bluuebunny/crossref_metadata_2025_split huggingface dataset:
Localization of the signal of dystonia-associated protein torsinA near the Golgi apparatus in cultured central neurons
Authors: Iwabuchi Sadahiro , Kawano Hiroyuki , Harata N. Charles 
Published: December 2019

ABSTRACTA single in-frame deletion of a codon for a glutamic acid residue within theTOR1Agene is linked to the autosomal-dominant movement disorder DYT1 dystonia, a condition characterized by involuntary muscle contractions that cause abnormal posture. This gene encodes the protein torsinA, and the functions of both wild-type and mutant (ΔE-torsinA) forms remain poorly understood. Previous studies based on overexpression systems indicated that wild-type torsinA resides mainly in the endoplasmic reticulum but that ΔE-torsinA is localized to the nuclear envelope or intracellular inclusions. This mutation-associated mis-localization has been proposed to underlie at least a part of the pathophysiology of DYT1 dystonia. However, the subcellular localization of torsinA has not been extensively studied when expressed at the endogenous level. Here we report an immunocytochemical analysis of torsinA proteins in cultured mouse neurons from a ΔE-torsinA knock-in model of DYT1 dystonia, where torsinA proteins are not upregulated. In all examined neurons of wild-type, heterozygous and homozygous mice, torsinA signal was found mainly near the Golgi apparatus, and only weakly in the endoplasmic reticulum and nuclear envelope. These results suggest that, in the absence of overexpression, torsinA proteins are localized near the Golgi apparatus and may influence cellular function involving the organelle.
Retrospective Assessment of Deep Neural Networks for Skin Tumor Diagnosis
Authors: Han Seung Seog , Moon Ik Jun , Na Jung-Im , Kim Myoung Shin , Park Gyeong Hun , Kim Seong Hwan , Kim Kiwon , Lee Ju Hee , Chang Sung Eun 
Published: December 2019

ABSTRACTBACKGROUNDThe aim of this study was to validate the performance of algorithm (http://rcnn.modelderm.com) for the diagnosis of benign and malignant skin tumors.METHODSWith external validation dataset (43 disorders; 40,331 clinical images from 10,426 patients; January 1, 2008 – March 31, 2019), we compared the prediction of algorithm with the clinical diagnosis of 65 attending physicians at the time of biopsy request.RESULTSFor binary-task classification of determining malignancy, the AUC of the algorithm was 0.863(95% CI 0.852-0.875) with unprocessed clinical photographs. The sensitivity/specificity of the algorithm at the predefined high-sensitivity and high-specificity threshold were 79.1%(76.9-81.4)/76.9%(76.1-77.8) and 62.7%(59.9- 65.5)/90.0%(89.4-90.6), respectively. The sensitivity/specificity calculated by the clinical diagnosis of attending physicians were 88.1%/83.8%(Top-3) and 70.2%/95.6%(Top-1), which were superior to those of algorithm.For multi-task classification, the mean Top-1,2,3 accuracies of the algorithm were 42.6±20.7%, 56.1±22.8%, 61.9±22.9%, and those of clinical diagnosis were 65.4±17.7%, 73.9±16.6%, 74.7±16.6%, respectively.In the reader test with images from 30-patients batches, the sensitivity / specificity of the algorithm at the predefined threshold were 66.9%±30.2% / 87.4%±16.5%. The sensitivity / specificity derived from the first diagnosis of 44 the participants were 65.8%±33.3% / 85.7%±11.0%, which were comparable with those of the algorithm (Wilcoxon signed-rank test; P=0.61 / 0.097).CONCLUSIONSOur algorithm could diagnose skin tumors at dermatologist-level when diagnosis was made solely with photographs, demonstrating its potential as a mass screening tool in telemedicine setting. However, due to limited data relevancy, the performance was inferior to that of actual medical examination. Clinical information should be integrated with imaging information to achieve more accurate predictions.
Temporal trends in public mass shootings: high-capacity magazines significantly increase fatality counts, and are becoming more prevalent
Authors: Towers Sherry , Wallace Danielle , Hemenway David 
Published: December 2019

