bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder
Authors: Salminen Lauren E. , Morey Rajendra A. , Riedel Brandalyn C. , Jahanshad Neda , Dennis Emily L. , Thompson Paul M. 
Published: November 2018

ABSTRACTBackground and PurposePosttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here we used a novel machine learning method - evolving partitions to improve classification (EPIC) - to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans.MethodsWe used EPIC with repeated cross-validation to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n=40) versus controls (n=57), and classification of ELS within the PTSD (ELS+ n=16; ELS-n=24) and control groups (ELS+ n=16; ELS- n=41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression.ResultsOn average, EPIC classified PTSD with 69% accuracy (SD=5%), and ELS with 64% accuracy in the PTSD group (SD=10%), and 62% accuracy in controls (SD=6%). EPIC selected unique sets of individual features that classified each group with 75-85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS.ConclusionsEPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.
Bottom-up sensory processing can decrease activity and functional connectivity in the default mode like network in rats
Authors: Hinz Rukun , Peeters Lore M. B. , Shah Disha , Missault Stephan , Belloy Michaël , Vanreusel Verdi , Malekzadeh Meriam , Verhoye Marleen , Van der Linden Annemie , Keliris Georgios A. 
Published: November 2018

AbstractThe default mode network is a large-scale brain network that is active during rest and internally focused states and deactivates as well as desynchronizes during externally oriented (top-down) attention demanding cognitive tasks. However, it is not sufficiently understood if unpredicted salient stimuli, able to trigger bottom-up attentional processes, could also result in similar reduction of activity and functional connectivity in the DMN. In this study, we investigated whether bottom-up sensory processing could influence the default mode like network (DMLN) in rats. DMLN activity was examined using block-design visual functional magnetic resonance imaging (fMRI) while its synchronization was investigated by comparing functional connectivity during a resting versus a continuously stimulated brain state by unpredicted light flashes. We demonstrated that activity in DMLN regions was decreased during visual stimulus blocks and increased during blanks. Furthermore, decreased inter-network functional connectivity between the DMLN and visual networks as well as decreased intra-network functional connectivity within the DMLN was observed during the continuous visual stimulation. These results suggest that triggering of bottom-up attention mechanisms in anesthetized rats can lead to a cascade similar to top-down orienting of attention in humans and is able to deactivate and desynchronize the DMLN.
Evaluating the Genome and Resistome of Extensively Drug-Resistant Klebsiella pneumoniae using Native DNA and RNA Nanopore Sequencing
Authors: Pitt Miranda E. , Nguyen Son H. , Duarte Tânia P.S. , Blaskovich Mark A.T. , Cooper Matthew A. , Coin Lachlan J.M. 
Published: November 2018

ABSTRACTKlebsiella pneumoniae frequently harbour multidrug resistance and current methodologies are struggling to rapidly discern feasible antibiotics to treat these infections. While rapid DNA sequencing has been proposed for prediction of resistance profile; the role of rapid RNA sequencing has yet to be fully explored. The MinION sequencer can sequence native DNA and RNA in real-time, providing an opportunity to contrast the utility of DNA and RNA for prediction of drug susceptibility. This study interrogated the genome and transcriptome of four extensively drug-resistant (XDR) K. pneumoniae clinical isolates. The majority of acquired resistance (≥75%) resided on plasmids including several megaplasmids (≥100 kbp). DNA sequencing identified most resistance genes (≥70%) within 2 hours of sequencing. Direct RNA sequencing (with a ∼6x slower pore translocation) was able to identify ≥35% of resistance genes, including aminoglycoside, β-lactam, trimethoprim and sulphonamide and also quinolone, rifampicin, fosfomycin and phenicol in some isolates, within 10 hours of sequencing. Polymyxin-resistant isolates showed a heightened transcription of phoPQ (≥2-fold) and the pmrHFIJKLM operon (≥8-fold). Expression levels estimated from direct RNA sequencing displayed strong correlation (Pearson: 0.86) compared to qRT-PCR across 11 resistance genes. Overall, MinION sequencing rapidly detected the XDR K. pneumoniae resistome and direct RNA sequencing revealed differential expression of these genes.
Active Receptor Tyrosine Kinases, but not Brachyury, are sufficient to trigger chordoma in zebrafish
Authors: D‘Agati Gianluca , Cabello Elena María , Frontzek Karl , Rushing Elisabeth J. , Klemm Robin , Robinson Mark D. , White Richard M. , Mosimann Christian , Burger Alexa 
Published: November 2018

