Association between a genetic index for digital dermatitis resistance and the presence of digital dermatitis, heel horn erosion, and interdigital hyperplasia in Holstein cows.

Digital dermatitis (DD) is a polybacterial disease endemic to most UK dairy farms. It poses a major financial and welfare threat and is characterized by high incidence and recurrence rates. We aimed to investigate the association between the UK EBV for resistance to digital dermatitis, the digital dermatitis index (DDI), and the frequency of DD, heel horn erosion (HHE), and interdigital hyperplasia (IH) in a population of Holstein dairy cows. We enrolled and genotyped 2,352 cows from 4 farms in a prospective cohort study. Foot lesion records were recorded by veterinary surgeons for each animal at 4 time points during a production cycle, starting at approximately 2 mo before calving and ending in late lactation. Importantly, these records were not used in the calculation of the DDI. Lesion records were matched to the animal's own DDI (n = 2,101) and their sire's DDI (n = 1,812). Digital dermatitis index values in our study population ranged from -1.41 to +1.2 and were transformed to represent distance from the mean expressed in SD. The relationship between the DDI and the presence of DD was investigated using a logistic regression model, with farm, parity, and a farm-parity interaction fitted as covariates. A multivariable logistic regression model was fitted to evaluate the relationship between HHE and DDI with farm fitted as a covariate. Finally, a univariable logistic regression model with DDI as explanatory variable was used to investigate the relationship between IH and DDI. The odds ratio of an animal being affected by DD was 0.69 for 1 SD increase in the animal's DDI (95% CI = 0.63-0.76). The odds of HHE and IH were 0.69 (95% CI = 0.62-0.76) and 0.58 (95% CI = 0.49-0.68) respectively for 1 SD increase in DDI. The adjusted probability of DD was 32% (95% CI = 27-36%) for cows with mean DDI value of 0, while it was 24% (95% CI = 20-29%) in cows with a DDI value of +1. Sire DDI breeding values were standardized in the same way and then binned into terciles creating an ordinal variable representing bulls of high, medium, and low genetic merit for DD resistance. The daughters of low genetic merit bulls were at 2.05 (95% CI = 1.60-2.64), 1.96 (95% CI = 1.53-2.50), and 2.85 (95% CI = 1.64-5.16) times greater odds of being affected by DD, HHE, and IH, respectively, compared with the daughters of high genetic merit bulls. The results of this study highlight the potential of digital dermatitis genetic indexes to aid herd management of DD, and suggest that breeding for resistance to DD, alongside environmental and management control practices, could reduce the prevalence of the disease.

Retention in Telehealth Treatment for Opioid Use Disorder Among Rural Populations: A Retrospective Cohort Study.

Introduction: There are limited studies to date on telemedicine treatment outcomes for opioid use disorder (OUD) among rural populations. Methods: This was a retrospective cohort study of rural adults enrolled in telemedicine OUD treatment. Study outcomes were percent retained in care and adherence to buprenorphine assessed by urine drug screens at 1, 3, and 6 months. Results: From April 1, 2020, through January 31, 2022, 1,816 rural patients across 14 states attended an initial telemedicine visit and received a clinical diagnosis of OUD. Participants had the following characteristics: mean age 37.7 years (±8.6); 52.4% female; and 66.7% Medicaid. At 1, 3, and 6 months, 74.8%, 61.5%, and 52.3% of participants were retained in care, and 69.0%, 56.0%, and 49.2% of participants were adherent, respectively. Conclusions: Telemedicine is an effective approach for treating OUD in rural populations, with retention comparable to in-person treatment.

Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning.

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

Recent Northern Hemisphere stratospheric HCl increase due to atmospheric circulation changes.

