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The existing physical layer security schemes, which are based on the key generation model and the wire-tap channel model, achieve security by utilizing channel reciprocity entropy and noise entropy, respectively. In contrast, we propose a novel secure transmission framework that combines noise entropy with reciprocity entropy, achieved by inserting reciprocity entropy into the frozen bits of polar codes. Note that in real-world scenarios, when eavesdroppers employ polynomial-time attacks, the bit error rate (BER) increases due to the introduction of computational entropy. To achieve indistinguishability security, we convert the practical physical layer security metric, BER, into the average min-entropy, a widely accepted concept in cryptography. The simulation results demonstrate that the eavesdropper's BER can be significantly increased without compromising the communication performance of the legitimate receiver. Under concrete parameters we selected, when compared to the joint scheme of physical layer key generation and one time pad, the modular semantically-secure scheme based on the wire-tap channel model, and the simple channel entropy combination scheme, our scheme achieves a message rate approximately 1.2 times, 3.8 times, and 1.4 times better, respectively. Experimental testing validates the feasibility of our scheme. [ABSTRACT FROM AUTHOR]

Atmospheric radiation is a major branch of atmospheric physics that encompasses the fundamental theories of atmospheric absorption, particle scattering (aerosols and clouds), and radiative transfer. Specifically, the simulations of atmospheric gaseous absorption and scattering properties of particles are the essential components of atmospheric radiative transfer models. Atmospheric radiation has important applications in weather, climate, data assimilation, remote sensing, and atmospheric detection studies. In Part I, a comprehensive review of the progress in the field of gas absorption and particle scattering research over the past 30 years with a particular emphasis on the contributions from Chinese scientists is presented. The review of gas absorption includes the construction of absorption databases, the impact of different atmospheric absorption algorithms on radiative calculations, and their applications in weather and climate models and remote sensing. The review on particle scattering starts with the theoretical and computational methods and subsequently explores the optical modeling of aerosols and clouds in remote sensing and atmospheric models. Additionally, the paper discusses potential future research directions in this field. [ABSTRACT FROM AUTHOR]

As a highly vascular eye part, the choroid is crucial in various eye disease diagnoses. However, limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data, particularly for the choroidal vessels. Meanwhile, the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data, while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks. Common cascaded structures grapple with error propagation during training. To address these challenges, we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper. Specifically, we propose Transformer-Assisted Cascade Learning Network (TACLNet) for choroidal vessel segmentation, which comprises a two-stage training strategy: pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation. We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC, capturing differential and detailed information simultaneously. Additionally, we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process. Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation. Besides, we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography (OCT) scans on a publicly available dataset. All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application. [ABSTRACT FROM AUTHOR]

Purpose: To optimize chemotherapy regimens and improve the effectiveness of chemotherapy combined with immunotherapy, a PET tracer specifically targeting the stimulator of interferon genes (STING), denoted as [18F]FBTA was used to monitor the early changes in tumor immunogenicity after chemotherapy in colorectal cancer (CRC) mice. Methods: The toluene sulfonate precursor was labeled with 18F to produce the STING targeted probe—[18F]FBTA. [18F]FBTA-PET imaging and biodistribution were performed using CRC mice treated with oxaliplatin (OXA) or cisplatin (CDDP). CRC mice were also treated with low (CDDP-LD: 1 mg/kg) or medium (CDDP-MD: 2.5 mg/kg) doses of CDDP, and subjected to PET imaging and biodistribution. The effects of different chemotherapeutic agents and different doses of CDDP on tumor innate immunity were verified by flow cytometry and immunohistochemistry. Results: PET imaging of CRC mice exhibited notably enhanced tumor uptake in the early phase of chemotherapy with treatment with OXA (3.09 ± 0.25%ID/g) and CDDP (4.01 ± 0.18%ID/g), especially in the CDDP group. The PET-derived tumor uptake values have strong correlations with STING immunohistochemical score. Flow cytometry showed both agents led to DCs and macrophages infiltration in tumors. Compared with OXA, CDDP treatment recruits more DCs and macrophages in CRC tumors. Both CDDP-LD and CDDP-MD treatment elevated uptake in CRC tumors, especially in CDDP-MD group. Immunohistochemistry and flow cytometry confirmed CDDP-MD treatment recruits more DCs and macrophages than CDDP-LD treatment. Conclusion: Overall, the STING-targeted tracer—[18F]FBTA was demonstrated to monitor early changes in tumor immunogenicity in CRC mice after chemotherapy. Besides, the STING-targeted strategy may help to select the appropriate chemotherapy regimen, including chemotherapeutic agents and doses, which further improve clinical decision making for combination immunotherapy after chemotherapy for CRC. [ABSTRACT FROM AUTHOR]

