Concentração plasmática de glutamina e glutamato em ciclistas de elite durante duas temporadas de treinamentos e competições

It is well known that athletes practicing exhaustive exercise may present, at the end of a competitive season, signs of “overtraining”/”overreaching”, that comprise loss of performance and many physiological, metabolic and psychological changes. In an attempt to identify possible signs of “overreaching” we studied a group of ten professional cyclists, mass 72.1 ± 3.5 kg, VO2 máx 73.96 ± 3.7 mL.kg-1.min-1, age 23 ± 4.01 yr, for 2 consecutive competitive seasons. The maximal consumption of oxygen and blood lactate concentration, during an maximal incremental test, as a mean to evaluate changes in athletes performance, and plasma cortisol, glutamine and glutamate concentrations, as metabolic markers for “overreaching” were assessed. Blood samples were collected from the antecubital vein 9 times during the two years period, after bouts of high intensity training and competition. The maximal consumption of oxygen (74.9 ± 1.69 mL.kg-1.min-1 and 77.62 ± 3.37 mL.kg-1.min-1, beginning and the end of first season) and plasma lactate concentration did not change during the experiment, but at the end of both seasons the athletes reported early fatigue symptoms, evaluated by using Borg scale, and could not reach the same load at the end of the tests (early exhaustion). Plasma glutamine (559.8 µmo.l-1 to 531.7 µmo.l-1 in the first season and 438.7 µmo.l-1 to 393.06 µmo.l-1 in the second season) and glutamate (214 µmo.l-1 to 167.2 µmo.l-1 in the first season and 244.2 µmo.l-1 to 205.64 µmo.l-1 in the second season) concentration were reduced at the end of both seasons, and plasma cortisol (363.15 µmo.l-1 to 569.66 µmo.l-1 in the second season), increased. Therefore, we conclude that the changes in plasma glutamine, glutamate and cortisol during a competitive season could be used as an early indicative of “overreaching”.É bem descrito que atletas que praticam atividade física exaustivas podem apresentar, ao final de um macrociclo de competições, sinais de “overtraining”/”overreaching” que incluem a diminuição no desempenho e muitas mudanças fisiológicas, metabólicas e psicológicas. Na tentativa de identificar possíveis sinais do “overreaching”, nós estudamos um grupo de 10 ciclistas profissionais, peso 72,1 ± 3,5 kg, consumo máximo de oxigênio (VO2 máx) 73,96 ± 3,7 mL.kg-1.min-1, idade 23 ± 4,01 anos, durante dois macrociclos anuais consecutivos. O VO2 máx e a concentração sangüínea de lactato, durante um teste incremental máximo, foram utilizados para avaliar o desempenho dos atletas e a concentração plasmática de cortisol, glutamina e glutamato, como marcadores metabólicos do “overreaching”. Durante o período de dois anos, nove amostras de sangue foram coletadas por punção venosa após os períodos de treinamento de alta intensidade e de competições. O VO² máx (74,9 ± 1,69 mL.kg-1.min-1 e 77,62 ± 3,37 mL.kg-1.min-1, respectivamente início e final do primeiro macrociclo) e a concentração plasmática de lactato não tiveram alterações durante o estudo, contudo, ao final de ambos macrociclos, os atletas apresentaram sinais de fadiga, percebidos através da escala de esforço subjetivo de Borg e pelo fato de não conseguirem suportar as mesmas cargas ao final dos testes (exaustão precoce). A concentração plasmática de glutamina (559,8 µmo.l-1 para 531,7 µmo.l-1 no primeiro macrociclo e 438,7 µmo.l-1 para 393,06 µmo.l-1 no segundo macrociclo) e do glutamato (214 µmo.l-1 para 167,2 µmo.l-1 no primeiro macrociclo e 244,2 µmo.l-1 para 205,64 µmo.l-1 no segundo macrociclo) diminuíram e a concentração plasmática de cortisol (363,15 µmo.l-1 para 569,66 µmo.l-1 no segundo macrociclo) aumentou. Com isso, nós concluímos que durante um macrociclo competitivo as mudanças na concentração plasmática de glutamina, glutamato e cortisol, podem ser utilizadas como marcadores precoces de um estágio de “overreaching”

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

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)

Evidence for the Higgs boson decay to a bottom quark–antiquark pair

info:eu-repo/semantics/publishe

Search for heavy resonances decaying to a top quark and a bottom quark in the lepton+jets final state in proton–proton collisions at 13 TeV

info:eu-repo/semantics/publishe

Long-term thermal sensitivity of Earth’s tropical forests

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate

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