AbstractThe aberrant activation of developmental processes triggers diverse cancer types. Chordoma is a rare, aggressive tumor arising from transformed notochord remnants. Several potentially oncogenic factors, including several receptor tyrosine kinase (RTK) genes, have been found deregulated in chordoma, yet causation remains uncertain. In particular, sustained expression of the developmental notochord transcription factor Brachyury is hypothesized as key driver of chordoma; nonetheless, experimental evidence for an oncogenic role of Brachyury in chordoma and its prognostic impact remains missing. Here, we developed and applied a zebrafish chordoma model to identify the notochord-transforming potential of implicated genesin vivo. We find that Brachyury, including a form with augmented transcriptional activity, is insufficient to initiate notochord hyperplasia. In contrast, the chordoma-implicated RTKs EGFR and KDR/VEGFR2 are sufficient to transform notochord cells within two to five days of development. Transcriptome and structural analysis of transformed notochords revealed that the aberrant activation of RTK/Ras signaling attenuates processes required for notochord differentiation, including of the unfolded protein response. Our results provide firstin vivoindication against a tumor-initiating potential of Brachyury in the notochord, and imply activated RTK signaling as possibly initiating event in chordoma. These results provide a mechanistic framework for the pursuit of chemotherapeutic compounds to combat this aggressive tumor type.
Integrative isotopic Paleoecology (δ13C, δ18O) of a Late Pleistocene vertebrate community from Sergipe, NE Brazil
Authors: Trindade Dantas Mário André , Cherkinsky Alexander , Lessa Carlos Micael Bonfim , Santos Luciano Vilaboim , Cozzuol Mario Alberto , Omena Érica Cavalcante , Silva Jorge Luiz Lopes da , Sial Alcides Nóbrega , Bocherens Hervé 
Published: November 2018

AbstractIsotopes are one of the best tools to reconstruct the Paleoecology of extinct taxa, yielding insights about their diet (through carbon; C3and C4plants), niche breadth (BA) and the environment in which they lived. In the present work we go deeper in the use of isotopes and explore a mathematical mixing model with the stable isotopes of two elements (carbon and oxygen) to (1) suggest the relative contribution of four types of food resources (leaves, fruits, roots and C4grass) for meso- and megaherbivores (weight > 100 kg) that lived in the Late Pleistocene of Poço Redondo, Sergipe, Brasil, and (2) evaluate which of these herbivores could be the potential prey for the carnivoresSmilodon populatorandCaiman latirostris. To explore the intra/interspecific competition of these fauna, we generate weight estimation, standardized niche breadth (BA) for the meso-megamammals from Sergipe and compare with data from the meso-megaherbivores from Africa, concluding thatEremotherium laurillardiandToxodon platensiswere the best resource competitors in the Late Pleistocene of Sergipe, and reinforcing their importance as key species in this extinct community. Finally, we reconstructed the paleoenvironment in which the vertebrate community of Sergipe lived, estimating Mean Annual Temperature (°C), Mean Annual Precipitation, Biomass and Energy Expendidure, noting that environments in the Late Pleistocene of Sergipe were similar to those of Africa nowadays, but hotter and with more energy expenditure for these meso-megamammals.
Spatial structure increases the benefits of antibiotic production in Streptomyces griseus
Authors: Westhoff Sanne , Otto Simon B. , Swinkels Aram , Bode Bo , van Wezel Gilles P. , Rozen Daniel E. 
Published: November 2018

AbstractBacteria in the soil compete for limited resources to survive and proliferate. One of the ways they might do this is by producing antibiotics, but the costs of antibiotic production and their low concentrations in soils have led to uncertainty about the role of these natural products for the bacteria that produce them. Here, we examine the fitness effects of streptomycin production by the filamentous soil bacterium Streptomyces griseus and the conditions that modify its ability to invade competitors. Using pairwise competion assays, we first provide direct evidence that streptomycin production enables S. griseus to kill and invade a population of the susceptible species, S. coelicolor, but not a streptomycin-resistant mutant of this species. Next we show that the fitness benefits of streptomycin production are density-dependent, because production scales positively with cell number, and frequency-dependent, with a threshold of invasion of S. griseus at around 1%. Finally, using serial transfer experiments where spatial structure is either maintained or periodically destroyed, we show that spatial structure reduces the threshold frequency of invasion by more than 100-fold, indicating that antibiotic production can permit invasion from extreme rarity. Our results provide clear evidence that streptomycin is both an offensive and defensive weapon that facilitates invasion into occupied habitats and also protects against invasion by competitors. They also indicate that the benefits of antibiotic production rely on ecological interactions occurring at small local scales, suggesting that low antibiotic concentrations in bulk soil are unlikely to be representative of their effective concentrations in nature.
Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series
Authors: Crowell Alexander M , Loros Jennifer J. , Dunlap Jay C 
Published: November 2018

