No abstract

Delayed stem senescence (DSS) in soybean plants is defined as retarded leaf and stem maturity, even if pods mature normally. This phenomenon is a serious problem for the combine harvesting of soybean in Japan. Our previous research showed that DSS occurred more frequently in June sowing than in July sowing, and the plants sown in June were exposed to higher temperature and higher solar radiation intensity during reproductive growth stage as compared to those sown in July. We additionally observed higher levels of cytokinins, plant hormones with senescence inhibition activity, in xylem exudate after pod‐setting stage (R4) under the DSS‐induced condition. Thus, this study investigated the effects of the respective environmental factors, temperature and light intensity, after R4 on cytokinin levels in the xylem exudate and the occurrence of DSS. Pot experiments were conducted in the experimental field of Nihon University (Fujisawa‐city, Kanagawa, Japan). In temperature treatments, soybean plants grown in the field environment were moved to an air‐conditioned room at R4 stage, whereas control of light intensity was achieved by shading using cheese clothes under the field condition. The temperature control experiments revealed that high temperature after R4 promoted the DSS symptom, while low temperature after R4 inhibited the occurrence of DSS. On the other hand, decreased light intensity after R4 did not affect the severity of DSS. The cytokinin levels in the xylem exudate at R5 correlated with the severity of DSS in the temperature treatments, but the change in light intensity did not influence the cytokinin levels. These results indicated that high temperature after pod‐setting stage have a profound effect on the occurrence of DSS, and this effect is mediated by the increased cytokinin levels in the xylem exudate.

Data augmentation is reported as a useful technique to generate a large amount of image datasets from a small image dataset. The aim of this study is to clarify the effect of data augmentation for leukocyte recognition with deep learning. We performed three different data augmentation methods (rotation, scaling, and distortion) as pretreatment on the original images. The subjects of clinical assessment were 51 healthy persons. The thin-layer blood smears were prepared from peripheral blood and stained with MG. The effect of data augmentation with rotation was the only significant effective technique in AI model generation for leukocyte recognition. On contrast, the effect of data augmentation with image distortion or image scaling was poor, and accuracy improvement was limited to specific leukocyte categories. Although data augmentation is one effective method for high accuracy in AI training, we consider that a highly effective method should be selected.

The step-and-shoot (SS) mode and continuous mode are currently used for single-photon emission computed tomography (SPECT) scan mode, and a new scan mode that combines both modes, step-and-shoot plus continuous (SSC) mode, was developed. It is expected to allow a shorter scan time and lower injected dose because the SSC mode is more sensitive than the SS mode. We confirmed the image quality of this scan mode, including various quantitative correction methods for scatter (SC), attenuation (AC), and resolution recovery (RR) in a phantom study and clinical case study. Image quality was evaluated by the count, contrast-to-noise ratio (CNR), and percent of the coefficient of variation (%CV). Independent of the correction methods, the count, CNR, and %CV of the SSC mode were superior to those of the SS mode. The ACSCRR was the best method, with a maximum increased rate of 66.4% in counts and 57.8% in CNR for the 13-mm sphere and 19.6% in CNR for other sphere sizes. The %CV for the SSC mode was the best for AC and ACRR, which was at 15.1%. With regards to attaining short bone SPECT scan time, the combination of the SSC mode and ACRR or ACSCRR demonstrated the best physical performance.