Analysis of the Genetic Parameters for Dairy Linear Appraisal and Zoometric Traits: A Tool to Enhance the Applicability of Murciano-Granadina Goats Major Areas Evaluation System

Selection for zoometrics defines individuals’ productive longevity, endurance, enhanced productive abilities and consequently, their long-term profitability. When zoometric analysis is aimed at large highly selected populations or in those at different levels of selection, linear appraisal systems (LAS) provide a timely response. This study estimates genetic and phenotypic parameters for zoometric/LAS traits in Murciano-Granadina goats, estimating genetic and phenotypic correlations among all traits, and determining whether major area selection would be appropriate or if adaptability strategies may need to be followed. Heritability estimates for the zoometric/LAS traits were low to high, ranging from 0.09 to 0.43, and the accuracy of estimation has improved after decades, rendering standard errors negligible. Scale inversion of specific traits may need to be performed before major areas selection strategies are implemented. Genetic and phenotypic correlations suggests that negative selection against thicker bones and higher rear insertion heights indirectly results in the optimization of selection practices in the rest of the traits, especially those in the structure, capacity and mammary system major areas. The integration and implementation of the strategies proposed within the Murciano-Granadina breeding program maximizes selection opportunities and the sustainable international competitiveness of the Murciano-Granadina goat in the dairy goat breed panorama

Optimization and Validation of a Linear Appraisal Scoring System for Milk Production-Linked Zoometric Traits in Murciano-Granadina Dairy Goats and Bucks

Implementing linear appraisal systems (LAS) may reduce time, personnel and resource costs when performing large-scale zoometric collection. However, optimizing complex zoometric variable panels and validating the resulting reduced outputs may still be necessary. The lack of cross-validation may result in the loss of accuracy and value of the practices implemented. Special attention should be paid when zoometric panels are connected to economically-relevant traits such as dairy performance. This methodological proposal aims to optimize and validate LAS in opposition to the traditional measuring protocols routinely implemented in Murciano-Granadina goats. The sample comprises 41,323 LAS and traditional measuring records from 22,727 herdbook-registered primipara does, 17,111 multipara does and 1485 bucks. Each record includes information on 17 linear traits for primipara/multipara does and 10 traits for bucks. All zoometric parameters are scored on a nine-point scale. Cronbach’s alpha values suggest a high internal consistency of the optimized variable panels. Model fit, variability explanation power and predictive power (mean square error (MSE), Akaike (AIC)/corrected Akaike (AICc) and Bayesian information criteria (BIC), respectively) suggest the model comprising zoometric LAS scores performs better than traditional zoometry. Optimized reduced models are able to capture variability for dairy-related zoometric traits without noticeable detrimental effects on model validity properties

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats

Software-Automatized Individual Lactation Model Fitting, Peak and Persistence and Bayesian Criteria Comparison for Milk Yield Genetic Studies in Murciano-Granadina Goats

SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the lactation curve of 159 Murciano-Granadina goats selected for genotyping analyses. Lactation curve shape, peak and persistence were evaluated for each model using 3107 milk yield controls with an average of 3.78 ± 2.05 lactations per goat. Best fit (Adjusted R2) values (0.47) were reached by the five-parameter logarithmic model of Ali and Schaeffer. Three main possibilities were detected: non-fitting (did not converge), standard (Adjusted R2 over 75%) and atypical curves (Adjusted R2 below 75%). All the goats fitted for 38 models. The ability to fit different possible functional forms for each goat, which progressively increased with the number of parameters comprised in each model, translated into a higher sensitivity to explaining curve shape individual variability. However, for models for which all goats fitted, only moderate increases in explanatory and predictive potential (AIC, AICc or BIC) were found. The Ali and Schaeffer model reported the best fitting results to study the genetic variability behind goat milk yield and perhaps enhance the evaluation of curve parameters as trustable future selection criteria to face the future challenges offered by the goat dairy industry

Does the Acknowledgement of αS1-Casein Genotype Affect the Estimation of Genetic Parameters and Prediction of Breeding Values for Milk Yield and Composition Quality-Related Traits in Murciano-Granadina?

