Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Network growth for enhanced natural selection

Natural selection and random drift are competing phenomena for explaining the evolution of populations. Combining a highly fit mutant with a population structure that improves the odds that the mutation spreads through the whole population tips the balance in favor of natural selection. The probability that the spread occurs, known as the fixation probability, depends heavily on how the population is structured. Certain topologies, albeit highly artificially contrived, have been shown to exist that favor fixation. We present a randomized mechanism for network growth that is loosely inspired in some of these topologies’ key properties and demonstrate, through simulations, that it is capable of giving rise to structured populations for which the fixation probability significantly surpasses that of an unstructured population. This discovery provides important support to the notion that natural selection can be enhanced over random drift in naturally occurring population structures

Network growth for enhanced natural selection

Natural selection and random drift are competing phenomena for explaining the evolution of populations. Combining a highly fit mutant with a population structure that improves the odds that the mutation spreads through the whole population tips the balance in favor of natural selection. The probability that the spread occurs, known as the fixation probability, depends heavily on how the population is structured. Certain topologies, albeit highly artificially contrived, have been shown to exist that favor fixation. We present a randomized mechanism for network growth that is loosely inspired in some of these topologies’ key properties and demonstrate, through simulations, that it is capable of giving rise to structured populations for which the fixation probability significantly surpasses that of an unstructured population. This discovery provides important support to the notion that natural selection can be enhanced over random drift in naturally occurring population structures

Ellipsoid clustering machine: a front line to aid in disease diagnosis

Made available in DSpace on 2017-01-30T12:01:02Z (GMT). No. of bitstreams: 2 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) 13.pdf: 1122813 bytes, checksum: 3850fc68537318894332cad2238295a8 (MD5) Previous issue date: 2007Universidade Federal do Rio de Janeiro. Programa de Engenharia de Sistemas e Computação. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Centro Industrial de Curitiba, Curitiba, PR, Brasil.Universidade Federal do Rio de Janeiro. Programa de Engenharia de Sistemas e Computação. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Química, Departamento de Bioquímica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Genômica Funcional e Bioinformática. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Biofísica Carlos Chagas Filho. Rio de Janeiro, RJ, Brasil.Este estudo apresenta nova estratégia de inferência direcionada a detectar presença de doenças em amostras biológicas. Diferencialmente dos métodos existentes, esta técnica é aplicável quando o número de patologias e as mesmas são desconhecidos. Esta é exemplificada através de software que denominamos “Máquina de Agrupamento por Elipsóide”, do inglês, Ellipsoid Clustering Machine (ECM). O mesmo identifica regiões conservadas em perfis proteômicos obtidos por espectrometria de massa de amostras biológicas de indivíduos controles e estima limites para classifica- ção baseando-se na variância da expressão protéica. O software também pode ser utilizado para inspeção visual de reprodutibilidade de dados. O ECM foi avaliado utilizando perfis protéicos do soro de pacientes com a doença de Hodgkin e de indivíduos controle. De acordo com a validação cruzada leave-one-out, o ECM separou corretamente os grupos se baseando apenas na informação de quatro picos espectrais selecionados. Este trabalho descreve o algoritmo e apresenta imagens de modelos 3D representativos da separação. O software está disponível na página do projeto na internet junto com modelos interativos e uma animação demonstrando o método.This study presents a new machine learning strategy to address the disease diagnosis classification problem that comprises an unknown number of disease classes. This is exemplified by a software called Ellipsoid Clustering Machine (ECM) that identifies conserved regions in mass spectrometry proteomic profiles obtained from control subjects and uses these to estimate classification boundaries based on sample variance. The software can also be used for visual inspection of data reproducibility. ECM was evaluated using mass spectrometry protein profiles obtained from serum of Hodgkin’s disease patients (HD) and control subjects. According to the leave-one-out cross validation, ECM completely separated both groups based only on the information derived from four selected mass spectral peaks. Classification details and a 3D graphical model showing the separation between the control subject cluster and HD patients is also presented. The software is available on the project website together with online interactive models of the dataset and an animation demonstrating the method

Pinpointing differentially expressed domains in complex protein mixtures with the cloud service of PatternLab for Proteomics

