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BACKGROUND & AIMS Drug-induced liver injury (DILI) has features similar to those of other liver diseases including autoimmune hepatitis (AIH). We aimed to characterize the clinical and autoimmune features of liver injury caused by nitrofurantoin, minocycline, methyldopa, or hydralazine. METHODS We analyzed data from 88 cases of DILI attributed to nitrofurantoin, minocycline, methyldopa, or hydralazine included in the Drug-Induced Liver Injury Network prospective study from 2004 through 2014. Sera were collected from patients at baseline and follow-up examination and tested for levels of immunoglobulin G (IgG), antibodies to nuclear antigen (ANA), smooth muscle (SMA), and soluble liver antigen (SLA). An autoimmune score was derived on the basis of increases in levels of IgG, ANA, SMA, and SLA (assigned values of 0, 1+, or 2+). AIH-associated HLA DRB1*03:01 and DRB1*04:01 allele frequencies were compared with those of the general population (controls). RESULTS Of the 88 cases, 80 were women (91%), 74% had hepatocellular injury, and 25% had severe injury. At the onset of DILI, 39% of cases had increased levels of IgG, 72% had increased levels of ANA, 60% had increased levels of SMA, and none had increases in SLA. A phenotype of autoimmunity (autoimmune score ≥2) was observed in 82% of cases attributed to nitrofurantoin and 73% of cases attributed to minocycline (73%) but only 55% of cases attributed to methyldopa and 43% of cases attributed to hydralazine (P = .16 for nitrofurantoin and minocycline vs methyldopa and hydralazine). We observed a decrease in numbers of serum samples positive for ANA (P = .01) or SMA (P < .001) and in autoimmune scores (P < .001) between DILI onset and follow-up. Similar percentages of patients with DILI had HLA-DRB1*03:01 (15%) and HLA-DRB1*04:01 (9%) as controls (12% and 9%, respectively). CONCLUSIONS In analysis of data from the DILIN, we found that most cases of DILI attributed to nitrofurantoin or minocycline and about half of cases that were due to methyldopa and hydralazine have a phenotype of autoimmunity similar to AIH. These features decrease with recovery of the injury and are not associated with the typical HLA alleles found in patients with idiopathic AIH.

The objective of the present study was to assess the predictive ability of subsets of single nucleotide polymorphism (SNP) markers for development of low-cost, low-density genotyping assays in dairy cattle. Dense SNP genotypes of 4,703 Holstein bulls were provided by the USDA Agricultural Research Service. A subset of 3,305 bulls born from 1952 to 1998 was used to fit various models (training set), and a subset of 1,398 bulls born from 1999 to 2002 was used to evaluate their predictive ability (testing set). After editing, data included genotypes for 32,518 SNP and August 2003 and April 2008 predicted transmitting abilities (PTA) for lifetime net merit (LNM$), the latter resulting from progeny testing. The Bayesian least absolute shrinkage and selection operator method was used to regress August 2003 PTA on marker covariates in the training set to arrive at estimates of marker effects and direct genomic PTA. The coefficient of determination (R(2)) from regressing the April 2008 progeny test PTA of bulls in the testing set on their August 2003 direct genomic PTA was 0.375. Subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP were created by choosing equally spaced and highly ranked SNP, with the latter based on the absolute value of their estimated effects obtained from the training set. The SNP effects were re-estimated from the training set for each subset of SNP, and the 2008 progeny test PTA of bulls in the testing set were regressed on corresponding direct genomic PTA. The R(2) values for subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP with largest effects (evenly spaced SNP) were 0.184 (0.064), 0.236 (0.111), 0.269 (0.190), 0.289 (0.179), 0.307 (0.228), 0.313 (0.268), and 0.322 (0.291), respectively. These results indicate that a low-density assay comprising selected SNP could be a cost-effective alternative for selection decisions and that significant gains in predictive ability may be achieved by increasing the number of SNP allocated to such an assay from 300 or fewer to 1,000 or more.

There is an old aphorism that fire is a good servant but a bad master. Something like this aphorism is frequently applied to the appropriate role of the bureaucracy in government. Because bureaucracy is often viewed as tainted with an ineradicable lust for power, it is alleged that, like fire, it needs constant control to prevent its erupting from beneficient servitude into dangerous and tyrannical mastery.The folklore of constitutional theory relegates the bureaucracy to somewhat the same low but necessary estate as Plato does the appetitive element of the soul. In the conventional dichotomy between policy and administration, administration is the Aristotelian slave, properly an instrument of action for the will of another, capable of receiving the commands of reason but incapable of reasoning. The amoral concept of administrative neutrality is the natural complement of the concept of bureaucracy as instrument; for according to this view the seat of reason and conscience resides in the legislature, whatever grudging concession may be made to the claims of the political executive, and a major, if not the major, task of constitutionalism is the maintenance of the supremacy of the legislature over the bureaucracy. The latter's sole constitutional role is one of neutral docility to the wishes of the day's legislative majority.

