Search

Aging is a systemic physiological degenerative process, with alterations in gut microbiota and host metabolism. However, due to the interference of multiple confounding factors, aging-associated molecular characteristics have not been elucidated completely. Therefore, based on 16S ribosomal RNA (rRNA) gene sequencing and non-targeted metabolomic detection, our study systematically analyzed the composition and function of the gut microbiome, serum, and fecal metabolome of 36 male rhesus monkeys spanning from 3 to 26 years old, which completely covers juvenile, adult, and old stages. We observed significant correlations between 41 gut genera and age. Moreover, 86 fecal and 49 serum metabolites exhibited significant age-related correlations, primarily categorized into lipids and lipid-like molecules, organic oxygen compounds, organic acids and derivatives, and organoheterocyclic compounds. Further results suggested that aging is associated with significant downregulation of various amino acids constituting proteins, elevation of lipids, particularly saturated fatty acids, and steroids. Additionally, age-dependent changes were observed in multiple immune-regulatory molecules, antioxidant stress metabolites, and neurotransmitters. Notably, multiple age-dependent genera showed strong correlations in these changes. Together, our results provided new evidence for changing characteristics of gut microbes and host metabolism during aging. However, more research is needed in the future to verify our findings. [ABSTRACT FROM AUTHOR]

We present a liquid-level measurement system based on second-confirm recognition algorithm. Compared with the traditional methods of the liquid-level measurement system, which has low accuracy, slow measurement speed, and limited range of measurements, this system can accurately and quickly measure the liquid level. Moreover, it can automatically complete the measurement of the liquid-level line position. The main component of the system includes a vision detection system and a tracking control system. The vision detection system mainly includes image preprocessing, numeral recognition, and liquid-level line recognition. Among the vision detection system, numeral recognition is the key task. Therefore, we propose a numeral recognition algorithm that employs a second-confirm algorithm to recognize numerals. The algorithm first utilizes template matching to find one numeral from the image. Afterward, we can divide the numeral image out of the rule image. This can greatly optimize the issues of time-consumption. The numerals can be segmented from the numeral image when the numeral image is obtained. After the numerals are captured, the numerals and template image difference is computed to complete numeral recognition. In addition, tracking control system tracks the liquid line in real time through the level value feedback by the vision system, which calculates the position of the liquid level in the image and feeds back the level value to the tracking control system. After experimental verification, the system can meet the expected requirements of liquid-level measurement. [ABSTRACT FROM AUTHOR]