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BackgroundNitrogen dioxide (NO2) is one of the main air pollutants, and though China's NO2 pollution has been improving year by year, it maintains at a high level, threatening the health of the population. ObjectiveTo investigate the effect of short-term exposure to atmospheric NO2 on the coagulation indexes in obese and normal-weight young individuals and potential modification effect of temperature. MethodsBased on a parallel control panel study design, this study recruited 53 normal-weight and 44 obese young individuals. Three prospective follow-ups were conducted. Air pollution data were obtained from the fixed monitoring station closest to the participant's residences, and personal air pollution exposure was simulated based on time-activity log and infiltration factor for the week before every follow-up. Temperature was collected from China Meteorological Data Service Center. Venous blood samples were taken to measure platelet (PLT) count, mean platelet volume (MPV), soluble CD40 ligand (sCD40L), soluble P-selectin (sP-selectin), platelet aggregation rate (PAgT), and plasminogen activator inhibitor type-1 (PAI-1) during every follow-up. A linear mixed-effect model was used to assess the association between short-term atmospheric NO2 exposure and the coagulation indexes of weight grouped young individuals, and a stratified analysis was used to explore potential modification effect of temperature. ResultsThe median [interquartile range (IQR)] of personal atmospheric NO2 exposure concentrations was 21.47 (8.01) µg·m−3. Short-term exposure to atmospheric NO2 was significantly associated the increase of sCD40L and PAgT in the obese individuals, while the most significant association appeared at 5 d lag, and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 5 d lag, sCD40L increased by 27.4% (95%CI: 4.2%, 56.6%) and PAgT increased by 37.5% (95%CI: 12.2%, 68.6%); short-term exposure to atmospheric NO2 was significantly associated with the decrease of PLT and PAgT in the normal-weight individuals, while the most significant association appeared at 5 d lag or 7 d lag, and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 5 d lag, PLT decreased by 11.8% (95%CI: −17.8%, −5.3%), and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 7 d lag, PAgT decreased by 16.8% (95%CI: −30.6%, −0.4%). We didn't find statistically significant association of short-term exposure to atmospheric NO2 with PLT in the obese individuals or sCD40L in the normal-weight individuals, nor statistically significant association between short-term exposure to atmospheric NO2 and PAI-1, MPV, and sP-selectin in different weight grouped individuals. The stratified analysis found that short-term exposure to atmospheric NO2 was significantly associated with PAgT in the normal-weight individuals, or with PLT, sCD40L, and sP-selectin in the obese individuals only at high temperature. ConclusionsShort-term exposure to atmospheric NO2 has adverse effects on the coagulation indexes of different weight grouped young individuals, and the obese individuals are more sensitive to it than the normal-weight individuals. High temperature can enhance the adverse health effect of short-term exposure to atmospheric NO2.

There have been 2,585 county-level converged media centers established in mainland China. Enhancing the attractiveness of information published on WeChat official accounts to users is a major challenge faced by these centers. This research selected articles published by WeChat official accounts of five representative county-level media converged centers in the Guangxi Zhuang Autonomous Region (from June to October 2023, totaling 4045 WeChat articles). The negative binomial regression method was used to explore the impact of title features (including punctuations, Arabic numerals, internet slang, and emotions) on the readership of WeChat articles. The results indicate that title length, square brackets, dashes, book title marks, and vertical bars are negatively correlated with readership. Titles containing Arabic numerals and reader addressing correlate positively with readership. Positive emotion tones have a positive impact on the reading behavior of users, but the effects of different emotions are complex. This research offers valuable insights for the design of WeChat article titles at county-level converged media centers and provides a new case study for research into the title features of WeChat official accounts.

Penthiopyrad (PO), a succinate dehydrogenase inhibitor (SDHI) fungicide, poses a potential risk to fish. Here, we investigated the adverse effects of PO on endocrine regulation and reproductive capacity in zebrafish during a 21-d sublethal exposure to PO concentrations ranging from 0.02 to 2.00 mg/L. Following exposure to PO (0.20 and 2.00 mg/L), female-specific effects including follicle necrosis, structural disturbance of the yolk follicle, fusion of cortical follicles appeared in ovarian tissue of adult females, which led to a significant reduction in fertility. Correspondingly, 0.20 and 2.00 mg/L PO led to a marked reduction in the GSI values of females, and 2.00 mg/L PO caused a 31% decline in the proportion of perinucleolar oocytes (PCO) in oocytes. In addition, testosterone (T) level was obviously suppressed and 17β-estradiol (E2) level was increased in females after exposure to 2.00 mg/L PO. Male zebrafish treated with 0.20 and 2.00 mg/L of PO exhibited significant interstitial enlargement, edema in the testes, and reduced diameter of seminiferous tubules, along with a thinner basement membrane. The effects of PO on males were associated with significant increase in E2 level, suggesting that PO has an estrogenic effect on male fish. Greater E2 levels in serum were further supported by increased transcription levels of genes linked to the hypothalamic-pituitary-gonad-liver (HPGL) axis. Notably, transcription levels of cyp19a, er2b, era, and cyp19b was remarkably increased, exhibiting a clear link with variations in E2 levels. Overall, the present study demonstrates that PO induces reproductive impairment in zebrafish by promoting steroidogenesis.

