ere around how typically how close clinical trial clinical research sites are to an individual. I believe the average time something around 70% of participants that are accessing a clinical trial, typically need to travel around two hours to get there so you're thinking about now, how do they take time off, how do they access the site and so on and so forth. There's also resource dependent issues in terms of building that study coordinator team ensuring that you have expertise and of course having trained professionals for things like bio sample collection. And a lot of the data that's collected is limited to the time that you have that participant in that brick and mortar facility, which makes it very episodic in terms of the collection. And so COVID-19 really kind of ushered in a new lens that really catalyzed a paradigm shift in terms of how we can potentially move clinical trials out of these brick and mortar facilities and more into people's homes. And this is typically referred to as the decentralization of clinical trials. So a decentralized clinical trial doesn't necessarily have to have a digital component and a digital research study doesn't necessarily have to be decentralized. But it's really the marriage of these two where we start seeing some exciting things in terms of how we can view the construction of these study designs in a different way. So moving from that potentially restricted patient population to really anyone anywhere and opening that up in terms of access, removing the burden of having the participant get to a physical site, but really being able to do it in the comfort of their own home. Leveraging technologies like a smart phone to be able to build a study app and say and provide a self-guided experience. And with that has the positive kind of downstream impact of not necessarily needing to build a huge study coordinator team but really focusing on the participant experience as it's delivered digitally through a smart phone study app. And also leveraging innovations for things that you may not consider as something that can be done in a decentralized way like by the sample collection we have studies that collects saliva and blood and stool again all things that are done in a self-administered way. And then of course most germane to our conversation today in this workshop is talking about all these technologies really give you this ability to not only collect data in a continuous way, but do it in such a way that it's collected in a real world context and that real world data allows for an eventual kind of analysis to get towards and drive towards real world evidence for a variety of different research questions that we may have. And again specific to the environmental space as it relates to this particular workshop. So proof of concept. I'll just kind of leave you with the digital trial center at Scripps Research has been successful in implementing this across a variety of different research modalities. As Rima mentioned, we have interest in the sleep medicine space the precision nutrition space the maternal health space infectious disease. And hopefully you can easily see how these can be adapted for a variety of different research context, especially environmental science and the impact on the public health perspective so appreciate the time and just wanted to give that quick setup and excited to jump into conversation here. Thank you very much. And, and thank you both, Dr. Wang and Dr. Ramos. I'd like to just remind our audience quickly that the Q&A function is below the video player and slide those so please put in your questions there. And with that, I'll take us to our Q&A session. Thank you both speakers and honestly I don't think we'll manage to ask you all the questions that are so interesting coming from our discussions today but maybe to start us off. You know I know Dr. Ramos, you mentioned this and it's very important in your work the issues of diversity, equity, inclusion and accessibility remain front and center. In the space where was her precision environmental health and for medical care delivery. So maybe starting with you and then we'll move to Dr. Wang. What in your opinion, or how are we, how well are we doing in this space in your opinion. Are we being sort of proactive in thinking and designing for diversity, equity, inclusion, accessibility. Are we lacking anything in this vision. What could help accelerate us getting there from any dimensions or examples or sort of lessons learned from your work. Yeah, absolutely I mean as you know, we could have a whole another workshop on this particular topic but it's important to raise and there was some great comments in the previous session with regards to recognizing the need for diverse cohorts. So to answer your question quite bluntly, I don't think we're ever doing a really good job in the health disparities space. I don't know what we could potentially do but where I get excited is that this really is a paradigm shift. So we really are rethinking how we do clinical trials and clinical research. So we're at this beginning stages of a decentralized digital clinical research model that we're still figuring out how to do it and we, and I think we owe it to underrepresented and underserved populations to prioritize them. And to say, you know, I will figure this out in the majority populations first, and then we'll get back to you and figure it out. And the problem there is that we start developing an infrastructure that is suitable for only a subset of the representative population. And by the time you get to other populations and other communities, there needs to be additional activation energy and additional efforts and resources to put forward addressing some of the very specific contexts that they bring within their communities. And it's like recognizing that not everyone has an Apple watch that there are gaps in service with regards to bandwidth issues and rural communities that there may be trust issues, specific to certain communities around their sharing of digital health data that we have to examine and what our security principles are with regards to how we not only ingest data but also how do we secure it as a research team. In addition to recognizing that we should be figuring out how to communicate what are these, what these new technologies are and I think there's, I've had a lot of conversations in which we can easily slip into an agist kind of framework where we say, oh, you know, the older people know how to do this. So we're going to set them aside. And I think some of the success that we've had is that we've shown and that that's just not true.  And she's been my number one participant in darn near all of my studies. And I've been pleasantly surprised at really her ability to navigate and it's been a tremendous amount of feedback from her to say this isn't clear. This isn't right or you have to think about finger strength for this particular device. So a variety of different things. And again, I can go on and on in this particular topic area, but it's, I'll end by reiterating this is our opportunity to prioritize a lot of populations and communities that we typically skip over and wait for the second round. The second round to come around. And so I think we have an opportunity to really make that our primary loans. Thank you so much. And Dr Wang, I'd love to also hear your thoughts on this issue and perhaps from your, you know, technology and global perspective as well. I think I'd cover it nicely. I mean, we developing the technology. The key is that the personnel in my lab are very diverse. My student postdoc who will be in the field are all from very diverse population here in the UC. And again, some of the luxury item, you know, if you do wearable for wellness or nutrition, this is more depend on the, on the, how rich you are, but most of the one for protection of the civilian or soldier. Here there is no discrimination between different, different populations. So it's not like it. As you mentioned the apple watch people use it for wellness, recreation, personal nutrition. This we need to improve that it will be available to. But when it comes to protection against environmental hazard or nerve agent, this should be a general available to all the population. And on that note, perhaps I'll ask one more of my questions and then we'll make sure to get the audience questions in as well. So you mentioned, you know, your team is highly interdisciplinary and I think from all the examples we've seen today, this is such a highly transdisciplinary field and you need so much expertise as Dr. And Sally also mentioned before. And as you mentioned, perhaps like the teams at play and running these studies now are shifting and changing and so on. So at least in my experience, I still feel like it's very challenging or the systems we work within are not very well built for interdisciplinary true collaboration and I'd love to hear both your thoughts. And what is it that we need to kind of help us break through these challenges and really work in highly transdisciplinary, you know, functioning environments. Perhaps Dr. Wang, if you'd like to start. You know, in UCSD we have a fantastic multidisciplinary we work closely with the physician and, and again the old environment is for equity and diversity, both in terms of training and accessibility. So we see no major barrier I mean the barrier is when people need to get expensive gadgets like Apple Watch and other many for wellness and nutrition but not for medical or security application. Yeah, so I'll add a perspective slightly different although I agree with, with all of these points I think the workforce is extremely important to recognize diversity in thought, as well as in background. But for us, we've landed on one of the greatest assets in developing our research efforts and that's putting together our virtual advisory team. So not interdisciplinary in the sense of professional backgrounds and disciplines, whether it's engineers or technologists or computer sciences, but really interdisciplinary if you think about the different roles that people have in our lives so patient advocates and industry and non profit organizers, grassroots organizers, the stay at home mom. So that's, that's our interdisciplinary lens in terms of ensuring that we get those perspectives because our expertise really