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Abstract A simple and flexible mass balance approach was applied to observations of XCH4 from TROPOMI to estimate CH4 emissions over Shanxi Province, including the impacts of advective transport, pressure transport, and atmospheric diffusion. High-frequency eddy-covariance flux observations were used to constrain the driving terms of the mass balance equation. This equation was then used to calculate day-to-day and 5 km × 5 km grided CH4 emissions from May 2018 to July 2022 based on TROPOMI RPRO column CH4 observations. The Shanxi-wide emissions of CH4, 126 ± 58.8 ug/m2/s, shows a fat tail distribution and high variability on a daily time scale (the 90th percentile is 2.14 times the mean and 2.74 times the median). As the number of days in the rolling average increases, the change in the variation decreases to 128 ± 35.7 ug/m2/s at 10-day, 128 ± 19.8 ug/m2/s at 30-day and 127 ± 13.9 ug/m2/s at 90-day. The range of values of the annual mean emissions on coal mine grids within Shanxi for the years 2018 to 2022 was 122 ± 58.2, 131 ± 71.2, 111 ± 63.6, 129 ± 87.1, and 138 ± 63.4 ug/m2/s, respectively. The 5-year average emissions from TROPOMI are 131 ± 68.0 ug/m2/s versus 125 ± 94.6 ug/m2/s on the grids where the EDGAR bottom-up database also has data, indicating that those pixels with mines dominate the overall emissions in terms of both magnitude and variability. The results show that high-frequency observation-based campaigns can produce a less biased result in terms of both the spatial and temporal distribution of CH4 emissions as compared with approaches using either low-frequency data or bottom-up databases, that coal mines dominate the sources of CH4 in Shanxi, and that the observed fat tail distribution can be accounted for using this approach.

Abstract Background Preoperative discrimination between non-muscle-invasive bladder cancer (NMIBC) and the muscle invasive bladder cancer (MIBC) is a determinant of management. The purpose of this research is to employ radiomics to evaluate the diagnostic value in determining muscle invasiveness of compressed sensing (CS) accelerated 3D T2-weighted-SPACE sequence with high resolution and short acquisition time. Methods This prospective study involved 108 participants who underwent preoperative 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted sequences. The cohort was divided into training and validation cohorts in a 7:3 ratio. In the training cohort, a Rad-score was constructed based on radiomic features selected by intraclass correlation coefficients, pearson correlation coefficient and least absolute shrinkage and selection operator . Multivariate logistic regression was used to develop a nomogram combined radiomics and clinical indices. In the validation cohort, the performances of the models were evaluated by ROC, calibration, and decision curves. Results In the validation cohort, the area under ROC curve of 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted models were 0.87(95% confidence interval (CI):0.73-1.00), 0.79(95%CI:0.63–0.96) and 0.77(95%CI:0.60–0.93), respectively. The differences in signal-to-noise ratio and contrast-to-noise ratio between 3D-CS-T2-weighted-SPACE and 3D-T2-weighted-SPACE sequences were not statistically significant(p > 0.05). While the clinical model composed of three clinical indices was 0.74(95%CI:0.55–0.94) and the radiomics-clinical nomogram model was 0.88(95%CI:0.75-1.00). The calibration curves confirmed high goodness of fit, and the decision curve also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. Conclusion The radiomics model based on compressed sensing 3D T2WI sequence, which was acquired within a shorter acquisition time, showed superior diagnostic efficacy in muscle invasion of bladder cancer. Additionally, the nomogram model could enhance the diagnostic performance.

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage.

Existing group recommendation models often have a monotonous approach when solving user representation, and only simple social relationships between users are utilized. This makes user representation inaccurate and most models do not consider the impact of user common intention and social interaction on group preferences. As a result, recommended items are not aligned with user needs. To address these issues, a new group recommendation model based on user common intention and social interaction (GR-UCISI) is proposed. Firstly, a user intention separation model that combines user-item interaction history with social interaction is constructed. Graph neural networks are utilized to collect user-item interaction and social interaction information, and to solve user intention and item representation. Secondly, by utilizing the social network random walk algorithm and the [K-means] clustering algorithm, users can be grouped. User group, user intention and group intention aggregation process are combined to obtain group common intention representation. Finally, group common intention representation and item representation are calculated to obtain the list of recommended items for the group. This method fully considers the impact of user individuality and commonality among group members on group preferences. It also utilizes social relationships to alleviate the problem of data sparsity and improve model performance. The experimental results show that compared with the model with the best recommendation effect of nine models, on the Gowalla dataset, the Precision and NDCG of the GR-UCISI model are increased by 3.01% and 5.26% respectively, on the Yelp-2018 dataset, the Precision and NDCG of the GR-UCISI model are increased by 2.96% and 1.12% respectively.

