BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database.

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.

Effects of stochastic coding on olfactory discrimination in flies and mice.

Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.

Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies.

Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learning (retaining associations between inputs and outputs, even after good representations are learned) plays an important function in the brain; however, its role in continual learning has not been carefully studied. Here, we identified a two-layer neural circuit in the fruit fly olfactory system that performs continual associative learning between odors and their associated valences. In the first layer, inputs (odors) are encoded using sparse, high-dimensional representations, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer, only the synapses between odor-activated neurons and the odor's associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We prove theoretically that these two perceptron-like layers help reduce catastrophic forgetting compared to the original perceptron algorithm, under continual learning. We then show empirically on benchmark data sets that this simple and lightweight architecture outperforms other popular neural-inspired algorithms when also using a two-layer feedforward architecture. Overall, fruit flies evolved an efficient continual associative learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation.

A neural theory for counting memories.

Keeping track of the number of times different stimuli have been experienced is a critical computation for behavior. Here, we propose a theoretical two-layer neural circuit that stores counts of stimulus occurrence frequencies. This circuit implements a data structure, called a count sketch, that is commonly used in computer science to maintain item frequencies in streaming data. Our first model implements a count sketch using Hebbian synapses and outputs stimulus-specific frequencies. Our second model uses anti-Hebbian plasticity and only tracks frequencies within four count categories ("1-2-3-many"), which trades-off the number of categories that need to be distinguished with the potential ethological value of those categories. We show how both models can robustly track stimulus occurrence frequencies, thus expanding the traditional novelty-familiarity memory axis from binary to discrete with more than two possible values. Finally, we show that an implementation of the "1-2-3-many" count sketch exists in the insect mushroom body.

A feedback control principle common to several biological and engineered systems.

Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules-additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease-are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.

Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.

Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes.

Better tired than lost: Turtle ant trail networks favor coherence over short edges.

Creating a routing backbone is a fundamental problem in both biology and engineering. The routing backbone of the trail networks of arboreal turtle ants (Cephalotes goniodontus) connects many nests and food sources using trail pheromone deposited by ants as they walk. Unlike species that forage on the ground, the trail networks of arboreal ants are constrained by the vegetation. We examined what objectives the trail networks meet by comparing the observed ant trail networks with networks of random, hypothetical trail networks in the same surrounding vegetation and with trails optimized for four objectives: minimizing path length, minimizing average edge length, minimizing number of nodes, and minimizing opportunities to get lost. The ants' trails minimized path length by minimizing the number of nodes traversed rather than choosing short edges. In addition, the ants' trails reduced the opportunity for ants to get lost at each node, favoring nodes with 3D configurations most likely to be reinforced by pheromone. Thus, rather than finding the shortest edges, turtle ant trail networks take advantage of natural variation in the environment to favor coherence, keeping the ants together on the trails.

A Correspondence Between Normalization Strategies in Artificial and Biological Neural Networks.

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalization, weight normalization, and their many variants help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this article, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neuron's activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent-that is, both push activation patterns of hidden units toward a homeostatic state, where all neurons are equally used-and we argue that such representations can improve coding capacity, discrimination, and regularization. As a proof of concept, we develop an algorithm, inspired by a neural normalization technique called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several data sets. Overall, we hope this bidirectional connection will inspire neuroscientists and machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear. METHODS: We used machine learning algorithms to predict COVID-19 severity in 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables collected on or before a patient's COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support). RESULTS: Our algorithm classified patients into these classes with an area under the receiver operating characteristic curve (AUROC) ranging from 70 to 85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables - including basic patient information, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type - suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors. CONCLUSIONS: Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.

A Statistical Growth Property of Plant Root Architectures.

Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar. This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions. Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations. Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three (x, y, z) growth directions. Plant shoot architectures also follow this design form, suggesting that two basic plant transport systems may use similar growth strategies.

Habituation as a neural algorithm for online odor discrimination.

Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.

Plant 3D (P3D): a plant phenotyping toolkit for 3D point clouds.

MOTIVATION: Developing methods to efficiently analyze 3D point cloud data of plant architectures remain challenging for many phenotyping applications. Here, we describe a tool that tackles four core phenotyping tasks: classification of cloud points into stem and lamina points, graph skeletonization of the stem points, segmentation of individual lamina and whole leaf labeling. These four tasks are critical for numerous downstream phenotyping goals, such as quantifying plant biomass, performing morphological analyses of plant shapes and uncovering genotype to phenotype relationships. The Plant 3D tool provides an intuitive graphical user interface, a fast 3D rendering engine for visualizing plants with millions of cloud points, and several graph-theoretic and machine-learning algorithms for 3D architecture analyses. AVAILABILITY AND IMPLEMENTATION: P3D is open-source and implemented in C++. Source code and Windows installer are freely available at https://github.com/iziamtso/P3D/. CONTACT: iziamtso@ucsd.edu or navlakha@cshl.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ten-Hour Time-Restricted Eating Reduces Weight, Blood Pressure, and Atherogenic Lipids in Patients with Metabolic Syndrome.

In animal models, time-restricted feeding (TRF) can prevent and reverse aspects of metabolic diseases. Time-restricted eating (TRE) in human pilot studies reduces the risks of metabolic diseases in otherwise healthy individuals. However, patients with diagnosed metabolic syndrome often undergo pharmacotherapy, and it has never been tested whether TRE can act synergistically with pharmacotherapy in animal models or humans. In a single-arm, paired-sample trial, 19 participants with metabolic syndrome and a baseline mean daily eating window of ≥14 h, the majority of whom were on a statin and/or antihypertensive therapy, underwent 10 h of TRE (all dietary intake within a consistent self-selected 10 h window) for 12 weeks. We found this TRE intervention improves cardiometabolic health for patients with metabolic syndrome receiving standard medical care including high rates of statin and anti-hypertensive use. TRE is a potentially powerful lifestyle intervention that can be added to standard medical practice to treat metabolic syndrome. VIDEO ABSTRACT.

