Association Study of Candidate Gene Polymorphisms and Obesity in a Young Mexican-American Population from South Texas

Background and Aims Obesity is increasingly a health problem and a risk factor for diabetes in young Mexican-American populations. Genetic association studies in older, mostly non-Hispanic populations have reported that polymorphisms in the candidate genes HSD11B1, CRP, ADIPOQ, PPARG, ANKK1, ABCC8 and SERPINF1 are associated with obesity or diabetes. We analyzed the polymorphisms rs846910, rs1205, rs1501299, rs1801282, rs1800497, rs757110 and rs1136287in these candidate genes, for association with obesity and metabolic traits in a young Mexican-American population from south Texas. Methods Genotyping of the seven common SNPs were performed by allelic discrimination assays in 448 unrelated Mexican Americans (median age = 16 years) from south Texas. χ2 tests and regression analyses using additive models were used for genetic association analyses adjusting for covariates; p values were corrected for multiple testing by permutation analyses. Results rs1800497 (ANKK1) shows association with waist circumference (p = 0.009) and retains the association (p = 0.03) after permutation testing. Analysis of metabolic quantitative traits shows that rs846910 (HSD11B1) was associated with HOMA-IR (p = 0.04) and triglycerides (p = 0.03), and rs1205 (CRP) with HOMA-IR (p = 0.03) and fasting glucose levels (p = 0.007). However, the quantitative traits associations are not maintained after permutation analysis. None of the other SNPs in this study showed associations with obesity or metabolic traits in this young Mexican-American population. Conclusions We report a potential association between rs1800497 (linked to changes in brain dopamine receptor levels) and central obesity in a young Mexican-American population

A study on multi-scale kernel optimisation via centered kernel-target alignment

Kernel mapping is one of the most widespread approaches to intrinsically deriving nonlinear classifiers. With the aim of better suiting a given dataset, different kernels have been proposed and different bounds and methodologies have been studied to optimise them. We focus on the optimisation of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, although it has been shown to achieve better performance in the presence of heterogeneous attributes. The large number of parameters in multi-scale kernels makes it computationally unaffordable to optimise them by applying traditional cross-validation. Instead, an analytical measure known as centered kernel-target alignment (CKTA) can be used to align the kernel to the so-called ideal kernel matrix. This paper analyses and compares this and other alternatives, providing a review of the literature in kernel optimisation and some insights into the usefulness of multi-scale kernel optimisation via CKTA. When applied to the binary support vector machine paradigm (SVM), the results using 24 datasets show that CKTA with a multi-scale kernel leads to the construction of a well-defined feature space and simpler SVM models, provides an implicit filtering of non-informative features and achieves robust and comparable performance to other methods even when using random initialisations. Finally, we derive some considerations about when a multi-scale approach could be, in general, useful and propose a distance-based initialisation technique for the gradient-ascent method, which shows promising results

Evaluation of bread quality and volatile compounds of breads made by sourdoughs fermented by sediments of pulque (xaxtle) as starter culture

Sourdough is an important modern fermentation method of cereal flour and water. The fermentation process is carried out by lactic acid bacteria (LAB) and yeasts which confer specific flavor characteristics to the bread. The main aim of this research was to investigate the bread quality and volatile compounds of breads made by sourdoughs inoculated with sediments of pulque (xaxtle) used it as starter culture. Fifty five volatile compounds were found in the bread made with sourdoughs inoculated with xaxtle from three different regions of Mexico. Using gas chromatography-mass spectrometry, compounds as 3-hydroxy-2-butanone; 3-methyl-1-butanol; 2-methyl, 1-butanol; dimethyl disulfide; furfural, nonanal, phenyl ethyl alcohol and butanoic acid were presented in the flavor profile of the breads and having a positive response to sensory analysis made by evaluators. The xaxtle of Nanacamilpa (XN) and the xaxtle of Villa Alta (XV) were the best breads getting 8.3±0.03, 8.8±0.02, 6.2±0.08 and 8.2±0.01 scores in a scale from 0 to 10 in color, smell, texture and flavor attributes respectively which are positive attributes in favor of the quality bread. As a result of fermentation sourdough with LAB and yeasts from the xaxtle during 24 hours (30° C), the bread made with the sourdough inoculated with xaxtle of Milpa Alta (XM) showed the major acid flavor therefore its sample was less acceptable getting 8.1±0.01, 7.8±0.02, 5.3±0.01 and 7.9±0.01 in the same attributes evaluated. The xaxtle of Nanacamilpa, Tlaxcala (XN) run better than the others as starter fermentation culture for sourdoughs

