bluuebunny/crossref_metadata_2025_split huggingface dataset:
TH2BS12P histone mark is enriched in the unsynapsed axes of the XY body and predominantly associates with H3K4me3-containing genomic regions in mammalian spermatocytes
Authors: Mahadevan Iyer Aditya , Pentakota Satyakrishna , Roy Raktim , Bhaduri Utsa , Satyanarayana Rao Manchanahalli R. 
Published: November 2018

AbstractVarious studies have focussed on understanding the repertoire and biological function of the post-translational modifications that occur on testis-specific histone variants like TH2B, Transition Proteins etc. In our attempt to decipher the unique functions of histone variant TH2B, we discovered a new modification Serine 12 phosphorylation on TH2B (TH2BS12P) in spermatocytes. Our present study is aimed at understanding the function of the TH2BS12P modification in the context of processes that occur during meiotic prophase I. Immunofluorescence studies revealed that TH2BS12P histone mark is enriched in the unsynapsed axes of the sex body and is associated with XY body axes associated proteins like Scp3, γH2AX, pATM, ATR etc. We also observe that TH2BS12P is associated with DSB initiator Spo11 and with several recombination related proteins like pATM, ATR, Rad51, γH2AX etcin vivo. This modification was also found to associate with transcription and recombination related histone marks like H3K4me3 and H3K36me3 in the context of mononucleosomes. Genome-wide occupancy studies as determined by ChIP sequencing experiments revealed that TH2BS12P is localised to subset of recombination hotspots, but majorly associated with H3K4me3 containing genomic regions like gene promoters. Mass spectrometry analysis of proteins that bind to TH2BS12P containing mononucleosomes revealed many proteins linked with the functions of pericentric heterochromatin, transcription and recombination related pathways. We propose that TH2BS12P modification could act alone or in concert with other histone marks for recruitment of appropriate transcription or recombination protein machinery at specific genomic loci. This is the first report documenting the role of a post-translational modification of a germ cell specific histone variant in meiotic prophase I related events.
Predicting response to motor therapy in chronic stroke patients using Machine Learning
Authors: Tozlu Ceren , Edwards Dylan , Boes Aaron , Labar Douglas , Tsagaris K. Zoe , Silverstein Joshua , Lane Heather Pepper , Sabuncu Mert R. , Liu Charles , Kuceyeski Amy 
Published: November 2018

AbstractAccurate predictions of motor improvement resulting from intensive therapy in chronic stroke patients is a difficult task for clinicians, but is key in prescribing appropriate therapeutic strategies. Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The first main objective of this study was to use machine learning methods to predict a chronic stroke individual’s motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data. The second main objective was to identify which data elements were most important in predicting chronic stroke patients’ impairment after 6 weeks of intervention. Data from one hundred and two patients (Female: 31%, age 61±11 years) who suffered first ischemic stroke 3-12 months prior were included in this study. After enrollment, patients underwent 6 weeks of intensive motor and transcranial magnetic stimulation therapy. Age, gender, handedness, time since stroke, pre-intervention Fugl-Meyer Assessment, stroke lateralization, the difference in motor threshold between the unaffected and affected hemispheres, absence or presence of motor evoked potential in the affected hemisphere and various imaging metrics were used as predictors of post-intervention Fugl-Meyer Assessment. Five machine learning methods, including Elastic-Net, Support Vector Machines, Artificial Neural Networks, Classification and Regression Trees, and Random Forest, were used to predict post-intervention Fugl-Meyer Assessment based on either demographic, clinical and neurophysiological data alone or in combination with the imaging metrics. Cross-validated R-squared and root of mean squared error were used to assess the prediction accuracy and compare the performance of methods. Elastic-Net performed significantly better than the other methods for the model containing pre-intervention Fugl-Meyer Assessment, demographic, clinical and neurophysiological data as predictors of post-intervention Fugl-Meyer Assessment (). Pre-intervention Fugl-Meyer Assessment and difference in motor threshold between affected and unaffected hemispheres were commonly found as the strongest two predictors in the clinical model. The difference in motor threshold had greater importance than the absence or presence of motor evoked potential in the affected hemisphere. The various imaging metrics, including lesion overlap with the spinal cord, largely did not improve the model performance. The approach implemented here may enable clinicians to more accurately predict a chronic stroke patient’s individual response to intervention. The predictive models used in this study could assist clinicians in making treatment decisions and improve the accuracy of prognosis in chronic stroke patients.
Comparison of three variant callers for human whole genome sequencing
Authors: Supernat Anna , Vidarsson Oskar Valdimar , Steen Vidar M. , Stokowy Tomasz 
Published: November 2018

