Recovery of lower limb function following 6 weeks of non-weight bearing

Skeletal muscle weakness and atrophy occur following an extended period of decreased use, including space flight and limb unloading. It is also likely that affected muscles will be susceptible to a re-loading injury when they begin return to earth or weight bearing. However, there is a paucity of literature evaluating the response of human unloaded muscle to exercise and return to activity.

The purpose of this pilot study was to evaluate the soreness, function and strength response of muscle to re-loading in seven patients who were non-weight bearing for 6 weeks, compared to five healthy subjects.

Function improved significantly over time for the patients but was still less than the healthy subjects over 12 weeks of physiotherapy. Concentric quadriceps muscle strength increased significantly over time for the patients. There was considerable variability in the patients’ reports of muscle soreness but there were no significant changes over time or between groups.     

The Mawrth Vallis region of Mars: A potential landing site for the Mars Science Laboratory (MSL) mission

The primary objective of NASA's Mars Science Laboratory (MSL) mission, which will launch in 2011, is to characterize the habitability of a site on Mars through detailed analyses of the composition and geological context of surface materials. Within the framework of established mission goals, we have evaluated the value of a possible landing site in the Mawrth Vallis region of Mars that is targeted directly on some of the most geologically and astrobiologically enticing materials in the Solar System. The area around Mawrth Vallis contains a vast (>1?×?10? km2) deposit of phyllosilicate-rich, ancient, layered rocks. A thick (>150?m) stratigraphic section that exhibits spectral evidence for nontronite, montmorillonite, amorphous silica, kaolinite, saponite, other smectite clay minerals, ferrous mica, and sulfate minerals indicates a rich geological history that may have included multiple aqueous environments. Because phyllosilicates are strong indicators of ancient aqueous activity, and the preservation potential of biosignatures within sedimentary clay deposits is high, martian phyllosilicate deposits are desirable astrobiological targets. The proposed MSL landing site at Mawrth Vallis is located directly on the largest and most phyllosilicate-rich deposit on Mars and is therefore an excellent place to explore for evidence of life or habitability.     

Preliminary application of a novel algorithm to monitor changes in pre-flight total peripheral resistance for prediction of post-flight orthostatic intolerance in astronauts

Orthostatic intolerance (OI) is a significant challenge for astronauts after long-duration spaceflight. Depending on flight duration, 20–80% of astronauts suffer from post-flight OI, which is associated with reduced vascular resistance. This paper introduces a novel algorithm for continuously monitoring changes in total peripheral resistance (TPR) by processing the peripheral arterial blood pressure (ABP). To validate, we applied our novel mathematical algorithm to the pre-flight ABP data previously recorded from twelve astronauts ten days before launch. The TPR changes were calculated by our algorithm and compared with the TPR value estimated using cardiac output/heart rate before and after phenylephrine administration. The astronauts in the post-flight presyncopal group had lower pre-flight TPR changes (1.66 times) than those in the non-presyncopal group (2.15 times). The trend in TPR changes calculated with our algorithm agreed with the TPR trend calculated using measured cardiac output in the previous study. Further data collection and algorithm refinement are needed for pre-flight detection of OI and monitoring of continuous TPR by analysis of peripheral arterial blood pressure.     

Forecasting space weather: Predicting interplanetary shocks using neural networks

We are developing a system to predict the arrival of interplanetary (IP) shocks at the Earth. These events are routinely detected by the Electron, Proton, and Alpha Monitor (EPAM) instrument aboard NASA’s ACE spacecraft, which is positioned at Lagrange Point 1 (L1). In this work, we use historical EPAM data to train an IP shock forecasting algorithm. Our approach centers on the observation that these shocks are often preceded by identifiable signatures in the energetic particle intensity data. Using EPAM data, we trained an artificial neural network to predict the time remaining until the shock arrival. After training this algorithm on 37 events, it was able to forecast the arrival time for 19 previously unseen events. The average uncertainty in the prediction 24 h in advance was 8.9 h, while the uncertainty improved to 4.6 h when the event was 12 h away. This system is accessible online, where it provides predictions of shock arrival times using real-time EPAM data.