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https://www.readbyqxmd.com/read/28636461/genice-a-novel-framework-for-gene-network-inference-by-clustering-exhaustive-search-and-multivariate-analysis
#1
Ricardo de Souza Jacomini, David Correa Martins-Jr, Felipe Leno da Silva, Anna Helena Reali Costa
Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in systems biology. Expression data sets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, we propose a novel framework called GeNICE, based on probabilistic GNs; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles and to provide an approximate solution with reduced computational complexity...
June 21, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
https://www.readbyqxmd.com/read/28635674/exploring-wound-healing-genomic-machinery-with-a-network-based-approach
#2
Francesca Vitali, Simone Marini, Martina Balli, Hanne Grosemans, Maurilio Sampaolesi, Yves A Lussier, Maria Gabriella Cusella De Angelis, Riccardo Bellazzi
The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties...
June 21, 2017: Pharmaceuticals
https://www.readbyqxmd.com/read/28635625/soil-moisture-content-estimation-based-on-sentinel-1-and-auxiliary-earth-observation-products-a-hydrological-approach
#3
Dimitrios D Alexakis, Filippos-Dimitrios K Mexis, Anthi-Eirini K Vozinaki, Ioannis N Daliakopoulos, Ioannis K Tsanis
A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested...
June 21, 2017: Sensors
https://www.readbyqxmd.com/read/28633567/endogenous-bioelectric-signaling-networks-exploiting-voltage-gradients-for-control-of-growth-and-form
#4
Michael Levin, Giovanni Pezzulo, Joshua M Finkelstein
Living systems exhibit remarkable abilities to self-assemble, regenerate, and remodel complex shapes. How cellular networks construct and repair specific anatomical outcomes is an open question at the heart of the next-generation science of bioengineering. Developmental bioelectricity is an exciting emerging discipline that exploits endogenous bioelectric signaling among many cell types to regulate pattern formation. We provide a brief overview of this field, review recent data in which bioelectricity is used to control patterning in a range of model systems, and describe the molecular tools being used to probe the role of bioelectrics in the dynamic control of complex anatomy...
June 21, 2017: Annual Review of Biomedical Engineering
https://www.readbyqxmd.com/read/28629365/evaluation-and-improvement-of-the-regulatory-inference-for-large-co-expression-networks-with-limited-sample-size
#5
Wenbin Guo, Cristiane P G Calixto, Nikoleta Tzioutziou, Ping Lin, Robbie Waugh, John W S Brown, Runxuan Zhang
BACKGROUND: Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required...
June 19, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/28627424/channeling-in-native-microbial-pathways-implications-and-challenges-for-metabolic-engineering
#6
REVIEW
Mary H Abernathy, Lian He, Yinjie J Tang
Intracellular enzymes can be organized into a variety of assemblies, shuttling intermediates from one active site to the next. Eukaryotic compartmentalization within mitochondrion and peroxisomes and substrate tunneling within multi-enzyme complexes have been well recognized. Intriguingly, the central pathways in prokaryotes may also form extensive channels, including the heavily branched glycolysis pathway. In vivo channeling through cascade enzymes is difficult to directly measure, but can be inferred from in vitro tests, reaction thermodynamics, transport/reaction modeling, analysis of molecular diffusion and protein interactions, or steady state/dynamic isotopic labeling...
June 13, 2017: Biotechnology Advances
https://www.readbyqxmd.com/read/28627049/making-group-inferences-using-sparse-representation-of-resting-state-functional-mri-data-with-application-to-sleep-deprivation
#7
Hui Shen, Huaze Xu, Lubing Wang, Yu Lei, Liu Yang, Peng Zhang, Jian Qin, Ling-Li Zeng, Zongtan Zhou, Zheng Yang, Dewen Hu
Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage...
June 19, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28625780/enhanced-depolymerization-of-actin-filaments-by-adf-cofilin-and-monomer-funneling-by-capping-protein-cooperate-to-accelerate-barbed-end-growth
#8
Shashank Shekhar, Marie-France Carlier
A living cell's ability to assemble actin filaments in intracellular motile processes is directly dependent on the availability of polymerizable actin monomers, which feed polarized filament growth [1, 2]. Continued generation of the monomer pool by filament disassembly is therefore crucial. Disassemblers like actin depolymerizing factor (ADF)/cofilin and filament cappers like capping protein (CP) are essential agonists of motility [3-8], but the exact molecular mechanisms by which they accelerate actin polymerization at the leading edge and filament turnover has been debated for over two decades [9-12]...
