RESUMO
Biowaste from slaughterhouses can be recovered to benefit food security and reduce contamination potential. More than 3 billion heads of livestock are consumed worldwide, which will increase by 17% by 2028, generating more biowaste, increasing infectious agents, and causing economic losses due to circular economy principles not being applied. This work evaluated the nutritional quality of four types of biowaste from bovine slaughter which were transformed into a meal for guinea pigs (rumen content (RCM), ears (EaM), blood (BM), and cheeks (CM)) according to their chemical composition, digestible components, energy contribution, and voluntary consumption. For the animal model, adult male guinea pigs were arranged in metabolic cages for feces collection without urinary contamination. Nine guinea pigs were used in each digestibility test. First, a direct digestibility test was conducted using a meal of barley as a reference diet (RD), the indigestibility coefficient of which allowed for the estimation of the digestibility of biowaste meals through indirect calculations; for this, diets composed of 80% of the RD and 20% of the corresponding biowaste meals were evaluated. The difference method was suitable for determining the digestibility of beef biowaste using the indigestibility coefficients of the reference diet to calculate the digestibility of ingredients which could not be offered as 100% of the meal but were incorporated as 20%. The digestible protein and metabolizable energy contents of RCM, EaM, BM, and CM were 10.2% and 2853 kcal/kg, 44.5% and 3325 kcal/kg, 70.7% and 2583 kcal/kg, and 80.8% and 3386 kcal/kg, respectively. The CM and BM feeds had the highest contributions of digestible protein due to their higher nitrogen content, and the CM and EaM feeds had the highest ME contents due to their higher fat contents. The biowaste meal consumption in descending order was CM > RCM > EaM > BM, which were consumed without problems. These results are indicative that these components can be part of guinea pigs' diets, and it is recommended to continue studies into guinea pig growth and fattening diets with different levels of these biowaste meals.
RESUMO
Circulating recombinant forms (CRFs) are important components of the HIV-1 pandemic. Those derived from recombination between subtype B and subsubtype F1, with 18 reported, most of them of South American origin, are among the most diverse. In this study, we identified a HIV-1 BF1 recombinant cluster that is expanding in Spain, transmitted mainly via heterosexual contact, which, analyzed in near full-length genomes in four viruses, exhibited a coincident BF1 mosaic structure, with 12 breakpoints, that fully coincided with that of two viruses (10BR_MG003 and 10BR_MG005) from Brazil, previously classified as CRF72_BF1. The three remaining Brazilian viruses (10BR_MG002, 10BR_MG004, and 10BR_MG008) previously identified as CRF72_BF1 exhibited mosaic structures highly similar, but not identical, to that of the Spanish viruses and to 10BR_MG003 and 10BR_MG005, with discrepant subtypes in two short genome segments, located in pol and gp120env. Based on these results, we propose that the five viruses from Brazil previously identified as CRF72_BF1 actually belong to two closely related CRFs, one comprising 10BR_MG002, 10BR_MG004, and 10BR_MG008, which keep their CRF72_BF1 designation, and the other, designated CRF122_BF1, comprising 10BR_MG003, 10BR_MG005, and the viruses of the identified Spanish cluster. Three other BF1 recombinant genomes, two from Brazil and one from Italy, previously identified as unique recombinant forms, were classified as CRF72_BF1. CRF122_BF1, but not CRF72_BF1, was associated with protease L89M substitution, which was reported to contribute to antiretroviral drug resistance. Phylodynamic analyses estimate the emergence of CRF122_BF1 in Brazil around 1987. Given their close phylogenetic relationship and similar structures, the grouping of CRF72_BF1 and CRF122_BF1 in a CRF family is proposed.
RESUMO
The high frequency data in intensive care unit is flashed on a screen for a few seconds and never used again. However, this data can be used by machine learning and data mining techniques to predict patient outcomes. Learning finite-state transducers (FSTs) have been widely used in problems where sequences need to be manipulated and insertions, deletions and substitutions need to be modeled. In this paper, we learned the edit distance costs of a symbolic univariate time series representation through a stochastic finite-state transducer to predict patient outcomes in intensive care units. The Nearest-Neighbor method with these learned costs was used to classify the patient status within an hour after 10 h of data. Several experiments were developed to estimate the parameters that better fit the model regarding the prediction metrics. Our best results are compared with published works, where most of the metrics (i.e., Accuracy, Precision and F-measure) were improved.