Hello and welcome to the National Human Genome Research Institute's Strategic Planning Virtual Town Hall. The National Human Genome Research Institute, or NHGRI, is part of the U.S. National Institutes of Health. NHGRI Director Dr. Eric Green will give a brief presentation, and then participants will be invited to ask questions and provide feedback or other comments. To be placed in the queue to ask a question during this discussion portion of this event, please press the star and one keys on your touchtone keypad. When asking a question, please start by telling us your name and organization. You may remove yourself from the queue at any time by pressing the pound key. Slides can be seen through the WebEx information made available upon registration for this event. The entire presentation will be recorded and made available within one week on the NHGRI website, genome.gov. Now I will turn the program over to the moderator, Dr. Laura Lyman Rodriguez, Director of the Division of Policy, Communications, and Education at NHGRI. Good afternoon. As the operator just said, I am Laura Lyman Rodriguez, and I'd like to welcome you to today's Virtual Town Hall. Our primary focus this afternoon will be to gather ideas and feedback from you, our stakeholders, about the proposed elements of the strategic planning process, as well as any immediate thoughts or ideas that you have about the opportunities in genomics and the application of genomic technologies and knowledge for the decade beginning in 2020. To begin, NHGRI Director, Dr. Eric Green, will provide an overview of the current round of strategic planning and how we envision it will inform the development of a 2020 vision for genomics. Following Dr. Green's presentation, we will open up the phone lines to answer your questions and to hear your input, which will help to guide planning activities going forward and inform the discussions to come. To help field questions and respond to your comments, we are also joined in the room by the experts from NHGRI that you see shown on the screen. As you can see, these individuals come from across the institute and bring their varied perspectives and expertise to our internal conversations about the ongoing strategic planning process. So without further delay, I will now turn it over to Dr. Green. Thank you, Laura. Hello, everybody. Thank you so much for joining us from the various places that you're at. What I thought I would do is really to set a context by providing a background about this strategic planning process and its importance within the context of the institute and also the field of genomics. This process actually began officially just under three months ago, back in February of this year at a meeting of the National Advisory Council for Human Genome Research, we announced the kicking off of a new round of strategic planning for the institute, one that aims to deliver a new vision for genomics in 2020. Now, if those of you are interested in getting significant background on what I'm about to give you a reader's digest version of, I would refer you to available web, I mean, available video at this website so that you can access the entire presentation. What I'm going to just simply do in a few minutes is just sort of give you the highlights of this, which will be very important for the discussion that we're going to have. When I presented this back in February, I think the real theme to set up the discussion was just simply the fact that the world of genomics has changed substantially. We sort of think back, the field was named and the word genomics was coined about 31 years ago. The National Human Genome Research Institute was created as an organization about 29 years ago and then 28 years ago, we kicked off the human genome project, our flagship effort for the field. We just celebrated a milestone in that just last week was the 15th anniversary of the completion of the human genome project and needless to say, over that entire span of 31 years, the field of genomics has advanced and matured considerably. And if you just think about what has happened in the 15 years since the end of the human genome project, it has been nothing short of breathtaking all of the advances that have taken place. Now, one of the key things I think for the field of genomics and the Institute specifically has been part of our culture is the creation and utilization of strategic plans that really guide us through our endeavors and I think have been a key element of our success, shown here are sort of the three official documents and strategic visions that guided the human genome project from beginning to end. When the genome project ended in 2003, NHGRI published a strategic vision for genomics beyond the human genome project. And it very much provided a very nice framework for what was going forward for the first decade of this new century. But then in 2011, we put out a new strategic vision and one that specifically spoke to a broadening of genomics that included not only understanding how genomes work, but thinking about how much of this was going to be eventually applied to the practice of medicine. So I think there's a cultural aspect of strategic planning that very much has served the Institute well in thinking about our programs, but I think has also served the field of genomics well in being strategic on a regular basis and then documenting what our current thinking is for going forward. Focusing in as an example on our current strategic plan, which as I mentioned was published in 2011, it was really one that articulated a vision for how to move beyond fundamental understanding about the structure and function of genomes to using that information to inform our understanding of human disease, eventually applying it to medicine and hopefully improving the effectiveness of health care. And as sort of reflected by the centerpiece figure from that strategic plan through a series of sequential density plots that reflected over different time intervals where accomplishments were taking place, especially if you look at the third one down, it really very much illustrated the expanding spectrum of activities and accomplishments across multiple different progressive areas of genomics, both this decade and then looking forward to beyond this decade and that is beyond 2020. But and this, what I will tell you is that while we're probably biased here, since we wrote this strategic plan in 2011, I think it has served the Institute well. I think it has served the field well. I can tell you that this strategic plan and even these five domains that are described along the top very much have provided an incredibly effective framework for us to think about our scientific programs here at the Institute and virtually everything we do fits very nicely within this framework. But there's a simple fact that is worth emphasizing and that is that when we start thinking about the future, you start thinking about the new decade on the horizon and we will in only a couple of years are actually less than that be in the year 2020, one can't help but reflect on what genomics is going to be like in the next decade. And in particular, I think it's important to emphasize that genomics remains incredibly hot. Spectacular advances are happening all the time. Genomics is finding its way across almost all areas of biomedicine and it really in some ways represents some of the most cutting edge activities in biomedical research. And so with all of that enthusiasm in mind and then recognizing that the current strategic plan that we published in 2011 wasn't really written in 2011. It was really written in 2010 and strategically came together in about 2009. Thinking forward to 2020, we just sort of come to a conclusion that it's hard to believe or claim or defend the fact that a 2011 strategic plan would represent a suitable blueprint for genomics or for us as an institute in 2020 and beyond. And so with that as a conclusion that we very much believe, however much we still like our 2011 strategic plan, we simply don't want to operate with that same plan going into the new decade. The other reality, of course, having gone through this multiple times now is that a good, robust strategic plan does not come together in a month or even a year. It actually takes a couple of years to properly get the input, synthesize that input and put it out into a document that will be well read and well appreciated by the community. And so with that in mind and with 2020, not that far away, we recognized it was time to get going. And so what I announced back in February was a plan for moving forward and actually having a general timeline for this new vision, which we're referring to as Genomics 2020 because this will be a plan that will be published in 2020. We started to plan for this last year and then we formally announced it as I told you in February. And knowing we needed a couple years and then thinking about 2020, we said, well, when in 2020 should we try to publish this? And we did identify sort of a very symbolically important odometer moment, if you will, because October 1 of 2020 will be the 30th anniversary of the launch of the Human Genome Project, which in many ways symbolically can be considered sort of the launching of the field. And so we thought that would be a good point in time to publish a new strategic vision. If we were going to publish it in October, we'd need to get a manuscript together and submit it to a journal and sometime in the summer, we would need to have a final meeting of the community to look critically at a draft version of that document sometime in the fall, I mean in the spring. And so that basically gives us from February 2018 until about April or so of 2020 to basically bring into the fold a number of elements of strategic planning that will provide us the input that we need for putting such a document together. And thinking about what are the elements we want to use, we have experience at what works well or what has worked well in the past, but we're also in a new place now than we were back in 2009 and 2010. And so we sort of thought about a mixture of things that we would be doing. A list of those are shown here. We will be having various types of workshops. We will be having some