bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation of parental effects using polygenic scores
Authors: Balbona Jared , Kim Yongkang , Keller Matthew C. 
Published: August 2020

AbstractOffspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents’ nontransmitted alleles, one can estimate the effect of “genetic nurture”— a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to a) account for the influence of assortative mating at both equilibrium and disequilibrium (after a single generation of assortment), and b) include measured parental phenotypes, allowing for the estimation of the total variation due to additive genetic effects and their covariance with the familial environment. By utilizing path analysis techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.
Two pathways drive meiotic chromosome axis assembly inSaccharomyces cerevisiae
Authors: Heldrich Jonna , Milano Carolyn R. , Markowitz Tovah E. , Ur Sarah N. , Vale-Silva Luis A. , Corbett Kevin D. , Hochwagen Andreas 
Published: August 2020

ABSTRACTSuccessful meiotic recombination, and thus fertility, depends on conserved axis proteins that organize chromosomes into arrays of anchored chromatin loops and provide a protected environment for DNA exchange. Here, we show that the stereotypic chromosomal distribution of axis proteins inS. cerevisiaeis the additive result of two independent pathways: a cohesin-dependent pathway, which was previously identified and mediates focal enrichment of axis proteins at gene ends, and a parallel cohesin-independent pathway that recruits axis proteins to broad genomic islands with high gene density. These islands exhibit elevated markers of crossover recombination as well as increased nucleosome density, which we show is a direct consequence of the underlying DNA sequence. A predicted PHD domain in the center of the axis factor Hop1 specifically mediates cohesin-independent axis recruitment. Intriguingly, other chromosome organizers, including cohesin, condensin, and topoisomerases, are differentially depleted from the same regions even in non-meiotic cells, indicating that these DNA sequence-defined chromatin islands exert a general influence on the patterning of chromosome structure.
Elicitation of potent neutralizing antibody responses by designed protein nanoparticle vaccines for SARS-CoV-2
Authors: Walls Alexandra C. , Fiala Brooke , Schäfer Alexandra , Wrenn Samuel , Pham Minh N. , Murphy Michael , Tse Longping V. , Shehata Laila , O'Connor Megan A. , Chen Chengbo , Navarro Mary Jane , Miranda Marcos C. , Pettie Deleah , Ravichandran Rashmi , Kraft John C. , Ogohara Cassandra , Palser Anne , Chalk Sara , Lee E-Chiang , Kepl Elizabeth , Chow Cameron M. , Sydeman Claire , Hodge Edgar A. , Brown Brieann , Fuller Jim T. , Dinnon Kenneth H. , Gralinski Lisa E. , Leist Sarah R. , Gully Kendra L. , Lewis Thomas B. , Guttman Miklos , Chu Helen Y. , Lee Kelly K. , Fuller Deborah H. , Baric Ralph S. , Kellam Paul , Carter Lauren , Pepper Marion , Sheahan Timothy P. , Veesler David , King Neil P. 
Published: August 2020

SUMMARYA safe, effective, and scalable vaccine is urgently needed to halt the ongoing SARS-CoV-2 pandemic. Here, we describe the structure-based design of self-assembling protein nanoparticle immunogens that elicit potent and protective antibody responses against SARS-CoV-2 in mice. The nanoparticle vaccines display 60 copies of the SARS-CoV-2 spike (S) glycoprotein receptor-binding domain (RBD) in a highly immunogenic array and induce neutralizing antibody titers roughly ten-fold higher than the prefusion-stabilized S ectodomain trimer despite a more than five-fold lower dose. Antibodies elicited by the nanoparticle immunogens target multiple distinct epitopes on the RBD, suggesting that they may not be easily susceptible to escape mutations, and exhibit a significantly lower binding:neutralizing ratio than convalescent human sera, which may minimize the risk of vaccine-associated enhanced respiratory disease. The high yield and stability of the protein components and assembled nanoparticles, especially compared to the SARS-CoV-2 prefusion-stabilized S trimer, suggest that manufacture of the nanoparticle vaccines will be highly scalable. These results highlight the utility of robust antigen display platforms for inducing potent neutralizing antibody responses and have launched cGMP manufacturing efforts to advance the lead RBD nanoparticle vaccine into the clinic.
Phylogeography and population genetic structure of red muntjacs: Evidence of enigmatic Himalayan red muntjac from India
Authors: Singh Bhim , Kumar Ajit , Uniyal Virendra Prasad , Gupta Sandeep Kumar 
Published: August 2020

