bluuebunny/crossref_metadata_2025_split huggingface dataset:
Well-Differentiated Papillary Mesothelioma of the Peritoneum is Genetically Distinct from Malignant Mesothelioma
Authors: Shrestha Raunak , Nabavi Noushin , Volik Stanislav , Anderson Shawn , Haegert Anne , McConeghy Brian , Brahmbhatt Sonal , Bell Robert H. , Bihan Stephane Le , Wang Yuzhuo , Collins Colin C. , Churg Andrew 
Published: May 2019

AbstractWell-differentiated papillary mesothelioma (WDPM) is an uncommon mesothelial proliferation that is most commonly encountered as an incidental finding in the peritoneal cavity. There is controversy in the literature about whether WDPM is a neoplasm or a reactive process, and if neoplastic, whether it is a variant or precursor of epithelial malignant mesothelioma or is a different entity. Using whole exome sequencing of five WDPM of the peritoneum, we have identified distinct mutations in EHD1, ATM, FBXO10, SH2D2A, CDH5, MAGED1, and TP73 shared by WDPM cases but not reported in malignant mesotheliomas. Furthermore, we show that WDPM is strongly enriched with C>A transversion substitution mutations, a pattern that is also not found in malignant mesotheliomas. The WDPMs lacked alterations involving BAP1, SETD2, NF2, CDKN2A/B, LASTS1/2, PBRM1, and SMARCC1 that are frequently altered in malignant mesotheliomas. We conclude that WDPMs are neoplasms that are genetically distinct from malignant mesotheliomas, and based on observed mutations do not appear to be precursors of malignant mesotheliomas.
A new Bayesian methodology for nonlinear model calibration in Computational Systems Biology
Authors: Bianconi Fortunato , Tomassoni Lorenzo , Antonini Chiara , Valigi Paolo 
Published: May 2019

AbstractComputational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e. estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function (pdf) of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC.
A comparative assessment of adult mosquito trapping methods to estimate spatial patterns of abundance and community composition in southern Africa
Authors: Gorsich Erin E. , Beechler Brianna R. , van Bodegom Peter M. , Govender Danny , Guarido Milehna M. , Venter Marietjie , Schrama Maarten 
Published: May 2019

AbstractBackgroundAssessing adult mosquito populations is an important component of disease surveillance programs and ecosystem health assessments. Inference from adult trapping datasets involves comparing populations across space and time, but comparisons based on different trapping methods may be biased if traps have different efficiencies or sample different subsets of the mosquito community.MethodsWe compared four widely-used trapping methods for adult mosquito data collection in Kruger National Park (KNP), South Africa: Centers for Disease Control miniature light trap (CDC), Biogents Sentinel trap (BG), Biogents gravid Aedes trap (GAT) and a net trap. We quantified how trap choice and sampling effort influence inferences on the regional distribution of mosquito abundance, richness and community composition.ResultsThe CDC and net traps together collected 96% (47% and 49% individually) of the 955 female mosquitoes sampled and 100% (85% and 78% individually) of the 40 species or species complexes identified. The CDC and net trap also identified similar regional patterns of community composition. However, inference on the regional patterns of abundance differed between these traps because mosquito abundance in the net trap was influenced by variation in weather conditions. The BG and GAT traps collected significantly fewer mosquitoes, limiting regional comparisons of abundance and community composition.ConclusionsThis study represents the first systematic assessment of trapping methods in natural savanna ecosystems in southern Africa. We recommend the CDC trap or the net trap for future monitoring and surveillance programs.
Molecular Expression Profiles of Morphologically Defined Hippocampal Neuron Types: Empirical Evidence and Relational Inferences
Authors: White Charise M. , Rees Christopher L. , Wheeler Diek W. , Hamilton David J. , Ascoli Giorgio A. 
Published: May 2019

