bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors associated with the stigma-discrimination complex towards healthcare workers among university students during the coronavirus pandemic in Mexico
Authors: Cassiani-Miranda Carlos Arturo , Álvarez-Solorza Isabel , Campo-Arias Adalberto , Arismendy-López Yinneth Andrea , Tirado-Otálvaro Andrés Felipe , Bustamante-Montes Lilia Patricia , Toxqui-Tlachino María Juana Gloria 
Published: March 2021

AbstractThe COVID-19-related stigma towards healthcare workers negatively influences their performance and job satisfaction, and well-being. The frequency of COVID-19-related stigma towards healthcare workers and its associated factors has not been sufficiently investigated. The objective was to determine the frequency and variables associated with COVID-19-related stigmatisation towards health workers in emerging-age university adults in Mexico. Analytical and cross-sectional study using an online questionnaire in 1,054 students between 18 and 29 years of age. Demographic variables, religiosity, fear of COVID-19 and stigma-discrimination related to COVID-19 towards healthcare workers were analysed. The latter was set as the dependent variable, while demographic variables, religiosity and high fear of COVID-19 were the independent variables. For the association between the variables, a binomial and logarithmic generalised linear model was designed to calculate the adjusted prevalence ratios. The proportion of high stigma-discrimination was 12.4%, and this was associated with a high fear of COVID-19 (APR 1.51, 95% CI 1.06 to 2.23). The main limitations were the cross-sectional nature, social desirability bias, non-probabilistic sampling. The results highlight the importance of establishing programmes to reduce COVID-19-related stigmatisation towards healthcare workers.
Characterizing altruistic motivation in potential volunteers for SARS-CoV-2 challenge trials
Authors: Rose Sophie M , Schmit Virginia L , Darton Thomas C , Eyal Nir , Magalhaes Monica , Morrison Josh , Peeler Matthew , Shah Seema K , Marsh Abigail A 
Published: March 2021

AbstractIn human challenge trials, volunteers are deliberately infected with a pathogen to accelerate vaccine development and answer key scientific questions. In the U.S., preparations for challenge trials with the novel coronavirus are complete, and in the U.K., challenge trials have recently begun. However, ethical concerns have been raised about the potential for invalid consent or exploitation. These concerns largely reflect worries that challenge trial volunteers may be unusually risk-seeking or too economically vulnerable to refuse the payments these trials provide, rather than being motivated primarily by altruistic goals. We conducted the first large-scale survey of intended human challenge trial volunteers and found that SARS-CoV-2 challenge trial volunteers exhibit high levels of altruistic motivations without any special indication of poor risk perception or economic vulnerability. Findings indicate that challenge trials with the novel coronavirus can attract volunteers with background conditions, attitudes, and motivations that should allay key ethical concerns.
A minimal robophysical model of quadriflagellate self-propulsion
Authors: Diaz Kelimar , Robinson Tommie L. , Aydin Yasemin Ozkan , Aydin Enes , Goldman Daniel I. , Wan Kirsty Y. 
Published: March 2021

AbstractLocomotion at the microscale is remarkably sophisticated. Microorganisms have evolved diverse strategies to move within highly viscous environments, using deformable, propulsion-generating appendages such as cilia and flagella to drive helical or undulatory motion. In single-celled algae, these appendages can be arranged in different ways around an approximately 10µm cell body, and coordinated in distinct temporal patterns. Inspired by the observation that some quadriflagellates (bearing four flagella) have an outwardly similar morphology and flagellar beat pattern, yet swim at different speeds, this study seeks to determine whether variations in swimming performance could arise solely from differences in swimming gait. Robotics approaches are particularly suited to such investigations, where the phase relationships between appendages can be readily manipulated. Here, we developed autonomous, algae-inspired robophysical models that can self-propel in a viscous fluid. These macroscopic robots (length and width = 8.5 cm, height = 2 cm) have four independently actuated ‘flagella’ that oscillate back and forth under low-Reynolds number conditions (Re∼ 𝒪(10−1)). We tested the swimming performance of these robot models with appendages arranged in one of two distinct configurations, and coordinated in one of three distinct gaits. The gaits, namely the pronk, the trot, and the gallop, correspond to gaits adopted by distinct microalgal species. When the appendages are inserted perpendicularly around a central ‘body’, the robot achieved a net performance of 0.15−0.63 body lengths per cycle, with the trot gait being the fastest. Robotic swimming performance was found to be comparable to that of the algal microswimmers across all gaits. By creating a minimal robot that can successfully reproduce cilia-inspired drag-based swimming, our work paves the way for the design of next-generation devices that have the capacity to autonomously navigate aqueous environments.
Deficiency of peroxisomal L-bifunctional protein (EHHADH) causes male-specific kidney hypertrophy and proximal tubular injury in mice
Authors: Ranea-Robles Pablo , Portman Kensey , Bender Aaron , Lee Kyung , He John Cijiang , Mulholland David J , Argmann Carmen , Houten Sander M 
Published: March 2021