ABSTRACTTesting of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample.According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively.We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.
Magnetoelectric materials for miniature, wireless neural stimulation at therapeutic frequencies
Authors: Singer Amanda , Dutta Shayok , Lewis Eric , Chen Ziying , Chen Joshua C. , Verma Nishant , Avants Benjamin , Feldman Ariel K. , O’Malley John , Beierlein Michael , Kemere Caleb , Robinson Jacob T. 
Published: November 2018

AbstractA fundamental challenge for bioelectronics is to deliver power to miniature devices inside the body. Wires are common failure points and limit device placement. On the other hand, wireless power by electromagnetic or ultrasound waves must overcome absorption by the body and impedance mismatches between air, bone, and tissue. In contrast, magnetic fields suffer little absorption by the body or differences in impedance at interfaces between air, bone, and tissue. These advantages have led to magnetically-powered stimulators based on induction or magnetothermal effects. However, fundamental limitations in these power transfer technologies have prevented miniature magnetically-powered stimulators from applications in many therapies and disease models because they do not operate in clinical “high-frequency” ranges above 50 Hz. Here we show that magnetoelectric materials – applied in bioelectronic devices – enable miniature magnetically-powered neural stimulators that can operate up to clinically-relevant high-frequencies. As an example, we show that ME neural stimulators can effectively treat the symptoms of a hemi-Parkinson’s disease model in freely behaving rodents. We further demonstrate that ME-powered devices can be miniaturized to mm-sized devices, fully implanted, and wirelessly powered in freely behaving rodents. These results suggest that ME materials are an excellent candidate for wireless power delivery that will enable miniature bioelectronics for both clinical and research applications.
Label-Free Density-Based Detection of Adipocytes of Bone Marrow Origin Using Magnetic Levitation
Authors: Sarigil Oyku , Anil-Inevi Muge , Mese Gulistan , Cumhur Tekin H. , Ozcivici Engin 
Published: November 2018

ABSTRACTAdipocyte hypertrophy and hyperplasia are important parameters in describing abnormalities in adipogenesis that are concomitant to diseases such as obesity, diabetes, anorexia nervosa and osteoporosis. Therefore, technical developments in detection of adipocytes become an important driving factor in adipogenesis research. Current techniques such as optical microscopy and flow cytometry are available in detection and examination of adipocytes, driving cell- and molecular-based research of adipogenesis. Even though microscopy techniques are common and straightforward, they are restricted for manipulation and separation of the cells. Flow cytometry is an alternative, but mature adipocytes are fragile and cannot withstand the flow process. Other separation methods usually require labeling of the cells or usage of microfluidic platforms that utilize fluids with different densities. Magnetic levitation is a novel label-free technology with the principle of movement of cells towards the lower magnetic field in a paramagnetic medium depending on their individual densities. In this study, we used magnetic levitation device for density-based single cell detection of differentiated adipogenic cells in heterogeneous populations. Results showed that magnetic levitation platform was sensitive to changes in lipid content of mesenchymal stem cells committed to adipogenesis and it could be successfully used to detect adipogenic differentiation of cells.
Analysis of cholesterol export from endo-lysosomes by Niemann Pick C2 protein using combined fluorescence and X-ray microscopy
Authors: Dupont Alice , Lund Frederik W. , Jensen Maria Louise V. , Szomek Maria , Nielsen Gitte K. , Heegaard Christian W. , Guttmann Peter , Werner Stephan , McNally James , Schneider Gerd , Kapishnikov Sergey , Wüstner Daniel 
Published: November 2018

