and the first think tech broadcast day of the year, January 3rd, while exciting. This is heading into our, what, 22nd year already about that. And we have Heli Halodnik and Ellie Halodnik and she's here to help us celebrate that and to talk about transitional justice, which is very important for us to understand on a global basis. Title of our show on transitional justice today is transitional justice in the Seychelles, okay? And the first order of business is to introduce Ellie. She's with Project Expedite Justice. Who are you, Ellie? And how did you get into our studio? Yeah, so I was a former intern, legal intern with Project Expedite Justice. And that's how I was introduced to think tech Hawaii. Why are you doing transitional justice? Why are you focusing on the Seychelles today? So I actually just completed my fellowship in international human rights law with my school, my program in international human rights law at Indiana University McKinney School of Law. And through that fellowship, I was actually fortunate enough to complete a legal internship with the Truth Reconciliation and National Unity Commission in the Seychelles. So we don't know where we're all going. We're at a time where it's hard to predict. But are you committed to a life in the law? Are you committed to a life in the law about transitional justice and human rights? Is that a decision you've made? Sure, I think it was a decision I made a really long time ago. I was definitely one of those kids who just knew what they wanted to do and stuck to their guns. So I've always wanted to pursue a career in international human rights law. What that career looked like has changed quite a bit over the years, but... I'm sure. Why? Why have you dedicated yourself to this subject? Sure, mainly out of just family history. I grew up hearing stories of relatives, my step-grandfather. His family died in the Holocaust as Roma victims. And that really inspired me to learn more about what human rights meant, historically and within the current landscape. And it sort of took off from there. Yeah, good. Very nice. I admire people like you. Okay, let's talk about the Seychelles. First, we want to put them a map up there. And for those people who cannot pinpoint where the Seychelles are, you can help them understand. So here's the map. And why don't you talk us through the map, okay? Yes, so the Seychelles is actually an archipelago or a makeup of about 115 different islands off the coast of Africa, just there in the Indian Ocean. The biggest island is going to be Mahi Island. That's where the commission is actually based in the capital city of Victoria. While the Seychelles is a makeup of 115 different islands, really they're only three to four, it inhabited islands. There's Mahi, Crawlin, and I'm trying to see on the map. I will probably pronounce it wrong. Ladinga, don't hold me to that. I won't be able to correct you at all, Ellie. Yeah, but the main island that we're going to be talking about is Mahi Island. Okay, is it a separate country, the Seychelles? No, it's not a separate country. It's really just considered the main island. That's where the International Airport is located. That's where the capital city is located, and that's where the majority of the population resides. And what's the racial makeup of the Seychelles? I know you have some of these islands on the Indian Ocean, west of India and east of India, and it's sort of a racial combination, hapahali kind of thing. What is it like in the Seychelles? Sure, so the Seychelles, it has a unique history and the fact that prior to becoming a French colony, it wasn't an inhabited, the islands weren't inhabited, and I'm probably not the best person to discuss the specific details of the history of the country, but to my understanding, it was a French colony before becoming a British colony, before gaining independence in 1976, which really leads us into the events surrounding that the violations committed in relation to the commission itself. Let me drop a footnote on that and say that, this is another story that began with colonialism, and people should be aware that colonialism wasn't all that was cracked up to be, and when it ended, and often in the world it ended because the colonialism itself was a failed phenomenon, and then afterward it left a vacuum, and the vacuum was filled by political forces that were not particularly democratic. So in 1976, they weaned themselves away from colonialism, but only to follow a coup, right? Can you talk about that? Yes, so the coup occurred in June, 1977. The leader of the coup was Albert Rene, and at the time he was the vice president of the country, and he overthrew James Mancham, who held the position of president. And that was actually the first government that formed following the establishment of the country as an independent country. So essentially the first established government was overthrown, then the coup d'état occurred in 1977. It was a one-party state from 1977 until 1993. However, Albert Rene held the presidency from 1977 until 2004. Paul, can we say it was a democracy? You know, I think that's kind of debatable. You know, up until 1993, I think that's gonna be a hard argument to make since it was a one-party state. It is to a certain degree a dictatorship within that point. Following the dual state and the collapse of the transition from the one-party system, there are more arguments that could be made for a democracy. And really the transition of government, to a certain extent, the opposition didn't gain a real footing until 2016 when they won a majority in the National Assembly. Okay, so we had what amounted to a dictatorship one-party system from the 70s until what, the year 2000 or so. And then after that, it became more democratic, is that fair to say? And as you see it evolve into the 21st century, it became more democratic, is that right? I think so. There's an argument that while the country became democratic, there's also certain allegations of corruption. And that to an extent always calls into question the democracy aspect of a government. But the dual-party system following 1993 was really when democracy had a footing in the country, I would say. Okay, well, that's good. And I suppose they maintained a certain connection with their former colonial parents, so to speak, France. Well, France was- Or was it the UK, which one? Yes, so the United Kingdom was really in charge of the colony up into independence. I don't know how long the French had the influence, but the language that's spoken in Seychelles is Seychelles Creole. Does that mean Seychelles English? No, it is a dialect of French. It's not exactly similar to, but you can relate it to the Creole spoken and the Caribbean. The closest dialect from what I've heard is the Creole spoken and Mauritius, which is the nearest country. Mauritius is a similar island state. Yes, yes. So, okay, so looking at it now at some point along the way, and this is why we're here today, there were violations of human rights in the Seychelles. And most people who follow the violations of human rights don't necessarily think of the Seychelles as a place where human rights were violated, but why don't you tell us what happened and what was the circumstance of this violation of human rights? Sure, so the commission was mandated in 2018, like I said, to investigate human rights violations committed during or in relation to the coup d'etat in 1977. Whoever the violations are alleged to have been committed from 1977 up into the mid to late 2000s. So really it's not just the- It's more than 20 years. Oh, I think just over 40 years, almost 60 years. 40 years. Because like I said, former president Albert Rene was in power from 1977 until 2004 and his political party was in power or they held the majority until 2016 and held the presidency until 2020. So you can see how certain specific acts were committed within certain timeframes, but really as a whole, different acts and different violations were committed throughout that time period. More alleged violations- That's the connection between the coup in 1976 and these human rights violations. What I'm thinking is that if you have a coup, you have a vacuum of power, you have usually a military coup, they have the guns and it's predictable that there would be violence in the coup. But you're talking about violence and human rights violations that lasted long after the coup itself. So what was the nature of that violence? What was the nature of those violations of human rights in all this long, multi-decade period? Sure, absolutely. So the Truth Reconciliation and National Unity Commission is similar to most truth commissions that it was built after a transition and political power. So the scope of the acts that there are the more, what we might consider gross serious violations of human rights law. So torture, rape, enforced disappearances, unlawful killings, deprivation of physical liberty, arbitrary arrests, all of these acts are included within the mandate. And these acts are acts by the military? Not necessarily. They could be acts committed by the military, by any branch of the government. And how a lot of, the Seychelles is a very small community. It's a very small country. In 1977, when the coup occurred, the residency was about, there was population of 60,000, around 60,000, I think. And currently there's just about a population of 100,000. So not a lot of, not a huge country by any means, population-wise. So a lot of, there are allegations and certain cases, potentially where acts were committed by civilians or they were committed within the umbrella of politics, by a politician, by a civilian, acting within politics. It doesn't necessarily, there is no limitation as to- Well, I'm thinking of Rwanda. With Project Expedite Justice, we've had a couple of shows about Rwanda. And the remarkable thing, people don't realize is that in the genocide in Rwanda, everybody was involved. You picked sides. It was sort of like the country was divided into two sides. And what did our guests say?  And that's how you took your sides. You were operating on one side or the other. Is it like that where people, ordinary civilian people, even not government employees or officials would go out and kill people or make them disappear or just sort of throw out the rule of law? No, it's my knowledge that, and there could be a different conclusion when the commission releases its findings in October of 2022 this year. There aren't necessarily a huge number of unlawful killings. Since the coup and since the acts that the commission is violating was largely out of this political transition, one way to view it is you're either affiliated with the party in power or you're not. And that's a good way to understand the different violations and alleged violations before the commission as well. There are obviously these violent acts that are, like you said, committed in most instances of coups and military takeovers, but there's also violations related to forcefully evictions and wrongful terminations of employment, forced exile, unjustified acquisition or loss of property, where these alleged violations occurred to victims because they were not affiliated with the government or they were considered, they were not affiliated with the political party in power or they were considered to be, they were seen to be against the party. Some of it doesn't sound like a violation of human rights. Maybe it's a violation of decency and good order. Maybe it's a violation of the norms you would expect in any civilized country, but some of those things you described don't sound like violations of human rights. So how did the commission get started? Who is the commission? Why did the commission get started? How long has it existed and what is it doing? So the commission was mandated in 2018. Like I said, in 2016, there was a transition in the National Assembly where the opposition coalition became the majority party, but up until 2020, the presidency was always held by a member of the same political party as former president Albert Renee, the United States Shelfs Party. And in 2018, the majority party in the National Assembly was the opposition coalition, but at the same time, there's still the presidency of the same political party. So there are some allegations that not allegations and potentially criticism is just internal debate that the commission is run in politics or it's seen as political because it was created under the previous political party that did commit or allegedly committed, a large part of these violations. So it came out of that, it's not external. It's like there's nobody in the international court of criminal justice or anything involved. This is a national experience rather than an international one. So yes and no, most of the commissioners are national commissioners, but the chairperson of the commission is actually an international citizen, she's Australian. And she is an expert in international and human rights law and international criminal law. So she does fill that role with a high level of expertise and a lot of experience. So this commission is trying to find the truth. It's a truth commission. Yes. It's trying to find out who acted badly and who the victims were. To what end? They have the ability to indict violators. They have the ability to prosecute and put them in jail. How does that work? So the commission does not hold the power.  