Importantly, at least four days after the exposure, the transcriptional levels of those genes are still deregulated. ASN007 Our results highlight the deep impact that short exposures to hypergravity and vibration have in organisms, and thus the implications that space flight launch could have. These phenomena should be taken into account when planning for well-controlled microgravity studies.Nanopore sequencing, as represented by Oxford Nanopore Technologies' MinION, is a promising technology for in situ life detection and for microbial monitoring including in support of human space exploration, due to its small size, low mass (~100 g) and low power (~1 W). Now ubiquitous on Earth and previously demonstrated on the International Space Station (ISS), nanopore sequencing involves translocation of DNA through a biological nanopore on timescales of milliseconds per base. Nanopore sequencing is now being done in both controlled lab settings as well as in diverse environments that include ground, air, and space vehicles. Future space missions may also utilize nanopore sequencing in reduced gravity environments, such as in the search for life on Mars (Earth-relative gravito-inertial acceleration (GIA) g = 0.378), or at icy moons such as Europa (g = 0.134) or Enceladus (g = 0.012). We confirm the ability to sequence at Mars as well as near Europa or Lunar (g = 0.166) and lower g levels, demonstrate the functionality of updated chemistry and sequencing protocols under parabolic flight, and reveal consistent performance across g level, during dynamic accelerations, and despite vibrations with significant power at translocation-relevant frequencies. Our work strengthens the use case for nanopore sequencing in dynamic environments on Earth and in space, including as part of the search for nucleic-acid based life beyond Earth.High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson's disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.Genetic risk for complex diseases very rarely reflects only Mendelian-inherited phenotypes where single-gene mutations can be followed in families by linkage analysis. More commonly, a large set of low-penetrance, small effect-size variants combine to confer risk; they are normally revealed in genome-wide association studies (GWAS), which compare large population groups. Whereas Mendelian inheritance points toward disease mechanisms arising from the mutated genes, in the case of GWAS signals, the effector proteins and even general risk mechanism are mostly unknown. Instead, the utility of GWAS currently lies primarily in predictive and diagnostic information. Although an amazing body of GWAS-based knowledge now exists, we advocate for more funding towards the exploration of the fundamental biology in post-GWAS studies; this research will bring us closer to causality and risk gene identification. Using Parkinson's Disease as an example, we ask, how, where, and when do risk loci contribute to disease?Gait deficits are a common feature of Parkinson's disease (PD) and predictors of future motor and cognitive impairment. Understanding how muscle activity contributes to gait impairment and effects of therapeutic interventions on motor behaviour is crucial for identifying potential biomarkers and developing rehabilitation strategies. This article reviews sixteen studies that investigate the electromyographic (EMG) activity of lower limb muscles in people with PD during walking and reports on their quality. The weight of evidence establishing differences in motor activity between people with PD and healthy older adults (HOAs) is considered. Additionally, the effect of dopaminergic medication and deep brain stimulation (DBS) on modifying motor activity is assessed. Results indicated greater proximal and decreased distal activity of lower limb muscles during walking in individuals with PD compared to HOA. Dopaminergic medication was associated with increased distal lower limb muscle activity whereas subthalamic nucleus DBS increased activity of both proximal and distal lower limb muscles. Tibialis anterior was impacted most by the interventions. Quality of the studies was not strong, with a median score of 61%. Most studies investigated only distal muscles, involved small sample sizes, extracted limited EMG features and lacked rigorous signal processing. Few studies related changes in motor activity with functional gait measures. Understanding mechanisms underpinning gait impairment in PD is essential for development of personalised rehabilitative interventions. Recommendations for future studies include greater participant numbers, recording more functionally diverse muscles, applying multi-muscle analyses, and relating EMG to functional gait measures.