Plants rely on environmental signals, such as gravity, to regulate their growth and gather resources. Hypergravity, an increased gravitational force beyond Earth's 1 x g, has been reported to induce various phenotypic, physiological, and structural changes in plants. In this study, mung beans (Vigna radiata) were subjected to hypergravity treatment using a standard laboratory centrifuge. Seeds were exposed to various gravitational values (100 and 1000 x g) with different durations (1, 2, 4 hours). The seeds were then germinated under normal growth conditions. After 7 days, germination percentages and root lengths were assessed to determine the impact of hypergravity on early plant development. Regardless of duration, all seeds exposed to 1000 x g showed a significant decrease in root length compared to the control. However, exposure to 100 x g for 4 hours showed a significant increase in root length by 3.17% (p < 0.05). The results indicate that mung beans respond differently depending on the strength and duration of hypergravity. Higher gravitational forces (1000 x g) slowed down root growth, suggesting that low to moderate gravity may enhance root development in early stages. These findings suggest that hypergravity treatment could confer changes in growth traits in mung beans, offering a potential strategy for improving crop resilience to various environmental stressors. Further investigations involving molecular and biochemical analyses are warranted to fully understand the underlying mechanisms and broader implications of hypergravity-induced plant responses.

The impact of different stress factors on plant growth can be assessed through the measurement of sprout length. Measuring the sprout length can be done through traditional methods (e.g. use of calipers) or through image analysis. The use of ImageJ, an image analysis program developed at National Institute of Health, is an established validated way to do the measurement (Schneider et al., 2012). But the line segmentation measure feature to measure the sprouts is very inefficient and time consuming when dealing with hundreds of samples. In this work we developed a computer vision-based approach, SproutCV, to automate the measurement process. After image preprocessing, where mean shift filtering, grayscale, Gaussian blur, and morphological operations were applied, Otsu's thresholding was implemented to identify sprout regions. We then skeletonized the output image and converted it to a network graph, wherein each pixel in an 8-connectivity is the node and the Euclidean distance between each pixel is the weighted edge. The skeleton lines were simplified further using Douglas-Peucker algorithm to remove unnecessary pixels. Dijkstra’s algorithm was utilized to find the two farthest nodes in the graph and the shortest path between them. The length of this final graph path was measured, and this represented the sprout length. SproutCV achieved a coefficient of determination (R2) of 0.9991 for 566 sprouts in comparison to measurements done in ImageJ, with a mean absolute percent error of 1.74%. This shows that SproutCV is an effective and accurate way of automating measurements of sprouts. Future improvements to this algorithm can expand its applications on automating measurements of various objects, from red blood cells in medical imaging to fishes in marine biology.

Spaceflight has diverse impacts on the human body. But due to the complexity of biological data, these changes are sometimes difficult to detect. Thus, in this study, network analysis is used to uncover important hub genes affected by short duration spaceflight to civilians. Differentially expressed genes from the transcriptome profiles of the Inspiration4 mission crew members were mapped in a network. And through degrees and centrality measures, hub genes were identified. Functional enrichment analysis (FEA) was done hereafter to understand their biological functions and were validated with other datasets in SOMA. Results uncovered eight hub genes namely UBA52, FAU, RACK1, RPS2, RPS11, RPS14, RPS15 and RPL35. FEA showed that these hub genes were all linked to ribosomal function. SOMA validation revealed that these ribosomal protein genes show consistent altered gene expression during spaceflight, suggesting potential links to interference in ribosome biogenesis and protein synthesis. These findings can help future studies in understanding the effect of spaceflight on humans.