Novel of MEMS Resonant Gyroscope using DETF as Sensing Structure

Nowadays MEMS product is widely used in all fields such as industry, automation, aerospace, marine, etc. With advances fabrication and lowering cost, MEMS product is very useful and its simple design enabling the user use it easily. This research introduced a new design of MEMS resonant gyroscope using DETF as sensing structure. The decoupled DETF has been designed to be able to produce the natural frequency of 80 KHz, while the whole gyroscope structures generate natural frequency of 3511,8 Hz. Above condition is done to avoid force miss detection in sense mode and drive mode, which has become the biggest problem in gyroscope design. The result showed that the new design of MEMS Resonant Gyroscope is feasible

EGTSyn: Edge-based Graph Transformer for Anti-Cancer Drug Combination Synergy Prediction

Combination therapy with multiple drugs is a potent therapy strategy for complex diseases such as cancer, due to its therapeutic efficacy and potential for reducing side effects. However, the extensive search space of drug combinations makes it challenging to screen all combinations experimentally. To address this issue, computational methods have been developed to identify prioritized drug combinations. Recently, Convolutional Neural Networks based deep learning methods have shown great potential in this community. Although the significant progress has been achieved by existing computational models, they have overlooked the important high-level semantic information and significant chemical bond features of drugs. It is worth noting that such information is rich and it can be represented by the edges of graphs in drug combination predictions. In this work, we propose a novel Edge-based Graph Transformer, named EGTSyn, for effective anti-cancer drug combination synergy prediction. In EGTSyn, a special Edge-based Graph Neural Network (EGNN) is designed to capture the global structural information of chemicals and the important information of chemical bonds, which have been neglected by most previous studies. Furthermore, we design a Graph Transformer for drugs (GTD) that combines the EGNN module with a Transformer-architecture encoder to extract high-level semantic information of drugs.Comment: 15 pages,4 figures,6 table

The Implications of Relationships between Human Diseases and Metabolic Subpathways

One of the challenging problems in the etiology of diseases is to explore the relationships between initiation and progression of diseases and abnormalities in local regions of metabolic pathways. To gain insight into such relationships, we applied the “k-clique” subpathway identification method to all disease-related gene sets. For each disease, the disease risk regions of metabolic pathways were then identified and considered as subpathways associated with the disease. We finally built a disease-metabolic subpathway network (DMSPN). Through analyses based on network biology, we found that a few subpathways, such as that of cytochrome P450, were highly connected with many diseases, and most belonged to fundamental metabolisms, suggesting that abnormalities of fundamental metabolic processes tend to cause more types of diseases. According to the categories of diseases and subpathways, we tested the clustering phenomenon of diseases and metabolic subpathways in the DMSPN. The results showed that both disease nodes and subpathway nodes displayed slight clustering phenomenon. We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased. Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged. Furthermore, the coexpression levels between disease genes and other types of genes were calculated for each subpathway in the DMSPN. The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway

Table_1_Identification of microsatellite instability and immune-related prognostic biomarkers in colon adenocarcinoma.xlsx

BackgroundColon adenocarcinoma (COAD) is a prevalent malignancy that causes significant mortality. Microsatellite instability plays a pivotal function in COAD development and immunotherapy resistance. However, the detailed underlying mechanism requires further investigation. Consequently, identifying molecular biomarkers with prognostic significance and revealing the role of MSI in COAD is important for addressing key obstacles in the available treatments.MethodsCIBERSORT and ESTIMATE analyses were performed to evaluate immune infiltration in COAD samples, followed by correlation analysis for MSI and immune infiltration. Then, differentially expressed genes (DEGs) in MSI and microsatellite stability (MSS) samples were identified and subjected to weighted gene co-expression network analysis (WGCNA). A prognostic model was established with univariate cox regression and LASSO analyses, then evaluated with Kaplan-Meier analysis. The correlation between the prognostic model and immune checkpoint inhibitor (ICI) response was also analyzed.ResultsIn total, 701 significant DEGs related to MSI status were identified, and WGCNA revealed two modules associated with the immune score. Then, a seven-gene prognostic model was constructed using LASSO and univariate cox regression analyses to predict survival and ICI response. The high-risk score patients in TCGA and GEO cohorts presented a poor prognosis, as well as a high immune checkpoint expression, so they are more likely to benefit from ICI treatment.ConclusionThe seven-gene prognostic model constructed could predict the survival of COAD and ICI response and serve as a reference for immunotherapy decisions.</p

Table_3_Identification of microsatellite instability and immune-related prognostic biomarkers in colon adenocarcinoma.xlsx

BackgroundColon adenocarcinoma (COAD) is a prevalent malignancy that causes significant mortality. Microsatellite instability plays a pivotal function in COAD development and immunotherapy resistance. However, the detailed underlying mechanism requires further investigation. Consequently, identifying molecular biomarkers with prognostic significance and revealing the role of MSI in COAD is important for addressing key obstacles in the available treatments.MethodsCIBERSORT and ESTIMATE analyses were performed to evaluate immune infiltration in COAD samples, followed by correlation analysis for MSI and immune infiltration. Then, differentially expressed genes (DEGs) in MSI and microsatellite stability (MSS) samples were identified and subjected to weighted gene co-expression network analysis (WGCNA). A prognostic model was established with univariate cox regression and LASSO analyses, then evaluated with Kaplan-Meier analysis. The correlation between the prognostic model and immune checkpoint inhibitor (ICI) response was also analyzed.ResultsIn total, 701 significant DEGs related to MSI status were identified, and WGCNA revealed two modules associated with the immune score. Then, a seven-gene prognostic model was constructed using LASSO and univariate cox regression analyses to predict survival and ICI response. The high-risk score patients in TCGA and GEO cohorts presented a poor prognosis, as well as a high immune checkpoint expression, so they are more likely to benefit from ICI treatment.ConclusionThe seven-gene prognostic model constructed could predict the survival of COAD and ICI response and serve as a reference for immunotherapy decisions.</p