It is formed as a truth commission. So its purpose is to investigate the complaints brought before it by victims, victim family members, representative of victims. Determine, you know, investigate these complaints, determine whether or not, human rights violations were committed, you know, in what context? Because there is really a large question of what is the truth. In a lot of, of say, Showa, you know, people's minds, there is the question of, well, what is the truth regarding, you know, How do they find the truth out? They have investigators. Yes. They, and the investigators, I guess, since it's not necessarily a prosecutorial organization, the investigators come before the tribunal, before the commission, and they present evidence, they have findings that they, you know, found when they investigated and they, and they tell the commission on what happened. And then what happens? What does the commission do with that? Well, the investigative teams are, are within the commission. And, you know, say Showa citizens are, anybody is free to, to bring evidence before the commission. And, and not just evidence, but any, you know, they can be heard really on, on any matter relevant to, to the mandate of the commission. Is it all public? Most of the hearings are public. They are broadcasted. You know, on the, they, I think believe there's like a YouTube channel, you know, that there may be via website, but they are all broadcasted. Well, not all I should say, but, but most are publicly broadcasted. Okay. They, they planning to make a report. Do they have an obligation to wrap it up by a certain day and make a report? Yes. So, so within the mandate, but the commission has three years to, you know, investigate these allegations and, and produce a final report with recommendations on reparations, remedies, different avenues, you know, for, for reparations for victims. And the commission also has, you know, for, for reparations for victims. And the commission also has the ability to grant amnesty, which I, I know you know, with the previous episode on truth commissions, it is a big criticism facing truth commissions, but within its mandate, the commission does have the ability to, to grant. So, so what, you know, what, what kind of resistance, if any, does the commission meet? I mean, does it got government support right now? Has it got the support of the people?  We have that in this country, by the way. I just, I wonder, you know, how well it's being received. Sure. So I guess I'll address each of those, you know, individually. I believe the first point was, is there, is there support? Was that? Yeah, it's a government. First point is, is the government supporting? I, I think that, that is a big issue. The commission is, you know, they do not have a big budget. They're, their recommended budget is repeatedly slashed on. So they don't get a lot of financial support from the government. And they also don't get a lot of, and, you know, this is just based on, on my observations. They don't get a lot of support from, from the government and the different government agencies. Despite the fact that, you know, there was this transition of power from the political party where, you know, who was in power for these alleged human rights violations to the opposition coalition, the resistance has been met really throughout the entire mandate. But it was the government that made the mandate, right? Yes. Yes. So how come the government makes the mandate, then then the government doesn't support the commission they mandated. So I think that, that's a huge question that, that is asked in most. Truth commissions. You know, and you know, I think that answer may depend on, on the individual circumstances within each truth commission. In the Seychelles, you know, I think that that is the right question to ask, you know, why, why wouldn't the government want to, to support this commission? Yeah. I don't.  It may be there are people in the government that have exposure. Just the way there are people in the United States, Congress who may have exposure for what they did and connection with our own homegrown insurrection. It could be there are people in the, in the Seychelles government who are afraid and they, they don't, they don't, they want to subvert this commission. Am I right? Is there any talk of that? I think there could, there could be a lot of different possibilities. In most truth commissions, you know, there, there's evidence of, you know, politicians who, who were involved in, in the allegations who, who, you know, are hesitant to, to cooperate because of other criminal acts like, you know, corruption and, and there is an anti-corruption commission within the Seychelles as well that that's operating, you know, in a similar mandate and within a similar timeline. So, you know, and, and the Seychelles is, is unique in the fact that it's a very, very small country, population wise and, you know, so. They got a lot of commissions there, that's for sure. They got more commissions than they need. What about the other factors that I mentioned? What, what about the public? Is the public supporting this commission and are people responding to, you know, subpoena type process requests that they come down and testify and speak before the commission? Is there public support? I, I believe that there's public support. Yes. Because, you know, the, the time that that's elapsed between a lot of these violations, you know, truth commissions are, are essential for fulfilling the right to truth.  So I think that there is a lot of participation within the community to, to assist the commission's investigations. And, and to, to bring complaints themselves. Well, there are two possible remedies. One is civil, one is criminal. And I understand the commission, although it may have the ability to pardon somebody, it may not have the ability to either issue a money judgment or to prosecute specifically can only make recommendations. And since it's a national mandate rather than international one, they can make recommendations to the same government who mandated them, but who isn't necessarily supporting them. Those, those recommendations, Ellie, tell me if I'm wrong may not go anywhere. Well, the, I think we can, we can look at those from as two different issues. So the biggest, you know, remedy that, that, that a lot of the cases that the commission is dealing with, obviously the, the biggest remedy sought is going to be compensation. You know, a lot of these alleged violations were, you know, committed within the circumstances of some sort of financial harm, like unjustified acquisition of property, you know, wrongful termination of employment. So there is a financial aspect tied to the crime. So reparations and, and, you know, financial reparations is obviously a big incentive. And a big responsibility that, that the commission has to meet. Well, let me, let me, let me, so if I get wrongfully terminated treated badly. In this country, theoretically, it doesn't always happen, theoretically, I get a lawyer and I sue the guy or the company or the government for wrongful termination. And that's been going on for a while. Why do you have to have a truth commission to give a financial remedy to someone who is wrongfully terminated? It seems like the long way around the horn to do something that could be done in a court proceeding any day of the week. Well, so how, how a lot of the, the cases before the commission are, you know, there isn't an overlap of multiple violations.  You may also have an alleged violation of forced exile. So, and a lot of that stems again, I think it's, you know, going back to, to sort of the political aspects of the commission. A lot of that is based on discrimination, political discrimination, because you're, you're perceived to be, you know, either against the government, against the party, you know, political affiliation, political discrimination in general. It may also, you know, forced exile. But a lot of these aren't just isolated incidents. So, so there is an overlap.  Or there was corruption in whatever pretended to be the courts. There was corruption.  And therefore the only way to have any remedy would be by a truth commission. So what is, what is your role in the role of project expedite justice and what's going on in, say, shells and this commission and their investigation? Are you helping individuals? Are you helping the commission? Are you an observer or a participant in the process? So I completed a legal internship with the commission as part of my fellowship in international human rights law. I was a remote intern for about six months. And then this past December, 2021. That's still funny to say. I still, you know, it's still weird saying we're in 2022. Right. But this past December, I was able to visit, say, shells. And, and, you know, work in the office. Really just as a, as a consultant to, to assist in any way that I could while I was in the country. So I got to, you know, observe the commission, observe the country in general, the different stakeholders, you know, I got to meet a lot of the staff. It was a really great opportunity. Very valuable experience. Yes, very. Let me, let me add why one of the reasons I think it's valuable. And it's my last question to you today is this, you know, you mentioned that there was an Australian chair of the commission that it was mandated locally, as opposed to many other commissions that have international routes, you know, international mandates, international connections with the International Court of Criminal Justice and the like. This is in the se shell. This is intramural in its own way. And I, it's very interesting. You said that this woman does this sort of thing as, as an occupation, as a career. She goes from place to place. Right. And she acts as the chair of a commission that would, that would do investigations and seek the truth on human rights violations. Well, the chairperson is an expert in international human rights law and international criminal law. I don't know if she has, has been the chair of any other truth commissions. I don't believe so. But, but she's had roles and, and other human rights mechanisms and other criminal mechanisms. So she has the experience in, in the field of law in general. And it sounds, it sounds like it could happen though. I mean, even if she's not doing that, it could be a new generation of international criminal justice people like your own self, where you hold yourself out to be somebody who could advise or chair a truth commission in any country where there is a political room to allow to create a mandated commission.  