RoughSet-DDPM: An Image Super-Resolution Method Based on Rough set Denoising Diffusion Probability Model

Image super-resolution aims to generate high-resolution (HR) images from low-resolution (LR) inputs. Existing methods like autoregressive models, generative adversarial networks (GANs), and denoising diffusion probability models (DDPMs) have limitations in image quality or sampling efficiency. This paper proposes Rough Set-DDPM, a new super-resolution technique combining rough set theory and DDPMs. The rough set formulation divides the DDPM sampling sequence into optimal sub-columns by minimizing roughness of sample sets. Particle swarm optimization identifies the sub-columns with lowest roughness. Rough Set-DDPM applies iterative denoising on these optimal columns to output HR images. Experiments on the FFHQ dataset validate that Rough Set-DDPM improves DDPM sampling efficiency while maintaining image fidelity. Quantitative results show Rough Set-DDPM requires fewer sampling steps and generates higher quality HR images compared to autoregressive models and GANs. By enhancing DDPM sampling, Rough Set-DDPM provides an effective approach to super-resolution that balances image quality and sampling speed. The key contributions include introducing rough sets to optimize DDPM sampling and demonstrating superior performance over existing methods

Eriodictyol modulates glioma cell autophagy and apoptosis by inhibition of PI3K/Akt/mTOR signaling pathway

Purpose: To investigate the effects of eriodictyol (ERD) on U251 human glioma cell cycle and viability, autophagy and apoptosis by modulation of PI3/Akt/mTOR signaling cascade. Methods: 740 Y-P was used to activate U251 human glioma cells. For exploring ERD effects, the U251 cells were treated with ERD and 740 Y-P together. MTT assay was used to elucidate cell viability and apoptosis. The expression of autophagic proteins (LC3B and Beclin-1), and apoptotic proteins (Bcl-2 and Bax) were quantified using Western blotting. To explore the role of PI3K/Akt/mTOR signaling pathway, their expression was measured in comparison to their respective phosphorylated derivatives by Western blotting. Results: ERD exposure downregulated p-PI3K and p-Akt protein expression. The results also indicate that ERD reduced cell viability and stimulated apoptosis in U251 cells (p < 0.05). Consequently, Bax expression was upregulated and the expression of Bcl-2 was downregulated. ERD enhanced the autophagy of glioma cells U251 by enhancing LC3B and Beclin-1 expression (p < 0.05). These effects were opposite to that revealed by 740 Y-P exposure alone. Conclusion: ERD reduces U251 human glioma cell viability, and triggers cell autophagy and apoptosis, which is significantly correlated to downregulation of PI3K/Akt/mTOR signalling cascade. Thus, the compound can potentially be used for the treatment of glioma

Diaqua­bis[5-(2-pyrid­yl)tetra­zolato-κ2 N 1,N 5]iron(II)

The title complex, [Fe(C6H4N5)2(H2O)2], was synthesized by the reaction of ferrous sulfate with 5-(2-pyrid­yl)-2H-tetra­zole (HL). The FeII atom, located on a crystallographic center of inversion, is coordinated by four N-atom donors from two planar trans-related deprotonated L ligands and two O atoms from two axial water mol­ecules in a distorted octa­hedral geometry. The FeII mononuclear units are further connected by inter­molecular O—H⋯N and C—H⋯O hydrogen-bonding inter­actions, forming a three-dimensional framework

KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demonstrated superior predictive performance on KGC tasks, these models infer missing links in a black-box manner that lacks transparency and accountability, preventing researchers from developing accountable models. Existing KGE-based explanation methods focus on exploring key paths or isolated edges as explanations, which is information-less to reason target prediction. Additionally, the missing ground truth leads to these explanation methods being ineffective in quantitatively evaluating explored explanations. To overcome these limitations, we propose KGExplainer, a model-agnostic method that identifies connected subgraph explanations and distills an evaluator to assess them quantitatively. KGExplainer employs a perturbation-based greedy search algorithm to find key connected subgraphs as explanations within the local structure of target predictions. To evaluate the quality of the explored explanations, KGExplainer distills an evaluator from the target KGE model. By forwarding the explanations to the evaluator, our method can examine the fidelity of them. Extensive experiments on benchmark datasets demonstrate that KGExplainer yields promising improvement and achieves an optimal ratio of 83.3% in human evaluation.Comment: 13 pages, 7 figures, 11 tables. Under Revie

Tricyclo­hex­yl(3,5-dibromo-2-hy­droxy­benzoato-κO)tin(IV)

In the title compound, [Sn(C6H11)3(C7H3Br2O3)], the Sn atom is four-coordinate and possesses a distorted Sn(C3O) tetra­hedral geometry, with Sn—C bond lengths in the range 2.132 (6)–2.144 (6) Å and with Sn—O = 2.086 (4) Å. The uncoordinated carboxyl­ate O atom forms a weak contact with the Sn atom, with an Sn⋯O separation of 2.962 (2) Å