June 9, 2017: Current Biology: CB
https://www.readbyqxmd.com/read/28624637/developing-a-new-bayesian-risk-index-for-risk-evaluation-of-soil-contamination
#9
M T D Albuquerque, S Gerassis, C Sierra, J Taboada, J E Martín, I M H R Antunes, J R Gallego
Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain)...
June 15, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/28623595/inferring-gene-regulatory-networks-in-the-arabidopsis-root-using-a-dynamic-bayesian-network-approach
#10
Maria Angels de Luis Balaguer, Rosangela Sozzani
Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infer GRNs based on dynamic Bayesian networks (DBNs), and we then explain how to approximate DBN-based GRN models with continuous models. In addition, we show a MATLAB implementation of the key steps of this method, which we use to infer an Arabidopsis root GRN.
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28623592/computational-approaches-to-study-gene-regulatory-networks
#11
Nooshin Omranian, Zoran Nikoloski
The goal of the gene regulatory network (GRN) inference is to determine the interactions between genes given heterogeneous data capturing spatiotemporal gene expression. Since transcription underlines all cellular processes, the inference of GRN is the first step in deciphering the determinants of the dynamics of biological systems. Here, we first describe the generic steps of the inference approaches that rely on similarity measures and group the similarity measures based on the computational methodology used...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28623589/aranet-a-network-biology-server-for-arabidopsis-thaliana-and-other-non-model-plant-species
#12
Tak Lee, Insuk Lee
Functional gene networks link genes based on their functional relatedness, which is inferred from various complementary biological datasets. Gene networks comprising vast amounts of data can be used to predict which genes are associated with complex traits. Decades of studies in plant biology using the model organism Arabidopsis thaliana have generated large amounts of information, enabling the development of a system-level molecular network. AraNet (currently version 2) is a genome-scale functional gene network for Arabidopsis thaliana, constructed by integrating 19 types of genomics data and can be explored through a web-server (http://www...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28623316/revealing-protein-networks-and-gene-drug-connectivity-in-cancer-from-direct-information
#13
Xian-Li Jiang, Emmanuel Martinez-Ledesma, Faruck Morcos
The connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs...
June 16, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28621973/gw170104-observation-of-a-50-solar-mass-binary-black-hole-coalescence-at-redshift-0-2
#14
