bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conductive polypyrrole-coated electrospun chitosan nanoparticles/poly(D,L-lactide) fibrous mat: influence of drug delivery and Schwann cells proliferation
Authors: Li Siqi , Yu Xiaoling , Li Yuan , Zhang Tian 
Published: February 2022

Abstract
For nerve tissue engineering (NTE), scaffolds with the ability to release drugs under control and support the rapid proliferation of cells are very important for the repair of nerve defects. This study aimed to fabricate a conductive drug-loaded fiber mat by electrospinning and assess its potential as a scaffold for Schwann cells proliferation. The conductive polypyrrole (PPy) was coated on an electrospun poly (D, L-lactide) (PLA) fibrous mat, which was simultaneously embedded with protein-loaded chitosan nanoparticles and ibuprofen as a model small molecule drug. The fibrous mat shows suitable conductivity, mechanical properties, and hydrophilicity for NTE. For drug release and degradation studies, the fibrous mat can achieve sustained release of bovine serum albumin (BSA) and ibuprofen, and the PPy coating can increase the surface wettability and conductivity while slowing down the degradation of the fibrous mat. The application of electrical stimulation (ES) to the fibrous mat can accelerate the release of ibuprofen, but there was no significant effect on the release rate of the protein. The fibrous mat showed no cytotoxicity in vitro, and Schwann cells (SCs) can adhere, grow, and proliferate well on mats. At the 120th hour of culture in vitro, the relative growth rate of SCs on the conductive drug-loaded fibrous mat reached 198.22 ± 2.34%, which was an increase of 37.93% compared to the SCs on the drug-loaded fibrous mat with ES. The density and elongation of SCs on the conductive drug-loaded fibrous mat were greater than those on the PLA fibrous mat, indicating that the conductive polypyrrole-coated electrospun chitosan nanoparticles/PLA fibrous mat has good potential for application in nerve regeneration.
HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure
Authors: Xiang Ju , Meng Xiangmao , Zhao Yichao , Wu Fang-Xiang , Li Min 
Published: February 2022

AbstractMotivationIdentifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction.ResultsWe propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM’s predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation.ConclusionsThe results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.
ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders
Authors: Heydari A Ali , Davalos Oscar A , Zhao Lihong , Hoyer Katrina K , Sindi Suzanne S 
Published: February 2022

AbstractMotivationSingle-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.ResultsWe train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models.Availability and implementationThe codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA.Supplementary informationSupplementary data are available at Bioinformatics online.
CNGPLD: case–control copy-number analysis using Gaussian process latent difference
Authors: Shih David J H , Li Ruoxing , Müller Peter , Zheng W Jim , Do Kim-Anh , Lin Shiaw-Yih , Carter Scott L 
Published: February 2022

AbstractMotivationCross-sectional analyses of primary cancer genomes have identified regions of recurrent somatic copy-number alteration, many of which result from positive selection during cancer formation and contain driver genes. However, no effective approach exists for identifying genomic loci under significantly different degrees of selection in cancers of different subtypes, anatomic sites or disease stages.ResultsCNGPLD is a new tool for performing case–control somatic copy-number analysis that facilitates the discovery of differentially amplified or deleted copy-number aberrations in a case group of cancer compared with a control group of cancer. This tool uses a Gaussian process statistical framework in order to account for the covariance structure of copy-number data along genomic coordinates and to control the false discovery rate at the region level.Availability and implementationCNGPLD is freely available at https://bitbucket.org/djhshih/cngpld as an R package.Supplementary informationSupplementary data are available at Bioinformatics online.
Fast and accurate gene regulatory network inference by normalized least squares regression
Authors: Hillerton Thomas , Seçilmiş Deniz , Nelander Sven , Sonnhammer Erik L L 
Published: February 2022

AbstractMotivationInferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The Least Squares Cut-Off (LSCO) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes.ResultsWe developed a GRN inference method called Least Squares Cut-Off with Normalization (LSCON) that tackles this problem. LSCON extends the LSCO algorithm by regularization to avoid hyper-connected genes and thereby reduce false positives. The regularization used is based on normalization, which removes effects of extreme values on the fit. We benchmarked LSCON and compared it to Genie3, LASSO, LSCO and Ridge regression, in terms of accuracy, speed and tendency to predict hyper-connected genes. The results show that LSCON achieves better or equal accuracy compared to LASSO, the best existing method, especially for data with extreme values. Thanks to the speed of least squares regression, LSCON does this an order of magnitude faster than LASSO.Availability and implementationData: https://bitbucket.org/sonnhammergrni/lscon; Code: https://bitbucket.org/sonnhammergrni/genespider.Supplementary informationSupplementary data are available at Bioinformatics online.
NeuCA web server: a neural network-based cell annotation tool with web-app and GUI
Authors: Duan Daoyu , He Sijia , Huang Emina , Li Ziyi , Feng Hao 
Published: February 2022

Abstract

Summary
Correctly annotating individual cell’s type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data.