Submitted by Luciane Willcox ([email protected]) on 2016-09-22T16:39:25Z No. of bitstreams: 1 Pinpointing differentially expressed domains.pdf: 427914 bytes, checksum: 3cdab1198bf568cad2a313276677c0f0 (MD5)Approved for entry into archive by Luciane Willcox ([email protected]) on 2016-09-22T17:42:23Z (GMT) No. of bitstreams: 1 Pinpointing differentially expressed domains.pdf: 427914 bytes, checksum: 3cdab1198bf568cad2a313276677c0f0 (MD5)Made available in DSpace on 2016-09-22T17:42:23Z (GMT). No. of bitstreams: 1 Pinpointing differentially expressed domains.pdf: 427914 bytes, checksum: 3cdab1198bf568cad2a313276677c0f0 (MD5) Previous issue date: 2013-06-21CNPq, CAPES, FAPERJ, FAPESP, Fundação Araucária, Fiocruz–PDTISFundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Curitiba, PR, Brasil.Universidade de São Paulo. Instituto de Química. Laboratório de Regulação da Expressão Gênica em Microrganismos. São Paulo, SP, Brasil.Center for Genetic Engineering and Biotechnology. Department of Proteomics. Ciudad de la Habana, Cuba. / Wellcome Trust Genome Campus. European Bioinformatics Institute. EMBL Outstation, Cambridge, UK.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Curitiba, PR, Brasil.Universidade Federal do Rio de Janeiro. COPPE. Systems Engineering and Computer Science Program, Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Curitiba, PR, Brasil.Mass-spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. Here we describe a new module integrated into PatternLab for Proteomics that allows the pinpointing of differentially expressed domains. This is accomplished by inferring functional domains through our cloud service, using HMMER3 and Pfam remotely, and then mapping the quantitation values into domains for downstream analysis. In all, spotting which functional domains are changing when comparing biological states serves as a complementary approach to facilitate the understanding of a system's biology. We exemplify the new module's use by reanalyzing a previously published MudPIT dataset of Cryptococcus gattii cultivated under iron-depleted and replete conditions. We show how the differential analysis of functional domains can facilitate the interpretation of proteomic data by providing further valuable insight

Effectively addressing complex proteomic search spaces with peptide spectrum matching

Submitted by Luciane Willcox ([email protected]) on 2016-09-05T13:32:50Z No. of bitstreams: 1 Effectively addressing complex proteomic search spaces with.pdf: 108543 bytes, checksum: 06bee82184b8a632ed6b020a1e605322 (MD5)Approved for entry into archive by Luciane Willcox ([email protected]) on 2016-09-05T13:59:55Z (GMT) No. of bitstreams: 1 Effectively addressing complex proteomic search spaces with.pdf: 108543 bytes, checksum: 06bee82184b8a632ed6b020a1e605322 (MD5)Made available in DSpace on 2016-09-05T13:59:55Z (GMT). No. of bitstreams: 1 Effectively addressing complex proteomic search spaces with.pdf: 108543 bytes, checksum: 06bee82184b8a632ed6b020a1e605322 (MD5) Previous issue date: 2013-02-27Fundação de Amparo a Pesquisa do Rio de Janeiro (FAPERJ), Programa de Desenolvimento Tecnológico de Insumos para a Saúde (PDTIS), and Conselho Nacional de Pesquisa (CNPq).Universidade Federal do Rio de Janeiro. Programa em Ciência da Computação e Engenharia de Sistemas, Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Curitiba, PR, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Química. Unidade de Proteômica. Rio de Janeiro, RJ, BrasilUniversidade Federal do Rio de Janeiro. Instituto de Química. Unidade de Proteômica. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Curitiba, PR, Brasil.Universidade Federal do Rio de Janeiro. Programa em Ciência da Computação e Engenharia de Sistemas, Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Programa em Ciência da Computação e Engenharia de Sistemas, Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Proteômica e Engenharia de Proteínas. Curitiba, PR, Brasil.Center for Genetic Engineering and Biotechnology. Department of Proteomics. Ciudad de la Habana, Cuba / Wellcome Trust Genome Campus. European Bioinformatics Institute. EMBL Outstation. Proteomic Services, Hinxton, Cambridge, UK.Center for Genetic Engineering and Biotechnology. Department of Proteomics. Ciudad de la Habana, Cuba.Center for Genetic Engineering and Biotechnology. Department of Proteomics. Ciudad de la Habana, Cuba.Center for Genetic Engineering and Biotechnology. Department of Proteomics. Ciudad de la Habana, Cuba.Protein identification by mass spectrometry is commonly accomplished using a peptide sequence matching search algorithm, whose sensitivity varies inversely with the size of the sequence database and the number of post-translational modifications considered. We present the Spectrum Identification Machine, a peptide sequence matching tool that capitalizes on the high-intensity b1-fragment ion of tandem mass spectra of peptides coupled in solution with phenylisotiocyanate to confidently sequence the first amino acid and ultimately reduce the search space. We demonstrate that in complex search spaces, a gain of some 120% in sensitivity can be achieved