Summary: Here, we present a protocol to generate a murine model of liver metastasis by directly injecting tumor cells into the portal vein under ultrasound guidance. We describe steps for animal and cell preparation and two techniques for injecting tumor cells. One technique is freehand, while the other technique is device-assisted using a 3D-printed prototype device. Finally, we describe tumor surveillance with bioluminescent imaging. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Pansharpening techniques fuse the complementary information from panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution MS image. However, the majority of existing pansharpening techniques suffer from spectral distortion owing to the low correlation between the MS and PAN images, and difficulties in obtaining appropriate injection gains. To address these issues, this article presents a novel pansharpening method based on the variational fractional-order geometry (VFOG) model and optimized injection gains. Specifically, to improve the correlation between the PAN and MS images, the VFOG model is constructed to generate a refined PAN image with a similar spatial structure to the MS image, while maintaining the gradient information of the original PAN image. Furthermore, to obtain accurate injection gains, and considering that the vegetated and nonvegetated regions should be dissimilar, an optimized adaptive injection gain based on the normalized differential vegetation index is designed. The final pansharpened image is obtained by an injection model using the refined PAN image and optimized injection gains. Extensive experiments on various satellite datasets demonstrate that the proposed method offers superior spectral and spatial fidelity compared to existing state-of-the-art algorithms.

Purine nucleoside phosphorylase (PNP) enables the breakdown and recycling of guanine nucleosides. PNP insufficiency in humans is paradoxically associated with both immunodeficiency and autoimmunity, but the mechanistic basis for these outcomes is incompletely understood. Here, we identify two immune lineage-dependent consequences of PNP inactivation dictated by distinct gene interactions. During T cell development, PNP inactivation is synthetically lethal with downregulation of the dNTP triphosphohydrolase SAMHD1. This interaction requires deoxycytidine kinase activity and is antagonized by microenvironmental deoxycytidine. In B lymphocytes and macrophages, PNP regulates Toll-like receptor 7 signaling by controlling the levels of its (deoxy)guanosine nucleoside ligands. Overriding this regulatory mechanism promotes germinal center formation in the absence of exogenous antigen and accelerates disease in a mouse model of autoimmunity. This work reveals that one purine metabolism gene protects against immunodeficiency and autoimmunity via independent mechanisms operating in distinct immune lineages and identifies PNP as a potentially novel metabolic immune checkpoint.

Abstract Background The adverse effects of particulate air pollution on heart rate variability (HRV) have been reported. However, it remains unclear whether they differ by the weight status as well as between wake and sleep. Methods A repeated-measure study was conducted in 97 young adults in Beijing, China, and they were classified by body mass index (BMI) as normal-weight (BMI, 18.5–24.0 kg/m2) and obese (BMI ≥ 28.0 kg/m2) groups. Personal exposures to fine particulate matter (PM2.5) and black carbon (BC) were measured with portable exposure monitors, and the ambient PM2.5/BC concentrations were obtained from the fixed monitoring sites near the subjects’ residences. HRV and heart rate (HR) were monitored by 24-h Holter electrocardiography. The study period was divided into waking and sleeping hours according to time-activity diaries. Linear mixed-effects models were used to investigate the effects of PM2.5/BC on HRV and HR in both groups during wake and sleep. Results The effects of short-term exposure to PM2.5/BC on HRV were more pronounced among obese participants. In the normal-weight group, the positive association between personal PM2.5/BC exposure and high-frequency power (HF) as well as the ratio of low-frequency power to high-frequency power (LF/HF) was observed during wakefulness. In the obese group, personal PM2.5/BC exposure was negatively associated with HF but positively associated with LF/HF during wakefulness, whereas it was negatively correlated to total power and standard deviation of all NN intervals (SDNN) during sleep. An interquartile range (IQR) increase in BC at 2-h moving average was associated with 37.64% (95% confidence interval [CI]: 25.03, 51.51%) increases in LF/HF during wakefulness and associated with 6.28% (95% CI: − 17.26, 6.15%) decreases in SDNN during sleep in obese individuals, and the interaction terms between BC and obesity in LF/HF and SDNN were both statistically significant (p

