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Recognizing human emotions based on electroencephalogram (EEG) signals has received a great deal of attentions. Most of the existing studies focused on offline analysis, and real-time emotion recognition using a brain computer interface (BCI) approach remains to be further investigated. In this paper, we proposed an EEG-based BCI system for emotion recognition. Specifically, two classes of video clips that represented positive and negative emotions were presented to the subjects one by one, while the EEG data were collected and processed simultaneously, and instant feedback was provided after each clip. Ten healthy subjects participated in the experiment and achieved a high average online accuracy of 91.5% ± 6.34%. The experimental results demonstrated that the subjects emotions had been sufficiently evoked and efficiently recognized by our system. Clinically, patients with disorder of consciousness (DOC), such as coma, vegetative state, and minimally conscious state, suffer from motor impairment and generally cannot provide adequate emotion expressions. Therefore, we applied our emotion recognition BCI system to patients with DOC. Eight DOC patients participated in our experiment, and three of them achieved significant online accuracy. The experimental results show that the proposed BCI system could be a promising tool to detect the emotional states of patients with DOC.

Objective To investigate the effect of a bone substitute material combined with fibroblast growth factor-2 (FGF-2) loaded barrier membrane on the preservation of alveolar ridge after tooth extraction. Materials and methods Four dogs were included. Six extraction sockets of each animal received the 3 treatments and were randomly divided into three groups. Group A: negative control; Group B: bovine xenografts + membrane; Group C: bovine xenografts +FGF-2-loaded membrane. CBCT and histological analysis were performed to evaluate changes in the width and height of alveolar ridges and extraction socket bone healing 8 weeks post-extraction. Results CBCT showed that the alveolar bone in Group A was significantly thinner than that in Group B and Group C at 1 and 3 mm apically from the alveolar crest. The alveolar width at 1 mm in Group C (60.99 ± 15.36%) was significantly thicker than that in Group B (39.75 ± 30.18%). Histomorphmetrical measurements showed that the buccal alveolar width at 1 mm was significantly thicker in Groups B and C than in Group A. Additionally, buccal bone height and lingual bone width at 1 mm in Group C (87.06 ± 10.34%, 89.09 ± 10.56%) were significantly greater than in Group A (53.48 ± 23.94%, 82.72 ± 12.59%). Conclusion The present findings indicate that application of bovine bone combined with barrier membrane with or without FGF-2 over tooth sockets can effectively reduce ridge absorption, especially in terms of ridge width and FGF-2 modified membrane seems to improve the outcomes obtained with membrane alone.

Background Type 2 diabetes mellitus (T2DM) has a harmful effect on the stability and osseointegration of dental implants. T2DM induces mitochondrial damage by inhibiting AMPK signaling, resulting in oxidative stress and poor osteogenesis in the peri-implant bone area. Genipin is a major component of gardenia fruits with strong antioxidant, anti-inflammation, and antidiabetic actions, and it also can activate mitochondrial quality control via the AMPK pathway. The purpose of this study was to investigate the effects of genipin and insulin treatment on implant osseointegration in T2DM rats and explore the underlying mechanisms. Methods Streptozotocin-induced diabetic rats received implant surgery in their femurs and were then assigned to five groups that were subjected to different treatments for three months: control group, T2DM group, insulin-treated T2DM group (10 IU/kg), genipin-treated T2DM group (50 mg/kg), and the genipin and insulin combination-treated T2DM group. Then, we regularly assessed the weight and glucose levels of the animals. Rats were euthanized at 3 months after the implantation procedure, and the femora were harvested for microscopic computerized tomography analysis, biomechanical tests, and different histomorphometric assessment. Results The results indicated that the highest blood glucose and oxidative stress levels were measured for the T2DM group, resulting in the poorest osseointegration. The combination-treated T2DM group mitigated hyperglycemia and normalized, reactivated AMPK signaling, and alleviated oxidative stress as well as reversed the negative effect of osseointegration. There were beneficial changes observed in the T2DM-genipin and T2DM-insulin groups, but these were less in comparison to the combination treatment group. Conclusion Our study suggests that treatment with genipin in combination with insulin could be an effective method for promoting implant osseointegration in T2DM rats, which may be related to AMPK signaling.