A total of 2090 lactation records for 710 Murciano-Granadina goats were collected during the years 2005–2016 and analyzed to investigate the influence of the αS1-CN genotype on milk yield and components (protein, fat, and dry matter). Goats were genetically evaluated, including and excluding the αS1-CN genotype, in order to assess its repercussion on the efficiency of breeding models. Despite no significant differences being found for milk yield, fat and dry matter heritabilities, protein production heritability considerably increased after aS1-CN genotype was included in the breeding model (+0.23). Standard errors suggest that the consideration of genotype may improve the model’s efficiency, translating into more accurate genetic parameters and breeding values (PBV). Genetic correlations ranged from −0.15 to −0.01 between protein/dry matter and milk yield/protein and fat content, while phenotypic correlations were −0.02 for milk/protein and −0.01 for milk/fat or protein content. For males, the broadest range for reliability (RAP) (0.45–0.71) was similar to that of females (0.37–0.86) when the genotype was included. PBV ranges broadened while the maximum remained similar (0.61–0.77) for males and females (0.62–0.81) when the genotype was excluded, respectively. Including the αS1-CN genotype can increase production efficiency, milk profitability, milk yield, fat, protein and dry matter contents in Murciano-Granadina dairy breeding programs

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry

Cost‐effectiveness and cost‐utility evaluation of individual vs. group transdiagnostic psychological treatment for emotional disorders in primary care (PsicAP‐Costs): a multicentre randomized controlled trial protocol

Background: Emotional disorders are common, and they have become more prevalent since the COVID‐19 pan‐ demic. Due to a high attendance burden at the specialized level, most emotional disorders in Spain are treated in primary care, where they are usually misdiagnosed and treated using psychotropic drugs. This contributes to perpetu‐ ate their illness and increase health care costs. Following the IAPT programme and the transdiagnostic approach, the PsicAP project developed a brief group transdiagnostic cognitive‐behavioural therapy (tCBT) as a cost‐effective alternative. However, it is not suitable for everyone; in some cases, one‐on‐one sessions may be more effective. The objective of the present study is to compare, in cost‐benefit terms, group and individual tCBT with the treatment usu‐ ally administered in Spanish primary care (TAU). Methods: A randomized, controlled, multicentre, and single‐blinded trial will be performed. Adults with mild to moderate emotional disorders will be recruited and placed in one of three arms: group tCBT, individual tCBT, or TAU. Medical data and outcomes regarding emotional symptoms, disability, quality of life, and emotion regulation biases will be collected at baseline, immediately after treatment, and 6 and 12 months later. The data will be used to calcu‐ late incremental cost‐effectiveness and cost‐utility ratios. Discussion: This trial aims to contribute to clinical practice research. The involvement of psychologists in primary care and the implementation of a stepped‐care model for mental disorders are recommended. Group therapy and a transdiagnostic approach may help optimize health system resources and unblock waiting lists so that people can spend less time experiencing mental health problems. Trial registration: ClinicalTrials.gov: NCT04847310; Protocols.io: bx2npqde. (April 19, 2021

Multidisciplinary Prehabilitation and Postoperative Rehabilitation for Avoiding Complications in Patients Undergoing Resection of Colon Cancer: Rationale, Design, and Methodology of the ONCOFIT Study

ONCOFIT is a randomized clinical trial with a two-arm parallel design aimed at determining the influence of a multidisciplinary Prehabilitation and Postoperative Program (PPP) on post-surgery complications in patients undergoing resection of colon cancer. This intervention will include supervised physical exercise, dietary behavior change, and psychological support comparing its influence to the standard care. Primary and secondary endpoints will be assessed at baseline, at preoperative conditions, at the end of the PPP intervention (after 12 weeks) and 1-year post-surgery, and will include: post-surgery complications (primary endpoint); prolonged hospital length of stay; readmissions and emergency department call within 1-year after surgery; functional capacity; patient reported outcome measures targeted; anthropometry and body composition; clinical/tumor parameters; physical activity levels and sedentariness; dietary habits; other unhealthy habits; sleep quality; and fecal microbiota diversity and composition. Considering the feasibility of the present intervention in a real-life scenario, ONCOFIT will contribute to the standardization of a cost-effective strategy for preventing and improving health-related consequences in patients undergoing resection of colon cancer with an important clinical and economic impact, not only in the scientific community, but also in clinical practice.This study was funded by the University of Granada, Plan Propio de Investigación 2016-Excellence actions: Unit of Excellence on Exercise and Health (UCEES). P.C. was supported by the Margarita Salas postdoctoral grant, convened by de University of the Basque Country (UPV/EHU), funded by the Ministry of Universities of Spain and the European Union-Next Generation EU

The RADMED monitoring program as a tool for MSFD implementation: toward an ecosystem based approach

In the western Mediterranean Sea, the RADMED monitoring programme is already conducting several of the evaluations required under the Marine Strategy Framework Directive (MFSD) along the Spanish Mediterranean coast. The different aspects of the ecosystem that are regularly sampled under this monitoring programme are the physical environment and the chemical and biological variables of the water column, together with the planktonic communities, biomass and structure. Moreover, determinations of some anthropogenic stressors on the marine environment, such as contaminants and microplastics, are under development. Data are managed and stored at the Instituto Español de Oceanografía (IEO) Data Centre that works under the Sea- DataNet infrastructure, and are also stored in the IBAMar database. In combination with remote sensing data, they are used to address open questions on the ecosystems in the western Mediterranean Sea.Postprint2,293