Cofunctional Subpathways Were Regulated by Transcription Factor with Common Motif, Common Family, or Common Tissue

Dissecting the characteristics of the transcription factor (TF) regulatory subpathway is helpful for understanding the TF underlying regulatory function in complex biological systems. To gain insight into the influence of TFs on their regulatory subpathways, we constructed a global TF-subpathways network (TSN) to analyze systematically the regulatory effect of common-motif, common-family, or common-tissue TFs on subpathways. We performed cluster analysis to show that the common-motif, common-family, or common-tissue TFs that regulated the same pathway classes tended to cluster together and contribute to the same biological function that led to disease initiation and progression. We analyzed the Jaccard coefficient to show that the functional consistency of subpathways regulated by the TF pairs with common motif, common family, or common tissue was significantly greater than the random TF pairs at the subpathway level, pathway level, and pathway class level. For example, HNF4A (hepatocyte nuclear factor 4, alpha) and NR1I3 (nuclear receptor subfamily 1, group I, member 3) were a pair of TFs with common motif, common family, and common tissue. They were involved in drug metabolism pathways and were liver-specific factors required for physiological transcription. In short, we inferred that the cofunctional subpathways were regulated by common-motif, common-family, or common-tissue TFs

DataSheet_1_Identification of microsatellite instability and immune-related prognostic biomarkers in colon adenocarcinoma.pdf

BackgroundColon adenocarcinoma (COAD) is a prevalent malignancy that causes significant mortality. Microsatellite instability plays a pivotal function in COAD development and immunotherapy resistance. However, the detailed underlying mechanism requires further investigation. Consequently, identifying molecular biomarkers with prognostic significance and revealing the role of MSI in COAD is important for addressing key obstacles in the available treatments.MethodsCIBERSORT and ESTIMATE analyses were performed to evaluate immune infiltration in COAD samples, followed by correlation analysis for MSI and immune infiltration. Then, differentially expressed genes (DEGs) in MSI and microsatellite stability (MSS) samples were identified and subjected to weighted gene co-expression network analysis (WGCNA). A prognostic model was established with univariate cox regression and LASSO analyses, then evaluated with Kaplan-Meier analysis. The correlation between the prognostic model and immune checkpoint inhibitor (ICI) response was also analyzed.ResultsIn total, 701 significant DEGs related to MSI status were identified, and WGCNA revealed two modules associated with the immune score. Then, a seven-gene prognostic model was constructed using LASSO and univariate cox regression analyses to predict survival and ICI response. The high-risk score patients in TCGA and GEO cohorts presented a poor prognosis, as well as a high immune checkpoint expression, so they are more likely to benefit from ICI treatment.ConclusionThe seven-gene prognostic model constructed could predict the survival of COAD and ICI response and serve as a reference for immunotherapy decisions.</p

Table_2_Identification of microsatellite instability and immune-related prognostic biomarkers in colon adenocarcinoma.xlsx

BackgroundColon adenocarcinoma (COAD) is a prevalent malignancy that causes significant mortality. Microsatellite instability plays a pivotal function in COAD development and immunotherapy resistance. However, the detailed underlying mechanism requires further investigation. Consequently, identifying molecular biomarkers with prognostic significance and revealing the role of MSI in COAD is important for addressing key obstacles in the available treatments.MethodsCIBERSORT and ESTIMATE analyses were performed to evaluate immune infiltration in COAD samples, followed by correlation analysis for MSI and immune infiltration. Then, differentially expressed genes (DEGs) in MSI and microsatellite stability (MSS) samples were identified and subjected to weighted gene co-expression network analysis (WGCNA). A prognostic model was established with univariate cox regression and LASSO analyses, then evaluated with Kaplan-Meier analysis. The correlation between the prognostic model and immune checkpoint inhibitor (ICI) response was also analyzed.ResultsIn total, 701 significant DEGs related to MSI status were identified, and WGCNA revealed two modules associated with the immune score. Then, a seven-gene prognostic model was constructed using LASSO and univariate cox regression analyses to predict survival and ICI response. The high-risk score patients in TCGA and GEO cohorts presented a poor prognosis, as well as a high immune checkpoint expression, so they are more likely to benefit from ICI treatment.ConclusionThe seven-gene prognostic model constructed could predict the survival of COAD and ICI response and serve as a reference for immunotherapy decisions.</p

Landscape and significance of human super enhancer-driven core transcription regulatory circuitry

A core transcription regulatory circuitry (CRC) is an interconnected self-regulatory circuitry that is formed by a group of core transcription factors (TFs). These core TFs collectively regulate gene expression by binding not only to their own super enhancers (SEs) but also to the SEs of one another. For most human tissue/cell types, a global view of CRCs and core TFs has not been generated. Here, we identified numerous CRCs using two identification methods and detailed the landscape of the CRCs driven by SEs in large cell/tissue samples. The comprehensive biological analyses, including sequence conservation, CRC activity and genome binding affinity were conducted for common TFs, moderate TFs, and specific TFs, which exhibit different biological features. The local module located from the common CRC network highlighted the essential functions and prognostic performance. The tissue-specific CRC network was highly related to cell identity. Core TFs in tissue-specific CRC networks exhibited disease markers, and had regulatory potential for cancer immunotherapy. Moreover, a user-friendly resource named CRCdb (http://www.licpathway.net/crcdb/index.html) was developed, which contained the detailed information of CRCs and core TFs used in this study, as well as other interesting results, such as the most representative CRC, frequency of TFs, and indegree/outdegree of TFs