bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex
Authors: Seeman Stephanie C. , Campagnola Luke , Davoudian Pasha A. , Hoggarth Alex , Hage Travis A. , Bosma-Moody Alice , Baker Christopher A. , Lee Jung Hoon , Mihalas Stefan , Teeter Corinne , Ko Andrew L. , Ojemann Jeffrey G. , Gwinn Ryder P. , Silbergeld Daniel L. , Cobbs Charles , Phillips John , Lein Ed , Murphy Gabe J. , Koch Christof , Zeng Hongkui , Jarsky Tim 
Published: April 2018

AbstractGenerating a comprehensive description of cortical networks requires a large-scale, systematic approach. To that end, the Allen Institute is engaged in a pipeline project using multipatch electrophysiology, supplemented with 2-photon optogenetics, to characterize connectivity and synaptic signaling between classes of neurons in adult mouse and human cortex. We focus on producing results detailed enough for the generation of computational models and enabling comparison with future studies. Here we report our examination of intralaminar connectivity within each of several classes of excitatory neurons. We find that connections are sparse but present among all excitatory cell types and layers we sampled, with the most sparse connections in layers 5 and 6. Almost all mouse synapses exhibited short-term depression with similar dynamics. Synaptic signaling between a subset of layer 2/3 neurons; however, exhibited facilitation. These results contribute to a body of evidence describing recurrent excitatory connectivity as a conserved feature of cortical microcircuits.
Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections
Authors: Marsh Jon N. , Matlock Matthew K. , Kudose Satoru , Liu Ta-Chiang , Stappenbeck Thaddeus S. , Gaut Joseph P. , Swamidass S. Joshua 
Published: April 2018

AbstractTransplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. The model substantially outperforms a model trained on image patches of isolated glomeruli. Encouragingly, the model’s performance is robust to slide preparation artifacts associated with frozen section preparation. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.
A continental system for forecasting bird migration
Authors: Van Doren Benjamin M. , Horton Kyle G. 
Published: April 2018

AbstractBillions of animals cross the globe each year during seasonal migrations, but efforts to monitor them are hampered by the irregularity and relative unpredictability of their movements. We developed a bird migration forecast system with continental scope by leveraging 23 years of spring observations to learn associations between atmospheric conditions and bird migration intensity. Our models explained up to 81% of variation in migration intensity across the United States at altitudes of 0-3000 m, and performance remained high when forecasting events 24-72 h into the future (68-72% variation explained). We infer that avian migratory movements across the United States frequently exceed 200 million individuals per night and exceed 500 million individuals per night during peak passage. Accurately forecasting bird migration will allow stakeholders to reduce collisions with illuminated buildings, airplanes, and wind turbines, predict movements under climate change scenarios, and engage the public.
Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification
Authors: Hett Kilian , Ta Vinh-Thong , Catheline Gwenaëlle , Tourdias Thomas , Manjón José V. , Coupé Pierrick ,   Alzheimer’s Disease Neuroimaging Initiative
Published: April 2018

ABSTRACTNumerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer’s disease (AD). While anatomical MRI captures structural alterations, studies demonstrated the ability of diffusion MRI to capture microstructural modifications at an earlier stage. Several methods have focused on hippocampus structure to detect AD. To date, the patch-based grading framework provides the best biomarker based on the hippocampus. However, this structure is complex since the hippocampus is divided into several heterogeneous subfields not equally impacted by AD. Former in-vivo imaging studies only investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to study the efficiency of hippocampal subfields compared to the whole hippocampus structure with a multimodal patch-based framework that enables to capture subtler structural and microstructural alterations. To this end, we analyze the significance of the different hippocampal subfields for AD diagnosis and prognosis with volumetric, diffusivity measurements and a novel multimodal patch-based grading framework that combines structural and diffusion MRI. The experiments conducted in this work showed that the whole hippocampus provides the most discriminant biomarkers for advanced AD detection while biomarkers applied into subiculum obtain the best results for AD prediction, improving by 2% the accuracy compared to the whole hippocampus.
Consumption of carotenoids not increased by bacterial infection in brown trout embryos (Salmo trutta)
Authors: Marques da Cunha Lucas , Wilkins Laetitia G. E. , Menin Laure , Ortiz Daniel , Vocat-Mottier Véronique , Wedekind Claus 
Published: April 2018