The abundance of chlorine in the Earth's atmosphere increased considerably during the 1970s to 1990s, following large emissions of anthropogenic long-lived chlorine-containing source gases, notably the chlorofluorocarbons. The chemical inertness of chlorofluorocarbons allows their transport and mixing throughout the troposphere on a global scale, before they reach the stratosphere where they release chlorine atoms that cause ozone depletion. The large ozone loss over Antarctica was the key observation that stimulated the definition and signing in 1987 of the Montreal Protocol, an international treaty establishing a schedule to reduce the production of the major chlorine- and bromine-containing halocarbons. Owing to its implementation, the near-surface total chlorine concentration showed a maximum in 1993, followed by a decrease of half a per cent to one per cent per year, in line with expectations. Remote-sensing data have revealed a peak in stratospheric chlorine after 1996, then a decrease of close to one per cent per year, in agreement with the surface observations of the chlorine source gases and model calculations. Here we present ground-based and satellite data that show a recent and significant increase, at the 2σ level, in hydrogen chloride (HCl), the main stratospheric chlorine reservoir, starting around 2007 in the lower stratosphere of the Northern Hemisphere, in contrast with the ongoing monotonic decrease of near-surface source gases. Using model simulations, we attribute this trend anomaly to a slowdown in the Northern Hemisphere atmospheric circulation, occurring over several consecutive years, transporting more aged air to the lower stratosphere, and characterized by a larger relative conversion of source gases to HCl. This short-term dynamical variability will also affect other stratospheric tracers and needs to be accounted for when studying the evolution of the stratospheric ozone layer.

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra.

Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.

Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning.

Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and protein concentration, and is also a robust predictor of several other economically important traits including individual fatty acids and body energy. This study predicted bTB status of UK dairy cows using their MIR spectral profiles collected as part of routine milk recording. Bovine tuberculosis data were collected as part of the national bTB testing program for Scotland, England, and Wales; these data provided information from over 40,500 bTB herd breakdowns. Corresponding individual cow life-history data were also available and provided information on births, movements, and deaths of all cows in the study. Data relating to single intradermal comparative cervical tuberculin (SICCT) skin-test results, culture, slaughter status, and presence of lesions were combined to create a binary bTB phenotype labeled 0 to represent nonresponders (i.e., healthy cows) and 1 to represent responders (i.e., bTB-affected cows). Contemporaneous individual milk MIR spectral data were collected as part of monthly routine milk recording and matched to bTB status of individual animals on the single intradermal comparative cervical tuberculin test date (±15 d). Deep learning, a sub-branch of machine learning, was used to train artificial neural networks and develop a prediction pipeline for subsequent use in national herds as part of routine milk recording. Spectra were first converted to 53 × 20-pixel PNG images, then used to train a deep convolutional neural network. Deep convolutional neural networks resulted in a bTB prediction accuracy (i.e., the number of correct predictions divided by the total number of predictions) of 71% after training for 278 epochs. This was accompanied by both a low validation loss (0.71) and moderate sensitivity and specificity (0.79 and 0.65, respectively). To balance data in each class, additional training data were synthesized using the synthetic minority over sampling technique. Accuracy was further increased to 95% (after 295 epochs), with corresponding validation loss minimized (0.26), when synthesized data were included during training of the network. Sensitivity and specificity also saw a 1.22- and 1.45-fold increase to 0.96 and 0.94, respectively, when synthesized data were included during training. We believe this study to be the first of its kind to predict bTB status from milk MIR spectral data. We also believe it to be the first study to use milk MIR spectral data to predict a disease phenotype, and posit that the automated prediction of bTB status at routine milk recording could provide farmers with a robust tool that enables them to make early management decisions on potential reactor cows, and thus help slow the spread of bTB.

Anti-N-Methyl-D-Aspartate Receptor Encephalitis: A Review of Psychiatric Phenotypes and Management Considerations: A Report of the American Neuropsychiatric Association Committee on Research.

OBJECTIVE: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune disorder characterized by prominent neuropsychiatric symptoms. Given the nature of its pathophysiology, psychiatrists tend to be one of the first clinicians encountering patients with the disease. METHODS: In the present review of patients described in the literature with psychiatric symptoms, the authors aimed to characterize the psychiatric symptoms of the disease and its management in adults and adolescents as well as children (≤12 years old). A total of 544 patients fulfilled the inclusion criteria. RESULTS: The authors found that 77% of patients with NMDAR encephalitis presented initially with psychiatric symptoms. These were mostly agitation (59%) and psychotic symptoms (in 54%, especially disorganized behavior and visual-auditory hallucinations), with agitation even more commonly being the presenting symptom in children (66%). Where psychotic symptoms were detailed, visual (64%) and auditory (59%) hallucinations were the most common, as well as persecutory delusions (73%). However, delusions were not clearly characterized in most cases. Catatonia was described in 42% of adult patients and 35% of children. Of the patients with documented exposure to antipsychotics, 33% were suspected to have an adverse drug reaction (notably, neuroleptic malignant syndrome in 22% of the cases). CONCLUSIONS: On the basis of these findings, it is important to consider anti-NMDAR encephalitis in the differential diagnosis of patients with an acute onset psychosis, especially in association with agitation, catatonia, or adverse response to antipsychotics. Furthermore, it is important to use antipsychotics with caution in patients with suspected or confirmed anti-NMDAR encephalitis.