AbstractWe investigated the phylogeographic pattern of red muntjac across its distribution range, intending to address the presence of distinct lineages from Northwest India. The Complete mitogenome analysis revealed that India holds three mitochondrial lineages of red muntjac, whereas four were identified from its entire distribution range: Himalayan red muntjac (M. (m.) aureus), Northern red muntjac (M. vaginalis), Srilankan and Western Ghat India (M. malabaricus) and Southern red muntjac (M. muntjak) from Sundaland. The newly identified Himalayan red muntjac found in the Northwestern part of India, which was previously described based on their morphological differences. Estimates of the divergence dating indicate that the Northwest and Northern lineage split during the late Pleistocene approximately 0.83 Myr (CI95%:0.53 to 1.26), which is the younger lineages, whereasM. malabaricusis the most primitive lineage among all the red muntjac. Microsatellite results also supported the mitochondrial data and evident the presence of three distinct genetic clusters within India. The pronounced climate fluctuation during the Quaternary period was considered as a critical factor influencing the current spatial distribution of forest-dwelling species that restricted themselves in northwest areas. Based on molecular data, this study provided evidence of a new lineage within the red muntjac group from India that required to be managed as an evolutionary significant unit (ESUs). It highlighted a need for the taxonomic revision of Himalayan red muntjac (M. (m.) aureus) and also suggested its conservation status under IUCN Red List.
DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network
Authors: Chen Cheng , Shi Han , Han Yu , Jiang Zhiwen , Cui Xuefeng , Yu Bin 
Published: August 2020

ABSTRACTResearch, analysis, and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad, composition, transition and distribution, Moreau-Broto autocorrelation, and structure feature. Then, the drug compounds are encoded using substructure fingerprint. Next, we utilize XGBoost to determine nonredundant and important feature subset, then the optimized and balanced sample vectors could be obtained through SMOTE. Finally, a DTIs predictor, DNN-DTIs, is developed based on deep neural network (DNN) via layer-by-layer learning. Experimental results indicate that DNN-DTIs achieves outstanding performance than other predictors with the ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's dataset. Therefore, DNN-DTIs's accurate prediction performance on Network1 and Network2 make it logical choice for contributing to the study of DTIs, especially, the drug repositioning and new usage of old drugs.
Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG
Authors: Shamsi Foroogh , Haddad Ali , Najafizadeh Laleh 
Published: August 2020

AbstractObjectiveClassification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.ApproachThe proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification.Main resultsFeatures extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% about only 500 ms after stimulus presentation.SignificanceOur results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms).This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.
Deep and superficial layers of the primary somatosensory cortex are critical for whisker-based texture discrimination in mice
Authors: Park Jung M , Hong Y Kate , Rodgers Chris C , Dahan Jacob B , Harano Nina , Schmidt Ewoud RE , Bruno Randy M 
Published: August 2020

SummaryThe neocortex, comprised of multiple distinct layers, processes sensory input from the periphery, makes decisions, and executes actions. Despite extensive investigation of cortical anatomy and physiology, the contributions of different cortical layers to sensory guided behaviors remain unknown. Here, we developed a two-alternative forced choice (2AFC) paradigm in which head-fixed mice use a single whisker to either discriminate textures of parametrically varied roughness or detect the same textured surfaces. Lesioning the barrel cortex revealed that 2AFC texture discrimination, but not detection, was cortex-dependent. Paralyzing the whisker pad had little effect on performance, demonstrating that passive can rival active perception and cortical dependence is not movement-related. Transgenic Cre lines were used to target inhibitory opsins to excitatory cortical neurons of specific layers for selective perturbations. Both deep and superficial layers were critical for texture discrimination. We conclude that even basic cortical computations require coordinated transformation of sensory information across layers.Abstract Figure
Src-dependent NM2A tyrosine-phosphorylation regulates actomyosin dynamics
Authors: Brito Cláudia , Mesquita Francisco S. , Osório Daniel S. , Pereira Joana Maria , Billington Neil , Sellers James R. , Carvalho Ana X. , Cabanes Didier , Sousa Sandra 
Published: August 2020

