bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep-sea plastisphere: long-term colonization by plastic-associated bacterial and archaeal communities in the Southwest Atlantic Ocean
Authors: Agostini Luana , Moreira Julio Cezar Fornazier , Bendia Amanda Gonçalves , Kmit Maria Carolina Pezzo , Waters Linda Gwen , Santana Marina Ferreira Mourão , Sumida Paulo Yukio Gomes , Turra Alexander , Pellizari Vivian Helena 
Published: January 2021

AbstractMarine plastic pollution is a global concern because of continuous release into the oceans over the last several decades. Although recent studies have made efforts to characterize the so-called plastisphere, or microbial community inhabiting plastic substrates, it is not clear whether the plastisphere is defined as a core community or as a random attachment of microbial cells. Likewise, little is known about the influence of the deep-sea environment on the plastisphere. In our experimental study, we evaluated the microbial colonization on polypropylene pellets and two types of plastic bags: regular high density polyethylene (HDPE) and HDPE with the oxo-biodegradable additive BDA. Gravel was used as control. Samples were deployed at three sites at 3,300 m depth in the Southwest Atlantic Ocean and left for microbial colonization for 719 days. For microbial communities analysis, DNA was extracted from the biofilm on plastic and gravel substrates, and then the 16S rRNA was sequenced through the Illumina Miseq platform. Cultivation was performed to isolate strains from the plastic and gravel substrates. Substrate type strongly influenced the microbial composition and structure, while no difference between sites was detected. Although several taxa were shared among plastics, we observed some groups specific for each plastic substrate. These communities comprised taxa previously reported from both epipelagic zones and deep-sea benthic ecosystems. The core microbiome (microbial taxa shared by all plastic substrates) was exclusively composed by low abundance taxa, with some members well-described in the plastisphere and with known plastic-degradation capabilities. Additionally, we obtained bacterial strains that have been previously reported inhabiting plastic substrates and/or degrading hydrocarbon compounds, which corroborates our metabarcoding data and suggests the presence of microbial members potentially active and involved with degradation of these plastics in the deep sea.
A Rapid and Low-Cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-Based RT-qPCR
Authors: Sater Fadi Abdel , Younes Mahmoud , Nassar Hassan , Nguewa Paul , Hamze Kassem 
Published: January 2021

AbstractBackgroundThe new SARS-CoV-2 variant VUI (202012/01), identified recently in the United Kingdom (UK), exhibits a higher transmissibility rate compared to other variants, and a reproductive number 0.4 higher. In the UK, scientists were able to identify the increase of this new variant through the rise of false negative results for the spike (S) target using a three-target RT-PCR assay (TaqPath kit).MethodsTo control and study the current coronavirus pandemic, it is important to develop a rapid and low-cost molecular test to identify the aforementioned variant. In this work, we designed primer sets specific to SARS-CoV-2 variant VUI (202012/01) to be used by SYBR Green-based RT-PCR. These primers were specifically designed to confirm the deletion mutations Δ69/Δ70 in the spike and the Δ106/Δ107/Δ108 in the NSP6 gene. We studied 20 samples from positive patients, 16 samples displayed an S-negative profile (negative for S target and positive for N and ORF1ab targets) and four samples with S, N and ORF1ab positive profile.ResultsOur results emphasized that all S-negative samples harbored the mutations Δ69/Δ70 and Δ106/Δ107/Δ108. This protocol could be used as a second test to confirm the diagnosis in patients who were already positive to COVID-19 but showed false negative results for S-gene.ConclusionsThis technique may allow to identify patients carrying the VUI (202012/01) variant or a closely related variant, in case of shortage in sequencing.
Objective adherence to an online FAVAS therapeutic game for treating amblyopia in children
Authors: Bocqué Catheline , Wang Jingyun , Rickmann Annekatrin , Julich-Haertel Henrike , Kaempf Uwe , Januschowski Kai 
Published: January 2021

