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Biological machines

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https://www.readbyqxmd.com/read/28733902/clustering-and-candidate-motif-detection-in-exosomal-mirnas-by-application-of-machine-learning-algorithms
#1
Pallavi Gaur, Anoop Chaturvedi
BACKGROUND: The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes...
July 22, 2017: Interdisciplinary Sciences, Computational Life Sciences
https://www.readbyqxmd.com/read/28729956/predicting-the-host-of-influenza-viruses-based-on-the-word-vector
#2
Beibei Xu, Zhiying Tan, Kenli Li, Taijiao Jiang, Yousong Peng
Newly emerging influenza viruses continue to threaten public health. A rapid determination of the host range of newly discovered influenza viruses would assist in early assessment of their risk. Here, we attempted to predict the host of influenza viruses using the Support Vector Machine (SVM) classifier based on the word vector, a new representation and feature extraction method for biological sequences. The results show that the length of the word within the word vector, the sequence type (DNA or protein) and the species from which the sequences were derived for generating the word vector all influence the performance of models in predicting the host of influenza viruses...
2017: PeerJ
https://www.readbyqxmd.com/read/28728937/the-impact-of-machine-learning-techniques-in-the-study-of-bipolar-disorder-a-systematic-review
#3
REVIEW
Diego Librenza-Garcia, Bruno Jaskulski Kotzian, Jessica Yang, Benson Mwangi, Bo Cao, Luiza Nunes Pereira Lima, Mariane Bagatin Bermudez, Manuela Vianna Boeira, Flávio Kapczinski, Ives Cavalcante Passos
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls...
July 17, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28728020/neuroscience-inspired-artificial-intelligence
#4
REVIEW
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick
The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals...
July 19, 2017: Neuron
https://www.readbyqxmd.com/read/28727228/actin-gamma-1-a-new-skin-cancer-pathogenic-gene-identified-by-the-biological-feature-based-classification
#5
Xinqian Dong, Yingsheng Han, Zhen Sun, Junlong Xu
Skin cancer is the most common form of cancer that accounting for at least 40% of cancer cases around the world. This study aimed to identify skin cancer-related biological features and predict skin cancer candidate genes by employing machine learning based on biological features of known skin cancer genes. The known skin cancer-related genes were fetched from database and encoded by the enrichment scores of gene ontology and pathways. The optimal features of the skin cancer related genes were selected with a series of feature selection methods, such as mRMR, IFS, and Random Forest algorithm...
July 20, 2017: Journal of Cellular Biochemistry
https://www.readbyqxmd.com/read/28721040/effect-of-ultraviolet-treatment-on-bacterial-attachment-and-osteogenic-activity-to-alkali-treated-titanium-with-nanonetwork-structures
#6
Honghao Zhang, Satoshi Komasa, Chiho Mashimo, Tohru Sekino, Joji Okazaki
PURPOSE: Alkali-treated titanium with nanonetwork structures (TNS) possesses good osteogenic activity; however, the resistance of this material to bacterial contamination remains inadequate. As such, TNS implants are prone to postoperative infection. In this work, we attempted to alter the biological properties of TNS by treatment with short-duration high-intensity ultraviolet (UV) irradiation. METHODS: TNS discs were treated with UV light (wavelength =254 nm, strength =100 mW/cm(2)) for 15 minutes using a UV-irradiation machine...
2017: International Journal of Nanomedicine
https://www.readbyqxmd.com/read/28719054/evolutionary-cell-biology-of-proteins-from-protists-to-humans-and-plants
#7
REVIEW
Helmut Plattner
During evolution, the cell as a fine-tuned machine had to undergo permanent adjustments to match changes in its environment, while "closed for repair work" was not possible. Evolution from protists (protozoa and unicellular algae) to multicellular organisms may have occurred in basically two lineages, Unikonta and Bikonta, culminating in mammals and angiosperms (flowering plants), respectively. Unicellular models for unikont evolution are myxamoebae (Dictyostelium) and increasingly also choanoflagellates, whereas, for bikonts, ciliates are preferred models...
