Goto

Collaborating Authors

England


NVIDIA Is Building an AI Supercomputer for Healthcare Research in England

#artificialintelligence

We Fools may not all hold the same opinions, but we all believe that considering a diverse range of insights makes us better investors. Follow Anders on Twitter, LinkedIn, and Google . Semiconductor designer NVIDIA (NASDAQ:NVDA) today announced that it is building the United Kingdom's most powerful supercomputer ever. Dubbed Cambridge-1, the system will give healthcare researchers access to impressive artificial intelligence (AI) tools. The $52 million Cambridge-1 will be installed at the university it's named after, and is scheduled to come online by the end of 2020.


How Close Is Humanity to the Edge?

The New Yorker

In mid-January, Toby Ord, a philosopher and senior research fellow at Oxford University, was reviewing the final proofs for his first book, "The Precipice: Existential Risk and the Future of Humanity." Ord works in the university's Future of Humanity Institute, which specializes in considering our collective fate. He had noticed that a few of his colleagues--those who worked on "bio-risk"--were tracking a new virus in Asia. Occasionally, they e-mailed around projections, which Ord found intriguing, in a hypothetical way. Among other subjects, "The Precipice" deals with the risk posed to our species by pandemics both natural and engineered.


Medical technology gives healthcare a shot in the arm

#artificialintelligence

Coronavirus has killed hundreds of thousands of people and has strained health systems around the world, but for Tony Young there may be a patch of a silver lining. The pandemic is accelerating use of technology to radically advance medicine and save lives in the future. "There are so many fantastic examples of the way in which technology is empowering our patients and our professionals," says Prof Young, a surgeon and national clinical lead for NHS England. Having launched his own medical-technology start-ups, he is helping to introduce innovations across the UK health service. Digital tools, whether for data management and drug development or enhanced diagnosis and treatment, have sharply improved the response to the threat of infection and all sorts of disease.


