Growth performance and body composition of giant trahira fingerlings fed diets with different protein and energy levels

The objective of this work was to determine the proper levels of protein and energy in diets of Hoplias lacerdae fingerlings. The dietary crude protein (CP) and gross energy (GE) levels for fingerlings of giant trahira were evaluated in a completely randomized 4x3 factorial design with 35, 39, 43 and 47% CP and 4,100, 4,300 and 4,500 kcal kg-1 of GE, and four replicates. The survival rate was 99.22%, and a linear improvement on the performance parameters was detected after increasing diet crude protein levels. Feed conversion ratio decreased with increasing levels of dietary protein and energy in the diets. A significant interaction between crude protein and gross energy was observed over body protein and mineral matter. Body lipid has increased linearly as gross energy in the diet increased. The retention of crude protein and energy showed a linear increasing with rising of crude protein levels in the diet. Crude protein level at 47% provides the best performance and energy retention, independently of the gross energy levels in the diet.

Tamanho de amostra para estimação do coeficiente de correlação linear de Pearson entre caracteres de milho

O objetivo deste trabalho foi determinar o tamanho de amostra para a estimação do coeficiente de correlação linear de Pearson entre caracteres de três híbridos de milho. Para as análises, foram tomadas aleatoriamente 361, 373 e 416 plantas, respectivamente, de híbridos simples, triplo e duplo. Para cada planta, os seguintes caracteres foram mensurados: diâmetro maior e menor do colmo, altura da planta e altura, peso, comprimento e diâmetro da espiga, número de fileiras por espiga, peso e diâmetro de sabugo, massa de cem grãos, número de grãos por espiga, comprimento e produtividade de grãos. Para cada um dos 91 pares de caracteres e híbridos, foi determinado o tamanho de amostra a partir de "bootstrap", com reposição de 1.000 amostras, de cada tamanho de amostra simulado. Na estimação do coeficiente de correlação linear de Pearson com a mesma precisão, o tamanho de amostra (número de plantas) aumenta na direção de pares de caracteres com menor intensidade de relação linear, independentemente do tipo de híbrido. Para os 91 pares de caracteres estudados, 252 plantas são suficientes para a estimação do coeficiente de correlação linear de Pearson, no intervalo de confiança de "bootstrap" de 95%, igual a 0,30

Bonnes pratiques de promotion de la santé des personnes âgées : développement des théories d'action des interventions = Best Practice Gesundheitsförderung im Alter (BPGF) : Entwicklung von Wirkungsmodellen.

Le projet "Bonnes pratiques de promotion de la santé des personnes âgées" vise à définir des recommandations pour promouvoir la santé des personnes âgées dans les différents cantons suisses adhérents au projet. Cinq instituts suisses ont ainsi été mandatés à produire un rapport faisant état de la situation relative à cinq domaines :- mesures visant à stimuler l'activité physique;- prévention des chutes;- participation du corps médical (médecins de famille);- conseils en matière de santé, manifestations, cours;- accès aux groupes-cibles et outils de recrutement.Dans le but de définir les bases pour une évaluation future des interventions recommandées, le présent rapport, sollicité par Promotion Santé Suisse, détaille le travail effectué avec les équipes responsables de chaque domaine pour élaborer une série de trois synthèses de leur travail, à savoir :- une analyse de la situation en question (problèmes constatés);- une théorie d'action définissant les objectifs intermédiaires et finaux à atteindre à partir des recommandations faites dans leur domaine;- une liste des indicateurs associés aux objectifs prioritaires.Ces synthèses ont été réalisées à l'aide du modèle de catégorisation des résultats de promotion de la santé et de la prévention (SMOC) un outil développé conjointement par les Instituts universitaires de médecine sociale et préventive de Berne et de Lausanne, en collaboration avec Promotion Santé Suisse. Les théories d'action ainsi élaborées ont ensuite été intégrées dans une théorie d'action globale correspondant à l'ensemble du projet " Bonnes pratiques de promotion de la santé des personnes âgées ". [P. 5]

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Smooth height/age curves from stem analysis with linear programming

[Abstract]

Dry matter production of Tanzania grass as a function of agrometeorological variables

