bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diversity of function and higher-order structure within HWE sensor histidine kinases
Authors: Dikiy Igor , Swingle Danielle , Toy Kaitlyn , Edupuganti Uthama R. , Rivera-Cancel Giomar , Gardner Kevin H. 
Published: July 2020

AbstractIntegral to the protein structure/function paradigm, oligomeric state is typically conserved along with function across evolution. However, notable exceptions such as the hemoglobins show how evolution can alter oligomerization to enable new regulatory mechanisms. Here we examine this linkage in histidine kinases (HKs), a large class of widely distributed prokaryotic environmental sensors. While the majority of HKs are transmembrane homodimers, members of the HWE/HisKA2 family can deviate from this architecture as exemplified by our finding of a monomeric soluble HWE/HisKA2 HK (EL346, a photosensing Light-Oxygen-Voltage (LOV)-HK). To further explore the diversity of oligomerization states and regulation within this family, we biophysically and biochemically characterized multiple EL346 homologs and found a range of HK oligomeric states and functions. Three LOV-HK homologs are primarily dimeric with differing structural and functional responses to light, while two Per-ARNT-Sim (PAS)-HKs interconvert between differentially active monomers and dimers, suggesting dimerization might control enzymatic activity for these proteins. Finally, we examined putative interfaces in a dimeric LOV-HK, finding that multiple regions contribute to dimerization. Our findings suggest the potential for novel regulatory modes and oligomeric states beyond those traditionally defined for this important family of environmental sensors.
Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance
Authors: Malec Scott A. , Bernstam Elmer V. , Wei Peng , Boyce Richard D. , Cohen Trevor 
Published: July 2020

ABSTRACTIntroductionConfounding bias threatens the reliability of observational studies and poses a significant scientific challenge. This paper introduces a framework for identifying confounding factors by exploiting literature-derived computable knowledge. In previous work, we have shown that semantic constraint search over computable knowledge extracted from the literature can be useful for reducing confounding bias in statistical models of EHR-derived observational clinical data. We hypothesize that adjustment sets of literature-derived confounders could also improve causal inference.MethodsWe introduce two methods (semantic vectors and string-based confounder search) that query the literature for potential confounders and use this information to build models from EHR-derived data to more accurately estimate causal effects. These methods search SemMedDB for indications TREATED BY the drug that is also known to CAUSE the adverse event. For evaluation, we attempt to rediscover associations in a publicly available reference dataset containing expected pairwise relationships between drugs and adverse events from empirical data derived from a corpus of 2.2M EHR-derived clinical notes. For our knowledge-base, we use SemMedDB, a database of computable knowledge mined from the biomedical literature. Using standard adjustment and causal inference procedures on dichotomous drug exposures, confounders, and adverse event outcomes, varying numbers of literature-derived confounders are combined with EHR data to predict and estimate causal effects in light of the literature-derived confounders. We then compare the performance of the new methods with naive (χ2, reporting odds ratio) measures of association.Results and ConclusionsLogistic regression with ten vector space-derived confounders achieved the most improvement with AUROC of 0.628 (95% CI: [0.556,0.720]), compared with baselineχ20.507 (95% CI: [0.431,0.583]). Bias reduction was improved more often in modeling methods using more rather than less information, and using semantic vector rather than string-based search. We found computable knowledge useful for improving automated causal inference, and identified opportunities for further improvement, including a role for adjudicating literature-derived confounders by subject matter experts.Graphical AbstractHighlightsAccess to causal background knowledge is required for causal learning to scale to large datasets.We introduce a framework for identifying confounders to enhance causal inference from EHR.We search computable knowledge for indications TREATED BY the drug that CAUSE the outcome.Literature-derived confounders reduce confounding bias in EHR data.Structured knowledge helps interpret and explain data captured in clinical narratives.
Acoustic and language analysis of speech for suicide ideation among US veterans
Authors: Belouali Anas , Gupta Samir , Sourirajan Vaibhav , Yu Jiawei , Allen Nathaniel , Alaoui Adil , Dutton Mary Ann , Reinhard Matthew J. 
Published: July 2020

