Seasonal changes in weight and body composition of yak grazing on alpine-meadow grassland in the Qinghai-Tibetan plateau of China

Forty-five male yaks (born April 2001) were studied to determine how seasonal changes on the Qinghai-Tibetan plateau affected BW and body composition. Thirty yaks were weighed monthly from birth to 26 mo of age to determine seasonal changes in BW. The remaining 15 yaks were allocated randomly to five groups (three yaks per group), designated for slaughter at 13, 15, 18, 22, and 25 mo to measure seasonal effects on body chemical composition. All yaks were grazed on the alpine-meadow grassland of the plateau without any supplementation. All BW and body composition data were calculated on an individual basis. Body weight and body composition data were both compared across seven growth periods spanning 2 yr and defined by season. From April (birth) to December 2001 of the first growing season, yak BW increased (P < 0.01); however, during the subsequent cold season (December 2001 to May 2002), BW decreased (P < 0.01). The second growing season ran from May 2002 (13 mo of age) to October 2002 (18 mo of age), and the second live weight-loss season ran from October 2002 until May 2003. The weight loss experienced by yaks during the first weight loss season was 25.64% of the total weight gain in the first growing season. The weight loss experienced by yaks during the second weight-loss season was 29.73% of the total weight gain in the second growing season. Energy retention in the second growing season was 291.07 MJ, 50.8% of which was consumed during the subsequent cold season. Energy accumulation in the summer (from May to July) and fall (from July to October) of the second growing season did not differ (5.01 and 6.30 MJ/kg of EBW gain, respectively; P = 0.63). The energy mobilized during the second winter (from October 2002 to February 2003) was 16.49 MJ/kg of EBW, and in the second spring (from February to May 2003), it was 9.06 MJ/kg of EBW. These data suggest that the decrease in grazing yak BW during the first cold season is much less than during the second cold season, and that the energy content per unit of BW mobilized is greater (P = 0.02) in winter than in spring. Results from this study demonstrate highly efficient compensatory growth in grazing yaks following the first weight loss period during the first cold season. This benefit could be exploited by herders to improve yak production. Yaks may have developed a type of self-protection mechanism to overcome the long cold seasons in the Qinghai-Tibetan plateau.

冀东油田柳南地区新近系油藏剩余油形成及分布规律研究

This thesis is based on the research project of Study on the Geological Characteristics and Remaining Oil Distribution Law of Neogene Reservoirs in Liunan Area, which is one of the key research projects set by PetroChina Jidong Oilfield Company in 2006. The determination of remaining oil distribution and its saturation changes are the most important research contents for the development and production modification of oilfields in high water-cut phases. Liunan oilfield, located in Tangshan of Hebei Province geographically and in Gaoliu structural belt of Nanpu sag in Bohai Bay Basin structurally, is one of the earliest fields put into production of Jidong oilfield. Focusing on the development problems encountered during the production of the field, this thesis establishes the fine geological reservoir model through the study of reservoir properties such as fine beds correlation, sedimentary facies, micro structures, micro reservoir architecture, flow units and fluid properties. Using routine method of reservoir engineering and technology of reservoir numerical modeling, remaining oil distribution in the target beds of Liunan area is predicted successfully, while the controling factors of remaining oil distribution are illustrated, and the model of remaining oil distribution for fault-block structure reservoirs is established. Using staged-subdivision reservoir correlation and FZI study, the Strata in Liunan Area is subdivided step by step; oil sand body data-list is recompiled; diagram databases are established; plane and section configuration of monolayer sandstone body, and combination pattern of sandstone bodys are summarized. The study of multi-level staged subdivision for sedimentary micro-facies shows that the Lower member of Minghuazhen formation and the whole Guantao formation in Liunan Area belong to meandering river and braided river sedimentary facies respectively, including 8 micro facies such as after point bar, channel bar, channel, natural levee, crevasse splay, abandoned channel, flood plain and flood basin. Fine 3D geological modeling is performed through the application of advanced software and integration of geological, seismic logging and reservoir engineering data. High resolution numerical simulation is performed with a reserve fitting error less than 3%, an average pressure fitting fluctuation range lower than 2Mpa and an accumulate water cut fitting error less than 5%. In this way, the distribution law of the target reservoir in the study area is basically recognized. Eight major remaining oil distribution models are established after analysis of production status and production features in different blocks and different layers. In addition, fuzzy mathematics method is used to the integreted evaluation and prediction of abundant remaining oil accumulation area in major production beds and key sedimentary time units of the shallow strata in Liunan Area and corresponding modification comments are put forward. In summary, the establishment of fine reservoir geological model, reservoir numerical simulation and distribution prediction of remaining oil make a sound foundation for further stimulation of oilfield development performance.

