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https://www.readbyqxmd.com/read/29144590/metabolic-profiling-of-normal-hepatocyte-and-hepatocellular-carcinoma-cells-via-1-h-nuclear-magnetic-resonance-spectroscopy
#1
Yang Chen, Zhong Chen, Jiang-Hua Feng, Yun-Bin Chen, Nai-Shun Liao, Ying Su, Chang-Yan Zou
Hepatocellular carcinoma (HCC) causes death mainly by disseminated metastasis progression from the organ being confined. Different metastatic stages are closely related to cellular metabolic profiles. Normal hepatocyte and HepG2 cell line from low metastatic HCC were studied by NMR-based metabolomic techniques. Multivariate and univariate statistical analyses were utilized to identify characteristic metabolites from cells and cultured media. Elevated levels of acetate, creatine, isoleucine, leucine, and phenylalanine were observed in HepG2 cells, suggesting more active in gathering nutrient components along with altered amino acid metabolisms and enhanced lipid metabolism...
November 16, 2017: Cell Biology International
https://www.readbyqxmd.com/read/29140375/plasma-of-argon-increases-cell-attachment-and-bacterial-decontamination-on-different-implant-surfaces
#2
Luigi Canullo, Tullio Genova, Hom-Lay Wang, Stefano Carossa, Federico Mussano
PURPOSE: This in vitro study tested the effects of argon atmospheric pressure dielectric barrier discharge (APDBD) on different implant surfaces with regard to physical changes, bacterial decontamination, and osteoblast adhesion. MATERIALS AND METHODS: Seven hundred twenty disks with three different surface topographies-machined (MAC), titanium plasma-sprayed (TPS), and zirconia-blasted and acid-etched (ZRT)-were tested in this experiment. Bacterial adhesion tests were performed repeatedly on a simplified biofilm of Streptococcus mitis...
November 2017: International Journal of Oral & Maxillofacial Implants
https://www.readbyqxmd.com/read/29140077/superior-robust-ultra-thin-single-crystalline-silicon-carbide-membrane-as-a-versatile-platform-for-biological-applications
#3
Tuan-Khoa Nguyen, Hoang-Phuong Phan, Harshad Kamble, Raja Vadivelu, Toan Khac Dinh, Alan Iacopi, Glenn Walker, Leonie Hold, Nam-Trung Nguyen, Dzung Viet Dao
Micro-machined membranes are promising platforms for cell culture thanks to their miniaturization and integration capabilities. Possessing chemical inertness, bio compatibility and integration, silicon carbide (SiC) membranes have attracted great interest towards biological applications. In this paper, we present the batch fabrication, mechanical characterizations, and cell culture demonstration of robust ultra-thin epitaxial deposited SiC membranes. The as-fabricated ultra-thin SiC membranes, with an ultra-high aspect ratio (length/thickness) of up to 20,000, possess high a fracture strength up to 2...
November 15, 2017: ACS Applied Materials & Interfaces
https://www.readbyqxmd.com/read/29137603/clustertad-an-unsupervised-machine-learning-approach-to-detecting-topologically-associated-domains-of-chromosomes-from-hi-c-data
#4
Oluwatosin Oluwadare, Jianlin Cheng
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts...
November 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29137180/metabolomic-modularity-analysis-mma-to-quantify-human-liver-perfusion-dynamics
#5
Gautham Vivek Sridharan, Bote Bruinsma, Shyam Sundhar Bale, Anandh Swaminathan, Nima Saeidi, Martin L Yarmush, Korkut Uygun
Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant...
November 13, 2017: Metabolites
https://www.readbyqxmd.com/read/29136580/advancing-the-large-scale-ccs-database-for-metabolomics-and-lipidomics-at-the-machine-learning-era
#6
REVIEW
Zhiwei Zhou, Jia Tu, Zheng-Jiang Zhu
Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS...
November 11, 2017: Current Opinion in Chemical Biology
https://www.readbyqxmd.com/read/29136241/wikipathways-a-multifaceted-pathway-database-bridging-metabolomics-to-other-omics-research
#7
Denise N Slenter, Martina Kutmon, Kristina Hanspers, Anders Riutta, Jacob Windsor, Nuno Nunes, Jonathan Mélius, Elisa Cirillo, Susan L Coort, Daniela Digles, Friederike Ehrhart, Pieter Giesbertz, Marianthi Kalafati, Marvin Martens, Ryan Miller, Kozo Nishida, Linda Rieswijk, Andra Waagmeester, Lars M T Eijssen, Chris T Evelo, Alexander R Pico, Egon L Willighagen
WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation...
November 10, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/29134060/modelling-pyruvate-dehydrogenase-under-hypoxia-and-its-role-in-cancer-metabolism
#8
Filmon Eyassu, Claudio Angione
Metabolism is the only biological system that can be fully modelled at genome scale. As a result, metabolic models have been increasingly used to study the molecular mechanisms of various diseases. Hypoxia, a low-oxygen tension, is a well-known characteristic of many cancer cells. Pyruvate dehydrogenase (PDH) controls the flux of metabolites between glycolysis and the tricarboxylic acid cycle and is a key enzyme in metabolic reprogramming in cancer metabolism. Here, we develop and manually curate a constraint-based metabolic model to investigate the mechanism of pyruvate dehydrogenase under hypoxia...
October 2017: Royal Society Open Science
https://www.readbyqxmd.com/read/29131760/deep-learning-a-primer-for-radiologists
#9
Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, An Tang
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance...