Brain mapping, from molecules to networks

Science

CATEGORY WINNER: CELL AND MOLECULAR BIOLOGY William E. Allen William E. Allen received his undergraduate degree from Brown University in 2012, M.Phil. in Computational Biology from the University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's life span. [ www.sciencemag.org/content/370/6519/925.3 ][1] Charting what the pioneering neuroanatomist Santiago Ramón y Cajal called the “impenetrable jungle” of the brain ([ 1 ][2]) presents one of biology's greatest challenges. How do billions of neurons, wired through trillions of connections, work together to produce cognition and behavior? Like an orchestra, wherein many instruments played simultaneously produce a sound greater than the sum of its parts, thought and behavior emerge from communication between ensembles of molecularly distinct neurons distributed throughout vast neural circuits. Although we know much about the properties of individual genes, cells, and circuits (see the figure, panel A), a vast gap lies between the function of each brain component and an animal's behavior. Bridging this gap has proven technically and conceptually difficult. Inspired by the fact that the development of high-throughput DNA sequencing led geneticists to shift focus from individual genes to the entire genome, I wanted to develop approaches that could simultaneously link multiple levels of the brain, from molecules to neurons to brain-wide neural networks. My goal was to capture a global perspective while maintaining the high resolution and specificity necessary to understand the function of individual components at each level. This new viewpoint, I hoped, would reveal how the collective properties of the brain's building blocks give rise to behavior. During my doctoral studies at Stanford University with Karl Deisseroth and Liqun Luo, I developed new methods to map the architecture and activity of mammalian neural circuits. I applied these approaches to understand the neural basis of thirst, a fundamental regulator of behavior ([ 2 ][3]). Need-based motivational drives, such as hunger and thirst, direct animals to satisfy specific physiological imperatives important for survival ([ 3 ][4]). Despite decades of research, at the beginning of my studies it was unclear how the activity of neurons that sense these needs causes an animal to engage in specific motivated behaviors (e.g., eating or drinking) to maintain homeostasis ([ 3 ][4]). Thirst, a relatively simple yet important drive, thus seemed the perfect model system for investigating multiple levels in the brain. I first traced thirst motivational drive from cellular gene expression to a circuit mechanism. Using a new version of targeted recombination in active populations (TRAP2), a tool to genetically label neurons according to their activity, I found that neurons in the median preoptic nucleus (MnPO) of the hypothalamus became activated in thirsty mice ([ 4 ][5]) (see the figure, panel C). Single-cell RNA sequencing revealed that these neurons formed a single molecularly defined cell type. Artificial activation of these neurons caused mice to drink water within seconds, whereas their inhibition prevented mice from drinking, which suggested that these MnPO neurons were master regulators of thirst. Drinking water also gradually reduced the activity of these neurons. Finally, activation of these neurons was aversive. Together, these results suggested a surprising “drive reduction” model of thirst motivation: Genetically hard-wired thirst neurons become active when mice need hydration, which causes mice to drink water. This ability to ascribe specific functional relevance to genetically defined neurons inspired me to develop new techniques to map cells within their native tissue architecture in even greater molecular detail. To this end, I co-developed STARmap, an approach for highly multiplexed in situ RNA sequencing to measure the expression of hundreds of genes simultaneously within a brain section at the level of single mRNA molecules ([ 5 ][6]) (see the figure, panel B ). In combination with genetic markers of activity, this technique powerfully describes the molecular identity of behaviorally activated neurons and their neighbors at single-cell resolution. ![Figure][7] New large-scale, high-resolution approaches to bridging multiple levels of brain function A new approach to brain function mapping. (A) An illustration of the levels of brain function and how they are interlinked. (B to D) New approaches to bridging levels: (B) STARm ap amplicons barcoding 1020 RNA species simultaneously with single-molecule resolution in the mouse visual cortex. (C) Genetic labeling of neurons according to activity reveals thirst neurons in the median preoptic nucleus of the hypothalamus, used to identify the motivational mechanism of thirst drive. (D) Brain-wide activity map of the response of thousands of neurons across dozens of brain regions to a water-predicting sensory cue, in thirsty or sated mice, reveals widespread broadcasting of thirst state. GRAPHIC: N. DESAI/ SCIENCE FROM W. ALLEN, WANG ET AL . ([ 5 ][6]), ALLEN ET AL . ( 4 ), ALLEN ET AL . ([ 9 ][8]) Despite these insights, a question remained: How do thirst-sensitive neurons deep in the brain coordinate activity in distributed circuits spanning sensory perception, cognition, and motor output to produce motivated behavior? I found that MnPO thirst neurons projected to many brain regions potentially serving different behavioral roles ([ 4 ][5]), but the gap between individual neurons and brain-wide networks was daunting. Earlier in graduate school, I had developed several new microscopy techniques to characterize brain-wide ([ 6 ][9]) or neocortex- wide ([ 7 ][10]) activity, which revealed that global neural activity was present during even simple motivated behaviors. However, because of the mammalian brain's opacity, these approaches were limited in their ability to record fast neural activity throughout the brain at the scale required to understand thirst motivation. Fortunately, however, developments in microelectronics enabled me to construct global maps of neuronal activity with microsecond-level temporal resolution. Using advanced “Neuropixels” probes ([ 8 ][11]), thin silicon needles that can be acutely inserted into the brain to record the electrical