AbstractU.S. veterans are 1.5 times more likely to die by suicide than Americans who never served in the military. Considering such high rates, there is an urgent need to develop innovative approaches for objective and clinically applicable assessments to detect individuals at high risk. We hypothesize that speech in suicidal veterans has a range of distinctive acoustic and linguistic features. The purpose of this work is to build an automated machine learning and natural language processing tool to screen for suicidality. Veterans made 588 narrative audio recordings via a mobile app in a real-life setting. In addition, veterans completed self-report psychiatric scales and questionnaires. Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal. The audios were also transcribed to extract textual features for linguistic analysis. We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection. We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios. Random Forest classifier correctly identified suicidal ideation in veterans based on the combined set of acoustic and linguistic features of speech with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80%. Speech analysis of audios collected from veterans in everyday life settings using smartphones is a promising approach for suicidal ideation detection. A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans.
Predictors of Anxiety Regarding The COVID-19 Pandemic Among Health-care Workers in a Hospital Not Assigned to Manage COVID-19 Patients in Nepal
Authors: Thapa Lekhjung , Ghimire Aparna , Ghimire Sulochana , Sharma Nooma , Shrestha Shakti , Devkota Medha , Bhattarai Suman , Singh Maharjan Anzil Man , Lohani Subash , Phuyal Subash , Maharjan Pratibha 
Published: July 2020

AbstractIntroductionWe studied the levels and severity of anxiety caused by COVID-19 amongst frontline health-care workers (HCWs) in a tertiary care neurological hospital in Nepal, not assigned to manage COVID-19 cases.MethodsA cross-sectional study was conducted on 100 frontline Health Care Workers (HCWs) using a 10-point subjective assessment tool, the Anxiety Level Index (ALI), and the Zung Self Rating Anxiety Score (SAS), to assess the level of anxiety regarding COVID-19.ResultsOn ALI 55% of HCW were found to have marked severe anxiety (6-9), however, on SAS 44% of HCW’s reported anxiety. The majority HCW’s were female (n=78) with nurses forming 62% of the sample size. The mean age (±SD) was 26.8 years (± 8.17). Factors associated with significantly higher levels of anxiety regarding COVID-19 on ALI were age (p=0.002), sex (p=0.001), receiving regular COVID-19 updates via social media (p=0.011) and a high frequency of checking for COVID-19 information authenticity (p=0.039). Work experience (p=0.026) and frequency of checking for information authenticity (p=0.029) were factors found to increase SAS measured anxiety and were found to be associated with significantly higher levels of anxiety. Multivariate logistic regression analysis showed that respondents with work experience of ≤2 years were 0.380 (95% CI 0.158 to 0.910) times less likely to have anxiety than those with work experience of ≥2 years. Similarly, the odds of having anxiety among those who checked information authenticity less frequently was 0.377 (95% CI 0.153 to 0.931) times less than those who often did.ConclusionThe COVID-19 outbreak has caused a substantial impact on the mental health of frontline HCWs in a hospital not assigned to manage COVID-19 patients. Length of time of worked in healthcare and the frequency of checking for COVID-19-related information were significant predictors of anxiety.
Clinical, Behavioral and Social Factors Associated with Racial Disparities in Hospitalized and Ambulatory COVID-19 Patients from an Integrated Health Care System in Georgia
Authors: Lobelo Felipe , Bienvenida Alan , Leung Serena , Mbanya Armand , Leslie Elizabeth J , Koplan Kate E , Shin S. Ryan 
Published: July 2020