is in the implementation science, and we can't be experts in that implementation science if we don't have a recognition of what the risks are. Ultimately, this has to be useful at the human general population level. When we talk about public health and so we really try to kind of reverse engineer that and Sarah well who's actually using it, and how can we extract a lot of those insights interdisciplinary, transdisciplinary in terms of professional disciplines 100%. We need technologists, we need clinicians, we need experts in public health, we need regulatory experts, we need all of that. What I'm just suggesting is that additional layer of what is the diversity and thought in terms of discipline with regards to the general public. It's very interesting. Thank you and perhaps I'll take a question from the audience. This is directed directed sorry to Dr Wang. What is it at the future of using tattoos or lab on the skin for monitoring analytes like glucose or environmental factors. And again glucose is the best example has been a, we all learned from glucose from three decades of glucose remember started the 80s people were dreaming about implantable glucose. So it took two decades until we realize this CGM continuous glucose, but this is driven by the market I don't see the environmental market is big enough. But the same we can use the same technology for other metabolite electrolyte, but it's each of these need the investment and I bet there is a big company like here Dexcom or about the investing billion because of the market so environmental market is relatively limited The US government for security applications investing a lot for protecting the soldier but I doubt that just the regular environmental will be major investment, unless there is some requirement. But what was the second question, it's mainly I think, I think you addressed it it's basically also for environmental factors let's say similar types of sensing. There is a really limited mind the technology is available and we need somebody to invest in it to make I mean, even the glucose company like apple is dreaming to have a glucose on the watch and the investing million is still drawn. So everything is challenging especially for the chemical sensing. So the chemical sensing is a different challenge and mobility and vital sign really the stability of this bio receptor the end of the end, they are not so stable for prolonged operation outdoor extreme condition in the desert and so on. Thank you and I believe being a mention that as well. We have a question specifically for ads. What is the type of information that you are collecting with decentralized trials and is it self reported behavioral information. How confident are you have the accuracy of responses when there is no coordinator interviewing these respondents. That's a great question and dovetails a little bit with the previous one in terms of we can have specific sensing for environmental measures. But it's also important to recognize that we can layer on top of that other digital biomarkers that are still helpful to kind of understand the context, whether it's measuring physical activity or sleep duration or resting heart rate or respiratory rate. All of these things I think could nicely complement more environmental specific sense and metrics that that we could glean and so those are examples to the initial part of that question in terms of different types of data we collect, you know, things that you would normally have a wearable from a Fitbit or Garmin or whatever. We have the opportunity to use a study after deploy a number of different surveys and so with regards to participant reported outcomes that can be in the context of validated survey instruments and we can get into things like anxiety disorder assessments or depressive disorder assessments stress and mood. All of these things that have validated instruments that have been deployed in the field in traditional studies. The last part is the trickiest one with regards to how how much can we really put stock into the accuracy of the of the answers that are being delivered. So a couple of things. One, I think this is probably most a question that surface the most in a regulatory context. Right. So when we're thinking about shifting this from a traditional research study to tradition to a traditional and formal clinical model, you now have the lens of the FDA on top of that where they're asking. The initial model was you're bringing 1000 people into one spot and one person's collecting the blood pressure. Now you're telling me that you're giving me 1000 blood pressure readings that were taken by 1000 different people. What does that offer up in terms of accuracy and validity. And those are things that we just have to think through and those are things that our team thinks through and this is what we get excited about where are the challenges how far can we push this and how much can we really determine a signal above the noise of potential inaccurate readings or readings that wouldn't necessarily be valid. And with that kind of blends the strategy of in ideal situations in which you can mimic the gold standard collection of a metric. Yes, let's bring that into the workflow but in instances in which you can. Does a silver or bronze standard metric serve the appropriate purpose and so for something like steps. I wouldn't put any stock into the exact number of steps, but I would put stock into the delta of steps so it may not be 10,000 but when you have recorded and create a baseline when an individual 10,000 steps. Now all of a sudden they're walking 6000 steps or 6000 steps are being recorded. It's really that delta that we get most interested in that we can do quite a bit with in terms of profile analysis trend analysis. And potentially even predictive analysis so it's a great question. I wish I could say I figured it out completely but it's something that we certainly are a front brain of all the time. It's very interesting thank you think the methodological sort of questions and ways we approach things are really fascinating now and it's sort of a new world of how we think about doing these studies. The next question is from cloud you and our planning committee and for both our speakers, maybe starting with Dr wrong in a futuristic world where we are all wearing sensors providing us with information on real time environmental exposures and health indicators. What do you think will be the balance between desensitization from data overload and changes in the use of consumer products available for everyday use in the marketplace. Meaning will people sort of reach a point how do you reach a point where people are not just overloaded with information and they're actually moving towards changes in their behaviors in a useful way. I mean there are different purpose one purpose is to give you warning and alert in case of any exposure sudden exposure that you need to take some precaution. So this is different than have the continuous data temporal a profile of this environmental pollution in San Diego, but the main concern when there is a real sudden exposure and the warning if you ever. As we say in the military for Norwegian explosive but the same for a civilian. So I don't think that the normal population worry about the long term temporal change as long as I was in the limit of normal and not exceeding levels so the main goal of this is to give an alert and warning for sudden sudden exposure from my point of view. But another area that we think about quite a bit in terms of behavior of the participant and I think there is quite a bit of desensitization that we have to contend with because there is a significant amount of data overload that we get on a daily basis. So where we land and this allows me to kind of bring up another important aspect that we focus on is return of information return of data return of insights back to the participant. So if you start including them into this process of don't just give me all your data but let me actually provide something back to you I think that starts not only establishes trust and I think a meaningful relationship with the participant. But also gets them engaged and motivated and understanding I think one of our successes. And the glucose keeps coming up because it's a great exemplar, but we have a precision nutrition study in which we launched and we provided a DEXCOM G six. But we really needed to incentivize and motivate the participant to log their food to where they're fit fit and do all of these other things that we were asking of them. In addition to the monetary incentive financial incentive which can help it really ultimately what motivate them was showing them the real time continuous glucose monitor readings. Overlaid with the macro nutritional information that they were recording from the food so that they can very intimately because it's personal to them see what this data means. And they actually were starting to understand the whole purpose of the study I think back at some of my older studies that I used to run. And it was very much fly in fly out and the participant really didn't their head was left spinning in terms of what exactly that I actually just participate in. But being able to be a part of, oh, this is my information and you're providing it back to me in a way that I can understand it and making meaningful. I think that's what can start chipping away at the desensitization because it starts really bringing this element of being meaningful for the participant. It's really really challenging and really difficult. And I will say thank you so much because that was going to be my next question and you're already answered it, especially about returning information and getting to actionable results in an ethical sort of, you know, useful context sensitive way. We are at time for our Q&A session and as I predicted, you know, I wish we could go on for another two hours. But I'd like to really thank Dr Wang and Dr Ramos for your time and for sharing your insights with us. This has been tremendous. For now, I'd like to move us all into closing remarks and thank you again to our two wonderful speakers. Thank you very much. Thank you very much. Thank you. So, I hope everyone is still online with us. And this brings us to the last day and the last session and closing of our wearables workshop.  