signals of hundreds of neurons simultaneously, I developed an experimental approach to record the activity of huge neuronal ensembles across the brain and reconstruct the anatomical location of each recorded cell ([ 9 ][8]). Applying this technique, I mapped the brain-wide flow of activity through ∼24,000 single neurons during thirst-motivated behavior ([ 9 ][8]) (see the figure, panel D). My experiments revealed that this simple behavior produced an unexpectedly global coordination of activity throughout the brain. By observing how activity changed as mice drank water, as well as directly stimulating hypothalamic thirst neurons, I showed that this activity wave was dependent on the animal's motivational state. Surprisingly, the activity of a few hundred thirst neurons instantly modulated the state of the entire brain. Even more surprisingly, I found many neurons, distributed throughout the brain, that directly encoded thirst. These results suggest that even simple behaviors, such as thirst, are emergent properties of the entire brain. I hope these new approaches will at last enable us to comprehend the rules that transform distributed patterns of electrical activity in neural circuits into thoughts, emotions, and perceptions. Understanding how molecules, neurons, and networks interact to shape these rules will have a sweeping impact on our understanding of brain function in health and disease. 1. [↵][12]“Mas, por desgracia, faltábanos el arma poderosa con que descuajar la selva impenetrable de la substancia gris…” ([ 10 ][13]). 2. [↵][14]1. C. A. Zimmerman, 2. D. E. Leib, 3. Z. A. Knight , Nat. Rev. Neurosci. 18, 459 (2017). [OpenUrl][15][CrossRef][16][PubMed][17] 3. [↵][18]1. S. M. Sternson , Neuron 77, 810 (2013). [OpenUrl][19][CrossRef][20][PubMed][21][Web of Science][22] 4. [↵][23]1. W. E. Allen et al ., Science 357, 1149 (2017). [OpenUrl][24][Abstract/FREE Full Text][25] 5. [↵][26]1. X. Wang et al ., Science 361, eaat5691 (2018). [OpenUrl][27][Abstract/FREE Full Text][28] 6. [↵][29]1. L. Ye et al ., Cell 165, 1776 (2016). [OpenUrl][30][CrossRef][31][PubMed][32] 7. [↵][33]1. W. E. Allen et al ., Neuron 94, 891 (2017). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. J. J. Jun et al ., Nature 551, 232 (2017). [OpenUrl][38][CrossRef][39][PubMed][40] 9. [↵][41]1. W. E. Allen et al ., Science 364, eeav3932 (2019). [OpenUrl][42] 10. [↵][43]1. S. Ramón y Cajal , Recuerdos de mi vida: Historia de mi labor científica (Moya, Madrid, 1917). [1]: http://www.sciencemag.org/content/370/6519/925.3 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: pending:yes [8]: #ref-9 [9]: #ref-6 [10]: #ref-7 [11]: #ref-8 [12]: #xref-ref-1-1 "View reference 1 in text" [13]: #ref-10 [14]: #xref-ref-2-1 "View reference 2 in text" [15]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BNeurosci%26rft.volume%253D18%26rft.spage%253D459%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrn.2017.71%26rft_id%253Dinfo%253Apmid%252F28638120%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [16]: /lookup/external-ref?access_num=10.1038/nrn.2017.71&link_type=DOI [17]: /lookup/external-ref?access_num=28638120&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [18]: #xref-ref-3-1 "View reference 3 in text" [19]: {openurl}?query=rft.jtitle%253DNeuron%26rft.volume%253D77%26rft.spage%253D810%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.neuron.2013.02.018%26rft_id%253Dinfo%253Apmid%252F23473313%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/external-ref?access_num=10.1016/j.neuron.2013.02.018&link_type=DOI [21]: /lookup/external-ref?access_num=23473313&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [22]: /lookup/external-ref?access_num=000316162600004&link_type=ISI [23]: #xref-ref-4-1 "View reference 4 in text" [24]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DAllen%26rft.auinit1%253DW.%2BE.%26rft.volume%253D357%26rft.issue%253D6356%26rft.spage%253D1149%26rft.epage%253D1155%26rft.atitle%253DThirst-associated%2Bpreoptic%2Bneurons%2Bencode%2Ban%2Baversive%2Bmotivational%2Bdrive%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aan6747%26rft_id%253Dinfo%253Apmid%252F28912243%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [25]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNTcvNjM1Ni8xMTQ5IjtzOjQ6ImF0b20iO3M6MjQ6Ii9zY2kvMzcwLzY1MTkvOTI1LjMuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [26]: #xref-ref-5-1 "View reference 5 in text" [27]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aat5691%26rft_id%253Dinfo%253Apmid%252F29930089%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjE3OiIzNjEvNjQwMC9lYWF0NTY5MSI7czo0OiJhdG9tIjtzOjI0OiIvc2NpLzM3MC82NTE5LzkyNS4zLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [29]: #xref-ref-6-1 "View reference 6 in text" [30]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D165%26rft.spage%253D1776%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cell.2016.05.010%26rft_id%253Dinfo%253Apmid%252F27238022%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [31]: /lookup/external-ref?access_num=10.1016/j.cell.2016.05.010&link_type=DOI [32]: /lookup/external-ref?access_num=27238022&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [33]: #xref-ref-7-1 "View reference 7 in text" [34]: {openurl}?query=rft.jtitle%253DNeuron%26rft.volume%253D94%26rft.spage%253D891%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.neuron.2017.04.017%26rft_id%253Dinfo%253Apmid%252F28521139%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: /lookup/external-ref?access_num=10.1016/j.neuron.2017.04.017&link_type=DOI [36]: /lookup/external-ref?access_num=28521139&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [37]: #xref-ref-8-1 "View reference 8 in text" [38]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D551%26rft.spage%253D232%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature24636%26rft_id%253Dinfo%253Apmid%252F29120427%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: /lookup/external-ref?access_num=10.1038/nature24636&link_type=DOI [40]: /lookup/external-ref?access_num=29120427&link_type=MED&atom=%2Fsci%2F370%2F6519%2F925.3.atom [41]: #xref-ref-9-1 "View reference 9 in text" [42]: {openurl}?query=rft.jtitle%253DScience%26rft.volume%253D364%26rft.spage%253Deeav3932%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [43]: #xref-ref-10-1 "View reference 10 in text"


