More Challenging Diets Sustain Feeding Performance: Applications Toward the Captive Rearing of Wildlife

The rescue and rehabilitation of young fauna is of substantial importance to conservation. However, it has been suggested that incongruous diets offered in captive environments may alter craniofacial morphology and hinder the success of reintroduced animals. Despite these claims, to what extent dietary variation throughout ontogeny impacts intrapopulation cranial biomechanics has not yet been tested. Here, finite element models were generated from the adult crania of 40 rats (n = 10 per group) that were reared on 4 different diet regimes and stress magnitudes compared during incisor bite simulations. The diets consisted of (1) exclusively hard pellets from weaning, (2) exclusively soft ground pellet meal from weaning, (3) a juvenile switch from pellets to meal, and (4) a juvenile switch from meal to pellets. We hypothesized that a diet of exclusively soft meal would result in the weakest adult skulls, represented by significantly greater stress magnitudes at the muzzle, palate, and zygomatic arch. Our hypothesis was supported at the muzzle and palate, indicating that a diet limited to soft food inhibits bone deposition throughout ontogeny. This finding presents a strong case for a more variable and challenging diet during development. However, rather than the "soft" diet group resulting in the weakest zygomatic arch as predicted, this region instead showed the highest stress among rats that switched as juveniles from hard pellets to soft meal. We attribute this to a potential reduction in number and activity of osteoblasts, as demonstrated in studies of sudden and prolonged disuse of bone. A shift to softer foods in captivity, during rehabilitation after injury in the wild for example, can therefore be detrimental to healthy development of the skull in some growing animals, potentially increasing the risk of injury and impacting the ability to access full ranges of wild foods upon release. We suggest captive diet plans consider not just nutritional requirements but also food mechanical properties when rearing wildlife to adulthood for reintroduction

Mitochondrial phylogeography and demographic history of the Vicuña: implications for conservation

The vicuña (Vicugna vicugna; Miller, 1924) is a conservation success story, having recovered from near extinction in the 1960s to current population levels estimated at 275 000. However, lack of information about its demographic history and genetic diversity has limited both our understanding of its recovery and the development of science-based conservation measures. To examine the evolution and recent demographic history of the vicuña across its current range and to assess its genetic variation and population structure, we sequenced mitochondrial DNA from the control region (CR) for 261 individuals from 29 populations across Peru, Chile and Argentina. Our results suggest that populations currently designated as Vicugna vicugna vicugna and Vicugna vicugna mensalis comprise separate mitochondrial lineages. The current population distribution appears to be the result of a recent demographic expansion associated with the last major glacial event of the Pleistocene in the northern (18 to 22°S) dry Andes 14–12 000 years ago and the establishment of an extremely arid belt known as the 'Dry Diagonal' to 29°S. Within the Dry Diagonal, small populations of V. v. vicugna appear to have survived showing the genetic signature of demographic isolation, whereas to the north V. v. mensalis populations underwent a rapid demographic expansion before recent anthropogenic impacts

NICE : A Computational solution to close the gap from colour perception to colour categorization

The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms

Multiscale sample entropy for time resolved epileptic seizure detection and fingerprinting

Early detection of epileptic seizures is still a challenge in the state-of-the-art. The proposed method exploits multiresolution sample entropy for both seizure detection and fingerprinting. First, a SVM classifier is used to detect the seizures' onset with high temporal accuracy, then the seizures fingerprints across the subband structure are derived exploiting sample entropy non stationarity. Over 8 hours of EEG data recordings from patients suffering from temporal lobe epilepsy were used for training and testing the system, and validation was performed based on annotation by one expert neurophysiologist. All the seizures were successfully detected and provides an effective time-scale fingerprinting of their evolution. A prominent impact in high (\u3b3) frequency band was observed whose neurophysiological ground is currently under investigation. \ua9 2014 IEEE

Early detection of epileptic seizures by entropy-based methods

This work presents a novel method for early detection of epileptic seizures from EEG data. Seizure detection was accomplished in three stages: multiresolution overcomplete decomposition by the a-trous algorithm, feature extraction by computing power spectral density and sample entropy values of sub-bands and detection by using z-test and support vector machine (SVM). Results highlight large differences between the subband sample entropy values for the epileptic and the control subjects, respectively, reveling a substantial increase of such parameter during the crisis. This enables high detection accuracy and specificity especially in beta and gamma bands (16-125 Hz). The detection performance of the proposed method was evaluated based on the ground truth provided by the expert neurophysiologist as well as by objective indexes when two crisis had been recorded

Early detection of epileptic seizures in sparse domains

This work presents a method for early detection of epileptic seizures from EEG data, taking into account information about both the temporal and the spatial evolution of the seizures. The system was designed using over 8 hours of EEG, including 10 seizures in 5 patients. Seizure detection was accomplished in three main stages: multiresolution overcomplete decomposition by the \ue0-trous algorithm, feature extraction by computing power spectral density and sample entropy values of subbands and detection by using z-test and support vector machines (SVM). Results highlight large differences between the sub-band sample entropy values during ictal and normal EEG epochs, respectively, reveling a substantial increase of such parameter during the crisis. This enables high detection accuracy and specificity especially in beta and gamma bands (16-125 Hz). The detection performance of the proposed method was evaluated based on the ground truth provided by the expert neurophysiologist, and the results show that our technique is capable to obtain a high accuracy (above the 95% on average), with a high temporal resolution. This enables reaching very low detection latency and early detection of the seizures onset. Furthermore, spatial information, within the limits of the acquisition, on the evolution of the crisis is maintained since all the channels are separately processed

Early detection of epileptic seizures by entropy-based methods

This work presents a novel method for early detection of epileptic seizures from EEG data. Seizure detection was accomplished in three stages: multiresolution overcomplete decomposition by the a-trous algorithm, feature extraction by computing power spectral density and sample entropy values of sub-bands and detection by using z-test and support vector machine (SVM). Results highlight large differences between the subband sample entropy values for the epileptic and the control subjects, respectively, reveling a substantial increase of such parameter during the crisis. This enables high detection accuracy and specificity especially in beta and gamma bands (16-125 Hz).The detection performance of the proposed methodwas evaluated based on the ground truth provided by the expert neurophysiologist as well as by objective indexes when two crisis had been recorded