Dietary soy and meat proteins induce distinct physiological and gene expression changes in rats

This study reports on a comprehensive comparison of the effects of soy and meat proteins given at the recommended level on physiological markers of metabolic syndrome and the hepatic transcriptome. Male rats were fed semi-synthetic diets for 1 wk that differed only regarding protein source, with casein serving as reference. Body weight gain and adipose tissue mass were significantly reduced by soy but not meat proteins. The insulin resistance index was improved by soy, and to a lesser extent by meat proteins. Liver triacylglycerol contents were reduced by both protein sources, which coincided with increased plasma triacylglycerol concentrations. Both soy and meat proteins changed plasma amino acid patterns. The expression of 1571 and 1369 genes were altered by soy and meat proteins respectively. Functional classification revealed that lipid, energy and amino acid metabolic pathways, as well as insulin signaling pathways were regulated differently by soy and meat proteins. Several transcriptional regulators, including NFE2L2, ATF4, Srebf1 and Rictor were identified as potential key upstream regulators. These results suggest that soy and meat proteins induce distinct physiological and gene expression responses in rats and provide novel evidence and suggestions for the health effects of different protein sources in human diets

Bird communities and feeding guilds in Monaragala, an isolated hill in the eastern intermediate zone of Sri Lanka

The bird communities and foraging guilds were studied in the entire forest of Monaragala hill, by recording species and their abundances, in 33 plots (each 100x20 m), in four transects laid along its altitudinal gradient. Bird calls and sightings were noted for 30 minutes between 7.00-9.30 am, twice each month from 2004-2008.The communities were determined, by cluster and ordination analyses of data in all plots. For each community, relative abundance (RA) and frequency (RF) were calculated. Species were assigned to bird guilds based on their habitats, main food types and feeding strategies, from published informationWithin and outside the plots sampled, 112 bird species (23% of Sri Lanka‟s avifauna, including eight endemics) in 84 genera, 44 families and 13 orders, were recorded. Three communities were identified: i. a low/mid-elevation disturbed forest community (LDFC), inhabited by 40 species (including seven endemics). The Crimson-Fronted Barbet, Black Crested Bulbul and Tickell‟s Blue Flycatcher co-dominated it. ii. a ridge/upper-elevation undisturbed forest community (RUFC) of 45 species that included eight endemics, nine restricted species, the wet zone Sri Lanka Yellow Fronted Barbet and Sri Lanka Wood Pigeon. The Black Bulbul and Sri Lanka Yellow Fronted Barbet were its dominants. Thirty three species were common to both forest communities. iii. a grassland community, with only 10 non-endemic species, dominated by the Crested Tree Swift and Indian Swiftlet. Three grassland species were also seen in the forest communities.In each forest community ten bird guilds were present. The arboreal frugivore and gleaning insectivore guilds ranked highest in them, followed by the hawking/hovering insectivore guild in the LDFC, and the omnivore gleaning guild in the RUFC. The grassland community had only three guilds. Based on RA the sweeping insectivore guild and on RF the aerial carnivore and the sweeping insectivore guilds were co-dominant. The arboreal granivore guild was restricted to the grasslandThe study revealed that this intermediate zone, 43 km2 isolated hill (1,100 m amsl) harbors a rich avifauna, including some typical wet zone species, and rich populations of the rare Sri Lanka Spur fowl and the Sri Lanka Wood pigeon, justifying its high conservation value

A participant-led programme for field veterinary training to identify bacteriological quality of milk from the farmer to the retail outlet

The training of field veterinarians in veterinary public health needs an in-depth understanding of the in-situ problems, social and economic barriers that prevent problem solving and a relevant pedagogical approach to suit the mature learner. A participatory approach is necessary to develop such training. A course designed on the principles of adult learning theory and utilizing the experience of the field veterinarian's local knowledge combined with the expertise of the training provider can be very effective. Forty-eight field veterinarians were trained using a collaborative, participatory approach to understand the issues in clean milk production in Sri Lanka. The veterinarians developed a Hazard Analysis Critical Control Point-based decision framework to identify and evaluate the evidence of bacterial contamination points in the milk chain from the farm to the processing plant. Samples and swabs were collected for bacterial culture and results showed high bacterial counts that showed contamination of milk starting from the farm, through milk collection and chilling centers ending with 2 × 106–3 × 107 bacteria per ml of milk. Chemical and physical hazards were also identified. Lack of appropriate hygienic procedures, chilling at the farm and at the collection center, together with the delays at the chilling center was identified as main contributing factors for high bacterial counts. This problem-based training approach facilitated collaborative inquiry, experiential learning and critical analytical skills. The training enabled the veterinarians to understand the scale of the problem and how they can intervene directly and indirectly to ensure clean milk production in Sri Lanka

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best results for LSTMs are often based on large-scale training of an ensemble of network instances. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (convolutional neural network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently scaled input images and different feature map sizes. Three datasets are used: the standard benchmark RIMES dataset (French); a historical handwritten dataset KdK (Dutch); the standard benchmark George Washington (GW) dataset (English). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches which did not use a pre-trained network. On the KdK and GW datasets, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections