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[Display omitted] • The incorporation of SiF 6 2− anion-pillars enhances CO 2 adsorption. • CuSiF 6 -TPPY reduces the energy barrier for *CO-to-*CHO while inhibiting desorption of CO. • Layer-to-layer model for photoinduced charge transfer suppresses electron-hole recombination. • Remarkable CH 4 yield of 13.84 μmol g−1 h−1 with a high selectivity of 88.78 %. The highly selective photoreduction of CO 2 to CH 4 is hindered by the sluggish eight-electron transfer process and rapid recombination of charge carriers. Herein, an anion-pillared metal–organic framework photocatalyst, CuSiF 6 -TPPY, is judiciously constructed for the highly selective photoreduction of CO 2 to CH 4. The polar SiF 6 2− pillars significantly enhance the CO 2 adsorption capacity to 96.56 cm3 g−1. Furthermore, the incorporation of SiF 6 2− pillars separates the HOMO and LUMO energy levels in distinct MOF layers, leading to a lower energy barrier for *CHO generation and a higher energy barrier for CO desorption. As a result, CuSiF 6 -TPPY delivers a CH 4 generation rate of 13.84 μmol g−1 h−1 with a high selectivity of 88.78 %, 4-times higher than TPPY ligands. Moreover, in-situ IR spectra and computational simulations further confirm the photoreduction pathways. [ABSTRACT FROM AUTHOR]

Probabilistic site characterization is the cornerstone of data-centric geotechnics, which underpins the digitalization in geotechnical engineering. The essential aspect of probabilistic site characterization is to reasonably infer the probability density distributions of geotechnical parameters, based on field and laboratory data. In this paper, a clay property database, SZ-CLAY/11/5130, was established by collecting geotechnical investigation reports from 52 engineering projects in Shenzhen city. This SZ-CLAY/11/5130 database was first compared with the global clay database CLAY/10/7490 and the Shanghai clay database SH-CLAY/11/4051, in terms of their sample ranges, correlations, and scatter plots. Then, the multivariate probability distribution of eight clay parameters was established using the training records selected from SZ-CLAY/11/5130, to characterize the generic correlations between multiple clay parameters. Based on Bayesian theory, the posterior distributions of clay mechanical parameters from a construction site in Shenzhen city were updated using both the site-specific correlations and the generic correlations given by SZ-CLAY/11/5130. The constructed multivariate probability density distribution is necessary for this updating step. [ABSTRACT FROM AUTHOR]

Learning causal structure in a complex system is crucial for causal inference and decision-making. Yet it remains a challenging problem due to the presence of unknown and varying feature spaces in most real-world scenes. Existing methods, including constraint-based and score-based methods, mainly rely on strict constraints, e.g., faithfulness, and cannot handle varying feature spaces. We propose a new gradient-based method ( C 2 S L I F S ) for learning causal structures from dynamic observational data with incremental instances and feature spaces. The C 2 S L I F S is designed as a nonlinear model with hidden variables, and utilizes the strategies of continuously optimizing reconstruction loss using dynamic weighting coefficients and equality constraints to update causal relationships, ensure acyclicity and avoid faithfulness constraints. We demonstrate the competitive performance of the proposed method through empirical evaluations against existing state-of-the-art methods. Besides that, two real-world case studies on two Bayesian network datasets (BNs), i.e., a protein signal network (Sachs) and a medical diagnostic alarm message network (Alarm), are conducted to elaborate on the effectiveness of C 2 S L I F S in real-world scenes. Code implementing the proposed algorithm is open-source and publicly available at https://github.com/youdianlong/C2SLIFS. • Initially study CSL from incremental instances and feature spaces instead of fixed ones. • Propose a nonlinear CSL model with hidden variables to improve performance and avoid faithfulness constraints. • Leverage gradient-based continuous optimization to adapt changes of feature spaces. • Relax the constraint bound using ζ instead of zero to enforce acyclicity in dynamic scenes. [ABSTRACT FROM AUTHOR]

Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer. [ABSTRACT FROM AUTHOR]

Video dehazing is a critical research area in computer vision that aims to enhance the quality of hazy frames, which benefits many downstream tasks, e.g. semantic segmentation. Recent work devise CNN-based structure or attention mechanism to fuse temporal information, while some others utilize offset between frames to align frames explicitly. Another significant line of video dehazing research focuses on constructing paired datasets by synthesizing foggy effect on clear video or generating real haze effect on indoor scenes. Despite the significant contributions of these dehazing networks and datasets to the advancement of video dehazing, current methods still suffer from spatial–temporal inconsistency and poor generalization ability. We address the aforementioned issues by proposing a triplane smoothing module to explicitly benefit from spatial–temporal smooth prior of the input video and generate temporally coherent dehazing results. We further devise a query base decoder to extract haze-relevant information while also aggregate temporal clues implicitly. To increase the generalization ability of our dehazing model we utilize CLIP guidance with a rich and high-level understanding of hazy effect. We conduct extensive experiments to verify the effectiveness of our model to generate spatial–temporally consistent dehazing results and produce pleasing dehazing results of real-world data. [ABSTRACT FROM AUTHOR]