The Effects of Resistance Training Volume on Skeletal Muscle Proteome

International Journal of Exercise Science 10(7): 1051-1066, 2017. Studies are conflicting to whether low volume resistance training (RT) is as effective as high-volume RT protocols with respect to promoting morphological and molecular adaptations. Thus, the aim of the present study was to compare, using a climbing a vertical ladder, the effects of 8 weeks, 3 times per week, resistance training with 4 sets (RT4), resistance training with 8 sets (RT8) and without resistance training control (CON) on gastrocnemius muscle proteome using liquid chromatography mass spectrometry (LC-MS/MS) and cross sectional area (CSA) of rats. Fifty-two proteins were identified by LC-MS/MS, with 39 in common between the three groups, two in common between RT8 and CON, one in common between RT8 and RT4, four exclusive in the CON, one in the RT8, and four in the RT4. The RT8 group had a reduced abundance of 12 proteins, mostly involved in muscle protein synthesis, carbohydrate metabolism, tricarboxylic acid cycle, anti-oxidant defense, and oxygen transport. Otherwise one protein involved with energy transduction as compared with CON group showed high abundance. There was no qualitative protein abundance difference between RT4 and CON groups. These results revealed that high volume RT induced undesirable disturbances on skeletal muscle proteins, while lower volume RT resulted in similar gains in skeletal muscle hypertrophy without impairment of proteome. The CSA was significantly higher in RT8 group when compared to RT4 group, which was significantly higher than CON group. However, no differences were found between trained groups when the gastrocnemius CSA were normalized by the total body weight

Ciência ativa: a experimentação como recurso didático para o ensino de ciências

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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

The complete genome sequence of Chromobacterium violaceum reveals remarkable and exploitable bacterial adaptability

Chromobacterium violaceum is one of millions of species of free-living microorganisms that populate the soil and water in the extant areas of tropical biodiversity around the world. Its complete genome sequence reveals (i) extensive alternative pathways for energy generation, (ii) ≈500 ORFs for transport-related proteins, (iii) complex and extensive systems for stress adaptation and motility, and (iv) wide-spread utilization of quorum sensing for control of inducible systems, all of which underpin the versatility and adaptability of the organism. The genome also contains extensive but incomplete arrays of ORFs coding for proteins associated with mammalian pathogenicity, possibly involved in the occasional but often fatal cases of human C. violaceum infection. There is, in addition, a series of previously unknown but important enzymes and secondary metabolites including paraquat-inducible proteins, drug and heavy-metal-resistance proteins, multiple chitinases, and proteins for the detoxification of xenobiotics that may have biotechnological applications

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