RESUMEN
Information on slaughter procedures for farmed fish in aquaculture is limited, both in Europe and in Italy, due to a general lack of field data. The aim of this study was to gather information on the procedures used to slaughter fish in Italy and to discuss them considering the WOAH and EFSA recommendations on fish welfare. Using a questionnaire survey, data were collected by official veterinarians in 64 slaughtering facilities where 20 different species of fish were slaughtered. The main species slaughtered were rainbow trout (Oncorhynchus mykiss; 29/64), followed by European sea bass (Dicentrarchus labrax; 21/64), sea bream (Sparus aurata; 21/64), Arctic char (Salvelinus alpinus; 14/64), European eel (Anguilla anguilla; 11/64), sturgeon (Acipenser spp; 11/64), common carp (Cyprinus carpio; 6/64), and brown trout (Salmo trutta fario L.; 5/64). The most applied stunning/killing methods were "asphyxia in ice/thermal shock" and "electric in water bath," followed by "percussion," "asphyxia in air," and "electric dry system." After the application of the method, the assessment of the fish level of unconsciousness was practiced in 72% of the facilities using more than one indicator, with "breathing" and "coordinated movements" the most practiced. The collected data showed a discrepancy between the available recommendations about the welfare of fish at slaughter and what is practiced in many production sites, but for many species precise recommendations are still not available.
RESUMEN
Heart rate (HR) and heart rate variability (HRV) are commonly used physiological measures in animals. While several studies exist on horse HRV, less information is available for donkeys. This scoping review aims to understand the extent and type of published evidence on donkey HR and HRV, their clinical and research applications, the devices used, and the analysis performed. Only quantitative primary studies published in English were considered. Four different databases were queried through the Web of Science platform, with additional evidence identified by citation chasing. After a two-stage screening phase, data were extracted considering study and population characteristics, information on HR/HRV analysis, and applications. The majority of the 87 included articles (about 80%) concerned a sample size of up to 20 individuals and were published since 2011 (about 65%). Forty-one articles employed an electronic device for signal acquisition (mainly electrocardiographs and heart rate monitors), yet only two articles reported HRV parameters. The literature on donkey HRV is lacking, and this gap can be filled by gaining knowledge on donkey characteristics and finding useful tools for welfare assessment. Comparison with what is known about the horse allows a discussion of the technical and interpretative difficulties that can be encountered with donkeys.
RESUMEN
The visual system needs to extract the most important elements of the external world from a large flux of information in a short time for survival purposes. It is widely believed that in performing this task, it operates a strong data reduction at an early stage, by creating a compact summary of relevant information that can be handled by further levels of processing. In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP). This allows a much stronger data reduction than models based just on redundancy reduction. We show that optimizing this model for best information preservation under tight constraints on computational resources yields surprisingly specific a-priori predictions for the shape of biologically plausible features, and for experimental observations on fast extraction of salient visual features by human observers. Interestingly, applying the same optimized model to HEP data acquisition systems based on pattern-filtering architectures leads to specific a-priori predictions for the relevant data patterns that these devices extract from their inputs. These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as "meaningful features" in the input data.