Angular-Distance Based Channel Estimation for Holographic MIMO

This paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO's electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.Comment: This paper has been accepted for publication in IEEE JSA

Differences in Apoptosis and Cell Cycle Distribution between Human Melanoma Cell Lines UACC903 and UACC903(+6), before and after UV Irradiation

Introduction of human chromosome 6 into malignant melanoma cell line UACC903 resulted in generation of the chromosome 6-mediated suppressed cell subline UACC903(+6) that displays attenuated growth rate, anchorage-dependency, and reduced tumorigenicity. We have showed that overexpression of a chromosome 6-encoded tumor suppressor gene led to partial suppression to UACC903 cell growth. We now describe the differences in apoptosis and cell cycle between UACC903 and UACC903(+6) before and after UV irradiation. MTT assay revealed 86.92±8.24% of UACC903 cells viable, significantly (p<0.01) higher than 48.76±5.31% of UACC903(+6), at 24 hr after 254-nm UV irradiation (40 J/M2). Before UV treatment, flow cytometry analysis revealed 6.06±0.20% apoptosis in UACC903, significantly (p=0.01) lower than 6.67±0.15% in UACC903(+6). The G0/G1, S and G2/M phase cells of UACC903 were, respectively, 54.10±0.59%, 22.31±0.50% and 16.85±0.25%, all significantly (p<0.01) different from the corresponding percentages (58.82±0.35%, 20.48±0.05%, and 13.17±0.45%) of UACC903(+6). After the UV treatment, UACC903 cells in apoptosis, G0/G1, S, and G2/M became 12.59±0.17%, 38.90±0.67%, 19.74±0.70%, and 27.01±0.66%, respectively, while UACC903(+6) cells were 24.16±0.48%, 37.97±0.62%, 19.20±0.52%, and 15.69±0.14%. TUNEL assay revealed 2.31±0.62% apoptosis in UACC903, significantly (p<0.01) lower than 9.60±1.14% of UACC903(+6), and a linear and exponential increase of apoptosis, respectively, in response to the UV treatment. These results indicate that UACC903(+6) cells have a greater tendency to undergo apoptosis and are thus much more sensitive to UV irradiation. Our findings further suggest a novel mechanism for chromosome 6-mediated suppression of tumorigenesis and metastasis, i.e., through increased cell death

Near-Field Sparse Channel Estimation for Extremely Large-Scale RIS-Aided Wireless Communications

A significant increase in the number of reconfigurable intelligent surface (RIS) elements results in a spherical wavefront in the near field of extremely large-scale RIS (XL-RIS). Although the channel matrix of the cascaded two-hop link may become sparse in the polar-domain representation, their accurate estimation of these polar-domain parameters cannot be readily guaranteed. To tackle this challenge, we exploit the sparsity inherent in the cascaded channel. To elaborate, we first estimate the significant path-angles and distances corresponding to the common paths between the BS and the XL-RIS. Then, the individual path parameters associated with different users are recovered. This results in a two-stage channel estimation scheme, in which distinct learning-based networks are used for channel training at each stage. More explicitly, in stage I, a denoising convolutional neural network (DnCNN) is employed for treating the grid mismatches as noise to determine the true grid index of the angles and distances. By contrast, an iterative shrinkage thresholding algorithm (ISTA) based network is proposed for adaptively adjusting the column coherence of the dictionary matrix in stage II. Finally, our simulation results demonstrate that the proposed two-stage learning-based channel estimation outperforms the state-of-the-art benchmarks.Comment: This paper has been accepted for publication in the IEEE GLOBECOM 2023 Workshops Proceeding

Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation

Medical image segmentation methods often rely on fully supervised approaches to achieve excellent performance, which is contingent upon having an extensive set of labeled images for training. However, annotating medical images is both expensive and time-consuming. Semi-supervised learning offers a solution by leveraging numerous unlabeled images alongside a limited set of annotated ones. In this paper, we introduce a semi-supervised medical image segmentation method based on the mean-teacher model, referred to as Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA). This method combines consistency regularization, pseudo-labels, and data augmentation to enhance the efficacy of semi-supervised segmentation. Firstly, the proposed model comprises both student and teacher models with a shared encoder and two distinct decoders employing different up-sampling strategies. Minimizing the output discrepancy between decoders enforces the generation of consistent representations, serving as regularization during student model training. Secondly, we introduce mixup operations to blend unlabeled data with labeled data, creating mixed data and thereby achieving data augmentation. Lastly, pseudo-labels are generated by the teacher model and utilized as labels for mixed data to compute unsupervised loss. We compare the segmentation results of the DCPA model with six state-of-the-art semi-supervised methods on three publicly available medical datasets. Beyond classical 10\% and 20\% semi-supervised settings, we investigate performance with less supervision (5\% labeled data). Experimental outcomes demonstrate that our approach consistently outperforms existing semi-supervised medical image segmentation methods across the three semi-supervised settings

Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data. However, annotating medical image datasets is a laborious and time-consuming process. Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on three publicly available datasets demonstrate that the PLGDF framework can largely improve performance by incorporating the unlabeled data. Meanwhile, our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods. The codes of this study are available at https://github.com/ortonwang/PLGDF

Self-crosslinkable chitosan-hyaluronic acid dialdehyde nanoparticles for CD44-targeted siRNA delivery to treat bladder cancer

Bladder cancer is one of the concerning malignancies worldwide, which is lacking effective targeted therapy. Gene therapy is a potential approach for bladder cancer treatment. While, a safe and effective targeted gene delivery system is urgently needed for prompting the bladder cancer treatment in vivo. In this study, we confirmed that the bladder cancer had CD44 overexpression and small interfering RNAs (siRNA) with high interfere to Bcl2 oncogene were designed and screened. Then hyaluronic acid dialdehyde (HAD) was prepared in an ethanol-water mixture and covalently conjugated to the chitosan nanoparticles (CS-HAD NPs) to achieve CD44 targeted siRNA delivery. The in vitro and in vivo evaluations indicated that the siRNA-loaded CS-HAD NPs (siRNA@CS-HAD NPs) were approximately 100 nm in size, with improved stability, high siRNA encapsulation efficiency and low cytotoxicity. CS-HAD NPs could target to CD44 receptor and deliver the therapeutic siRNA into T24 bladder cancer cells through a ligand-receptor-mediated targeting mechanism and had a specific accumulation capacity in vivo to interfere the targeted oncogene Bcl2 in bladder cancer. Overall, a CD44 targeted gene delivery system based on natural macromolecules was developed for effective bladder cancer treatment, which could be more conducive to clinical application due to its simple preparation and high biological safety

Durvalumab in Combination with Olaparib in Patients with Relapsed SCLC: Results from a Phase II Study

Purpose: Despite high tumor mutationburden, immune checkpoint blockade has limited efficacy in SCLC. We hypothesized that poly (ADP-ribose) polymerase inhibition could render SCLC more susceptible to immune checkpoint blockade. Methods: A single-arm, phase II trial (NCT02484404) enrolled patients with relapsed SCLC who received durvalumab, 1500 mg every 4 weeks, and olaparib, 300 mg twice a day. The primary outcome was objective response rate. Correlative studies included mandatory collection of pretreatment and during-treatment biopsy specimens, which were assessed to define SCLC immunephenotypes: desert (CD8-positive T-cell prevalence low), excluded (CD8-positive T cells in stroma immediately adjacent/within tumor), and inflamed (CD8-positive T cells in direct contact with tumor). Results: A total of 20 patients were enrolled. Their median age was 64 years, and most patients (60%) had platinum-resistant/refractory disease. Of 19 evaluable patients, two were observed to have partial or complete responses (10.5%), including a patient with EGFR-transformed SCLC. Clinical benefit was observed in four patients (21.1% [95% confidence interval: 6.1%–45.6%]) with confirmed responses or prolonged stable disease (≥8 months). The most common treatment-related adverse events were anemia (80%), lymphopenia (60%), and leukopenia (50%). Nine of 14 tumors (64%) exhibited an excluded phenotype; 21% and 14% of tumors exhibited the inflamed and desert phenotypes, respectively. Tumor responses were observed in all instances in which pretreatment tumors showed an inflamed phenotype. Of the five tumors without an inflamed phenotype at baseline, no during-treatment increase in T-cell infiltration or programmed death ligand 1 expression on tumor-infiltrating immune cells was observed. Conclusions: The study combination did not meet the preset bar for efficacy. Pretreatment and during-treatment biopsy specimens suggested that tumor immune phenotypes may be relevant for SCLC responses to immune checkpoint blockade combinations. The predictive value of preexisting CD8-positive T-cell infiltrates observed in this study needs to be confirmed in larger cohorts