Visualization analysis is crucial in healthcare as it provides insights into complex data and aids healthcare professionals in efficiency. Information visualization leverages algorithms to reduce the complexity of high-dimensional heterogeneous data, thereby enhancing healthcare professionals’ understanding of the hidden associations among data structures. In the field of healthcare visualization, efforts have been made to refine and enhance the utility of data through diverse algorithms and visualization techniques. This review aims to summarize the existing research in this domain and identify future research directions. We searched Web of Science, Google Scholar and IEEE Xplore databases, and ultimately, 76 articles were included in our analysis. We collected and synthesized the research findings from these articles, with a focus on visualization, artificial intelligence and supporting tasks in healthcare. Our study revealed that researchers from diverse fields have employed a wide range of visualization techniques to visualize various types of data. We summarized these visualization methods and proposed recommendations for future research. We anticipate that our findings will promote further development and application of visualization techniques in healthcare. [ABSTRACT FROM AUTHOR]

Recent theoretically predicted strong anisotropic spin Hall effect (SHE) or inverse SHE (ISHE) in chiral antiferromagnetic compounds Mn3X (X = Ge, Sn, Ga, Ir, Rh, and Pt) could potentially expand the horizons of the antiferromagnet spintronics; however, it has not been experimentally observed yet. For achieving this goal, we have first successfully fabricated high-quality Kagome phase Mn3Sn films with smooth surface by a combination of room-temperature magnetron sputtering and high-temperate annealing. These Mn3Sn films were proved to be epitaxially grown on MgO (110) single crystal substrate with a seldom reported (101-0\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${1}{\text{0}}\stackrel{\text{-}}{1}{\text{0}}$$\end{document}) orientation that could serve as a good platform for the studies of the crystalline orientation-related anisotropic phenomenon. Then, by employing spin pumping-induced inverse spin Hall effect (SP-ISHE) voltage measurements, we have experimentally proved the existence of crystalline orientation-related anisotropic ISHE with an amplitude of more than 35% in our Mn3Sn films. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy (HR) can potentially lead to vision loss if left untreated. Early screening and treatment are critical in reducing the risk of vision loss. The computer-aided diagnostic system presents an opportunity to improve the efficiency and reliability of HR screening and diagnosis, particularly given the shortage of specialized medical professionals and the challenges faced by primary care physicians in making precise diagnoses. A notable barrier to the development of such diagnostic algorithms is the lack of publicly available benchmarks and datasets. To address these issues, we organized a challenge named “HRDC—Hypertensive Retinopathy Diagnosis Challenge” in conjunction with the Computer Graphics International (CGI) 2023 conference. The challenge provided a fundus image dataset for two clinical tasks: hypertension classification and HR classification, with each task containing 1000 images. This paper presents a concise summary and analysis of the submitted methods and results for the two challenge tasks. For hypertension classification, the best performing algorithm achieved a Kappa score of 0.3819, an F1 score of 0.6337, and a specificity of 0.8472. For HR classification, the best performing algorithm achieved a Kappa score of 0.4154, an F1 score of 0.6122, and a specificity of 0.8444. We also explored an ensemble approach to the top-ranking methods, which further improved performance beyond the individual best performing algorithm for each task. The challenge results show that there is room for further optimization of these methods, but the insights and methodologies derived from this challenge provide valuable directions for developing more precise and reliable classification models for hypertension and HR. [ABSTRACT FROM AUTHOR]

Swept-source optical coherence tomography (SS-OCT) is widely used to diagnose high myopia due to its advantage in imaging the ocular anatomic structures. Although the scleral deformation provides information on the risk of the high myopia, further validation of these highly promising findings in clinical studies has been limited by the current semi-automated software, which requires human input, and the automatic analysis of the scleral structure is quite challenging due to the ambiguous boundaries. To address these challenges, we propose a deep contour attention network (DCANet) for automatic segmentation of scleral deformation structure. Specifically, we design a scale-aware attention feature fusion module to achieve cross-scale feature fusion, which can facilitate the network to learn complementary information from multi-scale features. In addition, we develop a pyramid feature enhancement module to allow the network to learn global contextual features through the combination of receptive field and attention mechanism, and we also propose a boundary heatmap label to enrich boundary information. We evaluate the performance of the proposed method on two in-house SS-OCT datasets. In addition to the multiple metrics that are used for evaluating the segmentation performance, including Jaccard similarity coefficient, dice similarity coefficient and boundary distance error, we also propose length similarity coefficient and angle similarity coefficient to evaluate the length estimation and angle estimation, respectively. The experimental results show that our method can effectively improve the segmentation performance, and our DCANet achieves the overall best performance on two datasets compared with other state-of-the-art networks. Our findings motivate the development of clinically applicable deep learning systems for the prediction of high myopia progression on the basis of the scleral phenotypes from SS-OCT images. [ABSTRACT FROM AUTHOR]