Consumo e custo da alimentação para recuperação da atividade ovariana luteal cíclica de vacas mestiças leiteiras com anestro

O objetivo do trabalho foi verificar o consumo e o custo de alimentos para recuperação da atividade ovariana luteal cíclica (AOLC) em vacas mestiças Holandês x Zebu com anestro. Foram usadas 18 vacas, não-gestantes, não-lactantes, magras, apresentando ovários sem função luteal, de tamanho normal e sem folículos palpáveis na superfície. Doze animais permaneceram em confinamento e receberam alimentação para ganho de peso até o reinício da AOLC. Os seis animais restantes, constituindo o grupo controle, receberam alimentação de mantença para o baixo peso apresentado e permaneceram em anestro durante o período experimental. A AOLC foi avaliada pela concentração de progesterona no soro sangüíneo (coleta de sangue a cada sete dias), pelo exame dos ovários por palpação retal a cada 12 dias, e observação visual dos sinais do estro três vezes ao dia. A recuperação da AOLC nas vacas com anestro exigiu um consumo médio de 722,4 kg de matéria seca, 50,4 kg de proteína bruta e 402,4 kg de NDT, relativos à ingestão média de 2.374,2 kg de volumoso e 194,1 kg de concentrado. O custo desses alimentos somado à perda estimada da produção de leite numa vaca de 3.000 litros de leite/lactação, provocada pelo prolongamento do intervalo de partos, foi equivalente a 1.364,2 litros de leite (preço recebido pelo produtor = R$ 0,20/litro). Esse custo elevado da recuperação do anestro onera o custo final da produção de leite, tendo em vista a alta incidência de anestro nos rebanhos brasileiros.

Peso para recuperação da atividade ovariana luteal cíclica em vacas leiteiras mestiças em anestro

Procurou-se conhecer o ganho de peso necessário e o peso mínimo para restabelecimento da atividade ovariana luteal cíclica (AOLC) em vacas adultas, mestiças Holandês x Zebu, não-lactantes, magras e com ovários inativos, anestro adquirido após longo período de restrição alimentar. Seis animais, com peso médio de 322,0 ± 27,0 kg, receberam dieta de mantença para o baixo peso apresentado (grupo I) e 12 animais, com peso médio de 315,0 ± 29,4 kg, foram alimentados para ganho de peso até a recuperação da AOLC (grupo II). O sangue foi coletado (dosagem de progesterona-RIA), e os animais, pesados semanalmente. A AOLC foi avaliada pela concentração de progesterona no sangue, exame dos ovários por palpação retal a cada 12 dias e observação visual do estro três vezes ao dia. O reinício da AOLC ocorreu nos animais do grupo II quando pesaram em média 392,7 ± 29,4 kg. A média do ganho de peso total nesse grupo foi de 77,7 ± 11,2 kg, correspondendo a 24,7 ± 4,5% do peso desses animais em anestro, ou 37,7% dos 206,2 kg perdidos na fase de restrição alimentar para adquirir anestro. Os seis animais do grupo I permaneceram em anestro. Os resultados mostram a influência do nível alimentar sobre a função luteal ovariana e a necessidade de ganho de peso para o restabelecimento do ciclo estral em vacas mestiças leiteiras, magras e em anestro.

Looking for Validity or Testing It? The Perils of Stepwise Regression, Extreme-Scores Analysis, Heteroscedasticity, and Measurement Error

When researchers introduce a new test they have to demonstrate that it is valid, using unbiased designs and suitable statistical procedures. In this article we use Monte Carlo analyses to highlight how incorrect statistical procedures (i.e., stepwise regression, extreme scores analyses) or ignoring regression assumptions (e.g., heteroscedasticity) contribute to wrong validity estimates. Beyond these demonstrations, and as an example, we re-examined the results reported by Warwick, Nettelbeck, and Ward (2010) concerning the validity of the Ability Emotional Intelligence Measure (AEIM). Warwick et al. used the wrong statistical procedures to conclude that the AEIM was incrementally valid beyond intelligence and personality traits in predicting various outcomes. In our re-analysis, we found that the reliability-corrected multiple correlation of their measures with personality and intelligence was up to .69. Using robust statistical procedures and appropriate controls, we also found that the AEIM did not predict incremental variance in GPA, stress, loneliness, or well-being, demonstrating the importance for testing validity instead of looking for it.

Adapting football to the child: an application of the logistic regression model in observational mewthodology.

Logistic regression is included into the analysis techniques which are valid for observationalmethodology. However, its presence at the heart of thismethodology, and more specifically in physical activity and sports studies, is scarce. With a view to highlighting the possibilities this technique offers within the scope of observational methodology applied to physical activity and sports, an application of the logistic regression model is presented. The model is applied in the context of an observational design which aims to determine, from the analysis of use of the playing area, which football discipline (7 a side football, 9 a side football or 11 a side football) is best adapted to the child"s possibilities. A multiple logistic regression model can provide an effective prognosis regarding the probability of a move being successful (reaching the opposing goal area) depending on the sector in which the move commenced and the football discipline which is being played.

