Avaliação das mudanças no uso e ocupação do solo do Município de Eunápolis-BA através da análise da eficiência dos índices espectrais de NDVI, NDBI e Built-Up/ Evaluation of changes in soil use in the city of Eunápolis-BA through analysis of the efficiency of spectrical indices of NDVI, NDBI and Built-Up

Em Eunápolis, o processo de antropização das paisagens tem se intensificado e vem causando inúmeras preocupações. Dessa forma, esse trabalho tem como objetivo analisar, através de equações, as variações nos índices espectrais do NDVI (Normalized Density Vegetation Index), NDBI (Normalized Difference Built Index) e Built-Up Index para os anos de 2007 e 2021, utilizando como parâmetro as alterações no uso do solo do município nos anos de 1996, 2007 e 2018. Para elaboração dos mapas de uso e ocupação do solo, foram utilizados dados vetoriais secundários oriundos de imagens do satélite Landsat 5  para os anos de 1996 e 2007 e do satélite RapidEye para o ano de 2018, classificadas pelo Fórum Florestal do Extremo Sul da Bahia. Para avaliação dos índices espectrais, foram utilizadas imagens do satélite Landsat 5 para o ano de 2007 e do satélite Landsat 8 para o ano de 2021. Mesmo os índices espectrais de NDBI não apresentando resultados satisfatórios para o município, a elaboração dos índices de NDVI e Built-Up Index apresentou resultados compatíveis com outros estudos, confirmando assim a eficiência na aplicabilidade da técnica em sensoriamento remoto com a finalidade de análise das transformações espaciais.

The use of Open Reading frame ESTs (ORESTES) for analysis of the honey bee transcriptome

BACKGROUND: The ongoing efforts to sequence the honey bee genome require additional initiatives to define its transcriptome. Towards this end, we employed the Open Reading frame ESTs (ORESTES) strategy to generate profiles for the life cycle of Apis mellifera workers. RESULTS: Of the 5,021 ORESTES, 35.2% matched with previously deposited Apis ESTs. The analysis of the remaining sequences defined a set of putative orthologs whose majority had their best-match hits with Anopheles and Drosophila genes. CAP3 assembly of the Apis ORESTES with the already existing 15,500 Apis ESTs generated 3,408 contigs. BLASTX comparison of these contigs with protein sets of organisms representing distinct phylogenetic clades revealed a total of 1,629 contigs that Apis mellifera shares with different taxa. Most (41%) represent genes that are in common to all taxa, another 21% are shared between metazoans (Bilateria), and 16% are shared only within the Insecta clade. A set of 23 putative genes presented a best match with human genes, many of which encode factors related to cell signaling/signal transduction. 1,779 contigs (52%) did not match any known sequence. Applying a correction factor deduced from a parallel analysis performed with Drosophila melanogaster ORESTES, we estimate that approximately half of these no-match ESTs contigs (22%) should represent Apis-specific genes. CONCLUSIONS: The versatile and cost-efficient ORESTES approach produced minilibraries for honey bee life cycle stages. Such information on central gene regions contributes to genome annotation and also lends itself to cross-transcriptome comparisons to reveal evolutionary trends in insect genomes

Fishery of the Uçá Crab Ucides Cordatus (Linnaeus, 1763) in a Mangrove Area in Cananéia, State of São Paulo, Brazil: Fishery Performance, Exploitation Patterns and Factors Affecting the Catches

The fishery of the mangrove crab (Ucides cordatus) is one of the oldest sources of food, income and extractive activity in the estuarine systems of Brazil. The state of São Paulo has the largest population of any Brazilian state, and the city of Cananéia, in the Brazilian southeast has the highest recorded level of exploitation of the uçá-crab. Since 1990, this species has been under intense exploitation pressure due to the unauthorized use of a type of trap called 'redinha'. This type of fishing gear is considered harmful and is prohibited by Brazilian law, although its use is very common throughout the country. This study aims to evaluate the exploitation patterns of U. cordatus based on landing data and monitoring of the crab fishermen to verify the population structure of the crab stock and to identify the factors that influence the catches. A general view of the sustainability of the fishery for this resource is also provided for five defined mangrove sectors (areas A to E) at Cananéia. For this purpose, fishery data were recorded during 2009-2010 by the Instituto de Pesca (APTA/SAA-SP), and monitoring of the capture procedures used by two fishermen was conducted to obtain biometry data (CW, carapace width) and gender data for the captured crabs. The redinha trap was very efficient (86.4%) and produced sustainable catches because the trapped crabs were legal-sized males (CW>60 mm), although some traps are lost or remain in the mangrove swamps and can cause pollution by introducing plastic debris. The fishery data were evaluated with a General Linear Model (GLM) based on six factors: the characteristics of the crab fishermen, the time of capture (by month and year), the lunar phase, the productive sector and the reproductive period. The individual crab fishermen's empirical knowledge, the year of capture and the productive sector were the strongest influences on the crab catch per unit effort (CPUE). Differing extraction patterns were found in the five sectors examined in the Cananéia estuary. These findings underscore the need for a reassessment of the prohibition of the trap's use, raising discussion as to its possible construction with biodegradable materials, thus ensuring profitable and sustainable catches through a local participatory management process

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

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

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