Partial order label decomposition approaches for melanoma diagnosis

Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. Melanoma recognition is a challenging task that nowadays is performed by well trained dermatologists who may produce varying diagnosis due to the task complexity. This motivates the development of automated diagnosis tools, in spite of the inherent difficulties (intra-class variation, visual similarity between melanoma and non-melanoma lesions, among others). In the present work, we propose a system combining image analysis and machine learning to detect melanoma presence and severity. The severity is assessed in terms of melanoma thickness, which is measured by the Breslow index. Previous works mainly focus on the binary problem of detecting the presence of the melanoma. However, the system proposed in this paper goes a step further by also considering the stage of the lesion in the classification task. To do so, we extract 100 features that consider the shape, colour, pigment network and texture of the benign and malignant lesions. The problem is tackled as a five-class classification problem, where the first class represents benign lesions, and the remaining four classes represent the different stages of the melanoma (via the Breslow index). Based on the problem definition, we identify the learning setting as a partial order problem, in which the patterns belonging to the different melanoma stages present an order relationship, but where there is no order arrangement with respect to the benign lesions. Under this assumption about the class topology, we design several proposals to exploit this structure and improve data preprocessing. In this sense, we experimentally demonstrate that those proposals exploiting the partial order assumption achieve better performance than 12 baseline nominal and ordinal classifiers (including a deep learning model) which do not consider this partial order. To deal with class imbalance, we additionally propose specific over-sampling techniques that consider the structure of the problem for the creation of synthetic patterns. The experimental study is carried out with clinician-curated images from the Interactive Atlas of Dermoscopy, which eases reproducibility of experiments. Concerning the results obtained, in spite of having augmented the complexity of the classification problem with more classes, the performance of our proposals in the binary problem is similar to the one reported in the literature

Dietary assessment methods for micronutrient intake in pregnant women : a systematic review

The EURopean micronutrient RECommendations Aligned (EURRECA) Network of Excellence needs clear guidelines for assessing the validity of reported micronutrient intakes among vulnerable population groups. A systematic literature search identified studies validating the methodology used for measuring usual dietary intake during pregnancy. The quality of each validation study selected was assessed using a EURRECA-developed scoring system. The validation studies were categorised according to whether the study used a reference method that reflected short-term intake (,7 d) long-term intake ($7 d) or used biomarkers (BM). A correlation coefficient for each micronutrient was calculated from the mean of the correlation coefficients from each study weighted by the quality of the study. Seventeen papers were selected, which included the validation of fifteen FFQ, two dietary records (DR), one diet history and a Fe intake checklist. Estimates of twenty-six micronutrients by six FFQ were validated against 24-h recalls indicating good correlation for six micronutrients. Estimates of twenty-four micronutrients by two FFQ were validated against estimated DR and all had good or acceptable correlations. Estimates of fourteen micronutrients by three FFQ were validated against weighed DR indicating good correlations for five. Six FFQ were validated against BM, presenting good correlations only for folic acid. FFQ appear to be most reliable for measuring short-term intakes of vitamins E and B6 and long-term intakes of thiamin. Apart from folic acid, BM do not add any more certainty in terms of intake method reliability. When frequency methods are used, the inclusion of dietary supplements improves their reliability for most micronutrients