town halls such as this one. This is the first of our town halls. This one being virtual. We have two other town halls already scheduled, one in June in the Pacific Northwest out in Seattle. We have one scheduled in July in the Bay Area being hosted by Stanford University. And there'll be others that we will be scheduling starting in the fall and beyond. We're already getting scheduled to gather people at existing professional meetings and consortium meetings and various other ways of capturing input. We have a dedicated web page as you might imagine that will as much as possible capture as best we can all the input we're getting summaries of those et cetera and have it available for the community to review. One big difference between now and the last strategic planning around the use of social media will be very prominent. There's a hashtag we'll be using in various ways and we'll be doing other means of reaching out through social media. As always, we'll engage our various advisory groups and we envision having at the end a finale meeting as we've done previously to sort of really get a final last input from the community about our written strategic plan. So that's the main background, but there's one other thing I wanted to cover because I firmly believe this will be an important challenge that we will grapple with throughout the strategic planning process just based on past experience and based on the widespread dissemination of genomics across the biomedical research landscape. And this overarching and I will admit it'll be a difficult issue really relates to scope and the fundamental question and that I want to unpack a little bit for you because I think it does help frame the conversations going forward for the next two years is is this genomics 2020 strategic planning process about the future of genomics completely as a field anything genomics we're willing to discuss and strategically plan around or is it about the future of NHGRI supported genomics research? And every time we've done strategic planning the answer to this has been a little bit different but sort of reflecting the increasing complexity and widespread dissemination of genomics. And what I would tell you is our conclusion for this round is we really have to bound this in some way we can't have this be all expansive and anything genomic we can deal with that would simply be impractical and has to have some bounds around it. So as we thought about this since we were going to be talking about what NHGRI was going to lead in genomics beginning next decade so 2020 and beyond you know what would that look like and how would we bound this so we can sort of make sure we have the most appropriate conversations and and what I would tell you is that in thinking about this it was very timely because we as an institute had recently sort of gone through a couple year process of internally and externally trying to think about our identity within the genomics ecosystem. Now it was easy 25 even you know 29 years ago even 15 years ago it was easy. NHGRI was largely synonymous with genomics that's just not true anymore there's a lot of genomics going on beyond what NHGRI leads or supports and so we're no longer synonymous with genomics but I do think the institute plays a very important role and through this self-identity considerations and through our our discussions internally and externally we came to the conclusion that really what our institute is all about is about being at the forefront of genomics we're not all of genomics but we certainly are at the forefront of genomics with a particular emphasis on our role at the National Institutes of Health so when it comes to human health and disease we are at the forefront of genomics and that's been very helpful to then help us bound what we want to gain input on through the strategic planning process so let me just unpack this a little bit more and you'll see what what our thinking has been around this and the metaphor we sort of came up with is a sort of a series of boats in the water and we're sort of the forefront we're the lead boat if you will and of course we create a wake as we progress forward as do other boats we've simply come to the conclusion that there are just certain areas that are just beyond our wake if you will these are mature areas that maybe once upon a time we had an involvement with but but that other institutes at NIH and other major funders have taken up responsibility for and these areas are so important and so critical to these other research communities that they almost have their own center of gravity they they need their own strategic planning process around them and so we these will sort of be out of scope things like cancer genomics something we once upon a time were very involved in but that that's sort of going to be out of scope for this round of strategic planning the same for micro microbial genomics and microbiome research again areas we very much respect and think are really important and we I think have played a role in helping advance those areas but now we largely regard them at we're seeding leadership of those to others and and I think that's really important that they get that kind of attention and there'll probably other areas along the way that will probably be identified that will probably just say you know what that's probably beyond the scope of what we will do so that's one class of area the second class of area I think that's very important will be areas that are sort of gray they're sort of at the intermediate zone they're we're involved with them but we want to be strategically involved in thinking about these areas but they're also areas that many others are working on and many cases in collaboration with us and those will be in scope even though they're sort of intermediate in nature we won't completely by any means be responsible for funding these areas but we certainly want to be involved in strategically thinking about it and we will be partially funding in these areas areas like the genomic basis of rare and common diseases and computational genomics and data science so these will certainly be in scope but we will also want to work with others to help us think strategically about these areas because we and others will be involved in leading those areas in 2020 and beyond and then of course right in the center of our wake will be our mainstream areas this is stuff we've been involved in almost from the beginning and these little areas will probably be heavily emphasized throughout our strategic planning process I will show you a non-comprehensive list these are just some prototypic areas I would list as being within the mainstream and you can read this list it ranges from technology development to fundamentals in genome structure and function including epigenomics gene environment interactions genomic medicine implementation thinking about workforce in genomics thinking about policy development and implementation in genomics and of course thinking about the ethical legal and social application of genomic advances these have been fundamental to our mission from the beginning they will continue to be they will represent mainstream areas for our strategic planning process so I hope that helps then to sort of answer we have things that are out of scope things that are gray and intermediate but in scope and then our mainstream areas which are certainly in scope and so returning to this question we sort of view this as an evolving answer but this round of strategic planning will focus on the forefront of genomics particularly as it pertains to human health and disease since we are at the National Institutes of Health so in summary let me just tell you that what we hope to get out of this strategic planning process is that we aim to basically continue to be the driving force for much of genomics at NIH and around the world we hope that the strategic plan will provide a clear I'm using the metaphor a 2020 vision for using genomics to advance human health I will assure you that the strategic plan will guide the institute's scientific priorities and shape our research portfolio and we hope that it will foster partnerships beyond groups we've necessarily had partnerships with maybe in previous rounds of strategic planning I mean that includes the research community health care community education policy and various general public communities you will see this round of strategic planning will have a broader engagement than previous rounds because of the nature of genomics especially as it's getting applied to medical care and of course as I implied from my previous slide I think this going through the strategic planning process will really help define NHGRI's position at the forefront of genomics and in doing so at a very practical level will help us understand really what it is that we're going to be doing next decade so last thing I just want to just briefly mentioned and then we will turn this over to others on the phone for input and questions and comments is that we couldn't bite all of this off sort of in one big massive amount and we wanted to have staff here at the institute starting to think about things and starting to organize the input that we're going to be getting from various interactions with the community and so in coming up with ways to brainstorm we found that if we sort of initially looked at five different areas although there's incredible overlap among these it was just a way to start having five conversations and having generating five different lists we immediately found there was a lot of overlap of items on one or more different of these areas but it was a good way to start and in fact some of the people around the table here with me are people leaders at NHGRI who have been helping us strategize initially around these five areas that will certainly be of high priority during the strategic planning process so I just want to throw those out there although there are other elements as well some of which that cut across all of them and lastly and it is the last slide I'll show you and it really relates to this first of many engagements with the community we want your input we hope you follow all of this by going to our website if you have thoughts questions