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Submitted by Juliana Barroso Silva (juliana.barrososilva@yahoo.com.br) on 2018-03-22T19:35:28Z No. of bitstreams: 1 TESE_Juliana_Barroso_Silva.pdf: 1323265 bytes, checksum: cad99fe998c4e341d6385e39ea20e249 (MD5) / Approved for entry into archive by Alexandra Maria Donadon Lusser Segali null (alexmar@fcav.unesp.br) on 2018-03-23T10:34:18Z (GMT) No. of bitstreams: 1 silva_jb_dr_jabo.pdf: 1323265 bytes, checksum: cad99fe998c4e341d6385e39ea20e249 (MD5) / Made available in DSpace on 2018-03-23T10:34:18Z (GMT). No. of bitstreams: 1 silva_jb_dr_jabo.pdf: 1323265 bytes, checksum: cad99fe998c4e341d6385e39ea20e249 (MD5) Previous issue date: 2018-02-20 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os parasitoides são insetos que utilizam outros insetos para se desenvolverem e completarem seu ciclo imaturo de desenvolvimento. Os parasitoides podem manipular a fisiologia e afetar o crescimento e desenvolvimento de seus hospedeiros, o que frequentemente resulta na morte de suas vítimas. Durante o parasitismo de Cotesia flavipes Cameron, 1891 (Hymenoptera: Braconidae) sobre Diatraea saccharalis (Fabricius, 1794) (Lepidoptera: Crambidae), teratócitos são liberados após eclosão da larva do parasitoide no interior do hospedeiro e esses teratócitos produzem um transcrito que codifica uma serpina (CfSerpina). As serpinas são inibidores de serino proteases muitas vezes associadas à regulação da cascata de ativação de profenoloxidases em fenoloxidases, enzimas relacionadas com a melanização de corpos estranhos no hospedeiro. O presente estudo foi conduzido para investigar a função biológica da CfSerpina e avaliar seu potencial biotecnológico como agente de controle de D. saccharalis após ingestão. A CfSerpina foi produzida em sistema heterólogo em células de E. coli BL 21, purificada em coluna de níquel-sepharose e quantificada pelo método de Bradford para posterior detecção por Western blot. A proteína foi produzida em quantidades suficientes para a realização da avaliação biológica e biotecnológica. Após os ensaios, verificou-se que a CfSerpina não pode ser associada à inibição da ativação da cascata de profenoloxidases na hemolinfa de D. saccharalis. Além disso, a exploração biotecnológica da CfSerpina como uma proteína tóxica para D. saccharalis não causou mortalidade ou alterações no ganho de peso larval de D. saccharalis após ingestão e não resultou em inibição in vitro da atividade de proteases digestivas de D. saccharalis. Investigações sobre outras possibilidades de regulação da fisiologia de D. saccharalis por CfSerpina precisam ser conduzidos para definição do papel dessa proteína no processo de regulação hospedeira. / Parasitoids are insects which use other insects to develop and complete their immature cycle. Parasitoids can manipulate the physiology and affect the growth and development of their hosts, which often results in the death of their victims. During parasitism of Diatraea saccharalis (Fabricius, 1794) (Lepidoptera: Crambidae) by the parasitoid Cotesia flavipes Cameron, 1891 (Hymenoptera: Braconidae) teratocytes are released after the eclosion of parasitoid larvae inside the host and these teratocytes produce a transcript that encodes a putative serpin (CfSerpin). Serpins are serine proteases inhibitors often associated with the regulation of the activation of prophenoloxidases into phenoloxidases, enzymes responsible for melanization of foreign bodies in the host. The study was conducted to investigate the biological function of CfSerpin, as well to evaluate its biotechnological potential to disrupt the development of D. saccharalis larvae after ingestion. CfSerpin was produced in a heterologous system using E. coli BL 21 cells, purified on a nickel-sepharose column, quantified by the Bradford method, and detected by Western blot. Purified CfSerpin was produced in sufficient amount for biological and biotechnological evaluation. CfSerpin could not be associated with the inhibition of activation of prophenoloxidases cascade in the host insect hemolymph. In addition, biotechnological exploitation of CfSerpin as a toxic protein after ingestion did not resulted in the disruption of the development of D. saccharalis larvae and no in vitro inhibition of digestive proteases of D. saccharalis by CfSerpin was observed. Investigations to unravel the role of CfSerpin on the regulation of D. saccharalis physiology still need to be conducted. / 141853/2016-6; 141604/2017-4; 159376/2014-9; 141283/2014-9 / 1747192; 1452546

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The hypothesis that similar chemicals exert similar biological activities has been widely adopted in the field of drug discovery and development. Quantitative Structure-Activity Relationship (QSAR) models have been used ubiquitously in drug discovery to understand the function of chemicals in biological systems. A common QSAR modeling method calculates similarity scores between chemicals to assess their biological function. However, due to the fact that some chemicals can be similar and yet have different biological activities, or conversely can be structurally different yet have similar biological functions, various methods have instead been developed to quantify chemical similarity at the functional level. Quantmap is one such method, which utilizes biological databases to quantify the biological similarity between chemicals. Quantmap uses quantitative molecular network topology analysis to cluster chemical substances based on their bioactivities. This method by itself, unfortunately, cannot assign new chemicals (those which may not yet have biological data) to the derived clusters. Owing to the fact that there is a lack of biological data for many chemicals, deep learning models were explored in this project with respect to their ability to correctly assign unknown chemicals to Quantmap clusters. The deep learning methods explored included both convolutional and recurrent neural networks. Transfer learning/pretraining based approaches and data augmentation methods were also investigated. The best performing model, among those considered, was the Seq2seq model (a recurrent neural network containing two joint networks, a perceiver and an interpreter network) without pretraining, but including data augmentation.

The aim of those thesis was an analysis of a off - producing function the fast - growing growths namely in the several standpoints. I deal with a prosecution of the fast - growing poplars in a theoretic part at first. Then I specifyed the three main functions the fast - growing timber species namely at an off - producing usage. It means a function ameliorative, sanitation, aesthetic, biological, produce and insulative here. Mostly I then attended to an water and windy erosion.

Stem cells differentiate through an organized hierarchy of intermediate cell types to terminally differentiated cell types. This process is largely guided by master transcriptional regulators, but it also depends on the expression of many other types of genes. The discrete cell types in the differentiation hierarchy are often identified based on the expression or non-expression of certain marker genes. Historically, these have often been various cell-surface proteins, which are fairly easy to assay biochemically but are not necessarily causative of the cell type, in the sense of being master transcriptional regulators. This raises important questions about how gene expression across the whole genome controls or reflects cell state, and in particular, differentiation hierarchies. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering and maximization of parsimony can organize the cell types into a tree, but in general this tree is different from the differentiation hierarchy. Using hematopoietic differentiation as an example, we demonstrate how many genes other than marker genes are able to discriminate between different branches of the differentiation tree by proposing two models for detecting genes that are up-regulated or down-regulated in distinct lineages. We then propose a novel approach to solving the following problem: Given the differentiation hierarchy and gene expression data at each node, construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming framework to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a sparse weighted Euclidean metric that uses just 175 genes. These 175 genes are different than the marker genes that were used to identify the 38 cell types, hence offering a novel alternative way of discriminating different branches of the tree. A DAVID functional annotation analysis shows that the 175 genes reflect major processes and pathways active in different parts of the tree. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. Our results show that the 175 genes frequently appear in the random metrics, implicating their significance from an empirical point of view as well. Finally, we show how our linear programming method is able to identify columns that were selected to build minimum spanning trees on the nodes of random variable-size matrices.