The traditional concerns about farm animal welfare have centred around the impact of intensive environments and management practices on the animal. This emphasis on the physical environment is changing, however, with greater consideration being given to animal factors and in particular to the selective breeding of farm animals. In this paper we use examples from our own research on dairy cattle and sheep breeding that have made positive and practical contributions towards reducing welfare problems by creating more balanced breeding programmes. In both examples, inclusion of health and fitness traits into breeding indexes can be shown to be more profitable than selecting on production traits alone. In addition, we propose that in principle animal breeding combined with economics research can make a more general contribution towards resolving animal welfare issues, by providing a framework for the quantitative evaluation of the costs and benefits of an animal production system. The advantage of the approaches currently used in multi-trait selection is that they transform all traits (production-based or welfare-based) to a common currency allowing direct comparisons of costs and benefits. Currently the weights applied to traits reflect their economic value to the producer. This approach is likely to underestimate non-economic welfare aspects such as the pain or discomfort associated with lameness, and new approaches are needed to more fully account for these non-economic welfare costs. We therefore propose that consideration is given to the use of approaches such as contingent valuation, which has been widely used in economics to derive values for non-economic activities. The question of who would pay for the addition of these welfare costs to a breeding index remains open, but it would seem most reasonable to treat these as a public good and pay for them as such through appropriate mechanisms.

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

The Stac Fada Member of the Stoer Group, within the Torridonian succession of NW Scotland, is a melt-rich, impact-related deposit that has not been conclusively correlated with any known impact structure. However, a gravity low approximately 50 km east of the preserved Stac Fada Member outcrops has recently been proposed as the associated impact site. We investigate the location of the impact structure through a provenance study of detrital zircon and apatite in five samples from the Stoer Group. Our zircon U–Pb data are dominated by Archaean grains (> 2.5 Ga), consistent with earlier interpretations that the detritus was largely derived from local Lewisian Gneiss Complex, whereas the apatite data (the first for the Stoer Group) display a single major peak at c. 1.7 Ga, consistent with regional Laxfordian metamorphism. The almost complete absence of Archaean-aged apatite is best explained by later heating of the > 2.5 Ga Lewisian basement (the likely source region) above the closure temperature of the apatite U–Pb system (c. 375–450°C). The U–Pb age distributions for zircon and apatite show no significant variation with stratigraphic height. This may be interpreted as evidence that there was no major change in provenance during the course of deposition of the Stoer Group or, if there was any significant change, the different source regions were characterized by similar apatite and zircon U–Pb age populations. Consequently, the new data do not provide independent constraints on the location of the structure associated with the Stac Fada Member impact event.

The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA×) and Limousin (LIM×) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA× and 106 LIM× were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P < 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA× meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM× meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R2 (SECV, mg/100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM× samples (R2 = 0.12 to 0.62; SECV = 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA× beef samples. In particular for LIM×, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.

The potential of the composition of the forerib measured by X-ray computed tomography (CT) as a predictor of carcass composition was evaluated using data recorded on 30 Aberdeen Angus and 43 Limousin crossbred heifers and steers. The left sides of the carcasses were split into 20 cuts, which were CT scanned and fully dissected into fat, muscle and bone. Carcass and forerib tissue weights were assessed by dissection and CT. Carcass composition was assessed very accurately by CT scanning of the primal cuts (adj-R2 = 0.97 for the three tissues). CT scanning predicted weights of fat, muscle and bone of the forerib with adj-R2 of 0.95, 0.91 and 0.75, respectively. Single regression models with the weights of fat, muscle or bone in the forerib measured by CT as the only predictors to estimate fat, muscle or bone of the left carcass obtained by CT showed adjusted coefficients of determination (adj-R2) of 0.79, 0.60 and 0.52, respectively. By additionally fitting breed and sex, accuracy increased to 0.85, 0.73 and 0.67. Using carcass and forerib weights in addition to the previous predictors improved significantly the prediction accuracy of carcass fat and muscle weights to adj-R2 values of 0.92 and 0.96, respectively, while the highest value for carcass bone weight was 0.77. In general, equations derived using CT data had lower adj-R2 values for bone, but better accuracies for fat and muscle compared to those obtained using dissection. CT scanning could be considered as an alternative very accurate and fast method to assess beef carcass composition that could be very useful for breeding programmes and research studies involving a large number of animals, including the calibration of other indirect methods (e.g. in vivo and carcass video image analysis).

