Podcast appearances and mentions of jonathan liu

Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.Thank you for reading Ground Truths. This post is public so feel free to share it.Transcript with audio and external linksEric Topol (00:05):Hello, it's Eric Topol with Ground Truths, and I am really thrilled to have with me Professor Faisal Mahmood, who is lighting it up in the field of pathology with AI. He is on the faculty at Harvard Medical School, also a pathologist at Mass General Brigham and with the Broad Institute, and he has been publishing at a pace that I just can't believe we're going to review that in chronological order. So welcome, Faisal.Faisal Mahmood (00:37):Thanks so much for having me, Eric. I do want to mention I'm not a pathologist. My background is in biomedical imaging and computer science. But yeah, I work very closely with pathologists, both at Mass General and at the Brigham.Eric Topol (00:51):Okay. Well, you know so much about pathology. I just assume that you were actually, but you are taking computational biology to new levels and you're in the pathology department at Harvard, I take it, right?Faisal Mahmood (01:08):Yeah, I'm at the pathology department at Mass General Brigham. So the two hospitals are now integrated, so I'm at the joint department.Eric Topol (01:19):Good. Okay. Well, I'm glad to clarify that because as far as I knew you were hardcore pathologist, so you're changing the field in a way that is quite unique, I should say, because a number of years ago, deep learning was starting to get applied to pathology just like it was and radiology and ophthalmology. And we saw some early studies with deep learning whereby you could find so much more on a slide that otherwise would be not even looked at or considered or even that humans wouldn't be able to see. So maybe you could just take us back first to the deep learning phase before these foundation models that you've been building, just to give us a flavor for what was the warmup in this field?Faisal Mahmood (02:13):Yeah, so I think around 2016 and 2017, it was very clear to the computer vision community that deep learning was really the state of the art where you could have abstract feature representations that were rich enough to solve some of these fundamental classification problems in conventional vision. And that's around the time when deep learning started to be applied to everything in medicine, including pathology. So we saw some earlier cities in 2016 and 2017, mostly in machine learning conferences, applying this to very basic patch level pathology dataset. So then in 2018 and 2019, there were some studies in major journals including in Nature Medicine, showing that you could take large amounts of pathology data and classify what's known to us and including predicting what's now commonly referred to as non-human identifiable features where you could take a label and this could come from molecular data, other kinds of data like treatment response and so forth, and use that label to classify these images as responders versus non-responders or having a certain kind of mutation or not.(03:34):And what that does is that if there is a morphologic signal within the image, it would pick up on that morphologic signal even though humans may not have picked up on it. So it was a very exciting time of developing all of these supervised, supervised foundation models. And then I started working in this area around 2019, and one of the first studies we did was to try to see if we can make this a little bit more data efficient. And that's the CLAM method that we published in 2021. And then we took that method and applied it to the problem of cancers of unknown primary, that was also in 2021.Eric Topol (04:17):So just to review, in the phase of deep learning, which was largely we're talking about supervised with ground truth images, there already was a sign that you could pick up things like the driver mutation, the prognosis of the patient from the slide, you could structural variations, the origin of the tumor, things that would never have been conceived as a pathologist. Now with that, I guess the question is, was all this confined to whole slide imaging or could you somehow take an H&E slide conventional slide and be able to do these things without having to have a whole slide image?Faisal Mahmood (05:05):So at the time, most of the work was done on slides that were fully digital. So taking a slide and then digitizing the image and creating a whole slide image. But we did show in 2021 that you could put the slide under a microscope and then just capture it with a camera or just with a cell phone coupled to a camera, and then still make those predictions. So these models were quite robust to that kind of domain adaptation. And still I think that even today the slide digitization rate in the US remains at around 4%, and the standard of care is just looking at a glass light under a microscope. So it's very important to see how we can further democratize these models by just using the microscope, because most microscopes that pathologists use do have a camera attached to them. So can we somehow leverage that camera to just use a model that might be trained on a whole slide image, still work with the slide under a microscope?Eric Topol (06:12):Well, what you just said is actually a profound point that is only 4% of the slides are being reviewed digitally, and that means that we're still an old pathology era without the enlightenment of machine eyes. I mean these digital eyes that can be trained even without supervised learning as we'll get to see things that we'll never see. And to make, and I know we'll be recalling back in 2022, you and I wrote a Lancet piece about the work that you had done, which is very exciting with cardiac biopsies to detect whether a heart transplant was a rejection. This is a matter of life or death because you have to give more immunosuppression drugs if it's a rejection. But if you do that and it's not a rejection or you miss it, and there's lots of disagreement among pathologists, cardiac pathologists, regarding whether there's a transplant. So you had done some early work back then, and because much of what we're going to talk about, I think relates more to cancer, but it's across the board in pathology. Can you talk about the inner observer variability of pathologists when they look at regular slides?Faisal Mahmood (07:36):Yeah. So when I first started working in this field, my kind of thinking was that the slide digitization rate is very low. So how do we get people to embrace and adapt digital pathology and machine learning models that are trained on digital data if the data is not routinely digitized? So one of my kind of line of thinking was that if we focus on problems that are inherently so difficult that there isn't a good solution for them currently, and machine learning provides, or deep learning provides a tangible solution, people will be kind of forced to use these models. So along those lines, we started focusing on the cancers of unknown primary problem and the myocardial biopsy problem. So we know that the Cohen's kappa or the intra-observer variability that also takes into account agreement by chance is around 0.22. So it's very, very low for endomyocardial biopsies. So that just means that there are a large number of patients who have a diagnosis that other pathologists might not agree with, and the downstream treatment regimen that's given is entirely based on that diagnosis. The same patient being diagnosed by a different cardiac pathologist could be receiving a very different regimen and could have a very, very different outcome.(09:14):So the goal for that study is published in Nature of Medicine in 2022, was to see if we could use deep learning to standardize that and have it act as an assistive tool for cardiac pathologists and whether they give more standardized responses when they're given a machine learning based response. So that's what we showed, and it was a pleasure to write that corresponding piece with you in the Lancet.Eric Topol (09:43):Yeah, no, I mean I think that was two years ago and so much has happened since then. So now I want to get into this. You've been on a tear every month publishing major papers and leading journals, and I want to just go back to March and we'll talk about April, May, and June. So back in March, you published two foundation models, UNI and CONCH, I believe, both of these and back-to-back papers in Nature Medicine. And so, maybe first if you could explain the foundation model, the principle, how that's different than the deep learning network in terms of transformers and also what these two different, these were mega models that you built, how they contributed to help advance the field.Faisal Mahmood (10:37):So a lot of the early work that we did relied on extracting features from a resonant trained on real world images. So by having these features extracted, we didn't need to train these models end to end and allowed us to train a lot of models and investigate a lot of different aspects. But those features that we used were still based on real world images. What foundation models led us do is they leveraged self supervised learning and large amounts of data that would be essentially unlabeled to extract rich feature representations from pathology images that can then be used for a variety of different downstream tasks. So we basically collected as much data as we could from the Brigham and MGH and some public sources while trying to keep it as diverse as possible. So the goal was to include infectious, inflammatory, neoplastic all everything across the pathology department while still being as diverse as possible, including normal tissue, everything.