AbstractThe Niemann-Pick C2 protein (NPC2) is a sterol transfer protein in late endosomes and lysosomes (LE/LYSs). How its capacity to transport cholesterol between membranes is linked to endo-lysosomal membrane trafficking is not known. Using quantitative fluorescence imaging combined with soft X-ray tomography (SXT); we show that NPC2 mediated sterol efflux is accompanied by large changes in distribution, size and ultrastructure of endocytic organelles. We observed clearance of intra-luminal lipid deposits, a decrease in number of autophagosomes, formation of membrane contact sites (MCSs) to the endoplasmic reticulum and extensive tubulation of LE/LYSs in three-dimensional SXT reconstructions of NPC2 treated human fibroblasts. The cells could recycle the cholesterol analog dehydroergosterol (DHE) from LE/LYSs slowly also in the absence of NPC2 protein but internalized NPC2 synchronized and accelerated this process significantly. Most fluorescent NPC2 was retained in LE/LYSs while DHE was selectively removed from these organelles, at least partially by non-vesicular exchange with other membranes. During sterol efflux LE/LYSs were reallocated to the cell periphery, where they could fuse with newly formed endosomes. Surface shedding of micro-vesicles was found, suggesting a pathway for cellular sterol release. We conclude that NPC2 mediated sterol efflux from LE/LYSs controls membrane traffic through the endo-lysosomal pathway.
Comprehensive profiling of antibodies against multiple infectious diseases in serum and the airway mucosa using synthetic peptide-based linear epitope microarrays
Authors: Sande Charles J , Chege Timothy , Mutunga Martin N , Gicheru Elijah , Nokes D James , Green Christopher A , Tuju James 
Published: November 2018

AbstractBackgroundDespite the considerable progress that has been achieved in the development of vaccines, infectious diseases continue to be the major cause of morbidity and mortality in developing countries. The first year of life remains the time of greatest risk with more than half of all deaths caused by infectious diseases in children under the age of five years occurring during this period. For most infections, antibodies are the strongest immune correlate of protective immunity and characterizing the breadth of the antibody repertoire against infectious diseases in early life is key to profiling individual disease risk.MethodsIn order to comprehensively profile the antibody repertoire against infectious diseases in early life, we developed a synthetic peptide-based microarray to simultaneously measure antibodies against forty one common infectious diseases. 82 empirically-validated linear B-cell epitopes were selected from the Immune Epitope Database (IEBD), expressed as synthetic peptides and printed onto microarray glass slides (microarray chip). The chip was used to simultaneously measure antigen-specific IgG and IgA in serum and mucosal samples from thirty eight infants and young children presenting to hospital with different illnesses. We also used the data from the microarray to estimate antibody decay kinetics for the five most frequently detected antigens during the first year of life.ResultsA synthetic peptide microarray to measure antibodies against forty one infectious diseases was successfully developed. Although the combination of antigens that were recognized were different for each child, antigens derived from Epstein-Barr virus, poliovirus,Streptococcus pneumoniae, plasmodium falciparum and varicella zoster were recognized by most study participants. While the combination of antigens recognized in serum was generally similar as those recognized in mucosal samples, antibodies to antigens such as herpesvirus, rubella and echinococcus were more predominant in either serum or mucosal samples. With the exception of the pneumococcus, we observed a progressive decline in serum IgG specific to all the infections above in the first six months of life. Pneumococcal IgG in serum exhibited a continuous rise in the first six months of life.ConclusionsWe successfully developed a synthetic peptide microarray and used it to profile the diversity of systemic and mucosal antibody against multiple infectious diseases in children. The data from the slide provide a snapshot overview of the breadth and kinetics of infectious disease antibodies early in life and opens the opportunity for conducting in-depth multi, multi-target serological studies of infectious diseases in future.
A highly divergent picornavirus infecting the gut epithelia of zebrafish (Danio rerio) in research institutions world-wide
Authors: Altan Eda , Kubiski Steven V. , Boros Ákos , Reuter Gábor , Sadeghi Mohammadreza , Deng Xutao , Creighton Erica K. , Crim Marcus J. , Delwart Eric 
Published: November 2018

