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Biological machines

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https://www.readbyqxmd.com/read/28934693/enzyme-classification-using-multiclass-support-vector-machine-and-feature-subset-selection
#1
Debasmita Pradhan, Sudarsan Padhy, Biswajit Sahoo
Proteins are the macromolecules responsible for almost all biological processes in a cell. With the availability of large number of protein sequences from different sequencing projects, the challenge with the scientist is to characterize their functions. As the wet lab methods are time consuming and expensive, many computational methods such as FASTA, PSI-BLAST, DNA microarray clustering, and Nearest Neighborhood classification on protein-protein interaction network have been proposed. Support vector machine is one such method that has been used successfully for several problems such as protein fold recognition, protein structure prediction etc...
August 31, 2017: Computational Biology and Chemistry
https://www.readbyqxmd.com/read/28933436/stereodivergent-synthesis-with-a-programmable-molecular-machine
#2
Salma Kassem, Alan T L Lee, David A Leigh, Vanesa Marcos, Leoni I Palmer, Simone Pisano
It has been convincingly argued that molecular machines that manipulate individual atoms, or highly reactive clusters of atoms, with Ångström precision are unlikely to be realized. However, biological molecular machines routinely position rather less reactive substrates in order to direct chemical reaction sequences, from sequence-specific synthesis by the ribosome to polyketide synthases, where tethered molecules are passed from active site to active site in multi-enzyme complexes. Artificial molecular machines have been developed for tasks that include sequence-specific oligomer synthesis and the switching of product chirality, a photo-responsive host molecule has been described that is able to mechanically twist a bound molecular guest, and molecular fragments have been selectively transported in either direction between sites on a molecular platform through a ratchet mechanism...
September 20, 2017: Nature
https://www.readbyqxmd.com/read/28931937/high-accuracy-label-free-classification-of-single-cell-kinetic-states-from-holographic-cytometry-of-human-melanoma-cells
#3
Miroslav Hejna, Aparna Jorapur, Jun S Song, Robert L Judson
Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of standardization metrics has hindered independent experimental comparison and validation. Here we identify twenty-six DHC-derived features that provide biologically independent information across a variety of mammalian cell state transitions...
September 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28927581/differential-aging-signals-in-abdominal-ct-scans
#4
Nikita V Orlov, Sokratis Makrogiannis, Luigi Ferrucci, Ilya G Goldberg
RATIONALE AND OBJECTIVES: Changes in the composition of body tissues are major aging phenotypes, but they have been difficult to study in depth. Here we describe age-related change in abdominal tissues observable in computed tomography (CT) scans. We used pattern recognition and machine learning to detect and quantify these changes in a model-agnostic fashion. MATERIALS AND METHODS: CT scans of abdominal L4 sections were obtained from Baltimore Longitudinal Study of Aging (BLSA) participants...
September 15, 2017: Academic Radiology
https://www.readbyqxmd.com/read/28923123/-a-machine-for-recreating-life-an-introduction-to-reproduction-on-film
#5
Jesse Olszynko-Gryn, Patrick Ellis
Reproduction is one of the most persistently generative themes in the history of science and cinema. Cabbage fairies, clones and monstrous creations have fascinated filmmakers and audiences for more than a century. Today we have grown accustomed not only to the once controversial portrayals of sperm, eggs and embryos in biology and medicine, but also to the artificial wombs and dystopian futures of science fiction and fantasy. Yet, while scholars have examined key films and genres, especially in response to the recent cycle of Hollywood 'mom coms', the analytic potential of reproduction on film as a larger theme remains largely untapped...
September 2017: British Journal for the History of Science
https://www.readbyqxmd.com/read/28918672/protein-complexes-big-data-machine-learning-and-integrative-proteomics-lessons-learned-over-a-decade-of-systematic-analysis-of-protein-interaction-networks
#6
Pierre C Havugimana, Pingzhao Hu, Andrew Emili
Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'...
September 18, 2017: Expert Review of Proteomics
https://www.readbyqxmd.com/read/28917842/multi-scale-simulations-of-biological-systems-using-the-opep-coarse-grained-model
#7
Fabio Sterpone, Sébastien Doutreligne, Thanh Thuy Tran, Simone Melchionna, Marc Baaden, Phuong H Nguyen, Philippe Derreumaux
Biomolecules are complex machines that are optimized by evolution to properly fulfill or contribute to a variety of biochemical tasks in the cellular environment. Computer simulations based on quantum mechanics and atomistic force fields have been proven to be a powerful microscope for obtaining valuable insights into many biological, physical, and chemical processes. Many interesting phenomena involve, however, a time scale and a number of degrees of freedom, notably if crowding is considered, that cannot be explored at an atomistic resolution...
