International breeding programs to improve health in pedigree dogs

Implementation of breeding programs in order to reduce incidence of inherited disorders and their impact on welfare should be a priority for dog breeders and breeding organizations. In that respect, exchange of breeding animals between countries constitutes a critical point to be taken into account. The purpose of this thesis was to investigate management strategies to improve breed health in an international context, concerning both genetic evaluation and management of genetic variability. A survey, which was completely filled in by 15 national kennel clubs (KCs), demonstrated shared concerns among KCs about health in pedigree dogs and a shared intent of improving breeding and health status, especially among European national KCs. In addition, with data provided by the French, Swedish and British kennel clubs, including pedigree databases and phenotypic records of hip dysplasia (HD), the feasibility of joint evaluations across countries and the efficiency of international breeding programs were investigated. The benefits of exchanging breeding animals across countries were clearly shown in terms of improved genetic variability and increased genetic progress, especially for breeds in countries with small populations. Further, the efficiency of breeding programs including importation of breeding males concerning genetic improvement of complex traits and inbreeding management was tested by simulation. We concluded that international breeding programs are useful and alternative options to improving canine genetic health and their benefits will be amplified with an expected increase in exchange of breeding animals in the future. Importing male dogs could lead to higher genetic progress, however, it is necessary to have a high genetic correlation between countries and high accuracy of estimated breeding values of imported dogs

Flow Patterns and Reaction Rate Estimation of RedOx Electrolyte in the Presence of Natural Convection

Transport processes in an upright, concentric, annular, electrochemical reactor filled with RedOx electrolyte solution are studied experimentally and theoretically. The electrodes form the two vertical surfaces of the reactor. The theoretical calculations consist of the solution of the Navier-Stokes and the Nernst-Planck equations accounting for species\u27 diffusion, migration, convection, and electrochemical reactions on the electrodes\u27 surfaces as a function of the difference in the electrodes\u27 potentials and the average concentration of the electrolyte. Since the convection is driven by density gradients, the momentum and mass transport equations are strongly coupled. In spite of the small dimensions (mm-scale) of the reactor, the current transmitted through the electrolyte is significantly enhanced by natural convection. The current is measured as a function of the difference in the electrodes\u27 potentials. To obtain the reaction rate constants, an inverse problem is solved and the reaction rate constants are determined by minimizing the discrepancy between theoretical predictions and experimental observations. As an example, we study the reversible electrochemical reaction Fe++++e- = Fe++ on platinum electrodes. The paper demonstrates that natural convection plays a significant role even when the reactor’s dimensions are on the millimeter scale and that it is possible to predict reaction rate constants while accounting for significant mass transfer effects

Thermally-actuated, phase change flow control for microfluidic systems

An easy to implement, thermally-actuated, noninvasive method for flow control in microfluidic devices is described. This technique takes advantage of the phase change of the working liquid itself—the freezing and melting of a portion of a liquid slug—to noninvasively close and open flow passages (referred to as a phase change valve). The valve was designed for use in a miniature diagnostic system for detecting pathogens in oral fluids at the point of care. The paper describes the modeling, construction, and characteristics of the valve. The experimental results favorably agree with theoretical predictions. In addition, the paper demonstrates the use of the phase change valves for flow control, sample metering and distribution into multiple analysis paths, sealing of a polymerase chain reaction (PCR) chamber, and sample introduction into and withdrawal from a closed loop. The phase change valve is electronically addressable, does not require any moving parts, introduces only minimal dead volume, is leakage and contamination free, and is biocompatible

Genome-wide association studies for canine hip dysplasia in single and multiple populations – implications and potential novel risk loci

Background Association mapping studies of quantitative trait loci (QTL) for canine hip dysplasia (CHD) can contribute to the understanding of the genetic background of this common and debilitating disease and might contribute to its genetic improvement. The power of association studies for CHD is limited by relatively small sample numbers for CHD records within countries, suggesting potential benefits of joining data across countries. However, this is complicated due to the use of different scoring systems across countries. In this study, we incorporated routinely assessed CHD records and genotype data of German Shepherd dogs from two countries (UK and Sweden) to perform genome-wide association studies (GWAS) within populations using different variations of CHD phenotypes. As phenotypes, dogs were either classified into cases and controls based on the Federation Cynologique Internationale (FCI) five-level grading of the worst hip or the FCI grade was treated as an ordinal trait. In a subsequent meta-analysis, we added publicly available data from a Finnish population and performed the GWAS across all populations. Genetic associations for the CHD phenotypes were evaluated in a linear mixed model using 62,089 SNPs. Results Multiple SNPs with genome-wide significant and suggestive associations were detected in single-population GWAS and the meta-analysis. Few of these SNPs overlapped between populations or between single-population GWAS and the meta-analysis, suggesting that many CHD-related QTL are population-specific. More significant or suggestive SNPs were identified when FCI grades were used as phenotypes in comparison to the case-control approach. MED13 (Chr 9) and PLEKHA7 (Chr 21) emerged as novel positional candidate genes associated with hip dysplasia. Conclusions Our findings confirm the complex genetic nature of hip dysplasia in dogs, with multiple loci associated with the trait, most of which are population-specific. Routinely assessed CHD information collected across countries provide an opportunity to increase sample sizes and statistical power for association studies. While the lack of standardisation of CHD assessment schemes across countries poses a challenge, we showed that conversion of traits can be utilised to overcome this obstacle

