bluuebunny/crossref_metadata_2025_split huggingface dataset:
Infrared variability due to magnetic pressure-driven jets, dust ejection and quasi-puffed-up inner rims
Authors: Liffman Kurt , Bryan Geoffrey , Hutchison Mark , Maddison Sarah T 
Published: February 2020

ABSTRACT
The interaction between a YSO stellar magnetic field and its protostellar disc can result in stellar accretional flows and outflows from the inner disc rim. Gas flows with a velocity component perpendicular to disc mid-plane subject particles to centrifugal acceleration away from the protostar, resulting in particles being catapulted across the face of the disc. The ejected material can produce a ‘dust fan’, which may be dense enough to mimic the appearance of a ‘puffed-up’ inner disc rim. We derive analytical equations for the time-dependent disc toroidal field, the disc magnetic twist, the size of the stable toroidal disc region, the jet speed, and the disc region of maximal jet flow speed. We show how the observed infrared variability of the pre-transition disc system LRLL 31 can be modelled by a dust ejecta fan from the inner-most regions of the disc whose height is partially dependent on the jet flow speed. The greater the jet flow speed, the higher is the potential dust fan scale height. An increase in mass accretion on to the star tends to increase the height and optical depth of the dust ejection fan, increasing the amount of 1–8 µm radiation. The subsequent shadow reduces the amount of light falling on the outer disc and decreases the 8–40 µm radiation. A decrease in the accretion rate reverses this scenario, thereby producing the observed ‘see-saw’ infrared variability.
Pilocytic astrocytoma demethylation and transcriptional landscapes link bZIP transcription factors to immune response
Authors: Aichmüller Christian F , Iskar Murat , Jones David T W , Korshunov Andrey , Radlwimmer Bernhard , Kool Marcel , Ernst Aurelie , Pfister Stefan M , Lichter Peter , Zapatka Marc 
Published: February 2020

Abstract

Background
Pilocytic astrocytoma (PA) is the most common pediatric brain tumor. While genome and transcriptome landscapes are well studied, data of the complete methylome, tumor cell composition, and immune infiltration are scarce.

Methods
We generated whole genome bisulfite sequence (WGBS) data of 9 PAs and 16 control samples and integrated available 154 PA and 57 control methylation array data. RNA sequence data of 49 PAs and 11 control samples as well as gene expression arrays of 248 PAs and 28 controls were used to assess transcriptional activity.

Results
DNA-methylation patterns of partially methylated domains suggested high stability of the methylomes during tumorigenesis. Comparing tumor and control tissues of infra- and supratentorial location using methylation arrays revealed a site specific pattern. Analysis of WGBS data revealed 9381 significantly differentially methylated regions (DMRs) in PA versus control tissue. Enhancers and transcription factor (TF) motifs of five distinct TF families were found to be enriched in DMRs. Methylation together with gene expression data–based in silico tissue deconvolution analysis indicated a striking variation in the immune cell infiltration in PA. A TF network analysis showed a regulatory relation between basic leucine zipper (bZIP) transcription factors and genes involved in immune-related processes.

Conclusion
We provide evidence for a link of focal methylation differences and differential gene expression to immune infiltration.
Downregulation of a Mitochondrial NAD+ Transporter (NDT2) Alters Seed Production and Germination in Arabidopsis
Authors: Feitosa-Araujo Elias , de Souza Chaves Izabel , Florian Alexandra , da Fonseca-Pereira Paula , Condori Apfata Jorge Alberto , Heyneke Elmien , Medeiros David Barbosa , Pires Marcel Viana , Mettler-Altmann Tabea , Neuhaus H Ekkehard , Palmieri Ferdinando , Ara�jo Wagner L , Obata Toshihiro , Weber Andreas P M , Linka Nicole , Fernie Alisdair R , Nunes-Nesi Adriano 
Published: February 2020

Abstract
Despite the fundamental importance of nicotinamide adenine dinucleotide (NAD+) for metabolism, the physiological roles of NAD+ carriers in plants remain unclear. We previously characterized the Arabidopsis thaliana gene (At1g25380), named AtNDT2, encoding a protein located in the mitochondrial inner membrane, which imports NAD+ from the cytosol using ADP and AMP as counter-exchange substrates for NAD+. Here, we further investigated the physiological roles of NDT2, by isolating a T-DNA insertion line, generating an antisense line and characterizing these genotypes in detail. Reduced NDT2 expression affected reproductive phase by reducing total seed yield. In addition, reduced seed germination and retardation in seedling establishment were observed in the mutant lines. Moreover, remarkable changes in primary metabolism were observed in dry and germinated seeds and an increase in fatty acid levels was verified during seedling establishment. Furthermore, flowers and seedlings of NDT2 mutants displayed upregulation of de novo and salvage pathway genes encoding NAD+ biosynthesis enzymes, demonstrating the transcriptional control mediated by NDT2 activity over these genes. Taken together, our results suggest that NDT2 expression is fundamental for maintaining NAD+ balance amongst organelles that modulate metabolism, physiology and developmental processes of heterotrophic tissues.
Disciplining Difference(s): Reproducing Inequalities through Disciplinary Interactions in Preschool
Authors: Gansen Heidi M 
Published: February 2020

