bluuebunny/crossref_metadata_2025_split huggingface dataset:
Legionella septentrionalis sp. nov., isolated from aquatic environments in the northern PR China
Authors: Li Liang-Hui , Zhang Lei , Wu Hai-Yan , Qu Ping-Hua , Chen Jia-Chang , Zhan Xiao-Yong , Zhu Qing-Yi , Chen Cha , Hu Chao-Hui 
Published: December 2020

Four strains (km711T, km714, km542 and km524), representing a novel

Legionella

species, were isolated from aquatic environments in northern PR China. Cells were Gram-stain-negative, rod-shaped, microaerobic, motile and growth depended on l-cysteine. They grew at 25‒42 °C (optimum, 35‒37 °C) and could tolerate up to 1.5 % (w/v) NaCl (optimum, 0.5 %). The major fatty acids (>5 %) of the type strain km711T were C17 : 0 anteiso, C15 : 0 anteiso, iso-C16 : 0 and C16 : 1
ω7c and/or iso-C15 : 0 2OH. The pairwise comparison values were <96.1 % for 16S rRNA gene sequences, 23.3‒28.7 % interspecies variation for mip gene sequences, <93.6 % average nucleotide identity and <72.8 % average amino acid identity between these four strains and related type strains within the genus

Legionella

. The phylogenetic tree based on the four concatenated genes (16S rRNA, mip, rpoB and rnpB) and protein-concatamer tree based on concatenation of 21 protein markers both revealed that these four strains formed a separate phylogenetic branch cluster within the genus

Legionella

. The results of phenotypic and genotypic features suggest that these four strains represent a novel species of the genus

Legionella

, for which the name Legionella septentrionalis sp. nov. is proposed (type strain km711T=KCTC 15655T=NBRC 113219T).
Development and Validation of an Obstetric Early Warning System model for use in low resource settings
Authors: Umar Aminu , Manu Alexander , Mathai Matthews , Ameh Charles 
Published: December 2020

AbstractBackgroundThe use of obstetric early-warning-systems (EWS) has been recommended to improve timely recognition, management and early referral of women who have or are developing a critical illness. Development of such prediction models should involve a statistical combination of predictor clinical observations into a multivariable model which should be validated. No obstetric EWS has been developed and validated for low resource settings. We report on the development and validation of a simple prediction model for obstetric morbidity and mortality in resource-limited settings.MethodsWe performed a multivariate logistic regression analysis using a retrospective case-control analysis of secondary data with clinical indices predictive of severe maternal outcome (SMO). Cases for design and validation were randomly selected (n=500) from 4360 women diagnosed with SMO in 42 Nigerian tertiary-hospitals between June 2012 and mid-August 2013. Controls were 1000 obstetric admissions without SMO diagnosis. We used clinical observations collected within 24 hours of SMO occurrence for cases, and normal births for controls. We created a combined dataset with two controls per case, split randomly into development (n=600) and validation (n=900) datasets. We assessed the model’s validity using sensitivity and specificity measures and its overall performance in predicting SMO using receiver operator characteristic (ROC) curves. We then fitted the final developmental model on the validation dataset and assessed its performance. Using the reference range proposed in the United Kingdom Confidential-Enquiry-into-Maternal-and-Child-Health 2007-report, we converted the model into a simple score-based obstetric EWS algorithm.ResultsThe final developmental model comprised abnormal systolic blood pressure-(SBP>140mm Hg or <90mmHg), high diastolic blood pressure-(DBP>90mmHg), respiratory rate-(RR>40/min), temperature-(>38°C), pulse rate-(PR>120/min), caesarean-birth, and the number of previous caesarean-births. The model was 86 % (95% CI 81-90) sensitive and 92%-(95% CI 89-94) specific in predicting SMO with area under ROC of 92% (95% CI 90% – 95%). All parameters were significant in the validation model except DBP. The model maintained good discriminatory power in the validation (n=900) dataset (AUC 92, 95% CI 88-94%) and had good screening characteristics. Low urine output (300mls/24hours) and conscious level (prolonged unconsciousness-GCS<8/15) were strong predictors of SMO in the univariate analysis.ConclusionWe developed and validated statistical models that performed well in predicting SMO using data from a low resource settings. Based on these, we proposed a simple score based obstetric EWS algorithm with RR, temperature, systolic BP, pulse rate, consciousness level, urinary output and mode of birth that has a potential for clinical use in low-resource settings.
Improved Sensitivity of Digits-in-Noise Test to High-Frequency Hearing Loss
Authors: Zadeh Lina Motlagh , Silbert Noah H. , Swanepoel De Wet , Moore David R. 
Published: December 2020

