699 research outputs found
Molecular cloning and expression profiling of a chalcone synthase gene from hairy root cultures of Scutellaria viscidula Bunge
A cDNA encoding chalcone synthase (CHS), the key enzyme in flavonoid biosynthesis, was isolated from hairy root cultures of Scutellaria viscidula Bunge by rapid amplification of cDNA ends (RACE). The full-length cDNA of S. viscidula CHS, designated as Svchs (GenBank accession no. EU386767), was 1649 bp with a 1170 bp open reading frame (ORF) that corresponded to a deduced protein of 390 amino acid residues, a calculated molecular mass of 42.56 kDa and a theoretical isoelectric point (pI) of 5.79. Multiple sequence alignments showed that SvCHS shared high homology with CHS from other plants. Functional analysis in silico indicated that SvCHS was a hydrophilic protein most likely associated with intermediate metabolism. The active sites of the malonyl-CoA binding motif, coumaroyl pocket and cyclization pocket in CHS of Medicago sativa were also found in SvCHS. Molecular modeling indicated that the secondary structure of SvCHS contained mainly α-helixes and random coils. Phylogenetic analysis showed that SvCHS was most closely related to CHS from Scutellaria baicalensis. In agreement with its function as an elicitor-responsive gene, the expression of Svchs was induced and coordinated by methyl jasmonate. To our knowledge, this is the first report to describe the isolation and expression of a gene from S. viscidula
An integrated approach to the interpretation of Single Amino Acid Polymorphisms within the framework of CATH and Gene3D
Background
The phenotypic effects of sequence variations in protein-coding regions come about primarily via their effects on the resulting structures, for example by disrupting active sites or affecting structural stability. In order better to understand the mechanisms behind known mutant phenotypes, and predict the effects of novel variations, biologists need tools to gauge the impacts of DNA mutations in terms of their structural manifestation. Although many mutations occur within domains whose structure has been solved, many more occur within genes whose protein products have not been structurally characterized.<p></p>
Results
Here we present 3DSim (3D Structural Implication of Mutations), a database and web application facilitating the localization and visualization of single amino acid polymorphisms (SAAPs) mapped to protein structures even where the structure of the protein of interest is unknown. The server displays information on 6514 point mutations, 4865 of them known to be associated with disease. These polymorphisms are drawn from SAAPdb, which aggregates data from various sources including dbSNP and several pathogenic mutation databases. While the SAAPdb interface displays mutations on known structures, 3DSim projects mutations onto known sequence domains in Gene3D. This resource contains sequences annotated with domains predicted to belong to structural families in the CATH database. Mappings between domain sequences in Gene3D and known structures in CATH are obtained using a MUSCLE alignment. 1210 three-dimensional structures corresponding to CATH structural domains are currently included in 3DSim; these domains are distributed across 396 CATH superfamilies, and provide a comprehensive overview of the distribution of mutations in structural space.<p></p>
Conclusion
The server is publicly available at http://3DSim.bioinfo.cnio.es/ webcite. In addition, the database containing the mapping between SAAPdb, Gene3D and CATH is available on request and most of the functionality is available through programmatic web service access.<p></p>
Epistasis of Transcriptomes Reveals Synergism between Transcriptional Activators Hnf1α and Hnf4α
