bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digitising point of care HIV test results to accurately measure, and improve performance towards, the UNAIDS 90-90-90 targets
Authors: Jacob Nisha , Rice Brian , Kalk Emma , Heekes Alexa , Morgan Jennie , Hargreaves James , Boulle Andrew 
Published: November 2019

AbstractIntroductionHigh rates of pre-treatment loss to care among persons diagnosed with HIV persist. Linkage to care can be improved through active digitally-based surveillance. Currently, record-keeping for HIV diagnoses in South Africa is paper-based. Aggregated testing data are reported routinely, and only discordant findings result in a specimen being tested at a laboratory and digitised.MethodsThe Western Cape Province in South Africa has a Provincial Health Data Centre (PHDC) where person-level routine data are consolidated in a single database, leveraging the existence of a unique patient identifier. To facilitate improved surveillance, a pre-carbonated point-of-care test (PoCT) form was piloted, where one copy was routed to the centralised laboratory and digitised for PHDC inclusion. We evaluated the utility of the intervention using cross-sectional and retrospective cohort analyses, as well as comparisons with reported aggregate data.ResultsFrom May 2017 to June 2018, 11337 digitised point-of-care HIV testing records were linked to the PHDC. Overall, 96% of records in the aggregate dataset were digitised, with 97% linked to the PHDC. Of those tested, 79% were female (median age 27 years). Linkage demonstrated that 51.3% of patients testing HIV-positive were retesting. Of those truly newly diagnosed, 81% were linked to HIV care and 25% were initiated on antiretroviral therapy immediately.ConclusionDigitisation of PoCT results is feasible and provides individuated HIV testing data to assist in linkage to care and in differentiating newly diagnosed patients from positive patients retesting. Actionable and accurate data can improve the measurement of performance towards the UNAIDS 90-90-90 targets.
EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease
Authors: Tait Luke , Tamagnini Francesco , Stothart George , Barvas Edoardo , Monaldini Chiara , Frusciante Roberto , Volpini Mirco , Guttmann Susanna , Coulthard Elizabeth , Brown Jon T , Kazanina Nina , Goodfellow Marc 
Published: November 2019

AbstractIntroductionElectroencephalogram (EEG) is a potentially useful clinical tool for aiding diagnosis of Alzheimer’s disease (AD). We hypothesized we can increase the accuracy of EEG for aiding diagnosis of AD using microstates, which are epochs of quasi-stability at the millisecond scale.MethodsEEG was collected from two independent cohorts of AD and control participants and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. Microstates were analysed, including a novel measure of complexity.ResultsMicrostate complexity significantly decreased in AD, and when combined with a spectral EEG measure, could classify AD with sensitivity and specificity >80%. These results were validated on an independent cohort and were also found to be generalizable to predict progression from MCI to AD. Additionally, microstates associated with the frontoparietal network were altered in AD.DiscussionEEG has the potential to be a non-invasive functional biomarker that predicts progression from MCI to AD.
MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm
Authors: Guo Zhen-Hao , You Zhu-Hong , Yi Hai-Cheng , Zheng Kai , Wang Yan-Bin 
Published: November 2019

AbstractMotivationEffectively representing the MeSH headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify.ResultsIn this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships) which can be constructed by the rule of tree num. Then, five graph embedding algorithms including DeepWalk (DW), LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed method, we carried out the node classification and relationship prediction tasks. The experimental results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the distinguishable ability of vectors. Thus, it can act as input and continue to play a significant role in any disease-, drug-, microbe- and etc.-related computational models. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.Contactzhuhongyou@ms.xjb.ac.cn
Local intracerebral Inhibition of IRE1 by MKC8866 sensitizes glioblastoma to irradiation/chemotherapy in vivo
Authors: Le Reste Pierre Jean , Pineau Raphael , Voutetakis Konstantinos , Samal Juhi , Jégou Gwénaële , Lhomond Stéphanie , Gorman Adrienne M. , Samali Afshin , Patterson John B , Zeng Qingping , Pandit Abhay , Aubry Marc , Soriano Nicolas , Etcheverry Amandine , Chatziioannou Aristotelis , Mosser Jean , Avril Tony , Chevet Eric 
Published: November 2019

AbstractGlioblastoma multiforme (GBM) is the most severe primary brain cancer. Despite an aggressive treatment comprising surgical resection and radio/chemotherapy patient’s survival post diagnosis remains short. A limitation for success in finding novel improved therapeutic options for such dismal disease partly lies in the lack of a relevant animal model that accurately recapitulates patient disease and standard of care. In the present study, we have developed a novel immunocompetent GBM model that includes tumor surgery and a radio/chemotherapy regimen resembling the Stupp protocol and we have used this model to test the impact of the pharmacological inhibition of the endoplasmic reticulum (ER) stress sensor IRE1, on treatment efficacy.
Arbitration between insula and temporoparietal junction subserves framing-induced boosts in generosity during social discounting
Authors: Sellitto Manuela , Neufang Susanne , Schweda Adam , Weber Bernd , Kalenscher Tobias 
Published: November 2019

