This study aimed to investigate the effect of photobiomodulation therapy (PBMT) on muscle fatigue (i.e., peripheral and central fatigue) after performing the grip exercise. Eleven healthy college students were recruited to participate in a crossover, randomized, double-blind, placebo-controlled experiment. To induce muscle fatigue, all subjects performed the first grip exercise with a load of the 50% of maximum voluntary contraction force until exhaustion, and then they received PBMT or placebo on the hand immediately to perform the same second grip exercise. Surface electromyography (sEMG) signals were recorded from the first dorsal interossei of right hand to evaluate peripheral fatigue using muscle fatigue index (MFI), and functional Near-Infrared Spectroscopy (fNIRS) signals were recorded from cortical regions to evaluate central fatigue using functional connectivity (FC) analysis. Results showed that muscle fatigue index (MFI) was significantly lower in the PBMT group (p < 0.05) in the second grip exercise, when compared to the placebo group, but there was no significant difference in grip strength duration between the two groups. The PBMT group and the placebo group showed different FC patterns in the second grip exercise, which was manifested by the range of FC between the seed point left primary motor cortex (lM1) and other regions of interest (ROIs) in the placebo group was wider than that in the PBMT group. We concluded that using PBMT immediately after performing the grip exercise had a relieving effect on muscle fatigue in the second grip exercise.
{"title":"Effects of Photobiomodulation therapy on relieving peripheral and central fatigue in the grip exercise","authors":"Siyu Hong, Lin Chen, Shuai Feng, Xin Zhang, Li-quan Yang, Jiangjie Zhou, Qi Wang, Kaige Zheng, Si-qi Zhang","doi":"10.1145/3571532.3571547","DOIUrl":"https://doi.org/10.1145/3571532.3571547","url":null,"abstract":"This study aimed to investigate the effect of photobiomodulation therapy (PBMT) on muscle fatigue (i.e., peripheral and central fatigue) after performing the grip exercise. Eleven healthy college students were recruited to participate in a crossover, randomized, double-blind, placebo-controlled experiment. To induce muscle fatigue, all subjects performed the first grip exercise with a load of the 50% of maximum voluntary contraction force until exhaustion, and then they received PBMT or placebo on the hand immediately to perform the same second grip exercise. Surface electromyography (sEMG) signals were recorded from the first dorsal interossei of right hand to evaluate peripheral fatigue using muscle fatigue index (MFI), and functional Near-Infrared Spectroscopy (fNIRS) signals were recorded from cortical regions to evaluate central fatigue using functional connectivity (FC) analysis. Results showed that muscle fatigue index (MFI) was significantly lower in the PBMT group (p < 0.05) in the second grip exercise, when compared to the placebo group, but there was no significant difference in grip strength duration between the two groups. The PBMT group and the placebo group showed different FC patterns in the second grip exercise, which was manifested by the range of FC between the seed point left primary motor cortex (lM1) and other regions of interest (ROIs) in the placebo group was wider than that in the PBMT group. We concluded that using PBMT immediately after performing the grip exercise had a relieving effect on muscle fatigue in the second grip exercise.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133064091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marta Rosaria Marino, E. Raiola, Giuseppe Russo, A. Lombardi, A. Borrelli, M. Triassi, Teresa Angela Trunfio
The Covid-19 pandemic that began in December 2019 and is still underway in 2022 has changed many habits and protocols in different economic industries including healthcare. In the specific case, the change in protocols and management of work activities also affected the health sector. Among the sectors in which the pandemic has influenced the flow of events is the cardiology sector. In the specific case, the present work will present how coronary bypass interventions have been influenced in their different aspects by the Covid-19 pandemic. The work will be based on a comparison between the 2019 data in the period prior to the pandemic and in 2020 in the post-pandemic period for two major public hospitals in the Campania region: the university hospital of Salerno (Italy) "San Giovanni di Dio and Ruggi D'Aragona” and AORN “ A. Cardarelli ”of Naples (Italy). Both the structures considered have an Emergency Department and First Aid Acceptance for surgical pathologies.