Your valuable insights and active engagement are really shaping this innovative field of wearable technologies for precision environmental health and for biomedical research, leaving a lasting and meaningful impression on our collective journey together. And I'm sure everybody online and watching us today feels the same. The first day of the wearables workshop covered a range of topics including non-invasive monitoring techniques and the development of wearable devices. We explored discussions on improving personalized health tracking, monitoring indoor office design, which we learned of course as an area we spend a lot of our time in, over 90% of our days. We learned about utilizing silicone wristbands for personal exposure detection, particularly for vulnerable populations, about wearables for air pollution monitoring and extreme heat, about monitoring warfighters exposure to chemical hazards and so much more. And today on our second day, we just heard from leaders and innovators in interdisciplinary fields and the biomedical space. The session three speakers highlighted a variety of wearables and sensors to detect and collect physiological biochemical health and environmental data. Data derived from wearables and other health data sources can then be used to train and develop machine learning algorithms for disease predictive models for diagnostics and for treatment. As a deeper dive, we heard from Dr. Mahalinga and Dr. Chang about the women's health study with Harvard School of Public Health and Apple, which was used to track fertility and even exposure to wildfire smoke in women. Dr. Jesselyn Dunn's presentation focused on digital biomarkers and utilizing smartphone collected data transformed into predictive health indicators. So Dr. Dunn's use of machine learning classifiers, wearable measurements and case studies further demonstrated the power of digital biomarkers in cardiovascular disease and diabetes monitoring. We also heard from Dr. David Armstrong, who highlighted the significance of wearables and managing diabetic food complications, a common and often neglected issue with frequent amputations occurring every 20 seconds. Dr. Armstrong touched on the importance of providing patients with devices that compensate for their lack of pain sensation, such as smart boots and exploring innovative sensors and approaches, including smart socks, smart shoes, insoles and injectables to positively impact patient outcomes. And then we heard from Dr. Vina Misra on the assist program that highlighted the advantages of long term health monitoring and the need to understand the relationship between health and environmental toxins through innovative personalized wearable technologies. And then we just had our engaging panel discussion on understanding how technology adoption, implementation, report back and science communication factor into advancing biomedical and environmental health research. So, excuse me, I'm talking about session four, not our session five speakers just now, but session four speakers highlighted areas concerning data privacy, cognitive liberty, data equity, workforce development, community engagement, standards and regulations and so much more. So just to give you a recap with a little bit more granularity, Dr. Nita Farahani's presentation explored the convergence of wearables and brain technology, highlighting the embedding of brain sensors into everyday devices and the promising potentials of wearables or newer tech and the considerations around cognitive liberty. Dr. Ritika Chativerdi discussed health data for equitable precision health. Dr. Chativerdi's research focuses on biases and wearable studies emphasizing the need for socio-demographic representation for developing benchmark data sets and adherence to fair data standards and principles in AI and machine learning research. You then heard from Dr. Shikhar Bansali on the use of wearables in public health and the challenges of interdisciplinary training, highlighting the need to update and enhance educational curricula to effectively address these areas and attract talented individuals and students and trainees, but also discussing the influence of social media and data-driven hype. Dr. Tiffany Powell Wiley discussed her community-engaged research approaches in several Washington DC communities at high risk for cardiovascular diseases. She is using wearable technology to discover how neighborhoods and other environments influence the development of obesity, diabetes and other markers of cardiometabolic risk. And then we heard from Ms. Deborah Prince about the importance and the reasons why UL developed standards for the safety of virtual reality, augmented reality and other wearable technologies, highlighting the process for how to develop appropriate standards. And lastly, moving on to our session five group discussion that we just had on the future of wearable technologies. We discussed the current state of wearables, future approaches and gaps and challenges we expect in getting there to the promise of wearables for all these applications. We had Dr. Joseph Wang discuss innovative approaches to wearables such as gloves for chemical detection, innovative data collection and so much more. And we also had Dr. Ed Ramos during his lightning talk discuss a future beyond clinical trials, one that is decentralized and really focused on the patient and participant experience. So over the course of these two days, we have witnessed the conversion of brilliant minds and groundbreaking ideas in the realm of wearable technologies. As we bring this wearables workshop to a close, we do though with a we do so with a deep appreciation for the knowledge we gained for the perspectives shared for the collective commitment to advancing this field. I would like to extend a huge thanks to everyone involved in making this workshop such a huge success. And we encourage you all to join the standing committee on the use of emergence emerging sciences for environmental decision, June 14 through June 15 for our sister workshop on advances in multimodal artificial intelligence to enhance environmental and biomedical data integration. And also personally, I'd like to thank our amazing planning committee and program managers and leaders in getting us here. And with that, we hope you have a great rest of your day. And this concludes our final day two of the National Academy of Sciences wearables workshop. Thank you, everyone.


Alright, now that we have a good idea of what subtraction really is and how we can do this without having any sort of formal algorithm for it, let's see if we can develop a formal algorithm. And for that we want to go back and start with our concrete representation of numbers using our place value chart. And so let's consider a problem like 432 base 5 minus 231 base 5. And so I can set down my place value chart. Again, I don't really need to show what the units look like. It's just a convenient visual picture there. However, the thing to remember is since we're working base 5, each unit is going to be 5 of the previous unit. So 5 of these make this, 5 of these make this, 5 of these make that, and so on. And the numbers that I have here are going to be 432 base 5. Let's see, that's 4 of these, 3 of these, 2 of those. And I want to remove 231 base 5, 2 of these, 3 of these, 1 of these. So let's take a look at that. So I'm going to set down my 432 base 5. And because I want to work with the concrete representation, I know I have 4 of these, 3 of these, 2 of these, but let's go ahead and draw those in. So there's my 4, 3, and 2. And then I want to remove 231, 2 of these, 3 of these, 1 of these. So I'll pick out 2 of these to get rid of, and they're gone. I want to take 3 of these, well I'll pick all 3 of these. I want to remove these 3, and they're gone. And then finally, 1 of these, so I'll pick 1 and get rid of it. And so this is what I have left. And now I want to change back and record using my abstract symbols. I have 2 of these, 0 of these, 1 of these. So my remainder is going to be 201 base 5. Well, what if I try something else? How about 1012 base 4 minus 323 base 4. And so again, I'll set down an empty place value chart just to be able to organize everything. And again, I don't really need to do much more than remember that any one of these is 4 of these and so on. I don't have to show what those look like, it's just convenient to do so. And so I'm going to have my 1012 base 4, that's 4 digits, so that's going to be 1 of these, 0 of these, 1 of these, 2 of these. And from that, I want to remove 323 base 4, that's 3 of these, 2 of these, and 3 of these. So I'm going to remove that. And, well, I have a problem. I don't have 3 of these. I don't have 2 of these, and I don't have 3 of these. I can't remove this from here. I can't remove this from here or this from here. Fortunately, I can borrow, well, actually I don't want to use that term anymore. You might have been told to borrow, but the reality is you're never going to return what you borrow. It's gone, so the proper term is we're going to trade, and we're going to use the fact that our trade rate we're working base 4 is 4 for 1. So any one of these I trade, I'm going to get 4 of the next thing over. So I can trade any unit for 4 of the next. So I'll trade this for 4 of these, and this is gone now. No longer exist. And now I'm in a position to remove 3, because I have 3 I can take away. But I still have the problem over in this column. So I'm going to, again, pick one of these to trade for 4 more in the next place. So I'll trade. This is gone, and now I have 4 of these. And I have the one that I had originally. Keep track of that one. And we're good. Now I'm able to remove 2 of these because I have enough. But now I still have this problem at the last column. So again, I'm going to trade one of these for 4 more in the next place over. And again, this thing that I've traded, that's gone. No longer exist. And I can do the required removals. So I'm going to remove 3 from here, and that leaves me with nothing. I'm going to remove 2 from here, and then I'm going to remove 3 from here. And now I am in a position that I can actually write my answer down using my abstract number symbols. I have 2 of these and 3 of these. So I can write my answer 2, 3, and all together that difference 101, 2 minus 3, 2, 3, base 4 is going to be 2, 3, base 4.