concerns ideas you can always email them to us at this dedicated email and just those who use social media this is the hashtag we'll be using to engage folks via social media in various ways over time in various venues and so forth so with that I think we're ready to take any questions and to hear from you about how best to engage the community and other stakeholders to inform our strategic planning process and once again that is star one to ask a question and we do have a question from Karen Miga of the University of California Santa Cruz Hi this is Karen Miga from Santa Cruz my research program really focuses on some of the most challenging regions of the human genome these are the repeat-rich millions of spaces that span human centromeres and I want to say in light of moving this beautiful metaphor you have a forefront of genetics I want to demand that we look backward and recognize that the human genome is still incomplete and a lot of this maybe in the past hasn't been managed to focus or the funding of NHGRI because lack of genes or maybe lack of functional elements but we know now based on a lot of dedicated funding and research that this is no longer true there are genes in these particular regions or transcripts there are the satellites themselves that are now implicated in all kinds of human disease and biomedical things that could be of interest and one other question could be we don't have the correct technology but now we know that's not even true some of my colleagues are involved in generating these ultra long reads that are now 2.2 megabases in length my group has been involved with NHGRI and GRC and putting together the reference models to complete at least generate some sequence in human centromere accretions so we can start to build off of that and we've also gone through and created the first map of human centromere so I think although it's not it seems like it's very narrow and focused I think it's wide in its breadth in the sense that we need to create tools and resources to complete the human genome and so I'm moving forward and we have this wide assessment of human genetics and genomics to include these regions so thank you for that question I'm going to turn this over to at least one maybe even a couple people around the table to further amplify what I'm about to say which is I completely agree with you and as somebody who during the genome project was sort of dealing with trying to build maps of some of these complicated regions to try to get them ready it was a clear recognition that this was going to be a hard problem and here we are now 15-20 years later it's still a hard problem and one that is very much I think in the sweet spot of what NHGRI should be doing because if we don't solve this problem I can assure you no other instituted NIH is going to take this on because it doesn't align with a specific disease area and yes you're right there are genes there and they're probably medically but we don't know what they are so we can't assign them to a particular disease area so let me ask Adam Felzenfeld to a couple comments because this is something that he's been thinking a lot about and programmatically oversees some of the activities that try to address this yes good afternoon so yeah we think about this a lot I would say that this discussion comes up in every strategic or scientific meeting that we've had that I've been involved with at NIH for the last NHGRI for the last over 15 years and you're right the technology is getting better over time it's a constant evaluation of how first of all how valuable the information would be and we're learning more biologically about that and how expensive it would be to get and on what scale and all of those things are changing constantly but I agree with you that the time seems riper than ever for doing this we have been thinking about this most recently in the context of genome reference resources where it's important to get haplotype resolved references and to represent at a high quality enough genomes from enough populations to make something really useful we don't have any conclusions yet but it is it is an active and closely related area and I will tell you that the institute I don't know maybe a couple years ago took a look at our technology development efforts and engaged our advisors and actually spoke to people in the community and came to the conclusion that we should not let up on the gas pedal when it comes to technology development that while certainly there is an entire industry in the private sector that has come up over the last 15 years in particular that's making spectacular advances in getting wonderful new technologies developed there are certain technology niches that are not being pursued by the private sector and that we need to continue to stimulate this area in the way we are able to mostly in the academic but even even fund grants in the private sector as well because we recognize that there are inaccessible regions of human genome that we want to access so our technologies are not good enough yet I am quite certain this will continue to be emphasized as it has been previously in the new strategic plan good fantastic and once again if you would like to ask a question please press star one on your touch tone phone one moment while we cue we have a question from Todd Lowe of the University of California Santa Cruz Hi so my question is we're we're really focused on non-coding RNA research in a particular RNA modifications and one of the problems that we're running up against is that there are a lot of enzymes that need to be made one of them that that is of particular interest is Alph B that we've been using in our labs to remove modifications from tRNAs and it seems like a resource that might not have a home somewhere else is basically making you know these enzymes in as a resource for people to manipulate their sequencing libraries before sequencing I was just wondering if there's if there's any interest in providing sort of reagents or getting the reagent resources started so that play you know the biotech companies can say hey people are going to use these enzymes to explore this new area for which you know we don't know what a lot of these modifications and enzymes are doing so so hi Todd how are you this is Eric I mean my immediate so it's an interesting idea my immediate reaction to that is that that has traditionally been so well served by the the private sector I mean sort of sort of and you know when they see a commercial opportunity I mean you sort of think about the history of restriction enzymes yes there was that brief phase where investigators made restriction enzymes or that but that went away pretty quick and then there was a phase when you know everybody was making tech polymerase mostly illegally but you know that went away pretty quick because so but I'm just trying to imagine you know we always ask the question would we do something better than what the private sector would and I'm not sure I know the answer in this particular case it's that certainly is worth teeing up and thinking about and seeing what others think but but is do you have an explanation of why those enzymes are you know why the sort of New England bio labs and the promegas are not stepping into that area yeah I talked to New England several times and try to get them interested in this and and there are now at least six or seven labs that have made this out the enzyme independently and and the results from from these athletes might be a little bit variable because all the different preps and so my thought is that it it is sort of leading edge and the diagram you showed as an H.C.R.I. being on the leading edge I think it would be a short term kind of a primer type of thing where you prime the pump and you show hey you know this resources there and it it really improves the technology for detecting or building better libraries that can detect molecules that weren't detectable before and so it would be more it it could be a project that that has a limited time but that simply puts these resources into not people's hands that you can find okay you know there are 20 different modifications and I'm you know New England Biolabs is not going to make all those until they they know there are a few winners and so I imagine there are going to be other things in the future especially because the specificity of law these modifications and times for DNA RNA et cetera are very incomplete and it's sort of at the it's at the exploration stage still so that's interesting thought I mean the one thing I'd say is that falls under our technology development bin of activities and so that's one thought I had so I mean clearly we'd be receptive if it was sort of built within of this is how it would advance the technology or the technological approaches for site the other thing is if it ends up being something that there were small companies working in that kind of a space our small business program SBIR program STTR program I mean that's a for small businesses that's a way we can sort of as you called it prime the pump right yeah okay that's a good point thanks our next question comes from Bob Wilden of the University of Vermont Health Network Hi everybody good to hear your voices again I'm just sort of going to come in on the clinical side of things and but also with regard to actually the technology and now that I'm out in the in the trenches it is even more apparent to me that we we still need greater advances in sequencing technology and other DNA a sort of manipulation or or variant identification technologies because it's still as as wonderful as massively parallel short read sequencing is it's still a complex and time consuming process and I guess my my my plea is to consider supporting research that advances those with the intent that they may be useful in a production environment not just a research environment so can can research be done to create something that is suitable more suitable for clinical environments rather than what we've experienced which is technologies developed primarily to support big research endeavors that then get co-opted in clinical context I'll stop there No thanks Bob maybe I'll have less be sick or make a couple comments about this so Bob are