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Designing a high‐performance cathode is essential for the development of proton‐conducting solid oxide fuel cells (H‐SOFCs), and nanocomposite cathodes have proven to be an effective means of achieving this. However, the mechanism behind the nanocomposite cathodes' remarkable performance remains unknown. Doping the Co element into BaZrO3 can result in the development of BaCoO3 and BaZr0.7Co0.3O3 nanocomposites when the doping concentration exceeds 30%, according to the present study. The construction of the BaCoO3/BaZr0.7Co0.3O3 interface is essential for the enhancement of the cathode catalytic activity, as demonstrated by thin‐film studies using pulsed laser deposition to simulate the interface of the BCO and BZCO individual particles and first‐principles calculations to predict the oxygen reduction reaction steps. Eventually, the H‐SOFC with a BaZr0.4Co0.6O3 cathode produces a record‐breaking power density of 2253 mW cm−2 at 700°C. [ABSTRACT FROM AUTHOR]

In this study, we present a new forward polarimetric radar operator called the Accurate and Efficient Radar Operator designed by ZheJiang University (ZJU-AERO). This operator was designed to interface with the numerical weather prediction (NWP) model of the global forecast system/regional mesoscale model of the China Meteorological Administration (CMA-GFS/MESO). The main objective of developing this observation operator was to assimilate observations from the precipitation measurement radar (PMR). It is also capable of simulating the ground-based radar's polarimetric radar variables, excluding the Doppler variables such as radial velocity and spectrum width. To calculate the hydrometeor optical properties of ZJU-AERO, we utilize the invariant-imbedding T-matrix (IITM) method, which can handle non-spherical and inhomogeneous hydrometeor particles in the atmosphere. The optical database of ZJU-AERO was designed with a multi-layered architecture to ensure the flexibility in hydrometeor morphology and orientation specifications while maintaining operational efficiency. Specifically, three levels of databases are created that store the single-scattering properties for different shapes at discrete sizes for various fixed orientations, integrated single-scattering properties over shapes and orientations, and bulk-scattering properties incorporating the size average, respectively. In this work, we elaborate on the design concepts, physical basis, and hydrometeor specifications of ZJU-AERO. Additionally, we present a case study demonstrating the application of ZJU-AERO in simulating the radar observations of Typhoon Haishen. [ABSTRACT FROM AUTHOR]

Background: The efficacy of optimal medical therapy (OMT) with or without revascularization therapy in patients with stable coronary artery disease (SCAD) remains controversial. We performed a meta-analysis of randomized controlled trials (RCTs) that compared OMT with or without revascularization therapy for SCAD patients. Methods: Studies were searched in PubMed, EMBASE, and the Cochrane Central Register of Clinical Trials from January 1, 2005, to December 30, 2023. The main efficacy outcome was a composite of all-cause death, myocadiac infarction, revascularization, and cerebrovascular accident. Results were pooled using random effects model and fixed effects model and are presented as odd ratios (ORs) with 95% confidence intervals (CI). Results: Ten studies involving 12,790 participants were included. The arm of OMT with revascularization compared with OMT alone was associated with decreased risks for MACCE (OR 0.55 [95% CI 0.38–0.80], I²=93%, P = 0.002), CV death (OR 0.84 [95% CI 0.73–0.97], I²=36%, P = 0.02), revascularization (OR 0.32 [95% CI 0.20–0.50], I²=92%, P < 0.001), and MI (OR 0.85 [95% CI 0.76–0.96], I²=45%, P = 0.007). While there was no significant difference between OMT with revascularization and OMT alone in the odds of all-cause death (OR 0.94 [95% CI 0.84–1.05], I²=0%, P = 0.30). Conclusions: The current updated meta-analysis of 10 RCTs shows that in patients with SCAD, OMT with revascularization would reduce the risk for MACCE, cardiovascular death, and MI. However, the invasive strategy does not decrease the risks for all-cause mortality when comparing with OMT alone. [ABSTRACT FROM AUTHOR]

Novel magneto‐optical (MO) properties can be actualized through well‐designed nanostructures, greatly improving the application value of MO effects. As only p‐polarized light can generate transverse magneto‐optical Kerr effect (TMOKE) in traditional MO materials, it is quite challenging and valuable to effectuate TMOKE under s‐polarization incidence at optical frequencies. The constructed nanopillar structures in this work can realize a clear TMOKE under both s‐polarization and p‐polarization incidences, as proved by experiments and simulations. Under s‐polarized incident light with an elevation angle of 45°, electric dipole resonance (EDR) and electric quadrupole resonance (EQR) are excited, resulting in the significant enhancement of TMOKE (five orders of magnitude higher than for the planar Co film). The intensity of s‐polarized TMOKE shows an increase with the enhancing diameter and height of the nanopillar structure. The amplitude of p‐polarized TMOKE is also appreciably increased due to the excitation of surface plasmon polaritons (SPPs) and localized surface plasmons (LSPs). Moreover, the Fe and Ni nanopillars display similar reflectivity and s‐polarized TMOKE behaviors in simulations. The s‐polarized TMOKE is unexpectedly observed in ferromagnetic metals for the first time, which builds up a considerable platform for the development and application of novel MO phenomena. [ABSTRACT FROM AUTHOR]

