Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats.

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

Increased Elastase and Matrix Metalloproteinase Levels in the Pulmonary Arteries of Infants With Congenital Diaphragmatic Hernia.

BACKGROUND: Pulmonary vascular disease (PVD) complicated with pulmonary hypertension (PH) is a leading cause of mortality in congenital diaphragmatic hernia (CDH). Unfortunately, CDH patients are often resistant to PH therapy. Using the nitrogen CDH rat model, we previously demonstrated that CDH-associated PVD involves an induction of elastase and matrix metalloproteinase (MMP) activities, increased osteopontin and epidermal growth factor (EGF) levels, and enhanced smooth muscle cell (SMC) proliferation. Here, we aimed to determine whether the levels of the key members of this proteinase-induced pathway are also elevated in the pulmonary arteries (PAs) of CDH patients. METHODS: Neutrophil elastase (NE), matrix metalloproteinase-2 (MMP-2), epidermal growth factor (EGF), tenascin-C, and osteopontin levels were assessed by immunohistochemistry in the PAs from the lungs of 11 CDH patients and 5 normal age-matched controls. Markers of proliferation (proliferating cell nuclear antigen (PCNA)) and apoptosis (cleaved (active) caspase-3) were also used. RESULTS: While expressed by both control and CDH lungs, the levels of NE, MMP-2, EGF, as well as tenascin-C and osteopontin were significantly increased in the PAs from CDH patients. The percentage of PCNA-positive PA SMCs were also enhanced, while those positive for caspase-3 were slightly decreased. CONCLUSIONS: These results suggest that increased elastase and MMPs, together with elevated tenascin-C and osteopontin levels in an EGF-rich environment may contribute to the PVD in CDH infants. The next step of this study is to expand our analysis to a larger cohort, and determine the potential of targeting this pathway for the treatment of CDH-associated PVD and PH. TYPE OF STUDY: Therapeutic. LEVEL OF EVIDENCE: LEVEL III.

How honeybees respond to heat stress from the individual to colony level.

A honey bee colony functions as an integrated collective, with individuals coordinating their behaviour to adapt and respond to unexpected disturbances. Nest homeostasis is critical for colony function; when ambient temperatures increase, individuals switch to thermoregulatory roles to cool the nest, such as fanning and water collection. While prior work has focused on bees engaged in specific behaviours, less is known about how responses are coordinated at the colony level, and how previous tasks predict behavioural changes during a heat stress. Using BeesBook automated tracking, we follow thousands of individuals during an experimentally induced heat stress, and analyse their behavioural changes from the individual to colony level. We show that heat stress causes an overall increase in activity levels and a spatial reorganization of bees away from the brood area. Using a generalized framework to analyse individual behaviour, we find that individuals differ in their response to heat stress, which depends on their prior behaviour and correlates with age. Examining the correlation of behavioural metrics over time suggests that heat stress perturbation does not have a long-lasting effect on an individual's future behaviour. These results demonstrate how thousands of individuals within a colony change their behaviour to achieve a coordinated response to an environmental disturbance.

Machine learning reveals the waggle drift's role in the honey bee dance communication system.

The honey bee waggle dance is one of the most prominent examples of abstract communication among animals: successful foragers convey new resource locations to interested followers via characteristic "dance" movements in the nest, where dances advertise different locations on different overlapping subregions of the "dance floor." To this day, this spatial separation has not been described in detail, and it remains unknown how it affects the dance communication. Here, we evaluate long-term recordings of Apis mellifera foraging at natural and artificial food sites. Using machine learning, we detect and decode waggle dances, and we individually identify and track dancers and dance followers in the hive and at artificial feeders. We record more than a hundred thousand waggle phases, and thousands of dances and dance-following interactions to quantitatively describe the spatial separation of dances on the dance floor. We find that the separation of dancers increases throughout a dance and present a motion model based on a positional drift of the dancer between subsequent waggle phases that fits our observations. We show that this separation affects follower bees as well and results in them more likely following subsequent dances to similar food source locations, constituting a positive feedback loop. Our work provides evidence that the positional drift between subsequent waggle phases modulates the information that is available to dance followers, leading to an emergent optimization of the waggle dance communication system.

Metabolomic profiles predict individual multidisease outcomes.

Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.

Behavioral variation across the days and lives of honey bees.

In honey bee colonies, workers generally change tasks with age (from brood care, to nest work, to foraging). While these trends are well established, our understanding of how individuals distribute tasks during a day, and how individuals differ in their lifetime behavioral trajectories, is limited. Here, we use automated tracking to obtain long-term data on 4,100+ bees tracked continuously at 3 Hz, across an entire summer, and use behavioral metrics to compare behavior at different timescales. Considering single days, we describe how bees differ in space use, detection, and movement. Analyzing the behavior exhibited across their entire lives, we find consistent inter-individual differences in the movement characteristics of individuals. Bees also differ in how quickly they transition through behavioral space to ultimately become foragers, with fast-transitioning bees living the shortest lives. Our analysis framework provides a quantitative approach to describe individual behavioral variation within a colony from single days to entire lifetimes.

