Miracle-dz/UniFAR-FTUs huggingface dataset:
The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study

Introduction. Pragmatic clinical trials test interventions in patients representative of real-world medical practice and reduce data collection costs by using data recorded in the electronic health record (EHR) during usual care. We describe our experience using the EHR to measure the primary outcome of a pragmatic trial, hospital readmissions, and important clinical covariates. Methods. The trial enrolled patients recently discharged from the hospital for treatment of heart failure to test whether automated daily monitoring integrated into the EHR could reduce readmissions. The study team used data from the EHR and several data systems that drew on the EHR, supplemented by the hospital admissions files of three states. Results. Almost three-quarters of enrollees’ readmissions over the 12-mo trial period were captured by the EHRs of the study hospitals. State data, which took 7 mo to more than 2 y from first contact to receipt of first data, provided the remaining one-quarter. Considerable expertise was required to resolve differences between the 2 data sources. Common covariates used in trial analyses, such as weight and body mass index during the index hospital stay, were available for >97% of enrollees from the EHR. Ejection fraction, obtained from echocardiograms, was available for only 47.6% of enrollees within the 6-mo window that would likely be expected in a traditional trial. Discussion. In this trial, patient characteristics and outcomes were collected from existing EHR systems, but, as usual for EHRs, they could not be standardized for date or method of measurement and required substantial time and expertise to collect and curate. Hospital admissions, the primary trial outcome, required additional effort to locate and use supplementary sources of data. Highlights Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently. Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not. Acquiring essential supplementary data—in this trial, state data on hospital admission—can be a lengthy and difficult process.
Principles of LGBTQ+ Evaluation

The time is long overdue for the field of evaluation to critically reckon with how we have failed to appropriately consider the needs and experiences of lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) people. Perhaps even more importantly, there is a dire need for work that moves us forward in new directions which are more affirming and inclusive of LGBTQ+ people. To achieve this idealistic change in LGBTQ+ Evaluation will require a genuine, transformative paradigm shift within the evaluation field, encompassing everything from pedagogy to practice and all activities in between. As a first step toward a unified paradigm of LGBTQ+ Evaluation, this chapter proposes eight Principles of LGBTQ+ Evaluation to guide evaluators’ work in partnership with and in service of LGBTQ+ communities, organizations, and individuals. Here we are not seeking to provide a script or a rigid framework but rather to create guiding signposts that light the way for evaluators new to LGBTQ+ Evaluation.
The ER tether VAPA is required for proper cell motility and anchors ER-PM contact sites to focal adhesions

Cell motility processes highly depend on the membrane distribution of Phosphoinositides (PInst), giving rise to cytoskeleton reshaping and membrane trafficking events. Membrane contact sites serve as platforms for lipid exchange and calcium fluxes between two organelles. Here, we show that VAPA, an ER membrane-resident contact site tether, plays a crucial role during cell motility. CaCo2 adenocarcinoma epithelial cells depleted for VAPA exhibit several collective and individual motility defects, disorganized actin cytoskeleton and altered protrusive activity. During migration, VAPA is required for the maintenance of PI(4,5)P2 levels at the plasma membrane, but not for PI(4)P homeostasis in the Golgi and endosomal compartments. Importantly, we show that VAPA regulates the dynamics of focal adhesions (FA) through its MSP domain, and is essential to stabilize and anchor ventral ER-PM contact sites to FA, thus mediating microtubule-dependent FA disassembly. To conclude, our results reveal unprecedented functions for VAPA-mediated membrane contact sites during cell motility and provides a dynamic picture of ER-PM contact sites connection with FA mediated by VAPA.
Shape primed AC-electrophoretic microrobots

