Effect of Litter Treatment on the Occurrence of Foot Pad Lesions

Pododermatitis (foot pad lesions) is one of the main welfare problems in modern broiler production in countries with developed poultry farming. Factors contributing to the occurrence of these lesions are nutrition, stocking density and material used for litter. There are a number of different procedures that can be applied in order to prevent and reduce the number of lesions with the most pronounced defects. The experiment was conducted on broiler chickens grown in 10 buildings of 240 m2 each. The stocking density was 35 kg/m2. The experiment was set up in five treatments with two replicates. Treatment one (T1) - control with a straw litter, treatment two (T2) - litter treated with microbial preparation Micropan®, treatment three (T3) – litter with addition of lignin, treatment four (T4) – litter with addition of lignin and Micropan® and treatment five (T5) - chopped straw without supplements. At the end of the experiment, on day 42 the intensity of the lesions was scored on the slaughter line. The presence of lesions was scored using scale from 0 (no lesions) to 3 (plantar pads with more than 50% damage). Based on the results of the trial it can be concluded that litter has a significant impact on the presence and the degree of foot pad lesions. The lowest score of foot pad lesions was observed in the treatment T5 (chopped straw). Different treatments of litters may also contribute to the solution of the problem of pododermatitis since the results in all treated groups (T2, T3, T4 and T5) were better when compared to the control

Morphological Characteristics of Breast and Thigh Muscles of Autochthonous Breeds of Chickens

Morphological characteristics of skeletal muscles of autochthonous breeds of chickens are very important for meat quality and comparison with current hybrids for intensive production. The autochthonous breeds used in the experiment were Sombor crested and Banat naked neck, both sexes. For the purposes of morphological examination, tissue samples were taken from the thigh muscle (m. biceps femoris) and muscles of the breast (m. pectoralis profundus) of 5 male and female animals of each breed. After a standard histological procedure for conventional light microscopy, samples were stained with hematoxylin - eosin. After the processing of the samples for the histochemical analysis, samples were stained with the enzyme succinate - dehydrogenase (SDH) with the aim of determining the presence of different muscle cell types (red, white and intermediate). Morphological parameters, in this study, were diameter of muscle cells, nucleocytoplasmic ratio of muscle cells, volume density of connective tissue within the muscle and the presence of red, white and intermediate muscle cell types. Comparison of diameters of muscle cells thigh and breast muscles between Sombor crested and Banat naked neck have showed that kind of muscle, race or gender have no significant effect on the differences in this parameter. There were no statistically significant differences in the nucleo-cytoplasmic ratio of the volume density of the connective tissue of muscles. Red muscle cells were, in both autochthonous breeds, significantly more represented in m. biceps femoris than m. pectoralis profundus. The results of this study indicate that no differences were observed between autochthonous breeds in morphological parameters for examined breast and thigh muscle

Automatic quality control of cardiac MRI segmentation in large-scale population imaging

The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study

Decision Forests, Convolutional Networks and the Models in-Between

This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree, the nodes along a single branch). CNNs achieve state of the art accuracy, thanks to their representation learning capabilities. We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency. We call this new family of hybrid models conditional networks. Conditional networks can be thought of as: i) decision trees augmented with data transformation operators, or ii) CNNs, with block-diagonal sparse weight matrices, and explicit data routing functions. Experimental validation is performed on the common task of image classification on both the CIFAR and Imagenet datasets. Compared to state of the art CNNs, our hybrid models yield the same accuracy with a fraction of the compute cost and much smaller number of parameters

Evidence of phonon-assisted tunnelling in electrical conduction through DNA molecules

We propose a phonon-assisted tunnelling model for explanation of conductivity dependence on temperature and temperature-dependent I-V characteristics in deoxyribonucleic acid (DNA) molecules. The capability of this model for explanation of conductivity peculiarities in DNA is illustrated by comparison of the temperature dependent I-V data extracted from some articles with tunnelling rate dependences on temperature and field strength computed according to the phonon-assisted tunnelling theory. PACS Codes: 87.15.-v, 71.38.-k, 73.40.GkComment: 6 pages, 3 figure

Careers in context: An international study of career goals as mesostructure between societies' career-related human potential and proactive career behaviour

Careers exist in a societal context that offers both constraints and opportunities for career actors. Whereas most studies focus on proximal individual and/or organisational-level variables, we provide insights into how career goals and behaviours are understood and embedded in the more distal societal context. More specifically, we operationalise societal context using the career-related human potential composite and aim to understand if and why career goals and behaviours vary between countries. Drawing on a model of career structuration and using multilevel mediation modelling, we draw on a survey of 17,986 employees from 27 countries, covering nine of GLOBE's 10 cultural clusters, and national statistical data to examine the relationship between societal context (macrostructure building the career-opportunity structure) and actors' career goals (career mesostructure) and career behaviour (actions). We show that societal context in terms of societies' career-related human potential composite is negatively associated with the importance given to financial achievements as a specific career mesostructure in a society that is positively related to individuals' proactive career behaviour. Our career mesostructure fully mediates the relationship between societal context and individuals' proactive career behaviour. In this way, we expand career theory's scope beyond occupation- and organisation-related factors

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

PURPOSE: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). METHODS: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. RESULTS: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. CONCLUSIONS: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management