Genomic analysis of recombinant Sabin clinical isolates

Recombination in Poliovirus vaccine strains is a very frequent phenomenon. In this report 23 polio/Sabin strains isolated from healthy vaccinees or from VAPP patients after OPV administration, were investigated in order to identify recombination sites from 2C to 3D regions of the poliovirus genome. RT-PCR, followed by Restriction Fragment Length Polymorphism (RFLP) screening analysis were applied in four distant genomic regions (5' UTR, VP1, 2C and 3C-3D) in order to detect any putative recombinant. The detected recombinants were sequenced from 2C to the end of the genome (3' UTR) and the exact recombination sites were determined with computational analysis. Five of the 23 isolated strains were recombinant in one genomic region, two of them in 2C, isolates EP16:S3/S2, EP23:S3/S1, two in 3D isolates EP6:S2/S1, EP12:S2/S1 and one in 3A isolate EP9:S2/Sl. Point mutations were found in strains EP3, EP6, EP9 and EP12. Recombination specific types and sites re-occurrence along with point mutations are discussed concerning the polioviruses evolution.

Cortical Bone Stress Distribution in Mandibles with Different Configurations Restored with Prefabricated Bar-Prosthesis Protocol: A Three-Dimensional Finite-Element Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Application of dissociation curve analysis to radiation hybrid panel marker scoring: generation of a map of river buffalo (B-bubalis) chromosome 20

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Global analysis of the sugarcane microtranscriptome reveals a unique composition of small RNAs associated with axillary bud outgrowth

Axillary bud outgrowth determines shoot architecture and is under the control of endogenous hormones and a fine-tuned gene-expression network, which probably includes small RNAs (sRNAs). Although it is well known that sRNAs act broadly in plant development, our understanding about their roles in vegetative bud outgrowth remains limited. Moreover, the expression profiles of microRNAs (miRNAs) and their targets within axillary buds are largely unknown. Here, we employed sRNA next-generation sequencing as well as computational and gene-expression analysis to identify and quantify sRNAs and their targets in vegetative axillary buds of the biofuel crop sugarcane (Saccharum spp.). Computational analysis allowed the identification of 26 conserved miRNA families and two putative novel miRNAs, as well as a number of trans-acting small interfering RNAs. sRNAs associated with transposable elements and protein-encoding genes were similarly represented in both inactive and developing bud libraries. Conversely, sequencing and quantitative reverse transcription-PCR results revealed that specific miRNAs were differentially expressed in developing buds, and some correlated negatively with the expression of their targets at specific stages of axillary bud development. For instance, the expression patterns of miR159 and its target GAMYB suggested that they may play roles in regulating abscisic acid-signalling pathways during sugarcane bud outgrowth. Our work reveals, for the first time, differences in the composition and expression profiles of diverse sRNAs and targets between inactive and developing vegetative buds that, together with the endogenous balance of specific hormones, may be important in regulating axillary bud outgrowth. © 2013 © The Author(2) [2013].

Integrated analysis environment for the Movement Assessment Battery for Children

Developmental Coordination Disorder (DCD), a chronic and usually permanent condition found in children, is characterized by motor impairment that interferes with a child's activities of daily living and with academic achievement. One of the most popular tests for the quantitative diagnosis of DCD is the Movement Assessment Battery for Children (MABC). Based on the Battery's standardized scores, it is possible to identify children with typical development, children at risk of developing DCD, and children with DCD. This article describes a computational system we developed to assist with the analysis of results obtained in the MABC test. The tool was developed for the web environment and its database provides integration of MABC data. Thus, researchers around the world can share data and develop collaborative work in the DCD field. In order to help analysis processes, our system provides services for filtering data to show more specific sets of information and present the results in textual, table, and graphic formats, allowing easier and more comprehensive evaluation of the results.

Differential Proteomic Analysis of Noncardia Gastric Cancer from Individuals of Northern Brazil

Gastric cancer is the second leading cause of cancer-related death worldwide. The identification of new cancer biomarkers is necessary to reduce the mortality rates through the development of new screening assays and early diagnosis, as well as new target therapies. In this study, we performed a proteomic analysis of noncardia gastric neoplasias of individuals from Northern Brazil. The proteins were analyzed by two-dimensional electrophoresis and mass spectrometry. For the identification of differentially expressed proteins, we used statistical tests with bootstrapping resampling to control the type I error in the multiple comparison analyses. We identified 111 proteins involved in gastric carcinogenesis. The computational analysis revealed several proteins involved in the energy production processes and reinforced the Warburg effect in gastric cancer. ENO1 and HSPB1 expression were further evaluated. ENO1 was selected due to its role in aerobic glycolysis that may contribute to the Warburg effect. Although we observed two up-regulated spots of ENO1 in the proteomic analysis, the mean expression of ENO1 was reduced in gastric tumors by western blot. However, mean ENO1 expression seems to increase in more invasive tumors. This lack of correlation between proteomic and western blot analyses may be due to the presence of other ENO1 spots that present a slightly reduced expression, but with a high impact in the mean protein expression. In neoplasias, HSPB1 is induced by cellular stress to protect cells against apoptosis. In the present study, HSPB1 presented an elevated protein and mRNA expression in a subset of gastric cancer samples. However, no association was observed between HSPB1 expression and clinicopathological characteristics. Here, we identified several possible biomarkers of gastric cancer in individuals from Northern Brazil. These biomarkers may be useful for the assessment of prognosis and stratification for therapy if validated in larger clinical study sets.

