The Iberian pork meat Industry in Jabugo (Huelva, Spain), 1997-2016

El municipio de Jabugo es uno de los más importantes en la transformación del cerdo ibérico. En las últimas décadas el sector ha sufrido una transformación, con procesos de concentración, relocalización y posicionamiento entre sus empresas. Como resultado de ello, las grandes industrias han salido fortalecidas, y la Denominación de Origen Protegida “Jabugo” aparece como una oportunidad para el sector.The municipality of Jabugo is one of the most important in the transformation of the Iberian pork. In the last decades, the industry has undergone a transformation, with processes of concentration, relocation and positioning among the companies in the industry. As a result, large companies have been strengthened, and the Protected Designation of Origin “Jabugo" emerges as an opportunity for the sector

Emerging Actors in Diabetic Cardiomyopathy: Heartbreaker Biomarkers or Therapeutic Targets?

The diabetic heart is characterized by metabolic disturbances that are often accompanied by local inflammation, oxidative stress, myocardial fibrosis, and cardiomyocyte apoptosis. Overall changes result in contractile dysfunction, concentric left ventricular (LV) hypertrophy, and dilated cardiomyopathy, that together affect cardiac output and eventually lead to heart failure, the foremost cause of death in diabetic patients. There are currently several validated biomarkers for the diagnosis and risk assessment of cardiac diseases, but none is capable of discriminating patients with diabetic cardiomyopathy (DCM). In this review we point to several novel candidate biomarkers from new activated molecular pathways (including microRNAs) with the potential to detect or prevent DCM in its early stages, or even to treat it once established. The prospective use of selected biomarkers that integrate inflammation, oxidative stress, fibrosis, and metabolic dysregulation is widely discussed

Endoplasmic reticulum stress downregulates PGC-1α in skeletal muscle through ATF4 and an mTOR-mediated reduction of CRTC2

Background Peroxisome proliferator-activated receptor γ (PPARγ) coactivator 1α (PGC-1α) downregulation in skeletal muscle contributes to insulin resistance and type 2 diabetes mellitus. Here, we examined the effects of endoplasmic reticulum (ER) stress on PGC-1α levels in muscle and the potential mechanisms involved. Methods The human skeletal muscle cell line LHCN-M2 and mice exposed to different inducers of ER stress were used. Results Palmitate- or tunicamycin-induced ER stress resulted in PGC-1α downregulation and enhanced expression of activating transcription factor 4 (ATF4) in human myotubes and mouse skeletal muscle. Overexpression of ATF4 decreased basal PCG-1α expression, whereas ATF4 knockdown abrogated the reduction of PCG-1α caused by tunicamycin in myotubes. ER stress induction also activated mammalian target of rapamycin (mTOR) in myotubes and reduced the nuclear levels of cAMP response element-binding protein (CREB)-regulated transcription co-activator 2 (CRTC2), a positive modulator of PGC-1α transcription. The mTOR inhibitor torin 1 restored PCG-1α and CRTC2 protein levels. Moreover, siRNA against S6 kinase, an mTORC1 downstream target, prevented the reduction in the expression of CRTC2 and PGC-1α caused by the ER stressor tunicamycin. Conclusions Collectively, these findings demonstrate that ATF4 and the mTOR-CRTC2 axis regulates PGC-1α transcription under ER stress conditions in skeletal muscle, suggesting that its inhibition might be a therapeutic target for insulin resistant states

Dinámica espacio temporal de la biomasa aérea en pastizales altoandinos basado en NDVI-MODIS validado por espectrometría in situ

Moderate resolution imagery (MODIS) data from the Normalized Difference Vegetation Index (NDVI) can be used to estimate aboveground biomass at large spatial scales; however, validation of the information with fieldwork is required to make more accurate grassland vegetation predictions. The study was conducted in three districts of the central highlands of Peru. In total, 153 grass samples (high grassland and low grassland) were collected after reading NDVI in situ within a pixel of 250x250 m, with a frequency of three months during a three year period. Satellite images were downloaded from the MODIS sensor to obtain the NDVI. The NDVI-MODIS values were calibrated with the NDVI registered in situ, using regression models. The calibrated equations modelled the dynamic trends of vegetation between 2000 and 2018 for the central highlands. The NDVI in situ of the low grassland ranged between 0.36 ± 0.13 and 0.24 ± 0.05 in the wet and dry seasons, respectively, while the high grassland ranged between 0.42 ± 0.14 and 0.26 ± 0.10 in the wet and dry seasons, respectively. The NDVI of the MODIS sensor for the low grassland ranged between 0.41 ± 0.14 and 0.27 ± 0.06 in the wet and dry seasons, respectively, and for the high grassland between 0.44 ± 0.14 and 0.41 ± 0.10 in the wet and dry seasons, respectively. The quadratic model obtained better estimators both for the NDVI calibration (RMSE: 0.06 and R2: 0.91) and for the biomass prediction (RMSE: 1300 and R2: 0.61). It is concluded that it is possible to use satellite information to evaluate the high Andean grasslands.Es posible utilizar datos del Índice de Vegetación de Diferencia Normalizada (NDVI) de imágenes de resolución moderada (MODIS) para estimar la biomasa aérea a grandes escalas espaciales; sin embargo, se requiere validar la información con trabajo in situ para hacer predicciones de la vegetación de pastizales más acertadas. El estudio se realizó en tres distritos de la sierra central del Perú. Se colectaron 153 muestras de pasto (pajonal alto y pajonal bajo) previa lectura de NDVI in situ dentro de un pixel de 250x250 m, con una frecuencia de tres meses en tres años de evaluaciones. Se descargaron imágenes satelitales del sensor MODIS para obtener el NDVI. Los valores de NDVI-MODIS fueron calibrados con el NDVI registrado in situ, mediante modelos de regresión. Las ecuaciones calibradas modelaron las tendencias dinámicas de la vegetación entre 2000 y 2018 para la sierra central. El NDVI in situ del pajonal bajo osciló entre 0.36 ±0.13 y 0.24±0.05 en las épocas húmeda y seca, respectivamente, mientras que el pajonal alto osciló entre 0.42±0.14 y 0.26±0.10 en las épocas húmeda y seca, respectivamente. El NDVI del sensor MODIS del pajonal bajo osciló entre 0.41±0.14 y 0.27±0.06 en las épocas húmeda y seca, respectivamente, y para el pajonal alto entre 0.44±0.14 y 0.41 ±0.10 en épocas húmeda y seca, respectivamente. El modelo cuadrático obtuvo mejores estimadores tanto para la calibración del NDVI (RMSE: 0.06 y R2: 0.91), como para la predicción de la biomasa (RMSE: 1300 y R2: 0.61). Se concluye que es posible utilizar información satelital para evaluar los pastizales altoandinos

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 R 2 ) 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 R 2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R 2 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

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

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

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

PPARβ/δ: A Key Therapeutic Target in Metabolic Disorders

Research in recent years on peroxisome proliferator-activated receptor (PPAR)β/δ indicates that it plays a key role in the maintenance of energy homeostasis, both at the cellular level and within the organism as a whole. PPARβ/δ activation might help prevent the development of metabolic disorders, including obesity, dyslipidaemia, type 2 diabetes mellitus and non-alcoholic fatty liver disease. This review highlights research findings on the PPARβ/δ regulation of energy metabolism and the development of diseases related to altered cellular and body metabolism. It also describes the potential of the pharmacological activation of PPARβ/δ as a treatment for human metabolic disorder