July 18, 2017: Journal of Eukaryotic Microbiology
https://www.readbyqxmd.com/read/28715209/comparison-of-the-predictive-performance-and-interpretability-of-random-forest-and-linear-models-on-benchmark-datasets
#8
Richard Liam Marchese Robinson, Anna Palczewska, Jan Palczewski, Nathan Kidley
The ability to interpret the predictions made by quantitative structure activity relationships (QSARs) offers a number of advantages. Whilst QSARs built using non-linear modelling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modelling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting non-linear QSAR models in general and Random Forest in particular...
July 17, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28713810/a-biomechanical-comparison-of-proportional-electromyography-control-to-biological-torque-control-using-a-powered-hip-exoskeleton
#9
Aaron J Young, Hannah Gannon, Daniel P Ferris
BACKGROUND: Despite a large increase in robotic exoskeleton research, there are few studies that have examined human performance with different control strategies on the same exoskeleton device. Direct comparison studies are needed to determine how users respond to different types of control. The purpose of this study was to compare user performance using a robotic hip exoskeleton with two different controllers: a controller that targeted a biological hip torque profile and a proportional myoelectric controller...
2017: Frontiers in Bioengineering and Biotechnology
https://www.readbyqxmd.com/read/28713235/connecting-the-brain-to-itself-through-an-emulation
#10
Mijail D Serruya
Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28712921/selective-regulation-of-biological-processes-by-vitamin-d-based-on-the-spatio-temporal-cistrome-of-its-receptor
#11
Antonio Neme, Sabine Seuter, Carsten Carlberg
The transcription factor vitamin D receptor (VDR) is the exclusive nuclear target of the biologically active form of vitamin D (1,25(OH)2D3). In THP-1 human monocytes we obtained a highly accurate VDR cistrome after 2 and 24 h ligand stimulation comprising more than 11,600 genomic loci, 78% of which were detected exclusively after 24 h. In contrast, a group of 510 persistent VDR sites occurred at all conditions and some 2,100 VDR loci were only transiently occupied. Machine learning and statistical analysis as well as a comparison with the re-analyzed B cell VDR cistrome indicated a subgroup of 339 highly conserved persistent VDR sites that were suited best for describing vitamin D-triggered gene regulatory scenarios...
July 13, 2017: Biochimica et Biophysica Acta
https://www.readbyqxmd.com/read/28712030/predictive-modelling-of-eutrophication-in-the-poz%C3%A3-n-de-la-dolores-lake-northern-spain-by-using-an-evolutionary-support-vector-machines-approach
#12
P J García-Nieto, E García-Gonzalo, J R Alonso Fernández, C Díaz Muñiz
Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes and deterioration of water quality and all its uses in general, when the production of algae and other aquatic vegetations are increased. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes...
July 15, 2017: Journal of Mathematical Biology
https://www.readbyqxmd.com/read/28711053/representations-in-neural-network-based-empirical-potentials
#13
Ekin D Cubuk, Brad D Malone, Berk Onat, Amos Waterland, Efthimios Kaxiras
Many structural and mechanical properties of crystals, glasses, and biological macromolecules can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approximate complex functions. For example, neural networks can be trained to reproduce results of density functional theory calculations at a much lower cost...
July 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28709899/prediction-of-protein-n-formylation-using-the-composition-of-k-spaced-amino-acid-pairs
#14
Zhe Ju, Jun-Zhe Cao
As one of important protein post-translational modifications, N-formylation has been reported to be involved in various biological processes. The accurate identification of N-formylation sites is crucial for understanding the underlying mechanisms of N-formylation. Since the traditional experimental methods are generally labor-intensive and expensive, it is important to develop computational methods to predict N-formylation sites. In this paper, a predictor named NformPred is proposed to improve the prediction of N-formylation sites by using composition of k-spaced amino acid pairs encoding scheme and support vector machine algorithm...
July 11, 2017: Analytical Biochemistry
https://www.readbyqxmd.com/read/28705738/identification-of-novel-potential-scaffold-for-class-i-hdacs-inhibition-an-in-silico-protocol-based-on-virtual-screening-molecular-dynamics-mathematical-analysis-and-machine-learning
#15
Cong Fan, Yanxin Huang
Histone deacetylases (HDACs) family has been widely reported as an important class of enzyme targets for cancer therapy. Much effort has been made in discovery of novel scaffolds for HDACs inhibition besides existing hydroxamic acids, cyclic peptides, benzamides, and short-chain fatty acids. Herein we set up an in-silico protocol which not only could detect potential Zn(2+) chelation bonds but also still adopted non-bonded model to be effective in discovery of Class I HDACs inhibitors, with little human's subjective visual judgment involved...
