I joined Catalysts a year ago, and this is my first time publicly speaking to a big auditorium, so bear with me. I've also forgot to bring my teleprompter in, so I'm just going to read from the papers. Okay, let's start. How many of you know what the cron job is? Oh, great. Okay, for everyone that doesn't know what a cron job is, in a nutshell, a cron job is a command to an operating system or server for a job that is to be executed at a specified time. In other words, a system to schedule the execution of various scripts. Just about every framework or system has a main cron script. Moodle does too. How many of you know what Moodle cron does? Okay, great. Let's have a closer look. Cron makes Moodle fun. Cron is Moodle's heartbeat and bio rhythm. It generates reports, sends forum messages, and just about everything you can think of that happens under the hood without your direct interaction. Thanks, cron. But look, oh, he doesn't look very happy down there. Does he? No, he does not. I was given the task to try to make the poor guy's life better. And to do this, I first had to find out what's wrong. I began talking to our team to try to collect more information about the symptoms as possible. I then looked at the Moodle tracker to see what other people were experiencing. And finally I started looking at the code to see what causes those symptoms. This is what I found. As seen in the previous slide, Moodle cron is a one-man show. He does one task after the other in a serial way, meaning all tasks wait for the one in front of it to finish before it begins. A way to better visualize this is through a metaphorical real life example. Let's imagine that instead of double-decker buses, we would use a one-person bus that would pick up people every minute or so. Inevitably huge waiting lines would form at the bus stop, and the people would experience delays. This is generally okay for systems that don't require real-time results, and the time aspect of a task execution is relevant. But this is not the case here. We want those foreign emails to be sent as soon as possible, instantly quite. We need those reports and those grades done fast. Nobody likes waiting, especially the young people. Unfortunately, there's more. Moodle cron relies on starting multiple instances of itself one every minute to handle the amount of work, to prevent it from running the same thing multiple times a locking mechanism was implemented. Meet the lock API. The locking system to code the official documentation is not meant to be fast, it's meant to be always correct, and hence it will never be super quick. So apart from dealing with one thing at a time, cron also locks and unlocks each task, plus locks and unlocks itself each time it does this, trying to ensure consistency. Just imagine the time lost just fidgeting with the keys. But that's not the only issue here. Now imagine two instances of cron fighting for the right to use the keys. One usually wins, and the other one waits for the first one to finish. But waiting is a limited process. If the time runs out, an error is thrown and the task is blocked. Sometimes, for some reason, usually something bad happens in the task, an instance of cron forgets to unlock one of its locks, in other words, fails to release a lock. Guess what happens? An error is thrown and the task is blocked. And these are two cases that are very frequent. This double locking and unlocking system is slow, plus isn't very efficient after all moving on. As said before, model cron relies on starting multiple instances of itself one every minute to handle the amount of work. If you were to start two instances of cron at the same time, they would have the same amount of work they would see and receive the same task to execute. Yes, they could not work on the same task because of the locking mechanism. But the whole situation would be an errorful mess. There's no clean way of dividing work between two or more instances. Plus, some of the scheduled tasks handle a huge chunk of data each time and as the number of users and courses increases, those chunks of data increase exponentially. So the more data a task handles, the more time and resources it needs to finish and the more time spent on a task, the bigger the delay on the next task is and so on. Currently, there's no easy way of dividing workload hence no provisions for sharding. So this raises a big question. If sharding is not an option, how do we scale? What happens when the number of users increases when we have thousands and tens of thousands of courses, activities, forum posts and so on? What happens when there are multiple plugins with Chrome tasks to be run as well? What happens when there's a busy period and a huge amount of ad hoc tasks are created? Will Chrome fail or become so clogged up that nothing will be done without delay? The current way Chrome is built will have serious difficulties under load. But don't worry, there's still hope. So we set out to fix all of the above and manage to do so. The solution that best fit our requirements was the implementation of a queue and the use of a queue management system. In simple terms, a queue is a waiting line and a queue management system is a process that dictates how that waiting line is dealt with. We have so far identified two major elements of our solution. The queue, a list of queue items and the manager, the system that deals with the queue. But in order to work on the queue items, the manager, like any real-life business manager, had to hire workers. Workers are entities that following a certain job instruction execute a task in a specified environment and in a particular way. One way of better visualizing this would be to imagine the whole solution as a parcel distribution company. The manager hires drivers to deliver the parcels. Each parcel requires a specific type of car for the delivery. We have large items, small items, et cetera, and different transportation conditions because we have fragile items, extra-long items, volatile items, and so on. With this picture in mind, let's look at how this solves the crown's problem. Knowing that in the future there will be a huge load of parcels to distribute, the serial execution would bring the debt of our business. The alternative is parallel execution. Instead of one worker delivering one parcel after the other, multiple workers deliver multiple parcels at the same time. Serial execution equals one way street. Parallel execution equals multi-lane highway. Which way would you go? But to do this, we needed something more. Having all the parcels in the room and making the workers lock the door behind them and lock the parcel in the box to mark it as due to be delivered by them made no sense. When adding an item to a better way had to be used unique single and three skew items. When adding an item to the system, each parcel was given a unique identity code. The items code would then be kept in the records until it gets delivered. So a new item of that exact type cannot be added to the system until the previous one is delivered. After the registration, the item is then placed on a conveyor belt and delivered to a loading dock. The first worker to get to the loading dock picks the first item, the second worker, the second item, and so on. So each item could only be picked up once by one worker. Having a system like described before can be very useful and can be extended even further. Imagine a client wants a dedicated line of parcels delivery so only their items get on the conveyor belt and get delivered as they arrive. This means an even shorter waiting line. What if you would want to have a crown that only delivers forum emails? Now you can. A crown dedicated to generating reports? Sure. No more waiting in line for the reports starts to finish before working on the grades. But wait, there's more. Busy period approaching? No problem. Hire more workers. Decide how many they should be and with a simple press of a button behold your crown increases in size. Or you just want the task to be picked up faster? No problem. Decrease the waiting time between the worker's visit to the loading dock, aka the queue cycle. Busy period over? Or you just... There's not so much work to be done? No problem. Send some workers on leave or increase the time between the queue cycles. All this is done from the user interface without stopping the queue or changing something on the server. I'm sure that by now you realize how all of this benefits the end user. Here is just the big one. Real-time results. Just try to imagine this in all the specific areas of Moodle that usually take time. Happy students equals happy teachers equals happy Moodle community. But wait, there's more. Having the freedom to innovate, we found more ways to improve the system so we created a bunch of new features. Moodle has a lot of nice modules and components, but not all of them have the same importance during certain periods. Sometimes we would need certain tasks more than others or we would like some tasks to be executed before others. In other words, we need to prioritize the way things are done and these priorities change over time. Like I mentioned before, all tasks are different and sometimes they become more important and thus require more attention. We made it possible for you to customize the way each task is handled by setting their priority, changing the amount of information collected about each execution and what happens next, setting the maximum number of fail attempts so it doesn't... to prevent it from causing issues to the system and also choosing the environment they are running. Each task is executed in an isolated environment, aka container. Each container can have specific configuration like different database connections, global variables, etc. This allows us to fine-tune the way each task is executed, which ultimately increases