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

Submitted by Luciane Willcox ([email protected]) on 2016-09-14T18:42:23Z No. of bitstreams: 1 Integrated analysis of shotgun proteomic data.pdf: 5689932 bytes, checksum: d05baca4adc848b8a5eca5ff7f53f2f8 (MD5)Approved for entry into archive by Luciane Willcox ([email protected]) on 2016-10-07T17:50:10Z (GMT) No. of bitstreams: 1 Integrated analysis of shotgun proteomic data.pdf: 5689932 bytes, checksum: d05baca4adc848b8a5eca5ff7f53f2f8 (MD5)Made available in DSpace on 2016-10-07T17:50:10Z (GMT). No. of bitstreams: 1 Integrated analysis of shotgun proteomic data.pdf: 5689932 bytes, checksum: d05baca4adc848b8a5eca5ff7f53f2f8 (MD5) Previous issue date: 2015-12-10Fundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Toxinologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil / Department of Pathology. University of Michigan. Ann Arbor, Michigan, USA.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Manaus, AM, Brasil.Universidade Federal do Rio de Janeiro. Programa de Engenharia de Sistemas de Computação. Rio de Janeiro, RJ, Brasil.Laboratory for Biological Mass Spectrometry. The Scripps Research Institute. La Jolla, California, USA.Laboratory for Biological Mass Spectrometry. The Scripps Research Institute. La Jolla, California, USA

A Geografia na base nacional comum curricular: Inconsistências e impropriedades da proposta do MEC

Professores de Geografia das redes de educação básica, de universidades e estudantes universitários do Ceará reuniram-se no Departamento de Geografia da Universidade Federal do Ceará para debater a proposta da Base Nacional Comum Curricular (BNCC), divulgada pelo Ministério da Educação (MEC), através da Secretaria de Educação Básica, em setembro de 2015. A BNCC apresenta formulações de componentes curriculares para a educação básica e foi submetida à consulta pública através do portal online www.basenacionalcomum.mec.gov.br. Ao final dos trabalhos, foi sistematizado o presente parecer que registra preocupação em relação à proposição da BNCC quanto à finalidade, à metodologia, aos conteúdos apresentados e, em especial, quanto ao componente curricular Geografia

Phase angle as an indicator of body composition and physical performance in handball players

Abstract Background Phase angle (PhA), obtained from the bioimpedance analysis, is widely used in clinical situations and in sports. This study evaluated the association between PhA with body composition and physical performance of handball athletes. Methods 43 national-level players (22.19 ± 3.86 years) of both sexes were evaluated regarding anthropometry, body composition, squat (SJ) and countermovement (CMJ) jumps, handgrip strength, and cardiorespiratory fitness. Results We verified a correlation between PhA of the whole body and fat-free mass (r = 0.511), body mass index (r = 0.307), and body fat % (r = -0.303). There was a positive correlation between PhA of the whole body and SJ (r = 0.376), CMJ (r = 0.419), and handgrip for the dominant hand (r = 0.448). Moreover, PhA of the upper limbs was more strongly correlated with handgrip for the dominant (r = 0.630) and non-dominant hand (r = 0.575) compared to PhA of the whole body considering both sexes. Similarly, segmental PhA had a stronger significant correlation with SJ (r = 0.402) and handgrip for the dominant hand (r = 0.482) in males, as well as CMJ (r = 0.602) in females, compared to PhA of the whole body. Conclusion PhA of the whole body was positively related to fat-free mass, body mass index, body fat %, and lower- and upper-limbs strength in handball athletes. Segmental PhA might be used as a tool for estimating lower and upper limbs performance considering the sex, in preference to the PhA of the whole body

A multi-protease, multi-dissociation, bottom-up-to-top-down proteomic view of the Loxosceles intermedia venom

Submitted by Manoel Barata ([email protected]) on 2017-09-04T16:00:24Z No. of bitstreams: 1 Trevisan-Mproteas.pdf: 349753 bytes, checksum: a53487aa6a3bb4e7e558a4867496c7a1 (MD5)Approved for entry into archive by Manoel Barata ([email protected]) on 2017-09-06T18:49:14Z (GMT) No. of bitstreams: 1 Trevisan-Mproteas.pdf: 349753 bytes, checksum: a53487aa6a3bb4e7e558a4867496c7a1 (MD5)Made available in DSpace on 2017-09-06T18:49:14Z (GMT). No. of bitstreams: 1 Trevisan-Mproteas.pdf: 349753 bytes, checksum: a53487aa6a3bb4e7e558a4867496c7a1 (MD5) Previous issue date: 2017Universidade Federal do Paraná. Departamento de Biologia Celular. Curitiba, PR, Brasil.Universidade Federal do Paraná. Departamento de Biologia Celular. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil.Universidade Federal do Paraná. Departamento de Biologia Celular. Curitiba, PR, Brasil.Center for Computational Mass Spectrometry. University of California. San Diego, USA.Center for Computational Mass Spectrometry. University of California. San Diego, USA.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Genômica Funcional. Curitiba, PR, Brasil. / Fundação Oswaldo Cruz. Instituto Carlos Chagas. Plataforma de Espectrometria de Massas RPT02H. Curitiba, PR, BrasilFundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil.Federal University of Rio de Janeiro. COPPE. Systems Engineering and Computer Science Program. Rio de Janeiro, RJ, Brasil.Universidade Federal do Paraná. Departamento de Biologia Celular. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Grupo de Proteômica e Espectrometria de Massas Computacional. Curitiba, PR, Brasil.Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523