Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

ObjectiveCandidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.Research Design and MethodsBy integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).Results273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.ConclusionsUsing a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.

Phosphorus availability in oxidic soils treated with lime and silicate applications

Based on the assumption that silicate application can raise soil P availability for crops, the aim of this research was to compare the effect of silicate application on soil P desorption with that of liming, in evaluations based on two extractors and plant growth. The experiment was carried out in randomized blocks with four replications, in a 3 × 3 × 5 factorial design, in which three soil types, three P rates, and four soil acidity correctives were evaluated in 180 experimental plots. Trials were performed in a greenhouse using corn plants in 20-dm³ pots. Three P rates (0, 50 and 150 mg dm-3) were applied in the form of powder triple superphosphate and the soil was incubated for 90 days. After this period, soil samples were collected for routine chemical analysis and P content determination by the extraction methods resin, Mehlich-1 and remaining P. Based on the results, acidity correctives were applied at rates calculated for base saturation increased to 70 %, with subsequent incubation for 60 more days, when P content was determined again. The acidity correctives consisted of: dolomitic lime, steelmaking slag, ladle furnace slag, and wollastonite. Therefore, our results showed that slags raised the soil P content more than lime, suggesting a positive correlation between P and Si in soil. Silicon did not affect the extractor choice since both Mehlich-1 and resin had the same behavior regarding extracted P when silicon was applied to the soil. For all evaluated plant parameters, there was significant interaction between P rates and correctives; highest values were obtained with silicate.

LAYERED DOUBLE HYDROXIDES: NANOMATERIALS FOR APPLICATIONS IN AGRICULTURE

The current research aims to introduce Layered Double Hydroxides (LDH) as nanomaterials to be used in agriculture, with particular reference to its use as storage and slow release matrix of nutrients and agrochemicals for plant growing. Structural characteristics, main properties, synthesis methods and characterization of LDH were covered in this study. Moreover, some literature data have been reported to demonstrate their potential for storage and slow release of nitrate, phosphate, agrochemicals, besides as being used as adsorbent for the wastewater treatment. This research aims to expand, in near future, the investigation field on these materials, with application in agriculture, increasing the interface between chemistry and agronomy.

CHROMIUM IN SOIL ORGANIC MATTER AND COWPEA AFTER FOUR CONSECUTIVE ANNUAL APPLICATIONS OF COMPOSTED TANNERY SLUDGE

Tannery sludge contains high concentrations of inorganic elements, such as chromium (Cr), which may lead to environmental pollution and affect human health The behavior of Cr in organic matter fractions and in the growth of cowpea (Vigna unguiculata L.) was studied in a sandy soil after four consecutive annual applications of composted tannery sludge (CTS). Over a four-year period, CTS was applied on permanent plots (2 × 5 m) and incorporated in the soil (0-20 cm) at the rates of 0, 2.5, 5.0, 10.0, and 20.0 Mg ha-1 (dry weight basis). These treatments were replicated four times in a randomized block design. In the fourth year, cowpea was planted and grown for 50 days, at which time we analyzed the Cr concentrations in the soil, in the fulvic acid, humic acid, and humin fractions, and in the leaves, pods, and grains of cowpea. Composted tannery sludge led to an increase in Cr concentration in the soil. Among the humic substances, the highest Cr concentration was found in humin. The application rates of CTS significantly increased Cr concentration in leaves and grains.

Comparative sensitivity of tumor and non-tumor cell lines as reliable approach for in vitro cytotoxicity screening of lysine-based surfactants with potential pharmaceutical applications

Surfactants are used as additives in topical pharmaceuticals and drug delivery systems. The biocompatibility of amino acid-based surfactants makes them highly suitable for use in these fields, but tests are needed to evaluate their potential toxicity. Here we addressed the sensitivity of tumor (HeLa, MCF-7) and non-tumor (3T3, 3T6, HaCaT, NCTC 2544) cell lines to the toxic effects of lysine-based surfactants by means of two in vitro endpoints (MTT and NRU). This comparative assay may serve as a reliable approach for predictive toxicity screening of chemicals prior to pharmaceutical applications. After 24-h of cell exposure to surfactants, differing toxic responses were observed. NCTC 2544 and 3T6 cell lines were the most sensitive, while both tumor cells and 3T3 fibroblasts were more resistant to the cytotoxic effects of surfactants. IC50-values revealed that cytotoxicity was detected earlier by MTT assay than by NRU assay, regardless of the compound or cell line. The overall results showed that surfactants with organic counterions were less cytotoxic than those with inorganic counterions. Our findings highlight the relevance of the correct choice and combination of cell lines and bioassays in toxicity studies for a safe and reliable screen of chemicals with potential interest in pharmaceutical industry.