Summary: In traumatic brain injury (TBI), a diversity of brain resident and peripherally derived myeloid cells have the potential to worsen damage and/or to assist in healing. We define the heterogeneity of microglia and macrophage phenotypes during TBI in wild-type (WT) mice and Ccr2−/− mice, which lack macrophage influx following TBI and are resistant to brain damage. We use unbiased single-cell RNA sequencing methods to uncover 25 microglia, monocyte/macrophage, and dendritic cell subsets in acute TBI and normal brains. We find alterations in transcriptional profiles of microglia subsets in Ccr2−/− TBI mice compared to WT TBI mice indicating that infiltrating monocytes/macrophages influence microglia activation to promote a type I IFN response. Preclinical pharmacological blockade of hCCR2 after injury reduces expression of IFN-responsive gene, Irf7, and improves outcomes. These data extend our understanding of myeloid cell diversity and crosstalk in brain trauma and identify therapeutic targets in myeloid subsets.

As with the extensive use of complex software in many fields, such as finance, transportation, aeronautics, and astronautics, software plays an increasingly more important role in society. Software reliability becomes a critical bottleneck for system reliability and draws increasingly greater attention from software engineers. Many researchers find that traditional software verification and validation techniques are not sufficient to ensure complex software reliability, especially after being deployed. In this paper, therefore, we propose a novel software runtime monitoring method that can help with software runtime verification and software failure prediction. This method aims to monitor software execution trace that reveal the software runtime status. First, the framework of this method is proposed to introduce the entire process and two key problems. Second, an instrumentation method based on srcML, an open source tool used to extract the software abstract syntax tree, is presented. By this instrumentation technique, monitoring code can be inserted into source code and runtime data can be exported during the software execution process. Then, a runtime data-collection technique is proposed to collect runtime data exported by monitoring code. On one hand, the file stream mechanism is used to export data files that can be used to support offline analysis, while on the other hand a client-server structure is proposed to support online analysis. Finally, a case study on Nginx is used to show the feasibility of the proposed method.

Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes.

Cervical cancer (CESC) is a gynecologic malignant tumor associated with high incidence and mortality rates because of its distinctive management complexity. Herein, we characterized the molecular features of CESC based on the metabolic gene expression profile by establishing a novel classification system and a scoring system termed as METAscore. Integrative analysis was performed on human CESC samples from TCGA dataset. Unsupervised clustering of RNA sequencing data on 2,752 formerly described metabolic genes identified three METAclusters. These METAclusters for overall survival time, immune characteristics, metabolic features, transcriptome features, and immunotherapeutic effectiveness existed distinct differences. Then we analyzed 207 DEGs among the three METAclusters and as well identified three geneclusters. Correspondingly, these three geneclusters also differently expressed among the aforementioned features, supporting the reliability of the metabolism-relevant molecular classification. Finally METAscore was constructed which emerged as an independent prognostic biomarker, related to CESC transcriptome features, metabolic features, immune characteristics, and linked to the sensitivity of immunotherapy for individual patient. These findings depicted a new classification and a scoring system in CESC based on the metabolic pattern, thereby furthering the understanding of CESC genetic signatures and aiding in the prediction of the effectiveness to anticancer immunotherapies.

Multi-scale geometric analysis is a popular tool that is widely used in the field of multi-focus image fusion. It plays a large role in extracting the features from input images. In this paper, a novel multi-focus image fusion method based on a non-fixed-base dictionary and multi-measure optimization is presented in the non-subsampled shearlet transform (NSST) domain. The proposed approach contains the following four steps. The input images are first decomposed by NSST into low- and high-frequency coefficients. Second, a sparse representation (SR)-based framework is proposed and applied to merge the low-frequency coefficients of the input images. In this framework, a non-fixed-base dictionary iteratively trained by the previous dictionary is constructed, which represents the complex details of the source images. Third, a type-2 fuzzy logic scheme is introduced for the high-frequency coefficient fusion, which can effectively select the high-quality coefficients from the source images so that it can achieve better performance. Finally, an inverse NSST operation is conducted in the merged low- and high-pass subbands and thus obtains the initial fused image. A multi-measure optimization method is then employed to optimize the initial fused image, and thus, the final fused result is achieved. In this method, three different measures, namely, pixel difference, visual saliency, and similarity, are designed to select the focus regions more completely. The experimental results demonstrate that our proposed approach yields a better effect than other methods in both the visual quality and the objective assessment.