Abstract
This article offers an expansive conceptualization and examination of young children’s experiences of school discipline inequalities, which includes the variety of behavior management approaches (i.e., punitive discipline and positive discipline) that preschool teachers differentially use in response to students’ behavior (e.g., noncompliance). I draw on data from ethnographic observations in three preschools (nine classrooms total) with different racial and socioeconomic classroom compositions. I find that teachers’ varying approaches to behavior management for students from particular racial, gender, and socioeconomic backgrounds (i.e., low-SES girls and middle-class black boys) contributes to children’s hierarchical identity groups within preschool classrooms: “good kids” vs. “troublemaking kids.” Specifically, the messages children at Imagination Center received from teachers’ differential disciplinary responses to low-SES girls’ behaviors, and the messages children at Kids Company received from teachers’ differential disciplinary responses to middle-class black boys’ behaviors, shaped their understandings of whom they should or should not associate with. The findings reveal how school-level disciplinary practices/cultures (e.g., positive discipline rather than punitive discipline) can help to alleviate disciplinary inequalities apparent in some preschool classrooms. Additionally, these data illuminate how preschool teachers’ differential approaches to behavior management affect some marginalized students’ experiences of discipline inequalities as early as preschool.
Longer is Not Always Better: Optimizing Barcode Length for Large-Scale Species Discovery and Identification
Authors: Yeo Darren , Srivathsan Amrita , Meier Rudolf 
Published: February 2020

AbstractNew techniques for the species-level sorting of millions of specimens are needed in order to accelerate species discovery, determine how many species live on earth, and develop efficient biomonitoring techniques. These sorting methods should be reliable, scalable, and cost-effective, as well as being largely insensitive to low-quality genomic DNA, given that this is usually all that can be obtained from museum specimens. Mini-barcodes seem to satisfy these criteria, but it is unclear how well they perform for species-level sorting when compared with full-length barcodes. This is here tested based on 20 empirical data sets covering ca. 30,000 specimens (5500 species) and six clade-specific data sets from GenBank covering ca. 98,000 specimens ($&gt;$20,000 species). All specimens in these data sets had full-length barcodes and had been sorted to species-level based on morphology. Mini-barcodes of different lengths and positions were obtained in silico from full-length barcodes using a sliding window approach (three windows: 100 bp, 200 bp, and 300 bp) and by excising nine mini-barcodes with established primers (length: 94–407 bp). We then tested whether barcode length and/or position reduces species-level congruence between morphospecies and molecular operational taxonomic units (mOTUs) that were obtained using three different species delimitation techniques (Poisson Tree Process, Automatic Barcode Gap Discovery, and Objective Clustering). Surprisingly, we find no significant differences in performance for both species- or specimen-level identification between full-length and mini-barcodes as long as they are of moderate length ($&gt;$200 bp). Only very short mini-barcodes (&lt;200 bp) perform poorly, especially when they are located near the 5$^\prime$ end of the Folmer region. The mean congruence between morphospecies and mOTUs was ca. 75% for barcodes $&gt;$200 bp and the congruent mOTUs contain ca. 75% of all specimens. Most conflict is caused by ca. 10% of the specimens that can be identified and should be targeted for re-examination in order to efficiently resolve conflict. Our study suggests that large-scale species discovery, identification, and metabarcoding can utilize mini-barcodes without any demonstrable loss of information compared to full-length barcodes. [DNA barcoding; metabarcoding; mini-barcodes; species discovery.]
Predicting successes and failures of clinical trials with an ensemble LS-SVR
Authors: Hong Zhen-Yu , Shim Jooyong , Son Woo Chan , Hwang Changha 
Published: February 2020

AbstractFor a variety of reasons, most drug candidates cannot eventually pass the drug approval process. Thus, developing reliable methods for predicting clinical trial outcomes of drug candidates is crucial in improving the success rate of drug discovery and development. In this study, we propose an ensemble classifier based on weighted least squares support vector regression (LS-SVR) for predicting successes and failures of clinical trials. The efficacy of the proposed ensemble classifier is demonstrated through an experimental study on PrOCTOR dataset, which consists of informative chemical features of the drugs and target-based features. Comparing random forest and other models, the proposed ensemble classifier obtains the highest value for the area under the receiver operator curve (AUC). The results of this study demonstrate that the proposed ensemble classifier can be used to effectively predict drug approvals.
Nomogram for the prediction of in-hospital incidence of acute respiratory distress syndrome in patients with acute pancreatitis
Authors: Ding Ning , Guo Cuirong , Huang Yuchai , Li Changluo , Zhou Yang , Yang Guifang , Chai Xiangping 
Published: February 2020

