Search

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]

Purpose: Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients. Methods: A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan–Meier method and compared with the observed PFS probability. Results: Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785–0.851), 0.752 (95% CI: 0.638–0.865), and 0.717 (95% CI: 0.641–0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822–0.895), 0.769 (95% CI: 0.642–0.896), and 0.730 (95% CI: 0.634–0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups. Conclusion: Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial spine; however, the quantitative detection of inflammation in AS remains a challenge in clinical settings. We aimed to investigate the feasibility of using a specific P2X7R-targeting 18F-labeled tracer [18F]GSK1482160 for positron emission tomography (PET) imaging and the quantification of AS. Methods: The radioligand [18F]GSK1482160 was obtained based on nucleophilic aliphatic substitution. Dynamic [18F]GSK1482160 and [18F]FDG micro-PET/CT imaging were performed on AS mice (n = 8) and age-matched controls (n = 8). Tracer kinetics modeling was performed using Logan's graphical arterial input function analysis to quantify the in vivo expression of P2X7R. The post-PET tissues were collected for hematoxylin–eosin (H&E), immunohistochemical (IHC), and immunofluorescence (IF) staining. Results: [18F]GSK1482160 PET/CT imaging revealed that the specific binding in the ankle joint and sacroiliac joint (SIJ) of the AS at 8 weeks group (BPNDankle−AS−8W (non-displaceable binding potential of the ankle) 3.931 ± 0.74; BPNDSIJ−AS−8W (BPBD of the SIJ) 4.225 ± 0.84) were significantly higher than the controls at 8 weeks group (BPNDankle−Ctr−8W 0.325 ± 0.15, BPNDSJJ−Ctr−8W 0.319 ± 0.17) respectively, and the AS at 14 weeks group (BPNDankle−AS−14W 12.212 ± 2.25; BPNDSJJ−AS−14W 13.389 ± 3.60) were significantly higher than the controls at 14 weeks group (BPNDankle−Ctr−14W 0.204 ± 0.16, BPNDSJJ−Ctr−14W 0.655 ± 0.35) respectively. The four groups had no significant difference in the [18F]FDG uptake of ankle and SIJ. IHC and IF staining revealed that the overexpression of P2X7R was colocalized with activated macrophages from the ankle synovium and spinal endplate in mice with AS, indicating that quantification of P2X7R may contribute to the understanding of the pathogenesis of inflammation in human AS. Conclusion: This study developed a novel P2X7R-targeting PET tracer [18F]GSK1482160 to detect the expression of P2X7R in AS mouse models and provided powerful non-invasive PET imaging and quantification for AS. [ABSTRACT FROM AUTHOR]

The Learning With Errors (LWE) problem is widely used in lattice-based cryptography, which is the most promising post-quantum cryptography direction. There are a variety of LWE-solving methods, which can be classified into four groups: lattice methods, algebraic methods, combinatorial methods, and exhaustive searching. The Blum–Kalai–Wasserman (BKW) algorithm is an important variety of combinatorial algorithms, which was first presented for solving the Learning Parity With Noise (LPN) problem and then extended to solve LWE. In this paper, we give an overview of BKW algorithms for solving LWE. We introduce the framework and key techniques of BKW algorithms and make comparisons between different BKW algorithms and also with lattice methods by estimating concrete security of specific LWE instances. We also briefly discuss the current problems and potential future directions of BKW algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Here, we aim to identify a CEACAM5-targeted nanobody and demonstrate its application in positron emission tomography (PET) imaging and near-infrared (NIR) fluorescence imaging in colorectal cancer (CRC). Methods: Immunohistochemistry was applied to verify CEACAM5 expression in CRC and metastatic lymph nodes (mLNs). CEACAM5-targeted nanobodies were obtained by immunization of human CEACAM5 protein in a dromedary, followed by several rounds of phage screenings. Immunofluorescence staining and flow cytometry was carried out to determine the binding affinity of the nanobodies. The nanobodies were radiolabeled by coupling 18F-SFB for PET imaging of CRC subcutaneous xenografts and lymph node metastasis (LNM). IRDye800CW (IR800) were conjugated to form NIR probes for NIR imaging in CRC subcutaneous models. Results: CEACAM5 was overexpressed in either human CRC tissues or mLNs. A CEACAM5 targeted nanobody, Nb41 was successfully generated, with excellent in vitro binding properties. Incorporation of albumin binding domain (ABD) did not affect the affinity of Nb41. In vivo imaging showed that both 18F-FB-Nb41 and 18F-FB-Nb41-ABD showed obvious accumulation in the tumor. Due to the longer retention in the blood, 18F-FB-Nb41-ABD enrichment in tumors was significantly delayed but higher compared to 18F-FB-Nb41. Both 18F-FB-Nb41 and 18F-FB-Nb41-ABD showed prominent LNM enrichment. Similarly, the IR800-conjugated nanobodies Nb41-IR800 and Nb41-ABD-IR800 exhibited superior imaging effects in subcutaneous models, while Nb41-ABD-IR800 exhibited higher fluorescence intensity in the tumor accompanied with a remarkedly delay compared to Nb41-IR800. Conclusion: Collectively, we presented the identification and in vivo validation of a CEACAM5-targeted nanobody and a fused nanobody with an ABD, which enabled to the non-invasive visualization of malignancy of CRC using PET imaging and NIR imaging in subcutaneous models as well as LNM models. [ABSTRACT FROM AUTHOR]

Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (∼127 GB) to that of the DNN parameters (∼20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models. [ABSTRACT FROM AUTHOR]

Purpose: Early diagnosis of sepsis-associated encephalopathy (SAE) is essential for the treatment and prognosis of septic patients. Static PET and MRI have shown promise for early diagnosis, while pharmacokinetic parameters from dynamic PET may provide better quantification for SAE. This study aims to compare the performance of dynamic 2-deoxy-2-[18F]fluoro-D-glucose ([18F]F-FDG) PET and multiparametric MRI in early imaging SAE with a view to providing guidance for the early diagnosis of SAE. Procedures: Dynamic [18F]F-FDG-PET/CT scans and multiparametric MRI were performed in SAE mice induced by LPS. Standardized uptake value (SUV) was measured in static scan images and [18F]F-FDG pharmacokinetic parameters were analyzed with two-tissue compartment model and Patlak plot. MRI relative signal intensity (rT1) derived from T1-weighted images (pre and post contrast) and 4 parameters originating from diffusion-weighted data were measured. Results: Both SUV and dephosphorylation rate constant (k4) increased in SAE model as early as 6 h post sepsis induction, while k4 increased with the relative value (SAE/normal) significantly stronger than that of SUV. Moreover, the net influx constant (Ki) showed significant decrease in SAE as early as 6 h compared with normal mice. Increased signal intensity was identified in T1-weighted contrast enhanced images and rT1 value increased at 12 h post induction. Diffusion tensor imaging (DTI) revealed fractional anisotropy (FA) decreased at 12 h and 24 h in external capsule (ec) and declined axial diffusivity (AD) was shown in white matter at 24 h. Conclusions: The dynamic PET (k4) was more sensitive than static PET (SUV) for early diagnosis of SAE and declined Ki was firstly found in murine SAE, which indicated the advantage of dynamic PET/CT in early detection and differential diagnosis of SAE. While MRI has a higher soft tissue resolution than PET/CT and can classify more subtle brain areas, the comprehensive utilization of the two modalities is helpful for managing SAE. [ABSTRACT FROM AUTHOR]

Purpose: Sigma-1 receptor (Sig-1R), a chaperone that resides at the mitochondrion-associated endoplasmic reticulum (ER) membrane, is an ER stress biomarker. It is thought that ER stress plays a critical role in the progression of metabolic-associated fatty liver disease (MAFLD). The aim of this study was to evaluate a positron emission tomography (PET) tracer [18F]F-TZ3108 targeting Sig-1R for MAFLD. Procedures: The mouse model of MAFLD was established by feeding high-fat diet (HFD) for 12 weeks. Dynamic (0–60 min) PET/CT scans were performed after intravenous injection of 2-deoxy-2[18F]fluoro-D-glucose ([18F]-FDG) and [18F]F-TZ3108. Tracer kinetic modeling was performed for quantification of the PET/CT imaging of the liver. Post-PET biodistribution, the liver tissue western blotting (WB), and immunofluorescence (IF) were performed to compare the expression of Sig-1R levels in the organs harvested from both MAFLD and age-matched control mice. Results: The micro PET/CT imaging revealed a significantly decreased uptake of [18F]F-TZ3108 in the livers of the MAFLD group compared to the healthy controls, while the uptake of [18F]-FDG in the livers was not significantly different between the two groups. Based on the tracer kinetic modeling, the binding disassociate rate (k4) for [18F]F-TZ3108 was significantly increased in MAFLD group compared to healthy controls. The volume distribution (VT), and the non-displacement binding potential (BPND) revealed significantly decrease in MAFLD compared to healthy controls respectively. The post-PET biodistribution (%ID/g) of [18F]F-TZ3108 in the livers of MAFLD mice was significantly reduced nearly twofold than that in the livers of control mice. WB and IF experiments further confirmed the reduction of Sig-1R expression in the MAFLD group. Conclusions: The expression of Sig-1R in the liver, measured by the PET tracer, [18F]F-TZ3108, was significantly decreased in mouse model of MAFLD. The [18F]F-TZ3108 PET/CT imaging may provide a novel means of visualization for ER stress in MAFLD or other diseases in vivo. [ABSTRACT FROM AUTHOR]

