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Background Nowadays, body and tail lesions and respiratory disease are some of the greatest problems affecting the health and welfare of pigs. The aim of the study was to measure the prevalence of pleurisy, bronchopneumonia (enzootic pneumonia like lesions) and lesions on tail and body of heavy pigs subjected or not to tail docking through the inspection in Italian abattoirs. Additionally, the effect of tail docking and season was investigated on carcass quality (weight, % of lean meat, and Protected Designation of Origin (PDO) classification). For this purpose, a total 17.256 carcasses belonging to 171 batches from 103 farms were inspected in an Italian abattoir between 2019 and 2022. Enzootic pneumonia (EP) like lesions were scored according to the Madec and Derrien method, while pleurisy was scored using the Italian Slaughterhouse pleuritic evaluation system (SPES). For the tail and body, the lesions were scored according to Welfare Quality. The lesion score index (LSI) was calculated for each area. Data were analysed using a general linear model (GLM) including tail caudectomy, season and distance of the farm from the abattoir. Results The warm season increased the percentage of lesions in carcasses in all parts of the body observed (P P P P P = 0.027; P P P Conclusions In conclusion, the presence of the undocked tail and the warm season can be considered risk factors for the prevalence of tail lesions, while long transport can increase limb lesions. Furthermore, the carcass weight and meat quality were negatively influenced by tail lesions.

This study examined associations between school sector (Government, Catholic or Independent) and depressive symptomology over the secondary school years. Six waves of data collected annually from a representative Australian sample were examined. Multilevel piecewise linear and logistic regression controlling for a variety of demographic variables and protective factors was undertaken. In all sectors, depressive symptomology decreased between 10 and 13 years of age, but significantly increased for girls at age 13. Adolescents in Catholic schools reported significantly fewer symptoms of depression compared to those in Government and Independent schools. Adolescents in Catholic schools were less likely to report clinical levels of depressed mood compared to adolescents in Government schools.

An extensive rearing system (ERS) for poultry requires an outdoor run, which enhances the foraging activity of chickens. Slow-growing (SG) strains are more adapted to ERS than fast-growing (FG); and generally, have higher levels of bioactive compounds in their meat. The aim of this paper was to assess the storage efficiency of n-3 and n-6 polyunsaturated fatty acids (PUFA), tocols and carotenes in the meat of seven commercial SG genotypes (SG1-7). One hundred SG chicks/strain of both sexes were included and their walking activity (High- or Low-W) was classified: SG1-4, HW comprised more than 10% of the time budget, and SG5-7, less than 10% (LW). Chickens were reared in pens (4 pens/strain) with indoor (0.10 m2/bird) and outdoor (4 m2/bird) areas, and they were fed the same dietad libitum(starter feed for 1–21 d, grower feed from 22 d to slaughter at 81 d). The chickens were weighed weekly; feed consumption and grass intake were also estimated. At 81 days of age, 32 chickens/genotype were selected on the basis of the average weight (1:1, M:F) and slaughtered. The breast, thigh and drumstick meat were excised from 30 carcasses/genotype, sampled and stored at -20°C until analysis. Nutrients (e.g., n-3, n-6, carotenes and tocols) of feed, grass and meat were analyzed. The storage efficiency of nutrients was estimated as the ratio between the amount deposited in the body muscles (OUT) and the dietary intake (feed and grass, IN). The genotype affected chickens foraging behavior and the intake of nutrients. For SG1, SG2 and SG3, more than 50% of the intake of n-3 came from grass, whereas in the other genotypes, less than 20%. Accordingly, chickens that foraged more showed better meat nutritional profiles (less fat, more n-3 and antioxidants), which, in ERS, was ascribed to grass ingestion. However, the storage efficiency of nutrients into meat was inversely correlated with the grass intake: strains with higher grass intake (SG1, SG2, and SG3) had lower storage rates. Several hypotheses were proposed to explain these trends.

The possibility of raising awareness about misbehaviour online, such as hate speech, especially in young generations could help society to reduce their impact, and thus, their consequences. The Computer Science Department of the University of Turin has designed various technologies that support educational projects and activities in this perspective. We implemented an annotation platform for Italian tweets employed in a laboratory called #DeactivHate, specifically designed for secondary school students. The laboratory aims at countering hateful phenomena online and making students aware of technologies that they use on a daily basis. We describe our teaching experience in high schools and the usefulness of the technologies and activities tested.

