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Biological machines

Hendrik Göddeke, M Hadi Timachi, Cedric A Hutter, Laura Galazzo, Markus A Seeger, Mikko Karttunen, Enrica Bordignon, Lars V Schäfer
ATP-binding cassette (ABC) transporters are ATP-driven molecular machines, in which ATP binding and hydrolysis in the nucleotide-binding domains (NBDs) is chemo-mechanically coupled to large-scale, alternating access conformational changes in the transmembrane domains (TMDs), ultimately leading to the translocation of substrates across biological membranes. The precise nature of the structural dynamics behind the large-scale conformational transition as well as the coupling of NBD and TMD motions is still unresolved...
March 16, 2018: Journal of the American Chemical Society
Thai M Hoang, Rui Pan, Jonghoon Ahn, Jaehoon Bang, H T Quan, Tongcang Li
Nonequilibrium processes of small systems such as molecular machines are ubiquitous in biology, chemistry, and physics but are often challenging to comprehend. In the past two decades, several exact thermodynamic relations of nonequilibrium processes, collectively known as fluctuation theorems, have been discovered and provided critical insights. These fluctuation theorems are generalizations of the second law and can be unified by a differential fluctuation theorem. Here we perform the first experimental test of the differential fluctuation theorem using an optically levitated nanosphere in both underdamped and overdamped regimes and in both spatial and velocity spaces...
February 23, 2018: Physical Review Letters
Maurizio Giordano, Kumar Parijat Tripathi, Mario Rosario Guarracino
BACKGROUND: System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models...
March 8, 2018: BMC Bioinformatics
Vasyl Kovalishyn, Julie Grouleff, Ivan Semenyuta, Vitaliy O Sinenko, Sergiy R Slivchuk, Diana Hodyna, Volodymyr Brovarets, Volodymyr Blagodatny, Gennady Poda, Igor V Tetko, Larysa Metelytsia
The problem of designing new anti-tubercular drugs against multiple-drug-resistant tuberculosis (MDR-TB) was addressed using advanced machine learning methods. Since there are only few published measurements against MDR-TB, we collected a large literature dataset and developed models against the non-resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2 = 0.7-0.8 (regression models), and balanced accuracies of about 80% (classification models) with cross-validation and independent test sets...
March 14, 2018: Chemical Biology & Drug Design
Jing Lu, Dong Lu, Zunyun Fu, Mingyue Zheng, Xiaomin Luo
Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accurate alternatives, such as in silico prediction models. In this review, we discuss the practical use of some prediction models on three toxicity end points, including acute toxicity, carcinogenicity, and inhibition of the human ether-a-go-go-related gene ion channel (hERG)...
2018: Methods in Molecular Biology
Xiang-Tian Yu, Lu Wang, Tao Zeng
Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies...
2018: Methods in Molecular Biology
Niloofar Yousefi Moteghaed, Keivan Maghooli, Masoud Garshasbi
Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier...
January 2018: Journal of Medical Signals and Sensors
Christophe Gardella, Olivier Marre, Thierry Mora
The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains...
March 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
Jia Shi, Yuye Wang, Tunan Chen, Degang Xu, Hengli Zhao, Linyu Chen, Chao Yan, Longhuang Tang, Yixin He, Hua Feng, Jianquan Yao
The imaging diagnosis and prognostication of different degrees of traumatic brain injury (TBI) is very important for early care and clinical treatment. Especially, the exact recognition of mild TBI is the bottleneck for current label-free imaging technologies in neurosurgery. Here, we report an automatic evaluation method for TBI recognition with terahertz (THz) continuous-wave (CW) transmission imaging based on machine learning (ML). We propose a new feature extraction method for biological THz images combined with the transmittance distribution features in spatial domain and statistical distribution features in normalized gray histogram...
March 5, 2018: Optics Express
Jinbo Chen, Uwe Scholz, Ruonan Zhou, Matthias Lange
In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes...
March 12, 2018: PLoS Computational Biology
Isaac R Galatzer-Levy, Kelly Ruggles, Zhe Chen
Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships...
January 2018: Chronic Stress
Ronghui You, Zihan Zhang, Yi Xiong, Fengzhu Sun, Hiroshi Mamitsuka, Shanfeng Zhu
Motivation: Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only <1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input)...