OLLIE: Derivation-based Tensor Program Optimizer

Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra expression of a DNN only consider expressions representable by a fixed set of predefined operators, missing possible optimization opportunities between general expressions. We propose OLLIE, the first derivation-based tensor program optimizer. OLLIE optimizes tensor programs by leveraging transformations between general tensor algebra expressions, enabling a significantly larger expression search space that includes those supported by prior work as special cases. OLLIE uses a hybrid derivation-based optimizer that effectively combines explorative and guided derivations to quickly discover highly optimized expressions. Evaluation on seven DNNs shows that OLLIE can outperform existing optimizers by up to 2.73$\times$ (1.46$\times$ on average) on an A100 GPU and up to 2.68$\times$ (1.51$\times$) on a V100 GPU, respectively

Modeling RedOx-Based Magnetohydrodynamics in Three-Dimensional Microfluidic Channels

RedOx-based magnetohydrodynamic MHD[1] flows in three-dimensional microfluidic channels are investigated theoretically with a coupled mathematical model consisting of the Nernst-Planck equations for the concentrations of ionic species, the local electroneutrality condition for the electric potential, and the Navier-Stokes equations for the flow field. A potential difference is externally applied across two planar electrodes positioned along the opposing walls of a microchannel that is filled with a dilute RedOx electrolyte solution, and a Faradaic current transmitted through the solution results. The entire device is positioned under a magnetic field which can be provided by either a permanent magnet or an electromagnet. The interaction between the current density and the magnetic field induces Lorentz forces, which can be used to pump and/or stir fluids for microfluidic applications. The induced currents and flow rates in three-dimensional 3D[1] planar channels obtained from the full 3D model are compared with the experimental data obtained from the literature and those obtained from our previous two-dimensional mathematical model.Aclosed form approximation for the average velocity flow rate[1] in 3D planar microchannels is derived and validated by comparing its predictions with the results obtained from the full 3D model and the experimental data obtained from the literature. The closed form approximation can be used to optimize the dimensions of the channel and to determine the magnitudes and polarities of the prescribed currents in MHD networks so as to achieve the desired flow patterns and flow rates

PowerFusion: A Tensor Compiler with Explicit Data Movement Description and Instruction-level Graph IR

Deep neural networks (DNNs) are of critical use in different domains. To accelerate DNN computation, tensor compilers are proposed to generate efficient code on different domain-specific accelerators. Existing tensor compilers mainly focus on optimizing computation efficiency. However, memory access is becoming a key performance bottleneck because the computational performance of accelerators is increasing much faster than memory performance. The lack of direct description of memory access and data dependence in current tensor compilers' intermediate representation (IR) brings significant challenges to generate memory-efficient code. In this paper, we propose IntelliGen, a tensor compiler that can generate high-performance code for memory-intensive operators by considering both computation and data movement optimizations. IntelliGen represent a DNN program using GIR, which includes primitives indicating its computation, data movement, and parallel strategies. This information will be further composed as an instruction-level dataflow graph to perform holistic optimizations by searching different memory access patterns and computation operations, and generating memory-efficient code on different hardware. We evaluate IntelliGen on NVIDIA GPU, AMD GPU, and Cambricon MLU, showing speedup up to 1.97x, 2.93x, and 16.91x(1.28x, 1.23x, and 2.31x on average), respectively, compared to current most performant frameworks.Comment: 12 pages, 14 figure

Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice

Non-destructive and rapid estimation of canopy variables is imperative for predicting crop growth and managing nitrogen (N) application. Hyperspectral remote sensing can be used for timely and accurate estimation of canopy physical and chemical properties; however, discrepancies associated with soil and water backgrounds complicate the estimation of crop N status using canopy spectral reflectance (CSR). This study established the quantitative relationships between dynamic canopy nitrogen (CN) status indicators, leaf dry weight (LDW), leaf N concentration (LNC), leaf N accumulation (LNA), and CSR-derived new hyperspectral vegetation indices (HVIs), and to access the plausibility of using these relationships to make in-season estimations of CN variables at the elongation (EL), booting (BT), and heading (HD) stages of rice crop growth. Two-year multi-N rate field experiments were conducted in 2015 and 2016 in Hubei Province, China, using the rice cultivar Japonica. The results showed that the sensitive spectral regions were negatively correlated with CN variables in the visible (400–720 nm and 560–710 nm) regions, and positively correlated (r > 0.50, r > 0.60) with red and NIR (720–900 nm) regions. These sensitive regions are used to formulate the new (SR777/759, SR768/750) HVIs to predict CN variables at the EL, BT, and HD stages. The newly developed stepwise multiple linear regression (SMLR) models could efficiently estimate the dynamic LDW at the BT stage and LNC and LNA at the HD stage. The SMLR models performed accurately and robustly when used with a validation data set. The projected results offer a suitable approach for rapid and accurate estimation of canopy N-indices for the precise management of N application during the rice growth period