AbstractBackgroundThere has been disagreement in the literature regarding whether public mass shootings are becoming more frequent, or more deadly, and whether high-capacity magazine (HCM) weaponry is associated with increased fatalities.ObjectiveWe examined the 85 non-felony- and non-gang-related mass shootings with at least four people shot and killed that occurred in public places between 1995 to 2018 to examine the frequency and deadliness of public mass shootings, as well as the role of HCM weaponry in fatalities.FindingsThe per-capita rate of public mass shootings with at least four people killed has not significantly increased since 1995 (Negative Binomial linear regression p=0.20), but public mass shootings with at least six people killed have been increasing by 6.9% per year (p=0.01). Public mass shootings are becoming significantly more deadly, with fatalities per incident increasing by 4.5% per year (truncated Negative Binomial log-linear regression p<0.001).Use of HCM weaponry banned during the 1994 to 2004 Federal Assault Weapons Ban (FAWB) increased fatality counts on average by 1.9 times, and total number shot by 3.3 times (non-parametric bootstrap comparison of means (NPBCM), p < 0.001 in both cases). In these incidents, HCM pistols were as deadly as assault rifles. The fraction of firearms involving HCMs used by perpetrators more than doubled after the lapse of the federal ban, from 25% during the ban to 59% after (Binomial factor regression, p<0.001).Our online visual analytics application, https://mass-shooting-analytics.shinyapps.io/mass_shootings/, allows interested parties to examine and download our data, replicate our analysis results and explore the analysis with alternate selections.Policy recommendationsA re-instatement of the federal ban on high-capacity magazines in pistols and rifles, or special taxes on such weaponry, may reduce public mass shooting fatalities.
Integrated Bioinformatic Approach to Identify Potential Biomarkers against Idiopathic Pulmonary Fibrosis
Authors: Ullah Md. Asad , Sarkar Bishajit , Araf Yusha , Islam Prottoy Md. Nazmul , Saha Ananna , Jahan Tanjila , Boby Aisha Siddiqua , Islam Md. Shariful 
Published: December 2019

AbstractIdiopathic Pulmonary Fibrosis (IPF) is a chronic and progressive lung disease that leads to gradual decline in lung function. The molecular mechanism and risk factors of this disease are still obscure. Poorly understood etiology of this disease is the major obstacle in the identification of potential biomarkers and drug targets. In this study, microarray gene expression data of normal and IPF patient has been utilized for the statistical analysis of differentially expressed genes (DEGs) with a view to identifying potential molecular signatures using network-based system. Then their functional enrichment analysis revealed their predominant involvement in transcription, protein acetylation, extracellular matrix organization, apoptic process, inflammatory response etc. Protein-Protein Interaction (PPI) network revealed (UBC, PTEN, SOS1, PTK2, FGFR1, YAP1, FOXO1, RACK1, BMP4 and CD44) as hub proteins in IPF. Subsequent regulatory network analysis suggested (E2F1, STAT3, PPARG, MEF2A, FOXC1, GATA3, YY1, GATA2, NFKB1, and FOXL1) as the best regulatory transcriptional signatures and (hsa-mir-155-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-19a-3p, hsa-mir-192-5p, hsa-mir-92a-3p, hsa-mir-26b-5p, hsa-mir-335-5p, hsa-mir-124-3p, and hsa-let-7b-5p) as the best post-transcriptional signatures. This study represents proteome and RNA signatures of IPF which might be useful to uphold the present efforts in the discovery of potential biomarkers and treatments of this disease.
Screening of Diabetes and Hypertension Based on Retinal Fundus Photographs Using Deep Learning
Authors: Dai Guangzheng , Zhang Chenguang , He Wei 
Published: December 2019

