Avaliação da destreza dos dedos e da força de preensão máxima em crianças com dislexia desenvolvimental

A dislexia caracteriza-se pela dificuldade de aprendizagem da leitura, escrita e soletração, sem uma causa aparente específica. Crianças e adultos com dislexia apresentam também déficits em diferentes tarefas sensório-motoras. Porém, não existe consenso quanto o efeito da dislexia na destreza dos dedos e se há alterações puramente motoras em indivíduos acometidos por essa desordem. O objetivo do estudo foi comparar crianças com e sem dislexia quanto à destreza dos dedos e à capacidade de geração de força máxima. Trinta crianças com dislexia e 30 sem dislexia, entre 8 e 14 anos, realizaram o teste dos nove pinos nos buracos (9-PnB) para avaliação da destreza manual e o teste força de preensão palmar máxima, ambos com a mão dominante. Elas foram instruídas a realizar o teste dos 9-PnB o mais rápido possível e em seguida produzir força de preensão máxima (FPMax) no dinamômetro hidráulico Jamar®. O menor tempo e a maior FPMax registradas em três tentativas foram utilizadas para as análises estatísticas. Os resultados revelaram que as crianças com dislexia são mais lentas na execução do teste dos 9-PnB, porém apresentam similar capacidade de geração de FPMax que crianças não disléxicas. Esses resultados indicam que as diferenças no desempenho em testes motores observadas entre crianças com dislexia e sem dislexia não têm origem no sistema motor e sim no modo com que a criança com dislexia processa as informações sensoriais e as transforma em respostas motoras para produzir ações

Adaptabilidade e estabilidade de híbridos de milho para o sul do bioma Amazônia via GGE biplot

The objective of this work was to select maize hybrids using the GGE biplot analysis, as well as to evaluate their stability and adaptability in different environments of the North and Midwest regions of Brazil. Thirty-six maize hybrids were evaluated in 2018, in the following five environments in the Northern and Midwestern regions, respectively: in the municipality of Vilhena, in the state of Rondônia; and in the municipalities of Sorriso, Sinop, Alta Floresta, and Carlinda, in the Northern region of the state of Mato Grosso. The experimental design was a randomized complete block design. The analysis of variance was performed, and adaptability and stability were estimated by the GGE biplot method based on grain yield performance. A significant interaction between genotypes and environments was detected, and the biplot analysis was efficient in explaining 62.74% of the total variation in the first two principal components, with the formation of three macroenvironments. The 1P2227, 'BRS 3042', and 1P2265 hybrids showed high yield, responsiveness, and stability in the evaluated environments. The DKB310VTPRO2 hybrid was the most unstable genotype. The recommended hybrids are: DKB310 for the Sorriso and Vilhena macroenvironment; 1M1810 and 1O2106 for the Carlinda environment; and 1M1807 for the Sinop environment.O objetivo deste trabalho foi selecionar híbridos de milho, por meio da análise GGE biplot, bem como avaliar sua estabilidade e adaptabilidade em diferentes ambientes das regiões Centro-Oeste e Norte do Brasil. Trinta e seis híbridos de milho foram avaliados em 2018, nos seguintes cinco ambientes das regiões Norte e Centro-Oeste, respectivamente: no município de Vilhena, no estado de Rondônia; e nos municípios de Sorriso, Sinop, Alta Floresta e Carlinda, na região norte do estado de Mato Grosso. O delineamento experimental foi em blocos completos ao acaso. Realizou-se a análise de variância, e estimaram-se a adaptabilidade e a estabilidade pelo método GGE biplot com base na produtividade. Detectou-se interação significativa entre genótipos e ambientes, e a análise biplot foi eficiente para explicar 62,74% da variação total nos dois primeiros componentes principais, com a formação de três macroambientes. Os híbridos 1P2227, 'BRS 3042' e 1P2265 apresentam alta produtividade, capacidade de resposta e estabilidade nos ambientes avaliados. O híbrido DKB310VTPRO2 foi o genótipo mais instável. Os híbridos recomendados são: DKB310 para o macroambiente Sorriso e Vilhena; 1M1810 e 1O2106 para o ambiente Carlinda; e 1M1807 para o ambiente Sinop

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

Many Labs 5:Testing pre-data collection peer review as an intervention to increase replicability

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3?9; median total sample = 1,279.5, range = 276?3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (?r = .002 or .014, depending on analytic approach). The median effect size for the revised protocols (r = .05) was similar to that of the RP:P protocols (r = .04) and the original RP:P replications (r = .11), and smaller than that of the original studies (r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00?.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19?.50)

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

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570