Restrição alimentar e atividade ovariana luteal cíclica pós-parto em vacas girolanda

Objetivou-se neste trabalho verificar se a restrição alimentar no pós-partoem vacas Girolanda, multíparas, de bom escore de condição corporal (ECC = 3,5 a 4,5) ao parto será suficiente para impedir o reinício da atividade ovariana luteal cíclica (AOLC) pós-parto.Os animais foram distribuídos em três tratamentos: Grupo I (n = 15), mantença; Grupo II (n = 10) e Grupo III (n = 13), sendo que os grupos II e III receberam restrição alimentar até 90 e 180 dias pós-parto, respectivamente. As pesagens e avaliações do ECC foram efetuadas logo após o parto, e depois semanalmente. A AOLC foi avaliada por palpação retal, observação de cio e concentração de progesterona no leite. Os intervalos do parto ao primeiro cio foram de 53,1, 63,2 e 51,2 dias (P>0,05), respectivamente para os Grupos I, II e III, que apresentaram perdas de peso de 7,3 kg, 57,0 kg e 63,7 kg nesse período; e de 14,2 kg, 63,8 kg e 78,4 kg do parto até 90 dias pós-parto, repectivamente. Em vacas Girolanda de bom escore de condição corporal ao parto, a perda de 15,2% do peso nos três primeiros meses de lactação, e de 16,3% do peso até 180 dias pós-parto, não é suficiente para atrasar o reinício ou interromper a AOLC nos respectivos períodos.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Evaluation of a multicomponent worksite health promotion program for cardiovascular risk factors-correcting for the regression towards the mean effect.

BACKGROUND: We assessed the impact of a multicomponent worksite health promotion program for0 reducing cardiovascular risk factors (CVRF) with short intervention, adjusting for regression towards the mean (RTM) affecting such nonexperimental study without control group. METHODS: A cohort of 4,198 workers (aged 42 +/- 10 years, range 16-76 years, 27% women) were analyzed at 3.7-year interval and stratified by each CVRF risk category (low/medium/high blood pressure [BP], total cholesterol [TC], body mass index [BMI], and smoking) with RTM and secular trend adjustments. Intervention consisted of 15 min CVRF screening and individualized counseling by health professionals to medium- and high-risk individuals, with eventual physician referral. RESULTS: High-risk groups participants improved diastolic BP (-3.4 mm Hg [95%CI: -5.1, -1.7]) in 190 hypertensive patients, TC (-0.58 mmol/l [-0.71, -0.44]) in 693 hypercholesterolemic patients, and smoking (-3.1 cig/day [-3.9, -2.3]) in 808 smokers, while systolic BP changes reflected RTM. Low-risk individuals without counseling deteriorated TC and BMI. Body weight increased uniformly in all risk groups (+0.35 kg/year). CONCLUSIONS: In real-world conditions, short intervention program participants in high-risk groups for diastolic BP, TC, and smoking improved their CVRF, whereas low-risk TC and BMI groups deteriorated. Future programs may include specific advises to low-risk groups to maintain a favorable CVRF profile.

Copeptin is not associated with menstrual cycle hormone.

Background: Copeptin (CP), a derivate from the antidiuretic hormone (ADH) precursor pre-pro-vasopressin, stochiometrically mirrors ADH secretion. CP is increasingly evaluated as a diagnostic and prognostic biomarker in different diseases. It is therefore important to recognize possible confounding factors when interpreting CP levels. In healthy regularly menstruating women, there is a small but measurable physiological variability of hormones involved in fluid regulation. ADH plasma levels have been found to be lowest at menstruation, increasing during the follicular phase with a peak at ovulation and a drop in the luteal phase. We investigated the variability of CP during the menstrual cycle (MC) and its correlation to MC hormones. Methods: In total, 15 healthy women with regular MC (from 26 to 33 days) were included in this study. Ovulation was confirmed by progesterone (prog) levels on day 21 of the MC before entering the study and during the study. Blood collection was performed on days 3, 5, 8-16, 18, 21, 24 and 27 of their MC. Serums were assayed for prog, estradiol (E2), LH, and CP. Mixed linear regression analysis for repeated measures was performed to study the changes of CP, prog, E2 and LH during the MC, and to test the correlation of CP with sex hormones during the MC. Results: Mean MC length in all subjects was 28.5±2.2 d. E2, prog, and LH exhibited characteristic changes during the MC (all P< 0.05). All cycles were ovulatory (peak prog 54±15 nmol/l). CP levels did not change significantly throughout the MC, and were not associated with changes in prog, E2 or LH-levels (all P=ns). Conclusion: CP levels remain stable during the MC and are not influenced by changes in sex hormones. This implicates that it is not necessary to consider MC phases when using CP as a biomarker in premenopausal women.

Childhood epilepsy with neuropsychological regression and continuous spike waves during sleep: epilepsy surgery in a young adult.

We describe the case of a man with a history of complex partial seizures and severe language, cognitive and behavioural regression during early childhood (3.5 years), who underwent epilepsy surgery at the age of 25 years. His early epilepsy had clinical and electroencephalogram features of the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia (Landau-Kleffner syndrome), which we considered initially to be of idiopathic origin. Seizures recurred at 19 years and presurgical investigations at 25 years showed a lateral frontal epileptic focus with spread to Broca's area and the frontal orbital regions. Histopathology revealed a focal cortical dysplasia, not visible on magnetic resonance imaging. The prolonged but reversible early regression and the residual neuropsychological disorders during adulthood were probably the result of an active left frontal epilepsy, which interfered with language and behaviour during development. Our findings raise the question of the role of focal cortical dysplasia as an aetiology in the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.