The size of juxtaluminal hypoechoic area in ultrasound images of asymptomatic carotid plaques predicts the occurrence of stroke

Objective: To test the hypothesis that the size of a juxtaluminal black (hypoechoic) area (JBA) in ultrasound images of asymptomatic carotid artery plaques predicts future ipsilateral ischemic stroke. Methods: A JBA was defined as an area of pixels with a grayscale value <25 adjacent to the lumen without a visible echogenic cap after image normalization. The size of a JBA was measured in the carotid plaque images of 1121 patients with asymptomatic carotid stenosis 50% to 99% in relation to the bulb (Asymptomatic Carotid Stenosis and Risk of Stroke study); the patients were followed for up to 8 years. Results: The JBA had a linear association with future stroke rate. The area under the receiver-operating characteristic curve was 0.816. Using Kaplan-Meier curves, the mean annual stroke rate was 0.4% in 706 patients with a JBA <4 mm 2, 1.4% in 171 patients with a JBA 4 to 8 mm2, 3.2% in 46 patients with a JBA 8 to 10 mm2, and 5% in 198 patients with a JBA >10 mm2 (P <.001). In a Cox model with ipsilateral ischemic events (amaurosis fugax, transient ischemic attack [TIA], or stroke) as the dependent variable, the JBA (<4 mm2, 4-8 mm2, >8 mm2) was still significant after adjusting for other plaque features known to be associated with increased risk, including stenosis, grayscale median, presence of discrete white areas without acoustic shadowing indicating neovascularization, plaque area, and history of contralateral TIA or stroke. Plaque area and grayscale median were not significant. Using the significant variables (stenosis, discrete white areas without acoustic shadowing, JBA, and history of contralateral TIA or stroke), this model predicted the annual risk of stroke for each patient (range, 0.1%-10.0%). The average annual stroke risk was <1% in 734 patients, 1% to 1.9% in 94 patients, 2% to 3.9% in 134 patients, 4% to 5.9% in 125 patients, and 6% to 10% in 34 patients. Conclusions: The size of a JBA is linearly related to the risk of stroke and can be used in risk stratification models. These findings need to be confirmed in future prospective studies or in the medical arm of randomized controlled studies in the presence of optimal medical therapy. In the meantime, the JBA may be used to select asymptomatic patients at high stroke risk for carotid endarterectomy and spare patients at low risk from an unnecessary operation

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

We aimed to investigate the performance of three deregression methods (VanRaden, VR; Wiggans, WG; and Garrick, GR) of cows’ and bulls’ breeding values to be used as pseudophenotypes in the genomic evaluation of test‐day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value (RELEBV ) higher than the average of parent reliability (RELPA ) in the training and validation populations; (ii) including only animals with RELEBV higher than 0.50 in the training and RELEBV higher than RELPA in the validation population; and (iii) including only animals with RELEBV higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction (GBLUP), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test‐day traits without need for weighting in the genomic analysis, unless large differences in RELEBV between training population animals exist

Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle

The success and sustainability of a breeding program incorporating genomic information is largely dependent on the accuracy of predictions. For low heritability traits, large training populations are required to achieve high accuracies of genomic estimated breeding values (GEBV). By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (ssGBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. The aim of this study was to compare the accuracy and bias of genomic predictions for various traits in Canadian Holstein cattle using ssGBLUP and multi-step genomic BLUP (msGBLUP) under different strategies, such as (1) adding genomic information of cows in the analysis, (2) testing different adjustments of the genomic relationship matrix, and (3) using a blending approach to obtain GEBV from msGBLUP. The following genomic predictions were evaluated regarding accuracy and bias: (1) GEBV estimated by ssGBLUP; (2) direct genomic value estimated by msGBLUP with polygenic effects of 5 and 20%; and (3) GEBV calculated by a blending approach of direct genomic value with estimated breeding values using polygenic effects of 5 and 20%. The effect of adding genomic information of cows in the evaluation was also assessed for each approach. When genomic information was included in the analyses, the average improvement in observed reliability of predictions was observed to be 7 and 13 percentage points for reproductive and workability traits, respectively, compared with traditional BLUP. Absolute deviation from 1 of the regression coefficient of the linear regression of de-regressed estimated breeding values on genomic predictions went from 0.19 when using traditional BLUP to 0.22 when using the msGBLUP method, and to 0.14 when using the ssGBLUP method. The use of polygenic weight of 20% in the msGBLUP slightly improved the reliability of predictions, while reducing the bias. A similar trend was observed when a blending approach was used. Adding genomic information of cows increased reliabilities, while decreasing bias of genomic predictions when using the ssGBLUP method. Differences between using a training population with cows and bulls or with only bulls for the msGBLUP method were small, likely due to the small number of cows included in the analysis. Predictions for lowly heritable traits benefit greatly from genomic information, especially when all phenotypes, pedigrees, and genotypes are used in a single-step approach