ABSTRACTObjectivesHearing loss is most commonly observed at high frequencies. High-frequency hearing loss (HFHL) precedes and predicts hearing loss at lower frequencies. It was previously shown that an automated, self-administered digits-in-noise (DIN) test can be sensitized for detection of HFHL by low-pass filtering the speech-shaped masking noise at 1.5 kHz. This study was designed to investigate whether sensitivity of the DIN to HFHL can be enhanced further using low-pass noise filters with higher cutoff frequencies.DesignUS-English digits 0-9, homogenized for audibility, were binaurally presented in different noise maskers including one broadband and three low-pass (cut-off at 2, 4, 8 kHz) filtered speech-shaped noises. DIN-Speech reception thresholds (SRTs) were obtained from 60 normal hearing (NH), and 40 mildly hearing impaired (HI) listeners with bilateral symmetric sensorineural hearing-loss. Standard and extended high frequency audiometric pure tone averages (PTAs) were compared with the DIN-SRTs.ResultsNarrower masking noise bandwidth generally produced better (more sensitive) mean DIN-SRTs. There were strong and significant correlations between SRT and PTA in the HI group. Lower frequency, PTALF 0.5,1, 2, 4 kHz had the highest correlation and steepest slope with SRTs obtained from the 2 kHz filter. Higher frequency, PTAHF 4,8,10,12.5 kHz correlated best with SRTs obtained from 4 and 8 kHz filtered noise. The 4 kHz low-pass filter also had the highest sensitivity (92%) and equally-highest (with the 8 kHz filter) specificity (90%) for detecting an average PTAHF of 20 dB or more.ConclusionsOf the filters used, DIN sensitivity to higher frequency hearing loss was greatest using the 4 kHz low-pass filter. These results suggest that low-pass filtered noise may be usefully substituted for broadband noise to improve earlier detection of HFHL using DIN.
PipeMaster: inferring population divergence and demographic history with approximate Bayesian computation and supervised machine-learning in R
Authors: Gehara Marcelo , Mazzochinni Guilherme G. , Burbrink Frank 
Published: December 2020

AbstractUnderstanding population divergence involves testing diversification scenarios and estimating historical parameters, such as divergence time, population size and migration rate. There is, however, an immense space of possible highly parameterized scenarios that are difsficult or impossible to solve analytically. To overcome this problem researchers have used alternative simulation-based approaches, such as approximate Bayesian computation (ABC) and supervised machine learning (SML), to approximate posterior probabilities of hypotheses. In this study we demonstrate the utility of our newly developed R-package to simulate summary statistics to perform ABC and SML inferences. We compare the power of both ABC and SML methods and the influence of the number of loci in the accuracy of inferences; and we show three empirical examples: (i) the Muller’s termite frog genomic data from Southamerica; (ii) the cottonmouth and (iii) and the copperhead snakes sanger data from Northamerica. We found that SML is more efficient than ABC. It is generally more accurate and needs fewer simulations to perform an inference. We found support for a divergence model without migration, with a recent bottleneck for one of the populations of the southamerican frog. For the cottonmouth we found support for divergence with migration and recent expansion and for the copperhead we found support for a model of divergence with migration and recent bottleneck. Interestingly, by using an SML method it was possible to achieve high accuracy in model selection even when several models were compared in a single inference. We also found a higher accuracy when inferring parameters with SML.
Large-scale prediction of key dynamic interacting proteins in multiple cancers
Authors: Zhang Jifeng , Wang Xiao , Ji Zhicheng , Tian Weidong 
Published: December 2020

