Accurate identification of microseismic P- and S-phase arrivals using the multi-step AIC algorithm

Zhu, Mengbo; Wang, Liguan; Liu, Xiaoming; Zhao, Jiaxuan; Peng, Pingan

doi:10.1016/j.jappgeo.2018.01.007

Cited by 24 publications

(7 citation statements)

References 36 publications

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“…Cox regression analysis of the multistep AIC (stepAIC) algorithm [ 22 ] was used to screen the prognosis-related DEGs. Significant items were identified when p < 0.05.…”

Section: Methodsmentioning

confidence: 99%

“…DEGs in colon cancer were identified from the TCGA data. [22] was used to screen the prognosis-related DEGs. Significant items were identified when p < 0.05. en, a prognostic model was constructed for risk assessment, and the risk score of each sample in the TCGA and GSE39582 datasets was calculated.…”

Section: Identification Of Differentially Expressed Genes (Degs)mentioning

confidence: 99%

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An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Lai

Feng

2022

Evidence-Based Complementary and Alternative Medicine

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Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan–Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.

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“…Cox regression analysis of the multistep AIC (stepAIC) algorithm [ 22 ] was used to screen the prognosis-related DEGs. Significant items were identified when p < 0.05.…”

Section: Methodsmentioning

confidence: 99%

Section: Identification Of Differentially Expressed Genes (Degs)mentioning

confidence: 99%

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Lai

Feng

2022

Evidence-Based Complementary and Alternative Medicine

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“…e stepAIC algorithm [23] was a stepwise logistic regression method. We used the stepAIC algorithm to identify prognostic genes in the top 20 hub nodes (multivariate Cox regression analysis) and construct a prognostic risk model.…”

Section: Construction and Assessment Of The Prognostic Risk Modelmentioning

confidence: 99%

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Wang

Han

2022

Evidence-Based Complementary and Alternative Medicine

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Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( p < 0.05 ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

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“…The accurate identification of the initiation of the P-phase within the continuously streaming seismic signals is the most crucial aspect of a successful EEW system. Many algorithms have been developed in the past for picking the P-phase onset in seismic signals [101][102][103][104][105][106][107][108][109][110]. The UEEWS utilizes an enhanced version of the standard short-term average (STA) and long-term average (LTA) algorithm.…”

Section: Event Detectionmentioning

confidence: 99%

Uttarakhand State Earthquake Early Warning System: A Case Study of Himalayan Environment

Kumar,

Kamal,

Sharma

et al. 2024

Preprint

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High seismic activity in the Himalayas and the increasing urbanization of its surrounding areas in the northern Indian region pose significant threats to both lives and properties. To mitigate the risk of seismic events, an earthquake early warning system can be used in the region. This system would be particularly beneficial to cities and towns in the mountainous and foothill regions that are located near earthquake sources. The government and the science and engineering community have re-sponded to this call by establishing the Uttarakhand State Earthquake Early Warning System (UEEWS). The system became fully operational on August 4, 2021, after being launched by the government of Uttarakhand. The UEEWS includes 170 accelerometers in the seismogenic part of the Uttarakhand region of the Himalayas. Ground motion data is transmitted to a central server through a dedicated private telecommunication network 24 hours a day, seven days a week.The system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile application installed on users' devices and sirens installed in government-owned public buildings. The UEEWS has successfully issued alerts for light earthquakes that occurred in the instrumented region and warnings for moderate earthquakes that occurred in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of the instrumentation, adaptation of attributes and their relation to earthquake parameters, the operational flow of the system, and information about the dissemination of warnings

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Accurate identification of microseismic P- and S-phase arrivals using the multi-step AIC algorithm

Cited by 24 publications

References 36 publications

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Uttarakhand State Earthquake Early Warning System: A Case Study of Himalayan Environment

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