Today, my guess is that they're increasing. In fact, we, we discussed this before the show. They're increasing. You know, if you look at the, the vectors in the last 10 or 20 years, we have more of this. We have more people behind barbed wire. I'm thinking of the movie by Ai Wei Wei, well, human flow where he, he tells us there are 65 million people who, who are perpetually behind barbed wire in camps around the world. And to me, that's a, you know, a violation of human rights right there. And so it seems to me it could be a new occupation, a new place for you and people with your training and interest to, you know, help and be the chair of a commission or be counsel to a commission and make the thing work, make it work in terms of its internal procedures, make it work in terms of its investigatory functions, the writing of its report, the dealing with the public, the dealing with the government. This is a hard, hard thing. And it's always going to have some resistance points, as I mentioned, that I've discussed with you. So what do you think about that? You think this could be a career. You don't have to be Australian, you know. Well, I think that there, there's definitely an argument for sort of the key role that international consultants and international experts have played in the commission and in, and in a lot of truth commissions in the past, because truth commissions do exist within, you know, a pretty particularized field and a specialized field in international human rights law and transitional justice. And in general, to my understanding is really, you know, a recent development. And it's a recent field where, you know, a lot of people don't have training on these topics. But, you know, with that, I think that while international consultants and, you know, international chairpersons and, you know, international experts all have important roles that they can play. Truth commissions in general, the real purpose and the real value and truth commissions themselves is, is the importance that they hold really within the domestic community. Yeah, well, if everybody's watching them, watching, participating, you know, and to some degree while, you know, staff may not have the background or the training going into roles and truth commissions and, you know, other international human rights bodies. You know, training obviously helps with a lot of that and can build capacity. But the unique knowledge that the Seishawa, you know, worker, the Seishawa staff and, you know, other domestic staff and other truth commissions have is that they understand the context that these violations were committed within and they recognize the importance to building the truth and investigating these complaints in order to build the truth and, you know, essentially fill the role that that truth commissions were, you know, mandated to create, which was, you know, uniting the country. Well, yeah, that's true. But I would say that the country may get behind them to a certain degree and just thinking, not necessarily the Seishawa's, but anywhere, and people may support them. Maybe the government gives them at least lip service. The real question, and I don't know if you have been involved in this, the real question is when the report is written, when the commission goes home, when the Australian lady goes back to Sydney, whatever, what happens then? And if you, you know, if you have a staff that will stay behind, represent the will, the findings, the recommendations of that commission, then you have a follow through. But it writes a report and the report goes on the back shelf and nobody gives a rip about the report. That's a failure. And so to me, you know, at least half of the energy that would go into a commission has to be on the follow through to make something happen so that the people who had confidence in it while it was going on can have confidence that it wasn't a waste of time. Do you agree? I think that, you know, one of the key pillars of transitional justice is, you know, memorialization. And I think that that has to do with a lot of the points that you're making. It's not just, you know, investigating and establishing the truth of these alleged human rights violations, but it's, okay, well, what are we going to do with the truth? And whether that is, you know, criminal accountability, whether that is, you know, granting amnesty when there is full and frank disclosure and, you know, and other guidelines and conditions have been met. Or whether it's, okay, we're going to, you know, incorporate this into the education, you know, let's teach the next generation what happened.  I don't mind everything they find. And if I had to restyle the title of our show today, the title of our show is transitional justice in the Seychelles, I would change it to the following quote, what are we going to do with the truth? Question mark end quote. Well, thank you, Ellie. It's been great to talk to you and explore these things. And I very much admire your interest and your participation in what's going on in the Seychelles. And I think it's only a beginning. And I hope we can circle back on this and other countries that you are involved with. Thank you so much, Ellie. Thank you. Happy New Year. Thank you too.


New York Assistant Professor and Principal Investigator in the Department of Biostatistics at the University of Washington. She develops tools for microbiome and biodiversity analysis. She is interested in reproducible research, meaningful data analysis and collaborates with scientists who share those values. She has been a recipient of NIH Outstanding Investigator Awards and University of Washington Outstanding Mentor Awards. Please check her Twitter and her websites. And on this, Dr. Willis, please take it away. Excellent. Well, thank you so much for the very kind introduction. I was just saying to folks who were here a bit earlier that, as was just mentioned, my home is Seattle. I love living in Seattle. I'm very sorry I can't be there in person today. I had a prior commitment on the East Coast for traveling. So feel free to connect with me and any of the ways here through Twitter or email. And I look forward to... It's been terrific to see the programming that's been happening at Bioconductor 2022. So this is actually my first time presenting at a Bioconductor conference. I'm a passionate misguided for the open source community. But I thought that since this is the first time I'm presenting here, I might start off just by introducing myself in some of the work that I do. So I'm a statistician. I have a favorite of training in statistics at this point. And I really believe that statistics exists to serve the sciences. And so to that end, collaborated with folks on a number of different projects. I'm really passionate about biodiversity. So I've got some projects here looking at analytics and looking at an Arctic boolean mammoth reconstruction of its lifetime movement from isotope data as well as looking at very hard to detect members of the oral microbiome. To support some of this work in the analysis of biodiversity data, I developed methods for its analysis. And so you can take a look at some of these methods here. These are mostly focused on developing new methods for the analysis of emerging structures in the analysis of microbiome data. So I'll talk more about that in a second. I think that one of the most important things statisticians and data analysts can do is to support modern sciences to bridge disciplinary divides. And so I take this outreach and interdisciplinary communication work really seriously. And I think that some of the tools that have allowed me to better collaborate and better communicate with folks in biological fields include having some sort of perspective pieces trying to frame some of the questions that statisticians think about for microbial ecologists. And I've got a couple of open source software projects but I'm really excited about listed on the bottom as well. So what I'm going to talk about today is more recent methodological work that I've done while this is accompanied by open source software course. And I'm particularly going to focus on a preprint that we put up a couple of months ago. It's called model and complex method and error in microbiome experiments. So feel free to check out that paper on archive if you would like more details. And I'm happy to answer questions. Of course, sounds like at the end is how we're doing them.  But this story really begins with the advent of high throughput sequencing. And as we mentioned many times yesterday, high throughput sequencing is this incredible technology that allows us to analyze genomic data sets at this unprecedented scale and also at an unprecedented low cost. So the advent of high throughput sequencing has really revolutionized the way that we study biology as well as the way that we study medicine. And so one thing that distinguishes high throughput sequencing from sort of classical approaches to studying ecological communities is that it is an indirect way of obtaining information about the genomic source or the community that's under study. So if I think back to how ecological surveys were done in the 50s, 60s and 70s, folks with a lot of sunscreen and bug nets were sitting there counting their frogs on their clipboards and counting different fishes in the net or different spiders that were in these traps. High throughput sequencing is totally unlike that. There are all of these different steps in the laboratory preparation procedure which I'll talk more about in a second. Before I do that, I'll give a little introduction to microbiomes and communities of microscopic organisms which are gonna be the focus of this talk today and indeed the rest of my current work. So you may or may not know that you're not wholly comprised of your eukaryotic self. You actually carry approximately 10 times more bacterial cells on your body than human cells. If we think about what that bacterial complement is able to help you with, it actually adds an enormous amount of function to what you can do. And so for example, we've got 22, about 22,000 genes in the human genome but the combined complement of genes from the microbes that you walk around with is maybe on the order of eight to 10 million. So what do these bacteria do for you? They help fight off viruses. They help you break down your food and metabolize and absorb nutrients. And I guess maybe one of my favorite factors that eukaryotic cells are descended from bacterial and archaeal cells and so I like to think of them on my good days as my distant cousins as well. So these are, we haven't think about pathogens but our commensal organisms help us a great deal with living and existing. Okay. And so a common data structure that we see in, that we often see in the analysis of microbiome data is count data, it could also be coverage data. This is not such an important distinction but we're often thinking about matrices of the form of samples by taxa or taxonomic groups species or strains. And so I'm gonna throughout this talk think about WIJ as the number of times a given strain, J is observed in sample I. So what we see in this example data set from Brooks and co-authors is that in sample one we observed lactobacillus chisputis 19 times and lactobacillus inners about four times. In contrast, lactobacillus chisputis was observed zero times in this second sample. Okay. And so a assumption that underpins a lot of the work in statistics on the modeling of microbiome data is concerned with true relative abundances of strains, species or taxa in sample. So I'm gonna let PIJ reflect the true relative abundance of taxon J in sample I. And a common assumption that underpins a lot of methods including concave and de-seq and many more that we can chat about in detail in question time is that the expected number of counts we observe in the sample for the Jth strain is proportional to this true relative abundance with some sample specific scaling factor that I'm gonna call CI here. And so if this is a core assumption of many methods for estimating abundance in microbial communities and looking at differential abundance across different sample types, I think it's really important to investigate this assumption and see do we have good evidence for this working assumption being the case? So how can we do that? So one really interesting data structure that exists in microbiome sequencing and I think it's kind of unique in microbiome sequencing as opposed to other high throughput sequencing technologies such as RNA-seq for example is we have this validation data that we can use called mock communities. So mock communities are artificially constructed communities of known composition and they've been really well used in the bioinformatics and computational biology literature but not so much in the statistical literature. So this is sort of where we've been today. And so mock communities have true known composition, right? So what I'm showing here is that I'm gonna show four samples and seven species and we see that that first sample is comprised 100% of streptococcus agalactiae and none of the other six strains are present in that sample. In contrast, the second sample here is comprised in equal or one-to-one parts by Apotropia vaginium prevotella bivia and then this third and fourth sample have one-to-one-to-one ratios of three taxa each. Okay, so this is a great tool that we can use to investigate these are some of the assumptions that underpin a lot of the methods that are used in microbiome analysis. And so it may or may not surprise you that that data table that I showed earlier actually originated from this community. So I'll let that, I'll let you all look at that for a second