Showing papers by "Gustavo Ferreira Martins published in 2022"

Artificial Intelligence-Aided Meta-Analysis of Toxicological Assessment of Agrochemicals in Bees

Abstract: The lack of consensus regarding pollinator decline in various parts of the planet has generated intense debates in different spheres. Consequently, much research has attempted to identify the leading causes of this decline, and a multifactorial synergism (i.e., different stressors acting together and mutually potentiating the harmful effects) seems to be the emerging consensus explaining this phenomenon. The emphasis on some stressor groups such as agrochemicals, and pollinators such as the honey bee Apis mellifera, can hide the real risk of anthropogenic stressors on pollinating insects. In the present study, we conducted a systematic review of the literature to identify general and temporal trends in publications, considering the different groups of pollinators and their exposure to agrochemicals over the last 76 years. Through an artificial intelligence (AI)-aided meta-analysis, we quantitatively assessed trends in publications on bee groups and agrochemicals. Using AI tools through machine learning enabled efficient evaluation of a large volume of published articles. Toxicological assessment of the impact of agrochemicals on insect pollinators is dominated by the order Hymenoptera, which includes honey bees. Although honey bees are well-explored, there is a lack of published articles exploring the toxicological assessment of agrochemicals for bumble bees, solitary bees, and stingless bees. The data gathered provide insights into the current scenario of the risk of pollinator decline imposed by agrochemicals and serve to guide further research in this area. Systematic Review Registration https://asreview.nl/.

Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology

Abstract: Modern techniques that enable high-precision and rapid identification/elimination of wheat seeds infected by Fusarium head blight (FHB) can help to prevent human and animal health risks while improving agricultural sustainability. Robust pattern-recognition methods, such as deep learning, can achieve higher precision in detecting infected seeds using more accessible solutions, such as ordinary RGB cameras. This study used different deep-learning approaches based on RGB images, combining hyperparameter optimization, and fine-tuning strategies with different pretrained convolutional neural networks (convnets) to discriminate wheat seeds of the TBIO Toruk cultivar infected by FHB. The models achieved an accuracy of 97% using a low-complexity design architecture with hyperparameter optimization and 99% accuracy in detecting FHB in seeds. These findings suggest the potential of low-cost imaging technology and deep-learning models for the accurate classification of wheat seeds infected by FHB. However, FHB symptoms are genotype-dependent, and therefore the accuracy of the detection method may vary depending on phenotypic variations among wheat cultivars.

Extract from Opuntia ficus-indica cladode delays the Aedes aegypti larval development by inducing an axenic midgut environment.

Abstract: This study evaluated the effects of acute exposure of Aedes aegypti third instar (L3 ) larvae to the saline extract of Opuntia ficus-indica cladodes on the biological cycle and fertility of the emerging adults. For this, larvae were treated for 24 h with the extract at ¼ LC50 (lethal concentration to kill 50% of larvae), ½ LC50 or LC50 ; the development and reproduction of the emerged adults were evaluated after a recovery period of 9 days. The resistance of proteins in the extract to hydrolysis by L3 digestive enzymes and histomorphological alterations in the larval midgut were also investigated. The extract contained lectin, flavonoids, cinnamic derivatives, terpenes, steroids, and reducing sugars. It showed a LC50 of 3.71% for 48 h. The data indicated mean survival times similar in control and extract treatments. It was observed development delay in extract-treated groups, with a lower number of adults than in control. However, the females that emerged laid similar number of eggs in control and treatments. Histological evaluation revealed absence of bacterial and fungal microorganisms in the food content in midguts from larvae treated with cladode extract. Electrophoresis revealed that three polypeptides in the extract resisted to hydrolysis by L3 digestive proteases for 90 min. The lectin activity was not altered even after 24-h incubation with the enzymes. In conclusion, the extract from O. ficus-indica can delay the development of Ae. aegypti larvae, which may be linked to induction of an axenic environment at larval midgut and permanence of lectin activity even after proteolysis.