bluuebunny/crossref_metadata_2025_split huggingface dataset:
A data compendium of Mycobacterium tuberculosis antibiotic resistance
Authors: Brankin Alice , Malone Kerri M ,   The CRyPTIC Consortium
Published: September 2021

AbstractThe Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) presents here a compendium of 15,211 Mycobacterium tuberculosis global clinical isolates, all of which have undergone whole genome sequencing (WGS) and have had their minimum inhibitory concentrations to 13 antitubercular drugs measured in a single assay. It is the largest matched phenotypic and genotypic dataset for M. tuberculosis to date. Here, we provide a summary detailing the breadth of data collected, along with a description of how the isolates were collected and uniformly processed in CRyPTIC partner laboratories across 23 countries. The compendium contains 6,814 isolates resistant to at least one drug, including 2,129 samples that fully satisfy the clinical definitions of rifampicin resistant (RR), multi-drug resistant (MDR), pre-extensively drug resistant (pre-XDR) or extensively drug resistant (XDR). Accurate prediction of resistance status (sensitive/resistant) to eight antitubercular drugs by using a genetic mutation catalogue is presented along with the presence of suspected resistance-conferring mutations for isolates resistant to the newly introduced drugs bedaquiline, clofazimine, delamanid and linezolid. Finally, a case study of rifampicin mono-resistance demonstrates how this compendium could be used to advance our genetic understanding of rare resistance phenotypes. The compendium is fully open-source and it is hoped that the dataset will facilitate and inspire future research for years to come.
Reward enhances memory via age-varying online and offline neural mechanisms across development
Authors: Cohen Alexandra O. , Glover Morgan M. , Shen Xinxu , Phaneuf Camille V. , Avallone Kristen N. , Davachi Lila , Hartley Catherine A. 
Published: September 2021

AbstractReward motivation enhances memory through interactions between mesolimbic, hippocampal, and cortical systems — both during and after encoding. Developmental changes in these distributed neural circuits may lead to age-related differences in reward-motivated memory and the underlying neural mechanisms. Converging evidence from cross-species studies suggests that subcortical dopamine signaling is increased during adolescence, which may lead to stronger memory representations of rewarding, relative to mundane, events and changes in the contributions of underlying subcortical and cortical brain mechanisms across age. Here, we used fMRI to examine how reward motivation influences the “online” encoding and “offline” post-encoding brain mechanisms that support long-term associative memory from childhood to adulthood in human participants of both sexes. We found that reward motivation led to both age-invariant enhancements and nonlinear age-related differences in associative memory after 24 hours. Furthermore, reward-related memory benefits were linked to age-varying neural mechanisms. During encoding, interactions between the prefrontal cortex and ventral tegmental area (VTA) were associated with better high-reward memory to a greater degree with increasing age. Pre- to post-encoding changes in functional connectivity between the anterior hippocampus and VTA were also associated with better high-reward memory, but more so at younger ages. Our findings suggest that there may be developmental differences in the contributions of offline subcortical and online cortical brain mechanisms supporting reward-motivated memory.Significance StatementA substantial body of research has examined the neural mechanisms through which reward influences memory formation in adults. However, despite extensive evidence that both reward processing and associative memory undergo dynamic change across development, few studies have examined age-related changes in these processes. We found both age-invariant and nonlinear age-related differences in reward-motivated memory. Moreover, our findings point to developmental differences in the processes through which reward modulates the prioritization of information in long-term memory – with greater early reliance on offline subcortical consolidation mechanisms and increased contribution of systems-level online encoding circuitry with increasing age. These results highlight dynamic developmental changes in the cognitive and neural mechanisms through which motivationally salient information is prioritized in memory from childhood to adulthood.
The creation of ghost forests is driven by physical, ecological, and disturbance factors but remains a rare phenomenon
Authors: Field Christopher R. 
Published: September 2021

