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Background ALKBH5 regulated the malignant behavior of breast cancer and glioblastoma. However, the expression and function of ALKBH5 in epithelial ovarian cancer have not yet been determined. In the present study, we investigated the expression and function of ALKBH5 in epithelial ovarian cancer with respect to its potential role in the tumorigenesis of the disease as well as an early diagnostic marker. Methods Immunohistochemistry and western blot were used to detect protein expression. Gene silencing and over-expression experiment were used to study gene function. Cell proliferation assay and Matrigel invasion assays were used to detect cell proliferation and invasion, respectively. The nude mouse tumor formation experiment was used to evaluate the growth of cells in vivo. Results The expression of ALKBH5 was found to be increased in epithelial ovarian cancer tissue as compared to the normal ovarian tissues. The silencing of ALKBH5 in SKOV3 cells enhanced the autophagy and inhibited the proliferation and invasion in vitro and in vivo, whereas the ectopic expression of ALKBH5 in A2780 cells exerted an opposite effect. Mechanical study revealed that ALKBH5 physically interacted with HuR. ALKBH5 activated EGFR-PIK3CA-AKT-mTOR signaling pathway. Also, ALKBH5 enhanced the stability of BCL-2 mRNA and promoted the interaction between Bcl-2 and Beclin1. Conclusion Overall, the present study identified ALKBH5 as a candidate oncogene in epithelial ovarian cancer and a potential target for ovarian cancer therapy.

Background Circular RNA (circRNA) has been proven to play a significant role in multiple types of cancer. However, the expression and role of circRNAs in epithelial ovarian cancer (EOC) remains elusive. Methods CircRNA and mRNA expression profiles of EOC were screened with sequencing analysis. Gene silencing and over-expression were used to study circRNA function. Cell proliferation and Matrigel invasion assays were used to detect cell proliferation and invasion, respectively. The expression of circRNAs, mRNAs and miRNAs was detected using qPCR. The location of circRNAs was detected using FISH. The expression of proteins was detected using western blot and immunohistochemistry. Results CircMUC16 had increased expression in EOC tissues as compared to healthy ovarian tissues. The expression of circMUC16 was linked to the progression in stage and grade of EOC. Hence, silencing circMUC16 suppressed autophagy flux of SKOV3 cells. In contrast, ectopic expression of circMUC16 promoted autophagy flux of A2780 cells. CircMUC16-mediated autophagy exacerbated EOC invasion and metastasis. Mechanistically, circMUC16 could directly bind to miR-199a-5p and relieve suppression of target Beclin1 and RUNX1. In turn, RUNX1 elevated the expression of circMUC16 via promotion of its transcription. CircMUC16 could directly bind to ATG13 and promote its expression. Conclusion This study demonstrated that circMUC16 regulated Beclin1 and RUNX1 by sponging miR-199a-5p. The data suggested that circMUC16 could be a potential target for EOC diagnosis and therapy.

The NOD-like receptor protein (NLRP)3 inflammasome plays a critical role in acute respiratory distress syndrome (ARDS) by activating caspase-1, which cleaves the precursor forms of IL-1β and IL-18 into active cytokines and induces pyroptosis by cleaving gasdermin D (GSDMD). LBH589, a pan-histone deacetylase inhibitor, exhibits promising anti-inflammatory and immunomodulatory properties. However, the protective effect and underlying mechanism of LBH589 against ARDS is still unclear. In this study, we aim to determine whether and how LBH589 inhibits NLRP3 inflammasome activation while exerting its anti-inflammatory effect. Our data demonstrated that LBH589 effectively suppressed NLRP3 inflammasome activation in lipopolysaccharides (LPS)-primed and adenosine triphosphate (ATP)-stimulated J774A.1 cells and bone marrow-derived macrophages (BMDMs), evidenced by attenuated cleaved caspase-1 and IL-1β, IL-18, IL-16 release, as along with reduced GSDMD-mediated pyroptosis and ASC speck formation. Additionally, LBH589 significantly decreased mitochondrial reactive oxygen species (mtROS) and oxidized mitochondrial DNA (Ox-mtDNA), key triggers of inflammasome activation. Importantly, both prophylactic and therapeutic administration of LBH589 inhibited the pro-inflammatory cytokines secretion in lung tissue and ameliorated lipopolysaccharide (LPS)-induced ARDS in mice. These findings suggest that LBH589 may provide therapeutic benefits in ARDS by attenuating NLRP3 inflammasome activation and pyroptosis.