Energy profiling of dairy cows from routine milk mid-infrared analysis.

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.

Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency.

Genome-wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with one based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and nongenotyped cows. Estimated effects for single SNP markers were small and not statistically important but virtually identical for either FE measure (RFI vs. DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within nonoverlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of Bos taurus autosomes BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of orthogonal relationships clearly defined between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.

Regulation of Electroconvulsive Therapy: A Systematic Review of US State Laws.

OBJECTIVES: The goal of this study was to systematically review current US state laws on electroconvulsive therapy (ECT) in order to provide a comprehensive resource to educate practitioners, potential patients, and lawmakers. METHODS: Individual state legislative Web sites were searched by 2 independent authors using the following search terms: "electroconvulsive therapy," "convulsive therapy," "electroconvulsant therapy," "electroshock therapy," and "shock therapy" from March 2017 to May 2017. All sections of state law pertaining to ECT were reviewed, and pertinent data regarding consent, age restrictions, treatment limitations, required reporting, defined qualified professionals, fees, and other information were extracted. RESULTS: State regulation on ECT widely varied from none to stringent requirements. There were 6 states without any laws pertaining to ECT. California, Illinois, Massachusetts, Missouri, New York, South Dakota, Tennessee, and Texas were noted to be the most regulatory on ECT. CONCLUSIONS: There are no US national laws on ECT leaving individual state governments to regulate treatment. Whereas some states have detailed restrictions on use, other states have no regulation at all. This variation applies to multiple areas of ECT practice, including who can receive ECT, who can provide informed consent, who can prescribe or perform ECT, and what administrative requirements (eg, fees, reporting) must be met by ECT practitioners. Knowledge of these state laws will help providers not only to be aware of their own state's regulations, but also to have a general awareness of what other states mandate for better patient care and utilization of ECT.

Prime-boost using separate oncolytic viruses in combination with checkpoint blockade improves anti-tumour therapy.

The anti-tumour effects associated with oncolytic virus therapy are mediated significantly through immune-mediated mechanisms, which depend both on the type of virus and the route of delivery. Here, we show that intra-tumoral oncolysis by Reovirus induced the priming of a CD8+, Th1-type anti-tumour response. By contrast, systemically delivered Vesicular Stomatitis Virus expressing a cDNA library of melanoma antigens (VSV-ASMEL) promoted a potent anti-tumour CD4+ Th17 response. Therefore, we hypothesised that combining the Reovirus-induced CD8+ T cell response, with the VSV-ASMEL CD4+ Th17 helper response, would produce enhanced anti-tumour activity. Consistent with this, priming with intra-tumoral Reovirus, followed by an intra-venous VSV-ASMEL Th17 boost, significantly improved survival of mice bearing established subcutaneous B16 melanoma tumours. We also show that combination of either therapy alone with anti-PD-1 immune checkpoint blockade augmented both the Th1 response induced by systemically delivered Reovirus in combination with GM-CSF, and also the Th17 response induced by VSV-ASMEL. Significantly, anti-PD-1 also uncovered an anti-tumour Th1 response following VSV-ASMEL treatment that was not seen in the absence of checkpoint blockade. Finally, the combination of all three treatments (priming with systemically delivered Reovirus, followed by double boosting with systemic VSV-ASMEL and anti-PD-1) significantly enhanced survival, with long-term cures, compared to any individual, or double, combination therapies, associated with strong Th1 and Th17 responses to tumour antigens. Our data show that it is possible to generate fully systemic, highly effective anti-tumour immunovirotherapy by combining oncolytic viruses, along with immune checkpoint blockade, to induce complementary mechanisms of anti-tumour immune responses.

Suspected Delirium Predicts the Thoroughness of Catatonia Evaluation.

