Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry

Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single pass MB acquisition with the advantages of high coherence and simple phase components has a more practical capability in 3D reconstruction than conventional repeat-pass MB acquisition. Using an asymptotic 3D phase unwrapping (PU), it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline design. However, current spaceborne SAR system doesn't satisfy this principle, inducing more difficulties in practical application. In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed. Its optimal MB acquisition is analyzed to achieve both good relative height precision and flexible baseline design. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters and evaluate the performance of various baseline configurations. Additionally, simulation-based demonstrations are conducted to evaluate the performance in typical scenarios and investigate the impact of various error sources. The results indicate that the proposed TDA-InSAR is able to get the specified MB acquisition for the asymptotic 3D PU, which offers a feasible solution for single-pass 3D SAR imaging.Comment: 16 pages, 20 figure

ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method

SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results

Single-cell transcriptomics reveals receptor transformations during olfactory neurogenesis

The sense of smell allows chemicals to be perceived as diverse scents. We used single neuron RNA-Sequencing (RNA-Seq) to explore developmental mechanisms that shape this ability as nasal olfactory neurons mature in mice. Most mature neurons expressed only one of the roughly 1000 odorant receptor genes (Olfrs) available, and that at high levels. However, many immature neurons expressed low levels of multiple Olfrs. Coexpressed Olfrs localized to overlapping zones of the nasal epithelium, suggesting regional biases, but not to single genomic loci. A single immature neuron could express Olfrs from up to seven different chromosomes. The mature state in which expression of Olfr genes is restricted to one per neuron emerges over a developmental progression that appears independent of neuronal activity requiring sensory transduction molecules

A new simplified and robust Surface Reflectance Estimation Method (SREM) for use over diverse land surfaces using multi-sensor data

Surface reflectance (SR) estimation is the most critical pre-processing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) Radiative Transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in-situ SR measurements collected by Analytical Spectral Devices (ASD) for the South Dakota State University (SDSU) site, USA (ii) cross-comparison between the SREM and Landsat spectral SR products, i.e., Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Landsat 8 Surface Reflectance Code (LaSRC), are conducted over 11 urban (2013-2018), 13 vegetated (2013-2018), and 11 desert/arid (2000 to 2018) sites located over different climatic zones at global scale, (iii) the performance of the SREM spectral SR retrievals for low to high aerosol loadings is evaluated, (iv) spatio-temporal cross-comparison is conducted for six Landsat paths/rows located in Asia, Africa, Europe, and the USA from 2013 to 2018 to consider a large variety of land surfaces and atmospheric conditions, (v) cross-comparison is also performed for the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Soil Adjusted Vegetation Index (SAVI) calculated from both the SREM and Landsat SR data, (vi) the SREM is also applied to the Sentinel-2A and Moderate Resolution Imaging Spectrometer (MODIS) data to explore its applicability, and (vii) errors in the SR retrievals are reported using the Mean Bias Error (MBE), Root Mean Squared Deviation (RMSD) and Mean Systematic Error (MSE). Results depict significant and strong positive Pearson’s correlation (r), small MBE, RMSD, and MSE for each spectral band against in-situ ASD data and Landsat (LEDAPS and LaSRC) SR products. Consistency in SREM performance against Sentinel-2A (r = 0.994, MBE = - 0.009, and RMSD = 0.014) and MODIS (r = 0.925, MBE = 0.007, and RMSD = 0.014) data suggests that SREM can be applied to other multispectral satellites data. Overall, the findings demonstrate the potential and promise of SREM for use over diverse surfaces and under varying atmospheric conditions using multi-sensor data on a global scale

The role of tumor-associated macrophages in glioma cohort: through both traditional RNA sequencing and single cell RNA sequencing

