Stefania Chessa

Mastitis is one of the major diseases affecting dairy herds, with significant impacts on animal health, milk production, and farm economics. Current sensor-based approaches mainly focus on detecting ongoing infections rather than anticipating their clinical evolution. This gap limits the ability of farmers to tailor timely and proportionate interventions. Therefore, the aim of this study was to develop machine learning models capable of predicting the clinical progression of mastitis following an initial increase in somatic cell count, using data from automatic milking systems. Data were collected from October 2023 to September 2024 on nine commercial dairy farms equipped with automatic milking systems, monitoring approximately 2,000 cows. A total of 440 mastitis events were identified and classified into four severity classes (subclinical, mild, moderate, severe) based on standardized clinical examinations. Data from 10 milkings before and 3 milkings after the mastitis event were collected, including milk yield and composition, electrical conductivity, rumination time, and milking dynamics. Time-series features were extracted using the MiniRocket algorithm and used to train multiple machine learning classifiers, including logistic regression, random forest, gradient boosting, support vector machines, and k-nearest neighbors. Models were evaluated for their ability to predict mastitis progression into subclinical, mild, or severe forms. Somatic cell count, electrical conductivity, lactose content, and milk flow time emerged as the most informative predictors. The best-performing models achieved moderate predictive performance (AUC up to 0.75) and were able to anticipate the development of clinical mastitis approximately one day before the appearance of clinical signs, with sensitivity up to 0.70. These results demonstrate that machine learning models applied to automatic milking system-derived time-series data can provide early indications of mastitis severity, supporting targeted interventions. Such predictive tools may improve mastitis management by reducing unnecessary treatments, limiting disease progression, and enhancing overall herd health.

Aggression is a behavioral phenomenon observed not only in wild animals but also in companion animals, including the domestic cat. Although feline aggression represents a common welfare and public health concern, its genetic and environmental causes remain not fully understood. The aim of this systematic review was to identify and synthesize the current evidence regarding the genetic and environmental factors associated with aggressive behavior in domestic cats. Original articles published in English between 2005 and 2025 were retrieved from Scopus and PubMed using predefined search strategies. After a two-stage screening and eligibility assessment, 25 studies retrieved though the database searches met the inclusion criteria. One additional eligible study was identified through reference screening, resulting in a total of 26 included studies. No formal risk-of-bias assessment was performed. Findings were synthesized narratively because of the substantial heterogeneity in study design, behavioral assessment methods, and outcome measures. The available evidence indicates that feline aggression is influenced by a complex interaction between genetic predisposition and environmental factors. Associations have been reported with polymorphisms in genes such as OXTR and AR, chromosomal abnormalities, breed, sex, coat color, early-life experiences, owner management, and environmental conditions. Considerable variability in behavioral assessment methods and the absence of a universally accepted classification of feline aggression currently limit direct comparison across studies. Overall, feline aggression should be regarded as a multifactorial trait whose accurate interpretation requires consideration of both inherited and environmental influences.

This study investigates the reliability and potential genetic utility of data recorded by automatic milking systems by comparing them with official milk recording data. Analyses focused on phenotypic distributions, correlations, systematic differences, and heritability estimates for milk production and quality traits including milk yield, fat and protein percentage, somatic cell count, and electrical conductivity. Automatic milking system data and official milk recording data shared similar distributions. Correlations between the two systems were high for milk yield (r = 0.93), but moderate for fat (r = 0.52) and protein percentage (r = 0.48), and somatic cell count (r = 0.62), suggesting that while the former provides consistent data for quantity traits, quality-related ones may be less reliable. Systematic deviations between automatic milking systems and official milk recordings emerged across different lactation stages. Heritability estimates based on automatic milking system data were generally higher than the official control for production traits, supporting their use in genetic evaluations. Electrical conductivity displayed a similar heritability to somatic cell count, but its measure is insufficiently detailed and its use as an indirect indicator of udder health is not recommended. Automatic milking system data demonstrates potential for integration into genetic selection programs, although further refinement of sensor accuracy is recommended.

Aggressiveness in dogs is a complex behavioral trait with implications for animal welfare and public safety. Despite domestication, dogs retain aggressive tendencies shaped by both genetic and environmental factors. This systematic review synthesizes the literature from 2000 to 2024 on the genetic and environmental bases of canine aggression. Using PRISMA 2020 guidelines, 144 articles were retrieved from Scopus and PubMed and screened in two phases, resulting in 33 studies selected for analysis. These were evaluated using a 20-question grid across seven categories, including phenotyping, genetic analysis, population structure, and future directions. The studies support a polygenic model of aggressiveness, with associations reported for genes involved in neurotransmission, hormone signaling, and brain function. However, inconsistencies in phenotyping, small sample sizes, and a limited consideration of environmental factors hinder robust conclusions. Most studies focused on popular companion breeds, while those commonly labeled as aggressive were underrepresented. The findings highlight the relevance of gene–environment interactions but underscore that aggression is often poorly defined and measured across studies. Future research should prioritize standardized phenotyping tools, broader breed inclusion, and the functional validation of genetic findings. These efforts will improve the understanding of dog aggression and inform breeding, behavioral assessment, and public policy.

