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In this paper, the model for predicting yields of chinese cabbages of each cultivar (joined-up in 2015 and wrapped-up in 2016) was developed after the reflectance of hyperspectral imagery was merged as 10 nm, 25 nm and 50 nm of FWHM (full width at half maximum). Band rationing was employed to minimize the unstable reflectance of multi-temporal hyperspectral imagery. The stepwise analysis was employed to select key band ratios to predict yields in all cultivars. The key band ratios selected for each of FWHM were used to develop the yield prediction models of chinese cabbage for all cultivars (joined-up & wrapped-up) and each cultivar (joined-up, wrapped-up). Effective accumulated temperature (EAT) was added in the models to evaluate its improvement of performances. In all models, the performance of models was improved with adding of EAT. The models with EAT for each of FWHM showed the predictability of yields in all cultivars as R2≥0.80, RMSE≤694 g/plant and RE≤28.3%. Such as this result, if the yield can be predicted regardless of the cultivar, it is considered to be advantageous when predicting the yield over a wide area because it is not require a cultivar classification work as pre-processing in imagery.

The aim of the study was to develop a new diagnostic biomarker for identifying serum exosomal miRNAs specific to epithelial ovarian cancer (EOC) and to find out target gene of the miRNA for exploring the molecular mechanisms in EOC. A total of 84 cases of ovarian masses and sera were enrolled, comprising EOC (n = 71), benign ovarian neoplasms (n = 13). We detected expression of candidate miRNAs in the serum and tissue of both benign ovarian neoplasm group and EOC group using real-time polymerase chain reaction. Immunohistochemistry were constructed using formalin fixed paraffin embedded (FFPE) tissue to detect expression level of suppressor of cytokine signaling 4 (SOCS4). In the EOC group, miRNA-1290 was significantly overexpressed in serum exosomes and tissues as compared to benign ovarian neoplasm group (fold change ≥ 2, p < 0.05). We observed area under the receiver operating characteristic curve (AUC) for miR-1290, using a cut-off of 0.73, the exosomal miR-1290 from serum had AUC, sensitivity, and specificity values of 0.794, 69.2 and 87.3, respectively. In immunohistochemical study, expression of SOCS4 in EOC was lower than that in benign ovarian neoplasm. Serum exosomal miR-1290 could be considered as a biomarker for differential diagnosis of EOC from benign ovarian neoplasm and SOCS4 might be potential target gene of miR-1290 in EOC.

Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

Lymph node metastasis plays a crucial role in predicting prognosis in advanced gastric cancer (AGC). In the present study, we formulated a fibrosis ratio (FR), defined as the number of metastatic lymph nodes with fibrosis divided by the total number of lymph nodes, and sought to determine whether it can be used to predict the prognosis of patients with AGC and improve on existing node staging. We retrospectively analyzed 161 patients who underwent curative resection for node-positive AGC between 2001 and 2010, evaluating the association between FR, lymph node ratio (LNR), and micrometastasis, and the relationship between FR and clinicopathologic findings, overall survival (OS) and disease-free survival (DFS). A high FR was significantly related to T stage (P