Collaborating Authors


Tracking development at the cellular level


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. [][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. [][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. [][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). 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Playing video games BENEFITS mental health


Playing video games could have a positive impact on a person's wellbeing, scientists at the University of Oxford have claimed. Researchers at the Oxford Internet Institute accessed the data of two games, Plants vs Zombies: Battle for Neighborville and Animal Crossing: New Horizons, in order to investigate the relationship between game play behaviour and mental health. The scientists, who worked with Electronic Arts and Nintendo of America, found that players experiencing genuine enjoyment from the games saw an improvement in their mental health. Professor Andrew Przybylski, lead author of the study and director of research at the Oxford Internet Institute, said the findings show'video games aren't necessarily bad for your health' and there are other psychological factors which have a significant effect on a person's wellbeing. Scientists at the University of Oxford found that the players experiencing genuine enjoyment from the games experienced a more positive wellbeing.

Graph Kernels: State-of-the-Art and Future Challenges Machine Learning

Among the data structures commonly used in machine learning, graphs are arguably one of the most general. Graphs allow modelling complex objects as a collection of entities (nodes) and of relationships between such entities (edges), each of which can be annotated by metadata such as categorical or vectorial node and edge features. Many ubiquitous data types can be understood as particular cases of graphs, including unstructured vectorial data as well as structured data types such as time series, images, volumetric data, point clouds or bags of entities, to name a few. Most importantly, numerous applications benefit from the extra flexibility that graph-based representations provide. In chemoinformatics, graphs have been used extensively to represent molecular compounds (Trinajstic, 2018), with nodes corresponding to atoms, edges to chemical bonds, and node and edge features encoding known chemical properties of each atom and bond in the molecule. Machine learning approaches operating on such graph-based representations of molecules are becoming increasingly successful in learning to predict complex molecular properties from large annotated data sets (Duvenaud et al., 2015; Gilmer et al., 2017; Wu et al., 2018), offering a promising set of tools for drug discovery (Vamathevan et al., 2019). In computational biology, graphs have likewise risen to prominence due to their ability to describe multifaceted interactions between (biological) entities.

How humans store memories is 'cornerstone of our intelligence'

Daily Mail - Science & tech

Scientists believe they may have discovered the'cornerstone of human intelligence', and it is all down to how we create and store memories. Previous research shows animals use a technique called'pattern separation' which stores memories in separate groups of neurons in the hippocampus. This stops them from getting mixed up, and it was believed humans probably use this technique as well. But a new study by experts at the University of Leicester shows the same group of neurons in the hippocampus store all memories. This key difference, the researchers say, could be the single factor which allowed our intellect to surpass that of other animals.

Contrastive Topographic Models: Energy-based density models applied to the understanding of sensory coding and cortical topography Machine Learning

We address the problem of building theoretical models that help elucidate the function of the visual brain at computational/algorithmic and structural/mechanistic levels. We seek to understand how the receptive fields and topographic maps found in visual cortical areas relate to underlying computational desiderata. We view the development of sensory systems from the popular perspective of probability density estimation; this is motivated by the notion that an effective internal representational scheme is likely to reflect the statistical structure of the environment in which an organism lives. We apply biologically based constraints on elements of the model. The thesis begins by surveying the relevant literature from the fields of neurobiology, theoretical neuroscience, and machine learning. After this review we present our main theoretical and algorithmic developments: we propose a class of probabilistic models, which we refer to as "energy-based models", and show equivalences between this framework and various other types of probabilistic model such as Markov random fields and factor graphs; we also develop and discuss approximate algorithms for performing maximum likelihood learning and inference in our energy based models. The rest of the thesis is then concerned with exploring specific instantiations of such models. By performing constrained optimisation of model parameters to maximise the likelihood of appropriate, naturalistic datasets we are able to qualitatively reproduce many of the receptive field and map properties found in vivo, whilst simultaneously learning about statistical regularities in the data.

