if so, move in that direction.” So automatically, it tries to take steps to get to the bottom. And optimization problem with that is that you can configure the step size. So, you could take tiny, tiny steps and take forever to get to the bottom or you could take massive steps and completely miss the bottom. So, you can imagine it like walking down a hill. If you're a minion then it will take a really long time because you're tiny. And if you're a giant, you might never get to the valley. CHARLES: Right. You might just leap right across the chasm. KATHARINE: Yeah, just miss it completely. So, that's linear regression where we're looking. And that's really, even though we have a two-dimensional graph that's a one-dimensional problem because we just have the one input feature. Now, when we're looking at neural networks and we're looking at gradient descent in neural networks, each one of those connections is something that we're trying to configure to reduce the error. So suddenly, you have maybe a hundred-dimension landscape and you're trying to get to the bottom of a hill. And there might be several local optimas and one deepest valley, but you might have lots of other valleys that you could get stuck in. So, it becomes a very difficult problem. Does that make sense? CHARLES: Yes. No, that does make sense. I'm just trying to let it sink in. KATHARINE: It's completely impossible to visualize a hundred dimensions, yeah. CHARLES: I actually had to sit back and kind of close my eyes and stare up at the ceiling. KATHARINE: I think the trick is not to think about it. I heard someone say, there's another fantastic course on Coursera by a famous computer scientist studying neural networks called Geoffrey Hinton. And his advice in one of the videos on visualizing these multidimensional landscapes, say it's 15 dimensions, is to close your eyes and shout, “15!” And that hasn't worked for me, but I'm sure it's worked for some people. CHARLES: But it certainly probably makes you feel better. KATHARINE: Yeah. I think it's one of those things that's just beyond comprehension. But we can just quietly accept it. CHARLES: Right. It's just – yeah, what's nice about I guess math is you just don't have to understand it. I mean, you do. You just understand that there really is no mapping to our physical experience. And that's okay. And just let that go. It's just like, this is just some… KATHARINE: Yeah. CHARLES: We had some numbers that existed in the domain of understanding, that we can understand. And there are just some rules that we follow and if you look at the intermediate steps, well the points don't really exist in that domain of physical experience and understanding. And that's okay. We just accept and let it go. We just hope that at some point, we can translate that model back into the domain of ‘we can understand it'. KATHARINE: Oh, completely. There's a lot of faith. CHARLES: Yeah. KATHARINE: And also, I remember coming into machine learning and thinking, “This is like magic. It's amazing.” And then you study it a bit and you're like, “This is so easy. This is just basic – this is just functions. These are just glorified function machines.” And then you look into it some more and you're like, “Nope. It's definitely magic.” CHARLES: Yeah. It's a phase of, every time you come up against the wall, right? And then you realize, “Oh no, it's actually something that I can close my mind over,” until you come to the next hurdle of magic. KATHARINE: Yeah. You think you've got a grasp on things and you think, “I know the landscape,” and then you suddenly realize how much more there is to learn. And that sinking feeling that you'll never learn it all. CHARLES: Yeah, yeah. Yup. Unfortunately, it seems like in tech that's like, that's just the condition. KATHARINE: Yeah, like all of my sad, unread books. CHARLES: If I wanted to get into just really start experimenting with this stuff and start saying, “Maybe I can utilize some of these techniques for some of these problems that I'm encountering,” Where would be a good place to get started? What libraries? What online resources? What people are good to follow and ask questions of? KATHARINE: Okay. Let's start with websites. So for a start, this website called Kaggle which I mentioned earlier, and that is K-A-G-G-L-E dot com. And that has a lot of dataset resources. It also has a community of people discussing how they use the datasets. It has competitions. And it has a lot of links. I discovered recently as well a really good blog. It's on Medium and it is called ‘Machine Learning for Humans'. And that's really well-written. I really like it, actually, and it has a good section on resources called ‘The Best Machine Learning Resources'. And I should probably plug my own blog, but this one's so much better. CHARLES: But please do. KATHARINE: No, no. I have to actually write stuff for it. But there's a lot of things there about, well, if you want to learn linear algebra, what if you want to learn probability and statistics, calculus, and then just go straight to machine learning and pick up the math on the way. I would say go on Coursera, because there are courses like Andrew Ng's course on machine learning from Stanford. And Geoffrey Hinton from the University of Toronto. But there are also courses there on things like calculus and probability and statistics if you want to level up your math. If you don't want to do anything to do with the math, I would say go to the vendor websites, like AWS, Google. If you're into Java, go on deep learning for J. And a lot of them have tutorials that complement their products. Deep learning for J is one of my favorites at the moment. It's an open source Java library. It's pretty plug and play, actually. You don't need to understand a lot of it to get started with it. But it helps. And obviously then there's TensorFlow although personally, I find just other Python libraries like SciPy a lot easier than using TensorFlow. And I think naturally you'll find the resources and the people to follow on Twitter from that. But the crucial thing I'd say is don't get hung up on which language to start playing around with. So, a lot of people say, “Oh, I must need to know Python. I must need to know math.” And really, you don't. It just depends on what level you want to approach things at. So, if you want to write your own gradient descent algorithms, then Python is probably more for you, or Matlab or R or something like that. But there are libraries where you can do it in Java. I've heard rumors that there's a JavaScript library and I wouldn't be surprised. So, I would just have a look at what's out there. But try and get a grasp of the fundamentals just from an intuition point of view, because it will make your life so much easier. You might for example realize that you're using the completely wrong algorithm for the problem that you're looking at. And that's invaluable. CHARLES: Yeah. Knowing what not to do certainly is. Alright. Well, thank you so much for that, Katharine. Thank you for being on the show. Thank you for curing us of at least a small portion of our ignorance. And if people want to get in touch with you perhaps and continue the conversation, or follow you, what's a good place to get in touch? KATHARINE: Sure. Probably tweet me on Twitter. I'm @KatharineCodes but it's spelled like Katharine Hepburn. It's K-A-T-H-A-R-I-N-E codes. Because when I joined Twitter, I didn't have much of an imagination. I still don't. So, it's not particularly clever. But it's there. CHARLES: Alright. Well, fantastic. And for everybody listening at home, you can also get in touch with us. We're @TheFrontside on Twitter. Or you can just drop us a line at info@frontside.io. Thanks for listening and we will see you all next time.

