Magnetic probe array with high sensitivity for fluctuating field

A magnetic probe array is constructed to measure precisely the spatial structure of a small fluctuating field included in a strong confinement field that varies with time. To exclude the effect of the confinement field, the magnetic probes consisting of figure-eight-wound coils are prepared. The spatial structure of the fluctuating field is obtained from a Fourier analysis of the probe signal. It is found that the probe array is more sensitive to the fluctuating field with a high mode number than that with a low mode number. An experimental demonstration of the present method is attempted using a field-reversed configuration plasma, where the fluctuating field with 0.1% of the confinement field is successfully detected.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Predicting bull fertility using genomic data and biological information

The genomic prediction of unobserved genetic values or future phenotypes for complex traits has revolutionized agriculture and human medicine. Fertility traits are undoubtedly complex traits of great economic importance to the dairy industry. Although genomic prediction for improved cow fertility has received much attention, bull fertility largely has been ignored. The first aim of this study was to investigate the feasibility of genomic prediction of sire conception rate (SCR) in US Holstein dairy cattle. Standard genomic prediction often ignores any available information about functional features of the genome, although it is believed that such information can yield more accurate and more persistent predictions. Hence, the second objective was to incorporate prior biological information into predictive models and evaluate their performance. The analyses included the use of kernel-based models fitting either all single nucleotide polymorphisms (SNP; 55K) or only markers with presumed functional roles, such as SNP linked to Gene Ontology or Medical Subject Heading terms related to male fertility, or SNP significantly associated with SCR. Both single- and multikernel models were evaluated using linear and Gaussian kernels. Predictive ability was evaluated in 5-fold cross-validation. The entire set of SNP exhibited predictive correlations around 0.35. Neither Gene Ontology nor Medical Subject Heading gene sets achieved predictive abilities higher than their counterparts using random sets of SNP. Notably, kernel models fitting significant SNP achieved the best performance with increases in accuracy up to 5% compared with the standard whole-genome approach. Models fitting Gaussian kernels outperformed their counterparts fitting linear kernels irrespective of the set of SNP. Overall, our findings suggest that genomic prediction of bull fertility is feasible in dairy cattle. This provides potential for accurate genome-guided decisions, such as early culling of bull calves with low SCR predictions. In addition, exploiting nonlinear effects through the use of Gaussian kernels together with the incorporation of relevant markers seems to be a promising alternative to the standard approach. The inclusion of gene set results into prediction models deserves further research.     

ELECTROFORCED SEDIMENTATION OF THICK SLURRIES IN CONSOLIDATION REGION

It is shown that the settling rates of thickened slurries can be greatly enhanced by application of D.C. voltages. It is demonstrated that the settling rates increase remarkably with increasing electric field intensity. The basic differential equation for gravitational settling of thick slurries is derived and this is extended to electroforced sedimentation. The theoretical' settling rates and the porosity distributions in settling sediments of both gravitatinal and electroforced settling compare favorably with experimental observations. In addition, it is shown that the compression-permeability characteristics in the low compressive pressure region can be closely determined analytically by using batch sedimentation data.     

Large-Scale Shake Table Experiment and Numerical Simulation on the Nonlinear Behavior of Pile-Groups Subjected to Large-Scale Earthquakes

This paper describes the results of large-scale shake-table experiments involving a 3×3 pile-group. The pile-group was embedded in dry sand and subjected to sinusoidal waves and an earthquake motion recorded from the 1995 Hyogo-ken Nanbu (Kobe) earthquake. The load transfer between soil and pile was derived and the group effect was captured. Numerical simulations were also performed using a Beam-on-Nonlinear-Winkler-Foundation approach with a new hysteretic p-y curve. A comparison of the experimental and numerical results revealed that the numerical simulation is capable of accounting for the soil-pile interaction observed in the experiment.     

Recurrent type III membranoproliferative glomerulonephritis after kidney transplantation

The first well-documented case of recurrent type III membranoproliferative glomerulonephritis after kidney transplantation is reported in this article. A 48-year-old man was admitted to the hospital because of nephrotic syndrome and moderate renal failure. The renal biopsy showed double-contour images at light microscopy. Electron microscopy revealed electron-dense deposits in the mesangium and in both the subepithelial and subendothelial sides of the basement membrane. Subepithelial deposits were sometimes hump-like and produced an irregular disruption of the lamina densa. A diagnosis of type III membranoproliferative glomerulonephritis was suggested. The patient had a rapid decrease in renal function and received dialysis in 3 months. Three years later, he received a cadaveric kidney transplant, and subsequently recovered normal renal function. Proteinuria appeared after 13 months, and a biopsy of the graft demonstrated recurrence of the original disease. Seven years after transplantation, he returned to hemodialysis.     

Eosinophil adhesion to cultured airway epithelial cells from guinea pig lungs

We examined the effects of histamine, tumor necrosis factor-alpha (TNF alpha), interferon-gamma (IFN gamma), indomethacin and dexamethasone on the adherence of eosinophils to airway epithelial cells. Epithelial cells were isolated from guinea pig trachea through enzymatic digestion, and cultured until they showed confluence. Eosinophils were taken by peritoneal lavage from guinea pigs sensitized with horse serum and purified. The adhesion of eosinophils to cultured epithelial cells was examined using 51Cr. The percentage adhesion was obtained by dividing counts per minute of lysed eosinophils adhered to epithelial cells by suspended eosinophils. Histamine significantly increased the adhesion of eosinophils to epithelial cells, while TNF alpha or IFN gamma did not significantly alter the adhesion. Indomethacin significantly inhibited the adhesion of eosinophils to epithelial cells in both nontreated and histamine-induced conditions. Dexamethasone also reduced adhesion, although it did not reach statistical significance. These findings indicate that histamine induced eosinophil adherence to airway epithelial cells, suggesting a role of endogenous prostaglandins in adherence.     

Three-dimensional quantitative coronary angiography

A method for reconstructing the three-dimensional coronary arterial tree structure from biplane two-dimensional angiographic images is presented. This method exploits the geometrical mathematics of X-ray imaging and the tracking of leading edges of injected contrast material into each vessel for identification of corresponding points on two images taken from orthogonal views. Accurate spatial position and dimension of each vessel in three-dimensional space can be obtained by this reconstruction procedure. The reconstructed arterial configuration is displayed as a shaded surface model, which can be viewed from various angles. Such three-dimensional vascular information provides accurate and reproducible measurements of vascular morphology and function. Flow measurements are obtained by tracking the leading edge of contrast material down the three-dimensional arterial tree. A quantitative analysis of coronary stenosis based on transverse area narrowing and regional blood flow, including the effect of vasoactive drugs, is described. Reconstruction experiments on actual angiographic images of the human coronary artery yield encouraging results toward a realization of computer-assisted three-dimensional quantitative angiography.