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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]

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]

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]

[Display omitted] Metal sulfides are regarded as efficient scavengers for heavy metals. However, the heavy metal adsorption capacity of metal sulfides is far from its theoretical values due to the insufficient exposure of adsorption sites. Surface modification of metal sulfides is considered one of the most effective strategies for improving heavy metal removal performance. Here, microalgae-derived carbon quantum dots (CQDs) were used as a green modifier for mediating nano-MnS/FeS formation to enhance Cd2+ removal. With the addition of 1 wt% CQDs, the Cd2+ adsorption capacity of 1 %CQDs-MnS reached 481 mg/g at 25 °C and 648.6 mg/g at 45 °C, which surpassed most of the previously reported metal sulfides. Furthermore, the CQDs-modified MnS displayed a better Cd2+ removal capacity than the commercial modifier sodium alginate. The mechanism analysis suggested that decreasing the particle size to expose more adsorption sites and providing additional chelating sites derived from the CQDs are two main reasons why CQDs enhance the Cd2+ adsorption capacity of metal sulfides. This study presents an exceptional cadmium nano-adsorbent of 1 %CQDs-MnS and provides a new perspective on the enhancement of heavy metal removal by using CQDs as a promising and universal green modifier that mediates the formation of metal sulfides. [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]

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]

• The 5-HT level was higher in the pulp than in the peel. • The distribution of 5-HT in apple fruits was skewed. • MdT5H5 may play an important role in the synthesis of 5-HT in apple. • 5-HT accumulated gradually during apple fruit development. • The bagging reduced the 5-HT content in apple fruits. Serotonin (5-hydroxytryptamine, 5-HT), a tryptophan derivative, is an important functional component in fruits. Although 5-HT was found in most fruits, the accumulation characteristics of its biosynthesis in apple fruits remain unclear. Here, we conducted a large-scale survey of 148 apple fruits in 5-HT content, and investigated the biosynthesis differences of 5-HT. Our results displayed a broad skewed normal distribution of 5-HT in apple fruits. 5-HT accumulated in all tissues of apple plants, with the highest content in the seeds and the higher content in the pulp than that of the peel. A significant increase of 5-HT at ripening stage was observed in the fruits with high 5-HT content, however, the 5-HT content did not change significantly during fruit development in the fruits with low 5-HT content. The bagging reduced the 5-HT biosynthesis compared to the fruits exposed to light conditions. In addition, the expression level of MdT5H5 was positively correlated with the 5-HT content, suggesting that MdT5H5 may play a key regulatory role in the 5-HT biosynthesis in apple fruits. [ABSTRACT FROM AUTHOR]

Background: Despite deeper understanding of the genetic landscape of acute myeloid leukemia (AML), the improvement of survival is still a great challenge. STK10 is overexpressed in several cancers with functions varying according to cancer types. But the functions of STK10 in AML has never been reported. Methods: We analyzed the expression, prognosis and potential functions of STK10 utilizing public web servers. Metascape and the String database were used for functional and protein–protein interaction analyses. Results: We found STK10 was enriched in blood & immune cells and overexpressed in AML. High STK10 expression was associated with poor overall survival, which was also identified in the subgroups of patients ≤ 60 years old and patients with non-high-risk cytogenetics. We demonstrated genes associated with STK10 were enriched in blood, spleen and bone marrow, influencing the immune function and biological process of AML. ITGB2 and ITGAM might directly interact with STK10 and were associated with poor prognosis. Besides, STK10 was associated with the infiltration of immune cells and immune checkpoints, like HLA-E, CD274 and GAL-9. Conclusions: The present study was the original description of STK10 in AML and set the stage for developing STK10 as a new prognostic marker or therapeutic target for AML. [ABSTRACT FROM AUTHOR]

• Machine learning excelled at extrapolating optical properties of non-spherical particles with larger size parameters. • Deep Neural Networks (DNN) outperformed the improved geometric optics method for particles with moderate size parameters, accounting for edge effects. • DNN efficiently computed optical properties of super-spheroids across a wide range of size parameters. This study investigates the use of machine learning techniques, specifically deep neural networks (DNN), to compute the optical properties of non-spherical particles across a wide range of size parameters. The approach involves training a DNN using a T-matrix database of super-spheroids with size parameters below 50. The DNN is then able to predict optical properties for size parameters beyond this limit, with exceptional accuracy observed for size parameters between 50–100. These predictions outperform the improved geometric optics method (IGOM). To further enhance accuracy and broaden the range of size parameters, databases are merged, combining the T-matrix results for small (0.1–50) and some moderate size parameters (50–100), the DNN predicted values for moderate size parameters (50–100), and the IGOM results for large size parameters (>100). This merged database is used to train a new DNN. This comprehensive training enables the neural networks to calculate the optical properties for super-spheroids across the entire size parameter spectrum, considering various shape parameters and refractive indices. This technique effectively bridges the gap between full-electromagnetic wave results and geometric-optics approximations, providing an efficient method for computing the optical properties of non-spherical particles from Rayleigh to geometric optics domains. Notably, the DNNs automatically consider edge effects, eliminating the need for deriving complex analytical formulas for non-spherical particles. [ABSTRACT FROM AUTHOR]

The aim of this study was to computationally model, in an unsupervised manner, a manifold of symmetry variations in normal brains, such that the learned manifold can be used to segment brain tumors from magnetic resonance (MR) images that fail to exhibit symmetry. An unsupervised brain tumor segmentation method, named as symmetric driven generative adversarial network (SD-GAN), was proposed. SD-GAN model was trained to learn a non-linear mapping between the left and right brain images, and thus being able to present the variability of the (symmetry) normal brains. The trained SD-GAN was then used to reconstruct normal brains and to segment brain tumors based on higher reconstruction errors arising from their being unsymmetrical. SD-GAN was evaluated on two public benchmark datasets (Multi-modal Brain Tumor Image Segmentation (BRATS) 2012 and 2018). SD-GAN provided best performance with tumor segmentation accuracy superior to the state-of-the-art unsupervised segmentation methods and performed comparably (<3% lower in Dice score) to the supervised U-Net (the most widely used supervised method for medical images). This study demonstrated that symmetric features presenting variations (i.e., inherent anatomical variations) can be modelled using unannotated normal MR images and thus be used in segmenting tumors. [ABSTRACT FROM AUTHOR]