The transcription of individual genes is determined by combinatorial interactions between DNAâbinding transcription factors. The current challenge is to understand how such combinatorial interactions regulate broad genetic programs that underlie cellular functions and disease. The transcription factors Hnf1α and Hnf4α control pancreatic islet ÎČ-cell function and growth, and mutations in their genes cause closely related forms of diabetes. We have now exploited genetic epistasis to examine how Hnf1α and Hnf4α functionally interact in pancreatic islets. Expression profiling in islets from either Hnf1a+/â or pancreas-specific Hnf4a mutant mice showed that the two transcription factors regulate a strikingly similar set of genes. We integrated expression and genomic binding studies and show that the shared transcriptional phenotype of these two mutant models is linked to common direct targets, rather than to known effects of Hnf1α on Hnf4a gene transcription. Epistasis analysis with transcriptomes of single- and double-mutant islets revealed that Hnf1α and Hnf4α regulate common targets synergistically. Hnf1α binding in Hnf4a-deficient islets was decreased in selected targets, but remained unaltered in others, thus suggesting that the mechanisms for synergistic regulation are gene-specific. These findings provide an in vivo strategy to study combinatorial gene regulation and reveal how Hnf1α and Hnf4α control a common islet-cell regulatory program that is defective in human monogenic diabetes
Characterization of pathogenic germline mutations in human Protein Kinases
Background
Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.<p></p>
Results
Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.<p></p>
Conclusions
Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.<p></p>
Tuning the electrical conductance of metalloporphyrin supramolecular wires
In contrast with conventional single-molecule junctions, in which the current flows parallel to the long axis or plane of a molecule, we investigate the transport properties of M(II)-5,15-diphenylporphyrin (M-DPP) single-molecule junctions (M=Co, Ni, Cu, or Zn divalent metal ions), in which the current flows perpendicular to the plane of the porphyrin. Novel STM-based conductance measurements combined with quantum transport calculations demonstrate that current-perpendicular-to-the-plane (CPP) junctions have three-orders-of-magnitude higher electrical conductanc than their current in-plane (CIP) counterparts, ranging from 2.10â2 G0 for Ni-DPP up to 8.10â2 G0 for Zn-DPP. The metal ion in the center of the DPP skeletons is strongly coordinated with the nitrogens of the pyridyl coated electrodes, with a binding energy that is sensitive to the choice of metal ion. We find that the binding energies of Zn-DPP and Co-DPP are significantly higher than those of Ni-DPP and Cu-DPP. Therefore when combined with its higher conductance, we identify Zn-DPP as the favoured candidate for high conductance CPP single-molecule devices
Predicting disease-associated substitution of a single amino acid by analyzing residue interactions
<p>Abstract</p> <p>Background</p> <p>The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.</p> <p>Results</p> <p>We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.</p> <p>Conclusions</p> <p>The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.</p
An examination of the psychometric structure of the Multidimensional Pain Inventory in temporomandibular disorder patients: a confirmatory factor analysis
BACKGROUND: This paper seeks to analyse the psychometric and structural properties of the Multidimensional Pain Inventory (MPI) in a sample of temporomandibular disorder patients. METHODS: The internal consistency of the scales was obtained. Confirmatory Factor Analysis was carried out to test the MPI structure section by section in a sample of 114 temporomandibular disorder patients. RESULTS: Nearly all scales obtained good reliability indexes. The original structure could not be totally confirmed. However, with a few adjustments we obtained a satisfactory structural model of the MPI which was slightly different from the original: certain items and the Self control scale were eliminated; in two cases, two original scales were grouped in one factor, Solicitous and Distracting responses on the one hand, and Social activities and Away from home activities, on the other. CONCLUSION: The MPI has been demonstrated to be a reliable tool for the assessment of pain in temporomandibular disorder patients. Some divergences to be taken into account have been clarified
Topology analysis and visualization of Potyvirus protein-protein interaction network
Background: One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses.
Results: After collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins.
By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects.
