Agricultural soil is the primary N
2
O sink limiting the emission of N
2
O gas into the atmosphere. Although
Gemmatimonadetes
bacteria are abundant in agricultural soils, limited information is currently available on N
2
O reduction by
Gemmatimonadetes
bacteria. Therefore, the effects of pH and temperature on N
2
O reduction activities and affinity constants for N
2
O reduction were examined by performing batch experiments using an isolate of
Gemmatimonadetes
bacteria,
Gemmatimonas aurantiaca
(NBRC100505
T
).
G. aurantiaca
reduced N
2
O at pH 5–9 and 4–50°C, with the highest activity being observed at pH 7 and 30°C. The affinity constant of
G. aurantiaca
cells for N
2
O was 4.4 μM. The abundance and diversity of the
Gemmatimonadetes
16S rRNA gene and
nosZ
encoding nitrous oxide reductase in agricultural soil samples were also investigated by quantitative PCR (qPCR) and amplicon sequencing analyses. Four N
2
O-reducing agricultural soil samples were assessed, and the copy numbers of the
Gemmatimonadetes
16S rRNA gene (clades G1 and G3),
nosZ
DNA, and
nosZ
mRNA were 8.62–9.65×10
8
, 5.35–7.15×10
8
, and 2.23–4.31×10
9
copies (g dry soil)
–1
, respectively. The abundance of the
nosZ
mRNA of
Gemmatimonadetes
bacteria and OTU91, OUT332, and OTU122 correlated with the N
2
O reduction rates of the soil samples tested, suggesting N
2
O reduction by
Gemmatimonadetes
bacteria.
Gemmatimonadetes
16S rRNA gene reads affiliated with OTU4572 and OTU3759 were predominant among the soil samples examined, and these
Gemmatimonadetes
OTUs have been identified in various types of soil samples.
Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 (p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway.
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