ABSTRACTPurposeThe aim of this study was to use deep learning to screen for hypertension and diabetes based on retinal fundus images.MethodsWe collected 1160 retinal photographs which included 580 from patients with a diagnosis of hypertension or diabetes and 580 from normotensive and non-diabetic control. We divided this image dataset into (i) a development dataset to develop model and (ii) test dataset which were not present during the training process to assess model’s performance. A binary classification model was trained by fine-tuning the classifier and the last convolution layer of deep residual network. Precision, recall, the area under the ROC (AUC), and the area under the Precision-Recall curve (AUPR) were used to evaluate the performance of the learned model.ResultsWhen we used 3-channel color retinal photographs to train and test model, its prediction precision for diabetes or hypertension was 65.3%, the recall was 82.5%, the AUC was 0.745, and the AUPR was 0.742. When we used grayscale retinal photographs to train and test model, its prediction precision was 70.0%, the recall was 87.5%, the AUC was 0.803, and the AUPR was 0.779.ConclusionsOur study shows that trained deep learning model based on the retinal fundus photographs alone can be used to screen for diabetes and hypertension, although its current performance was not ideal.
Type 2 diabetes risks and determinants in 2nd generation migrants and mixed ethnicity people of South Asian and African Caribbean descent in the UK
Authors: Farmaki Aliki-Eleni , Garfield Victoria , Eastwood Sophie V. , Farmer Ruth E. , Mathur Rohini , Patalay Praveetha , Sattar Naveed , Hughes Alun , Bhaskaran Krishnan , Smeeth Liam , Chaturvedi Nish 
Published: December 2019

AbstractBackgroundType 2 diabetes mellitus (T2DM) risk is markedly higher in UK South Asians (SA) and African Caribbeans (AC) compared to Europeans. Explanations for this excess are unclear. We therefore compared risks and determinants of T2DM in first- and second-generation (born in the UK) migrants, and in those of mixed ethnicity populations.MethodsData from the UK Biobank, a large population-based cohort of volunteers aged 40-69, were used. T2DM was assigned using self-report and glycated haemoglobin. Ethnicity was self-assigned. Using logistic regression and mediation analysis, we compared T2DM between first- and second-generation migrants, and between mixed European/South Asians (MixESA), or mixed European/African Caribbeans (MixEAC) with both Europeans and SA or AC respectively.ResultsT2DM prevalence was three to five times higher in SA and AC compared with Europeans [OR (95%CI): 4·80(3·60,6·40) and 3·30(2·70,4·10) respectively]. T2DM was 20-30% lower in second versus first generation SA and AC migrants [0·78(0·60,1·01) and 0·71(0·57,0·87) respectively]. T2DM in mixed populations was lower than comparator ethnic minority groups [MixESA versus SA 0·29(0·21,0·39), MixEAC versus AC 0·48(0·37,0·62)] and higher than Europeans, in MixESA 1·55(1·11, 2·17), and in MixEAC 2·06 (1·53, 2·78). Improved adiposity patterns in second generation migrants made an important contribution to risk reduction. Greater socioeconomic deprivation accounted for 17% and 42% of the excess risk of T2DM in MixESA and MixEAC compared to Europeans, respectively.ConclusionExcess T2DM risks in South Asians and African Caribbeans compared with Europeans in the UK are attenuated by ∼20% in second-generation migrants, demonstrating the marked benefits of favourable changes in environmental risk factors. T2DM prevalence in people of mixed ethnicity was also raised compared with Europeans, but considerably less than in the ethnic minority group; persistent socioeconomic disadvantage accounted for some of the residual excess.
Survey of Public Assay Data: Opportunities and Challenges to Understanding Antimicrobial Resistance
Authors: Agarwal Akshay , Nayar Gowri , Kaufman James 
Published: December 2019

