bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consensus clustering for Bayesian mixture models
Authors: Coleman Stephen , Kirk Paul D.W. , Wallace Chris 
Published: December 2020

AbstractCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness.In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three ‘omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis. These clusters can help assign likely function to understudied genes, for exampleGAS3clusters with histones active in S-phase, suggesting a role in DNA replication.Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.
Impact of blindness onset on the representation of sound categories in occipital and temporal cortices
Authors: Stefania Mattioni , Mohamed Rezk , Ceren Battal , Jyothirmayi Vadlamudi , Olivier Collignon 
Published: December 2020

AbstractHow does blindness onset impact on the organization of cortical regions coding for the deprived and the remaining senses? We show that the coding of sound categories in the occipital cortex is enhanced and more stable within and across blind individuals when compared to sighted controls, while a reverse group difference is found in the temporal cortex. Importantly, occipital and temporal regions share a more similar representational structure in blind people, suggesting an interplay between the reorganization of occipital and temporal regions following visual deprivation. We suggest that early, and to some extent late blindness, induces network-level reorganization of the neurobiology of auditory categories by concomitantly increasing/decreasing the respective computational load of occipital/temporal regions. These results highlight the interactive nature of regional brain development in case of sensory deprivation.
Targeted DamID inC. elegansreveals a role for LIN-22 and NHR-25 in epidermal cell differentiation
Authors: Katsanos Dimitris , Barkoulas Michalis 
Published: December 2020

AbstractTranscription factors are key players in gene networks controlling cell fate specification during development. In multicellular organisms, they can display complex patterns of expression and binding to their targets, which necessitates tissue-specific characterisation of transcription factor-target interactions. Here, we focus onC. elegansseam cell development, which is used as a model of robust epidermal stem cell patterning. Despite our knowledge of multiple transcription factors playing a role in epidermal development, the composition of the gene network underlying cell fate patterning remains largely unknown. We introduce Targeted DamID (TaDa) that allows tissue-specific transcription factor target identification in intactC. elegansanimals without cell isolation. We employ this method to recover putative targets in the epidermis for two transcription factors, the HES1 homologue LIN-22 and the NR5A1/2 nuclear hormone receptor NHR-25. Using single-molecule FISH (smFISH), we validate TaDa predictions and reveal a role for these transcription factors in promoting cell differentiation, as well as an unusual link between a HES factor and the Wnt signalling pathway.Our results expand our understanding of the epidermal gene network and highlight the power of TaDa to dissect the architecture of tissue-specific gene regulatory networks.
BLOCKADE OF M4 MUSCARINIC RECEPTORS ON STRIATAL CHOLINERGIC INTERNEURONS NORMALIZES STRIATAL DOPAMINE RELEASE IN A MOUSE MODEL OFTOR1ADYSTONIA
Authors: Downs Anthony M. , Donsante Yuping , Jinnah H.A. , Hess Ellen J. 
Published: December 2020

ABSTRACTTrihexyphenidyl (THP), a non-selective muscarinic receptor (mAChR) antagonist, is commonly used for the treatment of dystonia associated withTOR1A, otherwise known asDYT1dystonia. A better understanding of the mechanism of action of THP is a critical step in the development of better therapeutics with fewer side effects. We previously found that THP normalizes the deficit in striatal dopamine (DA) release in a mouse model ofTOR1Adystonia (Tor1a+/ΔEknockin (KI) mice), revealing a plausible mechanism of action for this compound, considering that abnormal DA neurotransmission is consistently associated with many forms of dystonia. However, the mAChR subtype(s) that mediate the rescue of striatal dopamine release remain unclear. In this study we used a combination of pharmacological challenges and cell-type specific mAChR conditional knockout mice of either sex to determine which mAChR subtypes mediate the DA release-enhancing effects of THP. We determined that THP acts in part at M4 mAChR on striatal cholinergic interneurons to rescue DA release in bothTor1a+/+andTor1a+/ΔEKI mice. Further, we found that the subtype selective M4 antagonist VU6021625 recapitulates the effects of THP. These data implicate a principal role for M4 mAChR located on striatal cholinergic interneurons in the mechanism of action of THP and suggest that subtype selective M4 mAChR antagonists may be effective therapeutics with fewer side effects than THP for the treatment ofTOR1Adystonia.
Probing T-cell response by sequence-based probabilistic modeling
Authors: Bravi Barbara , Balachandran Vinod P. , Greenbaum Benjamin D. , Walczak Aleksandra M. , Mora Thierry , Monasson Rémi , Cocco Simona 
Published: December 2020

