bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computational identification of natural senotherapeutic compounds that mimic dasatinib based on gene expression data
Authors: Meiners Franziska , Secci Riccardo , Sueto Salem , Fuellen Georg , Barrantes Israel 
Published: May 2022

ABSTRACTThe highest risk factor for chronic diseases is chronological age, and age-related chronic diseases account for the majority of deaths worldwide. Targeting senescent cells that accumulate in disease-related tissues presents a strategy to reduce disease burden and to increase healthspan.Our goal was the computational identification of senotherapeutic repurposing candidates that potentially eliminate senescent cells, based on their similarity in gene expression effects to dasatinib, a tyrosine-kinase inhibitor that induces apoptosis in certain senescent cell types, and that is frequently used as a senolytic together with quercetin.The natural senolytic piperlongumine (a compound found inlong pepper), and the natural senomorphics parthenolide, phloretin and curcumin (found in various edible plants) were identified as potential substitutes of dasatinib. The gene expression changes underlying the repositioning highlight apoptosis-related genes and pathways. The four compounds, and in particular the top-runner piperlongumine, may be combined with quercetin to obtain natural formulas emulating the dasatinib + quercetin (D+Q) formula that is frequently used in clinical trials targeting senescent cells.
A topography-based predictive framework for naturalistic viewing fMRI
Authors: Li Xuan , Friedrich Patrick , Patil Kaustubh R. , Eickhoff Simon B. , Weis Susanne 
Published: May 2022

AbstractRecent work has shown great interest in understanding individual differences in complex brain function under naturalistic viewing (NV) conditions. However, methods specifically designed for achieving this goal remain limited. Here, we propose a novel approach, called TOpography-based Predictive Framework (TOPF), to investigate individual differences in evoked brain activity on NV fMRI data. Specifically, TOPF identifies individual-specific evoked activity topographies in a data- driven manner and examines their behavioural relevance using a machine learning predictive framework. Our results show that these topographies successfully predict individual phenotypes across cognition, emotion and personality on unseen subjects, and the identified predictive brain regions are neurobiologically interpretable. Further, the prediction accuracy exceeds that of the commonly-used functional connectivity-based features. Conceptually, we highlight the importance of examining multivariate evoked activity patterns for studying brain-behaviour relationships. In summary, we provide a powerful tool for understanding individual differences and brain-behaviour relationships on NV fMRI data.
An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears
Authors: Lewis Joshua E. , Shebelut Conrad W. , Drumheller Bradley R. , Zhang Xuebao , Shanmugam Nithya , Attieh Michel , Horwath Michael C. , Khanna Anurag , Smith Geoffrey H. , Gutman David A. , Aljudi Ahmed , Cooper Lee A.D. , Jaye David L. 
Published: May 2022

ABSTRACTPathologic diagnosis of bone marrow disorders relies in part on microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including analysis of only a small subset of optimal slide areas and nucleated cells, and inter-observer variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline utilizes a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into one of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produces 11-component DCCs that demonstrate high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we show that automated analysis can reduce intra-slide variance in DCCs by analyzing the whole slide and marrow nucleated cells within optimal regions. Finally, pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully-automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.
yEvo: a modular eukaryotic genetics and evolution research experience for high school students
Authors: Taylor M. Bryce , Warwick Alexa R. , Skophammer Ryan , Boyer Josephine M. , Geck Renee C. , Gunkelman Kristin , Walson Margaux , Rowley Paul A. , Dunham Maitreya J. 
Published: May 2022