AbstractIntroductionRacial and ethnic minorities have shouldered a disproportioned burden of coronavirus disease 2019 (COVID-19) infection to date in the US, but data on the various drivers of these disparities is limited.ObjectivesTo describe the characteristics and outcomes of COVID-19 patients and explore factors associated with hospitalization risk by race.MethodsCase series of 448 consecutive patients with confirmed COVID-19 seen at Kaiser Permanente Georgia (KPGA), an integrated health care system serving the Atlanta metropolitan area, from March 3 to May 12, 2020. KPGA members with laboratory-confirmed COVID-19. Multivariable analyses for hospitalization risk also included an additional 3489 persons under investigation (PUI) with suspected infection. COVID-19 treatment and outcomes, underlying comorbidities and quality of care management metrics, socio-demographic and other individual and community-level social determinants of health (SDOH) indicators.ResultsOf 448 COVID-19 positive members, 68,3% was non-Hispanic Black (n=306), 18% non-Hispanic White (n=81) and 13,7% Other race (n=61). Median age was 54 [IQR 43-63) years. Overall, 224 patients were hospitalized, median age 60 (50-69) years. Black race was a significant factor in the Confirmed + PUI, female and male models (ORs from 1.98 to 2.19). Obesity was associated with higher hospitalization odds in the confirmed, confirmed + PUI, Black and male models (ORs from 1.78 to 2.77). Chronic disease control metrics (diabetes, hypertension, hyperlipidemia) were associated with lower odds of hospitalization ranging from 48% to 35% in the confirmed + PUI and Black models. Self-reported physical inactivity was associated with 50% higher hospitalization odds in the Black and Female models. Residence in the Northeast region of Atlanta was associated with lower hospitalization odds in the Confirmed + PUI, White and female models (ORs from 0.22 to 0.64)ConclusionsWe found that non-Hispanic Black KPGA members had a disproportionately higher risk of infection and, after adjusting for covariates, twice the risk of hospitalization compared to other race groups. We found no significant differences in clinical outcomes or mortality across race/ethnicity groups. In addition to age, sex and comorbidity burden, pre-pandemic self-reported exercise, metrics on quality of care and control of underlying cardio-metabolic diseases, and location of residence in Atlanta were significantly associated with hospitalization risk by race groups. Beyond well-known physiologic and clinical factors, individual and community-level social indicators and health behaviors must be considered as interventions designed to reduce COVID-19 disparities and the systemic effects of racism are implemented.
A model of COVID-19 propagation based on a gamma subordinated negative binomial branching process
Authors: Levesque Jérôme , Maybury David W. , Shaw R. H. A. David 
Published: July 2020

AbstractWe build a parsimonious Crump-Mode-Jagers continuous time branching process of COVID-19 propagation based on a negative binomial process subordinated by a gamma subordinator. By focusing on the stochastic nature of the process in small populations, our model provides decision making insight into mitigation strategies as an outbreak begins. Our model accommodates contact tracing and isolation, allowing for comparisons between different types of intervention. We emphasize a physical interpretation of the disease propagation throughout which affords analytical results for comparison to simulations. Our model provides a basis for decision makers to understand the likely trade-offs and consequences between alternative outbreak mitigation strategies particularly in office environments and confined work-spaces. Combining the asymptotic limit of our model with Bayesian hierarchical techniques, we provide US county level inferences for the reproduction number from cumulative case count data over July and August of this year.
Zika Virus Congenital Microcephaly Severity Classification and the Association of Severity with Neuropsychomotor Development
Authors: Esper Nathalia Bianchini , Franco Alexandre Rosa , Soder Ricardo Bernardi , Bomfim Rodrigo Cerqueira , Nunes Magda Lahorgue , Radaelli Graciane , Esper Katherine Bianchini , Kotoski Aline , Pripp Willian , Neto Felipe Kalil , Azambuja Luciana Schermann , Mathias Nathália Alves , da Costa Danielle Irigoyen , Portuguez Mirna Wetters , da Costa Jaderson Costa , Buchweitz Augusto 
Published: July 2020