An accurate Modulation Transfer Function (MTF) is essential for Synthetic Aperture Radar (SAR) wave spectra retrieval. This study aimed to investigate the performance of a quad-polarized wave retrieval algorithm based on fully polarimetric SAR image data using the improved tilt MTF and considering the influence of wind speed. The tilt MTF is the key factor in the wave retrieval scheme from quad-polarized (Vertical-Vertical (VV), Horizontal-Horizontal (HH), Vertical-Horizontal (VH), and Horizontal-Vertical (HV)) SAR images. In this study, the waves were inverted from more than 1300 Gaofen-3 (GF-3) images acquired in quad-polarization strip mode with a spatial resolution of 16 m and a swath coverage of 50 km. The winds were retrieved using the Geophysical Model Function (GMF) C-band SAR model for Gaofen-3 (CSARMOD-GF), which is suitable for re-calibrated GF-3 images in VV-polarization. The comparison of the wind speed yielded a Root Mean Square Error (RMSE) of 1.73 m/s and a Correlation Coefficient (COR) of 0.94. The validation of the Significant Wave Height (SWH) simulated using the Waves Nearshore (SWAN) model against Haiyang-2B (HY-2B) altimeter data yielded an RMSE of 0.56 m and a COR of 0.87. The results reveal that the SAR-derived wind and SWAN-simulated SWH are suitable for analysis of SAR wave retrieval. The full polarimetric technique was applied to the collected images, and the statistical analysis yielded a RMSE of 0.51 m, a COR of 0.75, and a Scatter Index (SI) of 0.44 compared with the SWHs retrieved using the simulations from the SWAN model. The non-polarized contribution in the Normalized Radar Cross Section (NRCS; unit: dB) caused by wave breaking $\\sigma _{\\rm{0}}^{wb}$ σ 0 w b was calculated using a theoretical approach that employs the VV-polarized calibrated NRCS $\\sigma _{\\rm{0}}^{{\\rm{vv}}}$ σ 0 v v and HH-polarized calibrated NRCS. The effect of wave breaking on the SAR retrieval waves was studied. The bias (SAR-derived minus SWAN-simulated SWH) increased as the ( $\\sigma _{\\rm{0}}^{wb}$ σ 0 w b / $\\sigma _{\\rm{0}}^{{\\rm{vv}}}$ σ 0 v v ) ratio (>0.4) increased, and the accuracy improved when the ratio was less than 0.4. This behavior is reasonable since the wave breaking inevitably affects the tilt modulation. Therefore, wave breaking should be considered in SAR wave retrieval using the approach proposed in this paper under extreme sea states, such as typhoons and hurricanes.

In this work, three types of machine learning algorithms are applied for synthetic aperture radar (SAR) wind retrieval in tropical cyclones (TCs), and the optimal method is confirmed. In total, 30 Sentinel-1 (S-1) images in dual-polarization (vertical–vertical [VV] and vertical–horizontal [VH] were collected during the period from 2016 to 2021, which were acquired in interferometric-wide and extra-wide modes with pixels of 10 m and 40 m, respectively. More than 100,000 sub-scenes with a spatial coverage of 3 km are extracted from these images. The dependences of variables estimated from sub-scenes, i.e., VV-polarized and VH-polarized normalized radar cross-section (NRCS), as well as the azimuthal wave cutoff wavelength, on wind speeds from the stepped-frequency microwave radiometer (SFMR) and the soil moisture active passive (SMAP) radiometer are studied, showing the linear relations between wind speed and these three parameters; however, the saturation of VV-polarized NRCS and the azimuthal wave cutoff wavelength is observed. This is the foundation of selecting input variables in machine learning algorithms. Two-thirds of the collocated dataset (20 images) are used for training the process using three machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), Multi-layer Perceptron, and K-Nearest Neighbor, and the coefficients are fitted after training completion through 20 images collocated with SFMR and SMAP data. Another 10 images are taken for validation up to 70 m/s, yielding a 2.53 m/s root mean square error (RMSE) with a 0.96 correlation and 0.12 scatter index (SI) using XGBoost. The result is better than the >5 m/s error achieved using the existing cross-polarized geophysical model function and the other two machine learning algorithms; moreover, the comparison between wind retrievals using XGBoost and Level-2 CyclObs products shows about 4 m/s RMSE and 0.18 SI. This suggests that the machine learning algorithm XGBoost is an effective method for inverting the TC wind field utilizing SAR measurements in dual-polarization.

The purpose of this study is to include rain effects in wind field retrieval from C-band synthetic aperture radar (SAR) imagery collected under tropical cyclone conditions. An effective and operationally attractive approach to detect rain cells in SAR imagery is proposed and verified using four Sentinel-1 (S-1) SAR images collected in dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) interferometric-wide swath imaging mode during the Satellite Hurricane Observation Campaign. SAR images were collocated with ancillary observations that include sea surface wind and rain rate from the Stepped-Frequency Microwave Radiometer (SFMR) on board of the National Oceanic and Atmospheric Administration aircraft. The winds are inverted from VV- and VH-polarized S-1 image using the CMOD5.N and S1IW.NR geophysical model functions (GMFs), respectively. Location and radius of cyclone’s eye, together with the TC central pressure, are calculated from the VV-polarized SAR-derived wind and a parametric model. A cost function is proposed that consists of the difference between the measured VV-polarized SAR normalized radar cross section (NRCS) and the NRCS predicted using CMOD5.N forced with the wind speed retrieved by the VH-polarized SAR images using S1IW.NR GMF and the wind direction retrieved from the patterns visible in the SAR image. This cost function is related to the SFMR rain rate. Experimental results show that the difference between measured and predicted NRCS values range from 0.5 dB to 5 dB within a distance of 100 km from the cyclone’s eye, while the difference increases spanning from 3 dB to 6 dB for distances larger than 100 km. Following this rationale, first the rain bands are extracted from SAR imagery and, then, the composite wind fields are reconstructed by replacing: (1) dual-polarized SAR-derived winds over the rain-free regions; (2) winds simulated using the radial-vortex model over the rain-affected regions. The validation of the composite wind speed against SFMR winds yields a <2 m s−1 and >0.7 correlation (COR) at all flow directions up to retrieval speeds of 70 m s−1. This result outperforms the winds estimated using the VH-polarized S1IW.NR GMF, which call for high error accuracy, such as about 4 m s−1 with a 0.45 COR ranged from 330° to 360°.