Hello everybody and welcome back to the Average, just ignore this backdrop, we are in the process of moving so yeah. Anyway, Arteza sent me this amazing box of art supplies so I'm going to test them out. Full disclosure, this video is sponsored by Arteza so thanks again for Arteza for sending me these items that I'm going to check out. Kind of like a mystery box because I'm not really sure what they sent me, so let's check them out. First up we have these 36 gouache colours which I'm super excited about. There's some really nice colours in here, it seems so let's let's try them out and also watchy if you could just get out of my shot because you're kind of ruining it. She's like, this box is for me. Pesto do etaca, so mean. Then they also sent me these triangular grip, ooh, fancy, 48 coloured pencils. Nice, I love pencils, I love gouache so they're pretty on brand of what to send me. And then finally they sent me this little bucket thing which is looking super cute and very handy when you're moving to have little containers, trust me. They have like different ones, that one actually looks really cool if it focuses. I would, I would like that one too, they look really nice. Yeah some cool stuff. Okay so let's get into opening the pencils first. Triangular grip, I love that if I can open them, my nails are completely dead so I have no nails. Looking like some nice colours, looking like some nice colours. Pretty cool and let's have a look at these triangular grip. I don't really get the point of triangular grip, I guess it's more dexterity, you get more dexterity in your pencil I guess. Yeah these are looking nice, I will test them out in a moment. Then some gouache, as you guys know if you follow my channel or not, I don't know. Guache is kind of the main paint that I like to use at the moment. I also do like watercolor but I feel like you can use gouache in the same sort of manner so it's a win-win and I do like using pencils in my like comic work and stuff like that so like texture and things. So these are like really handy things to be sent actually. I feel like the company did their research on what I like which is really cool to see. Okay these are looking really nice. Hell sky. So I think I don't know if I'm mistaken or if there are some metallicky colours in here.  Okay cool. I don't really have any metallicky gouache stuff so this is going to be interesting to try and use them. Let's see. Oh my gosh how many colours? 36 colours so there's quite a lot of colours in here. Nice. It will be really interesting to try and use metallics because I haven't really got like such a good background in metallicky stuff like I do enjoy using it but I don't know where to put this. I want to try them out now. So here we go. I really am attracted to this pearl bubblegum colour. I don't know that just sounds delicious to me. Pearl bubblegum. Yes please. Okay let's test them out. Okie dokie. I'm just going to test out some of the paints first on like this page possibly to just see and feel how they work a little bit just like not really swatching because you guys might know that I don't really like swatching so much but just to like get a handle on them and just push them around the page a little bit and you know I've got to try that bubblegum pink first because it is sounding delicious to me and I like this colour pearl emerald green are they all like similar they're all called pearl okay I think is that all metallic oops a little bit too much there don't really mind though don't really care can it get a bit crazy up in here is this white here and what's this one this is pearl white what's the difference pearl white and pearl white rose okay I missed I didn't read it properly maybe some like ochre they have this is gold I think but let's use it as the ochre of our this one's called pearl golden hour which sounds like a nice shade to be honest and I want some blues so let's try out pearl glacia blue I really like these names they just sound really fantastical and interesting to me um pearl lavender I want it I want it I want it I need it I want it they're all quite light colours so far but I think that's me choosing so let's get this I think this is probably the one of the darkest one is this midnight pearl midnight blue okay okay Arteza I'm seeing you and I also want my pencils here because I want to use the pencils over the top of the gouache or you know something I don't know well hi mochi have you come to be in a way okay this is so this is with water super pearlescent and then this is without water so you can see like it can be used quite opaquely which you know which I like I like it a lot boom boom boom let's try this midnight blue okay this is looking nice I really usually I'm I kind of steer clear a little bit of metallics but I think these ones are a bit more subtle I don't know how to describe it they seem like you can use them kind of as if they were normal paints and then just there's just a hint of like a shininess to it rather than it's just a gimmick if that makes sense I feel like they're quite nice in that sense um let's go to painting can't be bothered to test anymore that's me done with this yep they're good let's go see how long they take to dry actually very quick um I mean they're a little bit still wet there but they're mostly dry from when I when you saw me just put them down so I really like that because I would I like to work quite quickly and so I'm just gonna do some painting in my sketchbook um like I usually do with wash I really like the way this paper is quite thin but it reacts quite nicely to paint because of just the what she I don't know if you can hear much you growling but she just when she plays he's a little tiny cat but she plays and she gets really growling um I really like the texture it gives me which is probably something that people find the demon in my house uh odd but I do I really like playing with texture so I want to use this sketchbook for these gouache mochi mochi's going berserker what mochi's gone full berserker mode thank you doshy have a bite of course I don't know delivery boy comes and eats the burgers yeah we got painted by them so it's time to get into the painting I went to blow dry it because he's got some lumps and stuff on it but I really like the way that it turned out because it looks like the metallics kind of give a bit of a warmth and that feeling of just the heat in the desert I want to go in now with some pencils and just add some details and stuff to this yeah I'm actually more than impressed with these because I think they layer really nicely and they have just a glow to them the metallics I've used in the past have just been totally overpowering but as you can see this ones are just kind of a subtle I don't know just a subtle shine which I'm really enjoying so I'm going to just go in with some pencils now and do some detail work and just have fun with it and let loose and be kind of expressionate and impressionistic like I'm trying to be here I'm trying to be more loose with my paintings and I feel like when I've done that in the past it's really worked out for instance early on in this sketchbook like when I'm just a bit more loose I really like the way that it looks um where's one that I did oh maybe it wasn't in the sketchbook oh yeah here it is like I really really like this one it's probably more definition here than there is to this painting but there's sort of a similar vibe there of just like blocking out different shapes of things and I I do enjoy it so I'm gonna see whoops didn't realize that was still wet I'm gonna see how it turns out with the pencils now and just uh yeah try to add a little bit of definition just to sort of um show what we are looking at here a bit better because if you couldn't tell it is kind of like a desert scene with cactus and the sunset behind the mountains and stuff but let's try some pencils I'm really liking the feel of these they layering on this gouache I'm just gonna zoom in so you guys can see at a certain point they're layering down really nice like over the gouache it just very smooth pencil which is really nice to work with I really like that yeah I think that's looking kind of cool actually with the definition a bit more helping definitely so all I've done it is roughed out the shapes with the gouache paint and now I'm going in just trying to define roughly sort of these areas and maybe some more like twigs and shapes like that and just having fun with it making marks trying to give the impression of what you're looking at here and that's something I'm really enjoying doing and I think it has a very like dramatic effect and it's also something that I don't know I just find satisfying to create who that's kind of what I'm doing it like in my catchphrases follow the enjoyment of what you're doing and because I enjoy this mark making process that's kind of where I'm at for my creative style so at the moment I'm still in that place where I'm just like do I have a style I don't really think so so I'm just exploring everything just following what I find nice and what I think looks good as I go and yeah I'm