{"title":"Study of coronary artery bypass grafting admission in Covid-19 era: a bicentric study","authors":"Marta Rosaria Marino, E. Raiola, Giuseppe Russo, A. Lombardi, A. Borrelli, M. Triassi, Teresa Angela Trunfio","doi":"10.1145/3571532.3571549","DOIUrl":"https://doi.org/10.1145/3571532.3571549","url":null,"abstract":"The Covid-19 pandemic that began in December 2019 and is still underway in 2022 has changed many habits and protocols in different economic industries including healthcare. In the specific case, the change in protocols and management of work activities also affected the health sector. Among the sectors in which the pandemic has influenced the flow of events is the cardiology sector. In the specific case, the present work will present how coronary bypass interventions have been influenced in their different aspects by the Covid-19 pandemic. The work will be based on a comparison between the 2019 data in the period prior to the pandemic and in 2020 in the post-pandemic period for two major public hospitals in the Campania region: the university hospital of Salerno (Italy) \"San Giovanni di Dio and Ruggi D'Aragona” and AORN “ A. Cardarelli ”of Naples (Italy). Both the structures considered have an Emergency Department and First Aid Acceptance for surgical pathologies.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121013074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Colorectal cancer (CRC) has seriously threatened human life and health. Nowadays, research on the pathogenesis, diagnosis and treatment of CRC is still ongoing. Finding safe, convenient and reliable protein biomarkers of CRC will contribute to CRC diagnosis and treatment. In this study, label-free quantitative proteomics was used to profile the colorectal tissues of CRC in mice. Bioinformatics was used to fine the differentially expressed proteins. We identified 57 differentially expressed proteins with 29 significantly up-regulated and 28 significantly down-regulated. Results of Gene Ontology (GO) showed that most of the differentially expressed proteins located in the cytoplasm and extracellular exosomes, and they were involved in specific metabolic processes. Further metabolic pathway enrichment analysis showed that most of the differentially expressed proteins were related to metabolism of arginine and proline. In addition, protein-protein interaction (PPI) network analysis indicated that the up-regulated protein ALDH1B1 stayed at the key position in the network. Taking all the results together, it can be speculated that AMPN, AOC1, MYO1A and MAOB may be potential proteic biomarkers of CRC.
{"title":"Label-free based proteomics analysis of tissue profiles reveals the pathogenesis of colorectal cancer","authors":"Jingjing Liu, Xiaofeng Song, C. Qiu, Si-Yu Wu","doi":"10.1145/3571532.3571537","DOIUrl":"https://doi.org/10.1145/3571532.3571537","url":null,"abstract":"Colorectal cancer (CRC) has seriously threatened human life and health. Nowadays, research on the pathogenesis, diagnosis and treatment of CRC is still ongoing. Finding safe, convenient and reliable protein biomarkers of CRC will contribute to CRC diagnosis and treatment. In this study, label-free quantitative proteomics was used to profile the colorectal tissues of CRC in mice. Bioinformatics was used to fine the differentially expressed proteins. We identified 57 differentially expressed proteins with 29 significantly up-regulated and 28 significantly down-regulated. Results of Gene Ontology (GO) showed that most of the differentially expressed proteins located in the cytoplasm and extracellular exosomes, and they were involved in specific metabolic processes. Further metabolic pathway enrichment analysis showed that most of the differentially expressed proteins were related to metabolism of arginine and proline. In addition, protein-protein interaction (PPI) network analysis indicated that the up-regulated protein ALDH1B1 stayed at the key position in the network. Taking all the results together, it can be speculated that AMPN, AOC1, MYO1A and MAOB may be potential proteic biomarkers of CRC.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133440978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, a novel approach called EPNBC has been proposed by combining the biological information with the naïve Bayesian classifier and PageRank algorithm to predict potential essential proteins. In EPNBC, the naïve Bayesian classifier is used to process the original PPI network, and a new protein interaction network with more interaction relationships is obtained. Then, two similarity matrices were obtained by using Gaussian interaction profile kernel similarity based on the protein interaction relationship and gene expression data, and a weighted protein interaction network was obtained. The improved PageRank algorithm was used to score the nodes in the network and output the protein scores in descending order. Experimental results showed that EPNBC was superior to dozens of other methods in identifying essential proteins.
