Development of growth rate, body lipid, moisture, and energy models for white sturgeon (Acipenser transmontanus) fed at various feeding rates.

The objectives were to develop and evaluate: 1) growth rate models, 2) body lipid, moisture, and energy models for white sturgeon fed at various feeding rates (FR; % body weight [BW] per day) and then evaluate responses at proportions of optimum feeding rate (OFR) across increasing BW (g). For objective 1, 19 datasets from the literature containing initial BW, FR and specific growth rate (SGR; % BW increase per day) were used. For objective 2, 12 datasets from the literature (11 from objective 1) containing SGR, FR, final BW, body lipid (%), protein (%), ash (%), moisture (%), and energy (kJ/g) were used. The average rearing temperatures was 19.2 ± 1.5 °C (mean ± SD). The average nutrient compositions and gross energy of the diets were 45.7 ± 4.3% protein, 14.8 ± 3.2% lipid, and 20.4 ± 1.3 kJ/g, respectively. The logistic model was used for objectives 1 and 2 to develop a statistical relationship between SGR and FR, then an iterative technique was used to estimate OFR for each dataset. For objective 2, the statistical relationship between body lipid, energy, and moisture and FR was established. Using the OFR estimate, SGR, body lipid, energy and moisture were computed at various FR as a proportion of OFR. Finally, a nonparametric fitting procedure was used to establish relationships between SGR, body lipid, energy and moisture (responses) compared with BW (predictor) at various proportions of OFR. This allows visualization of the effect of under- or over-feeding on the various responses. When examining the differences between OFR at 100% and various proportions of OFR, SGR differences decrease and moisture differences increase as BW increases. Lipid and energy differences decrease as BW increases. To our knowledge, these are the first description of changes in nutrient compositions when white sturgeon are fed at various FR. Because physiological and behavioral properties that are unique to sturgeon, results from this study are specific to sturgeon under the conditions of this study and cannot be compared directly with salmonids even if some of the results are similar. This research provides insight to designing future nutritional studies in sturgeon

e-Distance Weighted Support Vector Regression

We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle these two issues. In addition, we use both dual coordinate descent (CD) and averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable to large scale problems. We report promising results obtained by e-DWSVR in comparison with existing methods on several benchmark datasets