ABSTRACTBackgroundZika virus infection during pregnancy is linked to birth defects, most notably, microcephaly, which in its turn, is associated with neurodevelopmental delays.ObjectiveThe goal of the study is to propose a method for severity classification of congenital microcephaly based on neuroradiological findings of MRI scans, and to investigate the association of severity with neuropsychomotor developmental scores. We also propose a semi-automated method for MRI-based severity classification of microcephaly.MethodsCross-sectional investigation of 42 infants born with congenital Zika infection. Bayley-III developmental evaluations and MRI scans were carried out at ages 13-39 months (mean: 24.8, SD: 5.8). The severity score was generated based on neuroradiologist evaluations of brain malformations. Next, we established a distribution of Zika virus-microcephaly severity score into mild, moderate, and severe and investigated the association of severity with neuropsychomotor developmental scores. Finally, we propose a simplified semi-automated procedure for estimating the severity score, based only on volumetric measures.ResultsResults showed a correlation of r = 0.89 (p < 0.001) between the Zika virus-microcephaly severity score and the semi-automated method. The trimester of infection did not correlate with the semi-automated method. Neuropsychomotor development correlated with the severity classification based on radiological readings and with the semi-automated method; the more severe the imaging scores, the lower neuropsychomotor developmental scores.ConclusionThe severity classification methods may be used to evaluate severity of microcephaly and possible association with developmental consequences. The semi-automated methods thus may be an alternative for prediction of severity of microcephaly using only one MRI sequence.
The heterogeneous nature of the Coronavirus receptor, angiotensin-converting enzyme 2 (ACE2) in differentiating airway epithelia
Authors: Manna Vincent J. , Caradonna Salvatore J. 
Published: July 2020

ABSTRACTCoronavirus Disease 2019 (COVID-19) is transmitted through respiratory droplets containing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) particles. Once inhaled, SARS-CoV-2 particles gain entry into respiratory ciliated cells by interacting with angiotensin converting enzyme 2 (ACE2). It is known that ACE2 functions within the renin-angiotensin system to regulate blood pressure, fluid homeostasis and inflammation. However, it is largely unknown what roles ACE2 has in ciliated cells of the airway. Therefore, understanding the function and nature of ACE2 within airway tissue has become an essential element in combatting the COVID-19 pandemic. Airway mucociliary tissue was generatedin-vitrousing primary human nasal epithelial cells isolated from nasal turbinates of donors and the air-liquid interface (ALI) model of differentiation. Using ALI tissue we cloned transcripts for three distinct variants of ACE2, one of which encodes the full-length ACE2 protein, the other two transcripts are truncated isoforms that had only been predicted to exist via sequence analysis software. We demonstrate that all three isoforms have the capacity to be glycosylated, a known modification of full-length ACE2. Immunofluorescence microscopy of individual ACE2 isoform transfected cells reveals distinct localization of variant 1 relative to X1 and X2. Double staining immunohistochemistry of ALI tissue using antibodies to either the N-term or C-term region of ACE2 revealed distinct and overlapping signals in the apical cytosol of ciliated cells. Most notably only the ACE2 C-term antibody displayed plasma-membrane localization in ciliated cells. We also observed a decrease in the total amount of ACE2 in ALI tissue derived from a 33 year-old male donor when compared to a 34 year-old female donor, thus there may be variation in the abundance of ACE2 protein in the airway among the population. Together, our data begins to highlight the dynamic status of the ACE2 protein in airway mucociliary tissue and we propose multiple ACE2 parameters that may impact an individual’s susceptibility to SARS-CoV-2. These parameters include the balance of cytosolic versus membrane bound ACE2, isoform expression levels, maintenance of post-translational modifications and the impact of genetic, environmental and lifestyle factors on these processes.
Developing a method for real-time visualization of cellulase activity
Authors: Kumari Pallavi , Sayas Tali , Bucki Patricia , Miyara Sigal Brown , Kleiman Maya 
Published: July 2020