AbstractBackgroundProximal tubular (PT) cells are enriched in mitochondria and peroxisomes. Whereas mitochondrial fatty acid oxidation (FAO) plays an important role in kidney function by supporting the high-energy requirements of PT cells, the role of peroxisomal metabolism remains largely unknown. EHHADH, also known as L-bifunctional protein, catalyzes the second and third step of peroxisomal FAO.MethodsWe studied kidneys of WT andEhhadhKO mice using histology, immunohistochemistry, immunofluorescence, immunoblot, RNA-sequencing, metabolomics and orchiectomy.ResultsWe observed male-specific kidney hypertrophy and glomerular filtration rate reduction in adultEhhadhKO mice. Transcriptome analysis unveiled a gene expression signature similar to PT injury in acute kidney injury mouse models. This was further illustrated by the presence of KIM-1 (kidney injury molecule-1), SOX-9, and Ki67-positive cells in the PT of maleEhhadhKO kidneys. MaleEhhadhKO kidneys had metabolite changes consistent with peroxisomal dysfunction as well as an elevation in glycosphingolipids levels. Orchiectomy ofEhhadhKO mice reversed kidney enlargement and decreased the number of KIM-1 positive cells. We reveal a pronounced sexual dimorphism in the expression of peroxisomal FAO proteins in mouse kidney, underlining a role of androgens in the kidney phenotype ofEhhadhKO mice.ConclusionsOur data highlight the importance of EHHADH and peroxisomal metabolism in male kidney physiology and reveal peroxisomal FAO as a sexual dimorphic metabolic pathway in mouse kidneys.
3D genomic capture of regulatory immuno-genetic profiles in COVID-19 patients for prognosis of severe COVID disease outcome
Authors: Hunter Ewan , Koutsothanasi Christina , Wilson Adam , Santos Francisco C. , Salter Matthew , Powell Ryan , Dring Ann , Brajer Paulina , Egan Benedict , Westra Jurjen W. , Ramadass Aroul , Messer William , Brunton Amanda , Lyski Zoe , Vancheeswaran Rama , Barlow Andrew , Pchejetski Dmitri , Robbins Peter A. , Mellor Jane , Akoulitchev Alexandre 
Published: March 2021

AbstractHuman infection with the SARS-CoV-2 virus leads to coronavirus disease (COVID-19). A striking characteristic of COVID-19 infection in humans is the highly variable host response and the diverse clinical outcomes, ranging from clinically asymptomatic to severe immune reactions leading to hospitalization and death. Here we used a 3D genomic approach to analyse blood samples at the time of COVID diagnosis, from a global cohort of 80 COVID-19 patients, with different degrees of clinical disease outcomes. Using 3D whole genomeEpiSwitch®arrays to generate over 1 million data points per patient, we identified a distinct and measurable set of differences in genomic organization at immune-related loci that demonstrated prognostic power at baseline to stratify patients with mild forms of illness and those with severe forms that required hospitalization and intensive care unit (ICU) support. Further analysis revealed both well established and new COVID-related dysregulated pathways and loci, including innate and adaptive immunity; ACE2; olfactory, Gβψ, Ca2+and nitric oxide (NO) signalling; prostaglandin E2 (PGE2), the acute inflammatory cytokine CCL3, and the T-cell derived chemotactic cytokine CCL5. We identified potential therapeutic agents for mitigation of severe disease outcome, with several already being tested independently, including mTOR inhibitors (rapamycin and tacrolimus) and general immunosuppressants (dexamethasone and hydrocortisone). Machine learning algorithms based on establishedEpiSwitch®methodology further identified a subset of 3D genomic changes that could be used as prognostic molecular biomarker leads for the development of a COVID-19 disease severity test.
In Poetry, if Meter has to Help Memory, it Takes its Time
Authors: Andreetta Sara , Soldatkina Oleksandra , Boboeva Vezha , Treves Alessandro 
Published: March 2021