Muscle function and body composition profile in adolescents withrestrictive anorexia nervosa: Does resistance training help?

The aim of this study was to analyze the effects of short-term resistance training on the body composition profile and muscle function in a group of Anorexia Nervosa restricting type (AN-R) patients. The sample consisted of AN-R female adolescents (12.8 ± 0.6 years) allocated into the control and intervention groups (n¼18 each). Body composition and relative strength were assessed at baseline, after 8 weeks and 4 weeks following the intervention. Body mass index (BMI) increased throughout the study (p = 0.011). Significant skeletal muscle mass (SMM) gains were found in the intervention group (p = 0.045, d = 0.6) that correlated to the change in BMI (r = 0.51, p < 0.031). Meanwhile, fat mass (FM) gains were significant in the control group (p = 0.047, d = 0.6) and correlated (r > 0.60) with change in BMI in both the groups. Significant relative strength increases (p < 0.001) were found in the intervention group and were sustained over time.

Body composition and bone mineral density changes during a premier league season as measured by dual-energy X-ray absorptiometry

Wallace, Joanne, et al., 'Body composition and bone mineral density changes during a premier league season as measured by dual-energy X-ray absorptiometry', International Journal of Body Composition Research (2006) 4(2) pp.61-66 RAE2008

Clergy, crusified within the body of Christ - a challenge to pastoral care

M.A. Thesis / University of Pretoria / Department of Practical Theology / Advised by Prof M Masango

Estimating 3D Body Pose Using Uncalibrated Cameras

An approach for estimating 3D body pose from multiple, uncalibrated views is proposed. First, a mapping from image features to 2D body joint locations is computed using a statistical framework that yields a set of several body pose hypotheses. The concept of a "virtual camera" is introduced that makes this mapping invariant to translation, image-plane rotation, and scaling of the input. As a consequence, the calibration matrices (intrinsics) of the virtual cameras can be considered completely known, and their poses are known up to a single angular displacement parameter. Given pose hypotheses obtained in the multiple virtual camera views, the recovery of 3D body pose and camera relative orientations is formulated as a stochastic optimization problem. An Expectation-Maximization algorithm is derived that can obtain the locally most likely (self-consistent) combination of body pose hypotheses. Performance of the approach is evaluated with synthetic sequences as well as real video sequences of human motion.

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.

Inferring Body Pose without Tracking Body Parts

A novel approach for estimating articulated body posture and motion from monocular video sequences is proposed. Human pose is defined as the instantaneous two dimensional configuration (i.e., the projection onto the image plane) of a single articulated body in terms of the position of a predetermined set of joints. First, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. The goal is to be able to map these, generally low level, visual features to body configurations. The system estimates different mappings, each one with a specific cluster in the visual feature space. Given a set of body motion sequences for training, unsupervised clustering is obtained via the Expectation Maximation algorithm. Then, for each of the clusters, a function is estimated to build the mapping between low-level features to 3D pose. Currently this mapping is modeled by a neural network. Given new visual features, a mapping from each cluster is performed to yield a set of possible poses. From this set, the system selects the most likely pose given the learned probability distribution and the visual feature similarity between hypothesis and input. Performance of the proposed approach is characterized using a new set of known body postures, showing promising results.

Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture

A non-linear supervised learning architecture, the Specialized Mapping Architecture (SMA) and its application to articulated body pose reconstruction from single monocular images is described. The architecture is formed by a number of specialized mapping functions, each of them with the purpose of mapping certain portions (connected or not) of the input space, and a feedback matching process. A probabilistic model for the architecture is described along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present Expectation Maximization (EM) algorithms for two different instances of the likelihood probability. Performance is characterized by estimating human body postures from low level visual features, showing promising results.

Tracking Human Body Pose on a Learned Smooth Space

Particle filtering is a popular method used in systems for tracking human body pose in video. One key difficulty in using particle filtering is caused by the curse of dimensionality: generally a very large number of particles is required to adequately approximate the underlying pose distribution in a high-dimensional state space. Although the number of degrees of freedom in the human body is quite large, in reality, the subset of allowable configurations in state space is generally restricted by human biomechanics, and the trajectories in this allowable subspace tend to be smooth. Therefore, a framework is proposed to learn a low-dimensional representation of the high-dimensional human poses state space. This mapping can be learned using a Gaussian Process Latent Variable Model (GPLVM) framework. One important advantage of the GPLVM framework is that both the mapping to, and mapping from the embedded space are smooth; this facilitates sampling in the low-dimensional space, and samples generated in the low-dimensional embedded space are easily mapped back into the original highdimensional space. Moreover, human body poses that are similar in the original space tend to be mapped close to each other in the embedded space; this property can be exploited when sampling in the embedded space. The proposed framework is tested in tracking 2D human body pose using a Scaled Prismatic Model. Experiments on real life video sequences demonstrate the strength of the approach. In comparison with the Multiple Hypothesis Tracking and the standard Condensation algorithm, the proposed algorithm is able to maintain tracking reliably throughout the long test sequences. It also handles singularity and self occlusion robustly.

A Self-Organizing Neural Network for Learning a Body-Centered Invariant Representation of 3-D Target Position

This paper describes a self-organizing neural network that rapidly learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets (Bullock, Grossberg, and Guenther, 1993).

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.

Longitudinal study of changes in body mass index, anthropometric measures, dietary intake and physical activity in cohorts of school-going Irish adolescents

The prevalence of obesity worldwide has increased dramatically over the last few decades. Poor dietary habits and low levels of exercise in adolescence are often maintained into adulthood where they can impact on the incidence of obesity and chronic diseases. A 3-year longitudinal study of anthropometric, dietary and exercise parameters was carried out annually (2005 - 2007) in 3 Irish secondary schools. Anthropometric measurements were taken in each year and analysed longitudinally. Overweight and obesity were at relatively low levels in these adolescents. Height, weight, BMI, waist and hip circumferences and TST increased significantly over the 3 years. Waist-to-hip ratio (WHR) decreased significantly over time. Boys were significantly taller than girls across the 3 years. A 3-day weighed food diary was used to assess food intake by the adolescents. Analysis of dietary intake data was determined using WISP©. Mean daily energy and nutrient intakes were reported. Mean daily energy and macronutrient intakes were analysed longitudinally. The adolescents’ diet was characterised by relatively high saturated fat intakes and insufficient fruit and vegetable consumption. The dietary pattern did not change significantly over the 3 years. Boys consumed more energy than girls over the study period. A validated questionnaire was used to assess physical activity and sedentary activity levels. Boys were substantially more active and had higher energy expenditure estimates than girls throughout the study. A significant longitudinal decrease in physical activity levels among the adolescents was observed. Both genders spent more than the recommended amount of time (hrs/day) pursing sedentary activities. The dietary pattern in these Irish adolescents is relatively poor. Of additional concern is the overall longitudinal decrease in physical activity levels. Promoting consumption of a balanced diet and increased exercise levels among adolescents will help to reduce future public health care costs due to weight-related diseases.