AbstractStudying the interactions between microorganisms and plant roots is crucial for understanding a variety of phenomena concerning crop yield and health. The role of root surface properties in these interactions, is rarely addressed. To this end, we previously built a synthetic system, from the inert polymer polydimethyl siloxane (PDMS), mimicking the root surface microstructure, using a replication technique. This replica enables the study of isolated effects of surface structure on microorganism-plant interactions. Since the root surface is composed mostly of cellulose, using cellulose-like materials as our replica, instead of PDMS, is the next logical step. This will enable following the hydrolysis of such surfaces as a result of microorganisms secreting Plant Cell Wall Degrading Enzymes (PCWDE), and in particular, cellulase. Visualization of such hydrolysis in a synthetic system can assist in studying the localization and activity of microorganisms and how they correlate with surface microtopography, separately from chemical plant signals.In this work, we modified the known carboxymethyl cellulase (CMC) hydrolysis visualization method to enable real-time tracking of cellulase activity of microorganisms on the surface. Surface was formed from pure CMC, rather than CMC incorporated in agar as is often done, and by that, eliminating diffusion issues. Acridine orange dye, which is compatible, at low concentrations, with microorganisms, as opposed to other routinely used dyes, was incorporated into the film. The dye disassociated from the film when hydrolysis occurred, forming a halo surrounding the point of hydrolysis. This enabled real-time visualization since the common need for post hydrolysis dyeing was negated. Using Root Knot Nematode (RKN) as a model organism that penetrates the plant root, we showed it was possible to follow microorganism cellulase secretion on the surface in the form of CMC film hydrolysis. Furthermore, the addition of natural additives, in the form of root extract was also shown to be an option and resulted in an increased RKN response. We tested our newly developed method by changing temperature and pH conditions and by characterization of the hydrolyzed surface using both Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM).This method will be implemented in the future on a root surface microstructure replica. We believe the combination of this new method with our previously developed root surface microstructure replication technique can open a new avenue of research in the field of plant root-microorganism interactions.
Phylogenomics of 8,839Clostridioides difficilegenomes reveals recombination-driven evolution and diversification of toxin A and B
Authors: Mansfield Michael J. , Tremblay Benjamin J-M , Zeng Ji , Wei Xin , Hodgins Harold , Worley Jay , Bry Lynn , Dong Min , Doxey Andrew C. 
Published: July 2020

AbstractClostridioides difficileis the major worldwide cause of antibiotic-associated gastrointestinal infection. A pathogenicity locus (PaLoc) encoding one or two homologous toxins, toxin A (TcdA) and toxin B (TcdB) is essential forC. difficilepathogenicity. However, toxin sequence variation poses major challenges for the development of diagnostic assays, therapeutics, and vaccines. Here, we present a comprehensive phylogenomic analysis 8,839C. difficilestrains and their toxins including 6,492 genomes that we assembled from the NCBI short read archive. A total of 5,175tcdAand 8,022tcdBgenes clustered into 7 (A1-A7) and 12 (B1-B12) distinct subtypes, which form the basis of a new method for toxin-based subtyping ofC. difficile. We developed a haplotype coloring algorithm to visualize amino acid variation across all toxin sequences, which revealed that TcdB has diversified through extensive homologous recombination throughout its entire sequence, and formed new subtypes through distinct recombination events. In contrast, TcdA varies mainly in the number of repeats in its C-terminal repetitive region, suggesting that recombination-mediated diversification of TcdB provides a selective advantage inC. difficileevolution. The application of toxin subtyping is then validated by classifying 351C. difficileclinical isolates from Brigham and Women’s Hospital in Boston, demonstrating its clinical utility. Subtyping partitions TcdB into binary functional and antigenic groups generated by intragenic recombinations, including two distinct cell-rounding phenotypes, whether recognizing frizzled proteins as receptors, and whether it can be efficiently neutralized by monoclonal antibody bezlotoxumab, the only FDA-approved therapeutic antibody. Our analysis also identifies eight universally conserved surface patches across the TcdB structure, representing ideal targets for developing broad-spectrum therapeutics. Finally, we established an open online database (DiffBase) as a central hub for collection and classification ofC. difficiletoxins, which will help clinicians decide on therapeutic strategies targeting specific toxin variants, and allow researchers to monitor the ongoing evolution and diversification ofC. difficile.