AbstractWith the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion.Author SummaryLarge repertoires of immune cells, such as T cells, are increasingly made available by high-throughput sequencing. Exploiting such datasets to infer how T-cell respond to antigens could help design vaccines and adoptive T-cell therapies. We here propose an approach based on probabilistic machine learning to identify and characterize responding T cells. After learning, this approach is able to distinguish clones that specifically respond to different antigen stimulations. The model parameters and the low-dimensional representations of the T-cell sequences identify sequence motifs underlying T-cell recognition at the molecular level. The approach is illustrated on repertoire data describingin vitrostimulation of T cells by cancer-related neoantigens, as well as on data for common infectious diseases.
Megadepth: efficient coverage quantification for BigWigs and BAMs
Authors: Wilks Christopher , Ahmed Omar , Baker Daniel N. , Zhang David , Collado-Torres Leonardo , Langmead Ben 
Published: December 2020

AbstractMotivationA common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types.ResultsMegadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19,000 GTExV8 BigWig files in approximately one hour using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package.Availabilityhttps://github.com/ChristopherWilks/megadepth,https://bioconductor.org/packages/megadepth.Contactchris.wilks@jhu.edu
Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson’s disease
Authors: Abbass Mohamad , Gilmore Greydon , Taha Alaa , Chevalier Ryan , Jach Magdalena , Peters Terry M. , Khan Ali R. , Lau Jonathan C. 
Published: December 2020

AbstractEstablishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducials (AFIDs) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinson’s Disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.
The chaperonin GroESL facilitatesCaulobacter crescentuscell division by supporting the function of the actin homologue FtsA
Authors: Schroeder Kristen , Heinrich Kristina , Neuwirth Ines , Jonas Kristina 
Published: December 2020

AbstractThe highly conserved chaperonin GroESL performs a crucial role in protein folding, however the essential cellular pathways that rely on this chaperone are underexplored. Loss of GroESL leads to severe septation defects in diverse bacteria, suggesting the folding function of GroESL may be integrated with the bacterial cell cycle at the point of cell division. Here, we describe new connections between GroESL and the bacterial cell cycle, using the model organismCaulobacter crescentus. Using a proteomics approach, we identify candidate GroESL client proteins that become insoluble or are degraded specifically when GroESL folding is insufficient, revealing several essential proteins that participate in cell division and peptidoglycan biosynthesis. We demonstrate that other cell cycle events such as DNA replication and chromosome segregation are able to continue when GroESL folding is insufficient, and find that deficiency of the bacterial actin homologue FtsA function mediates the GroESL-dependent block in cell division. Our data suggest that a GroESL-FtsA interaction is required to maintain normal dynamics of the FtsZ scaffold and divisome functionality inC. crescentus. In addition to supporting FtsA function, we show that GroESL is required to maintain the flow of peptidoglycan precursors into the growing cell wall. Linking a chaperone to cell division may be a conserved way to coordinate environmental and internal cues that signal when it is safe to divide.ImportanceAll organisms depend on mechanisms that protect proteins from misfolding and aggregation. GroESL is a highly conserved molecular chaperone that functions to prevent protein aggregation in organisms ranging from bacteria to humans. Despite detailed biochemical understanding of GroESL function, thein vivopathways that strictly depend on this chaperone remain poorly defined in most species. This study provides new insights into how GroESL is linked to the bacterial cell division machinery, a crucial target of current and future antimicrobial agents. We identify a functional interaction between GroESL and FtsA, a conserved bacterial actin homologue, suggesting that as in eukaryotes, some bacteria exhibit a connection between cytoskeletal actin proteins and chaperonins. Our work further defines how GroESL is integrated with cell wall synthesis, and illustrates how highly conserved folding machines ensure the functioning of fundamental cellular processes during stress.
Risk of thrombotic complications in influenza versus COVID-19 hospitalized patients
Authors:   Dutch COVID & Thrombosis Coalition (DCTC), Huisman MV , Stals Milou AM , Grootenboers Marco JJH , van Guldener Coen , Kaptein Fleur HJ , Braken Sander JE , Chen Qingui , Chu Gordon , van Driel Erik M , Iglesias del Sol Antonio , de Jonge Evert , Kant K Merijn , Pals Fleur , Toorop Myrthe MA , Cannegieter Suzanne C , Klok Frederikus A 
Published: December 2020