Tracking development at the cellular level

Science

We each developed from a single cell—a fertilized egg—that divided and divided and eventually gave rise to the trillions of cells, of hundreds of types, that constitute the tissues and organs of our adult bodies. Advancing our understanding of the molecular programs underlying the emergence and differentiation of these diverse cell types is of fundamental interest and will affect almost every aspect of biology and medicine. Recently, technological advances have made it possible to directly measure the gene expression patterns of individual cells ([ 1 ][1]). Such methods can be used to clarify cell types and to determine the developmental stage of individual cells ([ 2 ][2]). Single-cell transcriptional profiling of successive developmental stages has the potential to be particularly informative, as the data can be used to reconstruct developmental processes, as well as characterize the underlying genetic programs ([ 3 ][3], [ 4 ][4]). ![Figure][5] A genomic technique for tracking cellular development High-throughput single-cell genomic methods enable a global view of cell type diversifcation by transcriptome and epigenome CREDIT: N. DESAI/ SCIENCE FROM CAO ET AL. ([7][6]) AND BIORENDER When I began my doctoral studies in Jay Shendure's lab at the University of Washington, available single-cell sequencing techniques relied on the isolation of individual cells within physical compartments and thus were limited in terms of both throughput and cost. As a graduate student, I developed four high-throughput single-cell genomic techniques to overcome these limitations ([ 5 ][7]–[ 8 ][8]). Leveraging these methods, I profiled millions of single-cell transcriptomes from organisms, in species that included worms, mice, and humans. By quantifying the dynamics of embryonic development at single-cell resolution, I was able to map out the global genetic programs that control cell proliferation and differentiation at the whole-organism scale. By the 1980s, biologists had documented every developmental step in the nematode Caenorhabditis elegans , from a single-cell embryo to the adult worm, and mapped the connections of all of the worm's neurons ([ 9 ][9]). However, although the nematode worm has a relatively small cell number (558 cells at hatching), a comprehensive understanding of the molecular basis for the specification of these cell types remains difficult. To resolve cellular heterogeneity, I first developed a method to specifically label the transcriptomes of large numbers of single cells, which we called sci-RNA-seq (single-cell combinatorial indexing RNA sequencing) ([ 5 ][7]). This method is based on combinatorial indexing, a strategy using split-pool barcoding of nucleic acids to label vast numbers of single cells within a single experiment ([ 9 ][9]). In this study, I profiled nearly 50,000 cells from C. elegans at the L2 stage, which is more than 50-fold “shotgun cellular coverage” of its somatic cell composition. We further defined consensus expression profiles for 27 cell types and identified rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. This was the first study to show that single-cell transcriptional profiling is sufficient to separate all major cell types from an entire animal. C. elegans development follows a tightly controlled genetic program. Other multicellular organisms, such as mice and humans, have much more developmental flexibility. However, conventional approaches for mammalian single-cell profiling lack the throughput and resolution to obtain a global view of the molecular states and trajectories of the rapidly diversifying and expanding cell types. To investigate cell state dynamics in mammalian development, I developed an even more scalable single-cell profiling technique, sci-RNA-seq3 ([ 7 ][6]), and used it to trace the development path of 2 million mouse cells as they traversed diverse paths in a 4-day window of development corresponding to organogenesis (embryonic day 9.5 to embryonic day 13.5). From these data, we characterized the dynamics of cell proliferation and key regulators for each cell lineage, a potentially foundational resource for understanding how the hundreds of cell types forming a mammalian body are generated in development. This was, and remains, the largest publicly available single-cell transcriptional dataset. The sci-RNA-seq3 method enabled this dataset to be generated rapidly, within a few weeks, by a single individual. A major challenge regarding current single-cell assays is that nearly all such methods capture just one aspect of cellular biology (typically mRNA