Cross-platform urine metabolomics of experimental hyperglycemia in type 2 diabetes

Hyperglycemia causes diabetic nephropathy, a condition for which there are no specific diagnostic markers thatpredict progression to renal failure. Here we describe a multiplatform metabolomic analysis of urine from individualswith type 2 diabetes, collected before and immediately following experimental hyperglycemia. We used targetednuclear magnetic resonance spectroscopy (NMR), liquid chromatography - mass spectrometry (LC-MS) and gaschromatography - MS (GC-MS) to identify markers of hyperglycemia. Following optimization of data normalisation andstatistical analysis, we identified a reproducible NMR and LC-MS based urine signature of hyperglycemia. Significantincreases of alanine, alloisoleucine, isoleucine, leucine, N-isovaleroylglycine, valine, choline, lactate and taurine anddecreases of arginine, gamma-aminobutyric acid, hippurate, suberate and N-acetylglutamate were observed. GC-MSanalysis identified a number of metabolites differentially present in post-glucose versus baseline urine, but these could not be identified using current metabolite libraries. This analysis is an important first step towards identifying biomarkers of early-stage diabetic nephropathy.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.

A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany.

A stochastic discount factor approach to asset pricing using panel data asymptotics

Using the Pricing Equation in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) which relies on the fact that its logarithm is the "common feature" in every asset return of the economy. Our estimator is a simple function of asset returns and does not depend on any parametric function representing preferences. The techniques discussed in this paper were applied to two relevant issues in macroeconomics and finance: the first asks what type of parametric preference-representation could be validated by asset-return data, and the second asks whether or not our SDF estimator can price returns in an out-of-sample forecasting exercise. In formal testing, we cannot reject standard preference specifications used in the macro/finance literature. Estimates of the relative risk-aversion coefficient are between 1 and 2, and statistically equal to unity. We also show that our SDF proxy can price reasonably well the returns of stocks with a higher capitalization level, whereas it shows some difficulty in pricing stocks with a lower level of capitalization.

Factors affecting the technical efficiency of production of the Brazilian banking system : a comparison of four statistical models in the context of data envelopment analysis

This paper uses an output oriented Data Envelopment Analysis (DEA) measure of technical efficiency to assess the technical efficiencies of the Brazilian banking system. Four approaches to estimation are compared in order to assess the significance of factors affecting inefficiency. These are nonparametric Analysis of Covariance, maximum likelihood using a family of exponential distributions, maximum likelihood using a family of truncated normal distributions, and the normal Tobit model. The sole focus of the paper is on a combined measure of output and the data analyzed refers to the year 2001. The factors of interest in the analysis and likely to affect efficiency are bank nature (multiple and commercial), bank type (credit, business, bursary and retail), bank size (large, medium, small and micro), bank control (private and public), bank origin (domestic and foreign), and non-performing loans. The latter is a measure of bank risk. All quantitative variables, including non-performing loans, are measured on a per employee basis. The best fits to the data are provided by the exponential family and the nonparametric Analysis of Covariance. The significance of a factor however varies according to the model fit although it can be said that there is some agreements between the best models. A highly significant association in all models fitted is observed only for nonperforming loans. The nonparametric Analysis of Covariance is more consistent with the inefficiency median responses observed for the qualitative factors. The findings of the analysis reinforce the significant association of the level of bank inefficiency, measured by DEA residuals, with the risk of bank failure.

Application of chemometric methods in the evaluation of chemical and spectroscopic data on organic matter from Oxisols in sewage sludge applications

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

Influence of genetic type and level of concentrate in the finishing diet on carcass and meat quality traits in beef heifers

Carcass and meat quality traits of thirty-six feedlot beef heifers from different genetic groups (GG) fed at two concentrate levels (CL) were evaluated using 12- Nellore (NE), 12 - 1/2Angus x 1/2Nellore (AN) and 12 - 1/2Simmental x 1/2Nellore (SN) animals. Six heifers of each GG were randomly assigned into one of two treatments: concentrate at 0.8% or 1.2% of body weight (BW). Heifers fed concentrate at 0.8% of BW had greater (P<0.05) dressing percentage. None of the proximate analysis components of the beef were affected (P>0.05) by either CL or GG. Heifers from the AN group had higher (P<0.05) carcass weights, 12th rib fat thickness and lower dressing percentage (P<0.05) compared to the other groups. NE heifers had greater WBSF values (P<0.05) than the other genetic groups. Data suggest that the concentrate level can be reduced without compromising meat quality traits. (C) 2011 Elsevier Ltd. All rights reserved.

Mapping Macrophyte Species in the Amazon Floodplain Wetlands Using Fully Polarimetric ALOS/PALSAR Data

The purpose of this paper was to evaluate attributes derived from fully polarimetric PALSAR data to discriminate and map macrophyte species in the Amazon floodplain wetlands. Fieldwork was carried out almost simultaneously to the radar acquisition, and macrophyte biomass and morphological variables were measured in the field. Attributes were calculated from the covariance matrix [C] derived from the single-look complex data. Image attributes and macrophyte variables were compared and analyzed to investigate the sensitivity of the attributes for discriminating among species. Based on these analyses, a rule-based classification was applied to map macrophyte species. Other classification approaches were tested and compared to the rule-based method: a classification based on the Freeman-Durden and Cloude-Pottier decomposition models, a hybrid classification (Wishart classifier with the input classes based on the H/a plane), and a statistical-based classification (supervised classification using Wishart distance measures). The findings show that attributes derived from fully polarimetric L-band data have good potential for discriminating herbaceous plant species based on morphology and that estimation of plant biomass and productivity could be improved by using these polarimetric attributes.

