Cloud Cover in the Australian Region: Development and Validation of a Cloud Masking, Classification and Optical Depth Retrieval Algorithm for the Advanced Himawari Imager

This paper presents a cloud masking, cloud classification and optical depth retrieval algorithm and its application to the Advanced Himawari Imager (AHI) on the Himawari-8/9 satellites using visible, near infrared and thermal infrared bands. A time-series-based approach was developed for cloud masking which was visually assessed and quantitatively validated over 1 year of daytime data for both land and ocean against the level 2 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product (version 4.10). An overall hit rate (the proportion of pixels identified by both sensors as either clear or cloudy) of 87% was found. However, analysis revealed that, when partially cloudy conditions were experienced, the small footprint of the CALIOP sensor (70 meters beam size sampling every 330 meters along the ground track) had a major impact on the hit rate. When partially cloudy pixels are excluded a hit rate of ~98% was found, even for thin clouds with optical depth less than 0.25. A two-way confidence index for the cloud mask was developed which could be used to reclassify the pixels depending on applications, either biasing toward clearness or cloudiness. On the basis of the cloud masking, classification and optical depth retrieval was performed based on radiative transfer modeling. Small modeling error was found, and inspection of typical cloud classification examples showed that the results were consistent with cloud texture and cloud top temperatures. While difficult to validate retrieved cloud properties directly, an indirect quantitative validation was performed by comparing surface-level solar flux computed from the retrieved cloud properties with in-situ measurements at 11 sites across Australia for up to 3 years. Excellent agreement between calculated and measured solar flux was found, with a mean monthly bias of 2.96 W/m2 and RMSE of 8.91 W/m2, and the correlation coefficient exceeding 0.98 at all sites. Further assessment was conducted by comparing seasonal and annual cloud fraction with that of ISCCP (International Satellite Cloud Climatology Project) over Australia and surrounding region. It showed high degree of resemblance between the two datasets in their total cloud fraction. The geographical distribution of cloud classes also showed broad resemblance, though detailed differences exist, especially for high clouds, which is probably due to the use of different cloud classification systems in the two datasets. The products generated from this study are being used in several applications including ocean color remote sensing, solar energy, vegetation monitoring and detection of smoke for the study of their health impacts, and aerosol and land surface bidirectional reflectance distribution function (BRDF) retrieval. The method developed herein can be applied to other geostationary sensors

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Strawberry Maturity Recognition Algorithm Combining Dark Channel Enhancement and YOLOv5

Aiming at the problems of low accuracy of strawberry fruit picking and large rate of mispicking or missed picking, YOLOv5 combined with dark channel enhancement is proposed. In “Fengxiang” strawberry, the criterion of “bad fruit” is added to the conventional three criteria of ripeness, near-ripeness, and immaturity, because some of the bad fruits are close to the color of ripe fruits, but the fruits are small and dry. The training accuracy of the four kinds of strawberries with different ripeness is above 85%, and the testing accuracy is above 90%. Then, to meet the demand of all-day picking and address the problem of low illumination of images collected at night, an enhancement algorithm is proposed to enhance the images, which are recognized. We compare the actual detection results of the five enhancement algorithms, i.e., histogram equalization, Laplace transform, gamma transform, logarithmic variation, and dark channel enhancement processing under the different numbers of fruits, periods, and video tests. The results show that combined with dark channel enhancement, YOLOv5 has the highest recognition rate. Finally, the experimental results demonstrate that YOLOv5 is better than SSD, DSSD, and EfficientDet in terms of recognition accuracy, and the correct rate can reach more than 90%. Meanwhile, the method has good robustness in complex environments such as partial occlusion and multiple fruits

Association between Chinese Dietary Guidelines Compliance Index for Pregnant Women and Risks of Pregnancy Complications in the Tongji Maternal and Child Health Cohort