just enjoying it I really like these paints actually and I really like these pencils so I'm really pleased that they arrived because I kind of packed up all my other art supplies and gave a lot of our old art supplies away to my nieces and so these are really nice for the interim as well to have something to use was before I go to our new place and pick up our boxes full of stuff so super nice thing to do to be able to do and if you see if I'm like I'm just trying to like bring out texture as well in some areas because I made the gouache lumpier that's the saying you should use in some areas like here I've just lumped on some some gouache and I think that it's really created something quite interesting so I want to just like work with that and still it's still a bit wet so maybe I can push that round with my my pencil really get in there make some different textures and different energy in the painting and I think this is just adding to the energy and the feeling and the emotion of the painting and it's just it's just fun you know it's just something I enjoy doing so this is where I'm at right now and I hope that you guys are enjoying what you're doing with your artwork as well and I hope that you can enjoy what you're creating I know it can be super tough and sometimes there's that pressure on us and all we can think about is the pressure so you have to make something and you know you want to be good because you're going to post it somewhere or somebody's going to look at it or somebody's going to read it or somebody's going to consume whatever you've created and it's hard to block those thoughts out of that final thing that final moment where you've just pinpointed what everything accumulated has become but you forget the process and you forget the joy of what you're doing if that's all you can see and that's all you can think about so maybe this when I'm finished with it oops will be absolute garbage and I'll be afraid to show you so that could always happen so we've got to let go of that fear and just enjoy what we're doing and block out those thoughts and maybe listen to maybe listen to an audio book maybe listen to some music just to stop thinking and meditate a bit more because in the way creative process is meditation right it's not all just a means to an end it's about the doing of the thing that's my TED talk for today so it's just nice to have a moment sit down with a coffee chat with you guys and get on with it oh we made a mess of this pencil oopsie oh dear so how is everybody doing because I know it's been a while since we spoke and it'll probably be a while since the next video after this one comes out so just let me know down in the comments how you're doing and what's happening with you guys because I miss you guys and I feel like I haven't spoken in a while this is kind of a nice way for me to chill out and relax what do you guys think I think this is actually turning out to be a really nice piece so yeah I think that's it I think I'm just gonna say that's the final piece and thank you to Arteza basically for sending me this stuff and also shout out to my friend Tom who was the interim sender of all this stuff because they couldn't send it to my country at the time so thanks Tom for helping me get this video done and thank you guys for watching let me know what you think thanks to my patrons as well ever ever faithful patrons and really kind of you to look after me I think this will probably be the patron print because I really like the way that it turned out so this might be the patron print this month if you guys want to sign up then check it out in the link down below if you guys want to get any of these Arteza paint supplies and stuff there is a discount link down in my description so go check that out I think you get 10% off and the code is also there so thank you guys so much thank you Arteza and hopefully see you guys soon in the future whenever I can film again but thank you for watching this and bye have a good good old day I guess yep bye


Hello everyone, this is Amit Don and I'm on my way to Taipei on EVA Air Business class, but before that I'm taking you to the Plaza Premium Lounge at Toronto Pearson Airport.


Mr. Chairperson, Excellencies, Honorable Ministers, Heads of Delegations, Ladies and Gentlemen, I wish to thank our host, the Governor Sri Lanka, most especially President Shirasena, for organizing this timely conference. Let me apologize for not being with you personally, as another equally compelling matter requires my presence in Geneva. Let me make three points in my brief remarks. First of all, the need to understand the link between migration and health. Secondly, the need to implement migrant sensitive policies that are aligned with the person-centered universal health coverage principles. And thirdly, the need for partnerships and coordination. First point I would like to highlight is the need to more deeply understand the nexus between migration and health and to take action accordingly. We live in an era of unprecedented human mobility, a period in which more people are on the move than ever before. Today there are nearly 250 million international migrants, 750 million internal migrants, one in seven of the global population. Migration is inevitable, it's necessary, and if well managed, it's desirable. It is a reality to be managed and not a problem to be solved. The volume of current migration is an important indicator of the global importance of the health of those moving across and between borders and of the importance to consider and address the health of migrants as a global health agenda for all. Despite migration being a factor of life, large groups of migrants remain at high risk of social inclusion, discrimination and exploitation. Many still lack access to adequate health service or have to pay higher prices for it. Additionally, the realization of equitable standards of health for migrants, their families and communities, hosting them, remains an often neglected theme in both health migration and health dialogues and goals. We need to work together to overcome this reality and your leadership is essential. Let me highlight that migrants and mobile populations do not generally pose a risk to health for hosting communities and they should not be stignified and associated with the risk of importing diseases. As we know, it is rather the conditions surrounding the migration process that can increase the vulnerability of migrant and mobile populations and communities to ill health, particularly for those forced to move and those who find themselves in so-called irregular situations. This brings me to my second point. The need to implement migrant sensitive policies, policies that are aligned with the person-centered universal health coverage principles that promote inclusion and integration and address avoidable causes of vulnerability to ill health. Universal health coverage, UHC, or leaving no one behind, is very high on the global health agenda. We can all agree that UHC only exists if health systems account for all of its community members, including migrants. Successful integration of migrant and mobile populations into health systems is therefore critical to predict and prevent the spread of diseases, design evidence-based responses along mobility pathways, enhance national capacities to better promote the health of migrants, and to enhance the contribution of migrants to sustainable development. All these will only be possible if we work together, which is the last point I wish to make. The need to improve partnerships and coordination, to properly address challenges relating to the health and well-being of migrants. We need to work together to better understand the health aspects of mobility. Migrant health issues cannot be resolved at all by the health sector alone. Partnerships, health diplomacy, and leadership across all sectors and across all borders are essential. Moreover, it's important we work towards a unified agenda across regions on the health of migrants, including those who are forcibly displaced. In IOM, we much value our strong collaboration with WHO and all our member states, civil society, and other actors toward this goal. Our task is to find commonalities and unifying principles to promote strengthened health systems and health for all, where at an opportune time, estates begin work on the global compact for migrants, which sets to lay down common principles pertaining to human mobility. In conclusion, health is a basic human right, an essential component of sustainable development. Being and staying healthy is a fundamental precondition for migrants to be productive contributors to the social and economic development of their communities of origin and destination. I trust that health ministries and representatives of government, civil society organizations, community members, the private sector, and development partners all stand ready to promote the health of migrants and lead the mainstreaming of migrant health into all policies. Time is opportune for coming together and to achieve results. I wish you a most successful consultation.