Meat tenderness is the single most important quality attribute in consumer acceptance studies of meat eating quality. Several mechanical-based techniques have been developed to provide an objective measure of meat tenderness that have good correlation with sensory tenderness, as assessed by trained taste panel. The classic Warner-Bratzler shear force technique (WBSF) has shown correlations with sensory tenderness in the range of -0.39 to -0.77 (Van Oeckel et al., 1999; Shackelford et al., 1999a), whilst the Volodkevitch bite test attempts to imitate the incisor biting action by a compression method. A rapid slice shear force (SSF) test, which uses only one steak and hot meat (significant benefits in a commercial environment), had a stronger correlation with taste panel tenderness scores than WBSF (Shackelford et al., 1999b). The Meat Industry Research Institute of New Zealand (MIRINZ) test results, transformed into categories, was highly correlated (-0.97) with sensory tenderness (Bickerstaffe et al., 2001). The objective of this study was to evaluate the associations between objective physical measures (SSF, MIRINZ and Volodkevitch) and taste panel tenderness scores in beef.

Due to an increasingly discerning consumer demand, the eating quality of beef has become a major focus of research effort for the beef supply chain and some pricing schemes currently reward Aberdeen Angus sired animals over animals sired by other breeds. However, the animal performance characteristics of beef cattle at a farm level must also be examined in studies on beef eating quality since they will remain an integral part of primary producer returns for the foreseeable future. As part of a wider beef eating quality project, the objective of the current study was to assess the on-farm and carcass animal performance characteristics in suckler-bred steers and heifers during the finishing phase.

Previous work has suggested that dimensional data obtained from digital image analysis (DIA) has potential to predict the slaughter liveweights of finished beef cattle (Hyslop et al, 2008). Both farmers and cattle buyers may benefit from being able to select animals to meet slaughter specifications more accurately on-farm. The objective of the current study was to assess the feasibility of an on-farm DIA system to predict a number of characteristics in beef steers at slaughter.

X-ray computed tomography (CT) scanning makes use of the different rates at which the tissues attenuate X-rays depending on their densities. CT muscle density (MD) is the average pixel value for this tissue in the CT images and is related to real density of the tissue, which depends on its chemical composition. One of the components determining muscle density is intramuscular fat (IMF). Studies in sheep have shown that MD, measured in vivo, was phenotypically and genetically correlated to IMF and to fatty acid (FA) composition in Scottish Blackface lambs (Karamichou et al. 2006). Intramuscular fat and FA are important traits to measure due to their association with the nutritional value and eating quality of meat. A novel automatic image analysis for spiral CT scans (SCTS) of beef primal cuts allows very accurate estimations of primal cut and carcass composition with R2 of 0.90 to 0.99 (Navajas et al., 2008). The aim of this study was to investigate the associations of MD of beef primal cuts with IMF and FA profile in Aberdeen Angus (AA) and Limousin (LIM) cattle, based on the CT thresholds estimated as part of the development of the image analysis described by Navajas et al. (2008).