(11:52):And the hypothesis there, and that's been just recently confirmed that the hypothesis was that diversity would matter much more than the quantity of data. So if you have lots and lots of screening biopsies and you use all of them to train the foundation model, there isn't enough diversity there that it would begin to learn those fundamental feature representations that you would want it to learn. So we used all of this data and then trained the UNI model and then together with it was an image text model where it starts with UNI and then reinforces the feature representations using images and texts. And that sort of mimics how humans learn about pathology. So a new resident, new trainee learning pathology has a lot of knowledge of the world, but it's perhaps looking at a pathology image for the first time. But besides looking at the image, they're also being reinforced by all these language cues from, whether it's from text or from audio signals. So the hope there was that text would kind of reinforce that and generate better feature representation. So the two studies were made available together. They were published in Nature Medicine back in March, and with that we made both those models public. So at the time we obviously had no idea that they would generate so much interest in this field, downloaded 350,000 times on Hugging Face and used for all kinds of different applications that I would've never thought of. So that's been very exciting to see.Eric Topol (13:29):Can you give some examples of some of the things you wouldn't have thought of? Because it seems like you think of everything.Faisal Mahmood (13:35):Yeah, people have used it to when there was a challenge for detecting tuberculosis, I think in a very, very different kind of a dataset. It was from the Nightingale Foundation and they have large data sets. So that was very interesting to see. People have used it to create newer data sets that can then be used for training additional foundation models. It's being used to extract rich feature representations from pathology images, corresponding spatial transcriptomic data, trying to predict spatial transcriptomics directly from histology. And there's a number of other options.Eric Topol (14:27):Well, yeah, that was March. Before we get to April, you slipped in the spatial omics thing, which is a big deal that is ability to look at tissue, human tissue over time and space. I mean the spatial temporal, it will tell us so much whether an evolution of a cancer process or so many things. Can you just comment because this is one of the major parts of this new era of applying AI to biology?Faisal Mahmood (15:05):So I think there are a number of things we can do if we have spatial data spatially resolved omic data with histology images. So the first thing that comes to my mind as a computer scientist would be that can we train a joint foundation model where we would use the spatially resolved transcriptomics to further enforce the pathology signal as a ground truth in a contrastive manner, similar to what we do with text, and can we use that to extract even richer feature representation? So we're doing that. In fact, we made a data set of about a thousand pathology images with corresponding spatial transcriptomic information, both curated from public resources as well as some internal data publicly available so people could investigate that question further. We're entrusted in other aspects of this because there is some indication including a study from James Zou's group at Stanford showing that we can predict histology, predict the spatial transcriptomic signal directly from histology. So there's early indications that we might also be able to do that in three dimensions. So yeah, it's definitely very interesting. More and more of that data is becoming available and how machine learning can sort of augment that is very exciting.Eric Topol (16:37):Yeah, I mean, most of the spatial omics has been a product of single cell sequencing, whether it's single nuclei and different omics, not just DNA, of course, RNA and even methylation, whatnot. So the fact that you could try to impute that from the histologies is pretty striking. Now, that was March and then in April you published to me an extraordinary paper about demographic bias and how generative AI, we're in the generative AI year now since as we discussed with foundation models, here again that gen AI could actually reduce biases and enhance fairness, which of course is so counterintuitive to everything that's been written to date. So maybe you can take us through how we can get a reduction in bias in pathology.Faisal Mahmood (17:34):Yeah, so in the study, the study was about, this had been investigated in other fields, but what we try to show is that a model trained on large, diverse, publicly available data. When that's applied internally and we stratify it based on demographic differences, race and so forth, we see these very clear disparities and biases. And we investigated a lot of different solutions that were out there to equalize the distribution of the data to balance the distribution using or sampling and some of these simple techniques. And none of them worked quite well. And then we observed that using foundation models or just having richer feature representations eliminates some of those biases. In parallel, there was another study from Google where they use generative AI to synthesize additional images from those underrepresented groups and then use those images to enhance the training signal. And then they also showed that you could reduce those biases.(18:49):So I think the common denominator there is that richer feature representations contribute to reduced biases. So the biases not because there is some inherent signal tied to these subgroups, but the bias is essentially there because the feature representations are not strong enough. Another general observation is that there's some kind of a confounder often there that leads to the bias. And one example would be that patients with socioeconomic disparities might just be diagnosed late and there might not be enough advanced cases in the training dataset. So quite often when you go in and look at what your training distribution looks like and how it varies from your test distribution and what that dataset shift is, you're able to figure out where the bias inherently comes from. But as a general principle, if you use the richest possible feature representation or focus on making your feature representations richer by using better foundation models and so forth, you are able to reduce a lot of the bias.Eric Topol (19:58):Yeah, that's really another key point here is about the richer features and the ability counterintuitively to actually reduce bias. And what is important in interrogating data inputs, as you said before, you wind up with a problem with bias. Now, then it comes May since we're just March and April, in May you published TriPath, which is now bringing in the 3D world of pathology. So maybe you can give us a little skinny on that one.Faisal Mahmood (20:36):Yeah. So just looking at the spectrum of where pathology is today, I think that we all agree in the community that pathologists often look at extremely sampled tissue. So human tissue is inherently three-dimensional, and by the time it gets to a pathologist, it's been sampled and cut so many times that it often would lack that signal. And there are a number of studies that have shown that if you subsequently cut sections, you get to a different outcome. If you look at multiple slides for a prostate biopsy, you get to a different Gleason score. There are all of these studies that have shown that 3D pathology is important. And with that, there's been a growing effort to build tools, microscopes, imaging tools that can image tissue in 3D. And there are about 10 startups who've built all these different technologies, open-top light-sheet microscopy, microCT and so forth that can image tissue really well in three dimensions, but none of them have had clinical adoption.(21:39):And we think that a key reason is that there isn't a good way for a pathologist to examine such a large volume of tissue. If they spend so much time examining this large volume of tissue, they would never be able to get through all the, so the goal here really was to develop a computational tool that would look through the large volume and highlight key regions that a pathologist can then examine. And the secondary goal was that does using three dimensional tissue actually improve patient stratification and does using, essentially using three 3D deep learning, having 3D convolutions extract richer features from the three dimensions that can then be used to separate patients into distinct risk groups. So that's what we did in this particular case. The study relied on a lot of data from Jonathan Liu's group at University of Washington, and also data that we collected at Harvard from tissue that came from the Brigham and Women's Hospital. So it was very exciting to show that what the value of 3D pathology can be and how it can actually translate into the clinic using some of these computational tools.Eric Topol (22:58):Do you think ultimately someday that will be the standard that you'll have a 3D assessment of a biopsy sample?Faisal Mahmood (23:06):Yeah, I'm really convinced that ultimately 3D would become the standard because the technology to image these tissue is becoming better and better every year, and it's getting closer to a point where the imaging can be fast enough to get to clinical deployment. And then on the computational end, we're increasingly making a lot of progress.Eric Topol (23:32):And it seems, again, it's something that human eyes couldn't do because you'd have to look at hundreds of slides to try to get some loose sense of what's going on in a 3D piece of tissue. Whereas here you're again taking advantage, exploiting the digital eyes. Now this culminates to your June big paper PathChat in Nature, and this was a culmination of a lot of work you've been doing. I don't know if you do any sleep or your team, but then you published a really landmark paper. Can you take us through that?Faisal Mahmood (24:12):Yeah, so I think that with the foundation models, we could extract very rich feature representation. So to us, the obvious next step was to take those feature representations and link them with language. So a human would start to communicate with a generative AI model where we could ask questions about what's going on in a pathology image, it would be capable of making a diagnosis, it would be capable of writing a report, all of those things. And the reason we thought that this was really possible is because pathology knowledge is a subset of the world's knowledge. And companies like OpenAI are trying to build singular, multimodal, large language models that would harbor the world's information, the world knowledge and pathology is much, much more finite. And if we have the right kind of training data, we should be able to build a multimodal large language model that given any pathology image, it can interpret what's going on in the image, it can make a diagnosis, it can run through grading, prognosis, everything that's currently done, but also be an assistant for research, analyzing lots of images to see if there's anything common across them, cohorts of responders versus non-responders and so forth.(25:35):So we started by collecting a lot of instruction data. So we started with the foundation models. We had strong pathology image foundation models, and then we collected a lot of instruction data where we have images, questions, corresponding answers. And we really leveraged a lot of the data that we had here at Brigham and MGH. We're obviously teaching hospitals. We have questions, we have existing teaching training materials and work closely with pathologists at multiple institutions to collect that data. And then finally trained a multimodal large language model where we could give it a whole slide image, start asking questions, what was in the image, and then it started generating all these entrusting morphologic descriptions. But then the challenge of course is that how do you validate this? So then we created validation data sets, validated on what multiple choice questions on free flowing questions where multiple pathologists, we had a panel of seven pathologists look through every response from our model as well as more generic models like the OpenAI, GPT-4 and BiomedCLIP and other models that are publicly available, and then compare how well this pathology specific model does in comparison to some of those other models.