Okay, so welcome all to this SIV Virtual Computational Biology Seminar Series, which is the last one before the summer break, so we're going to see you back in September. And today for this last talk, we have the pleasure to host here in Lausanne, Hubert Rehauer, from the Functional Genomics Center Zurich at the Affiliate to the ETH Zurich and the University of Zurich. Hubert studied physics at the University of Wurzburg in Germany, and his main interest there was in the computational modeling of statistical physics. After his diploma, sorry, he joined the ETH Zurich for a PhD in Image Data Mining, and in 2000 he shifted to Life Sciences and joined the company Gene Data as a scientific consultant where he developed algorithm and software solution for the bi-emerging field of expression microarrays. And in 2005, he joined as bioinformatician the Functional Genomics Center Zurich, where he currently holds the group leader position in Genome Informatics, and Hubert is also a group leader of the Swiss Institute of Bioinformatics. So his group at the Functional Genomics Center collaborates with researchers on the analysis of next-generation sequencing data, and the group provides web-based analysis to the research community, as well as custom development of analysis pipelines. The group supports the analysis of virtually all NGS applications based on data produced by any recent sequencing technology, and the group also trains researchers and bioinformaticians on various aspects of data analysis and provide access to their computing infrastructure for running analysis. So today, Hubert will tell us how single-state RNA sequencing platforms perform, and I want to thank you again for accepting this invitation, and the floor is yours. Hello. Hello, everybody. Hello, everybody here in this room, and hello, everybody who is virtually online and follows this presentation in a different place. So yes, today I'm talking about some performance comparison that we did to gather with our research groups on platforms that allow single-cell RNA seed. The outline of my talk is as follows, I will quickly repeat what we are doing, but it's not going to be too long when I will introduce these single-cell platforms and shortly mention how this has been evolved, also mention quickly existing comparisons, and then I will come to our study that we did and the quality metrics that we have done so far, and this is still some work in progress, and we will still contribute to that. Now, if you talk about genome informatics, here's what we do at the Functional Genomic Center. Our mission is to provide bioinformatics services and collaborations in the area of omics, so this focus on next-generation sequencing data. So all types of bioinformatics challenges that involves next-generation sequencing, but can also involve metabolomics, proteomics, or clinical data where the provide support. And we also maintain and run a cluster, high-performance computing environment, cluster and cloud-based, and we also do some software and pipelines. And here on the next graph, I'll just show what is in the brain, what is our visualized roughly, what are the ways, how we want to make bioinformatics easier for researchers. So it, in my view, I think it depends, A, on the bioinformatics skills of the researchers and on the depth of bioinformatics support that we can provide. And so where we have a series of shiny web applications, they serve for visualization of various data, and where this is really for every researcher and pretty much independent of the bioinformatics skills. The idea is that these web interfaces are intuitive and can be easily used by virtually everybody who knows what he or she wants to do. When one level higher, we have our web pipelines, where we have a sushi environment, but a framework where we can run next generation sequencing data analysis. And if you know what parameters to choose and how to run this, and if you really know what to do, and you are able to express it, then this is a great tool that gives, lets you from a web browser generate QC reports from your data, realign your data, call snips and so on. And at the end of the, if you run both things, then at the end of the analysis, you will get a folder with the shell script that has been executed, the parameters, log files, and finally also all the results. So everything is fully documented, you can take it away and use it further for downstream analysis and interpretation. And there we report, support really everything, RNA-seq, the NOVO, metagenomics, all types of applications. And then on top, even for the highly sophisticated users who really need their own environment, there we also have a collection of Linux boxes where they can develop their own scripts on the command line. And finally on top, we offer also collaborations and there we, from our side, provide the most input and this is again helpful for any type of research of whether he has limited bioinformatics skills or whether this is an expert in bioinformatics, this can be fruitful collaboration in all sense. That was already all and here now there is the overview of the study that we did, that was performed, the study was performed by the genomics research group of the ABIF. ABIF is the Association of Biomolecular Research Facilities. This is an association of core facilities like ours, the Functional Genomics Center. And there have been people from the University of Rochester, Sunny Albany, Harvard and the University of Zurich involved and we got also support from BioRide, Illumina, Pan-X Genomics, Waveforgen, Fluidine and ABIF, the ABIF itself. And just because we name, I name these companies, I don't want to express that these are the best companies for single cell technologies, these are just the technologies that we had at hand. Now let's quickly review what are, what is the status of single cell RNA-SIG? It's now already eight years ago that it has first developed and it has been improved over the last years and there has been some major steps, especially also the introduction of free prime and tagging so that you no longer do full length pre-amplification and also with free prime and tagging and sequencing you can get very good expression estimates. Also the introduction of UMI's we've seen and these, with these UMI's you have unique molecular identifiers that help you to get rid of any PCR duplicates that have been introduced during the amplification and if you figure it out you have to do a lot of amplifications because a cell will have something like a quarter of a million molecules that you want to sequence and on the sequence cell you want to produce something like a million reads and there you need some substantial amplification. Now these protocols have been compared by an excellent paper from Sigenheim and collaborators which has been published this year in molecular cell was also already available in bio archives last year and what we see from there is if you map all protocols that they investigated they map, they have a substantial amount of reads that actually map to exonic regions of the genome and that can therefore contribute to gene expression values. There's also a substantial amount of reads that are mapped but non-exonic also unmapped reads these all these fractions are larger than you would expect from bulk sequencing with bulk sequencing we expect for example easily to have up to 70% of the reads or even higher can be anything between 60 and 90% of the reads that map to exonic reads. The single cell RNA is the number of reads that really come from protein coding exons are smaller and what you can also see the top something like eight protocols that have been analyzed and there is substantial amount of PCR duplication because the light blue shows you the number of reads that are mapped exonic and the dark blue shows you the number of different UMI's that are those so this light blue basically are the redundant fraction that you have. This means if you have for cell sig 2 in total if you have got one million reads you can get something like an estimate when 400,000 reads mapped to exonic and something like only 20,000 reads are really unique in this example. And what this comparison also showed is that something around one million reads are enough the number of detected genes on the right figure shows that saturates around one million. There are different views on that but there can be something like between half a million and five million you reach saturation and the graph also shows that there is a substantial difference that for example the drop-seq this is the brownish graph at the bottom end has one of the lowest sensitivity of it has the lowest number of detected genes if you fix for example the sequencing depth. While SmartSec 2 also in this comparison had the highest sensitivity and where with a sequencing depth of one million reads you easily get something like eight or six, nine thousand genes detected. And they further checked okay now every cell is different we see only 8,000 genes in one cell but how does it look like if we have a set of cells if we do a set of cells do we detect in each cell all those the same genes or not and what has been shown a lot of times in different cells you detect different genes and this is a due to different biology due to the way transcription works but also due to dropouts and these dropouts can be also involved caused by the protocol that just misses these few molecules in the one cell but not in the other. And the fact that if you check what is the cumulative detection of genes in for example 100 cells this still increases this shows that this dropout process is somewhat random. So and this also shows that it's really worth sequencing more genes. And then in a second paper by Swenson which was also published this year they checked what is the actual sensitivity of the protocols and they used spike in model for that where we had a spike in data also and with that model they get estimation that the protocols detect the best protocols like the smart or the cell sec 2 and the start sec may detect down to something like a 5 molecules in a million reads they can easily detect the expression for both. So they have really a high sensitivity and but what this also shows again this droplet based approaches like drop sec or gem code they tend to have a higher detection limit