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

Effects of sleep deprivation on skeletal muscle recovery parameters after eccentric exercise-induced damage

Introduction: Popularly, sleep is known as an essential issue to maintain muscle physiology, as well as for recovery of its functions after various types of damage, as well as those seen after bouts of exercise. However, it is not known how some variables related to muscle recovery respond under sleep deprivation. Aim: To evaluate if total sleep deprivation for 60 hours, after muscle damage induced by eccentric action, modify de response of some parameters related to muscle recovery, such as: isometric strength, thigh girth, pain perception, pro- and antiinflammatory cytokines, and hormones. Materials and methods: In two separate conditions, with cross-over model and washout of at least four weeks, ten men (age = 24.5 ± 2.9 years; body mass index = 22.7 ± 2.3 kg / m2) performed a unilateral muscle damage protocol for lower limb, comprising 240 eccentric contractions for knee extensor muscles, performed in the isokinetic dynamometer. In the first condition, muscle damage protocol was followed by 60 hours of total sleep deprivation + 12 hours of normal sleep (2 sleep deprivation nights + 1 sleep night - Condition PS). In the second condition, the same protocol for muscle damage was conducted, but followed by 3 nights of normal sleep (Condition S). Blood samples were collected serially throughout the protocol for: creatine kinase (CK), testosterone (T), IGF-1, cortisol (C), tumor necrosis factor (TNF)-alpha, interleukin (IL)-1 beta, IL- 6 receptor antagonist of IL-1 (IL-1ra) and IL-10. After that, it was calculated the 24 h area under the curve (AUC) between hours 24 and 48 after the muscle damage protocol). Results: Peak torque reduced at all times after muscle damage in both conditions (F (4,72) = 70.57, p 1.0) . The pain perception showed only effect of time (F (4,56) = 17.3, p 1,0). A percepção de dor demonstrou somente efeito do tempo (F(4,56) = 17,3, p < 0,001), sendo todos os momentos após o dano muscular maiores que o período basal, em ambas as condições (p < 0,05). Por meio da análise do tamanho do efeito, a Condição PS apresentou maior valor nos momentos 36 horas (p = 0,9, d = 0,8) e 60 horas (p = 0,99, d = 0,5). A IL-6 e o IL- 1ra se elevaram entre zero e 2 horas após o dano muscular, em ambas as condições (p < 0,05) e, quando analisada a AUC de 24 horas, houve tendência de aumento da IL-6 na Condição PS, com elevado tamanho do efeito (t = -1,97, gl = 8, p = 0,08, d = 0,8). A análise da AUC de 24 horas demonstrou que o IGF-1 (t = - 2,76, gl = 8, p = 0,02, d = 0,7) e o cortisol (t = -7,69, gl = 8, p < 0,001, d = 2,7) foram maiores na Condição PS, juntamente com maior razão cortisol:testosterona nesta mesma Condição (elevado (t = -3,4, gl = 8, p = 0,009, d = 1,6). Conclusão: xvii Foi possível concluir que a privação de sono total, nos primeiros dois dias após o dano muscular, retarda a recuperação da força, alterando também o perfil hormonal e inflamatório, marcados pela elevação do cortisol, IGF-1 e IL-6.Associação Fundo de Incentivo à Pesquisa (AFIP)Fundo de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP: 2010/07005-0FAPESP: CEPID#98/14303-3CNPq: 142060/2012-7Dados abertos - Sucupira - Teses e dissertações (2013 a 2016

Paradoxical sleep deprivation is related to muscle atrophy and damages muscle recovery