(26:58):And we found that it was very good at morphologic description.Eric Topol (27:05):It's striking though to think now that you have this large language model where you're basically interacting with the slide, and this is rich, but in another way, just to ask you, we talk about multimodal, but what about if you have electronic health record, the person's genome, gut microbiome, the immune status and social demographic factors, and all these layers of data, environmental exposures, and the pathology. Are we going to get to that point eventually?Faisal Mahmood (27:45):Yeah, absolutely. So that's what we're trying to do now. So I think that it's obviously one step at a time. There are some data types that we can very easily integrate, and we're trying to integrate those and really have PathChat as being a binder to all of that data. And pathology is a very good binder because pathology is medicine's ground truth, a lot of the fundamental decisions around diagnosis and prognosis and treatment trajectory is all sort of made in pathology. So having everything else bind around the pathology is a very good idea and indication. So for some of these data types that you just mentioned, like electronic medical records and radiology, we could very easily go that next step and build integrative models, both in terms of building the foundation model and then linking with language and getting it to generate responses and so forth. And for other data types, we might need to do some more specific training data types that we don't have enough data to build foundation models and so forth. So we're trying to expand out to other data types and see how pathology can act as a binder.Eric Topol (28:57):Well if anybody's going to build it, I'm betting on you and your team there, Faisal. Now what this gets us to is the point that, was it 96% or 95% of pathologists in this country are basically in an old era, we're not eking out so much information from slides that they could, and here you're kind of in another orbit, you're in another world here whereby you're coming up with information. I mean things I never thought really the prognosis of a patient over extended period of time, the sensitivity of drugs to the tumor from the slide, no less the driver mutations to be able to, so you wouldn't even have to necessarily send for mutations of the cancer because you get it from the slide. There's so much there that isn't being used. It's just to me unfathomable. Can you help me understand why the pathology community, now that I know you're not actually a pathologist, but you're actually trying to bring them along, what is the reason for this resistance? Because there's just so much information here.Faisal Mahmood (30:16):So there are a number of different reasons. I mean, if you go into details for why digital pathology is not actively happening. Digitizing an entire department is expensive, retaining large amounts of slides is expensive. And then the value proposition in terms of patient care is definitely there. But the financial incentives, reimbursement around AI is not quite there yet. It's slowly getting there, but it's not quite there yet. In the meantime, I think what we can really focus on, and what my group is thinking a lot about is that how can we democratize these models by using what the pathologists already have and they all have a microscope and most of them have a microscope with a camera attached to it. Can we train these models on whole slide images like we have them and adapt them to just a camera coupled to a microscope? And that's what we have done for PathChat2.(31:23):I think one of the demos that we showed after the article came out was that you could use PathChat on your computer with the whole slide image, but you can also use it with a microscope just coupled to a camera and you put a glass light underneath. And in an extreme lower source setting, you can also use it with just a cell phone coupled to a microscope. We're also building a lighter weight version of it that wouldn't require internet, so it would just be completely locally deployed. And then it could be active in lower source settings where sometimes sending a consult can take a really, really long time, and quite often it's not very easy for hospitals in lower source settings to track down a patient again once they've actually left because they might've traveled a long distance to get to the clinic and so forth. So the value of having PathChat deployed in a lower source setting where it can run locally without internet is just huge because it can accelerate the diagnosis so much. In particular for very simple things, which it's very, very good at making a diagnosis for those cases.Eric Topol (32:33):Oh, sure. And it can help bridge inequities, I mean, all sorts of things that could be an outgrowth of that. But what I still having a problem with from the work that you've done and some of the other people that well that are working assiduously in this field, if I had a biopsy, I want all the information. I don't want to just have the old, I would assume you feel the same way. We're not helping patients by not providing the information that's there just with a little help from AI. If it's going to take years for this transformation to occur, a lot of patients are going to miss out because their pathologists are not coming along.Faisal Mahmood (33:28):I think that one way to of course solve this would be to have it congressionally mandated like we had for electronic medical records. And there are other arguments to be made. It's been the case for a number of different hospitals have been sued for losing slides. So if you digitize all your slides and you're not going to lose them, but I think it will take time. So a lot of hospitals are making these large investments, including here at the Brigham and MGH, but it will take time for all the scanners, all the storage solutions, everything to be in place, and then it will also take time for pathologists to adapt. So a lot of pathologists are very excited about the new technology, but there are also a lot of pathologists who feel that their entire career has been diagnosing cases or using a microscope and slide. So it's too big of a transition for them. So I think there'll obviously be some transition period where both would coexist and that's happening at a lot of different institutions.Eric Topol (34:44):Yeah, I get what you're saying, Faisal, but when I wrote Deep Medicine and I was studying what was the pathology uptake then of deep learning, it was about 2% and now it's five years later and it's 4% or 5% or whatever. This is a glacial type evolution. This is not keeping up with how the progress that's been made. Now, the other thing I just want to ask you before finishing up, there are some AI pathology companies like PathAI. I think you have a startup model Modella AI, but what can the companies do when there's just so much reluctance to go into the digital era of pathology?Faisal Mahmood (35:31):So I think that this has been a big barrier for most pathology startups because around seven to eight years ago when most of these companies started, the hope was that digital pathology would happen much faster than it actually has. So I think one thing that we're doing at Modella is that we understand that the adoption of digital pathology is slow. So everything that we are building, we're trying to enable it to work with the current solutions that exist. So a pathologist can capture images from a pathology slide right in their office with a camera with a microscope and PathChat, for example, works with that. And then the next series of tools that we're developing around generative AI would also be developed in a manner that it would be possible to use just a camera coupled to a microscope. So I think that I do feel that all of these pathology AI companies would have been doing much, much better if everything was digital, because adopting the tools that they developed would very straightforward. Right now, the barrier is that even if you want to deploy an AI driven solution, if your hospital is not entirely digital, it's not possible to do that. So it requires this huge upfront investment.Eric Topol (37:06):Yeah, no, it's extraordinary to me. This is such an exciting time and it's just not getting actualized like it could. Now, if somebody who's listening to our conversation has a relative or even a patient or whatever that has a biopsy and would like to get an enlightened interpretation with all the things that could be found that are not being detected, is there a way to send that to a center that is facile with this? Or if that's a no go right now?Faisal Mahmood (37:51):So I think at the moment it's not possible. And the reason is that a lot of the generic AI tools are not ready for this. The models are very, very specific for specific purposes. The generalist models are just getting started, but I think that in the years to come, this would be a competitive edge for institutions who do adopt AI. They would definitely have a competitive edge over those who do not. We do from time to time, receive requests from patients who want us to run their slides on the cancers of unknown primary tool that we built. And it depends on whether we are allowed to do so or not, because it has to go through a regular diagnostic first and how much information can we get from the patient? But it's on a case by case basis.Eric Topol (38:52):Well, I hope that's going to change soon because you have been, your team there has just been working so hard to eke out all that we can learn from a path slide, and it's extraordinary. And it made me think about what we knew five years ago, which already was exciting, and you've taken that to the fifth power now or whatever. So anyway, just to congratulate you for your efforts, I just hope that it will get translated Faisal. I'm very frustrated to learn how little this is being adopted here in this country, a rich country, which is ignoring the benefits that it could provide for patients.Faisal Mahmood (39:40):Yeah. That's our goal over the next five years. So the hope really is to take everything that we have developed so far and then get it in aligned with where the technology currently is, and then eventually deploy it both at our institution and then across the country. So we're working hard to do that.Eric Topol (40:03):Well, maybe patients and consumers can get active about this and demand their medical centers to go digital instead of living in an analog glass slide world, right? Yeah, maybe that's the route. Anyway, thank you so much for reviewing at this pace of your publications. It's pretty much unparalleled, not just in pathology AI, but in many parts of life science. So kudos to you, Richard Chen, and your group and so many others that have been working so hard to enlighten us. So thanks. I'll be checking in with you again on whatever the next model that you build, because I know it will be another really important contribution.Faisal Mahmood (40:49):Thank you so much, Eric. Thanks.**************************Thanks for listening, reading or watching!The Ground Truths newsletters and podcasts are all free, open-access, without ads.Please share this post/podcast with your friends and network if you found it informativeVoluntary paid subscriptions all go to support Scripps Research. Many thanks for that—they greatly helped fund our summer internship programs for 2023 and 2024.Thanks to my producer Jessica Nguyen and Sinjun Balabanoff for audio and video support at Scripps Research.Side note: My X/twitter account @erictopol was hacked yesterday, 27 July, with no help from the platform to regain access despite many attempts. Please don't get scammed! Get full access to Ground Truths at erictopol.substack.com/subscribe

