Animal Welfare Assessment and Meat Quality through Assessment of Stress Biomarkers in Fattening Pigs with and without Visible Damage during Slaughter.

The study aimed to investigate the physiological and meat quality differences between Non-Ambulatory, Non-Injured (NANI), and without apparent abnormalities (non-NANI) pigs in a commercial slaughterhouse setting, focusing on the impact of stress and health conditions on the overall well-being and meat quality of the animals. A total of 241 surgically castrated crossbred male pigs from Southern Brazil were analyzed, with 131 non-NANI pigs and 110 NANI pigs. Infrared orbital temperature, rectal temperature, hematological parameters, and meat quality measurements were collected. Statistical analysis included ANOVA tests and principal component analysis (PCA). NANI pigs exhibited significantly higher infrared orbital temperatures and rectal temperature (p < 0.01). Hematological analysis revealed higher levels of hemoglobin, hematocrit, and red blood cells in NANI pigs (p < 0.05). White blood cell count and lactate dehydrogenase were significantly elevated in NANI pigs (p < 0.01), indicating potential infections or inflammatory responses. Meat quality parameters showed that NANI pigs had lower pH values, higher luminosity, and increased drip loss (p < 0.01), reflecting poorer water retention and potential muscle glycogen depletion. The study highlights the physiological and meat quality differences between NANI and non-NANI pigs, emphasizing the impact of stress, health conditions, and handling procedures on the animals. Blood biomarkers proved valuable in assessing physiological stress, immune response, and potential health issues in pigs, correlating with meat quality abnormalities. Utilizing these biomarkers as predictive tools can enhance animal welfare practices and contribute to improving meat quality in the swine industry.

How people interact with a chatbot against disinformation and fake news in COVID-19 in Brazil: The CoronaAI case.

BACKGROUND: The search for valid information was one of the main challenges encountered during the COVID-19 pandemic, which resulted in the development of several online alternatives. OBJECTIVES: To describe the development of a computational solution to interact with users of different levels of digital literacy on topics related to COVID-19 and to map the correlations between user behavior and events and news that occurred throughout the pandemic. METHOD: CoronaAI, a chatbot based on Google's Dialogflow technology, was developed at a public university in Brazil and made available on WhatsApp. The dataset with users' interactions with the chatbot comprises approximately 7,000 hits recorded throughout eleven months of CoronaAI usage. RESULTS: CoronaAI was widely accessed by users in search of valuable and updated information on COVID-19, including checking the veracity of possible fake news about the spread of cases, deaths, symptoms, tests and protocols, among others. The mapping of users' behavior revealed that as the number of cases and deaths increased and as COVID-19 became closer, users showed a greater need for information applicable to self-care compared to following the statistical data. In addition, they showed that the constant updating of this technology may contribute to public health by enhancing general information on the pandemic and at the individual level by clarifying specific doubts about COVID-19. CONCLUSION: Our findings reinforce the potential usefulness of chatbot technology to resolve a wide spectrum of citizens' doubts about COVID-19, acting as a cost-effective tool against the parallel pandemic of misinformation and fake news.

White striping degree assessment using computer vision system and consumer acceptance test.

OBJECTIVE: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. METHODS: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). RESULTS: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. CONCLUSION: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.