B P Abbott, R Abbott, T D Abbott, F Acernese, K Ackley, C Adams, T Adams, P Addesso, R X Adhikari, V B Adya, C Affeldt, M Afrough, B Agarwal, M Agathos, K Agatsuma, N Aggarwal, O D Aguiar, L Aiello, A Ain, P Ajith, B Allen, G Allen, A Allocca, P A Altin, A Amato, A Ananyeva, S B Anderson, W G Anderson, S Antier, S Appert, K Arai, M C Araya, J S Areeda, N Arnaud, K G Arun, S Ascenzi, G Ashton, M Ast, S M Aston, P Astone, P Aufmuth, C Aulbert, K AultONeal, A Avila-Alvarez, S Babak, P Bacon, M K M Bader, S Bae, P T Baker, F Baldaccini, G Ballardin, S W Ballmer, S Banagiri, J C Barayoga, S E Barclay, B C Barish, D Barker, F Barone, B Barr, L Barsotti, M Barsuglia, D Barta, J Bartlett, I Bartos, R Bassiri, A Basti, J C Batch, C Baune, M Bawaj, M Bazzan, B Bécsy, C Beer, M Bejger, I Belahcene, A S Bell, B K Berger, G Bergmann, C P L Berry, D Bersanetti, A Bertolini, J Betzwieser, S Bhagwat, R Bhandare, I A Bilenko, G Billingsley, C R Billman, J Birch, R Birney, O Birnholtz, S Biscans, A Bisht, M Bitossi, C Biwer, M A Bizouard, J K Blackburn, J Blackman, C D Blair, D G Blair, R M Blair, S Bloemen, O Bock, N Bode, M Boer, G Bogaert, A Bohe, F Bondu, R Bonnand, B A Boom, R Bork, V Boschi, S Bose, Y Bouffanais, A Bozzi, C Bradaschia, P R Brady, V B Braginsky, M Branchesi, J E Brau, T Briant, A Brillet, M Brinkmann, V Brisson, P Brockill, J E Broida, A F Brooks, D A Brown, D D Brown, N M Brown, S Brunett, C C Buchanan, A Buikema, T Bulik, H J Bulten, A Buonanno, D Buskulic, C Buy, R L Byer, M Cabero, L Cadonati, G Cagnoli, C Cahillane, J Calderón Bustillo, T A Callister, E Calloni, J B Camp, M Canepa, P Canizares, K C Cannon, H Cao, J Cao, C D Capano, E Capocasa, F Carbognani, S Caride, M F Carney, J Casanueva Diaz, C Casentini, S Caudill, M Cavaglià, F Cavalier, R Cavalieri, G Cella, C B Cepeda, L Cerboni Baiardi, G Cerretani, E Cesarini, S J Chamberlin, M Chan, S Chao, P Charlton, E Chassande-Mottin, D Chatterjee, K Chatziioannou, B D Cheeseboro, H Y Chen, Y Chen, H-P Cheng, A Chincarini, A Chiummo, T Chmiel, H S Cho, M Cho, J H Chow, N Christensen, Q Chu, A J K Chua, S Chua, A K W Chung, S Chung, G Ciani, R Ciolfi, C E Cirelli, A Cirone, F Clara, J A Clark, F Cleva, C Cocchieri, E Coccia, P-F Cohadon, A Colla, C G Collette, L R Cominsky, M Constancio, L Conti, S J Cooper, P Corban, T R Corbitt, K R Corley, N Cornish, A Corsi, S Cortese, C A Costa, M W Coughlin, S B Coughlin, J-P Coulon, S T Countryman, P Couvares, P B Covas, E E Cowan, D M Coward, M J Cowart, D C Coyne, R Coyne, J D E Creighton, T D Creighton, J Cripe, S G Crowder, T J Cullen, A Cumming, L Cunningham, E Cuoco, T Dal Canton, S L Danilishin, S D'Antonio, K Danzmann, A Dasgupta, C F Da Silva Costa, V Dattilo, I Dave, M Davier, D Davis, E J Daw, B Day, S De, D DeBra, E Deelman, J Degallaix, M De Laurentis, S Deléglise, W Del Pozzo, T Denker, T Dent, V Dergachev, R De Rosa, R T DeRosa, R DeSalvo, J Devenson, R C Devine, S Dhurandhar, M C Díaz, L Di Fiore, M Di Giovanni, T Di Girolamo, A Di Lieto, S Di Pace, I Di Palma, F Di Renzo, Z Doctor, V Dolique, F Donovan, K L Dooley, S Doravari, I Dorrington, R Douglas, M Dovale Álvarez, T P Downes, M Drago, R W P Drever, J C Driggers, Z Du, M Ducrot, J Duncan, S E Dwyer, T B Edo, M C Edwards, A Effler, H-B Eggenstein, P Ehrens, J Eichholz, S S Eikenberry, R A Eisenstein, R C Essick, Z B Etienne, T Etzel, M Evans, T M Evans, M Factourovich, V Fafone, H Fair, S Fairhurst, X Fan, S Farinon, B Farr, W M Farr, E J Fauchon-Jones, M Favata, M Fays, H Fehrmann, J Feicht, M M Fejer, A Fernandez-Galiana, I Ferrante, E C Ferreira, F Ferrini, F Fidecaro, I Fiori, D Fiorucci, R P Fisher, R Flaminio, M