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Despite tremendous advances in cancer research, breast cancer (BC) remains a major health concern and is the most common cancer affecting women worldwide. Breast cancer is a highly heterogeneous cancer with potentially aggressive and complex biology, and precision treatment for specific subtypes may improve survival in breast cancer patients. Sphingolipids are important components of lipids that play a key role in the growth and death of tumor cells and are increasingly the subject of new anti-cancer therapies. Key enzymes and intermediates of sphingolipid metabolism (SM) play an important role in regulating tumor cells and further influencing clinical prognosis. We downloaded BC data from the TCGA database and GEO database, on which we performed in depth single-cell sequencing analysis (scRNA-seq), weighted co-expression network analysis, and transcriptome differential expression analysis. Then seven sphingolipid-related genes (SRGs) were identified using Cox regression, least absolute shrinkage, and selection operator (Lasso) regression analysis to construct a prognostic model for BC patients. Finally, the expression and function of the key gene PGK1 in the model were verified by experiments. This prognostic model allows for the classification of BC patients into high-risk and low-risk groups, with a statistically significant difference in survival time between the two groups. The model is also able to show high prediction accuracy in both internal and external validation sets. After further analysis of the immune microenvironment and immunotherapy, it was found that this risk grouping could be used as a guide for the immunotherapy of BC. The proliferation, migration, and invasive ability of MDA-MB-231 and MCF-7 cell lines were dramatically reduced after knocking down the key gene PGK1 in the model through cellular experiments. This study suggests that prognostic features based on genes related to SM are associated with clinical outcomes, tumor progression, and immune alterations in BC patients. Our findings may provide insights for the development of new strategies for early intervention and prognostic prediction in BC.

Sub-meter-resolution Polarimetric Synthetic Aperture Radar (PolSAR) imagery enables precise building footprint extraction but introduces complex scattering correlated with fine spatial structures. This change renders both traditional methods, which rely on simplified scattering models, and existing deep learning approaches, which sacrifice spatial detail through multi-looking, inadequate for high-precision extraction tasks. To address this, we propose an Orthogonal Dual-Resolution Network (ODRNet) for end-to-end, precise segmentation directly from single-look complex (SLC) data. Unlike complex-valued neural networks that suffer from high computational cost and optimization difficulties, our approach decomposes complex-valued data into its orthogonal real and imaginary components, which are then concurrently fed into a Dual-Resolution Branch (DRB) with Bilateral Information Fusion (BIF) to effectively balance the trade-off between semantic and spatial details. Crucially, we introduce an auxiliary Polarization Orientation Angle (POA) regression task to enforce physical consistency between the orthogonal branches. To tackle the challenge of diverse building scales, we designed a Multi-scale Aggregation Pyramid Pooling Module (MAPPM) to enhance contextual awareness and a Pixel-attention Fusion (PAF) module to adaptively fuse dual-branch features. Furthermore, we have constructed a VHR PolSAR building footprint segmentation dataset to support related research. Experimental results demonstrate that ODRNet achieves 64.3% IoU and 78.27% F1-score on our dataset, and 73.61% IoU with 84.8% F1-score on a large-scale SLC scene, confirming the method’s significant potential and effectiveness in high-precision building extraction directly from SLC.

Based on Digital Beamforming (DBF) technology, spaceborne SAR systems can achieve high-resolution and wide-swath (HRWS) imaging. When combined with reflector antennas, the DBF-SCORE (Digital Beamforming-SCan On REceive) system also features light weight and low cost, making it an important choice for spaceborne HRWS SAR. This paper firstly proposes an advanced Full-chain Real-time Internal Calibration (FRIC) scheme, where the calibration path covers the entire receive chain from the antenna feed port to the input port of the Analog-to-Digital Converter (ADC) and achieves high-precision internal calibration concurrently with data acquisition. Secondly, based on the L-band reflector antenna DBF-SCORE system architecture, the design of radio frequency (RF) front end, namely the Transmit-Receive-Calibration Module (TRCM), is carried out. We propose the implementation of azimuth encoding modulation of the calibration signal through periodic switch control within the TRCM. Subsequently, the calibration signal is extracted using waveform diversity technology and its Signal-to-Noise Ratio (SNR) is improved through azimuth coherent integration technology. In addition, a ground verification system is established using the TRCM to evaluate the comprehensive performance of transmitting, receiving, and real-time internal calibration. Experimental results verify the effectiveness of the FRIC scheme and provide valuable insights for future spaceborne DBF SAR systems.

Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments.

Surgery is essential for controlling the symptoms and complications of stage IV breast cancer. However, locoregional treatment of primary tumors often results in distant progression, including lung metastasis, the most common type of visceral metastasis. As a minimally invasive thermal therapy, microwave ablation (MWA) has been attempted in the treatment of breast cancer, but the innate immune response after MWA has not yet been reported. Using two murine models of stage IV breast cancer, we found that MWA of primary breast cancer inhibited the progression of lung metastasis and improved survival. NK cells were activated after MWA of the primary tumor and exhibited enhanced cytotoxic functions, and the cytotoxic pathways of NK cells were activated. Depletion experiments showed that NK cells but not CD4+ or CD8+ T cells played a pivotal role in prolonging survival. Then, we found that compared with surgery or control treatment, MWA of the primary tumor induced completely different NK-cell-related cytokine profiles. Macrophages were activated after MWA of the primary tumor and produced IL-15 that activated NK cells to inhibit the progression of metastasis. In addition, MWA of human breast cancer stimulated an autologous NK-cell response. These results demonstrate that MWA of the primary tumor in metastatic breast cancer inhibits metastatic progression via the macrophage/IL-15/NK-cell axis. MWA of the primary tumor may be a promising treatment strategy for de novo stage IV breast cancer, although further substantiation is essential for clinical testing.

Activation of oncogenes by promoter hypomethylation plays an important role in tumorigenesis. Zinc finger protein 57 (ZFP57), a member of KRAB-ZFPs, could maintain DNA methylation in embryonic stem cells (ESCs), although its role and underlying mechanisms in breast cancer are not well understood. In this study, we found that ZFP57 had low expression in breast cancer, and overexpression of ZFP57 could inhibit the proliferation of breast cancer cells by inhibiting the Wnt/β-catenin pathway. MEST was validated as the direct target gene of ZFP57 and MEST may be down-regulated by ZFP57 through conserving DNA methylation. Furthermore, overexpression of MEST could restore the tumour-suppressed and the Wnt/β-catenin pathway inactivated effects of ZFP57. ZFP57-MEST and the Wnt/β-catenin pathway axis are involved in breast tumorigenesis, which may represent a potential diagnostic biomarker, and provide a new insight into a novel therapeutic strategy for breast cancer patients.

As nuclear structural materials, austenitic and ferritic/martensitic (F/M) steels will face inevitable irradiation swelling when fulfilling a role in nuclear reactors, especially under high-dose irradiation. For this work, a coupled machine learning rate theory (ML-RT) model for the swelling of austenitic and F/M steels was developed. In this model, ML was introduced to predict the steady-state irradiation swelling onset dose (Donset), while the improved RT was developed to simulate the swelling behavior after the incubation period. More than 200 series of data on the Donset of different structures of steel were collected for the ML prediction. The coefficient of determination (R) of the results in ML is more than 0.9. In the RT, the evolutions of the dislocation loop and void were described and calculated rather than using the fitting parameters. Cascade efficiency was employed to describe the cascade process. The coupled ML-RT model was verified with the swelling data from neutron irradiation experiments for various steels. The theoretical results of the swelling peak temperatures and swelling behavior are more accurate and reasonable, compared with those from the previous RT model. Using the ML-RT model, the swelling performance of CLAM steel under neutron irradiation of up to 180 dpa was predicted. The differences between the swelling performance of austenitic steels and F/M steels were analyzed and the differences were mainly associated with the bias. These results will be helpful for evaluating the neutron irradiation swelling behavior of candidate structural materials.