{"title":"Method for Essential Protein Prediction Based on the Naïve Bayesian Classifier and Bioinformation Fusion","authors":"Jingjuan Tan, Linai Kuang, Lei Wang","doi":"10.1145/3571532.3571533","DOIUrl":"https://doi.org/10.1145/3571532.3571533","url":null,"abstract":"In this article, a novel approach called EPNBC has been proposed by combining the biological information with the naïve Bayesian classifier and PageRank algorithm to predict potential essential proteins. In EPNBC, the naïve Bayesian classifier is used to process the original PPI network, and a new protein interaction network with more interaction relationships is obtained. Then, two similarity matrices were obtained by using Gaussian interaction profile kernel similarity based on the protein interaction relationship and gene expression data, and a weighted protein interaction network was obtained. The improved PageRank algorithm was used to score the nodes in the network and output the protein scores in descending order. Experimental results showed that EPNBC was superior to dozens of other methods in identifying essential proteins.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130287686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alzheimer's disease (AD) is an age-related disorder characterised by the degeneration of neurons, which leads to cognitive function. Recent research on the genetic basis of AD found some evidence of the potential implication of several risk genes in AD. Specifically, aberrant immune regulation in the brain could cause an increased risk of developing AD via damaging neurons and synapses. However, establishing a potential causal relationship between a gene and the risk of developing AD is hard to achieve in humans because this would require clinical trials. Here, we leverage intrinsic genetic variabilities in AD patients compared to non-diseased patients to propose genes that can cause AD. We used Mendelian randomisation to screen for genes causally associated with AD risk, starting with 36 potential genes identified in the most recent GWAS. We found a cluster of genes, CR1, PLCG2 and HLA-DQA1, associated with immune activation. Using single-cell sequencing data from AD patients, we found that they are significantly increased in microglia. Our results suggest that these higher levels of CR1, PLCG2 and HLA-DQA1 are associated with an increased risk of developing AD. We identified that 7-nitro-N-phenethyl-1H-indole-2-carboxamide is an inhibitor of PLCG2 and could act as a potential drug candidate for patients with genetic predispositions to significantly higher levels of PLCG2. We conclude that inhibitors of inflammation in patients who are genetically more susceptible to aberrant microglial activation could constitute as a strategy for personalised treatment. This study also establishes a workflow for further investigations of personalised treatment solutions using MR and causal inference.
{"title":"Causal associations between neuroinflammation and Alzheimer's disease risk","authors":"K-M Lin, Yizhou Yu","doi":"10.1145/3571532.3571536","DOIUrl":"https://doi.org/10.1145/3571532.3571536","url":null,"abstract":"Alzheimer's disease (AD) is an age-related disorder characterised by the degeneration of neurons, which leads to cognitive function. Recent research on the genetic basis of AD found some evidence of the potential implication of several risk genes in AD. Specifically, aberrant immune regulation in the brain could cause an increased risk of developing AD via damaging neurons and synapses. However, establishing a potential causal relationship between a gene and the risk of developing AD is hard to achieve in humans because this would require clinical trials. Here, we leverage intrinsic genetic variabilities in AD patients compared to non-diseased patients to propose genes that can cause AD. We used Mendelian randomisation to screen for genes causally associated with AD risk, starting with 36 potential genes identified in the most recent GWAS. We found a cluster of genes, CR1, PLCG2 and HLA-DQA1, associated with immune activation. Using single-cell sequencing data from AD patients, we found that they are significantly increased in microglia. Our results suggest that these higher levels of CR1, PLCG2 and HLA-DQA1 are associated with an increased risk of developing AD. We identified that 7-nitro-N-phenethyl-1H-indole-2-carboxamide is an inhibitor of PLCG2 and could act as a potential drug candidate for patients with genetic predispositions to significantly higher levels of PLCG2. We conclude that inhibitors of inflammation in patients who are genetically more susceptible to aberrant microglial activation could constitute as a strategy for personalised treatment. This study also establishes a workflow for further investigations of personalised treatment solutions using MR and causal inference.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122546613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saurav K. Aryal, Teanna Barrett, Gloria J. Washington
The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.