It has been a big challenge to distinguish synchronous multiple primary lung cancer (sMPLC) from primary lung cancer with intrapulmonary metastases (IPM). We aimed to assess the clinical application of dynamic 18F-FDG PET/CT in patients with multiple lung cancer nodules. We enrolled patients with multiple pulmonary nodules who had undergone dynamic 18F-FDG PET/CT and divided them into sMPLC and IPM groups based on comprehensive features. The SUVmax, fitted K i value based on dynamic scanning, and corresponding maximum diameter ( D max ) from the two largest tumors were determined in each patient. We determined the absolute between-tumor difference of SU V max / D max and K i / D max (Δ SU V max / D max ; Δ K i / D max ) and assessed the between-group differences. Further, the diagnostic accuracy was evaluated by ROC analysis and the correlation between Δ SU V max / D max and Δ K i / D max from all groups was determined. There was no significant difference for Δ SU V max / D max between the IPM and sMPLC groups, while the IPM group had a significantly higher Δ K i / D max than the sMPLC group. The AUC of Δ K i / D max for differentiating sMPLC from IPM was 0.80 (cut-off value of K i = 0.0059 , sensitivity 79%, specificity 75%, p < 0.001). There was a good correlation (Pearson r = 0.91 , 95% CI: 0.79-0.96, p < 0.0001) between Δ SU V max / D max and Δ K i / D max in the IPM group but not in the sMPLC group (Pearson r = 0.45 , p > 0.05). Dynamic 18F-FDG PET/CT could be a useful tool for distinguishing sMPLC from IPM. K i calculation based on Patlak graphic analysis could be more sensitive than SUVmax in discriminating IPM from sMPLC in patients with multiple lung cancer nodules. [ABSTRACT FROM AUTHOR]

The AI-based Nonspherical Aerosol Optical Scheme (AI-NAOS) is a newly developed aerosol optical module that improves the representation of aerosol optical properties for radiative transfer simulations in atmospheric models. It incorporates the nonsphericity and inhomogeneity (NSIH) of internally mixed aerosol particles through a deep learning method. Specifically, the AI-NAOS considers black carbon (BC) as fractal aggregates and models soil dust (SD) as super-spheroids, encapsulated partially or completely with hygroscopic aerosols such as sulfate, nitrate, and aerosol water. To obtain AI-NAOS, a database of the optical properties for the models was constructed using the invariant imbedding T-matrix method (IITM), and deep neural networks (DNN) were trained based on this database. In this study, the AI-NAOS was integrated into the mesoscale version 5.1 of Global/Regional Assimilation and Prediction System with Chinese Unified Atmospheric Chemistry Environment (GRAPES_Meso5.1/CUACE). Real-case simulations were conducted during a winter with high pollution, comparing BC aerosols evaluated using three schemes with spherical aerosol models (external-mixing, core-shell, and volume-mixing) and the AI-NAOS scheme. The results showed that NSIH effect led to a moderate estimation of absorbing aerosol optical depth (AAOD) and obvious changes in aerosol radiative effects, short-wave heating rates, temperature profiles, and boundary layer height. The AAOD values based on three spherical schemes were 70.4 %, 125.3 %, and 129.3 % over Sichuan Basin, benchmarked to the AI-NAOS results. Compared to the external-mixing scheme, the direct radiative effect (DRE) induced by the NSIH effect reached +1.6 W/m2 at the top-of-atmosphere (TOA) and -2.9 W/m2 at surface. The NSIH effect could enhance the short-wave heating rate, reaching 20 %. Thus, the warming effect at 700 hPa and the cooling effect on the ground were strengthened by 21 % and 13 %, reaching +0.04 and –0.10 K, which led to a reduction in the height of the Planetary Boundary Layer (PBL) by –11 meters. In addition, the precipitation was inhibited by the NSIH effect, causing a 15 % further decrease. Therefore, the NSIH effects demonstrated their non-negligible impacts and highlighted the importance of incorporating them into chemical weather models. [ABSTRACT FROM AUTHOR]

Mineral dust particles are nonspherical and inhomogeneous; however, they are often simplified as homogeneous spherical particles for retrieving the refractive indices from laboratory measurements of scattering and absorption coefficients. The retrieved refractive indices are then employed for computing the optical properties of spherical or nonspherical dust model particles with downstream applications. This study aims to theoretically investigate uncertainties involved in the aforementioned rationale based on numerical simulations and focuses on a wavelength range of 355–1064 nm. Initially, the optical properties of nonspherical and inhomogeneous dust aerosols are computed as baseline cases. Subsequently, the scattering and absorption coefficients of homogeneous spheres and super-spheroids are computed at various refractive indices and compared with those of inhomogeneous dust aerosols to determine the dust refractive index. To mimic the real laboratory measurement, the size distribution of the baseline case is assumed to be unknown and determined through a process akin to using optical particle counters for sizing. The resulting size distribution differs from the original one of the baseline cases. The impact of discrepancies in size distributions on retrieving the dust refractive index is also investigated. Our findings reveal that these discrepancies affect scattering and absorption coefficients, presenting challenges in accurately determining the refractive index, particularly for the real parts. Additionally, the retrieved refractive indices are noted to vary with particle size primarily due to differences in size distribution, with imaginary parts decreasing as the particle size increases. A comparison between sphere models and super-spheroid models shows that the former tend to underestimate the imaginary parts, leading to an overestimation of single-scattering albedo. This study underscores the importance of employing consistent nonspherical models for both refractive index retrieval and subsequent optical simulation in downstream applications. Nevertheless, the impact of refractive index uncertainties on the asymmetry factor and phase matrix is found to be minimal, with particle shape playing a more significant role than differences in the imaginary parts of the dust refractive index. [ABSTRACT FROM AUTHOR]