Smooth muscle cell-specific myosin heavy chain, encoded by MYH11 , is selectively expressed in smooth muscle cells ( SMC s). Pathogenic variants in MYH11 predispose to a number of disorders, including heritable thoracic aortic disease associated with patent ductus arteriosus, visceral myopathy, and megacystis-microcolon-intestinal hypoperistalsis syndrome. Rare variants of uncertain significance occur throughout the gene, including MYH11 p.Glu1892Asp, and we sought to determine if this variant causes thoracic aortic disease in mice. Genomic editing was used to generate Myh11 E1892D/E1892D mice. Wild-type ( WT ) and mutant mice underwent cardiovascular phenotyping with and without transverse aortic constriction ( TAC ). Myh11 E1892D/E1892D and WT mice displayed similar growth, blood pressure, root and ascending aortic diameters, and cardiac function up to 13 months of age, along with similar contraction and relaxation on myographic testing. The hypertension induced by TAC was similarly in Myh11 E1892D/E1892D and WT mice, but mutant mice showed augmented ascending aortic enlargement and increased elastic fiber fragmentation on histology. Unexpectedly, male Myh11 E1892D/E1892D mice undergoing TAC had decreased ejection fraction, stroke volume, fractional shortening, and cardiac output compared to similarly treated male WT mice. Importantly, left ventricular mass increased significantly due to primarily posterior wall thickening, and cardiac histology confirmed cardiomyocyte hypertrophy and increased collagen deposition in the myocardium and surrounding arteries. These results further highlight the phenotypic heterogeneity associated with MYH11 rare variants. Given that MYH11 is selectively expressed in SMCs, these results implicate a role of SMCs in the arteries of the heart contributing to cardiac hypertrophy and failure with pressure overload.

This article investigates channel phase mismatch calibration during the application of displaced-phase-center antenna (DPCA) in dual-channel sliding spotlight synthetic aperture radar (SAR) for ground moving target indication (GMTI). In sliding spotlight SAR, the utilization of beam progressive sweeping in azimuth causes antenna phase centers to be misaligned from the sensor path, resulting in the phase mismatch between channels. Then, spatial channel co-registration required in the DPCA cannot be achieved directly by an azimuth time shift. In this study, a calibration method based on scanning geometry of the dual-channel sliding spotlight SAR is developed to address this issue. Moreover, the effect of the phase mismatch calibration on the estimation of azimuth time difference between the two channels is derived and analyzed in depth. The clutter suppression results processed from experimental data acquired by a C-band dual-channel SAR system (Gaofen-3) operated in sliding spotlight mode are shown for the first time to demonstrate the effective phase mismatch calibration.

Moving targets are usually smeared and imaged at incorrect positions in synthetic aperture radar (SAR) images due to the target motions during the illumination time. Moreover, a moving target will cause multiple artifacts in the reconstructed image since pulse repetition frequency (PRF) operated in high-resolution wide-swath (HRWS) SAR is very low. In order to reliably indicate moving targets, a robust cancellation algorithm is derived in this paper for clutter suppression in multichannel HRWS SAR, which is free by velocity searching and covariance matrix estimation of clutter plus noise. The proposed multilayer channel-cancellation combined with the deramp processing is designed to sequentially suppress the seriously aliased clutter in HRWS SAR. Experimental results show that the proposed algorithm is efficient and robust in tough situations, and have a superior detection ability in weak targets and low-velocity targets. In addition, the radial velocity estimation algorithm combined with the channel cancellation is exploited to relocate moving targets. The effectiveness of the proposed algorithms is validated by actual spaceborne SAR data acquired by a coordination experiment with two controllable vehicles.