Visible and near infrared reflectance spectroscopy (Vis-NIR) has been widely used by the industry research-base for large-scale meat quality evaluation to predict the chemical composition of meat quickly and accurately. Meat tenderness is measured by means of slow and destructive methods (e.g. Warner-Bratzler shear force). Similarly, sensory analysis, using trained panellists, requires large meat samples and is a complex, expensive and time-consuming technique. Nevertheless, these characteristics are important criteria that affect consumers’ evaluation of beef quality. Vis-NIR technique provides information about the molecular bonds (chemical constituents) and tissue ultra-structure in a scanned sample and thus can indirectly predict physical or sensory parameters of meat samples. Applications of Vis-NIR spectroscopy in an abattoir for prediction of physical and sensory characteristics have been less developed than in other fields. Therefore, the aim of this study was to test the on-line Vis-NIR spectroscopy for the prediction of beef quality characteristics such as colour, instrumental texture, water holding capacity (WHC) and sensory traits, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

The amount and fatty acid (FA) composition of beef intramuscular fat (IMF) are key factors that influence technological and sensory quality, especially shelf-life (lipid and pigment oxidation) and flavour. Furthermore, consumers are interested in the fat composition of meat, as nutritional guidelines are recommending a lower saturated FA (SFA) intake due to its association with cardiovascular diseases. The amount and composition of ruminant IMF, which depends on factors such as the genetic origin of the animals, feeding regime, age or live weight, influences the final quality of the product, which also explains the increasing interest in defining the FA profile of meat. However, quantitative chemical techniques for the determination of FA involve extraction of total lipids and determination of FA methyl esters by gas chromatography, so that this procedure is costly, time-consuming and generates hazardous waste. The use of near infrared reflectance spectroscopy (NIR) is increasing in food analysis because it offers several advantages over conventional methods, giving fast, non-destructive, clean and cost effective measurements. Therefore, the aim of this study was to test the on-line estimation of the concentration of major individual FA (C16:0, C18:0 and C18:1) and main groups of FA (SFA, monounsaturated FA (MUFA) and polyunsaturated FA (PUFA)) of beef IMF using NIR, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

X-ray computed tomography (CT) scanning of live animals and carcasses provides a very accurate assessment of the weights of fat, muscle and bone in pigs (Dobrowolski et al., 2003) and sheep (Johansen et al., 2007). Although the limiting size of the CT gantry prevents CT scanning of live beef cattle, beef primal joints are small enough to be scanned. Navajas et al. (2008) explored the use of spiral CT scanning (SCTS) to quantify beef carcass composition. The automatic image analysis developed provided very accurate assessments of primal and carcass composition (Navajas et al, 2008). The objective of this study was to evaluate the accuracy of this novel method for assessing beef primal cut and carcass composition in an independent dataset.

Breeding indices that combine carcass and maternal performance traits have been tested in practice and result in significant improvements in lamb growth. However, subjective carcass grades for fatness and conformation did not differ as a result of 7 years of selection on the index (Conington et al., 2006). Instrumental grading systems with the potential to objectively and accurately predict carcass composition and muscularity using video image scanning (VIA) may become standard in the UK for lamb carcass grading in the future. The use of computer tomography (CT) scanning of lambs in sheep breeding programmes is likely to better predict the carcass and prime cut VIA-assessed traits and hence lead to accelerated progress in carcass traits. This paper assesses the impact of including CT-derived carcass traits in breeding programmes for hill sheep and the consequences on traits related to maternal traits and lamb survival.