Conclusions: Our findings confirm that potyvirus protein-protein interaction networks are highly connected, with some proteins playing the role of hubs. Several topological parameters depend linearly on the protein degree. Some viral proteins focus their effect in only host hubs while others diversify its effect among several proteins at the first step. Future new data will help to refine our model and to improve our predictions.This work was supported by the Spanish Ministerio de Economia y Competitividad grants BFU2012-30805 (to SFE), DPI2011-28112-C04-02 (to AF) and DPI2011-28112-C04-01 (to JP). The first two authors are recipients of fellowships from the Spanish Ministerio de Economia y Competitividad: BES-2012-053772 (to GB) and BES-2012-057812 (to AF-F).Bosque, G.; Folch Fortuny, A.; PicĂł Marco, JA.; Ferrer, A.; Elena Fito, SF. (2014). Topology analysis and visualization of Potyvirus protein-protein interaction network. BMC Systems Biology. 129(8):1-15. doi:10.1186/s12918-014-0129-8S1151298Gibbs A, Ohshima K: Potyviruses and the digital revolution. Annu Rev Phytopathol. 2010, 48: 205-223. 10.1146/annurev-phyto-073009-114404.Spence NJ, Phiri NA, Hughes SL, Mwaniki A, Simons S, Oduor G, Chacha D, Kuria A, Ndirangu S, Kibata GN, Marris GC: Economic impact of turnip mosaic virus, cauliflower mosaic virus and beet mosaic virus in three Kenyan vegetables. Plant Pathol. 2007, 56: 317-323. 10.1111/j.1365-3059.2006.01498.x.Ward CW, Shukla DD: Taxonomy of potyviruses: current problems and some solutions. Intervirology. 1991, 32: 269-296.Riechmann JL, LaĂn S, GarcĂa JA: Highlights and prospects of potyvirus molecular biology. J Gen Virol. 1992, 73 (Pt 1): 1-16. 10.1099/0022-1317-73-1-1.Elena SF, Rodrigo G: Towards an integrated molecular model of plant-virus interactions. Curr Opin Virol. 2012, 2: 719-724. 10.1016/j.coviro.2012.09.004.Wei T, Zhang C, Hong J, Xiong R, Kasschau KD, Zhou X, Carrington JC, Wang A: Formation of complexes at plasmodesmata for potyvirus intercellular movement is mediated by the viral protein P3N-PIPO. PLoS Pathog. 2010, 6: e1000962-10.1371/journal.ppat.1000962.Chung BY-W, Miller WA, Atkins JF, Firth AE: An overlapping essential gene in the Potyviridae. Proc Natl Acad Sci. 2008, 105: 5897-5902. 10.1073/pnas.0800468105.Allison R, Johnston RE, Dougherty WG: The nucleotide sequence of the coding region of tobacco etch virus genomic RNA: evidence for the synthesis of a single polyprotein. Virology. 1986, 154: 9-20. 10.1016/0042-6822(86)90425-3.Domier LL, Franklin KM, Shahabuddin M, Hellmann GM, Overmeyer JH, Hiremath ST, Siaw MF, Lomonossoff GP, Shaw JG, Rhoads RE: The nucleotide sequence of tobacco vein mottling virus RNA. Nucleic Acids Res. 1986, 14: 5417-5430. 10.1093/nar/14.13.5417.Revers F, Le Gall O, Candresse T, Maule AJ: New advances in understanding the molecular biology of plant/potyvirus interactions. Mol Plant Microbe Interact. 1999, 12: 367-376. 10.1094/MPMI.1999.12.5.367.Urcuqui-Inchima S, Haenni AL, Bernardi F: Potyvirus proteins: a wealth of functions. Virus Res. 2001, 74: 157-175. 10.1016/S0168-1702(01)00220-9.Merits A, RajamĂ€ki M-L, Lindholm P, Runeberg-Roos P, Kekarainen T, Puustinen P, MĂ€kelĂ€inen K, Valkonen JPT, Saarma M: Proteolytic processing of potyviral proteins and polyprotein processing intermediates in insect and plant cells. J Gen Virol. 2002, 83: 1211-1221.Adams MJ, Antoniw JF, Beaudoin F: Overview and analysis of the polyprotein cleavage sites in the family Potyviridae. Mol Plant Pathol. 2005, 6: 471-487. 10.1111/j.1364-3703.2005.00296.x.Zheng H, Yan F, Lu Y, Sun L, Lin L, Cai L, Hou M, Chen J: Mapping the self-interacting domains of TuMV HC-Pro and the subcellular localization of the protein. Virus Genes. 2011, 42: 110-116. 10.1007/s11262-010-0538-8.Culver JN, Padmanabhan MS: Virus-induced disease: altering host physiology one interaction at a time. Annu Rev Phytopathol. 2007, 45: 221-243. 10.1146/annurev.phyto.45.062806.094422.De Las Rivas J, Fontanillo C: Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol. 2010, 6: e1000807-10.1371/journal.pcbi.1000807.Bornke F: Protein Interaction Networks. Anal Biol Netw. Edited by: Junker BH, Schreiber F. 2008, John Wiley & Sons, Inc, Hoboken, NJ, USA, 207-232. 