Due to the relentless exploitation of nonrenewable resources, humanity is faced with a resource depletion crisis in the coming decades and serious environmental issues. Achieving efficient removal and upcycling of pollutants (ERUP) may become a potential strategy to address these issues. Wastewater, characterized by its large production volume and fluidity, can easily cause widespread environmental pollution through natural water networks. Due to solubility constraints, pollutants in wastewater typically exhibit low concentrations and complex compositions, thereby impeding effective recovery. Therefore, achieving ERUP in wastewater is both highly significant and extremely challenging. Unlike conventional wastewater treatment strategies that are focused on removing pollutants, ERUP strategies can not only realize the efficient removal of pollutants from water but also convert pollutants into valuable and functional products. Herein, we enumerated the latest research progress on ERUP in wastewater and highlighted studies that demonstrate the simultaneous achievement of pollutant removal and the direct conversion of these contaminants into high-efficiency catalysts, hydrogen energy, electrical energy, and other high-value chemicals. Finally, we identified the problems and challenges in the development of ERUP in wastewater and outlined potential research directions for future studies. [ABSTRACT FROM AUTHOR]

Myopia is a serious threat to eye health and can even cause blindness. It is important to grade myopia and carry out targeted intervention. Nowadays, various studies using deep learning models based on optical coherence tomography (OCT) images to screen for high myopia. However, since regions of interest (ROIs) of pre-myopia and low myopia on OCT images are relatively small, it is rather difficult to use OCT images to conduct detailed myopia grading. There are few studies using OCT images for more detailed myopia grading. To address these problems, we propose a novel attention-based multi-scale feature fusion network named AMFF for myopia grading using OCT images. The proposed AMFF mainly consists of five modules: a pre-trained vision transformer (ViT) module, a multi-scale convolutional module, an attention feature fusion module, an Avg-TopK pooling module and a fully connected (FC) classifier. Firstly, unsupervised pre-training of ViT on the training set can better extract feature maps. Secondly, multi-scale convolutional layers further extract multi-scale feature maps to obtain more receptive fields and extract scale-invariant features. Thirdly, feature maps of different scales are fused through channel attention and spatial attention to further obtain more meaningful features. Lastly, the most prominent features are obtained by the weighted average of the highest activation values of each channel, and then they are used to classify myopia through a fully connected layer. Extensive experiments show that our proposed model has the superior performance compared with other state-of-the-art myopia grading models. [ABSTRACT FROM AUTHOR]

As a benchmark triple‐conducting cathode, BaCo0.4Fe0.4Zr0.1Y0.1O3−δ (BCFZY) has been widely investigated for protonic ceramic fuel cells (PCFC) in recent years. However, the reported electrochemical performance of BCFZY cathode differs, which is determined in this work to originate from the thermal expansion mismatch between BCFZY and electrolyte. Accordingly, two strategies for enhanced thermo‐mechanical compatibility are examined: impregnation and thermal expansion offset. In contrast to the impregnation of BCFZY nanoparticles on electrolyte backbones that only helps improve electrochemical performance, negative thermal expansion oxide Sm0.85Cu0.15MnO3−δ (SCM)‐offset BCFZY exhibits superior durability and activity simultaneously. Specifically, the polarization resistance decay rate of the SCM‐offset BCFZY is only ~0.2%/100 h, compared with ~18.75%/100 h for "impregnated BCFZY." Moreover, pure SCM generates moderate cathodic performance (area‐specific resistance = 0.11 Ω cm2, 700 °C), X‐ray diffraction and transmission electron microscopy reveal an in‐situ formed intergranular Ba2Cu3SmO7−δ phase at the boundaries of BCFZY and SCM. Thus, SCM can serve as a "three‐effect" additive, i) offset thermo‐expansion, ii) strengthen electrode structure and adhesion, and iii) provide acceptable oxygen‐reduction‐reaction activity, being favorable for superior performance. A PCFC using a SCM‐offset BCFZY cathode achieves the highest power density (1455 mW cm−2) yet recorded for PCFCs with BCFZY‐based cathodes. [ABSTRACT FROM AUTHOR]

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]

