Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning For Sepsis Treatment: A Continuous Action Space Solution

Sepsis is the leading cause of death in intensive care units. It is challenging to treat sepsis because the optimal treatment is still unclear, and individual patients respond differently to treatments. Recent attempts to use reinforcement learning to provide real-time personalized treatment recommendations have shown promising results. However, the discrete action design (i.e., discretizing the continuum of action space into coarse-grained decisions) poses problems in policy learning and evaluation, and limits the effectiveness of the treatment recommendations. In this work, we proposed a continuous state and action space solution inspired by the Deep Deterministic Policy Gradient (DDPG) algorithm. We performed qualitative evaluations and applied the direct method for off-policy evaluations. Our results match clinician performance and are more clinically reasonable and explainable than the state of the art.
A QP-Free Algorithm for Finite Minimax Problems

The nonlinear minimax problems without constraints are discussed. Due to the expensive computation for solving QP subproblems with inequality constraints of SQP algorithms, in this paper, a QP-free algorithm which is also called sequential systems of linear equations algorithm is presented. At each iteration, only two systems of linear equations with the same coefficient matrix need to be solved, and the dimension of each subproblem is not of full dimension. The proposed algorithm does not need any penalty parameters and barrier parameters, and it has small computation cost. In addition, the parameters in the proposed algorithm are few, and the stability of the algorithm is well. Convergence property is described and some numerical results are provided.
Assessing the performance of the Cell Painting assay across different imaging systems

Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.
Label-Wise Graph Convolutional Network for Heterophilic Graphs

Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications. However, most existing GNNs assume the graphs exhibit strong homophily in node labels, i.e., nodes with similar labels are connected in the graphs. They fail to generalize to heterophilic graphs where linked nodes may have dissimilar labels and attributes. Therefore, in this paper, we investigate a novel framework that performs well on graphs with either homophily or heterophily. More specifically, we propose a label-wise message passing mechanism to avoid the negative effects caused by aggregating dissimilar node representations and preserve the heterophilic contexts for representation learning. We further propose a bi-level optimization method to automatically select the model for graphs with homophily/heterophily. Theoretical analysis and extensive experiments demonstrate the effectiveness of our proposed framework for node classification on both homophilic and heterophilic graphs.
Learning Skills Diverse in Value-Relevant Features

Behavioural abstraction via temporally extended actions is vital to solving large-scale reinforcement learning problems. Skills structure exploration, speed up credit assignment, and can be used in transfer learning. However, such abstraction is often difficult or expensive for experts to craft by hand. Unsupervised information-theoretic methods (Gregor et al., 2016; Eysenbach et al., 2019; Sharma et al., 2020) address this problem by learning a set of skills without using environment rewards, typically by maximizing discriminability of the states visited by individual skills. However, since only some features of the state matter in complex environments, these methods often discover behaviours that are trivially diverse, learning skills that are not helpful for downstream tasks. To overcome this limitation, we propose a method for learning skills that only control features important to the tasks of interest. First, by training on a small set of source tasks, the agent learns which features are most relevant. Then, the discriminability objective for an unsupervised information-theoretic method is defined for this learned feature space. This allows the construction of sets of diverse and useful skills that can control the most important features. Experimental results in continuous control domains validate our method, demonstrating that it yields skills that substantially improve learning on downstream locomotion tasks with sparse rewards.
A Note on the Information-Theoretic Basis for Fitts’ Law

Fitts' law is an information-theoretic view of human motor behavior developed from Shannon's Theorem, 17, a fundamental theorem of communications systems. Using data from Fitts' original experiments, we demonstrate that Fitts' choice of an equation that deviates slightly from the underlying principal is perhaps unfounded, and that the relationship is improved by using an exact adaptation of Shannon's equation.
German Ixodes inopinatus samples may not actually represent this tick species

Ticks are important vectors of human and animal pathogens, but many questions remain unanswered regarding their taxonomy. Molecular sequencing methods have allowed research to start understanding the evolutionary history of even closely related tick species. Ixodes inopinatus is considered a sister species and highly similar to Ixodes ricinus, an important vector of many tick-borne pathogens in Europe, but identification between these species remains ambiguous with disagreement on the geographic extent of I. inopinatus. In 2018-2019, 1583 ticks were collected from breeding great tits (Parus major) in southern Germany, of which 45 were later morphologically identified as I. inopinatus. We aimed to confirm morphological identification using molecular tools. Utilizing two genetic markers (16S rRNA, TROSPA) (n=37) and whole genome sequencing of specific ticks (n=8), we were able to determine that German samples morphologically identified as I. inopinatus, genetically represent I. ricinus regardless of previous morphological identification and most likely are not I. ricinus/I. inopinatus hybrids. Further, our results showed that the entire mitochondrial genome, let alone singular mitochondrial genes (i.e., 16S), is unable to distinguish between I. ricinus and I. inopinatus. As most examples of I. inopinatus in Germany were based on morphology and mitochondrial sequences, the results of the current study brings into question whether I. inopinatus was properly identified in previous research and if this species exists in Central Europe. Our results highlight the power of utilizing genomic data in answering questions regarding tick taxonomy even when closely related species are considered. Highlights German Ixodes inopinatus samples represent I. ricinus based on genomic data The mitochondrial genome is not sufficient for delineation of I. inopinatus and I. ricinus German samples most likely do not represent I. ricinus/I. inopinatus hybrids
Simultaneous measurement of nascent transcriptome and translatome using 4-thiouridine metabolic RNA labeling and translating ribosome affinity purification

Regulation of gene expression in response to various biological processes, including extracellular stimulation and environmental adaptation requires de novo RNA synthesis and translation. Analysis of the coordinated and circular regulation of dynamic RNA synthesis and translation is required to determine functional protein production. However, reliable methods for the simultaneous measurement of nascent RNA synthesis and translation at the gene level are limited. Here, we developed a novel method for the simultaneous assessment of nascent RNA synthesis and translation by combining 4-thiouridine (4sU) metabolic RNA labeling and translating ribosome affinity purification (TRAP) using a monoclonal antibody against evolutionarily conserved ribosomal P-stalk proteins. The P-stalk-mediated TRAP (P-TRAP) technique recovered endogenous translating ribosomes, allowing easy translatome analysis of various eukaryotes. We validated this method in mammalian cells by demonstrating that acute unfolded protein response (UPR) in the endoplasmic reticulum (ER) induces dynamic reprograming of nascent RNA synthesis and translation. Our method can serve as a simple and powerful tool for analyzing the coordinated regulation of transcription and translation of individual genes in various eukaryotes.
Error Amplification When Updating Deployed Machine Learning Models

As machine learning (ML) shows vast potential in real world applications, the number of deployed models has been increasing substantially, but little attention has been devoted to validating/improving model performance over time. Model updates, sometimes frequent, are essential to dealing with data shift, policy changes and in general, to improving the model performance. Updating also presents significant risks to amplifying model errors if effort is not put into preventing it. Unfortunately very little analysis is done to date of what can happen as models are deployed and become a part of the decision making process, where there is no longer a way to disentangle human from machine error in the collected labels. The phenomenon of interest is termed error amplification where model errors corrupt future labels, and are reinforced by updates eventually causing the model to predict its own outputs instead of the labels of interest. We analyze various factors influencing the magnitude of error amplification, and provide guidance for model and threshold selection when error amplification is a risk. We demonstrate that a variety of learning techniques cannot handle the systematic way in which error amplification corrupts observed outcomes. Additionally, we discuss both procedural and modeling solutions to reduce model deterioration over time based on our empirical evaluations.
Hall MHD simulations of collisionless multiple X line reconnection

A Hall MHD simulation of multiple X line reconnection, in which a diffusion region around the standing X line and the moving plasmoids are involved, is presented. The observed features of Hall magnetic and electric fields in the diffusion region and the direction reversal of the electron flow in the vicinity of the magnetic separatrices are displayed in case 1 with the guide field B-y0 = 0. During the passage of a tailward moving plasmoid the bipolar B-y waveform signatures caused by the Hall current and the Hall electric field signatures in case 1 are qualitatively in line with the observations from the Geotail and Cluster spacecraft. Case 2 with B-y0 = 0.15 is carried out to investigate the reconnection configuration on the earthward side of the X line. The peaking signature of the positive B-y correlated with the bipolar (-/+) B-z is observed while an earthward moving plasmoid passes though the given point below the current sheet and close to the midplane; but the negative B-y is recorded at the given point above the current sheet. Such positive and negative B-y signatures, which are comparable with the observations from Cluster 3 and other Cluster spacecraft, are attributed to the B-y asymmetric structures associated with the Hall effect. The enhancement and variation of the electric field detected by Cluster 3 might be related to the -V' x B electric field associated with the earthward movement of the X line.
The HRA Organ Gallery affords immersive superpowers for building and exploring the Human Reference Atlas with virtual reality