Second-order electrokinetic flow around colloidal particles caused by concentration polarization electro-osmosis can be utilized to controllably move asymmetric particle dimers in AC electrical fields. To demonstrate this actua-tion mechanism, we created particle dimers from micron-sized silica spheres with sizes 1 . 01 µm and 2 . 12 µm by connecting them with DNA linker molecules. The dimers can be steered along arbitrarily chosen paths within a 2D plane by controlling the direction of the AC electric field in a fluidic chamber with the joystick of a gamepad. Further utilizing induced dipole-dipole interactions, we demonstrate that particle dimers can be used to controllably pick up monomeric particles and release them at any desired position, and also to assemble several particles into groups. Systematic experiments exploring the dependence of the movement direction and velocity on buffer composition, frequency, and field strength further elucidate the underlying physical mechanism, and provide operational parameter ranges for our micro robotic swimmers which we termed ‘SPACE-bots’.
Effects of Neurofeedback training on performance in laboratory tasks: A systematic review

Neurofeedback procedures are attracting increasing attention in the neuroscience community. Based on the principle that participants, through suitable feedback, may learn to affect specific aspects of their brain activity, neurofeedback has been applied to basic research, translational, and clinical science alike. A large segment of the extant empirical research as well as review articles have focused on the extent to which neurofeedback interventions affect mental health outcomes, cognitive capacity, aging, and other complex behaviors. Another segment has aimed to characterize the extent to which neurofeedback affects the targeted neural processes. At this time, there is no current systematic review of the effects of neurofeedback on healthy participants’ performance in experimental tasks. Such a review is relevant in this rapidly evolving field because changes in experimental task performance are traditionally considered a hallmark of changing neurocognitive processes, often established in neurotypical individuals. This systematic review addresses this gap in the literature using the PRISMA method, building on earlier reviews on the same topic. Empirical studies using EEG or fMRI to alter brain processes linked to established, well-defined cognitive and affective laboratory tasks were reviewed. Substantial variability was found regarding the nature of the control for placebo effects, the implementation of the feedback, and the neural targets of feedback. Importantly, only a minority of the studies reported statistically meaningful effects of neurofeedback on performance in cognitive and affective tasks. Examining effect sizes and p-values in a subset of studies found no evidence for reporting bias, while also not finding systematic relations between study characteristics such as sample size or experimental control on the one hand and efficacy on the other. Implications for future work are discussed.
Antiviral immune response reveals host-specific virus infections in natural ant populations

Hosts can carry many viruses in their bodies, but not all of them cause disease. We studied ants as a social host to determine both their overall viral repertoire and the subset of actively infecting viruses across natural populations of three subfamilies: the Argentine ant (Linepithema humile, Dolichoderinae), the invasive garden ant (Lasius neglectus, Formicinae) and the red ant (Myrmica rubra, Myrmicinae). We used a dual sequencing strategy to reconstruct complete virus genomes by RNA-seq and to simultaneously determine the small interfering RNAs (siRNAs) by small RNA sequencing (sRNA-seq), which constitute the host antiviral RNAi immune response. This approach led to the discovery of 41 novel viruses in ants and revealed a host-ant specific RNAi response (21 vs. 22 nt siRNAs) in the different ant species. The efficiency of the RNAi response (sRNA/RNA read count ratio) depended on the virus and the respective ant species, but not its population. Overall, we found the highest virus abundance and diversity per population in Li. humile, followed by La. neglectus and M. rubra. Argentine ants also shared a high proportion of viruses between populations, whilst overlap was nearly absent in M. rubra. Only a single of the total 59 viruses in our study caused active infection in more than one ant species, whilst six viruses infected one, but only contaminated another ant species. Disentangling active infection from contamination thus allowed us to show high host-specificity of active viral infections versus a decent degree of spillover of non-infecting viral contaminants across ant species, providing relevant information for ecosystem management.
Solving Anscombe’s Quartet using a Transfer Learning Approach

