Genetic variability in the humoral immune response to bovine herpesvirus-1 infection in dairy cattle and genetic correlations with performance traits

Bovine herpesvirus-1 (BoHV-1) is a viral pathogen of global significance that is known to instigate several diseases in cattle, the most notable of which include infectious bovine rhinotracheitis and bovine respiratory disease. The genetic variability in the humoral immune response to BoHV-1 has, to our knowledge, not ever been quantified. Therefore, the objectives of the present study were to estimate the genetic parameters for the humoral immune response to BoHV-1 in Irish female dairy cattle, as well as to investigate the genetic relationship between the humoral immune response to BoHV-1 with milk production performance, fertility performance, and animal mortality. Information on antibody response to BoHV-1 was available to the present study from 2 BoHV-1 sero-prevalence research studies conducted between the years 2010 to 2015, inclusive; after edits, BoHV-1 antibody test results were available on a total of 7,501 female cattle from 58 dairy herds. National records of milk production (i.e., 305-d milk yield, fat yield, protein yield, and somatic cell score; n = 1,211,905 milk-recorded cows), fertility performance (i.e., calving performance, pregnancy diagnosis, and insemination data; n = 2,365,657 cows) together with animal mortality data (i.e., birth, farm movement, death, slaughter, and export events; n = 12,853,257 animals) were also available. Animal linear mixed models were used to quantify variance components for BoHV-1 as well as to estimate genetic correlations among traits. The estimated genetic parameters for the humoral immune response to BoHV-1 in the present study (i.e., heritability range: 0.09 to 0.16) were similar to estimates previously reported for clinical signs of bovine respiratory disease in dairy and beef cattle (i.e., heritability range: 0.05 to 0.11). Results from the present study suggest that breeding for resistance to BoHV-1 infection could reduce the incidence of respiratory disease in cattle while having little or no effect on genetic selection for milk yield or milk constituents (i.e., genetic correlations ranged from ?0.13 to 0.17). Moreover, even though standard errors were large, results also suggest that breeding for resistance to BoHV-1 infection may indirectly improve fertility performance while also reducing the incidence of mortality in older animals (i.e., animals >182 d of age). Results can be used to inform breeding programs of potential genetic gains achievable for resistance to BoHV-1 infection in cattle.     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

Genetic selection for hoof health traits and cow mobility scores can accelerate the rate of genetic gain in producer-scored lameness in dairy cows

Cattle breeding programs that strive to reduce the animal-level incidence of lameness are often hindered by the availability of informative phenotypes. As a result, indicator traits of lameness (i.e., hoof health and morphological conformation scores) can be used to improve the accuracy of selection and subsequent genetic gain. Therefore, the objectives of the present study were to estimate the variance components for hoof health traits using various phenotypes collected from a representative sample of Irish dairy cows. Also of interest to the present study was the genetic relationship between both hoof health traits and conformation traits with producer-scored lameness. Producer-recorded lameness events and linear conformation scores from 307,657 and 117,859 Irish dairy cows, respectively, were used. Data on hoof health (i.e., overgrown sole, white line disease, and sole hemorrhage), mobility scores, and body condition scores were also available from a research study on up to 11,282 Irish commercial dairy cows. Linear mixed models were used to quantify variance components for each trait and to estimate genetic correlations among traits. The estimated genetic parameters for hoof health traits in the present study were greater (i.e., heritability range: 0.005 to 0.27) than previously reported in dairy cows. With the exception of analyses that considered hoof health traits in repeatability models, little difference in estimated variance components existed among the various hoof-health phenotypes. Results also suggest that producer-recorded lameness is correlated with both hoof health (i.e., genetic correlation up to 0.48) and cow mobility (i.e., genetic correlation = 0.64). Moreover, cows that genetically tend to have rear feet that appear more parallel when viewed from the rear are also genetically more predisposed to lameness (genetic correlation = 0.39); genetic correlations between lameness and other feet and leg type traits, as well as between lameness and frame type traits, were not different from zero. Results suggest that if the population breeding goal was to reduce lameness incidence, improve hoof health, or improve cow mobility, genetic selection for either of these traits should indirectly benefit the other traits. Results were used to quantify the genetic gains achievable for lameness when alternative phenotypes are available.     

The Struggle to Compete: From comparative to competitive advantage in Northern British Columbia

The social, economic, and political landscape of northern British Columbia (BC), Canada, has undergone considerable transformation since a recession in the early 1980s. From this, there is an emerging recognition of the need to move from an economy based upon comparative advantage to one embracing competitive advantage. The purpose of this paper, drawn from ongoing regional research, is twofold. First, we apply a rural lens to the regional planning and development literature, which highlights the significance of competitive advantage as a tool for regional rejuvenation. Second, we add to this dialogue by exploring the relevance and meaning of competitive advantage in the non-metropolitan setting of northern BC.     

Preparation of highly conductive reduced graphite oxide/poly(styrene-co-butyl acrylate) composites via miniemulsion polymerization

Polymer/reduced graphite oxide (rGO) composite nanoparticles with a high electrical conductivity were synthesized using the miniemulsion polymerization technique. The rGO was modified with a reactive surfactant, 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS), to facilitate monomer intercalation into the rGO nanogalleries. The AMPS-modified rGO was emulsified in the presence of styrene (St) and butyl acrylate (BA) monomers, and the stable miniemulsion was polymerized to form poly(St-co-BA)/rGO composite latex nanoparticles. The transition in the composite nanoparticles from an electrical insulator to an electrical conductor occurred at an rGO content of 10 wt% (relative to the monolayer content), yielding an electrical conductivity of 0.49 S/cm. The electrical conductivity of the composite nanoparticles reached 2.22 S/cm at 20 wt% rGO, yielding a much better conductivity than other polymer composites prepared using a GO filler. Importantly, the miniemulsion polymerization method for fabricating poly(St-co-BA)/rGO composite nanoparticles is easy, green, low-cost, and scalable, providing a universal route to the rational design and engineering of highly conductive polymer composites.     

Physicochemical and retrogradation properties of modified chestnut starches

Food Science and Biotechnology - Physicochemical properties of acetylated (AC), cross-linked (CL), and hydroxypropylated (HP) chestnut starches were investigated. Modified chestnut starch showed...     

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

Abstract

Human Factors Analysis and Classification System (HFACS) is based upon Reason’s organizational model of human error which suggests that there is a ‘one to many’ mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations.

Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.     

Commercial process for mass production of synthetic natural gas through the adiabatic reactors: operational characteristics of a 50‐kW pilot‐plant,influence of steam,and CO2

In synthetic natural gas (SNG) reaction process, the water gas shift (WGS) reaction and methanation reaction take place simultaneously, and an insufficient supply of steam might deactivate the catalyst. In this study, the characteristics of the methanation reaction with a commercial catalyst and using a low [H₂]/[CO] mole ratio in SNG synthesis are evaluated. The reaction characteristics at various possible process parameters are evaluated varying different process parameters such as the [H₂O]/[CO] mole ratio, [H₂]/[CO] mole ratio, flow of different % CO₂, and reaction temperature. Temperature profiles on catalyst bed are monitored as a function of the [H₂O]/[CO] mole ratio, [H₂]/[CO] mole ratio, and flow of different % CO₂. Through a lab‐scale optimization process, suitable optimum conditions are selected and in the same condition a 50‐kW pilot‐scale SNG production process through adiabatic reactors is carried out. The pilot scale SNG reaction is stable through overnight and the CO conversion efficiency and CH₄ selectivity are 100% and 97.3%, respectively, while the maximum CH₄ productivity is 0.654 m³/kg_cat · h. Copyright © 2016 John Wiley & Sons, Ltd.