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  1. The term “microgravity” is used to describe the “weightlessness” or “zero-g” circumstances that can only be found in space beyond earth’s atmosphere. Rhodobacter sphaeroides is a gram-negative purple phototroph, used as a model organism for this study due to its genomic complexity and metabolic versatility. Its genome has been completely sequenced, and profiles of the differential gene expression under aerobic, semi-aerobic, and photosynthetic conditions were examined. In this study, we hypothesized that R. sphaeroides will show altered growth characteristics, morphological properties, and gene expression patterns when grown under simulated microgravity. To test that, we measured the optical density and colony-forming units of cell cultures grown under both microgravity and normal gravity conditions. Differences in the cell morphology were observed using scanning electron microscopy (SEM) images by measuring the length and the surface area of the cells under both conditions. Furthermore, we also identified homologous genes of R. spheroides using the differential gene expression study of Acidovorax under microgravity in our laboratory. Growth kinetics results showed that R. sphaeroides cells grown under microgravity experience a shorter log phase and early stationary phase compared to the cells growing under normal gravity conditions. The length and surface area of the cells under microgravity were significantly higher confirming that bacterial cells experience altered morphological features when grown under microgravity conditions. Differentially expressed homologous gene analysis indicated that genes coding for several COG and GO functions, such as metabolism, signal-transduction, transcription, translation, chemotaxis, and cell motility are differentially expressed to adapt and survive microgravity. 
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    Free, publicly-accessible full text available November 1, 2024
  2. Small ribonucleic acid (sRNA) sequences are 50–500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism’s genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs previously identified in Salmonella typhimurium LT2 (SLT2) and Escherichia coli K12 ( E. coli K12) genomes. Second, as a preliminary study, we characterized the sRNA-size distribution with the conformity test for Benford’s law. Third, we applied six traditional classification algorithms to sRNA features and assessed classification performance with seven metrics, varying positive-to-negative instance ratios, and utilizing stratified 10-fold cross-validation. We revisited important individual features and feature groups and found that classification with combined features perform better than with either an individual feature or a single feature group in terms of Area Under Precision-Recall curve (AUPR). We reconfirmed that AUPR properly measures classification performance on imbalanced data with varying imbalance ratios, which is consistent with previous studies on classification metrics for imbalanced data. Overall, eXtreme Gradient Boosting (XGBoost), even without exploiting optimal hyperparameter values, performed better than the other five algorithms with specific optimal parameter settings. As a future work, we plan to extend XGBoost further to a large amount of published sRNAs in bacterial genomes and compare its classification performance with recent machine learning models’ performance. 
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