March 7, 2018: Bioinformatics
Timothy O'Connor, Siddharth Rawat, Adam Markman, Bahram Javidi
We propose a compact imaging system that integrates an augmented reality head mounted device with digital holographic microscopy for automated cell identification and visualization. A shearing interferometer is used to produce holograms of biological cells, which are recorded using customized smart glasses containing an external camera. After image acquisition, segmentation is performed to isolate regions of interest containing biological cells in the field-of-view, followed by digital reconstruction of the cells, which is used to generate a three-dimensional (3D) pseudocolor optical path length profile...
March 1, 2018: Applied Optics
Marc Lenoir, Cansel Ustunel, Sandya Rajesh, Jaswant Kaur, Dimitri Moreau, Jean Gruenberg, Michael Overduin
Sorting nexins anchor trafficking machines to membranes by binding phospholipids. The paradigm of the superfamily is sorting nexin 3 (SNX3), which localizes to early endosomes by recognizing phosphatidylinositol 3-phosphate (PI3P) to initiate retromer-mediated segregation of cargoes to the trans-Golgi network (TGN). Here we report the solution structure of full length human SNX3, and show that PI3P recognition is accompanied by bilayer insertion of a proximal loop in its extended Phox homology (PX) domain. Phosphoinositide (PIP) binding is completely blocked by cancer-linked phosphorylation of a conserved serine beside the stereospecific PI3P pocket...
March 8, 2018: Nature Communications
Adilya Dagkesamanskaya, Krzysztof Langer, Alexandra Tauzin, Catherine Rouzeau, Delphine Lestrade, Gabrielle Potocki-Veronese, Laurent Boitard, Jérôme Bibette, Jean Baudry, Denis Pompon, Véronique Anton-Leberre
Application of droplet-based microfluidics for the screening of microbial libraries is one of the important ongoing developments in functional genomics/metagenomics. In this article, we propose a new method that can be employed for high-throughput profiling of cell growth. It consists of light-driven labelling droplets that contain growing cells directly in a microfluidics observation chamber, followed by recovery of the labelled cells. This method is based on intracellular expression of green-to-red switchable fluorescent proteins...
March 5, 2018: Journal of Microbiological Methods
Jiří Kléma, František Malinka, Filip Železný
BACKGROUND: One of the major challenges in the analysis of gene expression data is to identify local patterns composed of genes showing coherent expression across subsets of experimental conditions. Such patterns may provide an understanding of underlying biological processes related to these conditions. This understanding can further be improved by providing concise characterizations of the genes and situations delimiting the pattern. RESULTS: We propose a method called semantic biclustering with the aim to detect interpretable rectangular patterns in binary data matrices...
October 16, 2017: BMC Genomics
Maria Paraskevaidi, Camilo L M Morais, Olivia Raglan, Kássio M G Lima, Evangelos Paraskevaidis, Pierre L Martin-Hirsch, Maria Kyrgiou, Francis L Martin
Biospectroscopy has the potential to investigate and characterise biological samples and could, therefore, be utilised to diagnose various diseases in a clinical environment. An important consideration in spectrochemical studies is the cost-effectiveness of the substrate used to support the sample, as high expense would limit their translation into clinic. In this paper, the performance of low-cost aluminium (Al) foil substrates was compared with the commonly used low-emissivity (low-E) slides. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy was used to analyse blood plasma and serum samples from women with endometrial cancer and healthy controls...
March 7, 2018: Journal of Biophotonics
Kirill Veselkov, Jonathan Sleeman, Emmanuelle Claude, Johannes P C Vissers, Dieter Galea, Anna Mroz, Ivan Laponogov, Mark Towers, Robert Tonge, Reza Mirnezami, Zoltan Takats, Jeremy K Nicholson, James I Langridge
Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets...
March 6, 2018: Scientific Reports
Crhisllane Rafaele Dos Santos Vasconcelos, Túlio de Lima Campos, Antonio Mauro Rezende
BACKGROUND: Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum...
March 6, 2018: BMC Bioinformatics
Robert W Eyre, Thomas House, F Xavier Gómez-Olivé, Frances E Griffiths
Background: Central to the study of populations, and therefore to the analysis of the development of countries undergoing major transitions, is the calculation of fertility patterns and their dependence on different variables such as age, education, and socio-economic status. Most epidemiological research on these matters rely on the often unjustified assumption of (generalised) linearity, or alternatively makes a parametric assumption (e.g. for age-patterns). Methods: We consider nonlinearity of fertility in the covariates by combining an established nonlinear parametric model for fertility over age with nonlinear modelling of fertility over other covariates...
2018: Emerging Themes in Epidemiology
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