AbstractNon-muscle myosin 2A (NM2A) is a key cytoskeletal enzyme that along with actin assembles into actomyosin filaments inside cells. NM2A is fundamental in cellular processes requiring force generation such as cell adhesion, motility and cell division, and plays important functions in different stages of development and during the progression of viral and bacterial infections. We previously identified a novel tyrosine phosphorylation on residue 158 (pTyr158) in the motor domain of NM2A. This phosphorylation is dependent on Src kinase and is promoted byListeria monocytogenesinfection of epithelial cells, however its role is unknown. Here we show that Listeriolysin O (LLO), the pore-forming toxin (PFT) secreted byL. monocytogenes, is sufficient to trigger NM2A pTyr158by activating Src, an upstream regulator of actomyosin remodeling. We further address the role of NM2A pTyr158on the organization and dynamics of the actomyosin cytoskeleton and find that, by controlling the activation of the NM2A, the status of the pTyr158alters cytoskeletal organization, dynamics of focal adhesions and cell motility.In vitro, we observe that non-phosphorylatable and phospho-mimetic versions of NM2A at Tyr158display motor and ATPase activities similar to the wild-type NM2A, which indicates that the phenotype of these mutants in cells is independent of their ability to translocate actin filaments. Importantly, we find the regulation of this phosphorylation site to be of physiological relevance inCaenorhabditis elegans, in particular in response to intoxication by a PFT and to heat shock. We conclude that the control of the phosphorylation status at NM2A Tyr158is a conserved trait that contributes to the regulation of actomyosin dynamics and the ability of cells to respond to bacterial infection. We propose Src-dependent NM2A pTyr158as a novel layer of regulation of the actomyosin cytoskeleton.
Multimodal Label-free Monitoring of Adipogenic Stem Cell Differentiation using Endogenous Optical Biomarkers
Authors: Mehta Nishir , Shaik Shahensha , Prasad Alisha , Chaichi Ardalan , Sahu Sushant P. , Hasan Syed Mohammad Abid , Donnarumma Fabrizio , Murray Kermit K. , Devireddy Ram , Gartia Manas Ranjan 
Published: August 2020

ABSTRACTStem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective therapy requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (< 0.001% of the number of cells). Here, we report a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping (SAM)-based machine learning analysis to distinguish differentiating human adipose derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enabled identification of differentiated cells at single cell resolution. The label-free HSI method was compared with the standard methods such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. Further, we performed Raman microscopy and multiphoton-based metabolic imaging to provide complimentary information for the functional imaging of the hASCs. Finally, the HSI method was validated using matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution. This could provide a powerful tool to assess the safety and efficacy of stem cell-based regenerative therapies.
The Cell Wall Integrity MAPK pathway controls actin cytoskeleton assembly during fungal somatic cell fusion
Authors: Serrano Antonio , Hammadeh Hamzeh H. , Schwarz Natalie , Brandt Ulrike , Fleißner André 
Published: August 2020

AbstractSomatic cell fusion is widely studied in the filamentous fungusNeurospora crassa. The interaction of genetically identical germlings is mediated by a signaling mechanism in which the cells take turns in signal-sending and receiving. The switch between these physiological states is represented by the alternating membrane recruitment of the SO protein and the MAPK MAK-2. This dialog-like behavior is observed until the cells establish physical contact, when the cell-wall-integrity MAK-1 is recruited to the contact area to control the final steps of the cell fusion process. This work revealed, for the first-time, an additional MAK-1-function during the tropic growth phase. Specific inhibition of MAK-1 during tropic-growth resulted in disassembly of the actin-aster, and mislocalization of SO and MAK-2. Similar defects were observed after the inhibition of the Rho-GTPase RAC-1, suggesting a functional link between them, being MAK-1 upstream of RAC-1. In contrast, after inhibition of MAK-2, the actin-aster stayed intact, however, its subcellular localization became instable within the cell-membrane. Together these observations led to a new working model, in which MAK-1 promotes the formation and stability of the actin-aster, while MAK-2 controls its positionning and cell growth directionality.Summary statementThe CWI MAPK MAK-1 pathway controls actin cytoskeleton assembly at the cell tips through activation of the Rho-GTPase RAC-1 exclusively on somatic cell fusion.
DeepPSC (protein structure camera): computer vision-based protein backbone structure reconstruction from alpha carbon trace as a case study
Authors: Zhang Xing , Luo Junwen , Cai Yi , Zhu Wei , Yang Xiaofeng , Cai Hongmin , Lin Zhanglin 
Published: August 2020