An insightful sitdown with Jonathan Liu , a visual artist and lecturer based in Singapore. __________ Jonathan Liu is a Visual Artist & Lecturer working primarily with photography within his practice. He is also a founding member of NFT Asia - a community aimed at uplifting asian artists within the NFT Space. He is interested in the narratives formed through text and photographs. Drawn from his fascination with narratives and the relationship between the artist and the poet, his recent works attempt to mirror and question our reality through representation and fragmentation of the landscape. His work deals with concepts such as memory, post-memory and the search for the layers in-between, with works exhibited in the United Kingdom, China and Singapore.

On this Northwest Florida Fishing Report, Joe and Angelo "The Coastal Connection" DePaola talks fishing with some of the best anglers on the Florida Emerald Coast. This week's fishing contributors are Wendall Hall and Barton Barton. Plus, Butch Thierry joins the show, and we talk with Jonathan Liu, Product Manager at Mustang Survival, about how to choose the best inflatable life vest for fisherman. Enjoy the show! 10% OFF AFTCO Products NWFFR has partnered with AFTCO, and we are offering all of our listeners 10% off AFTCO products. Text the word "fishing" to 647-558-9895 to subscribe to our email list, and we'll send you the promo code via email! This Report is Presented By: Angelo DePaola - The Coastal Connection - eXp Realty Sponsors: AFTCO Admiral Shellfish Company Boaters List Bajio Sunglasses L&M Marina United Bank Photonis MB Ranch King Blinds Hunting Exchange Fishing Chaos Buck's Island Hilton's Real-Time Navigator Test Calibration Dixie Supply / Baker Metal Works Fishbites Great Days Outdoors Killerdock

Jonathan is best known for his 2008 Honda S2000 and his track prepped 2018 Forester XT. We talk about his start with tuning, parts development, his time at Galpin Auto Sports (from the famous Pimp My Ride), and much more.