ABSTRACTComputational learning methods allow researchers to make predictions, draw inferences, and automate generation of mathematical models. These models are crucial to solving real world problems, such as antimicrobial resistance, pathogen detection, and protein evolution. Machine learning methods depend upon ground truth data to achieve specificity and sensitivity. Since the data is limited in this case, as we will show during the course of this paper, and as the size of available data increases super-linearly, it is of paramount importance to understand the distribution of ground truth data and the analyses it is suited and where it may have limitations that bias downstream learning methods. In this paper, we focus on training data required to model antimicrobial resistance (AR). We report an analysis of bacterial biochemical assay data associated with whole genome sequencing (WGS) from the National Center for Biotechnology Information (NCBI), and discuss important implications when making use of assay data, utilizing genetic features as training data for machine learning models. Complete discussion of machine learning model implementation is outside the scope of this paper and the subject to a later publication.The antimicrobial assay data was obtained from NCBI BioSample, which contains descriptive information about the physical biological specimen from which experimental data is obtained and the results of those experiments themselves.[1] Assay data includes minimum inhibitory concentrations (MIC) of antibiotics, links to associated microbial WGS data, and treatment of a particular microorganism with antibiotics.We observe that there is minimal microbial data available for many antibiotics and for targeted taxonomic groups. The antibiotics with the highest number of assays have less than 1500 measurements each. Corresponding bias in available assays makes machine learning problematic for some important microbes and for building more advanced models that can work across microbial genera. In this study we focus, therefore, on the antibiotic with most assay data (tetracycline) and the corresponding genus with the most available sequence (Acinetobacterwith 14000 measurements across 49 antibiotic compounds). Using this data for training and testing, we observed contradictions in the distribution of assay outcomes and report methods to identify and resolve such conflicts. Per antibiotic, we find that there can be up to 30% of (resolvable) conflicting measurements. As more data becomes available, automated training data curation will be an important part of creating useful machine learning models to predict antibiotic resistance.CCS CONCEPTS• Applied computing → Computational biology; Computational genomics; Bioinformatics;
The urinary microbiota composition remains stable over time and under various storage conditions
Authors: Bundgaard-Nielsen Caspar , Ammitzbøll Nadia , Isse Yusuf Abdi , Muqtar Abdisalam , Jensen Ann-Maria , Leutscher Peter , Arenholt Louise Thomsen Schmidt , Hagstrøm Søren , Sørensen Suzette 
Published: December 2019

AbstractBackgroundNew sensitive techniques have revealed a large population of bacteria in the human urinary tract, challenging the perception of the urine of healthy humans being sterile. While the role of this urinary microbiota is unknown, dysbiosis has been linked to disorders like urgency urinary incontinence and interstitial cystitis. When comparing studies it is crucial to account for possible confounders introduced due to methodological differences. Here we investigated whether storage condition or time of collection, had any impact on the urinary microbial composition.ResultsFor comparison of different storage conditions, urine was collected from five healthy adult female donors, and analyzed by 16S rRNA gene sequencing. Using the same methods, the daily or day-to-day variation in urinary microbiota was investigated in nineteen healthy donors, including four women, five men, five girls, and five boys. With the exception of two male adult donors, none of the tested conditions gave rise to significant differences in alpha and beta diversities between individuals.Conclusion: The composition of the urinary microbiota was found to be highly resilient to changes introduced by storage temperature and duration. In addition, we did not observe any intrapersonal daily or day-to-day variations in microbiota composition in women, girls or boys.Together our study supports flexibility in study design, when conducting urinary microbiota studies.Author summaryThe discovery of bacteria native to the urinary tract in healthy people, a location previously believed to be sterile, has prompted research into the clinical potential of these bacteria. However, methodological weaknesses can significantly influence such studies, and thus development of robust techniques for investigating these bacteria are needed. In the present study, we investigated whether differences in storage following collection, could affect the bacterial composition of urine samples. Next, we investigated if this composition exhibited daily or day-to-day variations.Firstly, we found, that the bacterial composition of urine could be maintained by storage at −80 °C, −20 °C, or refrigerated at 4 °C. Secondly, the bacterial composition of urine remained stable over time. Overall, the results of this study provide information important to study design in future investigations into the clinical implications of urinary bacteria.
Estimating densities of larval Salmonflies (Pteronarcys californica) through multiple pass removal of post-emergent exuvia in Colorado rivers
Authors: Heinold Brian D. , Kowalski Dan A. , Nehring R. Barry 
Published: December 2019

AbstractTraditional methods of collecting and sorting benthic macroinvertebrate samples are useful for stream biomonitoring and ecological studies; however, these methods are time consuming, expensive, and require taxonomic expertise. Estimating larval densities through collection of post-emergent exuvia can be a practical and time efficient alternative. We evaluated the use of multiple pass depletion techniques of the post-emergent exuvia ofPteronarcys californicato estimate larval densities at ten sites in three Colorado rivers. Exuvia density was highly correlated with both final-instar larval density (R2= 0.90) and total larval density (R2= 0.88) and the multiple pass removal technique performed well. Exuvia surveys foundP. californicaat three low density sites where benthic sampling failed to detect it. At moderate and high density sites the exuvia surveys always produced lower density estimates than benthic surveys. Multiple pass depletion estimates of exuvia proved to be an accurate and efficient technique at estimating larval densities and provided an effective alternative for traditional benthic sampling when objectives are monitoringP. californicaand detecting populations, especially at low density sites.
RosettaGPCR: Multiple Template Homology Modeling of GPCRs with Rosetta
Authors: Bender Brian Joseph , Marlow Brennica , Meiler Jens 
Published: December 2019