September 13, 2017: Biochemical and Biophysical Research Communications
https://www.readbyqxmd.com/read/28913654/discriminating-cirrnas-from-other-lncrnas-using-a-hierarchical-extreme-learning-machine-h-elm-algorithm-with-feature-selection
#8
Lei Chen, Yu-Hang Zhang, Guohua Huang, Xiaoyong Pan, ShaoPeng Wang, Tao Huang, Yu-Dong Cai
As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques...
September 14, 2017: Molecular Genetics and Genomics: MGG
https://www.readbyqxmd.com/read/28901446/a-support-vector-machine-classifier-for-the-prediction-of-osteosarcoma-metastasis-with-high-accuracy
#9
Yunfei He, Jun Ma, Xiaojian Ye
In this study, gene expression profiles of osteosarcoma (OS) were analyzed to identify critical genes associated with metastasis. Five gene expression datasets were screened and downloaded from Gene Expression Omnibus (GEO). Following assessment by MetaQC, the dataset GSE9508 was excluded for poor quality. Subsequently, differentially expressed genes (DEGs) between metastatic and non-metastatic OS were identified using meta‑analysis. A protein-protein interaction (PPI) network was constructed with information from Human Protein Reference Database (HPRD) for the DEGs...
September 7, 2017: International Journal of Molecular Medicine
https://www.readbyqxmd.com/read/28900125/mergeable-nervous-systems-for-robots
#10
Nithin Mathews, Anders Lyhne Christensen, Rehan O'Grady, Francesco Mondada, Marco Dorigo
Robots have the potential to display a higher degree of lifetime morphological adaptation than natural organisms. By adopting a modular approach, robots with different capabilities, shapes, and sizes could, in theory, construct and reconfigure themselves as required. However, current modular robots have only been able to display a limited range of hardwired behaviors because they rely solely on distributed control. Here, we present robots whose bodies and control systems can merge to form entirely new robots that retain full sensorimotor control...
September 12, 2017: Nature Communications
https://www.readbyqxmd.com/read/28894088/a-de-novo-substructure-generation-algorithm-for-identifying-the-privileged-chemical-fragments-of-liver-x-receptor%C3%AE-agonists
#11
He Peng, Zhihong Liu, Xin Yan, Jian Ren, Jun Xu
Liver X receptorβ (LXRβ) is a promising therapeutic target for lipid disorders, atherosclerosis, chronic inflammation, autoimmunity, cancer and neurodegenerative diseases. Druggable LXRβ agonists have been explored over the past decades. However, the pocket of LXRβ ligand-binding domain (LBD) is too large to predict LXRβ agonists with novel scaffolds based on either receptor or agonist structures. In this paper, we report a de novo algorithm which drives privileged LXRβ agonist fragments by starting with individual chemical bonds (de novo) from every molecule in a LXRβ agonist library, growing the bonds into substructures based on the agonist structures with isomorphic and homomorphic restrictions, and electing the privileged fragments from the substructures with a popularity threshold and background chemical and biological knowledge...
September 11, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28893404/3d-quantitative-chemical-imaging-of-tissues-by-spectromics
#12
REVIEW
Cyril Petibois
Mid-infrared (IR), Raman, and X-ray fluorescence (XRF) spectroscopy methods, as well as mass spectrometry (MS), can be used for 3D chemical imaging. These techniques offer an invaluable opportunity to access chemical features of biological samples in a nonsupervised way. The global chemical information they provide enables the exploitation of a large array of chemical species or parameters, so-called 'spectromics'. Extracting chemical data from spectra is critical for the high-quality chemical analysis of biosamples...
September 8, 2017: Trends in Biotechnology
https://www.readbyqxmd.com/read/28892030/expedited-radiation-biodosimetry-by-automated-dicentric-chromosome-identification-adci-and-dose-estimation
#13
Ben Shirley, Yanxin Li, Joan H M Knoll, Peter K Rogan
Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques...