November 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/29128545/massive-parallel-sequencing-of-mitochondrial-dna-genomes-from-mother-child-pairs-using-the-ion-torrent-personal-genome-machine-pgm
#10
Ke Ma, Xueying Zhao, Hui Li, Yu Cao, Wei Li, Jian Ouyang, Lu Xie, Wenbin Liu
Mitochondrial genome analysis is a potent tool in forensic practice and in the understanding of human phylogeny in the maternal lineage. With the development of molecular biology and bioinformatics techniques, high-throughput sequencing has enabled mtDNA analysis during whole genome sequencing, which provides more comprehensive information and raises the power of discrimination. In this study, peripheral blood samples were taken from 194 mother-offspring pairs and sequenced by Ion Torrent Personal Genome Machine and obtained high-coverage mitochondrial sequencing data, demonstrating the mutation levels at each position in the mitochondrial DNA (mtDNA) between maternally related pairs...
November 6, 2017: Forensic Science International. Genetics
https://www.readbyqxmd.com/read/29124837/synthetic-ion-channels-and-dna-logic-gates-as-components-of-molecular-robots
#11
Ryuji Kawano
A molecular robot is a next-generation biological robot consisting of biomaterials such as DNA, proteins, and lipids that imitates the actions of microorganisms. Three prerequisites have been proposed for the construction of such a robot: sensor, intelligence, and actuator. This minireview focuses on recent research on synthetic ion channels and DNA computing technologies, which are viewed as potential candidate components of molecular robots. Synthetic ion channels, which are embedded in an artificial cell membrane (lipid bilayer), sense ambient ions or chemicals and incorporate the molecules...
November 9, 2017: Chemphyschem: a European Journal of Chemical Physics and Physical Chemistry
https://www.readbyqxmd.com/read/29122011/entity-recognition-in-the-biomedical-domain-using-a-hybrid-approach
#12
Marco Basaldella, Lenz Furrer, Carlo Tasso, Fabio Rinaldi
BACKGROUND: This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. METHOD: The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only...
November 9, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/29120497/book-review-on-molecular-biology-of-assemblies-and-machines-by-alasdair-steven-wolfgang-baumeister-louise-johnson-and-richard-perham-published-by-garland-science-taylor-and-francis-group
#13
https://www.readbyqxmd.com/read/29118099/the-potential-of-cryo-electron-microscopy-for-structure-based-drug-design
#14
REVIEW
Andreas Boland, Leifu Chang, David Barford
Structure-based drug design plays a central role in therapeutic development. Until recently, protein crystallography and NMR have dominated experimental approaches to obtain structural information of biological molecules. However, in recent years rapid technical developments in single particle cryo-electron microscopy (cryo-EM) have enabled the determination to near-atomic resolution of macromolecules ranging from large multi-subunit molecular machines to proteins as small as 64 kDa. These advances have revolutionized structural biology by hugely expanding both the range of macromolecules whose structures can be determined, and by providing a description of macromolecular dynamics...
November 8, 2017: Essays in Biochemistry
https://www.readbyqxmd.com/read/29114182/application-of-deep-learning-in-automated-analysis-of-molecular-images-in-cancer-a-survey
#15
REVIEW
Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen
Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically...
2017: Contrast Media & Molecular Imaging
https://www.readbyqxmd.com/read/29111979/machine-learning-and-data-science-in-soft-materials-engineering
#16
Andrew Ferguson
In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by ``de-jargonizing'' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy...
November 7, 2017: Journal of Physics. Condensed Matter: An Institute of Physics Journal
https://www.readbyqxmd.com/read/29110491/deep-learning-accurately-predicts-estrogen-receptor-status-in-breast-cancer-metabolomics-data
#17
Fadhl M Alkawaa, Kumardeep Chaudhary, Lana X Garmire
Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if the deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM)...
November 7, 2017: Journal of Proteome Research
https://www.readbyqxmd.com/read/29106441/orchid-a-novel-management-annotation-and-machine-learning-framework-for-analyzing-cancer-mutations
#18
Clinton L Cario, John S Witte
Motivation: As whole-genome tumor sequence and biological annotation datasets grow in size, number, and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments, and machine learning algorithms, there is also a need for the integration of functionality across frameworks. Results: We present orchid, a python based software package for the management, annotation, and machine learning of cancer mutations...
November 2, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29106044/an-improved-binary-differential-evolution-algorithm-for-feature-selection-in-molecular-signatures
#19
X S Zhao, L L Bao, Q Ning, J C Ji, X W Zhao
The discovery of biomarkers from high-dimensional data is a very challenging task in cancer diagnoses. On the one hand, biomarker discovery is the so-called high-dimensional small-sample problem. On the other hand, these data are redundant and noisy. In recent years, biomarker discovery from high-throughput biological data has become an increasingly important emerging topic in the field of bioinformatics. In this study, we propose a binary differential evolution algorithm for feature selection. Firstly, we suggest using a two-stage approach, where three filter methods including the Fisher score, T-statistics, and Information gain are used to generate the feature pool for input to differential evolution (DE)...
November 6, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/29102828/reduced-electron-exposure-for-energy-dispersive-spectroscopy-using-dynamic-sampling
#20
Yan Zhang, G M Dilshan Godaliyadda, Nicola Ferrier, Emine B Gulsoy, Charles A Bouman, Charudatta Phatak
Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. In this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope...
October 23, 2017: Ultramicroscopy
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