AbstractMicrobial experimental evolution paired with whole-genome sequencing allows researchers to observe evolutionary processes in real-time. The resources for carrying out and analyzing microbial evolution experiments have become more accessible. It is now possible to expand these studies beyond the research laboratory and into the classroom. We have developed a series of five connected and standards-aligned yeast evolution laboratory modules, called “yEvo,” for high school biology students. The modules have been designed to enable students to take agency in answering open-ended research questions. In Module 1, students evolve the baker’s yeast Saccharomyces cerevisiae to tolerate an over-the-counter antifungal drug, and in subsequent modules, investigate how evolved yeasts adapted to this stressful condition at both the phenotype and genotype levels. Pre- and post-surveys from 72 students at two different schools and one-on-one interviews with students and teachers were used to assess our program goals to iteratively improve these modules over three years. We also measured changes in student conceptions of mutation and evolution, confidence in scientific practices, and interest in STEM and biology careers. Students who participated in our experimental evolution module showed improvements in activity-specific concepts, including the importance of variation in evolution and the random nature of mutation. They additionally reported increased confidence in their ability to design a valid biology experiment. Student experimental data replicated literature findings on mechanisms of clotrimazole resistance and has led to new insights into this phenomenon. This collaborative endeavor will serve as a model for other university researchers and K-16 classrooms interested in engaging in open-ended research questions using yeast as a model system.
A Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State
Authors: Radhakrishnan Adityanarayanan , Friedman Sam Freesun , Khurshid Shaan , Ng Kenney , Batra Puneet , Lubitz Steven , Philippakis Anthony , Uhler Caroline 
Published: May 2022

AbstractA fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardio-vascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results provide a framework for integrating distinct diagnostic modalities into a common representation that better characterizes physiologic state.
BioTEA: containerized methods of analysis for microarray-based transcriptomics data
Authors: Visentin Luca , Munaron Luca , Ruffinatti Federico Alessandro 
Published: May 2022

AbstractTens of thousands of gene expression data sets describing a variety of model organisms in a many different pathophysiological conditions are currently stored in publicly available databases such as Gene Expression Omnibus (GEO) and ArrayExpress. As microarray technology is giving way to RNA-Seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring at the same time the reproducibility of the results. To meet this need, here we present bioTEA, a novel software tool that combines the ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options—including gene filtering, batch effect handling, sample pairing, statistical test type—to adapt the algorithm flow to the structure of the particular data set. All these options are saved in single text file which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of analysis. Overall, these features make bioTEA an invaluable tool for both bioiformaticians and wet-lab biologists interested in transcriptomics.
Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics
Authors: Miao Jiacheng , Guo Hanmin , Song Gefei , Zhao Zijie , Hou Lin , Lu Qiongshi 
Published: May 2022

AbstractPolygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting its clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a novel statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs a novel annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 18.7%-122.1% gain in predictive R2 compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic prediction.
Spinal Fusion Properties of Mechanically-Reinforced, Osteomodulatory Chitosan Hydrogels
Authors: Darkow Blake T. , Herbert Joseph P. , Messler Mark J. , Grisolano Abigail , Hemmerla August J. , Kimes Austin D. , Lanza Julien , Sun Yisheng , Crim Julia R. , Stensby Derek , Wan Caixia , Moore Don K. , Ulery Bret D. 
Published: May 2022

