bluuebunny/crossref_metadata_2025_split huggingface dataset:
Two original observations concerning bacterial infections in COVID-19 patients hospitalized in intensive care units during the first wave of the epidemic in France
Authors: d’Humières Camille , Patrier Juliette , Lortat-Jacob Brice , Tran-dinh Alexy , Chemali Lotfi , Maataoui Naouale , Rondinaud Emilie , Ruppé Etienne , Burdet Charles , Ruckly Stéphane , Montravers Philippe , Timsit Jean-François , Armand-Lefevre Laurence 
Published: January 2021

AbstractAmong 197 COVID-19 patients hospitalized in ICU, 88 (44.7%) experienced at least one bacterial infection, with pneumonia (39.1%) and bloodstream infections (15,7%) being the most frequent. Unusual findings include frequent suspicion of bacterial translocations originating from the digestive tract as well as bacterial persistence in the lungs despite adequate therapy.
A Genetic Risk Score for Glioblastoma Multiforme Based on Copy Number Variations
Authors: Ko Charmeine , Brody James P. 
Published: January 2021

AbstractGlioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer.Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations.We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile.The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.
Factors influencing nursing students’ intention to accept COVID-19 vaccination – A pooled analysis of seven countries
Authors: Patelarou Evridiki , Galanis Petros , Mechili Enkeleint A. , Argyriadi Agathi , Argyriadis Alexandros , Asimakopoulou Evanthia , Brokaj Stiliana , Bucaj Jorgjia , Carmona-Torres Juan Manuel , Cobo-Cuenca Ana Isabel , Doležel Jakub , Finotto Stefano , Jarošová Darja , Kalokairinou Athina , Mecugni Daniela , Pulomenaj Velide , Saliaj Aurela , Sopjani Idriz , Zahaj Majlinda , Patelarou Athina 
Published: January 2021

AbstractExperiencing the second wave of COVID-19 pandemic, high vaccination coverage by a safe and effective vaccine globally would be a great achievement. Acceptance of vaccination by healthcare students is an important issue as they have a key role as future professionals in educating patients, informing and guiding them to the right clinical decision. The aim of this study was to explore the intention of nursing students to get vaccinated for SARS-CoV-2 infection and the factors acting either as motivators or barriers towards vaccination. A multicenter cross-sectional study was conducted in 7 countries (Greece, Albania, Cyprus, Spain, Italy, Czech Republic and Kosovo) through a web survey. In total 2249 undergraduate nursing students participated. Forty three point eight percent of students agreed to accept a safe and effective COVID-19 vaccine, while the acceptance was higher among Italian students. The factors for intention to get vaccinated were male gender (p=0.008), no working experience in healthcare facilities during the pandemic (p=0.001), vaccination for influenza in 2019 and 2020 (p<0.001), trust in doctors (p<0.001), governments and experts (p=0.012), high level of knowledge (p<0.001) and fear of COVID-19 (p<0.001). Understanding of factors that influence students’ decision to accept COVID-19 vaccination could increase the acceptance rate contributing to a management of the pandemic.HighlightsLess than half of the sample intended to accept COVID-19 vaccinationFactors that influenced nursing students to get vaccinated against COVID-19 were male gender, no working experience in healthcare facilities during the pandemic, vaccination for influenza in 2019 and 2020, trust in doctors, governments and experts, high level of knowledge and fear of COVID-19.
Pooled Sample Testing for SARS-CoV-2 Using Rapid RT-PCR COVID-19 Tests
Authors: Hyde Bethany , Verma Prat , Berke Ethan M. 
Published: January 2021

AbstractWe tested an operationally efficient way to pool samples on a rapid, point-of-care PCR device and examined the limit of detection of SARS-CoV-2 for various pool sizes. Pooled testing maintained testing performance similar to individual sample PCR testing, offering the potential for scalable rapid testing at lower cost with less supplies.
Linked supergenes underlie split sex ratio and social organization in an ant
Authors: Lagunas-Robles German , Purcell Jessica , Brelsford Alan 
Published: January 2021

