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The thermal expansion effects of GNSS stations are influenced by not only temperature variations, but also bedrock depths and types. Unfortunately, the current studies treat the subsurface GNSS monument and their nearby bedrock as a whole, without taking into account the inconsistencies among bedrock depths and types, while the existing full-spectrum finite element method (FEM) cannot be easily extended to consider the bedrock information. To solve this problem, we propose a refined full-spectrum temperature-induced subsurface thermal expansion model (FSHBDT) that considers both seasonal and non-seasonal temperature variations as well as bedrock information based on the half-space harmonic model. Results show that the full-spectrum half-space harmonic model (FSH), which considers only seasonal and non-seasonal temperature variations, can obtain comparable results to the FEM and even outperform the FEM for inland stations. In addition, the depth and type of bedrock have significant effects on the annual amplitude and phase of thermal expansion-induced vertical displacement. In particular, we find that the station displacement increases by more than 1 mm and the annual phase delays by up to 10° for high-latitude and deeper bedrock stations when bedrock depths are taken into account. The FSHBDT improves the correlation coefficient between GNSS height and mass load displacements by up to 42.3% compared to the FEM and explains up to 8.2% of the nonlinear variation in the GNSS height time series. Our work confirms the advantage of rigorous subsurface thermal expansion modeling to correct the nonlinear variations of global GNSS stations, which might provide a potential opportunity to improve the terrestrial reference frame toward the goal of 1 mm accuracy. [ABSTRACT FROM AUTHOR]

The new generation of BDS-3 broadcasted open service signals B1C and B2a, which are compatible and interoperable with GPS and Galileo overlapping frequency signals, are suitable for multi-constellation global navigation satellite system (multi-GNSS) precise point positioning ambiguity resolution (PPP-AR). However, multipath errors caused by an actual complex environment can affect the ability of ambiguity resolution, thereby restricting the positioning performance of multi-GNSS. Due to different orbital repeat periods of GNSS systems, the implementation complexity of a multipath correction method based on time-domain repeatability is relatively high, while that based on spatial-domain repeatability are research hotspot at present, thanks to the advantages of simple algorithms, easy implementation, and real-time correction. Based on the original multipath hemispherical map (MHM) and trend-surface analysis MHM (TMHM) methods, four multipath processing schemes, namely, the independent modeling and correction (I-MHM, I-TMHM), together with the joint modeling and correction (C-MHM, C-TMHM) of different GNSS systems are proposed in this paper. We find that the residuals of GPS, BDS-3, and Galileo overlapping frequency show a strong correlation at the same spatial position after considering the GNSS inter-system biases in static PPP-AR modes, while the multipath joint modeling and correction method can improve the positioning performance more than the independent modeling and correction. This can be attributed to the ability of multi-GNSS to improve the space coverage within grids, making the modeling results more explanatory. Compared to C-MHM, the C-TMHM derived positioning accuracy and convergence time of combined GCE in 3D component can be improved by up to 29.3% and 40.7%, respectively. In addition, through using multi-GNSS data for multipath modeling, the modeling time can be shortened by more than half to obtain a correction effect similar to that of full orbit period modeling, specifically, 3-day data for GC modeling, while 4-day data for GE, CE, and GCE modeling. Finally, the performances of our improved multipath modeling method were verified and evaluated by using the observation data in environment with fewer blind areas. Compared with the uncorrected cases, the positioning accuracies of GC, GE, CE, and GCE in 3D component improve by 51.7, 63.8, 59.7, and 65.7%, after correcting the multipath error by the proposed C-TMHM method, while the convergence time can also be shortened by 55.3, 51.0, 52.2, and 64.2%, respectively. This research has significant applicability for mitigating multipath errors in various scenarios to improve positioning accuracy and reliability. [ABSTRACT FROM AUTHOR]