Beyond the tree of texts: Building an empirical model of scribal variation through graph analysis of texts and stemmata

Stemmatology, or the reconstruction of the transmission history of texts, is a field that stands particularly to gain from digital methods. Many scholars already take stemmatic approaches that rely heavily on computational analysis of the collated text (e.g. Robinson and O’Hara 1996; Salemans 2000; Heikkilä 2005; Windram et al. 2008 among many others). Although there is great value in computationally assisted stemmatology, providing as it does a reproducible result and allowing access to the relevant methodological process in related fields such as evolutionary biology, computational stemmatics is not without its critics. The current state-of-the-art effectively forces scholars to choose between a preconceived judgment of the significance of textual differences (the Lachmannian or neo-Lachmannian approach, and the weighted phylogenetic approach) or to make no judgment at all (the unweighted phylogenetic approach). Some basis for judgment of the significance of variation is sorely needed for medieval text criticism in particular. By this, we mean that there is a need for a statistical empirical profile of the text-genealogical significance of the different sorts of variation in different sorts of medieval texts. The rules that apply to copies of Greek and Latin classics may not apply to copies of medieval Dutch story collections; the practices of copying authoritative texts such as the Bible will most likely have been different from the practices of copying the Lives of local saints and other commonly adapted texts. It is nevertheless imperative that we have a consistent, flexible, and analytically tractable model for capturing these phenomena of transmission. In this article, we present a computational model that captures most of the phenomena of text variation, and a method for analysis of one or more stemma hypotheses against the variation model. We apply this method to three ‘artificial traditions’ (i.e. texts copied under laboratory conditions by scholars to study the properties of text variation) and four genuine medieval traditions whose transmission history is known or deduced in varying degrees. Although our findings are necessarily limited by the small number of texts at our disposal, we demonstrate here some of the wide variety of calculations that can be made using our model. Certain of our results call sharply into question the utility of excluding ‘trivial’ variation such as orthographic and spelling changes from stemmatic analysis.

Protein–RNA interactions: a structural analysis

A detailed computational analysis of 32 protein–RNA complexes is presented. A number of physical and chemical properties of the intermolecular interfaces are calculated and compared with those observed in protein–double-stranded DNA and protein–single-stranded DNA complexes. The interface properties of the protein–RNA complexes reveal the diverse nature of the binding sites. van der Waals contacts played a more prevalent role than hydrogen bond contacts, and preferential binding to guanine and uracil was observed. The positively charged residue, arginine, and the single aromatic residues, phenylalanine and tyrosine, all played key roles in the RNA binding sites. A comparison between protein–RNA and protein–DNA complexes showed that whilst base and backbone contacts (both hydrogen bonding and van der Waals) were observed with equal frequency in the protein–RNA complexes, backbone contacts were more dominant in the protein–DNA complexes. Although similar modes of secondary structure interactions have been observed in RNA and DNA binding proteins, the current analysis emphasises the differences that exist between the two types of nucleic acid binding protein at the atomic contact level.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

Computational resources for structural and metagenomic characterization of functions in bacteria and bacterial communities. Antimicrobial resistance, pathways for xenobiotic degradation and relationship between gut microbiome and ageing.

Prokaryotic organisms are one of the most successful forms of life, they are present in all known ecosystems. The deluge diversity of bacteria reflects their ability to colonise every environment. Also, human beings host trillions of microorganisms in their body districts, including skin, mucosae, and gut. This symbiosis is active for all other terrestrial and marine animals, as well as plants. With the term holobiont we refer, with a single word, to the systems including both the host and its symbiotic microbial species. The coevolution of bacteria within their ecological niches reflects the adaptation of both host and guest species, and it is shaped by complex interactions that are pivotal for determining the host state. Nowadays, thanks to the current sequencing technologies, Next Generation Sequencing, we have unprecedented tools for investigating the bacterial life by studying the prokaryotic genome sequences. NGS revolution has been sustained by the advancements in computational performance, in terms of speed, storage capacity, algorithm development and hardware costs decreasing following the Moore’s Law. Bioinformaticians and computational biologists design and implement ad hoc tools able to analyse high-throughput data and extract valuable biological information. Metagenomics requires the integration of life and computational sciences and it is uncovering the deluge diversity of the bacterial world. The present thesis work focuses mainly on the analysis of prokaryotic genomes under different aspects. Being supervised by two groups at the University of Bologna, the Biocomputing group and the group of Microbial Ecology of Health, I investigated three different topics: i) antimicrobial resistance, particularly with respect to missense point mutations involved in the resistant phenotype, ii) bacterial mechanisms involved in xenobiotic degradation via the computational analysis of metagenomic samples, and iii) the variation of the human gut microbiota through ageing, in elderly and longevous individuals.

