HIERARCHICAL BAYESIAN METHODS TO MODEL HETEROGENEITY IN COW- AND HERD-LEVEL RELATIONSHIPS BETWEEN MILK PRODUCTION AND REPRODUCTION IN DAIRY COWS

Two of the most important broad classifications of phenotypes for successful dairy production are milk yield and fertility. The nature of the relationship between milk production and reproductive performance of dairy cows is uncertain due to conflicting results reported in many studies. A common deficiency in many such studies is an underappreciation of the dual dimension of the production-reproduction relationship, as defined by herd (random or u) level and cow (residual or e) level sources of (co)variation. Our overall hypothesis is that the e- and u- level relationships between milk production and reproduction in dairy cows are heterogeneous and depend upon various herd-related and management factors. Our objective is to develop hierarchical Bayesian extensions that capture heterogeneity in the relationships between traits by mixed effects modeling of u level and e level covariances between traits of interest. We specify a bivariate Bayesian model to jointly model two continuous traits and we apply a square-root free Cholesky decomposition to the variance-covariance matrices of the residuals (cow-level) and random effects (herd-level). As a result, the e- and u-level covariances among the traits are reparameterized into unconstrained and easily interpretable e- and u- regression parameters, respectively. These regression parameters specify the cow- and herd-level relationships, respectively, between the traits and can be easily modeled as functions of relevant fixed and random effects, thereby providing a mixed model extension of Pourahmadi’s method. We validate our method using a simulation study and apply it to data on 305-day milk yield and calving interval of Michigan dairy cows

MATHEMATICAL OPTIMIZATION: APPLICATION TO THE DESIGN OF OPTIMAL MICRO-CHANNEL HEAT SINKS

This paper documents the geometrical optimization of a micro-channel heatsink embedded inside a highly conductive solid, with the intent of developing optimal solutions for thermal management in microelectronic devices. The objective is to minimize the peak wall temperature of the heat sink subject to various constraints such as manufacturing restraints, fixed pressure drop and total fixed volume. A gradient based multi-variable optimization algorithm is used as it adequately handles the numerical objective function obtained from the computational fluid dynamics simulation. Optimal geometric parameters defining the micro-channel were obtained for a pressure drop ranging from 10 kPa to 60 kPa corresponding to a dimensionless pressure drop of 6.5 × 107 to 4 × 108 for fixed volumes ranging from 0.7 mm3 of 0.9 mm3. The effect of pressure drop on the aspect ratio, solid volume fraction, channel hydraulic diameter and the minimized peak temperature are reported. Results also show that as the dimensionless pressure drop increases the maximised dimensionless global thermal conductance also increases. These results are in agreement with previous work found in literature

Ground-plane screening of Coulomb interactions in two-dimensional systems: How effectively can one two-dimensional system screen interactions in another?

The use of a nearby metallic ground-plane to limit the range of the Coulomb interactions between carriers is a useful approach in studying the physics of two-dimensional (2D) systems. This approach has been used to study Wigner crystallization of electrons on the surface of liquid helium, and most recently, the insulating and metallic states of semiconductor-based two-dimensional systems. In this paper, we perform calculations of the screening effect of one 2D system on another and show that a 2D system is at least as effective as a metal in screening Coulomb interactions. We also show that the recent observation of the reduced effect of the ground-plane when the 2D system is in the metallic regime is due to intralayer screening.Comment: 14 pages, 7 figures Accepted in PR

Indications and outcome of admission of diabetic patients into the medical wards in a Nigerian tertiary hospital- Atwo year review

Background/ objectives: In 2013, two – third of the estimated 318 million individuals living with diabetes resided in developing countries. The determination of the burden of diabetes in hospitals will help in designing an efficient tool for planning, delivery and evaluation of targeted interventions.Patient/materials and methods: A retrospective description of both types 1 and 2 diabetics at the medical wards of Federal Medical Centre, Makurdi from January 1, 2012 to December 31, 2013. Age, gender, diagnosis and outcome were extracted fromadmissions/discharge logs. Diagnosis of diabetes was based on fasting blood glucose =7mmol/L on =2 occasions. Severe hypertension was defined as =160/100 mmHg. Analysis of data was done using Epi Info version 2.3. The qualitative data were expressed as frequencies and percentages and quantitative data were expressed as mean and standard deviation (SD).Results: A total of 195 diabetics were admitted with 187 (95.9%) type 2 diabetics and 8 (4.1%) type 1 diabetics. There were 113 (57.9%) males and 82 (42.1%) females with a gender difference of 1.3:1. The mean age was 53.5±15.7 years. The age range 51 – 60 years had the highest number of patients on admission. Majority (76.9%) of admissions was through the emergency unit. The commonest indication for admission was hyperglycemia (60.5%). Most (76.9%) were discharged, 8.2% died, 6.2% were transferred-out, 5.6% were referred to other centres and 3.1% discharged against medical advice.Keywords: indication, outcome, admission, diabetes, hyperglycemi

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Evidence for ACTN3 as a genetic modifier of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is characterized by muscle degeneration and progressive weakness. There is considerable inter-patient variability in disease onset and progression, which can confound the results of clinical trials. Here we show that a common null polymorphism (R577X) in ACTN3 results in significantly reduced muscle strength and a longer 10\u2009m walk test time in young, ambulant patients with DMD; both of which are primary outcome measures in clinical trials. We have developed a double knockout mouse model, which also shows reduced muscle strength, but is protected from stretch-induced eccentric damage with age. This suggests that \u3b1-actinin-3 deficiency reduces muscle performance at baseline, but ameliorates the progression of dystrophic pathology. Mechanistically, we show that \u3b1-actinin-3 deficiency triggers an increase in oxidative muscle metabolism through activation of calcineurin, which likely confers the protective effect. Our studies suggest that ACTN3 R577X genotype is a modifier of clinical phenotype in DMD patients