AbstractTo test the idea that poetic meter emerged as a cognitive schema to aid verbal memory, we have focused on classical Italian poetry and on its three basic components of meter: rhyme, accent and verse length. Meaningless poems were generated by introducing prosody-invariant non-words into passages from Dante’sDivina Commediaand Ariosto’sOrlando Furioso, which were then further manipulated by selectively ablating rhymes, modifying accent patterns or altering the number of syllables. The resulting four versions of each non-poem were presented in a fully balanced design to cohorts of high school educated Italian native speakers, who were then asked to retrieve 3 target non-words. Surprisingly, we found that the integrity of Dante’s meter has no significant effect on memory performance. With passages derived from Ariosto, instead, removing each component downgrades memory by an amount proportional to its contribution to perceived metric plausibility, with rhymes having the strongest effects, followed by accents and then by verse length. Counterintuitively, the fully metric versions required longer reaction times, implying that activating metric schemata involves a cognitive cost. Within schema theories, this finding provides evidence for high-level interactions between procedural and episodic memory.
Altered effective connectivity in sensorimotor cortices: a novel signature of severity and clinical course in depression
Authors: Ray Dipanjan , Bezmaternykh Dmitry , Mel’nikov Mikhail , Friston Karl J , Das Moumita 
Published: March 2021

Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and Dynamic Causal Modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex: an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (non-pharmacological) treatment, depression associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.Significance StatementResearch into neurobiology of depression primarily focuses on its complex psychological aspects. Here, we propose an alternative approach and target sensorimotor alterations - a prominent but often neglected feature of depression. We demonstrated using resting-state fMRI data and computational modelling that top-down and bottom-up information flow in sensory and motor cortices is altered with increasing depression severity in a way that is consistent with depression symptoms. Depression associated changes were found to be consistent across sessions, amenable to treatment and of effect size sufficiently large to predict whether somebody has mild or severe depression. These results pave the way for a new avenue of research into the neural underpinnings of mental health conditions.
Tracing evolution history of 100 whole genome sequences of diffuse stem brain tumor
Authors: Zou Lihua 
Published: March 2021

AbstractDiffuse intrinsic pontine glioma (DIPG) is a deadly disease among young children. The evolution path and mutational processes giving rise to DIPG remain elusive. We analyzed 100 whole genome sequences (WGS) from 60 DIPG patients. This revealed 25% DIPGs acquired whole-genome duplications (WGD) early during tumor evolution. WGD samples are associated with loss of TP53 and poorer survival. In addition, almost all WGD samplers harbor complex structural variations (SVs) and show characteristic short microhomology at SV breakpoints. Mutation analysis revealed that H3K27M driver mutation is acquired early during tumor clonal evolution. Mutation signature analysis identified a unique mutational process at a late stage of tumor evolution. This study revealed that tumor evolution of DIPG is characterized by chromosomal instability shaped by DNA repair defects and dynamic mutational processes. Our work shed new insights on the disease pathogenesis of DIPG and provided rationale for designing novel therapy for this deadly disease.
Time dependent proinflammatory responses shape virus interference during coinfections of influenza A virus and influenza D virus
Authors: Guan Minhui , Blackmon Sherry , Olivier Alicia K. , Zhang Xiaojian , Liu Liyuan , Woolums Amelia , Crenshaw Mark A. , Liao Shengfa F. , Webby Richard , Epperson William , Wan Xiu-Feng 
Published: March 2021

