Soil and water losses in eucalyptus plantation and natural forest and determination of the USLE factors at a pilot sub-basin in Rio Grande do Sul, Brazil

Monitoring water erosion and the factors that control soil and water loss are essential for soil conservation planning. The objective of this study was to evaluate soil and water losses by water erosion under natural rainfall in eucalyptus plantations established in 2001 (EF2), and 2004 (EF1), native forest (NF) and bare soil (BS), during the period of 2007 to 2012; and to determine the USLE factors: rain erosivity (R), erodibility (K) of a Red Argisol and the cover-management factor (C) for EF1, EF2 and NF at a pilot sub-basin, in Eldorado do Sul, RS, Brazil. The R factor was estimated by the EI30 index, using rainfall data from a gauging station located at the sub-basin. The soil and water losses were monitored in erosion plots, providing consistent data for the estimation of the K and C factors. The sub-basin presented an average erosivity of 4,228.52 MJ mm ha-1 h-1 yr-1. The average annual soil losses em EF1 and EF2 (0.81 e 0.12 Mg ha-1 year-1, respectively) were below of the limit of tolerance, 12.9 Mg ha-1 year-1. The percentage values of water loss relating to the total rainfall decreased annually, approaching the values observed at the NF. From the 5th year on after the implantation of the eucalyptus systems, soil losses values were similar to the ones from NF. The erodibility of the Red Argisol was of 0.0026 Mg ha h ha-1 MJ-1mm-1 and the C factor presented values of 0.121, 0.016 and 0.015 for EF1, EF2 and NF, respectively

Assessing sediment yield and streamflow with SWAT model in a small sub-basin of the Cantareira System

Hydro-sedimentological models might be useful tools for investigating the effectiveness of soil and water conservation practices. However, evaluating the usefulness of such models requires that predictions are tested against observational data and that uncertainty from model parameterization is addressed. Here we aimed to evaluate the capacity of the SWAT model to simulate monthly streamflow and sediment load in the Posses creek catchment (12 km2), Southeast Brazil. The SUFI-2 algorithm from SWAT-CUP was applied for calibration, testing, uncertainty, and sensitivity analysis. The model was calibrated and initially tested using discharge and sediment load data, which were measured at the catchment outlet. Additionally, we used soil loss measurements from erosion plots within the catchment as independent data for model evaluation. Average monthly streamflow simulations obtained satisfactory results, with Nash-Sutcliffe coefficient (NSE) values of 0.75 and 0.51 for the calibration and testing periods, respectively. Sediment load simulations also displayed satisfactory results for calibration (NSE = 0.65) and testing (NSE = 0.52). However, the comparison with independent plot data revealed that SWAT severely overestimated hillslope erosion rates and compensated it with high sediment channel deposition. Moreover, the model was not sensitive to the parameters used for calculating hillslope sediment yields. Therefore, it should be used with caution for evaluating the interactions between land use, soil erosion, and sediment delivery. We found that the commonly used outlet-based approach for model calibration and testing can lead to internal misrepresentations, and models can reproduce the right answer for the wrong reasons

Desenvolvimento de um software de gerenciamento de produção para os piscicultores do estado de Roraima / Development of a production management software for fish farmers in the state of Roraima

O tambaqui representa 98% do total de peixes produzidos em cativeiro no estado de Roraima. O município de Amajari é destaque na produção nacional, sendo o principal polo produtor de pescados de cultivo do estado. Fatores como variações no clima, incidência de doenças e comportamento dos preços nos mercados, aliados ao baixo nível de conhecimento técnico, principalmente dos pequenos produtores, torna necessária a difusão de técnicas e ferramentas com a finalidade de melhorar as atividades operacionais dentro de uma piscicultura. Com isso, o uso de ferramentas computacionais, como por exemplo planilhas eletrônicas, pode ser útil para melhorar a organização das informações dos recursos utilizados na produção, com a possibilidade de realizar um planejamento e decisões estratégicas conforme a realidade da fazenda. Sendo assim, o objetivo deste trabalho é desenvolver um programa utilizando a linguagem de programação Visual Basic for Application (VBA) que permita ao usuário realizar o acompanhamento de sua produção por meio de um ambiente personalizado a fim de melhorar práticas dentro de uma piscicultura. O trabalho foi dividido em duas etapas: elaboração da planilha eletrônica no ambiente Microsoft Excel e desenvolvimento do software de acompanhamento. Os parâmetros de produção foram definidos tendo como base os principais índices zootécnicos utilizados na literatura. Para o desenvolvimento desse sistema foram elaborados códigos de programação em VBA e formulários para interação entre usuário e software. O programa desenvolvido é formado por cinco abas, sendo elas: “Cadastrar povoamento”, "Cadastrar acompanhamento”, “Consultar viveiro”, “Editar viveiro” e “Encerrar viveiro”. Assim, esse sistema permite que o produtor cadastre, consulte, visualize e imprima os dados referentes ao acompanhamento de sua produção, possibilitando que técnicos e produtores implementem em suas pisciculturas o gerenciamento zootécnico por meio ferramentas computacionais, resultando em melhores práticas e atividades gerenciais

Photobiomodulation reduces the cytokine storm syndrome associated with Covid-19 in the zebrafish model

Although the exact mechanism of the pathogenesis of COVID-19 is not fully understood, oxidative stress and the release of pro-inflammatory cytokines have been highlighted as playing a vital role in the pathogenesis of the disease. In this sense, alternative treatments are needed to reduce the inflammation caused by COVID-19. Therefore, this study aimed to investigate the potential effect of red PBM as an attractive therapy to downregulate the cytokine storm caused by COVID-19 from a zebrafish model. RT-PCR analyses and protein-protein interaction prediction among SARS-CoV-2 and Danio rerio proteins showed that rSpike was responsible for generating systemic inflammatory processes with significantly increased pro-inflammatory (il1b, il6, tnfa, and nfkbiab), oxidative stress (romo1) and energy metabolism (slc2a1a, coa1) mRNA markers, with a pattern like those observed in COVID-19 cases in humans. On the other hand, PBM treatment decreased the mRNA levels of these pro-inflammatory and oxidative stress markers compared with rSpike in various tissues, promoting an anti-inflammatory response. Conversely, PBM promotes cellular and tissue repair of injured tissues and significantly increases the survival rate of rSpike-inoculated individuals. Additionally, metabolomics analysis showed that the most impacted metabolic pathways between PBM and the rSpike-treated groups were related to steroid metabolism, immune system, and lipids metabolism. Together, our findings suggest that the inflammatory process is an incisive feature of COVID-19, and red PBM can be used as a novel therapeutic agent for COVID-19 by regulating the inflammatory response. Nevertheless, the need for more clinical trials remains, and there is a significant gap to overcome before clinical trials.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 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