Objective: Patients with systemic lupus erythematosus (SLE) often develop multi-organ damages including heart and kidney complications. We sought to better define the underlying mechanisms with a focus on the chemokine receptor CX3CR1. Methods: We generated Cx3cr1-deficient MRL/lpr lupus-prone mice through backcrossing. We then employed heterozygous intercross to generate MRL/lpr littermates that were either sufficient or deficient of CX3CR1. The mice were also treated with either Lactobacillus spp. or a high-fat diet (HFD) followed by assessments of the kidney and heart, respectively. Results: Cx3cr1–/– MRL/lpr mice exhibited a distinct phenotype of exacerbated glomerulonephritis compared to Cx3cr1+/+ littermates, which was associated with a decrease of spleen tolerogenic marginal zone macrophages and an increase of double-negative T cells. Interestingly, upon correction of the gut microbiota with Lactobacillus administration, the phenotype of exacerbated glomerulonephritis was reversed, suggesting that CX3CR1 controls glomerulonephritis in MRL/lpr mice through a gut microbiota-dependent mechanism. Upon treatment with HFD, Cx3cr1–/– MRL/lpr mice developed significantly more atherosclerotic plaques that were promoted by Ly6C+ monocytes. Activated monocytes expressed ICOS-L that interacted with ICOS-expressing follicular T-helper cells, which in turn facilitated a germinal center reaction to produce more autoantibodies. Through a positive feedback mechanism, the increased circulatory autoantibodies further promoted the activation of Ly6C+ monocytes and their display of ICOS-L. Conclusions: We uncovered novel, Cx3cr1 deficiency-mediated pathogenic mechanisms contributing to SLE-associated glomerulonephritis and cardiovascular disease. [ABSTRACT FROM AUTHOR]

Fibroblasts and myofibroblasts are major mesenchymal cells in the lamina propria of colon mucosa and in colon cancer tissues. Detailed insight into the highly specific populations of fibroblasts and myofibroblasts is required to understand the integrity and homeostasis of human colon mucosa and colon cancer. Based on gene expression profiles of single cells, we identified fibroblast populations that produce extracellular matrix components, Wnt ligand- and BMP-secreting fibroblasts, chemokine- and chemokine ligand-generating fibroblasts, highly activated fibroblasts, immune-modulating fibroblasts, epithelial cell-modulating myofibroblasts, stimuli-responsive myofibroblasts, proliferating myofibroblasts, fibroblast-like myofibroblasts, matrix producing myofibroblasts, and contractile myofibroblasts in human colon mucosa. In colon cancer tissue, the compositions of fibroblasts and myofibroblasts were highly altered, as were the expressing patterns of genes including BMPs, Wnt ligands, chemokines, chemokine ligands, growth factors and extracellular matrix components in fibroblasts and myofibroblasts. Our work expands the working atlas of fibroblasts and myofibroblasts and provides a framework for interrogating the complexity of stromal cells in human healthy colon mucosa and colon cancer tissues. The heterogeneity of fibroblasts in normal colon mucosa and colorectal cancer (CRC) is examined using single cell data, showing their potential function in normal gut homeostasis and in CRC. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment classification has been widely used by researchers as a fine-grained sentiment classification task to predict the sentiment polarity of specific aspect words in a given sentence. Previous studies have shown relatively good experimental results using graph convolutional networks, so more and more approaches are beginning to exploit sentence structure information for this task. However, these methods do not link aspect word and context well. To address this problem, we propose a method that utilizes a hierarchical multi-head attention mechanism and a graph convolutional network (MHAGCN). It fully considers syntactic dependencies and combines semantic information to achieve interaction between aspect words and context. To fully validate the effectiveness of the method proposed in this paper, we conduct extensive experiments on three benchmark datasets, which, according to the experimental results, show that the method outperforms current methods. [ABSTRACT FROM AUTHOR]