Periodontal diseases can lead to chronic inflammation affecting the integrity of the tooth supporting tissues. Recently, a striking association has been made between periodontal diseases and primary cancers in the absence of a mechanistic understanding. Here we address the effect of periodontal inflammation (PI) on tumor progression, metastasis, and possible underlining mechanisms. We show that an experimental model of PI in mice can promote lymph node (LN) micrometastasis, as well as head and neck metastasis of 4T1 breast cancer cells, both in early and late stages of cancer progression. The cervical LNs had a greater tumor burden and infiltration of MDSC and M2 macrophages compared with LNs at other sites. Pyroptosis and the resultant IL-1β production were detected in patients with PI, mirrored in mouse models. Anakinra, IL-1 receptor antagonist, limited metastasis, and MDSC recruitment at early stages of tumor progression, but failed to reverse established metastatic tumors. PI and the resulting production of IL-1β was found to promote CCL5, CXCL12, CCL2, and CXCL5 expression. These chemokines recruit MDSC and macrophages, finally enabling the generation of a premetastatic niche in the inflammatory site. These findings support the idea that periodontal inflammation promotes metastasis of breast cancer by recruiting MDSC in part by pyroptosis-induced IL-1β generation and downstream CCL2, CCL5, and CXCL5 signaling in the early steps of metastasis. These studies define the role for IL-1β in the metastatic progression of breast cancer and highlight the need to control PI, a pervasive inflammatory condition in older patients.

Long noncoding RNA (LncRNA) small nucleolar RNA host gene 16 (SNHG16) is correlated with cell injuries, including pneumonia. However, its role and mechanism remain vague in pneumonia. The interplay among genes was confirmed by dual-luciferase reporter assay, RNA immunoprecipitation, and RNA pull-down assay. SNHG16 and sushi domain containing 2 (SUSD2) were upregulated, and miRNA (miR)-141-3p was downregulated in the serum of acute pneumonia patients and lipopolysaccharide (LPS)-challenged human lung fibroblasts WI-38. LPS induced apoptosis, autophagy, and inflammatory response in WI-38 cells, which was significantly attenuated by SNHG16 knockdown and/or miR-141-3p overexpression. Notably, both SNHG16 and SUSD2 were identified as target genes of miR-141-3p. Besides, the suppressive role of SNHG16 knockdown in LPS-induced in WI-38 cells was partially abolished by miR-141-3p silencing, and the similar inhibition of miR-141-3p overexpression was further blocked by SUSD2 restoration. In conclusion, knockdown of SNHG16 could alleviate LPS-induced apoptosis, autophagy, and inflammation in WI-38 cells partially though the SNHG16/miR-141-3p/SUSD2 pathway.

Meditation aims to improve individuals’ core psychological capacities, such as attentional and emotional self-regulation. In this study, we experimented to explore the utility of using permutation entropy (PE) features on electroencephalogram (EEG) data to distinguish among states in human meditation, attention, and relaxation. Twenty advanced yogis with above three years yoga experience and twenty nonmeditators were recruited in our experiment. The whole experiment contained 7 trials. During each trial, subjects were requested to maintain meditation, attention, and relaxation state for three minutes in random order, respectively, after the corresponding voice prompted. In the meantime, 30-channel EEG data were collected in real-time. We calculated PE features from different EEG frequency bands and selected the most correlative features using Fisher’s ratio technique. After that, we fed the feature vectors into a three-class SVM classifier. Twenty yogis and twenty non-meditators achieved average offline classification accuracies of 74.31% and 62.16%, respectively. These results indicated that PE features could be useful in discriminating meditation state and might bring new insight in building meditation-related Brain-Computer Interfaces (BCIs).

To construct aptamer AS1411-functionalized targeted lipid nanobubbles that could simultaneously target abnormally highly expressed nucleolin (NCL) on tumor tissue and neovasculature. Additionally, the study of their contrast-enhanced ultrasound molecular imaging capabilities in vitro and in vivo to explore new methods and approaches for the early and accurate diagnosis of triple-negative breast cancer (TNBC). First, the targeted lipid-nucleic acid molecules were constructed by an amide reaction. Then, the targeted lipid nanobubbles (AS1411-NBs) and nontargeted lipid nanobubbles (NBs) were prepared by membrane hydration, mechanical vibration and centrifugal floatation. The physicochemical characteristics and contrast-enhanced ultrasound imaging capabilities of AS1411-NBs and NBs were compared and analyzed in vitro and in vivo. There were no significant differences between the AS1411-NBs and NBs in their concentration, average particle size or ultrasound imaging capabilities in vitro (P > 0.05). However, AS1411-NBs could simultaneously target NCL in tumor tissue and neovasculature to effectively prolong the duration of contrast-enhanced ultrasound imaging compared to NBs in vivo. The area under the time-intensity curve was significantly different between AS1411-NBs and NBs (P

Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised (CRS-R). Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness (DOCs). Brain-computer interface (BCI) can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expression. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R.