AbstractLower back pain is a considerable medical problem that will impact 80% of the U.S. population at some point in their life. For the most severe cases, surgical repair is necessary and is associated with costs upwards of $10.2 billion annually in the United States. To alleviate back pain, spine fusions are a common treatment in which two or more vertebrae are biologically fused together often through the use of a graft material. Unfortunately, iliac crest bone autograft, the current gold standard graft material, can yield insufficient fusion and is associated with considerable donor site morbidity and pain as well as limited supply. Therefore, new materials need to be developed in order to better coordinate healing and new bone growth in the affected area to reduce unnecessary patient burden. In order to address this issue, the incorporation of allograft and one of two types of cellulose (i.e., 0CNCs and CNFs) into a dual-crosslinked chitosan hydrogel loaded with bioactive calcium phosphate was investigated. Hydrogels were then tested for both their material and biological properties. Specifically, hydrogel swelling ratio, mass loss, ion release profile, compressive strength, in vitro biocompatibility and osteoinduction as well as in vivo biocompatibility, and effectiveness in a spine fusion model were determined. Cellulose and allograft incorporation significantly improved hydrogel compressive strength and biocompatibility and CNFs were found to be a significantly more biocompatible form of cellulose than 0CNCs. Additionally, through the controlled delivery of osteoinductive simple signaling molecules (i.e., calcium and phosphate ions), DCF-loaded CNF/Chitosan hydrogels were able to induce osteoblast-like activity in murine mesenchymal stem cells. When evaluated in vivo, these hydrogels were found to be non-toxic though the subacute phase (14 days). A 6-week rabbit spine fusion found these materials to achieve near complete fusion when assessed radiographically. This research provides considerable support for the utility of our novel material for spine fusion procedures as well as other future bone applications.
Effects of source sample amount on biodiversity surveys of bacteria, fungi, and nematodes in soil ecosystems
Authors: Kageyama Takuya , Toju Hirokazu 
Published: May 2022

AbstractBacteria, fungi, and nematodes are major components of soil ecosystems, playing pivotal roles in belowground material cycles and biological community processes. A number of studies have recently uncovered the diversity and community structure of those organisms in various types of soil ecosystems based on DNA metabarcoding (amplicon sequencing). However, because each of the most previous studies examined only one or two of the three organismal groups, it remains an important challenge to reveal the entire picture of soil community structure. We herein examined how we could standardize DNA extraction protocols for simultaneous DNA metabarcoding of bacteria, fungi, and nematodes. Specifically, in an Illumina sequencing analysis of forest and farmland soil samples, we performed DNA extraction at five levels of soil-amount settings (0.5 g, 2 g, 5 g, 10 g, and 20 g). We then found that DNA extraction with the 0.5-g soil setting, which had been applied as default in many commercial DNA extraction kits, could lead to underestimation of α-diversity in nematode community. The detected family-level diversity of nematodes, in particular, increased with increasing soil amounts in DNA extraction. Our analyses also indicated that dissimilarity (β-diversity) of community structure among replicate samples were higher in fungi and nematodes than in bacteria, suggesting difference in basic levels of spatial heterogeneity among the three organismal groups. These findings provide platforms for optimizing DNA metabarcoding protocols commonly applicable to major groups of organisms in soil ecosystems.
Reduced memory precision in older age is associated with functional and structural differences in the angular gyrus
Authors: Korkki S. M. , Richter F. R. , Gellersen H. M. , Simons J. S. 
Published: May 2022

AbstractDecreased fidelity of mnemonic representations plays a critical role in age-related episodic memory deficits, yet the brain mechanisms underlying such reductions remain unclear. Using functional and structural neuroimaging, we examined how changes in two key nodes of the posterior-medial network, the hippocampus and the angular gyrus, might underpin loss of memory precision in older age. Healthy young and older adults completed a memory task that involved reconstructing object features on a continuous scale. Investigation of BOLD activity during retrieval revealed an age-related reduction in activity reflecting successful recovery of object features in the hippocampus, whereas trial-wise modulation of BOLD signal by graded memory precision was diminished in the angular gyrus. Grey matter volume of the angular gyrus further predicted individual differences in memory precision in older age, beyond likelihood of successful retrieval. These findings provide converging evidence for a role of functional and structural integrity of the angular gyrus in constraining the fidelity of episodic remembering in older age, yielding new insights into parietal contributions to age-related episodic memory decline.
The right inferior frontal gyrus as pivotal node and effective regulator of the basal ganglia-thalamocortical response inhibition circuit
Authors: Zhuang Qian , Qiao Lei , Xu Lei , Yao Shuxia , Chen Shuaiyu , Zheng Xiaoxiao , Li Jialin , Fu Meina , Li Keshuang , Vatansever Deniz , Ferraro Stefania , Kendrick Keith M. , Becker Benjamin 
Published: May 2022