AbstractMotivationIdentification of constitutive reference genes is critical for analysis of gene expression. Large numbers of high throughput time series expression data are available, but current methods for identifying invariant expression are not tailored for time series. Identification of reference genes from these data sets can benefit from methods which incorporate the additional information they provide.ResultsHere we show that we can improve identification of invariant expression from time series by modelling the time component of the data. We implement the Prediction Interval Ranking Score (PIRS) software, which screens high throughput time series data and provides a ranked list of reference candidates. We expect that PIRS will improve the quality of gene expression analysis by allowing researchers to identify the best reference genes for their system from publicly available time series.AvailabilityPIRS can be downloaded and installed with dependencies using ‘pip install pirs’ and Python code and documentation is available for download at https://github.com/aleccrowell/PIRS.Contactalexander.m.crowell@gmail.com
Bioinformatics Approach to Identify Diseasome and Co-morbidities Effect of Mitochondrial Dysfunctions on the Progression of Neurological Disorders
Authors: Satu Md. Shahriare , Howlader Koushik Chandra , Ullah Akhund Tajim Md. Niamat , Huq Fazlul , Quinn Julian M.W. , Moni Mohammad Ali 
Published: November 2018

AbstractMitochondrial dysfunction can cause various neurological diseases. We therefore developed a quantitative framework to explore how mitochondrial dysfunction may influence the progression of Alzheimer’s, Parkinson’s, Huntington’s and Lou Gehrig’s diseases and cerebral palsy through analysis of genes showing altered expression in these conditions. We sought insights about the gene profiles of mitochondrial and associated neurological diseases by investigating gene-disease networks, KEGG pathways, gene ontologies and protein-protein interaction network. Gene disease networks were constructed to connect shared genes which are commonly found between the neurological diseases and Mitochondrial Dysfunction. We also generated KEGG pathways and gene ontologies to explore functional enrichment among them, and protein-protein interaction networks to identify the shared protein groups of these diseases. Finally, we verified our biomarkers using gold benchmark databases (e.g., OMIM and dbGaP) which identified effective reasons of it. Our network-based methodologies are useful to investigate disease mechanisms, predictions for comorbidities and identified distinct similarities among different neurological disorders for mitochondrial dysfunction.
A simple approximation to bias in the genetic effect estimates when multiple disease states share a clinical diagnosis
Authors: Lobach Iryna , Kim Inyoung , Alekseyenko Alexander , Lobach Siarhei , Zhang Li 
Published: November 2018

ABSTRACTCase-control genome-wide association (CC-GWAS) studies might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease, cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished in a biomarker evaluation that might not be feasible on the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or non-genetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a logistic regression model with the clinical diagnosis as an outcome variable to the estimates in the relationship to the true disease state of interest. We performed simulation studies to assess accuracy of the approximation that we’ve derived. We next applied the derived approximation to the analysis of the genetic basis of innate immune system of Alzheimer’s disease.
A learned embedding for efficient joint analysis of millions of mass spectra
Authors: Bittremieux Wout , May Damon H. , Bilmes Jeffrey , Noble William Stafford 
Published: November 2018

AbstractComputational methods that aim to exploit publicly available mass spectrometry repositories primarily rely on unsupervised clustering of spectra. Here, we propose to train a deep neural network in a supervised fashion based on previous assignments of peptides to spectra. The network, called “GLEAMS,” learns to embed spectra into a low-dimensional space in which spectra generated by the same peptide are close to one another. We use GLEAMS as the basis for a large-scale spectrum clustering, detecting groups of unidentified, proximal spectra representing the same peptide, and we show how to use these clusters to explore the dark proteome of repeatedly observed yet consistently unidentified mass spectra. We provide a software implementation of our approach, along with a tool to quickly embed additional spectra using a pre-trained model, to facilitate large-scale analyses.
Asymmetric secretion in budding yeast reinforces daughter cell identity
Authors: Thomas Valerie N. , Weiss Eric L. , Brace Jennifer L. 
Published: November 2018