AbstractGene and protein expressions are key determinants of cellular function. Neurons are the building blocks of brain circuits, yet the relationship between their molecular identity and the spatial distribution of their dendritic inputs and axonal outputs remain incompletely understood. The open-source knowledge base Hippocampome.org amasses such transcriptomic data from the scientific literature for morphologically defined neuron types in the rodent hippocampal formation: dentate gyrus (DG), CA3, CA2, CA1, subiculum (SUB), and entorhinal cortex (EC). Positive, negative, or mixed expression reports were initially obtained from published articles directly connecting molecular evidence to neurons with known axonal and dendritic patterns across hippocampal layers. Here, we supplement this information by collating, formalizing, and leveraging relational expression inferences (REIs) that link a gene or protein expression or lack thereof to that of another molecule or to an anatomical location. With these additional interpretations, we freely release online a comprehensive human- and machine-readable molecular profile for the more than 100 neuron types in Hippocampome.org. Analysis of these data ascertains the ability to distinguish unequivocally most neuron types in each of the major subdivisions of the hippocampus based on currently known biochemical markers. Moreover, grouping neuron types by expression similarity reveals eight super-families characterized by a few defining molecules.Significance StatementThe molecular composition of cells underlies their structure, activity, and function. Neurons are arguably the most diverse cell types with their characteristic tree-like shapes mediating synaptic communication throughout the brain. Biochemical marker data are available online for hundreds of morphologically identified neuron types in the mammalian hippocampus, including expression of calcium-binding proteins, receptors, and enzymes. Here, we augment this evidence by systematically applying logical rules empirically derived from the published literature (e.g. “presence of molecule X implies lack of molecule Y”). The resulting substantially expanded expression profiles provide nearly unique molecular identities for most known hippocampal neuron types while revealing previously unrecognized genetic similarities across anatomical regions and morphological phenotypes.
HIPPIE2: a method for fine-scale identification of physically interacting chromatin regions
Authors: Kuksa Pavel P. , Amlie-Wolf Alexandre , Hwang Yih-Chii , Valladares Otto , Gregory Brian D. , Wang Li-San 
Published: May 2019

AbstractMost regulatory chromatin interactions are mediated by various transcription factors (TFs) and involve physically-interacting elements such as enhancers, insulators, or promoters. To map these elements and interactions, we developed HIPPIE2 which analyzes raw reads from high-throughput chromosome conformation (Hi-C) experiments to identify fine-scale physically-interacting regions (PIRs). Unlike standard genome binning approaches (e.g., 10K-1Mbp bins), HIPPIE2 dynamically calls physical locations of PIRs with better precision and higher resolution based on the pattern of restriction events and relative locations of interacting sites inferred from the sequencing readout.We applied HIPPIE2 to in situ Hi-C datasets across 6 human cell lines (GM12878, IMR90, K562, HMEC, HUVEC, NHEK) with matched ENCODE and Roadmap functional genomic data. HIPPIE2 detected 1,042,738 distinct PIRs across cell lines, with high resolution (average PIR length of 1,006bps) and high reproducibility (92.3% in GM12878 replicates). 32.8% of PIRs were shared among cell lines. PIRs are enriched for epigenetic marks (H3K27ac, H3K4me1) and open chromatin, suggesting active regulatory roles. HIPPIE2 identified 2.8M significant intrachromosomal PIR–PIR interactions, 27.2% of which were enriched for TF binding sites. 50,608 interactions were enhancer–promoter interactions and were enriched for 33 TFs (31 in enhancers/29 in promoters), several of which are known to mediate DNA looping/long-distance regulation. 29 TFs were enriched in >1 cell line and 4 were cell line-specific. These findings demonstrate that the dynamic approach used in HIPPIE2 (https://bitbucket.com/wanglab-upenn/HIPPIE2) characterizes PIR–PIR interactions with high resolution and reproducibility.
Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter, Stress Hormone, and Male Sex Hormone Systems
Authors: Camacho Mariam Bonyadi , Vijitbenjaronk Warut D. , Anastasio Thomas J 
Published: May 2019