AbstractG-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies. However, to date, only about 10% of druggable GPCRs have had their structures characterized at atomic resolution. Further, because of the flexibility of GPCRs, alternative conformations remain to be modeled, even after an experimental structure is available. Thus, computational modeling of GPCRs is a crucial component for understanding biological function and to aid development of new therapeutics. Previous single- and multi-template homology modeling protocols in Rosetta often generated non-native-like conformations of transmembrane α-helices and/or extracellular loops. Here we present a new Rosetta protocol for modeling GPCRs that is improved in two critical ways: Firstly, it uses a blended sequence- and structure-based alignment that now accounts for structure conservation in extracellular loops. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target GPCR can be used expanding the conformational space sampled in a meaningful way. This new method allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available atwww.rosettagpcr.org.Author SummaryStructure-based drug discovery is among the new technologies driving the development of next generation therapeutics. Inherent to this process is the availability of a protein structure for virtual screening. The most heavily drugged protein family, G-protein coupled receptors (GPCRs), however suffers from a lack of experimental structures that could hinder drug development. Technical challenges prevent the determination of every protein structure, so we turn to computational modeling to predict the structures of the remaining proteins. Again, traditional techniques fail due to the high divergence of this family. Here, we build on available methods specifically for the challenge of modeling GPCRs. This new method outperforms other methods and allows for the ability to accurately model nearly 90% of the entire GPCR family. We therefore generate a model database of all GPCRs (www.rosettagpcr.org) for use in future drug development.
Visualization of two architectures in class-II CAP-dependent transcription activation
Authors: Shi Wei , Jiang Yanan , Deng Yibin , Dong Zigang , Liu Bin 
Published: December 2019

AbstractTranscription activation by cyclic AMP receptor protein (CAP) is the classic paradigm of transcription regulation in bacteria. CAP was suggested to activate transcription on class-II promoters via a recruitment and isomerization mechanism. However, whether and how it modifies RNA polymerase (RNAP) to initiate transcription remains unclear. Here we report cryo-EM structures of an intactE. coliclass-II CAP-dependent transcription activation complex (TAC) with and withoutde novoRNA transcript. The structures reveal two distinct architectures of TAC and show that CAP-binding induces substantial conformational changes in all the subunits of RNAP and consequently widens the main cleft of RNAP considerably to facilitate DNA promoter entering and formation of initiation open complex. These structural changes vanish during further RNA transcript synthesis. The observations in this study suggest a unique activation mechanism on class-II promoters that CAP activates transcription by first remodeling RNAP conformation and then stabilizing initiation complex.
Intellectual disability-associated factor Zbtb11 cooperates with NRF-2/GABP to control mitochondrial function
Authors: Wilson Brooke C. , Boehme Lena , Annibali Ambra , Hodgkinson Alan , Carroll Thomas S. , Oakey Rebecca J. , Seitan Vlad C. 
Published: December 2019

AbstractZbtb11 is a conserved transcription factor mutated in families with hereditary intellectual disability. Its precise molecular and cellular functions are currently unknown, precluding our understanding of the aetiology of this disease. Using a combination of functional genomics, genetic and biochemical approaches here we show that Zbtb11 plays essential roles in maintaining the homeostasis of mitochondrial function. Mechanistically, we find Zbtb11 facilitates the recruitment of Nuclear Respiratory Factor 2 (NRF-2) to its target promoters, activating a subset of nuclear genes with roles in the biogenesis of respiratory complex I and the mitoribosome. Genetic inactivation ofZbtb11resulted in a severe complex I assembly defect, impaired mitochondrial respiration, mitochondrial depolarisation, and ultimately proliferation arrest and cell death. Experimental modelling of the pathogenic human mutations showed these have a destabilising effect on the protein, resulting in reduced Zbtb11 dosage, down-regulation of its target genes, and impaired complex I biogenesis. Our study establishes Zbtb11 as a novel essential mitochondrial regulator, improves our understanding of the transcriptional mechanisms of nuclear control over mitochondria, and provides a rationale for the aetiology of Zbtb11-associated intellectual disability.