September 4, 2017: Journal of Visualized Experiments: JoVE
https://www.readbyqxmd.com/read/28890685/emg-based-continuous-and-simultaneous-estimation-of-arm-kinematics-in-able-bodied-individuals-and-stroke-survivors
#14
Jie Liu, Sang Hoon Kang, Dali Xu, Yupeng Ren, Song Joo Lee, Li-Qun Zhang
Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28890183/radiomics-analysis-on-ultrasound-for-prediction-of-biologic-behavior-in-breast-invasive-ductal-carcinoma
#15
Yi Guo, Yuzhou Hu, Mengyun Qiao, Yuanyuan Wang, Jinhua Yu, Jiawei Li, Cai Chang
INTRODUCTION: In current clinical practice, invasive ductal carcinoma is always screened using medical imaging techniques and diagnosed using immunohistochemistry. Recent studies have illustrated that radiomics approaches provide a comprehensive characterization of entire tumors and can reveal predictive or prognostic associations between the images and medical outcomes. To better reveal the underlying biology, an improved understanding between objective image features and biologic characteristics is urgently required...
August 18, 2017: Clinical Breast Cancer
https://www.readbyqxmd.com/read/28890089/-tumour-sequencing-evolutions-and-revolutions
#16
N Piton, A Lamy, J-C Sabourin
For some years now, we have entered the genomic age of tumour genotyping from a medical point of view. Technological breakthroughs in both biology and information science now allow a genomic analysis of cancers in everyday medical practice with, in some case, a major impact on patient care not only for the choice of therapy (i.e. EGFR mutations in lung adenocarcinoma), but also for diagnosis and monitoring of the disease. Tumour genotyping is performed from formalin-fixed paraffin-embedded tissues used for diagnosis of cancer...
September 7, 2017: Cancer Radiothérapie: Journal de la Société Française de Radiothérapie Oncologique
https://www.readbyqxmd.com/read/28888744/-stereotactic-body-radiotherapy-for-liver-tumors-state-of-the-art
#17
O Riou, D Azria, F Mornex
Thanks to the improvement in radiotherapy physics, biology, computing and imaging, patients presenting with liver tumors can be efficiently treated by radiation. Radiotherapy has been included in liver tumors treatment guidelines at all disease stages. Liver stereotactic radiotherapy has to be preferred to standard fractionated radiotherapy whenever possible, as potentially more efficient because of higher biological equivalent dose. Liver stereotactic radiotherapy planning and delivery require extensive experience and optimal treatment quality at every step, thus limiting its availability to specialized centres...
September 6, 2017: Cancer Radiothérapie: Journal de la Société Française de Radiothérapie Oncologique
https://www.readbyqxmd.com/read/28886434/prediction-of-lysine-crotonylation-sites-by-incorporating-the-composition-of-k-spaced-amino-acid-pairs-into-chou-s-general-pseaac
#18
Zhe Ju, Jian-Jun He
As one of the most important and common histones post-translational modifications, crotonylation plays a key role in regulating various biological processes. The accurate identification of crotonylation sites is crucial to elucidate the underlying molecular mechanisms of crotonylation. In this study, a novel bioinformatics tool named CKSAAP_CrotSite is developed to predict crotonylation sites. The highlight of CKSAAP_CrotSite is to adopt the composition of k-spaced amino acid pairs as input encoding, and the support vector machine is employed as the classifier...
August 24, 2017: Journal of Molecular Graphics & Modelling
https://www.readbyqxmd.com/read/28881755/identification-of-long-non-coding-rnas-biomarkers-associated-with-progression-of-endometrial-carcinoma-and-patient-outcomes
#19
Yanan Sun, Xiaoyan Zou, Jun He, Yuqin Mao
Endometrial carcinoma is a complex disease characterized by both genetic, epigenetic and environmental factors. Increasing evidence has suggested that long non-coding RNAs (lncRNAs) play important roles in the development and progression of cancers. In this study, we performed a comparison analysis for lncRNA expression between patients with early-stage (stage I/II) and those with advanced-stage (stage III/IV) derived from The Cancer Genome Atlas (TCGA) project and identified 17 differentially expressed lncRNAs using student t-test...
August 8, 2017: Oncotarget
https://www.readbyqxmd.com/read/28881183/from-machine-learning-to-deep-learning-progress-in-machine-intelligence-for-rational-drug-discovery
#20
REVIEW
Lu Zhang, Jianjun Tan, Dan Han, Hao Zhu
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply...
September 4, 2017: Drug Discovery Today
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