Podcasts about residues

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

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South African food in retail stores is generally safe from harmful pesticide residues, with most levels below maximum residue limits (MRLs) set by the Department of Health and aligned with Codex standards. Hosted on Acast. See acast.com/privacy for more information.

In this episode, I share intuitive insights on the power of turning to face our so-called "monsters of mind" and how doing so frees us and allows us to reclaim the once lost aspects of our wounded inner children.Disclaimer: This podcast is intended for entertainment and informational purposes only and does not substitute individual psychological advice. No AI—all content and episodes created and written by Ashley Melillo. *This is an affiliate link. Purchasing through affiliate links supports The Soul Horizon at no extra cost to you. Thanks for your support!

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview hereBarry O'Reilly - Founder at Black Tulip Tech and Author of "Residues" & "The Architect's Paradox"Jacqui Read - Software Architect, Speaker & Author of "Communication Patterns"RESOURCESBarryhttps://bsky.app/profile/technologytulip.bsky.socialhttps://www.linkedin.com/in/barry-o-reilly-b924657https://www.blacktulip.seJacquihttps://bsky.app/profile/tekiegirl.bsky.socialhttps://jacquiread.comhttps://fosstodon.org/@tekiegirlhttps://www.linkedin.com/in/jacquelinereadhttps://github.com/tekiegirlDESCRIPTIONIn this GOTO Book Club interview, Jacqui Read discusses with Barry O'Reilly his books "Residues: Time, Uncertainty, and Change in Software Architecture" and "The Architect's Paradox". He explains how uncertainty defines the architect's role and introduces residuality—a method where architects deliberately stress their conceptual models until they collapse, then optimize the resulting "residues" or leftovers to create more resilient systems.Unlike traditional software engineering approaches, that try to eliminate uncertainty through rigid requirements, residuality embraces random stressors (even far-fetched scenarios like giant lizards) to uncover architectural fault lines.O'Reilly argues that this playful yet mathematically sound approach produces more robust architectures than conventional methods, and his second book explores how inherited philosophical thinking often undermines software architecture's effectiveness in complex business contexts.RECOMMENDED BOOKSBarry O'Reilly • ResiduesBarry O'Reilly • The Architect's ParadoxJacqui Read • Communication PatternsAnne Currie & Jamie Dobson • The Cloud Native AttitudeGregor Hohpe • The Software Architect ElevatorGregor Hohpe • Enterprise Integration Patterns, Vol 2BlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

What percentage of meat samples test positive for drug residue violations?

Technological University Dublin (TU Dublin) and BioAtlantis have announced the launch of an innovative new research initiative, VASEACAD (Valorising Seafood Side Streams, Residues, Unwanted Catches and Discards). The project is funded under the EU-SBEP-2 Call (Second Sustainable Blue Economy Partnership) and is supported by the Marine Institute. With a total funding award of €1.6 million, including €299,525 granted to TU Dublin, the VASEACAD project brings together a consortium of 10 partners from across the EU. The project aims to transform fish processing by-products (materials that are typically discarded), into bioactive protein hydrolysates and other high value biomolecules through state-of-the-art bioprocessing techniques. The goal is to create functional and sustainable ingredients for commercial use, supporting a circular and resource-efficient bioeconomy. Leading the research at TU Dublin is Dr Azza Silotry Naik, Principal Investigator, lecturer and expert in food biotechnology and marine byproduct valorisation who stated: 'This project represents an exciting opportunity to develop sustainable solutions for marine by-products by leveraging bioprocessing to create ingredients with functional and commercial potential. I'm proud to collaborate with BioAtlantis and grateful to the Marine Institute for recognising the importance of this work in supporting both sustainability and innovation' Dr Naik brings substantial expertise to the initiative, having previously worked on several EU and nationally funded marine research projects, and led the development of functional ingredients in both academic and commercial R&D environments. Headquartered in County Kerry, BioAtlantis is a pioneering biotech company specialising in bioactives derived from marine and terrestrial sources. It is home to the largest seaweed extraction facility in Ireland and Britian and has a long track record of collaborating with academic institutions. Discussing the partnership, John T. O'Sullivan, CEO of BioAtlantis said: 'BioAtlantis is delighted to partner with Technological University Dublin in the VASEACAD project, focusing on converting fish by-products into valuable ingredients utilising bioprocessing techniques for different market segments. This project not only supports the circular blue bioeconomy, but also aligns with our commitment to developing sustainable, science-based solutions'. The Marine Institute, Ireland's national agency for marine research and innovation, welcomed the project's alignment with national priorities for sustainability and resource efficiency. Veronica Cunningham, Research Funding Office Manager; Marine Institute commented: 'We are pleased to support the VASEACAD project under the EU Sustainable Blue Economy Partnership. Valorisation of marine side streams is critical to reducing waste, supporting innovation, and strengthening Ireland's marine bioeconomy. Projects like this demonstrate the strength of collaborative research in delivering solutions that benefit the environment and provide opportunities for industry too.' The project is also receiving strategic support from Professor Christine O'Connor, Head of Research and Innovation, Faculty of Sciences and Health at TU Dublin. Prof O'Connor, with her expertise in waste valorisation and chemical analysis, will act as a senior advisor on the project, helping guide its scientific direction and impact. VASEACAD reflects TU Dublin's commitment to research with real-world impact, combining academic expertise, industry collaboration, and sustainability driven innovation to contribute to a more resilient and circular blue economy. The VASEACAD project is carried out with the support of the Marine Institute funded by the Government of Ireland under the Sustainable Blue Economy Partnership co-funded by the European Union, and co-branded by the UN Decade of Ocean Science 2021-2030. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and ofte...

