Immune cell-based therapies remain constrained in solid tumors by inefficient trafficking, heterogeneous tumor recognition, limited persistence, functional exhaustion, and suppressive tumor microenvironments. Surface functionalization provides a direct approach to regulate immune cell–tumor and immune cell–microenvironment interactions without necessarily altering intrinsic cellular programs. This review focuses on immune cell surface engineering strategies based on defined chemical reactions, physical assembly, and biological ligand presentation. We discuss how functional groups, coupling mechanisms, modification density, ligand selection, and surface stability influence immune cell viability, phenotype, migration, activation, and resistance to tumor-induced suppression. Particular attention is given to how rationally designed surface ligands and functional cargos improve tumor homing, intratumoral retention, local immune stimulation, and coordinated antitumor responses. We further compare the characteristics, advantages, and limitations of different modification approaches across immune cell types and solid tumor contexts. By linking surface modification principles with antitumor functions, this review provides a methodological framework for selecting immune cell surface engineering strategies to improve the therapeutic performance of cell-based treatments against solid tumors.

[This retracts the article DOI: 10.3892/ol.2019.10110.].

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method.

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future.

Over-fitting-based image compression requires weights compactness for compression and fast convergence for practical use, posing challenges for deep convolutional neural networks (CNNs) based methods. This paper presents a simple re-parameterization method to train CNNs with reduced weights storage and accelerated convergence. The convolution kernels are re-parameterized as a weighted sum of discrete cosine transform (DCT) kernels enabling direct optimization in the frequency domain. Combined with L1 regularization, the proposed method surpasses vanilla convolutions by achieving a significantly improved rate-distortion with low computational cost. The proposed method is verified with extensive experiments of over-fitting-based image restoration on various datasets, achieving up to -46.12% BD-rate on top of HEIF with only 200 iterations.

We propose a novel method, named DanceU, to automate dance video editing in an intelligent way. Our method fully leverages two folds of a dance video, the background music and the dancer's motion, to determine where to add effects and what kind of effects to be added. We parameterize dance motion into two metrics: motion amplitude and motion intensity, to represent the motion characteristics of different dance types. Then, we design algorithms to align the dance motions with music beats and automatically decide dance effects for the dance video. To our best knowledge it is the first proposed method for automatic dance video effect generation. Results show that our method can generate fluent, performative, and music-matched effects, which achieves similar editing level to professionals. (A demo video: https://f.io/fgQUxehZ)

In this paper, we introduce a refined three-stage inversion algorithm (TSIA) for forest height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR). Specifically, the iterative extraction of the boundary of the coherence region (IEBCR) and iterative look-up table (ILUT) are proposed to improve the efficiency of traditional TSIA. A class of refined TSIA utilizes the boundary of the coherence region (BCR) to alleviate the underestimation phenomenon in forest height estimation. Given many eigendecompositions in the extraction of BCR (EBCR), we analyze the relationship of eigenvectors between the adjacent points on the BCR and propose the IEBCR utilizing the power methods. In the final inversion stage of TSIA, the look-up table (LUT) uses the exhaustive search method to minimize the loss function in the 2-D grid with defined step sizes and thus costs high computational complexity. To alleviate the deficiency, we define the random volume over ground (RVoG) function based on the RVoG model and prove its monotonicity and convergence from the analytical and numerical points of view. After analyzing the relationship between the RVoG function and the loss function, we propose the ILUT for the inversion stage. The simulation and experiments based on the BioSAR 2008 campaign data illustrate that the IEBCR and ILUT greatly improve the computational efficiency almost without compromising on accuracy.

TH-2 is a bistatic synthetic aperture radar (SAR) satellite system in formation flight. Compared with traditional InSAR systems, it can eliminate decoherent sources such as time and atmosphere, besides, it can generate highly coherent SAR image pairs. This paper firstly describe the extended chirp scaling (ECS) imaging algorithm based on the hyperbolic equivalent method, and also introduces pre-filtering to deal with problems such as reduced coherence and interference phase errors caused by mixed baselines. Secondly, it introduces the interference processing method and the technical process of DSM reconstruction in the bistatic mode. Finally, an interference imaging experiment is performed using the original echo data of a certain mountainous experimental area, and the 3D reconstruction experiment is performed by using the generated SAR image pair, which analyzes the coherence of the image, the phase unwrapping results and the DSM reconstruction results. The experimental results verify that the interference imaging algorithm in this paper has good focusing effect and phase preservation capacity. At the same time, the interferometry and 3D reconstruction capabilities of the data are verified as well.