AbstractBoth influenza A virus (IAV) and influenza D virus (IDV) are enzootic in pigs. IAV causes approximately 100% morbidity with low mortality, whereas IDV leads to only mild respiratory diseases in pigs. In this study, we performed a series of coinfection experimentsin vitroandin vivoto understand how IAV and IDV interact and cause pathogenesis during coinfection. Results showed that IAV inhibited IDV replication when infecting swine tracheal epithelial cells (STEC) with IAV 24- or 48-hours prior to IDV inoculation, and that IDV suppressed IAV replication when IDV preceded IAV inoculation by 48 hours. Virus interference was not identified during simultaneous IAV/IDV infections or with 6 hours between the two viral infections, regardless of their order. The interference pattern at 24- and 48-hours correlated with proinflammatory responses induced by the first infection, which was about 24-hours slower for IDV than IAV. The viruses did not interfere with each other if both infected the cells before proinflammatory responses were induced. Coinfection in pigs further demonstrated that IAV interfered both viral shedding and virus replication of IDV, especially in the upper respiratory tract. Clinically, coinfection of IDV and IAV did not show significant enhancement of disease pathogenesis, compared with the pigs infected with IAV alone. In summary, this study suggests that interference during coinfection of IAV and IDV is primarily due to the proinflammatory response and is therefore dependent on the time between infection, and the order of infection.ImportanceBoth IAV and IDV are enzootic in pigs, and feral pigs have a higher risk for both IAV and IDV exposures than IDV exposure alone. This study suggests that in coinfection with IAV and IDV either virus can interfere with the replication of the other virus by stimulating proinflammatory responses; however, the proinflammatory response was 24 hours slower for IDV than IAV.In vitrothere was no interference during simultaneous coinfection, regardless of infection order. Coinfection of IDV and IAV in pigs did not show enhanced pathogenesis, compared with those infected only with IAV. This study can facilitate our understanding of virus epidemiology and pathogenesis associated with IAV and IDV coinfection.
SARS-CoV-2 Epidemiology on a Public University Campus in Washington State
Authors: Weil Ana A. , Sohlberg Sarah L. , O’Hanlon Jessica A. , Casto Amanda M. , Emanuels Anne W , Lo Natalie K. , Greismer Emily P. , Magedson Ariana M. , Wilcox Naomi C. , Kim Ashley E. , Back Lewis , Frazar Christian D. , Pelle Ben , Sibley Thomas R. , Ilcisin Misja , Lee Jover , Ryke Erica L. , Chris Craft J. , Schwabe-Fry Kristen M. , Fay Kairsten A. , Cho Shari , Han Peter D. , Heidl Sarah J. , Pfau Brian A. , Truong Melissa , Zhong Weizhi , Srivatsan Sanjay R. , Harb Katia F. , Gottlieb Geoffrey S. , Hughes James P. , Nickerson Deborah A. , Lockwood Christina M. , Starita Lea M. , Bedford Trevor , Shendure Jay A. , Chu Helen Y. 
Published: March 2021