you suggesting something more along the lines of improving variant identification or are you talking about more along the lines of variant interpretation science well what's the gap that you're pushing on here I think it's all all the way from you know from you know extracted DNA through the sequencing process to you know make the coverage more reliable to make copy number variation detection more reliable to cover areas that are not well covered by the short read technology so that we can really offer a more complete picture and then I think the other thing is sort of the cost and the time it takes to sort of move the pipe move through the entire pipeline for a clinical quality result is something that at least for indication based testing is a barrier agree and you know I think these are all things that the field has been talking about a lot and as many specific suggestions as people like you can come up with as to the specific gaps that exist all along that entire spectrum that you've described specific suggestions about what could be a research challenge that would solve this problem those are the kind of inputs we would love to get in in building this strategic vision at least thank you we're gonna have at least fine gold make a comment right so this is I think one area that we very specifically have identified that crosses the different groups so in front the basic genomic technologies into genomics of medicine and disease where we realize we can do a lot on the front end for technology development that can directly impact the implementation of genomics as well as our first question that we got in this call right as the yes the uncharted area the genome we don't even know how to look at before before we go to the next question on the phone maybe being a little shy I just want while you're sitting there I want to just re-emphasize one thing maybe you could be thinking about and if you're willing to sort of give us some feedback we'd love to hear it is we like hearing these specific questions about scientific and clinical areas but we also very much want to get any input you have about how we're conducting this strategic planning process but I mean part of the purpose of this first call was also to just sort of gain input about any ideas that are out there about other ways of collecting feedback input and so forth I listed on the slide the elements we currently have in place that we're we're using and that we're going to continue to use but we also want to very early in the process to make sure we ask the question are there things we haven't thought of that we should be doing mechanically or otherwise are there other ways of collecting input that people think we should try so as much about the substance we want that input but we're also in this particular call want to know if you have any ideas about the mechanical ways we're collecting that input our next question is from Robert Green of the Harvard Medical School Hi everyone um Eric to that as best I can see you're doing an amazing job of of broadly collecting feedback you might consider exploring with some of the scientists in industry if you're not already doing that and that pertains to one of my comments but basically I have a question and a comment the question is would would a sort of multi-omics integration be part of one of the leading edge trails or would that be off somewhere closer to microbiome and in thinking with other and the reason is because building on the genome for better understanding of penicillin and expressivities probably surely going to involve metabolomics and proteomics and epigenetics and so forth so I would personally hope that would be among our leading edge things and so that's that's one comment and then my second comment is that as you all know I came from neurology where I was involved in the in the ad me the Alzheimer world and this ad me project that I was very involved with successfully partnered with private industry through the fnih to get an additional 40 million over the last decade that it was able to use and I worry a big picture I worry about the messages that society is getting from all of these companies that are launching and advertising and marketing and how we in science keep compete with that and keep up with that and in some cases reined in so I just wanted to throw out there the possibility that one of the strategic partners that could deliberately be in a in a thoughtful considered way some sort of collaborations with industry like the ad me has done with its private partner scientific board so those are my my two kind of question comments so let me take the first pass but I welcome others who want to weigh in on either of those my immediate we certainly are thinking about industry and in fact I think it is one if you sort of list the things what's different now than than 10 years ago and what's different now compared to you know 20 years oh my goodness I mean the fact that that there's a significant industry has been built up on the technology side and the application I mean it's just an amazing amount of other groups that we would absolutely need to touch base with in some cases we're being going to be very strategic no pun intended I mean when we're out in the Bay Area for example where there's a high concentration we're making of industry folks that work in genomic space we're actually making a clear reach out to them to try to get them to come over and talk to us and other and other venues like that we're going to be really trying to make sure to engage the industrial folks so I think that's what they work fully cognizant of although we are very receptive to some of your ideas and this idea of maybe creating some group to go on a scouting party or something to get a little bit more detailed might be worthwhile the other thing you asked about again I'll I'll give you my my input and or my my reaction and I'm sure others may want to weigh in as well the the other omics is a constant source of discussion slash debate at this institute I think that has been and will continue to be we are absolutely not unified in our in our views on this and that's fine and I would absolutely say that in general in recent years we've sort of held the line to nucleic acid based omics and I don't want to say we're absolutely going to hold that line in the future I guess part of the strategic planning process will be to see what people think maybe that's going to become an unpopular view and I although I by the way intellectually I completely agree with you that we we do need to capture lots of omic information and try to understand it relative to the genomic information one of the practical things that I admit I fall on the more conservative side of that spectrum is just the size of our institute and and the scale of the problem and I get a little worried about since we are a small NIH institute whether we use practical for us to take on things as big as proteomics metabolomics glycomic all the other ones so I don't know what that's going to look like and maybe part of this is maybe we could during the strategic planning process try to get our brains wrapped around this and maybe try to frame this in a fashion that would at least be provide a bit of a blueprint about what needs to be done and at least acknowledge we can't fund it all but try to look for funding partners or try to get others to be willing to sort of go in you know in some collaborative way I will say that there's other parts of NIH that are embracing other aspects of omics beyond genomics as they should and and probably we could imagine integrating with that a little bit better in the future so I think it's one of the areas we'll want input on during the strategic planning process anybody want to make any comments either about just to say we are doing a lot in the epigenomics world epigenomics but still on the nucleic acid world so not quite done yes anesthesia wise and I can just comment that on the the clinical side of things when we're talking about being able to integrate this into medicine one of the things we've been talking about is how can you take the genomic information and integrate it with these other types of omic information in order to be able to better serve the patients no question yeah good and that serves the genomic medicine component which I'm thrilled to see is emerging more prominently so that's great thank you and this is Laura Rodriguez and I just wanted to chime in on your comment about messages that the public is hearing whether they're coming from industry or or wherever they're coming from and and reflect back on Eric's comments during his slides about the active engagement we are hoping to find and and really seeking out with with quite a bit of purpose in terms of different partners than we have usually talked to in our strategic planning so really a purposeful outreach to communities to hear from them what does genomics mean to them what are they hearing what are they interested in seeing from research what what would make genomics research meaningful genomic medicine meaningful to them so I think we do want to be very attentive to that and so your idea is coming forth and how to do that both whether it's talking to industry but also really going out into communities and hearing from the public and particular sectors within the public directly is a big part of what we want to do this time Thanks Laura and our just frontline providers part of those communities because they are kind of the last to know sometimes and the first to get stuck with material that they don't fully understand Yes and I would imagine that they would we would want to touch them from a couple of different perspectives both from the clinical side as well as again from the engagement and outreach side and what do they need to interact with their patients and understand what they need to know so that we're really looking and how do we do that effectively because that's something we need to learn to do so there are some research related questions potentially to that Great thank you Our next question is from Gigan Zhang of the University of Kentucky Hi very Thank you very much Yes this is a Wigan Wiggins on a Kentucky I'm on the technology side My question is I really appreciate