There are more ways than ever to be a chef today, something that's become a bit of a sub-theme this season on The Front Burner. This week, we talk to Brian Bistrong about the myriad opportunities available to chefs in 2017, something he knows intimately, having been chef de cuisine to David Bouley, owner of his own restaurant (Braeburn), chef of Wolfgang Puck's Test Kitchen, and now Corporate Executive Chef for Dean & DeLuca. Brian discusses all of these roles and as a bonus, was once executive chef of Jimmy's own restaurant The Harrison, so we tackle the unique give and take of that relationship as well.

Character generation and an unscheduled stop on the road

This time on the podcast, Mel chats with Lawrence MacLellan. He is a reflexologist from Canada who has been doing this work for twenty-four years. Don’t miss this fascinating interview. Unfortunately, the particular book he mentioned by Kevin and Barbara Kunz is out of print. However, you can find other books by them, and on reflexology in general from Bookshare, Recordings for the Blind and Dyslexic, and anywhere electronic books are sold. Then, Peggy is joined by her daughter for “Let’s Eat.” This time, they’re making an apple cake recipe from Pampered Chef. The recipe follows: THIS APPLESAUCE CAKE HAS ALL THE FLAVORS OF FALL - PLUS A SIMPLE HOMEMADE CARAMEL SAUCE! INGREDIENTS · Cake · Oil for brushing pan · 1 red apple such as Gala, Braeburn, or Fuji · 1 pkg (15.25 oz or 515 g) spice cake mix · 1 ½ cups (375 mL) unsweetened applesauce · Salted Caramel Sauce · ½ cup (125 mL) pure maple syrup · 2 tbsp (30 mL) butter · Coarse sea salt DIRECTIONS 1 Brush the bottom and sides of the Rockcrok® Everyday Pan with oil using the Chef’s Silicone Basting Brush. 2 Core the apple using The Corer™. Cut the apple in half crosswise, then slice the apple halves using the Simple Slicer on the #3 setting. Cut slices in half. 3 Starting at the edges of the pan, carefully arrange the apple slices, slightly overlapping, in the bottom of the pan. 4 In a large bowl, whisk the cake mix and applesauce until blended. Spoon the batter over the apples and spread evenly. 5 Microwave, covered, on HIGH for 10 minutes, or until a wooden pick inserted in the center comes out clean. Remove from the microwave, uncover, and let stand for 10 minutes. 6 Place the syrup in a small saucepan. Heat over medium-high heat until small bubbles form, stirring occasionally. Add the butter; stir until butter is melted. Bring the mixture to a boil. Boil 1 minute, stirring constantly. 7 Remove from the heat; let cool 15-20 minutes or until thickened (similar to the consistency of honey). 8 Carefully invert the cake onto a serving platter. Just before serving, drizzle with the caramel and sprinkle with salt. Yield: 12servings of 1 slice Nutrients per serving: Calories 210, Total Fat 5 g, Saturated Fat 2 g, Cholesterol 5 mg, Sodium 300 mg, Carbohydrate 43 g, Fiber 1 g, Protein 2 g Cook's Tips: To prepare in a conventional oven, preheat oven to 375°F (190°C). Follow steps 1-4 as directed. Bake on center rack for 32-35 minutes or until wooden pick inserted near the center comes out clean. Remove from the oven and loosen the edges of the cake. Cool for 10 minutes, then invert onto a platter. Prepare the sauce as directed while cake is baking; continue as directed. We welcome your feedback or questions! Find us on Facebook, Twitter, YouTube, LinkedIn and our BlindAlive Community on Facebook. Be the first to know of new and exclusive promotions by Subscribing to our Newsletter. For more information on Eyes-Free Fitness® Workouts go to www.BlindAlive.com