10.1002/9780470253489.ch9.Phizicky EM, Fields S: Protein-protein interactions: methods for detection and analysis. Microbiol Rev. 1995, 59: 94-123.BrĂŒckner A, Polge C, Lentze N, Auerbach D, Schlattner U: Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci. 2009, 10: 2763-2788. 10.3390/ijms10062763.Fields S, Song O: A novel genetic system to detect protein-protein interactions. Nature. 1989, 340: 245-246. 10.1038/340245a0.Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams S-L, Millar A, Taylor P, Bennett K, Boutilier K, Yang L, Wolting C, Donaldson I, Schandorff S, Shewnarane J, Vo M, Taggart J, Goudreault M, Muskat B, Alfarano C, Dewar D, Lin Z, Michalickova K, Willems AR, Sassi H, Nielsen PA, Rasmussen KJ, Andersen JR, Johansen LE, Hansen LH, et al: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002, 415: 180-183. 10.1038/415180a.Hu C-D, Chinenov Y, Kerppola TK: Visualization of interactions among bZIP and Rel family proteins in living cells using bimolecular fluorescence complementation. Mol Cell. 2002, 9: 789-798. 10.1016/S1097-2765(02)00496-3.Kodama Y, Hu C-D: An improved bimolecular fluorescence complementation assay with a high signal-to-noise ratio. Biotechniques. 2010, 49: 793-805. 10.2144/000113519.Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, et al: Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005, 437: 1173-1178. 10.1038/nature04209.Venkatesan K, Rual J-F, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh K-I, Yildirim MA, Simonis N, Heinzmann K, Gebreab F, Sahalie JM, Cevik S, Simon C, de Smet A-S, Dann E, Smolyar A, Vinayagam A, Yu H, Szeto D, Borick H, Dricot A, Klitgord N, Murray RR, Lin C, Lalowski M, Timm J, et al: An empirical framework for binary interactome mapping. Nat Methods. 2008, 6: 83-90. 10.1038/nmeth.1280.Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000, 403: 623-627. 10.1038/35001009.Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci. 2001, 98: 4569-4574. 10.1073/pnas.061034498.Uetz P, Dong Y-A, Zeretzke C, Atzler C, Baiker A, Berger B, Rajagopala SV, Roupelieva M, Rose D, Fossum E, Haas J: Herpesviral protein networks and their interaction with the human proteome. Science. 2006, 311: 239-242. 10.1126/science.1116804.Fossum E, Friedel CC, Rajagopala SV, Titz B, Baiker A, Schmidt T, Kraus T, Stellberger T, Rutenberg C, Suthram S, Bandyopadhyay S, Rose D, von Brunn A, Uhlmann M, Zeretzke C, Dong Y-A, Boulet H, Koegl M, Bailer SM, Koszinowski U, Ideker T, Uetz P, Zimmer R, Haas J: Evolutionarily conserved herpesviral protein interaction networks. PLoS Pathog. 2009, 5: e1000570-10.1371/journal.ppat.1000570.Rodrigo G, Carrera J, Ruiz-Ferrer V, del Toro FJ, Llave C, Voinnet O, Elena SF: A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS One. 2012, 7: e40526-10.1371/journal.pone.0040526.Newman MEJ: The structure and function of complex networks. SIAM Rev. 2003, 45: 167-256. 10.1137/S003614450342480.Watts DJ, Strogatz SH: Collective dynamics of "small-world" networks. Nature. 1998, 393: 440-442. 10.1038/30918.Albert R, BarabĂĄsi A-L: Statistical mechanics of complex networks. Rev Mod Phys. 2002, 74: 47-97. 10.1103/RevModPhys.74.47.Boccaletti S, Latora V, Moreno Y, ChĂĄvez M, Hwang D: Complex networks: structure and dynamics. Phys Rep. 2006, 424: 175-308. 10.1016/j.physrep.2005.10.009.BarabĂĄsi A-L, Oltvai ZN: Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004, 5: 101-113. 10.1038/nrg1272.Albert R, DasGupta B, Hegde R, Sivanathan GS, Gitter A, GĂŒrsoy G, Paul P, Sontag E: Computationally efficient measure of topological redundancy of biological and social networks. Phys Rev E. 2011, 84: 036117-10.1103/PhysRevE.84.036117.Cho D-Y, Kim Y-A, Przytycka TM: Chapter 5: network biology approach to complex diseases. PLoS Comput Biol. 2012, 8: e1002820-10.1371/journal.pcbi.1002820.Russell RB, Aloy P: Targeting and tinkering with interaction networks. Nat Chem Biol. 2008, 4: 666-673. 10.1038/nchembio.119.Winterbach W, Mieghem PV, Reinders M, Wang H, de Ridder D: Topology of molecular interaction networks. BMC Syst Biol. 2013, 7: 90-10.1186/1752-0509-7-90.PrĆŸulj N: Protein-protein interactions: making sense of networks via graph-theoretic modeling. Bioessays. 2011, 33: 115-123. 10.1002/bies.201000044.Yook S-H, Oltvai ZN, BarabĂĄsi A-L: Functional and topological characterization of protein interaction networks. Proteomics. 2004, 4: 928-942. 10.1002/pmic.200300636.PrĆŸulj N, Wigle DA, Jurisica I: Functional topology in a network of protein interactions. Bioinformatics. 