AbstractTracking cancer dynamic protein-protein interactions(PPIs) and deciphering their pathogenesis remain a challenge. Here, we presented a dynamic PPIs’ hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted more than 1,400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. Two prominent functional characteristics, “Cell cycle-related” and “Immune-related”, were presented, suggesting that it might be a general characteristic of KCG. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.
Uncovering the ideal plant ideotype for maximising seed yield inBrassica napus
Authors: Siles Laura , Hassall Kirsty L. , Sanchis-Gritsch Cristina , Eastmond Peter J. , Kurup Smita 
Published: December 2020

AbstractSeed yield is a complex trait for many crop species including oilseed rape (Brassica napus), the second most important oilseed crop worldwide. Studies have focused on the contribution of distinct factors in seed yield such as environmental cues, agronomical practices, growth conditions or specific phenotypic traits at the whole plant level, such as number of pods in a plant. However, in spite of the immense economic importance of oilseeds, none of these studies have comprehensively analysed individual traits and their combined contribution to seed yield. Here, we describe the analysis and contribution of 33 phenotypic traits within aB. napusdiversity set population and their trade-offs on seed yield not only at the whole plant level but also the less studied female reproductive traits. Our results revealed that both winter and spring oilseed rape; the two more economically important oilseed rape groups in terms of oil production; were found to share a common dominant reproductive strategy for seed yield. In this strategy the main inflorescence is the principal source of seed yield, producing a good number of ovules, a large number of long pods with a concomitantly high number of seeds per pod. We observed that winter oilseed rape opted for more reproductive strategies than spring oilseed rape, presenting more environmental flexibility to maximise seed yield. Overall, we conclude that, oilseed rape adopts a similar strategy that is key for maximal seed yield and propose an ideal ideotype highlighting crucial phenotypic traits that could be potential targets for breeding.One sentence summaryThe main florescence is the principal source of seed yield in winter and spring oilseed rape, with winter oilseed rape following several reproductive strategies to maximise seed yield.
Neural mechanisms underlying the temporal control of sequential saccade planning in the frontal eye fields
Authors: Basu Debaleena , Sendhilnathan Naveen , Murthy Aditya 
Published: December 2020

SummarySequences of saccadic eye movements are instrumental in navigating our visual environment. While neural activity has been shown to ramp up to a threshold before single saccades, the neural underpinnings of multiple saccades is unknown. To understand the neural control of rapid saccade sequences, we recorded from the frontal eye field (FEF) of macaque monkeys while they performed a sequential saccade task. We show that concurrent planning of two saccade plans brings forth processing bottlenecks, specifically by decreasing the growth rate and increasing the threshold of saccade-related ramping activity. The rate disruption affected both saccade plans, and a computational model wherein activity related to the two saccade plans bilaterally and asymmetrically inhibited each other, predicted the behavioral and neural results observed experimentally. Borrowing from models in psychology, our results demonstrate a capacity-sharing mechanism of processing bottlenecks, wherein multiple saccade plans in a sequence, compete for the processing capacity by perturbation of the saccade-related ramping activity. Finally, we show that in contrast to movement related neurons, visual activity in FEF neurons is not affected by the presence of multiple saccade targets, indicating that for perceptually simple tasks, inhibition amongst movement-related neurons mainly instantiates capacity sharing. Taken together, we show how psychology-inspired models of capacity sharing can be mapped onto neural responses to understand the control of rapid saccade sequences.
Identification of High risk nsSNPs in Human TP53 Gene Associated with Li–Fraumeni Syndrome: An In Silico Analysis Approach
Authors: Mustafa Mujahed I. , Murshed Naseem S. , Elbasher Mazen A. , Makhawi Abdelrafie M. 
Published: December 2020