Fredrik talks to Barry O’Reilly about software architecture. Barry has spent a lot of time and energy connecting software architecture to actual code and development work, and finding good ways of actually training new generations of software architects. Architecture is a level above programming, it is a different skill, and it needs to be properly taught so that more people can think and make active decisions about it. Oh, and architecture happens at a group level. You can’t really do it alone. Barry’s quest led him to complexity science, a PhD to actually prove his ideas hold up, and two books. The idea that you have to understand what goes on in the code in order to do good architecture is more controversial than one might think. Thank you Cloudnet for sponsoring our VPS! Comments, questions or tips? We a re @kodsnack, @tobiashieta, @oferlund and @bjoreman on Twitter, have a page on Facebook and can be emailed at info@kodsnack.se if you want to write longer. We read everything we receive. If you enjoy Kodsnack we would love a review in iTunes! You can also support the podcast by buying us a coffee (or two!) through Ko-fi. Links Barry Black tulip Complexity science IDE Antifragile Nassim Taleb Nassim guesting Econtalk talking about antifragility while the book was in progress Barry’s papers: No More Snake Oil: Architecting Agility through Antifragility (2019) An introduction to residuality theory: Software design heuristics for complex systems (2020) The Machine in the Ghost: Autonomy, Hyperconnectivity, and Residual Causality (2021) The Philosophy of Residuality Theory (2021) Residuality Theory, random simulation, and attractor networks (2022) Residuality and Representation: Toward a Coherent Philosophy of Software Architecture (2023) Domain driven design Europe Leanpub Residues - Barry’s first book Barry’s NDC talks - on process and on philosophy Support us on Ko-fi Our agile release train engineer stickers The architect’s paradox - Barry’s second book Accelerate Øredev Kodsnack 346 - Tomer Gabel about the golden age of tomfoolery Dataföreningen Dataföreningen kompetens Titles How we design and think about structure Climbed the greasy pole Keep close to the code Remove themselves from the code as a status symbol I would see a lot of grey There’s a generation missing A level of thinking above programming When you look up from your IDE We had to rescue architecture When they say “architect” Headed for that ivory tower A self-titling profession Comfortable in uncertainty Multiple books, and a PhD How does this thing break Everything will always break Patching those cracks Do you have any proof of this? The key to good software architecture is pessimism The mincing of academic criticism Typing furiously Hope for the future He’s from the real world!

AABP Executive Director Dr. Fred Gingrich is joined by AABP Past President Dr. Pat Gorden, a professor of dairy production medicine and clinical pharmacology at Iowa State University College of Veterinary Medicine. We review the meaning of violative drug residues and the most common medications used in cattle that have historically been associated with violative drug residues. Gorden also reviews both how animals are selected for testing and how withdrawal intervals are determined by drug sponsors when going through the drug approval process. Of particular importance is the effect of disease on the clearance of drugs from the animal since the residue studies are performed on healthy animals for FDA submission. Gorden reviews basic pharmacologic mechanisms for drugs and how disease state may impact drug clearance. He also reviews a study he performed that looked at severe clinical mastitis cows and the impact on the pharmacokinetics of ceftiofur in these animals. Veterinarians should utilize FARAD for withdrawal determinations and consider disease state on drug clearance.  Finally, Gorden offers suggestions for practicing veterinarians to review with farm managers and employees, including observations in the hospital pen or when treatments are administered. Ensuring correct protocol compliance, applying correct therapy, estimating the correct weight, extending the withdrawal interval on sick animals, ensuring the correct dose, route and volume of injection, and fully mixing suspensions prior to filling the syringe are all important items to check. Veterinarians are tasked with ensuring appropriate oversight on drug use on farms and regularly reviewing these steps is an important aspect of this stewardship principle as well as creating billable hours that are valued by the client. P.J. Gorden, M.D. Kleinhenz, L.W. Wulf, B. KuKanich, C.J. Lee, C. Wang, J.F. Coetzee, Altered plasma pharmacokinetics of ceftiofur hydrochloride in cows affected with severe clinical mastitis, J Dairy Sci. Volume 99, Issue 1, 2016, Pages 505-514, https://doi.org/10.3168/jds.2015-10239. Gorden PJ, Ydstie JA, Kleinhenz MD, et al. Comparative plasma and interstitial fluid pharmacokinetics and tissue residues of ceftiofur crystalline-free acid in cattle with induced coliform mastitis. J Vet Pharmacol Therap. 2018; 41: 848–860. https://doi.org/10.1111/jvp.12688.  

The topic of this podcast episode is the liver and its importance for bodily function. Nurse Doza emphasizes how common it is for people to have a fatty liver due to their diet, specifically fast food consumption. He explains how the sugar and fructose in these foods directly affect the liver and contribute to non-alcoholic fatty liver disease. He also encourages listeners to take control of their diet and make choices that support a healthy liver. This episode provides practical tips for improving liver health and acknowledges the positive impact the podcast is having on listeners' lives.   TIMESTAMPS: 00:00 START 06:20 The liver and its importance. 09:07 Non-alcoholic fatty liver disease. 12:09 Importance of food and nutrients. 15:44 Alcohol's contribution to diabetes. 21:46 Liver-supporting supplements. 23:05 Resveratrol in the Mediterranean diet. 29:38 Liver health and supplements. 32:45 Gut and liver relationship. 34:07 Fasting for a healthier liver. 37:22 Liver health practices. Introducing Liver Love: Every vibrant life begins with a healthy core. Cleanse, rejuvenate, and love your liver with our premium supplement, "Liver Love". Designed meticulously for Phase 1 and 2 of liver detoxing. Begin your journey to a healthier you. Click here to get Liver Love now! Show Notes:   The Importance of Supporting Antioxidant Production.[^1^] - The crux of health issues: Inflammation. - The origin of inflammation: Stress. - Consequences of chronic stress: Bodily dysfunction. - The liver: A powerhouse of antioxidant production[^2^]. - Glutathione: Liver's potent gift and its profound benefits[^3^]. - Introducing NAC: Glutathione's precursor and its significance[^4^]. - The need for NAC and glutathione supplementation. - The liver-enhancing power of B vitamins[^5^].   Hormone Regulation & The Liver[^6^] - The liver's pivotal role in hormone regulation. - The communicative power of hormones. - Liver: The body's natural storage facility. - Better hormones equate to a healthier liver[^7^]. - Early menopause's potential link to liver health[^8^]. - The underappreciated link: Liver and insulin. - The domino effect: Insulin issues leading to hormonal imbalances[^9^].   The Perils of Fast Food on Liver Health[^10^] - The challenges in processing fast food. - Residues of past unhealthy diets lingering in the liver. - Beyond fast food: The toll of an unhealthy diet on the liver[^11^]. - The equation of good fats and a healthier liver. - Avocado: The liver's best friend. - Monounsaturated fat: A top-tier dietary inclusion[^12^]. - The liver's role in cholesterol production[^13^]. - The promise of fish oil for liver wellness[^14^]. - The connection: Fatty liver, omega 3, and choline deficiencies[^15^].   Decoding the Relationship: Liver & Estrogen[^16^] - Fatty liver's association with compromised estrogen. - Estrogen production's direct tie to the liver[^17^]. - The toll of birth control on liver health and estrogen quality[^18^]. - The malleability of epigenetics[^19^]. - Stress, liver health, and its implications on estrogen[^20^]. - The genetic connection to liver health and detoxification needs[^21^]. - Delving into the COMT gene's role in hormone regulation[^22^]. - The intersection of cholesterol, liver, and menopause-associated estrogen[^23^].   Methylation & Its Influence on Liver Function[^24^] - The expression of the MTHFR gene in the liver. - Prevalence and implications of MTHFR gene mutation[^25^]. - The methylation cycle's role in vitamin metabolism[^26^]. - Significance of B9 in methylation and liver functions[^27^]. - The interconnected web: MTHFR gene's impact on various bodily processes[^28^]. - Glutathione production's link to correct methylation[^29^]. - Methylation's role in disease risk[^30^]. - The importance of methylated vitamins for MTHFR gene support[^31^]. - The intertwined roles of MTHFR and COMT genes in methylation[^32^]. Discover Liver Love: Let's face it, our livers undergo a lot, daily. Toxins, processed foods, medications, and more. It's time to give back. Show your liver some love with our specially formulated detox supplement, "Liver Love". The first step towards a healthier tomorrow starts with a cleanse today. Tap here to give your liver the love it deserves!   ---   **REFERENCES**:   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320789/figure/molecules-23-03305-f001/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637678/#B4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125908/ https://pubmed.ncbi.nlm.nih.gov/14973104/ https://pubmed.ncbi.nlm.nih.gov/19095062/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6726297/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637678/ https://pubmed.ncbi.nlm.nih.gov/334126/ https://www.ahajournals.org/doi/10.1161/JAHA.120.020560 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3228367/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5123374/#MOESM3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2674329/#R28 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531579/#B30

