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Large ham weight losses (WL) in dry-curing are undesired as they lead to a loss of marketable product and penalise the quality of the dry-cured ham. The availability of early predictions of WL may ease the adaptation of the dry-curing process to the characteristics of the thighs and increase the effectiveness of selective breeding in enhancing WL. Aims of this study were (i) to develop Bayesian and Random Forests (RFs) regression models for the prediction of ham WL during dry-curing using on-site infrared spectra of raw ham subcutaneous fat, carcass and raw ham traits as predictors and (ii) to estimate genetic parameters for WL and their predictions (P-WL). Visible-near infrared spectra were collected on the transversal section of the subcutaneous fat of raw hams. Carcass traits were carcass weight, carcass backfat depth, lean meat content and weight of raw hams. Raw ham traits included measures of ham subcutaneous fat depth and linear scores for round shape, subcutaneous fat thickness and marbling of the visible muscles of the thigh. Measures of WL were available for 1672 hams. The best prediction accuracies were those of a Bayesian regression model including the average spectrum, carcass and raw ham traits, with R2 values in validation of 0.46, 0.55 and 0.62, for WL at end of salting (23 days), resting (90 days) and curing (12 months), respectively. When WL at salting was used as an additional predictor of total WL, the R2 in validation was 0.67. Bayesian regressions were more accurate than RFs models in predicting all the investigated traits. Restricted maximum likelihood (REML) estimates of genetic parameters for WL and P-WL at the end of curing were estimated through a bivariate animal model including 1672 measures of WL and 8819 P-WL records. Results evidenced that the traits are heritable (h2 ± SE was 0.27 ± 0.04 for WL and 0.39 ± 0.04 for P-WL), and the additive genetic correlation is positive and high (ra = 0.88 ± 0.03). Prediction accuracy of ham WL is high enough to envisage a future use of prediction models in identifying batches of hams requiring an adaptation of the processing conditions to optimise results of the manufacturing process. The positive and high genetic correlation detected between WL and P-WL at the end of dry-curing, as well as the estimated heritability for P-WL, suggests that P-WL can be successfully used as an indicator trait of the measured WL in pig breeding programs.

Male reproductive performances are often ignored in cattle breeding programmes, although semen traits might be used to improve bull breeding soundness. Effects of genetic and environmental factors on semen production and quality traits were estimated in 693 Piemontese bulls with the aim of providing the first estimates of genetic parameters for semen traits for this breed. Volume and concentrations of individual ejaculates (up to three per each test-day), and volume, concentration, total number of spermatozoa and post-thawing progressive motility of within test-day pooled semen were available for 19 060 ejaculates. Bulls reached the maximum amount of daily semen production after their third year of age, with concentration rapidly increasing until 23 months of age, and then slowly decreasing. Semen volume was at its highest when collection days were at least 15 days apart, whereas the maximum concentration was reached when the interval was 6 days. Heritability estimates were generally moderate (0.14–0.26), and low for progressive motility (0.08). Estimates of genetic correlation among the volumes of the individual ejaculates were high and positive (≥0.79), as were the genetic correlations among their concentrations (≥0.46). Genetic correlations among volume and concentration traits varied from −0.47 (with a 95% high posterior density interval ranging from −0.65 to −0.23) to −0.32 (with a 95% high posterior density interval ranging from −0.55 to −0.09). Progressive motility was unrelated with the other traits, but moderately positively correlated with volumes of the second and third ejaculates. The magnitude of heritabilities showed that selection for semen traits is possible. However, the unfavourable relationship between volume and concentration must be taken into account if a future selection programme is to be established.

