Water-Soluble 3D Covalent Organic Framework that Displays an Enhanced Enrichment Effect of Photosensitizers and Catalysts for the Reduction of Protons to H2.

Covalent organic frameworks (COFs) are emerging porous polymers that have 2D or 3D long-range ordering. Currently available COFs are typically insoluble or decompose upon dissolution, which remarkably restricts their practical implementations. For 3D COFs, the achievement of noninterpenetration, which maximizes their porosity-derived applications, also remains a challenge synthetically. Here, we report the synthesis of the first highly water-soluble 3D COF (sCOF-101) from irreversible polymerization of a preorganized supramolecular organic framework through cucurbit[8]uril (CB[8])-controlled [2 + 2] photodimerization. Synchrotron X-ray scattering and diffraction analyses confirm that sCOF-101 exhibits porosity periodicity, with a channel diameter of 2.3 nm, in both water and the solid state and retains the periodicity under both strongly acidic and basic conditions. As an ordered 3D polymer, sCOF-101 can enrich [Ru(bpy)3]2+ photosensitizers and redox-active polyoxometalates in water, which leads to remarkable increase of their photocatalytic activity for proton reduction to produce H2

Element Geochemical Analysis of the Contribution of Aeolian Sand to Suspended Sediment in Desert Stream Flash Floods

The interaction of wind and water in semiarid and arid areas usually leads to low-frequency flash flood events in desert rivers, which have adverse effects on river systems and ecology. In arid zones, many aeolian dune-fields terminate in stream channels and deliver aeolian sand to the channels. Although aeolian processes are common to many desert rivers, whether the aeolian processes contribute to fluvial sediment loss is still unknown. Here, we identified the aeolian-fluvial cycling process responsible for the high rate of suspended sediment transport in the Sudalaer desert stream in the Ordos plateau of China. On the basis of element geochemistry data analysis, we found that aeolian sand was similar to suspended sediment in element composition, which suggests that aeolian sand contributes to suspended sediment in flash floods. Scatter plots of some elements further confirm that aeolian sand is the major source of the suspended sediment. Factor analysis and the relation between some elements and suspended sediment concentration prove that the greater the aeolian process, the higher the suspended sediment concentration and the greater the contribution of aeolian sand to suspended sediment yield. We conclude that aeolian sand is the greatest contributor to flash floods in the Sudalaer desert stream

GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy

Online adaptive radiation therapy (ART) has great promise to significantly reduce normal tissue toxicity and/or improve tumor control through real-time treatment adaptations based on the current patient anatomy. However, the major technical obstacle for clinical realization of online ART, namely the inability to achieve real-time efficiency in treatment re-planning, has yet to be solved. To overcome this challenge, this paper presents our work on the implementation of an intensity modulated radiation therapy (IMRT) direct aperture optimization (DAO) algorithm on graphics processing unit (GPU) based on our previous work on CPU. We formulate the DAO problem as a large-scale convex programming problem, and use an exact method called column generation approach to deal with its extremely large dimensionality on GPU. Five 9-field prostate and five 5-field head-and-neck IMRT clinical cases with 5\times5 mm2 beamlet size and 2.5\times2.5\times2.5 mm3 voxel size were used to evaluate our algorithm on GPU. It takes only 0.7~2.5 seconds for our implementation to generate optimal treatment plans using 50 MLC apertures on an NVIDIA Tesla C1060 GPU card. Our work has therefore solved a major problem in developing ultra-fast (re-)planning technologies for online ART

Firm-Generated Online Content in Social Media and Stock Performance: An Event Window Study of Twitter and the S&P 500

This study explores how different types of firm-generated online content (FGOC) on social media affect stock performance. Employing signaling theory and limited attention theory, we analyze stock market data from 141 companies in the S&P 500 index and categorize FGOC on Twitter into distinct signal types through semantic analysis. Using econometric models, we estimate the relationships between these FGOC signals and abnormal stock returns. Our findings reveal that disseminating a greater number of strong image-enhancing FGOC signals, particularly those related to new products and financial matters, significantly enhances stock performance, resulting in higher abnormal stock returns. In contrast, weak image-enhancing FGOC signals not only fail to improve stock performance but also diminish the positive relationship between strong image-enhancing signals, especially those pertaining to financial information, and stock performance. This study contributes to the literature by illuminating the interplay between different types of FGOC, addressing the need for research on how varying informational elements interact in social media contexts. It provides practical guidance for managers on managing digital communication strategies to enhance investor engagement and optimize market outcomes

Correlation between childhood tuberculosis and abundance of T cell gene transcription and impaired T cell function

Purpose: To investigate the relationship amongst childhood tuberculosis, abundance of T cell gene transcription and impairment of T cell function. Methods: A total of 329 pediatric patients treated for tuberculosis in Central Hospital of Zibo, Zibo, China from 2017 to 2019 were enrolled in the study. Among them, 167 cases of tuberculosis-hospitalized children were assigned to the TB group. Additionally, 162 well- and adequately-treated patients with a previous history of tuberculosis were selected as the control group. The abundance of continuous gene transcripts in the peripheral blood of the children was analyzed. The RNA profiles were analyzed via microarray, while interferon (IFN) level was measured by enzyme linked immunosorbent assay (ELISA). The T cell proliferation was determined by thymidine assay. Results: Within 6 months of the commencement of treatment, the differentially expressed transcripts returned the expression in children in the control group. The abundance of Talipes equinovarus, atrial septal defect, robin sequence, and the persistence of the left superior vena cava (TARP) gene transcription in the TB group was lower than in the control group on days 30, 120 and 180 (p < 0.05), while IL1R2 gene transcription abundance in the TB group was higher than in the control group on days 30, 120 ,180 (p < 0.05). The proliferation of T cells and IFNγ in tuberculosis children (TB group) were lower than in healthy controls (p < 0.05). In this study, a total of 129 genes were found to have significant differences in expression, and hence it is speculated that changes in RNA abundance altered the immune pathway. Conclusion: The reduced abundance of T cell gene transcription and renovated T cell function in children with tuberculosis are related to acquired immunodeficiency. The results of this study provide a theoretical basis for the clinical diagnosis and treatment of tuberculosis in children

Pattern Recognition for Steam Flooding Field Applications based on Hierarchical Clustering and Principal Component Analysis

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward\u27s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance