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Background: Antioxidant therapy is gaining traction in managing sepsis and septic shock, owing to its perceived positive impact on patient outcomes. This study sought to compare the efficacy of five antioxidant therapies (melatonin, vitamin C, vitamin E, selenium, and N-acetylcysteine, both individually and in combination with other compounds such as vitamin B1, hydrocortisone, propolis, and glutamine) in treating sepsis or septic shock in the intensive care unit (ICU). Methods: The study involved randomized and multi-arm trials with sepsis or septic shock patients using melatonin, vitamin C, vitamin E, selenium, or N-acetylcysteine. Studies were sourced from PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and WHO - Clinical Trials Registry Platform for the frequentist network meta-analysis on 28-day mortality and Sequential Organ Failure Assessment (SOFA) scores. The risk of bias was assessed using the Physiotherapy Evidence Database scale. Therapies were compared directly and indirectly using R software. Results: The study of 56 trials involving 9,366 patients was included. Bias assessment revealed that 89.3 % of trials achieved excellent or good quality. Based on treatment ranking and pairwise comparisons, melatonin with propolis (SUCRA = 93.29 %) is effective in improving SOFA scores, statistically significant, with no publication bias (p= 0.73). High-dose vitamin C (SUCRA = 83.97 %), vitamin C with vitamin B1 (SUCRA = 78.72 %), and melatonin (SUCRA = 67.03 %) are potential therapies for organ dysfunction. Melatonin (SUCRA = 88.22 %) and high-dose vitamin C (SUCRA = 80.75 %) were the most effective in reducing 28-day mortality rates. However, analysis indicated that the results for 28-day mortality rates were not statistically significant. Also, these results contained publication bias (p= 0.02). Conclusion: The study offers fresh perspectives on antioxidant therapy treatments for sepsis or septic shock in ICU, emphasizing the combination of melatonin and propolis notably reduces SOFA scores for those patients.

Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image processing and traditional machine learning often fall short of achieving high accuracy. Recently, deep convolutional neural networks have demonstrated their capability to ensure both time efficiency and accuracy in this task. However, deep convolutional networks typically have a large number of parameters, requiring substantial training time, and the convolutional operations lack attentional mechanisms. Therefore, in this paper, we propose the AttentionConvMixer neural network with Priority Channel Attention (PCA) and Priority Spatial Attention (PSA). The proposed approach exhibits good performance across all three fish classification datasets without introducing any additional parameters, thus demonstrating the effectiveness of our proposed method.

Accurate measurements and statistics of fish data are important for sustainable development of aqua-enviroment and marine fisheries. For data measurements and statistics, automatic segmentation of fish is one of key tasks. The fish segmentation however is a challenging task due to arterfacts in underwater images. In this study, we introduce a deep-learning approach, namely FCN-WRN-WASP for automatic fish segmentation from the underwater images. In particular, we introduce a computational-efficient variation called Waterfall Atrous Spatial Pooling (WASP) module into a Fully convolutional network with Wide ResNet baseline. We also proposed a loss function inspired from active contour approach that can exploit the local intensity information from the input image. The approach has been validated on the DeepFish data and the SIUM data set. The results are promissing for fish segmentation, with higher Intersection over Union (IoU) scores compared to state of the arts. The evaluation results showed that the incorporation of the image based active contour loss helps increase the segmentation performance. In addition, the use of the WASP in the architecture is effective especially for forground fish segmentation.

Payments for environmental services (PES) have been used popularly to manage natural resources, and their contribution to conservation and socio-economic objectives has still received concern from researchers. This study uses data from the household survey and focus group discussions on assessing the perceptions of indigenous people about payments for forest environmental services (PFES) policy in Vietnam. The results show that this policy still has some limitations and faces many challenges although indigenous people's perceptions on the effect of PFES on livelihood, forest conservation, and activities of the PFES implementation are positive. The main issues are legal rights for households to participate in forest protection, mechanisms and regulations for forest patrol and protection at the household and community level, and some issues in the payment norm to ensure equality in implementing the PFES program.

Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image processing and traditional machine learning often fall short of achieving high accuracy. Recently, deep convolutional neural networks have demonstrated their capability to ensure both time efficiency and accuracy in this task. However, deep convolutional networks typically have a large number of parameters, requiring substantial training time, and the convolutional operations lack attentional mechanisms. Therefore, in this paper, we propose the AttentionConvMixer neural network with Priority Channel Attention (PCA) and Priority Spatial Attention (PSA). The proposed approach exhibits good performance across all three fish classification datasets without introducing any additional parameters, thus demonstrating the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]