{"title":"Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation","authors":"Saurav K. Aryal, Teanna Barrett, Gloria J. Washington","doi":"10.1145/3571532.3571538","DOIUrl":"https://doi.org/10.1145/3571532.3571538","url":null,"abstract":"The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124180963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer and does not benefit from endocrine therapy targeting the estrogen receptor (ER). Increasing the expression of ER in TNBC cells using pharmacological tools may make endocrine therapy available for the treatment of TNBC. We aimed to identify molecules that may reverse ER expression in TNBC. Methods: The mRNA profiles of breast cancer tissues were downloaded from The Cancer Genome Atlas. The gene modules that were correlated between TNBC and luminal subtype were extracted based on mRNA profiles using weighted gene co-expression network analysis (WGCNA). The Connective Map (CMap) was used to predict small molecular compounds that could reverse the expression levels of hub genes in TNBC. The viability of MDA-MB-231 cells incubated with the combinations of indicated compounds and tamoxifen was determined using Cell Counting Kit-8 and clone formation assays. Results: In total, 3868 differentially expressed genes in breast cancer were analyzed using WGCNA. When the TNBC and luminal subtypes were attributed to specific phenotypes, gene modules derived from the co-expression network were identified. A total of 176 genes that were positively correlated with TNBC and 109 genes that were negatively correlated with TNBC were identified as hub genes. The hub genes in TNBC and the luminal subtype were distinct from each other, and the hub genes in TNBC showed a dysfunction in the cell cycle. CMap analysis demonstrated that GW-8510 was a leading candidate for reversing hub gene expression in TNBC. The upregulation of ER as well as progesterone receptor expression in MDA-MB-231 cells by GW-8510 was verified. Moreover, the combined application of GW-8510 and tamoxifen resulted in a synthetic loss of viability in MDA-MB-231 cells. Conclusion: GW-8510 re-sensitized TNBC to tamoxifen-based hormone therapy, providing a new opportunity for TNBC therapy.
背景:三阴性乳腺癌(TNBC)是最具侵袭性的乳腺癌类型，并且不能从针对雌激素受体(ER)的内分泌治疗中获益。使用药理学工具增加TNBC细胞中ER的表达可能使内分泌疗法可用于治疗TNBC。我们的目标是鉴定可能逆转三阴癌中ER表达的分子。方法:从The cancer Genome Atlas下载乳腺癌组织mRNA谱。采用加权基因共表达网络分析(WGCNA)，基于mRNA谱提取TNBC与luminal亚型相关的基因模块。结缔组织图谱(CMap)用于预测可以逆转TNBC中枢纽基因表达水平的小分子化合物。使用Cell Counting Kit-8和克隆形成实验测定MDA-MB-231细胞与指定化合物和他莫昔芬联合孵育的活力。结果:WGCNA共分析3868个乳腺癌差异表达基因。当TNBC和luminal亚型归因于特定表型时，鉴定了来自共表达网络的基因模块。共鉴定出176个与TNBC呈正相关的基因和109个与TNBC负相关的基因为枢纽基因。TNBC和luminal亚型的枢纽基因不同，TNBC的枢纽基因在细胞周期中表现出功能障碍。CMap分析表明，GW-8510是逆转TNBC中hub基因表达的主要候选基因。证实GW-8510可上调MDA-MB-231细胞内质网和孕酮受体的表达。此外，GW-8510和他莫昔芬联合应用导致MDA-MB-231细胞的合成活力丧失。结论:GW-8510使TNBC对以他莫昔芬为基础的激素治疗再敏感，为TNBC治疗提供了新的契机。
{"title":"Network Analysis Identifies Potential Small-Molecule Drugs Sensitizing Triple-Negative Breast Cancer to Tamoxifen: Small-Molecule Drugs Re-sensitizing TNBC to Tamoxifen","authors":"Mengying Zhou, Xinghua Liao, Tao Xu","doi":"10.1145/3571532.3571535","DOIUrl":"https://doi.org/10.1145/3571532.3571535","url":null,"abstract":"Background: Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer and does not benefit from endocrine therapy targeting the estrogen receptor (ER). Increasing the expression of ER in TNBC cells using pharmacological tools may make endocrine therapy available for the treatment of TNBC. We aimed to identify molecules that may reverse ER expression in TNBC. Methods: The mRNA profiles of breast cancer tissues were downloaded from The Cancer Genome Atlas. The gene modules that were correlated between TNBC and luminal subtype were extracted based on mRNA profiles using weighted gene co-expression network analysis (WGCNA). The Connective Map (CMap) was used to predict small molecular compounds that could reverse the expression levels of hub genes in TNBC. The viability of MDA-MB-231 cells incubated with the combinations of indicated compounds and tamoxifen was determined using Cell Counting Kit-8 and clone formation assays. Results: In total, 3868 differentially expressed genes in breast cancer were analyzed using WGCNA. When the TNBC and luminal subtypes were attributed to specific phenotypes, gene modules derived from the co-expression network were identified. A total of 176 genes that were positively correlated with TNBC and 109 genes that were negatively correlated with TNBC were identified as hub genes. The hub genes in TNBC and the luminal subtype were distinct from each other, and the hub genes in TNBC showed a dysfunction in the cell cycle. CMap analysis demonstrated that GW-8510 was a leading candidate for reversing hub gene expression in TNBC. The upregulation of ER as well as progesterone receptor expression in MDA-MB-231 cells by GW-8510 was verified. Moreover, the combined application of GW-8510 and tamoxifen resulted in a synthetic loss of viability in MDA-MB-231 cells. Conclusion: GW-8510 re-sensitized TNBC to tamoxifen-based hormone therapy, providing a new opportunity for TNBC therapy.