Fletcher, H Fong, P W F Forsyth, S S Forsyth, J-D Fournier, S Frasca, F Frasconi, Z Frei, A Freise, R Frey, V Frey, E M Fries, P Fritschel, V V Frolov, P Fulda, M Fyffe, H Gabbard, M Gabel, B U Gadre, S M Gaebel, J R Gair, L Gammaitoni, M R Ganija, S G Gaonkar, F Garufi, S Gaudio, G Gaur, V Gayathri, N Gehrels, G Gemme, E Genin, A Gennai, D George, J George, L Gergely, V Germain, S Ghonge, Abhirup Ghosh, Archisman Ghosh, S Ghosh, J A Giaime, K D Giardina, A Giazotto, K Gill, L Glover, E Goetz, R Goetz, S Gomes, G González, J M Gonzalez Castro, A Gopakumar, M L Gorodetsky, S E Gossan, M Gosselin, R Gouaty, A Grado, C Graef, M Granata, A Grant, S Gras, C Gray, G Greco, A C Green, P Groot, H Grote, S Grunewald, P Gruning, G M Guidi, X Guo, A Gupta, M K Gupta, K E Gushwa, E K Gustafson, R Gustafson, B R Hall, E D Hall, G Hammond, M Haney, M M Hanke, J Hanks, C Hanna, M D Hannam, O A Hannuksela, J Hanson, T Hardwick, J Harms, G M Harry, I W Harry, M J Hart, C-J Haster, K Haughian, J Healy, A Heidmann, M C Heintze, H Heitmann, P Hello, G Hemming, M Hendry, I S Heng, J Hennig, J Henry, A W Heptonstall, M Heurs, S Hild, D Hoak, D Hofman, K Holt, D E Holz, P Hopkins, C Horst, J Hough, E A Houston, E J Howell, Y M Hu, E A Huerta, D Huet, B Hughey, S Husa, S H Huttner, T Huynh-Dinh, N Indik, D R Ingram, R Inta, G Intini, H N Isa, J-M Isac, M Isi, B R Iyer, K Izumi, T Jacqmin, K Jani, P Jaranowski, S Jawahar, F Jiménez-Forteza, W W Johnson, N K Johnson-McDaniel, D I Jones, R Jones, R J G Jonker, L Ju, J Junker, C V Kalaghatgi, V Kalogera, S Kandhasamy, G Kang, J B Kanner, S Karki, K S Karvinen, M Kasprzack, M Katolik, E Katsavounidis, W Katzman, S Kaufer, K Kawabe, F Kéfélian, D Keitel, A J Kemball, R Kennedy, C Kent, J S Key, F Y Khalili, I Khan, S Khan, Z Khan, E A Khazanov, N Kijbunchoo, Chunglee Kim, J C Kim, W Kim, W S Kim, Y-M Kim, S J Kimbrell, E J King, P J King, R Kirchhoff, J S Kissel, L Kleybolte, S Klimenko, P Koch, S M Koehlenbeck, S Koley, V Kondrashov, A Kontos, M Korobko, W Z Korth, I Kowalska, D B Kozak, C Krämer, V Kringel, B Krishnan, A Królak, G Kuehn, P Kumar, R Kumar, S Kumar, L Kuo, A Kutynia, S Kwang, B D Lackey, K H Lai, M Landry, R N Lang, J Lange, B Lantz, R K Lanza, A Lartaux-Vollard, P D Lasky, M Laxen, A Lazzarini, C Lazzaro, P Leaci, S Leavey, C H Lee, H K Lee, H M Lee, H W Lee, K Lee, J Lehmann, A Lenon, M Leonardi, N Leroy, N Letendre, Y Levin, T G F Li, A Libson, T B Littenberg, J Liu, R K L Lo, N A Lockerbie, L T London, J E Lord, M Lorenzini, V Loriette, M Lormand, G Losurdo, J D Lough, G Lovelace, H Lück, D Lumaca, A P Lundgren, R Lynch, Y Ma, S Macfoy, B Machenschalk, M MacInnis, D M Macleod, I Magaña Hernandez, F Magaña-Sandoval, L Magaña Zertuche, R M Magee, E Majorana, I Maksimovic, N Man, V Mandic, V Mangano, G L Mansell, M Manske, M Mantovani, F Marchesoni, F Marion, S Márka, Z Márka, C Markakis, A S Markosyan, E Maros, F Martelli, L Martellini, I W Martin, D V Martynov, J N Marx, K Mason, A Masserot, T J Massinger, M Masso-Reid, S Mastrogiovanni, A Matas, F Matichard, L Matone, N Mavalvala, R Mayani, N Mazumder, R McCarthy, D E McClelland, S McCormick, L McCuller, S C McGuire, G McIntyre, J McIver, D J McManus, T McRae, S T McWilliams, D