Analysis of high-dimensional datasets often involves usage of summary statistics, one of which is the correlation coefficient. These values are then used to inform downstream analysis, whether in feature selection or in subsequent construction of networks and heatmaps. Condensing pairwise scatterplots into these singular values however, often results in a loss of information. Originally proposed by F. J. Anscombe in his famous ‘Anscombe’s Quartet,’ this phenomenon has been canonically used to demonstrate the importance of plotting and the limitations of summary statistics such as correlation or variance [F.J. Anscombe, (1973) American Statistician. 27 (1), 17-21]. While numerous methods exist for the generation of visually distinct datasets that share similar summary statistics, the converse has not been extensively studied. To address this gap, we propose ICLUST (Image CLUSTering), an image classifier tool that can visually distinguish correlations with similar summary statistics in simulations and identify meaningful clusters in real data. Such a tool can potentially benefit those performing exploratory analysis or feature selection in a complementary fashion by identifying relationships between variables that traditional summary metrics cannot provide. Significance Statement Distilling large-scale, multidimensional datasets via analysis of pairwise relationships often employs a single value to describe the relationship between variables. However, as demonstrated through simulations, such summarization fails to retain the nuances of the data. Characteristics such as the type of relationship (linear versus nonlinear, etc.) and the spread of the data are commonly lost when using correlations. Here we propose a transfer learning framework, borrowing from image clustering and classification software, to visually classify graphs. We apply our method towards separation of scatterplots with similar correlation statistics but visually distinctive patterns in both simulations and real data, demonstrating its broad applicability.
The Prophylactic and Therapeutic Efficacy of the Broadly Active Antiviral Ribonucleoside N4-Hydroxycytidine (EIDD-1931) in a Mouse Model of Lethal Ebola Virus Infection

The unprecedented magnitude of the 2013-2016 Ebola virus (EBOV) epidemic in West Africa resulted in over 11,000 deaths and spurred an international public health emergency. A second outbreak in 2018-2020 in DRC resulted in an additional >3400 cases and nearly 2300 deaths (WHO Disease Outbreak News: Update 26 June, 2020). These large outbreaks across geographically diverse regions highlight the need for the development of effective oral therapeutic agents that can be easily distributed for self-administration to populations with active disease or at risk of infection. Herein, we report the in vivo efficacy of N4-hydroxycytidine (EIDD-1931), a broadly active ribonucleoside analog and the active metabolite of the prodrug EIDD-2801 (molnupiravir), in murine models of lethal EBOV infection. Twice daily oral dosing with EIDD-1931 at 200 mg/kg for 7 days, initiated either with a prophylactic dose 2 hours before infection, or as therapeutic treatment starting 6 hours post-infection, resulted in 92-100% survival of mice challenged with lethal doses of EBOV, reduced clinical signs of Ebola virus disease (EVD), reduced serum virus titers, and facilitated weight loss recovery. These results support further investigation of molnupiravir as a potential therapeutic or prophylactic treatment for EVD.
Endothelial-derived FABP4 constitutes the majority of basal circulating hormone and regulates lipolysis-driven insulin secretion

Fatty acid binding protein 4 (FABP4) is a lipid chaperone secreted from adipocytes upon stimulation of lipolysis. Circulating FABP4 levels strongly correlate with body mass index and obesity-related pathologies in experimental models and humans. While adipocytes have been presumed to be the major source of hormonal FABP4, this question has not been addressed definitively in vivo. We generated mice with FABP4 deletion in cells known to express the gene; adipocytes (Adipo-KO), endothelial cells (Endo-KO), myeloid cells (Myeloid-KO), and the whole body (Total-KO) to examine the contribution of these cell types to basal and stimulated plasma FABP4 levels. Unexpectedly, baseline plasma FABP4 was only reduced by ∼25% in Adipo-KO mice, whereas Endo-KO mice showed ∼75% decreases compared to wildtype controls. In contrast, Adipo-KO mice exhibited ∼62% reduction in FABP4 responses to lipolysis, while there was minimal reduction in Endo-KO mice, indicating that adipocytes are the main FABP4 source in lipolysis. We did not detect any myeloid cell contribution to circulating FABP4. Surprisingly, despite the nearly intact FABP4 responses, Endo-KO mice showed blunted lipolysis-induced insulin secretion, identical to Total-KO mice. We conclude that the endothelium is the major source of baseline hormonal FABP4 and is required for the insulin response to lipolysis.
Refphase: Multi-sample reference phasing reveals haplotype-specific copy number heterogeneity

Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patient’s disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample reference phasing. We demonstrate Refphase’s ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours.
rBahadur: Efficient simulation of high-dimensional genotype data with global dependence structures

Existing methods for generating synthetic genotype data are ill-suited for replicating the effects of assortative mating (AM). We propose rb_dplr, a novel and computationally efficient algorithm for generating high-dimensional binary random variates that effectively recapitulates AM-induced genetic architectures using the Bahadur order-2 approximation of the multivariate Bernoulli distribution. Availability and implementation The rBahadur R library is available at https://github.com/rborder/rBahadur.
NeuroWRAP: integrating, validating, and sharing neurodata analysis workflows

Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP, which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.
Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning

Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology that provide critical insights into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale quantitative neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition. Author Summary Bees, despite their small brains, possess a rich behavioural repertoire and show significant variations among individuals. In social bees this variability is key to the division of labour that maintains their complex social organizations, and has been linked to the maturation of specific brain areas as a result of development and foraging experience. This makes bees an ideal model for understanding insect cognitive functions and the neural mechanisms that underlie them. However, due to the scarcity of comparative data, the relationship between brain neuro-architecture and behavioural variance remains unclear. To address this problem, we developed an AI-based approach for automated analysis of brain images and analysed an unprecedentedly large dataset of honey bee and bumblebee brains. Through this process, we were able to identify previously undescribed anatomical features that correlate with known behaviours, supporting recent evidence of lateralized behaviour in foraging and pollination. Our method is open-source, easily accessible online, user-friendly, fast, accurate, and robust to different species, enabling large-scale comparative analyses across the animal kingdom. This includes investigating the impact of external stressors such as environmental pollution and climate change on cognitive development, helping us understand the mechanisms underlying the cognitive abilities of animals and the implications for their survival and adaptation.
Optimal cosmic microwave background map-making in the presence of cross-correlated noise

Aims. We present an extension of the ROMA map-making algorithm for the generation of optimal cosmic microwave background polarization maps. The new code allows for a possible cross-correlated noise component among the detectors of a CMB experiment. A promising application is the forthcoming LSPE balloon-borne experiment, which is devoted to the accurate observation of CMB polarization at large angular scales. Methods. We generalized the noise covariance matrix in time domain to account for all the off-diagonal terms due to the detector cross-talk. Hence, we performed preliminary forecasts of the LSPE-SWIPE instrument. Results. We found that considering the noise cross-correlation among the detectors results in a more realistic estimate of the angular power spectra. In particular, the extended ROMA algorithm has provided a considerable reduction of the spectra error bars. We expect that this improvement could be crucial in constraining the B-mode polarization at the largest scales.
Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

Background The cerebellum critically contributes to higher-order cognitive and emotional functions such fear learning and memory. Prior research on cerebellar volume in PTSD is scant and has neglected neuroanatomical subdivisions of the cerebellum that differentially map on to motor, cognitive, and affective functions. Methods We quantified cerebellar lobule volumes using structural magnetic resonance imaging in 4,215 adults (PTSD n= 1640; Control n=2575) across 40 sites from the from the ENIGMA-PGC PTSD working group. Using a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation, we obtained volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum total and subregional volume in PTSD compared to healthy controls. The Benjamini-Hochberg procedure was used to control the false discovery rate (p-FDR < .05). Results PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume. In addition, people with PTSD showed reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), but also the vermis (VI, VIII), flocculonodular lobe (lobule X), and cerebellar white matter (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. Conclusions These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in high-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.