A novel homozygous splice acceptor site mutation of KISS1R in two siblings with normosmic isolated hypogonadotropic hypogonadism

Context: Loss-of-function mutations of the kisspeptin-1 receptor gene, KISS1R, have been identified in patients with normosmic isolated hypogonadotropic hypogonadism (nIHH). Objective: To investigate KISS1R defects in patients with absent or delayed puberty. Patients: We investigated KISS1R gene defects in a cohort of 99 Brazilian patients with nIHH or constitutional delay of puberty (CDP). Methods: The entire coding region of KISS1R was amplified by PCR followed by automatic sequencing. In addition, screening for KISS1R exonic deletions was performed by multiplex ligation-dependent probe amplification. Results: One novel homozygous KISS1R mutation was identified in two siblings with nIHH. This variant was an insertion/deletion (indel) mutation characterized by the deletion of three nucleotides (GCA) at position -2 to -4, and by the insertion of seven nucleotides (ACCGGCT) at the same position, within the 30 splice acceptor site of intron 2 of KISS1R. The brothers who carried this KISS1R mutation had no clinical evidence of pubertal development at the ages of 14 and 20 years. Computational analysis of this indel mutation predicted the generation of an abnormal protein. In addition, a new heterozygous KISS1R variant (p.E252Q) was identified in a male patient with sporadic nIHH. However, in vitro studies of this variant did not demonstrate functional impairment. Only known polymorphisms were identified in patients with CDP. Conclusion: Loss-of-function mutations of KISS1R represents a rare cause of nIHH, and was absent in patients with CDP. We have described a novel KISS1R homozygous splice acceptor site mutation in the familial form of nIHH.

Coletores de distribuição e sua otimização hidráulica, aplicados a uma instalação de produção de água arrefecida em sistemas de AVAC ( Fórum Picoas)

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

New insights on integer-programming models for the kidney exchange problem

In recent years several countries have set up policies that allow exchange of kidneys between two or more incompatible patient–donor pairs. These policies lead to what is commonly known as kidney exchange programs. The underlying optimization problems can be formulated as integer programming models. Previously proposed models for kidney exchange programs have exponential numbers of constraints or variables, which makes them fairly difficult to solve when the problem size is large. In this work we propose two compact formulations for the problem, explain how these formulations can be adapted to address some problem variants, and provide results on the dominance of some models over others. Finally we present a systematic comparison between our models and two previously proposed ones via thorough computational analysis. Results show that compact formulations have advantages over non-compact ones when the problem size is large.

S100A6 Amyloid Fibril Formation Is Calcium-modulated and Enhances Superoxide Dismutase-1 (SOD1) Aggregation

S100A6 is a small EF-hand calcium- and zinc-binding protein involved in the regulation of cell proliferation and cytoskeletal dynamics. It is overexpressed in neurodegenerative disorders and a proposed marker for Amyotrophic Lateral Sclerosis (ALS). Following recent reports of amyloid formation by S100 proteins, we investigated the aggregation properties of S100A6. Computational analysis using aggregation predictors Waltz and Zyggregator revealed increased propensity within S100A6 helices HI and HIV. Subsequent analysis of Thioflavin-T binding kinetics under acidic conditions elicited a very fast process with no lag phase and extensive formation of aggregates and stacked fibrils as observed by electron microscopy. Ca2+ exerted an inhibitory effect on the aggregation kinetics, which could be reverted upon chelation. An FT-IR investigation of the early conformational changes occurring under these conditions showed that Ca2+ promotes anti-parallel β-sheet conformations that repress fibrillation. At pH 7, Ca2+ rendered the fibril formation kinetics slower: time-resolved imaging showed that fibril formation is highly suppressed, with aggregates forming instead. In the absence of metals an extensive network of fibrils is formed. S100A6 oligomers, but not fibrils, were found to be cytotoxic, decreasing cell viability by up to 40%. This effect was not observed when the aggregates were formed in the presence of Ca2+. Interestingly, native S1006 seeds SOD1 aggregation, shortening its nucleation process. This suggests a cross-talk between these two proteins involved in ALS. Overall, these results put forward novel roles for S100 proteins, whose metal-modulated aggregation propensity may be a key aspect in their physiology and function.