AbstractMarine transgression and the landward migration of coastlines in response to sea-level rise will determine the future of coastal ecosystems and species worldwide, yet no complete model of these processes exist. Ghost forests, areas where coastal forest has been killed by saltwater inundation, are an attention-grabbing early indicator of marine transgression. Research on marine transgression to date has largely been limited to place-based studies that, due to logistical constraints and foci of expertise, investigate subsets of the potential drivers of marine transgression. Here I take advantage of new open source datasets to provide the first systematic analysis of marine transgression across large scales, focusing on the creation of ghost forests across a region of ecological and scientific importance, the northeast U.S. My analysis provides the first synthesis of the physical, ecological, and disturbance factors that influence marine transgression. It also provides crucial regional context for more intensive studies that have a limited geographic scope. I found that ghost forests are a rare phenomenon in the landscape context: 95% of recent forest loss is concentrated in less than 3.86% of marshes, and between 2000 and 2018, only 0.88% of the entire forested area of the transgression zone experienced loss. As a result of this rarity, regional variation in marine transgression is largely driven by opportunities for rare events, which are more numerous when suitable conditions, such as shallow slopes, cover extensive areas. Quantifying recent trends in the rate of forest loss found little evidence of acceleration, with evidence instead suggesting that fewer ghost forests are being created. I also found that physical, ecological, and disturbance factors, including hurricane impacts, were all important for understanding recent trends in forest loss, suggesting that an interdisciplinary approach is warranted for future analyses and modeling of marine transgression. Such interdisciplinary research is urgently needed, as the current rate of marine transgression points to the likelihood of near-term losses of coastal wetlands, with dire implications for the species that depend on them.
Analysis of Multisensory-Motor Integration in Olfactory Navigation of Silkmoth, Bombyx mori, using Virtual Reality System
Authors: Yamada Mayu , Ohashi Hirono , Hosoda Koh , Kurabayashi Daisuke , Shigaki Shunsuke 
Published: September 2021

AbstractMost animals survive and thrive due to navigation behavior to reach their destinations. In order to navigate, it is important for animals to integrate information obtained from multisensory inputs and use that information to modulate their behavior. In this study, by using a virtual reality (VR) system for an insect, we investigated how an adult silkmoth integrates visual and wind direction information during female search behavior (olfactory behavior). According to the behavioral experiments using the VR system, the silkmoth had the highest navigation success rate when odor, vision, and wind information were correctly provided. However, we found that the success rate of the search significantly reduced if wind direction information was provided that was incorrect from the direction actually detected. This indicates that it is important to acquire not only odor information, but also wind direction information correctly. In other words, Behavior was modulated by the degree of co-incidence between the direction of arrival of the odor and the direction of arrival of the wind, and posture control (angular velocity control) was modulated by visual information. We mathematically modeled the modulation of behavior using multisensory information and evaluated it by simulation. As a result, the mathematical model not only succeeded in reproducing the actual female search behavior of the silkmoth, but can also improve search success relative to the conventional odor source search algorithm.
Aberrant epigenetic and transcriptional events associated with breast cancer risk
Authors: Marino Natascia , German Rana , Podicheti Ram , Rush Douglas B. , Rockey Pam , Huang Jie , Sandusky George E. , Temm Constance J. , Althouse Sandra K. , Nephew Kenneth P. , Nakshatri Harikrishna , Liu Jun , Vode Ashley , Cao Sha , Storniolo Anna Maria 
Published: September 2021

ABSTRACTBackgroundGenome-wide association studies have identified several breast cancer susceptibility loci. However, biomarkers for risk assessment are still missing. Here, we investigated cancer-related molecular changes detected in tissues from women at high risk for breast cancer prior to disease manifestation. Disease-free breast tissue cores donated by healthy women (N=146, median age=39 years) were processed for both methylome (MethylCap) and transcriptome (Illumina’s HiSeq4000) sequencing. Analysis of tissue microarray and primary breast epithelial cells was used to confirm gene expression dysregulation.ResultsTranscriptomic analysis identified 69 differentially expressed genes between women at either high and those at average risk of breast cancer (Tyrer-Cuzick model) at FDR<0.05 and fold change≥2. The majority of the identified genes were involved in DNA damage checkpoint, cell cycle, and cell adhesion. Two genes, FAM83A and NEK2, were overexpressed in tissue sections (FDR<0.01) and primary epithelial cells (p<0.05) from high-risk breasts. Moreover, 1698 DNA methylation aberrations were identified in high-risk breast tissues (FDR<0.05), partially overlapped with cancer-related signatures and correlated with transcriptional changes (p<0.05, r≤0.5). Finally, among the participants, 35 women donated breast biopsies at two time points, and age-related molecular alterations enhanced in high-risk subjects were identified.ConclusionsNormal breast tissue from women at high risk of breast cancer bears molecular aberrations that may contribute to breast cancer susceptibility. This study is the first molecular characterization of the true normal breast tissues and provides an opportunity to investigate molecular markers of breast cancer risk, which may lead to new preventive approaches.
Sensitivity of discrete symmetry metrics: implications for metric choice
Authors: Hill Allen D , Nantel Julie 
Published: September 2021

