Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data.
View Article and Find Full Text PDFExtensive spaceflight life investigations (SLIs) have revealed observable space effects on plants, particularly their growth, nutrition yield, and secondary metabolite production. Knowledge of these effects not only facilitates space agricultural and biopharmaceutical technology development but also provides unique perspectives to ground-based investigations. SLIs are specialized experimental protocols and notable biological phenomena.
View Article and Find Full Text PDFBiological experiments performed in space crafts like space stations, space shuttles, and recoverable satellites has enabled extensive spaceflight life investigations (SLIs). In particular, SLIs have revealed distinguished space effects on microbial growth, survival, metabolite production, biofilm formation, virulence development and drug resistant mutations. These provide unique perspectives to ground-based microbiology and new opportunities for industrial pharmaceutical and metabolite productions.
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