Polarization imaging enables the capture of material properties and surface characteristics of objects, finding applications in remote sensing and defense. However, most existing polarization imaging systems rely on polarization filtering elements, resulting in energy loss exceeding 50% and compromising intensity imaging performance. Conventionally, a rotary device equipped with different polarizers and a blank area is used to acquire both polarization and intensity images, which is cumbersome for integration and miniaturization purposes. In this study, a miniaturized and easily integrated active metasurface based on VO2 grating structure for switchable detection of intensity and polarization in the far‐infrared band (8–14 µm) is proposed. With VO2 in the dielectric state, the device exhibits high transmittance for both TE‐ and TM‐polarized light (Experimental transmittance of up to 65%). Upon phase transition to the metallic state, the device demonstrates low transmittance for TE‐polarized light while maintaining high transmittance for TM‐polarized light (linear polarization extinction ratio up to 10 dB). Finally, the tunable thermal intensity and polarization imaging capabilities of the custom‐made setup are characterized. The metasurface showcases comparable performance to standard commercial polarizers in terms of polarization imaging. The developed tunable far‐infrared polarization imaging system holds promise for next‐generation multidimensional information perception. [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]

Background: Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine‐learning methodology to characterize and validate enhancements applied to the grey‐level co‐occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO. Methods: One hundred patients diagnosed with age‐related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high‐resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland–Altman plot, while agreement between them was assessed through the Bland–Altman plot. Results: Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05). Conclusions: Overall, our findings suggest that a machine‐learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications. [ABSTRACT FROM AUTHOR]

Background: Heavy metal pollution has emerged as a significant concern for human health, prompting increased awareness of its potential adverse effects. While previous research has established a connection between heavy metals and liver function biomarkers, the specific relationship between heavy metals and HBV infection remains unexplored. This cross-sectional study aims to investigate the potential correlations between five blood heavy metals - lead, cadmium, mercury, manganese, and selenium - and the presence of HBsAg, HBsAb, and HBcAb in adults. Methods: The study utilized data from NHANES 2007–2018. Participants were classified into four groups based on their infectious status, and the association between heavy metals and HBV infection was analyzed using multiple logistic regression and stratification analysis. Results: A total of 8431 participants were included, with 5 436 classified as Susceptible, 1 765 as Vaccinated, 865 as Natural Infection, and 103 as Acute/Chronic HBV Infection. The Vaccinated group exhibited a lower mean age (34.52 ± 14.16 years) compared to the other groups. Statistically significant differences in heavy metal concentrations (except selenium) were observed among the groups (P < 0.001). After adjusting for covariates, lead was significantly associated with HBV infection (Q2: OR 2.37, 95%CI 1.04–5.39; Q3: OR 2.34, 95%CI 1.01–5.40), and positive trends were observed for high blood concentrations of mercury (Q4: OR 3.03, 95%CI 1.31–7.04) and manganese (Q4: OR 2.52, 95%CI 1.20–5.28). Furtherly, the presence of lead reduced the protection of HBsAb (Q2: OR 0.84, 95%CI 0.73–0.97; Q3: OR 0.77, 95%CI 0.66–0.90; Q4: OR 0.83, 95%CI 0.70–0.98). Subgroup analysis indicated that cadmium was associated with an increased risk of HBV infection in Asians (OR 1.36, 95%CI 1.03–1.78) and individuals with a BMI range of 25 to 30 (OR 1.60, 95%CI 1.17–2.18). Conclusions: The study's findings suggest a correlation between elevated blood Pb concentrations and reduced immunization rates against hepatitis B. Individuals with a positive HBsAg exhibit lower blood Se concentrations and higher blood Hg and Mn concentrations. [ABSTRACT FROM AUTHOR]

Mass spectrometry (MS) has become an essential analytical method in every sector of science and technology. Because of its unique ability to provide direct molecular structure information on analytes, an extra method is rarely required. This review describes fabrication of a variable-pressure hollow cathode discharge (HCD) ion source for MS in detection, quantification and investigation of gas-phase ion molecule reactions of explosives and related compounds using air as a carrier gas. The HCD ion source has been designed in such a way that by altering the ion source pressures, the system can generate both HCD and conventional GD. This design enables for the selective detection and quantification of explosives at trace to ultra-trace levels. The pressure-dependent HCD ion source has also been used to investigate ion-molecule reactions in the gas phase of explosives and related compounds. The mechanism of ion formation in explosive reactions is also discussed. [ABSTRACT FROM AUTHOR]