Plus Manufacturing, Inc.'s Procyon Soap Free Multi-Purpose Cleaner & Degreaser offers superior cleaning results with no sticky soapy residues. Check it out at https://soapfreeprocyon.com/shop Plus Manufacturing, Inc. City: Spokane Address: 2704 N Madelia St Website: https://soapfreeprocyon.com/ Phone: +18007762966 Email: press@soapfree.net

Terry McElvaney, Veterinary Inspector at the Veterinary Medicines, Antimicrobial Resistance, Byproducts and TSE Division, joins Stuart Childs on this week's Dairy Edge podcast to discuss residues in milk. Terry first explains the role of the division in which he works and how it is important in facilitating trade. Terry says that the division tests 15,000 samples annually and carries out 70,000 tests on these samples for many different types of residues in order to report to the EU on our compliance with the regulations around the use of veterinary products in animals that go into the food chain. He says that while a miniscule percentage of samples test positive each year, this year there has been an increase in the positives associated with Ivermectin and Levamisole, active ingredients in wormers and flukicides. Terry advises farmers to test before treating and to ensure the right product is used at the right rate at the right time.  Observing withdrawals is important and people need to carefully record dates of administration to ensure those withdrawals are observed. Terry also warns people that stock will often calve ahead of time and when that does happen, the withdrawal period still needs to be observed. He finishes by recommending people avoid unnecessary use of any veterinary medicinal products when possible and where they have to be used, to get good advice on the product to use.    For more episodes from the Dairy Edge podcast go to the show page at: https://www.teagasc.ie/animals/dairy/the-dairy-edge-podcast/ The Dairy Edge is a co-production with LastCastMedia.com

For this week's OviCast, we're joined by Terry McElvaney, Veterinary Inspector with Department of Agriculture, Food and the Marine, to discuss some issues that have occurred with flukicide residues in lamb carcasses.  We discuss the problems encountered and the follow up investigations with Terry explaining some of the issues caused and issues they have encountered.  We also discuss the key areas to focus on when using anthelminthic and antibiotics to avoid these type of issues occurring.   Details of licensed veterinary products can be found on the HPRA veterinary medicines webpage: https://www.hpra.ie/homepage/veterinary/veterinary-medicines-information For more episodes from the OviCast podcast, visit the show page at:https://www.teagasc.ie/animals/sheep/ovicast-sheep-podcast

Today we look at some of the colonial legacies in discourses around girls' education. With me are Chris Kirchgasler and Karishma Desai. They've recently published an article entitled, “'Girl' in Crisis: Colonial Residues of Domesticity in Transnational School Reforms,” which was published in the Comparative Education Review. Chris Kirchgasler is an Assistant Professor at the University of Wisconsin-Madison and Karishma Desai is an assistant Professor at Rutgers Graduate School of Education. freshedpodcast.com/kirchgasler-desai/ -- Get in touch! Twitter: @FreshEdpodcast Facebook: FreshEd Email: info@freshedpodcast.com

(Dhamma Stream Online Sessions)

(Dhamma Stream Online Sessions)

The USDA published the 2022 Pesticide Data Program Annual Summary that shows over 99% of tested samples had pesticide residues BELOW EPA benchmark levels, and House lawmakers launched a new Agricultural Trade Caucus, seeking to advance and promote policies vital to U.S. agriculture.

The USDA published the 2022 Pesticide Data Program Annual Summary that shows over 99% of tested samples had pesticide residues BELOW EPA benchmark levels, and House lawmakers launched a new Agricultural Trade Caucus, seeking to advance and promote policies vital to U.S. agriculture.

Join us as we unravel a critical consumer concern—the presence of antibiotic residues in our food. Delve into the potential health risks and the serious illnesses they might lead to. By the end of this podcast, you'll acquire a thorough understanding of the dangers posed by antibiotic residues and discover effective ways to minimize their adverse effects.Please visit https://healthfactsdiva.com for resources.Support the showPlease be advised that the following program is for entertainment purposes only. Consult your doctor for medical advice.

Join us as we unravel a critical consumer concern—the presence of antibiotic residues in our food. Delve into the potential health risks and the serious illnesses they might lead to. By the end of this podcast, you'll acquire a thorough understanding of the dangers posed by antibiotic residues and discover effective ways to minimize their adverse effects.Please visit https://healthfactsdiva.com for resources.Support the showPlease be advised that the following program is for entertainment purposes only. Consult your doctor for medical advice.

Impressions stay in your memory. For example, if I have watched a film in the evening, individual sequences or images from the film keep coming back to me the next day, even though I may have only watched it to switch off and not really consciously or with much intention. I sometimes notice the same thing with things I've heard or when I cycle past an advert. Much of what our senses perceive is left behind. Or as the Latin proverb aptly puts it: semper aliquid haeret - something always sticks. This makes it all the more important to consciously pay attention to what we impose and inflict on our senses. We do this more or less carefully and consciously when it comes to food. How is it with you? Do you have a healthy approach to consuming information or would it perhaps be time for a diet for your eyes or ears? I wish you an extraordinary day!

Welcome back to another Q and A episode of the Nutrition Science Podcast. We have some exciting topics today, we will be discussing:  Whether air fryers are safe or not How to remove pesticide residues from food If colostrum has any benefits  Whether I think misinformation should be illegal  And the health effects of deli meats  Tune in to the show to hear more. Links  Take Advantage of Limited Time Discounted Course Offer Legion Supplements BOGO Sale (Use code Chavez at checkout)  Paper on removing pesticide residues in food ----> https://pubmed.ncbi.nlm.nih.gov/29222908/ Research on colostrum ---> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255475/  

We will address a crucial issue that impacts us as consumers. We will delve into the subject of antibiotic residues in the food we eat, which can be harmful to our health and potentially lead to serious illnesses, such as cancer. By the end of this podcast, you will gain a comprehensive understanding of the risks associated with antibiotic residues and learn ways to minimize their adverse effects.Support the showPlease be advised that the following program is for entertainment purposes only. Consult your doctor for medical advice.

