The effect of cumulative endurance exercise on leptin and adiponectin and their role as markers to monitor training load

Leptin and adiponectin play an essential role in energy metabolism. Leptin has also been proposed as a marker for monitoring training load. So far, no studies have investigated the variability of these hormones in athletes and how they are regulated during cumulative exercise. This study monitored leptin and adiponectin in 15 endurance athletes twice daily in the days before, during and after a 9-day simulated cycling stage race. Adiponectin significantly increased during the race (p = 0.001) and recovery periods (p = 0.002) when compared to the baseline, while leptin decreased significantly during the race (p < 0.0001) and returned to baseline levels during the recovery period. Intra-individual variability was substantially lower than inter-individual variability for both hormones (leptin 34.1 vs. 53.5%, adiponectin 19% vs. 37.2%). With regards to exercise, this study demonstrated that with sufficient, sustained energy expenditure, leptin concentrations can decrease within the first 24 hours. Under the investigated conditions there also appears to be an optimal leptin concentration which ensures stable energy homeostasis, as there was no significant decrease over the subsequent race days. In healthy endurance athletes the recovery of leptin takes 48-72 hours and may even show a supercompensation-like effect. For adiponectin, significant increases were observed within 5 days of commencing racing, with these elevated values failing to return to baseline levels after 3 days of recovery. Additionally, when using leptin and adiponectin to monitor training loads, establishing individual threshold values improves their sensitivity.     

De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures

The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta''s FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5′ UTR; the reverse complement of the 5′ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3′ UTR. For eleven of these elements (the stems in SL1–8, reverse complement of SL1–4, FSE, s2m and 3′ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.     

Multiple Bypass: Interposition Agents for Distributed Computing

Interposition agents are a well-known device for attaching legacy applications to distributed systems. However, agents are difficult to build and are often large, monolithic pieces of software which are suited only to limited applications or systems. We solve this problem with Bypass, a language and a tool for quickly building multiple small agents that can be combined together to create powerful yet manageable software.