AbstractSexually reproducing organisms usually invest equally in male and female offspring. Deviations from this pattern have led researchers to new discoveries in the study of parent-offspring conflict, genomic conflict, and cooperation. Some social insect species exhibit the unusual population-level pattern of split sex ratio, wherein some colonies specialize in the production of future queens and others specialize in the production of males. Theoretical work focused on the relatedness asymmetries emerging from haplodiploid inheritance, whereby queens are equally related to daughters and sons, but their daughter workers are more closely related to sisters than to brothers, led to a series of testable predictions and spawned many empirical studies of this phenomenon. However, not all empirical systems follow predicted patterns, so questions remain about how split sex ratio emerges. Here, we sequence the genomes of 138Formica glacialisworkers from 34 male-producing and 34 gyne-producing colonies to determine whether split sex ratio is under genetic control. We identify a supergene spanning 5.5 Mbp that is closely associated with sex allocation in this system. Strikingly, this supergene is adjacent to another supergene spanning 5 Mbp that is associated with variation in colony queen number. We identify a similar pattern in a second related species,Formica podzolica.The discovery that split sex ratio is determined, at least in part, by a supergene in two species opens a new line of research on the evolutionary drivers of split sex ratio.Significance StatementSome social insects exhibit split sex ratio, wherein some colonies produce future queens and others produce males. This phenomenon spawned many influential theoretical studies and empirical tests, both of which have advanced our understanding of parent-offspring conflicts and cooperation. However, some empirical systems did not follow theoretical predictions, indicating that researchers lack a comprehensive understanding of the drivers of split sex ratio. Here, we show that split sex ratio is associated with a large genomic region in two ant species. The discovery of a genetic basis for sex allocation in ants provides a novel explanation for this phenomenon, particularly in systems where empirical observations deviate from theoretical predictions.
Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model
Authors: Sledzieski Samuel , Singh Rohit , Cowen Lenore , Berger Bonnie 
Published: January 2021

AbstractProtein-protein interaction (PPI) networks have proven to be a valuable tool in systems biology to facilitate the discovery and understanding of protein function. Unfortunately, experimental PPI data remains sparse in most model organisms and even more so in other species. Existing methods for computational prediction of PPIs seek to address this limitation, and while they perform well when sufficient within-species training data is available, they generalize poorly to new species or often require specific types and sizes of training data that may not be available in the species of interest. We therefore present D-SCRIPT, a deep learning method for predicting a physical interaction between two proteins given just their sequences. Compared to existing methods, D-SCRIPT generalizes better to new species and is robust to limitations in training data size. Our approach encodes the intuition that for two proteins to physically interact, a subset of amino acids from each protein should be in contact with the other. The intermediate stages of D-SCRIPT directly implement this intuition; the penultimate stage in D-SCRIPT is a rough estimate of the inter-protein contact map of the protein dimer. This structurally-motivated design enables interpretability of our model and, since structure is more conserved evolutionarily than sequence, improves generalizability across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared to the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3-D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. Our work suggests that recent advances in deep learning language modeling of protein structure can be leveraged for protein interaction prediction from sequence. D-SCRIPT is available athttp://dscript.csail.mit.edu.
Genetic overlap between Alzheimer’s disease and depression mapped onto the brain
Authors: Sánchez Jennifer Monereo , Schram Miranda T. , Frei Oleksandr , O’Connell Kevin , Shadrin Alexey A. , Smeland Olav B. , Westlye Lars T. , Andreassen Ole A. , Kaufmann Tobias , Linden David E.J. , van der Meer Dennis 
Published: January 2021

ABSTRACTBackgroundAlzheimer’s disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterizing their genetic overlap may provide etiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects.MethodsWe applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n=79,145) and depression (n=450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (mean age 57.21 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.ResultsMiXer estimated 98 causal genetic variants overlapping between the two disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B=-0.002, p=9.1×10−4) and depression (B=0.007, p=3.2×10−9) in the UK Biobank. This SNP was also associated with several regions of the corpus callosum volume anterior (B>0.024, p<8.6×10−4), third ventricle volume ventricle (B=-0.025, p=5.0×10−6), and inferior temporal gyrus surface area (B=0.017, p=5.3×10−4).DiscussionOur results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.
Opportunistic Assessment of Ischemic Heart Disease Risk Using Abdominopelvic Computed Tomography and Medical Record Data: a Multimodal Explainable Artificial Intelligence Approach
Authors: Zambrano Chaves Juan M , Chaudhari Akshay S , Wentland Andrew L , Desai Arjun D , Banerjee Imon , Boutin Robert D , Maron David J , Rodriguez Fatima , Sandhu Alexander T , Jeffrey R Brooke , Rubin Daniel , Patel Bhavik 
Published: January 2021