The topic of this podcast episode is the liver and its importance for bodily function. Nurse Doza emphasizes how common it is for people to have a fatty liver due to their diet, specifically fast food consumption. He explains how the sugar and fructose in these foods directly affect the liver and contribute to non-alcoholic fatty liver disease. He also encourages listeners to take control of their diet and make choices that support a healthy liver. This episode provides practical tips for improving liver health and acknowledges the positive impact the podcast is having on listeners' lives.   TIMESTAMPS: 00:00 START 06:20 The liver and its importance. 09:07 Non-alcoholic fatty liver disease. 12:09 Importance of food and nutrients. 15:44 Alcohol's contribution to diabetes. 21:46 Liver-supporting supplements. 23:05 Resveratrol in the Mediterranean diet. 29:38 Liver health and supplements. 32:45 Gut and liver relationship. 34:07 Fasting for a healthier liver. 37:22 Liver health practices. Introducing Liver Love: Every vibrant life begins with a healthy core. Cleanse, rejuvenate, and love your liver with our premium supplement, "Liver Love". Designed meticulously for Phase 1 and 2 of liver detoxing. Begin your journey to a healthier you. Click here to get Liver Love now! Show Notes:   The Importance of Supporting Antioxidant Production.[^1^] - The crux of health issues: Inflammation. - The origin of inflammation: Stress. - Consequences of chronic stress: Bodily dysfunction. - The liver: A powerhouse of antioxidant production[^2^]. - Glutathione: Liver's potent gift and its profound benefits[^3^]. - Introducing NAC: Glutathione's precursor and its significance[^4^]. - The need for NAC and glutathione supplementation. - The liver-enhancing power of B vitamins[^5^].   Hormone Regulation & The Liver[^6^] - The liver's pivotal role in hormone regulation. - The communicative power of hormones. - Liver: The body's natural storage facility. - Better hormones equate to a healthier liver[^7^]. - Early menopause's potential link to liver health[^8^]. - The underappreciated link: Liver and insulin. - The domino effect: Insulin issues leading to hormonal imbalances[^9^].   The Perils of Fast Food on Liver Health[^10^] - The challenges in processing fast food. - Residues of past unhealthy diets lingering in the liver. - Beyond fast food: The toll of an unhealthy diet on the liver[^11^]. - The equation of good fats and a healthier liver. - Avocado: The liver's best friend. - Monounsaturated fat: A top-tier dietary inclusion[^12^]. - The liver's role in cholesterol production[^13^]. - The promise of fish oil for liver wellness[^14^]. - The connection: Fatty liver, omega 3, and choline deficiencies[^15^].   Decoding the Relationship: Liver & Estrogen[^16^] - Fatty liver's association with compromised estrogen. - Estrogen production's direct tie to the liver[^17^]. - The toll of birth control on liver health and estrogen quality[^18^]. - The malleability of epigenetics[^19^]. - Stress, liver health, and its implications on estrogen[^20^]. - The genetic connection to liver health and detoxification needs[^21^]. - Delving into the COMT gene's role in hormone regulation[^22^]. - The intersection of cholesterol, liver, and menopause-associated estrogen[^23^].   Methylation & Its Influence on Liver Function[^24^] - The expression of the MTHFR gene in the liver. - Prevalence and implications of MTHFR gene mutation[^25^]. - The methylation cycle's role in vitamin metabolism[^26^]. - Significance of B9 in methylation and liver functions[^27^]. - The interconnected web: MTHFR gene's impact on various bodily processes[^28^]. - Glutathione production's link to correct methylation[^29^]. - Methylation's role in disease risk[^30^]. - The importance of methylated vitamins for MTHFR gene support[^31^]. - The intertwined roles of MTHFR and COMT genes in methylation[^32^]. Discover Liver Love: Let's face it, our livers undergo a lot, daily. Toxins, processed foods, medications, and more. It's time to give back. Show your liver some love with our specially formulated detox supplement, "Liver Love". The first step towards a healthier tomorrow starts with a cleanse today. Tap here to give your liver the love it deserves!   ---   **REFERENCES**:   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320789/figure/molecules-23-03305-f001/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637678/#B4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125908/ https://pubmed.ncbi.nlm.nih.gov/14973104/ https://pubmed.ncbi.nlm.nih.gov/19095062/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6726297/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637678/ https://pubmed.ncbi.nlm.nih.gov/334126/ https://www.ahajournals.org/doi/10.1161/JAHA.120.020560 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3228367/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5123374/#MOESM3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2674329/#R28 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531579/#B30