Efficient and accurate computation of the single‐scattering properties of black carbon (BC) aerosols is fundamental in various fields, including remote sensing and climate simulations. In this study, we developed a composite model of fractal aggregates of BC encapsulated with hygroscopic aerosols to represent the ambient BC. We used the invariant imbedding T‐matrix method to compute the optical properties of fully and partially encapsulated BC aerosols. In this new model, the traditional assumption of unoverlapped surfaces in the super‐position T‐matrix method is unnecessary. After extensive simulations, we established a database of single‐scattering properties, including the extinction efficiency, the single‐scattering albedo, the asymmetry factor and six phase matrix elements. Moreover, we obtained deep neural networks (DNNs) from this database using a deep learning method. These DNN models provide a universal interface for predicting the optical properties of ambient BC aerosols. Specifically, through a modified architecture of the DNN, we trained two models based on the database to predict three integrated optical properties (extinction efficiency, single‐scattering albedo, and asymmetry factor) and six phase matrix elements. We performed statistical assessments based on the true values in the database and the predicted values from the DNNs, demonstrating that the DNNs accurately predicted all single‐scattering properties. Therefore, the developed DNN models can be conveniently implemented in aerosol optical parameterization for remote sensing studies and atmospheric models. Plain Language Summary: Black carbon (BC) aerosol plays a significant role in the atmosphere, influencing climate through its interactions with radiation, such as scattering and absorbing. The accurate determination of the single‐scattering properties of BC, including the extinction efficiency, the single‐scattering albedo, the asymmetry factor and six phase matrix elements, is crucial for understanding its radiation effects. In this study, we developed a comprehensive model for BC particles that incorporates complex morphological characteristics and the mixing of BC with other hygroscopic aerosols. We established a database of single‐scattering properties for these models using the invariant imbedding T‐matrix method. However, the large storage space required by this database (over 20 GB) makes it impractical for widespread applications. Therefore, we employed a deep learning method to compress the storage consumption. Two deep neural networks (DNNs) were trained using the database, enabling the prediction of all single‐scattering properties. Remarkably, the performance of the DNNs was excellent, with a coefficient of determination greater than 0.99 for all single‐scattering properties, as determined through statistical assessment. These DNN models hold great potential for various atmospheric radiation and remote sensing studies in the future. Key Points: A flexible model of partially and fully encapsulated fractal black carbon particles was developedA database of single‐scattering properties of encapsulated fractal black carbon particles was constructedTwo deep neural networks were obtained, offering a flexible approach to calculating the optical properties of black carbon aerosols [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]

Active thermal radiation control of an object requires adaptive and flexible strategies to cope with emissivity engineering and its temperature dependence. On the other hand, the concept of digital coding allows a high degree of freedom in manipulating the interacting physical quantities based on local discretized features. Learning from this idea, this study put forward the manipulation of spatial infrared emission based on tungsten (W)‐doped VO2 films toward thermal coding. Here, 1‐bit coding worksheets created by low‐emissivity, that is, code '0′, and high‐emissivity, that is, code '1′, shift with materials metal‐insulator phase transition to encode emissive information in the temperature domain. It is shown experimentally that this concept has marked implications on thermal radiation control at Mid‐infrared, among which are as follows: digital anti‐counterfeiting by in‐plane spatial coding of a group of VO2 thin films with various W‐doping density and infrared camouflage by out‐of‐plane spatial coding of them. With a W‐doping gradient of 0%, 0.8%, and 1.7%, the three‐phase transition points in the temperature domain, that is, 72, 63, and 45 °C, give a remarkable modulation range of 27 °C. These phenomena pave the way for new classes of emissivity engineering for more accurate, flexible, and adaptive thermal radiation control. [ABSTRACT FROM AUTHOR]

In the current operational four‐dimensional variational (4DVar) data assimilation (DA) system of the Global Forecast System developed by the China Meteorology Administration (CMA‐GFS), all microwave observation data in cloud and precipitation regions are discarded during pre‐processing. This study implemented a Pseudo All‐Sky DA (referred to as PAS‐DA hereafter) subsystem in the operational cycle version of the CMA‐GFS (CMA‐GFS v3.2). The term "pseudo" in this study indicates that the Jacobians of brightness temperature with respect to hydrometeors from the adjoint radiative transfer model were temporarily neglected. Specifically, a liquid hydrometeor sensitive channel (23.8 GHz V pol.) of the MicroWave Radiation Imager on the platform of FengYun‐3D (FY3D‐MWRI) was selected to assess the impact of the all‐sky assimilation approach using the PAS‐DA subsystem. In the observation error model, we proposed a new cumulative distribution function (CDF) bias correction method for the cloud proxy in consideration of large discrepancies between the probability density functions (PDFs) of the observed‐cloud‐proxy and simulated‐cloud‐proxy (known as "cloud bias"). Results of single‐observation experiments justified that the present PAS‐DA subsystem could extend analysis increments to cloud regions, meanwhile correcting the errors of humidity analysis according to the mislocation of cloud distributions. In addition, the forecast experiments that were run for 1 month of the 6 hr PAS‐DA cycle in July and August 2021 demonstrate obvious superiority of the PAS‐DA over the current operational clear‐sky DA cycle: (a) root‐mean‐square errors (RMSEs) of humidity analysis were reduced by about 10% in the tropics, (b) significant improvements in humidity forecasts could be sustained for 96 hr and (c) many other forecast scores in the tropics and Southern Hemisphere also benefit from the PAS‐DA approach. Therefore, the PAS‐DA could be used for all‐sky assimilation studies and particularly for understanding how the all‐sky assimilation approach works, although the PAS‐DA serves as a transition scheme from the clear‐sky to "true" all‐sky DA. [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 development of proton, oxygen‐ion, and electron mixed conducting materials, known as triple‐conduction materials, as cathodes for proton‐conducting solid oxide fuel cells (H‐SOFCs) is highly desired because they can increase fuel cell performance by extending the reaction active area. Although oxygen‐ion and electron conductions can be measured directly, proton conduction in these oxides is usually estimated indirectly. Because of the instability of cathode materials in a reducing environment, direct measurement of proton conduction in cathode oxide is difficult. The La0.8Sr0.2Sc0.5Fe0.5O3–δ (LSSF) cathode material is proposed for H‐SOFCs in this study, which can survive in an H2‐containing atmosphere, allowing measurement of proton conduction in LSSF by hydrogen permeation technology. Furthermore, LSSF is discovered to be a unique proton and electron mixed‐conductive material with limited oxygen diffusion capability that is specifically designed for H‐SOFCs. The LSSF is an appealing cathode choice for H‐SOFCs due to its outstanding CO2 tolerance and matched thermal expansion coefficient, producing a record‐high performance of 2032 mW cm−2 at 700°C and good long‐term stability under operational conditions. The current study reveals that a new type of proton–electron mixed conducting cathode can provide promising performance for H‐SOFCs, opening the way for developing high‐performance cathodes. [ABSTRACT FROM AUTHOR]

