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This study explored the impact of various label information (extrinsic attributes) and sociodemographic and attitudinal factors (intrinsic attributes) on Brazilian consumer choices, using simulated traditional and plant-based muçarela cheese as the model product. The research was conducted in two phases: the first involved a structured questionnaire assessing attitudinal dimensions such as Health Consciousness, Climate Change, Plant-based Diets, and Food Neophobia, along with sociodemographic data collection. The second phase comprised a discrete choice experiment with (n = 52) and without (n = 509) eye tracking. The term "Cheese" on labels increased choice probability by 7.6% in a general survey and 15.1% in an eye tracking study. A prolonged gaze at "Cheese" did not affect choice, while more views of "Plant-based product" slightly raised choice likelihood by 2.5%. Repeatedly revisiting these terms reduced the choice probability by 3.7% for "Cheese" and 1% for "Plant-based product". Nutritional claims like "Source of Vitamins B6 and B12" and "Source of Proteins and Calcium" boosted choice probabilities by 4.97% and 5.69% in the general and 8.4% and 6.9% in the eye-tracking experiment, respectively. Conversely, front-of-package labeling indicating high undesirable nutrient content decreased choice by 13% for magnifying presentations and 15.6% for text. In a plant-based subsample, higher environmental concerns and openness to plant-based diets increased choice probabilities by 5.31% and 5.1%, respectively. These results highlight the complex dynamics between label information, consumer understanding, and decision-making.

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Among the emerging prebiotics, galactooligosaccharide (GOS) has a remarkable value with health-promoting properties confirmed by several studies. In addition, the application of ohmic heating has been gaining prominence in food processing, due to its various technological and nutritional benefits. This study focuses on the transformative potential of ohmic heating processing (OH, voltage values 30 and 60 V, frequencies 100, 300, and 500 Hz, respectively) in prebiotic chocolate milk beverage (3.0 %w/v galactooligosaccharide) processing. Chemical stability of GOS was assessed along all the ohmic conditions. In addition, microbiological analysis (predictive modeling), physical analysis (color and rheology), thermal load indicators assessment, bioactivity values, and volatile compound was performed. HPAEC-PAD analysis confirmed GOS stability and volatile compound evaluation supported OH's ability to preserve flavor-associated compounds. Besides, OH treatments demonstrated superior microbial reduction and decreased thermal load indicators as well as the assessment of the bioactivity. In conclusion, OH presented was able to preserve the GOS chemical stability on chocolate milk beverages processing with positive effects of the intrinsic quality parameters of the product.

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Climate change mitigation and biodiversity conservation are two major environmental actions that need to be effectively performed this century, alongside ensuring food supply for a growing global human population. These three issues are highly interlinked through land management systems. Thus, major global food production regions located in biodiversity hotpots and with potential for carbon sequestration face trade-offs between these valuable land-based ecosystem services. The state of Mato Grosso in Brazil is one such region, where private lands that have been illegally used for agriculture could be restored to natural vegetation - with potential benefits for climate change mitigation and biodiversity conservation, although with potentially negative effects on food production. To address this challenge, in this study we used a multicriteria nexus modeling approach that considers carbon stocks, priority areas for biodiversity conservation, and the opportunity for food production, to develop scenarios of land allocation that aim to balance the benefits and drawbacks of ecosystem restoration. Results show that forcing landowners to restore their individual lands compromises the potential for a "green land market" throughout the Amazon biome in which private landowners with lower food production capacities (e.g., less connected to markets and infrastructure) would benefit from restoration programs that compensate them for the inclusion of environmental restoration among their economic activities, instead of taking large economic risks to produce more food. We additionally highlight that strategic ecosystem restoration can achieve higher gains in biodiversity and carbon with lower costs of restoration actions and with minimal impacts on agriculture. Analyses like ours demonstrate how scenarios of land allocation that simultaneously address climate mitigation and biodiversity conservation through ecosystem restoration, while also minimizing possible impacts on food production, can be sought to move the world towards a sustainable future.

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Genetic diversity and population structuring for the species Haemogogus leucocelaenus, a sylvatic vector of yellow fever virus, were found to vary with the degree of agricultural land use and isolation of fragments of Atlantic Forest in municipalities in the state of São Paulo where specimens were collected. Genotyping of 115 mitochondrial SNPs showed that the populations with the highest indices of genetic diversity (polymorphic loci and mean pairwise differences between the sequences) are found in areas with high levels of agricultural land use (northeast of the State). Most populations exhibited statistically significant negative values for the Tajima D and Fu FS neutrality tests, suggesting recent expansion. The results show an association between genetic diversity in this species and the degree of agricultural land use in the sampled sites, as well as signs of population expansion of this species in most areas, particularly those with the highest forest edge densities. A clear association between population structuring and the distance between the sampled fragments (isolation by distance) was observed: samples from a large fragment of Atlantic Forest extending along the coast of the state of São Paulo exhibited greater similarity with each other than with populations in the northwest of the state.

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Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK's layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation.