Although commonly linked to psychiatric disorders, catatonia is frequently identified secondary to neurological and general medical conditions (GMCs). The present study aimed to characterize the diagnostic workup of cases of catatonia in a general hospital setting. The authors performed a retrospective chart review of 54 cases of catatonia, over 3 years. Clinical suspicion of comorbid delirium was the strongest predictor of a more thorough general medical workup. Attribution of catatonia to a psychiatric etiology was associated with significantly less diagnostic workup. Prospective studies should help clarify the relationship between catatonia and delirium and standardize the diagnostic approach to patients presenting with catatonia.

The genetics of antibody response to paratuberculosis in dairy cattle.

Genetic parameters were estimated for antibody response to paratuberculosis (Mycobacterium avium ssp. paratuberculosis) using milk ELISA test results, collected and analyzed by National Milk Records, from Holstein Friesian cows on UK dairy farms in their first 3 lactations. Milk ELISA test results were obtained from 2007 to 2012 and combined with milk recording data and pedigree information. The reduced data set edited for the purposes of genetic parameter estimation consisted of 148,054 milk ELISA records from 64,645 lactations in 40,142 cows of 908 sires, recorded in 641 herds. Milk ELISA test results were loge-transformed and univariate analysis of 3 alternative animal models and equivalent sire models were considered. The most appropriate model included additive genetic and permanent environmental random effects, whereas maternal effects were significant according to likelihood ratio test and Akaike's information criterion but not for Bayesian information criterion. Heritability and repeatability estimates were 0.06 and 0.37, respectively, for the chosen animal model and its equivalent sire model. A subset of the data including herds with greater than 10% positive tests gave a slightly higher heritability of 0.08. Favorable but generally low significant genetic correlations were obtained between antibody response with 305-d milk yield (-0.16), 305-d protein yield (-0.16), loge-transformed lactation-average somatic cell count (0.15), and the number of mastitis episodes (0.22). Thus, selection on the antibody response to paratuberculosis, should not be detrimental to production or udder health traits. Testing cattle for paratuberculosis is important for its use in control programs and although the heritability of antibody response was low, breeding against the disease might be a good prospect as a preventative measure to assist together with other approaches in an overall control strategy.

Genetic evaluation for bovine tuberculosis resistance in dairy cattle.

Genetic evaluations for resistance to bovine tuberculosis (bTB) were calculated based on British national data including individual animal tuberculin skin test results, postmortem examination (presence of bTB lesions and bacteriological culture for Mycobacterium bovis), animal movement and location information, production history, and pedigree records. Holstein cows with identified sires in herds with bTB breakdowns (new herd incidents) occurring between the years 2000 and 2014 were considered. In the first instance, cows with a positive reaction to the skin test and a positive postmortem examination were defined as infected. Values of 0 and 1 were assigned to healthy and infected animal records, respectively. Data were analyzed with mixed models. Linear and logit function heritability estimates were 0.092 and 0.172, respectively. In subsequent analyses, breakdowns were split into 2-mo intervals to better model time of exposure and infection in the contemporary group. Intervals with at least one infected individual were retained and multiple intervals within the same breakdown were included. Healthy animal records were assigned values of 0, and infected records a value of 1 in the interval of infection and values reflecting a diminishing probability of infection in the preceding intervals. Heritability and repeatability estimates were 0.115 and 0.699, respectively. Reliabilities and across time stability of the genetic evaluation were improved with the interval model. Subsequently, 2 more definitions of "infected" were analyzed with the interval model: (1) all positive skin test reactors regardless of postmortem examination, and (2) all positive skin test reactors plus nonreactors with positive postmortem examination. Estimated heritability was 0.085 and 0.089, respectively; corresponding repeatability estimates were 0.701 and 0.697. Genetic evaluation reliabilities and across time stability did not change. Correlations of genetic evaluations for bTB with other traits in the current breeding goal were mostly not different from zero. Correlation with the UK Profitable Lifetime Index was moderate, significant, and favorable. Results demonstrated the feasibility of a national genetic evaluation for bTB resistance. Selection for enhanced resistance will have a positive effect on profitability and no antagonistic effects on current breeding goal traits. Official genetic evaluations are now based on the interval model and the last bTB trait definition.

Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors.