expression), limiting the ability to relate different components to one another and to infer causal relationships. Another technique that I developed, sci-CAR (single-cell combinatorial indexing chromatin accessibility and mRNA) ([ 6 ][10]), was created with the goal of overcoming this limitation, allowing the user to jointly profile the epigenome (chromatin accessibility) and transcriptome (mRNA). I applied sci-CAR to the mouse whole kidneys, recovering all major cell types and linking cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells. To further explore the gene regulatory mechanisms, I invented sci-fate ([ 8 ][8]), a new method that identifies the temporal dynamics of transcription by distinguishing newly synthesized mRNA transcripts from “older” mRNA transcripts in thousands of individual cells. Applying the strategy to cancer cell state dynamics in response to glucocorticoids, we were able to link transcription factors (TFs) with their target genes on the basis of the covariance between TF expression and the amount of newly synthesized RNA across thousands of cells. In summary, my dissertation involved developing the technical framework for quantifying gene expression and chromatin dynamics across thousands to millions of single cells and applying these technologies to profile complex, developing organisms. The methods that I developed enable such projects to be achievable by a single individual, rather than requiring large consortia. Looking ahead, I anticipate that the integration of single-cell views of the transcriptome, epigenome, proteome, and spatial-temporal information throughout development will enable an increasingly complete view of how life is formed. GRAND PRIZE WINNER Junyue Cao Junyue Cao received his undergraduate degree from Peking University and a Ph.D. from the University of Washington. After completing his postdoctoral fellowship at the University of Washington, Junyue Cao started his lab as an assistant professor and lab head of single-cell genomics and population dynamics at the Rockefeller University in 2020. His current research focuses on studying how a cell population in our body maintains homeostasis by developing genomic techniques to profile and perturb cell dynamics at single-cell resolution. CATEGORY WINNER: ECOLOGY AND EVOLUTION Orsi Decker Orsi Decker completed her undergraduate degree at Eötvös Loránd University in Budapest, Hungary. She went on to receive her master's degree in Ecology and Evolution at the University of Amsterdam. Decker completed her doctoral research at La Trobe University in Melbourne, Australia, where she investigated the extinctions of native digging mammals and their context-dependent impacts on soil processes. She is currently a postdoctoral researcher at La Trobe University, where she is examining how land restoration efforts could be improved to regain soil functions through the introduction of soil fauna to degraded areas. [www.sciencemag.org/content/370/6519/925.1][11] CATEGORY WINNER: MOLECULAR MEDICINE Dasha Nelidova Dasha Nelidova completed her undergraduate degrees at the University of Auckland, New Zealand. She completed her Ph.D. in neurobiology at the Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland. Nelidova is currently a postdoctoral researcher at the Institute of Molecular and Clinical Ophthalmology Basel, where she is working to develop new translational technologies for treating retinal diseases that lead to blindness. [www.sciencemag.org/content/370/6519/925.2][12] CATEGORY WINNER: CELL AND MOLECULAR BIOLOGY William E. Allen William E. Allen received his undergraduate degree from Brown University in 2012, M.Phil. in Computational Biology from the University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's life span. [www.sciencemag.org/content/370/6519/925.3][13] 1. [↵][14]1. D. Ramsköld et al ., Nat. Biotechnol. 30, 777 (2012). [OpenUrl][15][CrossRef][16][PubMed][17] 2. [↵][18]1. C. Trapnell , Genome Res. 25, 1491 (2015). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. D. E. Wagner et al ., Science 360, 981 (2018). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. K. Davie et al ., Cell 174, 982 (2018). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. J. Cao et al ., Science 357, 661 (2017). [OpenUrl][29][Abstract/FREE Full Text][30] 6. [↵][31]1. J. Cao et al ., Science 361, 1380 (2018). [OpenUrl][32][Abstract/FREE Full Text][33] 7. [↵][34]1. J. Cao et al ., Nature 566, 496 (2019). [OpenUrl][35][CrossRef][36][PubMed][37] 8. [↵][38]1. J. Cao, 2. W. Zhou, 3. F. Steemers, 4. C. Trapnell, 5. J. Shendure , Nat. Biotechnol. 