they need more molecules in the input to be detected. And this is the channel the previous slides was the average but here in this graph they showed that even you have a well defined average but every cell the detection limit can be very different from cell to cell so you have a considerable variation from cell to cell. And in this protocol and this paper it's also worth reading so they compare these full length protocols versus the tag counting protocols and check the individual performances. Now these protocols were focused mainly on what are the med lab steps that you do after you have the cell isolated but now we go one step back and want to say what are the ways how you can you get actually single cells. So where single cells manipulation is around since years you can do micro pipetting or laser capture micro dissection or you can do fact sorting but the first thing micro pipetting and laser capture this only works for low number of cells so if you want to have 10, 20, 100 cells that's okay if you want to have thousands of cells that doesn't fly. But the good thing is this would work with any tissue the bad thing when it comes to cost these reactions are in micro little volumes and the consumables are usually expensive. And there have been approaches around that do the whole reactions in micro droplets and if you go into micro droplets when this reaction goes into nano little volumes and the cost for the reagents drop. And then there is also as a special product there's this commercially available micro fluidics chips from Fluidime C1 and they are designed to handle 100s of cells and they can also visualize the cells they very very fast and they do the action in nano little volumes. So there have been these two approaches around for a few years micro droplets and this micro fluidic stations and but this obviously triggered some companies to further follow on and that here I'll come first to this C1 auto prep I'll show the specs. It's a micro fluidic cell manipulation device you can have it with 96 cells or 800 cells you can do full length transcript as much as two protocol basically or you can do free run tech counting a big drawback is that you have doublets and that you have different chips for specific cell sizes. So you cannot have if you do a tissue if you do a tissue disruption and then you have small and small cells you can't put them all together in the same chip. And then the micro droplets there has been the 10x company that developed this chromium system and where all reactions go in an emulsion system in small drops where the necessary reagents for CDNA generation are contained and the cell is added and when you have the first step of the RNA sequencing in these drops and obviously you can easily have thousands, 10 thousands of drops 10 thousands of cells that you can handle and the reagents are quite cheap and you can process them all in parallel together. So after these two products have been established I was joined the market Illumina together with BioRed developed with DDSec Shure Cell Protocol it also works on droplet it also has a droplet approach it can capture between something like 300 and 1200 cells it is more flexible in the cell size up to 5 micron so it has the protocol that's free run tech counting only but has UMI's also and the big plus on that is it gets away without any pre-amplification step. And then another player that joined the market is WaferGen with this iCell 8 system and that's a different approach it's a nano dispensing array and here you have an array of this 5000 wells and these wells contain cell bar codes and the dispenser dispenses individual cells on both wells and this goes according to a process distribution so with the dispenser you usually fill up to 34% of these wells so that's why you can get between 1000 and 2000 cells on one well. The big plus on that is display after you dispense the cells you can visualize the cells you can check if they are the ones that you want if they are in a good shape and you can also select those and sequence only those you're interested in and while with the other devices for example with the drop sick device all of those drops are end up in the pool and you're going to sequence all. Now the study design that we used we started out with a press cancel cell line and we had a vehicle treatment and we had an H-Tech inhibition with TSA and we did on both also bulk RNA seek and we used these protocols like these platforms WaferGen iCell 8 with Illumina, BioRed, DD6, the Fluidime C1 chip that has 96 cells and does full length and the Fluidime C1 chip high throughput that has 800 cells and does only free run tagging and the 10x genomics chromium solution and these the different as you've seen before these are very different technologies and they've got different strengths in terms of throughput and sensitivity and our design also somehow matched that so for the WaferGen iCell 8 the typical batch size would be that you can use 900 cells in one run and we did 900 times two on one chip and we did this the whole thing twice and for the Illumina BioRed we had 600 in the both in both batches Fluidime we had you can have up to 96 but we found the failure rate the count on in 80 and with the Fluidime C1 the count on 350 and the 10x genomics we think about 8 the targeted around 1000 G cells. The target sequencing depth was also 100,000 reads per cell and we did the same thing with the Fluidime C1 and we targeted around 1000 G cells. The target sequencing depth was also 100,000 reads per cell for each technology except for the Fluidime C1 where it was 1 million reads per cell because that protocol does full length RNA transcript that has a coverage of the full length transcript so you obviously need more reads to capture the molecules. When evaluating such technology also from a core facility standpoint out these are the aspects that we were interested in a how reliable is such a system how how much can we trust it from this and from the scientific point it's more important what transcript sensitivity do we want is free time taking enough or do I need full length transcripts do I also need non-coding do I need to worry about doublets and from a technical point is what are UMI's can I include UMI's can I do de-duplication with UMI's and also from a technical thing is how many cells can I get and what are the total in total the costs. For the evaluation we looked at the following metrics what is the transcript home that we cover what is the diversity of this transcript home and what is the sensitivity of this detection how reliable and efficient is it what fraction of cells and reads are actually usable and we also checked in out do we rather want to sequence more reads per cell or do we have more a higher number of cells with fewer reads and the things that we didn't cover here is doublets and we also didn't cover ERCC's or other spygans because not every technology allows the spygans and we also didn't cover normalization aspects there are also publications on that there's been a recent publication again where I think normalization for single cell RNA-seq is still a major issue especially if you have a low read and if you have a large number of genes that have that the expression is exactly zero. Now the first thing we looked at what is the strand specificity so here all protocols that claim to be strand specific were also highly strand specific except for the C1 high throughput chip there was a number of cells that had a considerable amount of reads but were on the wrong strand and C1 96 has the strand specificity is 50% because this is an unstranded protocol and same for the bulk RNA-seq protocol that we used. Now for the mapping rates or mapping targets that we looked at do where do the reads go if you are able to align them and the majority of the reads in our example went to protein coding regions and this was basically true for all protocols. So the majority of the reads really come from protein coding experts with this treatment. There are also some reads from pseudogenes there are some reads short non-coding and mitochondria, but in none of the protocol these had really a major impact. And then here this is a complicated slide. Here I compare what are the informative reads and what are the usable cells in the end. Now if you look on the X axis I plot the number of reads and on the Y axis I plot the number of usable cells the number of cells and for the I cell 8 you see at the top the dot that shows you that we have nominal we have 4,000 cells so we have a device that claims it can do up to 4,000 cells and we sequenced that with around 200 million reads and now in the end we looked how many reads were ending up on protein coding axons and how many reads could be actually used and this was this you can deduct from the triangle at the bottom so something like 150 million reads would end up on protein coding axons which I consider a high efficiency but the device is made to capture up to 4,000 cells we had only 1,600 cells where we could get reliable expression values from. So a large number of cells were just lost during processes did not create libraries did not create we didn't detect cell barcodes for that for both genes we didn't have enough material for that. So let's say with that the circle always shows what the manufacturer tells you what you can read and the triangle shows you what you will actually read and for example what is also remarkable here for the C196 chip so we had a large fraction of cells that nearly all cells were really usable we had enough reads we had consistent expression profiles only a low number of cells were excluded in the QC step and also a large fraction of the reads actually contributed to the expression values for the C1H2 the efficiency on the sequencing is not as high we have more reads that come somewhere else that are not protein coding and the outlier here is a little bit the Schur cell which in terms of cells most of the cells are usable but this data set has been completely over sequenced so if because the reads were all duplicate reads that would have and all of these duplicate reads were eliminated in the UMI step so we only had something like 50,000 different unique molecules that we could see with sorry 50 million in total for all cells different unique molecules that we sequenced but we have this has been sequenced