UK Researchers Say AI Needs More Animal Sense


The incomplete understanding of human brains and how to endow computers with common sense are among AI's most enduring challenges. New research from DeepMind London, Imperial College London and the University of Cambridge argues that common sense in humans is founded on a set of basic capacities that are also possessed by many other animals, and that animal cognition can therefore serve as inspiration for many AI tasks and curricula. In a paper published in Trends in Cognitive Sciences journal this month, the researchers identify just how much AI research might benefit from the field of animal cognition. There is no universally accepted definition of "common sense." While much research has used language as a touchstone, the new paper temporarily sets language aside to focus on other common sense capacities found in non-human animals. They such believe capacities pertaining to the understanding of everyday concepts such as objects, space, and causality are also a baseline for humans, and this "foundational layer of common sense, which is a prerequisite for human-level intelligence" could provide something that's lacking in today's AI systems.

Sparse Symplectically Integrated Neural Networks Machine Learning

We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data. SSINNs combine fourth-order symplectic integration with a learned parameterization of the Hamiltonian obtained using sparse regression through a mathematically elegant function space. This allows for interpretable models that incorporate symplectic inductive biases and have low memory requirements. We evaluate SSINNs on four classical Hamiltonian dynamical problems: the H\'enon-Heiles system, nonlinearly coupled oscillators, a multi-particle mass-spring system, and a pendulum system. Our results demonstrate promise in both system prediction and conservation of energy, often outperforming the current state-of-the-art black-box prediction techniques by an order of magnitude. Further, SSINNs successfully converge to true governing equations from highly limited and noisy data, demonstrating potential applicability in the discovery of new physical governing equations.

Machine learning helps to predict new violence hot spots


Violence reduction strategies, which have traditionally focused on pubs and nightclubs, need to be broadened to include places where alcohol is not served if they are to be effective, a new study has suggested. Researchers from Cardiff University's Violence Research Group gathered data from 10 city centers across England and Wales and used machine learning to map the distribution of reported incidents of violent crime against alcohol outlets and, crucially, locations where alcohol is not sold. When they compared their analysis to a model mapping only places where alcohol is sold, the researchers discovered their new combined model more accurately predicted levels of violence. The study found that alongside pubs and bars several other destinations often associated with typical "nights out" were hot spots for violent crime, including fast-food outlets, takeaways, bus stops and cash machines. The researchers say this is the first time such a wide area has been analyzed and their study has uncovered previously unmapped violence hot spots.

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control Machine Learning

Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to compatible settings, where the state and action space dimensions are the same across tasks. Graph Neural Networks (GNN) are one way to address incompatible environments, because they can process graphs of arbitrary size. They also allow practitioners to inject biases encoded in the structure of the input graph. Existing work in graph-based continuous control uses the physical morphology of the agent to construct the input graph, i.e., encoding limb features as node labels and using edges to connect the nodes if their corresponded limbs are physically connected. In this work, we present a series of ablations on existing methods that show that morphological information encoded in the graph does not improve their performance. Motivated by the hypothesis that any benefits GNNs extract from the graph structure are outweighed by difficulties they create for message passing, we also propose Amorpheus, a transformer-based approach. Further results show that, while Amorpheus ignores the morphological information that GNNs encode, it nonetheless substantially outperforms GNN-based methods.

Is Artificial Intelligence White?


The "whiteness" of artificial intelligence (AI) removes people of colour from the way humanity thinks about its technology-enhanced future, researchers argue. University of Cambridge experts suggest current portrayals and stereotypes about AI risk creating a "racially homogenous" workforce of aspiring technologists, creating machines with bias baked into their algorithms. The scientists say cultural depictions of AI as white need to be challenged, as they do not offer a "post-racial" future but rather one from which people of colour are simply erased. In their paper, "The Whiteness of AI" published in the journal, Philosophy and Technology, Leverhulme CFI Executive Director, Stephen Cave and Dr Kanta Dihal offer insights into the ways in which portrayals of AI stem from, and perpetuate, racial inequalities. Cave and Dihal cite research showing that people perceive race in AI, not only in human-like robots, but also in abstracted and disembodied AI.