AbstractDeep learning has been increasingly used in protein tertiary structure prediction, a major goal in life science. However, all the algorithms developed so far mostly use protein sequences as input, whereas the vast amount of protein tertiary structure information available in the Protein Data Bank (PDB) database remains largely unused, because of the inherent complexity of 3D data computation. In this study, we propose Protein Structure Camera (PSC) as an approach to convert protein structures into images. As a case study, we developed a deep learning method incorporating PSC (DeepPSC) to reconstruct protein backbone structures from alpha carbon traces. DeepPSC outperformed all the methods currently available for this task. This PSC approach provides a useful tool for protein structure representation, and for the application of deep learning in protein structure prediction and protein engineering.
Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ)
Authors: Balbuena Juan A. , Montlleó Clara , Llopis-Belenguer Cristina , Blasco-Costa Isabel , Sarabeev Volodimir L. , Morand Serge 
Published: August 2020

Abstract1. Most species in ecological communities are rare whereas only a few are common. This distributional paradox has intrigued ecologists for decades but the interpretation of species abundance distributions remains elusive.2. We present Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ) as an R package. FuzzyQ shifts the focus from the prevailing species-categorization approach to develop a quantitative framework that seeks to place each species along a rare-commonness gradient. Given a community surveyed over a number of sites, quadrats, or any other convenient sampling unit, FuzzyQ uses a fuzzy clustering algorithm that estimates a probability for each species to be common or rare based on abundance-occupancy information. Such as probability can be interpreted as a commonness index ranging from 0 to 1. FuzzyQ also provides community-level metrics about the coherence of the allocation of species into the common and rare clusters that are informative of the nature of the community under study.3. The functionality of FuzzyQ is shown with two real datasets. We demonstrate how FuzzyQ can effectively be used to monitor and model spatio-temporal changes in species commonness, and assess the impact of species introductions on ecological communities. We also show that the approach works satisfactorily with a wide range of communities varying in species richness, dispersion and abundance currencies.4. FuzzyQ produces ecological indicators easy to measure and interpret that can give both clear, actionable insights into the nature of ecological communities and provides a powerful way to monitor environmental change on ecosystems. Comparison among communities is greatly facilitated by the fact that the method is relatively independent of the number of sites or sampling units considered. Thus, we consider FuzzyQ as a potentially valuable analytical tool in community ecology and conservation biology.
Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis
Authors: Wang Shixiang , Tao Ziyu , Wu Tao , Liu Xue-Song 
Published: August 2020

AbstractSummaryMutational signatures are recurring DNA alteration patterns caused by distinct mutational events during the evolution of cancer. In recent years, several bioinformatics tools are available for mutational signature analysis. However, most of them focus on specific type of mutation, or have limited scope of application. A pipeline tool for comprehensive mutational signature analysis is still lacking. Here we present Sigflow pipeline, which provides an one-stop solution forde novosignature extraction, reference signature fitting, signature stability analysis, sample clustering based on signature exposure in different types of genome DNA alterations including single base substitution (SBS), doublet base substitution (DBS), small insertion and deletion (INDEL), and copy number alteration. A Docker image is constructed to solve the complex and time-consuming installation issues, and this enables reproducible research by version control of all dependent tools along with their environments. The Sigflow pipeline can be applied to both human and mouse genomes.Availability and implementationSigflow is an open source software under academic free license (AFL) v3.0 and it is freely available athttps://github.com/ShixiangWang/sigminer.workfloworhttps://hub.docker.com/r/shixiangwang/sigflow.Contactliuxs@shanghaitech.edu.cn