the performance of the site. We also made it possible for an easy integration of our advanced queuing solutions like RebitMQ, Redis, or Amazon SQS by following their standards and making sure that this does not alter the queue manager's workflow. While designing the system, it becomes clear to us that the queue can be used for other things, not just for the Chrome. It offers a new way of handling the work in real-time, faster than it was previously possible. It offers a chance to switch from a synchronous to an asynchronous way of doing things. Think of all the functionalities that take a lot of time to finish and you can't do anything else but wait for it to finish. Think of the backup and restore process. Instead of waiting in line, waiting in front of a loading page while the server finishes your restore, why not use that time to do something else like finishing an assignment or creating a quiz for your students or even start multiple restores. Imagine a moodle where you start the process like backup and restore and then be able to continue to working on anything else and after a short while you get a notification that lets you know that your restore has finished or that your report has been generated. With the queue, this is now closer to becoming a reality. Thank you. Questions? I wanted to share a question. Where can we get this plug-in? I didn't quite get it. Where can we get this plug-in? Currently it's not released, we're still testing it and making sure that it runs as we expected. It will be released soon, not entirely sure this month or next month and then it's easily available. We will also have a small demonstration at our stand to actually see it in action if anybody's interested. From my understanding, a use case could be to make, say, backups on specific timing. Even with different queues, I cannot see the elimination of locking mechanisms if they refer, for example, to very similar records.  You still do not eliminate the error of the locking mechanisms. Well, basically we have the unique queue items that have a unique identity code. So it's only once, and only one queue item of that type can be in the queue and be started and run and worked on. So basically we've eliminated the possibility of the same task to be run at the same time we started at a different stage to have two of them running in parallel ish. So basically that handles the locking problem because it's based on what you're currently doing. Just one question. You mentioned using containers to spin up each of these different workers. Does it work without containers in the sense of if you've got a more traditional Moodle server with, you know, actually, can you do it? Well, does this plug-in work with that? Well, I use the term container just to make it more clear that the task is run in an isolated environment. They're not exactly containers like you have with Docker or something like that. It's just the script itself, the task execution is isolated. So any configuration that we have, we can specify to that particular task and all the other ones don't get affected by any changes to one task. Okay, so you could still run it? Yes. Yes. Multiple instances of cron calls on a single server. Yeah. Yeah. For this plug-in to be fully functional, does it require you to use the Redis or another queue system or MQ or you can rely only on... No. We have a built-in queue solution that it's basically using the database to store queue items and pick them up from there and so on. Was there a good reason for doing this as a plug-in? It strikes me that a lot of these improvements would be better in the Moodlecore cron system so everyone just benefits. Well, basically we went with the idea that this is optional. Not everybody would require this solution and maybe not everybody has so many users who require such an advanced or more performant solution. We started as a plug-in. We might and we do consider pushing it to core but that's at a later stage. Thank you very much. Okay. Thank you.


All right, so we're going to record this session for our participants online. There's been technical difficulties with sharing it for everybody in person. Welcome to Dr. Scott Furlan from Seattle Children's. I'll be presenting on bringing single cell genomics closer to the clinic for patients with leukemia.  So, thanks for the organizers of the meeting today, Tim, for inviting me to talk. Sean and Mark Carlson also for their support. It's a real pleasure to be here. I'm a physician, scientist, an MD. I use genomic technologies to better understand relapse disease, relapse of leukemia in children. And I am a bone marrow transplant, so I attend at the Seattle Children's Hospital and perform bone marrow transplants on our patients who need them. Today, I want to share with you really a project in the lab that's just been building over the last year or so, and was really motivated by one patient's sample. And I'll show you most of the data from that sample today. But I do want to just take the time right now and acknowledge his sacrifice and willingness to participate in research. He really opened our eyes to the possibilities of using single cell RNA-seq for residual disease measurements in AML. And unfortunately, last month, he died after a long battle with AML. And he was a great kid and had a love of science and will really, really miss him. I devote this talk to him and his family, and thank you for listening. So overview, I want to introduce the concept of measurable residual disease, as I said, from the lens of one of our patients. Provide a rationale for the use of single cell genomic technologies to potentially improve measurable residual disease assessment after transplant. For some of our preliminary data, highlight some novel molecular and computational approaches we're taking in the lab to enhance MRD, enhance the confidence of MRD assessments. And I won't touch on it today, but by explaining our work in MRD, I hope you'll kind of see the broader applicability of single cell genomics to understanding biology of AML, acute leukemias, and mechanisms of relapse. So really, we're just kind of building that toolbox. And hopefully in the next few years, we'll be able to get even more samples and have a greater understanding of relapse leukemia. So I have no relevant conflicts of interest or disclosures to make.  And our history begins with us when he was diagnosed with myelodysplastic syndrome in 2018. Myelodysplastic syndrome is a disorder of blood-producing stem cells that produces dysmorphic or malformed or strangely shaped red blood cells, white blood cells, as evidenced by the picture here shown in this diagram. And unfortunately, he went on and evolved to what we call acute myeloid leukemia, which is not uncommon for patients with MDS. He received a standard of care, which is a match-unrelated bone marrow transplant in April of 2019. And two years later, unfortunately, he developed low blood counts again on routine monitoring and was found to have a relapse of his disease. After relapse, he underwent re-induction chemotherapy and was being considered for a second transplant, this time using a different donor source of cord blood donor. And there was a conundrum, and we were struggling to figure out what to do next. We were measuring his disease in the marrow using flow cytometry. And by doing so, we were achieving measurements in the 1% to 2% range. So 1% to 2% of the cells in his marrow were leukemic. But when we were testing for mutational burden using bulk NGS testing, the variant allele frequencies of those data seemed to suggest that he actually had a much higher level of disease, around 10%. And so we really were struggling with this conundrum, and it was very frustrating for both the treating team and the family, as you can imagine. And you might say, OK, well, he's got relapsed disease, we'll just treat him, move on. And there's an important reason why we can't necessarily do that or why we need to be very careful here. So let me explain the sort of nuances in clinical care for patients who have low levels of disease after transplant. So if you have levels of disease that are less than 1%, you can consider maneuvers to try to boost that graft versus leukemia effect. And those maneuvers include altering immunosuppression, giving more donor lymphocytes, potentially treating with chromatin modifying agents to try to boost antigen production by the leukemia in hopes that the graft will destroy the leukemic cells. But we generally don't consider those maneuvers unless the disease level is low. So greater than 1%, they won't work. It's just not enough. And when we do this, we can alter the trajectory of relapse for a large number of patients, and we can cure them with just these small maneuvers. On the other hand, if the burden of disease is high, so if we have greater than 5% of disease, it is generally not considered a good idea to go to a second transplant. The outcomes for patients who have levels of disease greater than 5% are dismal, and therefore we don't even consider really taking those patients to second transplant. And so the paradigm or the clinical practice is to reduce the burden of disease by chemotherapy, and then once we achieve less than 5%, taking the patient. So you can imagine, we have two tests. One is saying go to transplant, and the other one is saying don't. And this was a real struggle. And so this really opened my eyes into really how the clinical tests that we have for measurable residual disease are not good enough, and we need to do better. And so this motivated us to try and use single cell RNA sequencing to try to potentially understand what the real level of disease is. Now, I'd like to start back up and just give you a little bit of background about how we test for measurable residual disease. Generally the tests fall into four categories, PCR-based