Control of the Intracellular Redox State by Glucose Participates in the Insulin Secretion Mechanism

Background: Production of reactive oxygen species (ROS) due to chronic exposure to glucose has been associated with impaired beta cell function and diabetes. However, physiologically, beta cells are well equipped to deal with episodic glucose loads, to which they respond with a fine tuned glucose-stimulated insulin secretion (GSIS). In the present study, a systematic investigation in rat pancreatic islets about the changes in the redox environment induced by acute exposure to glucose was carried out. Methodology/Principal Findings: Short term incubations were performed in isolated rat pancreatic islets. Glucose dose- and time-dependently reduced the intracellular ROS content in pancreatic islets as assayed by fluorescence in a confocal microscope. This decrease was due to activation of pentose-phosphate pathway (PPP). Inhibition of PPP blunted the redox control as well as GSIS in a dose-dependent manner. The addition of low doses of ROS scavengers at high glucose concentration acutely improved beta cell function. The ROS scavenger N-acetyl-L-cysteine increased the intracellular calcium response to glucose that was associated with a small decrease in ROS content. Additionally, the presence of the hydrogen peroxide-specific scavenger catalase, in its membrane-permeable form, nearly doubled glucose metabolism. Interestingly, though an increase in GSIS was also observed, this did not match the effect on glucose metabolism. Conclusions: The control of ROS content via PPP activation by glucose importantly contributes to the mechanisms that couple the glucose stimulus to insulin secretion. Moreover, we identified intracellular hydrogen peroxide as an inhibitor of glucose metabolism intrinsic to rat pancreatic islets. These findings suggest that the intracellular adjustment of the redox environment by glucose plays an important role in the mechanism of GSIS.Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)(CAPES) Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazi

Explaining Support Vector Machines: A Color Based Nomogram.

PROBLEM SETTING: Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. OBJECTIVE: In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. RESULTS: Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. CONCLUSIONS: This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method

SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification

A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality if the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. In this work we developed a novel method for multivariate feature selection based on the Partial Least Squares algorithm. We compared the method's variants with common feature selection techniques across a large number of real case-control datasets, using several classifiers. We demonstrate the advantages of the method and the preferable combinations of classifier and feature selection technique

Aquarium Nitrification Revisited: Thaumarchaeota Are the Dominant Ammonia Oxidizers in Freshwater Aquarium Biofilters

Ammonia-oxidizing archaea (AOA) outnumber ammonia-oxidizing bacteria (AOB) in many terrestrial and aquatic environments. Although nitrification is the primary function of aquarium biofilters, very few studies have investigated the microorganisms responsible for this process in aquaria. This study used quantitative real-time PCR (qPCR) to quantify the ammonia monooxygenase (amoA) and 16S rRNA genes of Bacteria and Thaumarchaeota in freshwater aquarium biofilters, in addition to assessing the diversity of AOA amoA genes by denaturing gradient gel electrophoresis (DGGE) and clone libraries. AOA were numerically dominant in 23 of 27 freshwater biofilters, and in 12 of these biofilters AOA contributed all detectable amoA genes. Eight saltwater aquaria and two commercial aquarium nitrifier supplements were included for comparison. Both thaumarchaeal and bacterial amoA genes were detected in all saltwater samples, with AOA genes outnumbering AOB genes in five of eight biofilters. Bacterial amoA genes were abundant in both supplements, but thaumarchaeal amoA and 16S rRNA genes could not be detected. For freshwater aquaria, the proportion of amoA genes from AOA relative to AOB was inversely correlated with ammonium concentration. DGGE of AOA amoA genes revealed variable diversity across samples, with nonmetric multidimensional scaling (NMDS) indicating separation of freshwater and saltwater fingerprints. Composite clone libraries of AOA amoA genes revealed distinct freshwater and saltwater clusters, as well as mixed clusters containing both freshwater and saltwater amoA gene sequences. These results reveal insight into commonplace residential biofilters and suggest that aquarium biofilters may represent valuable biofilm microcosms for future studies of AOA ecology

Climate-Based Models for Understanding and Forecasting Dengue Epidemics

Dengue fever is a major public health problem in the tropics and subtropics. Since no vaccine exists, understanding and predicting outbreaks remain of crucial interest. Climate influences the mosquito-vector biology and the viral transmission cycle. Its impact on dengue dynamics is of growing interest. We analyzed the epidemiology of dengue in Noumea (New Caledonia) from 1971 to 2010 and its relationships with local and remote climate conditions using an original approach combining a comparison of epidemic and non epidemic years, bivariate and multivariate analyses. We found that the occurrence of outbreaks in Noumea was strongly influenced by climate during the last forty years. Efficient models were developed to estimate the yearly risk of outbreak as a function of two meteorological variables that were contemporaneous (explicative model) or prior (predictive model) to the outbreak onset. Local threshold values of maximal temperature and relative humidity were identified. Our results provide new insights to understand the link between climate and dengue outbreaks, and have a substantial impact on dengue management in New Caledonia since the health authorities have integrated these models into their decision making process and vector control policies. This raises the possibility to provide similar early warning systems in other countries

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

X-ray diffraction and Raman spectroscopic studies of Zn-substituted carrboydite-like compounds

Hydrotalcite-like compounds of the formula NixZn6-xAl2(OH)(16)(SO4)(.)4H(2)O where x varies from 0 to 6, equivalent to a zinc-substituted carrboydite have been synthesised and characterised by X-ray diffraction, electron microscopy and vibrational spectroscopy. Both the d (003) spacing and the crystallite size are a function of the amount of zinc replacement for nickel in the carrboydite-like compounds. Transmission electron microscopy (TEM) shows the clay-like crystal structure for these hydrotalcite compounds. These compounds were characterised by vibrational spectroscopic techniques and a comparison made with the naturally occurring minerals. Additional bands in the sulphate antisymmetric stretching and bending region leads to the conclusion that the symmetry of the sulphate anion is reduced inferring the bonding of the sulphate anion to the hydrotalcite hydroxyl surface. (c) 2005 Elsevier B.V. All rights reserved