ABSTRACTAimThis retrospective study was to evaluate whether an updated version of attention binding digital therapeutic games based on the principle of Focal Ambient Visual Acuity Stimulation (FAVAS) would result in an improved patient adherence of patching in 4- to 12-year-old patients with amblyopia.MethodsWe analyzed pseudonymised electronically recorded data from patients treated with two different versions of attention binding digital therapeutic games in 2015 and 2020. Two groups of children treated with occlusion therapy and attention binding digital therapeutic games, divided in treatment version, were compared. Patients in Group 2015 used the old version of therapeutic games without tablet computer functionality, while Group 2020 used more attractive therapeutic games with tablet computer functionality. Objective adherence was calculated by comparing the amount of minutes using the therapeutic games as monitored in the automatized logbook versus prescribed minutes of using the games.ResultsChildren in Group 2015 spent on average 2009.3±1372.1 (36 to 5472) minutes using FAVAS; children in Group 2020 spent on average 2695.5±1526.8 (37.5 to 5672) minutes using the improved therapy. Meaning, Group 2020 spent 686.2 more minutes on FAVAS than Group 2015 (t=3.87, P<0.001). Although patient adherence was very variable, it significantly improved up to 78% ± 46% in Group 2020 compared to the 57% ± 34% in Group 2015 (t=4.3, P<0.001).ConclusionFAVAS 2020 with an improved gamification aspect as well as tablet computer functionality increased adherence significantly compared to the earlier version FAVAS 2015, indicating that FAVAS 2020 could be an effective approach to support patching amblyopia treatment.
Diagnostic accuracy and utility of SARS-CoV-2 antigen lateral flow assays in medical admissions with possible COVID-19
Authors: Houston Hamish , Gupta-Wright Ankur , Toke-Bjolgerud Edward , Biggin-Lamming James , John Laurence 
Published: January 2021

AbstractWe evaluated diagnostic accuracy of the Innova SARS-CoV-2 Antigen Rapid Qualitative Test compared to SARS-CoV-2 RT-PCR from nasopharyngeal swabs in adult admissions who met the COVID-19 case definition at a busy acute hospital in the UK. We found the Innova SARS-CoV-2 Antigen Rapid Qualitative Test had a good specificity in patients with symptoms of COVID-19 presenting to hospital. The Innova LFA can be used to rapidly identify COVID-19 cases amongst hospital admissions meeting the COVID-19 case definition, allowing patients to be allocated to COVID-19 cohort areas.
Baseline functional connectivity in resting state networks associated with depression and remission status after 16 weeks of pharmacotherapy: A CAN-BIND Report
Authors: van der Wijk Gwen , Harris Jacqueline K. , Hassel Stefanie , Davis Andrew D. , Zamyadi Mojdeh , Arnott Stephen R. , Milev Roumen , Lam Raymond W. , Frey Benicio N. , Hall Geoffrey B. , Müller Daniel J. , Rotzinger Susan , Kennedy Sidney H. , Strother Stephen C. , MacQueen Glenda M. , Protzner Andrea B. 
Published: January 2021

AbstractUnderstanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. While numerous studies have been conducted, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity findings in the default mode, salience and cognitive control networks (DMN, SN, and CCN respectively) associated with MDD and pharmacotherapy outcomes in a large, multi-site sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression (CAN-BIND) initiative. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior DMN, SN and CCN) and all other brain voxels across participants. Partial least squares, a multivariate statistical technique, was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission early (MADRS ≤ 10 within 8 weeks), late (MADRS ≤ 10 within 16 weeks) or not at all. We replicated previous findings of altered connectivity in the DMN, SN and CCN in patients. In addition, baseline connectivity of the anterior/posterior DMN and SN seeds differentiated patients with different treatment outcomes. Weaker connectivity within the anterior DMN and between the anterior DMN and the SN and CCN characterised early remission; stronger connectivity within the SN and weaker connectivity between the SN and the DMN and CCN was related to late remission, of which the weaker SN – anterior DMN connectivity might specifically be associated with remission to dual pharmacotherapy; and connectivity strength between the posterior DMN and cingulate areas distinguished all three groups, with early remitters showing the strongest connections and non-remitters the weakest. The stability of these baseline patient differences was established in the largest single-site subsample of the data. Our replication and extension of altered connectivity within and between the DMN, SN and CCN highlighted previously reported and new differences between patients with MDD and controls, and revealed features that might predict remission prior to pharmacotherapy.Trial registrationClinicalTrials.gov:NCT01655706.
New-Onset IgG Autoantibodies in Hospitalized Patients with COVID-19
Authors: Chang Sarah Esther , Feng Allan , Meng Wenzhao , Apostolidis Sokratis A. , Mack Elisabeth , Artandi Maja , Barman Linda , Bennett Kate , Chakraborty Saborni , Chang Iris , Cheung Peggie , Chinthrajah Sharon , Dhingra Shaurya , Do Evan , Finck Amanda , Gaano Andrew , Geßner Reinhard , Giannini Heather M. , Gonzalez Joyce , Greib Sarah , Gündisch Margrit , Hsu Alex Ren , Kuo Alex , Manohar Monali , Mao Rong , Neeli Indira , Neubauer Andreas , Oniyide Oluwatosin , Powell Abigail Elizabeth , Puri Rajan , Renz Harald , Schapiro Jeffrey M. , Weidenbacher Payton A , Wittman Rich , Ahuja Neera , Chung Ho-Ryun , Jagannathan Pras , James Judith , Kim Peter S. , Meyer Nuala J. , Nadeau Kari , Radic Marko , Robinson William H. , Singh Upinder , Wang Taia T. , Wherry E. John , Skevaki Chrysanthi , Prak Eline T. Luning , Utz PJ 
Published: January 2021