AbstractObjectiveThe aim of this study was to formulate and validate an individualized predictive nomogram for in-hospital incidence of acute respiratory distress syndrome (ARDS) in patients with acute pancreatitis(AP).DesignIt was a retrospective cohort study.SettingDepartments of Emergency Medicine of two university-affiliated tertiary hospitals, Changsha, China.ParticipantsFrom January 2017 to December 2018, 779 individuals with AP were included in this study.Primary outcome measuresThe in-hospital incidence of ARDS was assessed.ResultsAP patients were randomly distributed into primary cohort(n=560)and validation cohort(n=219). Based on the primary cohort, risk factors were identified by logistic regression model and a nomogram was performed. Five independent factors including white blood counts(WBC),prothrombin time(PT),albumin(ALB),serum creatinine(SCR) and triglyceride (TG) were associated with in-hospital incidence of ARDS in AP patients. A nomogram was constructed based on the five independent factors with primary cohort of AUC 0.821 and validation cohort of AUC 0.822. Calibration curve analysis indicated that the predicted probability was in accordance with the observed probability in both primary and validation cohorts.ConclusionsThe study developed an intuitive nomogram with easily available laboratory parameters for the prediction of in-hospital incidence of ARDS in patients with AP. The incidence of ARDS for an individual patient can be fast and conveniently evaluated by our nomogram.Strengths and limitations of this studyThis is the first study to develop a nomogram for predicting the in-hospital incidence of ARDS in AP patients.For each AP patient, our nomogram enables physicians to directly and conveniently calculate a numeric probability of ARDS.There might be patient selection biases owing to retrospective nature of our study.
Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines
Authors: Gao Mingxuan , Ling Mingyi , Tang Xinwei , Wang Shun , Xiao Xu , Qiao Ying , Yang Wenxian , Yu Rongshan 
Published: February 2020

AbstractWith the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.
Geometric Potentials from Deep Learning Improve Prediction of CDR H3 Loop Structures
Authors: Ruffolo Jeffrey A. , Guerra Carlos , Mahajan Sai Pooja , Sulam Jeremias , Gray Jeffrey J. 
Published: February 2020

AbstractAntibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody. When evaluated on the Rosetta Antibody Benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same, and worse structures (measured by root-mean-squared distance [RMSD] from the experimental CDR H3 loop structure) than the standard Rosetta energy function for 30, 13, and 6 targets, respectively, and improved the average RMSD of predictions by 21.3% (0.48 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loop structures.
Low-Bias Amplification for Robust DNA Data Readout
Authors: Gao Yanmin , Chen Xin , Hao Jianye , Zhang Chengwei , Qiao Hongyan , Ke Yonggang , Qi Hao 
Published: February 2020

AbstractIn DNA data storage, the massive sequence complexity creates challenges in repeatable and efficient information readout. Here, our study clearly demonstrated that canonical polymerase chain reaction (PCR) created significant DNA amplification biases, which greatly hinder fast and stable data retrieving from hundred-thousand synthetic DNA sequences encoding over 2.85 megabyte (MB) digital data. To mitigate the amplification bias, we adapted an isothermal DNA amplification for low-bias amplification of DNA pool with massive sequence complexity, and named the new method isothermal DNA reading (iDR). By using iDR, we were able to robustly and repeatedly retrieve the data stored in DNA strands attached on magnetic beads (MB) with significantly decreased sequencing reads, compared with the PCR method. Therefore, we believe that the low-bias iDR method provides an ideal platform for robust DNA data storage, and fast and reliable data readout.
Urn models for regulated gene expression yield physically intuitive solutions for probability distributions of single-cell counts
Authors: Choudhary Krishna , Narang Atul 
Published: February 2020

