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Calculated Bouguer gravity anomalies from the Andean orogenic belt interpreted as derived from regional gravity data to aid understanding of the lithospheric structure and tectonic evolution of the belt. These anomalies reveal lithospheric structures distributed throughout the belt, including linear and circular structures. NE‐trending structures reflect sinistral transpression across the northern part of the belt, and NW‐trending structures represent dextral transtension in the southern part. These results are supported by gravity‐anomaly patterns that demonstrate mantle flow in a trench‐parallel direction both northward and southward away from the stagnation band that is beneath the subducting Nazca slab. This mantle flow has served as an important driving force in the evolution of the Andean orogenic belt. Features of the modified tectonic model of the Andean orogenic belt are consistent with the spatial variation in and interpretation of Bouguer gravity anomalies. [ABSTRACT FROM AUTHOR]

Seismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, constrained by known geological laws and drilling and logging data. The principle of seismic inversion based on deep learning is to learn the mapping between seismic data and rock properties by training a neural network using logging data as labels. However, due to high cost, the number of logging curves is often limited, leading to a trained model with poor generalization. Multi-task learning (MTL) provides an effective way to mitigate this problem. Learning multiple related tasks at the same time can improve the generalization ability of the model, thereby improving the performance of the main task on the same amount of labeled data. However, the performance of multi-task learning is highly dependent on the relative weights for the loss of each task, and manual tuning of the weights is often time-consuming and laborious. In this paper, a Fully Convolutional Residual Network (FCRN) is proposed to achieve seismic impedance inversion and seismic data reconstruction simultaneously, and a method based on the homoscedastic uncertainty of the Bayesian model is used to balance the weights of the loss function for the two tasks. The test results on the synthetic datasets of Marmousi2, Overthrust, and Volve field data show that the proposed method can automatically determine the optimal weight of the two tasks, and predicts impedance with higher accuracy than single-task FCRN model. [ABSTRACT FROM AUTHOR]

The mechanism for uplift of the eastern Tibetan Plateau is still a matter of debate. There are two main models: extrusion and crustal flow. These models have been tested by surface observations, but questions about the uplift remain. In addition, the devastating 2008 Mw 7.9 Wenchuan earthquake along the Longmen Shan fault zone (LMSFZ) reminds us that the tectonic activity within eastern Tibet is complex and poses a major natural hazard. This activity is accompanied by dramatic uplift along the LMSFZ, but only minor convergence (<4 mm year–1) against the Sichuan basin is observed. In order to investigate the mechanism for uplift of Longmen Shan (LMS) area, we explored the lithospheric structure across the Songpan–Ganzi terrane (SGT), LMS, and western Sichuan basin by undertaking an integrated analysis of a variety of data including new, logistically challenging controlled-source seismic profiling (reflection and refraction) results, receiver function estimates of crustal thickness, gravity and magnetic data, GPS data, and geologic constraints. Our analysis of crustal structure indicates that the crust is not thick enough to support its current elevation and that the crust is essentially composed of three layers of similar thickness. Thus, based on our crustal structure model, 2D numerical modelling was conducted to investigate uplift mechanisms. The modelling results indicate that the middle crust beneath the SGT is the most ductile layer, which is the key factor responsible for the crustal-scale faulting, earthquake behaviour, and periods of uplift. In addition, the modelling results indicate that the strong Sichuan block acts as a backstop for the thrusting along the LMS and crustal thickening to the west. [ABSTRACT FROM AUTHOR]