38, 980 (2020). [OpenUrl][39][CrossRef][40][PubMed][41] 9. [↵][42]1. D. A. Cusanovich et al ., Science 348, 910 (2015). [OpenUrl][43][Abstract/FREE Full Text][44] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: pending:yes [6]: #ref-7 [7]: #ref-5 [8]: #ref-8 [9]: #ref-9 [10]: #ref-6 [11]: http://www.sciencemag.org/content/370/6519/925.1 [12]: http://www.sciencemag.org/content/370/6519/925.2 [13]: http://www.sciencemag.org/content/370/6519/925.3 [14]: #xref-ref-1-1 "View reference 1 in text" [15]: {openurl}?query=rft.jtitle%253DNature%2Bbiotechnology%26rft.stitle%253DNat%2BBiotechnol%26rft.aulast%253DRamskold%26rft.auinit1%253DD.%26rft.volume%253D30%26rft.issue%253D8%26rft.spage%253D777%26rft.epage%253D782%26rft.atitle%253DFull-length%2BmRNA-Seq%2Bfrom%2Bsingle-cell%2Blevels%2Bof%2BRNA%2Band%2Bindividual%2Bcirculating%2Btumor%2Bcells.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnbt.2282%26rft_id%253Dinfo%253Apmid%252F22820318%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [16]: /lookup/external-ref?access_num=10.1038/nbt.2282&link_type=DOI [17]: /lookup/external-ref?access_num=22820318&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [18]: #xref-ref-2-1 "View reference 2 in text" [19]: {openurl}?query=rft.jtitle%253DGenome%2BRes.%26rft_id%253Dinfo%253Adoi%252F10.1101%252Fgr.190595.115%26rft_id%253Dinfo%253Apmid%252F26430159%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NjoiZ2Vub21lIjtzOjU6InJlc2lkIjtzOjEwOiIyNS8xMC8xNDkxIjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcwLzY1MTkvOTI0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [21]: #xref-ref-3-1 "View reference 3 in text" [22]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aar4362%26rft_id%253Dinfo%253Apmid%252F29700229%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNjAvNjM5Mi85ODEiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxOS85MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [24]: #xref-ref-4-1 "View reference 4 in text" [25]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D174%26rft.spage%253D982%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cell.2018.05.057%26rft_id%253Dinfo%253Apmid%252F29909982%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: /lookup/external-ref?access_num=10.1016/j.cell.2018.05.057&link_type=DOI [27]: /lookup/external-ref?access_num=29909982&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [28]: #xref-ref-5-1 "View reference 5 in text" [29]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DCao%26rft.auinit1%253DJ.%26rft.volume%253D357%26rft.issue%253D6352%26rft.spage%253D661%26rft.epage%253D667%26rft.atitle%253DComprehensive%2Bsingle-cell%2Btranscriptional%2Bprofiling%2Bof%2Ba%2Bmulticellular%2Borganism%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aam8940%26rft_id%253Dinfo%253Apmid%252F28818938%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNTcvNjM1Mi82NjEiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxOS85MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [31]: #xref-ref-6-1 "View reference 6 in text" [32]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aau0730%26rft_id%253Dinfo%253Apmid%252F30166440%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [33]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNjEvNjQwOS8xMzgwIjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcwLzY1MTkvOTI0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [34]: #xref-ref-7-1 "View reference 7 in text" [35]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D566%26rft.spage%253D496%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41586-019-0969-x%26rft_id%253Dinfo%253Apmid%252F30787437%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [36]: /lookup/external-ref?access_num=10.1038/s41586-019-0969-x&link_type=DOI [37]: /lookup/external-ref?access_num=30787437&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [38]: #xref-ref-8-1 "View reference 8 in text" [39]: {openurl}?query=rft.jtitle%253DNat.%2BBiotechnol.%26rft.volume%253D38%26rft.spage%253D980%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41587-020-0480-9%26rft_id%253Dinfo%253Apmid%252F32284584%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [40]: /lookup/external-ref?access_num=10.1038/s41587-020-0480-9&link_type=DOI [41]: /lookup/external-ref?access_num=32284584&link_type=MED&atom=%2Fsci%2F370%2F6519%2F924.atom [42]: #xref-ref-9-1 "View reference 9 in text" [43]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aab1601%26rft_id%253Dinfo%253Apmid%252F25953818%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNDgvNjIzNy85MTAiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxOS85MjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9