The Human Reference Atlas (HRA, https://humanatlas.io) funded by the NIH Human Biomolecular Atlas Program (HuBMAP, https://commonfund.nih.gov/hubmap) and other projects engages 17 international consortia to create a spatial reference of the healthy adult human body at single-cell resolution. The specimen, biological structure, and spatial data that define the HRA are disparate in nature and benefit from a visually explicit method of data integration. Virtual reality (VR) offers unique means to enable users to explore complex data structures in a threedimensional (3D) immersive environment. On a 2D desktop application, the 3D spatiality and real-world size of the 3D reference organs of the atlas is hard to understand. If viewed in VR, the spatiality of the organs and tissue blocks mapped to the HRA can be explored in their true size and in a way that goes beyond traditional 2D user interfaces. Added 2D and 3D visualizations can then provide data-rich context. In this paper, we present the HRA Organ Gallery, a VR application to explore the atlas in an integrated VR environment. Presently, the HRA Organ Gallery features 55 3D reference organs,1,203 mapped tissue blocks from 292 demographically diverse donors and 15 providers that link to 5,000+ datasets; it also features prototype visualizations of cell type distributions and 3D protein structures. We outline our plans to support two biological use cases: on-ramping novice and expert users to HuBMAP data available via the Data Portal (https://portal.hubmapconsortium.org), and quality assurance/quality control (QA/QC) for HRA data providers. Code and onboarding materials are available at https://github.com/cns-iu/ccf-organ-vr-gallery#readme.
On test horizon for model validation by output error

We derive necessary and sufficient conditions under which the parameters of the model are identical to those of the true system which is supposed to be linear and of finite dimension. These conditions are that the cross-correlations between the output error and the input as well as the model output should be asymptotically null in a finite horizon. This horizon may be used as a minimum necessary test horizon in model validation. >
Quantitative classification of energy landscapes inferred from single nanoparticle tracking of membrane receptors inside nanodomains reveals confinement functional and molecular features

The cell membrane organization has been hypothesized for a long time to have an essential functional role, through the control of membrane receptor confinement in micro- or nanodomains. Several mechanisms have been proposed to account for these properties, though some features of the resulting organization have remained controversial, notably the nature, size, and stability of cholesterol- and sphingolipid-rich domains called rafts. Here, we quantitatively probed the energy landscape experienced by single nanoparticle-labeled membrane receptors - epidermal growth factor receptors (EGFR), transferrin receptors (TfR), and receptors of ε-toxin produced by C. perfringens and α-toxin of C.Septicum (CPεTR and CSαTR, respectively) - through the development of new computational methods. By establishing a new analysis pipeline combining Bayesian inference, decision trees and clustering approaches, we indeed systematically classified single protein trajectories according to the type of confining energy landscape. This revealed the existence of only two distinct organization modalities: (A) confinement in a quadratic energy landscape for EGF, CPεT and CSαT receptors and (B) free diffusion in confinement domains resulting from the steric hindrance due to F-actin barriers for transferrin receptors. The characterization of confinement energy landscapes by Bayesian inference furthermore revealed the role of interactions with the domain environment in cholesterol- and sphingolipid-rich domains with (in the case of EGFR) or without (for CPεT and CSαT receptors) parallel interactions with F-actin, to regulate the confinement energy depth. Strikingly, these two distinct mechanisms result in the same organization type (A). We furthermore revealed that the apparent domain sizes for these receptor trajectories resulted from Brownian exploration of the energy landscape in a steady-state like regime at a common effective temperature, independently of the underlying molecular mechanisms. These results highlight that the membrane organization in confinement domains may be more adequately described as interaction hotspots rather than rafts with abrupt domain boundaries. Altogether, these results establish a new computational approach, which paves the way to the constitution of an atlas of energy landscape of membrane proteins and of their control mechanisms, and support a new general model for functional receptor confinement in membrane nanodomains.
Development and characterization of a multimeric recombinant protein based on the spike protein receptor binding domain of SARS-CoV-2 that can neutralize virus infection