Objectives: The objectives of this study were to investigate the c.1024G>T SNP in the TRPV4 gene in Scottish Straight and Fold cats, and to evaluate the pattern of skeletal phenotype and the evolution of radiological signs of Scottish Fold osteochondrodysplasia (SFOCD) over time in heterozygous subjects. Methods: DNA was obtained from blood samples of 17 cats (Scottish Fold: n = 12; Scottish Straight: n = 5) and subsequently genotyped by sequencing in a 249 bp region of the TRPV4 gene (exon 6), including the known c.1024G>T causative mutation for osteochondrodysplasia. Orthopaedic and radiographic analyses were performed on animals carrying the mutant allele. Results: Genotyping by sequencing confirmed that all and only the Scottish Fold cats carried the mutant allele in a heterozygous asset. Furthermore, two other exon variants, already described in the literature as silent variants, were found in some of the sampled cats. Comparative orthogonal radiographic views of the shoulder, elbow, carpus, hip, stifle and tarsus were obtained. A mediolateral projection of the thoracic and lumbar column was also performed. Three out of four cats were clinically and radiographically examined again 1.5 years later. Conclusions and relevance: Although the presence of the mutant allele in all the tested Scottish Fold cats was confirmed, only 1/12 showed clinical signs of SFOCD. Furthermore, no cats in the 1.5-year follow-up showed skeletal changes. Although significant, the c.1024G>T mutation in the TRPV4 gene, supposedly, is not the only cause or risk of developing SFOCD.

Heifers are a fundamental resource on farms, and their importance is reflected in both farm management and economy. Therefore, the selection of heifers to be reared on a farm should be carefully performed to select only the best animals. Genomic selection is available nowadays to evaluate animals in a fast and economic way. However, it is mainly used on the sire line and on performance traits. Ten farms were selected based on their 5-year records of average somatic cell count and evenly classified into high (>300,000 cells/mL) and low somatic cell count (<150,000 cells/mL). Genomic indexes (regarding both wellness and productive traits) were evaluated in 157 Italian Holstein heifers reared in the selected ten farms (90 from high-cells farms and 67 from low-cells ones). Linear mixed models were fitted to analyze the effects of the abovementioned genomic indexes on related phenotypes. Results have shown that farms classified into low somatic cell count had an overall better animal genomic pool compared to high somatic cell count ones. Additionally, the results shown in this study highlighted a difference in wellness genomic indexes in animals from farms with either a high or a low average somatic cell count. Applying genomic tools directly to heifer selection could improve economic aspects related to herd turnover.

Small ruminant lentiviruses (SRLV) belong to the Retroviridae family and can cause various diseases. One of the most impacting diseases is visna-maedi, a complex disease characterized by long latencies and chronic progressive inflammatory events affecting the nervous system, lungs, mammary gland, and articular joints. A single nucleotide polymorphism (rs408593969, c.103G>A, missense mutation E35K) in the ovine transmembrane protein gene 154 (TMEM154) was identified as protective against small ruminant lentivirus infection in different herds worldwide. However, there is evidence in the scientific literature of a breed-specificity of this protective effect and, furthermore, there are still limited studies regarding the association between the animal genotype and the infecting virus genotype. Thus, the aim of this study was to further investigate the association between the animal genotype for the suggested protective mutation and the infecting virus genotype, in three different sheep breeds reared in northern Italy. The results obtained only partially confirmed the data available in the literature, as the protective effect was confirmed only for SRLV genotype A clusters, while other genotypes (namely B and E) infected AA and GA animals. Further studies with an experimental infection of specific virus genotypes in hosts with specific genotypes are required to confirm the larger number of cases the results obtained in this study.

Food authentication in local breeds has important implications from both an economic and a qualitative point of view. Meat products from autochthonous breeds are of premium value, but can easily incur fraudulent or accidental substitution or mislabeling. The aim of this study was to identify a small number of SNPs using the Illumina PorcineSNP60 BeadChip for breed traceability, in particular of the Italian Nero Siciliano pig and its derived products. A panel of 12 SNPs was sufficient to discriminate Nero Siciliano pig from cosmopolitan breeds and wild boars. After adding 8 SNPs, the final panel of 20 SNPs allowed us to discriminate all the breeds involved in the study, to correctly assign each individual to its breed, and, moreover, to discriminate Nero Siciliano from first-generation hybrids. Almost all livestock breeds are being genotyped with medium- or high-density SNP panels, providing a large amount of information for many applications. Here, we proposed a method to select a reduced SNP panel to be used for the traceability of pig breeds.

One of the major issues in Scottish and Highland Fold cats is osteochondrodysplasia, a bone and cartilage development disorder. A point mutation in TRPV4 gene, associated to the ear-folded phenotype, is responsible of osteochondrodysplasia in those cat breeds. DNA amplification and genotyping by sequencing are currently the standard techniques to identify carrier animals. We developed a Ligase Detection Reaction technique, which is cheaper, more affordable and time saving analysis, obtaining 100% of correct genotyping and identification of all the fold animals as heterozygous for the c.1024G > T mutation. Ligase Detection Reaction can be used to determine the genotype of kittens even before the manifestation of the folded ears character. Despite the presence of the mutation, none of the fold animals presented clinical or radiographic signs of the disease. Thus, the association between the c.1024G > T genotype and osteochondrodysplasia, at least in young animals, is not evident. Further studies are required to fully evaluate this association.