Purpose: Development of best practices for dealing with incidental findings on neuroimaging requires insight in their frequency and clinical relevance. Methods: Here, we delineate prevalence estimates with 95% confidence intervals and clinical management of incidental findings, based on the first 3589 participants of the population-based Rhineland Study (age range 30–95 years) who underwent 3 Tesla structural neuroimaging (3D, 0.8 mm3 isotropic resolution). Two trained raters independently assessed all scans for abnormalities, with confirmation and adjudication where needed by neuroradiologists. Participants were referred for diagnostic work-up depending on the potential benefit. Results: Of 3589 participants (mean age 55 ± 14 years, 2072 women), 867 had at least one possible incidental finding (24.2%). Most common were pituitary abnormalities (12.3%), arachnoid cysts (4.1%), developmental venous anomalies (2.5%), non-acute infarcts (1.8%), cavernomas (1.0%), and meningiomas (0.7%). Forty-six participants were informed about their findings, which was hitherto unknown in 40 of them (1.1%). Of these, in 19 participants (48%), a wait-and-see policy was applied and nine (23%) received treatment, while lesions in the remainder were benign, could not be confirmed, or the participant refused to inform us about their clinical diagnosis. Conclusion: Nearly one-quarter of participants had an incidental finding, but only 5% of those required referral, that mostly remained without direct clinical consequences. [ABSTRACT FROM AUTHOR]

Levan is a type of fructan with β-(2, 6) fructosyl linkage. In recent years, levan has been used in the medicinal, food and chemical industries for its excellent physicochemical properties of good thermal stability, low viscosity, pseudoplasticity, and non-swelling in water. The levansucrase is key enzyme responsible for biosynthesis of levan from substrate sucrose. In the present study cell lysate of Escherichia coli was used as support for convenient immobilization of Ba-SacB (i.e., Ba-SacB-Cell-Lysate) under the action of dopamine. Compared with the free Ba-SacB, the Ba-SacB-Cell-Lysate showed surprisingly enhanced pH stability in the pH range 6–9, and improved relative activities in the pH ranges 4–5 and 7–10. With the presence of 10% (v/v) butyl alcohol, 5mmol/L Fe3+, and after vortex for 90 min, the Ba-SacB-Cell-Lysate showed up to 14.32 times, 4.63 times, and 9.2 times higher relative activities than the free Ba-SacB. The Ba-SacB-Cell-Lysate kept nearly 81% of the residual activity after 9 cycles of reuse. The fructo-oligosaccharide syrup (FOS) was efficiently produced through the catalysis of levanase with the substrate of levan produced by Ba-SacB-Cell-Lysate, and the preparation conditions were optimized by Box-Behnken response surface methodology. [ABSTRACT FROM AUTHOR]

Recently, social networks have been regarded as auxiliary information to mitigate the data sparsity issue in recommender systems. However, most existing social recommendation methods fail to effectively capture the relations between multiple behaviors, resulting in the correlated behaviors being unable to make semantic complements to the target behavior and sparse behavior data features. To alleviate the above problems, we propose a novel method based on graph neural network, namely Multiple Behavior Interactive Enhanced Social-aware Recommendation (MISR), which can dynamically acquire more fine-grained relations and differences between different behaviors and combine features of temporal sequences to capture potential interactions. In addition, we develop a global enhanced module to fully learn the enhanced user representation, empowering MISR to capture jointly the heterogeneous strengths of global social context and social relations. Extensive experiments on three real-world recommendation datasets validate the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions. [ABSTRACT FROM AUTHOR]

Sequential Recommendation systems play a crucial role in predicting users’ preferences based on their behavioral history. However, the existing methods ignore the extrapolation nature of sequences and do not make deep use of item provider information. This oversight limits the model’s ability to fully utilize relevant external properties. To alleviate these limitations, we design a recommendation model that incorporates Position encoding and external reinforcement (Item -Provider), named DPSRec. Specifically, we design an Embed Encoding layer, in order to distinguish the Position Embedding of previous sequence models, we combine the time variability with the position encoding with extrapolation property, and encode the item and item provider sequences accordingly. Meanwhile, to avoid the noise that the initial item embeddings might cause with Position Encoding, we calculate the position Encoding separately from the item embedding. In addition, we design a Cross Propagation layer to capture implicit higher-order dependencies between item sequences. Extensive experiments on three real-world datasets demonstrate that the proposed model generally outperforms the baselines by about 1–12.5%. Our source code will be published after the paper is published. [ABSTRACT FROM AUTHOR]

