Physically optimizing inference

Data is scaling exponentially in fields ranging from genomics to neuroscience to economics. A central question is: can modern machine learning methods be applied to construct predictive models of natural systems like cells and brains based on large data sets? In this paper, we examine how inference is impacted when training data is generated by the statistical behavior of a physical system, and hence outside direct control by the experimentalist. We develop an information-theoretic analysis for the canonical problem of spin-network inference. Our analysis reveals the essential role that the physical properties of the spin network and its environment play in determining the difficulty of the underlying machine learning problem. Specifically, stochastic fluctuations drive a system to explore a range of configurations providing `raw' information for a learning algorithm to construct an accurate model; yet they also blur energetic differences between network states and thereby degrade information. This competition leads spin networks to generically have an intrinsic optimal temperature at which stochastic spin fluctuations provide maximal information for discriminating among competing models, maximizing inference efficiency. We demonstrate a simple active learning protocol that optimizes network temperature to boost inference efficiency and dramatically increases the efficiency of inference on a neural circuit reconstruction task. Our results reveal a fundamental link between physics and information and show how the physical environment can be tuned to optimize the efficiency of machine learning

Edible Connections: A Model for Citizen Dialogue Used to Discuss Local Food, Farm, and Community Issues

Edible Connections: Changing the Way We Talk About Food, Farm, and Community introduces a model created to facilitate public dialogue on local food system issues such as farmland preservation, food safety, and hunger. Overall, this article describes and compares Edible Connections to other public discourse strategies used to engage individuals within a community in discussions regarding concerns about their local food system. Two characteristics set Edible Connections apart from other public dialogue strategies. First, the media—print, broadcast, e-commerce—are forum participants. Second is Edible Connections’ clear focus on food system issues. Its format allows those carrying out forums the flexibility to structure the dialogue to meet specific local objectives. Descriptions of how Pennsylvania communities defined and carried out Edible Connections to address locally important questions on the food system illustrate the ways in which Edible Connections helps to meet community interests and needs

Strengthening Community Engagement Toward Sustainable Local Food Systems

Perspectives of Extension educators relative to local food system (LFS) issues are examined. These educators perceived consumer food safety, viable ag-related businesses, land use planning, farm land preservation, loss of family-owned farms, and access to quality foods as important issues. Extension educators viewed county Extension directors, regional directors, and program advisory boards as the strongest supporters for the local LFS. Lack of program resources to support and carry out LFS programming was identified as a barrier. Significant differences were also found between Extension educators\u27 demographic and program characteristics and important LFS issues

Physically optimizing inference

Data is scaling exponentially in fields ranging from genomics to neuroscience to economics. A central question is: can modern machine learning methods be applied to construct predictive models of natural systems like cells and brains based on large data sets? In this paper, we examine how inference is impacted when training data is generated by the statistical behavior of a physical system, and hence outside direct control by the experimentalist. We develop an information-theoretic analysis for the canonical problem of spin-network inference. Our analysis reveals the essential role that the physical properties of the spin network and its environment play in determining the difficulty of the underlying machine learning problem. Specifically, stochastic fluctuations drive a system to explore a range of configurations providing `raw' information for a learning algorithm to construct an accurate model; yet they also blur energetic differences between network states and thereby degrade information. This competition leads spin networks to generically have an intrinsic optimal temperature at which stochastic spin fluctuations provide maximal information for discriminating among competing models, maximizing inference efficiency. We demonstrate a simple active learning protocol that optimizes network temperature to boost inference efficiency and dramatically increases the efficiency of inference on a neural circuit reconstruction task. Our results reveal a fundamental link between physics and information and show how the physical environment can be tuned to optimize the efficiency of machine learning

A primal-dual data-driven method for computational optical imaging with a photonic lantern

Optical fibres aim to image in-vivo biological processes. In this context, high spatial resolution and stability to fibre movements are key to enable decision-making processes (e.g., for microendoscopy). Recently, a single-pixel imaging technique based on a multicore fibre photonic lantern has been designed, named computational optical imaging using a lantern (COIL). A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to enable image reconstructions for high resolution COIL microendoscopy. In this work, we develop a data-driven approach for COIL. We replace the sparsity prior in the proximal algorithm by a learned denoiser, leading to a plug-and-play (PnP) algorithm. We use recent results in learning theory to train a network with desirable Lipschitz properties. We show that the resulting primal-dual PnP algorithm converges to a solution to a monotone inclusion problem. Our simulations highlight that the proposed data-driven approach improves the reconstruction quality over variational SARA-COIL method on both simulated and real data

Study Protocol for RESORP – Resolution of Organ Injury in Acute Pancreatitis – an observational prospective cohort study