Segmentation of regions of interest (ROIs) such as tumors and bones plays an essential role in the analysis of musculoskeletal (MSK) images. Segmentation results can help with orthopaedic surgeons in surgical outcomes assessment and patient's gait cycle simulation. Deep learning-based automatic segmentation methods, particularly those using fully convolutional networks (FCNs), are considered as the state-of-the-art. However, in scenarios where the training data is insufficient to account for all the variations in ROIs, these methods struggle to segment the challenging ROIs that with less common image characteristics. Such characteristics might include low contrast to the background, inhomogeneous textures, and fuzzy boundaries. we propose a hybrid convolutional neural network – transformer network (HCTN) for semi-automatic segmentation to overcome the limitations of segmenting challenging MSK images. Specifically, we propose to fuse user-inputs (manual, e.g., mouse clicks) with high-level semantic image features derived from the neural network (automatic) where the user-inputs are used in an interactive training for uncommon image characteristics. In addition, we propose to leverage the transformer network (TN) – a deep learning model designed for handling sequence data, in together with features derived from FCNs for segmentation; this addresses the limitation of FCNs that can only operate on small kernels, which tends to dismiss global context and only focus on local patterns. We purposely selected three MSK imaging datasets covering a variety of structures to evaluate the generalizability of the proposed method. Our semi-automatic HCTN method achieved a dice coefficient score (DSC) of 88.46 ± 9.41 for segmenting the soft-tissue sarcoma tumors from magnetic resonance (MR) images, 73.32 ± 11.97 for segmenting the osteosarcoma tumors from MR images and 93.93 ± 1.84 for segmenting the clavicle bones from chest radiographs. When compared to the current state-of-the-art automatic segmentation method, our HCTN method is 11.7%, 19.11% and 7.36% higher in DSC on the three datasets, respectively. Our experimental results demonstrate that HCTN achieved more generalizable results than the current methods, especially with challenging MSK studies. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) have been reported to play important roles in human cancers. Circular RNA homeodomain interacting protein kinase 3 (Circ-HIPK3) was investigated to be involved in tumorigenesis. However, the functions of circ-HIPK3 in oral squamous cell carcinoma (OSCC) remain vague. The expression of circ-HIPK3, microRNA (miR)-381-3p and Yes-associated protein1 (YAP1) was detected by qRT-PCR or western blot. Cell proliferation, apoptosis, invasion and migration were measured by MTT assay, flow cytometry, or transwell assay. The dual-luciferase reporter assay was employed to test the target correlations miR-381-3p and circ-HIPK3 or YAP1. Murine xenograft model was established to conduct in vivo assay. Circ-HIPK3 was elevated in OSCC tissues and cell lines, and decrease of circ-HIPK3 suppressed OSCC cell proliferation, invasion, migration and induced apoptosis in vitro as well as inhibited tumor growth in vivo. Rescue assay indicated circ-HIPK3 silence mediated OSCC progression inhibition by sponging miR-381-3p, which was a target of circ-HIPK3. Furthermore, miR-381-3p directly interacted with YAP1 and miR-381-3p inhibition could attenuate YAP1 deletion-induced suppression on cell malignant biological behavior in OSCC. Meanwhile, co-expression analysis showed circ-HIPK3 could regulate YAP1 expression by competing for miR-381-3p. Circ-HIPK3 contributed to OSCC growth and development through regulating YAP1 expression by sponging miR-381-3p, indicating a promising therapeutic strategy for OSCC. [ABSTRACT FROM AUTHOR]

Dust shortwave absorbability suffers from significant uncertainties. We investigated the radiative effects of dust aerosols from the circum‐Tibet region using four shortwave absorbability schemes and examined their impacts on the East Asian summer monsoon by using the Community Earth System Model (CESM). We adopted superspheroidal models to compute dust aerosol optical properties and found that more absorptive particles have larger asymmetry factor and lower single‐scattering albedo. Meiyu precipitation, as a part of the East Asian summer monsoon, is weakened by dust aerosols, particularly for the convective precipitation. The mechanism is that dust aerosols transported mainly from the Gobi desert radiatively heat the lower atmosphere to the north of the Meiyu rainband and decrease the meridional temperature gradient, leading to the weakening of the midtropospheric westerly jet over the Meiyu rainband, which is important for convection. More absorptive dust aerosols with a stronger radiative heating effect inhibit more convective Meiyu precipitation. Plain Language Summary: The circum‐Tibet region including the Taklimakan and Gobi deserts is the major source region of dust aerosols in East Asia particularly during the spring and summer seasons. Meiyu, as a part of the East Asian summer monsoon, generally occurs from mid‐June to mid‐July and often shows a tilt rain band extending from southwest to east China. The Letter reports on our finding about the relationship between dust shortwave absorbability (the amount of solar radiation absorbed by dust) and Meiyu precipitation. For weakly absorptive dust aerosols, convective Meiyu precipitation is rarely changed by dust aerosols; however, for strongly absorptive dust aerosols, convective Meiyu precipitation is significantly decreased. The impact of dust absorbability on Meiyu precipitation is explained through atmospheric heating rate, temperature, and wind field changes. This Letter emphasizes the urgent need of a more accurate knowledge of dust shortwave absorbability for climate modeling since significant uncertainties of refractive indices exist and new evidences show that Asian dust could be less absorptive than previously believed. Key Points: Dust shortwave absorbability affects single‐scattering albedo and asymmetry factorDust aerosols inhibit convective Meiyu precipitation depending on absorbabilityMidtropospheric westerly jet is weakened by dust aerosols causing decreased convective precipitation [ABSTRACT FROM AUTHOR]

In this study, four functional β-cyclodextrin derivatives including amidoxime linear-linked and cross-linked β-cyclodextrin/acrylonitrile/acrylic terpolymer (l-CD-AO-AA and c-CD-AO-AA), citric acid cross-linked poly-β-cyclodextrin (PCD), amidoxime cross-linked β-cyclodextrin and acrylonitrile–acrylic block copolymer (CD-b-AO-AA) were synthesized and properties of β-CD derivatives were characterized by FT-IR, SEM, TGA and element analysis. The effect of solid liquid ratio of the adsorbents, ionic strength, pH, contact time, initial concentration of U(VI), adsorption temperature, selective adsorption for different metal ions and the reasonable desorption conditions were studied. It was found when content of U(VI) was 22 mg/L, the four functional materials l-CD-AO-AA, c-CD-AO-AA, PCD and CD-AO-b-AA completely adsorbed U(VI) at 0.06 g/L (R ≈ 97%), 0.10 g/L (R ≈ 96%), 0.08 g/L (R ≈ 94%) and 0.52 g/L (R ≈ 94%), respectively. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to evaluate the efficacy of intracameral preservative-free triamcinolone acetonide (TA) on reducing inflammatory reactions after pediatric cataract surgery. Methods: Researches were comprehensively searched in PubMed, Embase, and the Cochrane Central Register of Controlled Trials up to 30 November, 2019. Data of interest were extracted and analyzed by Review Manager 5.2. The primary outcome was cell deposits. The secondary outcomes included posterior synechias, fibrinous anterior chamber reaction, visual axis obscuration, and posterior capsule opacification. The odds ratio (OR), weight mean difference (WMD), and their 95% confidence intervals (95% CI) were used to assess the strength of the association. Results: In total, 45 articles were obtained. Finally, 5 studies with a total of 308 eyes were enrolled in this meta-analysis. Among these, 134 eyes underwent intracameral TA treatment and 174 eyes did not. In intracameral TA treatment group, the incidence of cell deposits (OR = 0.35, 95% CI 0.16–0.76, P = 0.008), posterior synechias (OR = 0.44, 95% CI 0.23–0.86, P = 0.02), and fibrinous anterior chamber reaction (OR = 0.12, 95% CI 0.01–1.05, P = 0.05) were lower compared to those in patients without intracameral TA treatment. Meanwhile, the incidence of visual axis obscuration (OR = 0.10, 95% CI 0.01–0.85, P = 0.04) and posterior capsule opacification (OR = 0.09, 95% CI 0.02–0.55, P = 0.009) were also reduced significantly. The intraocular pressure (IOP) was evaluated at the first month postoperatively (WMD = 0.84 mmHg, 95% CI 0.14–1.53, P = 0.02), but within the normal range. There was no difference of IOP between two groups at 6 months or above after operation (P = 0.93). Conclusions: The intracameral preservative-free TA in pediatric cataract surgery contributed to much less inflammatory reaction with the normal IOP range. [ABSTRACT FROM AUTHOR]

Hollow perovskite oxide La 0·5 Sr 0·5 MnO 3 nanospheres are synthesized by using carbon spheres as template in the presence of citric acid. Among the samples investigated in this work, the one obtained at a sintering temperature of 700 °C exhibits the highest catalytic activity in the oxygen reduction reaction (ORR) in an alkaline electrolyte. The electrochemical measurement data shows that the sample has a positive onset potential (0.860 V) and a high limiting current density (6.48 mA cm−2). Furthermore, the catalyst shows a high selectivity towards methanol and a better stability than a commercial Pt/C catalyst. The hollow La 0·5 Sr 0·5 MnO 3 nanoshperes described in this manuscript hold a great promise for applications in metal-air batteries and alkaline fuel cells. Image 1 • Hollow La 0·5 Sr 0·5 MnO 3 nanospheres were synthesized using template method. • The nanospheres exhibited high ORR activity, good selectivity and durability. • The hollow La 0·5 Sr 0·5 MnO 3 nanospheres are promising noble-metal-free catalyst. [ABSTRACT FROM AUTHOR]

Graphitic carbon nitride, as a popular material in the field of environmental remediation, still suffers from unsatisfactory performance for heavy metals adsorption owing to lack of specific adsorption sites. In this study, molybdenum (Mo) and sulphur (S) were simultaneously introduced onto the surface of oxygen-doped graphitic carbon nitride (OCN) for the enhancement of Cd2+ adsorption. The synthesized MOS/OCN-1 exhibited substantially increased maximum adsorption capacity of 293.8 mg/g, calculated from Sips isotherm model, which was 8.7 times higher than that for pristine OCN (33.9 mg/g). The adsorption efficiency of MOS/OCN-1 was >94% even under high concentration of coexisting ions (i.e., Ca2+, Mg2+ and Zn2+). MoO 3 and MoS 2 on the surface of OCN were proven to interact with Cd2+ by forming CdMoO 4 and CdS species. OCN provided a stable matrix with a large surface area making more active sites exposed, which greatly facilitated Mo(IV) oxidation and Cd2+ precipitation. Our findings revealed that as well as the well-known Cd-S interaction, Mo atoms in the hybrid composites also played an important role in Cd2+ removal, which opened up the application possibility of OCN with Mo and S hybridization for in-situ Cd2+ remediation. [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]

Abstract Ethnopharmacological relevance In traditional Chinese medicine, supplementing Qi and strengthening body resistance are an important principle of anticancer treatment. Panax ginseng C.A.Mey. (ginseng) and Astragalus membranaceus Bunge (astragalus) are the representative herbs for this therapeutic principle. Aim of the study This study aims to explore the effect of the water extract of ginseng and astragalus (WEGA) on regulating macrophage polarization and mediating anticancer in the tumor microenvironment. Materials and methods A549 cells were cultured in tumor-associated macrophage (TAM) supernatant with various concentrations of WEGA (0, 5, 10, 20 mg/mL). A549 cell proliferation was determined through methyl thiazole tetrazolium (MTT) assay and real-time cell analysis (RTCA), respectively. In vivo experiments were performed with a Lewis lung cancer (LLC) xenograft mouse model. Forty-eight mice were divided into six groups and treated with saline, WEGA, or cis-diamine dichloro platinum (DDP) with dosage of WEGA (0, 30, 60, 120 mg/kg body weight/day). The different groups were administered with drugs via oral or intraperitoneal injection once a day for 21 consecutive days. Tumor inhibition rate, spleen index, thymus index, cytokine, protein, and mRNA expression levels were detected in mice. Results In a co-culture system, WEGA remarkably inhibited A549 cell proliferation, promoted the expression of M1 macrophage markers and inhibited M2 TAMs markers. Therefore, WEGA affected the biological behavior of cancer cells by regulating the expression of some markers relevant to macrophage polarization. In addition, the group of WEGA and DDP chemotherapy effectively inhibited the transplanted tumor growth in mice and improved weight loss and immunosuppressive with the cisplatin inducing. Conclusions This study provides mechanistic insights into the anticancer effect of WEGA through the regulation of macrophage polarization and highlights that WEGA could be a novel option for integrative cancer therapies. Graphical abstract fx1 [ABSTRACT FROM AUTHOR]

Sea salt aerosols were assumed to be homogeneous spheres in most climate models. However, observations show that sea salt particles are inhomogeneous during the deliquesce and crystallization processes. Using a two‐layer sphere model, we found that backscattering of solar radiation associated with sea salts is underestimated in homogeneous sea salt models. The Community Earth System Model is used to assess the inhomogeneity effect on direct radiative forcing. For global climate model simulation, the inhomogeneity effect on radiative transfer is found to be small as high RHs over widespread oceans suppress the impact of inhomogeneity. On the other hand, in coastal regions, the inhomogeneity effect can cause up to 10% radiative forcing difference of sea salt aerosols. The inhomogeneity effect of sea salt aerosols has to be considered over coastal regions, especially in the Mediterranean, Australia, and the eastern coast of South America. Plain Language Summary: In a humid environment, solid sea salt particles can be coated with water. Sea salt aerosol has a cooling effect on the Earth, but coated sea salt has even stronger cooling effect. However, in most climate models, coated sea salt is not considered. This study confirms that coated sea salt aerosols may not significantly affect the global radiation budget, but they should definitely be considered in the coastal climate where large changes in relative humidity are evident. Key Points: For some range of relative humidity (50‐80%), sea salt is assumed to be an inhomogeneous two‐layer sphereThe inhomogeneity of sea salt decreases the asymmetry factor and increases backscattering of solar radiationInhomogeneity has a relatively large impact (up to 10%) on sea salt radiative forcing over coastal waters [ABSTRACT FROM AUTHOR]

Abstract Hazardous outdoor air pollution has severely affected indoor air quality, threatening the health of billions of people. However, existing indoor air purification technologies are unsatisfactory due to some inherent limitations such as poor efficiency, limited target pollutants, the need to frequently replace filters or adsorbents, or the generation of harmful by-products. Here, we studied the effect and mechanism of cryogenic circulation for indoor air purification. Experimental results show that up to 99% of indoor PM 2.5 from ambient air was removed at −18 °C. The morphological measurements indicate that micrometer-sized particles are formed concomitantly with the reduction of nanometer- or submicron-sized particles, suggesting that condensational growth of fine particles is responsible for the removal. Applying the method to gaseous pollutant purification demonstrates that 98% of NO 2 is condensed and removed from the ambient air at −50 °C, implying that the method would be effective for multiple indoor pollutants with higher boiling points. Cryogenic condensation may provide a principle for continuous indoor air purification via modified air conditioners and humidifiers in cases where health benefits outweigh energy consumption concerns. Graphical abstract Unlabelled Image Highlights • Cryogenic condensation can remove indoor fine particulate and gaseous pollutants. • Phase transition of gaseous components induces haze pollutants removal. • Obtain clean indoor air for health through reasonable energy consumption. [ABSTRACT FROM AUTHOR]

Highlights • A new dermoscopic segmentation method based on learning class-wise deep features. • A class-specific model helps balance the contribution of melanoma and non-melanoma data. • A step-wise refinement approach iteratively maximises the segmentation map agreement. • A probability based integration approach combines the relevant complementary segmentation results. • We achieve higher accuracy compared to the state-of-the-art methods on 3 datasets. Abstract The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs. non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our experiments on three well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation. [ABSTRACT FROM AUTHOR]

Abstract A microwave sintering technique is reported for fabricating co-sintered proton-conducting solid oxide fuel cells. With this method, high-quality ceramic electrolyte membranes can be prepared at 1100 °C, thus enabling the fabrication of entire cells in a single step. The microwave sintering method not only enhances electrolyte densification but also improves the cathode/electrolyte interface, which is critical for improving fuel cell performance. The power output of the co-sintered cell prepared under the microwave conditions (up to 449 mW cm−2 at 700 °C) was significantly higher than that of the cell fabricated using the traditional co-sintering method (approximately 292 mW cm−2 at the same temperature). Electrochemical analysis revealed that the enhanced electrolyte density and the improved cathode/electrolyte interface achieved by using the microwave sintering technique decrease both the ohmic resistance and the polarisation resistance of the cell, leading to good fuel cell performance. [ABSTRACT FROM AUTHOR]

Abstract NiO is used as a sintering aid to modify BaZr 0.1 Ce 0.7 Y 0.2 O 3-δ by an external addition method and by an internal doping strategy to improve the sinterability of BaZr 0.1 Ce 0.7 Y 0.2 O 3-δ. In both cases, the modified BaZr 0.1 Ce 0.7 Y 0.2 O 3-δ materials show an improved sinterability compared with the original BaZr 0.1 Ce 0.7 Y 0.2 O 3-δ. However, doping BaZr 0.1 Ce 0.7 Y 0.2 O 3-δ with NiO to form BaZr 0.1 Ce 0.66 Ni 0.04 Y 0.2 O 3-δ is found to be an effective strategy to significantly improve the electrolyte properties. The BaZr 0.1 Ce 0.66 Ni 0.04 Y 0.2 O 3-δ sample shows a high density and large grain size after sintering at a relatively low temperature (1400 °C). Electrochemical studies reveal that the doping strategy offers a high proton conductivity in both the bulk and across grain boundaries. The conductivity of BaZr 0.1 Ce 0.66 Ni 0.04 Y 0.2 O 3-δ sintered at 1400 °C is observed to be higher than that of BaZr 0.1 Ce 0.7 Y 0.2 O 3-δ sintered at 1600 °C. With BaZr 0.1 Ce 0.66 Ni 0.04 Y 0.2 O 3-δ as the electrolyte, a proton-conducting solid oxide fuel cell displays a large peak power density of 477 mW cm-2 at 600 °C and a high electrolyte membrane conductivity of 6.3 × 10−3 S cm−1. Highlights • NiO was used as the dopant for BaZr 0.1 Ce 0.7 Y 0.2 O 3-δ proton-conducting oxide. • High sinterability with improved conductivity was achieved. • The doping strategy showed advantages over the traditional approach of using NiO. • The cell with BaZr 0.1 Ce 0.66 Ni 0.04 Y 0.2 O 3-δ electrolyte showed a high performance. [ABSTRACT FROM AUTHOR]

We study the Imbert-Fedorov (IF) effect in Kretschmann configuration with anisotropic metamaterial to explore a flexible method to enhance and modulate IF shift. The physical mechanism for large IF shifts in an anisotropic waveguide based on spin-orbit angular momentum coupling is explained. The influences of metamaterial thickness, anisotropy, and loss on IF shift are systematically discussed. This provides a theoretical prediction of IF shift in a Kretschmann configuration which is verified by simulation results in semiconductor metamaterial waveguide. The simulation results show that both metamaterial anisotropy and loss contributes significantly to the IF shift. Thus, reducing the loss and enhancing the metamaterial anisotropy are necessary and important measures to realize enhanced IF effect in the proposed Kretschmann configuration. [ABSTRACT FROM AUTHOR]

Magnetically doped CeO2 is a promising dilute magnetic semiconductor and may also be useful in magneto-optical applications. Ce1-xCoxO2-δ (x=0, 0.02, 0.06, 0.15, and 0.25) films were deposited by pulsed laser deposition on MgO(100) substrates and their structural, magnetic, and magneto-optical properties were characterized. The films show a textured ceria single phase with (111) preferred orientation. All the Co-doped samples show room temperature ferromagnetism and large magnetic anisotropy with an out-of-plane easy axis. Magneto-optical measurements indicate that the Co-doped films also have high saturation Faraday rotation ranging from 230 to 6000 deg/cm depending on the Co concentration, and their refractive index and extinction coefficient also increase with Co concentration. [ABSTRACT FROM AUTHOR]

The microwave sintering strategy was for the first time adopted to prepare proton-conducting electrolyte membranes for solid oxide fuel cells. The preparation of a dense proton-conducting BaCe 0.7 Zr 0.1 Y 0.2 O 3-δ (BCZY) electrolyte membrane can be achieved at 1200 °C with the microwave sintering method. In sharp contrast, a BCZY sample prepared at 1200 °C using the conventional thermal sintering method was found to be porous. In comparison with a dense BCZY sample prepared at 1400 °C using the conventional sintering method, the microwave-sintered BCZY electrolyte showed an improved proton conductivity, which is beneficial for fuel cell applications. Experimental results showed that the microwave sintering method enabled a homogenous elemental distribution and a suppression of barium evaporation, leading to the conductivity improvement in both bulk and grain boundaries. With the microwave sintered BCZY film as the electrolyte, a proton-conducting solid oxide fuel cell delivered a maximum power density of 838 mW cm −2 at 700 °C with an electrolyte film conductivity as high as 1.4 × 10 −2 S cm −1 . This study suggests that the microwave sintering method is a promising strategy to prepare electrolyte membranes at a relatively low temperature with high conductivity, which could advance the development of proton-conducting solid oxide fuel cells. [ABSTRACT FROM AUTHOR]

Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis in computer-aided diagnosis systems. In recent years, segmentation methods based on fully convolutional networks (FCNs) have achieved great success in general images. FCN performance is primarily due to it leveraging large labeled datasets to hierarchically learn the features that correspond to the shallow appearance as well as the deep semantics of the images. However, such dependence on large dataset does not translate well into medical images where there is a scarcity of annotated medical training data, and FCN results in coarse ROI detections and poor boundary definitions. To overcome this limitation, medical image-specific FCN methods have been introduced with post-processing techniques to refine the segmentation results; however, the performance of these methods is reliant on the appropriate tuning of a large number of parameters and dependence on data-specific post-processing techniques. In this study, we leverage the state-of-the-art image feature learning method of generative adversarial network (GAN) for its inherent ability to produce consistent and realistic images features by using deep neural networks and adversarial learning concept. We improve upon GAN such that ROI features can be learned at different levels of complexities (simple and complex), in a controlled manner, via our proposed dual-path adversarial learning (DAL). The outputs from our DAL are then augmented to the learned ROI features into the existing FCN training data, which increases the overall feature diversity. We conducted experiments on three public datasets with a variety of visual characteristics. Our results demonstrate that our DAL can improve FCN-based segmentation methods and outperform or be competitive in performances to the state-of-the-art methods without using medical image-specific optimizations. [ABSTRACT FROM AUTHOR]

A novel liquid-phase synthesis strategy is demonstrated for the preparation of the Nb-containing ceramic oxide SrCo 0.9 Nb 0.1 O 3-δ (SCN). In comparison with the traditional solid-state reaction (SSR) method, the liquid-phase synthesis route offers a couple of advantages, including a lower phase formation temperature and a smaller particle size of the SCN materials that are beneficial for applications as proton-conducting fuel cell cathode. With BaCe 0.4 Zr 0.4 Y 0.2 O 3-δ (BCZY442) as the electrolyte and the SCN synthesized in this work as the cathode, a proton-conducting solid oxide fuel cell (SOFC) shows a peak power density of 348 mW cm −2 at 700 °C, significantly higher than that of a SOFC fabricated with SCN cathode prepared using the SSR method, which can only deliver 204 mW cm −2 at the same temperature. Additionally, this new synthesis strategy allows impregnation of Sr 2+ , Co 3+ and Nb 5+ on the solid backbone in aqueous solution, further improving cell performance to reach a peak power density of 488 mW cm −2 at 700 °C. [ABSTRACT FROM AUTHOR]

The β-cyclodextrin-graft-(maleic anhydride-co-acrylonitrule) copolymer (β-CD-g-(MAHco-AN)) synthesized through radical polymerization reactions of β-cyclodextrin (β-CD) with maleic anhydride (MAH) and acrylonitrule (AN) in the special monomer proportion, chemically modify with amidoxime groups to obtained the new adsorbent, which was terms as amidoxime-functionalized β-cyclodextrin-graft-(maleic anhydride-co-acrylonitrule) copolymer (β-CD-g-(MAH-co-AO)). Based on the characteristic results of Fourier transform infrared spectra (FTIR), scanning electron microscopy (SEM), X-ray Diffraction (XRD), and thermalgravity analysis (TGA) techniques, the grafted nitrile groups were successfully converted to amidoxime groups by reaction with hydroxylamine. In this report, the influence of different factors such as pH value and ionic strength, solid-liquid ratio, contact time, initial U(VI) concentration, and temperature on adsorption was investigated by a batch adsorption experiment. The adsorption process fitting results show that the adsorption followed the Langmuir isotherm model and the maximum adsorption capacity was 0.747 g/g at pH 4.0. In addition, the regeneration performance was investigated by varying the concentration of eluent, temperature, and contact time. Under the desorption condition of 0.10 M HNO3, the adsorbents can be reused 12 times in the case that the adsorption capacity was not significantly reduced. The functionalized copolymer exhibits high selectivity under circumstance of other co-existing ions is present in the solution. [ABSTRACT FROM AUTHOR]

