Single image super resolution based on multi-scale structure and non-local smoothing

In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation

Spatial epidemiological approaches to inform leptospirosis surveillance and control: a systematic review and critical appraisal of methods

Leptospirosis is a global zoonotic disease that the transmission is driven by complex geographical and temporal variation in demographics, animal hosts and socioecological factors. This results in complex challenges for the identification of high‐risk areas. Spatial and temporal epidemiological tools could be used to support leptospirosis control programs, but the adequacy of its application has not been evaluated. We searched literature in six databases including PubMed, Web of Science, EMBASE, Scopus, SciELO and Zoological Record to systematically review and critically assess the use of spatial and temporal analytical tools for leptospirosis and to provide general framework for its application in future studies. We reviewed 115 articles published between 1930 and October 2018 from 41 different countries. Of these, 65 (56.52%) articles were on human leptospirosis, 39 (33.91%) on animal leptospirosis and 11 (9.5%) used data from both human and animal leptospirosis. Spatial analytical (n = 106) tools were used to describe the distribution of incidence/prevalence at various geographical scales (96.5%) and to explored spatial patterns to detect clustering and hot spots (33%). A total of 51 studies modelled the relationships of various variables on the risk of human (n = 31), animal (n = 17) and both human and animal infection (n = 3). Among those modelling studies, few studies had generated spatially structured models and predictive maps of human (n = 2/31) and animal leptospirosis (n = 1/17). In addition, nine studies applied time‐series analytical tools to predict leptospirosis incidence. Spatial and temporal analytical tools have been greatly utilized to improve our understanding on leptospirosis epidemiology. Yet the quality of the epidemiological data, the selection of covariates and spatial analytical techniques should be carefully considered in future studies to improve usefulness of evidence as tools to support leptospirosis control. A general framework for the application of spatial analytical tools for leptospirosis was proposed

Clinical implication of PD-L2 in the prognosis assessment of HNSCC immunotherapy

Background and purpose: Programmed death-1 (PD-1) monoclonal antibody therapy plays an increasingly important role in the treatment of head and neck squamous cell carcinoma (HNSCC). However, low response rate and lack of predictive biomarkers are still the challenging problems. This study aimed to confirm that programmed death ligand-2 (PD-L2) is a predictive biomarker for the outcome of HNSCC anti-PD-1 immunotherapy. Methods: The samples and clinical data of 50 HNSCC patients undergoing PD-1 monoclonal antibody immunotherapy were collected. Immunohistochemical staining was used to analyze the level of programmed death ligand-1 (PD-L1) and PD-L2. Kaplan-Meier overall survivals were analyzed using SPSS 26.0 software, grouped by the basic clinical characteristics and the PD-L1 and PD-L2 levels. Survival curves were plotted using GraphPad Prism. Results: HNSCC had a relatively high expression rate of PD-L2 with more than 80% of cases detected as PD-L2 positive. The expression of PD-L2 significantly correlated with the clinical outcome of immunotherapy, with a mean survival of 18.8 (16.0-21.7) months for patients with high PD-L2 expression and 11.0 (9.1-12.8) months for patients with low PD-L2 expression, this difference being statistically significant. Conclusion: PD-L2 has the potential to be used as a predictive biomarker for HNSCC anti-PD-1 immunotherapy

Upconversion NIR-II fluorophores for mitochondria-targeted cancer imaging and photothermal therapy

Acknowledgements: The work was supported by the National Key R&D Program of China (2020YFA0908800), NSFC (81773674, 81573383), Shenzhen Science and Technology Research Grant (JCYJ20190808152019182), Hubei Province Scientific and Technical Innovation Key Project, National Natural Science Foundation of Hubei Province (2017CFA024, 2017CFB711), the Applied Basic Research Program of Wuhan Municipal Bureau of Science and Technology (2019020701011429), Tibet Autonomous Region Science and Technology Plan Project Key Project (XZ201901-GB-11), the Local Development Funds of Science and Technology Department of Tibet (XZ202001YD0028C), Project First-Class Disciplines Development Supported by Chengdu University of Traditional Chinese Medicine (CZYJC1903), Health Commission of Hubei Province Scientific Research Project (WJ2019M177, WJ2019M178), the China Scholarship Council, and the Fundamental Research Funds for the Central Universities.Peer reviewedPublisher PD