The quality of subcutaneous fat of raw hams is a trait of interest in selective breeding programs for pig lines used in dry-cured ham production, and rapid, non-invasive methods for its assessment are available. However, the efficacy of such methods to provide indicator traits for breeding programs needs to be proven. The study investigated the accuracy of on-site visible–near IR spectroscopy predictions of iodine number and fatty acid (FA) composition of raw ham subcutaneous fat, and it evaluated their effectiveness as indicator traits of ham fat quality in a pig breeding program. Prediction equations were developed using visible–near IR spectra acquired at the slaughterhouse from five sites in subcutaneous fat of raw hams of 1025 crossbred pigs. Pigs were raised, under standardized rearing and feeding conditions, in the sib-testing program of the Goland C21 boar line and slaughtered at nine months of age and average body weight of 166 ± 15 kg. Accuracy was generally relatively poor, but R2 in external validation was >0.7 for iodine number and concentration of C18:2n-6, polyunsaturated FAs and omega-6 FAs. To assess the effectiveness of the on-site predictions as indicator traits in a breeding program, (co)variance components of the measured traits (OBS) and of their predictions using in-lab (in-lab-PR) or on-site (on-site-PR) spectrometers were estimated. Available records for OBS were 6814 and 2048, for iodine number and FA composition, respectively. Predictions using in-lab were available for pigs slaughtered between 2006 and 2014, for a total of 10 153 records. Predictions using on-site were obtained from spectra collected since 2011, for a total of 10 296 records. The estimated heritabilities for the investigated traits ranged from 0.34 to 0.50 and were greater for on-site-PR than for OBS. Genetic correlations between OBS and in-lab-PR were very close to 1.00 for all the investigated traits, whereas those between OBS and on-site-PRED ranged from 0.86 to 0.94. On-site visible-IR predictions are accurate enough to support the use of this technique for large-scale phenotyping of raw ham fat quality, even when dealing with animals of a single genetic line raised in standardized conditions, and may be implemented as indicator traits in breeding programs.

In recent years, test day models (TDm) have been proposed for genetic evaluation programs of dairy cattle. Advantages due to the use of TDm are known (Jensen, 2001). TDms differ for the (co)variance structure among observations: the first proposed model was a single trait repeatability model, whereas later studies investigated multitrait (MT) and random regression (RR) models (Swalve, 1999). RR assume that the shape of the lactation curve is influenced by random genetic and permanent environmental effects and differ for the number of parameters of the function used for fitting the shape of the individual lactation curve (Jamrozik et al., 1997). TDm are commonly considered in terms of accuracy of estimated breeding values but effects due to constraints on the observational structure of data, which result from the application of different models, are not considered. Such constraints are expected to affect the effective number of herds and cows providing records and, as a consequence, time at first evaluation of young bulls. Application of some TDm to a population with small average herd size might be troublesome because of the loss of herds involved in the genetic evaluation program, as reported by Dalzotto (2000) for the Italian Brown population.........

Dry cured ham is the most valuable product of the pig industry in Italy and quality of fat covering in fresh hams affects qualitative properties of the final product (Mordenti et al., 1992). High content of unsaturated fatty acids is a predisposing factor for fat with inadequate consistence and for oxydative processes during curing (Chizzolini et al., 1995). Iodine number and linoleic acid content are measures related to the degree of fat unsaturation and can be used to evaluate objectively quality aspects of fat covering of fresh hams (Bellati et al., 1996)...

The aim of this study was to estimate heritability of beef quality traits in Piemontese cattle. A total of 804 young bulls, progeny of 109 AI sires, were sampled from 124 fattening farms (FF) and slaughtered in different days at the same commercial abattoir. At slaughter, bulls were 523±73 d old and average carcasses weight (CW) was 417±45 kg. Carcasses were scored for fleshiness (EUS) and fatness. An individual beef sample was collected from Longissimus Thoracis 24 h after slaughter and held refrigerated at 4 °C for 8 d. Measured traits were pH at ageing (pH8d), beef colour (L*, a*, b*, Hue and Chroma), shear force (SF), drip (DL) and cooking loss (CL). A REML linear animal model including the fixed effects of FF, slaughter age and CW class and the random effect of the bull was used. The estimated heritability for EUS, SF, DL, Hue, L* and a* was of intermediate magnitude (from 0.22 to 0.49) whereas heritabilities for all other traits were low (from 0.04 to 0.16).