July 10, 2017: Biochemical and Biophysical Research Communications
https://www.readbyqxmd.com/read/28701799/a-robust-method-for-inferring-network-structures
#16
Yang Yang, Tingjin Luo, Zhoujun Li, Xiaoming Zhang, Philip S Yu
Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices...
July 12, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28700571/open-and-closed-structures-reveal-allostery-and-pliability-in-the-hiv-1-envelope-spike
#17
Gabriel Ozorowski, Jesper Pallesen, Natalia de Val, Dmitry Lyumkis, Christopher A Cottrell, Jonathan L Torres, Jeffrey Copps, Robyn L Stanfield, Albert Cupo, Pavel Pugach, John P Moore, Ian A Wilson, Andrew B Ward
For many enveloped viruses, binding to a receptor(s) on a host cell acts as the first step in a series of events culminating in fusion with the host cell membrane and transfer of genetic material for replication. The envelope glycoprotein (Env) trimer on the surface of HIV is responsible for receptor binding and fusion. Although Env can tolerate a high degree of mutation in five variable regions (V1-V5), and also at N-linked glycosylation sites that contribute roughly half the mass of Env, the functional sites for recognition of receptor CD4 and co-receptor CXCR4/CCR5 are conserved and essential for viral fitness...
July 12, 2017: Nature
https://www.readbyqxmd.com/read/28696674/lasso-peptide-benenodin-1-is-a-thermally-actuated-1-rotaxane-switch
#18
Chuhan Zong, Michelle J Wu, Jason Z Qin, A James Link
Mechanically interlocked molecules that change their conformation in response to stimuli have been developed by synthetic chemists as building blocks for molecular machines. Here we describe a natural product, the lasso peptide benenodin-1, which exhibits conformational switching between two distinct threaded conformers upon actuation by heat. We have determined the structures of both conformers and have characterized the kinetics and energetics of the conformational switch. Single amino acid substitutions to benenodin-1 generate peptides that are biased to a single conformer, showing that the switching behavior is potentially an evolvable trait in these peptides...
July 24, 2017: Journal of the American Chemical Society
https://www.readbyqxmd.com/read/28691387/cascades-in-compartments-en-route-to-machine-assisted-biotechnology
#19
Kersten S Rabe, Joachim Müller, Marc Skoupi, Christof M Niemeyer
Biological compartmentalization is a fundamental principle of life that allows cells to metabolize, propagate or communicate with their environment. Much research is devoted to the understanding of this basic principle and to harness biomimetic compartments and catalytic cascades as tools for technological processes. We summarize the current state-of-the-art of these developments with a special emphasis on length scales, mass transport phenomena and molecular scaffolding approaches, ranging from small crosslinkers over proteins and nucleic acids to colloids and patterned surfaces...
July 10, 2017: Angewandte Chemie
https://www.readbyqxmd.com/read/28688276/novel-2-2-alkylthiobenzenesulfonyl-3-phenylprop-2-ynylideneamino-guanidine-derivatives-as-potent-anticancer-agents-synthesis-molecular-structure-qsar-studies-and-metabolic-stability
#20
Aneta Pogorzelska, Jarosław Sławiński, Beata Żołnowska, Krzysztof Szafrański, Anna Kawiak, Jarosław Chojnacki, Szymon Ulenberg, Joanna Zielińska, Tomasz Bączek
A series of new 2-(2-alkylthiobenzenesulfonyl)-3-(phenylprop-2-ynylideneamino)guanidine derivatives have been synthesized and evaluated in vitro by MTT assays for their antiproliferative activity against cell lines of colon cancer HCT-116, cervical cancer HeLa and breast cancer MCF-7. The obtained results indicated that these compounds display prominent cytotoxic effect. The best anticancer properties have been observed for derivatives 44 (IC50 = 6-18 μM) and 45 (IC50 = 8-14 μM). Very good results of antiproliferative assays have been also shown for compounds 26, 36, and 46 and noticeable anticancer profile has been found for set of derivatives 34-39...
June 29, 2017: European Journal of Medicinal Chemistry
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