Introduction: Metastasis is a leading cause of cancer-related-deaths worldwide. Recently, cancer stem cells (CSCs) have been believed to be responsible for tumor initiation and metastasis, but till now, difference of cellular features and behaviors between CSCs from tumor tissues (TCSCs) and circulation (CCSCs) remains largely unknown, which hinders the progression of targeted therapies for metastasis. Methods and Results: Here, we provide the features of circulating gastric cancer stem cells (CGCSCs) isolated from human gastric adenocarcinoma. The CGCSCs and TGCSCs were culture in a same serum free stem cell culture medium, however the morphology are different with each other. EMT-associated markers were measured by Immunofluorescence, Western Blotting, and RT-PCR methods, and the results indicated that the CGCSCs and TGCSCs carry different epithelial-mesenchymal features. And then, proliferation and apoptosis assays revealed that the CGCSCs exhibited characteristics of higher proliferation and resistance to apoptosis in vitro. Soft agar assay and nude mice tumorigenicity assay displayed strong tumorigenicity of CGCSCs. Finally, Matrigel invasion assays and in vivo experimental metastasis assay were also performed, which demonstrated that CGCSCs carry high invasive and metastatic capabilities than TGCSCs. Conclusions: As expected, the CGCSCs indeed showed extremely invasive and metastatic properties. They also exhibited distinctive mesenchymal phenotypes, high self-renewal, proliferative capabilities, tumor induction and low apoptosis. Interestingly, CGCSCs show small cell-size than TGCSCs (tissue gastric cancer stem cells). The findings might help us to understand the biological characteristic of CGCSCs deeply, and give light to strategies for cancer therapies. [ABSTRACT FROM AUTHOR]

A micromirror actuated by three piezoelectric microcantilevers is presented for optical data tracking of high-density storage application. The microcantilevers are actuated by 2.5-μm-thick lead zirconate titanate (PZT) films which are deposited on the silicon-based substrate by a compatible sol-gel route. The X-ray diffraction result shows that the PZT film is perovskite structure and has a typical good ferroelectric loop. The quasi-static displacement of the mirror plate increases linearly with increasing the driving voltage and the tracking resolution on disk is as high as 8 nm/V. The micromirror also provides a high bandwidth of about 21 kHz, which is high enough to support the optical data tracking of future high-density storage. [ABSTRACT FROM AUTHOR]

The Tianhuashan Basin is one of the most important volcanic basins in the northern Wuyi, southeastern China, comprising two successive volcanic units, the Daguding Formation and the overlying Ehuling Formation, along with several small associated igneous intrusions. The Lengshuikeng super-large-scale Ag-Pb-Zn deposit, which is closely related to these volcanic-intrusive rocks, is located in the northwestern part of the basin. In order to understand the basin evolution and magmatism, we determined LA-ICP-MS U-Pb zircon ages for the volcanic successions and associated intrusive rocks. U-Pb zircon dating of volcanic units yielded precise ages of 144±1 Ma for crystal tuff in the lower member of the Daguding Formation, 142±1 Ma for andesite within the upper member of the Daguding Formation, 140±1 Ma for tuffite of the first (i.e., lowermost) member of the Ehuling Formation, and 137±1 Ma for rhyolitic ignimbrite within the third volcano-stratigraphic member of the Ehuling Formation. Three types of intrusive igneous rocks (quartz syenite porphyry, K-feldspar granite porphyry, and rhyolite porphyry) yielded precise weighted mean 206Pb/238U ages of 144±1, 140±1, and 140±1 Ma, respectively, suggesting that these intrusions along with the aforementioned volcanics were all emplaced during the Early Cretaceous. In addition, the weighted mean Pb/U ages determined on zircon from two samples of a granite porphyry intrusion, which hosts ore mineralization of the Lengshuikeng Ag-Pb-Zn deposit, are 158±1 and 157±1 Ma, indicating emplacement in the Late Jurassic. These new geochronological results for igneous rocks of the Tianhuashan Basin constrain the timing of volcanic and plutonic activity in the basin, and have important implications for our understanding the tectonic history of the region, and for identifying metallogenic types and the timing of ore deposition of the Lengshuikeng deposit. [ABSTRACT FROM AUTHOR]