Mineral dust particles are typically nonspherical and inhomogeneous; however, they are often simplified as homogeneous spherical particles for retrieving the refractive indices from laboratory measurements of scattering and absorption coefficients. This study theoretically investigated uncertainties in refractive indices and corresponding optical properties resulting from this simplification at various sizes within the wavelength range of 355 to 1064 nm. Different numerical experiments were conducted under both ideal and realistic scenarios, taking into account instrumental bias in the realistic scenarios. In the numerical experiments, the inhomogeneous super-spheroid models were considered as the dust samples, while the homogeneous super-spheroid models and sphere models were used to retrieve the refractive indices. Under the ideal scenario, the look-up tables for the homogeneous super-spheroid models satisfactorily covered the measurements at any size and wavelength, while those for the sphere models failed when considering large sizes. Under the realistic scenario, both the homogeneous super-spheroid models and sphere models were ineffective for large sizes due to discrepancies in size distribution resulting from the measurements using an optical particle counter. Nevertheless, it was possible to retrieve the imaginary parts of the refractive indices based solely on the absorption coefficients. The imaginary parts obtained from the sphere models were generally consistent with those from the super-spheroid models under ideal conditions, while the former was significantly smaller than the latter under the realistic conditions. In addition, the retrieved imaginary parts were found to be size-dependent, which could be attributed to the inherent limitations of homogeneous models in characterizing inhomogeneous particles. Results showed that the uncertainties in the imaginary part and single scattering albedo should be smaller than 0.002 (0.0007) and 0.03 (0.01), respectively, under conditions of high (low) absorption. The sphere models tended to overestimate the asymmetry factor. The uncertainty in the asymmetry factor exhibited a significant variation, reaching up to 0.04 or even larger. Nonetheless, the uncertainties in the phase matrices resulting from the uncertainties in refractive indices were generally acceptable within a specific model. [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]

Solid oxide electrolysis cells (SOECs) have captured worldwide attention as a promising energy conversion device, but durability at large current densities is still an issue. Here, the authors present an encouraging stable anode design for electrolysis at high current densities, in which a nano La0.6Sr0.4CoO3−δ (LSC) catalyst network is integrated with robust microchannels of the yttria-stabilized zirconia scaffold. Such a controlled structured electrode not only allows rapid gas diffusion, but also provides sufficient active sites as well as firm anode–electrolyte interface adhesion for achieving highly stable and efficient electrolysis. These unique advantages lead to an extremely low polarization resistance of 0.010 Ω cm2 and high durability for over 100 h at a large current density of 3.4 A cm−2 at 800 °C. The outstanding performance demonstrates that the as-prepared cell structure may be generally applicable in the design of high-efficiency energy conversion systems. [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]

A low loss optical interconnection between optical fibers and photonic integrated circuits is critical for high performance photonic systems. In the past decade, spot size converters, subwavelength waveguide grating (SWG) structures, and different refractive index materials have been applied to allow efficient coupling between the fiber and the photonic chips. However, it is still challenging to achieve low-loss coupling when interfacing high index contrast waveguides such as SiN with SMF-28 fibers. In this work, we report a multilayer edge-coupler using SiOxN materials with different indices to allow for efficient edge coupling between SMF-28 fiber and SiN single mode waveguides. A coupling loss of 0.068 dB for the TM mode was achieved theoretically at a 1550 nm wavelength, with a 1 dB alignment tolerance offset of 2.4 μm. [ABSTRACT FROM AUTHOR]

The photonic spin Hall effect (PSHE) is analogous to the electronic spin Hall effect, which has substantial potential for optoelectronic applications. However, PSHE-based devices are rarely studied, and manipulating the PSHE by charge current has remained elusive thus far. In this paper, we demonstrate current-induced switching of PSHE in the Ce1Dy2Al0.42Fe4.58O12 thin film capping with Pt electrodes, which is mediated by spin–orbit torques at the Pt/Ce1Dy2Al0.42Fe4.58O12 interface. The results show that the transverse beam shifts related to the PSHE can be reversed by applying a small order of magnitude charge current (∼108 A·m−2) to the Pt layer in opposite directions, which predicts low dissipation in our proposed heterostructure. In addition, by applying an in-plane magnetic field, the saturation beam shifts can be increased, which can significantly enhance the switching ratio. [ABSTRACT FROM AUTHOR]