and then I'll pull out a couple of really interesting pieces. So one thing that I'll notice here is that we're observing strains in samples in which they should be absent. So as I said earlier, we observed lactobacillus chryspatus 19 times in this first sample in which it should not be present or we know it not to be present. Another really interesting feature that we see looking here is that while we have known equal ratios, let's say in this third sample of lactobacillus chryspatus and lactobacillus inners as well as prevotella bivia, we observe many more reads from lactobacillus inners compared to that of lactobacillus chryspatus. We observe about two and a half times more, maybe around 11,000 lactobacillus inners reads in this third sample compared to about 4,800 lactobacillus chryspatus in samples in which they should be present in the same abundance. So I'll just make those two observations again because they're gonna be pretty important for the model that I go on to talk about today is firstly that we notice that despite equal mixing fractions, some taxa are observed many more times than others. And then also despite supposedly being absent, we observe taxa in which we shouldn't. Okay. And so what I'm gonna focus on for the rest of this talk is using this control data to propose and justify and validate a model that maybe better reflects underlying biology of these communities than the assumptions underpinned by this working assumption that I introduced earlier where we had that counts were proportional to true relative abundances. Okay. So this is work with David Clawson who's a fifth year PhD student in the Department of Biostatistics at the University of Washington and has just been an absolute joy to work with. And so David and I worked on the following model. So we're gonna decompose the expected number of counts in a sample attributable to a taxon as comprising of two pieces. So that first piece is contributions from what should be present in this environment. And then the second piece is contributions from what we can broadly think of as contamination, things that shouldn't actually be picked up in our sample because we know that they shouldn't be there. So I'll dive in more detail into this contribution of sample piece first. So what I'm showing here is more mock community data. And this is also mock community data from the Brooks and Co-authors paper that I mentioned earlier. And so what you'll notice is in common about these five communities. So in this top panel, I've got true relative abundances. We see that the first and second samples and third samples are comprised of one to one to one mixtures of three different taxa denoted by different colors. All of the five samples that I've pulled up here both have this green taxon and this blue taxon present in these communities. And what we observe is that we're consistently observing a lot more of these observations due to this green taxon compared to due to this blue taxon. So let's think about the ratio of number of observations attributable to each of these taxa. And I would claim that we're consistently observing about 18 times more observations due to lactobacillus inners, the green taxon, compared to streptococcus agilactiae. And so I think for folks who've looked at high throughput sequencing data, you might think or someone who's looked at a lot of high throughput sequencing data, I would say this is a pretty consistent single signal to noise ratio compared to a lot of what we see in this field. And so is this some fluke am I cherry picking the two taxa that I'm taking the ratios of? Well, let's take a look. So here's a pot where we've got pairs of taxa on the x-axis and I've showed my, we observe approximately 18 more copies of lactobacillus inners to each copy of streptococcus agilactiae in communities in which they're present in equal abundances. And if we look at all of the other pairs of taxa, I would say we also have pretty consistent over detection and in particular over detection of one taxon relative to another in a pretty constant multiplicative proportion. Okay, so observing in this first, the most left hand side of this plot, approximately 30 times more lactobacillus inners compared to gardenarella vaginalis. And this is true regardless of the number of species present in each of these mixtures. So this is, I think, a pretty strong signal and a pretty good indication of what sort of model we should be considering in this scenario. And so I'm gonna claim that this data, everything I've shown up until now, provides pretty compelling evidence against this model where we have expected counts as proportional to the true relative abundances and much stronger support for a model where we have our expected counts are proportional to the product of our true relative abundances as well as strain-specific detection effects or what I'm gonna call or what's commonly known in the microbiome field as detection efficiencies. So I'm gonna let that EJ term denote efficiencies here. For estimation purposes later, it'll be convenient to work with these on the log scale. So we've got an expedited form of this model as well. Of course we're gonna hang on to our sample-specific sampling intensities, CI, in addition. Okay, so I think it's one thing to sort of see this model and say like, yes, that all seems fine and good, but what are the real implications of failing to consider taxon-specific efficiencies when we go to do modeling? So the model that I'm claiming is that we're looking at, if we go from looking at counts to looking at relative proportions, we can consider observed relative proportions as proportional to true proportions of taxa in environments multiplied by taxon-specific efficiencies. So what's the impact of failing to account for these taxon-specific efficiencies? So let's consider just a toy example, noiseless setting where we're looking at maybe a treatment sample and a control sample. And this could be population level data. It's not so important here. So it would be very nice to look at this data that appears on our desk and conclude that the relative abundance of this orange taxon, which is maybe a pathogen, let's say, decreased in the treatment sample compared to in the control sample, because we observe a decrease from 42% to 24% in terms of these relative proportions. So, perniciously, what could have generated this exact observed data is the opposite where in fact, we had an increase in the relative abundance of this orange pathogen from 20% relative abundance to 33% relative abundance going from control to treatment. So a change in the opposite direction to what we're observing in our data. So what could have driven this effect? The following taxon-specific detection efficiencies could have given rise to this. So we had one taxon that was extremely easy to detect, this green taxon observed 18 times more easily than this blue taxon, and then, and also observed about three times or observed three times more easily than this orange taxon, so six to 18, one to three. And so what's happening here is that because this green taxon is also changing in relative abundance, going from 5% relative abundance to 33% relative abundance, and also because it's very easy to detect, it's sort of eating up a lot of this relative abundance pie that's available and edging out sort of this orange taxon in showing us which direction it's moving. So really critically here, what we're finding, what we see and what this example shows is that it's not the relative abundance of one taxon that's important, but the relative abundance of all taxa that are important as well as their detection efficiencies. So this is essentially why this detection efficiency problem poses such a big challenge to analyzing this data and making scientific conclusions, drawing scientific conclusions from it. Okay, so I'll wrap up this sort of subsection on talking about contributions from the samples. So what we know to be present in these communities are saying that this piece in expectation is the product of true relative abundances, sample specific intensities, reflecting that some of these samples have many more observations attributed to them than others. For example, this first row has about 51,000 observations observed in total and this second row has about 21,000 observations observed in total. And then in addition, we need this term that accounts for different degrees to which taxa are detected. So the fact that as we noted earlier, lactobacillus inners is observed about two and a half times more easily than lactobacillus chryspatus. So that's our contribution from sample piece. Let's move on to chatting about the reason why we have non-zero numbers of observations from taxa that shouldn't be present, that should be observed zero times. So the model that I'm gonna consider here is pretty similar to the one that I introduced earlier. We're gonna think of contamination profiles. So we're gonna think of potentially capital K tilde sources of contamination that could be impacting our reads. Each of these contaminant relative on its profiles have an intensity that they contribute. And then we also have that samples sequence more deeply in total also sort of blow up the number of the amount of contamination in absolute terms that we observe in them. So this is just a nice piece that gives us some explanation for why we're observing non-zero counts of taxa that should be absent. Okay, so let's put those pieces together.  Essentially, we've got this piece due to what should be there and this piece due to what shouldn't be there. And I'm a statistician, right? So I like to think very generally in terms of experimental design and possible ways that folks could be running these experiments. And so I'm gonna introduce just so you know that this is a fairly general model, a couple of different design matrices. So one that links samples to specimens. For example, if folks have replicates, technical replicates that they wanna sequence. Another that potentially links these different detection effects to different batches or different sample preparation methods. That's this X matrix. And then we've also got, you know, different ways that samples can share contamination profiles. And that's coming in through this Z tilde matrix. So that's a bunch of notation. Feel free to take a look at the paper if you wanna dive into more detail here. So how are we gonna estimate parameters in this model? Well, we're gonna propose using likelihood-based tools to do so. And so we're gonna model our counts conditional on our parameters as Poisson distributed with means given as stated previously. And critically, I'll talk about the Poisson assumption in a second and whether or not that's something we're really gonna lean on. Spoiler alert, we're not. But critically here, in order to fit this model we need at least some parameters to be known in this model. So for example, we're imagining this being most useful in a setting where folks are investigating samples of known composition as control data alongside samples of unknown composition. So maybe that's a setup or maybe we know that we're gonna set one, we're gonna sequence the same samples looking multiple different ways. And we're gonna set one of those as the baseline will be estimating different detection effects, one protocol relative to another. So one of the really nice features of using this estimation approach is that it results in estimators for our proportions as well as for our detection effects that are consistent under very general conditions. And in particular, we don't need to assume that these counts are actually Poisson distributed as long as we've correctly specified our mean model. So this is consistency in a very, very general sense that's not tethered to distributional assumptions about WIJ. So really we're thinking about this likelihood-based approach as giving us estimating equations. One thing that's worth noting is that the Poisson distribution makes strong assumptions about the relationship between the mean and variance of observations. And so a tool that we have to improve efficiency is to perform maximum weighted likelihood where we're looking at estimating some variance function as a result of looking at squared residuals, for example. And then we can make structural constraints like fitting that model with a isotomic regression as a reweighting approach. So some nice details in the paper if you'd like to look more closely at that. So estimation in this setting is really interesting because some of the parameters that we're trying to estimate do not fall in