with something like 600 million reads and why was this done when this was supported by Illumina and they said we just put it on a whole flow cell basically and where the sequencing was not a cost and they wanted to look very good in this comparison and so they spend a lot of reads in it so but I think it was this was over sequenced so you can produce a lot of reads but if the diversity is not in your library this is wasted but this is also nothing not that much new also that 10x genomics it also has been over sequenced to some sense because we have many much fewer UMI when we have reads now when we look at the expression diversity this shows this violin plots showed a number of reads that are spent on the top expressors so next generation sequencing is a competitive thing if some highly expressed genes eats all your reads you don't have sequencing capacity left to sequence the low express genes and how does this happen this happen if some if the high express genes or abundant genes get preferentially amplified when you will only see this gene and you will see nothing else and this is true for the IFC 800 which has the highest fraction of reads that go on the top 500 genes and here you will see we have done a pilot study of this already in 2006 these are labeled with prefix with 2006 and where the values were comparable so IFC 800 which is the C1H high throughput chip is inefficient because it spends many reads on it shows signs of over amplification and it spends the majority of the reads on very very few genes on 50 genes here and this is true for the high throughput this is also true for the 10X genomics where we did 100,000 cell loading and this is to a certain degree true also for the ISO 8 and with that respect the 96 C196 chip is good and the bulk sequencing obviously is also good you have a high diversity you have less over amplification now another aspect of diversity is how many genes are detected obviously with the bulk sequencing you detect most genes and the C196 chip comes closer this is also what the other papers from Siegenheim and Svensson have shown this SmartSec 2 protocol is really a sensitive protocol and after that the C1H high throughput chip is very sensitive and on less sensitive are the ISO 8, the 10X genomics and the sure cell but this obviously also depends on the sequencing depth the more you sequence the higher you have the chance to detect something and in order to correct for that we sub-sampled 20,000 reads or UMI from each data set and then we check what is the number of detected genes and therefore we try to get out the sequencing depth which influences the detection rates and now the numbers are more comparable but what you can still see is that bulk sequencing is best the ranking basically did not make a change except for sure cell which is really a high number of different genes found if you take only 20,000 reads and I think this high diversity comes from the fact that the sure cell protocol does not do any pre-amplification step and does not over-represent individual genes there now we check also if you go from one cell to the other cell if you look at different cells how many genes do we detect cumulatively in all cells do we always miss the same genes or do we detect more genes if you look at different cells now these are all the same cells from the cancer thing this is not like you have different cell types as you would have from within tissues so we expect smaller variability until you see the same plot at the log scale and at the linear scale again for comparability we have 20,000 reads but they are sub-sampled and what we can see here the 10x genomics this droplet approach has this lower sensitivity and detects fewer genes and if you look at a linear scale what you can learn from there is that the whole thing saturates in our thing with something like 200 to 500 genes so if you have something like 400 genes you have seen most of the expression diversity now things have been sub-sampled for 20,000 reads and with the 10x genomics technology if you have 5,000 cells you tend to sequence low but if you and it also as it shown before it doesn't really make sense to sequence more you don't have a major benefit but if you take only 96 cells as for example with the fluoridime approach then you still have enough money usually left or you can still afford to sequences with higher coverage and then we do the same plots where we take all reads but we have segments so this means about more than a million per cell for the C196 and what you see here is that you really have also a benefit it is worth to sequence cells that you have produced with the C196 with the SMOTSIC 2 protocol with more than a million reads and with the others protocols you start to saturate earlier now what we also wanted to look at there is a lot of amplification amplification can also be influenced by GC content of the genes and then we check what is the chance of a gene being detected if it has a low GC content or if it has a high GC content and what you see here the plots in red this is the chance of detection for genes with a low GC content and here we took an extremely low GC content below 0.4 the green plots show the genes that have a GC content around the center between 0.5 and 0.55 and the blue ones are both genes with very very high GC content and here what becomes apparent the C196 has a GC bias also the C1 high throughput chip but basically a very small GC bias present in the 10X genomics and in the Schur cell GC content is also in our bulk RNSIC protocol and in the isolate and we further stratified that according to length now we have been the whole we have been all genes in a space where we put on the X axis the GC content and on the Y axis the gene length in LOC2 scale and what we can see here that most protocols have a certain bias for example the C196 prefers short genes easily detects rather short genes with low GC content short genes are on the Y axis very rather on the low and on the X axis low GC content is on the left and the very short genes with less than 512 bases they have been detected but what we can see here also this Schur cell overall it has a low detection rate but it detects basically independent of the GC content and length now we looked at the big questions what one should do should I rather sequence 1,000 cells with a million reads each or should I sequence 10,000 cells with 100,000 reads each so these are the trade-offs but you can do cost-wise it's very very similar what we did there is we loaded the 10 X genomics machine with 1,000 cells, 2,000 cells, 5,000 cells and 10,000 cells and always sequence something like 150 million reads out of it around 150 million reads and then this means the number of reads per cell goes down the more cells you have the fewer reads per cell and what we wanted to know then if we vary the cell loading what is our detection rate and what we can see here if the UMI sequence per cell which goes up to 60,000 here if we increase our sequencing effort up to where we also detect more genes so it is true if you fill in with 10,000 cells the whole protocol works but if you spend only a low amount of sequence capacity for it you also detect only a few low number of genes so it is definitely worth to sequence up to 50,000 UMI per cell and which in our approach translated approximately into half a million reads per cell you could also bump it up we didn't do that I think it would have worked I think the information is there but this was limited also and when we did a T-cell, a T-SNE analysis where we tried to see if we can separate the cells and if you have a thousand cell loading we can easily separate these two conditions that was no problem at all if you use these 2,000 cells with lower sequencing depth then we could still separate the two conditions but what we saw here is that there are two subgroups and this is because the whole thing has been done in two batches and these are the two batches I don't have a plot for you but these are the two batches and here the green cloud is also clearly two batches that explains the substructure when in our sequencing if we use this 5,000 cell loading when this separation becomes unreliable and we can no longer clearly classify all the cells correctly and if you use this 10,000 cells loading that we use with low density coverage sequencing when everything was gone so when there was no sensitivity around to discriminate the two types but this is exactly what for example the 10x genomics people advertise but it's enough to sequence 10,000 reads or 20,000 reads or at most 50,000 reads for it because they want that you spend your money on the 10x reactions and not on the sequencing I guess if you ask an Illumina representative it's going to be exactly the other way around and I think there has been this paper out that really showed that with as many as low numbers of 20,000 reads you can classify different cell types from each other but I think from our study I think you really have to see what is your classification task at hand that you want, what sensitivity do you need and I would feel safer if you start with at least 100,000 reads or rather half a million reads per cell to get a feeling if what is the number of reads that you need for your research question but where the demands may definitely vary from case to case Conclusions All technologies allowed with successful single cell RNA-seq experiments I think that different technologies satisfy different needs in terms of detection sensitivity if you go for detection, if you want highest sensitivity you can also, we also had very good experience with this manual plate based SmartSec 2 protocol but if you have a higher demand on cell throughput then you might want to go for a different technology and this also depends on the costs that you have and on the batch sizes that these technologies provide There is a coverage bias present and this obviously is more stronger, more stronger than as compared to bulk RNA sequencing and the droplet based methods provide this highest throughput in terms of cells but at the cost of the gene detection sensitivity and with this I want to thank all those that have contributed to this study and I want to also acknowledge the whole function genomic center that supported this work and I am open for questions if they are here or from your online attendees