Survival analysis methodology was applied in order to analyse sources of variation of preweaning survival time and to estimate variance components using data from a crossbred piglets population. A frailty sire model was used with the litter effect treated as an additional random source of variation. All the variables considered had a significant effect on survivability: sex, cross-fostering, parity of the nurse-sow and litter size. The variance estimates of sire and litter were closed to 0.08 and 2 respectively and the heritability of pre-weaning survival was 0.03.

Pig breeding in Italy aims to obtain heavy pigs for production of typical cured end products. Detailed guidelines related to animals, weight, age, feeding regime, carcass and fresh ham traits impose constraints on this type of production.

Subaortic and pulmonic stenosis are congenital cardiac defects affecting several dog breeds (Jacobs, 1990; Buchanan, 1992; Tidholm, 1997). Subaortic stenosis (SAS) is characterized by a fibrous ring of tissue placed immediately below the aortic valve.

Dissecting the genetics of production traits in livestock is of outmost importance, both to understand biological mechanisms underlying those traits and to facilitate the design of selection programs incorporating that information. For the pig industry, traits related to curing are key for protected designation of origin productions. In particular, appropriate ham weight loss after dry-curing ensures high quality of the final product and avoids economic losses. In this study, we analyzed data (N = 410) of ham weight loss after approximately 20 months of dry-curing. The animals used for ham production were purebred pigs belonging to a commercial line. A genome-wide association study (GWAS) of 29,844 SNP markers revealed the polygenic nature of the trait: 221 loci explaining a small percentage of the variance (0.3–1.65%) were identified on almost all Sus scrofa chromosomes. Post-GWAS analyses revealed 32 windows located within regulatory regions and 94 windows located in intronic regions of specific genes. In total, 30 candidate genes encoding receptors and enzymes associated with ham weight loss (MTHFD1L, DUSP8), proteolysis (SPARCL1, MYH8), drip loss (TNNI2), growth (CDCA3, LSP1, CSMD1, AP2A2, TSPAN4), and fat metabolism (AGPAT4, IGF2R, PTDSS2, HRAS, TALDO1, BRSK2, TNNI2, SYT8, GTF2I, GTF2IRD1, LPCAT3, ATN1, GNB3, CMIP, SORCS2, CCSER1, SPP1) were detected.

Abstract Background The gilthead sea bream (Sparus aurata) has long been considered resistant to viral nervous necrosis (VNN), until recently, when significant mortalities caused by a reassortant nervous necrosis virus (NNV) strain were reported. Selective breeding to enhance resistance against NNV might be a preventive action. In this study, 972 sea bream larvae were subjected to a NNV challenge test and the symptomatology was recorded. All the experimental fish and their parents were genotyped using a genome-wide single nucleotide polymorphism (SNP) array consisting of over 26,000 markers. Results Estimates of pedigree-based and genomic heritabilities of VNN symptomatology were consistent with each other (0.21, highest posterior density interval at 95% (HPD95%): 0.1–0.4; 0.19, HPD95%: 0.1–0.3, respectively). The genome-wide association study suggested one genomic region, i.e., in linkage group (LG) 23 that might be involved in sea bream VNN resistance, although it was far from the genome-wide significance threshold. The accuracies (r) of the predicted estimated breeding values (EBV) provided by three Bayesian genomic regression models (Bayes B, Bayes C, and Ridge Regression) were consistent and on average were equal to 0.90 when assessed in a set of cross-validation (CV) procedures. When genomic relationships between training and testing sets were minimized, accuracy decreased greatly (r = 0.53 for a validation based on genomic clustering, r = 0.12 for a validation based on a leave-one-family-out approach focused on the parents of the challenged fish). Classification of the phenotype using the genomic predictions of the phenotype or using the genomic predictions of the pedigree-based, all data included, EBV as classifiers was moderately accurate (area under the ROC curve 0.60 and 0.66, respectively). Conclusions The estimate of the heritability for VNN symptomatology indicates that it is feasible to implement selective breeding programs for increased resistance to VNN of sea bream larvae/juveniles. Exploiting genomic information offers the opportunity of developing prediction tools for VNN resistance, and genomic models can be trained on EBV using all data or phenotypes, with minimal differences in classification performance of the trait phenotype. In a long-term view, the weakening of the genomic ties between animals in the training and test sets leads to decreased genomic prediction accuracies, thus periodical update of the reference population with new data is mandatory.