A multi-trait selection index designed to improve the overall economic performance of hill sheep, including both maternal and lamb traits, has been developed and tested in a selection experiment over 7 years. Two versions of the index were tested, with different economic weights applied to the traits, on two different hill farms: one version including maternal and growth traits; the other version with additional breeding goals of carcass weight, fatness and conformation scores. Responses to selection, using both versions of the index, suggest that improvements are being made in overall index score and lamb growth. This study investigated the indirect effects of these selection indices on lamb carcass composition and muscularity traits, as measured using X-ray computed tomography (CT) scanning. A total of 499 lambs from the two hill farms were CT scanned at weaning (approximately 120 days of age). Approximately half of the lambs from each farm were from the selection line (S, animals with highest index scores selected for breeding), while the other half were from a control line (C, animals with average index scores selected). Composition and muscularity traits were estimated on each lamb from CT data and differences between genetic lines investigated, within farm, using restricted maximum likelihood analyses, adjusting for either live weight or age. Results showed that the selection index without carcass traits produced lambs with carcass composition that was not significantly different to control lambs at a given live weight or age. Including carcass traits in the index resulted in lambs with no compositional differences (except for a slight increase in bone) at a set age compared with controls. At a given live weight however, selection lambs had less fat and lower carcass weights and killing-out percentage. Muscularity (3-D muscle shape) and muscle area shape (2-D) were not improved as a result of selection on either version of the index (including carcass weight and grades in the breeding goals or not) and, at a fixed live weight, muscularity in hind leg and lumbar regions tended to be higher in the C line. To accelerate changes in carcass composition and muscularity within the context of a multi-trait selection index for hill sheep, consideration should therefore be given to including objective CT-derived carcass traits in the index in addition to the Meat and Livestock Commission (MLC) carcass grades or ultrasound measurements.

Previous work has shown that dimensional information derived from visual images can be used to accurately estimate pig growth, in terms of size and shape (Doeschl et al, 2004). The use of visual images to derive accurate estimates of weight could be very useful information in the commercial environment within and across the livestock industries. The objective of the current study was to examine a small number of digital images of finished beef cattle to ascertain if digital image analysis (DIA) has potential to predict the liveweight (LW) of the animals at slaughter.

X-ray computed tomography (CT) measurements of live sheep have been used to predict carcass composition very accurately (Macfarlane et al., 2006). The utilisation of spiral CT scans (SCTS) for quantifying muscle volumes and weights, using automatic image analysis procedures has also been shown to be very accurate in sheep (Navajas et al., 2006). Although the limiting size of the CT gantry prevents CT scanning of live beef cattle, beef primal joints are small enough to be scanned. Hence, SCTS could be used to quantify beef carcass composition, and provide valuable information for breeding programmes including composition faster than by anatomical dissection. The objective of this study was to develop a CT image analysis procedure to assess fat, muscle and bone weights of beef carcasses and to evaluate its accuracy.

Genetic parameters were estimated between current UK hill sheep breeding goals and lamb carcass composition and muscularity traits derived using X-ray computed tomography (CT). To produce these estimates, a total of 648 lambs from two hill farms were CT scanned at weaning (ca 120 days of age), over 3 years, and total weights of carcass muscle (MUSC), fat (CFAT) and bone (BONE) and internal fat (IFAT) were predicted. Previously derived muscularity indices were also calculated for the hind leg (HLMI) and lumbar (LRMI) regions, to assess muscle shape. Data for current breeding goals (lamb performance and maternal traits) were also included from a total of 10 297 lamb records and 12 704 ewe records. Heritabilities were estimated for each trait and genetic and phenotypic correlations were calculated between each CT trait and other lamb or ewe traits. Moderate to high positive genetic correlations were found between CT-predicted tissue weights and breeding goals, which were also weights (lamb weaning weight, carcass weight, mature ewe weight, average weight of lambs reared by the ewe). CFAT was positively genetically correlated with ultrasound backfat depth at weaning (UFD) and subjective fatness grade at slaughter (MLCF), suggesting that carcass fat could be decreased using selection on any of these predictors. Ultrasound muscle depth at weaning (UMD) and subjective conformation score at slaughter (MLCC) had high genetic correlations with the muscularity indices (HLMI and LRMI), but correlations with MUSC were not significantly different from zero. This implies that selection to improve MLCC is likely to be increasing the ‘roundness’ of muscle shape in the high-priced carcass region, but having little impact on total lean meat yield. Correlations of CT traits with the other ewe traits (number of lambs weaned, number of lambs lost, longevity, fleece weight) were generally small or not significantly different from zero. The genetic parameters generated in this study can now be used in selection index calculations to assess the benefits of including lamb CT traits in future selection programmes for hill sheep.