tests, NGS-based tests, so the bulk NGS testing like I was explaining before. And then multi-parameter flow cytometry is the gold standard for pediatric AML. And then chimerism testing is another group of tests that we use. So let me just go through the strengths and deficiencies of each one. So PCR tests are really aimed at capturing fusion. So RNA fusion, chromosomal translocations, leukemias are often caused by chromosomal translocations, so we can PCR and capture those transcripts. They have very high sensitivity, but they're limited in their applicability, and you need to have a priori knowledge about what you're going after with the PCR test. You can't just fish for every fusion, and here's why. So we like to say that pediatric AML and ALL2 have what's called a long tail. And so these are data that Tim alluded to earlier in our collaboration with Tim and Sohail Mishinshi, my mentor. We have 1,210 patients who were profiled on the last cooperative children's oncology group trial, study AML-1031. And we just looked at all the different fusions that we see. You can see the major problem is that a large number of patients don't have a fusion at all. So there's nothing to actually PCR. And then the long tail problem is the other problem. You can't possibly go after all of these. I mean, you could, but it would be very challenging and hasn't been implemented clinically. So that's a problem for long tails, a problem for molecular assays. And similarly, we don't always for mutations. We don't also have, we have to have a priori knowledge. And there's a similar long tail of mutations that are associated with leukemia. So bulk NGS testings are even less sensitive, and so they don't work very well either. In this case, they did show that discrepancy. So that's interesting. So the standard of care really in pediatric AML is to use multi-parameter flow psychometry. It's more broadly applicable, it applies to many different kinds of leukemia. But it's limited in sensitivity. And I think what, what our experience with this patient has shown us is that it probably is, it underestimates the burden of disease. And that's probably for a lot of different reasons. But namely because I think there's clonal heterogeneity in AML in particular, ALL less so. And I think that confounds the ability to detect AML in clinical samples. So additional problems include the fact that it's really hard to run across multiple centers, right? You've got, everybody's using different antibodies, different instruments. Maybe someone didn't bleach it properly, who knows, right? And so it's a challenge. And chimerism testing is not currently sensitive enough to be used as an assay for MRD right now. It's plus or minus 5% on either side. So 10%, that's not good enough. So we, as I alluded to earlier, or I might have just said it, single cell RNA sequencing we think can meet this need. So because single cell RNA sequencing, and we're 10x genomics here, three prime and five prime measurements. And because of the massive parameter space in genes, a number of genes that we're able to detect in single cells, we think that this will give us a better ability to differentiate leukemia versus not. And so we'll compete with multi-parameter flow cytometers, the single cell method, and it can measure many, many, many new parameters. And what I'd like to share with you today is our experience using natural variation and mining the data for SNPs and single nucleotide variation to further differentiate recipient versus host, I'm sorry, recipient versus donor in samples. And I think this is a really important advance and will be a clinically useful tool for single cell chimerism. We have ongoing work using next generation detecting mutations using long read sequencing, and I think Tim alluded to that a little bit earlier. And we are working on trying to detect fusion transcripts in 10x data as well. So we think this is an exciting area for development. We're excited to talk about our experience with the molecular approaches and computational approaches, but we need help. We need help from computational people, from molecular people. So if there's anything you'd like to reach out to me, you want to get involved, love to hear from you, please do so. So what's the deliverable here? I think that we can deliver a more confident assessment of what that level of measurable residual disease is. And I think that's the direction that we need to move. Okay, so let me tell you about the experiment that we did on our patient that I described to you earlier. So we took a bone marrow sample. We really didn't know what the best method for processing the sample would be. There's lots of papers out there on single cell RNA-Seq. You usually use FICOL or gradient centrifugation, but we wanted to go take a step back and say, okay, what's best? So we did red blood cell lysis step on the marrow. We did gradient centrifugation. We also did CD34 enrichment. So we ran the cells through an immunomagnetic column enriching for CD34 with the idea that if flow was right, and we're only at 1%, hey, let's boost this up a little bit so that we can actually see some enough disease to be able to look at the genomic profile of it. And then we performed single cell capture using both 3' and 5' 10x, V3, and sequenced those six samples. So three different processing, and then two different library preps to get the data. So I want to talk about a little, I think as we begin to think about scaling these things clinically, I think we also have to think about, is this feasible? I won't get to cost, but is it feasible to actually do? We collected the sample in the afternoon on a Thursday, captured the cells later that afternoon. Library preparation we did in one day on a Friday. We lost the weekend because the core wasn't open, and we now have our own sequencer, but we didn't then. And so we started sequencing at 8am on Monday. Sequencing ran for 24 hours. There's a little delay there, as partly my fault, but the demultiplexing started in the afternoon on Tuesday, and CellRanger was done in a few hours, and I was able to look at the data on Thursday evening. So is this feasible for turnaround? Yes, I think that it is. Flow is a one to two-day turnaround time. Those molecular tests, much longer. I think this is feasible to do. And so I don't want to spend a lot of time talking about droplet partitioning, single cell RNA sequencing. I think a lot of you are familiar with it. Suffice to say that the microfluidic device is a workhorse that brings cells and specialized beads together that barcode the messenger RNA from those cells uniquely, and then we perform library preparation and were able to recover the transcripts that are associated with single cells because of those barcodes that were tagged onto the messenger RNA molecule early on. I want to just point out a fantastic team of people I have in the lab doing these experiments and who've been leading our molecular development work in this space. Sammy and Truti are great to have in the lab and they're doing a wonderful job. All right. So I'm just going to skip all over the QC. I know you've probably seen tons of single cell RNA sequencing QC. I'm not going to bore you with that. We know how to do QC. I'm assuming you do too. Let's just look at the data. So we know that this patient, this is the flow, this flow cytometry. The blasts here are high for CD34 and low for CD38, which is classic for AML. So what's the first thing we're going to look for? We're going to look for a CD34 transcript. So here's a dimension UMAP embedding of the data after filtering, cleaning the data up, removing doublets, et cetera, et cetera. And what we see is a large hook of cells over here. Now this is all of the cells, all of the conditions combined. So CD34 enriched, FICOL, and RBC lysis. And then I'm just throwing the three prime data. I'm just going to fast forward and tell you that the five prime data don't look much different than the three prime data. So we'll just get rid of that right now.  Now this includes the CD34 enrichment. So could we just be enriching for the leukemia? And the answer to that question appears to be yes, if these are leukemia, I'll get to that. But this hook, we see in the CD34 enriched sample, we see many cells in the gradient centrifugation sample and RBC lysis sample. And these are about 10%. So in more agreement with those NGS tests than flow cytometric measurements. But now we have a lot of cells to look at, which is good. Okay, so the next thing we really wanted to do was annotate these cell types. We know that the, we suspected leukemia shouldn't really cluster with healthy blood cells in the marrow. It should be different. It should be expressing a lot of different other genes. And so the method that we first used to look at this was Surat's RPCA integration. And what that allows us to do is, you know, the reference dataset that we're using here, which is from Greenleaf Labs publication in 2019, is a great reference dataset. I highly recommend it for marrow. We've seen really good performance with that as a reference dataset, but it used 10xv2 and we're using v3.  