AbstractGenerosity toward others declines across the perceived social distance to them. Here, participants chose between selfish and costly generous options in two conditions: in the gain frame, a generous choice yielded a gain to the other; in the loss frame, it entailed preventing the loss of a previous endowment to the other. Social discounting was reduced in the loss compared to the gain frame, implying increased generosity toward strangers. Using neuroimaging tools, we found that while the temporoparietal junction (TPJ) and the ventromedial prefrontal cortex (VMPFC) subserved generosity in the gain frame, the insular cortex was selectively recruited during generous choices in the loss frame. We provide support for a network-model according to which TPJ and insula differentially promote generosity by modulating value signals in the VMPFC in a frame-dependent fashion. These results extend our understanding of the insula role in nudging prosocial behavior in humans.
Efficient toolkit implementing best practices for principal component analysis of population genetic data
Authors: Privé Florian , Luu Keurcien , Blum Michael G.B. , McGrath John J. , Vilhjálmsson Bjarni J. 
Published: November 2019

AbstractPrincipal Component Analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (1) capturing Linkage Disequilibrium (LD) structure instead of population structure, (2) projected PCs that suffer from shrinkage bias, (3) detecting sample outliers, and (4) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr.For example, we find that PC19 to PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16-18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr.Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data.
A cooperative network of molecular “hot spots” highlights the complexity of LH3 collagen glycosyltransferase activities
Authors: Chiapparino Antonella , De Giorgi Francesca , Scietti Luigi , Faravelli Silvia , Roscioli Tony , Forneris Federico 
Published: November 2019

ABSTRACTHydroxylysine glycosylations are collagen-specific post-translational modifications essential for maturation and homeostasis of fibrillar as well as non-fibrillar collagen molecules. Lysyl hydroxylase 3 (LH3) is the only human enzyme capable of performing two chemically-distinct collagen glycosyltransferase reactions using the same catalytic site: inverting beta-1,O-galactosylation of hydroxylysines and retaining alpha-1,2-glycosylation of galactosyl hydroxylysines.Here, we used structure-based mutagenesis to show that both glycosyltransferase activities are strongly dependent on a broad cooperative network of amino acid side chains which includes the first-shell environment of the glycosyltransferase catalytic site and shares features with both retaining and inverting enzymes. We identified critical “hot spots” leading to selective loss of inverting activity without affecting the retaining reaction. Finally, we present molecular structures of LH3 in complex with UDP-sugar analogs which provide the first structural templates for LH3 glycosyltransferase inhibitor development.Collectively, our data provide a comprehensive overview of the complex network of shapes, charges and interactions that enable LH3 glycosyltransferase activities, expanding the molecular framework for the manipulation of glycosyltransferase functions in biomedical and biotechnological applications.
LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer
Authors: Zhang Junpeng , Xu Taosheng , Liu Lin , Zhang Wu , Zhao Chunwen , Li Sijing , Li Jiuyong , Rao Nini , Le Thuc Duy 
Published: November 2019

AbstractUntil now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline’s performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.Author summaryPrevious studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.
Investigating higher order interactions in single cell data with scHOT
Authors: Ghazanfar Shila , Lin Yingxin , Su Xianbin , Lin David M. , Patrick Ellis , Han Ze Guang , Marioni John C. , Yang Jean Yee Hwa 
Published: November 2019

ABSTRACTSingle-cell RNA-sequencing has transformed our ability to examine cell fate choice. For example, in the context of development and differentiation, computational ordering of cells along ‘pseudotime’ enables the expression profiles of individual genes, including key transcription factors, to be examined at fine scale temporal resolution. However, while cell fate decisions are typically marked by profound changes in expression, many such changes are observed in genes downstream of the initial cell fate decision. By contrast, the genes directly involved in the cell fate decision process are likely to interact in subtle ways, potentially resulting in observed changes in patterns of correlation and variation rather than mean expression prior to cell fate commitment. Herein, we describe a novel approach, scHOT – single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. We demonstrate the utility of scHOT by studying embryonic development of the liver, where we find coordinated changes in higher order interactions of programs related to differentiation and liver function. We also demonstrate its ability to find subtle changes in gene-gene correlation patterns across space using spatially-resolved expression data from the mouse olfactory bulb. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.
Assessing the shared variation among high-dimensional data matrices: a modified version of the Procrustean correlation coefficient
Authors: Coissac E. , Gonindard-Melodelima C. 
Published: November 2019

AbstractMotivationMolecular biology and ecology studies can produce high dimension data. Estimating correlations and shared variation between such data sets are an important step in disentangling the relationships between different elements of a biological system. Unfortunately, classical approaches are susceptible to producing falsely inferred correlations.ResultsHere we propose a corrected version of the Procrustean correlation coefficient that is robust to high dimensional data. This allows for a correct estimation of the shared variation between two data sets and the partial correlation coefficients between a set of matrix data.AvailabilityThe proposed corrected coefficients are implemented in the ProcMod R package available on CRAN. The git repository is hosted at https://git.metabarcoding.org/lecasofts/ProcModContacteric.coissac@metabarcoding.org
Estradiol promotes and progesterone reduces anxiety-like behavior produced by nicotine withdrawal in rats
Authors: Flores Rodolfo J. , Cruz Bryan , Uribe Kevin P. , Correa Victor L. , Arreguin Montserrat C. , Carcoba Luis M. , Mendez Ian A. , O’Dell Laura E. 
Published: November 2019