the interior of the parameter space in which they can live. And so we've got these relative abundances that are not only in the simplex. So given some of proportions has to add up to one, but we also wanna allow maximum likelihood estimates to potentially fall on the boundary. So specifically, we want to permit true relative abundances or true estimated relative abundances to be exactly zero. We don't wanna assume that every strain, every species occurs in every sample. That's not biologically plausible. So how are we gonna do that? So we consider breaking up our algorithm for estimation into two pieces. We've got this first set of steps, which is a barrier method, and then another then followed by a profiling step. So the idea here is that we've got this really unstable estimation problem. And so we're first going to consider this setting where we're gonna enforce that our estimated relative abundances fall in the interior of the simplex, interior of the space of observations between zero and one that add up to one. And then we're gonna at the final step allow those estimates of relative abundance to move on to the boundary if it increases the likelihood and that's done via a profiling step. And so essentially the way that this barrier method step works, so we wanna really carefully control the way that these, the way that we're increasing our likelihood and the way that our estimates are changing along the steps of this algorithm as we go forward is returning this quite unstable, constrained optimization problem into a sequence of unconstrained optimization problems. So we're maximizing not our likelihood, but our likelihood plus this other term controlled by a hyperparameter T. So we originally start with a small T and we say solve this problem and then initializing that the next step of our algorithm we're gonna increase T by for example, 10 fold. And so we're looking at maximizing a function that's more like our likelihood. And then we repeat that again until T is really large. At any individual step of this optimization we're gonna use regularized Fisher scoring. So second degree, second order approximation of our likelihood. So the interesting thing about this is that we're at each step of this algorithm only going to enforce that our relative abundances are greater than zero or between zero and one. And we can do that and enforce this, the simplex constraint that they add up to one, but while being strictly greater than zero by reparameterizing our relative abundances using long ratios. So this is just a nice reparameterization trick to allow this interior of the simplex constraint. So a profiling step once we've got, we've got estimates that approximate something that's really similar to our likelihood, but not quite. We wanna allow our estimated relative abundances to potentially fall on the boundary of the simplex. And we're gonna do that with a constraint Newton's method within this augmented Lagrangian term. So you'll notice that this is not your typical Lagrangian. We've got two pieces that enforce this constraint. And we're gonna solve this problem using non-negatively squares. Essentially we no longer have this ratio parameterization that's one piece that allows us to potentially have estimates on the boundary of the simplex. And of course we're only gonna take this update step if it increases our likelihood. So we have this additional step here to make sure that we're increasing our likelihood at this step. Happy to take more questions on this and yeah, very excited about this. So the final piece that I'll introduce before talking about a couple of examples is some really interesting considerations regarding statistical inference or hypothesis testing in this setting. And so, as I've talked about and one of the challenges is for estimation is that our parameters are not guaranteed to lie in the interior of the space in which they can fall. And so a lot of our conditions for... Our regularity conditions for doing asymptotic analysis break down here because looking at, you may remember from your first interest sets class, but that we're assuming that parameters fall in the interior of the space in which they can fall. So coming up with an asymptotic distribution or describing the asymptotic distribution of things like test statistics or estimators is not trivial here. And so a lot of digging into what possibilities we have here landed us on this really amazing bootstrap alternative. So we're not doing just your regular multinomial bootstrap. We're gonna consider a modification which is still giving us correct asymptotic distributions for estimators when we have parameters that potentially fall on the boundary of their space. And so what we've got here is a result showing that if we're looking at a subsampled bootstrap and so we're looking at P, N, Z distribution here. So this is a weighted empirical distribution where the weights of each of the observations that we draw are coming from a Dirichlet distribution with an M over N parameters and we need to have M and N becoming both becoming large and in particular, M becoming small relative to N. So there's some really interesting considerations here. If you're curious about these questions and I think this is just a really great example where theory and practice merge together in interesting ways. So feel free to check out those details if you're interested and I'm happy to take questions during question time. Okay, so I'll show you how this tool and this method can be used in a couple of different ways for the analysis of microbiome data. And so a common situation that folks are interested in is choosing between different possible experiments that they could run. And so this is a setting, this is data from Costea and co-authors published in 2017 where the authors were setting out to investigate which of three different ways that they could run their sequencing was the best one. And so what they did is they took 10 samples, I think believe they're human stool and into each of these samples, they mixed a synthetic community of 10 different bacteria and then they split their samples up, sequenced all samples according to each of these three protocols and then a sort of a validation approach looked at some flow cytometry data. And so one of the really interesting things about the analysis that these authors did is that they could look at their flow cytometry or relative abundance profiles and look at their sequencing profiles and say they don't quite match which looks to be the best. So that's sort of the old framing in terms of counts proportional to true relative abundances, but it was really hard to do anything with this. So let me show you how we can do a little more nuanced analysis with this new tool. So what I'm gonna show here are according to each of these three different protocols, relative abundances measured by this validation technology, flow cytometry against the estimated relative abundances for every one of our in colors, each of these 10 specimens and across the X axis, we essentially have different strains. So what we're observing is that some taxa are consistently observed way less than they should be. And very few, these lines are not on the X equals Y line. And so we're really struggling to see which if any taxa are estimated correctly with respect to their relative abundances. So if we fit a model that fails to account for detection effects across taxa, which explains why we could consistently be undetecting taxa in all of our samples, we end up with a model like this. So this is essentially correcting for contamination in samples. And we still see we've got some deviation from the X equals Y line. If we fit this full model, the one that I've introduced already, we I think can see something quite stark, which is that protocol W, while still biased in some sense towards detecting some taxa compared to others, is much lower variance than protocols H and Q. So I think this is a nice way of reframing, not how can we improve the, how can we choose a protocol that's accurate on average? But here we can start to say, we're never gonna get accuracy. We can correct for this bias in relative abundance estimates. And the challenge that we wanna do is minimize variance, okay? So we're optimizing precision now, we're prioritizing precision now over accuracy. And I think we're prioritizing both precision and accuracy, but we can choose a protocol that's an experimental protocol that's low variance because we now have a tool to improve accuracy. Another really important way in which this method can be used is to remove contamination in samples. And so the idea is that a lot of the tools that currently exist in the microbiome literature for removing contamination label taxa as contaminant or non-contaminant. And don't have sort of a, well maybe it's a contaminant in this sample from this other sample framing. And so what we're gonna advocate for here is a tool, an experimental tool called dilution series, where instead of just processing, where instead of biologists processing their sample, they process their sample as well as that same sample diluted down with say water in a one to three ratio, and then maybe again one to nine ratio and so on. Because what we can see sort of from this graphic here is that the proportion of contaminants, if contamination is constant across all samples, we see that the proportion of contaminants is greater in these more diluted samples. Okay, so let's take a look at what this looks like. This is data from castans and co-authors. I made this claim that we have contamination in our samples. And my evidence or one piece of evidence for this is that in a community in which we know there to be only eight strains present, we observed 248 strains in total. So 240 contaminants in this data set across I think nine samples or so. So what castans and co-authors we're looking at with eight bacterial strains mixed in, equal ratios, 12.5% each, and they have dilution series of each of these samples. And so what this model that we've introduced now can account for is not only these detection effects that we're not expecting to see 12% observed reads, but also contamination being greater in these diluted samples. So let's just take a look if we take a look on the left here, we've got observed relative abundances of each of these eight taxa along with other being contaminants. And then on the right-hand side, we've got what's known to be present in these communities. If we dilute this sample down three to one and sequence it again, you can see we observed slightly different proportions and maybe a slightly different proportion of these contaminants, slightly increased relative abundance of other over here. And as we continue to dilute again, one to three, so one to nine ratio of original, we're seeing more and more contamination. So we jump up to a five-fold and five rounds of dilution in one to three, we're now observing about 25% of our reads coming from contaminants. And if we're looking at an eight-fold, three to one dilution, we're getting almost 80% of our sequences coming from contaminants. So this is what that full dataset looks like. So what I'm gonna do, we've only got nine samples here, so we're gonna do what we can. We're gonna do three-fold cross-validation. And so if we fit this model that I've described earlier, we see a much improved estimates of relative composition. We're getting much closer to this 12.5% for each tax online. Again, we're learning on six samples and then training on three. And these folds are done in a way such that we're not communicating information across different folds. And then similarly for the second fold and so on for this third fold. So I would say that we're learning pretty as much as we can from a relatively small dataset and that this dilution series is a pretty low difficulty, low expense technique that folks can use in the wet lab that they can then combine with these analytical tools in order to reduce the impact of contaminants on their analysis. And in particular, one thing I was really excited about for this example was that if we look at 95% confidence intervals for our true relative abundances for 238 of the 240 contaminants, our true confidence intervals for their presence in the community were covered zero. So I think our in-practice empirical coverage is pretty great. Pretty good, I'm pretty pleased with that. So there's a lot of ongoing work that I'm really excited to do in this area.  