Order of strength exercises on the performance of judo athletes

OBJETIVO: El objetivo fue investigar el efecto del orden distintas de ejercicios de fuerza para extremidades superiores e inferiores en el rendimiento específico de judokas junior. MÉTODO: Se divide una muestra de 39 atletas masculinos en tres grupos: Experimental-EG1; EG2 y Control. Los Grupos realizaron ejercicios de fuerza máxima y potencia con intensidades del 80-90% para 12 semanas de intervención. El orden de los ejercicios para el EG1 fue del de extremidades superiores primero e inferiores después. El EG2 realiza los mismos ejercicios en secuencia inversa. Se utilizó la Prueba Especial Judo Fitness (SJFT) para evaluación. RESULTADOS: Los experimentos intra e inter grupos muestran diferencias (p<0.05) en los derribos-caídas y en el índice SJFT, con EG2 mostrando mejores resultados. CONCLUSIONES: Las variables de SJFT tuvieron mejores resultados en el orden de ejercicios seguidos por el EG2OBJECTIVE: To analyze the effect of strength exercises using different orders for upper and lower limbs on the specific performance of junior judo athletes. METHODS: 39 male athletes were divided into three groups: experimental- EG1, experimental-EG2 and control group. Experimental groups performed with intensities 80-90% of strength and power for 12 weeks. The exercise-order for EG1 followed an upper to lower limb sequence and EG2 performed the same exercises in reverse. The Special Judo Fitness Test (SJFT) was used in the assessment. RESULTS: Experimental intra and inter-groups showed differences (p<0.05) in the throws-falls and SJFT-index, but the EG2 showed best results. CONCLUSIONS: The SJFT-variables had better results to exercises order in EG

Análise de associação quanto à produtividade e seus caracteres componentes em linhagens e cultivares de arroz de terras altas

O objetivo deste trabalho foi identificar, por meio da análise de mapeamento associativo, os marcadores moleculares relacionados à produtividade do arroz de terras altas e aos seus caracteres componentes. Foram usadas 113 linhagens e cultivares de arroz de terras altas, da Coleção Nuclear de Arroz da Embrapa, com reduzido vínculo genético entre si. Os seguintes caracteres componentes da produtividade foram avaliados: número de panículas por metro, número de grãos por panícula e peso de 100 grãos. Dos 115 marcadores utilizados, 25 (21,7%) associaram-se significativamente a um ou mais caracteres. Entre os 29 SSR ("simple sequence repeats") colocalizados em QTL ("quantitative trait loci") de produtividade de arroz, 12 foram associados aos caracteres avaliados e considerados como candidatos para uso na seleção assistida por marcadores. Os marcadores NP914540, Q6ZGD1 e Q69JE3, associados ao número de grãos por panícula, ainda não foram anotados no arroz e podem constituir o ponto de partida para estudos de genômica funcional. Entre os marcadores derivados de sequências transcritas, NP914526 e NP914533 destacam-se por pertencer a rotas metabólicas relacionadas ao aumento do potencial produtivo de arroz

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

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