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Four approaches using single-nucleotide polymorphism (SNP) information (F ' -metric model, kernel regression, reproducing kernel Hilbert spaces (RKHS) regression, and a Bayesian regression) were compared with a standard procedure of genetic evaluation (E-BLUP) of sires using mortality rates in broilers as a response variable, working in a Bayesian framework. Late mortality (14-42 days of age) records on 12,167 progeny of 200 sires were precorrected for fixed and random (nongenetic) effects used in the model for genetic evaluation and for the mate effect. The average of the corrected records was computed for each sire. Twenty-four SNPs seemingly associated with late mortality were included in three methods used for genomic assisted evaluations. One thousand SNPs were included in the Bayesian regression, to account for markers along the whole genome. The posterior mean of heritability of mortality was 0.02 in the E-BLUP approach, suggesting that genetic evaluation could be improved if suitable molecular markers were available. Estimates of posterior means and standard deviations of the residual variance were 24.38 (3.88), 29.97 (3.22), 17.07 (3.02), and 20.74 (2.87) for E-BLUP, the linear model on SNPs, RKHS regression, and the Bayesian regression, respectively, suggesting that RKHS accounted for more variance in the data. The two nonparametric methods (kernel and RKHS regression) fitted the data better, having a lower residual sum of squares. Predictive ability, assessed by cross-validation, indicated advantages of the RKHS approach, where accuracy was increased from 25 to 150%, relative to other methods.

on behalf of Drug-Induced Liver Injury Network (DILIN) investigators and International DILI consortium (iDILIC)

Pearson's correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression in the tails of the distribution, where individuals are chosen for selection. This research used 14 maize and 16 wheat data sets with different trait–environment combinations. Six different models were evaluated by means of a cross-validation scheme (50 random partitions each, with 90% of the individuals in the training set and 10% in the testing set). The predictive accuracy of these algorithms for selecting individuals belonging to the best α=10, 15, 20, 25, 30, 35, 40% of the distribution was estimated using Cohen's kappa coefficient (κ) and an ad hoc measure, which we call relative efficiency (RE), which indicates the expected genetic gain due to selection when individuals are selected based on GS exclusively. We put special emphasis on the analysis for α=15%, because it is a percentile commonly used in plant breeding programmes (for example, at CIMMYT). We also used ρ as a criterion for overall success. The algorithms used were: Bayesian LASSO (BL), Ridge Regression (RR), Reproducing Kernel Hilbert Spaces (RHKS), Random Forest Regression (RFR), and Support Vector Regression (SVR) with linear (lin) and Gaussian kernels (rbf). The performance of regression methods for selecting the best individuals was compared with that of three supervised classification algorithms: Random Forest Classification (RFC) and Support Vector Classification (SVC) with linear (lin) and Gaussian (rbf) kernels. Classification methods were evaluated using the same cross-validation scheme but with the response vector of the original training sets dichotomised using a given threshold. For α=15%, SVC-lin presented the highest κ coefficients in 13 of the 14 maize data sets, with best values ranging from 0.131 to 0.722 (statistically significant in 9 data sets) and the best RE in the same 13 data sets, with values ranging from 0.393 to 0.948 (statistically significant in 12 data sets). RR produced the best mean for both κ and RE in one data set (0.148 and 0.381, respectively). Regarding the wheat data sets, SVC-lin presented the best κ in 12 of the 16 data sets, with outcomes ranging from 0.280 to 0.580 (statistically significant in 4 data sets) and the best RE in 9 data sets ranging from 0.484 to 0.821 (statistically significant in 5 data sets). SVC-rbf (0.235), RR (0.265) and RHKS (0.422) gave the best κ in one data set each, while RHKS and BL tied for the last one (0.234). Finally, BL presented the best RE in two data sets (0.738 and 0.750), RFR (0.636) and SVC-rbf (0.617) in one and RHKS in the remaining three (0.502, 0.458 and 0.586). The difference between the performance of SVC-lin and that of the rest of the models was not so pronounced at higher percentiles of the distribution. The behaviour of regression and classification algorithms varied markedly when selection was done at different thresholds, that is, κ a...