The Araçuaí Orogen, in eastern Brazil, was formed during the Neoproterozoic – Cambrian amalgamation of West Gondwana. During the Mesozoic – Cenozoic opening of the South Atlantic Ocean, and the associated divergent tectonics, the orogen developed as basement to the passive margin of South America and was progressively covered by thick offshore sedimentary basins, particularly the Espírito Santo, Mucuri, and Cumuruxatiba basins, in which hydrocarbon systems have been exploited. Our understanding of the Araçuaí Orogen's passive margin evolution, erosion, and sediment transfer to these basins ultimately depends on constraining the onshore exhumation in response to Mesozoic – Cenozoic events. Here, new and previously published data from apatite fission-track (AFT) analyses and inverse thermal history modelling of (Pre)Cambrian basement rocks from the Araçuaí Orogen resolve three discrete basement cooling and associated erosional exhumation episodes. In the Pre-Rift phase, Jurassic – Hauterivian erosion of the Araçuaí Orogen is most likely related to the adjoining intra-continental West Gondwana flexural subsidence, which increased hillslope and river erosion power. In the Rift and Transitional phases, Barremian – Albian accelerated phase of erosion is associated with the uplift of the Atlantic rift shoulders and the establishment of an oceanic base-level. In the Drift phase, reactivations in response to far-field stresses likely triggered a Late Cretaceous – Paleocene rapid erosion event. The rates at which these events unfolded vary spatially and are controlled by inherited structures. The Araçuaí Orogen experienced slower denudation rates in areas closer to the São Francisco Craton, which suggests that the tectonic reactivation and related surface uplift during the Mesozoic–Cenozoic is in first-order controlled by lithospheric rigidity. Furthermore, the structural framework of the Paramirim and Pirapora aulacogens and NE-oriented shear zones in the orogen’s southeast facilitated later reactivations. From the spatial pattern of denudation/exhumation of the Araçuaí Orogen during the Mesozoic – Cenozoic, we draw inferences on the tectonic development of the offshore basins regarding their hydrocarbon potentials.

The advent of the Internet of Things brought a new age of interconnected device functionality, ranging from personal devices and smart houses to industrial control systems. However, increased security risks have emerged in its wake, in particular self-replicating malware that exploits weak device security. Studies modeling malware epidemics aim to predict malware behavior in essential ways, usually assuming a number of simplifications, but they invariably simplify the single most important subdynamics of malware: random propagation. In our previous work, we derived and presented the first exact mathematical model of random propagation, defined as the subdynamics of propagation of a malware model. The propagation dynamics were derived for the SIS model in discrete form. In this work, we generalize the methodology of derivation and extend it to any Markov chain model of malware based on random propagation. We also propose a second method of derivation based on modifying the simplest form of the model and adjusting it for more complex models. We validated the two methodologies on three malware models, using simulations to confirm the exactness of the propagation dynamics. Stochastic errors of less than 0.2% were found in all simulations. In comparison, the standard nonlinear model of propagation (present in ∼95% of studies) has an average error of 5% and a maximum of 9.88% against simulations. Moreover, our model has a low mathematical trade-off of only two additional operations, being a proper substitute to the standard literature model whenever the dynamical equations are solved numerically.

Oncogenic neurotrophic tropomyosin receptor kinase gene fusions occur in less than 1% of common cancers. These mutations have emerged as new biomarkers in cancer genomic profiling with the approval of selective drugs against tropomyosin receptor kinase fusion proteins. Nevertheless, the optimal pathways and diagnostic platforms for this biomarker’s screening and genomic profiling have not been defined and remain a subject of debate. A panel of national experts in molecular cancer diagnosis and treatment was convened by videoconference and suggested topics to be addressed in the literature review. The authors proposed a testing algorithm for oncogenic neurotrophic tropomyosin receptor kinase gene fusion screening and diagnosis for the Brazilian health system. This review aims to discuss the latest literature evidence and international consensus on neurotrophic tropomyosin receptor kinase gene fusion diagnosis to devise clinical guidelines for testing this biomarker. We propose an algorithm in which testing for this biomarker should be requested to diagnose advanced metastatic tumors without known driver mutations. In this strategy, Immunohistochemistry should be used as a screening test followed by confirmatory next-generation sequencing in immunohistochemistry-positive cases.

