Jim Ramírez

Supplemental zinc in fattening lambs improves their health, performance, and meat quality. However, the Zn effect on grazing animals combined with different levels of concentrate should be known unknown. The objective was to evaluate the Zn-organic effect in the diet of grazing lambs supplemented with two levels of concentrate on growth, dry matter (DM) intake, carcass traits, body composition, meat quality, and fatty acid profile in Longissimus dorsi muscle. Twenty-eight lambs were used in a factorial arrangement of two levels of concentrate (C) feed intake (C-0.75 and C-1.5% of live weight) and two Zn-Met levels (0 and 80 ppm Zn kg−1 DM) on the grazing of Lolium perenne L. Digestibility and dry matter intake, weight gain, and productive performance were measured. At the end of the fattening period (90 d), the lambs were slaughtered and the carcass traits, body composition, instrumental quality, and lipid profile of meat were evaluated. The effect of treatment (T), measurement period (P), and T × P interaction was observed (p < 0.05) for dry matter intake (DMI). For the final live weight (FLW) and daily weight gain (DWG), there was an effect (p < 0.05) of T and P, with Zn-80 ppm + C-1.5% treatment being greater. The live weight at slaughter (LWS) and leg length (LL) showed an effect (p < 0.05) of C × Zn. Zn-80 ppm + C-1.5% treatment was higher in the kidney fat, empty body weight, carcass fat, fat and retained energy but lower in protein content (p < 0.05). The physicochemical characteristics and fatty acid content of meat were not affected (p > 0.05). It can be concluded that the concentrated-organic zinc synergy in grazing lambs improved the performance, weight gain, and body composition, which resulted in heavier carcasses with greater amounts of fat, protein, and energy deposited. Furthermore, the physical and chemical meat traits were not affected, but the n-3 fatty acid content and n-3/n-6 ratio in Longissimus dorsi was affected by the Zn level supplemented.

This paper presents a method for selecting Regions of Interest in brain Magnetic Resonance Imaging for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map, generating a set of representative prototypes, and the receptive field of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative which were previously segmented through Statistical Parametric Mapping. The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease. Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls and Alzheimer's disease patients, and 84% of accuracy for Mild Cognitive Impairment and AD patients.