So after integrating the data, we see cells, and I'm showing the patient cells here in gray, that cluster with T cells and NK cells, B cells. We see myeloid cells clustering with myeloid cells. We see a group of cells. We don't know their myeloid yet, but they cluster with them at least. It's hard to see on this plot, but behind the gray line there, there's some green cells. Those are red blood cells. So these are red blood cells. And then there's this kind of ugly blob again of cells that looks very suspicious. The other thing we did to annotate our cells, now where I'm coloring the reference cells gray, and I'm annotating the patient cells using UMAP Transform. So I'm taking the UMAP data from the reference and we rigorously went through their data and we're able to pretty precisely recover their exact embedding, I would say. This is our redo of their embedding. It looks really close. And then we were able to project our data, so the patient cells onto this UMAP using UMAP Transform, and then color the cells by which cells they were most intimate with after that UMAP transformation step. And so now we're actually able to annotate the cells using UMAP Transform, and that was really helpful. And what you see is a really disordered pattern of what appears, as best we can tell using this projection method, a pattern of cells that appear to have a disordered differentiation pathway within that putatively leukemic blob. And you see cells that are most resemble stem cells going to early erythroid cells and then growing straight to monocytes. That's not how red blood cells are supposed to develop. Now this is not development, right? This is just what the best fit is, but nevertheless, it looks disordered. So and again we see the labels that we got up for our cells using UMAP Transform were intimate with the cells that we obtained with the RPCA embedding. And so okay, that's great. It actually took a little while to do that. I'd say an afternoon, maybe more a day. And we want something faster. If we're going to turn this around, I'm not sure RPCA integration, while it's good and fast enough. We want something a little bit faster. So and we like to code. And so we wanted to also kind of create our own classifier. So we created a wish list of things that we wanted to do. And so now I'm going to just kind of take a break from the patient sample and talk to you about a little bit of a computational tool that we've developed in the lab to help us annotate cells. We wanted it to work in R. We wanted to be able to classify cells with one line of code. We wanted it to be fast. We wanted to be able to edit the models or the learning algorithms that the structure uses. And we wanted it to be UMI count-based. In other words, we didn't want to have to take external data sets and do dimensionality reduction and call clusters and see how well it aligned with their calls, et cetera, et cetera, et cetera. We just want to be able to quickly download a data set from GEO and see how our cells annotate relative to that reference and quickly. So we developed a... So I was looking around and if anyone has any ideas about why I should be using a Rayfire, please tell me now. But I was looking around and I was intrigued by a C++ library called a Rayfire, which is a machine learning library that works using... And I'm saying all this, I don't really know it. So I'll just say that out loud. It's supposed to work. It does work. We've tested it. It works really well on both CPUs and GPUs. Flips between the two. There's nothing you have to do. It's really nice. And fortunately, the RCPP teams have figured out a way to easily create our packages using Rayfire libraries. And so we can plug it into those C++ libraries. And it makes it really fast. And so this... To try to think of a name for this project or this classifier, I came up with the word view master. If you remember... I'm a child of the 80s. So if you remember that old thing, used to flip through the little wheel, the little wheel of pictures and quickly just change your view. And that's what we want to do with single cell RNA sequencing data. We want to take our data and quickly flip through a reel of references and see, okay, yeah, this looks pretty good. But let's look at a different reference. Let's look at a reference from someone who was sick. Let's look at a reference from a mouse. And just see how things are similar or different. And so it works really well. So let me just show you how we've implemented it and how it benchmarks a little bit. Okay, so fortunately it came pre-baked with some code. So I was able to quickly implement the softmax regression, which is otherwise known as multinomial regression, logistic regression. And I didn't have to do much other than edit the code a little bit to feed it a serrat object or a monocle object or single cell experiment object. And so the idea here is that we feed at a reference dataset. It quickly learns how to classify cells based on what we're using as the most variable genes, as most packages use, the most variable genes, about 2,500 of those. And then learn using probabilities, the, or spitting out probabilities, learn what the appropriate cell type is for each one. And because we have a reference already, those labels already there, we can learn. And then we can apply that model, the model that we get from ViewMaster, to take ourselves here and quickly annotate them to look, to give them labels according to the reference. And it works really well. Let me just give you an overview of an experiment we did to benchmark it. So the reference dataset, we held out 80, we trained on 80%, hold out 20 test set, feed it into ViewMaster, develop a model, and then we check it on another bone marrow dataset, another separate dataset, where there's an actual cell type. And I say actual because, you know, it's just the label that the authors gave. But then we can compare the actual cell type to the predicted cell type that ViewMaster spits out. And so the two datasets that we used, again, are the green leaf reference of bone marrow and stem cells. And then you may be familiar with the SROT BM site seek dataset. I harmonized the labels so that they would work. And let me show you what it spit out. So using the green leaf data as a reference and the SROT dataset as a query, accuracy on training is high as you would expect. But the hold out dataset is pretty impressive. I don't think it's overfitted. 98% accuracy and 29 seconds to actually do this classification, which I thought was reasonably fast. And then comparing the actual cell type to the predicted cell type across the range of different cell types, I think we do pretty well. There's some confounding here of lymphoid progenitors with pre-B cells. Those are very similar cell types. But I think there's actually another reason why this is confounded. The SROT dataset has very few numbers of lymphoid progenitors and very few numbers of B cell progenitors. I don't think this dataset was CD34 enriched very much. This dataset here on the left is CD34 enriched. So it has CD34 enriched and unenriched. So I think that's hurt the performance a little bit, especially around classifying that more rare cell type. And then when we do it the opposite way, we do see really nice confusion matrix and similar speed, et cetera. So we really want to use this more. People would like to weigh in on what they think of it. We'd love to hear it. I'm sure we've got a lot of room for improvement. So that's ViewMaster. And this is being led by a very talented technician in my lab, Olivia Waltner. Okay. So I alluded to this earlier, but how can we also... So going back to that patient sample now, how can we use natural genetic variation to predict whether or to figure out whether we can find out whether a cell is donor versus recipient? So we're mining the data for SNPs, essentially. And what I want to point out is that it's really critical to pair this with cell type. So as a transplanter, when I perform a transplant on someone, I'm less worried if their T-cells don't become 100% donor. That's not uncommon for T-cell engraftment to take a really long time. And they may never achieve 100% donor T-cell engraftment. But it's when those myeloid progenitors are anything but 100% donor, that's when I'm worried that it's been a relapse of leukemia. And so that's why pairing single cell chimerism, or at least a genotype demultiplexing, with cell type identification, they think it's where the advance is and where we're going to be able to more confidently measure residual disease, if that makes sense. So there's a tool, really great tool that was written by Haynes Heaton. And he's at Auburn now and he's agreed to collaborate with us. So we're working with him and extending this package to more data sets. And so we're excited to work with Haynes. But the idea is that we measure SNPs in reads that align to the genome. And because these reads are tagged with the cell that they came from, it's easy to cluster the cells, not by gene expression, but by SNP genotype. And so this is just data from his paper where there was five samples that were mixed together and then perform PCA on the SNP genotype matrix, the probability matrix that supercell spits out. And you can see clearly five different clusters of genotypes. And he went on even further to call cells that kind of had an intermediate genotype between two clusters as doublets. And those are genotype doublets. Presumably.  