AbstractCarotenoids are organic pigment molecules that play important roles in signalling, control of oxidative stress, and immunity. Fish allocate carotenoids to their eggs, which gives them the typical yellow to red colouration and supports their resistance against microbial infections. However, it is still unclear whether carotenoids act mainly as a shield against infection or are used up during the embryos’ immune defence. We investigated this question with experimental families produced from wild-caught brown trout (Salmo trutta). Singly raised embryos were either exposed to the bacterial pathogen Pseudomonas fluorescens or sham-treated at one of two stages during their development. A previous study on these experimental families reported positive effects of egg carotenoids on embryo growth and resistance against the infection. Here, we quantified carotenoid consumption in these infected and sham-infected maternal sib groups. We found that carotenoid contents mostly decreased during embryogenesis. However, these decreases were neither linked to the virulence induced by the pathogen nor dependent on the time point of infection. We conclude that egg carotenoids are not significantly used up by the embryos’ immune defence.
RobusTAD: A Tool for Robust Annotation of Topologically Associating Domain Boundaries
Authors: Dali Rola , Bourque Guillaume , Blanchette Mathieu 
Published: April 2018

AbstractMotivationTopologically Associating Domains (TADs) are chromatin structures that can be identified by analysis of Hi-C data. Tools currently available for TAD identification are sensitive to experimental conditions such as coverage, resolution and noise level.ResultsHere, we present RobusTAD, a tool to score TAD boundaries in a manner that is robust to these parameters. In doing so, RobusTAD eases comparative analysis of TAD structures across multiple heterogeneous samples.AvailabilityRobusTAD is implemented in R and released under a GPL license. RobusTAD can be downloaded from https://github.com/rdali/RobusTAD and runs on any standard desktop computer.Contactrola.dali@mail.mcgill.ca, blanchem@cs.mcgill.caSupplementary informationSupplementary data are available at Bioinformatics online.
Knockdown of GAS5 restores ox-LDL-induced impaired autophagy flux via upregulating miR-26a in human endothelial cells
Authors: Liang Weijie , Fan Taibing , Liu Lin , Zhang Lianzhong 
Published: April 2018

AbstractBackgroundOxidized low-density lipoprotein (ox-LDL)-induced endothelial cell (EC) injury and autophagy dysfunction play a vital role in the development of atherosclerosis. LncRNAs have been identified to participate in the regulation of pathogenesis of atherosclerosis. However, it remains largely undefined whether growth-arrest specific transcript 5 (GAS5) could influence ox-LDL-induced autophagy dysfunction in ECs.MethodsThe expressions of GAS5 and miR-26a in the plasma samples of patients with atherosclerosis and ox-LDL-treated human aortic endothelial cells (HAECs) were detected by qRT-PCR. Luciferase reporter assay, RNA immunoprecipitation (RIP), and RNA pull down were performed to validate whether GAS5 could directly interact with miR-26a. The effects of ox-LDL, GAS5 or combined with miR-26a on apoptosis and autophagy were evaluated by flow cytometry analysis and western blot, respectively.ResultsGAS5 expression was upregulated and miR-26a was downregulated in the plasma samples of patients with atherosclerosis and ox-LDL-treated HAECs. There was reciprocal inhibition between GAS5 and miR-26a expressions in ox-LDL-treated HAECs. We further demonstrated that GAS5 directly interacted with miR-26a in ox-LDL-treated HAECs. Additionally, ox-LDL administration induced apoptosis and impaired autophagy flux in HAECs. Rescue experiments demonstrated that GAS5 knockdown restored ox-LDL-induced impaired autophagy flux by upregulating miR-26a in HAECs.ConclusionKnockdown of GAS5 restores ox-LDL-induced impaired autophagy flux via upregulating miR-26a in human endothelial cells, revealing a novel regulatory mechanism for ox-LDL-induced impaired autophagy flux in ECs through ceRNA crosstalk.
Collective feature selection to identify crucial epistatic variants
Authors: Verma Shefali S. , Lucas Anastasia , Zhang Xinyuan , Veturi Yogasudha , Dudek Scott , Li Binglan , Li Ruowang , Urbanowicz Ryan , Moore Jason H. , Kim Dokyoon , Ritchie Marylyn D. 
Published: April 2018