How do we use these techniques and these insights in order to help biologists better spend their money and better allocate their energy across, for example, choosing between replicates and choosing between dilution series. How do we best encourage folks to collect the right type of control data? There's also all of these interesting questions sort of at the interface of biology and engineering about well, how conserved is the detection of a specific strain across phylogeny? So if we're looking at comparing two different species in the same genus, can we learn things about the detection, the ability to detect one species from another? You know, I alluded earlier to this, some of the mathematical challenges regarding estimation and I said sort of vaguely we need some things to be known whether we need our P's or our betas to be known and this is essentially getting towards identifiability. So some more general understanding of identifiability on the algebra side, I think is an important consideration. And then the holy grail of this research area is enabling biologists to do meta-analysis. So to combine data sets that have been constructed maybe in different continents, definitely in different labs, probably in different batches and combining them in a way to essentially improve the estimation of whether it's treatment effects or disease effects, as well as power to look for a differently abundant taxa across different sample types. So for folks who are not in the microbiome literature, I thought I'd, or not working in this area, I thought I'd give just a couple of sort of takeaways from the highest level of what I think we can learn from this quite specific example from microbial ecology. And the main one is about model validation. So statisticians and computational biologists and biologists have for many years used these models that weren't validated and were built on this assumption that the expected counts attributable to a species were proportional to their true relative abundance. And along through that example that I showed earlier where we had a decrease in one taxon, a decrease in a taxon that manifested as an increase, we saw that this is a model that can really mislead us, can really take us in the opposite direction from what biology is going on under the hood.  And so in our setting, we were looking at this control data that through mock communities, this had previously been largely gone unused by the statistical community, but we found to be really useful for evaluating model specification. And so alongside all of this work, as I've said before, they're really the only way to get useful tools, make useful tools is to distribute them as open source software in my opinion. So all of this methodology is implemented as an R package known as TinyVamp. Something I'm really excited to chat about with folks is how do you balance, how do you find the right balance of tutorials and vignettes that you put together when you're focused on developing a method that's very flexible? And so that's something that we're really sort of grappling with at the moment, how much support, all of the different, given that there are so many ways to use this method, what could we do to better support our users? So this research thread has come together over many years. This paper that I mostly talked about today is work with David Clausen, who's just someone, a student that I've been so lucky to work with. But some of the earlier ideas for this work were done in collaboration with Mike McLaren and Ben Callahan at NC State for Ben and now MIT for Mike, or actually, yeah, MIT for Mike, as well as with Jim Hughes and Brian Williamson at the University of Washington. And I'm really grateful to the National Institute for General Medical Science for funding this work. So that was all I had prepared for you today. I would really be excited to take questions. Thank you so much for being here. All right, thank you. This was great. Let's see if we have some questions from LifeOgins. Can you hear me okay? I can. I'd just like to say thank you for your talk. I am currently working at OHSU and I'm working on a metagenomic shock and sequencing microbial analysis of esophageal cancer progression. Lisa Karsten, who you referenced is, she's on our project right now, but this is kind of small world moment, but I've been personally working on the viral genome detection of the double-stranded DNA, non-RNA stage viruses that integrate into the human genome, and it's been difficult to normalize the counts based on all of the things that you talked about, about the high complexity regions and detection and things like that. So I'll be sending you probably a little bit of a long email after this, but I just wanted to- That's wonderful. Yeah, bring that up. Yeah, it's a lot of interplay what drives diseases in terms of the human microbiome, but this has been just a really wonderful talk, so thank you very much. Thank you so much. I really appreciate that, and I look forward to your email. I actually saw some data from some of my colleagues at Seattle Children's quite recently who were also looking at viral mock communities and this question of single-stranded, double-stranded DNA, RNA viruses and how does this impact their detection efficiencies is something that I at this point know very little about, and I'm hoping that this is an emerging research area and I really look forward to seeing what you find. Thanks so much for introducing yourself. Yes, thank you very much. Hello. So I have two questions for you. I really enjoyed your talk and my first question is the model that you proposed and fit to data sets on the order of a tennis species if I understood correctly. One of the things I wondered about it is it seems to have a lot of parameters and microbiome data oftentimes is very sparse. And so I'm wondering whether it's even possible to fit such a model to true microbiome data when you don't have the same organisms across many samples that you've sequenced. And so you're trying to fit a model if I understood correctly with many parameters to sparse data. Yeah, fantastic question. Thanks so much for asking it. So if you look at the dimension of our data and if you look at the dimension of our parameter space, I would say that in general, we're not gonna be able to estimate individual relative abundances for every taxon in every sample. And this sort of gets at this point of identifiability. So what sort of replicate data is needed if we see even the same sample multiple times that's giving us more information and how can we better leverage what we have? So that's one question or that's one piece of my response to your excellent question. The other thing that I think is important is that for the purposes of maybe comparing across samples or across sample types, this challenge of the WIJ is such that WIJ equals zero, this zero's problem is something that is really important to consider. I would say that given that we're trying to estimate relative abundances, that sounds like really good information to me that our true underlying relative abundances are also zero if we're failing to observe them in many samples. And so this doesn't worry me for the purposes of this calibration model as much as it does for differential abundance comparison, for example. So to speak to your question about dimension of our parameter space, you said that there are 10 species in this castans data. Actually, there were 248 species in this dataset and they could be fit relatively quickly. I just showed and then collapsed a bunch into other for the purposes of visualization. So I've been encouraged to realize how much we can learn from these datasets, even though of course we do have a parameter space dimension plus data dimension considerations, absolutely. But I think it mostly impacts asymptotics is my long answer to your points that you bring up. But happy to chat more about this. This is happy to go into more detail. Okay, thank you for that. So my second question is one source of contamination of microbiome samples in my experience is barcode crosstalk. So on Lumina when you multiplex, you get this problem where some samples bleed into other samples because of the demultiplexing error. So it seemed to me like if you were to just dilute the sample, the barcode crosstalk would remain at a constant abundance. It would not appear to increase in abundance. And so I was wondering whether your model at all fits barcode crosstalk as one possible source of contamination, how that would be dealt with. Yeah, fantastic. Thanks so much for bringing that up. So this question of barcodes that don't fully anneal and therefore hop from one sample to another. So index hopping is one term that this phenomenon has been given. Is something I've considered in a couple of settings and I've looked at it from a modeling perspective where we explicitly try to model hops from one read to another. That becomes computationally intractable very quickly. And so one of the nice things about this model or a place in which I'm optimistic is that if you know the true composition of some samples and you also have barcode hopping, that piece is going to be estimated as part of your background contamination profile. And so essentially you're gonna have small quantities of every one of your taxa in your contaminant profile even if those contaminants are coming in the experimental sample preparation process. And so this 238 out of 240, taxa being estimated or having confidence intervals for their true composition that covers zero gives me a lot of hope actually that we can still be estimating true zero abundances even in the presence of crosstalk or this index hopping phenomenon that you talk about. So I think that's a bit more detailed than I went into today but I'm happy to chat more about this offline. I would say that this method does, but it sort of gets at it indirectly that maybe better than nothing is where I would end that. Thanks for that great question. All right, we have few questions from virtual audience. So the first one is from my club. And his question is, will meta-analysis require inevitably original count data from all studies or is there a summary statistics solution that will be good enough? Great question, Mike. That is really interesting. I think depending in particular on the scale, I guess maybe there's some different people. So one is, what are we estimating? Are we estimating maybe like average treatment effect across different cohorts? Maybe in that case, doing meta-analysis through these tools could be enabled by the use of summary statistics if count data's been maybe not available, for example. I would have to think about that more though, that's my sort of off the cuff response to that question. I guess on the other side, if not everyone's publishing their count data and one of the nice things about this Poisson approach to giving us estimating equations is that we don't actually have to have count data. We could be looking at coverage data. I guess we could be looking at proportion data, though in that case, we're losing information about differential precision across samples and maybe some ability to estimate the mean variance relationship across the breadth of our counts. But I'll have to think more about this question of meta-analysis and summary statistics because I admit that's not something I've thought about in too much detail, but thanks so much for that very interesting thought. All right, another question from Alex Eamons. And this was fantastic. It was indeed. How did this modeling compare across sample types? Does it work equally well for less diverse sample types like human gods versus more diverse sample types like soils? Yeah, fantastic question. So thanks so much, Alex. I appreciate the kind words as well. So I guess there's a few different things that leave into my mind in response to this question. So one is that there are, of course, computational challenges with looking at more diverse environments. So the bigger the increases the dimension of your data, it increases the dimension of your parameter space. And so computation will inevitably take longer if you're looking at calibration with respect to, for example, high diversity soil environments compared to low diversity environments such as the human vaginal microbiome. The other thing that you bring up is this comparison across sample types. And so I think that an interesting question and something that we're looking at that is not well documented at this point is what is the impact of the extracellular matrix on these detection efficiencies? And so our efficiencies essentially the same if we're looking at like saline environment, saline environments for marine