And we can clearly identify two very different clusters of SNP genotypes. We missed some doublets, right? Because when we did our doublet discrimination using scrublet, which is what we usually use, it's looking for gene expression and modeling gene expression doublets doesn't know how to look for genotypic doublets, right? So we found a few extra. And we can color this graph now by cell type. And you can see that there's this sort of smeary cloud of cells that don't really cluster very well into a genotype here. And when we look at what cell types those are, not surprisingly, those are red blood cells. And red blood cells have RNA, but they don't have a diversity of RNA. And so therefore they wouldn't be expected to have a lot of SNPs in those RNA molecules. So it's reasonable to think that, okay, if this genetic demultiplexing gets it wrong, if it's in a red blood cell, I'm going to be a lot less worried about leukemia, right? We don't see mature red blood cells as leukemic. So we can remove them. We get really nice clusters of genotype. And now, and this was just an amazing like wow moment for us, was when we colored the cells now. So this is the cell type identification. When we color this using genotype, it's so clear that these cells are of recipient origin and that this is very likely to be leukemia. Now I'm just showing you what this looks like in the hook embedding, taking it out of integrated. So this is what the, these are the same cells that were in the hook. Okay, so we're also working to be able to detect mutations in single cell data. And we might have like, let's say we have this clinically relevant SNP, an X on one of the mutation. And during capture 10X, we use a poly T oligo binds poly A, and then we make, you know, cDNA using reverse transcriptase going this way. We might miss that G molecule if we're only sequencing 30, you know, sorry, 90 reads into the back of the molecule. So we're not going to capture that G, but if we take, you know, because we, and then we fragment the library, et cetera, et cetera. But if we take the full length cDNA that we get from 10X before fragmentation, we might be able to recover reads that have that mutation embedded in them. The other accident that we can sort of take advantage of when we look at this is the fact that poly T oligos will bind to internal A repeats within the molecule. And so you will actually get some coverage randomly in other parts of the molecule. And so we can take advantage of that. And there was a really nice algorithm called CB Sniffer that was written to do exactly this, is to mine 10X data specifically for mutations in molecules, just using the standard 10X and relying on this priming that happens internal to the molecule. So we did, we ran CB Sniffer. Again, here's the genotype calls on the left. And here I'm showing all of the calls for the TP53 mutation that we're able to identify. And you can very clearly see that the mutated TP53 cells are in the hook and wild type or not. So not too surprising, but still pretty clear, black and white. When we look at IDH1, what is really interesting is that there seems to be a lot more tumor heterogeneity with respect to IDH1 mutations. And so we see about 50% of the cells have wild type, even the leukemia have 50% have wild type IDH1. And so as I alluded to earlier, we went back to that unfragmented library. And with the help of Jason Underwood, who's a principal scientist at PACBio, we're working on using hybridization capture to pull down those molecules early on and then sequence them with long reads. And I only have a snippet of data to show you, I wish I had more. But here's our coverage of the IDH1 locus. And we see that nice 50% ratio of IDH1 mutant to IDH1 wild type. And so we're working on computational approaches to be able to pull the PACBio data and integrate it with the short read data. And so that's more to come on that front. But this looks really promising in our ability to actually recover molecules and increase coverage over clinically important areas of the genome. So how does this extend to another patient? So I've got another patient, another two-year-old. She had undifferentiated leukemia. So this is a really rare leukemia that doesn't fit under the ALL bucket or the AML bucket. And we were able, and her parents were kind enough to let us take extra marrow. And she had an unrelated cord blood transplant and looked like she had early evidence of relapse after transplant as well. So we used ViewMaster and we used Supercell. And we're clearly able to identify a large number of cells here that looked, I won't go step by step through this. It's pretty clean. These are recipient origin, and they all look like early erythroid to HSC-like cells. But in this experiment, we added SightSeq. And so for those of you who are maybe unfamiliar with SightSeq, SightSeq is using oligo-tagged antibodies to on 10x data or other platforms to measure cell surface proteome. And so what we're working on now is marrying clinical flow data to the SightSeq data that we get from the same sample. So here I have the clinical flow data on the left. We have high CD34, low CD38, et cetera. And then this is the, we've recapitulated the same looking plot using flow data, it's not flow data, sorry. Cell surface immunoproteome data. And this time we have 150 surface molecules that we've measured on these samples. So there's a lot of opportunity to discover new molecules that are associated with leukemia. And you can see very similar trends. So high for CD34, negative for CD38, high for CD34, intermediate for CD7, et cetera, 56 negative. And so this works. And I think this is another exciting area of development and how we can kind of marry clinical flow with molecular techniques in the future. So the other thing I think we can do with this is maybe we will learn new immunofenotypic markers that are associated with leukemia and potentially improve multi-parameter flow cytometry and really push that envelope as well. So I'm excited to see what happens there. This is being led by two scientists, Melinda Brionnacki, who's a medical oncologist. And Shuyen Chen is our resident, our collaborating hematopathologist. And her expertise in clinical flow has been really invaluable in this project. So where are we now? We've profiled seven samples, five I'm showing here. They show good correlation here, the number of blasts that we detect by single cell RNA sequencing on the x-axis, the number of blasts or leukemic cells we detect using flow cytometry. There's good correlation. I'm circling the samples that, where I think where flow is actually getting a little, is underestimating the burden of disease potentially. We have two patients who have PCR positivity but are flow negative. And we're working to analyze that. And we did intriguingly find one, in one of the samples, one cell of early myeloid lineage and recipient origin. So in summary, inconsistencies and clinical assays have been an important motivator for this project and I think have motivated me to improve diagnostics for leukemia. Integration of single cell data with genetic demultiplexing, and I think we'll ultimately provide a more confident assessment of relapse after transplant. I think we have promising preliminary data suggesting we can augment coverage using alternative molecular approaches on Tenex data. We have nice immunoproteomic data showing that we can recapitulate clinical flow. And we want to increase our sample numbers. We want to build our toolkit further, our computational toolkit, detect fusions, be able to mine those data, integrate it with UMAP embeddings and expand the toolkit even more. Ultimately, I haven't talked at all about this and I think, I hope that by talking about MRD, you can kind of see the potential for this to really explore mechanisms of relapse, HLA expression on the leukemia, antigen processing machinery, we can look at T cell exhaustion, myeloid suppressor cells. But we need money, so if you know anyone who has any, yeah, give it, please let me know. And we also need people. So we're hiring at SERI and I'll just put this up here. So Sean put Sean's email on there and Jay who I see sitting in the back, please contact them if you have any more information and are interested in working with us. We'd be delighted to hear from you. I'd like to acknowledge my lab, my clinical coordinators and clinical help mentors, of course, our patients and their families, funding sources, and with that I'll take a break and we can have questions. Yeah, very great presentation, really enjoyed. I was wondering it's, I mean seven samples, I guess it's still the beginning to draw patterns, but often you can have very fast algorithms, but if you need manual curation, that's the time consuming part. So if you have to spend a week to understand the, what maybe progenitor population you are dealing with, that's the pipeline. So I was wondering how much you think you can automate this process, how much you think Leukemia is such a terogeneous that you need a person for a week for each case that you have to analyze or... Yeah, it's a good question. I mean, right, we don't know because we haven't done enough samples, but it's pretty fast once we get it rolling. The ViewMaster has pretty confident assessment can differentiate a T cell and a red blood cell and a myeloid progenitor. That's really all we need. We're not asking a lot. So we see recipient chimerism in a T cell, we're going to be less worried. We see recipient chimerism in a red blood cell less worried. We see recipient chimerism in a myeloid progenitor for AML, at least. That's problematic. And so, yeah, we'll need to do more samples to see if the algorithm falls down in some cases, differentiating T cells from Leukemia. I don't think that's going to happen, but we'll only time until. I'm just going to squeeze in here. To be really accepted by this crowd, I think it needs to be ViewMastR. Okay, sure. Sure. Okay, yeah, I love it. We'll do that. Yeah, we'll change the name. Hi. Just a question about the mutation detection with IsoSeq. So like Pacfire saying that they've got much greater fidelity of detection of mutations, and there was also a whole recent Twitter thread saying how we expect to see a certain amount of missed calls within 10X just due to the actual artifacts within that. Are you seeing much difference between the mutation calls in 10X and the IsoSeq? And if so, which do you think has greater fidelity? Yeah, don't know enough to answer that question intelligently yet, but I will say that the TSO artifact that we think confounds the ability to sort of just PCR off of 10X libraries and put them on a short read sequence or we've done that experiment and put it on a Lumina, we see real problems with a TSO artifact. And so I didn't include that because it's just getting a little bit too inside baseball, but we have a TSO depletion step that we're doing to try to minimize that 10X artifact. So hopefully we're addressing it, but I think we need more data to answer that question fully. It was a great talk and then it was really great to see that our research can be applicable in the real patients. I just wonder whether you are, I mean, you are kind of playing to utilize this information from single cell assays in maybe future patient treatment or anything. Yeah, I think so. I mean, that's the goal. I think what would be my long-term vision is, and I don't know how well this would work in de novo ML or regular vanilla leukemia, but I think after transplant, mining the data for genetic variation is a huge advance and gives me a lot of confidence as a treating physician that we're seeing a true relapse. And so I think that as we think about mobilizing this clinically and what are the next steps, I think transplant patients make the most sense to sort of roll this out.  