Assembly structure of Mn 1-x Ni x Fe 2 O 4 (0 < x < 1) nanoparticles has been adjusted by introducing polyethylene glycol (PEG) into the synthesis procedure. Colloidal nanocrystal assembly (CNA) of Mn 0.5 Ni 0.5 Fe 2 O 4 (M1N1-PEG) with inverse spinel structure can be formed with the assistance of PEG, whereas other CNAs of Mn 1-x Ni x Fe 2 O 4 (0 < x < 1) with normal spinel structures are formed no matter PEG is added or not. The structural evolution of M 1-x Ni x Fe 2 O 4 CNAs can be confirmed by the Raman spectroscopy and magnetic measurements as well as the theoretical simulation. Inverse spinel M1N1-PEG and NiFe 2 O 4 CNAs only display an electrocatalytic peak toward the oxidation of L-histidine with high sensitivity in aqueous sodium sulfate electrolyte. While all the Mn 1-x Ni x Fe 2 O 4 CNAs (0 ≤ x < 1) with normal spinel structure demonstrated the intercalation/deintercalation of sodium ions except for the inverse spinel structured M1N1-PEG. [ABSTRACT FROM AUTHOR]

Several proton conductive ceramic oxides are evaluated for potential application in ceramic-NiO composite anodes for proton-conducting La 0.99 Ca 0.01 NbO 4 (LNO) electrolyte-based fuel cells. Chemical compatibility tests show that most of the existing proton-conducting oxides are unfavorable for application in LNO electrolyte-based fuel cells because of undesirable reactions at high temperatures. Further considering the chemical compatibility with NiO and the ability to promote the densification of the deposited LNO electrolyte, LNO-NiO composite anode proves to be the only suitable anode candidate. With humidified hydrogen (∼3%H 2 O) as the fuel and static air as the oxidant, fuel cells based on LNO electrolyte film deposited on LNO-NiO anodes show a peak power density of 24 mW cm −2 at 750 °C, this value being one of the largest ever reported for LNO-based cells. Further investigation reveals that the polarization resistance of the cell is the major contribution to the total cell resistance, limiting the overall cell performance. [ABSTRACT FROM AUTHOR]

Objective: Segmentation of skin lesions is an important step in the automated computer aided diagnosis of melanoma. However, existing segmentation methods have a tendency to over- or under-segment the lesions and perform poorly when the lesions have fuzzy boundaries, low contrast with the background, inhomogeneous textures, or contain artifacts. Furthermore, the performance of these methods are heavily reliant on the appropriate tuning of a large number of parameters as well as the use of effective preprocessing techniques, such as illumination correction and hair removal. Methods: We propose to leverage fully convolutional networks (FCNs) to automatically segment the skin lesions. FCNs are a neural network architecture that achieves object detection by hierarchically combining low-level appearance information with high-level semantic information. We address the issue of FCN producing coarse segmentation boundaries for challenging skin lesions (e.g., those with fuzzy boundaries and/or low difference in the textures between the foreground and the background) through a multistage segmentation approach in which multiple FCNs learn complementary visual characteristics of different skin lesions; early stage FCNs learn coarse appearance and localization information while late-stage FCNs learn the subtle characteristics of the lesion boundaries. We also introduce a new parallel integration method to combine the complementary information derived from individual segmentation stages to achieve a final segmentation result that has accurate localization and well-defined lesion boundaries, even for the most challenging skin lesions. Results: We achieved an average Dice coefficient of 91.18% on the ISBI 2016 Skin Lesion Challenge dataset and 90.66% on the PH2 dataset. Conclusion and Significance: Our extensive experimental results on two well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation. [ABSTRACT FROM AUTHOR]

[ 18 F]-Fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET–CT) scans of lymphoma patients usually show disease involvement as foci of increased radiotracer uptake. Existing methods for detecting abnormalities, model the characteristics of these foci; this is challenging due to the inconsistent shape and localization information about the lesions. Thresholding the degree of FDG uptake is the standard method to separate different sites of involvement. But may fragment sites into smaller regions, and may also incorrectly identify sites of normal physiological FDG uptake and normal FDG excretion (sFEPU) such as the kidneys, bladder, brain and heart. These sFEPU can obscure sites of abnormal uptake, which can make image interpretation problematic. Identifying sFEPU is therefore important for improving the sensitivity of lesion detection and image interpretation. Existing methods to identify sFEPU are inaccurate because they fail to account for the low inter-class differences between sFEPU fragments and their inconsistent localization information. In this study, we address this issue by using a multi-scale superpixel-based encoding (MSE) to group the individual sFEPU fragments into larger regions, thereby, enabling the extraction of highly discriminative image features via domain transferred convolutional neural networks. We then classify there regions into one of the sFEPU classes using a class-driven feature selection and classification model (CFSC) method that avoids overfitting to the most frequently occurring classes. Our experiments on 40 whole-body lymphoma PET-CT studies show that our method achieved better accuracy (an average F-score of 91.73%) compared to existing methods in the classification of sFEPU. [ABSTRACT FROM AUTHOR]

[Display omitted] • Microalgae-derived yolk-shell microspheres were prepared to achieve Cd2+ upcycling. • Cd2+ from wastewater was directly in-site converted into photocatalyst (ZSC) • ZSC was demonstrated to have core–shell ZnS@Zn 0.58 Cd 0.42 S heterostructure. • ZSC shows excellent performance for H 2 evolution and organic pollutant degradation. • Photocatalytic activity of ZSC was enhanced due to heterojunction. Uncontrolled exploitation of heavy metals has caused environmental pollution and mineral resource depletion crises. The first-choice approach for addressing these issues is heavy metal upcycling. However, efficient retrieval and conversion of heavy metals from contaminated environments into high-value-added products remains challenging. Here, we developed a strategy to simultaneously achieve efficient accumulation (256 mg/g), magnetic separation (∼1 min), and directly in-site conversion of Cd2+ from wastewater into core–shell ZnS@Zn 0.58 Cd 0.42 S heterojunction photocatalysts (ZSC). Microalgae-derived yolk-shell microspheres containing multicomponent nanocores (Fe 3 O 4 and ZnS) were developed to serve as adsorbent, magnetic carrier, and photocatalyst assembly platform to achieve Cd2+ upcycling into ZSC. In particularly, ZSC shows much higher photocatalytic activity for H 2 generation and organic pollutant degradation than do commercial photocatalysts (P25-TiO 2 and CdS) due to its greater utilization of solar energy and heterostructure. Altogether, we provide an appealing strategy for upcycling cadmium into high-performance heterojunction photocatalysts for environmental purification and clean energy generation. [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]

Y-doped BaZrO 3 (BZY) electrolyte films are successfully fabricated by utilizing the driving force from the anode substrate, aiming to circumvent the refractory nature of BZY materials. The BZY electrolyte film on the high shrinkage anode becomes dense after sintering even though no sintering aid is added, while the BZY electrolyte remains porous on the conventional anode substrate after the same treatment. The resulting BZY electrolyte shows a high conductivity of 4.5 × 10 − 3 S cm − 1 at 600 °C, which is 2 to 20 times higher than that for most of BZY electrolyte films in previous reports. In addition, the fuel cell with this BZY electrolyte generates a high power output of 267 mW cm − 2 at 600 °C. These results suggest the strategy presented in this study provides a promising way to prepare BZY electrolyte films for fuel cell applications. [ABSTRACT FROM AUTHOR]

The source to sink process of terrigenous sediment from East Asia continent to the marginal seas is crucial for the understanding of land–ocean interaction and marine sedimentary process in the West Pacific continental margin. This paper presents element geochemical and Sr–Nd isotopic records of core CM97 in the Changjiang (Yangtze River) Estuary and core MD06–3040 on the inner shelf of East China Sea, aiming to reconstruct the Changjiang sediment source–to–sink transport process and its major controlling mechanism during the Holocene. Driven by the asynchronous evolution of the Indian and East Asian summer monsoon, the provenance of sediments accumulated in the Changjiang Estuary gradually changed from the upper catchment during the late glacial period to the mid–lower valley in the early–mid Holocene. With the intensive agricultural cultivation and urbanization development in the catchment during the late Holocene, the major source of Changjiang sediment into the sea shifted back to the upper catchment, again. The high–frequency ENSO activity may superimpose the effect of human disturbances on sediment source–to–sink process in the late Holocene. Therefore, the Holocene provenance evolution of Changjiang sediments in the East China Sea witnessed the changes of governing sediment erosion process within a large catchment from monsoon climate–dominated to anthropogenic driving. Our study also suggests the strong effect of hydrodynamics in coastal and shelf environments on sedimentary geochemical compositions, which deserves more attention in further sediment provenance study. [ABSTRACT FROM AUTHOR]

The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

An ice crystal single-scattering property database is developed in the microwave spectral region (1 to 874 GHz) to provide the scattering, absorption, and polarization properties of 12 ice crystal habits (10-plate aggregate, 5-plate aggregate, 8-column aggregate, solid hexagonal column, hollow hexagonal column, hexagonal plate, solid bullet rosette, hollow bullet rosette, droxtal, oblate spheroid, prolate spheroid, and sphere) with particle maximum dimensions from 2 µm to 10 mm. For each habit, four temperatures (160, 200, 230, and 270 K) are selected to account for temperature dependence of the ice refractive index. The microphysical and scattering properties include projected area, volume, extinction efficiency, single-scattering albedo, asymmetry factor, and six independent nonzero phase matrix elements (i.e. P 11 , P 12 , P 22 , P 33 , P 43 and P 44 ). The scattering properties are computed by the Invariant Imbedding T-Matrix (II-TM) method and the Improved Geometric Optics Method (IGOM). The computation results show that the temperature dependence of the ice single-scattering properties in the microwave region is significant, particularly at high frequencies. Potential active and passive remote sensing applications of the database are illustrated through radar reflectivity and radiative transfer calculations. For cloud radar applications, ignoring temperature dependence has little effect on ice water content measurements. For passive microwave remote sensing, ignoring temperature dependence may lead to brightness temperature biases up to 5 K in the case of a large ice water path. [ABSTRACT FROM AUTHOR]

To derive the bulk radiative properties of ice clouds, aircraft contrails and snow grains, which are fundamental to atmospheric radiative transfer calculations in downstream applications, it is necessary to accurately simulate the scattering of light by individual ice particles. An ice particle optical property database reported in 2013 (hereafter, TAMUice2013) is updated (hereafter, TAMUice2016) to incorporate recent advances in computation of the optical properties of nonspherical particles. Specifically, we employ the invariant imbedding T-matrix (II-TM) method to compute the optical properties of particles with small to moderate size parameters. Both versions use the Improved Geometric Optics Method (IGOM) to compute the optical properties of large ice crystals, but TAMUice2016 improves the treatment of inhomogeneous waves inside the scattering particles in the case where ice is absorptive such as at infrared wavelengths. To bridge the gap between the extinction efficiencies computed from the II-TM and the IGOM, TAMUice2016 includes spectrally dependent higher order terms of the edge effect in addition to the first order counterpart considered in TAMUice2013. Furthermore, the differences between TAMUice2013 and TAMUice2016 are quantified with respect to the computation of the bulk optical properties of ice clouds. [ABSTRACT FROM AUTHOR]

Zhachong Shisanwei Pill (ZSP) is a commonly used Mongolian medicine in treating cerebrovascular diseases and plays a role in the clinical treatment of ischemic stroke (IS). Based on determining the protective effect of ZSP on cerebral ischemia, they adopted the proteomics method to explore the mechanism of ZSP against IS. Rats with middle cerebral artery occlusion (MCAO) model were prepared by wire embolization method, and divided into sham group, model group, ZSP high-dose group, medium-dose group, low-dose group and positive drug group. We collected the brain tissue of rats for 12 h after modeling. Neurological deficit score and cerebral infarction volume ratio evaluated pharmacodynamics, and we selected the optimal dose for subsequent experiments. Proteomics was used to screen out possible ZSP anti-IS mediated pathways and differentially expression proteins. Network pharmacology was used to verify the correlation between diseases and drugs. Hematoxylin-eosin (HE) staining and transmission electron microscope (TEM) were used to explore further the pharmacodynamic effect of ZSP against IS and its possible mechanism. The cerebral infarction rate and neurological function score in rats showed that the medium-dose ZSP group had the best efficacy. Proteomics results showed that the anti-IS action of ZSP was mainly through lysosome pathway. LAMP2, AP3M1, and SCARB2 were the differentially changed proteins in this pathway. Network pharmacology verified this. HE staining and TEM results showed that ZSP could improve the pathological state of neurons in MCAO rats and reduce the number of lysosomes in MCAO rats. Western blot (WB) results showed that compared with the model group, the protein expression levels of LAMP2 and AP3M1 in the ZSP group were significantly down-regulated, and the protein expression levels of SCARB2 were significantly up-regulated. This study confirms that ZSP regulates the lysosomal pathway, which may protect IS by down-regulating LAMP2 and AP3M1 and up-regulating SCARB2. [Display omitted] • ZSP has pharmacological effects on cerebral ischemia. • This study combined proteomics and bioinformatics. • ZSP may regulate the lysosomal pathway. • ZSP ameliorates cerebral ischemia by affecting multiple proteins. [ABSTRACT FROM AUTHOR]