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你有多久身體沒大掃除了，6大症狀代表你該排毒了！各器官過勞會積毒，如何排除？排毒看時辰？這樣做叫你排毒一身輕！

Jonathan is an ex-drug dealer, ex-gang member, and ex-convict. He went through a very dark and difficult time when he was young. Since then, Jonathan has transformed into an emotional freedom coach. He now helps people process through and release their trapped and stuck emotions. Jonathan's story is very impressive, and he has a lot of ideas to offer in today's interview. Let's dive in! Learn more about Jonathan Liu and his coaching, check out this site: https://www.thepowerfulyouniversity.com/

眼白出血會再發嗎？和春天火大有關還是太疲勞，反覆發作是血管破裂？留意血管警訊別中風,保護眼睛滅火又補水，中醫有妙方！

科學研究：病毒潛伏男性生殖系統中，会导致哪些功能障礙？對女性生殖器官有哪些影響？哪些不良習慣損害兩性生殖系統健康？怎樣清除體內藏匿的病毒，這樣做可快速改善 ！

輕症表現，也會帶來新冠長期損害？新冠病毒藏在人體不同器官，長新冠集中在身體哪些系統？怎樣清除體內藏匿的病毒，這樣做可快速改善 ！

健忘、注意力不集中、認知障礙、思維混亂——這是新冠後遺症嗎？失智症竟然可以通過音樂治療改善？！帕金森扔掉拐杖連，醫生都超讚的神奇療癒力量！

看視頻久了？眼睛痛、頭頸痛，3動作改善！滑手機肩頸痠痛，1道湯品可舒緩～ 放鬆肩頸還可以這樣拉筋！解除疼痛竟還有這一招，超簡單～

What happens when a person decides to worship Jesus outside of China's state-controlled churches?  "If you want to worship in a church that is not vetted by the state you have to worship in an illegal, underground church," Christian Post reporter Leah explained. Klett explores the issues surrounding Chinese government control and Rev. Fr. Jonathan Liu's harrowing story — as well as his warning for Christians.  Read Klett's story as well: https://www.christianpost.com/news/chinese-pastor-blacklisted-by-communist-party-shares-testimony.html 

糖尿病是因为胰腺不能分泌胰岛素？一旦得了糖尿病，就终生是糖尿病，一辈子必须吃药，打胰岛素？如果你吃過多的糖，你會得到糖尿病？！人工甜味劑對患有糖尿病的人很危險？需要打胰島素代表血糖控制不佳、病情加重？用胰島素治療最終會走向洗腎？這些迷思，專家會幫您解答！

人體細胞會衰老？ 怎麼保持血管年輕而乾淨、如何保護大腦活力，防失智，哪些生活習慣可以幫你不變老，吃哪些食物可以逆齡不老？人體有不老基因，堅持這樣做、你可以改變你的不良遺傳密碼，慢慢變老！

打疫苗後出現心絲絲拉拉痛、如冰針扎，且範圍繼續擴大是怎麼回事，該怎麼辦？急性心、腦血管病有前兆嗎，如何發現異常，及時送醫？怎樣在家進行緊急救護，爭取更多救治機會？風險因素有哪些？如何預防？

疫情下，全球糖尿病暴增16%，研究發現，疫情下這種行為使糖尿病和其他慢性病的風險大大增加，怎樣擺脫？

Omicron感染都是輕症？小心有些症狀並不輕 ！它更像大號感冒？專家提醒不要掉以輕心！究竟是什麼原因？感染Omicron 居家護理這樣做 方法簡單見效快

Omicron全球蔓延，驚現住院率增高，高峰尚未到來，美國住院率重症破紀錄！？CDC大型研究出爐，465家醫療機構推出結論：完成疫苗接種後，哪些人還有患重症高風險？

你有類似感冒的症狀嗎？如何識別是否感染了Omicron變種病毒？有哪些方法來保護自己和他人免受 Omicron 的侵害？感染了Omicron 出現症狀，有什麼居家的調理方法可以幫助他們？哪些食物和自然方法在疫情下會讓我保持健康？

如果失眠 你的免疫力會怎樣？你經常睡不好、失眠 難以入睡嗎？如何培養睡眠好習慣？神奇頻率助您入睡 掌握自然方法獲得一夜好眠！

好多人求改善法令紋的訴求，那確實法令紋一重啊人看起來就特別顯老顯醜，你有這個煩惱嘛？有的話看下去吧。 皮膚鬆弛、細紋、暗沉、眼袋、法令紋、美白、瘦臉…中醫醫美 調理身體更年輕｜針灸瘦身 清腸燃脂不反彈 中醫教你如何駐顏有術!

看手機看太多：眼乾澀、酸痛、眼睛提早老化、近視、老花、飛蚊症⋯⋯這些食物明目最有效，這幾個手法用對了眼疾立解！

你是焦慮情緒 or 焦慮症，一般人要怎麼區分？「心理」疫情別輕忽 ，《柳葉刀》大型研究 ，疫情下焦慮症和抑鬱症激增1/4 ，這種思維模式，其實有辦法擺脫！吃這些食物和抑鬱焦慮說再見

最易被忽略的新冠傳播途徑！打疫苗前後這些方法，調節免疫力！居家常用方法，幫你快速康復！怎麼用退燒藥才是正確的方式？

#008:  “What do I do now? Everything that I use to do doesn't work. Money can't solve this, I can't buy my happiness. It's about allowing those feelings when they start to come up...sit with them and allow them to come up and allow yourself to feel them.  It's very counterintuitive for most people."Today's guest is my old friend, Jonathan Liu, Soul Freedom Coach.  Why are emotions so important to activate your Intuition?  In today's interview, we dive into:Jonathan's origin story.What are the 4 bodies that everyone has?What's a common myth in his field of expertise?How can folks shift their perspectives of emotions?Hurt does NOT heal over time.What Jonathan would say to his younger self.Join our Boom Vision family and hit subscribe!  If you'd like to get the links and show notes for this episode, head to:https://www.benjaminyeh.com/episode-8-how-to-access-your-intuition-emotions-jonathan-liu