We're already spending a ton of money on these patients. What's, you know, three to $5,000 more for a 10X one. Scott, for clarification, just in case anybody here doesn't know how ridiculously expensive it is to treat this disease, what's the median cost to treat successfully or otherwise a pediatric AML patient? Yeah, so a transplant, you know, I don't know if I'm allowed to say this, but I'll say it. So we ask for patients who come in out of the country, don't have insurance within the country. We ask for usually between half a million and a million dollars up front to do a bone marrow transplant. So it's not cheap. And so, you know, a couple of 10X runs, it's like a drop in the bucket. So we got some questions online as well. I think Sean was going to take off. Yeah. So Scott, Jared Andrews, did we? Okay. Jared Andrews asks, why not use something like single R? And he has the capital R. I mean, we did, we did look at that. And there was a reason, but I don't remember what, single R is good. We've used it in other settings. We just kind of wanted to write something. Is that all right to say? And then Ryan Thompson asks, how does this method handle cells with ambiguous expression, i.e. expression that is intermediate between two or more cell types? And then you follow it up to say, to clarify, since several methods were discussed, I'm referring to the cell type annotation method. Yeah. So we're working on that. I mean, there's a max function at the end of the multinomial classifier that just takes the maximum. And so we're toying. This isn't really going to answer the question, but we've implemented a threshold. And so maybe we're thinking about using only the cells that have a probability greater than X and saying that those are more confident cell types. But yeah, we've more work to do on those sorts of questions. And so yeah, more to come there. More questions in person? There are more online if there's no in person. Yeah, jump jump in now before you get crowded out by this pesky virtual folks. Right. Yeah. Okay. All right. Eric Quavis asks, what data is used from SNPs to cluster cell types if present allele, position, LD? Yeah. So it's a it's a there's a variant calling step. You get a BCF file and you quantify the variants and get a matrix of reference and alt and then supercell takes the ref and alt to matrix and then does its magic and spits out? Nothing. The answer. Yeah. All right. Do we have time for over? Okay. May Woods asks, do you think there should be a standard set on the number of cells sequenced for single cell approaches? Since mutations in cells circulating at low frequency will be subject to sampling variants? I don't know. I mean, I don't think we would use and I can't envision a situation where we would say, you know, oh, one cell has one mutation and we're going to, you know, alter treatment based on that. So I think we I don't think we're in danger of that because I think what we need to do when we we implement single cell genomics clinically is we need to look at multiple parameters at the same time. And so I don't think we're going to be sort of hamstrung by, you know, one cell and an aberrant measurement in that way. I don't know if that really answers the question. And maybe you might, one possible answer might be is that as long reads start to potentially become more and more certain, we know that one cell can restart leukemia. So maybe at some point it does become feasible. But right now? Yeah. You said probably not. Right right now. Yeah, we got we got a slow slow roll into this. May follows up. She says, but it's not one cell if you sample 5000 cells. Yeah, I'm not I'm not sure I get the question, but sounds like email. Yeah, email me. Yeah, my work. Yeah, let's let's talk. Okay. Thanks everyone. I appreciate continuing the glorious streak of ending on time. Everybody enjoyed lunch. And please be sure and check out the workshop this afternoon. Thanks everybody. Another big hand for Scott. It'd be so kind. And our birds of a feather coming up this afternoon.


So I want to check in on the Senate race that's currently taking place in the state of Pennsylvania It is very fascinating on both sides. So on the GOP side predictably you have essentially what is a circus It features dr. Oz which is entertaining in and of itself because he's a clown but essentially that GOP primary has devolved into a debate about Brotherism and Dr. Oz has pledged to renounce his Turkish citizenship in the event He wins the primary and I guess he's a little bit disappointed and surprised that it's come to this which is weird because it's the GOP and This is what they do. So I don't get why you expect it any different But it's fascinating now on the Democratic Party side. You actually have some really interesting figures And when I say figures, I mean, there's one person that's interesting to me John Federman. He's the lieutenant governor and he's the more progressive candidate He's not a Bernie Sanders per se, but he actually has a message that's resonating with people He's less rehearsed as a politician.  I mean right wing Except he has since had to distance himself from Joe Manchin after trying to be compared to Joe Manchin because Joe Manchin Kirsten Sinema single-handedly have tarnished the corporate Democrat brand after obstructing Biden's entire presidency So Connor Lamb is now left with nothing to brag about and now he's getting desperate So in this desperation, he's choosing to go on a kamikaze mission that will inevitably help the GOP I think his thinking here is that if I can't win if I'm gonna lose then I at least want the next best person to win And it's not the Democrat. It's the Republican because we're talking about Connor Lamb here So he's a right-winger. So if he sees that John Federman is poised to win He's going to do what he can to help the Republican but simultaneously as he does their bidding He's gonna deny that he has any involvement because it's his super PAC that's doing these attacks not him wink wink Definitely not coordinating with them wink wink. So there's an article about this by Holly Otterbyn of Politico and it is fascinating She breaks it down. Let's get to that. She writes a super PAC backing Pennsylvania Senate candidate Connor lamb is warning perspective donors That he is trailing front-runner John Federman by 30 percentage points in the Democratic primary and that the public's perception of his opponent's Ideology must change for lamb to have a shot primary voters Don't see Federman as the liberal he is reads a memo circulated by the pro-lam group pen progress Which was obtained by Politico for Connor lamb to have a path in the primary this dynamic needs to change The document highlights the testing of aggressive negative messaging against Federman who is Pennsylvania's lieutenant governor Including that he is a dangerous radical who proudly calls himself a socialist Supports far-left policies like a thirty four trillion dollar government takeover of healthcare and has spoken at the defund the police rallies and Wants to release convicted felons back onto our streets the Federman campaign sought to blame any coming-on thought on the Democratic side on lamb himself rather than an independent super PAC Connor hasn't been able to gain ground with Democrats So he's decided to run like a Republican and use Fox News talking points to attack a fellow Democrat said Federman spokesperson Joe Calvello This is a desperate move from a campaign that hasn't been able to raise the money on its own and hasn't broken through with anyone except For some political insiders, so I love that response from the Federman campaign. That's exactly what they should be saying You know conflate him with Fox News talk about how he's only winning support from insiders basically the donors of the Democratic Party and yeah, hit back don't just Expect these attacks to kind of fall short. I think that a lot of times Democrats They will just expect to absorb the blows by their opponents and not respond But no you need to forcefully push back against these smear attacks because this is what this is This is a smear campaign now. Um, there's a couple of things to say about this first and foremost It's this ingenious at best but outright lies at worst So when it comes to whether or not John Federman is a socialist I wish but he's not he's never said that he's a socialist He's a social Democrat So, you know, this is the whole far-left bad attack that we're seeing albeit from a Democrat Connor Lamb and he's barely a Democrat. He's a Democrat in name only he's a mansion Democrat So he's basically a Republican for all intents and all intents and purposes now He talks about the 34 million dollar government takeover of healthcare He's referring to Medicare for all because John Federman endorsed Bernie Sanders back in 2016 Now, I don't know if John Federman is the most vocal advocate for Medicare for all But we need people in the Senate who support Medicare for all because Medicare for all is good It's not a radical far-left policy if every other developed country has healthcare that is free at the point of service Do you think that the conservatives in Canada or the Tories in UK would call their healthcare system far-left? No, they wouldn't get elected if they attacked it, but yet you have a Democrat calling healthcare Far-left in the United States.  