Machine Learning Approaches to Improve Three Basic Plant Phenotyping Tasks Using Three-Dimensional Point Clouds.

Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary challenges in plant phenotyping: lamina/stem classification, lamina counting, and stem skeletonization. For classification, we assessed and validated the accuracy of our methods on a dataset of 54 3D shoot architectures, representing multiple growth conditions and developmental time points for two Solanaceous species, tomato (Solanum lycopersicum cv 75 m82D) and Nicotiana benthamiana Using deep learning, we classified lamina versus stems with 97.8% accuracy. Critically, we also demonstrated the robustness of our method to growth conditions and species that have not been trained on, which is important in practical applications but is often untested. For lamina counting, we developed an enhanced region-growing algorithm to reduce oversegmentation; this method achieved 86.6% accuracy, outperforming prior methods developed for this problem. Finally, for stem skeletonization, we developed an enhanced tip detection technique, which ran an order of magnitude faster and generated more precise skeleton architectures than prior methods. Overall, our improvements enable higher throughput and accurate extraction of phenotypic properties from 3D point cloud data.

Network trade-offs and homeostasis in Arabidopsis shoot architectures.

Understanding the optimization objectives that shape shoot architectures remains a critical problem in plant biology. Here, we performed 3D scanning of 152 Arabidopsis shoot architectures, including wildtype and 10 mutant strains, and we uncovered a design principle that describes how architectures make trade-offs between competing objectives. First, we used graph-theoretic analysis to show that Arabidopsis shoot architectures strike a Pareto optimal that can be captured as maximizing performance in transporting nutrients and minimizing costs in building the architecture. Second, we identify small sets of genes that can be mutated to shift the weight prioritizing one objective over the other. Third, we show that this prioritization weight feature is significantly less variable across replicates of the same genotype compared to other common plant traits (e.g., number of rosette leaves, total volume occupied). This suggests that this feature is a robust descriptor of a genotype, and that local variability in structure may be compensated for globally in a homeostatic manner. Overall, our work provides a framework to understand optimization trade-offs made by shoot architectures and provides evidence that these trade-offs can be modified genetically, which may aid plant breeding and selection efforts.

Neural arbors are Pareto optimal.

Neural arbors (dendrites and axons) can be viewed as graphs connecting the cell body of a neuron to various pre- and post-synaptic partners. Several constraints have been proposed on the topology of these graphs, such as minimizing the amount of wire needed to construct the arbor (wiring cost), and minimizing the graph distances between the cell body and synaptic partners (conduction delay). These two objectives compete with each other-optimizing one results in poorer performance on the other. Here, we describe how well neural arbors resolve this network design trade-off using the theory of Pareto optimality. We develop an algorithm to generate arbors that near-optimally balance between these two objectives, and demonstrate that this algorithm improves over previous algorithms. We then use this algorithm to study how close neural arbors are to being Pareto optimal. Analysing 14 145 arbors across numerous brain regions, species and cell types, we find that neural arbors are much closer to being Pareto optimal than would be expected by chance and other reasonable baselines. We also investigate how the location of the arbor on the Pareto front, and the distance from the arbor to the Pareto front, can be used to classify between some arbor types (e.g. axons versus dendrites, or different cell types), highlighting a new potential connection between arbor structure and function. Finally, using this framework, we find that another biological branching structure-plant shoot architectures used to collect and distribute nutrients-are also Pareto optimal, suggesting shared principles of network design between two systems separated by millions of years of evolution.

Travel in city road networks follows similar transport trade-off principles to neural and plant arbors.

Both engineered and biological transportation networks face trade-offs in their design. Network users desire to quickly get from one location in the network to another, whereas network planners need to minimize costs in building infrastructure. Here, we use the theory of Pareto optimality to study this design trade-off in the road networks of 101 cities, with wide-ranging population sizes, land areas and geographies. Using a simple one parameter trade-off function, we find that most cities lie near the Pareto front and are significantly closer to the front than expected by alternate design structures. To account for other optimization dimensions or constraints that may be important (e.g. traffic congestion, geography), we performed a higher-order Pareto optimality analysis and found that most cities analysed lie within a region of design space bounded by only four archetypal cities. The trade-offs studied here are also faced and well-optimized by two biological transport networks-neural arbors in the brain and branching architectures of plant shoots-suggesting similar design principles across some biological and engineered transport systems.

Adjustment in tumbling rates improves bacterial chemotaxis on obstacle-laden terrains.

The mechanisms of bacterial chemotaxis have been extensively studied for several decades, but how the physical environment influences the collective migration of bacterial cells remains less understood. Previous models of bacterial chemotaxis have suggested that the movement of migrating bacteria across obstacle-laden terrains may be slower compared with terrains without them. Here, we show experimentally that the size or density of evenly spaced obstacles do not alter the average exit rate of Escherichia coli cells from microchambers in response to external attractants, a function that is dependent on intact cell-cell communication. We also show, both by analyzing a revised theoretical model and by experimentally following single cells, that the reduced exit time in the presence of obstacles is a consequence of reduced tumbling frequency that is adjusted by the E. coli cells in response to the topology of their environment. These findings imply operational short-term memory of bacteria while moving through complex environments in response to chemotactic stimuli and motivate improved algorithms for self-autonomous robotic swarms.