Constituted by various heterogeneous cells, the tumor microenvironment (TME) is capable of promoting tumor proliferation, invasion, and metastasis through extensive crosstalk. The pivotal factor influencing the survival time of patients and their response to immunotherapy lies in the intratumoral immune environment. We obtained 112 differential genes related to T cell-mediated tumor killing in LUAD by employing bioinformatics analysis on the basis of the TCGA and TISIDB databases. Then the 6-gene prognostic risk score model (CA9, OIP5, TIMP1, SEC11C, FURIN, and TLR10) was constructed by conducting univariate LASSO as well as multivariate Cox regression analyses. The median risk score was taken as the threshold to classify the samples into two groups. Survival analysis revealed that the low-risk group exhibited a more favorable prognosis. Subsequently, the Cox regression analysis combined with clinical information (age, gender, and pathological stage) and the risk score of LUAD patients demonstrated the potential of this model as an independent prognostic factor. The nomogram established based on clinical information and a risk score in combination with the calibration curve indicated that this model had good predictive ability. Notable enrichment of the differential genes from the high- and low-risk groups was discovered in immune-associated processes or pathways, as shown by the GO and KEGG enrichment analyses. The combined use of single-sample gene enrichment analysis (ssGSEA) and immunophenoscore (IPS) demonstrated heightened immune infiltration and IPS scores in the low-risk group, indicating that immunotherapy was likely to show good efficacy in patients from this group. To sum up, the prognostic model of LUAD constructed based on T-cell-mediated cell killing-related genes was not only capable of screening the prognosis of LUAD patients but was also used for screening those LUAD patients with high sensitivity to immunotherapy. Our study offered novel insights into the clinical treatment and prognostic prediction of LUAD patients. [ABSTRACT FROM AUTHOR]

Submergence stress can severely affect plant growth. Orchardgrass (Dactylis glomerata L.) is an important forage grass, and the molecular mechanisms of orchardgrass to submergence stress are not well understood. The roots of the flood-tolerant cultivar "Dian Bei" were harvested at 0 h, 8 h and 24 h of submergence stress. The combined transcriptomic and metabolomic analyses showed that β-alanine metabolism, flavonoid biosynthesis, and biosynthesis of amino acid pathways were significantly enriched at 8 h and 24 h of submergence stress and were more pronounced at 24 h. Most of the flavonoid biosynthesis-related genes were down-regulated for the synthesis of metabolites such as naringenin, apigenin, naringin, neohesperidin, naringenin chalcone, and liquiritigenin in response to submergence stress. Metabolites such as phenylalanine, tyrosine, and tryptophan were up-regulated under stress. The predominant response of flavonoid and amino acids biosynthesis to submergence stress suggests an important role of these pathways in the submergence tolerance of orchardgrass. [ABSTRACT FROM AUTHOR]

Objectives: Heart failure with mildly reduced ejection fraction (HFmrEF) or heart failure with preserved ejection fraction (HFpEF) are associated with significant morbidity and mortality, as well as growing health and economic burden. Sodium-glucose co-transporter 2 (SGLT2) inhibitors are very promising for the outcome improvement of patients with HFpEF or HFmrEF. The meta-analysis was performed to investigate the effects of SGLT2 inhibitors in HFpEF or HFmrEF, by pooling data from all clinically randomized controlled trials (RCTs) available to increase power to testify. Methods: Studies were searched in electronic databases from inception to November, 2022. We performed a meta-analysis to estimate the effect of SGLT2 inhibitors on clinical endpoints in patients with HFpEF or HFmrEF, using trial-level data with consistent endpoint definitions. The primary outcome was the composite of heart failure (HF) hospitalization or cardiovascular death. Hazard ratio (HR) was pooled with 95% confidence interval (CI) for dichotomous data. This study was registered with INPLASY 2022110095. Results: Six studies involving 15,989 participants were included into the final analysis. Pooled analyses revealed that SGLT2 inhibitors significantly reduced the composite of HF hospitalization or cardiovascular death [HR: 0.79 (0.72–0.85); I2 = 0%; P < 0.00001] and HF hospitalizations [HR: 0.74 (0.67–0.82); I2 = 0%; P < 0.00001]. This finding was seen in both HFmrEF trials [HR: 0.76 (0.67–0.87); I2 = 49%; P < 0.0001] and HFpEF subgroup studies [HR: 0.70 (0.53–0.93); I2 = 0%; P = 0.01]. The incidence of any serious adverse events [OR: 0.89 (0.83–0.96); I2 = 0%; P = 0.002] was significantly lower in the SGLT2 inhibitor arm. No significant differences were observed between the two groups with regard to cardiovascular death and all-cause death. Conclusions: This meta-analysis of patients with heart failure of left ventricular ejection fraction (LVEF) > 40% showed that SGLT2 inhibitors significantly reduce the risk of the composite of cardiovascular death and hospitalization for heart failure, but not cardiovascular death and all-cause death. Nevertheless, given that SGLT2 inhibitors may reduce the risk of hospitalization for heart failure, they should be considered the fundamental treatment for all patients with HFpEF or HFmrEF. [ABSTRACT FROM AUTHOR]