Availability and implementation
NeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Neural decoding of emotional prosody in voice-sensitive auditory cortex predicts social communication abilities in children
Authors: Leipold Simon , Abrams Daniel A , Karraker Shelby , Menon Vinod 
Published: February 2022

Abstract
During social interactions, speakers signal information about their emotional state through their voice, which is known as emotional prosody. Little is known regarding the precise brain systems underlying emotional prosody decoding in children and whether accurate neural decoding of these vocal cues is linked to social skills. Here, we address critical gaps in the developmental literature by investigating neural representations of prosody and their links to behavior in children. Multivariate pattern analysis revealed that representations in the bilateral middle and posterior superior temporal sulcus (STS) divisions of voice-sensitive auditory cortex decode emotional prosody information in children. Crucially, emotional prosody decoding in middle STS was correlated with standardized measures of social communication abilities; more accurate decoding of prosody stimuli in the STS was predictive of greater social communication abilities in children. Moreover, social communication abilities were specifically related to decoding sadness, highlighting the importance of tuning in to negative emotional vocal cues for strengthening social responsiveness and functioning. Findings bridge an important theoretical gap by showing that the ability of the voice-sensitive cortex to detect emotional cues in speech is predictive of a child’s social skills, including the ability to relate and interact with others.
Characteristics of Persons Treated for Hepatitis C Using National Pharmacy Claims Data, United States, 2014–2020
Authors: Teshale Eyasu H , Roberts Henry , Gupta Neil , Jiles Ruth 
Published: February 2022

Abstract
Using national pharmacy claims data for 2014–2020, 843 329 persons were treated for hepatitis C at least once. The proportion treated increased annually among persons aged &lt;40 years, insured by Medicaid, and treated by primary care providers. Monitoring hepatitis C treatment is essential to identify barriers to treatment access.
Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
Authors: Wright Adam J , Orlic-Milacic Marija , Rothfels Karen , Weiser Joel , Trinh Quang M , Jassal Bijay , Haw Robin A , Stein Lincoln D 
Published: February 2022

Abstract

Abstract
Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested.

Database URL
www.reactome.org
Proximate larval epidermal cell layer generates forces for Pupal thorax closure in Drosophila
Authors: Athilingam Thamarailingam , Parihar Saurabh S , Bhattacharya Rachita , Rizvi Mohd S , Kumar Amit , Sinha Pradip 
Published: February 2022

Abstract
During tissue closures, such as embryonic dorsal closure in Drosophila melanogaster, a proximate extra-embryonic layer, amnioserosa, generates forces that drive migration of the flanking lateral embryonic epidermis, thereby zip-shutting the embryo. Arguably, this paradigm of tissue closure is also recapitulated in mammalian wound healing wherein proximate fibroblasts transform into contractile myofibroblasts, develop cell junctions, and form a tissue layer de novo: contraction of the latter then aids in wound closure. Given this parallelism between disparate exemplars, we posit a general principle of tissue closure via proximate cell layer-generated forces. Here, we have tested this hypothesis in pupal thorax closure wherein 2 halves of the presumptive adult thorax of Drosophila, the contralateral heminotal epithelia, migrate over an underlying larval epidermal cell layer. We show that the proximate larval epidermal cell layer promotes thorax closure by its active contraction, orchestrated by its elaborate actomyosin network-driven epithelial cell dynamics, cell delamination, and death—the latter being prefigured by the activation of caspases. Larval epidermal cell dynamics generate contraction forces, which when relayed to the flanking heminota—via their mutual integrin-based adhesions—mediate thorax closure. Compromising any of these contraction force-generating mechanisms in the larval epidermal cell layer slows down heminotal migration, while loss of its relay to the flanking heminota abrogates the thorax closure altogether. Mathematical modeling further reconciles the biophysical underpinning of this emergent mechanism of thorax closure. Revealing mechanism of thorax closure apart, these findings show conservation of an essential principle of a proximate cell layer-driven tissue closure.
A Fat-Promoting Botanical Extract From Artemisia scoparia Exerts Geroprotective Effects on Caenorhabditis elegans Life Span and Stress Resistance
Authors: Ghosh Bhaswati , Guidry Hayden J , Johnston Maxwell , Bohnert K Adam 
Published: February 2022