ABSTRACTBackgroundTesting programs have been utilized as part of SARS-CoV-2 mitigation strategies on university campuses, and it is not known which strategies successfully identify cases and contain outbreaks.ObjectiveEvaluation of a testing program to control SARS-CoV-2 transmission at a large university.DesignProspective longitudinal study using remote contactless enrollment, daily mobile symptom and exposure tracking, and self-swab sample collection. Individuals were tested if the participant was (1) exposed to a known case, developed new symptoms, or reported high-risk behavior, (2) a member of a group experiencing an outbreak, or (3) at baseline upon enrollment.SettingAn urban, public university during Autumn quarter of 2020ParticipantsStudents, staff, and faculty.MeasurementsSARS-CoV-2 PCR testing was conducted, and viral genome sequencing was performed.ResultsWe enrolled 16,476 individuals, performed 29,783 SARS-CoV-2 tests, and detected 236 infections. Greek community affiliation was the strongest risk factor for testing positive. 75.0% of positive cases reported at least one of the following: symptoms (60.8%), exposure (34.7%), or high-risk behaviors (21.5%). 88.1% of viral genomes (52/59) sequenced from Greek-affiliated students were genetically identical to at least one other genome detected, indicative of rapid SARS-CoV-2 spread within this group, compared to 37.9% (11/29) of genomes from non-Greek students and employees.LimitationsObservational study.ConclusionIn a setting of limited resources during a pandemic, we prioritized testing of individuals with symptoms and high-risk exposure during outbreaks. Rapid spread of SARS- CoV-2 occurred within outbreaks without evidence of further spread to the surrounding community. A testing program focused on high-risk populations may be effective as part of a comprehensive university-wide mitigation strategy to control the SARS-CoV-2 pandemic.
Can we predict antibody responses in SARS-CoV-2? A cohort analysis
Authors: Gaeddert Mary , Kitchen Philip , Broger Tobias , Weber Stefan , Bartenschlager Ralf , Plaszczyca Anna , Kräusslich Hans-Georg , Müller Barbara , Souto-Carneiro Margarida , Janssen Maike , Müller-Tidow Carsten , Merle Uta , Herrmann Yannis , Raedeker Lukas , Sebastian Jakob , Brindl Niall , Starck Tim , Denkinger Claudia M. 
Published: March 2021

AbstractBackgroundAfter infection with severe acute respiratory syndrome coronavirus (SARS-CoV-2), Immunoglobulin G (IgG) antibodies and virus-specific neutralizing antibodies (nAbs) develop. This study describes antibody responses in a cohort of recovered COVID-19 patients to identify predictors.MethodsWe recruited patients with confirmed SARS-CoV-2 infection from Heidelberg, Germany. Blood samples were collected three weeks after COVID-19 symptoms ended. Participants with high antibody titers were invited for follow-up visits. IgG titers were measured by the Euroimmun Assay, and nAbs titers in a SARS-CoV-2 infection-based assay.Results281 participants were enrolled between April and August 2020 with IgG testing, 145 (51.6%) had nAbs, and 35 (12.5%) had follow-up. The median IgG optical density (OD) ratio was 3.1 (Interquartile range (IQR) 1.6-5.1), and 24.1% (35/145) had a nAb titer>1:80. Higher IgG titers were associated with increased age and more severe disease, and higher nAbs were associated with male gender and CT-value of 25-30 on RT-PCR at diagnosis. The median IgG OD ratio on follow-up was 3.7 (IQR 2.9-5.9), a median increase of 0.5 (IQR −0.3-1.7). Six participants with follow-up nAbs all had titers ≤ 1:80.ConclusionsWhile age and disease severity were correlated with IgG responses, predictive factors for nAbs in convalescent patients remain unclear.
Using Machine Learning along with Data Science algorithms to pre-process and forecast COVID-19 Cases and Deaths
Authors: Choudhary Avi 
Published: March 2021

AbstractThe Covid-19 pandemic has taken a major toll on the health and state of our global population. With tough decisions for allocating resources(i.e. vaccines)[1] are being made, forecasting through machine learning has become more important than ever. Moreover, as vaccines are being brought to the public and cases are going down, it is time that we reflect on where the pandemic has taken the most toll:for the purpose of future reform. This research illustrates two different models and algorithms for COVID-19 forecasting: Auto Regressive models and Recurrent Neural Networks(RNNs). The results show the true potential of RNNs to work with sequential and time-series data to forecast future cases and deaths in different states. As the paper utilizes the tanh activation function and multiple LSTM layers, the research will show the importance of machine learning and its ability to help politicians make decisions when it comes to helping states during the pandemic and future reform. The data will also pre-process the time-series data, using rolling statistics and will clean the data for the auto-regressive model and RNN layers. Thus, we show that along with Recurrent Neural Network layers, activation functions also play a crucial role in the accuracy of the forecast.