Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Down-regulation of protease activated receptor 2, interleukin-17 and other pro-inflammatory genes by subantimicrobial doxycycline dose in a rat periodontitis model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Clinical evaluation of multiple-surface ART restorations: three-year follow-up

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

Desempenho de bovinos cruzados terminados em confinamento e qualidade dos músculos longissimus e semitendinosus in natura e maturados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

An Extended Kernel for Generalized Multiple-Instance Learning

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly better or even with the old kernel in the other applications, showing that an SVM using this kernel does not overfit despite its richer representation.

Anxiolytic-Like Property of Risperidone and Olanzapine as Examined in Multiple Measures of Fear in Rats

Atypical antipsychotics are also used in the treatment of anxiety-related disorders. Clinical and preclinical evidence regarding their intrinsic anxiolytic efficacy has been mixed. In this study, we examined the potential anxiolytic-like effects of risperidone and olanzapine, and compared them with haloperidol, chlordiazepoxide (a prototype of sedative–anxiolytic drug) or citalopram (a selective serotonin reuptake inhibitor). We used a composite of two-way avoidance conditioning and acoustic startle reflex model and examined the effects of drug treatments during the acquisition phase (Experiment 1) or extinction phase (Experiments 2 and 3) on multiple measures of conditioned and unconditioned fear/anxiety-like responses. In Experiment 4, we further compared risperidone, olanzapine, haloperidol, citalopram and chlordiazepoxide in a standard elevated plus maze test. Results revealed three distinct anxiolytic-like profiles associated with risperidone, olanzapine and chlordiazepoxide. Risperidone, especially at 1.0 mg/kg, significantly decreased the number of avoidance responses, 22 kHz ultrasonic vocalization, avoidance conditioning-induced hyperthermia and startle reactivity, but did not affect defecations or time spent on the open arms. Olanzapine (2.0 mg/kg, sc) significantly decreased the number of avoidance responses, 22 kHz vocalization and amount of defecations, but it did not inhibit startle reactivity and time spent on the open arms. Chlordiazepoxide (10 mg/kg, ip) significantly decreased the number of 22 kHz vocalization, avoidance conditioning-induced hyperthermia and amount of defecations, and increased time spent on the open arms, but did not decrease avoidance responses or startle reactivity. Haloperidol and citalopram did not display any anxiolytic-like property in these tests. The results highlight the importance of using multiple measures of fear-related responses to delineate behavioral profiles of psychotherapeutic drugs.

QUANTITATIVE TRAIT LOCI FOR AGRONOMIC AND END-USE QUALITY PERFORMANCE AND THE EFFECT OF SOILBORNE WHEAT MOSAIC VIRUS IN A HARD WINTER WHEAT POPULATION IN NEBRASKA

To better understand agronomic and end-use quality in wheat (Triticum aestivum L.) we developed a population containing 154 F6:8 recombinant inbred lines (RILs) from the cross TAM107-R7/Arlin. The parental lines and RILs were phenotyped at six environments in Nebraska and differed for resistance to Wheat soilborne mosaic virus (WSBMV), morphological, agronomic, and end-use quality traits. Additionally, a 2300 cM genome-wide linkage map was created for quantitative trait loci (QTL) analysis. Based on our results across multiple environments, the best RILs could be used for cultivar improvement. The population and marker data are publicly available for interested researchers for future research. The population was used to determine the effect of WSBMV on agronomic and end-use quality and for the mapping of a resistance locus. Results from two infected environments showed that all but two agronomic traits were significantly affected by the disease. Specifically, the disease reduced grain yield by 30% of susceptible RILs and they flowered 5 d later and were 11 cm shorter. End-use quality traits were not negatively affected but flour protein content was increased in susceptible RILs. The resistance locus SbmTmr1 mapped to 27.1 cM near marker wPt-5870 on chromosome 5DL using ELISA data. Finally, we investigated how WSBMV affected QTL detection in the population. QTLs were mapped at two WSBMV infected environments, four uninfected environments, and in the resistant and susceptible RIL subpopulations in the infected environments. Fifty-two significant (LOD≥3) QTLs were mapped in RILs at uninfected environments. Many of the QTLs were pleiotropic or closely linked at 6 chromosomal regions. Forty-seven QTLs were mapped in RILs at WSBMV infected environments. Comparisons between uninfected and infected environments identified 20 common QTLs and 21 environmentally specific QTLs. Finally, 24 QTLs were determined to be affected by WSBMV by comparing the subpopulations in QTL analyses within the same environment. The comparisons were statistically validated using marker by disease interactions. These results showed that QTLs can be affected by WSBMV and careful interpretation of QTL results is needed where biotic stresses are present. Finally, beneficial QTLs not affected by WSBMV or the environment are candidates for marker-assisted selection.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.

Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Study of using marker assisted selection on a beef cattle breeding program by model comparison

A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.

Trends in aortic aneurysm- and dissection-related mortality in the state of Sao Paulo, Brazil, 1985-2009: multiple-cause-of-death analysis

Background: Aortic aneurysm and dissection are important causes of death in older people. Ruptured aneurysms show catastrophic fatality rates reaching near 80%. Few population-based mortality studies have been published in the world and none in Brazil. The objective of the present study was to use multiple-cause-of-death methodology in the analysis of mortality trends related to aortic aneurysm and dissection in the state of Sao Paulo, between 1985 and 2009. Methods: We analyzed mortality data from the Sao Paulo State Data Analysis System, selecting all death certificates on which aortic aneurysm and dissection were listed as a cause-of-death. The variables sex, age, season of the year, and underlying, associated or total mentions of causes of death were studied using standardized mortality rates, proportions and historical trends. Statistical analyses were performed by chi-square goodness-of-fit and H Kruskal-Wallis tests, and variance analysis. The joinpoint regression model was used to evaluate changes in age-standardized rates trends. A p value less than 0.05 was regarded as significant. Results: Over a 25-year period, there were 42,615 deaths related to aortic aneurysm and dissection, of which 36,088 (84.7%) were identified as underlying cause and 6,527 (15.3%) as an associated cause-of-death. Dissection and ruptured aneurysms were considered as an underlying cause of death in 93% of the deaths. For the entire period, a significant increased trend of age-standardized death rates was observed in men and women, while certain non-significant decreases occurred from 1996/2004 until 2009. Abdominal aortic aneurysms and aortic dissections prevailed among men and aortic dissections and aortic aneurysms of unspecified site among women. In 1985 and 2009 death rates ratios of men to women were respectively 2.86 and 2.19, corresponding to a difference decrease between rates of 23.4%. For aortic dissection, ruptured and non-ruptured aneurysms, the overall mean ages at death were, respectively, 63.2, 68.4 and 71.6 years; while, as the underlying cause, the main associated causes of death were as follows: hemorrhages (in 43.8%/40.5%/13.9%); hypertensive diseases (in 49.2%/22.43%/24.5%) and atherosclerosis (in 14.8%/25.5%/15.3%); and, as associated causes, their principal overall underlying causes of death were diseases of the circulatory (55.7%), and respiratory (13.8%) systems and neoplasms (7.8%). A significant seasonal variation, with highest frequency in winter, occurred in deaths identified as underlying cause for aortic dissection, ruptured and non-ruptured aneurysms. Conclusions: This study introduces the methodology of multiple-causes-of-death to enhance epidemiologic knowledge of aortic aneurysm and dissection in Sao Paulo, Brazil. The results presented confer light to the importance of mortality statistics and the need for epidemiologic studies to understand unique trends in our own population.