Demos These are: Quadratic residues: https://asecuritysite.com/primes/q_res Jacobi symbol: https://asecuritysite.com/primes/jac Jacobi and Legendre symbol: https://asecuritysite.com/primes/jacobi Introduction Remember at school that class where the teacher taught you about how to square something? It was great, and where we loved to take the square of 3 and get 9, and the square of 5 gave us 25. But, in the next lesson, we came back to earth with a bump, as it was time for the nasty little square root. Now, we have to find two numbers which when multiplied together, gave us 121, or 196. Luckily, there was a convenient button on the calculator that give us our quick answer. In the time before calculators, though, working out more complex square roots involved tables of logarithms. And, so, in this podcast, I will outline a difficult problem … find a square root in a modulo n world … aka quadratic residues. A hard problem In cryptography, we look for hard problems to solve. For this, we can create a backdoor into the problem and solve the problem. With discrete logarithms, we have a hard problem of: Y=g^x (mod p) and where it is difficult to determine x, even though we know g, Y and p, but as long as the prime number if large enough. Another hard problem is used in the RSA public key method, and this involves the difficulty in factorization at modulus (N) which is made up of two prime numbers.  Another hard problem is quadratic residues modulus n, and uses the form of: x²=a (mod p) and where we must find a value of x which results in a value of a (mod p). If a solution exists, the value of a is a quadratic residue (mod n).  In modular arithmetic, this operation is equivalent to a square root of a number (and where x is the modular square root of a modulo p). In the following, we will try and solve for the value of x, and also generate the Legendre symbol value. For example, if we have a=969 and p=1223, we get: Solve x²=968 (mod 1223) [Ans: 453] Try! and: Solve x²=1203 (mod 1223) [Ans: 375] Try! Thus 968 and 1203 are quadratic residues modulo 1223. The form of x²=a (mod p) is not always solvable. For example, if we have a=209 and p=1223 , we get: x²=209 (mod 1223) Also, if a shares a factor with p it is also not solvable. For example: x²=39 (mod 13) will return a zero value for x. If we take a value of p=53, we get the following values [here]: 0, 1, 4, 6, 7, 9, 10, 11, 13, 15, 16, 17, 24, 25, 28, 29, 36, 37, 38, 40, 42, 43, 44, 46, 47, 49, 52 A sample run of the code gives: Quadradic residue (mod n) solver a: 47 p: 53We need to solve for: val^2 = 47 (mod 53 )-----------------------Result: 10( 10 )^2 = 47 (mod 53 )-----------------------For a prime number of 53 here are the residues up to p (or 100)1 4 6 7 9 10 11 13 15 16 17 24 25 28 29 36 37 38 40 42 43 44 46 47 49 52 In this case, we see that 10 is a possible quadratic residue for a p of 53. The solution is thus: 10²=47(mod 53) You can see a demonstration here and here are some examples: Solve x²=12 (mod 13) [Ans: 8] Try! Solve x²=968 (mod 1223) [Ans: 453] Try! Solve x²=1203 (mod 1223) [Ans: 375] Try! Solve x²=47 (mod 53) [Ans: 10] Try! Solve x²=209 (mod 1223) [No solution!] Try! Solve x²=888 (mod 1223) [No solution!] Try! Solve x²=39 (mod 13) [No solution!] Try! Legendre symbol In science, it is difficult to avoid Adrien-Marie Legendre, as there are so many things named after him: Fourier–Legendre series; Gauss–Legendre algorithm; Legendre chi function; Legendre duplication formula; Legendre–Papoulis filter; Legendre form; Legendre polynomials; Legendre sieve; Legendre symbol; Legendre transformation; Legendre wavelet; Legendre–Clebsch condition; Legendre–Fenchel transformation; Legendre's constant; Legendrian knot; and Gamma function–Legendre formula. And, so, where does Legedre help with your online security? Well, you will find his method used in elliptic curve methods, which are used to protect your online identity, and the security of the communications that you have with this Web page. So, let's look at the Legendre Symbol. For this, we turn to Legendre who, in 1798, defined the Legendre symbol. In the following, we will try and solve for the value of x, and also generate the Legendre symbol value [link]: Solve x²=12 (mod 13) With his method, we can determine that the answer is 8, as 64 (mod 13) is 12. Some sample code is [here]: import sysimport libnumdef legendre_symbol(a, p): ls = pow(a, (p - 1) // 2, p) return -1 if ls == p - 1 else lsn=11if (len(sys.argv)>1): n=int(sys.argv[1])print ("Here are the Z*p (quadratic residues modulo n and coprime to n):")print ("nJacobi symbol")for a in range(1, n): rtn= libnum.jacobi(a,n) if (rtn==1): print (a,end=', ')print ("nLegendre symbol")for a in range(1, n): rtn= legendre_symbol(a,n) if (rtn==1): print (a,end=', ') A quadratic residue relates to the solving of the form: y=x² (mod n), and where we need to find values of y for different values of x and for a given modulus (n). For n=11, we get Z∗p={1, 3, 4, 5, 9}. This is because, 1² (mod11)=1, 2² (mod11)=4, 3² (mod11)=9, 4² (mod11)=5, 5² (mod11)=3, 6² (mod11)=3, 7² (mod11)=5, 8² (mod11)=9, 9² (mod11)=4, and 10² (mod11)=1. To find the quadratic residues for a given modulus, we can use the Jacobi symbol is: and is defined as: The legendre_symbol returns: 1. When a is a quadratic residue of p. -1. When a is a quadratic nonresidue of p. 0. When a shares a factor of p. The Jacobi symbol was defined by Carl Gustav Jacob Jacobi as a generalized form of the Legendre symbol. Elliptic Curves While we have a trivial example here, we can use it for more complex ones, such as finding a point on the elliptic curve [here]. A sample run is: Elliptic curve is: P-192Finding elliptic point closest to: 1Prime number: 6277101735386680763835789423207666416083908700390324961279a,b -3 2455155546008943817740293915197451784769108058161191238065(2, 1126956676167578795924565825825899020268914906345645360775L)(3, 2476168101441297080746512578325117519920374855425678540834L)(5, 936760824408609109609580731987662341845728162027345586443L)(6, 61374494507529673497365598443020935064779457192199494327L)(8, 1539168359597512271047259505090133446672063593980132990812L)(12, 3464753203279792192409824182683870253677262339932562461307L)(13, 3288234558942609973454802567986887155175778959720199156770L)(15, 4548834217212027584647316131131523554591911664904227806291L)(17, 2148916484007672061843886225501299518817815267521173400039L)(18, 1600977792967480259538850281480651298625682822208237361467L)(22, 1682016893107185458056834822961338463540516386180178478778L) The code for this is [here]: import mathimport sysimport libnumdef legendre_symbol(a, p): ls = pow(a, (p - 1) // 2, p) return -1 if ls == p - 1 else lsdef findit(start,p,a,b): x=start count=0 while True: val=((x*x*x) + a*x+ b) % p rtn= legendre_symbol(val, p) if (rtn==1): if (libnum.has_sqrtmod(val,{p: 1})): res=next(libnum.sqrtmod(val,{p: 1})) print(x,int(res)) count=count+1 x=x+1 if (count>20): return if (x-start>200): returnp = 2**256 - 2**224 + 2**192 + 2**96 - 1 a=-3b=41058363725152142129326129780047268409114441015993725554835256314039467401291startval=1type="P-192"if (len(sys.argv)>1): startval=int(sys.argv[1])if (len(sys.argv)>2): type=str(sys.argv[2])if (type=="P-192"): p = 2**192-2**64-1 a=-3 b=2455155546008943817740293915197451784769108058161191238065if (type=="P-224"): b=18958286285566608000408668544493926415504680968679321075787234672564 p = 2**224 - 2**96 + 1 a=-3if (type=="P-256"): p = 2**256 - 2**224 + 2**192 + 2**96 - 1 a=-3 b=41058363725152142129326129780047268409114441015993725554835256314039467401291if (type=="P-384"): a=-3 b=27580193559959705877849011840389048093056905856361568521428707301988689241309860865136260764883745107765439761230575 p = 2**384 - 2**128 - 2**96 + 2**32 - 1 if (type=="Curve25519"): a=486662 b=1 p = 2**255 - 19 if (type=="secp256k1"): a=0 b=7 p = 2**256 - 2**32 - 977 if (type=="M-221"): a=117050 b=1 p = 2**221 - 3 if (type=="BN(2,254)"): a=0 b=2 p = 16798108731015832284940804142231733909889187121439069848933715426072753864723 if (type=="M-383"): a=2065150 b=1 p = 2**383 - 187 print("Elliptic curve is:tt",type)print("Finding elliptic point closest to:t",startval)print("Prime number:ttt",p)print("a,b",a,b)findit(startval,p,a,b)