A new compoud based on carbazole functionalized β-diketone-boron difluoride ( 1) was synthesized. It is found that compoud 1 can gel toluene, ethylbenzene and some mixed solvents, and 3D networks consisting lots of nanofibers were observed using SEM and fluorescence microscopy. The molecular packing model in xerogel 1 was proposed according to the results of XRD, UV/Vis absorption and semiempirical calculations. The obtained nanofibers prepared through organogelation can emit strong orange light under irradiation. It is interesting that the fluorescence of the nanofiber-based film can be quenched quickly and significantly upon exposed to the vapors of n-butylamine, dibutylamine, triethylamine, triethylamine, cyclohexylamine, aniline and pyridine instead of other common reagents. Notably, the response time of the nanofiber-based film to aniline vapor reached ca. 0.59 s, and the detection limit for aniline was as low as ca. 0.83 mL/m. [ABSTRACT FROM AUTHOR]

NbO-modified PZT/ZnO nanowhisker (denoted as PZT/ZnO-NbO) piezoelectric composites were prepared by a solid state sintering technique. Effects of NbO addition on the microstructure, electrical, and mechanical properties of the PZT/ZnO composites were investigated. With increasing NbO content, the grain size of the composites was reduced and the fracture mode changed from intergranular to intragranular gradually. Compared with the PZT/ZnO composites, the dielectric, piezoelectric, and ferroelectric properties of the PZT/ZnO-NbO composites were improved significantly, while mechanical properties were enhanced slightly. The optimum electrical and mechanical properties were achieved for the PZT/ZnO composites modified with 0.75 wt% NbO sintered at 1150 °C, with dielectric permittivity ε, piezoelectric coefficient d, planar electromechanical coupling k, remnant polarization P, fracture toughness K, and flexural strength σ being on the order of 4930, 600 pC/N, 0.63, 29.2 μC/cm, 1.56 MPa m and 130 MPa, respectively. The NbO-modified PZT/ZnO piezoelectric composites, with comparable electrical properties and improved mechanical properties than those of commercial PZT-5H ceramics, are promising candidates for further applications. [ABSTRACT FROM AUTHOR]

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia. [ABSTRACT FROM AUTHOR]

Metformin or glucagon-like peptide-1 (GLP-1) analogue liraglutide has cardiovascular benefits. However, it is not clear whether their combined treatment have additive or synergistic effects on the vasculature. In this study, human umbilical vein endothelial cells (HUVECs), exposed to palmitic acid (PA) to induce endothelial dysfunction, were incubated with metformin, liraglutide or their combination. High fat diet (HFD)-fed ApoE−/− mice were randomized into control, metformin, liraglutide, and combination treatment groups. Results showed that in PA-treated HUVECs and HFD-fed ApoE−/− mice, combination of metformin and liraglutide at lower dose significantly improved endothelial dysfunction compared with the single treatment. Metformin upregulated GLP-1 receptor (GLP-1R) level and protein kinase A (PKA) phosphorylation. However, PKA inhibition but not GLP-1R blockade eliminated the protective effects of metformin on endothelial function. Furthermore, AMPK inhibitor compound C abolished the metformin-mediated upregulation of GLP-1R level and PKA phosphorylation. In conclusion, combination of metformin and liraglutide has synergistic protective effects on endothelial function. Moreover, metformin stimulates GLP-1R and PKA signalling via AMPK-dependent pathway, which may account for its synergistic protective effects with liraglutide. Our findings provide new insights on the interaction between metformin and GLP-1, and provide important information for designing new GLP-1-based therapy strategies in treating type 2 diabetes. [ABSTRACT FROM AUTHOR]