Gene Expression Profiles Stratified according to Type 1 Diabetes Mellitus Susceptibility Regions

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Involvement of L-Type Calcium Channel and SERCA2a in Myocardial Dysfunction Induced by Obesity

Obesity has been shown to impair myocardial performance. Nevertheless, the mechanisms underlying the participation of calcium (Ca2+) handling on cardiac dysfunction in obesity models remain unknown. L-type Ca2+ channels and sarcoplasmic reticulum (SR) Ca2+-ATPase (SERCA2a), may contribute to the cardiac dysfunction induced by obesity. The purpose of this study was to investigate whether myocardial dysfunction in obese rats is related to decreased activity and/or expression of L-type Ca2+ channels and SERCA2a. Male 30-day-old Wistar rats were fed standard (C) and alternately four palatable high-fat diets (Ob) for 15 weeks. Obesity was determined by adiposity index and comorbidities were evaluated. Myocardial function was evaluated in isolated left ventricle papillary muscles under basal conditions and after inotropic and lusitropic maneuvers. L-type Ca2+ channels and SERCA2a activity were determined using specific blockers, while changes in the amount of channels were evaluated by Western blot analysis. Phospholamban (PLB) protein expression and the SERCA2a/PLB ratio were also determined. Compared with C rats, the Ob rats had increased body fat, adiposity index and several comorbidities. The Ob muscles developed similar baseline data, but myocardial responsiveness to post-rest contraction stimulus and increased extracellular Ca2+ was compromised. The diltiazem promoted higher inhibition on developed tension in obese rats. In addition, there were no changes in the L-type Ca2+ channel protein content and SERCA2a behavior (activity and expression). In conclusion, the myocardial dysfunction caused by obesity is related to L-type Ca2+ channel activity impairment without significant changes in SERCA2a expression and function as well as L-type Ca2+ protein levels. J. Cell. Physiol. 226: 2934-2942, 2011. (C) 2011 Wiley-Liss, Inc.

International Recommendations for Training Future Toxicologic Pathologists Participating in Regulatory-Type, Nonclinical Toxicity Studies

The International Federation of Societies of Toxicologic Pathologists (IFSTP) proposes a common global framework for training future toxicologic pathologists who will support regulatory-type nonclinical toxicology studies. Trainees optimally should undertake a scientific curriculum of at least 5 years at an accredited institution leading to a clinical degree (veterinary medicine or medicine). Trainees should then obtain 4 or more years of intensive pathology practice during a residency and/or on-the-job "apprenticeship," at least 2 years of which must be focused on regulatory-type toxicologic pathology topics. Possession of a recognized pathology qualification (i.e., certification) is highly recommended. A non-clinical pathway (e.g., a graduate degree in medical biology or pathology) may be possible if medically trained pathologists are scarce, but this option is not optimal. Regular, lifelong continuing education (peer review of nonclinical studies, professional meetings, reading, short courses) will be necessary to maintain and enhance one's understanding of current toxicologic pathology knowledge, skills, and tools. This framework should provide a rigorous yet flexible way to reliably train future toxicologic pathologists to generate, interpret, integrate, and communicate data in regulatory-type, nonclinical toxicology studies. (J Toxicol Pathol 2010; 23: 171-181)

Absence of transforming growth factor-beta type II receptor is associated with poorer prognosis in HER2-negative breast tumours

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

Type C botulism in swine fed on restaurant waste

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Microwave Denture Disinfection Versus Nystatin in Treating Patients with Well-Controlled Type 2 Diabetes and Denture Stomatitis: A Randomized Clinical Trial

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

L-Type calcium channels mediate water intake induced by angiotensin injection into median preoptic nucleus

Calcium ions are widely accepted as critically important in responses of neurons to a stimulus. We have show previously the central involvement of angiotensin II (ANGII) in water intake. This study determined whether voltage-dependent calcium channels are involved in ANGII-induced behavioral drinking implicating nitrergic mechanism. The antidipsogenic actions of L-type calcium channel antagonists nifedipine, on ANGII-induced drinking behavior were studied when it is injected into the median preoptic nucleus (MnPO). The influence of nitric oxide (NO) on nifedipine antidipsogenic action was also studied by utilizing the N-W-nitro-L-arginine methyl ester (L-NAME) a constitutive nitric oxide synthase inhibitor constitutive (cNOSI) and 7-nitroindazol (7-NIT) a specific neuronal nitric oxide synthase inhibitor (nNOSI) and L-arginine a NO donor. Rats 200-250 g, with cannulae implanted into MnPO, pre-treated into MnPO with either nifedipine, followed by ANGII, drank significantly less water than controls during the first 15 min after injection. However, L-NAME potentiated the dipsogenic effect of ANGII that is blocked by prior injection of nifedipine and L-arginine. 7-NIT injected prior to ANGII into MnPO also potentiated the dipsogenic effect of ANGII but with a less intensity than L-NAME that it is also blocked by prior injection of nifedipine. The results described in this paper provide evidence that calcium channels play important roles in the ANGII-induced behavioral water intake. The structures containing NO in the brain such as MnPO include both endothelial cells and neurons might be responsible for the influence of nifedipine on dipsogenic effect of ANGII. These data shows the correlation between L-type calcium channel and a free radical gas NO produced endogenously from amino acids L-arginine by endothelial and neuronal NO synthase in the control of ANGII-dipsogenic effect. This suggests that an L-type calcium channel participates in both short- and longer-term neuronal actions of ANGII by nitrergic way. (c) 2006 Elsevier B.V. All rights reserved.