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The intense process of deforestation in tropical forests poses serious challenges for the survival of biodiversity, as well as for the human species itself. This scenario is supported by the increase in the incidence of epidemics of zoonotic origin observed over the last few decades. In the specific case of sylvatic yellow fever (YF), it has already been shown that an increase in the transmission risk of the causative agent (yellow fever virus - YFV) is associated with areas with a high degree of forest fragmentation, which can facilitate the spread of the virus. In this study we tested the hypothesis that areas with more fragmented landscapes and a higher edge density (ED) but a high degree of connectivity between forest patches favor YFV spread. To this end, we used YF epizootics in non-human primates (NHPs) in the state of São Paulo to build direct networks, and used a multi-selection approach to analyze which landscape features could facilitate YFV spread. Our results showed that municipalities with the potential to spread the virus exhibited a higher amount of forest edge. Additionally, the models with greater empirical support showed a strong association between forest edge density and the risk of occurrence of epizootic diseases, as well as the need for a minimum threshold of native vegetation cover to restrict their transmission. These findings corroborate our hypothesis that more fragmented landscapes with a higher degree of connectivity favor the spread of YFV, while landscapes with fewer connections tend to act as dead zones for the circulation of the virus.

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Over the last few years, understanding of the effects of increasingly interconnected global flows of agricultural commodities on coupled human and natural systems has significantly improved. However, many important factors in environmental change that are influenced by these commodity flows are still not well understood. Here, we present an empirical spatial modelling approach to assess how changes in forest cover are influenced by trade destination. Using data for soybean-producing municipalities in the state of Mato Grosso, Brazil, between 2004 and 2017, we evaluated the relationships between forest cover change and the annual soybean trade destination. Results show that although most of the soybean produced in Mato Grosso during the study period (60%) was destined for international markets, municipalities with greater and more consistent soybean production not destined for international markets during the study period were more strongly associated with deforestation. In these municipalities, soybean production was also significantly correlated with cattle and pasture expansion. These results have important implications for the sustainable management of natural resources in the face of an increasingly interconnected world, while also helping to identify the most suitable locations for implementing policies to reduce deforestation risks.

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Serratia marcescens is a Gram-negative bacterium presenting intrinsic resistance to polymyxins that has emerged as an important human pathogen. Although previous studies reported the occurrence of multidrug-resistance (MDR) S. marcescens isolates in the nosocomial settings, herein, we described isolates of this extensively drug-resistant (XDR) species recovered from stool samples of food-producing animals in the Brazilian Amazon region. Three carbapenem-resistant S. marcescens strains were recovered from stool samples of poultry and cattle. Genetic similarity analysis showed that these strains belonged to the same clone. Whole-genome sequencing of a representative strain (SMA412) revealed a resistome composed of genes encoding resistance to ß-lactams [blaKPC-2, blaSRT-2], aminoglycosides [aac(6')-Ib3, aac(6')-Ic, aph(3')-VIa], quinolones [aac(6')-Ib-cr], sulfonamides [sul2], and tetracyclines [tet(41)]. In addition, the analysis of the virulome demonstrated the presence of important genes involved in the pathogenicity of this species (lipBCD, pigP, flhC, flhD, phlA, shlA, and shlB). Our data demonstrate that food-animal production can act as reservoirs for MDR and virulent strains of S. marcescens.

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This study aimed to characterize a Klebsiella pneumoniae strain (KP411) recovered from the stool samples of poultry (Gallus gallus) in the Brazilian Amazon Region. The whole-genome sequencing of KP411 revealed the presence of an important arsenal of antimicrobial resistance genes to ß-lactams (blaCTX-M-14, blaTEM-1B, blaKPC-2, blaSVH-11), aminoglycosides [aph(3″)- Ib, aph(6)-Id, aph(3')-Ia], sulfonamides (sul1, sul2), quinolones (oqxAB), fosfomycin (fosAKP), and macrolides [mph(A)]. Furthermore, our analyses revealed that the KP411 strain belongs to the ST258 clonal lineage, which is one of the main epidemic clones responsible for the dissemination of KPC-2 worldwide. Our data suggest that food-producing animals may act as reservoirs of multidrug-resistant K. pneumoniae belonging to the ST258 clone, and, consequently, contribute to their dissemination to humans and the environment.

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The processing of high-protein vanilla-flavored milk was performed under different electric field strengths of ohmic heating (5.22 V/cm, OH6; 6.96 V/cm, OH8; 8.70 V/cm, OH10; 10.43 V/cm, OH12) to evaluate the energy consumption, processing parameters, and microbiological, rheological, and biological aspects, compared with the sample submitted to conventional pasteurization (PAST, 72 °C/15 s). All samples showed higher than 12 g/100 mL of protein, consisting of high-protein content products. In addition, Ohmic Heating (OH) generated lower energy expenditure and more significant microbial inactivation of lactic acid bacteria, molds and yeasts, total mesophiles, and psychotropics. Furthermore, OH at lower electric field strengths, mainly OH8, improved anti-diabetic, anti-oxidant, and anti-hypertensive activities and rheological properties, and resulted in lower hydroxymethylfurfural contents, and higher whey protein nitrogen index. The results suggest that OH is a technology that can be used in flavored milk with high-protein content, being recommended an electric field strength of 6.96 V/cm. However, more studies are necessary to evaluate the effect of OH on high-protein dairy products, mainly by studying other OH processing parameters.

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