early comment I've made about the industry of making a lot of progress but also the certain niche area that that neglected us So basically I want to get some sense of what is the most important I mean when we talk about technology there's so many different aspects you know reading lens speeds accuracy resolution coverage cost time so on but for you down to you know what is what is the most challenging I mean I would say you know if I list I'll probably would say accuracy is probably the most important challenge you know you have a multi-million dollars equipment down to you know couple of tens of thousand or even couple of thousand dollar equipment out there or machine there and but none of those actually reached to the high enough accuracy so that's one and also I like to hear from you guys to tell us what what are the area that really need to be pushed to the next level thank you anybody want to well I think that's what I think that's what we're we're hoping to hear from you from the community exactly I mean I mean this is Eric I mean I what I would certainly say about the issue around technology we it's all important right I mean we need accuracy we need long read legs we need simple simpler sample preps etc we need better algorithms etc etc some of this will be solved at a chemical level so we'll be solved at a physical level so it'll be solved at a software level and I mean where we support all of those things and we're always looking for new ideas and new ways to solve the problem but I think ultimately they need to be robust they need to be robust and and also at least for me it's always very helpful to hear what the purpose is so so accuracy is obviously very important but in the clinic we've often heard that speed is extremely important and of course for anything that's going to be high throughput cost is important so and once again if you would like to ask a question please press star one on your touch tone telephone and we have a follow-up from Bob from Bob Wilden University of Vermont Health Network Hi it's Bob again first of all this is really fantastic and your lead-in discussions were really helpful and informative of the process and I really appreciate that another topic that I wanted to bring up was the pharmacogenomics area as an example of a area that really has impact across multiple specialties specialties and what I've discovered is that it's sometimes hard to get them in anybody engaged in looking at the concept of pharmacogenomics in a general sense because each specialty is only interested in their particular piece of that and what their specialty organizations are including in guidelines and often there's nothing included in the guideline because I think there is less sort of cross-discipline cooperation between the specialty and the pharmacists and and so forth and so on I know that NHGRI did a pharmacogenomics meeting a couple of years ago which was really great and I think kick started some new interest and new funding and I just wanted to endorse that and hope that that that angle continues until it's picked up by other factors and if there's anything that can be done to try to break through the sort of specialty wall barriers for projects like this that cover multiple specialties where that are pretty isolated in the clinical space that would be great I'm not being being very specific I'm sorry about that but I hope that's helpful so Bob this is Eric I share with you concerns about pharmacogenomics I mean not horrible concerns but I mean a recognition that it's a complicated area even if you look you raise some issues about how it slips between the different specialties in a strange way and if you actually look at NIH I don't think we have this all right I mean we don't have this properly organized where it's traditionally been funded is not not necessarily where you would have expected and it's not where there's sort of long-term interest then we certainly are very interested in this as I'm sure you know and if you look at certain of our programs like our Emerge network where it's a sort of a very prominently featured part of and some of we have some of the world experts as grantees in that area studying aspects of it I guess to some extent even in the ignite network there's pharmacogenomics going on and then the other place where I think there's going to be some development although not quite yet is with all of us research cohort they often refer you know which is just getting off the ground has a national launch on Sunday already 40 something thousand people enrolled up to a million over the next few years they often talk about pharmacogenomics as being one of the early areas of intense work because once you get clinical information about individuals and genotypic information should create all sorts of opportunities but that's all just sort of let's just sort of headlines it's not nothing's been exactly figured out exactly how that's going to research is going to look like for all of us but it's going to clearly be something that over the next years I think it'll get a lot of attention with the all of us program but you know again we're chipping away of certain of our of our genomic medicine consortia we're trying to encourage and see how we could be helpful for other institutes in their areas to get more involved some of that has been slow going and this is Anastasia want to make comment and I would just add to what Eric said that definitely this is an area that we have done research in within genomic medicine at NHGRI trying to look at how pharmacogenomics could be implemented but I do think this is an area where we could really hear from the community more about what are really the challenges that are driving the issues that you're seeing and implementing pharmacogenomics in the clinic what what are those specific questions that you're seeing is being the barriers beyond just this concern about it falling between the specialties is it something where more education is potentially what could help with the issues it's something about developing new technologies is it about being able to bring that different types of information together in the electronic medical records so hearing more about what the challenges are could also be helpful to us yeah it's it's centrally I mean boiling it down it's about clinical utility data and and you know there's there's lots of analytical validity sanitary validity and even clinical validity but you know until the clinical utility data starts to come out and both in the cost and outcomes measures and people are appearing to be very reluctant to move forward and once again press star one if you would like to ask a question one moment while we queue and it appears we have no further questions at this time actually we do we have a follow-up from Todd Lowe UC Santa Cruz hi um I was curious to know since NHGRI is really good about supporting these new technologies how deliberate is are the efforts to communicate with other institutes you know for example to have sort of say add-on supplements that are directly applicable say to you know NTI or the Brain initiative or or other areas that sort of make it sort of a little bit less difficult once you you know you you're working on a new technology to get it you know applied in in other very specific areas that are only supported in other institutes so basically bridging the divide between the development and technology and getting it more quickly applied in other institutes so this is Eric I what I would say is it's I think it's heterogeneous like a lot of I think that's the answer a lot of questions about NIH is it's very heterogeneous I mean I think there are examples where there are technology development efforts associated with programs at other institutes there's been examples of this in common fund initiatives where there's been a component but and I think NHGRI is well regarded and well recognized as being having expertise in technology development so I think other institutes have come and talked to us about this but so you know we would continue to sort of be sort of on call for facility you know being helpful and and discussing and I mean there are some institutes that do a lot of technology development right okay thanks but but if I sorry this is Adam if I took your if I understood your question you're asking if there was a way to to stimulate sort of connecting the things more directly and it yeah so I mean for example if you have a technology and you suddenly see oh wow this could be applied in you know in many different areas but you you know it's not you can't just apply for a supplement for a grant that you got basically for the tech development to be applied and I think there's a little bit of a barrier once you're funded in one institute to get a complete new grants in another institute which so so I guess I'm just wondering it we need if there was some way of having a slightly more formalized way of saying these are the some of the best new things that we've got and and some kind of collaboration for transferring those over without having to get a complete the NOVO grant in another institute which oftentimes is very difficult I think that's something that idea for us to be thinking about our next question is from Marja Dundek of the University of Pennsylvania Hi my name is Maria from UPenn I'm from the end of physical sciences and also worked on some next generation sequencing technologies and so my general question as I have been in this sort of in between area between physics and biology and medicine and so on how can we think of mapping the best more efficient way our technical ability onto the most relevant issues from the clinic and so what I find is that there's a lot of not miscommunication but no communication between different fields so for example in the physical sciences you have the cutting edge materials cutting edge techniques from the hardware all the way to software and data analysis and frequently by talking to people on very remote fields I find for example even from astronomy they can be mapped onto the genome and so I guess my question is how can we best bridge these to get the best outcome from the both people who have technology to the users and understand each other needs and