Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution

Since the number of incident energies is limited, it is difficult to directly acquire hyperspectral images (HSI) with high spatial resolution. Considering the high dimensionality and correlation of HSI, super-resolution (SR) of HSI remains a challenge in the absence of auxiliary high-resolution images. Furthermore, it is very important to extract the spatial features effectively and make full use of the spectral information. This paper proposes a novel HSI super-resolution algorithm, termed dual-domain network based on hybrid convolution (SRDNet). Specifically, a dual-domain network is designed to fully exploit the spatial-spectral and frequency information among the hyper-spectral data. To capture inter-spectral self-similarity, a self-attention learning mechanism (HSL) is devised in the spatial domain. Meanwhile the pyramid structure is applied to increase the acceptance field of attention, which further reinforces the feature representation ability of the network. Moreover, to further improve the perceptual quality of HSI, a frequency loss(HFL) is introduced to optimize the model in the frequency domain. The dynamic weighting mechanism drives the network to gradually refine the generated frequency and excessive smoothing caused by spatial loss. Finally, In order to better fully obtain the mapping relationship between high-resolution space and low-resolution space, a hybrid module of 2D and 3D units with progressive upsampling strategy is utilized in our method. Experiments on a widely used benchmark dataset illustrate that the proposed SRDNet method enhances the texture information of HSI and is superior to state-of-the-art methods

Imaging through multimode fibres with physical prior

Imaging through perturbed multimode fibres based on deep learning has been widely researched. However, existing methods mainly use target-speckle pairs in different configurations. It is challenging to reconstruct targets without trained networks. In this paper, we propose a physics-assisted, unsupervised, learning-based fibre imaging scheme. The role of the physical prior is to simplify the mapping relationship between the speckle pattern and the target image, thereby reducing the computational complexity. The unsupervised network learns target features according to the optimized direction provided by the physical prior. Therefore, the reconstruction process of the online learning only requires a few speckle patterns and unpaired targets. The proposed scheme also increases the generalization ability of the learning-based method in perturbed multimode fibres. Our scheme has the potential to extend the application of multimode fibre imaging

Super-resolution imaging through a multimode fiber: the physical upsampling of speckle-driven

Following recent advancements in multimode fiber (MMF), miniaturization of imaging endoscopes has proven crucial for minimally invasive surgery in vivo. Recent progress enabled by super-resolution imaging methods with a data-driven deep learning (DL) framework has balanced the relationship between the core size and resolution. However, most of the DL approaches lack attention to the physical properties of the speckle, which is crucial for reconciling the relationship between the magnification of super-resolution imaging and the quality of reconstruction quality. In the paper, we find that the interferometric process of speckle formation is an essential basis for creating DL models with super-resolution imaging. It physically realizes the upsampling of low-resolution (LR) images and enhances the perceptual capabilities of the models. The finding experimentally validates the role played by the physical upsampling of speckle-driven, effectively complementing the lack of information in data-driven. Experimentally, we break the restriction of the poor reconstruction quality at great magnification by inputting the same size of the speckle with the size of the high-resolution (HR) image to the model. The guidance of our research for endoscopic imaging may accelerate the further development of minimally invasive surgery

Chemoradiotherapy-Induced CD4+ and CD8+ T-Cell Alterations to Predict Patient Outcomes in Esophageal Squamous Cell Carcinoma

Purpose and Objectives: Chemoradiotherapy (CRT) is an important component of treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Recent research findings support the role of CRT in activating an anti-tumor immune response. However, predictors of CRT efficacy are not fully understood. The aim of this study was to measure CRT-induced changes to lymphocyte subpopulations and to evaluate the prognostic value of lymphocyte alterations for patients with ESCC.Materials and Methods: In total, this pilot study enrolled 64 patients with ESCC who received neo-adjuvant CRT or definitive CRT. Peripheral blood samples were collected before and during treatment and were analyzed by flow cytometry for CD19, CD3, CD4, CD8, CD56, and CD16. Relationships between lymphocyte subset alterations and overall survival (OS) and progression-free survival (PFS) were evaluated using the log-rank test and a Cox regression model.Results: The median follow-up period was 11.8 months (range, 4.0–20.2 months). Compared to pre-treatment specimens, post-treatment blood samples had decreased proportions of CD19+ B-cells and increased proportions of CD3+ and CD8+ T-cells (all P < 0.05). Univariate and multivariate analysis showed that increased CD4+ T-cell ratios after CRT independently predicted superior PFS (hazard ratio [HR] = 0.383; 95% confidence interval [CI] = 0.173–0.848, P = 0.017) and that increased CD8+ T-cell ratios predicted improved OS (HR = 0.258; 95% CI = 0.083–0.802, P = 0.019). Patients with both increased CD4+ and CD8+ ratios had a superior PFS and OS, compared to patients with an increased CD4+ ratio only or CD8+ ratio only or neither (1-year PFS rate 63 vs. 25%, 1-year OS rate 80 vs. 62%, P = 0.005 and 0.025, respectively).Conclusions: CRT-induced increases in CD4+ and CD8+ T-cell ratios are reliable biomarker predictors of survival in patients with ESCC

Breast-cancer-secreted miR-122 reprograms glucose metabolism in premetastatic niche to promote metastasis

Reprogrammed glucose metabolism as a result of increased glycolysis and glucose uptake is a hallmark of cancer. Here we show that cancer cells can suppress glucose uptake by non-tumour cells in the pre-metastatic niche, by secreting vesicles that carry high levels of the miR-122 microRNA. High miR-122 levels in the circulation have been associated with metastasis in breast cancer patients and we show that cancer-cell-secreted miR-122 facilitates metastasis by increasing nutrient availability in the pre-metastatic niche. Mechanistically cancer-cell-derived miR-122 suppresses glucose uptake by niche cells in vitro and in vivo by downregulating the glycolytic enzyme pyruvate kinase (PKM). In vivo inhibition of miR-122 restores glucose uptake in distant organs, including brain and lungs, and decreases the incidence of metastasis. These results demonstrate that by modifying glucose utilization by recipient pre-metastatic niche cells, cancer-derived extracellular miR-122 is able to reprogram systemic energy metabolism to facilitate disease progression