Feed efficiency (FE), characterized as the fraction of feed nutrients converted into salable milk or meat, is of increasing economic importance in the dairy industry. We conjecture that FE is a complex trait whose variation and relationships or partial efficiencies (PE) involving the conversion of dry matter intake to milk energy and metabolic body weight may be highly heterogeneous across environments or management scenarios. In this study, a hierarchical Bayesian multivariate mixed model was proposed to jointly infer upon such heterogeneity at both genetic and nongenetic levels on PE and variance components (VC). The heterogeneity was modeled by embedding mixed effects specifications on PE and VC in addition to those directly specified on the component traits. We validated the model by simulation and applied it to a joint analysis of a dairy FE consortium data set with 5,088 Holstein cows from 13 research stations in Canada, the Netherlands, the United Kingdom, and the United States. Although no differences were detected among research stations for PE at the genetic level, some evidence was found of heterogeneity in residual PE. Furthermore, substantial heterogeneity in VC across stations, parities, and ration was observed with heritability estimates of FE ranging from 0.16 to 0.46 across stations.

The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows.

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain ß-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output.

Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle.

Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across-environment models.

Genetic parameters of linear conformation type traits and their relationship with milk yield throughout lactation in mixed-breed dairy goats.

Conformation traits are of interest to many dairy goat breeders not only as descriptive traits in their own right, but also because of their influence on production, longevity, and profitability. If these traits are to be considered for inclusion in future dairy goat breeding programs, relationships between them and production traits such as milk yield must be considered. With the increased use of regression models to estimate genetic parameters, an opportunity now exists to investigate correlations between conformation traits and milk yield throughout lactation in more detail. The aims of this study were therefore to (1) estimate genetic parameters for conformation traits in a population of crossbred dairy goats, (2) estimate correlations between all conformation traits, and (3) assess the relationship between conformation traits and milk yield throughout lactation. No information on milk composition was available. Data were collected from goats based on 2 commercial goat farms during August and September in 2013 and 2014. Ten conformation traits, relating to udder, teat, leg, and feet characteristics, were scored on a linear scale (1-9). The overall data set comprised data available for 4,229 goats, all in their first lactation. The population of goats used in the study was created using random crossings between 3 breeds: British Alpine, Saanen, and Toggenburg. In each generation, the best performing animals were selected for breeding, leading to the formation of a synthetic breed. The pedigree file used in the analyses contained sire and dam information for a total of 30,139 individuals. The models fitted relevant fixed and random effects. Heritability estimates for the conformation traits were low to moderate, ranging from 0.02 to 0.38. A range of positive and negative phenotypic and genetic correlations between the traits were observed, with the highest correlations found between udder depth and udder attachment (0.78), teat angle and teat placement (0.70), and back legs and back feet (0.64). The genetic correlations estimated between conformation traits and milk yield across the first lactation demonstrated changes during this period. The majority of correlations estimated between milk yield and the udder and teat traits were negative. Therefore, future breeding programs would benefit from including these traits to ensure that selection for increased productivity is not accompanied by any unwanted change in functional fitness.

Short communication: Genomic selection in a crossbred cattle population using data from the Dairy Genetics East Africa Project.

Due to the absence of accurate pedigree information, it has not been possible to implement genetic evaluations for crossbred cattle in African small-holder systems. Genomic selection techniques that do not rely on pedigree information could, therefore, be a useful alternative. The objective of this study was to examine the feasibility of using genomic selection techniques in a crossbred cattle population using data from Kenya provided by the Dairy Genetics East Africa Project. Genomic estimated breeding values for milk yield were estimated using 2 prediction methods, GBLUP and BayesC, and accuracies were calculated as the correlation between yield deviations and genomic breeding values included in the estimation process, mimicking the situation for young bulls. The accuracy of evaluation ranged from 0.28 to 0.41, depending on the validation population and prediction method used. No significant differences were found in accuracy between the 2 prediction methods. The results suggest that there is potential for implementing genomic selection for young bulls in crossbred small-holder cattle populations, and targeted genotyping and phenotyping should be pursued to facilitate this.

Patient-Centered Electroconvulsive Therapy Care: A Call to Action.

We present our experience applying the IOM's "10 Simple Rules" to our ECT Service at a major teaching hospital in order to achieve patient-centered care. We encourage all ECT providers to partner with their patients in engaging family members and significant others in each aspect of ECT care, especially the ECT treatment itself.