abilities it's a hard one anybody around the table want to you know make a small comment and say a lot of it does start with education and so NIH does have a number of programs bringing in very talented students who come from physics who come from engineering who come from undergraduate programs that traditionally don't involve biology at all but these are students who want to do exactly what it is that that you're saying and we we work on binding dual mentored programs for those students so that they can do exactly as you say and bring some of these state-of-the-art technologies that honestly as biologists we might not see an application for and then over their four or five years as when they're a trainee here actually see that kind of a collaboration come to fruition so it's certainly an issue that we're aware of and I think it's a ground-up issue for some of us I think some of the responsibility lies on the investigators themselves that being trained as a computer scientist myself and then when you do biology I wonder if you saw a biologist to talk to and find ways to apply so you can actively do that as an investigator I wonder if there's anyone on the phone who has experience doing that that might want to share it yeah please press star one and there are no questions nor responses at this time we'll give it another few seconds anybody last last bits and again as Eric said earlier we'd like in particular to get any input on the ways that we're planning to reach out if any of you have had success in reaching out to communities in your areas of research or different disciplines as Elise was just asking about please now would be a great time just to hear about things that have worked for you that we might want to consider that you don't see us thinking about and again whether that's a particular community or discipline or sectors within our own field or the population trainees versus others that we should particularly pay attention to we'd appreciate hearing that please press star one and we have a question from Jessica Friedman ma'am could you please state your organization I'm sure I'm a student at the John Hopkins Bloomberg School of Public Health hi what's your question oh okay sorry I thought go for it okay so I'm a master student in public health at Hopkins and I'm concentrating in health policy I was hoping that you might be able to give like a brief overview of what you envision policy development might look like in 2020 you know I've been thinking about what kind of input I can provide but I don't really know what the general plan looks like and then as a follow-up to that as I was thinking about this you know I'm specifically interested in policy related to implementation of genomic research and you know using these learnings in clinical medicine in particular I'd you know I really am thought by now that there'd be more widespread use among providers using a comprehensive family health history to help assess a patient's individual risk level you know this is something that doesn't have a lot of significant investment in technology it's pretty low cost and it's you know directly related to things that we've learned over the past 15 years you know I know there's concerns about the ethics and the privacy of you know the families genomics and when I look around on websites I see all these initiatives from 2010 2012 that we're looking at family history and it seems like nothing really has happened since then so thanks so this is Laura Rodriguez and I can start but I imagine others might have information to share or ideas to share as well in terms of how to integrate some of what you're talking about from an application perspective and how to learn how to make things like family history actually work in the clinic and so I'll just address the first question in terms of what does policy development look like in 2020 I wish I had a crystal ball and I wish it would I could say that it will finally be logical in 2020 but I think that you know we will be dealing with again much of the same situation where we have now where we're trying to do our best to both keep up with the science and keep up with the cultural shifts that are taking place to establish frameworks that can guide science forward responsibly in a manner that that takes advantage of the applications that the knowledge coming out of the research has to offer and at a speed that people can actually take it up and I think that's where we really struggle sometimes to for policy to not get in the way of the science but also make sure that we are thinking ahead to implications for the individuals where the science is going to be applied whether it's in the clinic or whether it's other applications out in society so I'm very glad to hear that that you're a student who's excited to take this on because there's a lot of work to be done and there's going to be I think even more complex questions two years from now as we know that much more about genomics and are still struggling I would predict with trying to think about what are the most effective and efficient ways to translate the use of the information into the clinic and again into other spheres as well and so to your your second question I think about ways that you might be able to help and things that I hope we hear through the strategic planning process and then Carl Ester and others may have ideas about how they want to try to collect some of this information but we do need to learn how to develop the evidence to understand what makes sense to move forward into the clinic at what speed and with what oversight and I think we need to learn how to do that science and then we need to also do that science and so building a research agenda around those kinds of questions around and applying implementation science techniques and methodologies to our area will be very important in hearing from you as a trainee and your colleagues and and other experts and users of the information will be critical to us being able to try and map out a vision for the big questions that we should pursue in this space and I'll just turn it over to others that I think Laura's point is excellent but I guess another aspect to this is in terms of diverse communities and the fact that tools like family health history have been are quite homogeneous although there are folks that are developing things that are a little more culturally sensitive I would be very interested in your thoughts on how to better apply things like family health history as you said which seems to be quite a simple tool but in many ways it does not get widely utilized but the question is are we just not utilizing it in a way in which communities find it comfortable or relevant so I would be very interested in your in your thoughts on that on what as Laura said are the ways to make it more useful for a wider diverse audience and this is a station I can add to that that this is also in an area that we've been interested in funding looking at some of the questions about family health history and other ways to be able to improve the process both for the actual participant or patient themselves to be able to provide some of this information or ways that some of this information could potentially be pulled out of a lot of electronic health records as well to be able to help facilitate the usage of some of this information because we have seen as well some of that lack of uptake in the clinic so since we're passing the round so so one last thought speaking to your comments about technology you know the the data from the family health history of course becomes most powerful when it correlates with with something genetic some molecular biology some marker some mutation that we have either biological knowledge about or we know is actionable in the clinic in some way so the one of the ways that we can gain a lot of traction of course is by epidemiology studies which for instance the National Cancer Institute does lots of where we can say this aspect of family history correlates with these markers or these mutations or alterations in these genes and I think that's where a lot of emphasis has gone on in the previous strategic plan and I would hope it would be something that would continue and we have a question from Linda Hunt of Michigan State University this is the last one Hi I'm interested in ethics arm going forward and I'm not sure how to put this question but it seems to me the tone of much of what's being said is that the problem is getting people to understand health and the meaning of genomic research in terms that fit with a kind of existing scientific agenda so the translation issue to me seems like it's been being talked about in a little bit of a unidirectional as a problem of uptake and I would like to see an agenda that really kind of problematized the delivery end of it and thinks more carefully about why the fit with kind of everyday thinking of perhaps challenge populations doesn't fit so well so the delivery issue might not be one of simply needing to educate people better but perhaps to think more carefully about what it is that we're delivering and how meaningful it is to people Kyla did you want to Yeah I was just thinking could you give me an example I just want to make sure I'm understanding what you're saying Well for example I've been doing some research with chronically ill patients and talking to them about their understanding of genetic factors and their susceptibility to the illness and their treatment response and I find that the concepts of genetics are not only not very meaningful to them in a practical sense but not to their clinicians as well so there's I think a really large gap between you know a simple problem of information transfer and a question of what what are the needs in the receiving population and the ability of them to kind of generate kind of resource base to be able to interact with this kind of science Yeah no that's very helpful I completely agree with that we've had a lot of conversations with community partners and leaders who talk about the fact that at this point genomic information for many communities is just in time information it's the type of thing that they think about when providing with provided with information about why they should