The objective of this work was to develop and validate linear regression models to estimate the production of dry matter by Tanzania grass (Megathyrsus maximus, cultivar Tanzania) as a function of agrometeorological variables. For this purpose, data on the growth of this forage grass from 2000 to 2005, under dry‑field conditions in São Carlos, SP, Brazil, were correlated to the following climatic parameters: minimum and mean temperatures, degree‑days, and potential and actual evapotranspiration. Simple linear regressions were performed between agrometeorological variables (independent) and the dry matter accumulation rate (dependent). The estimates were validated with independent data obtained in São Carlos and Piracicaba, SP, Brazil. The best statistical results in the development and validation of the models were obtained with the agrometeorological parameters that consider thermal and water availability effects together, such as actual evapotranspiration, accumulation of degree‑days corrected by water availability, and the climatic growth index, based on average temperature, solar radiation, and water availability. These variables can be used in simulations and models to predict the production of Tanzania grass.

Predicting the occurrence of alate aphids in Brassicaceae

The objective of this work was to predict the occurrence of alates of Brevicoryne brassicae, Lipaphis erysimi, and Myzus persicae (Hemiptera, Aphididae) in Brassicaceae. The alate aphids were collected in yellow water traps from July 1997 to August 2005. Aphid population peaks were predicted using a degree‑day model. The meteorological factors, temperature, air relative humidity, rainfall, and sunshine hours, were used to provide precision indexes to evaluate the best predictor for the date of the first capture of alate aphids by the traps. The degree‑day model indicated that the peak population of the evaluated aphid species can be predicted using one of the following biofix dates: January 1st, June 1st, and the date of the first capture of the alate aphid species by the yellow water traps. The best predictor of B. brassicae occurrence is the number of days with minimum temperature >15°C, and of L. erysimi and M. persicae, the number of days with rainfall occurrence.

TBS (Trabecular Bone Score) is More Sensitive Than BMD to Diabetes-Related Fracture Risk

Background:Type 2 diabetes (T2D) is associated with increased fracture risk but paradoxically greater BMD. TBS (trabecular bone score), a novel grey-level texture measurement extracted from DXA images, correlates with 3D parameters of bone micro-architecture. We evaluated the ability of lumbar spine (LS) TBS to account for the increased fracture risk in diabetes. Methods:29,407 women ≥50 years at the time of baseline hip and spine DXA were identified from a database containing all clinical BMD results for the Province of Manitoba, Canada. 2,356 of the women satisfied a well-validated definition for diabetes, the vast majority of whom (>90%) would have T2D. LS L14 TBS was derived for each spine DXA examination blinded to clinical parameters and outcomes. Health service records were assessed for incident non-traumatic major osteoporotic fracture codes (mean follow-up 4.7 years). Results:In linear regression adjusted for FRAX risk factors (age,BMI, glucocorticoids, prior major fracture, rheumatoid arthritis, COPD as a smoking proxy, alcohol abuse) and osteoporosis therapy, diabetes was associated with higher BMD for LS, femoral neck and total hip but lower LS TBS (all p<0.001). Similar results were seen after excluding obese subjects withBMI>30. In logistic regression (Figure), the adjusted odds ratio (OR) for a skeletal measurement in the lowest vs highest tertile was less than 1 for all BMD measurements but increased for LS TBS (adjusted OR 2.61, 95%CI 2.30-2.97). Major osteoporotic fractures were identified in 175 (7.4%) with and 1,493 (5.5%) without diabetes (p < 0.001). LS TBS predicted fractures in those with diabetes (adjusted HR 1.27, 95%CI 1.10-1.46) and without diabetes (HR 1.31, 95%CI 1.24-1.38). LS TBS was an independent predictor of fracture (p<0.05) when further adjusted for BMD (LS, femoral neck or total hip). The explanatory effect of diabetes in the fracture prediction model was greatly reduced when LS TBS was added to the model (indicating that TBS captured a large portion of the diabetes-associated risk), but was paradoxically increased from adding any of the BMD measurements. Conclusions:Lumbar spine TBS is sensitive to skeletal deterioration in postmenopausal women with diabetes, whereas BMD is paradoxically greater. LS TBS predicts osteoporotic fractures in those with diabetes, and captures a large portion of the diabetes-associated fracture risk. Combining LS TBS with BMD incrementally improves fracture prediction.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

A random linear functional approach to efficiency bounds

This paper suggests a method for obtaining efficiency bounds in models containing either only infinite-dimensional parameters or both finite- and infinite-dimensional parameters (semiparametric models). The method is based on a theory of random linear functionals applied to the gradient of the log-likelihood functional and is illustrated by computing the lower bound for Cox's regression model

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands

Abstract