Phosphoserine phosphatase is required for serine and one-carbon unit synthesis in Hydrogenobacter thermophilus

Hydrogenobacter thermophilus is an obligate chemolithoautotrophic bacterium of the phylum Aquificae and is capable of fixing carbon dioxide through the reductive tricarboxylic acid (TCA) cycle. The recent discovery of two novel-type phosphoserine phosphatases (PSPs) in H. thermophilus suggests the presence of a phosphorylated serine biosynthesis pathway; however, the physiological role of these novel-type metal-independent PSPs (iPSPs) in H. thermophilus has not been confirmed. In the present study, a mutant strain with a deletion of pspA, the catalytic subunit of iPSPs, was constructed and characterized. The generated mutant was a serine auxotroph, suggesting that the novel-type PSPs and phosphorylated serine synthesis pathway are essential for serine anabolism in H. thermophilus. As an autotrophic medium supplemented with glycine did not support the growth of the mutant, the reversible enzyme serine hydroxymethyltransferase does not appear to synthesize serine from glycine and may therefore generate glycine and 5,10-CH2-tetrahydrofolate (5,10-CH2-THF) from serine. This speculation is supported by the lack of glycine cleavage activity, which is needed to generate 5,10-CH2-THF, in H. thermophilus. Determining the mechanism of 5,10-CH2-THF synthesis is important for understanding the fundamental anabolic pathways of organisms, because 5,10-CH2-THF is a major one-carbon donor that is used for the synthesis of various essential compounds, including nucleic and amino acids. The findings from the present experiments using a pspA deletion mutant have confirmed the physiological role of iPSPs as serine producers and show that serine is a major donor of one-carbon units in H. thermophilus

Deep Learning-Based Algal Detection Model Development Considering Field Application

Algal blooms have various effects on drinking water supply systems; thus, proper monitoring is essential. Traditional visual identification using a microscope is a time-consuming method and requires extensive labor. Recently, advanced machine learning algorithms have been increasingly applied for the development of object detection models. The You-Only-Look-Once (YOLO) model is a novel machine learning algorithm used for object detection; it has been continuously improved in newer versions, and a tiny version of each standard model presented. The tiny versions applied a less complicated architecture using a smaller number of convolutional layers to enable faster object detection than the standard version. This study compared the applicability of the YOLO models for algal image detection from a practical aspect in terms of classification accuracy and inference time. Therefore, automated algal cell detection models were developed using YOLO v3 and YOLO v4, in which a tiny version of each model was also applied. The cell images of 30 algal genera were used for training and testing the models. The model performances were compared using the mean average precision (mAP). The mAP values of the four models were 40.9, 88.8, 84.4, and 89.8 for YOLO v3, YOLO v3-tiny, YOLO v4, and YOLO v4-tiny, respectively, demonstrating that YOLO v4 is more precise than YOLO v3. The tiny version models presented noticeably higher model accuracy than the standard models, allowing up to ten times faster object detection time. These results demonstrate the practical advantage of tiny version models for the application of object detection with a limited number of object classes

Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal

Wastewater-based epidemiology (WBE) is drawing increasing attention as a promising tool for an early warning of emerging infectious diseases such as COVID-19. This study demonstrated the utility of a spatial bisection method (SBM) and a global optimization algorithm (i.e., genetic algorithm, GA), to support better designing and operating a WBE program for disease surveillance and source identification. The performances of SBM and GA were compared in determining the optimal locations of sewer monitoring manholes to minimize the difference among the effective spatial monitoring scales of the selected manholes. While GA was more flexible in determining the spatial resolution of the monitoring areas, SBM allows stepwise selection of optimal sampling manholes with equiareal subcatchments and lowers computational cost. Upon detecting disease outbreaks at a regular sewer monitoring site, additional manholes within the catchment can be selected and monitored to identify source areas with a required spatial resolution. SBM offered an efficient method for rapidly searching for the optimal locations of additional sampling manholes to identify the source areas. This study provides strategic and technical elements of WBE including sampling site selection with required spatial resolution and a source identification method