AbstractGait asymmetry is present in several pathological populations, including those with Parkinson’s disease, Huntington’s disease, and stroke survivors. Previous studies suggest that commonly used discrete symmetry metrics, which compare single bilateral variables, may not be equally sensitive to underlying effects of asymmetry, and the use of a metric with low sensitivity could result in unnecessarily low statistical power. The purpose of this study was to provide a comprehensive assessment of the sensitivity of commonly used discrete symmetry metrics to better inform design of future studies. Monte Carlo simulations were used to estimate the statistical power of each symmetry metric at a range of asymmetry magnitudes, group/condition variabilities, and sample sizes. Power was estimated by repeated comparison of simulated symmetric and asymmetric data with a paired t-test, where the proportion of significant results is equivalent to the power. Simulation results confirmed that not all common discrete symmetry metrics are equally sensitive to reference effects of asymmetry. Multiple symmetry metrics exhibit equivalent sensitivities, but the most sensitive discrete symmetry metric in all cases is a bilateral difference (e.g. left - right). A ratio (e.g. left/right) has poor sensitivity when group/condition variability is not small, but a log-transformation produces increased sensitivity. Additionally, two metrics which included an absolute value in their definitions showed increased sensitivity when the absolute value was removed. Future studies should consider metric sensitivity when designing analyses to reduce the possibility of underpowered research.Summary statementStatistical power is an important factor in study design. Our results show that not all discrete symmetry metrics have similar or sufficient sensitivity to detect effects of asymmetry.
Naïve andin vitro-activated primary mouse CD8+T cells retainin vivoimmune responsiveness after electroporation-based CRISPR/Cas9 genetic engineering
Authors: Pfenninger Petra , Yerly Laura , Abe Jun 
Published: September 2021

AbstractCRISPR/Cas9 technology has revolutionized genetic engineering of primary cells. Although its use is gaining momentum in studies on CD8+T cell biology, it remains elusive to what extent CRISPR/Cas9 affectsin vivofunction of CD8+T cells. Here, we optimized nucleofection-based CRISPR/Cas9 genetic engineering of naïve andin vitro-activated primary mouse CD8+T cells and tested theirin vivoimmune responses. Nucleofection of naïve CD8+T cells preserved theirin vivoantiviral immune responsiveness to an extent that is indistinguishable from non-nucleofected cells, whereasin vitroactivation of CD8+T cells prior to nucleofection led to slightly impaired expansion/survival. Of note, different target proteins displayed distinct decay rates after gene editing. This is in stark contrast to a comparable period of time required to complete gene inactivation. Thus, for optimal experimental design, it is crucial to determine the kinetics of the loss of target gene product to adapt incubation period after gene editing. In sum, nucleofection-based CRISPR/Cas9 genome editing achieves efficient and rapid generation of mutant CD8+T cells without imposing detrimental constraints on theirin vivofunctions.
Examining Batch Effect in Histopathology as a Distributionally Robust Optimization Problem
Authors: Hari Surya Narayanan , Nyman Jackson , Mehta Nicita , Elmarakeby Haitham , Jiang Bowen , Dietlein Felix , Rosenthal Jacob , Sengupta Eshna , Chowdhury Alexander , Umeton Renato , Van Allen Eliezer M. 
Published: September 2021

AbstractComputer vision (CV) approaches applied to digital pathology have informed biological discovery and development of tools to help inform clinical decision-making. However, batch effects in the images have the potential to introduce spurious confounders and represent a major challenge to effective analysis and interpretation of these data. Standard methods to circumvent learning such confounders include (i) application of image augmentation techniques and (ii) examination of the learning process by evaluating through external validation (e.g., unseen data coming from a comparable dataset collected at another hospital). Here, we show that the source site of a histopathology slide can be learned from the image using CV algorithms in spite of image augmentation, and we explore these source site predictions using interpretability tools. A CV model trained using Empirical Risk Minimization (ERM) risks learning this source-site signal as a spurious correlate in the weak-label regime, which we abate by using a training method with abstention. We find that a patch based classifier trained using abstention outperformed a model trained using ERM by 9.9, 10 and 19.4% F1 in the binary classification tasks of identifying tumor versus normal tissue in lung adenocarcinoma, Gleason score in prostate adenocarcinoma, and tumor tissue grade in clear cell renal cell carcinoma, respectively, at the expense of up to 80% coverage (defined as the percent of tiles not abstained on by the model). Further, by examining the areas abstained by the model, we find that the model trained using abstention is more robust to heterogeneity, artifacts and spurious correlates in the tissue. Thus, a method trained with abstention may offer novel insights into relevant areas of the tissue contributing to a particular phenotype. Together, we suggest using data augmentation methods that help mitigate a digital pathology model’s reliance on potentially spurious visual features, as well as selecting models that can identify features truly relevant for translational discovery and clinical decision support.
Integrating Transcriptomics, Genomics, and Imaging in Alzheimer’s Disease: A Federated Model
Authors: Wu Jianfeng , Chen Yanxi , Wang Panwen , Caselli Richard J , Thompson Paul M , Wang Junwen , Wang Yalin ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: September 2021

