AVALIAÇÃO DE LINGUIÇAS DE TILÁPIAS DO NILO (Oreochromis niloticus)SUBMETIDAS A DIFERENTES MÉTODOS DE DEFUMAÇÃO

Uma forma de diversificar e estimular o consumo de peixes no Brasil pode ocorrer com a elaboração de produtos inovadores como as linguiças defumadas. Portanto, o objetivo do presente estudo foi avaliar aspectos nutricionais (proteína bruta, gordura, umidade e cinzas), físicas (perda de peso e encolhimento, capacidade de retenção de água, textura e cor instrumental e pH), química (atividade de água) e bacteriológicas(Escherichia coli, Salmonellaspp., Staphylococcus coagulase positiva ebactérias aeróbias psicrotróficas) de linguiças de filés de tilápias do Nilo submetidas a defumação tradicional e líquida. As linguiças submetidas a defumação líquida apresentaram maior percentual proteico, maior valor de L* (luminosidade), menor valor de a*(vermelho) e b* (amarelo) porém com maior perda de peso durante o processamento e menor capacidade de retenção de água. Os teores de umidade, gordura, cinzas, porcentagem de encolhimento, textura instrumental, pH, atividade de água e análises bacteriológicas não foram influenciadas por ambos os métodos de defumação aplicados.Portanto, a defumação líquida causamelhora nos percentuais de proteínas, pouca variação nos aspectos físicos, químicos e bacteriológicos de linguiças elaboradas com filés de tilápias do Nilo em relação ao método tradicional, além de apresentar maior facilidade de execução, menor poluição ambiental, mais fácil limpeza após o processamento e menor possibilidade de deposição de compostos químicos cancerígenos, sendo um potencial substituto do método tradicional de defumação de linguiças de tilápias do Nil

Endometriose no Brasil: perfil epidemiológico das internações nos últimos dez anos (2013-2022)

Introdução: Endometriose é uma doença crônica que afeta entre até 10% das mulheres em idade reprodutiva. Definida pela presença de tecido endometrial fora da cavidade uterina, essa doença causa um processo inflamatório na pelve que pode levar à fibrose e formação de aderências. Objetivo: Descrever o perfil epidemiológico das internações por endometriose no Brasil nos últimos dez anos (2013-2022). Métodos: Trata-se de um estudo transversal, observacional, descritivo, de caráter quantitativo, no qual os dados foram obtidos a partir do Departamento de Informática do Sistema Único de Saúde - DATASUS. As variáveis pesquisadas foram: total de internações, cor/raça, faixa etária, média de permanência e óbitos. O período da pesquisa foi delimitado entre os anos de 2013 e 2022. Resultados: Foram registradas 119.467 internações por endometriose entre 2013 e 2022. O maior número foi registrado no ano de 2015, 15.061. A região sudeste apontou o maior número de internações, 49.898. A cor/raça branca registrou 44.507 internações. A faixa etária com maior número de hospitalizações foi a de 40 a 49 anos. A média de permanência foi de 2,4 dias. Conclusão: As internações por endometriose desenham uma curva que oscila ao longo dos anos no Brasil. O perfil epidemiológico das internações foi caracterizado por mulheres brancas na faixa etária de 40 a 49 anos. A média de permanência das internações foi de 2,4 dias e a região com maior número de casos foi a região sudeste

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Wavelet gated multiformer for groundwater time series forecasting

Abstract Developing accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channeled through a mixing gate (linear combination) of sub-encoders (Transformer and Wavelet Crossformer) that output trending signatures to the decoder. This process improved the model’s predictive capabilities, reducing Mean Absolute Error by 31.26 % compared to the second-best performing transformer-like models evaluated. We have also used the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) to extract cyclical trends from pairs of stations across multifractal regimes by denoising the pair of signals with Daubechies wavelets. Our dataset was obtained from a network of eight wells for groundwater monitoring in Brazilian aquifers, six rainfall stations, eleven river flow stations, and three weather stations with atmospheric pressure, temperature, and humidity sensors

Multi-fractal detrended cross-correlation heatmaps for time series analysis

Complex systems in biology, climatology, medicine, and economy hold emergent properties such as non-linearity, adaptation, and self-organization. These emergent attributes can derive from large-scale relationships, connections, and interactive behavior despite not being apparent from their isolated components. It is possible to better comprehend complex systems by analyzing cross-correlations between time series. However, the accumulation of non-linear processes induces multiscale structures, therefore, a spectrum of power-law exponents (the fractal dimension) and distinct cyclical patterns. We propose the Multifractal detrended cross-correlation heatmaps (MF-DCCHM) based on the DCCA cross-correlation coefficients with sliding boxes, a systematic approach capable of mapping the relationships between fluctuations of signals on different scales and regimes. The MF-DCCHM uses the integrated series of magnitudes, sliding boxes with sizes of up to 5% of the entire series, and an average of DCCA coefficients on top of the heatmaps for the local analysis. The heatmaps have shown the same cyclical frequencies from the spectral analysis across different multifractal regimes. Our dataset is composed of sales and inventory from the Brazilian automotive sector and macroeconomic descriptors, namely the Gross Domestic Product (GDP) per capita, Nominal Exchange Rate (NER), and the Nominal Interest Rate (NIR) from the Central Bank of Brazil. Our results indicate cross-correlated patterns that can be directly compared with the power-law spectra for multiple regimes. We have also identified cyclical patterns of high intensities that coincide with the Brazilian presidential elections. The MF-DCCHM uncovers non-explicit cyclic patterns, quantifies the relations of two non-stationary signals (noise effect removed), and has outstanding potential for mapping cross-regime patterns in multiple domains.Funding Agencies|Swedish National Infrastructure for Computing (SNIC) [SNIC 2022/22-843]</p