Selecting pigs with reduced ability to accumulate boar taint (BT) compounds in their tissues is an alternative to male surgical castration. As the majority of slaughter pigs are crossbred, before selecting against BT in purebreds, it is essential to consider possible impacts on commercial traits in crossbreds. This study estimated the genetic correlations between BT compound levels measured in 1115 purebred pigs and carcass and ham quality traits collected in 26,577 crossbred Italian heavy pigs. Genetic correlations were estimated in bivariate Bayesian analyses including one BT trait and one production or ham quality trait at a time. Heritability of androstenone, skatole, and indole was 0.41, 0.49, and 0.37, respectively. A moderate negative correlation between skatole and carcass yield (−0.40), and between all BT compounds and backfat (from −0.26 to −0.55) was observed. Conversely, positive correlations (from 0.11 to 0.54) were found between skatole and ham fat thickness traits. Correlations between BT compounds and iodine number ranged from −0.07 (for androstenone) to −0.64 (for skatole), whereas those with PUFA ranged from −0.13 (for indole) to −0.33 (for skatole). Hence, reducing BT could decrease ham fat thickness and increase unsaturated fatty acids, with potential negative impacts on product quality.

With a perspective future ban on surgical castration in Europe, selecting pigs with reduced ability to accumulate boar taint (BT) compounds (androstenone, indole, skatole) in their tissues seems a promising strategy. BT compound concentrations were quantified in the adipose tissue of 1075 boars genotyped at 29,844 SNPs. Traditional and SNP-based breeding values were estimated using pedigree-based BLUP (PBLUP) and genomic BLUP (GBLUP), respectively. Heritabilities for BT compounds were moderate (0.30–0.52). The accuracies of GBLUP and PBLUP were significantly different for androstenone (0.58 and 0.36, respectively), but comparable for indole and skatole (~0.43 and ~0.47, respectively). Several SNP windows, each explaining a small percentage of the variance of BT compound concentrations, were identified in a genome-wide association study (GWAS). A total of 18 candidate genes previously associated with BT (MX1), reproduction traits (TCF21, NME5, PTGFR, KCNQ1, UMODL1), and fat metabolism (CTSD, SYT8, TNNI2, CD81, EGR1, GIPC2, MIGA1, NEGR1, CCSER1, MTMR2, LPL, ERFE) were identified in the post-GWAS analysis. The large number of genes related to fat metabolism might be explained by the relationship between sexual steroid levels and fat deposition and be partially ascribed to the pig line investigated, which is selected for ham quality and not for lean growth.

Strategies to control boar taint (BT) in meat relies on the reduction of skatole, indole, and androstenone concentration. This might have unfavorable effects on the libido of breeding boars. The association between BT compound concentration in backfat and libido was investigated in 391 commercial breeding boars. Six sexual behavior traits (SBT; sexual arousal, salivation, mounting performance, interest in the dummy sow, penis unsheathing, and overall libido score) were scored during the training of the boars with the dummy sow. Variation in SBT was analyzed by proportional-odds cumulative logistic models. Overall, indole, skatole, and androstenone concentrations were weakly associated with libido. Farm of origin, age at training or body weight, and BT compound levels were poor predictors of boar performance (the area under the ROC curve ranged from 0.60 to 0.69). This indicates that BT compound concentrations were weakly associated with libido, even though the probability of observing good SBT scores increased with high levels of androstenone, intermediate or low levels of skatole, and intermediate to high levels of indole. Hence, practices aiming at reducing androstenone, and controlling the concentrations of skatole and indole to intermediate levels are not expected to impair the libido of young boars.