AbstractZebrafish have been extensively used as a model system for research in vertebrate development and pathogen-host interactions. We describe the complete genome of a novel picornavirus identified during a viral metagenomics analysis of zebrafish gut tissue. The closest relatives of this virus showed identity of ≤19.8% in their P1 capsids and ≤35.4% in their RdRp qualifying zebrafish picornavirus 1 (ZfPV1) as member of a novel genus with a proposed name of Cyprivirus. RT-PCR testing of zebrafish from 41 institutions from North America, Europe, and Asia showed ZfPV1 to be highly prevalent world-wide. In situ hybridization of whole zebrafish showed viral RNA was restricted to a subset of enterocytes and cells in the subjacent lamina propria of the intestine and the intestinal mucosa. This naturally occurring and apparently asymptomatic infection (in wild type zebrafish lineage AB) provides a natural infection system to study picornavirus-host interactions in an advanced vertebrate model organism. Whether ZfPV1 infection affects any immunological, developmental or other biological processes in wild type or mutant zebrafish lineages remains to be determined.
High accuracy protein structures from minimal sparse paramagnetic solid-state NMR restraints
Authors: Perez Alberto , Gaalswyk Kari , Jaroniec Christopher P. , MacCallum Justin L. 
Published: November 2018

AbstractThere is a pressing need for new computational tools to integrate data from diverse experimental approaches in structural biology. We present a strategy that combines sparse paramagnetic solid-state NMR restraints with physics-based atomistic simulations. Our approach explicitly accounts for uncertainty in the interpretation of experimental data through the use of a semi-quantitative mapping between the data and the restraint energy that is calibrated by extensive simulations. We apply our approach to solid-state NMR data for the model protein GB1 labeled with Cu2+-EDTA at six different sites. We are able to determine the structure to ca. 1 Å accuracy within a single day of computation on a modest GPU cluster. We further show that in 4 of 6 cases, the data from only a single paramagnetic tag are sufficient to fold the protein to high accuracy.
Glycan cross-feeding drives mutualism betweenFusobacteriumand the vaginal microbiota
Authors: Agarwal Kavita , Robinson Lloyd , Perry Justin , Foster Lynne , Lin Hueylie , Tortelli Brett , O’Brien Valerie P. , Reno Hilary , Gilbert Nicole , Lewis Warren , Lewis Amanda 
Published: November 2018

SummaryDysbiosis of the vaginal microbiome is associated with vaginal colonization by potential pathogens includingFusobacterium nucleatum, a bacterium linked with intrauterine infection and preterm birth. However, mechanisms by which such pathogens gain a foothold in the dysbiotic vagina remain obscure. Here we demonstrate that sialidase activity, a biochemical marker of vaginal dysbiosis, promotedF. nucleatumforaging on mammalian sialoglycans, an otherwise inaccessible resource. In mice with sialidase-positive vaginal microbiomes, mutantF. nucleatumunable to consume sialic acids displayed impaired colonization. Furthermore, community- and co-culture experiments showed thatF. nucleatumdid not simply take advantage of sialidase-positive bacteria, but also “gave back” to the community, supporting robust outgrowth of sialidase-producers, includingGardnerella vaginalis. These results illustrate that mutualistic relationships between vaginal bacteria support pathogen colonization and reinforce dysbiosis, adding complexity to the simplistic dogma that the mere absence of “healthy” lactobacilli is what creates a permissive environment for pathogens during dysbiosis.
A comparison of deep learning and linear-nonlinear cascade approaches to neural encoding
Authors: Moskovitz Theodore H. , Roy Nicholas A. , Pillow Jonathan W. 
Published: November 2018