Zn0.58Co2.42O4 (ZCO), which is a novel spinel oxide, has been proposed as a cathode material for proton-conducting solid oxide fuel cells (H-SOFCs) to explore the possibility of using spinel oxide cathodes for H-SOFCs. The high oxygen-ion and proton migration kinetics of ZCO, which first-principles calculations and experiments have demonstrated, allow the ZCO cell to show high cell performance without impairing the stability of the material. Further optimizing the cathode/electrolyte interface, by utilizing the relatively low melting point of ZCO, allows a record-high performance to be obtained for the ZCO cell compared with other SOFCs using a spinel oxide cathode. [ABSTRACT FROM AUTHOR]

Noncollinear antiferromagnet Weyl semimetal Mn3Sn has recently attracted great research interest. Although large anomalous Hall effect (AHE), anomalous Nernst effect (ANE), and magneto-optical effect have been observed in Mn3Sn, most studies are based on single crystals. So far, it is still challenging to grow high quality epitaxial Mn3Sn thin films with transport and optical properties comparable to their single crystal counterparts. Here, we report the structure and magneto-optical and transport properties of epitaxial Mn3Sn thin films fabricated by pulsed laser deposition (PLD). Highly oriented Mn3+xSn1−x (0001) and (11 2 ¯ 0) epitaxial films are growth on single crystalline Al2O3 and MgO substrates. Large anomalous Hall effect up to Δ ρ H = 3.02 μ Ω cm and longitudinal magneto-optical Kerr effect with |θK| = 38.1 mdeg at 633 nm wavelength are measured at 300 K, which are comparable to Mn3Sn single crystals. Our work demonstrates that high quality Mn3Sn epitaxial thin films can be fabricated by PLD, paving the way for future device applications. [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]

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The identification of melanoma involves an integrated analysis of skin lesion images acquired using clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic details from clinical images. Visual melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC), which involves identifying specific characteristics of skin lesions. The 7PC contains intrinsic relationships between categories that can aid classification, such as shared features, correlations, and the contributions of categories towards diagnosis. Manual classification is subjective and prone to intra- and interobserver variability. This presents an opportunity for automated methods to aid in diagnostic decision support. Current state-of-the-art methods focus on a single image modality (either clinical or dermoscopy) and ignore information from the other, or do not fully leverage the complementary information from both modalities. Furthermore, there is not a method to exploit the ‘intercategory’ relationships in the 7PC. In this study, we address these issues by proposing a graph-based intercategory and intermodality network (GIIN) with two modules. A graph-based relational module (GRM) leverages intercategorical relations, intermodal relations, and prioritises the visual structure details from dermoscopy by encoding category representations in a graph network. The category embedding learning module (CELM) captures representations that are specialised for each category and support the GRM. We show that our modules are effective at enhancing classification performance using three public datasets (7PC, ISIC 2017, and ISIC 2018), and that our method outperforms state-of-the-art methods at classifying the 7PC categories and diagnosis. [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) is one of the most important basic parameters for studying the oceanographic processes and is of great significance in identifying oceanic currents. However, for a long time, the salinity observation in the estuary and coastal waters has not been well resolved due to the technology limitation. In this study, the SSS inversion models for the Changjiang Estuary and the adjacent sea waters were established based on machine learning methods, using SMAP (Soil Moisture Active and Passive) salinity data combined with the specific bands and bands ratios of MODIS (Moderate Resolution Imaging Spectroradiometer). The performance of the three machine learning methods (Random Forest, Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Automatic Machine Learning (TPOT)) are compared with accuracy verification by the in-situ measured SSS. Random Forest is proven to be effective for the SSS inversion in flood season, whereas TPOP performs the best for the dry season. The machine learning-based models effectively solve the problem of insufficient time span of SSS observation from salinity satellites. At the same time, an empirical algorithm was established for the SSS inversion for the sea areas with low salinity (<30 psu) where the machine learning based model fails with great errors. The average deviation of the complex SSS inversion models is −0.86 psu, validated with Copernicus Global Ocean Reanalysis Data. The long term series SSS dataset of March and August from 2003 to 2020 was then constructed to observe the salinity distribution characteristics of the flood season and the dry season, respectively. It is indicated that the distribution pattern of CDW can be divided into three categories: northeast-oriented expansion pattern, multi direction isotropic expansion pattern, and a turn pattern of which CDW shows changing direction, namely the northeast-southeast expansion pattern. The pattern of CDW expansion is indicated to be the comprehensive effect of the interaction of different currents. In addition, it is noteworthy that CDW shows increasing expansion with decreasing SSS in the front plume, especially in the flood season. This study not only gives a feasible solution for effective SSS observation, but also provides a dataset of basic oceanographic parameters for studying the coastal biogeochemical processes, evolution of land-sea interaction, and changing trend of material and energy transport by the CDW in the west Pacific boundary. [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]

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e.g., local lymph node metastasis and adjacent tissue invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation in advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background information. In addition, we also introduce a cascaded survival network to capture the prognostic information existing out of primary tumors and further leverage the global tumor information (e.g., tumor size, shape, and locations) derived from the segmentation backbone. Our experiments with two clinical datasets demonstrate that our DeepMTS can consistently outperform traditional radiomics-based survival prediction models and existing deep survival models. [ABSTRACT FROM AUTHOR]