A comparative transcriptome analysis reveals expression profiles conserved across three Eimeria spp. of domestic fowl and associated with multiple developmental stages

Coccidiosis of the domestic fowl is a worldwide disease caused by seven species of protozoan parasites of the genus Eimeria. The genome of the model species, Eimeria tenella, presents a complexity of 55-60 MB distributed in 14 chromosomes. Relatively few studies have been undertaken to unravel the complexity of the transcriptome of Eimeria parasites. We report here the generation of more than 45,000 open reading frame expressed sequence tag (ORESTES) cDNA reads of E. tenella, Eimeria maxima and Eimeria acervulina, covering several developmental stages: unsporulated oocysts, sporoblastic oocysts, sporulated oocysts, sporozoites and second generation merozoites. All reads were assembled to constitute gene indices and submitted to a comprehensive functional annotation pipeline. In the case of E. tenella, we also incorporated publicly available ESTs to generate an integrated body of information. Orthology analyses have identified genes conserved across different apicomplexan parasites, as well as genes restricted to the genus Eimeria. Digital expression profiles obtained from ORESTES/EST countings, submitted to clustering analyses, revealed a high conservation pattern across the three Eimeria spp. Distance trees showed that unsporulated and sporoblastic oocysts constitute a distinct clade in all species, with sporulated oocysts forming a more external branch. This latter stage also shows a close relationship with sporozoites, whereas first and second generation merozoites are more closely related to each other than to sporozoites. The profiles were unambiguously associated with the distinct developmental stages and strongly correlated with the order of the stages in the parasite life cycle. Finally, we present The Eimeria Transcript Database (http://www.coccidia.icb.usp.br/eimeriatdb), a website that provides open access to all sequencing data, annotation and comparative analysis. We expect this repository to represent a useful resource to the Eimeria scientific community, helping to define potential candidates for the development of new strategies to control coccidiosis of the domestic fowl. (C) 2011 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Experimental Model of Arthritis Induced by Paracoccidioides brasiliensis in Rats

Paracoccidioidomycosis (PCM), a disease caused by the fungus Paracoccidioides brasiliensis (Pb), is highly prevalent in Brazil, where it is the principal cause of death by systemic mycoses. The disease primarily affects men aged 30-50 year old and usually starts as a pulmonary focus and then may spread to other organs and systems, including the joints. The present study aimed to develop an experimental model of paracoccidioidomycotic arthritis. Two-month-old male Wistar rats (n = 48) were used, divided in 6 groups: test groups EG/15 and EG/45 (received one dose of 100 mu l of saline containing 10(5) Pb viable yeasts in the knee); heat killed Pb-group HK/15 and HK/45 (received a suspension of 10(5) Pb nonviable yeasts in the knee) and control groups CG/15 and CG/45 (received only sterile saline in the knee). The rats were killed 15 and 45 days postinoculation. In contrast with the control rats, the histopathology of the joints of rats of the test groups (EG/15 and EG/45) revealed a picture of well-established PCM arthritis characterized by extensive sclerosing granulomatous inflammation with numerous multiple budding fungal cells. The X-ray examination revealed joint alterations in these groups. Only metabolic active fungi evoked inflammation. The experimental model was able to induce fungal arthritis in the knees of the rats infected with metabolic active P. brasiliensis. The disease tended to be regressive and restrained by the immune system. No evidence of fungal dissemination to the lungs was observed.

Comparison of multiple-input single-output single-user ultra-wideband systems with pre-distortion

This paper presents a performance analysis of a baseband multiple-input single-output ultra-wideband system over scenarios CM1 and CM3 of the IEEE 802.15.3a channel model, incorporating four different schemes of pre-distortion: time reversal, zero-forcing pre-equaliser, constrained least squares pre-equaliser, and minimum mean square error pre-equaliser. For the third case, a simple solution based on the steepest-descent (gradient) algorithm is adopted and compared with theoretical results. The channel estimations at the transmitter are assumed to be truncated and noisy. Results show that the constrained least squares algorithm has a good trade-off between intersymbol interference reduction and signal-to-noise ratio preservation, providing a performance comparable to the minimum mean square error method but with lower computational complexity. Copyright (C) 2011 John Wiley & Sons, Ltd.