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.04.547731v1?rss=1 Authors: Tennakoon, M., Thotamune, W., Payton, J. L., Karunarathne, A. Abstract: Prenylation is a universal and irreversible post-translational modification that supports membrane interactions of proteins involved in various cellular processes, including migration, proliferation, and survival. Thus, dysregulation of prenylation contributes to multiple disorders, including cancers, vascular diseases, and neurodegenerative diseases. During prenylation, prenyltransferase enzymes tether metabolically produced isoprenoid lipids to proteins via a thioether linkage. Pharmacological inhibition of the lipid synthesis pathway by statins has long been a therapeutic approach to control hyperlipidemia. Building on our previous finding that statins inhibit membrane association of G protein {gamma} (G{gamma}) in a subtype-dependent manner, we investigated the molecular reasoning for this differential. We examined the prenylation efficacy of carboxy terminus (Ct) mutated G{gamma} in cells exposed to Fluvastatin and prenyl transferase inhibitors and monitored the subcellular localization of fluorescently tagged G{gamma} subunits and their mutants using live-cell confocal imaging. Reversible optogenetic unmasking-masking of Ct residues was used to probe their contribution to the prenylation process and membrane interactions of the prenylated proteins. Our findings suggest that specific Ct residues regulate membrane interactions of the G{gamma} polypeptide statin sensitivity and prenylation efficacy. Our results also show that a few hydrophobic and charged residues at the Ct are crucial determinants of a protein's prenylation ability, especially under suboptimal conditions. Given the cell and tissue-specific expression of different G{gamma} subtypes, our findings explain how and why statins differentially perturb heterotrimeric G protein signaling in specific cells and tissues. Our results may provide molecular reasoning for repurposing statins as Ras oncogene inhibitors and the failure of using prenyltransferase inhibitors in cancer treatment. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

https://paranormalhub.com/grizzly-hunt This Wednesday at 4:00 PM EST Time! Grizzly On The Hunt and FDL Paranormal discusses haunted items including dolls. Grizzly has so much activity in his studio around his dolls it's incredible. Activity being caught on live shows in progress. Grizzly also has poltergeist activity during a show us as well. Grizzly uses lighted up cat balls, Rim Pod and other ghost investigator equipment to document his activity that has continuously to ramp up activity that is present and documenting these occurrences. Due to the poltergeist activity in his studio Grizzly has to secure items down with type so devices will not be toss or thrown during live shows. Grizzly has reached out to several psychics mediums that are well known and confirmed that many of Grizzly's dolls have attachments! Haunted objects are inanimate items that are believed to possess supernatural or paranormal properties. These objects are said to be imbued with the presence of spirits or energies that can influence their surroundings or interact with people. Haunted objects can come in various forms, including jewelry, furniture, dolls, paintings, and even everyday items. These objects are often associated with stories of strange occurrences, unexplained phenomena, and negative or unsettling experiences reported by those who have come inte contact with them. One famous example of a haunted object is the Annabelle doll, which gained notoriety through the "Conjuring" film franchise. The real Annabelle doll, a Raggedy Ann doll, is kept in a glass case at the Warrens' Occult Museum in Connecticut. It is said to be possessed by a malevolent spirit and has been associated with a range of paranormal incidents. Another well-known haunted object is the Hope Diamond, a large blue diamond that is rumored to bring misfortune and tragedy to its owners. Legend has it that anyone who possesses the diamond will suffer from bad luck and experience various hardships. While the existence and nature of haunted objects are subjects of debate and skepticism, stories and accounts of their paranormal attributes continue to captivate the imagination of many people. The term "haunted attachments" can refer to various things depending on the context. In the paranormal sense, it often refers to objects that are believed to be connected to spirits or entities and can bring about paranormal activity or supernatural experiences It's important to note that beliefs in haunted attachments and their effects vary greatly among individuals, and skepticism is common. Some people find the idea intriguing and believe in the existence of such phenomena, while others view it as purely superstition or imagination. The concept of items being possessed or believed to have supernatural influence can be attributed to various cultural, religious, and paranormal beliefs. Different explanations and beliefs exist across different cultures and belief systems. Here are a few perspectives on why items may be perceived as possessed: #HauntedVessels #ParanormalObjects #GhostlyArtifacts #grizzlychris #EeriePossessions #SupernaturalArt #Curseditems #SpectralAntiques #SpiritInfusedObjects #Spooky Collectibles #EnchantedRelics #PhantomMemorabilia #OtherworldlyArt #SpiritedArtifacts #SupernaturalCollectibles #EtherealPossessions #GhostlyTreasures #HauntedArt #CursedCuriosities #SpectralKeepsakes #ParanormalArtifacts #EerieArtworks #PhantomArt #SupernaturalRelics #GhostlyOddities #EnchantedArt #HauntedMemorabilia @ParanormalObjects @GhostlyArtifacts @EeriePossessions @SupernaturalArt @Curseditems @SpectralAntiques @SpiritinfusedObjects @SpookyCollectibles @EnchantedRelics @PhantomMemorabilia @OtherworldlyArt @SpiritedArtifacts @SupernaturalCollectibles @EtherealPossessions @GhostlyTreasures @HauntedArt @CursedCuriosities @SpectralKeepsakes @ParanormalArtifacts @EerieArtworks @PhantomArt @SupernaturalRelics @GhostlyOddities @EnchantedArt @HauntedMemorabilia --- Send in a voice message: https://podcasters.spotify.com/pod/show/grizzly-onthehunt/message Support this podcast: https://podcasters.spotify.com/pod/show/grizzly-onthehunt/support

Spilled wine? No problem! Plus Manufacturing, Inc.'s soap-free Procyon Spot & Stain Remover will have your carpet looking like new in no time. Shop now at https://soapfreeprocyon.com/shop Plus Manufacturing, Inc. 2704 N Madelia St, Spokane, WA 99207, United States Website https://soapfreeprocyon.com/ Phone +1-800-776-2966 Email press@soapfree.net

Eric Camden (lead investigator with Foresite) and Mike Konrad discuss electrochemical migration (ECM) and other failure modes caused by residues on circuit assemblies.