Introduction Survivors of acute pancreatitis (AP) have shorter overall survival and increased incidence of new-onset cardiovascular, respiratory, liver and renal disease, diabetes mellitus and cancer compared with the general population, but the mechanisms that explain this are yet to be elucidated. Our aim is to characterise the precise nature and extent of organ dysfunction following an episode of AP.Methods and analysis This is an observational prospective cohort study in a single centre comprising a University hospital with an acute and emergency receiving unit and clinical research facility. Participants will be adult patient admitted with AP. Participants will undergo assessment at recruitment, 3 months and 3 years. At each time point, multiple biochemical and/or physiological assessments to measure cardiovascular, respiratory, liver, renal and cognitive function, diabetes mellitus and quality of life. Recruitment was from 30 November 2017 to 31 May 2020; last follow-up measurements is due on 31 May 2023. The primary outcome measure is the incidence of new-onset type 3c diabetes mellitus during follow-up. Secondary outcome measures include: quality of life analyses (SF-36, Gastrointestinal Quality of Life Index); montreal cognitive assessment; organ system physiological performance; multiomics predictors of AP severity, detection of premature cellular senescence. In a nested cohort within the main cohort, individuals may also consent to multiparameter MRI scan, echocardiography, pulmonary function testing, cardiopulmonary exercise testing and pulse-wave analysis.Ethics and dissemination This study has received the following approvals: UK IRAS Number 178615; South-east Scotland Research Ethics Committee number 16/SS/0065. Results will be made available to AP survivors, caregivers, funders and other researchers. Publications will be open-access.Trial registration numbers ClinicalTrials.gov Registry (NCT03342716) and ISRCTN50581876; Pre-results

Intracluster correlation coefficients and coefficients of variation for perinatal outcomes from five cluster-randomised controlled trials in low and middle-income countries: results and methodological implications

Background: Public health interventions are increasingly evaluated using cluster-randomised trials in which groups rather than individuals are allocated randomly to treatment and control arms. Outcomes for individuals within the same cluster are often more correlated than outcomes for individuals in different clusters. This needs to be taken into account in sample size estimations for planned trials, but most estimates of intracluster correlation for perinatal health outcomes come from hospital-based studies and may therefore not reflect outcomes in the community. In this study we report estimates for perinatal health outcomes from community-based trials to help researchers plan future evaluations.Methods: We estimated the intracluster correlation and the coefficient of variation for a range of outcomes using data from five community-based cluster randomised controlled trials in three low-income countries: India, Bangladesh and Malawi. We also performed a simulation exercise to investigate the impact of cluster size and number of clusters on the reliability of estimates of the coefficient of variation for rare outcomes.Results: Estimates of intracluster correlation for mortality outcomes were lower than those for process outcomes, with narrower confidence intervals throughout for trials with larger numbers of clusters. Estimates of intracluster correlation for maternal mortality were particularly variable with large confidence intervals. Stratified randomisation had the effect of reducing estimates of intracluster correlation. The simulation exercise showed that estimates of intracluster correlation are much less reliable for rare outcomes such as maternal mortality. The size of the cluster had a greater impact than the number of clusters on the reliability of estimates for rare outcomes.Conclusions: The breadth of intracluster correlation estimates reported here in terms of outcomes and contexts will help researchers plan future community-based public health interventions around maternal and newborn health. Our study confirms previous work finding that estimates of intracluster correlation are associated with the prevalence of the outcome of interest, the nature of the outcome of interest ( mortality or behavioural) and the size and number of clusters. Estimates of intracluster correlation for maternal mortality need to be treated with caution and a range of estimates should be used in planning future trials

Nuclear export competence of pre-40S subunits in fission yeast requires the ribosomal protein Rps2

Ribosome biogenesis is an evolutionarily conserved pathway that requires ribosomal and nonribosomal proteins. Here, we investigated the role of the ribosomal protein S2 (Rps2) in fission yeast ribosome synthesis. As for many budding yeast ribosomal proteins, Rps2 was essential for cell viability in fission yeast and the genetic depletion of Rps2 caused a complete inhibition of 40S ribosomal subunit production. The pattern of pre-rRNA processing upon depletion of Rps2 revealed a reduction of 27SA2 pre-rRNAs and the concomitant production of 21S rRNA precursors, consistent with a role for Rps2 in efficient cleavage at site A2 within the 32S pre-rRNA. Importantly, kinetics of pre-rRNA accumulation as determined by rRNA pulse-chases assays indicated that a small fraction of 35S precursors matured into 20S-containing particles, suggesting that most 40S precursors were rapidly degraded in the absence of Rps2. Analysis of steady-state RNA levels revealed that some pre-40S particles were produced in Rps2-depleted cells, but that these precursors were retained in the nucleolus. Our findings suggest a role for Rps2 in a mechanism that monitors pre-40S export competence

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy