Isobaric thermal expansivity and thermophysical characterization of liquids and liquid mixturesElectronic Supplementary Information available. See http://www.rsc.org/suppdata/cp/b1/b104891k/

2001 ◽  
Vol 3 (23) ◽  
pp. 5230-5236 ◽  
Author(s):  
Claudio A. Cerdeiriña ◽  
Clara A. Tovar ◽  
Diego González-Salgado ◽  
Enrique Carballo ◽  
Luis Romaní
Keyword(s):  
Thermal Expansivity ◽  
Supplementary Information ◽  
Thermophysical Characterization ◽  
Isobaric Thermal Expansivity

Liquid dibromomethane under pressure: a computational study

2021 ◽  
Vol 23 (4) ◽  
pp. 2964-2971
Author(s):  
Bernadeta Jasiok ◽  
Mirosław Chorążewski ◽  
Eugene B. Postnikov ◽  
Claude Millot
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Thermophysical Properties ◽  
Computational Study ◽  
Thermal Expansivity ◽  
Dynamics Simulations ◽  
Isobaric Thermal Expansivity

Thermophysical properties of liquid dibromomethane are investigated by molecular dynamics simulations between 268 and 328 K at pressures up to 3000 bar. Notably, the isotherms of the isobaric thermal expansivity cross around 800 bar.

scholarly journals CSMD: a computational subtraction-based microbiome discovery pipeline for species-level characterization of clinical metagenomic samples

2019 ◽  
Author(s):  
Yu Liu ◽  
Paul W Bible ◽  
Bin Zou ◽  
Qiaoxing Liang ◽  
Cong Dong ◽  
...  
Keyword(s):  
Species Level ◽  
Supplementary Information ◽  
Clinical Samples ◽  
Plasma Dna ◽  
Bioinformatics Pipeline ◽  
Microbial Identification ◽  
Reference Databases ◽  
Microbial Dna ◽  
Higher Sensitivity

Abstract Motivation Microbiome analyses of clinical samples with low microbial biomass are challenging because of the very small quantities of microbial DNA relative to the human host, ubiquitous contaminating DNA in sequencing experiments and the large and rapidly growing microbial reference databases. Results We present computational subtraction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate species-level microbiome profiles for clinical samples with low microbial loads. CSMD applies strategies for the maximal elimination of host sequences with minimal loss of microbial signal and effectively detects microorganisms present in the sample with minimal false positives using a stepwise convergent solution. CSMD was benchmarked in a comparative evaluation with other classic tools on previously published well-characterized datasets. It showed higher sensitivity and specificity in host sequence removal and higher specificity in microbial identification, which led to more accurate abundance estimation. All these features are integrated into a free and easy-to-use tool. Additionally, CSMD applied to cell-free plasma DNA showed that microbial diversity within these samples is substantially broader than previously believed. Availability and implementation CSMD is freely available at https://github.com/liuyu8721/csmd. Supplementary information Supplementary data are available at Bioinformatics online.

Thermophysical characterization of glassy, cross-linked heterocyclic polymers

1994 ◽  
Vol 238 ◽  
pp. 417-427 ◽  
Author(s):  
V.P. Azarenkov ◽  
A.V. Baibak ◽  
V.Yu. Kramarenko ◽  
V.P. Privalko
Keyword(s):  
Heterocyclic Polymers ◽  
Thermophysical Characterization

Synthesis and Thermophysical Characterization of New Biologically Friendly Agmatine-Based Ionic Liquids and Salts by Experimental and Computational Approach

2019 ◽  
Vol 7 (12) ◽  
pp. 10773-10783 ◽  
Author(s):  
Milan B. Vraneš ◽  
Jovana J. Panić ◽  
Aleksandar S. Tot ◽  
Sergej M. Ostojić ◽  
Dragana D. Četojević-Simin ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Computational Approach ◽  
Thermophysical Characterization

scholarly journals Jasmine: a Java pipeline for isomiR characterization in miRNA-Seq data

2019 ◽  
Author(s):  
Xiangfu Zhong ◽  
Albert Pla ◽  
Simon Rayner
Keyword(s):  
Population Structure ◽  
Software Tool ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Analysis Pipeline ◽  
Detailed Characterization ◽  
Fasta Format ◽  
Java Application

Abstract Motivation The existence of complex subpopulations of miRNA isoforms, or isomiRs, is well established. While many tools exist for investigating isomiR populations, they differ in how they characterize an isomiR, making it difficult to compare results across different tools. Thus, there is a need for a more comprehensive and systematic standard for defining isomiRs. Such a standard would allow investigation of isomiR population structure in progressively more refined sub-populations, permitting the identification of more subtle changes between conditions and leading to an improved understanding of the processes that generate these differences. Results We developed Jasmine, a software tool that incorporates a hierarchal framework for characterizing isomiR populations. Jasmine is a Java application that can process raw read data in fastq/fasta format, or mapped reads in SAM format to produce a detailed characterization of isomiR populations. Thus, Jasmine can reveal structure not apparent in a standard miRNA-Seq analysis pipeline. Availability and implementation Jasmine is implemented in Java and R and freely available at bitbucket https://bitbucket.org/bipous/jasmine/src/master/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i516-i524
Author(s):  
Midori Iida ◽  
Michio Iwata ◽  
Yoshihiro Yamanishi
Keyword(s):  
Large Scale ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Molecular Networks ◽  
Supplementary Information ◽  
New Associations ◽  
Disease States ◽  
Molecular Features ◽  
Novel Approach

Abstract Motivation Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease–disease relationships remain uncharacterized. Results In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. Supplementary information Supplementary data are available at Bioinformatics online.

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