Metamaterials with artificial optical properties have attracted significant research interest. In particular, artificial magnetic resonances with non-unity permeability tensor at optical frequencies in metamaterials have been reported. However, only non-unity diagonal elements of the permeability tensor have been demonstrated to date. A gyromagnetic permeability tensor with non-zero off-diagonal elements has not been observed at the optical frequencies. Here we report the observation of gyromagnetic properties in the near-infrared wavelength range in a magneto-plasmonic metamaterial. The non-zero off-diagonal permeability tensor element causes the transverse magneto-optical Kerr effect under s-polarized incidence that otherwise vanishes if the permeability tensor is not gyromagnetic. By retrieving the permeability tensor elements from reflection, transmission, and transverse magneto-optical Kerr effect spectra, we show that the effective off-diagonal permeability tensor elements reach 10−3 level at the resonance wavelength (~900 nm) of the split-ring resonators, which is at least two orders of magnitude higher than magneto-optical materials at the same wavelength. The artificial gyromagnetic permeability is attributed to the change in the local electric field direction modulated by the split-ring resonators. Our study demonstrates the possibility of engineering the permeability and permittivity tensors in metamaterials at arbitrary frequencies, thereby promising a variety of applications of next-generation nonreciprocal photonic devices, magneto-plasmonic sensors, and active metamaterials. Optical gyromagnetic properties are not observed in natural or metamaterials to date. Here, the authors experimentally demonstrated optical gyromagnetic properties in a magneto-plasmonic metamaterial, realizing the long-sought bi-gyrotropic medium. [ABSTRACT FROM AUTHOR]

In this paper, we study the hybrid dual attack over learning with errors (LWE) problems for any secret distribution. Prior to our work, hybrid attacks are only considered for sparse and/or small secrets. A new and interesting result from our analysis shows that for most cryptographic use cases a hybrid dual attack outperforms a standalone dual attack, regardless of the secret distribution. We formulate our results into a framework of predicting the performance of the hybrid dual attacks. We also present a few tricks that further improve our attack. To illustrate the effectiveness of our result, we re-evaluate the security of all LWE related proposals in round 3 of NIST's post-quantum cryptography process, and improve the state-of-the-art cryptanalysis results by 2-15 bits, under the BKZ-core-SVP model. [ABSTRACT FROM AUTHOR]

As the key link connecting the earth's spheres, continental weathering plays an important role in regulating the global biogeochemical cycle and long-term climate change. Siliciclastic sediments derived from large river basins can record continental weathering and erosion signals, and are thus widely used to investigate weathering processes. However, sediment grain size, hydrodynamic sorting and sedimentary recycling complicate the interpretation of sediment weathering proxies. This study presents elemental and lithium isotope compositions of estuarine surface sediments (SS) and suspended particulate matters (SPM) collected from the Changjiang (Yangtze River) Estuary. Based on a simple mass balance model, the proportions of different end-members (i.e., igneous rocks, modern weathering products and inherited weathering products) in sediments were quantitatively calculated and thus the silicate weathering process can be estimated. Overall, the sediments in the Changjiang Estuary are mainly eroded from un-weathered rock fragments (>60%), while modern weathering products account for less than 40%. The fine-grained SPM contain more shale components (52–66%), and the modern weathering products account for 21–40%. Comparatively, the coarse-grained surface sediments contain more un-weathered igneous rock fragments (63–84%) and less modern weathering products (only 4–18%). The comparison of δ7Li values with the weathering proxy (Chemical Index of Alteration, CIA) suggests that sediment weathering intensity declines with increasing proportion of un-weathered igneous rock fragments. Additionally, the occurrence of inherited weathering products (i.e., shale) in modern sediments makes it a challenge to simply use CIA and δ7Li as indicators of weathering intensity. This study confirms that fine-grained particles are more suitable for tracing contemporary weathering process, albeit with the influence of sedimentary recycling. Lithium isotopes combining with the mass balance model can quantitatively constrain the continental weathering processes in large river basins. [ABSTRACT FROM AUTHOR]

Ankle–foot orthoses (AFOs) are devices prescribed to improve mobility in people with neuromuscular disorders. Traditionally, AFOs are manually fabricated by an orthotist based on a plaster impression of the lower leg which is modified to correct for impairments. This study aimed to digitally analyse this manual modification process, an important first step in understanding the craftsmanship of AFO fabrication to inform the digital workflows (i.e. 3D scanning and 3D printing), as viable alternatives for AFO fabrication. Pre- and post-modified lower limb plaster casts of 50 children aged 1–18 years from a single orthotist were 3D scanned and registered. The Euclidean distance between the pre- and post-modified plaster casts was calculated, and relationships with participant characteristics (age, height, AFO type, and diagnosis) were analysed. Modification maps demonstrated that participant-specific modifications were combined with universally applied modifications on the cast's anterior and plantar surfaces. Positive differences (additions) ranged 2.12–3.81 mm, negative differences (subtractions) ranged 0.76–3.60 mm, with mean differences ranging from 1.37 to 3.12 mm. Height had a medium effect on plaster additions (rs = 0.35). We quantified the manual plaster modification process and demonstrated a reliable method to map and compare pre- and post-modified casts used to fabricate children's AFOs. [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]