Introduction: Paradoxical sleep deprivation (PSD) in rats is able to induce reductions in body mass and body fat, due to increased secretion of pituitary-adrenal axis hormones. Considering that PSD is also associated with a decrease in anabolic hormones, skeletal muscle can also be strongly influenced, causing atrophy damaging its recovery after a lesion. Objectives: To evaluate body mass, hormone profile and tibialis anterior (TA) muscle histomorphometry after cryolesion or no damage. Method: To achieve the proposed objectives, two experiments were conducted. In the first, 3-month-old male Wistar rats (9 to 10 per group) were subjected to 96 hours of PSD (PSD group) or 96 hours of sleep (CTRL group). In the second experiment, 3-month-old male Wistar rats (8 to 9 per group) were first subjected to cryolesion and then divided in the following groups: PSD for 96 hours (Group D-PSD) or sleep for 96 hours (D-CTRL group); or 2) PSD for 96 hours followed by 96 hours of sleep ad libitum (sleep rebound – D-RB group) or sleep for 192 hours (D-CTRL-RB group). Blood corticosterone and testosterone levels and TA muscle histomorphometry were determined, both in damaged and healthy muscles. Results: In experiment 1, we observed reductions in body mass, muscle mass and cross-sectional area of TA in the PSD group, and increased levels of corticosterone and decreased levels of testosterone. In the second experiment, the histomorphometry of the TA not damaged and the hormonal parameters of D-PSD group were similar to experiment 1, while the TA damaged showed decreases in its mass in comparison to DCTRL and D-CTRL-RB groups. Although the cross-sectional area of the damaged area was lower compared to healthy TA on the D-PSD group, it was not different from the damaged TA of CTRL-D group. However, the D-RB group presented the TA mass and AST significantly higher than the PSD-group, but lower than the D-CTRL-RB and D-RB groups. Conclusion: PSD is associated with TA atrophy and impaired ability to recover from damage induced by cryolesion, possibly because of hormonal responses associated with PSD.Introdução: A privação de sono paradoxal (PSP) em ratos é capaz de induzir reduções da massa corporal e de gordura corporal, fato este atribuído ao aumento da secreção de hormônios do eixo hipófise-adrenal. Considerando que a PSP também está associada com diminuição de hormônios anabólicos, a musculatura esquelética também pode ser fortemente influenciada, respondendo com atrofia e podendo prejudicar sua recuperação após determinado dano. Objetivos: Avaliar a massa corporal, perfil de secreção hormonal e a histomorfometria do músculo tibialis anterior (TA) submetido ou não a criolesão. Material e método: Para alcançar os objetivos propostos, foram realizados dois experimentos. No primeiro, 9-10 ratos Wistar machos, com aproximadamente 3 meses de idade foram submetidos a PSP por 96 horas (Grupo PSP) ou ao período de sono por 96 horas (Grupo CTRL). No segundo experimento, 8-9 ratos Wistar machos, com aproximadamente 3 meses de idade foram submetidos ao protocolo de criolesão, para indução da lesão no músculo TA e, subsequentemente, submetidos a: 1) PSP por 96 horas (Grupo D-PSP) ou período de sono por 96 horas (Grupo D-CTRL); ou 2) PSP por 96 horas seguida de 96 horas de sono ad libitum (período de rebote de sono – Grupo D-RB) ou ao período de sono por 192 horas (Grupo D-CTRL-RB). Foram realizadas dosagens de corticosterona e testosterona sanguíneas e análise histomorfométrica do músculo TA sadio e danificado. Resultados: No experimento 1 foi possível observar redução da massa corporal, da massa muscular e da AST do TA no grupo PSP, além de aumento da concentração de corticosterona e diminuição da concentração de testosterona. Já no experimento 2, a histomorfometria do músculo TA sadio e os parâmetros hormonais foram similares ao experimento 1 no grupo D-PSP, enquanto que o TA danificado apresentou redução da sua massa somente no grupo D-PSP. Embora a AST da região danificada tenha sido menor em relação ao TA sadio no grupo D-PSP, a mesma não foi diferente do TA danificado do grupo D-CTRL. Por outro lado, o grupo D-RB apresentou massa e AST do TA maior que o grupo D-PSP, mas menor que os grupos D-CTRL e D-CTRL-RB. Conclusão: A PSP está associada com atrofia do músculo TA e prejuízo da sua capacidade de recuperação após dano induzido por criolesão, possivelmente pelas respostas hormonais associadas a PSP.TEDEBV UNIFESP: Teses e dissertaçõe

Avaliação do conhecimento nutricional de mulheres fisicamente ativas e sua associação com o estado nutricional

O objetivo do presente estudo foi avaliar o conhecimento nutricional de mulheres fisicamente ativas e correlacionar a pontuação obtida com variáveis antropométricas e nível de escolaridade. A amostra foi composta por 60 mulheres e os dados antropométricos mensurados foram massa corporal, estatura, Índice de Massa Corporal (massa corporal/estatura²) e perímetro da cintura. A amostra apresentou conhecimento nutricional moderado e correlações significativas foram obtidas da pontuação do questionário de conhecimento nutricional com o Índice de Massa Corporal (r = - 0,38, p < 0,05), perímetro da cintura (r = - 0,30, p < 0,05) e nível de escolaridade (r = 0,36, p < 0,05). O conhecimento nutricional pode estar associado com o estado nutricional e o nível de escolaridade

High-Intensity Interval Training Attenuates Insulin Resistance Induced by Sleep Deprivation in Healthy Males

Introduction: Sleep deprivation can impair several physiological systems and recently, new evidence has pointed to the relationship between a lack of sleep and carbohydrate metabolism, consequently resulting in insulin resistance. To minimize this effect, High-Intensity Interval Training (HIIT) is emerging as a potential strategy.Objective: The aim of this study was to investigate the effects of HIIT on insulin resistance induced by sleep deprivation.Method: Eleven healthy male volunteers were recruited, aged 18–35 years, who declared taking 7–8 h sleep per night. All volunteers were submitted to four different conditions: a single night of regular sleep (RS condition), 24 h of total sleep deprivation (SD condition), HIIT training followed by regular sleep (HIIT+RS condition), and HIIT training followed by 24 h of total sleep deprivation (HIIT+SD condition). They performed six training sessions over 2 weeks and each session consisted of 8–12 × 60 s intervals at 100% of peak power output. In each experimental condition, tests for glucose, insulin, cortisol, free fatty acids, and insulin sensitivity, measured by oral glucose tolerance test (OGTT), were performed.Results: Sleep deprivation increased glycaemia and insulin levels, as well as the area under the curve. Furthermore, an increase in free fatty acids concentrations and basal metabolism was observed. There were no differences in the concentrations of cortisol. However, HIIT before 24 h of sleep deprivation attenuated the increase of glucose, insulin, and free fatty acids.Conclusion: Twenty-four hours of sleep deprivation resulted in acute insulin resistance. However, HIIT is an effective strategy to minimize the deleterious effects promoted by this condition