2004, 20: 340-348. 10.1093/bioinformatics/btg415.Elena SF, Carrera J, Rodrigo G: A systems biology approach to the evolution of plant-virus interactions. Curr Opin Plant Biol. 2011, 14: 372-377. 10.1016/j.pbi.2011.03.013.Zilian E, Maiss E: Detection of plum pox potyviral protein-protein interactions in planta using an optimized mRFP-based bimolecular fluorescence complementation system. J Gen Virol. 2011, 92: 2711-2723. 10.1099/vir.0.033811-0.Lin L, Shi Y, Luo Z, Lu Y, Zheng H, Yan F, Chen J, Chen J, Adams MJ, Wu Y: Protein-protein interactions in two potyviruses using the yeast two-hybrid system. Virus Res. 2009, 142: 36-40. 10.1016/j.virusres.2009.01.006.Guo D, RajamĂ€ki M-L, Saarma M, Valkonen JPT: Towards a protein interaction map of potyviruses: protein interaction matrixes of two potyviruses based on the yeast two-hybrid system. J Gen Virol. 2001, 82: 935-939.Shen WT, Wang MQ, Yan P, Gao L, Zhou P: Protein interaction matrix of papaya ringspot virus type P based on a yeast two-hybrid system. Acta Virol. 2010, 54: 49-54. 10.4149/av_2010_01_49.Kang S, Ws L, Kh K: A protein interaction map of soybean mosaic virus strain G7H based on the yeast two-hybrid system. Mol Cells. 2004, 18: 122-126.Yambao MLM, Masuta C, Nakahara K, Uyeda I: The central and C-terminal domains of VPg of Clover yellow vein virus are important for VPg-HCPro and VPg-VPg interactions. J Gen Virol. 2003, 84: 2861-2869. 10.1099/vir.0.19312-0.Evidence for network evolution in an Arabidopsis interactome map. Science. 2011, 333: 601-607. 10.1126/science.1203877.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.Fouss F, Francoisse K, Yen L, Pirotte A, Saerens M: An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification. Neural Netw Off J Int Neural Netw Soc. 2012, 31: 53-72. 10.1016/j.neunet.2012.03.001.Bass JIF, Diallo A, Nelson J, Soto JM, Myers CL, Walhout AJM: Using networks to measure similarity between genes: association index selection. Nat Methods. 2013, 10: 1169-1176. 10.1038/nmeth.2728.Newman MEJ: Assortative mixing in networks. Phys Rev Lett. 2002, 89: 208701-10.1103/PhysRevLett.89.208701
Reproductive failure, possible maternal infanticide, and cannibalism in wild moustached tamarins, Saguinus mystax
Maternal infanticide in wild non-human primates has only been reported twice. In this paper, we report a possible new case of infanticide and cannibalism within a series of four successive reproductive failures in wild moustached tamarins, Saguinus mystax. Necropsy and genetic analyses of the corpses enabled us to rule out any pathology, and to determine paternity. The mother was seen biting and then eating the head of its own infant during a period when another female was pregnant and gave birth just 1Â month later. Before that, the perpetrator had given birth to twins three times successfully when four to five adult and subadult males were present in the group. Although we do not know for certain that the infant was alive when the mother started biting it, our field observations preceding the event suggest it probably was. The possible infanticide case and the two cases of births and early death of the infants occurred while only two to three adult males were present in the group. This could be the second case of maternal infanticide reported in the genus Saguinus and the similar circumstances suggest a common pattern. We discuss these events in the light of the different functional explanations of infanticide and conclude that parental manipulation was the most likely: the mother could have terminated the investment in offspring that had low chances of survival in a group with low availability of helpers
Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at âs = 7 TeV with the ATLAS detector
This paper presents a measurement of the cross-section for high transverse momentum W and Z bosons produced in pp collisions and decaying to all-hadronic final states. The data used in the analysis were recorded by the ATLAS detector at the CERN Large Hadron Collider at a centre-of-mass energy of âs = 7 TeV;{\rm Te}{\rm V}4.6\;{\rm f}{{{\rm b}}^{-1}}{{p}_{{\rm T}}}\gt 320\;{\rm Ge}{\rm V}|\eta |\lt 1.9{{\sigma }_{W+Z}}=8.5\pm 1.7$ pb and is compared to next-to-leading-order calculations. The selected events are further used to study jet grooming techniques
- âŠ