now think about it but it's not something that's at the forefront of their thinking so I would be very interested in again thinking through how we can support the efforts of getting this information to communities early but in a way that is not necessarily just sort of preaching or making it seem as though this is the most important thing then having sensitivity to the fact that they have a lot of other things to think about but also recognizing that one of the worst times to have to understand this is when you've just been giving a diagnosis and again it becomes sort of just in time information so we can certainly appreciate that and we are having several conversations with our working group related to community engagement on how to address this issue okay and it appears that we have no further questions at this time okay so I believe at this point we can give it just one more second and see if anyone jumps in for the last comment and if not then we will have this conclude our town hall this afternoon and I would like to personally thank everyone for their time and participation and the thoughtful questions and dialogue that we were able to have today at this kickoff listening session as Eric mentioned at the beginning there will be several other such town halls around the country please if you want to stay informed and we hope that you do look up the information on the website you can find it at genome.gov backslash genomics 2020 you can email us with direct input or questions at our email address up on the screen genomics2020atnih.gov or follow us or put information out there for us to follow with the hashtag genomics 2020 we really value all of your input again we appreciate your time this afternoon and we thank all of you and I hope you have a great weekend


It's linear algebra time and we're continuing this look into this exciting look into vectors And what we're going to look at today specifically is a very simple idea of vector addition We're just going to do vector addition And we're going to look at it in two ways one is this as far as this geometric interpretations concerned If I have two axes and I have two vectors there is a vector and there is a vector and I just want to add them And you must have seen this before Remember this Vector has an x axis value. Let's call this x sub 1 and Here and x let's make it y sub 1 and of course it was in higher dimensional space in three dimensions and hyper dimensions Just carry on with these this one will have its own Let's make that x sub 2 and it's only y sub 2 And you can well imagine when we do this we just add those two together these two components So it'll be x 1 and x plus x 2 that will be the resultant and y 1 plus y 2 What we are really doing is the following we taking this vector and We are moving it Along so that its Tail is at the head of this first one. So it's going to be this parallel movement today. Let's make it there There we go and of the similar length and that's there this value here will be x sub 1 plus x sub 2 and This value here Let's make this that'll be y sub 1 plus y sub 2. That's very easy to see and what you actually have What you actually have is this parallelogram and the resultant vector is going to be that very simple notation Now we had scalar multiplication when it comes to matrices We also get scalar multiplication of a vector if I take this vector and I multiply it by 2 The direction its angle is never going to change. It is just going to get longer So multiplied by 2 but I can multiply it by a half and I can multiply it by negative 1 I Can multiply that by negative 1. It's just going to be in this direction and Before we carry on with that, let's just think about just doing this in Vector notation. I have my one vector vector v and that is going to have components x 1 and y 1 and That is a column vector and I have vector 2 and that is going to be x sub 2 y sub 2 Those values that we see there as I said in multiple multiple multiple dimensions So if we go v1 plus v2 That is just going to be x sub 1 plus x sub 2 and y sub 1 plus y sub 2 It's very simple idea Very simple to do that as I said in higher dimensions. It just carries on these two have to have the same dimensions They have to live in the same space though If they don't live in the same space two dimensional space one dimensional space for scalar two dimensional space Three dimensional space hyperspace They've got to live in the same space and then you can simply very simply just add These vectors these vectors to each other and that just really brings me to the or why I mentioned the scalar Multiplication is I can reverse I reverse the first one here, but let's reverse the second one here by multiplying throughout by negative 1 If I do that all I'm going to do is I'm reversing the direction of this So if I want v sub 1 minus v sub 2 if I want this That to happen very easy. It is just it is just the addition of Minus 1 the scalar multiplication of So I just reverse it and then I add it so don't get confused when you see these things being drawn this way And then some convoluted thingy happening happening with with that remembering remember that this we have this binary operation between two elements of some given set this is the set of vectors in that specific space and Edition is just the Subtraction is just addition With this scalar multiplication of negative 1 with the second element there after our binary operation So very easy to do to think about vector addition vector addition Just just as far as looking at it like this and having this very simple concept in your head as to what is happening there Once you represent of course vectors in this form and this column vector form It's a very simple thing to do vector addition and then together with that vector Subjection, let's go to methamedica and see how we can accomplish this So let's have a quick look at vector addition Let me just do two vectors my one vector is going to hold as a column vector 1 comma 2 and Plus my second one. Let's make that v obvious that we're going to do Let's make it 10 and 20 is my second one and all you can see is that it's element wise addition So 1 and 10 is 11 2 and 20 is 22 Remember if I wanted to see or one way to see it as a column vector. It's just to do this matrix form Don't think we show I showed that yesterday, but just want to it's my it looks like it is a row But it's not you can clearly see that it's not a nested list And in matrix form a Single row like this is going to be seen as a column vector So remember when we had this Just our little arrow there and I just wanted to show you how easy it would be just to turn around this one So we can do that negation So that will just be negative one times So that will be minus three and if I look at that my first one is still here from zero zero to one one Zero zero to one one, but instead of zero two three We have zero two negative three and that's how you're going to do the subtraction because if I were to say Let's say one comma two and I were to subtract for instance Let's make that then the ten comma twenty You're going to see that we have one minus ten which is negative nine and two minus twenty which is negative 18 So really vector addition and vector subtract subtraction Irrespective of the dimensions that you are in as long as it's the same dimensions It's a very very easy thing to do a vector addition inside of Mathematica


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Convolution is a measure of overlap between two functions as one slides over the other. Mathematically, it's a sum of products. The standard convolution operation is slow to perform. However, we can speed this up with an alternative method that is the topic of this video, Dep-wise separable convolution. Let's first very quickly go over the basics of convolution on an input volume. Consider an input volume f of shape df cross df cross m, where df is the width and height of the input volume and m is the number of input channels. If the color image was an input, then m would be equal to 3 for the rg and b channels. We apply convolution on a kernel k of shape dk cross dk cross m. This will give us an output of shape dg cross dg cross 1. If we apply n such kernels on the input, then we get an output volume g of shape dg cross dg cross n. A convolution operation takes the sum of products of the input and the kernel to return a scalar. This operation is continued by sliding the kernel over the input. I've explained this concept in detail on my video on convolution neural networks. Check that out for a clearer understanding. I'm more concerned now with the cost of this convolution operation. So let's take a look at that. We can measure the computation required for convolution by taking a look at the number of multiplications required. So why is that? It's because multiplication is an expensive operation relative to addition. So let's determine the number of multiplications. For one convolution operation, the number of multiplications is the number of elements in that kernel. So that would be dk times dk times m multiplications. But we slide this kernel over the input. We perform dg convolutions along the width and dg convolutions along the height. And hence, dg cross dg convolutions overall. So the number of multiplications in the convolution of one kernel over the entire input f is dg square times dk square times m. Now this is for just one kernel. But if we have n such kernels, which makes the absolute total number of multiplications become n times dg square times dk square times m multiplications. Let's now take a look at depth-wise separable convolutions. In standard convolution, the application of filters across all input channels and the combination of these values are done in a single step. Depth-wise separable convolution, on the other hand, breaks us down into two parts. The first is depth-wise convolution, that is, it performs the filtering stage, and then point-wise convolution, which performs the combining stage. Let's get into some details here. Depth-wise convolution applies convolution to a single input channel at a time. This is different from the standard convolution that applies convolution to all channels. Let us take the same input volume f to understand