BRACHIARIA IN SELENIUM-CONTAMINATED SOIL UNDER SULPHUR SOURCE APPLICATIONS

ABSTRACT High contents of plant-available selenium in the soil in the form of selenate, resulting from natural or anthropogenic action, jeopardizes agricultural areas and requires research for solutions to establish or re-establish agricultural or livestock operation, avoiding the risk of poisoning of plants, animals and humans. The purpose was to evaluate sulfur sources in the form of sulfate, e.g., ammonium sulfate, calcium sulfate, ferric sulfate, in the remediation of tropical soils anthropogenically contaminated with Se under the tropical forage grass Brachiaria brizantha (Hochst. ex A. Rich.) Stapf cv. Marandu. More clayey soils are less able to supply plants with Se, which influences the effects of S sources, but it was found that high soil Se concentrations negatively affected forage biomass production, regardless of the soil. Of the tested S sources, the highly soluble ammonium sulfate and ferric sulfate reduced plant Se uptake and raised the available sulfur content in the soil.

Do Rates and Splitting of Phosphogypsum Applications Influence the Soil and Annual Crops in a No-Tillage System?

ABSTRACT Applications of phosphogypsum (PG) provide nutrients to the soil and reduce Al3+ activity, favoring soil fertility and root growth, but allow Mg2+ mobilization through the soil profile, resulting in variations in the PG rate required to achieve the optimum crop yield. This study evaluated the effect of application rates and splitting of PG on soil fertility of a Typic Hapludox, as well as the influence on annual crops under no-tillage. Using a (4 × 3) + 1 factorial structure, the treatments consisted of four PG rates (3, 6, 9, and 12 Mg ha-1) and three split applications (P1 = 100 % in 2009; P2 = 50+50 % in 2009 and 2010; P3 = 33+33+33 % in 2009, 2010 and 2011), plus a control without PG. The soil was sampled six months after the last PG application, in stratified layers to a depth of 0.8 m. Corn, wheat and soybean were sown between November 2011 and December 2012, and leaf samples were collected for analysis when at least 50 % of the plants showed reproductive structures. The application of PG increased Ca2+ concentrations in all sampled soil layers and the soil pH between 0.2 and 0.8 m, and reduced the concentrations of Al3+ in all layers and of Mg2+ to a depth of 0.6 m, without any effect of splitting the applications. The soil Ca/Mg ratio increased linearly to a depth of 0.6 m with the rates and were found to be higher in the 0.0-0.1 m layer of the P2 and P3 treatments than without splitting (P1). Sulfur concentrations increased linearly by application rates to a depth of 0.8 m, decreasing in the order P3>P2>P1 to a depth of 0.4 m and were higher in the treatments P3 and P2 than P1 between 0.4-0.6 m, whereas no differences were observed in the 0.6-0.8 m layer. No effect was recorded for K, P and potential acidity (H+Al). The leaf Ca and S concentration increased, while Mg decreased for all crops treated with PG, and there was no effect of splitting the application. The yield response of corn to PG rates was quadratic, with the maximum technical efficiency achieved at 6.38 Mg ha-1 of PG, while wheat yield increased linearly in a growing season with a drought period. Soybean yield was not affected by the PG rate, and splitting had no effect on the yield of any of the crops. Phosphogypsum improved soil fertility in the profile, however, Mg2+ migrated downwards, regardless of application splitting. Splitting the PG application induced a higher Ca/Mg ratio in the 0.0-0.1 m layer and less S leaching, but did not affect the crop yield. The application rates had no effect on soybean yield, but were beneficial for corn and, especially, for wheat, which was affected by a drought period during growth.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Erosion Control for Highway Applications, Phase I: Review and Synthesis of Literature, Final Report, TR-464, May 2002

The project described herein has led to a convenient, computer-based expert system for identifying and evaluating potentially effective erosion- and sedimentation-control measures for use in roadway construction throughout Iowa and elsewhere in the Midwest. The expert system is intended to be an accessible and efficient practical resource to aid state, county, and municipal engineers in the selection of the best management practices for preventing unwanted erosion and sedimentation at roadway construction sites, during and after construction.

Field Evaluation of Timber Preservation Treatments for Highway Applications Final Report, December 2007

Timber material repair and replacement cost for timber bridges is a considerable expense to highway agencies in Iowa, especially to county road departments. To address these needs, the objectives of this investigation was to study the field effectiveness of various treatment alternatives used on Iowa roadway projects and to determine if the current specifications and testing are adequate for providing proper wood preservation. To satisfy the research needs, the project scope involved a literature review, identification of metrics, questionnaire survey of Iowa counties, onsite inspections, and a review of current specifications and testing procedures. Based on the preservative information obtained, the following general conclusions were made: Copper naphthenate is recommended as the plant-applied preservative treatment for timber bridges. Best Management Practices should be followed to ensure quality treatment of timber materials. Bridge maintenance programs need to be developed and implemented. The Iowa Department of Transportation specifications for preservative treatment are the regulating specification for bridges constructed with state or federal funding in Iowa and are also recommended for all other bridges.

Integrated methodologies to study human transcriptional regulation from functional genomics data

Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.