Background The SARS-CoV-2 virus, responsible for the COVID-19 pandemic, has four structural proteins and sixteen non-structural proteins. The S-protein is one of the structural proteins exposed on the surface of the virus and is the main target for producing neutralizing antibodies and vaccines. The S-protein forms a trimer that can bind the angiotensin-converting enzyme 2 (ACE2) through its receptor binding domain (RBD) for cell entry. Methods We stably expressed in a constitutive manner in HEK293 cells a new recombinant protein containing a signal sequence of immunoglobulin to produce an extended C-terminal portion of the RBD followed by a region responsible for the trimerization inducer of the bacteriophage T4, and a sequence of 6 histidines. The protein was produced and released in the culture supernatant of cells and was purified by Ni-agarose column and exclusion chromatography. It was then characterized by SDS-polyacrylamide gel and used as antigen to generate protective antibodies to inhibit ACE2 receptor interaction and virus entry into Vero cells. Results The purified protein displayed a molecular mass of 135 kDa and with a secondary structure like the monomeric RBD. Electrophoresis analysis in SDS-polyacrylamide gel with and without reducing agents, and in the presence of crosslinkers indicated that it forms a multimeric structure composed of trimers and hexamers. The purified protein was able to bind the ACE2 receptor and generated high antibody titers in mice (1:10000), capable of inhibiting the binding of biotin labeled ACE2 to the virus S1 subunit, and to neutralize the entry of the SARS-CoV-2 Wuhan strain into cells. Conclusion Our results characterize a new multimeric protein based on S1 subunit to combat COVID-19, as a possible immunogen or antigen for diagnosis.
Full–field analysis of a planar anisotropic layered half–plane for concentrated forces and edge dislocations

The problem of a planar anisotropic elastic layered half–plane subjected to concentrated forces and edge dislocations applied either in the layer or in the half–plane is analysed. One of the objectives of this study is to develop an effective analytical methodology to construct the exact full–field solution for this problem. By using the Lekhnitskii formalism for anisotropic elastic material with the Fourier–transformation technique, the explicit closed–form solutions for stresses in the layer and the half–plane are obtained. The solutions are suitable for loadings that are acting on the free surface or at the interface. The complete solutions for this problem consist only of the simplest solutions obtained from an infinite homogeneous medium with concentrated forces and edge dislocations. The solutions include Green's function for applied loadings in an infinite medium and an infinite number of image singularities that are induced to satisfy the boundary and interface conditions. It is shown that the physical meaning of the solution is the image method. The magnitudes and locations of image singularities are determined automatically from the mathematical method presented in this study. Numerical results for the full–field stress distribution in the layered half–plane medium subjected to concentrated forces or edge dislocations are discussed in detail.
When is warmer better? Disentangling within- and between-generation effects of thermal history on survival

Understanding the fitness consequences of thermal history is necessary to predict organismal responses to global warming. This is especially challenging for ectotherms with complex life cycles, since distinct life stages can differ in thermal sensitivity, acclimate to different thermal environments, and accrue responses to acclimation within and between generations. Although acclimation is widely hypothesized to benefit organisms by helping them (or their offspring) to compensate for negative impacts of environmental change, equivocal support for this hypothesis highlights the need to assess alternatives. However, assessments that do so in ways that explicitly dissect responses across life stages and generations remain limited. We assess alternative hypotheses for acclimation responses (none, beneficial, colder-is-better, and warmer-is-better) within and between generations of an externally-fertilizing marine tubeworm whose vulnerability to warming rests on survival at early planktonic stages (gametes, embryos, and larvae). We start by acclimating parents, gametes, and embryos to ambient and projected warmer temperatures (17 °C and 22 °C) factorially by life stage. We then rear individuals with differing acclimation histories to the end of larval development at test temperatures from 10 °C to 28 °C (upper and lower survival limits) to estimate thermal survival curves for development, and compare curves among acclimation histories. We show that survival curves are most responsive to parental acclimation followed by acclimation at embryogenesis, but are buffered against acclimation at fertilization. Moreover, curves respond independently to acclimation within and between generations, and respond largely as predicted by the warmer-is-better hypothesis, despite the semblance of beneficial acclimation after successive acclimations to warmer temperature. Our study demonstrates the varied nature of thermal acclimation, and the importance of considering how acclimation responses aggregate across complex life cycles when predicting vulnerability to warming.
Two‐layer flows past a semicircular obstruction

The flow past a semicircular cylinder at the bottom of a channel is considered. The channel is of finite vertical extent and of infinite horizontal extent. The fluid consists of two layers of constant densities. Both viscosity and surface tension are neglected. It is assumed that the flow is symmetric with respect to a vertical line through the center of the circular cross section and uniform in each layer at infinity. An integrodifferential equation for the unknown shape of the interface is derived, discretized, and solved numerically using Newton’s method. It is shown that there are flows that are perturbations of uniform streams and others that are perturbations of solitary waves. Progression along the various branches of solutions produce two different types of limiting configurations. One is characterized by an infinite broadening in the horizontal direction and the other by overhanging interfacial profiles.