Welcome to Episode 11 of The Poetry Gods! On this episode of The Poetry Gods, we skip our usual segment of "What's on Your Mind?" to talk about names, phases, brands, publishing, & so much more with genius poet Morgan Parker. As always, you can reach us at emailthepoetrygods@gmail.com. We are looking to book shows for Fall 2016. Bring The Poetry gods to your campus! MORGAN PARKER BIO: Morgan Parker is the author of Other People's Comfort Keeps Me Up At Night (Switchback Books 2015), selected by Eileen Myles for the 2013 Gatewood Prize. Her second collection, There Are More Beautiful things than Beyonce, is forthcoming from Tin House Books in February 2017. Morgan received her Bachelors in Anthropology and Creative Writing from Columbia University and her MFA in Poetry from NYU. Her work has been featured or is forthcoming in numerous publications, as well as anthologized in Why I Am Not A Painter (Argos Books), The BreakBeat Poets: New American Poetry in the Age of Hip-Hop, and Best American Poetry 2016. Winner of a 2016 Pushcart Prize and a Cave Canem graduate fellow, Morgan lives with her dog Braeburn in Brooklyn, NY. She works as an Editor for Amazon Publishing's imprint Little A and Day One. She also teaches Creative Writing at Columbia University and co-curates the Poets With Attitude (PWA) reading series with Tommy Pico. With poet and performer Angel Nafis, she is The Other Black Girl Collective. She is a Sagittarius. Follow Morgan Parker on twitter: @morganapple on instagram: @morganapple0 Follow The Poetry Gods on all social media: @jayohessee, @azizabarnes, @iamjonsands, @thepoetrygods & CHECK OUR WEBSITE: thepoetrygods.com/ (much thanks to José Ortiz for designing the website! shouts to Jess X Chen for making our logo)

Kris and Kole talk about medication, photography, and exertion. ALSO: In the shit. Selfie deaths. Hotel Hell. Pill Mills.

It's Wednesday, which means only one thing. No, put down that cider, because Canterlot Radio is just around the corner.  This week, Emily Jones of Everfree Radio's Pegasisters Live joins us in the NewsStable to discuss the new trailer for the upcoming MLP movie, "Equestria Girls"  We're also gonna be sharing a poem on the Settlers and Braeburn in an all new Bron-etry corner, and a letter about how not to let disagreements ruin a friendship in this week's Letter to Celestia. Plus, two hours of music that gets your hooves stomping and flank a moving! Join is for Canterlot Radio. Wednesday at 3PM EST!

