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Cowpea (Vigna unguiculata) is one of the traditional ingredients of Japan. The objective of this study was to investigate the nutritional and chemical composition and the biological activities of cowpea grown in Okinawa region, a subtropical area located in south west Japan. This study demonstrated that the protein and dietary fiber content of cowpea grown in the Okinawa region was similar to that of cowpeas grown in other countries. In addition, it was found that the sodium content and anthocyanin composition of cowpeas grown in the Okinawa region was different from that grown in other regions of Japan; however, no significant differences were observed in proanthocyanidin composition and content among the cowpeas grown in different cultivation areas in Japan. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the composition and contents of anthocyanins and proanthocyanidins, and the antioxidant capacity of black and red cowpeas grown in Japan, with comparison to grain legumes (black kidney beans, black soybeans, and azuki beans) commonly consumed in Japan. This study revealed that black cowpeas contained seven anthocyanins, the 3-O-galactosides and 3-O-glucosides of cyanidin and delphinidin, and the 3-O-glucosides of malvidin, peonidin, and petunidin, at higher levels than other grain legumes. In addition, black and red cowpeas were rich in proanthocyanidin oligomers (monomers to hexamers) when compared with other grain legumes. Among these, black and red cowpeas showed potent hydrophilic oxygen radical absorbance capacity (H-ORAC), reaching 540.6 and 367.5 µmol-Trolox equivalents/g-dry matter, respectively. The contributions of anthocyanins and proanthocyanidin oligomers to H-ORAC values were 45.8 % and 17.5 % in black and red cowpeas, respectively. [ABSTRACT FROM AUTHOR]

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation.

Sensory characteristics, colorimetric value and volatile components of shiikuwasha (Citrus depressa Hayata) juice and adulterated model juice using calamondin (Citrus madurensis LOUR.) juice were investigated. (1) A triangle test detected adulterated model juice ranging from 0% to 50%. The results of Scheffe's paired comparison test (Ura variation) suggested that the color and flavor of juice were used to differentiate the shiikuwasha juice from the adulterated model juices. (2) Color parameters (L*, a* and b*) were detected by a tristimulus reflectance colorimeter, and significant color differences (p < 0.05) were observed between shiikuwasha juice and other model juices. The results of (1) and (2) suggested consumers are able to identify pure shiikuwasha juice personally. (3) Volatile components were measured and identified by GC/MS; γ-terpinene and linalool were new and useful chemical markers for detecting small amounts of shiikuwasha juice mixed with calamondin juice. [ABSTRACT FROM AUTHOR]

Background: Serum β-cryptoxanthin levels are lower in overweight subjects than in normal subjects. Abnormalities of adipocytokine profiles in obesity subjects have been reported. There are several reports that serum β-cryptoxanthin levels in them were relatively lower than normal subjects. Objective: We hypothesize that supplementation of highly concentrated β-cryptoxanthin improves serum adipocytokine profiles in obese subjects. This study tested the association between β-cryptoxanthin intake and serum adipocytokine levels. Methods: An intervention study consisted of a 3-week long before-and-after controlled trial, where β-cryptoxanthin (4.7 mg/day) was given to 17 moderately obese postmenopausal women. Results: The results indicated no significant changes in body weight or body mass index (BMI). Serum β-cryptoxanthin levels increased significantly by 4-fold. Serum high molecular weight (HMW)-adiponectin levels increased significantly, while serum plasminogen activator inhibitor (PAI)-1 levels decreased. Conclusions: We concluded that increasing the intake of β-cryptoxanthin to approximately 4 mg per day for 3 weeks may have beneficial effects on the serum adipocytokine status and consequently alleviate progression of metabolic syndrome. [ABSTRACT FROM AUTHOR]

From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

This study examines the performance of radiation processes (shortwave and longwave radiations) using numerical weather prediction models (NWPs). NWP were calculated using four different horizontal resolutions (5, 2 and 1 km, and 500 m). Validation results on solar irradiance simulations with a horizontal resolution of 500 m indicated positive biases for direct normal irradiance dominate for the period from 09 JST (Japan Standard Time) to 15 JST. On the other hand, after 15 JST, negative biases were found. For diffused irradiance, weak negative biases were found. Validation results on upward longwave radiation found systematic negative biases of surface temperature (corresponding to approximately −2 K for summer and approximately −1 K for winter). Downward longwave radiation tended to be weak negative biases during both summer and winter. Frequency of solar irradiance suggested that the frequency of rapid variations of solar irradiance (ramp rates) from the NWP were less than those observed. Generally, GHI distributions between the four different horizontal resolutions resembled each other, although horizontal resolutions also became finer.

To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future.

A girl with congenital agammaglobulinemia and minor facial anomalies lacked B cells in peripheral blood: karyotypic analysis of white blood cells showed balanced translocation, t(9;20)(q33.2;q12). In the current study, we isolated a novel gene, leucine-rich repeat-containing 8 (LRRC8), at the translocation site on chromosome 9. It has four transmembrane helixes with one isolated and eight sequentially located leucine-rich repeats (LRRs) and constitutes a new protein family. It is expressed on T cells as well as on B-lineage cells. Translocation truncates the LRRC8 gene, resulting in deletion of the eighth, ninth, and half of the seventh LRR domains located close to the C-terminal. The truncated form of the LRRC8 gene is transcribed with sequences from the noncoding region adjacent to the truncated seventh LRR. Protein products derived from the truncated gene are coexpressed on white blood cells with the intact LRRC8 protein from the untranslocated allele. Transplantation experiments with murine bone marrow cells that were forced to express the truncated LRRC8 show that expression of the truncated protein inhibited B cell development. These results indicate that LRRC8 is responsible for the B cell deficiency in this patient and is required for B cell development. [ABSTRACT FROM AUTHOR]