Gliomas are the leading cause in more than 50% of malignant brain tumor cases. Prognoses, recurrences, and mortality are usually poor for gliomas that have malignant features. In gliomas, there are four grades, with grade IV gliomas known as glioblastomas (GBM). Currently, the primary methods employed for glioma treatment include surgical removal, followed by chemotherapy after the operation, and targeted therapy. However, the outcomes of these treatments are unsatisfactory. Gliomas have a high number of tumor-associated macrophages (TAM), which consist of brain microglia and macrophages, making them the predominant cell group in the tumor microenvironment (TME). The glioma cohort was analyzed using single-cell RNA sequencing to quantify the genes related to TAMs in this study. Furthermore, the ssGSEA analysis was utilized to assess the TAM-associated score in the glioma group. In the glioma cohort, we have successfully developed a prognostic model consisting of 12 genes, which is derived from the TAM-associated genes. The glioma cohort demonstrated the predictive significance of the TAM-based risk model through survival analysis and time-dependent ROC curve. Furthermore, the correlation analysis revealed the significance of the TAM-based risk model in the application of immunotherapy for individuals diagnosed with GBM. Ultimately, the additional examination unveiled the prognostic significance of PTX3 in the glioma group, establishing it as the utmost valuable prognostic indicator in patients with GBM. The PCR assay revealed the PTX3 is significantly up-regulated in GBM cohort. Additionally, the assessment of cell growth further confirms the involvement of PTX3 in the GBM group. The analysis of cell proliferation showed that the increased expression of PTX3 enhanced the ability of glioma cells to proliferate. The prognosis of glioblastomas and glioma is influenced by the proliferation of tumor-associated macrophages

Stability of SARS-CoV-2 in cold-chain transportation environments and the efficacy of disinfection measures

BackgroundLow temperature is conducive to the survival of COVID-19. Some studies suggest that cold-chain environment may prolong the survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and increase the risk of transmission. However, the effect of cold-chain environmental factors and packaging materials on SARS-CoV-2 stability remains unclear.MethodsThis study aimed to reveal cold-chain environmental factors that preserve the stability of SARS-CoV-2 and further explore effective disinfection measures for SARS-CoV-2 in the cold-chain environment. The decay rate of SARS-CoV-2 pseudovirus in the cold-chain environment, on various types of packaging material surfaces, i.e., polyethylene plastic, stainless steel, Teflon and cardboard, and in frozen seawater was investigated. The influence of visible light (wavelength 450 nm-780 nm) and airflow on the stability of SARS-CoV-2 pseudovirus at -18°C was subsequently assessed.ResultsExperimental data show that SARS-CoV-2 pseudovirus decayed more rapidly on porous cardboard surfaces than on nonporous surfaces, including polyethylene (PE) plastic, stainless steel, and Teflon. Compared with that at 25°C, the decay rate of SARS-CoV-2 pseudovirus was significantly lower at low temperatures. Seawater preserved viral stability both at -18°C and with repeated freeze−thaw cycles compared with that in deionized water. Visible light from light-emitting diode (LED) illumination and airflow at -18°C reduced SARS-CoV-2 pseudovirus stability.ConclusionOur studies indicate that temperature and seawater in the cold chain are risk factors for SARS-CoV-2 transmission, and LED visible light irradiation and increased airflow may be used as disinfection measures for SARS-CoV-2 in the cold-chain environment

The Effectiveness of Pay-It-Forward in Addressing HPV Vaccine Delay and Increasing Uptake Among 15–18-Year-Old Adolescent Girls Compared to User-Paid Vaccination: A Study Protocol for a Two-Arm Randomized Controlled Trial in China

Background Human papillomavirus (HPV) vaccination could prevent cervical and other HPV-associated cancers attributable to vaccine-associated HPV types. However, HPV vaccination coverage among women aged 9–18 years old is low in China. Common barriers include poor financial affordability, minimal public engagement, and low confidence in domestically produced HPV vaccines. Pay-it-forward offers an individual a free or subsidized service then an opportunity to voluntarily donate and/or create a postcard message to support future people. This study aims to assess the effectiveness of pay-it-forward as compared to standard-of-care self-paid vaccination to improve HPV vaccine uptake among adolescent girls aged 15–18 years, who are left out in the current pilot free HPV vaccination task force in some parts of China. Methods This is a two-arm randomized controlled trial in Chengdu, China. Eligible adolescent girls (via caregivers) will be randomly selected and recruited through four community health centers (one in the most developed urban areas, one in higher middle-income and one in lower middle-income suburban areas, and one in the least developed rural areas) using the resident registration list. A total of 320 participants will be randomized into two study arms (user-paid versus pay-it-forward vaccination) in a 1:1 ratio. The intervention assignment will be blinded to recruiters and participants using envelop concealment until the research assistants open the envelop to determine which treatment to deliver to each individual. The primary outcome of the study will be HPV vaccine uptake by administrative data. Secondary outcomes include costs, vaccine hesitancy, and the completion rates of the 3-dose HPV vaccination series. Discussion This study will investigate an innovative pay-it-forward strategy’s effectiveness and economic costs to improve HPV vaccination among 15–18-year-old adolescent girls. Study findings will have implications for increasing HPV vaccine uptake in places where HPV vaccines are provided for a fee