Current research on software vulnerability analysis mostly focus on source codes or executable programs. But these methods can only be applied after software is completely developed when source codes are available. This may lead to high costs and tremendous difficulties in software revision. On the other hand, as an important product of software design phase, architecture can depict not only the static structure of software, but also the information flow due to interaction of components. Architecture is crucial in determining the quality of software. As a result, by locating the architecture-level information flow that violates security policies, vulnerabilities can be found and fixed in the early phase of software development cycle when revision is easier with lower cost. In this paper, an approach for analyzing information flow vulnerability in software architecture is proposed. First, the concept of information flow vulnerability in software architecture is elaborated. Corresponding security policies are proposed. Then, a method for constructing service invocation diagrams based on graph theory is proposed, which can depict information flow in software architecture. Moreover, an algorithm for vulnerability determination is designed to locate architecture-level vulnerabilities. Finally, a case study is provided, which verifies the effectiveness and feasibility of the proposed methods.

An extending Borgonovo’s global sensitivity analysis is proposed to measure the influence of fuzzy distribution parameters on fuzzy failure probability by averaging the shift between the membership functions (MFs) of unconditional and conditional failure probability. The presented global sensitivity indices can reasonably reflect the influence of fuzzy-valued distribution parameters on the character of the failure probability, whereas solving the MFs of unconditional and conditional failure probability is time-consuming due to the involved multiple-loop sampling and optimization operators. To overcome the large computational cost, a single-loop simulation (SLS) is introduced to estimate the global sensitivity indices. By establishing a sampling probability density, only a set of samples of input variables are essential to evaluate the MFs of unconditional and conditional failure probability in the presented SLS method. Significance of the global sensitivity indices can be verified and demonstrated through several numerical and engineering examples.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is projected to rise to the second leading cause of U.S. cancer-related deaths by 2020. Novel therapeutic targets are desperately needed. MicroRNAs (miRs) are small noncoding RNAs that function by suppressing gene expression and are dysregulated in cancer. miR-21 is overexpressed in PDAC tumor cells (TC) and is associated with decreased survival, chemoresistance and invasion. Dysregulation of miR regulatory networks in PDAC tumor-associated fibroblasts (TAFs) have not been previously described. In this study, we show that miR-21 expression in TAFs promotes TC invasion. METHODS: In-situ hybridization for miR-21 was performed on the 153 PDAC patient UCLA tissue microarray and 23 patient-matched lymph node metastases. Stromal and TC histoscores were correlated with clinicopathologic parameters by univariate and multivariate Cox regression. miR-21 positive cells were further characterized by immunofluorescence for mesenchymal/epithelial markers. For in vitro studies, TAFs were isolated from freshly resected human PDAC tumors by the outgrowth method. miR-21 was overexpressed/inhibited in fibroblasts and then co-cultured with GFP-MiaPaCa TCs to assess TC invasion in modified Boyden chambers. RESULTS: miR-21 was upregulated in TAFs of 78% of tumors, and high miR-21 significantly correlated with decreased overall survival (P = 0.04). Stromal miR-21 expression was also significantly associated with lymph node invasion (P = 0.004), suggesting that it is driving TC spread. Co-immunofluorescence revealed that miR-21 colocalized with peritumoral fibroblasts expressing α-smooth muscle actin. Moreover, expression of miR-21 in primary TAFs correlated with miR-21 in TAFs from patient-matched LN metastases; evidence that PDAC tumor cells induce TAFs to express miR-21. miR-21 expression in TAFs and TCs promotes invasion of TCs and is inhibited with anti-miR-21. CONCLUSIONS: miR-21 expression in PDAC TAFs is associated with decreased overall survival and promotes TC invasion. Anti-miR-21 may represent a novel therapeutic strategy for dual targeting of both tumor and stroma in PDAC.

To efficiently evaluate the influence of the distribution parameters of the input variables on the failure probability of engineering structures and improve the reliability and safety of engineering structures in a targeted manner, new methods for the global reliability sensitivity analysis (RSA) of distribution parameters are proposed in this study based on the cubature formula (CF), surrogate sampling probability density function (SSPDF), and quasi-Monte Carlo (QMC) method. By introducing CF, the proposed methods can effectively improve the computational efficiency of the nested expectation and variance operators in the reliability sensitivity indices of the distribution parameters. Based on the concept of SSPDF, a surrogate importance sampling probability density function was developed. This not only overcomes the problem of the computational effort of propagating parameter uncertainty to the failure probability function (FPF), which depends on the dimensionality of the parameters; it also further improves the efficiency of the RSA of the parameters in the case of a small failure probability. Finally, by incorporating the idea of the QMC method, the process of calculating the reliability sensitivity indices of the parameters is reduced from a double-loop to a single-loop one. Three engineering examples are used in this study to demonstrate the efficiency and accuracy of the new algorithms. [ABSTRACT FROM AUTHOR]