AbstractSecond-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed patients still fail to respond even after months to years of searching to find an effective combination. To aid in the identification of potentially affective antidepressant combinations, we created a computational model of the monoaminergic neurotransmitter (serotonin, norepinephrine, and dopamine), stress-hormone (cortisol), and male sex-hormone (testosterone) systems. The model was trained via machine learning to represent a broad set of empirical observations. Neuroadaptation to chronic drug administration was simulated through incremental adjustments in model parameters that corresponded to key regulatory components of the neurotransmitter and neurohormone systems. Analysis revealed that neuroadaptation in the model depended on all of the regulatory components in complicated ways, and did not reveal any one or a few specific components that could be targeted in the design of combination antidepressant treatment. We used large sets of neuroadapted states of the model to screen 74 different drug and hormone combinations and identified several combinations that could potentially be therapeutic for a higher proportion of male patients than SSRIs by themselves.
The cell cycle in Staphylococcus aureus is regulated by an amidase that controls peptidoglycan synthesis
Authors: Do Truc , Schaefer Kaitlin , Santiago Ace George , Coe Kathryn A. , Fernandes Pedro B. , Kahne Daniel , Pinho Mariana G. , Walker Suzanne 
Published: May 2019

AbstractBacteria are protected by a polymer of peptidoglycan that serves as an exoskeleton. In Staphylococcus aureus, the enzymes that assemble peptidoglycan move during the cell cycle from the periphery, where they are active during growth, to the division site where they build the partition between daughter cells. But how peptidoglycan synthesis is regulated throughout the cell cycle is not understood. Here we identify a membrane protein complex that spatially regulates S. aureus peptidoglycan synthesis. This complex consists of an amidase that removes peptide chains from uncrosslinked peptidoglycan and a partner protein that controls its activity. Typical amidases act after cell division to hydrolyze peptidoglycan between daughter cells so they can separate. However, we show that this amidase controls cell growth. In its absence, excess peptidoglycan synthesis occurs at the cell periphery, causing cells to grow so large that cell division is defective. We show that cell growth and division defects due to loss of this amidase can be mitigated by attenuating the polymerase activity of the major S. aureus peptidoglycan synthase. Our findings lead to a model wherein the amidase complex regulates the density of peptidoglycan assembly sites to control peptidoglycan synthase activity at a given cellular location. Removal of peptide chains from peptidoglycan at the cell periphery promotes synthase movement to midcell during cell division. This mechanism ensures that cell expansion is properly coordinated with cell division.
CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
Authors: Bernstein Matthew N. , Ma Zhongjie , Gleicher Michael , Dewey Colin N. 
Published: May 2019

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract
Cross-talk between SIM2s and NFkB regulates cyclooxygenase 2 expression in breast cancer
Authors: Wyatt Garhett , Young Chloe , Crump Lyndsey , Wessells Veronica , Gustafson Tanya , Fan Yang-Yi , Chapkin Robert , Porter Weston , Lyons Traci R 
Published: May 2019

AbstractBackgroundBreast cancer is a leading cause of cancer-related death for women in the United States. Thus, there a need to investigate novel prognostic markers and therapeutic strategies. Inflammation raises challenges to both treating and preventing the spread of breast cancer. Specifically, the nuclear factor kappa b (NFkB) pathway contributes to cancer progression by stimulating proliferation and preventing apoptosis. One target gene of this pathway is PTGS2, the gene that encodes for cyclooxygenase 2 (COX-2), which is upregulated in 40% of human breast carcinomas. COX-2 is an enzyme involved in inflammation. Here we investigate the effect of Singleminded 2s, a transcriptional tumor suppressor that is implicated in inhibition of tumor growth and metastasis, in regulating NFkB and COX-2.MethodsWe utilized in vitro reporter assays, immunoblot analyses, qPCR and immunohistochemical analysis to dissect the relationship between NFκB, SIM2s, and COX-2. Furthermore, we utilized COX-2 targeting strategies to determine tumor suppressive activities.ResultsOur results reveal that SIM2s attenuates the activation of a NFκB via luciferase reporter assays. Furthermore, immunostaining of lysates from breast cancer cells over expressing SIM2s showed reduction in various NFκB signaling proteins, whereas knockdown of SIM2 revealed increases in the same NFκB signaling proteins. Additionally, by increasing NFκB translocation to the nucleus in DCIS.COM cells, we show that NFκB signaling can act in a reciprocal manner to decrease expression of SIM2s. Likewise, suppressing NFκB translocation in DCIS.COM cells increases SIM2s expression. We also found that NFκB/p65 represses SIM2 in via dose-dependent manner and when NFκB is suppressed the effect on the SIM2 is negated. Additionally, our CHIP analysis confirms that NFκB/p65 binds directly to SIM2 promoter site and that the NFκB sites in the SIM2 promoter are required for NFkB-mediated suppression of SIM2s. Finally, over expression of SIM2s decreases PTGS2 in vitro and COX-2 staining in vivo while decreasing PTGS2 and/or Cox-2 activity results in re-expression of SIM2. Our findings identify a novel role for SIM2s in NFκB signaling and COX-2 expression.ConclusionsThese findings provide evidence for a mechanism where SIM2s may represses COX-2 expression to provide an overall better prognosis for breast cancer patients.
The Biogeography of Coelurosaurian Theropods and its Impact on their Evolutionary History
Authors: Ding Anyang , Pittman Michael , Upchurch Paul , O’Connor Jingmai , Field Daniel J. , Xu Xing 
Published: May 2019