this process. f has a shape df cross df cross m, where df is the width and height of the input volume, and m is the number of input channels, like I mentioned before. For depth-wise convolution, we use filters, or kernels, k, of shape dk cross dk cross 1. Here, dk is the width and height of the square kernel, and it has a depth of 1, because this convolution is only applied to a channel, unlike standard convolution, which is applied throughout the entire depth. And since we apply one kernel to a single input channel, we require m, such dk cross dk cross 1 kernels, over the entire input volume f. For each of these m convolutions, we end up with an output dg cross dg cross 1 in shape. Now, stacking these outputs together, we have an output volume of g, which is of shape dg cross dg cross m. This is the end of the first phase, that is, the end of depth-wise convolution. Now, this is exceeded by point-wise convolution. Point-wise convolution involves performing the linear combination of each of these layers. Here, the input is the volume of shape dg cross dg cross m. The filter kpc has a shape 1 cross 1 cross m. This is basically a 1 cross 1 convolution operation over all m layers. The output will thus have the same input width and height as the input dg cross dg for each filter. Assuming that we want to use some n such filters, the output volume becomes dg cross dg cross n. So that's great, we got this down. Now, let's take a look at the complexity of this convolution. We can split this into two parts, as we have two phases. First, we compute the number of multiplications in depth-wise convolution. So here, the kernels have a shape dk cross dk cross 1. So the number of multiplications on one convolution operation is, well, dk times dk, dk square. When applied over the entire input channel, this convolution is performed dg times dg number of times. So the number of multiplications for the kernel over the input channel becomes dg square times dk square. Now, such multiplications are applied over all m input channels. For each channel, we have a different kernel. And hence, the total number of multiplications in the first phase that is depth-wise convolution is m times dg square times dk square. Next, we compute the number of multiplications in the second phase that is point-wise convolution. Here, the kernels have a shape 1 cross 1 cross m, where m is the depth of the input volume. And hence, the number of multiplications for one instance of convolution is m. This is applied to the entire output of the first phase, which has a width and height of dg. So the total number of multiplications for this kernel is dg times dg times m. So for some n kernels, we'll have n times dg times dg times m, such multiplications. And thus, the total number of multiplications is the sum of multiplications in the depth-wise convolution stage, plus the number of multiplications in the point-wise convolution stage. We can take m times dg squared common. Now, we compare the standard convolution with depth-wise convolution. We get the ratio as the sum of reciprocal of the depth of output volume, that is n, and the reciprocal of the squared dimensions of the kernel, dk. To put this into perspective of how effective depth-wise convolution is, let us take an example. So consider the output feature volume n of 1024, and a kernel of size 3. That's dk is equal to 3. Plugging these values into the relation, we get 0.112. In other words, standard convolution has 9 times more the number of multiplications as that of depth-wise separable convolution. This is a lot of computing power. We can also quickly compare the number of parameters in both convolutions. In standard convolution, each kernel has dk times dk times m learnable parameters. Since there are n such kernels, there are n times m times dk squared parameters. In depth-wise separable convolutions, we'll split this once again into two parts. In the depth-wise convolution phase, we use m kernels of shape dk cross dk. In point-wise convolution, we use n kernels of shape 1 cross 1 cross m.  Taking the ratio, we get the same ratio as we did for computational power required. So we understood exactly what depth-wise convolution is and also its computation power with respect to the traditional standard convolution. But where exactly has this been used? Well, there are some very interesting papers here. The first is on multimodal neural networks. These are networks designed to solve multiple problems using a single network. A multimodal network has four parts. The first is modality nets to convert different input types to a universal internal representation. Then we have an encoder to process inputs. We have a mixer to encode inputs with previous outputs. And we have a decoder to generate outputs. A fundamental component of each of these parts is depth-wise separable convolution. It works effectively in such large networks. Next up, we have exception. A convolution neural network architecture based entirely on depth-wise separable convolution layers. It has shown the state-of-the-art performance on large datasets like Google's JFT image dataset. It's a repository of 350 million images with 17,000 class labels. To put this into perspective, the popular ImageNet took three days to train. However, to train even a subset of this JFT dataset, it took a month and it didn't even converge. In fact, it would have approximately taken about three months to converge had they let it run to its full length. So that's useful. This paper is pushing convolution neural networks to use depth-wise separable convolution as a de facto. Up third, we have mobile nets. A neural network architecture that strives to minimize latency of smaller scale networks so that computer vision applications run well on mobile devices. Mobile nets use depth-wise separable convolutions in its 28-layer architecture. This paper compares the performance of mobile nets with fully connected layers versus depth-wise separable convolution layers. It turns out the accuracy on ImageNet only drops by 1% while using significantly less number of parameters. From 29.3 million, the number of parameters is down to just 4.2 million. We can see the MULT's ads. The number of multiplications and additions, which is a direct measure of computation, has also significantly decreased for depth-wise separable convolution mobile nets. So here are some things to remember in this video. Depth-wise separable convolution decreases the computation and number of parameters when compared to standard convolution. Second is that depth-wise separable convolution is a combination of depth-wise convolution followed by a point-wise convolution. Depth-wise convolution is the filtering step, and point-wise convolution can be thought of as the combination step. Finally, they have been successfully implemented in neural network architectures like multimodal networks, exception, and mobile nets. And that's all I have for you now. Thank you all for stopping by today. If you liked the video, hit that like button. If you want to stick around, hit that subscribe button. If you really want to stick around, hit that bell icon next to the subscribe button so as to be notified of my uploads immediately. Links to important papers are down below, so check them out. Have a good day, and I'll see you in the next one. Bye.


Okay, welcome. In this video, I want to show you an example of a navigation trade alert that we sent out to our subscribers. And back on 10.13, so this day right here, as you can see, applied volatility spiked up. It was over 60th percentile because of a little bit of a down move in EEM. So we put on a short strangle. We sold a one standard deviation short strangle in EEM. And as you can see, almost immediately after we did that, volatility contracted. And that's exactly what we want to do. That's what helps our positions when we're selling premium is that contraction in implied volatility. So you can see it moved up a little and then came back down. And so today is Friday, October 28th. So let's go to the analyze tab and take a look. So you can see here's where price is currently trading. We're up about almost $90 on the trade. We can get a max profit of $174, so we're over 50% of max profit, which is the target that we look for on these short strangles.  So let's go ahead and close out of that one. Great closing order by, we've tried to buy that back for $0.27. Probably have to pay $0.28, let's see if we can get filled. And yeah, we'll have to move that up a penny, let's move it up to $0.28, confirm and send. And hopefully we get filled at $0.28, we may have to go up one more, which looks like we are. Typically, I would let this order sit, but for the purpose of this video, I'm going to go ahead and kick it up one more penny, confirm and send. Oh, and before I even entered it, we did get filled at $0.28. So perfect. So if you go back to the analyze tab, you see the position is now zeroed out. So I'm going to send this alert out to my subscribers, let them know that we exited the trade. If you'd like to learn more about the different strategies that we use to make consistent returns, come see us at navigationtrading.com. We've got a ton of free resources, including the navigation watch list, which is a list of the most profitable symbols to trade for each type of strategy. You've got the volatility indicator, which you've seen on my charts. You can download this directly to your Thinkorswim trading platform. And we've got a free options course called Trading Options for Income, which is a step-by-step guide to get you making consistent trades right away. We look forward to seeing you there.


Meanwhile, a former chief judge of a numberous state, Peter Miadi, has marched as the presidential candidate of the all-progressive Grand Alliance, Apke. Justice Maadi, who is the sole presidential aspirant of Apke, was declared the winner of the primary election of the party on Wednesday. His candidacy was affirmed by 150 delegates who voted at a special convention of the party held in Abuja. The former chief judge, in his acceptance speech asked the Nigerians to respect the ballot and promise to secure votes at the polls that will transform the country.


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