My guest, Eric Camden (Lead Investigator with Foresite) and I discuss electrochemical migration (ECM) and other failure modes caused by residues on circuit assemblies.Eric Camden's Contact Information:ericc@foresiteinc.comhttps://www.foresiteinc.com

On this edition of Free City Radio we highlight a multimedia project called "Do Right by the Right Whale," which highlights the voice of Mi'kmaq ecological justice activist Brian Issac. Throughout the last summer Brian worked with the Office of Sustainability at Concordia University in Montreal to create this project, of which you can hear the audio narration on this program. Importantly this project speaks to the ways that Mi'kmaq have long held traditional practices through which practices of ecological and environmental sustainability have traveled across generations, a reality that is now important to draw on for building practices today to support and protect the Right Whale. In this episode we hear from Brian Issac who worked with the Office of Sustainability at Concordia. Also we hear from Christian Favreau who shares thoughts about supporting such work through the sustainability office at the university. Music on this edition is "Torn" from the album "Residues" by Jordan Christoff, my brother, out via @amekcollective Free City Radio is hosted and produced by Stefan @spirodon Christoff and airs on @radiockut 90.3FM at 11am on Wednesdays and @cjlo1690 AM in Tiohti:áke/Montréal on Tuesdays at 1pm on @ckuwradio 95.9FM in Winnipeg at 8am on Tuesdays, on @cfrc 101.9FM in Kingston, Ontario at 11:30am on Wednesdays and broadcasting on @cfuv 101.9 FM in Victoria, BC on Wednesdays at 9am. Also Free City Radio is a podcast through both Spotify and Apple Podcasts, please encourage a friend to tune-in !

Agricultural residue or agricultural waste is not waste, it's an asset. Because of biochar it's now valuable! Biochar is a substance that resembles charcoal that is produced from biomass and is sustainably sourced from agricultural and forestry waste. These can be from corn stalks, hulls, wood chips and much more. Check out our blog on “What Materials Has ARTi Successfully Pyrolysed (Turned into biochar)” at the following (Link). But is it really “waste”? Are they only agricultural residues? Firstly, due to the scale of our civilization and the growing human demands, there is more production of everything. This would for sure certainly include agriculturally grown food. More production means more waste, or unneeded materials from the process. That's the nature of production. Did you remember that potato plants have stalks and green leaves? The potato part we eat is just part of the root system in the soil. The stalks and leaves of potato plants for example have little use. Potato plants even generate flowers. Potato flowers, anyone? The global potato industry is enormous, producing 376 million metric tons in 2021. (Potato News Today, Jan. 21st, 2023). This is humongous. So, there's going to be a lot of unwanted biomasses generated.

Vidcast: https://youtu.be/beg61R9bj0o The FDA and Snack Innovations, Inc. now recall certain lots of Drizzilicious Mini Rice Cake Bites and Drizzled Popcorn. These products have undeclared peanut antigens. Those with peanut allergies could develop life-threatening allergic reactions should they ingest these snacks. If you bought any of these products, do not consume them until you call Snack Innovations at 1-888-445-5122 to check if your lot is affected. https://www.fda.gov/safety/recalls-market-withdrawals-safety-alerts/snack-innovations-inc-conducts-voluntary-recall-limited-quantity-drizzilicious-mini-rice-cakes-4oz #drizzilicious #ricecakes #popcorn #peanut #allergy #recall drizzilicious, ricecakes, popcorn, peanut, allergy, recall

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.04.519021v1?rss=1 Authors: Gil, B., Rose, J., Demurtas, D., Mancini, G.-F., Sordet-Dessimoz, J., Sorrentino, V., Rudinskiy, N., Frosh, M. P., Hyman, B. T., Moniatte, M., Spires-Jones, T. L., Herron, C. E., Schmid, A. W. Abstract: In Alzheimer's disease (AD), Amyloid-beta (A{beta}) oligomers are considered an appealing therapeutic- and diagnostic target. However, to date, the molecular mechanisms associated with the pathological accumulation or structure of A{beta} oligomers remains an enigma to the scientific community. Here we demonstrate the strong seeding properties of unique A{beta} fragment signatures and show that the truncated A{beta}peptides of residues A{beta}1-23, A{beta}1-24 and A{beta}1-25, rapidly seed to form small, SDS-PAGE stable assemblies of ~5kDa to ~14kDa molecular mass range. Mass spectrometry analysis of SDS-PAGE fractionated and gel extracted oligomers revealed that the truncated A{beta} isoforms of residues 1-23 to 1-25 form stable entities with low molecular weight (LMW) oligomers, which strongly resemble the regularly reported A{beta} entities of putative dimeric or trimeric assemblies found in human post-mortem AD and Tg mouse brain extracts. Furthermore, electrophysiological recordings in the mouse hippocampus indicate that LMW A{beta} assemblies formed by fragments A{beta}1-23 to A{beta}1-25 significantly impair long-term-potentiation (LTP) in the absence of full-length A{beta}1-42. Extensive antibody screening highlights the important observation, that the LMW A{beta} assemblies formed by these truncated A{beta} peptides escape immuno-detection using conventional, conformation specific antibodies but, more importantly, the clinical antibody aducanumab. Our novel findings suggest that there are new A{beta} target loopholes which can be exploited for the development of therapeutic antibodies with binding properties against stable target hotspots present in A{beta} oligomers. We provide here a first example of a new class of monoclonal antibody with unique binding properties against LMW A{beta} oligomers, in the absence of binding to large fibrillar A{beta} assemblies, or dense amyloid plaques. Our research supports a novel, unparalleled approach for targeting early, pathological A{beta} species during the insidious phase of AD and prior to the appearance of large oligomeric or protofibrilar assemblies. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.04.515179v1?rss=1 Authors: D'Augustin, O., Gaudon, V., Siberchicot, C., Smith, R., Chapuis, C., DEPAGNE, J., Veaute, X., BUSSO, D., Di-Guilmi, A.-M., Castaing, B., Radicella, J. P., Campalans, A., Huet, S. Abstract: The DNA-glycosylase OGG1 oversees the detection and clearance of the 7,8-dihydro-8-oxoguanine (8-oxoG), which is the most frequent form of oxidized base in the genome. This lesion is deeply buried within the double-helix and its detection requires careful inspection of the bases by OGG1 via a mechanism that remains only partially understood. By analyzing OGG1 dynamics in the nucleus of living human cells, we demonstrate that the glycosylase constantly scans the DNA by rapidly alternating between diffusion within the nucleoplasm and short transits on the DNA. This scanning process, that we find to be tightly regulated by the conserved residue G245, is crucial for the rapid recruitment of OGG1 at oxidative lesions induced by laser micro-irradiation. Furthermore, we show that residues Y203, N149 and N150, while being all involved in early stages of 8-oxoG probing by OGG1 based on previous structural data, differentially regulate the scanning of the DNA and recruitment to oxidative lesions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Heart-wrenching interview with chemist and 9/11 researcher Kevin Ryan who says additional research needed to pinpoint what's killing tens of thousands of 9/11 emergency responders isn't being funded because no one wants to "go there" and look further at evidence indicating that high-energy thermitic explosions are most likely causing the high death toll among first responders. 

