Technology almost 4.0 application in developing a conveyor belt with low-cost, reused and accessible materials for bagging grains

With the technological development of a new class of wastes, the technological ones were created. Many times, this waste is not processed correctly, having its hazardous disposal. Thus, recycling these materials is an alternative to end specific equipment. This work used this approach to develop a low-cost, affordable second-row conveyor belt. The conveyor belt was designed to bag, weigh, and monitor different volumes in a grain silo. Such equipment is of interest to smallholder applications as well as the integration between different areas of the Biosystems engineering course

Pereskia aculeata Miller leaves present in vivo topical anti-inflammatory activity in models of acute and chronic dermatitis

AbstractEthnopharmacological relevance: The leaves of Pereskia aculeata Miller (Cactaceae), known as Barbados gooseberry, are used in Brazilian traditional medicine as emollients and to treat skin wounds and inflammation. This study investigated the topical anti-inflammatory activity of the hexane fraction (HF) obtained from the methanol extract of the leaves of this species in models of acute and chronic ear dermatitis in mice.Material and methods: Mice ear edema was induced by topical application of croton oil, arachidonic acid, capsaicin, ethyl-phenylpropiolate and phenol; and by subcutaneous injection of histamine. Ear biopsies were obtained to determine the levels of IL-1β, IL-6 and TNF-α cytokines by ELISA assay. Histopathological analysis was also performed to evaluate the HF activity in croton oil multiple application test. In addition, acute dermal irritation/corrosion test in rats was accomplished. HF chemical characterization was performed by GC–MS analysis.Results: HF intensively reduced the inflammatory process induced by all irritant agents used, except for arachidonic acid. This activity is related, at least in part, to the reduction of IL-6 and TNF-α cytokines levels. Moreover, when the glucocorticoid receptor antagonist mifepristone was used, HF failed to respond to the croton oil application.The results strongly suggested a glucocorticoid-like effect, which was reinforced by the presence of considerable amounts of sterol compounds identified in HF. The acute dermal irritaton/corrosion test showed no signs of toxicity.Conclusions: This study showed that the acute and chronic anti-inflammatory activity of P. aculeata leaves is very promising, and corroborates to better understand their ethnopharmacological applications

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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

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

Software for classification of banana ripening stage using machine learning

Abstract: Pattern recognition aims to classify some datasets into specific classes or clusters, having several applications in agriculture. The objectification of the process minimizes errors since it reduces subjectivity, allowing a fairer remuneration to the producer and standardized products to the consumer. Thus,this work aimed to develop an embedded system with artificial intelligence to determine the ripening stage of bananas (outputs) from the insertion of physical (i.e., fruit weight, texture and diameter), physicochemical (i.e.,pH, titratable acidity (TA), soluble solids (SS) and SS/TA ratio) and biochemical (i.e., total sugars, phenolic compounds, ascorbic acid,quantification of pigments in fruit peel and pulp and antioxidant activity by DPPH and FRAP methods) data (inputs). The bananas were harvested at each evaluated stage according to the Von Loesecke ripening scale, as follows:stage 2, totally green; stage 4, more yellow than green; stage 6, yellow; and stage 7, yellow with brown spots. Subsequently, they were selected and submitted to quality analysis. The data obtained were then mined and the attributes were selected using WEKA software. The classifier software was developed using MATLAB. The most relevant attributes selected in the Bayes Net classifier for the Cross-Validation method were: apical, central, basal and mean textures (between apical, median and basal textures), pH, soluble solids, phenolic compounds, antioxidant activities by the FRAP and DPPH methods, vitamin C, anthocyanins from the pulp, chlorophyll a content in the fruit peel and sugar, resulting in a mean F-measure of 97.0%

Prediction of Bioactive Compounds and Antioxidant Activity in Bananas during Ripening Using Non-Destructive Parameters as Input Data

Vegetable quality parameters are established according to standards primarily based on visual characteristics. Although knowledge of biochemical changes in the secondary metabolism of plants throughout development is essential to guide decision-making about consumption, harvesting and processing, these determinations involve the use of reagents, specific equipment and sophisticated techniques, making them slow and costly. However, when non-destructive methods are employed to predict such determinations, a greater number of samples can be tested with adequate precision. Therefore, the aim of this work was to establish an association capable of modeling between non-destructive—physical and colorimetric aspects (predictive variables)—and destructive determinations—bioactive compounds and antioxidant activity (variables to be predicted), quantified spectrophotometrically and by HPLC in ‘Nanicão’ bananas during ripening. It was verified that to predict some parameters such as flavonoids, a regression equation using predictive parameters indicated the importance of R2, which varied from 83.43 to 98.25%, showing that some non-destructive parameters can be highly efficient as predictors