Seismic Displacements of Rigid Retaining Walls

Rigid retaining walls experience significant displacements during earthquakes. Several investigations have developed 1-D and 2-D models to predict displacements. A critical review of the state of the art shows that these model may not predict realistic displacement Wu (1999). A new 2-D model, which considers strain dependent soil stiffness and material damping, sliding and rocking motions, and practical field water conditions behind the wall as per Eurocode (1994) have been presented. Typical results are included. A comparison of prediction and performance of a centrifuge model has shown good agreement. This model represents a considerable advance over the existing solutions and is easily useable by the practicing engineer

On Seismic Displacements of Rigid Retaining Walls

Rigid retaining walls experience both sliding and rocking displacement during earthquakes. Richards and Elms\u27 (1979) method incorporates only sliding displacements in design. A realistic method of computing both sliding and rocking displacements of the walls based on nonlinear soil properties both for the base soil and backfill was developed by Rafnsson and Prakash (1994). A design procedure based on this method was then developed by Prakash. et al (1995, b). Comparisons of the displacements of typical walls by both the methods have been made. Also, typical field data from recent earthquakes has been analyzed to compare the observed displacements with the computed displacements

On Seismic Design Displacements of Rigid Retaining Walls

Correlation of the dynamic displacement and significant factors are presented. Three computer programs (Rafnsson, 1991) have been modified to develop design charts. The wall dimension computed by static condition and related displacement under dynamic loading can be estimated from the computer programs. Twenty-one combinations of base soil and back fill, 5 different ground motions and 7 different heights of wall are used in the analyses to develop design charts. These will help the designer to predict the dynamic behavior of retaining walls and to optimize the design work. Furthermore, equations have been fitted to predict the displacement without using the computer program in several cases

Economic Aseismic Design of Rigid Retaining Wall

Rigid retaining walls experience significant displacements during earthquakes. Several investigators have developed 1- D and 2-D models to predict displacements. A critical review of the state of the art shows that these model may not predict realistic displacements Wu (1999). A new 2-D model, which considers strain dependant soil stiffness and material damping, sliding and rocking motions, and practical field water conditions behind the wall as per Eurocode (1994) has been developed (Wu 1999). This model represents a considerable advance over the existing solutions and is easily useable by the practicing engineer. It has been shown that walls inclined on the back fill offer several technical advantage

Pressure-induced structural modulations in coesite

Silica phases, SiO2, have attracted significant attention as important phases in the fields of condensed-matter physics, materials science, and (in view of their abundance in the Earth's crust) geoscience. Here, we experimentally and theoretically demonstrate that coesite undergoes structural modulations under high pressure. Coesite transforms to a distorted modulated structure, coesite-II, at 22–25 GPa with modulation wave vector q=0.5b∗. Coesite-II displays further commensurate modulation along the y axis at 36–40 GPa and the long-range ordered crystalline structure collapses beyond ∼40GPa and starts amorphizing. First-principles calculations illuminate the nature of the modulated phase transitions of coesite and elucidate the modulated structures of coesite caused by modulations along the y-axis direction. The structural modulations are demonstrated to result from phonon instability, preceding pressured-induced amorphization. The recovered sample after decompression develops a rim of crystalline coesite structure, but its interior remains low crystalline or partially amorphous. Our results not only clarify that the pressure-induced reversible phase transitions and amorphization in coesite originate from structural modulations along the y-axis direction, but also shed light on the densification mechanism of silica under high pressure

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP).Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed.Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937–0.990) and 0.862 (95% CI 0.742–0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways.Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases

Association between gut microbiota and pan-dermatological diseases: a bidirectional Mendelian randomization research

BackgroundGut microbiota has been associated with dermatological problems in earlier observational studies. However, it is unclear whether gut microbiota has a causal function in dermatological diseases.MethodsThirteen dermatological diseases were the subject of bidirectional Mendelian randomization (MR) research aimed at identifying potential causal links between gut microbiota and these diseases. Summary statistics for the Genome-Wide Association Study (GWAS) of gut microbiota and dermatological diseases were obtained from public datasets. With the goal of evaluating the causal estimates, five acknowledged MR approaches were utilized along with multiple testing corrections, with inverse variance weighted (IVW) regression serving as the main methodology. Regarding the taxa that were causally linked with dermatological diseases in the forward MR analysis, reverse MR was performed. A series of sensitivity analyses were conducted to test the robustness of the causal estimates.ResultsThe combined results of the five MR methods and sensitivity analysis showed 94 suggestive and five significant causal relationships. In particular, the genus Eubacterium_fissicatena_group increased the risk of developing psoriasis vulgaris (odds ratio [OR] = 1.32, pFDR = 4.36 × 10−3), family Bacteroidaceae (OR = 2.25, pFDR = 4.39 × 10−3), genus Allisonella (OR = 1.42, pFDR = 1.29 × 10−2), and genus Bacteroides (OR = 2.25, pFDR = 1.29 × 10−2) increased the risk of developing acne; and the genus Intestinibacter increased the risk of urticaria (OR = 1.30, pFDR = 9.13 × 10−3). A reverse MR study revealed insufficient evidence for a significant causal relationship. In addition, there was no discernible horizontal pleiotropy or heterogeneity.ConclusionThis study provides novel insights into the causality of gut microbiota in dermatological diseases and therapeutic or preventive paradigms for cutaneous conditions

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

ObjectivesTo develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).MethodsA total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed.ResultsA total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts.ConclusionA novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images