Pyroptosis is a recently-identified pathway of host cell death that is stimulated by a range of microbial infections. Emerging evidence indicates pyroptosis plays crucial roles in tumor growth, disease progression, and migration of different cancer cells. However, the clinical significance of pyroptosis in tumor behavior prognosis, as well as the underlying mechanism in different cancers remains elusive. Here, by evaluating the expression level of pyroptosis genes in colorectal cancer (CRC) patients from the TCGA cohort and GEO cohort (GSE39582), we identified pyroptosis-related DEGs and then built a 13-gene risk model by applying the LASSO Cox regression algorithm. Furthermore, functional analysis using GSEA and GSEV revealed that our prognostic model may function through regulating immune responses and tumor biogenesis pathways. Significant infiltration of activated immune cells (e.g. cytotoxic T cells) was observed in the low risk score group. The selected gene set was further validated in the GEO cohort. Time-dependent ROC curves confirmed that our risk score model is robust in predicting 1, 3 and 5-year overall survival in CRC patients. Overall, we have identified a pyroptosis-related gene signature that consists of 13 genes, which serves as a potent indicator of CRC prognosis. Thus, our model provides insights in how to make better clinical decision in the future., Competing Interests: None., (IJCEP Copyright © 2022.)

Objective: Colorectal cancer (CRC) is the leading cause of mortality worldwide. Growing evidence suggests that the current pathological staging system is inadequate for efficient and accurate prognosis. In this study, we aim to build a prognosis model to predict the survival outcome of CRC patients by using gene expression profiles from The Cancer Genome Atlas (TCGA). Materials and Methods: Univariate and multivariate Cox regression analysis were used to assess the relationship between clinical factors and P4HA1 expression regarding the prognosis of patients with colon adenocarcinoma (COAD). The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic differential expression genes (DEGs) for the construction of prognostic risk score model. Kaplan-Meier and receiver operating characteristic (ROC) survival analysis were used to assess the performance of the model on both TCGA cohort and an independent dataset GSE39582. Results: Overexpression of P4HA1 was confirmed to be associated with poor clinical outcome of colon cancer patients in both TCGA and GSE39582 cohorts. Using the TCGA cohort, we identified 1528 DEGs related to elevated P4HA1 expression, and we established a 11-gene panel to construct the prognostic risk score model by LASSO Cox regression analysis based on their expression profiles. The 11-gene signature was further validated in the independent dataset GSE39582. Time-dependent ROC curves indicated good performance of our model in predicting 1, 2, and 3-years overall survival in COAD patients. Additionally, gene set enrichment analysis indicated that the 11-gene signature was related to pathways involved in tumor progression. Conclusions: Together, we have established a 11-gene signature significantly associated with prognosis in COAD patients, which could serve as a promising tool for clinical application in the future.

This study aims to explore the mechanism of glioblastoma multiforme (GBM) in hypoxia through metabolomic and proteomic analysis. We showed that the migration and invasiveness of LN18 cells was significantly enhanced after 24 h of hypoxia treatment. The metabolomic and proteomic profiling were conducted in LN18 cells cultured under hypoxia condition. Correlation analysis between significant differential metabolites and proteins revealed seven proteins and ten metabolites, of which metabolite L-Arg was negatively correlated with P4HA1 protein. Meanwhile, the expression of HIF1α, nNOS and P4HA1 was up-regulated, and the concentration of L-Arg and NO was decreased and increased respectively. Knockdown of HIF1α reduced the expression of nNOS and P4HA1, the concentration of NO and the invasiveness of cells, while increased the concentration of L-Arg. Similar changes on P4HA1 expression, the concentration of L-Arg and NO were observed when the expression of nNOS was disrupted. Lastly, knockdown of P4HA1 impaired the invasion of LN18 and T98G cells, probably through regulating the expression of Vimentin, MMP2, MMP9, Snail and E-cadherin. Consistent trends on both the overexpression of these relevant genes, as well as the concentration of L-Arg and NO were also observed in all our overexpression experiments. Besides, we investigated the relationship between P4HA1 expression and prognosis by MTA, CGGA and TCGA databases. Increased P4HA1 level was correlated poor prognosis with advanced histological grade. In summary, we found that hypoxia promotes the migration and invasion of GBM via the L-Arg/P4HA1 axis which maybe an effective molecular marker or predictor of clinical outcome in GBM patients., Competing Interests: None., (AJCR Copyright © 2021.)