AI startup Graphcore says most of the world won't train AI, just distill it

ZDNet

Simon Knowles, chief technology officer of the AI computing startup Graphcore, told a supercomputing conference on Wednesday most of the world won't have the dollars required to train neural network models, which are trending toward a trillion parameters, or weights, apiece. Instead, such huge models will be distilled down by users with less compute power, to make something purposeful and manageable. Simon Knowles, chief technologist for Bristol, England-based AI computing startup Graphcore, on Wednesday told an audience of supercomputing professionals that the large mass of AI work in years to come will be done by people distilling large deep learning models down to something useable that is more task-specific. "There are three user scales for people who might want to buy and own, or rent, an AI computer," said Knowles in the talk, a video of which is posted online. "There'll be a very small number of people who will train enormous tera-scale models from scratch," said Knowles, referring to deep learning models that have a trillion parameters, or weights.


AI3SD Winter Seminar Series: Robots, AI and NLP in Drug Discovery

#artificialintelligence

This seminar forms part of the AI3SD Online Seminar Series that will run across the winter (from November 2020 to April 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Robots, AI and NLP in Drug Discovery. Abstract: Natural Language Processing (NLP) has been used in drug discovery for decades.


DeepMind funds new post at Oxford University – the DeepMind Professorship of Artificial Intelligence

Oxford Comp Sci

Demis Hassabis, co-founder and CEO, DeepMind, says: 'I'm delighted to expand our support of AI research at Oxford with the DeepMind Professorship of Artificial Intelligence. I look forward to seeing who the University appoints and where they decide to focus their research with the support of Oxford's world-class AI research community.'


How Amazon became a pandemic giant – and why that could be a threat to us all

The Guardian

For the last year, Anna (not her real name) has been working as an Amazon "associate", in the kind of vast warehouse the company calls a fulfilment centre. For £10.50 an hour, she works four days a week, though, during busy periods, this sometimes goes up to five. Her shift begins at 7.15am and ends at 5.45pm. "When I get home," she says, "it's about 6.30. And I just go in, take a shower and go to bed. Anna is a picker in one of the company's most technologically advanced workplaces, in the south of England. This means she works in a metal enclosure in front of a screen that flashes up images of the products she has to put in the "totes" destined for the part of the warehouse where customer orders are made ready for posting out. Everything from DVDs to gardening equipment is brought to her by robot "drives": squat, droid-like devices that endlessly lift "pods" – tall fabric towers full of pockets that contain everything from DVDs to toys – and then speed them to the pickers. Everything has to happen quickly. According to the all-important metric by which a picker's performance is measured, Anna says she has to average 360 items an hour, or around 3,800 a day. In March, the Covid-19 lockdown meant that customer orders suddenly rocketed. Anna says that lots of her colleagues started putting in overtime, and new recruits arrived en masse. "They hired a lot of people," she says. "I thought there should have been fewer people in the warehouse, to have distancing." "They took out some of the tables because of 2-metre distancing, but it was impossible to find a free table or chair.


Ocado shops its way to a robotics platform for groceries and beyond

#artificialintelligence

BEGIN ARTICLE PREVIEW: Grocery retailer Ocado is not well known outside of the United Kingdom. Even in the U.K., Ocado commands a mere 1.8% market share. But in the 20 years since it launched as one of the country’s first online-only supermarkets, the brand has become synonymous with technology. This is thanks to investments in machine learning, robotics, automated warehouses, and R&D projects to develop robotic arms capable of picking and packing delicate items such as fruit. The company has gradually transformed into a platform that equips retailers like Kroger with the tech needed to challenge the likes of Amazon, whose expansion into groceries continues. And just as Amazon offers all kinds of goods on its platform, Ocado’s ambitions now stretch far beyond groceries. Last week, Hatfield, England-based Ocado made its first acquisitions when it snapped up not one but two U.S. robotics companies for a combined total of $287 million. One is Kindred Systems, a