AbstractCurrent risk scores for predicting ischemic heart disease (IHD) risk—the leading cause of global mortality—have limited efficacy. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8,197 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed improved multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient’s electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data can enhance IHD risk assessment and aid primary prevention efforts for IHD.
Attention Deficit and Hyperactivity Disorder and Enuresis co-occurrence in the pediatric population: a systematic review and meta-analysis
Authors: de Sena Oliveira Ana Cecília , da Silva Athanasio Bruno , de Carvalho Mrad Flávia Cristina , de Almeida Vasconcelos Monica Maria , Rodrigues Maicon Albuquerque , de Miranda Débora Marques , Simões e Silva Ana Cristina 
Published: January 2021

ABSTRACTBackgroundAttention Deficit and Hyperactivity/Impulsivity Disorder (ADHD) and Enuresis are common behavioral disorders in childhood, impacting in adolescence and adult life.ObjectiveWe systematically search the literature to verify the relationship between ADHD and enuresis and how these conditions can modify each other during development.MethodUsing PRISMA guidelines, we tried to answer the following question: How frequent is ADHD and Enuresis comorbidity?ResultsTwenty-five studies were fully read and show similar rates of ADHD in the Enuretic group than the frequency of Enuresis in the ADHD group. There is a two-times higher risk to have both conditions simultaneously.ConclusionEnuresis and ADHD seems to happen as a continuous of the same spectrum. Further studies are necessary to evaluate if gender, age, course and presence of comorbidities are similar in patients with both conditions in comparison to those with only one of these conditions.
TCA cycle remodeling drives proinflammatory signaling in humans with pulmonary tuberculosis
Authors: Collins Jeffrey M. , Jones Dean P. , Sharma Ashish , Khadka Manoj , Liu Ken , Kempker Russell R. , Prideaux Brendan , Maner-Smith Kristal , Tukvadze Nestani , Shah N. Sarita , Brust James C.M. , Sekaly Rafick P. , Gandhi Neel R. , Blumberg Henry M. , Ortlund Eric , Ziegler Thomas R. 
Published: January 2021

AbstractThe metabolic signaling pathways that drive pathologic tissue inflammation and damage in humans with pulmonary tuberculosis (TB) are not well understood. Using combined methods in plasma high-resolution metabolomics, lipidomics and cytokine profiling from a multicohort study of humans with pulmonary TB disease, we discovered that IL-1β-mediated inflammatory signaling was closely associated with TCA cycle remodeling, characterized by accumulation of the pro-inflammatory metabolite succinate and decreased concentrations of the anti-inflammatory metabolite itaconate. This inflammatory metabolic response was particularly active in persons with multidrug-resistant (MDR)-TB that received 2 months of ineffective treatment and was only reversed after 1 year of appropriate anti-TB chemotherapy. Both succinate and IL-1β were significantly associated with proinflammatory lipid signaling, including increases in the products of phospholipase A2, increased arachidonic acid formation, and metabolism of arachidonic acid to proinflammatory eicosanoids. Together, these results indicate that decreased itaconate and accumulation of succinate and other TCA cycle intermediates are important drivers of IL-1β-mediated proinflammatory eicosanoid signaling in humans with pulmonary TB disease. Host-directed therapies that mitigate such metabolic reprograming have potential to limit pulmonary inflammation and tissue damage.Graphical AbstractOne Sentence SummaryRemodeling of the tricarboxylic acid cycle, characterized by increases in the proinflammatory metabolite succinate and decreased itaconate, mediates proinflammatory eicosanoids signaling in humans with pulmonary tuberculosis through induction of IL-1β.
Assessing the impact of static and fluctuating ocean acidification on the behavior ofAmphiprion percula
Authors: Vaughan Matthew A. , Dixson Danielle L. 
Published: January 2021

AbstractCoral reef organisms are exposed to both an increasing magnitude ofpCO2, and natural fluctuations on a diel scale. For coral reef fishes, one of the most profound effects of ocean acidification is the impact on ecologically important behaviors. Previous behavioral research has primarily been conducted under staticpCO2conditions and have recently come under criticism. Recent studies have provided evidence that the negative impacts on behavior may be reduced under more environmentally realistic, fluctuating conditions. We investigated the impact of both present and future day, static (500 and 1000 μatm) and diel fluctuating (500 ± 200 and 1000 ± 200 μatm)pCO2on the lateralization and chemosensory behavior of juvenile anemonefish,Amphiprion percula. Our static experimental comparisons support previous findings that under elevatedpCO2, fish become un-lateralized and lose the ability to discriminate olfactory cues. Diel-fluctuatingpCO2may aid in mitigating the severity of some behavioral abnormalities such as the chemosensory response, where a preference for predator cues was significantly reduced under a future diel-fluctuatingpCO2regime. This research aids in ground truthing earlier findings and contributes to our growing knowledge of the role of fluctuating conditions.
Cellular crowd control: overriding endogenous cell coordination makes cell migration more susceptible to external programming
Authors: Shim Gawoon , Devenport Danelle , Cohen Daniel J. 
Published: January 2021