AbstractA large body of work on neural encoding has focused on “cascade” type models such as the linear-nonlinear-Poisson (LNP) model. This approach seeks to describe the encoding process in terms of a series of stages: (1) projection of the stimulus onto a bank of linear filters; (2) a nonlinear function combining these filter outputs; and (3) a noisy spike generation process. Here we explore the relationship of the LNP modeling framework to more recent approaches arising from the deep learning literature. Specifically, we show that deep neural network (DNN) and convolutional neural network (CNN) models of neural activity sit firmly within the LNP framework, and correspond to particular parametrizations of the nonlinear stage of the LNP model. Using data from primate retina and primary visual cortex, we compare the performance of LNP models fit with deep learning methods to LNP models fit with traditional estimators, including spike-triggered covariance (STC), information-theoretic spike-triggered averaging and covariance (iSTAC), and maximum likelihood estimators also known as “maximally informative dimensions” (MID). We show that models with nonlinearities parametrized by deep networks achieve higher accuracy for a fixed number of filters, and can extract a larger number of informative filters than traditional models. Finally, we perform a dimensionality analysis of LNP models trained with deep learning methods, revealing that a large number of filters are needed to accurately describe the neural responses of many cells even early in the visual pathway. This result overturns one of the central tenets of the LNP modeling framework: that neural computations are low-dimensional, or depend on the stimulus only via its projection onto a small number of linear filters. We discuss the implications of these findings for both the fitting and interpretation of LNP encoding models.
Nanoscale fracture of defective popgraphene monolayer
Authors: Meng Fanchao , Ni Ming , Chen Feng , Song Jun , Wei Dong 
Published: November 2018

ABSTRACTA new carbon allotrope, namely popgraphene, has been recently demonstrated to possess high potentials for nanodevice applications. Here, the fracture of defective popgraphene was studied using molecular dynamics simulations and continuum modeling. Three scenarios of defects were considered, including an individual point defect, distributed point defects, and nanocracks. It was found that the fracture stress of popgraphene with an individual point defect was governed by both the geometry of the defect and the critical bond where fracture initiates. Moreover, the fracture stress of popgraphene with distributed point defects was discovered to be inversely proportional to the defect density, showing a nice linear trend. Furthermore, for popgraphene with a nanocrack, it failed in a brittle fashion and exhibited a negligible lattice trapping effect. Griffith criterion was subsequently employed with the consideration of crack deflection to accurately predict the dependence of fracture stress on crack size. The present study lays a mechanistic foundation for nanoscale applications of popgraphene and offers a better understanding of the roles of defects in fracture of low-dimensional materials.
Cell rounding causes genomic instability by dissociation of single-stranded DNA-binding proteins
Authors: Guo Qian , Liao Xianglu , Wang Xingwu , Liu Ling , Song Bao 
Published: November 2018

AbstractGenomic instability can cause a wide range of diseases, including cancer and cellular senescence, which is also a major challenge in stem cell therapy. However, how a single event can cause extremely high levels of genomic instability remains unclear. Using our developed method, cell in situ electrophoresis (CISE), and models of normal, cancer, and embryonic stem cells, we found that cell rounding as a catastrophic source event ubiquitously observed in vivo and in vitro might lead to large-scale DNA deprotection, genomic instability, chromosomal shattering, cell heterogeneity, and senescent crisis by dissociation of single-stranded DNA-binding proteins (SSBs). Understanding the mechanism may facilitate the development of clinical strategies for cancer therapy, improve the safety of stem cell therapy, and prevent pathological aging.