AbstractAlzheimer’s disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics – the study of gene expression – also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person’s genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.
A genetic trap in yeast for inhibitors of SARS-CoV-2 main protease
Authors: Alalam Hanna , Sigurdardóttir Sunniva , Bourgard Catarina , Tiukova Ievgeniia , King Ross D. , Grøtli Morten , Sunnerhagen Per 
Published: September 2021

ABSTRACTThe ongoing COVID-19 pandemic urges searches for antiviral agents that can block infection or ameliorate its symptoms. Using dissimilar search strategies for new antivirals will improve our overall chances of finding effective treatments. Here, we have established an experimental platform for screening of small molecule inhibitors of SARS-CoV-2 main protease in Saccharomyces cerevisiae cells, genetically engineered to enhance cellular uptake of small molecules in the environment. The system consists of a fusion of the E. coli toxin MazF and its antitoxin MazE, with insertion of a protease cleavage site in the linker peptide connecting the MazE and MazF moieties. Expression of the viral protease confers cleavage of the MazEF fusion, releasing the MazF toxin from its antitoxin, resulting in growth inhibition. In the presence of a small molecule inhibiting the protease, cleavage is blocked and the MazF toxin remains inhibited, promoting growth. The system thus allows positive selection for inhibitors. The engineered yeast strain is tagged with a fluorescent marker protein, allowing precise monitoring of its growth in the presence or absence of inhibitor. We detect an established main protease inhibitor down to 10 μM by a robust growth increase. The system is suitable for robotized large-scale screens. It allows in vivo evaluation of drug candidates, and is rapidly adaptable for new variants of the protease with deviant site specificities.IMPORTANCEThe COVID-19 pandemic may continue several years before vaccination campaigns can put an end it globally. Thus, the need for discovery of new antiviral drug candidates will remain. We have engineered a system in yeast cells for detection of small molecule inhibitors of one attractive drug target of SARS-CoV-2, its main protease which is required for viral replication. To detect inhibitors in live cells brings the advantage that only compounds capable of entering the cell and remain stable there, will score in the system. Moreover, by its design in yeast, the system is rapidly adaptable for tuning of detection level, eventual modification of protease cleavage site in case of future mutant variants of the SARS-CoV-2 main protease, or even for other proteases.
Little hope for the polyploid endemic Pyrenean Larkspur (Delphinium montanum): evidences from population genomics and Ecological Niche Modelling
Authors: Salvado Pascaline , Aymerich Boixader Pere , Parera Josep , Bonfill Albert Vila , Martin Maria , Quélennec Céline , Lewin Jean-Marc , Delorme-Hinoux Valérie , Bertrand Joris A. M. 
Published: September 2021

AbstractSpecies endemic to restricted geographical ranges represent a particular conservation issue, be it for their heritage interest. In a context of global change, this is particularly the case for plants which belong to high-mountain ecosystems and, because of their ecological requirements, are doomed to survive or disappear on their ‘sky islands’. The Pyrenean Larkspur (Delphinium montanum, Ranunculaceae) is endemic to the Eastern part of the Pyrenees (France and Spain). It is now only observable at a dozen of localities and some populations show signs of decline, such as a recurrent lack of flowering. Implementing population genomic approach (e.g. RAD-seq like) is particularly useful to understand genomic patterns of diversity and differentiation in order to provide recommendations in term of conservation. However, it remains challenging for species such asD. montanumthat are autotetraploid with a large genome size (1C-value > 10 pg) as most methods currently available were developed for diploid species. A Bayesian framework able to call genotypes with uncertainty allowed us to assess genetic diversity and population structure in this system. Our results show evidence for inbreeding (meanGIS= 0.361) within all the populations and substantial population structure (meanGST= 0.403) at the metapopulation level. In addition to a lack of connectivity between populations, spatial projections of Ecological Niche Modelling analyses under different climatic scenarios predict a dramatic decrease of suitable habitat forD. montanumin the future. Based on these results, we discuss the relevance and feasibility of different conservation measures.