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Dynamic ensemble prediction of cognitive performance in spaceflight.

ABSTRACT: During spaceflight, astronauts face a unique set of stressors, including microgravity, isolation, and confinement, as well as environmental and operational hazards. These factors can negatively impact sleep, alertness, and neurobehavioral performance, all of which are critical to mission success. In this paper, we predict neurobehavioral performance over the course of a 6-month mission aboard the International Space Station (ISS), using ISS environmental data as well as self-reported and cognitive data collected longitudinally from 24 astronauts. Neurobehavioral performance was repeatedly assessed via a 3-min Psychomotor Vigilance Test (PVT-B) that is highly sensitive to the effects of sleep deprivation. To relate PVT-B performance to time-varying and discordantly-measured environmental, operational, and psychological covariates, we propose an ensemble prediction model comprising of linear mixed effects, random forest, and functional concurrent models. An extensive cross-validation procedure reveals that this ensemble outperforms any one of its components alone. We also identify the most important predictors of PVT-B performance, which include an individual's previous PVT-B performance, reported fatigue and stress, and temperature and radiation dose. This method is broadly applicable to settings where the main goal is accurate, individualized prediction of human behavior involving a mixture of person-level traits and irregularly measured time series.

SUBMITTER: Tu D

PROVIDER: S-EPMC9246897 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MotivationProtein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming. To expedite the discovery of novel substrates, computational approaches based on kinase recognition sequence (motifs) from known substrates, protein structure, interaction and co-localization have been proposed. However, rarely do these methods take into account the dynamic responses of signaling cascades measured from in vivo cellular systems. Given that recent advances in mass spectrometry-based technologies make it possible to quantify phosphorylation on a proteome-wide scale, computational approaches that can integrate static features with dynamic phosphoproteome data would greatly facilitate the prediction of biologically relevant kinase-specific substrates.ResultsHere, we propose a positive-unlabeled ensemble learning approach that integrates dynamic phosphoproteomics data with static kinase recognition motifs to predict novel substrates for kinases of interest. We extended a positive-unlabeled learning technique for an ensemble model, which significantly improves prediction sensitivity on novel substrates of kinases while retaining high specificity. We evaluated the performance of the proposed model using simulation studies and subsequently applied it to predict novel substrates of key kinases relevant to insulin signaling. Our analyses show that static sequence motifs and dynamic phosphoproteomics data are complementary and that the proposed integrated model performs better than methods relying only on static information for accurate prediction of kinase-specific substrates.Availability and implementationExecutable GUI tool, source code and documentation are freely available at https://github.com/PengyiYang/KSP-PUEL.Contactpengyi.yang@nih.gov or jothi@mail.nih.govSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:Long duration head down tilt bed rest (HDBR) has been widely used as a spaceflight analog environment to understand the effects of microgravity on human physiology and performance. Reports have indicated that crewmembers onboard the International Space Station (ISS) experience symptoms of elevated CO2 such as headaches at lower levels of CO2 than levels at which symptoms begin to appear on Earth. This suggests there may be combinatorial effects of elevated CO2 and the other physiological effects of microgravity including headward fluid shifts and body unloading. The purpose of the current study was to investigate these effects by evaluating the impact of 30 days of 6° HDBR and 0.5% CO2 (HDBR + CO2) on mission relevant cognitive and sensorimotor performance. We found a facilitation of processing speed and a decrement in functional mobility for subjects undergoing HDBR + CO2 relative to our previous study of HDBR in ambient air. In addition, nearly half of the participants in this study developed signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), a constellation of ocular structural and functional changes seen in approximately one third of long duration astronauts. This allowed us the unique opportunity to compare the two subgroups. We found that participants who exhibited signs of SANS became more visually dependent and shifted their speed-accuracy tradeoff, such that they were slower but more accurate than those that did not incur ocular changes. These small subgroup findings suggest that SANS may have an impact on mission relevant performance inflight via sensory reweighting. New And Noteworthy:We examined the effects of long duration head down tilt bed rest coupled with elevated CO2 as a spaceflight analog environment on human cognitive and sensorimotor performance. We found enhancements in processing speed and declines in functional mobility. A subset of participants exhibited signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), which affects approximately one in three astronauts. These individuals increased their visual reliance throughout the intervention in comparison to participants who did not show signs of SANS.

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Dynamic ensemble prediction of cognitive performance in spaceflight.

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