Meacher, G D Meadors, J Meidam, E Mejuto-Villa, A Melatos, G Mendell, R A Mercer, E L Merilh, M Merzougui, S Meshkov, C Messenger, C Messick, R Metzdorff, P M Meyers, F Mezzani, H Miao, C Michel, H Middleton, E E Mikhailov, L Milano, A L Miller, A Miller, B B Miller, J Miller, M Millhouse, O Minazzoli, Y Minenkov, J Ming, C Mishra, S Mitra, V P Mitrofanov, G Mitselmakher, R Mittleman, A Moggi, M Mohan, S R P Mohapatra, M Montani, B C Moore, C J Moore, D Moraru, G Moreno, S R Morriss, B Mours, C M Mow-Lowry, G Mueller, A W Muir, Arunava Mukherjee, D Mukherjee, S Mukherjee, N Mukund, A Mullavey, J Munch, E A M Muniz, P G Murray, K Napier, I Nardecchia, L Naticchioni, R K Nayak, G Nelemans, T J N Nelson, M Neri, M Nery, A Neunzert, J M Newport, G Newton, K K Y Ng, T T Nguyen, D Nichols, A B Nielsen, S Nissanke, A Nitz, A Noack, F Nocera, D Nolting, M E N Normandin, L K Nuttall, J Oberling, E Ochsner, E Oelker, G H Ogin, J J Oh, S H Oh, F Ohme, M Oliver, P Oppermann, Richard J Oram, B O'Reilly, R Ormiston, L F Ortega, R O'Shaughnessy, D J Ottaway, H Overmier, B J Owen, A E Pace, J Page, M A Page, A Pai, S A Pai, J R Palamos, O Palashov, C Palomba, A Pal-Singh, H Pan, B Pang, P T H Pang, C Pankow, F Pannarale, B C Pant, F Paoletti, A Paoli, M A Papa, H R Paris, W Parker, D Pascucci, A Pasqualetti, R Passaquieti, D Passuello, B Patricelli, B L Pearlstone, M Pedraza, R Pedurand, L Pekowsky, A Pele, S Penn, C J Perez, A Perreca, L M Perri, H P Pfeiffer, M Phelps, O J Piccinni, M Pichot, F Piergiovanni, V Pierro, G Pillant, L Pinard, I M Pinto, M Pitkin, R Poggiani, P Popolizio, E K Porter, A Post, J Powell, J Prasad, J W W Pratt, V Predoi, T Prestegard, M Prijatelj, M Principe, S Privitera, G A Prodi, L G Prokhorov, O Puncken, M Punturo, P Puppo, M Pürrer, H Qi, J Qin, S Qiu, V Quetschke, E A Quintero, R Quitzow-James, F J Raab, D S Rabeling, H Radkins, P Raffai, S Raja, C Rajan, M Rakhmanov, K E Ramirez, P Rapagnani, V Raymond, M Razzano, J Read, T Regimbau, L Rei, S Reid, D H Reitze, H Rew, S D Reyes, F Ricci, P M Ricker, S Rieger, K Riles, M Rizzo, N A Robertson, R Robie, F Robinet, A Rocchi, L Rolland, J G Rollins, V J Roma, J D Romano, R Romano, C L Romel, J H Romie, D Rosińska, M P Ross, S Rowan, A Rüdiger, P Ruggi, K Ryan, M Rynge, S Sachdev, T Sadecki, L Sadeghian, M Sakellariadou, L Salconi, M Saleem, F Salemi, A Samajdar, L Sammut, L M Sampson, E J Sanchez, V Sandberg, B Sandeen, J R Sanders, B Sassolas, B S Sathyaprakash, P R Saulson, O Sauter, R L Savage, A Sawadsky, P Schale, J Scheuer, E Schmidt, J Schmidt, P Schmidt, R Schnabel, R M S Schofield, A Schönbeck, E Schreiber, D Schuette, B W Schulte, B F Schutz, S G Schwalbe, J Scott, S M Scott, E Seidel, D Sellers, A S Sengupta, D Sentenac, V Sequino, A Sergeev, D A Shaddock, T J Shaffer, A A Shah, M S Shahriar, L Shao, B Shapiro, P Shawhan, A Sheperd, D H Shoemaker, D M Shoemaker, K Siellez, X Siemens, M Sieniawska, D Sigg, A D Silva, A Singer, L P Singer, A Singh, R Singh, A Singhal, A M Sintes, B J J Slagmolen, B Smith, J R Smith, R J E Smith, E J Son, J A Sonnenberg, B Sorazu, F Sorrentino, T Souradeep, A P Spencer, A K Srivastava, A Staley, M Steinke, J Steinlechner, S Steinlechner, D Steinmeyer, B C