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131880935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In view of the fact that the collected sEMG signal contains a lot of noise, which makes it impossible to accurately identify and analyze the signal, this paper proposes a method that complete ensemble empirical mode decomposition with adaptive noise and wavelet layered threshold denoising to achieve accurate signal identification and analysis. The method is to first calculate the correlation coefficient after CEEMDAN(Cemplete Ensemple Empirical Mode Decomposition with Adaptive Noise) decomposition, and then denoise the first three IMFs after decomposition, and then reconstruct, and then perform wavelet layered threshold denoising after reconstruction. After experimental comparison, it is found that the denoising effect of designing such a denoising algorithm is better than other different global thresholds and separate layered threshold denoising.
针对采集到的表面肌电信号中含有大量噪声，无法对信号进行准确识别和分析的问题，本文提出了一种采用自适应噪声和小波分层阈值去噪的方法来完成系综经验模态分解，实现对信号的准确识别和分析。该方法首先计算CEEMDAN(complete Ensemple Empirical Mode Decomposition with Adaptive Noise)分解后的相关系数，然后对分解后的前三个IMFs进行去噪，再进行重构，重构后进行小波分层阈值去噪。经过实验对比，发现设计的这种去噪算法的去噪效果优于其他不同的全局阈值和分层阈值去噪。
{"title":"CEEMDAN-Wavelet Threshold Denoising Method on sEMG","authors":"Jianwei Fang, Liye Ren, Junyi Tian, Guisong Li","doi":"10.1145/3571532.3571554","DOIUrl":"https://doi.org/10.1145/3571532.3571554","url":null,"abstract":"In view of the fact that the collected sEMG signal contains a lot of noise, which makes it impossible to accurately identify and analyze the signal, this paper proposes a method that complete ensemble empirical mode decomposition with adaptive noise and wavelet layered threshold denoising to achieve accurate signal identification and analysis. The method is to first calculate the correlation coefficient after CEEMDAN(Cemplete Ensemple Empirical Mode Decomposition with Adaptive Noise) decomposition, and then denoise the first three IMFs after decomposition, and then reconstruct, and then perform wavelet layered threshold denoising after reconstruction. After experimental comparison, it is found that the denoising effect of designing such a denoising algorithm is better than other different global thresholds and separate layered threshold denoising.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129506423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently clinical evidences have confirmed that human diseases are affected by the microbes inhabiting human bodies. Identifying latent microbe-disease associations can provide a deep insight into the pathogenesis of diseases. However, traditional biological experiments are inefficient and expensive to achieve pathogenic microbes for diseases, computational approaches become a new alternative choice. In this work, we introduce a graph neural network method (MLAGCNMDA) with multiple layers of graph convolutional network and attention mechanism to predict potential microbe-disease pairs. In MLAGCNMDA, a heterogeneous network is constructed based on the known microbe-disease associations and multiple similarities between microbes and diseases. Moreover, nodes embedding of the heterogeneous network are learned by a multi-layer graph convolutional network model, in which the attention mechanism is introduced in each graph convolutional layer to distinguish the importance of neighbor nodes. Finally, a bilinear decoder is used to decode the node embedding to reconstruct microbe-disease associations. The experiments show that our method outperforms the baseline methods with reliable average AUCs of 0.945 and 0.946 in the Leave-one-out and 5-fold cross validation assessment framework. Case studies on two diseases, i.e., colorectal carcinoma and liver cirrhosis, further confirm the reliability and effectiveness of our method.