A unique platform for combined photothermal and chemotherapy using PLA stereocomplex (PLA SC) micelles-induced hybrid gold nanocarriers is designed. The PLA SC micelles, made from the self-assembly of poly(ethylene glycol)-block-poly(l-lactide) (PEG-PLLA) and poly(2-(dimethylamino) ethyl methacrylate)-block-poly(d-lactide) (PDMAEMA-PDLA), for the first time are used as a template to fabricate the hybrid PLA SC@Au core-shell nanocarriers, in which the anticancer drugs are encapsulated within the core, while the Au nanoparticles are tethered in the shell via the in situ reduction of AuCl4- by PDMAEMA. The obtained PLA SC@Au hybrid nanocarriers exhibit low toxicity and remarkable photothermal effect. Upon near-infrared laser irradiation, the on-site photothermal therapy can further induce an accelerated drug release from the hybrid nanocarrier reservoir via hyperthermia heating of the nanocarriers, thus leading to a synergistic photothermal and chemotherapy toward a significantly improved efficacy in tumor shrinkage. The as-designed PLA SC@Au hybrid nanocarriers, with their biocompatible compositions, dual-drug delivery characteristics, and combined photothermal/chemotherapy, show high potential as a novel platform for cancer treatment.

Abstract This article aims to develop a hybrid approach of complementary ensemble empirical mode decomposition (CEEMD) and random decrement technique (RDT) for identifying the modal parameters (i.e...

Due to the complex marine environment and high-order frequency modulation (FM) on radar echo from the micromotion of the target, the effective and robust detection of a marine target with micromotion under heavy sea clutters’ background is a challenging task. In this article, we propose a novel detection and estimation algorithm based on adaptive sparse modified-Lv’s transform (ASMLVT). First, the micro-Doppler (m-D) characteristics of marine targets are employed and modeled as quadratic frequency-modulated (QFM) signals. Second, we modify the 2-D robust sparse Fourier transform (2-D-RSFT) and make it adaptive to the sea clutters’ background, namely, 2-D adaptive sparse Fourier transform (2-D-ASFT). Then, we substitute the 2-D Fast Fourier transform (2-D-FFT) operation with 2-D-ASFT in the modified-Lv’s transform (MLVT). The proposed algorithm can not only achieve good energy accumulation and accurate parametric estimation for marine targets with micromotion but is also robust to the heavy sea clutters and can greatly reduce false alarms. Besides, it has a good cross-term suppression ability to detect multitargets. Experiments with simulated and real radar datasets show that the proposed algorithm can effectively detect and estimate multitargets with micromotion under heavy sea clutter and low signal-to-clutter ratio (SCR) background.

Background Novel and non-invasive biomarkers with higher sensitivity and specificity for the diagnosis of prostate cancer (PCa) is urgently needed. In this study, we used next-generation sequencing (NGS) to characterize the genome-wide exosomal miRNA expression profiling in urine specimens and explored the diagnostic potential of urinary exosomal miRNAs for PCa. Methods Urinary exosomal microRNA expression profiling was performed by next-generation sequencing (NGS) and then validated by quantitative real-time PCR. Results Significant downregulation of urinary exosomal miR-375 was observed in PCa patients compared with healthy controls, while the expression levels of urinary exosomal miR-451a, miR-486-3p and miR-486-5p were found to be significantly up-regulated in the PCa patients. Furthermore, the expression level of urinary exosomal miR-375 showed a significant correlation with the clinical T-stage and bone metastasis of patients with PCa (P<0.05). Receiver operator characteristic curve demonstrated that the urinary exosomal miR-375, miR-451a, miR-486-3p and miR-486-5p levels can be used to differentiate PCa patients from healthy controls, with area under the curves (AUCs) of 0.788, 0.757, 0.704 and 0.796, respectively. The urinary exosomal miR-375 was found to be superior in discriminating between localized and metastatic PCa with an AUC of 0.806. Moreover, PCa patients can be distinguished from patients with benign prostatic hyperplasia by using a panel combining urinary exosomal miR-375 and miR-451a with an AUC of 0.726. Conclusion These findings demonstrate that the urinary exosomal miRNAs can serve as novel and non-invasive biomarkers for diagnosing and predicting the progression of PCa.