Stephens, S P Stevenson, R Stone, K A Strain, G Stratta, S E Strigin, R Sturani, A L Stuver, T Z Summerscales, L Sun, S Sunil, P J Sutton, B L Swinkels, M J Szczepańczyk, M Tacca, D Talukder, D B Tanner, M Tápai, A Taracchini, J A Taylor, R Taylor, T Theeg, E G Thomas, M Thomas, P Thomas, K A Thorne, K S Thorne, E Thrane, S Tiwari, V Tiwari, K V Tokmakov, K Toland, M Tonelli, Z Tornasi, C I Torrie, D Töyrä, F Travasso, G Traylor, D Trifirò, J Trinastic, M C Tringali, L Trozzo, K W Tsang, M Tse, R Tso, D Tuyenbayev, K Ueno, D Ugolini, C S Unnikrishnan, A L Urban, S A Usman, K Vahi, H Vahlbruch, G Vajente, G Valdes, M Vallisneri, N van Bakel, M van Beuzekom, J F J van den Brand, C Van Den Broeck, D C Vander-Hyde, L van der Schaaf, J V van Heijningen, A A van Veggel, M Vardaro, V Varma, S Vass, M Vasúth, A Vecchio, G Vedovato, J Veitch, P J Veitch, K Venkateswara, G Venugopalan, D Verkindt, F Vetrano, A Viceré, A D Viets, S Vinciguerra, D J Vine, J-Y Vinet, S Vitale, T Vo, H Vocca, C Vorvick, D V Voss, W D Vousden, S P Vyatchanin, A R Wade, L E Wade, M Wade, R M Wald, R Walet, M Walker, L Wallace, S Walsh, G Wang, H Wang, J Z Wang, M Wang, Y-F Wang, Y Wang, R L Ward, J Warner, M Was, J Watchi, B Weaver, L-W Wei, M Weinert, A J Weinstein, R Weiss, L Wen, E K Wessel, P Weßels, T Westphal, K Wette, J T Whelan, B F Whiting, C Whittle, D Williams, R D Williams, A R Williamson, J L Willis, B Willke, M H Wimmer, W Winkler, C C Wipf, H Wittel, G Woan, J Woehler, J Wofford, K W K Wong, J Worden, J L Wright, D S Wu, G Wu, W Yam, H Yamamoto, C C Yancey, M J Yap, Hang Yu, Haocun Yu, M Yvert, A Zadrożny, M Zanolin, T Zelenova, J-P Zendri, M Zevin, L Zhang, M Zhang, T Zhang, Y-H Zhang, C Zhao, M Zhou, Z Zhou, X J Zhu, A Zimmerman, M E Zucker, J Zweizig
We describe the observation of GW170104, a gravitational-wave signal produced by the coalescence of a pair of stellar-mass black holes. The signal was measured on January 4, 2017 at 10∶11:58.6 UTC by the twin advanced detectors of the Laser Interferometer Gravitational-Wave Observatory during their second observing run, with a network signal-to-noise ratio of 13 and a false alarm rate less than 1 in 70 000 years. The inferred component black hole masses are 31.2_{-6.0}^{+8.4}M_{⊙} and 19.4_{-5.9}^{+5...
June 2, 2017: Physical Review Letters
https://www.readbyqxmd.com/read/28620222/nitric-oxide-mediated-transcriptional-modulation-enhances-plant-adaptive-responses-to-arsenic-stress
#15
Pradyumna Kumar Singh, Yuvraj Indoliya, Abhisekh Singh Chauhan, Surendra Pratap Singh, Amit Pal Singh, Sanjay Dwivedi, Rudra Deo Tripathi, Debasis Chakrabarty
Arsenic (As) contamination in rice leads to yield decline and causes carcinogenic risk to human health. Although the role of nitric oxide (NO) in reducing As toxicity is known, NO-mediated genetic modulation in the plant during arsenic toxicity has not yet been established. We analyzed the key components of NO metabolism and the correlations between NO interaction and arsenic stress using rice as a relevant model plant. Illumina sequencing was used to investigate the NO-mediated genome-wide temporal transcriptomic modulation in rice root upon AsIII exposure during 12 days (d) of the growth period...