When Eberhard Müller and Paulette Satur first bought a farm on Long Island in 1997, the idea was for Satur Farms to be a place where the couple could grow vegetables for Lutèce. Müller was the chef of the famed restaurant at the time, and they thought the farm would simply be a weekend destination. Now, three decades later, Müller and Satur are full-time farmers with two farms — one on the North Fork and a second in South Florida. During the winter months, when fields in the Northeast are barren, specialty salad greens like frisée are still in season in Florida. Müller explained that having the location in the South allows the farm to supply restaurants and retailers with greens year-round, but there’s also a second and, possibly more important, reason for the bifurcation. “Our crew, the people who work for us, are really very well trained because it is specialty produce that we grow,” he said. “They also need to make a living. You know, it’s not that easy to be off for six months, and there [are] no jobs out on the North Fork in the wintertime so we decided we need to do something and we founded that farm in Florida.” Currently, the cool but temperate weather in Florida is ideal for growing the frizzy-looking lettuce frisée. Müller grew up eating frisée in Germany, but he had trouble finding the green when he moved to New York City three decades ago. It was one of the items he was most excited to start growing at Satur Farms. “It’s an endive and there [are] different variations of endives,” said Müller, who also used to be the chef of Le Bernardin. “There’s the flat-leaf endives that we typically call escarole, and then there is...  the curly endive that’s a coarser grained frisée and then there’s a fine leaf frisée, which is the one that we’re particularly fond of growing. It’s the most challenging.” Müller describes the fine-leaf frisée as tasting sweet with a subtle hint of bitterness and said that balance of flavors is particularly appealing for chefs. Growing techniques can have a major impact on that taste, he added. At Satur Farms, they place blanching caps on the heads of frisée four or five days before harvesting. That goes back to the traditional way the lettuce was overwintered in Europe. (Photo: Paulette Satur and Eberhard Müller/Shonna Valeska) “In olden times, they used to pack [the frisée heads] in straw,” Müller explained. “As you needed the frisée or the endives, you take the straw out and what happens, because you had this all covered up, you have no sunlight going to it. And so all the leaves turn yellow and tender.” Now, instead of straw, Satur Farms uses the caps particularly created for this purpose. “They look like Kaiser Wilhem’s hat,” he said. This process is important because the lighter colored parts of the leaves are the sweetest, most tender sections. The freshness of the frisée also affects sweetness. “Like with everything else, sugar is the culprit for sweetness, obviously, in plants,” Müller said. “As soon as you start harvesting it, sugar converts into starch and that happens in the frisée, as well.” That’s why, during this time of the year, Satur Farms harvests, trucks, and delivers produce on a tight schedule. Müller estimates it takes 36 to 42 hours to get frisée from the fields in Florida to the kitchens of New York City. “It takes 22 hours to drive from our location in Florida,” he said. “[Then] it takes us four to six hours to process everything and put it back on our trucks and send it back into New York City or to the restaurants on Long Island.” On the return trip, the trucks carry seedlings, plants and equipment needed at the farm in Florida. The two locations also means that Müller and Satur fly back and forth several times during the winter. That leaves little time for the chef  to spend in the kitchen, but when he does cook, here’s one recipe for frisée that he often uses. Frisée salad with apples and blue cheese, walnut-cider vinaigrette by Satur Farms Serves 4 people 2 heads of large frisée or 3-4 smaller ones 2 apples such as Granny Smith, Mutsu, Fuji or Braeburn, depending on time of year ¾ cup crumbled blue cheese (Maytag or similar) ½ cup walnut halves toasted and coarsely chopped 3 tbs. cider vinegar 1 lemon ½ tbs. Dijon mustard  1 ½ tbs. walnut oil 2 tbs. vegetable oil 3-4 tbs. water 2 tsp. sugar or honey Salt and pepper to taste Clean and trim the outer green leafs of the frisée and discard. Cut the inner yellow, creamy part into 1” pieces. You should obtain 3-4 cups of cleaned frisée. Thoroughly wash and spin dry several times and reserve. On a food mandolin, slice the apples into 1/8 inch julienne. Sprinkle with the juice of half of the lemon to prevent them from oxidizing. Reserve, as well. To prepare the vinaigrette dressing squeeze the remaining half of the lemon into a mixing bowl, add the cider vinegar, Dijon mustard, sugar or honey, water, salt and pepper and mix well to dissolve the ingredients. Slowly whisk in the vegetable and walnut oil. When you have obtained a homogenous dressing, add the crumbled blue-cheese and adjust the seasoning with salt and pepper. Dress the reserved frisée with this vinaigrette-dressing, add the apple julienne. Sprinkle with the toasted walnuts and serve immediately.

In this concert in the Library's "Homegrown" series, Tony Ellis and the Musicians of Braeburn play traditional banjo and stringband music from Ohio. Speaker Biography: Tony Ellis is a prominent bluegrass banjo and fiddle player. He performed with Bill Monroe and the Bluegrass Boys, the originators of the bluegrass style, both at the Grand Ole Opry and on tour, for over two years, recording some 25 songs with them.

Talking about one painting a day begining in October of 2005

Hello, lovers of computers old and new... This is show #005!  Thanks for sticking around - hope you enjoy the program. In addition to covering your e-mails and some interesting links, this show discusses a couple of my favorite (even older than show #003) computer languages, Fortran and COBOL.  If you'd like to dust off your old programs and give them a whirl, have a look at the links below! Braeburn provides articles about the history of computing and technology in general.  They have a recent article on the history of the Apple Lisa. The Replica I, a replica of the original Apple I computer! The Retrocomputing 2005 challenge - test your retrocomputing abilities... DOSBox is an environment to run older DOS games (and other programs) on modern systems and operating systems. Tiny COBOL is a modern COBOL compiler that is open-source. Salford FTN95 is a modern Fortran 95 compiler for Windows (with .NET support!). Be sure to send us any comments, questions or feedback to retrobits@gmail.com Our Theme Song is "Sweet" from the "Re-Think" album by Galigan Thanks for listening! - Earl