AbstractThe involvement of specific basal ganglia-thalamocortical circuits in response inhibition has been extensively mapped in the last few decades. However, the pivotal brain nodes and directed casual regulation within this inhibitory circuit in humans remains controversial. Here, we capitalize on recent progress in robust and biologically plausible directed causal modelling (DCM-PEB) and a large fMRI response inhibition dataset (n=218) to determine key nodes, their causal regulation and modulation via biological variables (sex) and inhibitory performance in the inhibitory control circuit encompassing the right inferior frontal gyrus (rIFG), caudate nucleus (rCau), globus pallidum (rGP) and thalamus (rThal).The entire neural circuit exhibited high intrinsic connectivity and an increasing rIFG inflow and its causal regulation over the rCau and rThal during response inhibition. In addition, sex and behavioral performance influenced the architecture of the regulatory circuits such that women displayed increased rThal self-inhibition and decreased rThal to GP modulation, while better inhibitory performance was associated with stronger rThal to rIFG communication. Furthermore, control analyses did not reveal a similar key communication in a left lateralized model. Together these findings indicate a pivotal role of the rIFG as input and causal regulator of subcortical response inhibition nodes.
Spectral flow cytometry reveals metabolic heterogeneity in tissue macrophages
Authors: Heieis Graham A , Patente Thiago A , Tak Tamar , Almeida Luís , Everts Bart 
Published: May 2022

SummaryTissue-macrophage populations are constituted by a mosaic of phenotypes, yet new methods are needed to link metabolic status to the range of phenotypes in vivo. We therefore designed a high-dimensional panel for spectral flow cytometry to investigate the heterogeneity of tissue macrophage metabolism at steady-state, and their metabolic adaptation in response to infection. Distinct metabolic profiles were observed between tissue macrophages from different peripheral organs, as well as within populations from a specific site. As such, our data show multiple metabolic states in macrophages corresponding to relative stages of maturity in both the peritoneal cavity and small intestine. Immune perturbation with helminth infection further showed that peritoneal macrophages acquire an overall highly metabolically active profile, whereas responding intestinal macrophages displayed minimal changes in their metabolic phenotype. Thus, we demonstrate that high-dimensional, flow-based analysis is an exciting method to interrogate the metabolic heterogeneity and dynamics of tissue-macrophage populations.
LoRTIS Software Suite: Transposon mutant analysis using long-read sequencing
Authors: Lott Martin , Yasir Muhammad , Turner A. Keith , Bastkowski Sarah , Page Andrew , Webber Mark A. , Charles Ian G. 
Published: May 2022

AbstractTo date transposon insertion sequencing (TIS) methodologies have used short-read nucleotide sequencing technology. However, short-read sequences are unlikely to be matched correctly within repeated genomic regions which are longer than the sequence read. This drawback may be overcome using long-read sequencing technology. We have developed a suite of new analysis tools, the “LoRTIS software suite” (LoRTIS-SS), that produce transposon insertion site mapping data for a reference genome using long-read nucleotide sequence data.Long-read nucleotide sequence data can be applied to TIS, this enables the unique mapping of transposon insertion sites within long genomic repeated sequences. Here we present long-read TIS analysis software, LoRTIS-SS, which uses the Snakemake framework to manage the workflow. A docker image is provided, complete with dependencies and ten scripts are included for experiment specific data processing before or after use of the main workflow. The workflow uses long-read nucleotide sequence data such as those generated by the MinION sequencer (Oxford Nanopore Technologies). The unique mapping properties of long-read sequence data were exemplified by reference to the ribosomal RNA genes of Escherichia coli strain BW25113, of which there are 7 copies of ∼4.9 kbases in length that are at least 99% similar. Of reads that matched within rRNA genes, approximately half matched uniquely. The software workflow outputs data compatible with the established Bio-TraDIS analysis toolkit allowing for existing workflows to be easily upgraded to support long-read sequencing.