AbstractThe present study assessed sex differences and the role of ovarian hormones in the behavioral effects of nicotine withdrawal. Study 1 compared physical signs, anxiety-like behavior, and corticosterone levels in male, intact female, and ovariectomized (OVX) female rats during nicotine withdrawal. Estradiol (E2) and progesterone levels were also assessed in intact females that were tested during different phases of the 4-day estrous cycle. Study 2 assessed the role of ovarian hormones in withdrawal by comparing the same measures in OVX rats that received vehicle, E2, or E2+progesterone prior to testing. Briefly, rats received a sham surgery or an ovariectomy procedure. Fifteen days later, rats were prepared with a pump that delivered nicotine for 14 days. On the test day, rats received saline or the nicotinic receptor antagonist, mecamylamine to precipitate withdrawal. Physical signs and anxiety-like behavior were assessed on the elevated plus maze (EPM) and light-dark transfer (LDT) tests. During withdrawal, intact females displayed greater anxiety-like behavior and corticosterone levels as compared to male and OVX rats. Females tested in estrus (when E2 is relatively low) displayed less anxiety-like behavior and corticosterone versus all other phases. Anxiety-like behavior and corticosterone were positively correlated with E2 and negatively correlated with progesterone. Intact females displaying high E2/low progesterone displayed greater anxiety-like behavior and corticosterone as compared to females displaying low E2/high progesterone. Lastly, OVX-E2 rats displayed greater anxiety-like behavior than OVX-E2+progesterone rat. These data suggest that E2 promotes and progesterone reduces anxiety-like behavior produced by withdrawal.
Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory
Authors: Kim Robert , Sejnowski Terrence J. 
Published: November 2019

AbstractCortical neurons process and integrate information on multiple timescales. In addition, these timescales or temporal receptive fields display functional and hierarchical organization. For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields and long timescales to support reliable representations of stimuli. Despite of the recent advances in experimental techniques, the underlying mechanisms for the emergence of neuronal timescales long enough to support WM are unclear and challenging to investigate experimentally. Here, we demonstrate that spiking recurrent neural networks (RNNs) designed to perform a WM task reproduce previously observed experimental findings and that these models could be utilized in the future to study how neuronal timescales specific to WM emerge.
A literature mining method to judge whether there are uncertainties in empirical-dependent antineoplastic drug distribution in specific clinical scenarios
Authors: Ji Xiaoyang , Feng Zhendong , Zhang Qiangzu , Zhang Zhonghai , Fan Yanhui , Na Renhua , Niu Gang 
Published: November 2019

AbstractCancer clinical practice guidelines recommend different treatment options for different cancer types and are mainly developed by clinicians. In theory, those recommendation schemes that are supported by scientific research should provide better efficacy for patients. However, in actual clinical practice: “Is the choice of a specific antineoplastic drug for a specific cancer supported by the results of molecular biology mechanisms or based on the subjective experience of the clinician?” Answering this question is of significant importance for guiding clinical practice, but there is currently no operational method to provide objective judgment in specific cases. This paper describes a literature mining method that collates information from specific antineoplastic drug-related literature to establish an antineoplastic drug-gene association matrix for global or specific cancer scenarios, and further establishes a standard model and scenario models. Based on the parameters of these models, we constructed a linear regression analysis method to evaluate whether the models in different scenarios deviated from a random distribution. Finally, we determined the possible efficacy of an antineoplastic drug in different cancer types, which was validated by the Genomics of Drug Sensitivity in Cancer (GDSC) database. Using our mining method, we tested 18 antineoplastic drugs in 16 cancer types. We found that cisplatin used in ovarian cancer was more efficacious and may benefit patients more than when used in breast cancer, which provides a new paradigm for rational knowledge-driven drug distribution patterns in clinical practice.
The Absence of Universally-Conserved Protein-coding Genes
Authors: Tan Change Laura 
Published: November 2019

AbstractPublic access to thousands of completely sequenced and annotated genomes provides a great opportunity to address the relationships of different organisms, at the molecular level and on a genome-wide scale. Via comparing the phylogenetic profiles of all protein-coding genes in 317 model species described in the OrthoInspector3.0 database, we found that approximately 29.8% of the total protein-coding genes were orphan genes (genes unique to a specific species) while < 0.01% were universal genes (genes with homologs in each of the 317 species analyzed). When weighted by potential birth event, the orphan genes comprised 82% of the total, while the universal genes accounted for less than 0.00008%. Strikingly, as the analyzed genomes increased, the sum total of universal and nearly-universal genes plateaued while that of orphan and nearly-orphan genes grew continuously. When the compared species increased to the inclusion of 3863 bacteria, 711 eukaryotes, and 179 archaea, not one of the universal genes remained. The results speak to a previously unappreciated degree of genetic biodiversity, which we propose to quantify using the birth-event-weighted gene count method.