Abstract
Like other biological processes, aging is not random but subject to molecular control. Natural products that modify core metabolic parameters, including fat content, may provide entry points to extend animal life span and promote healthy aging. Here, we show that a botanical extract from Artemisia scoparia (SCO), which promotes fat storage and metabolic resiliency in mice, extends the life span of the nematode Caenorhabditis elegans by up to 40%. Notably, this life-span extension depends significantly on SCO’s effects on fat; SCO-treated worms exhibit heightened levels of unsaturated fat, and inhibition of Δ9 desaturases, which oversee biosynthesis of monounsaturated fatty acids, prevents SCO-dependent fat accumulation and life-span extension. At an upstream signaling level, SCO prompts changes to C. elegans fat regulation by stimulating nuclear translocation of transcription factor DAF-16/FOXO, an event that requires AMP-activated protein kinase under this condition. Importantly, animals treated with SCO are not only long-lived but also show improved stress resistance in late adulthood, suggesting that this fat-promoting intervention may enhance some aspects of physiological health in older age. These findings identify SCO as a natural product that can modify fat regulation for longevity benefit and add to growing evidence indicating that elevated fat can be prolongevity in some circumstances.
How is inclusiveness in health systems research priority-setting affected when community organizations lead the process?
Authors: Pratt Bridget , Srinivas Prashanth N , Seshadri Tanya 
Published: February 2022

Abstract
Community engagement is gaining prominence in health research. But communities rarely have a say in the agendas or conduct of the very health research projects that aim to help them. One way thought to achieve greater inclusion for communities throughout health research projects, including during priority-setting, is for researchers to partner with community organizations (COs). This paper provides initial empirical evidence as to the complexities such partnerships bring to priority-setting practice. Case study research was undertaken on a three-stage CO-led priority-setting process for health systems research. The CO was the Zilla Budakattu Girijana Abhivrudhhi Sangha, a district-level community development organization representing the Soliga people in Karnataka, India. Data on the priority-setting process were collected in 2018 and 2019 through in-depth interviews with researchers, Sangha leaders and field investigators from the Soliga community who collected data as part of the priority-setting process. Direct observation and document collection were also performed, and data from all three sources were thematically analysed. The case study demonstrates that, when COs lead health research priority-setting, their strengths and weaknesses in terms of representation and voice will affect inclusion at each stage of the priority-setting process. CO strengths can deepen inclusion by the CO and its wider community. CO weaknesses can create limitations for inclusion if not mitigated, exacerbating or reinforcing the very hierarchies that impede the achievement of improved health outcomes, e.g. exclusion of women in decision-making processes related to their health. Based on these findings, recommendations are made to support the achievement of inclusive CO-led health research priority-setting processes.
Fully discrete best-approximation-type estimates inL∞ (I;L2(Ω)d) for finite element discretizations of the transient Stokes equations
Authors: Behringer Niklas , Vexler Boris , Leykekhman Dmitriy 
Published: February 2022

AbstractIn this article, we obtain an optimal best-approximation-type result for fully discrete approximations of the transient Stokes problem. For the time discretization, we use the discontinuous Galerkin method and for the spatial discretization we use standard finite elements for the Stokes problem satisfying the discrete inf-sup condition. The analysis uses the technique of discrete maximal parabolic regularity. The results require only natural assumptions on the data and do not assume any additional smoothness of the solutions.
Ivermectin for the prevention of COVID-19: addressing potential bias and medical fraud
Authors: Hill Andrew , Mirchandani Manya , Ellis Leah , Pilkington Victoria 
Published: February 2022

Abstract

Background
Ivermectin is an antiparasitic drug being investigated in clinical trials for the prevention of COVID-19. However, there are concerns about the quality of some of these trials.

Objectives
To conduct a meta-analysis with randomized controlled trials of ivermectin for the prevention of COVID-19, while controlling for the quality of data. The primary outcome was RT–PCR-confirmed COVID-19 infection. The secondary outcome was rate of symptomatic COVID-19 infection.

Methods
We conducted a subgroup analysis based on the quality of randomized controlled trials evaluating ivermectin for the prevention of COVID-19. Quality was assessed using the Cochrane risk of bias measures (RoB 2) and additional checks on raw data, where possible.

Results
Four studies were included in the meta-analysis. One was rated as being potentially fraudulent, two as having a high risk of bias and one as having some concerns for bias. Ivermectin did not have a significant effect on preventing RT–PCR-confirmed COVID-19 infection. Ivermectin had a significant effect on preventing symptomatic COVID-19 infection in one trial with some concerns of bias, but this result was based on post hoc analysis of a multi-arm study.

Conclusions
In this meta-analysis, the use of ivermectin was not associated with the prevention of RT–PCR-confirmed or symptomatic COVID-19. The currently available randomized trials evaluating ivermectin for the prevention of COVID-19 are insufficient and of poor quality.