This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state.

The purpose of our work is to analyze the effect of wave breaking on dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) synthetic aperture radar (SAR) image in the C-band during tropical cyclones (TCs) based on the machine learning method. In this study, more than 1300 Sentinel-1 (S-1) interferometric-wide (IW) and extra wide (EW) mode SAR images are collocated with wave simulations from the WAVEWATCH-III (WW3) model during 400 TCs. The validation of the significant wave height (SWH) simulated using the WW3 model against Jason-2 altimeter data. The winds for S-1 SAR images are reconstructed using wind retrievals in VV and VH polarization. The non-polarized (NP) contribution σ wb caused by wave breaking is assumed to be the result of the SAR-measured normalized radar cross-section (NRCS) σ 0 minus the Bragg resonant roughness σ br without the distortion of rain cells during TCs. The σ br is simulated by imputing wave spectra from the WW3 model into the theoretical backscattering model. It is found that the ratio (σ wb /σ 0 ) in VV polarization is related to the wind speed, the wind direction relative with the flight orientation, and radar incidence angle. Following this rationale, the Adaptive Boosting (AdaBoost) model was used for the estimation of NP contribution σ wb during TCs and are implemented for more than 300 dual-polarized S-1 images to validate the model. It is found that for the comparison between the sum of simulation NRCS and SAR observations, the root mean squared error (RMSE) is 1.95 dB and the coefficient (COR) is 0.86, which is better than a 2.83 dB RMSE and a 0.67 COR by empirical model. It is concluded that the AdaBoost model has a good performance on NP component simulation during TCs.

With the continuous increase in global energy consumption and the escalating severity of climate change, the development of high-performance thermal insulation materials is crucial for reducing energy waste and carbon emissions. In this work, a facile method was proposed to prepare thermal-insulating glass fiber wool/methyltrimethoxysilane aerogel (GFWA) composites through vacuum-assisted impregnation. The obtained results indicated that GFWA composites exhibited excellent thermal insulation and hydrophobic properties, with GFWA-30 containing 30 wt.% glass fiber wool having a thermal conductivity of 35.3 mW/m·K and a water contact angle of 125.8°. Additionally, the Young’s modulus of this composite was 21.2% higher than that of MTMS aerogel. In terms of thermal safety performance, compared to methyltrimethoxysilane aerogel, the GFWA-30 composite showed reductions of 21.6%, 18.8%, and 27.95% in peak heat release rate, total heat release, and gross calorific value, respectively. This study offers a simple and feasible approach to fabricating high-performance thermal insulation materials, which display huge potential for widespread application in the fields of building insulation and other fields with thermal insulation requirements.

The goal of this study was to investigate the performance of a spectral-transformation wave retrieval algorithm and confirm the accuracy of wave retrieval from C-band Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) images. More than 200 GF-3 SAR images of the coastal China Sea and the Japan Sea for dates from January to July 2020 were acquired in the Quad-Polarization Strip (QPS) mode. The images had a swath of 30 km and a spatial resolution of 8 m pixel size. They were processed to retrieve Significant Wave Height (SWH), which is simulated from a numerical wave model called Simulating WAves Nearshore (SWAN). The first-guess spectrum is essential to the accuracy of Synthetic Aperture Radar (SAR) wave spectrum retrieval. Therefore, we proposed a wave retrieval scheme combining the theocratic-based Max Planck Institute Algorithm (MPI), a Semi-Parametric Retrieval Algorithm (SPRA), and the Parameterized First-guess Spectrum Method (PFSM), in which a full wave-number spectrum and a non-empirical ocean spectrum proposed by Elfouhaily are applied. The PFSM can be driven using the wind speed without calculating the dominant wave phase speed. Wind speeds were retrieved using a Vertical-Vertical (VV) polarized geophysical model function C-SARMOD2. The proposed algorithm was implemented for all collected SAR images. A comparison of SAR-derived wind speeds with European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data showed a 1.95 m/s Root-Mean-Squared Error (RMSE). The comparison of retrieved SWH with SWAN-simulated results demonstrated a 0.47 m RMSE, which is less than the 0.68 m RMSE of SWH when using the PFSM algorithm.