AbstractBackgroundMachine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called “short fat data” problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.ResultsThrough our simulation study we propose a collective feature selection approach to select features that are in the “union” of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~44,000 samples obtained from Geisinger’s MyCode Community Health Initiative (on behalf of DiscovEHR collaboration).ConclusionsIn this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.
Weakly Supervised Learning of Single-Cell Feature Embeddings
Authors: Caicedo Juan C. , McQuin Claire , Goodman Allen , Singh Shantanu , Carpenter Anne E. 
Published: April 2018

AbstractWe study the problem of learning representations for single cells in microscopy images to discover biological relationships between their experimental conditions. Many new applications in drug discovery and functional genomics require capturing the morphology of individual cells as comprehensively as possible. Deep convolutional neural networks (CNNs) can learn powerful visual representations, but require ground truth for training; this is rarely available in biomedical profiling experiments. While we do not know which experimental treatments produce cells that look alike, we do know that cells exposed to the same experimental treatment should generally look similar. Thus, we explore training CNNs using a weakly supervised approach that uses this information for feature learning. In addition, the training stage is regularized to control for unwanted variations using mixup or RNNs. We conduct experiments on two different datasets; the proposed approach yields single-cell embeddings that are more accurate than the widely adopted classical features, and are competitive with previously proposed transfer learning approaches.
Animal models of chemotherapy-induced peripheral neuropathy: a machine-assisted systematic review and meta-analysis
Authors: Currie Gillian L. , Angel-Scott Helena N. , Colvin Lesley , Cramond Fala , Hair Kaitlyn , Khandoker Laila , Liao Jing , Macleod Malcolm , McCann Sarah K. , Morland Rosie , Sherratt Nicki , Stewart Robert , Tanriver-Ayder Ezgi , Thomas James , Wang Qianying , Wodarski Rachel , Xiong Ran , Rice Andrew S.C. , Sena Emily 
Published: April 2018

AbstractBackground and aimsChemotherapy-induced peripheral neuropathy (CIPN) can be a severely disabling side-effect of commonly used cancer chemotherapeutics, requiring cessation or dose reduction, impacting on survival and quality of life. Our aim was to conduct a systematic review and meta-analysis of research using animal models of CIPN to inform robust experimental design.MethodsWe systematically searched 5 online databases (PubMed, Web of Science, Citation Index, Biosis Previews and Embase (September 2012) to identify publications reporting in vivo CIPN modelling. Due to the number of publications and high accrual rate of new studies, we ran an updated search November 2015, using machine-learning and text mining to identify relevant studies.All data were abstracted by two independent reviewers. For each comparison we calculated a standardised mean difference effect size then combined effects in a random effects meta- analysis. The impact of study design factors and reporting of measures to reduce the risk of bias was assessed. We ran power analysis for the most commonly reported behavioural tests.Results341 publications were included. The majority (84%) of studies reported using male animals to model CIPN; the most commonly reported strain was Sprague Dawley rat. In modelling experiments, Vincristine was associated with the greatest increase in pain-related behaviour (−3.22 SD [−3.88; −2.56], n=152, p=0). The most commonly reported outcome measure was evoked limb withdrawal to mechanical monofilaments. Pain-related complex behaviours were rarely reported. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. Overall, studies were at moderate risk of bias, with modest reporting of measures to reduce the risk of bias.ConclusionsHere we provide a comprehensive summary of the field of animal models of CIPN and inform robust experimental design by highlighting measures to increase the internal and external validity of studies using animal models of CIPN. Power calculations and other factors, such as clinical relevance, should inform the choice of outcome measure in study design.
Distinct sequence patterns in the active postmortem transcriptome
Authors: Noble Peter A , Pozhitkov Alexander E. 
Published: April 2018