AbstractAsymmetric segregation of cellular factors during cell division produces two cells with different identities. This asymmetry underlies cell fate decisions as well as the ability to self-renew. Asymmetric segregation of protein and RNA to the growing bud ofSaccharomyces cerevisiaegenerates a daughter cell with features distinct from its mother. For example, asymmetric segregation of the transcription factor Ace2 to the newly formed daughter cell activates a gene expression program unique to daughters. Ace2 activates a cohort of genes, including degradative enzymes, which facilitate cell separation exclusively from the daughter. This asymmetric secretion leaves a characteristic ‘bud scar’ chitin ring on the mother. We sought to determine the sufficiency of Ace2 to define a daughter cell state by generating anACE2allele which localizes to both mother and daughter nuclei. When Ace2 asymmetry is lost, Ace2 target gene transcription and translation occur in both mother and daughter cells. However, we find that mother cells retain bud scars and maintain asymmetric daughter-specific secretion of the wall degrading enzyme Cts1. These findings demonstrate that while mothers are competent to transcribe and translate Ace2 targets, additional intrinsic factors reinforce the daughter cell state.TOC SummaryAsymmetric segregation of the transcription factor Ace2 drives daughter-specific cell separation after cytokinesis. Cells engineered to express Ace2 targets symmetrically produce the cell separation enzyme Cts1. However, secretion remains asymmetric suggesting other daughter-specific factors are required to reinforce the daughter cell identity.
Low oxygen post conditioning improves stroke-induced cognitive impairment
Authors: Zhao Zidan , Ong Lin Kooi , Pietrogrande Giovanni , Bezanilla Sonia Sanchez , Warren Kirby , Ilicic Marina , Kluge Murielle G. , TeBay Clifford , Ottersen Ole P. , Johnson Sarah J , Nilsson Michael , Walker Frederick R 
Published: November 2018

AbstractPost-stroke cognitive impairment has proven to be notoriously difficult to treat. In the current study, we sought to both better understand cellular changes that underpin cognitive deficits and to consider the potential restorative benefits of low oxygen post conditioning (LOPC). We were motivated to use LOPC as an intervention as it is one of the few experimental interventions previously shown to improve cognitive function post-stroke. Experimental stroke was induced by photothrombotic occlusion in adult male C57BL/6 mice. Mice were randomly assigned to either a normal atmospheric air exposure or low oxygen (11% O2) exposure groups three days post-occlusion. On day 17 post-stroke, mice were euthanized for histology or biochemical analyses. Stroked mice exposed to LOPC was associated with marked reductions in amyloid-beta both in its absolute level and in the extent of its oligomerization. Exposure to LOPC post-stroke also improved cellular deficits induced by stroke including an increase in vessel density, a reduction in vascular leakage, and restoration of AQP4 polarisation. Critically, stroked mice exposed to LOPC exhibited robust improvements in cognitive function post-stroke, assessed using a touchscreen based paired- associate learning task. These findings provide compelling pre-clinical evidence of the potential clinical utility of LOPC for enhancing recovery post-stroke.
Polymorphism-aware species trees with advanced mutation models, bootstrap and rate heterogeneity
Authors: Schrempf Dominik , Minh Bui Quang , Haeseler Arndt von , Kosiol Carolin 
Published: November 2018

AbstractMolecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Recently, multispecies coalescent based methods have increased in popularity, however, their application is limited to a small number of species and individuals. We introduced a polymorphism-aware phylogenetic model (PoMo), which overcomes this limitation and scales well with the increasing amount of sequence data while accounting for present and ancestral polymorphisms. PoMo circumvents handling of gene trees and directly infers species trees from allele frequency data. Here, we extend the PoMo implementation in IQ-TREE and integrate search for the statistically best-fit mutation model, the ability to infer mutation rate variation across sites, and assessment of branch support values. We exemplify an analysis of a hundred species with ten haploid individuals each, showing that PoMo can perform inference on large data sets. While PoMo is more accurate than standard substitution models applied to concatenated alignments, it is almost as fast. We also providebmm-simulate, a software package that allows simulation of sequences evolving under PoMo. The new options consolidate the value of PoMo for phylogenetic analyses with population data.
3D Structural Connectivity in Healthy Adults: Detecting Structural Connections and Their Underlying Substrates for Olfactory Saccadic Pathway
Authors: Elumalai Ganesh , Syed Imran Shareef , Chatterjee Harshita , Brown Valencia , Sober Lekesha Adele , Surya Nitya Akarsha , Ghanta Venkata , Chandrasekhar Pradeep , Venkatesan Anbarasan , Sewram Nadira , Muthukumaran Nitesh Raj , Harkrishna Golla , Vamsi Nanduri Mojess , Osakwe Nneoma Somtochukwu 
Published: November 2018

ABSTRACTThis confirmatory study aimed to unravel the neural structural connectivity of Olfactory-Saccadic pathways extending between Piriform and Entorhinal Cortices to Frontal Eye Field (FEF), and to correlate its functional importance with possible clinical implications, using Diffusion Imaging fiber Tractography. The confirmatory observational analysis used thirty-two healthy adults, ultra-high b-value, diffusion imaging datasets from an Open access platform in Human Connectome Project (HCP). In all the datasets from both the sexes, fibers were traced and the neural structural connectivity was confirmed. The hemispheric differences between male and female subjects were anaNumber of tracts refers to a bundle of fibres having lysed using independent sample t-test. Thus, the study confirmed the structural existences of Olfactory-saccadic pathways that may be involved in influencing the movements of the neck and eyeball gaze (saccadic eye movement), towards the spatial orientation of olfactory stimulus.