AbstractBackgroundWhereas accumulating studies on COVID-19 patients report high incidences of thrombotic complications, large studies on clinically relevant thrombosis in patients with other respiratory tract infections are lacking. How this high risk in COVID-19 patients compares to those observed in hospitalized patients with other viral pneumonias such as influenza is unknown.ObjectivesTo assess the incidence of venous and arterial thrombotic complications in hospitalized influenza patients as opposed to that observed in hospitalized COVID-19 patients.MethodsRetrospective cohort study; we used data from Statistics Netherlands (study period: 2018) on thrombotic complications in hospitalized influenza patients. In parallel, we assessed the cumulative incidence of thrombotic complications – adjusted for competing risk of death - in patients with COVID-19 in three Dutch hospitals (February 24th - April 26th 2020).ResultsOf the 13.217 hospitalized influenza patients, 437 (3.3%) were diagnosed with thrombotic complications, versus 66 (11%) of the 579 hospitalized COVID-19 patients. The 30-day cumulative incidence of any thrombotic complication in influenza was 11% (95%CI 9.4-12) versus 25% (95%CI 18-32) in COVID-19. For venous thrombotic complications (VTE) and arterial thrombotic complications alone, these numbers were respectively 3.6% (95%CI 2.7-4.6) and 7.5% (95%CI 6.3-8.8) in influenza versus 23% (95%CI 16-29) and 4.4% (95%CI 1.9-8.8) in COVID-19.ConclusionsThe incidence of thrombotic complications in hospitalized influenza patients was lower than in hospitalized COVID-19 patients. This difference was mainly driven by a high risk of VTE complications in the COVID-19 patients admitted to ICU. Remarkably, influenza patients were more often diagnosed with arterial thrombotic complications.EssentialsIt is unknown how COVID-19 compares to patients with other virus infections regarding thrombosis.Hospitalized patients with influenza and COVID-19 were evaluated and compared to each other.30-day cumulative incidence of thrombosis was lower in influenza (11%) than in COVID-19 (25%).Difference was mainly driven by a high risk of VTE in COVID-19 patients admitted to the ICU.
The COSEVAST Study: Unravelling the role of Arterial Stiffness in COVID-19 Disease severity
Authors: Kumar Sanjeev , Kumar Neeraj , Kumar Abhyuday , Bhushan Divendu , Kumar Amarjeet , Kumar Ajeet , Singh Veena , Singh Prabhat Kumar 
Published: December 2020

AbstractBackgroundBased on the detailed review of available research and case studies reported in reputed international journals, it can be concluded that endothelial damage (En-dotheliitis) both in small and large arteries may be an important factor of morbidity and mortality in COVID-19 patients. Arterial stiffness due to Endothelial Dysfunction has been established as an independent and specific marker of various chronic cardiovascular diseases.ObjectiveOur objective was to examine functional impairment of the arteries in COVID-19 disease and establish the non-invasive measurement of Arterial Stiffness as an independent marker of disease severity.MethodsWe recorded the Arterial Stiffness of 23 Mild, 21 Moderate and 20 Severe COVID-19 patients grouped on latest NIH severity criteria. We observed Arterial Stiffness of COVID-19 patients with standard parameters like non-invasive Carotid-Femoral Pulse Wave velocity (cfPWV), Age-Normalized increase in cfPWV (ANI_cfPWV).ResultsModerate and Severe COVID-19 patients have extremely elevated arterial stiffness than Mild patients. In Mild patients, cfPWV (829.1 ± 139.2 cm/s) was extremely significantly lower than both Moderate (1067 ± 152.5 cm/s, P< 0.0001) and Severe (1416 ± 253.9 cm/s, P < 0.0001) patients. ANI_cfPWV in Moderate and Severe patients was significantly higher than Mild patients. (Mild: 101.2 ± 126.1 cm/s; Moderate: 279 ± 114.4 cm/s; Severe: 580.1 ± 216.4 cm/s; intergroup P <0.0001). Conclusion: Our findings strongly suggest that arterial stiffness can be an independent and accurate marker for objective risk stratification and therapeutic alleviation of the acute cardiovascular complications like MODS in COVID-19.
Impact of MEK inhibitor therapy on neurocognition in neurofibromatosis type 1
Authors: Walsh Karin S. , Wolters Pamela L. , Widemann Brigitte C. , del Castillo Allison A. , Sady Maegan D. , Inker Tess , Roderick Marie Claire , Martin Staci , Toledo-Tamula Mary Anne , Struemph Kari , Paltin Iris , Collier Victoria , Mullin Kathy , Fisher Michael J. , Packer Roger J. 
Published: December 2020