It tells you everything that you need to know. He also Couldn't say that John Federman Supports to funding the police and he wants to tie him to that slogan, which is politically unpopular So since he couldn't say that because there's no evidence of that He I guess speaks to John Federman talking at Black Lives Matter rallies and just says oh well since there are some signs there That's a defund the police He supports defunding the police and he spoke at defund the police rallies. You mean Black Lives Matter rallies. I Mean you couldn't just say oh well He spoke at Black Lives Matter rallies because if you don't believe that Black Lives Matter then I don't think you're fit to Run for any office as a Republican or Democrat because of course Black Lives Matter So what does he do?  Oh, and he also wants to release convicted felons And put them back onto our streets some tough-on-crime Fear mongering that we see from the 90s. Conor Lamb is a Republican I don't get why he's running as a Democrat just run as a Republican now I think that a lot of people they see this and they think wait a second This strategy seems kind of contradictory because on one hand He knows that you can't just outright run as a corporate Democrat anymore because that brand is toxic Hence why Conor Lamb distanced himself from Joe Manchin, but at the same time he's trying to bring up this far-left attack Because he thinks that being far left is bad when what John Federman is saying is clearly resonating with the people so if Being a moderate and far left is bad then what idea what ideology is good, and it's not about ideology I think that Conor Lamb knows he's going to lose if you're 30 points behind it's gonna take a miracle to make that up I mean, it's possible, right? But it's gonna be very difficult. So here's what I think he's obviously doing This is a kamikaze mission. He knows that this attack is going to Hurt his campaign because people are going to see this as very disingenuous, especially Democratic Party primary voters in Pennsylvania, but He's trying to help the person who in his mind is the next best person if he can't win Then he'd rather have a Republican than somebody who might be a leftist like John Federman And that really is gross and again this is speculation, but I think that his motivations are pretty clear what he's doing here, I mean imagine if a Democrat was running in a primary who is progressive and they tried to do a Republican smear campaign against their opponent The Democratic Party establishment would come out and denounce that and say you're trying to help the Republican But here he is using talking points straight from Fox News and the GOP Against the front-runner So what else is he trying to do? I mean, it's not that big of a stretch to think that he wants the Republican to win when ideologically him and a Republican are Pretty similar anyway, so yeah now the response from Conor Lam's campaign is hilarious because they're basically Pretending to be outraged that Federman's campaign would dare accuse them directly of launching this attack because it's not them It's the super PAC obviously they would have nothing to do with this horrible smear campaign quote Abby Nassif Murphy Lam's campaign manager shot back This material did not come from our campaign But the fact that John Fetterman thinks only Fox News Republicans oppose socialism Defunding the police and banning all private health insurance shows how out of touch He is with reality and why the Republicans are dying to run against him in the fall. So I love that Oh, we definitely didn't say this. We had nothing to do with this, but here's all the same talking points again Well, if you had nothing to do with this and you're not coordinating with that super PAC that Started this mere campaign. Why are you saying the same thing as them? Seems a little bit suspicious, right? Maybe You're breaking the law. Maybe you are coordinating if you're just parroting what they're saying and I love how the name of this Super PAC is pen progress When this is basically a right-wing super PAC. It's so Orwellian. It's so Orwellia Orwellian It just is so disingenuous. I hate it but yeah, this is this is really interesting it's a desperate attempt and One thing that I've learned when it comes to attack campaigns is that usually candidates are very reluctant to do attack Campaigns against their opponents because that brings them down and they only resort to attack campaigns If they're desperate because it might bring down them in the thinking of voters But it also sometimes brings down their opponent. This is based on political science study So it's a little bit dated. Maybe that's changed over the years, but you know, it's a desperate attempt I don't know that it's gonna work. I hope that it doesn't but Conor Lam I genuinely don't understand why he'd run as a Democrat when he's functionally a Republican Just run as a Republican and you can be yourself. Why fake it.   I don't get it. But yeah, really interesting here definitely Hypocritical right because again if the Democratic Party establishment saw a progressive Using GOP talking points against the corporate Democrat They would come out and denounce it and say hey, you're helping the Republicans, but here Conor Lam does Just that and the establishment we hear nothing from them crickets. Is the DNC gonna denounce.  So this is interesting. I'm rooting for John Federman I don't think that he is you know as progressive as Bernie Sanders But you know, I think that he's a good candidate and out of everyone I'm rooting for him the most He has the best shot at winning and he's pretty progressive.


Alright, here we are out here with Animal Rights Activism Sydney. This is a very similar style to my brothers and sisters over at Anonymous for the voiceless, what they do. We've got the TV screens up here with the animal agriculture footage on there.  Let's go. I've been watching lots and lots of your videos. Amazing. Over and over again. And now you're doing activism. Yes. Well, I've been, I was a vegetarian activist for a long time and a vegetarian when I was 16. Yeah. I've been with the intention of going vegan for just to be a long time to get there. That's cool. Yeah. It's a horrible way. We're trying to stop it, bro. That's all, mate. We're just trying to stop people from buying our products so that they don't, you know, we don't want animals to be hurt, man. No, it's not true. It's not true. They didn't do nothing wrong to us. You know what I mean? They're innocent. That's right. We just want, when people buy it, they've got the knife in their hand, you know?  No animals are tortured and killed for it, bro. So take it with you, man. I've seen it before. You have? Yeah. Maybe it was a documentary on TV. It probably made me eight months ago. A dominion? On TV. Oh, documentary on TV. Yeah. Yeah. I was watching it. Fuck. I was watching it. Fuck. So the way they kill the animals, it's a lot of them. Yeah. Long, you know? They put them in a suit or whatever they do. Do it the right way. It'll be fair, you know? If you don't have a signal and it's dying, fair enough, you know what I mean?  You know what I mean? It's unjustified. For our citizens, for feeding.  And no one here sees what's going on in there. No. They go in the supermarket. Yeah, nice and dandy. They don't see the suffering in the blood. That's true. Exactly. Yeah. That's because the male chicks don't grow up to lay eggs. So they just dispose of them. They chuck them in there fully conscious. Yeah. That's when they first hatch. That happens at the hatchery. That's the egg industry. Free range organic. They all get rid of the males because they don't grow up to be egg laying hens, you know?   They're just products to them that, you know, people buy eggs and they push a button on the blender. Yeah. Look, why I changed?  I didn't want to be an animal abuser anymore. And I just went like this. I got to do something. And I was like, well, I can be a vegan. That's the best thing I can do. Well, what I thought was that, what I wanted to say was for fucking, why don't they shut the hatchery in, you know what I mean?   That's what he's doing. He's just wondering. Because the government legally, this is legal. They make a money off it.  We don't need to eat it. Like, we're not trying to be surviving in the tribe with hunting animals. We don't have no justification.   We don't have to stab anyone to death for food. We don't have anyone to death for food anymore. Yeah. Yeah, it does, eh? Got a good heart, mate. All right, so check out that stuff and do the right thing, buddy. Absolutely. On ya, buddy. On ya. A legend, eh? Good guy, I see. Honest. An honest, big-hearted guy. What's that guy there, man? Like, because he's got tattoos and he looks a bit rougher. Like, people won't, like, they judge a book by its cover. That guy had a bigger heart than some of the, you know, I don't know, how would I say, privileged people that walk around that haven't lived through any suffering, that look at these animals. Human privilege. It is. It's human privilege.  He walks past and goes, this is horrible. This is cruel. Why are we doing this? You know what I mean?  Like, I'm sitting there debating with them at a university about why this is wrong. It shouldn't even be a debate. Did that man there debate with me? No? He just went, wow, that's cruel. Yeah, exactly. Like, exactly.  