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Chilo sacchariphagus (Lepidoptera: Pyralidae) is an economically important sugarcane pest. Although numerous studies were conducted on the physiological responses in C. sacchariphagus, little is known regarding the genes regulating these physiological processes. Gene expression analysis by qRT-PCR can offer a significant indication for functional gene studies. To our knowledge, the reference genes of C. sacchariphagus have not been screened or evaluated, which hinders the functional gene study. In the present study, the stability of seven reference genes (β-ACT, GAPDH, BTF3, 28S, RPL7, EF1α, and SDHA) was evaluated in C. sacchariphagus under different experimental conditions, including tissues (antenna, head, thorax, abdomen, leg, and wing), temperatures (4 °C, 25 °C, and 37 °C) and sexes (male and female), through RefFinder, which integrates four algorithms (Normfinder, BestKeeper, ΔCt method, and geNorm). The findings suggested that the combination of β-ACT and RPL7 is ideal to analyze gene expressions in different tissues and at distinct temperatures, and EF1α and SDHA were suitable reference genes for comparing gene expressions between sexes. Finally, the expression profiles of CsacPBP1 gene were evaluated, and the outcomes further confirm the importance of selecting fitting reference genes for normalization of qRT-PCR data. This study represents the first kind in screening out suitable reference genes for gene expression analysis in C. sacchariphagus. Information from this study is poised to galvanize future inquiry into the gene expression of C. sacchariphagus, an economically important pest of sugarcane.

Introduction: The goal of this study is to compare the prognostic performance of NETPET scores, based on gallium-68 DOTANOC (68Ga-DOTANOC) and fluorine-18 fluorodeoxyglucose (18F-FDG) Positron Emission Tomography-Computed Tomography (PET-CT), and PET-CT metabolic parameters in metastatic gastrointestinal neuroendocrine tumors (GI-NET), while constructing and validating a nomogram derived from dual-scan PET-CT. Methods: In this retrospective study, G1–G3 GI-NET patients who underwent 68Ga-DOTANOC and 18F-FDG PET scans were enrolled and divided into training and internal validation cohorts. Three grading systems were constructed based on NETPET scores and standardized uptake value maximum (SUVmax). LASSO regression selected variables for a multivariable Cox model, and nomograms predicting progression-free survival (PFS) and overall survival (OS) were created. The prognostic performance of these systems was assessed using time-dependent receiver-operating characteristic (ROC) curves, concordance index (C-index), and other methods. Nomogram evaluation involved calibration curves, decision curve analysis (DCA), and the aforementioned methods in both cohorts. Results: In this study, 223 patients (130 males; mean age ± SD: 52.6 ± 12 years) were divided into training (148) and internal validation (75) cohorts. Dual scans were classified based on NETPET scores (D1–D3). Single 68Ga-DOTANOC and 18F-FDG PET-CT scans were stratified into S1-S3 and F1-F3 based on SUVmax. The NETPET score-based grading system demonstrated the best OS and PFS prediction (C-index, 0.763 vs. 0.727 vs. 0.566). Nomograms for OS and PFS exhibited superior prognostic performance in both cohorts (all AUCs > 0.8). Conclusions: New classification based on NETPET score predicts patient OS/PFS best. PET-CT-based nomograms show accurate OS/PFS forecasts.

As one of the most densely populated, economically developed, and outwardly open urban agglomerations in China, the Pearl River Delta (PRD) urban agglomeration is a key player in achieving China’s carbon peak and carbon neutrality targets. This study analyzes low-emission development by examining the evolutionary patterns of carbon dioxide (CO2) emissions and the decoupling relationship between economic growth and CO2 emissions, using the latest available data from 2000 to 2020. Here are the main findings: (1) We found a significant fluctuation in the decoupling statuses between economic advancements and CO2 emissions within the PRD domain. Predominantly, a weak decoupling scenario was observed, where economic proliferations were paralleled by nearly equivalent increments in CO2 emissions. (2) The growth rate of carbon emissions increased significantly relative to economic expansion during 2015–2020, especially pronounced in cities such as Guangdong, Zhuhai, Foshan, and Dongguan. This delineates the persistent challenges in steering towards a pathway of energy conservation and emission abatement in the region. (3) Furthermore, a differential role of elasticity factors was noted across cities: Guangzhou and Shenzhen witnessed a significant influence of energy-saving elasticity in fostering a decoupling between economic surge and CO2 emissions, whereas in other cities, the emphasis shifted towards emission-reduction elasticity as a more vital determinant. The results of this study are of great significance for guiding policy makers and stakeholders in urban clusters across China and in similar regions globally to achieve low carbon development goals.

This study aims to explore the joint usage of multisource scatterometer measurements in polar sea water and ice discrimination. All radar backscatter measurements from current operating satellite scatterometers are considered, including the C-band ASCAT scatterometer on board the MetOp series satellites, the Ku-band scatterometer on board the HY-2B satellite (HSCAT), and the Ku-band scatterometer on board the CFOSAT satellite (CSCAT). By performing seven experiments that use the same support vector machine (SVM) classifier method but with different input data, we find that the SVM model with all available HSCAT, CSCAT, and ASCAT scatterometer data as inputs gives the best performance. In addition to the SVM outputs, we employ the image erosion/dilation techniques and area growth method to reduce misclassifications of sea water and ice. The sea ice extent obtained in this study shows a good agreement with the National Snow and Ice Data Center (NSIDC) sea ice concentration data from the years 2019 to 2021. More specifically, the sea ice areas are closer to the sea ice areas calculated using 15% as the threshold for NSIDC sea ice concentration data in both Arctic and Antarctic. The sea ice edges acquired by the multisource scatterometer show a close correlation with sea ice edges from the Sentinel-1 Synthetic Aperture Radar (SAR) images. In addition, we found that the coverage of multisource scatterometer data in a half-day is usually above 97%, and more importantly, the sea ice areas obtained on the basis of half-day and daily multisource scatterometer data are very close to each other. The presented work can serve as guidance on the usage of all available scatterometer measurements in sea ice monitoring.