AbstractObjectiveNF1-associated cognitive impairments carry significant life-long morbidity. The lack of targeted biologic treatments remains a significant unmet need. We examine changes in cognition in patients with NF1 in the first 48 weeks of MEK inhibitor (MEKi) treatment.Methods59 NF1 patients ages 5-27 on a MEKi clinical trial treating plexiform neurofibroma underwent pre-treatment and follow-up cognitive assessments over 48-weeks of treatment. Performance tasks (Cogstate) and observer-reported functioning (BRIEF) were primary outcomes. Group-level (pairedt-tests) and individual-level analyses (reliable change index; RCI) were used.ResultsAnalysis showed statistically significant improvements on BRIEF compared to baseline (24-week BRI:t(58)=3.03,p=.004,d=0.24; 48-week MCI:t(39)=2.70,p=.01,d=0.27). RCI indicated more patients had clinically significant improvement at 48-weeks than expected by chance (Chi Square=11.95,p=.001, OR=6.3). Group-level analyses indicated stable performance on Cogstate (p>.05). RCI statistics showed high proportions of improved working memory (24-weeksChi Square=8.36,p=.004, OR=4.6 and 48-weeksChi Square=9.34,p=.004, OR=5.3) but not visual learning/memory. Patients with baseline impairments on BRIEF were more likely to show significant improvement than non-impaired patients (24-weeks 46% v. 8%;Chi Square=9.54,p=.008, OR=9.22; 48-weeks 63% v. 16%;Chi Square=7.50,p=.02, OR=9.0).InterpretationOur data shows no evidence of neurotoxicity in 48-weeks of treatment with a MEKi and a potential clinical signal supporting future research of MEKi as a cognitive intervention.
SARS-CoV-2 infection, risk perception, behaviour, and preventive measures at schools in Berlin, Germany, during the early post-lockdown phase: A cross-sectional study
Authors: Hommes Franziska , Loon Welmoed van , Thielecke Marlene , Abramovich Igor , Lieber Sascha , Hammerich Ralf , Gehrke-Beck Sabine , Linzbach Elisabeth , Schuster Angela , von dem Busche Katja , Theuring Stefanie , Gertler Maximilian , Martinez Gabriela Equihua , Richter Joachim , Bergmann Clara , Bölke Alisa , Böhringer Falko , Mall Marcus A. , Rosen Alexander , Krannich Alexander , Keller Jan , Bethke Norma , Kurzmann Marco , Kurth Tobias , Kirchberger Valerie , Seybold Joachim , Mockenhaupt Frank P. 
Published: December 2020

AbstractBackgroundBriefly before the first peak of the COVID-19 pandemic in Berlin, Germany, schools closed in mid-March 2020 for six weeks. Following re-opening, schools gradually resumed operation at a reduced level for nine weeks preceding the summer holidays.AimDuring this phase, we conducted a situational assessment in schools among students and teachers as to infection status, symptoms, affective, behavioural, educational issues, and preventive measures.MethodsAt twenty-four randomly selected primary and secondary schools, one class each was examined. Oro-nasopharyngeal swabs and capillary blood samples were collected to assess SARS-CoV-2 infection (PCR) and specific IgG (ELISA), respectively. Medical history, household and schooling characteristics, leisure time activities, fear of infection, risk perception, hand hygiene, physical distancing, and facemask wearing were assessed.ResultsAmong 535 participants (385 students, 150 staff), one teenager was SARS-CoV-2 infected (0.2%), and seven individuals exhibited specific IgG (1.3%); 16% reported symptoms upon examination, and 48% in the preceding 14 days. Compared to before the pandemic, the proportion of leisure time spent as screen-time increased, and the majority of primary school students reported reduced physical activity. Fear of infection and risk perception were relatively low, but acceptance of adapted health behaviours was high. Governmental preventive measures were adequately implemented, with primary schools performing better than secondary schools.ConclusionIn this phase of rare infection and low seroreactivity, individual and school-level infection prevention and control measures were largely adhered to. Nevertheless, vigilance, continued and proactive preventive measures, and well-rehearsed reaction options are essential to cope with increasing pandemic activity.