AbstractCoronavirus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. We developed three different protein arrays to measure hallmark IgG autoantibodies associated with Connective Tissue Diseases (CTDs), Anti-Cytokine Antibodies (ACA), and anti-viral antibody responses in 147 hospitalized COVID-19 patients in three different centers. Autoantibodies were identified in approximately 50% of patients, but in <15% of healthy controls. When present, autoantibodies largely targeted autoantigens associated with rare disorders such as myositis, systemic sclerosis and CTD overlap syndromes. Anti-nuclear antibodies (ANA) were observed in ∼25% of patients. Patients with autoantibodies tended to demonstrate one or a few specificities whereas ACA were even more prevalent, and patients often had antibodies to multiple cytokines. Rare patients were identified with IgG antibodies against angiotensin converting enzyme-2 (ACE-2). A subset of autoantibodies and ACA developed de novo following SARS-CoV-2 infection while others were transient. Autoantibodies tracked with longitudinal development of IgG antibodies that recognized SARS-CoV-2 structural proteins such as S1, S2, M, N and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. COVID-19 patients with one or more autoantibodies tended to have higher levels of antibodies against SARS-CoV-2 Nonstructural Protein 1 (NSP1) and Methyltransferase (ME). We conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized COVID-19 patients and are positively correlated with immune responses to SARS-CoV-2 proteins.
Operational response simulation tool for epidemics within refugee and IDP settlements
Authors: Aylett-Bullock Joseph , Cuesta-Lazaro Carolina , Quera-Bofarull Arnau , Katta Anjali , Pham Katherine Hoffmann , Hoover Benjamin , Strobelt Hendrik , Jimenez Rebeca Moreno , Sedgewick Aidan , Evers Egmond Samir , Kennedy David , Harlass Sandra , Kahindo Maina Allen Gidraf , Hussien Ahmad , Luengo-Oroz Miguel 
Published: January 2021

AbstractThe spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. Non-pharmaceutical public health interventions can be used to mitigate transmission, and modeling efforts can provide crucial insights on the potential effectiveness of such interventions to help inform decision making processes. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements. The model, based on the JUNE open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. Furthermore, we present a visual analytics tool which allows decision makers to distill insights by comparing the results of different simulations and scenarios. Through simulating their effects on the epidemiological development of COVID-19, we evaluate several public health interventions ranging from increasing mask wearing compliance to the reopening of learning institutions. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings.
High current production ofShewanella oneidensiswith electrospun carbon nanofiber anodes is directly linked to biofilm formation
Authors: Erben Johannes , Wang Xinyu , Kerzenmacher Sven 
Published: January 2021

1AbstractWe study the current production ofShewanella oneidensisMR-1 with electro-spun carbon fiber anode materials and analyze the effect of the anode morphology on the micro-, meso-and macroscale. The materials feature fiber diameters in the range between 108 nm and 623 nm, resulting in distinct macropore sizes and surface roughness factors. A maximum current density of (255± 71) µA cm−2was obtained with 286 nm fiber diameter material. Additionally, micro-and mesoporosity were introduced by CO2and steam activation which did not improve the current production significantly. The current production is directly linked to the biofilm dry mass under anaerobic and micro-aerobic conditions. Our findings suggest that either the surface area or the pore size related to the fiber diameter determines the attractiveness of the anodes as habitat and there-fore biofilm formation and current production. Reanalysis of previous works supports these findings.
Identifying drivers of spatio-temporal variation in survival in four blue tit populations
Authors: Bastianelli Olivier , Robert Alexandre , Doutrelant Claire , de Franceschi Christophe , Giovannini Pablo , Charmantier Anne 
Published: January 2021