June 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28617955/expression-atlas-and-comparative-coexpression-network-analyses-reveal-important-genes-involved-in-the-formation-of-lignified-cell-wall-in-brachypodium-distachyon
#16
Richard Sibout, Sebastian Proost, Bjoern Oest Hansen, Neha Vaid, Federico M Giorgi, Severine Ho-Yue-Kuang, Frédéric Legée, Laurent Cézart, Oumaya Bouchabké-Coussa, Camille Soulhat, Nicholas Provart, Asher Pasha, Philippe Le Bris, David Roujol, Herman Hofte, Elisabeth Jamet, Catherine Lapierre, Staffan Persson, Marek Mutwil
While Brachypodium distachyon (Brachypodium) is an emerging model for grasses, no expression atlas or gene coexpression network is available. Such tools are of high importance to provide insights into the function of Brachypodium genes. We present a detailed Brachypodium expression atlas, capturing gene expression in its major organs at different developmental stages. The data were integrated into a large-scale coexpression database ( www.gene2function.de), enabling identification of duplicated pathways and conserved processes across 10 plant species, thus allowing genome-wide inference of gene function...
June 15, 2017: New Phytologist
https://www.readbyqxmd.com/read/28617445/acquired-cross-linker-resistance-associated-with-a-novel-spliced-brca2-protein-variant-for-molecular-phenotyping-of-brca2-disruption
#17
Stefan Meyer, Adam Stevens, Roberto Paredes, Marion Schneider, Michael J Walker, Andrew J K Williamson, Maria-Belen Gonzalez-Sanchez, Stephanie Smetsers, Vineet Dalal, Hsiang Ying Teng, Daniel J White, Sam Taylor, Joanne Muter, Andrew Pierce, Chiara de Leonibus, Davy A P Rockx, Martin A Rooimans, Elaine Spooncer, Stacey Stauffer, Kajal Biswas, Barbara Godthelp, Josephine Dorsman, Peter E Clayton, Shyam K Sharan, Anthony D Whetton
BRCA2 encodes a protein with a fundamental role in homologous recombination that is essential for normal development. Carrier status of mutations in BRCA2 is associated with familial breast and ovarian cancer, while bi-allelic BRCA2 mutations can cause Fanconi anemia (FA), a cancer predisposition syndrome with cellular cross-linker hypersensitivity. Cancers associated with BRCA2 mutations can acquire chemo-resistance on relapse. We modeled acquired cross-linker resistance with an FA-derived BRCA2-mutated acute myeloid leukemia (AML) platform...
June 15, 2017: Cell Death & Disease
https://www.readbyqxmd.com/read/28617232/tigeri-modeling-and-visualizing-the-responses-to-perturbation-of-a-transcription-factor-network
#18
Namshik Han, Harry A Noyes, Andy Brass
BACKGROUND: Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimental and theoretical methodologies. Less work has focused on using these models to examine how TF networks respond to changes in the cellular environment. METHODS: In this paper, we have developed a simple, pragmatic methodology, TIGERi (Transcription-factor-activity Illustrator for Global Explanation of Regulatory interaction), to model the response of an inferred TF network to changes in cellular environment...
May 31, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28611422/bone-fusion-in-normal-and-pathological-development-is-constrained-by-the-network-architecture-of-the-human-skull
#19
Borja Esteve-Altava, Toni Vallès-Català, Roger Guimerà, Marta Sales-Pardo, Diego Rasskin-Gutman
Craniosynostosis, the premature fusion of cranial bones, affects the correct development of the skull producing morphological malformations in newborns. To assess the susceptibility of each craniofacial articulation to close prematurely, we used a network model of the skull to quantify the link reliability (an index based on stochastic block models and Bayesian inference) of each articulation. We show that, of the 93 human skull articulations at birth, the few articulations that are associated with non-syndromic craniosynostosis conditions have statistically significant lower reliability scores than the others...
June 13, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28607776/could-lc-ne-dependent-adjustment-of-neural-gain-drive-functional-brain-network-reorganization
#20
REVIEW
Carole Guedj, David Meunier, Martine Meunier, Fadila Hadj-Bouziane
The locus coeruleus-norepinephrine (LC-NE) system is thought to act at synaptic, cellular, microcircuit, and network levels to facilitate cognitive functions through at least two different processes, not mutually exclusive. Accordingly, as a reset signal, the LC-NE system could trigger brain network reorganizations in response to salient information in the environment and/or adjust the neural gain within its target regions to optimize behavioral responses. Here, we provide evidence of the co-occurrence of these two mechanisms at the whole-brain level, in resting-state conditions following a pharmacological stimulation of the LC-NE system...
2017: Neural Plasticity
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