Activation of Serine One-Carbon Metabolism by Calcineurin A beta 1 Reduces Myocardial Hypertrophy and Improves Ventricular Function

Background In response to pressure overload, the heart develops ventricular hypertrophy that progressively decompensates and leads to heart failure. This pathological hypertrophy is mediated, among others, by the phosphatase calcineurin and is characterized by metabolic changes that impair energy production by mitochondria. Objectives The authors aimed to determine the role of the calcineurin splicing variant CnAβ1 in the context of cardiac hypertrophy and its mechanism of action. Methods Transgenic mice overexpressing CnAβ1 specifically in cardiomyocytes and mice lacking the unique C-terminal domain in CnAβ1 (CnAβ1Δi12 mice) were used. Pressure overload hypertrophy was induced by transaortic constriction. Cardiac function was measured by echocardiography. Mice were characterized using various molecular analyses. Results In contrast to other calcineurin isoforms, the authors show here that cardiac-specific overexpression of CnAβ1 in transgenic mice reduces cardiac hypertrophy and improves cardiac function. This effect is mediated by activation of serine and one-carbon metabolism, and the production of antioxidant mediators that prevent mitochondrial protein oxidation and preserve ATP production. The induction of enzymes involved in this metabolic pathway by CnAβ1 is dependent on mTOR activity. Inhibition of serine and one-carbon metabolism blocks the beneficial effects of CnAβ1. CnAβ1Δi12 mice show increased cardiac hypertrophy and declined contractility. Conclusions The metabolic reprogramming induced by CnAβ1 redefines the role of calcineurin in the heart and shows for the first time that activation of the serine and one-carbon pathway has beneficial effects on cardiac hypertrophy and function, paving the way for new therapeutic approaches

Evaluation of milk production by cows on a diet supplemented with nopal (Opuntia ficus-indica) during the dry season

Evaluation was made of a bovine milk production system using a total of 11 cows in two groups: G1, which was fed a conventional type diet according to the season of the year –(a) grazing and concentrates during the rainy season, (b) corn stover and concentrates during the dry season; G2- which was fed as in G1 during the rainy season and with the addition of a supplement of cactus pear or nopal (Opuntia ficus-indica) at the rate of 1.77 kg of dry matter (14.88 kg of fresh material) daily. Milk production (MP) was measured at 15-d intervals and the resulting data were analyzed using mixed model with repeated measures and minimum least square means methodology. MP was affected by group (P < 0.03) and the interaction of stage of lactation by treatment (P <0.001), but not by lactation number. Mean daily production of the cows G2 and G1 was 8.53 and 7.44 kg of milk, respectively. Use of nopal as a dietary supplement for milk-producing bovines is a viable alternative in rural areas of the country that enables increased production during time of scarcity of quality forage

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido

Preferred growth direction by PbS nanoplatelets preserves perovskite infrared light harvesting for stable, reproducible, and efficient solar cells

Formamidinium-based perovskite solar cells (PSCs) present the maximum theoretical efficiency of the lead perovskite family. However, formamidinium perovskite exhibits significant degradation in air. The surface chemistry of PbS has been used to improve the formamidinium black phase stability. Here, the use of PbS nanoplatelets with (100) preferential crystal orientation is reported, to potentiate the repercussion on the crystal growth of perovskite grains and to improve the stability of the material and consequently of the solar cells. As a result, a vertical growth of perovskite grains, a stable current density of 23 mA cm(-2), and a stable incident photon to current efficiency in the infrared region of the spectrum for 4 months is obtained, one of the best stability achievements for planar PSCs. Moreover, a better reproducibility than the control device, by optimizing the PbS concentration in the perovskite matrix, is achieved. These outcomes validate the synergistic use of PbS nanoplatelets to improve formamidinium long-term stability and performance reproducibility, and pave the way for using metastable perovskite active phases preserving their light harvesting capability