{"title":"Predicting Microbe-Disease Associations via Multiple Layer Graph Convolutional Network and Attention Mechanism","authors":"K. Shi, Lin Li, Juehua Yu, Yi Zhang, Xiaolan Xie","doi":"10.1145/3571532.3571540","DOIUrl":"https://doi.org/10.1145/3571532.3571540","url":null,"abstract":"Recently clinical evidences have confirmed that human diseases are affected by the microbes inhabiting human bodies. Identifying latent microbe-disease associations can provide a deep insight into the pathogenesis of diseases. However, traditional biological experiments are inefficient and expensive to achieve pathogenic microbes for diseases, computational approaches become a new alternative choice. In this work, we introduce a graph neural network method (MLAGCNMDA) with multiple layers of graph convolutional network and attention mechanism to predict potential microbe-disease pairs. In MLAGCNMDA, a heterogeneous network is constructed based on the known microbe-disease associations and multiple similarities between microbes and diseases. Moreover, nodes embedding of the heterogeneous network are learned by a multi-layer graph convolutional network model, in which the attention mechanism is introduced in each graph convolutional layer to distinguish the importance of neighbor nodes. Finally, a bilinear decoder is used to decode the node embedding to reconstruct microbe-disease associations. The experiments show that our method outperforms the baseline methods with reliable average AUCs of 0.945 and 0.946 in the Leave-one-out and 5-fold cross validation assessment framework. Case studies on two diseases, i.e., colorectal carcinoma and liver cirrhosis, further confirm the reliability and effectiveness of our method.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133365199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neurodegeneration, network alteration and cognitive deficits are commonly observed in patients with Alzheimer's disease. To improve the altered brain networks, external stimulations at gamma frequency have been applied in animal models and human patients. However, whether these external stimuli can entrain brain activity remains elusive. In the present study, to investigate the neural mechanisms underlying gamma stimulation, we presented auditory and visual stimuli of gamma frequency and examined the changes in neural activity after gamma stimulation of different sensory modalities. We found that audio-visual gamma stimulations cause the strongest supra-linear gamma entrainment in the brain, followed by auditory gamma stimulations and visual gamma stimulations. Together, these data deepen our understanding of the neural mechanism of sensory gamma stimulation and will shed light on future research of other non-invasive brain stimulation to treat Alzheimer's disease.
{"title":"Brain entrainment by audio-visual gamma frequency stimulations","authors":"Ziwen Yi, Weijia Wang","doi":"10.1145/3571532.3571551","DOIUrl":"https://doi.org/10.1145/3571532.3571551","url":null,"abstract":"Neurodegeneration, network alteration and cognitive deficits are commonly observed in patients with Alzheimer's disease. To improve the altered brain networks, external stimulations at gamma frequency have been applied in animal models and human patients. However, whether these external stimuli can entrain brain activity remains elusive. In the present study, to investigate the neural mechanisms underlying gamma stimulation, we presented auditory and visual stimuli of gamma frequency and examined the changes in neural activity after gamma stimulation of different sensory modalities. We found that audio-visual gamma stimulations cause the strongest supra-linear gamma entrainment in the brain, followed by auditory gamma stimulations and visual gamma stimulations. Together, these data deepen our understanding of the neural mechanism of sensory gamma stimulation and will shed light on future research of other non-invasive brain stimulation to treat Alzheimer's disease.","PeriodicalId":355088,"journal":{"name":"Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115158972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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