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Abstract Esophageal cancer is a grave malignant condition. While radiotherapy, often in conjunction with chemotherapy, serves as a cornerstone in the management of locally advanced or metastatic cases, patient tolerance and treatment resistance frequently hinder its efficacy. Cell-in-cell structures, prevalent in various tumors, have been linked to prognosis. Hence, investigating the prognostic significance and regulatory mechanisms of genes related to these intracellular structures in esophageal cancer is imperative. The Cancer Genome Atlas (TCGA) Esophageal Cancer (ESCA) dataset served as the training set for the analysis. Differentially expressed genes (DEGs) in ESCA samples were identified, with those related to intercellular structures designated cell-in-cell-related differential expression genes (CIC-related DEGs). Cox regression analysis was employed to identify prognostic genes, categorizing samples into high- and low-risk groups based on median risk scores. Validation was conducted using the GSE53624 risk model. Established methodologies included morphological mapping, enrichment analysis, immune infiltration analysis, prognostic gene expression validation, molecular docking, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) validation. Thirty-eight intersecting genes were identified between the disease and normal groups in ESCA samples. Stepwise multivariate Cox analysis pinpointed three prognostic genes: androgen receptor (AR), C-X-C motif chemokine ligand 8 (CXCL8), and epidermal growth factor receptor (EGFR). The risk model’s applicability was confirmed in the GSE53624 dataset, revealing eight significantly different immune-related gene sets. Prognostic gene expression validation demonstrated significant differences between the disease and normal groups in both datasets. The proteins corresponding to the three prognostic genes interacted with gefitinib and osimertinib. RT-PCR results corroborated the differential expression of prognostic genes in esophageal cancer tissues. This study identified AR, CXCL8, and EGFR as prognostic genes and demonstrated their molecular interactions with gefitinib and osimertinib, providing a foundation for ESCA diagnosis and treatment.

Abstract Moiré superlattices in two-dimensional stacks have attracted worldwide interest due to their unique electronic properties. A typical example is the moiré ferroelectricity, where adjacent moirés exhibit opposite spontaneous polarization that can be switched through interlayer sliding. However, in contrast to ideal regular ferroelectric moiré domains (equilateral triangles) built in most theoretical models, the unavoidable irregular moiré supercells (non-equilateral triangles) induced by external strain fields during the transfer process have been given less attention. Manipulation of controllable polarization evolutions is also a big challenge due to an interlinked network of polarized domains. In this study, we employ a sliding-disturb measurement to examine and modulate these irregular moirés via mechanical force. By introducing a curved substrate, the irregular moirés are fabricated, and three distinct types of moiré domains with different patterns are identified and modulated by external mechanical force disturbing. They exhibit reduced pinning forces when the shear direction is not aligned with the strain direction. The shift of the moirés is observed to be orthogonal to the shear direction. This work offers an effective pathway for the controlled switch of the polarization in interfacial ferroelectricity.

Abstract The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. UCS is crucial in assessing the stability and durability of rock masses, which directly influences the safety, efficiency, and cost-effectiveness of construction and excavation operations. Here’s a refined version of your text for enhanced clarity and flow: in this part, the execution of the proposed model was compared for both single and hybrid configurations. Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). For training, 70% of the UCS dataset was utilized, while the remaining 30% was equally divided between testing (15%) and validation (15%). For the model evaluation, several metrics were considered in this work, including the R 2, RMSE, WAPE, MAE, and RAE, which ensure fairness in the analysis. The closer the R 2 value comes to 1, the better the performance. The error metrics should be close to 0 for better accuracy. From Table 2, one can observe that the result of the standalone SVR model gave an RMSE of 6.213 during training and 9.454 during testing, hence showing poor performance. However, the inclusion of optimization algorithms significantly improved the performance of the SVR framework. Among the hybrid models, the SVSH model had the best performance, with an R 2 value of 0.998 and an RMSE of 1.261 during training. The SVCO model performed moderately, with an R 2 value of 0.988 during training.

In rapidly developing regions, especially in emerging economies like Ghana, the decline of greenery is an urgent concern. This ongoing trend leads to the significant loss of land and green areas, which worsens environmental challenges and imposes substantial costs on Local as well as state governments. This study focuses on the Wa Municipality, examining the interplay between urbanization, regulatory policies, and human behavior in influencing green space loss.The innovation of this research lies in its integrated approach, combining qualitative insights with regression analysis to uncover complex dynamics affecting urban green spaces. Using statistical analysis and qualitative insights combined, the study revealed that while regulatory policies play a positive role in the preservation of green spaces (with a regression coefficient of Rp = 1.07, p

Abstract Separation of multi-component mixtures in an energy-efficient manner has important practical impact in chemical industry but is highly challenging. Especially, targeted simultaneous removal of multiple impurities to purify the desired product in one-step separation process is an extremely difficult task. We introduced a pore integration strategy of modularizing ordered pore structures with specific functions for on-demand assembly to deal with complex multi-component separation systems, which are unattainable by each individual pore. As a proof of concept, two ultramicroporous nanocrystals (one for C2H2-selective and the other for CO2-selective) as the shell pores were respectively grown on a C2H6-selective ordered porous material as the core pore. Both of the respective pore-integrated materials show excellent one-step ethylene production performance in dynamic breakthrough separation experiments of C2H2/C2H4/C2H6 and CO2/C2H4/C2H6 gas mixture, and even better than that from traditional tandem-packing processes originated from the optimized mass/heat transfer. Thermodynamic and dynamic simulation results explained that the pre-designed pore modules can perform specific target functions independently in the pore-integrated materials.