AbstractAs collective cell migration is essential in biological processes spanning development, healing, and cancer progression, methods to externally program cell migration are of great value. However, problems can arise if the external commands compete with strong, pre-existing collective behaviors in the tissue or system. We investigate this problem by applying a potent external migratory cue—electrical stimulation and electrotaxis—to primary mouse skin monolayers where we can tune cell-cell adhesion strength to modulate endogenous collectivity. Monolayers with high cell-cell adhesion showed strong natural coordination and resisted electrotactic control, with this conflict actively damaging the leading edge of the tissue. However, reducing pre-existing coordination in the tissue by specifically inhibiting E-cadherin-dependent cell-cell adhesion, either by disrupting the formation of cell-cell junctions with E-cadherin specific antibodies or rapidly dismantling E-cadherin junctions with calcium chelators, significantly improved controllability. Finally, we applied this paradigm of weakening existing coordination to improve control to demonstrate accelerated wound closurein vitro. These results are in keeping with those from diverse, non-cellular systems, and confirm that endogenous collectivity should be considered as a key, quantitative design variable when optimizing external control of collective migration.
Molecular Dynamics Simulations of Phytopthora sojae Avirulence Factor 5, PsAvh5, Define Membrane Binding, Inositolphosphate-3’-phosphate Interactions and Protein Mechanics
Authors: Watkins Stephan L. 
Published: January 2021

AbstractPhytopthora Avirulence proteins are a primary target for development of rational chemical and biological control of some of the most devastating plant pathogens. Despite the sequencing of entire genomes, and characterization of many of these proteins at the chemical level, many questions remain regarding actual chemical and biological interactions involved. In addition, disputed roles of ligands, such as Inositolphosphate-3’-phosphate and amino acids of important function remain unclear. To address some of these issues, we developed molecular models from structural elements and published data for Phytopthora sojae avirulence protein 5. Molecular dynamics simulations are used to study protein function, interactions involved primarily with lipids and membranes, and inositol derivatives. Our findings indicate that the protein is stable as a monomer, and in a dimeric form. Also, that these proteins interact with Inositolphosphate-3’phosphate as a necessary membrane element, in binding. We identified several amino acids of importance, additional to defining the mechanical features of the protein within the binding process to different membranes. A high affinity, comparable to other membrane surface binding molecules of −219.54 Kcal for the dimer, and −176.61 for the monomer were determined. With either form, we found the inositolphosphate-3’-phosphate to be essential in the membrane binding process. Our findings answer some of the debated questions while creating a point to further test avirulence proteins in general for functional aspects. Additionally, the structures and data can be utilized to provide a better starting point for rational design approaches to control this pathogen.
From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy
Authors: Rocchini Duccio , Marcantonio Matteo , Da Re Daniele , Bacaro Giovanni , Feoli Enrico , Foody Giles M. , Furrer Reinhard , Harrigan Ryan J. , Kleijn David , Iannacito Martina , Lenoir Jonathan , Lin Meixi , Malavasi Marco , Marchetto Elisa , Meyer Rachel S. , Moudrý Vítězslav , Payne Davnah , Schneider Fabian D. , Šímová Petra , Thornhill Andrew H. , Thouverai Elisa , Vicario Saverio , Wayne Robert K. , Ricotta Carlo 
Published: January 2021

AbstractAimThe majority of work done to gather information on Earth diversity has been carried out by in-situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannon’s Information Theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, namely they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time.InnovationThe parametric Rao’s quadratic entropy, coded in R, i) allows to represent the whole continuum of potential diversity indices in one formula, and ii) starting from the Rao’s quadratic entropy, allows to explicitly make use of distances among pixel reflectance values, together with relative abundances.Main conclusionsThe proposed unifying measure is an integration between abundance- and distance-based algorithms to map the continuum of diversity given a satellite image at any spatial scale.