Abstract Objective This study aims to screen the differentially expressed long non-coding RNAs (DELncRNAs) related to the regulation of epithelial-mesenchymal transition (EMT) in hypospadias in mesenchymal stem cell-derived exosomes (MSC-Exons) and explore the potential mechanism of these lncRNAs for the EMT in hypospadias. Methods In this study, the microarray data related to MSC-Exos and hypospadias were downloaded from Gene Expression Omnibus (GEO). Besides, the lncRNAs highly expressed in MSC-Exos and the differentially expressed mRNAs and lncRNAs in children with hypospadias were screened, respectively. In addition, the lncRNAs enriched in MSC-Exos and differentially expressed lncRNAs in hypospadias were intersected to obtain the final DElncRNAs. Moreover, the co-expression interaction pairs of differentially expressed lncRNAs and mRNAs were analyzed to construct a Competing Endogenous RNA (ceRNA) network. Finally, the candidate lncRNAs in exosomes were subjected to in vitro cell function verification. Results In this study, a total of 4 lncRNAs were obtained from the microarray data analysis. Further, a ceRNA regulatory network of MSC-Exo-derived lncRNAs related to the regulation of EMT in hypospadias was constructed, including 4 lncRNAs, 2 mRNAs, and 6 miRNAs. The cell function verification results indicated that the exosomes secreted by MSCs may transport HLA complex group 18 (HCG18) into target cells, which promoted the proliferation, migration, and EMT of these cells. Conclusion MSC-Exo-derived lncRNA HCG18 can enter target cells, and it may be involved in the regulation of EMT in hypospadias through the ceRNA network.

Since it was introduced, the Orbitrap has been proved to be a powerful ion analyzer whose resolution is affected by the fringing field in the electrode structure. Orbitrap is an ultra-high precision instrument. The gap between two outer half electrodes and the design of ion perforation on one side of the outer electrode will produce fringing field effect, resulting in the distortion of the ideal electrostatic field. The distorted field formed in the internal space of Orbitrap will have a serious impact on the resolution. Thermo Fisher Scientific is the only company that has a patent on Orbitrap mass spectrometers and related technologies, however, it has not provided relevant materials to explain the problem in detail and specific solutions, and few researchers have conducted in-depth research. Therefore, it is necessary to study the influence of the distortion field caused by the ion perforation and the outer electrode gap on the resolution, and propose a solution to the fringing field effect. In this work, ion optical simulation software (SIMION) was used to establish ideal, distorted and corrected models, in order to measure the limiting resolution of the orbital trap by measuring the axial vibration period dispersion corresponding to different initial axial amplitudes and different orbital radius. By comparing the experimental and theoretical results of the ideal model, the reliability of the selection of simulation parameters and the calculation method of ion motion were verified. In the distortion model experiment, the ion axial motion period dispersion caused by the fringing field effect arising from the outer electrode gap and ion perforation and its influence on the resolution was investigated. In the correction model experiment, it was proved that the distortion field can be corrected by adjusting the voltage of the two compensating electrodes, so as to overcome the fringing field effect caused by the ion perforation and the outer electrode gap. In addition, it was also found that the axial flight period error caused by radial dispersion in the corrected model is smaller than that in the ideal model, and the difference between the two is small in the axial dispersion. The comprehensive result of the corrected model is better than that of the ideal model. Therefore, the error caused by scratches on the surface of the electrodes can be corrected by adjusting the voltage of the compensating electrodes. The final simulation results showed that the limiting mass resolution for m/z 100 ion can reach to 2 500 000.

Abstract Wilms tumor (WT) is the most common malignancy of the genitourinary system in children. Currently, the Integration of single-cell RNA sequencing (scRNA-Seq) and Bulk RNA sequencing (RNA-Seq) analysis of heterogeneity between different cell types in pediatric WT tissues could more accurately find prognostic markers, but this is lacking. RNA-Seq and clinical data related to WT were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Small nucleolar RNA host gene 15 (SNHG15) was identified as a risk signature from the TARGET dataset by using weighted gene co-expression network analysis, differentially expressed analysis and univariate Cox analysis. After that, the functional mechanisms, immunological and molecular characterization of SNHG15 were investigated at the scRNA-seq, pan-cancer, and RNA-seq levels using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), ESTIMATE, and CIBERSORT. Based on scRNA-seq data, we identified 20 clusters in WT and annotated 10 cell types. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing M2 macrophages as hubs for intercellular communication. In addition, in vitro cellular experiments showed that siRNAs interfering with SNHG15 significantly inhibited the proliferation and migration of G401 cells and promoted the apoptosis of G401 cells compared with the control group. The effect of siRNAs interfering with SNHG15 on EMT-related protein expression was verified by Western blotting assay. Thus, our findings will improve our current understanding of the pathogenesis of WT, and they are potentially valuable in providing novel prognosis markers for the treatment of WT.

This research was to assess the dietary copper (Cu) requirement of broiler chickens fed a practical corn-soybean meal diet during 22–42 d of age. A total of 288 numbered Arbor Acres male broilers at 22 d of age were randomly allotted 6 treatments with 8 replicate cages (6 broilers per cage) per treatment. Broilers were fed a Cu-unsupplemented corn-soybean meal basal diet (control, containing 7.36 mg Cu/kg) or the basal diet added with 3, 6, 9, 12, or 15 mg Cu/kg from CuSO4·5H2O for 21 d. Quadratic, asymptotic and broken-line models were fitted and the best fitted models were selected to determine dietary Cu requirements. The results revealed that the contents of Cu in serum and liver, mRNA expression levels of Cu- and zinc-containing superoxide dismutase (CuZnSOD) in liver and monoamine oxidase b (MAO B) in heart, as well as protein expression level of CuZnSOD in liver were affected (P