Delta變種感染的症狀，和早期不同？更像一種常見病！這種首發症狀別輕忽！人體有自癒力，掌握這些方法，「長新冠」也不怕！

秋季咳嗽、咽喉不適、胸悶！你是得了流感，還是新冠？如何區分新冠和普通咳嗽？1招止咳的好方法！咳嗽也是過敏嗎？哪些咳嗽易誤診？

年輕女孩患心肌炎，差點沒命！腹瀉、嘔吐也可能是心肌炎？疫苗引發心肌炎，可以預防嗎？怎樣減少心肌炎副作用？有沒有得疫苗後心肌炎，怎麼判斷？不單是年輕男性，這些人是疫苗後心肌炎易得人群！

掌握3大方法，新冠病毒變種也不怕！打疫苗後出現不適症狀，怎麼調養？天然免疫力更持久！如何啟動？

小心！這種症狀 ，是新冠傷了肝！3茶飲、5穴位有解！病毒檢測不到、身體卻未恢復？是病沒去「根」？

除了疫苗、口罩，終結疫情還有1個關鍵！第2劑輝瑞BNT疫苗、莫德納心肌炎，是第1劑三倍？這樣做預防！

簡單幾招，不吃藥也能降血壓！天然食物能改善疾病？你身體就是醫生，教你提升身體的自癒力！

頻尿、尿急小心「前列腺炎」！尿不乾淨、起夜是「前列腺增生」？一不小心，可能進展成嚇人的「前列腺癌」？！教你免開刀、保養好前列腺（攝護腺）的好方法！

《尚氣》（Shang chi）電影大熱，主角名字「尚氣」原來是這個意思！氣賦予生命能量？對抗 新冠 、提升正氣，氣虛要怎麼補？有哪些迷思與誤區？

青蒿素 能抗新冠嗎？青蒿能對抗Delta變種病毒？鼻腔免疫力差可導致嚴重的新冠！感染Delta變種，無症狀時也帶傳染力，該怎麼辦

2021英國大型研究：約1/5新冠患者皮膚紅疹是唯一症狀！身體出現哪些皮膚問題，可能與新冠病毒有關？新冠紅疹和普通皮膚病紅疹要怎樣區分？這種情況及早就醫！

接種新冠疫苗會影響月經嗎？出現月經異常怎麼辦？ 英4千女通報接種後經期不良反應：經血比平常更多、經期延遲、經痛等月經異常狀況，生理期可以打疫苗嗎？

感染Delta變種最常見的這5個症狀！其中這個症狀最該警惕！如何判斷自己感染了新冠還是感冒？出現症狀該怎麼辦？

Tim Fung is CEO and co-founder of Airtasker – a marketplace enabling users to outsource everyday tasks.  Tim and his co-founder Jonathan Liu launched Airtasker back in 2012, and it has become synonymous with getting odd jobs and services done.  Fast forward to 2021, and it has become a listed company on the ASX.   Tim Fung and Mark Bouris discuss how to execute a convincing pitch to your audience, develop a marketplace business and why TV ads are still relevant.  Apply to be part of Survive & Thrive cast here Join the Facebook Group. Follow Mark Bouris on Instagram, LinkedIn & YouTube. Want to grow your business and stay ahead of the pack? Access Mark Bouris' Masterclasses. Got a question or comment for Mark? Send an email.  See omnystudio.com/listener for privacy information.

頭髮油 、身體胖又腫？自我辨識 體內濕氣 10大警訊！身體濕氣重，竟是 新冠 的易感因素？便捷方法甩掉肥肉痰濕，啟動「體內除濕機」！黃金飲食計劃大公開

徹底擺脫 新冠病毒 ，沒有那麼簡單？ 疲勞 感、噁心嘔吐、焦慮抑鬱，如何恢復？醫師教你擊退3大新冠後遺症！

AZ疫苗、強生疫苗血栓副作用，2方法預防！打疫苗後，這些情況一定要注意！出現血栓該怎麼辦？腦血栓、心肌梗塞發作，「1穴位」立刻急救、避免奪命

打 第2劑 疫苗 出現這些 症狀 ，警惕 心肌炎 副作用 ！簡單1方法，預防心肌炎！心肌炎可以自癒，但千萬別做這件事！否則當心致命？強心臟、改善心肌炎的食物

遠離 便秘 、 大腸癌 ，這樣做輕鬆清腸道！ 大便 8種形狀，暗示不同疾病！這種形狀最危險？1杯蔬果汁，排便、清宿便超順暢？按這2組穴位，快速改善便秘！

高血壓 不降， 中風 、 心血管疾病 嚴重恐奪命？簡單3招 自然降血壓 ！家裡5食材，是「天然降壓藥」！ 耳朵 上有特效 降壓穴 ？高血壓根本原因是它

三高、 癌症 等 6大 慢性病 ，讓 新冠 更危險！2大方法避免染疫變重症！肺癌染疫，死亡率達25%？1碗 藥膳 助抗癌，按摩3大穴位讓肺部變年輕！https://www.youtube.com/watch?v=5rfWCfBHQxY&t=0s (00:00) 三高等6大慢性病與癌症讓新冠更危險！ 2大方法避免染疫後變重症！ 1碗藥膳助抗肺癌，按摩3個穴位讓肺部變年輕！ https://www.youtube.com/watch?v=5rfWCfBHQxY&t=208s (03:28) 肥胖、高血壓、糖尿病患者為何容易演變為新冠重症？ https://www.youtube.com/watch?v=5rfWCfBHQxY&t=945s (15:45) 肺癌患者感染新冠病毒後風險更高？ https://www.youtube.com/watch?v=5rfWCfBHQxY&t=2152s (35:52) 哮喘患者在疫情中也是高危人群？ https://www.youtube.com/watch?v=5rfWCfBHQxY&t=2359s (39:19) 這款藥膳幫助哮喘患者安度疫情 https://www.youtube.com/watch?v=5rfWCfBHQxY&t=2480s (41:20) 這些穴位按摩幫助哮喘患者 https://www.youtube.com/watch?v=5rfWCfBHQxY&t=2709s (45:09) 抑鬱等精神疾病也增加新冠重症風險？ https://www.youtube.com/watch?v=5rfWCfBHQxY&t=3008s (50:08) 緩解抑鬱的三個穴位 https://www.youtube.com/watch?v=5rfWCfBHQxY&t=3413s (56:53) 改善抑鬱的藥膳