Road detection from images has emerged as an important way to obtain road information, thereby gaining much attention in recent years. However, most existing methods only focus on extracting road information from single temporal intensity images, which may cause a decrease in image resolution due to the use of spatial filter methods to avoid coherent speckle noises. Some newly developed methods take into account the multi-temporal information in the preprocessing stage to filter the coherent speckle noise in the SAR imagery. They ignore the temporal characteristic of road objects such as the temporal consistency for the road objects in the multitemporal SAR images that cover the same area and are taken at adjacent times, causing the limitation in detection performance. In this paper, we propose a multiscale and multitemporal network (MSMTHRNet) for road detection from SAR imagery, which contains the temporal consistency enhancement module (TCEM) and multiscale fusion module (MSFM) that are based on attention mechanism. In particular, we propose the TCEM to make full use of multitemporal information, which contains temporal attention submodule that applies attention mechanism to capture temporal contextual information. We enforce temporal consistency constraint by the TCEM to obtain the enhanced feature representations of SAR imagery that help to distinguish the real roads. Since the width of roads are various, incorporating multiscale features is a promising way to improve the results of road detection. We propose the MSFM that applies learned weights to combine predictions of different scale features. Since there is no public dataset, we build a multitemporal road detection dataset to evaluate our methods. State-of-the-art semantic segmentation network HRNetV2 is used as a baseline method to compare with MSHRNet that only has MSFM and the MSMTHRNet. The MSHRNet(TAF) whose input is the SAR image after the temporal filter is adopted to compare with our proposed MSMTHRNet. On our test dataset, MSHRNet and MSMTHRNet improve over the HRNetV2 by 2.1% and 14.19%, respectively, in the IoU metric and by 3.25% and 17.08%, respectively, in the APLS metric. MSMTHRNet improves over the MSMTHRNet(TAF) by 8.23% and 8.81% in the IoU metric and APLS metric, respectively.

Hepatic stellate cells (HSCs) activation is a key step that promotes hepatic fibrosis. Emerging evidence suggests that aerobic glycolysis is one of its important metabolic characteristics. Our previous study has reported that CD147, a glycosylated transmembrane protein, contributes significantly to the activation of HSCs. However, whether and how it is involved in the aerobic glycolysis of HSCs activation is unknown. The objective of the present study was to validate the effect of CD147 in HSCs activation and the underlying molecular mechanism. Our results showed that the silencing of CD147 decreased the expression of α-smooth muscle-actin (α-SMA) and collagen I at both mRNA and protein levels. Furthermore, CD147 silencing decreased the glucose uptake, lactate production in HSCs, and repressed the lactate dehydrogenase (LDH) activity, the expression of hexokinase 2 (HK2), glucose transporter 1 (Glut1). The effect of galloflavin, a well-defined glycolysis inhibitor, was similar to CD147 siRNA. Mechanistically, CD147 silencing suppressed glycolysis-associated HSCs activation through inhibiting the hedgehog signaling. Moreover, the hedgehog signaling agonist SAG could rescue the above effect of CD147 silencing. In conclusion, CD147 silencing blockade of aerobic glycolysis via suppression of hedgehog signaling inhibited HSCs activation, suggesting CD147 as a novel therapeutic target for hepatic fibrosis.

Central nervous system (CNS) cancers are among the most common and treatment-resistant diseases. The main reason for the low treatment efficiency of the disorders is the barriers against targeted delivery of anticancer agents to the site of interest, including the blood-brain barrier (BBB) and blood-brain tumor barrier (BBTB). BBB is a strong biological barrier separating circulating blood from brain extracellular fluid that selectively and actively prevents cytotoxic agents and majority of anticancer drugs from entering the brain. BBB and BBTB are the major impediments against targeted drug delivery into CNS tumors. Nanotechnology and its allied modalities offer interesting and effective delivery strategies to transport drugs across BBB to reach brain tissue. Integrating anticancer drugs into different nanocarriers improves the delivery performance of the resultant compounds across BBB. Surface engineering of nanovehicles using specific ligands, antibodies and proteins enhances the BBB crossing efficacy as well as selective and specific targeting to the target cancerous tissues in CNS tumors. Multifunctional nanoparticles (NPs) have brought revolutionary advances in targeted drug delivery to brain tumors. This study reviews the main anatomical, physiological and biological features of BBB and BBTB in drug delivery and the recent advances in targeting strategies in NPs-based drug delivery for CNS tumors. Moreover, we discuss advances in using specific ligands, antibodies, and surface proteins for designing and engineering of nanocarriers for targeted delivery of anticancer drugs to CNS tumors. Finally, the current clinical applications and the perspectives in the targeted delivery of therapeutic molecules and genes to CNS tumors are discussed.