ABSTRACTThe Coelurosauria are a group of mostly feathered theropods that gave rise to birds, the only dinosaurs that survived the Cretaceous-Paleogene extinction event and are still found today. Between their first appearance in the Middle Jurassic up to the end Cretaceous, coelurosaurs were party to dramatic geographic changes on the Earth’s surface, including the breakup of the supercontinent Pangaea, and the formation of the Atlantic Ocean. These plate tectonic events are thought to have caused vicariance or dispersal of coelurosaurian faunas, influencing their evolution. Unfortunately, few coelurosaurian biogeographic hypotheses are supported by quantitative evidence. Here, we report the first, broadly-sampled quantitative analysis of coelurosaurian biogeography using the likelihood-based package BioGeoBEARS. Mesozoic geographic configurations and changes are reconstructed and employed as constraints in this analysis, including their associated uncertainties. We use a comprehensive time-calibrated coelurosaurian evolutionary tree produced from the Theropod Working Group phylogenetic data matrix. Six biogeographic models in the BioGeoBEARS package with different assumptions about the evolution of spatial distribution are tested against the geographic constraints. Our results statistically favour the DIVALIKE+J and DEC+J models, which allow vicariance and founder events, supporting continental vicariance as an important factor in coelurosaurian evolution. Ancestral range estimation indicates frequent dispersal events via the Apulian Route (connecting Europe and Africa during the Early Cretaceous) and the Bering Land Bridge (connecting North America and Asia during the Late Cretaceous). These quantitative results are consistent with commonly inferred Mesozoic dinosaurian dispersals and continental-fragmentation-induced vicariance events. In addition, we recognise the importance of Europe as a dispersal centre and gateway in the Early Cretaceous, as well as other vicariance events like those triggered by the disappearance of land-bridges.
Microbially induced calcite precipitation usingBacillus velezensiswith guar gum
Authors: Dikshit Rashmi , Jain Animesh , Dey Arjun , Kamilya Sujit , Mondal Abhishake , Kumar Aloke 
Published: May 2019

AbstractThe present study was performed to explore the efficiency of microbially induced calcite precipitation (MICP) via locally isolated bacterial strains. Strains were isolated from soil and were screened for urease activity as well as microbial precipitation. Among all screened isolates, a carbonate precipitating soil bacterium was subjected for 16S rRNA gene sequencing. This strain was identified asBacillus velezensis. The MICP characteristics of this strain were explored under three different media compositions and significant amount of precipitation in all cases was observed. Highest amount of precipitation was seen with guar as a biopolymer additive medium under experimented conditions. Activity of isolated strain with reference to pH profile, and ammonia concentration and total reducing sugar was further explored under media supplemented with four concentrations of guar (0.25%, 0.5%, 0.75% and 1% w/v). Microstructural analysis of microbial precipitation was performed with the help of scanning electron microscopy (SEM) and X-ray powder diffraction (XRD) analysis, which confirmed the presence of calcium carbonate in different phases. The strain was subjected to bio-cementation and locally available sand was successfully consolidated. XRD results confirmed the presence of calcium carbonate on consolidated samples.