The progression of AIDS depends on the complex host and virus interactions. The most important disease progression hallmarks are immune activation and apoptosis. In this study, we address the prevalence of polymorphisms related to proinflammatory and apoptotic genes, such as IFNG (+874T/A), TNF (308G/A), IL6 (-174G/C), IL8 (-251A/T), FAS (-670A/G), and FASL (-124A/G) in 160 ethnically mixed HIV-1-infected patients from multicentre cohorts with different clinical outcomes (13 elite controllers [EC], 66 slow long-term non-progressors [LTNPs], and 81 progressors [P]). The genotyping was accomplished by TaqMan-qPCR. Among all the polymorphisms analyzed in the cytokines, the IL6 -174G/C polymorphism showed a higher frequency of GG genotype in the LTNP and LTNP+EC groups as compared to the P group. Moreover, there was a significantly higher frequency of the G allele in the LTNP and LTNP+EC groups as compared to the P group. On the other hand, the levels of CD4+ T lymphocytes were higher among individuals showing the AA and AG genotypes for the FASL -124A/G polymorphism as compared to the GG genotype. Furthermore, the AG and AA genotypes were more frequent, as compared to the GG genotype, in individuals showing a lower viral load. In contrast, for the FAS -670A/G polymorphism, a significantly higher viral load was observed in individuals with the AG genotype as compared to the GG genotype. In conclusion, we found three genetic allelic variants of the IL6 -174G/C, FASL -124A/G, and FAS -670A/G polymorphisms that were related to disease progression and immunological and virological markers in cohorts of HIV-1-positive ethnically mixed patients.

Background Temporal pattern discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources. Methods We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance. Results Similar to previous findings, we noted an increase in the Information Component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1–30 days as compared to the control period of − 180 to − 1 days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs. Conclusion Our OMOP replication matched the we can account forwe can account for of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

This study examined associations between school sector (Government, Catholic or Independent) and depressive symptomology over the secondary school years. Six waves of data collected annually from a representative Australian sample were examined. Multilevel piecewise linear and logistic regression controlling for a variety of demographic variables and protective factors was undertaken. In all sectors, depressive symptomology decreased between 10 and 13 years of age, but significantly increased for girls at age 13. Adolescents in Catholic schools reported significantly fewer symptoms of depression compared to those in Government and Independent schools. Adolescents in Catholic schools were less likely to report clinical levels of depressed mood compared to adolescents in Government schools.

In this paper we present a large corpus of Twitter data in Italian, manually annotated with hate speech in the political domain. Given the rising amount of hate messages in the public debate, we believe that this is a valuable resource for the NLP community. Here we describe the collection of data and test some baseline with classification algorithms. In questo articolo presentiamo un corpus di dati Twitter di dominio politico in Italiano, annotati manualmente con etichette di odio. Dato il dilagare di messaggi di odio nel diabattito pubblico, crediamo che questa risorsa sia preziosa per la comunità di ricerca nell’elaborazione automatica del linguaggio. Qui viene descritta la raccolta dei dati e vengono applicati algoritmi di classificazione per effettuare la valutazione di base.

Inside the NLP community there is a considerable amount of language resources created, annotated and released every day with the aim of studying specific linguistic phenomena. Despite a variety of attempts in order to organize such resources has been carried on, a lack of systematic methods and of possible interoperability between resources are still present. Furthermore, when storing linguistic information, still nowadays, the most common practice is the concept of "gold standard", which is in contrast with recent trends in NLP that aim at stressing the importance of different subjectivities and points of view when training machine learning and deep learning methods. In this paper we present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG) for the collection of linguistic annotated data. O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community. The ontology has also been designed to account for a perspectivist approach, since it provides a model for encoding both gold standard and single-annotator labels in the KG. The paper is structured as follows. In Section 1 the motivations of our work are outlined. Section 2 describes the O-Dang! Ontology, that provides a common semantic model for the integration of datasets in the KG. The Ontology Population stage with information about corpora, users, and annotations is presented in Section 3. Finally, in Section 4 an analysis of offensiveness across corpora is provided as a first case study for the resource.

Exploitation and circumvention of the mentally incapacitated is a criminal offence listed in the Italian penal code (Penal Code Art. 643). It involves the exploitation and circumvention of people who are fragile and vulnerable owing to their health conditions and/or mental status, and fits into the grey area between such subjects' freedom to make decisions about their physical conditions and economic assets, and the abuse of their trust perpetrated by third parties for personal gain. The authors describe a case of financial exploitation and circumven-tion of a married couple that is remarkable in view of both the huge sums extorted from the victims and the duration of the peculiar illicit activities (that lasted more than 10 years). These were perpetrated by a charismatic figure leading a "para-religious" group. He claimed to be a prophet, but his private life was highly questionable. The method of ascertainment used to verify the victims' mentally incapacitated state is described, analyzed and commented; the diagnosis could not be deferred in view of the characteristics of the prolonged penal offences committed.

The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.

The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.