samples compared to freshwater environments. Or is the amount of like carbohydrates some in stool impacting detection effects for the same taxa. So I think that my current on this are that through comparisons within relatively simple sample types, efficiencies are pretty consistent. And this is data from that paper with McLaren and co-authors. I think the question is a little more or I think we have less information about comparing across very different samples. And so these sort of gross, or like large scale extrapolations and large scale, comparing very heterogeneous data types is sort of a challenge on the biological and model building side more so than on the statistical side. But I'm looking to see as more data emerges what we can say about that. So I hope that address your question in a couple of different ways. All right. And then another question from Mike. Are the models and inference methods relevant for questions about single cell competition? For example, single types may have efficiency differences that may lead to over detection. I didn't fully catch that question. But if I understand it sounds like Mike was curious about generalizability of this model to single cell data, for example. And other put genomic data types. And I would say that I guess having spent many years working with microbial communities, I can attest to I think the strength of this model in that scenario. And I'm curious to hear from actually, from you and other folks who have expertise in both single cell and bulk RNA sequencing if a model such as this has been validated in that setting. My impression is that because of the way that like what we're looking at the scale of individual transcripts it seems much harder to develop the same kind of control data as what we're leveraging in this scenario. But again, my expertise in these other genomics areas is pretty limited. So I would defer to other folks but I would, I have been asking folks for years like has this been like carefully validated? Have folks really been, have folks thought about this in other settings? And I'm not yet convinced I have complete information on that. So happy to continue that conversation with you as well. All right, it looks like we have all questions answered. I'll pose one from the floor. Oh, okay. Peace. Thank you, wonderful presentation. Could you bring up that slide of the dilution data? I think it's like the third to last perhaps. Yeah, let me see what I can do. Is this the slide you're after? Yeah, that's right. I thought there was incredible fidelity in the right hand slide. So I'm just trying to get a handle on what's going on on the left hand side. These are other analyses being used. Let me see if I understand what you're asking about here. My understanding is that on the right hand side this is your model. That's right. And you're estimating the proportions extremely accurately for all of these. And on the left hand side, something else has happened. What is going on over there? Yeah, thanks so much for asking me to clarify. So what we've shown on the left hand side here is essentially the raw data that have been transformed into proportion scale. So if you look at just a single one of these dilution sets, this is the sum of all across all of these, eight taxa plus an unclassified and plus some other. We've got a total proportion of one. As we observed from our WIJs divided by the sum over all IWIJ. So this is essentially what we are estimating under this model where expected counts are proportional to true relative abundances. So plug in relative abundances from our data shown on the left hand side. And then I think of it as corrected relative abundances for both contamination and detection effects on the right hand side. Does that answer your question? I think so. What were the confidence interval widths like on the right hand side? Great question, yeah. And so I think as I mentioned, I believe we're learning from, I can't remember, I think we must be learning from three samples and trying to predict the composition of the sample here. So I would say that I'm trying to apply, pretend that n is large when n is three sounds unreliable to me and so I would, we didn't compute confidence intervals for this, because I'm not even sure if confidence interval construction throughout amount of n bootstrap approach is possible with three samples. So this is not a scenario where I'd encourage inference or hypothesis testing. This is more of a model fit illustration. But yeah, thanks for your question. If we were looking at more than nine samples undiluted and then up to eight prefold dilutions, we could definitely compute those interval estimates. But in this case, that's not possible, unfortunately. All right, any other questions? If not, let me say for our virtual audience, there is a reaction button. So please use those reactions. And for our live audience, let's thank our speaker. Thank you. Thank you so much for coming. Have a good, enjoy the rest of your conference.


Hello everyone, myself C.L. Rathod, Assistant Professor, Department of Mechanical Engineering Walsh and the Institute of Technology, SolarPool. Today, we are going to start a new topic that is Artographic Projections of Simple Object, Learning Outcomes. At the end of this session, student will be able to draw the Artographic Projections of Simple Object. So, this is the Simple Object which I have selected for this lecture. Now, we are going to see this, the problem is given, draw the Artographic View of the given drawing that is we are interested to draw the front view and top view. So, this is the image which we are going to selected for this video lecture. Here when we see front view in the x directions, you are going to see this block, this block and this block and this vertical slide and this vertical slide. So, in order to see the in front view, we are going to hatch this whatever the things which is visible in the front view. So, when we see from the x directions, this will be visible and this block will be visible, this block is completely visible and this block is completely visible when we see from the x directions. And when we see from the top view and another thing is that here we are when we see from the x directions, this portion that is v shape is visible when we see from the x direction. When we see from the top view, so when we see from the top view, we are going to see this complete block, this complete block and this complete block we are going to see and this block is visible when we see from the top view and when we see from the top view one thing is not visible that is this complete rectangular slot is not visible. So now in order to draw this one first we are going to require our reference line. So in order to draw this reference line here we are going to consider this. This is the xy line and another one is the x1 y1 line. So this is the xy x1 y1 and when we see from the front side in the x direction its base is 62 it is given 62 mm. So first we are going to consider this 62 mm base. So this is the 62 mm base we are going to consider and its height its complete block this block is its height is 62 by 16 mm. So here in this we are going to consider the 16 mm block here 16 mm block when we see from the front side. So 62 by 16 mm block I am going to draw when we see from the x direction. When we see from the x direction you can see here this 22 block is visible completely this side as well as this side. So I am going to mark 22 from this left hand side to 22 mm. So I am going to mark here 22 and this side also I am going to mark 22 mm this side. Then I have to complete this block of 22 by 16 on both side this is the 22 by 16 block and another block is visible. This one its length is 18 mm and 18 mm when we see from the front side we can see here this complete block is visible and this side another one block is visible. So we have to divide this 18 by 2 it means 9 mm each we are going to draw here. So I have to complete this block of 18 by 2 that is 9 mm each. Now you can see here this rectangular slidings block is it has a width of 9 mm both side. So and its height is 42 complete height is from base to its is 42. So I am going to draw the 42 mm height from this. So this is the 42 mm and its base you can see here its vertical sliding width is 9 mm. So I am going to complete this 9 mm here. So I am going to take 9 mm from this. So this side also I am going to take 9 mm and up to 45 mm height I am going to consider this vertical height. So this is the 42 and this vertical slide I am going to draw here. This side also I am going to draw and you can see here when we see from the front this height is its width is given 16, 30 and 16. So I am going to take 16 mm from this and also from this side also I am going to take 16 mm. Here you can see its length is 30 mm. So I am going to take 30 mm, 15 mm axis I am going to consider here. So here is the 15 mm and its v shape we are going to see v shape when we see from the front side v shape and its depth is 15 mm below. So I am going to consider the 15 mm from this below. So this is the 15 mm and then I am going to join this one v shape we are going to draw here after joining this one. Now when we see from the front view one thing is not visible. Now pause the video and think that which portion is not visible. I hope you understood that. So when we see from the x direction that is in front view this complete block is not visible. So its width is given 6 mm. So what I have to do here? So I am going to consider this 6 mm this block and whatever the things which is not visible we are going to represent that with the help of dotted line. So this is the dotted line we are going to represent. Now when we see from the x direction that is in the front view. So we have completed the view and now we can see here. So after drawing all this front view whatever the things which is visible to us is from the x direction is we have drawn. So now we are going to dimension our figure. So this is the base is given that is 62 mm. So this is the 62 mm and its complete height is 42. So we have to mention here 42 and its 16 mm block is visible from this one and 30 mm from this. So now I will complete this all these blocks of their sizes that is this is 16 mm, this is 30 mm and this is also 16 mm and its depth. So depth is given that is 15 mm from the top this is the 15 mm and its slot which is not visible this is 6 mm it is given. So we have to draw the 6 mm. Now after completing this front view so we are interested to draw the top view of this given object. So first we have to draw the projectors of all these one all the edges. So these are the edges we are going to interested to draw the projectors from the points and in order to draw the top view when we see from we will going to see this complete block of 16 by 20 16 plus 30 plus 16 by 20. So the base we are going to consider here is we are going to consider the 62 mm is the base we are going to consider and its height is 50 mm. So we are going to consider here 50 mm is the height. So here we are completed this 50 by 62 mm plan. So in this one when we see from the top we are going to see this complete block of 16 by 20. So I am going to measure from this here is 50 mm 20 mm we are going to consider mark here 20 mm and we are going to complete this by this complete block of 16 by 20 mm and when we see from the top this edge is visible. So here v shape is required for drawing this one.  So from this one we are going to consider the 9 mm depth. So this v block is completely we are going to draw here and when we see from the top this sliding edge is visible completely. So for that we are going to complete this sliding edge here and here also sliding edge is completely visible and this complete block of 30 by 16 we are going to visible here. So for that this 16 by 20 block is visible when we see from the top view and this 16 by 20 block is visible when we see from the top view. And another thing is that not visible when we see from the top that is this block. So its length is given 16 mm from both side. So I am going to measure this 16 mm from this here and from this also I am going to measure 16 mm from this side. So this is not visible when we see from the top view. So it is not visible then we are going to represent with the dotted line. So this is the height of 50 mm we are going to mark here and its base is 62 mm is given. So this is how we are going to draw the our top view. So these are the references which I have taken for this lecture that is Engineering Graphics by Sri Thakur Dinesing, Mahalakshmi Publication House. Thank you.