The Internet continues to grow at a phenomenal rate and the amount of information on the web is overwhelming. It provides us a great deal of information resource. Web text feature extraction is considered as the main problem in text mining. We use Vector Space Model (VSM) as the description of web text and present a novel feature extraction algorithm which is based on the improved Bean optimization Algorithm (BOA). This algorithm will greatly improve the efficiency of web texts processing.

In recent years, spaceborne Interferometric Synthetic Aperture Radar (InSAR) technology has shown increasing application potential in the field of geohazard monitoring. In this article, we first introduce the principle of InSAR technology, then systematically review the development of InSAR technology and analyze the technical characteristics and applicable scope of methods such as differential InSAR and time-series InSAR. We then discuss the application status and development trend of InSAR technology in geohazard monitoring with respect to earthquakes, landslides, hydropower projects, and ground subsidence. Finally, to guide future work in the dynamic monitoring and prevention of geohazards, we summarize the key issues and scientific problems faced by the application of InSAR to geohazard monitoring, which include atmospheric correction, complex-area deformation data acquisition, and the acquisition of multidimensional deformation data. Judging from the current applications of geological hazard monitoring, this technology is now at the point of extensive application. With the development of future spaceborne SAR satellite systems and the driving force of industry application, InSAR technology will develop into a sophisticated high-precision ground observation technology that will have a huge impact on geological hazard monitoring.

To achieve the three-dimensional surface deformation maps, interferometric synthetic aperture radar (InSAR) and global position system (GPS) data are integrated. In this letter, Markov random field-based L1 regularization is proposed to reconstruct the Gibbs energy equation for improving the previous three-dimensional optimized model. Furthermore, differential evolution algorithm is used to minimize the energy function for deriving the three optimal components of the ground motion. The new method proposed in this paper is then applied to derive the three-dimensional deformation maps of the 2016 Kumamoto earthquake. The Experimental results show that our new method achieves considerably higher performance than the more standard L2-based method. The root mean square errors for the three components of east, north and up are 1.45, 1.87 and 2.14 cm, respectively, which are smaller than the L2-based method, especially in the vertical direction.

The slant range errors caused by traditional hyperbolic range equation (THRE) with stop-and-go assumption will lead to image defocusing in high resolution spaceborne sliding-spotlight Bistatic SAR system (ST-BiSAR). In this paper, an accurate bistatic slant range model based on uniform acceleration curve motion (UARM) is proposed, which is precisely fitted with the actual range history. Then, a two-step imaging algorithm based on UARM and method of reversion series (MSR) is introduced to eliminate aliasing phenomenon and realize focus. Finally, simulation results verify the correctness and effectives of the proposed range model and imaging algorithms.

Background RASSF1A promoter methylation is consistent with clinicopathological data and has good accuracy in distinguishing tumors. However, the diagnostic parameters vary among previous studies. A systematic review was conducted to explore the diagnostic value of RASSF1A promoter methylation in prostate cancer. Methods A comprehensive search of the literature in the PubMed, Medline, Cochrane Library, Embase and ISI Web of Science databases up to May 21, 2020 was performed. STATA software version 12.0 and Meta-disc version 1.4 were used to analyze the data. Results The pooled sensitivity was 0.64 (95% CI 0.61–0.66), the pooled specificity was 0.80 (95% CI 0.77–0.83), the PLR was 3.82 (95% CI 1.96–7.44), and the NLR was 0.29 (95% CI 0.16–0.52). Furthermore, the pooled DOR of RASSF1A promoter methylation for prostate cancer was 13.08 (95% CI: 6.56–26.08). The area under the summary ROC curve was 0.87 (95% CI: 0.84–0.90). The results of the meta-regression suggested that heterogeneity was mainly derived from publication year. Fagan’s nomogram showed that the predictive accuracy was increased significantly by detecting RASSF1A promoter methylation for diagnosing prostate cancer. Conclusion This meta-analysis suggests that detection of the RASSF1A promoter methylation status can be used for the diagnosis of PCa. In the future, further analyses and studies of larger sample sizes in large centers are needed to confirm our conclusion.