ABSTRACTIn a context of rapid climate change, the influence of large-scale and local climate on population demography is increasingly scrutinized, yet studies are usually focused on one population. Demographic parameters, including survival, can vary significantly across populations of a given species, depending on global or local climatic fluctuations but also on many other population-specific parameters such as breeding density, habitat naturalness, predation or parasitism. Such ecological differences between populations could lead to different paces-of-life (POL), whereby populations where individuals display higher reproductive investment and bolder behaviours would have lower survival probabilities. We use here long-term (19 to 38 years) monitoring datasets from four Mediterranean populations of blue tits (Cyanistes caeruleus) to investigate the effects of sex, age class, large-scale and local climate temporal variation and population breeding density, on adult survival, using Capture-Mark-Recapture modelling. Environment heterogeneity in these four populations (two in evergreen and two in deciduous forests) has been linked to strong multi-trait phenotypic variation, suggesting blue tits in deciduous forests display faster POL compared to their conspecifics in evergreen habitats. The present results show heterogeneity in average survival probabilities across the four populations, with, as predicted, lower survival in the ‘fast’ blue tits occupying deciduous habitats. Interestingly, the year-to-year variation in survival probabilities was synchronous among populations. This suggests that regional environmental conditions could drive survival fluctuations across populations. However, breeding densities were not correlated across populations, and we found no evidence that adult survival is correlated with either large-scale or local, climate temporal variation in these four blue tit populations. Finally, two of the focal populations displayed a linear temporal decrease in adult survival over the study period, calling for further investigation to explain this decline. Overall, this multi-site study shows that blue tit parental survival from one spring to the next can vary substantially across years, in a synchronous way across populations, yet the climate indices we used are not correlated with the temporal variation. This calls for further investigations in other potential drivers such as resource (in particular insect) abundance, predation or parasitism.
Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model
Authors: Hosoda Shion , Fukunaga Tsukasa , Hamada Michiaki 
Published: January 2021

AbstractMotivationAccumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions.ResultsIn this study, we developedunsupervised learning basedmicrobialinteraction inference method usingBayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota.AvailabilityThe C++ and python source codes of the Umibato software are available athttps://github.com/shion-h/UmibatoContactshion_hosoda@asagi.waseda.jp,mhamada@waseda.jp
Statistical learning as reinforcement learning phenomena
Authors: Orpella J , Mas-Herrero E , Ripollés P , Marco-Pallarés J , de Diego-Balaguer R 
Published: January 2021

AbstractStatistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks theonlinestatistical learning of language rules combined with computational modelling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate - on two different cohorts - that a Temporal Difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the longstanding gap between language learning and reinforcement learning phenomena.
Estimating the societal disease burden of East Coast fever among rural cattle keeping households of Namwala District, Zambia
Authors: Mwila Natasha , Mumba Chisoni , Salih Omran , Sichibalo Karen , Simulundu Edgar , Changula Katendi , Chitanga Simbarashe 
Published: January 2021

AbstractThe study aimed to estimate the disease burden of East Coast fever (ECF) among rural cattle keeping households of Namwala District of Zambia using Productivity Adjusted Life Years (PALYs). We modified Disability Adjusted Life Year (DALY) equations for humans to PALYs to estimate the societal burden of animal diseases. We used a structured questionnaire to collect data on parameters that feed into PALY equations. We coded and entered data from questionnaires directly into Statistical Package of Social Sciences (IBM SPSS Version 20), and entered the estimated values of PALY parameters into Mathematical Calculus Software called Integral Calculator (https://www.integral-calculator.com/). We then used the integral calculator to calculate PALY equations, which we used to estimate the societal disease burden of ECF in cattle. PALYs calculations were done in three categories; PALYs without discounting and age weighting, PALYs with only discounting, and PALYs with discounting and age weighting.Results revealed that the years of productivity lost by a cow, bull, and ox that suffered from ECF were estimated at 15, 10, and 15 years, respectively. In the second category, the years of productivity lost by a cow, bull, and ox were seven, six, and seven years, respectively. In the final category, the years of productivity lost by a cow, bull, and ox were five years.ECF caused a total of 517,165 PALYs in Namwala District. The quality of life reduced in years due to disability (YLD) caused by ECF per cow, bull, and ox was 0.07, 0.07, and 0.02 percent of their life expectancy, respectively. The estimated values for the years of a lifetime lost due to mortality (YLL) caused by ECF were 35, 49, and 35 percent of the life expectancy per cow, bull, and ox. These results are important for measuring outcomes of animal health problems in terms of PALYs. The findings are helpful in future projections for the future burden of any disease and can be used as a basis in policy-making and decision-making, particularly on priorities in animal health research. We recommend that a classification of animal diseases of national economic importance should consider both the societal burden and economic impact instead of the common practice of only considering the economic impact.