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Join Tim and Kim as they talk about all  your favorite potential residues in beef, from hormones and antibiotics to genetically-modified crops, with Dr. Joe Schwarcz, Director of the Office for Science and Society at McGill University.CitationDoyle, E. (2000). Human Safety of Hormone Implants Used to Promote Growth in Cattle. 24.Hirpessa, B., Ulusoy, B., Hecer, C. (2020). Hormones and Hormonal Anabolics: Residues in Animal Source Food, Potential Public Health Impacts, and Methods of Analysis.  Retrieved August 9, 2022, from https://www.hindawi.com/journals/jfq/2020/5065386/Jeong, S.-H., Kang, D.-J., Lim, M.-W., Kang, C.-S., & Sung, H.-J. (2010). Risk Assessment of Growth Hormones and Antimicrobial Residues in Meat. Toxicological Research, 26(4), 301–313. https://doi.org/10.5487/TR.2010.26.4.301Kumar, S. (2018). Adverse effects on consumer's health caused by hormones administered in cattle. 10.Ramatla, T., Ngoma, L., Adetunji, M., & Mwanza, M. (2017). Evaluation of Antibiotic Residues in Raw Meat Using Different Analytical Methods. Antibiotics, 6(4), 34. https://doi.org/10.3390/antibiotics6040034Smith, Z. K., & Johnson, B. J. (2020). Mechanisms of steroidal implants to improve beef cattle growth: A review. Journal of Applied Animal Research, 48(1), 133–141. https://doi.org/10.1080/09712119.2020.1751642Thieme, D., & Hemmersbach, P. (Eds.). (2010). Doping in Sports (Vol. 195). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-79088-4

Max Tegmark is a cosmologist and renowned physicist. In this interview, we discuss his work on the history ofName-dropping residue purity Cygnus

The harrowing story of the GOOD MADAM, begins with the death of her grandmother, the woman who raised her, Tsidi (Chumisa Cosa) and her daughter are forced to move in with Tsidi's estranged mother, Mavis (Nosipho Mtebe), who has lived and worked in the wealthy suburbs of Cape Town for most of Tsidi's life. Residues of apartheid-era domestic servitude confront legacies of colonial land theft in South African auteur Jenna Cato Bass's daring horror-satire. Jenna Cato Bass (High Fantasy, Flatland) transforms the legacies of South Africa's colonial land theft and Black domestic service to white bosses into a gutsy psychological thriller. Co-written with Babalwa Baartman, Mlungu Wam (Good Madam) grapples with the daily violence that haunts the nation's most pressing political issues, long after the end of apartheid. Summoning horror-satire references from Ousmane Sembène's Black Girl to Jordan Peele's Get Out, Bass and Baartman's suspenseful descent into complex, searing allegory insists on reckoning with the enduring presence of traumas deceptively labelled “history.” Director Jenny Cato Bass and co-screenwriter Babalwa Baartman join us for a conversation on the inspiration for GOOD MADAM, impact and legacy on today's South Africa, their on-going collaboration, and the superb cast of actors who helped them realize their vision. For more on Good Madam go to: shudder.com

This week, I sat down with Sheryl Lindros Dolan, Senior Regulatory Consultant with B&C and Senior Regulatory Specialist with our consulting affiliate, The Acta Group, and Meibao Zhuang, Ph.D., Senior Scientist/Regulatory Consultant with B&C and Acta, to discuss pesticide tolerances, what are they, how does the U.S. Environmental Protection Agency (EPA) develop them, and how well government and industry stakeholders communicate their utility in ensuring a safe and reliable food supply. We also wander into the complex world of soil amendments and adjuvants, so if you do not know what these are, listen up. ALL MATERIALS IN THIS PODCAST ARE PROVIDED SOLELY FOR INFORMATIONAL  AND ENTERTAINMENT PURPOSES. THE MATERIALS ARE NOT INTENDED TO CONSTITUTE LEGAL ADVICE OR THE PROVISION OF LEGAL SERVICES. ALL LEGAL QUESTIONS SHOULD BE ANSWERED DIRECTLY BY A LICENSED ATTORNEY PRACTICING IN THE APPLICABLE AREA OF LAW. ©2022 Bergeson & Campbell, P.C.  All Rights Reserved

The use of pesticides in global agriculture brings with it many problems including the killing of non-target, beneficial species as well as reversing pest-management gains from the use of conservation agriculture methods. In a newly published study by researchers at Penn State University, the use of plant cover, such as cover crops, was shown to […]

On this episode of The Best of Bias Podcast Lydell Dinero joined by AKA, Sean_ E, and LBZ discuss various sports and pop culture topics from the last couple weeks. Tune in as they discuss NFL Free agency, The struggle in the Ukraine, update on Tory lanez and Meg the stallion's case, and much more. Don't miss an all new episode of the Best of Bias Podcast.

The herbicide glyphosate has been used for decades, with increased use paralleling the adoption of genetically engineered crops. The compound has a strong safety record and international regulatory consensus stating no unique health risks when [...] The post 307 – Glyphosate Residues and Dietary Exposure first appeared on Talking Biotech Podcast.

Today on Life well lived,Omobola host Rev.(Dr.) Marisha Stewart,a divorce Coach,licensed Minister,and a podcast host. Dr. Marisha will be sharing her real-life experience as a divorcee,a co-parent,and also fulfilling her life purpose after her divorce.She has received healing from the residue and emotional trauma of divorce and she's on a mission to help women alike work through the challenges of divorce,single mum,co-parenting, to living a purposeful life. These and more she's set to contribute on the show.Be ready for an amazing time.Welcome to Life well lived by Omobola Stephen.Rev.(Dr.) Marisha Stewart website: www.iamthelionessqueen.comFacebook: Lioness QueenInstagram: revdrlionessqueen Twitter: lionessqueen727 

Sean and Dave talk through the art of cleaning LV flooring, which is the main maintenance required.  This daily process replaces all of the polishing and refinishing of VCT flooring.   Oh, and Sean says the LV flooring is now 60 % of the flooring market!  Better get on track with your education on this!   Sean DeVore  District Manager c: 352-630-9884 e: sean.devore@mannington.com

The norm in conventional agricultural practice is to make the residue from old crops disappear, a practice that hasn't changed in over 70 years. Explore how California farmers and UC scientists are working together to perfect techniques to maximize the benefits of these crop residues to develop healthier more productive soils, reduce water consumption, and ensure sustainable agricultural production. Series: "Sustainable California" [Science] [Show ID: 32361]

When you bite into an apple you think you're just eating an apple right? Wrong! If your apple is a conventionally grown apple it's most likely come with a side serving of fries, and by fries I mean chemical residues such as pesticides and herbicides. Today we're chatting about chemical residues in fruit and vegetables and what you can do to reduce your exposure. In this Eco Chat episode I discuss: What pesticides are and why they're used Five ways to remove chemical toxins from fresh produce The Environmental Working Group's Dirty Dozen and Clean 15 lists Why you shouldn't avoid fruit and vegetables on the Dirty Dozen list and what you should do instead. To download your FREE Dirty Dozen Fridge Memo click here.