It's like, this isn't, you know, that's what I mean. I mean, people are so caught up in their own, like, it's a lot of ego. It's a lot of, like, I don't know, they're fighting to hold on to their beliefs, their steak there. I don't know. I don't really don't, sometimes I just lose hoping people away, but then someone like him walks past, I'm like, wow, what a legend, hey, what a good guy, you know? I don't know if he's going to go vegan, but at least he agreed.  You know what I mean? Speak English, yeah? Do you know what this is? It's against the bad, when the animals are bad in animals. Yeah, it's where people get their meat, cheese, milk and eggs from. Slaughterhouses and farms here. Yeah, where are you guys from? Germany. Germany. I've been to Germany, I've been out in front of German slaughterhouses as well, yeah? Yeah. Yeah. In Germany, they, I've had Fabide, I'm not from here. Fabide is, it's a whole country, yeah. Fabide what? They said in Germany, those kind of slaughterhouses have been. That's ridiculous, no, no, no, no, no. Germany, Berlin, I've been to a gas chamber in Berlin. Have you seen the Pigsby gas chamber? Yeah. Have you seen the Pigsby gas chamber? Yeah. It's, I don't know if you've seen it on the screen. Horrible. They don't prohibit any of this in Germany. No, no, not in the gas chamber. Yeah, yeah, yeah, and so is slaughtering animals with a bolt gun and a knife to the throat. Is it completely legal in Germany too? Yeah, they do this all around the world. Standard practice. Yeah. You know what they do at the top here? This is what they might have stopped in Germany. They put them in a mixer. The blender. Yeah, yeah. So you know about this? Chicken nuggets. Yeah, I've seen this before. So you must have changed your diet by now or your lifestyle? At least I only buy meat and I don't buy like super cheap meat and stuff. If I buy meat, I buy expensive one. I don't know. It makes you feel better? I don't know if the conditions are actually better. That's just what the blender makes me feel better. So those labels make you feel better, but that animal was murdered in a slaughterhouse just like these ones. You know they all go to the same slaughterhouse? No, I don't know. Well, they slaughter factory farmed animals at the same place. They slaughter the free range animals. And you're right, you don't know what happened to that animal.  So that should be evidence. Yeah. The reason we show people this is not for... We don't stand here for no reason. We're trying to convince the public. We're not convinced the public. We're trying to let the public understand that without their money, we want them to know they pay for animal abuse. It's really simple. Like without the public, they don't slaughter animals. So the blood is on the hands of people who buy it. Sure. Yeah. Who's organizing this? Animal rights activists in Sydney. Yeah. We're just animal rights activists.  When we have options, vegan options. I used to be vegan for a few months. Just to try. Yeah. A lot of people say they try vegan, but vegan's like a philosophy you adhere to. It's like being against racism. You're against animal abuse, you know? A similar thing. I wouldn't agree it's the same like being against racism. Not at all. I tried to see, you know, right now I'm not busy, but I'm super busy like finding work. So I tried to see if there's like a big threshold to just going full, full vegan. Yeah. So I did like two or three months of being vegan. And I just figured it was more expensive. It took more time. It took some more practice. Not if you live and build in, but perhaps not if you live in Sydney. But at least in my city, which is on the opposite side, it's just... It was a little bit inconvenient at first. Yeah. You don't like, in Berlin you don't have any, it doesn't make a difference whether you eat vegan or if you don't. Perhaps it doesn't here. But at least in Düsseldorf it's very hard to like, you don't have any shops where you can be sure of that they are vegan or if you have vegan ones, they're super expensive. And if you go to supermarkets where you only have like good vegetarian quality or vegan quality or things that are only vegan, it's super expensive. I agree that some vegan products are super expensive. But also, the most cheapest products in the supermarket, the whole food, plants, the beans, the rice, the potatoes, the corn, the vegetables and the fruits, those are the cheapest foods in the supermarket. By far. You know, so you can design a vegan diet that is cost effective for you. For sure. But the reason I said it's like being against racism is because it's an issue of injustice here. I mean, it's not the same as racism, but we're discriminating against some animals and we're cutting their heads off because of that. So it's an injustice. It's not right. It is an injustice. I was just not agreeing with the kind of... The analogy? Yeah. Well, both of them are evil. They are, but to a different extent. Of course. Racism is like, oh, they're different to us. Let's treat them differently. Treat them badly. Surely the underlying principle is the same. They're different to us. Let's treat them badly.  And let's just say you buy this free-range animal, right? I don't believe so because I've seen free-range farms. But let's just say, like, this animal, they're on the grass, then they get led into the slaughterhouse and get shot in the skull and have their head cut off, gutted, turned into meat. You know? That's the injustice. They're treated as... Surely the underlying principle, I get the analogy in terms of racism. I don't just... I just don't think that's to the same extent. It's worse. The result of human supremacy to animals is worse. Where are 3 trillion humans being holocausted right now? They're not. No, exactly. 3 trillion animals every year, including sea animals because 2.7 trillion marine animals are also murdered. Sure. So bad things have happened because of racism. We can all see into history it's evil. Racism is evil.  Human slavery still exists. Where is this type of treatment of humans legal? No, it isn't. Yeah. It's legal here to... It's legal here to do this to animals in Australia. This is actually standard legal practice. Yeah, probably. I mean, I don't need a lot of meat anywhere. Anywhere. Yeah. I eat maybe chicken once a week. Yeah, well... I hope that that you can still see from that bird's perspective that has their head cut off for that once a week and it's still an injustice, you know? Because it's really, like, not that inconvenient when you put the animals first. Well, not...  I'm saying, like, when you look at it from the victim's perspective, moving this way in the supermarket, moving that way in the supermarket, buying some, you know, beans and rice and some fruits and veggies and some soy milk, it's really not that bad compared to having your head cut off. No, it's not too bad. I know. It's good talking to you, mate. It's good to meet you. Good to meet you too, buddy. Yeah, that was a good chat. I don't know. It's a little bit too easy, I think. I don't know. I just don't know what he was thinking, that's all. It's hard because... Yeah, it seemed a little bit like apathy. Like, this is why some people might measure that as a successful conversation. I mean, I told, explained at the best I could. But you just don't know. And I felt from that conversation not confident in what he was going to do after it. But you can only try your best head. So, it was nice.


The Kellier Community Theater is an informal group full of familiar faces. Many of the cast and crew members are associated with the school district in some way or another as Mel Meyer reports for this week's in focus. The school superintendent leads staff, students and other community members as Doc in Doc's holiday. I think Kellier School has a tradition, a long standing tradition of community theater. The community theater does not have a set schedule. Calvin Mayers, who is a school custodian, had the idea to put on a performance and started asking around if anyone was interested. Derek Nisler says it's fun to be performing with his coworkers. We kind of joke with each other in the hall and make sure we always have our script on us and learning our lines. For the students, it's been interesting seeing the school staff in a different light. They're just a friend, they're just they're equal to you because they're all the same role. The staff of our school is already really close to all the students so it's just getting to know them a little bit more and more of the community members. For one of the show's stars, getting on stage in front of the students felt like second nature. I'm a graduate of Kellier High School. She was in every single school play that they had during her four years. And a lot of people remember me as a student in performing in the school plays. So it's kind of mind blowing. Showing audience members that the passions they pick up now can continue in the future. From this production, we can maybe get more people, students and adults interested in our next community play. In Kellier with this week's in focus, I'm Al Meyer, Lakeland News. And the show will go on at the Kellier Old School Center on Sunday at 7pm. If you've enjoyed this segment of Lakeland News, please consider making a tax deductible contribution to Lakeland Public Television.


Learning is changing. New technology allows us to think about learning and teaching in entirely new ways. The interactions in the print page of the book that would have sent you off to somewhere else can now keep you here on this page. The figures in the book can be interactive experiments. The measurements and challenges can be right there on the page. Anything you do here is yours. It's on your personal shelf for you to keep. Join me Peter Scott and my fellow OU professors Eileen Scanlon and Mike Sharples and our guest Professor Sean Bain in the University of Edinburgh in the first of Open Minds Talks.