飛蚊症、白內障、青光眼有救，這樣做讓眼睛回春！失明也能恢復？2茶飲改善飛蚊症、2招逆轉白內障！飛蚊症出現這症狀，警惕視網膜剝離

10個死於 新冠肺炎 的人，有8人是「這一族群」！2大方法 抗老化 、讓器官變年輕， 預防 新冠重症 ！2秘訣 補腎 ，逆轉衰老、強壯 免疫力 ！

最易傳播 新冠 病毒 的地方，不是餐館、公車，而是「這個地方」！千萬不要去！兩地醫生看 台灣 疫情 ，這幾點讓人深思！日本也曾栽在這裡？台最大爭議話題解析！

台灣 疫情 急速上升，連續4天 確診 破百！空城、搶購囤糧，台灣開始 封城 了嗎？接觸者、有症狀者，一定要這樣做！ 防疫 自保必知7件事

https://www.youtube.com/hashtag/%E5%8D%B0%E5%BA%A6%E7%96%AB%E6%83%85 (#印度疫情) 死亡慘烈，火葬場木頭不夠用❗️

https://www.youtube.com/hashtag/%E8%84%82%E8%82%AA%E8%82%9D (#脂肪肝)​ 4人中1人有！這樣做 https://www.youtube.com/hashtag/%E9%80%86%E8%BD%89%E8%84%82%E8%82%AA%E8%82%9D (#逆轉脂肪肝)​ ，遠離 https://www.youtube.com/hashtag/%E8%82%9D%E7%A1%AC%E5%8C%96 (#肝硬化)​ 、 肝癌 ！

高https://www.youtube.com/hashtag/%E8%A1%80%E5%A3%93 (#血壓)​ 、 https://www.youtube.com/hashtag/%E9%AB%98%E8%A1%80%E8%84%82 (#高血脂)​ 、高血糖不用怕！

睡覺7小時，白天一定精神好？

單2招，擊退 https://www.youtube.com/hashtag/%E8%80%81%E8%8A%B1%E7%9C%BC (#老花眼)​ 、 https://www.youtube.com/hashtag/%E8%BF%91%E8%A6%96 (#近視)​ 、飛蚊症！

Jonathan Liu hated public speaking and basically everything about litigation. Even so, he spent 7 years as a litigator before leaving the law for a career in real estate. On today's episode of the podcast, Jonathan shares how he figured out it was time for him to leave the law, and how he developed his career as a real estate agent and investor. Show notes at formerlawyer.com/084.

這樣做遠離腦退化！✨https://www.youtube.com/hashtag/%E5%81%A5%E8%85%A6 (#健腦)​ 4大招，改善https://www.youtube.com/hashtag/%E8%85%A6%E9%9C%A7 (#腦霧)​ 、失智、提升記憶力！

https://www.youtube.com/hashtag/%E6%96%B0%E5%86%A0 (#新冠)​ 攻擊https://www.youtube.com/hashtag/%E5%BF%83%E8%87%9F (#心臟)​ 恐致猝死？

https://www.youtube.com/hashtag/%E6%96%B0%E5%86%A0 (#新冠)​ 傷腎！

Jonathan Liu, Director of Revenue and Marketing Strategy with glh Hotels, joining Jason from London, England. Jonathan talks about glh's diverse 18 hotels, including the Hard Rock London, how and why revenue and marketing are falling under his purview, moving his website to a new CMS platform to have more dynamic activity, configuring a new CRM, and looking at revenue management systems, trying to bring everything they're doing from marketing perspective and relate it back to their revenue systems how they price and yield, trying to make a seamless eco-system of everything coming together.

50多歲女子患https://www.youtube.com/hashtag/%E7%B3%96%E5%B0%BF%E7%97%85 (#糖尿病)​ 、不控糖，突然昏迷過世！

https://www.youtube.com/hashtag/%E7%B6%A0%E8%8C%B6 (#綠茶)​ 、黑巧克力，可以 https://www.youtube.com/hashtag/%E9%A0%90%E9%98%B2%E6%96%B0%E5%86%A0 (#預防新冠)​ ？這些食物 你經常吃，卻不知有https://www.youtube.com/hashtag/%E9%98%B2%E7%96%AB%E5%8A%9F%E6%95%88 (#防疫功效)​

https://www.youtube.com/hashtag/%E6%96%B0%E5%86%A0 (#新冠)​ https://www.youtube.com/hashtag/%E7%97%87%E7%8B%80 (#症狀)​ 渾身疼痛、嗅味覺異常、發燒咳嗽等等，醫師2招 https://www.youtube.com/hashtag/%E8%87%AA%E6%88%91%E7%B7%A9%E8%A7%A3 (#自我緩解)​ ！

https://www.youtube.com/hashtag/%E9%81%8E%E5%B9%B4 (#過年)​ 除了年夜飯，少不了的就是各種零食小吃。過年 https://www.youtube.com/hashtag/%E9%9B%B6%E9%A3%9F%E6%8E%92%E5%90%8D (#零食排名)​，第1名能降三高、增強免疫力！防新冠病毒，年節一定要避免2件事？常給長輩送6樣養生禮品，人參、燕窩、魚油⋯⋯真的物有所值嗎？哪些能幫助 https://www.youtube.com/hashtag/%E9%98%B2%E7%96%AB (#防疫) https://www.youtube.com/watch?v=jwDWIhiqUsM&t=0s (00:00)​ 2月9日節目 https://www.youtube.com/watch?v=jwDWIhiqUsM&t=120s (02:00)​ 全球疫情更新 https://www.youtube.com/watch?v=jwDWIhiqUsM&t=308s (05:08)​ 過年養生零食大排行！逐一解析報你知！ https://www.youtube.com/watch?v=jwDWIhiqUsM&t=1816s (30:16)​ 疫情下如何過好年？圍爐吃火鍋會增加傳染危險？ https://www.youtube.com/watch?v=jwDWIhiqUsM&t=1369s (22:49)​ 英國打算使用混合疫苗，如何操作？是否有效？ https://www.youtube.com/watch?v=jwDWIhiqUsM&t=2135s (35:35)​ 加州疫情更新 https://www.youtube.com/watch?v=jwDWIhiqUsM&t=2491s (41:31)​ 過年防疫要注意哪些要點？推薦哪些藥膳與食材？ https://www.youtube.com/watch?v=jwDWIhiqUsM&t=3192s (53:12)​ 傳統的過年習俗能驅邪避疫？古代的年夜飯是怎樣的？

It’s Episode 100, and we’ve got a whole mess of people on the show. We’ve got Jason Hoban, Jonathan Liu, Bradley Stair, and Jeff Kurtenacker! We’re celebrating 100 episodes of the podcast, and some big news for the film. We get some background on how Documentary First is made, then we dig into some exciting news! We talk through the learning experience of the film, the amazing experience of “Just doing it”. The process of discovering if you can do something. Making something creative.