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

The launch of the Haiyang-2 (HY-2) satellite constellation fosters the quick acquisition of global sea surface vector winds from the perspective of remote sensing. This study intends to develop a six-hourly mesoscale analysis of sea surface winds based on the microwave scatterometers onboard the HY-2 satellite series, with the objective of meeting the considerable demand for accurate and gap-free ocean wind forcing products. First, the accuracy of HY-2 scatterometers (HSCATs) in measuring wind is evaluated. In particular, the standard deviation (SD) errors of HSCATs data are assessed using the collocated buoy measurements with different temporal windows in order to account for the temporal representativeness errors in the blending analysis. Afterwards, a two-dimensional variational (2D-Var) method is implemented to blend the HSCATs measured winds and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis winds over the global ocean surface. This approach is different from existing methods in that it takes both wind error and background error correlation into account. The results show that the blended wind product is of a promising quality compared with independent wind references. Interestingly, the blended winds derived from 2D-Var in combination with an empirical background error correlation show smaller SD errors than those using a Gaussian error correlation function. Overall, the blended wind product should be valuable for forcing global ocean models or describing air-sea interaction processes on a scale close to the scatterometer measurements.

The estimation of tropical cyclone (TC) intensity using Ku-band scatterometer data is challenging due to rain perturbation and signal saturation in the radar backscatter measurements. In this paper, an alternative approach to directly taking the maximum scatterometer-derived wind speed is proposed to assess the TC intensity. First, the TC center location is identified based on the unique characteristics of wind stress divergence/curl near the TC core. Then the radial extent of 17-m/s winds (i.e., R17) is calculated using the wind field data from the Haiyang-2B (HY-2B) scatterometer (HSCAT). The feasibility of HSCAT wind radii in determining TC intensity is evaluated using the maximum sustained wind speed (MSW) in the China Meteorological Administration best-track database. It shows that the HSCAT R17 value generally better correlates with the best-track MSW than the HSCAT maximum wind speed, therefore indicating the potential of using the HSCAT data to improve the TC nowcasting capabilities.

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). This paper performs sea ice monitoring with the CSCAT backscatter measurements in polar areas. The CSCAT measurements have the characteristics of diverse incidence and azimuth angles and separation between open water and sea ice. Hence, five microwave feature parameters, which show different sensitivity to ice or water, are defined and derived from the CSCAT measurements firstly. Then the random forest classifier is selected for sea ice monitoring because of its high overall accuracy of 99.66% and 93.31% in the Arctic and Antarctic, respectively. The difference of features ranked by importance in different seasons and regions shows that the combination of these parameters is effective in discriminating sea ice from water under various conditions. The performance of the algorithm is validated against the sea ice edge data from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) on a global scale in a period from 1 January 2019 to 10 May 2021. The mean sea ice area differences between CSCAT and OSI SAF product in the Arctic and Antarctic are 0.2673 million km2 and −0.4446 million km2, respectively, and the sea ice area relative errors of CSCAT are less than 10% except for summer season in both poles. However, the overall sea ice area derived from CSCAT is lower than the OSI SAF sea ice area in summer. This may be because the CSCAT is trained by radiometer sea ice concentration data while the radiometer measurement of sea ice is significantly affected by melting in the summer season. In conclusion, this research verifies the capability of CSCAT in monitoring polar sea ice using a machine learning-aided random forest classifier. This presented work can give guidance to sea ice monitoring with radar backscatter measurements from other spaceborne scatterometers, particular for the recently launched FY-3E scatterometer (called WindRad).

A sea ice detection algorithm based on Fisher’s linear discriminant analysis is developed to segment sea ice and open water for the Ku-band scatterometer onboard the China’s Hai Yang 2A Satellite (HY-2A/SCAT). Residual classification errors are reduced through image erosion/dilation techniques and sea ice growth/retreat constraint methods. The arctic sea-ice-type classification is estimated via a time-dependent threshold derived from the annual backscatter trends based on previous HY-2A/SCAT derived sea ice extent. The extent and edge of the sea ice obtained in this study is compared with the Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data and the Sentinel-1 SAR imagery for verification, respectively. Meanwhile, the classified sea ice type is compared with a multi-sensor sea ice type product based on data from the Advanced Scatterometer (ASCAT) and SSMIS. Results show that HY-2A/SCAT is powerful in providing sea ice extent and type information, while differences in the sensitivities of active/passive products are found. In addition, HY-2A/SCAT derived sea ice products are also proved to be valuable complements for existing polar sea ice data products.

An objectively trained model for tropical cyclone intensity estimation from routine satellite infrared images over the Northwestern Pacific Ocean is presented in this paper. The intensity is correlated to some critical signals extracted from the satellite infrared images, by training the 325 tropical cyclone cases from 1996 to 2007 typhoon seasons. To begin with, deviation angles and radial profiles of infrared images are calculated to extract as much potential predicators for intensity as possible. These predicators are examined strictly and included into (or excluded from) the initial predicator pool for regression manually. Then, the “thinned” potential predicators are regressed to the intensity by performing a stepwise regression procedure, according to their accumulated variance contribution rates to the model. Finally, the regressed model is verified using 52 cases from 2008 to 2009 typhoon seasons. The R2 and Root Mean Square Error are 0.77 and 12.01 knot in the independent validation tests, respectively. Analysis results demonstrate that this model performs well for strong typhoons, but produces relatively large errors for weak tropical cyclones.