ABSTRACTOur previous study found more than 500 transcripts significantly increased in abundance in the zebrafish and mouse several hours to days postmortem relative to live controls. The current literature suggests that most mRNAs are post-transcriptionally regulated in stressful conditions, we rationalized that the postmortem transcripts must contain sequence features (3 to 9 mers) that are unique from those in the rest of the transcriptome – specifically, binding sites for proteins and/or non-coding RNAs involved in regulation. Our new study identified 5117 and 2245 over-represented sequence features in the mouse and zebrafish, respectively. Some of these features were disproportionately distributed along the transcripts with high densities in the 3-UTR region of the zebrafish (0.3 mers/nt) and the ORFs of the mouse (0.6 mers/nt). Yet, the highest density (2.3 mers/nt) occurred in the ORFs of 11 mouse transcripts that lacked UTRs. Our results suggest that these transcripts might serve as ‘molecular sponges’ that sequester RNA binding proteins and/or microRNAs, increasing the stability and gene expression of other transcripts. In addition, some features were identified as binding sites forRbfoxandHudproteins that are also involved in increasing transcript stability and gene expression. Hence, our results are consistent with the hypothesis that transcripts involved in responding to extreme stress have sequence features that make them different from the rest of the transcriptome, which presumably has implications for post-transcriptional regulation in disease, starvation, and cancer.ABBREVIATIONSUTRuntranslated regionsORFsopen reading framesOPoverabundant transcript poolCPcontrol transcript poolFPfalse positiveRBPRNA binding proteinsncRNAnon-coding RNAmiRNAmicroRNA
Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments
Authors: Bloom Jesse D. 
Published: April 2018

ABSTRACTIn single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.
Distantly related crops are not better rotation partners for tomato
Authors: Ingerslew Kathryn S. , Kaplan Ian 
Published: April 2018

Abstract

Although crop rotation has been used for centuries to enhance agricultural production, there are surprisingly little data justifying the use of one rotation over another. Growers typically avoid growing plants in succession that belong to the same genus or family, but it is not clear whether closely related crops are indeed poor rotation partners and whether evolutionary history, overall, predicts legacy effects in agricultural soils.

Here, we use a plant–soil feedback framework to test the relationship between species relatedness and crop growth. Using tomato Solanum lycopersicum as our focal crop, we determined how 36 common crop and weed species that vary along a spectrum of phylogenetic relatedness influence tomato growth in subsequent plantings. We also tested whether soil conditioning affects the performance of an above‐ground insect herbivore, the tobacco hornworm Manduca sexta.

Phylogenetic relatedness did not predict plant–soil feedback effects on tomato biomass or hornworm performance; rather, impacts of soil conditioning were highly species‐ or family‐specific. For example, tomatoes growing in soil previously containing plants in the Asteraceae family were notably resistant to caterpillar feeding.

There was also a disconnect between which plant species caused negative feedbacks on tomatoes vs. hornworms (e.g. thistle Cirsium discolor soil had strong negative effects on herbivory but no impact on plant growth). Hence, negative feedbacks on hornworms are likely due to enhanced defence instead of simply reducing leaf availability.

Synthesis and applications. These data demonstrate that, despite being widely recommended by agronomists in most cropping systems, phylogenetic relatedness is a poor predictor for the success of crop pairings in rotation, especially in tomato. Better understanding of species‐specific effects of soil conditioning will lend insight into how polycultures can be better designed to optimize crop growth while reducing susceptibility to insect pests, which is particularly useful on diversified farms that cultivate a variety of crop species.