31歲媽媽感染新冠病毒後，11個月無法下床⋯⋯

台灣疫情「會失控」嗎？應該封院、封城嗎？

新冠病例1人傳染143人！

全球新冠病毒確診人數迅速攀升

Jonathan Liu is a former lawyer and now a real estate agent, investor, and entrepreneur. And all that sounds fancy, but really, as Jon will tell you, it was a pretty scary experience. If you are thinking of making the jump but are thinking “maybe one day,” this may be the episode for you. In our conversation, Jon talks about his greatest fears before making the jump and how he overcame them, and about his greatest challenges after making the jump and how he addressed it. Because for me, I’ve learned the most from the people who have walked the path that I want to be on. So if you want to be on the path of entrepreneurship and post-lawyerhood, stay tuned to hear more about Jon’s incredible story.   Show notes: Visit Jon’s Instagram here: https://www.instagram.com/jonliu7/ Visit Jon’s LinkedIn here: https://www.linkedin.com/in/jonathan-liu-21879a29/ Email Jon here: jon.liu@jliuhomes.com

立冬在11月7號，天氣也會開始漸漸轉涼，華人有句民間流行 的諺語「冬天進補，春天打老虎」。可見冬季進補相當重要，但是冬季到底怎麼補？ 那為什麼冬季特別強調養「腎」呢？要怎麼養？ 有慢性病的人也能補嗎？怎麼補？ 一般如果冬天手腳容易冷（像有些女性或是老人家）要怎麼補？ 今日嘉賓 中醫、針灸教學和臨床20餘年的加拿大公立學院中醫教授 Jonathan Liu 劉醫師 Support this podcast

We are used to respect when given to a figure of authority. But how is respect shown to those younger than us, or those with less power? Jonathan believes that respect flows like a river, and describes how he learns respect from an exemplary figure, and in turn be able to show respect to those who are younger.

What does success mean to you? For these 2, it means being happily married, with the cutest baby ever, and helping others achieve the same success! Bridget and Jonathan are the founders of Divinely Together. They help spiritual moms and women reclaim their power, speak their truths, and bring peace, love and harmony into their lives and their relationships. Their dream for their clients is to create a legacy of happy and healthy relationships for generations to come. https://www.facebook.com/groups/jbselflove/

Jonathan Liu is a realtor at H&M Realty Group and President of Res Ipsa Real Estate Solutions. As his company name hints, he's a lawyer turned real estate agent and investor. Jonathan practiced primarily in the employment law area. For a while it was the sensible decision to stick with something secure in light of his growing family. On the side, though, he had begun investing in rental properties. Eventually, it became clear to him that taking the riskier road was more aligned with what he wanted to do and the lessons he wanted to teach his daughter.  Topics we discussed: Lean on mentors, although ones within your firm may not have sufficient perspective  What to do when you realize that partnership is not your goal  Experimenting with different practice settings Different things make sense in different seasons of your life  “Achievement without fulfillment is the greatest failure” - Tony Robbins  Seeking alignment between your beliefs and actions; especially in light of trying to teach kids lessons We often lean on excuses (reasonable as they may be) to avoid the discomfort of taking risks  You have to be comfortable with being uncomfortable for a minute There may be moments of questioning your decision, but that just comes with the territory  Its challenging, scary, fun, and doable to figure out how to do new things Even with the risks, he wish he'd made the change earlier Website: www.thelawyersescapepod.com  Facebook Group: www.facebook.com/groups/465965087538268/

Jonathan Liu joins the hosts to talk about being a reviewer in the board game world. He is a lead games editor at geekdad.com, a prominent geek review site. You can reach out to Jonathan on twitter: @jonathanhliu   If you enjoy this podcast, please consider supporting our Patreon. https://www.patreon.com/breakingintoboardgames   Hosts: Tony - Designer - @beardedrogue Ian - Developer - @ianzangdesign Dan - Publisher - @letimangames   "There It Is" by Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/

Are you stuck and have no where to turn? Are you feeling like you have hit rock bottom and you need an answer to prayer? Do you want to turn your boat around and head in the direction for success once and for all?  If you said to one or all of these questions, I don't need to to tell you, you are tuned in, tapped in, and turned on to the right place with the right people! Join me as you learn about our Guest Today TEDX Speaker Jonathon Liu. OH By the way you will  be riveted as you hear him tell you how his life spiraled into unimaginable an unimaginable place...... So Listen up now and do Click and Share this with your friends. Jonathan Liu is an EX gang member, ex drug dealer, and ex convict, who changed his life when he discovered the power of energy healing.  Since then he has transformed the lives of people from all over the world.  Jonathan is an internationally acclaimed expert when it comes to healing with Sacred Geometry.  He coaches both men and women into becoming the best version of themselves, and he uses Sacred Geometry to eliminate blocks energetically and emotionally that are stopping them from becoming the best versions of themselves so they can have more success, more happiness, and more freedom in their lives.  Contact Jonathon Liu at www.speakingofjon.com

Jonathan gets 1 to 2 pitches a day to review a Kickstarter campaign on Geek Dad. That is a lot. Here he shares some tips on how to improve your chances of getting noticed and getting coverage.

Jonathan Liu is Editor for Geek Dad and shares insights into getting your project in front of the media.

Jonathan Liu, writer for GeekDad on Wired.com, is my guest as we discuss his Kicking Back program. The idea of giving to the Kickstarter community as a backer BEFORE you become a project owner. It is an interesting conversation as we chat with Jonathan about this and his Emperor's New Clothes project

Jonathan Liu has the dream job. He writes for Geek Dad covering the board gaming space. How cool is that? Jonathan joins me on the show to talk about resisting the Kickstarter urge to back all the great game projects.