A Flying Platform to Investigate Neuronal Correlates of Navigation in the Honey Bee (Apis mellifera).

Navigating animals combine multiple perceptual faculties, learn during exploration, retrieve multi-facetted memory contents, and exhibit goal-directedness as an expression of their current needs and motivations. Navigation in insects has been linked to a variety of underlying strategies such as path integration, view familiarity, visual beaconing, and goal-directed orientation with respect to previously learned ground structures. Most works, however, study navigation either from a field perspective, analyzing purely behavioral observations, or combine computational models with neurophysiological evidence obtained from lab experiments. The honey bee (Apis mellifera) has long been a popular model in the search for neural correlates of complex behaviors and exhibits extraordinary navigational capabilities. However, the neural basis for bee navigation has not yet been explored under natural conditions. Here, we propose a novel methodology to record from the brain of a copter-mounted honey bee. This way, the animal experiences natural multimodal sensory inputs in a natural environment that is familiar to her. We have developed a miniaturized electrophysiology recording system which is able to record spikes in the presence of time-varying electric noise from the copter's motors and rotors, and devised an experimental procedure to record from mushroom body extrinsic neurons (MBENs). We analyze the resulting electrophysiological data combined with a reconstruction of the animal's visual perception and find that the neural activity of MBENs is linked to sharp turns, possibly related to the relative motion of visual features. This method is a significant technological step toward recording brain activity of navigating honey bees under natural conditions. By providing all system specifications in an online repository, we hope to close a methodological gap and stimulate further research informing future computational models of insect navigation.

Social networks predict the life and death of honey bees.

In complex societies, individuals' roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual's social network that can be obtained without interfering with the colony. This 'network age' accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function.

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions.

Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. To help accelerate behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabeled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of our dataset is to be able to classify behaviors accurately using both labeled and unlabeled tracking data, as well as being able to generalize to new settings.

Post-stroke visual midline shift syndrome.

The state of research on the topic of visual midline shift syndrome following a cerebrovascular accident is unknown. A scoping review was conducted using the search terms of 'visual midline shift' (or equivalent) and 'cerebrovascular accident' (or equivalent). Articles were selected from eight academic and one grey literature database, and went through two levels of review, as per Arksey and O'Malley, before being deemed acceptable for inclusion. Of the 931 abstracts reviewed, 27 articles met the criteria for inclusion. Data extracted from the selected articles included terminology and definition, symptoms, underlying pathophysiology, duration, assessment method, and management of visual midline shift syndrome following cerebrovascular accident. There is agreement on the existence of a midline shift following a cerebrovascular accident resulting in poor posture and imbalance. Much uncertainty exists in the literature regarding terminology, underlying pathophysiology, assessment method and management of this condition. Further research is required.

Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence.

Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a recording setup customized to track up to 4,000 marked bees over several weeks. Due to detection and decoding errors of the bee markers, linking the correct correspondences through time is non-trivial. In this contribution we present an in-depth description of the underlying multi-step algorithm which produces motion paths, and also improves the marker decoding accuracy significantly. The proposed solution employs two classifiers to predict the correspondence of two consecutive detections in the first step, and two tracklets in the second. We automatically tracked ~2,000 marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ~13% to around 2% post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ~3 million images covering 3 days. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.

RenderGAN: Generating Realistic Labeled Data.

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.

Automatic detection and decoding of honey bee waggle dances.

The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer's movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07%. The decoded waggle orientation has an average error of -2.92° (± 7.37°), well within the range of human error. To evaluate and exemplify the system's performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.

Elastase and matrix metalloproteinase activities are associated with pulmonary vascular disease in the nitrofen rat model of congenital diaphragmatic hernia.

BACKGROUND/PURPOSE: Pulmonary vascular disease (PVD) is a leading cause of congenital diaphragmatic hernia (CDH) mortality. Progression of PVD involves extracellular matrix remodeling by elastases and matrix metalloproteinases (MMP), concomitant with proliferation of smooth muscle cells in a growth factor-enriched environment. Blockade of this pathway reversed primary pulmonary hypertension and improved survival. This study was designed to determine whether a similar pathway is induced in PVD secondary to CDH. METHODS: Fetal rats exposed to nitrofen at gestational day 9 developed left-sided CDH and were compared at term to their non-CDH littermates by assessing histologic and biochemical features of PVD. RESULTS: Rats with CDH displayed right ventricle hypertrophy, increased pulmonary artery medial wall thickness and muscularization, and decreased lumen size. As revealed by in situ zymography and immunohistochemistry, this was associated with an induction of elastolytic and MMP activities as well as an elevation of epidermal growth factor and osteopontin levels in the diseased lung vasculature. CONCLUSIONS: CDH-associated PVD involves an induction of elastase and MMP activities and increased osteopontin deposition in an epidermal growth factor-rich environment. Inhibition of this pathway may thus represent a novel therapeutic approach for the treatment of CDH-associated PVD. LEVEL OF EVIDENCE: Level I (Basic Science Study).