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs - Voxelmorph (VM), Diffeomorphic Voxelmorph (DifVM), and Laplacian Pyramid Image Registration Network (LapIRN) – on three well-established public datasets of 3D brain magnetic resonance imaging (MRI) - IBSR18, Mindboggle101, and LPBA40. The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs. [ABSTRACT FROM AUTHOR]

Objective: The accurate classification of mass lesions in the adrenal glands ('adrenal masses'), detected with computed tomography (CT), is important for diagnosis and patient management. Adrenal masses can be benign or malignant and benign masses have varying prevalence. Classification methods based on convolutional neural networks (CNNs) are the state-of-the-art in maximizing inter-class differences in large medical imaging training datasets. The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions.Methods: We developed a deep multi-scale resemblance network (DMRN) to overcome these limitations and leveraged paired CNNs to evaluate the intra-class similarities. We used multi-scale feature embedding to improve the inter-class separability by iteratively combining complementary information produced at different scales of the input to create structured feature descriptors. We augmented the training data with randomly sampled paired adrenal masses to reduce the influence of imbalanced training data.Results: We used 229 CT scans of patients with adrenal masses for evaluation. In a five-fold cross-validation, our method had the best results (89.52 % in accuracy) when compared to the state-of-the-art methods (p < 0.05). We conducted a generalizability analysis of our method on the ImageCLEF 2016 competition dataset for medical subfigure classification, which consists of a training set of 6776 images and a test set of 4166 images across 30 classes. Our method achieved better classification performance (85.90 % in accuracy) when compared to the existing methods and was competitive when compared with methods that require additional training data (1.47 % lower in accuracy).Conclusion: Our DMRN sub-classified adrenal masses on CT and was superior to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Constructing an appropriate particle morphology model is essential for realistic simulation of optical properties of atmospheric particles. This paper presents a model for generating surface roughness based on a combination of methods from discrete differential geometry combined with a stochastic partial differential equation for surface evolution introduced by Edwards and Wilkinson. Scattering of light by roughened particles is simulated using the Invariant Imbedding T-Matrix (II-TM) method. The effects of surface roughness on the single-scattering properties, namely, the phase matrix, asymmetry factor, and extinction efficiency, are investigated for a single wavelength in the visible range and for a range of size parameters up to x =50. Three different smooth shapes are considered: spherical, spheroidal, and hexagonal, the latter two in just the “compact particle” case of unit aspect ratio. It is shown that roughness has negligible effects on the optical scattering properties for size parameters less than 20. For size parameters ranging from 20 to 50, the phase matrix elements are more sensitive to the surface roughness than are two important integral optical properties, the extinction efficiency and asymmetry factor. As has been seen in studies using other forms of roughening, the phase function is progressively smoothed as roughness increases. The effect on extinction efficiency is to increase it, and on asymmetry factor is to decrease it. Each of these effects is relatively modest in the size range considered, but the trend of results suggests that greater effects will be seen for size parameters larger than ones considered here. [ABSTRACT FROM AUTHOR]

The accuracy of the physical-geometric optics (PG-O) approximation is examined for the simulation of electromagnetic scattering by nonspherical dielectric particles. This study seeks a better understanding of the tunneling effect on the phase matrix by employing the invariant imbedding method to rigorously compute the zeroth-order Debye series, from which the tunneling efficiency and the phase matrix corresponding to the diffraction and external reflection are obtained. The tunneling efficiency is shown to be a factor quantifying the relative importance of the tunneling effect over the Fraunhofer diffraction near the forward scattering direction. Due to the tunneling effect, different geometries with the same projected cross section might have different diffraction patterns, which are traditionally assumed to be identical according to the Babinet principle. For particles with a fixed orientation, the PG-O approximation yields the external reflection pattern with reasonable accuracy, but ordinarily fails to predict the locations of peaks and minima in the diffraction pattern. The larger the tunneling efficiency, the worse the PG-O accuracy is at scattering angles less than 90°. If the particles are assumed to be randomly oriented, the PG-O approximation yields the phase matrix close to the rigorous counterpart, primarily due to error cancellations in the orientation-average process. Furthermore, the PG-O approximation based on an electric field volume-integral equation is shown to usually be much more accurate than the Kirchhoff surface integral equation at side-scattering angles, particularly when the modulus of the complex refractive index is close to unity. Finally, tunneling efficiencies are tabulated for representative faceted particles. [ABSTRACT FROM AUTHOR]

Modified soils (MSs) are being increasingly used as geo-engineering materials for the sedimentation removal of cyanobacterial blooms. Cationic starch (CS) has been tested as an effective soil modifier, but little is known about its potential impacts on the treated water. This study investigated dissolved organic matters in the bloom water after algal removal using cationic starch modified soils (CS-MSs). Results showed that the dissolved organic carbon (DOC) could be decreased by CS-MS flocculation and the use of higher charge density CS yielded a greater DOC reduction. When CS with the charge density of 0.052, 0.102 and 0.293 meq/g were used, DOC was decreased from 3.4 to 3.0, 2.3 and 1.7 mg/L, respectively. The excitation–emission matrix fluorescence spectroscopy and UV 254 analysis indicated that CS-MS exhibits an ability to remove some soluble organics, which contributed to the DOC reduction. However, the use of low charge density CS posed a potential risk of DOC increase due to the high CS loading for effective algal removal. When CS with the charge density of 0.044 meq/g was used, DOC was increased from 3.4 to 3.9 mg/L. This study suggested, when CS-MS is used for cyanobacterial bloom removal, the content of dissolved organic matters in the treated water can be controlled by optimizing the charge density of CS. For the settled organic matters, other measures ( e.g. , capping treatments using oxygen loaded materials) should be jointly applied after algal flocculation. [ABSTRACT FROM AUTHOR]