Background: Compliance with dietary guidelines among pregnant women can positively influence not only their own health but also the health of their babies. Measuring the compliance requires professional skills in nutrition and dietary counseling. In China, few simple and effective techniques assess dietary quality among pregnant women, especially in rural areas. We aimed to establish a new simple and effective assessment technique, the “Chinese Dietary Guidelines Compliance Index for Pregnant Women (CDGCI-PW)” and assess the association between maternal dietary compliance and risks of pregnancy complications. Methods: The CDGCI-PW consists of 13 main components which were based on the 2016 edition of the Chinese dietary guidelines for pregnant women. Each component was assigned a different score range, and the overall score ranged from 0 to 100 points. The Tongji Maternal and Child Health Cohort study (from September 2013 to May 2016) was a prospective cohort study designed to examine maternal dietary and lifestyle effects on the health of pregnant women and their offspring. The maternal diet during the second trimester was compared with the corresponding recommended intake of the Chinese balanced dietary pagoda for pregnant women to verify their compliance with dietary guidelines. The association between maternal dietary quality and risks of pregnancy complications was estimated by regression analysis. Receiver operating characteristic (ROC) curves were constructed to identify the optimal cut-off values of CDGCI-PW for gestational hypertension and gestational diabetes mellitus (GDM). Results: Among the 2708 pregnant women, 1489 were eventually followed up. The mean CDGCI-PW score was 74.1 (standard deviation (SD) 7.5) in the second trimester. The majority of foods showed the following trend: the higher the CDGCI-PW score, the higher the proportion of pregnant women who reported food intake within the recommended range. Moreover, a higher maternal CDGCI-PW score was significantly associated with lower risks of gestational hypertension [odds ratio (OR) (95% confidence interval [(CI): 0.30 (0.20, 0.37)] and GDM [OR (95% CI): 0.38 (0.31, 0.48)]. The optimal CDGCI-PW cut-off value for gestational hypertension was ≥68.5 (sensitivity 82%; specificity: 61%; area under the ROC curve, AUC = 0.743), and the optimal CDGCI-PW cut-off score for GDM was ≥75.5 (sensitivity 43%; specificity: 81%; area under the ROC curve, AUC = 0.714). Conclusions: The CDGCI-PW is a simple and useful technique that assesses maternal diet quality during pregnancy, while adherence to the CDGCI-PW is associated with a lower risk of gestational hypertension and GDM

Image_1_To develop a prognostic model for neoadjuvant immunochemotherapy efficacy in esophageal squamous cell carcinoma by analyzing the immune microenvironment.tif

ObjectiveThe choice of neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC) is controversial. This study aims to provide a basis for clinical treatment selection by establishing a predictive model for the efficacy of neoadjuvant immunochemotherapy (NICT).MethodsA retrospective analysis of 30 patients was conducted, divided into Response and Non-response groups based on whether they achieved major pathological remission (MPR). Differences in genes and immune microenvironment between the two groups were analyzed through next-generation sequencing (NGS) and multiplex immunofluorescence (mIF). Variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves to establish a predictive model. An additional 48 patients were prospectively collected as a validation set to verify the model’s effectiveness.ResultsNGS suggested seven differential genes (ATM, ATR, BIVM-ERCC5, MAP3K1, PRG, RBM10, and TSHR) between the two groups (P ConclusionNICT treatment may exert anti-tumor effects by enriching immune cells and activating exhausted T cells. Tumor area CD3+, PD-L1+, and CD3+PD-L1+ are closely related to therapeutic efficacy. The model containing these three variables can accurately predict treatment outcomes, providing a reliable basis for the selection of neoadjuvant treatment plans.</p

Table_1_To develop a prognostic model for neoadjuvant immunochemotherapy efficacy in esophageal squamous cell carcinoma by analyzing the immune microenvironment.docx

ObjectiveThe choice of neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC) is controversial. This study aims to provide a basis for clinical treatment selection by establishing a predictive model for the efficacy of neoadjuvant immunochemotherapy (NICT).MethodsA retrospective analysis of 30 patients was conducted, divided into Response and Non-response groups based on whether they achieved major pathological remission (MPR). Differences in genes and immune microenvironment between the two groups were analyzed through next-generation sequencing (NGS) and multiplex immunofluorescence (mIF). Variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves to establish a predictive model. An additional 48 patients were prospectively collected as a validation set to verify the model’s effectiveness.ResultsNGS suggested seven differential genes (ATM, ATR, BIVM-ERCC5, MAP3K1, PRG, RBM10, and TSHR) between the two groups (P ConclusionNICT treatment may exert anti-tumor effects by enriching immune cells and activating exhausted T cells. Tumor area CD3+, PD-L1+, and CD3+PD-L1+ are closely related to therapeutic efficacy. The model containing these three variables can accurately predict treatment outcomes, providing a reliable basis for the selection of neoadjuvant treatment plans.</p

Image_4_To develop a prognostic model for neoadjuvant immunochemotherapy efficacy in esophageal squamous cell carcinoma by analyzing the immune microenvironment.tif

ObjectiveThe choice of neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC) is controversial. This study aims to provide a basis for clinical treatment selection by establishing a predictive model for the efficacy of neoadjuvant immunochemotherapy (NICT).MethodsA retrospective analysis of 30 patients was conducted, divided into Response and Non-response groups based on whether they achieved major pathological remission (MPR). Differences in genes and immune microenvironment between the two groups were analyzed through next-generation sequencing (NGS) and multiplex immunofluorescence (mIF). Variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves to establish a predictive model. An additional 48 patients were prospectively collected as a validation set to verify the model’s effectiveness.ResultsNGS suggested seven differential genes (ATM, ATR, BIVM-ERCC5, MAP3K1, PRG, RBM10, and TSHR) between the two groups (P ConclusionNICT treatment may exert anti-tumor effects by enriching immune cells and activating exhausted T cells. Tumor area CD3+, PD-L1+, and CD3+PD-L1+ are closely related to therapeutic efficacy. The model containing these three variables can accurately predict treatment outcomes, providing a reliable basis for the selection of neoadjuvant treatment plans.</p