AbstractBackgroundLi–Fraumeni syndrome (LFS) is a cancer–prone conditions caused by a germline mutation of the TP53 gene on chromosome 17p13.1. It has an autosomal dominant pattern of inheritance with high penetrance.PurposeThe aim of this study is to identify the high-risk pathogenic nsSNPs in PT53 gene that could be involved in the pathogenesis of Li–Fraumeni syndrome.MethodsThe nsSNPs in the human PT53 gene retrieved from NCBI, were analyzed for their functional and structural consequences using various in silico tools to predict the pathogenicity of each SNP. SIFT, Polyphen, PROVEAN, SNAP2, SNPs&Go, PHD-SNP, and P-Mut were chosen to study the functional inference while I-Mutant 3.0, and MUPro tools were used to test the impact of amino acid substitutions on protein stability by calculating ΔΔG value. The effects of the mutations on 3D structure of the PT53 protein were predicted using RaptorX and visualized by UCSF Chimera.ResultsA total of 845 PT53 nsSNPs were analyzed. Out of 7 nsSNPs of PT53 three of them (T118L, C242S, and I251N) were found high-risk pathogenic.ConclusionIn this study, out of 7 predicted high-risk pathogenic nsSNPs, three high-risk pathogenic nsSNPs of PT53 gene were identified, which could be used as diagnostic marker for this gene. The combination of sequence-based and structure-based approaches is highly effective for pointing pathogenic regions.
In silico identification of novel chemical compounds with anti-TB potential for the inhibition of InhA and EthR from Mycobacterium tuberculosis
Authors: Halder Sajal Kumar , Elma Fatiha 
Published: December 2020

ABSTRACTTuberculosis (TB) continuously pose a major public health concern around the globe, with a mounting death toll of approximately 1.4 million in 2019. The reduced bioavailability, increased toxicity and resistance of several first-line and second-line anti-TB drugs such as isoniazid, ethionamide have necessitated the search for new medications. In this research, we have identified several novel chemical compounds with anti-TB properties using various computational tools like molecular docking analysis, drug-likeness evaluation, ADMET profiling, P450 site of metabolism prediction and molecular dynamics simulation study. This study involves fifty drug-like compounds with antibacterial activity that inhibit InhA and EthR involved in the synthesis of one of the major lipid components, mycolic acid, which is crucial for the viability ofMycobacterium tuberculosis.Among these fifty compounds, 3-[3-(4-Fluorophenyl)-1,2,4-oxadiazol-5-yl]-N-(2-methylphenyl) piperidine-1-carboxamide (C22) and 5-(4-Ethyl-phenyl)-2-(1H-tetrazol-5-ylmethyl)-2H-tetrazole (C29) were found to pass the two-step molecular docking, P450 site of metabolism prediction and pharmacokinetics filtering analysis successfully. Their binding stability for target proteins have been evaluated through RMSD, RMSF, Radius of gyration analysis from 10 ns Molecular Dynamics Simulation (MDS) run. Our identified drugs could be a capable therapeutic for Tuberculosis drug discovery, having said that more in vitro and in vivo testing is required to justify their potential as novel drug and mode of action.
Scalable inference of transcriptional kinetic parameters from MS2 time series data
Authors: Bowles Jonathan R. , Hoppe Caroline , Ashe Hilary L. , Rattray Magnus 
Published: December 2020

AbstractMotivationThe MS2-McP (MS2 coat protein) live imaging system allows for visualisation of transcription dynamics through the introduction of hairpin stem-loop sequences into a gene. A fluorescent signal at the site of nascent transcription in the nucleus quantifies mRNA production. computational modelling can be used to infer the promoter states along with the kinetic parameters governing transcription, such as promoter switching frequency and polymerase loading rate. However, modelling of the fluorescent trace presents a challenge due its persistence; the observed fluorescence at a given time point depends on both current and previous promoter states. A memory-adjusted Hidden Markov Model (mHMM) was recently introduced to allow inference of promoter activity from MS2-McP data. However, the computational time for inference scales exponentially with gene length and the mHMM is therefore not currently practical for application to many eukaryotic genes.ResultsWe present a scalable implementation of the mHMM for fast inference of promoter activity and transcriptional kinetic parameters. This new method can model genes of arbitrary length through the use of a time-adaptive truncated compound state space. The truncated state space provides a good approximation to the full state space by retaining the most likely set of states at each time during the forward pass of the algorithm. Testing on MS2-MCP fluorescent data collected from earlyDrosophila melanogasterembryos indicates that the method provides accurate inference of kinetic parameters within a computationally feasible timeframe. The inferred promoter traces generated by the model can also be used to infer single-cell transcriptional parameters.AvailabilityPython implementation available athttps://github.com/ManchesterBioinference/burstInfer, along with code to reproduce the examples presented here.
Bridging the gap betweenin silicoandin vivo:modeling opioid disposition in a kidney proximal tubule microphysiological system
Authors: Imaoka Tomoki , Huang Weize , Shum Sara , Hailey Dale W. , Chang Shih-Yu , Chapron Alenka , Yeung Catherine K. , Himmelfarb Jonathan , Isoherranen Nina , Kelly Edward J. 
Published: December 2020