AbstractFitting the probability mass functions from analytical solutions of stochastic models of gene expression to the count distributions of mRNA and protein molecules in single cells can yield valuable insights into mechanisms of gene regulation. Solutions of chemical master equations are available for various kinetic schemes but, even for the models of regulation with a basic ON-OFF switch, they take complex forms with generating functions given as hypergeometric functions. Gene expression studies that have used these to fit the data have interpreted the parameters as burst size and frequency. However, this is consistent with the hypergeometric functions only if a gene stays active for short time intervals separated by relatively long intervals of inactivity. Physical insights into the probability mass functions are essential to ensure proper interpretations but are lacking for models of gene regulation. We fill this gap by developing urn models for regulated gene expression, which are of immense value to interpret probability distributions. Our model consists of a master urn, which represents the cytosol. We sample RNA polymerases and ribosomes from it and assign them to recipient urns of two or more colors, which represent time intervals with a homogeneous propensity for gene expression. Colors of the recipient urns represent sub-systems of the promoter states, and the assignments to urns of a specific color represent gene expression. We use elementary principles of discrete probability theory to derive the solutions for a range of kinetic models, including the Peccoud-Ycart model, the Shahrezaei-Swain model, and models with an arbitrary number of promoter states. For activated genes, we show that transcriptional lapses, which are events of gene inactivation for short time intervals separated by long active intervals, quantify the transcriptional dynamics better than bursts. Our approach reveals the physics underlying the solutions, which has important implications for single-cell data analysis.
Wheat pathogenZymoseptoria tritici N-myristoyltransferase inhibitors: on-target antifungal activity and an unusual metabolic defense mechanism
Authors: Fedoryshchak Roman O. , Ocasio Cory A. , Strutton Ben , Mattocks Jo , Corran Andrew J. , Tate Edward W. 
Published: February 2020

ABSTRACTZymoseptoria triticiis the causative agent ofSeptoria triticiblotch (STB), which costs billions of dollars annually to major wheat-producing countries in terms of both fungicide use and crop loss. Agricultural pathogenic fungi have acquired resistance to most commercially available fungicide classes, and the rate of discovery and development of new fungicides has stalled, demanding new approaches and insights. Here we investigate a potential mechanism of targeting an important wheat pathogenZ. triticivia inhibition ofN-myristoyltransferase (NMT). We characterizeZ. triticiNMT biochemically for the first time, profile thein vivo Z. triticimyristoylated proteome and identify and validate the firstZ. triticiNMT inhibitors. Proteomic investigation of the downstream effects of NMT inhibition identified an unusual and novel mechanism of defense against chemical toxicity inZ. triticithrough the application of comparative bioinformatics to deconvolute function from the previously largely unannotatedZ. triticiproteome. Research into novel fungicidal modes-of-action is essential to satisfy an urgent unmet need for novel fungicide targets, and we anticipate that this study will serve as a useful proteomics and bioinformatics resource for researchers studyingZ. tritici.
Differences in the free energies between the excited states of Aβ40 and Aβ42 monomers encode their distinct aggregation propensities
Authors: Chakraborty Debayan , Straub John E. , Thirumalai D. 
Published: February 2020

The early events in the aggregation of the intrinsically disordered peptide, Aβ, involve transitions from the disordered lowest free energy ground state to assembly-competent states. Are the finger-prints of order found in the amyloid fibrils encoded in the conformations that the monomers access at equilibrium? If so, could the enhanced aggregation rate of Aβ42 compared to Aβ40 be rationalized from the sparsely populated high free energy states of the monomers? Here, we answer these questions in the affirmative using coarse-grained simulations of the SOP-IDP model of Aβ40 and Aβ42. Although both the peptides have practically identical ensemble-averaged properties, characteristic of random coils (RCs), the conformational ensembles of the two monomers exhibit sequence-specific heterogeneity. Hierarchical clustering of conformations reveals that both the peptides populate high free energy aggregation-prone (N*) states, which resemble the monomers in the fibril structure. The free energy gap between the ground (RC) and theN* states of Aβ42 peptide is smaller than for Aβ40. By relating the populations of excited states of the two peptides to the fibril formation time scales using an empirical formula, we explain nearly quantitatively the faster aggregation rate of Aβ42 relative to Aβ40. TheN* concept accounts for fibril polymorphs, leading to the prediction that the less stableN* state of Aβ42, encoding for the U-bend fibril, should form earlier than the structure with the S-bend topology, which is in accord with the Ostwald’s rule rationalizing crystal polymorph formation.Significance StatementAlzheimer’s disease (AD), a rampant neurodegenerative disorder, is caused by the accumulation of pathological aggregates, primarily composed of the two isoforms Aβ40 and Aβ42. Experiments have shown that Aβ42 is more aggregation-prone compared to Aβ40. However, the molecular origin of this apparent anomaly remains elusive. Here, we provide a microscopic basis for the different aggregation rates in terms of the distinct populations of high free energy excited fibril-like states (N*) that are encoded in the monomer spectra. The N* theory explains the emergence of fibril polymorphs, and predicts the relative kinetic stabilities of Aβ42 fibrils using Ostwald’s rule of stages. Our work shows that sequence-specific conformational heterogeneity of the monomer ensembles provides important cues for understanding protein aggregation.