Your Excellency, President of Interpol, Excellency Secretary-General of Interpol, Distinguished Guests, Excellencies, Ladies and Gentlemen, a very good evening to all of you. And for friends from around the world, warm welcome to Singapore, literally. It's a pleasure to join all of you this evening. I'm delighted that this second edition of Interpol World held in Singapore continues to draw strong participation from law enforcement, government bodies, academia, security professionals and citizen providers. We have here with us today influential thought leaders and practitioners from both the public and private sectors converging to discuss safety and security issues. This comes at a time when the global community grapples with the rapidly changing and increasingly complex security landscape that presents unprecedented challenges. Interpol World reflects Interpol's efforts to build a safer world through an enhanced international cooperation and innovation on policing and security matters. Singapore strongly supports such efforts by Interpol to strengthen global safety and security. Our support for Interpol goes beyond Interpol World. Singapore hosts the Interpol Global Complex for Innovation or IGCI. Our increasingly global world connected by technology and powered by cross-border networks offers new economic and other opportunities but at the same time presents new security risks. We therefore support Interpol's global strategy to combat current and emergent security risks such as cyber crime, terrorism and transnational crime. We recognize the importance of capacity building and innovation to enable countries to fight transnational crime and security threats collectively and effectively. Indeed, technology and innovation are key enablers for law enforcement and security services too in this digital age. Policing of the future will increasingly rely on technology and data analytics to fight terrorism, cyber crime and organized and emergent crime. It is critical that both the public and private sectors collaborate closely to understand the current and emerging risks in the operating environment and examine how technology and innovation can be harnessed to the fullest potential to address and mitigate these risks. Just as law enforcement and security services are looking at different ways to integrate technology and innovation in their operating models, so too are terrorists and criminal organizations, particularly in the realm of cyberspace. Cyberspace offers the twin benefits of anonymity and mobility to terrorists and criminals alike. A criminal underground economy hidden in the internet has been operating for many years now. Terrorists and criminal networks can now operate globally, coordinating complex operations across territorial borders in real time. Although recent WannaCry ransomware outbreak is a stark reminder of this reality, the ransomware reportedly affected computer networks in more than 150 countries affecting more than 200,000 people. Just last week media reported a new, not pettier, ransomware outbreak. The outbreak wreaked havoc around the globe, crippling businesses in many sectors including banking, energy, and transport. It is therefore tiny that the dark net received the attention and focus of the Interpol World Congress earlier today. As a highly cyber-connected city, Singapore is enhancing its capabilities and resilience to deal with the cyber threat. So allow me this evening to take a bit of time to share some of our key initiatives against cyber crime in the spirit of Sharon. In July last year we launched our National Cyber Crime Action Plan, or NCEP for short. There are four key priorities in this action plan. First, educating and empowering the public to stay safe in the cyber space. Second, enhancing the government's capacity and capability to combat cyber crime. Third, strengthening legislation and the criminal justice framework. And fourth, stepping up partnerships and international negotiations. I will elaborate starting with the first priority, public education and empowerment. As part of its public cyber outreach and resilience program, the Singapore Police Force actively reaches up to members of the public to encourage safe online behaviour and to reduce opportunities for cyber criminals. These are customized to better target more vulnerable groups in society such as our young and our elderly. But the police has also partnered in Europe all in its no more ransom project. The project helps victims of ransomware retrieve their encrypted data without paying any ransom. It educates the public on how they can better protect themselves. Since December last year, more than 10,000 ransomware victims worldwide have benefited using tools available in the project's online portal to decrypt their affected devices. The police has been proactively promoting the project's online portal to our public and to expand the reach of the project in the currency, the process of translating the contents of the online portal into our other official languages. So that's education in our page. I now move on to the second pillar of our action plan, which is enhancing government's capacity and capability to combat cyber crime. Here we seek to enhance our cyber crime investigation as well as forensic capability. In 2015, the police established a cyber crime command within our criminal investigation department. Its role is to develop specialist expertise in cyber investigation, digital forensics and cyber crime policy, and also to improve police readiness for and response to emerging cyber threats. Full-time cyber crime response teams have been set up since December 2015 in all six of our frontline police divisions across the city to enhance our cyber crime response capabilities. Our third national priority is to strengthen laws and our criminal justice framework to tackle cyber threats. The Computer Misuse and Cyber Security Act is Singapore's primary legislation to deal with cyber crime. We amended it in June this year to address the increasing scale and transnational nature of cyber crime, as well as the evolving tactics of cyber criminals. So, for instance, it is now an offense under our law to deal with hacked personal information as well as hacking tools with criminal intent. The amendment has also given Singapore extra territorial jurisdiction over offenses where the Act causes or creates a significant risk of serious harm in Singapore. Move on to the final pillar of our action plan, which is building partnerships and international negotiations. Building strong partnerships with key public and private sector actors is this important thought he thrust. As Interpol's global hub for combating cyber crime, the IGCI is a natural partner for us. Collaborating with Interpol and other partner countries, Singapore has rolled out the Sioux of capacity building programs for the ASEAN region in the last few years, as we are the ASEAN niche of the cyber crime. Some contemporary examples include the ASEAN Cyber Capacity Development Project funded by Japan and implemented by Interpol that will run from 2016 to 2018. The Interpol Southeast Asian World Shop on Cyber Crime under the auspices of the Singapore-US third party training program in 2015 and 2016 and the ASEAN plus three cyber crime will shop in July and last year involving China, Japan and South Korea. We are privileged to have Interpol's presence in Singapore as well as strong support from key international partners in ASEAN member states. We hope to build an even more collaborative environment to exchange best practices and to forge even those operational needs. This has borne fruit, for example in February this year Interpol led an operation targeting cyber crime across ASEAN member states. The operation conducted out of IGCI brought together investigators from seven ASEAN member states to share information on specific cyber crime situations in their respective countries. It identified nearly 9,000 compromised servers and in hundreds of malware infected websites and investigations after ongoing. The private sector too is an important partner. Its investments in R&D, for example, provides a significant competitive advantage against cyber crime. Now, recognizing the value of private sector the Singapore Police Cyber Crime Command ruled out the alliance of public private cyber crime stakeholders initiative in February this year. This alliance serves as a dedicated platform for law enforcement and the private sector to meet to enhance cyber crime awareness in the private sector to prevent, deter and detect cyber crime and to forge active collaboration. There are currently 40 partners in the alliance comprising global IT companies, e-commerce platforms, telecommunications service providers, financial institutions as well as remittance agencies. Through this alliance, the stronger relations forged at the banks allow the police to quickly free bank accounts involved in the recent spades of impersonation scams. This prevented all of these fund transfers and loss for the victims. The Singapore Ministry of Home Affairs has also recently established a special interest group for cyber crime investigation in collaboration with the Singapore Cyber Security Consortium. Through this initiative, we can bring real world cyber crime and investigation challenges to the academia and our industry and leverage on their talent and innovation to deal with cyber crime. The fight against cyber crime cannot be fought by governments alone. The collaborative ecosystem involving the community, industry, academia and governments is crucial to tackling this formidable threat. I hope that all of us will leverage on Interpol World 2017 to forge deeper public private sector understanding and collaboration and contribute towards Interpol's vision to build a safer world. Once again, welcome to Singapore and I wish all of you an enjoyable networking evening and a productive and successful conference. Thank you.