AbstractBackgroundOpioid overdose, dependence, and addiction are a major public health crisis. Patients with chronic kidney disease (CKD) are at high risk of opioid overdose, therefore novel methods that provide accurate prediction of kidney clearance (CLr) and systemic disposition of opioids in CKD patients can facilitate the optimization of therapeutic regimens.MethodsWe conducted prediction of kidney clearance and systemic disposition of morphine and its active metabolite morphine-6-glucuronide (M6G) in CKD patients using a vascularized human proximal tubule microphysiological system (VPT-MPS) coupled with a parent-metabolite full body physiologically-based pharmacokinetic (PBPK) model.ResultsThe VPT-MPS, populated with a human umbilical vein endothelial cell (HUVEC) channel and an adjacent human primary proximal tubular epithelial cells (PTEC) channel, successfully demonstrated secretory transport of morphine and M6G from the HUVEC channel into the PTEC channel in a time-dependent manner; transporter inhibitors decreased translocation by 74.3% and 63.6%, respectively. Thein vitrodata generated by VPT-MPS were incorporated into a mechanistic kidney model and parent-metabolite full body PBPK model to predict CLrand systemic disposition of morphine and M6G. The model successfully predicted CLrwithin 1.5-fold, and the plasma concentration-time profiles of morphine and M6G in both healthy subjects and CKD patients, with absolute average fold error values <1.5.ConclusionsA microphysiological system together with mathematical modeling successfully predicted kidney clearance and systemic disposition of opioids in CKD patients and healthy subjects.
A Universal Deep Neural Network for In-Depth Cleaning of Single-Cell RNA-Seq Data
Authors: Li Hui , Brouwer Cory R. , Luo Weijun 
Published: December 2020

AbstractSingle cell RNA sequencing (scRNA-Seq) has been widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts and need thorough cleaning. The existing denoising and imputation methods largely focus on a single type of noise (i.e. dropouts) and have strong distribution assumptions which greatly limit their performance and application. We designed and developed the AutoClass model, integrating two deep neural network components, an autoencoder and a classifier, as to maximize both noise removal and signal retention. AutoClass is free of distribution assumptions, hence can effectively clean a wide range of noises and artifacts. AutoClass outperforms the state-of-art methods in multiple types of scRNA-Seq data analyses, including data recovery, differential expression analysis, clustering analysis and batch effect removal. Importantly, AutoClass is robust on key hyperparameter settings including bottleneck layer size, pre-clustering number and classifier weight. We have made AutoClass open source at:https://github.com/datapplab/AutoClass.
De novoconstruction of a transcriptome for the stink bug crop pestChinavia impicticornisduring late development
Authors: Genevcius Bruno C. , Torres Tatiana T. 
Published: December 2020

AbstractChinavia impicticornisis a Neotropical stink-bug (Hemiptera: Pentatomidae) with economic importance for different crops. Little is known about the development of the species, as well as the genetic mechanisms that may favor the establishment of populations in cultivated plants. Here we conduct the first large-scale molecular study withC. impicticornis. We generated RNA-seq data for males and females, at two immature stages, for the genitalia separately and for the rest of the body. We assembled the transcriptome and conduct a functional annotation.De novoassembled transcriptome based on whole bodies and genitalia of males and females contained around 400,000 contigs with an average length of 688 bp. After pruning duplicated sequences and conducting a functional annotation, the final annotated transcriptome comprised 39,478 transcripts of which 12,665 had GO terms assigned. These novel datasets will provide invaluable data for the discovery of molecular processes related to morphogenesis and immature biology. We hope to contribute to the growing research on stink bugevo-devoas well as the development of bio-rational solutions for pest management.