Genetics and genomics of reproductive disorders in Canadian Holstein cattle

In Canada, reproductive disorders known to affect the profitability of dairy cattle herds have been recorded by producers on a voluntary basis since 2007. Previous studies have shown the feasibility of using producer-recorded health data for genetic evaluations. Despite low heritability estimates and limited availability of phenotypic information, sufficient genetic variation has been observed for those traits to indicate that genetic progress, although slow, can be achieved. Pedigree- and genomic-based analyses were performed on producer-recorded health data of reproductive disorders, including retained placenta (RETP), metritis (METR), and cystic ovaries (CYST) using traditional BLUP and single-step genomic BLUP. Genome-wide association studies and functional analyses were carried out to unravel significant genomic regions and biological pathways, and to better understand the genetic mechanisms underlying RETP, METR, and CYST. Heritability estimates (posterior standard deviation in parentheses) were 0.02 (0.003), 0.01 (0.004), and 0.02 (0.003) for CYST, METR, and RETP, respectively. A moderate to strong genetic correlation of 0.69 (0.102) was found between METR and RETP. Averaged over all traits, sire proof reliabilities increased by approximately 11 percentage points with the incorporation of genomic data using a multiple-trait linear model. Biological pathways and associated genes underlying the studied traits were identified and will contribute to a better understanding of the biology of these 3 health disorders in dairy cattle

Estimating the effect of the deleterious recessive haplotypes AH1 and AH2 on reproduction performance of Ayrshire cattle

The effects of 2 deleterious recessive haplotypes on reproduction performance of Ayrshire cattle, Ayrshire Haplotype 1 (AH1) and Ayrshire Haplotype 2 (AH2), were investigated in Canadian Ayrshire cattle. We calculated their phenotypic effects on stillbirth (SB) rate and 56-d nonreturn rate (NRR) by estimating the interaction of service sire carrier status with maternal grandsire carrier status using the official Canadian evaluation models for those 2 traits. The interaction term included 9 subclasses for the 3 possible statuses of each bull: haplotype carrier, noncarrier, or not geno-typed. For AH1, 394 carriers and 1,433 noncarriers were available, whereas 313 carriers and 1,543 noncarriers were available for the AH2 haplotype. The number of matings considered for SB was 34,312 for heifers (first parity) and 115,935 for cows (later parities). For NRR, 49,479 matings for heifers and 160,528 for cows were used to estimate the haplotype effects. We observed a negative effect of AH1 on SB rates, which was 2.0% higher for matings of AH1-carrier sires to dams that had an AH1-carrier sire; this effect was found for both heifers and cows. However, AH1 had small, generally nonsignificant effects on NRR. The AH2 haplotype had a substantial negative effect on NRR, with 5.1% more heifers and 4.0% more cows returning to service, but the effects on SB rates were inconsistent and mostly small effects. Our results validate the harmful effects of AH1 and AH2 on reproduction traits in the Canadian Ayrshire population. This information will be of great interest for the dairy industry, allowing producers to make mating decisions that would reduce reproductive losses

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle

The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUPIM) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUPIM, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations

A step towards medical ethics modeling

Modeling of ethical reasoning has been a matter of discussion and research among distinct scientific fields, however no definite model has demonstrated undeniable global superiority over the others. However, the context of application of moral reasoning can require one methodology over the other. In areas such as medicine where quality of life and the life itself of a patient may be at stake, the ability to make the reasoning process understandable to staff and to change is of a paramount importance. In this paper we present some of the modeling lines of ethical reasoning applied to medicine, and defend that continuous logic programming presents potential for the development of trustworthy morally aware decision support systems. It is also presented a model of moral decision in two situations that emerge recurrently at the Intensive Care Units, a service where the moral complexity of regular decisions is a motivation for the analyze and development of moral decision support methodologies