Objective. For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), communication is challenging. Currently, the communication methods of DOC patients are limited to behavioral responses. However, patients with DOC cannot provide sufficient behavioral responses due to motor impairments and limited attention. In this study, we proposed a hybrid asynchronous brain–computer interface (BCI) system that provides a new communication channel for patients with DOC. Approach. Seven patients with DOC (3 VS and 4 MCS) and eleven healthy subjects participated in our experiment. Each subject was instructed to focus on the square with the Chinese words ‘Yes’ and ‘No’. Then, the BCI system determined the target square with both P300 and steady-state visual evoked potential (SSVEP) detections. For the healthy group, we tested the performance of the hybrid system and the single-modality BCI system. Main results. All healthy subjects achieved significant accuracy (ranging from 72% to 100%) in both the hybrid system and the single modality system. The hybrid asynchronous BCI system outperformed the P300-only and SSVEP-only systems. Furthermore, we employed the asynchronous approach to dynamically collect the electroencephalography signal. Compared with the synchronous system, there was a 21% reduction in the average required rounds and a reduction of 105 s in the online experiment time. This asynchronous system was applied to detect the ‘yes/no’ communication function of seven patients with DOC, and the results showed that three of the patients (3 MCS) not only showed significant accuracies (67 ± 3%) in the online experiment, and their Coma Recovery Scale-Revised scores were also improved compared with the scores before the experiment. This result demonstrated that 3 of 7 patients were able to communicate using our hybrid asynchronous BCI system. Significance. This hybrid asynchronous BCI system can be used as a useful auxiliary bedside tool for simple communication with DOC patients.

Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and k-mer frequencies) and structure features (e.g., minimum free energy, conserved stem-loop, or graph structures) are suggested to be combined with the next- and third-generation sequencing platforms to improve ncRNA prediction.

Objective As the most prevalent type of rhinitis, allergic rhinitis is consisted of seasonal allergic rhinitis (SAR) and perennial allergic rhinitis. This study is carried out for revealing the mechanisms of SAR. Methods Microarray data set GSE43523 (including 7 SAR nasal epithelial cells and 5 nonallergic control nasal epithelial cells) was extracted from Gene Expression Omnibus database. Based on limma package, differential expression analysis for the 2 groups was performed to obtain differentially expressed genes (DEGs). Using Multifaceted Analysis Tool for Human Transcriptome online tool, the functions involving the DEGs were predicted by enrichment analysis. Combined with Cytoscape software, protein–protein interaction (PPI) network was built and a significant network module was acquired. In addition, transcription factor (TF)-target and miRNA-target pairs were predicted using WebGestalt tool, and then TF-miRNA-target regulatory network was built by Cytoscape software. Results There were 274 DEGs between rhinitis and control samples, including 144 upregulated genes and 130 downregulated genes. After PPI for the DEGs was built, a significant network module was identified. In the TF-miRNA-target regulatory network, ABCA1, CPEB4, CD69, MIR-17-5P, and CREB had higher degrees. Furthermore, both ABCA1 and CD69 were targeted by MIR-17-5P in the regulatory network. Conclusion CPEB4 and CREB might be implicated in the pathogenesis of SAR. Besides, MIR-17-5P might also act in SAR via targeting ABCA1 and CD69.

Xiaoqi Zhang,* Hao Feng,* Ziyu Li, Dongfang Li, Shanshan Liu, Haiyun Huang, Minqi Li Department of Bone Metabolism, School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, China *These authors contributed equally to this work Purpose: Oral squamous cell carcinoma (OSCC) is one of the most common malignant diseases worldwide, yet its molecular mechanisms are largely unknown. We aimed to construct gene co-expression networks to identify key modules and hub genes involved in the pathogenesis of OSCC. Patients and methods: We used dataset GSE30784 to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). Hub genes were screened and validated by other datasets. Results: Turquoise and brown modules were found to be the most significantly related to tumorigenesis. Functional enrichment analysis showed that the turquoise module was associated with cell–cell adhesion, extracellular matrix and collagen catabolic process. A total of 10 hub genes (MMP1, TNFRSF12A, PLAU, FSCN1, PDPN, KRT78, EVPL, GGT6, SMIM5 and CYSRT1) were identified and validated at transcriptional and translational levels. Their genetic alteration and survival analysis were also revealed. Conclusion: We identified two modules and 10 hub genes, which were associated with the tumorigenesis of OSCC. The two modules provided references that will advance the understanding of mechanisms of tumorigenesis in OSCC. Moreover, the hub genes may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of OSCC in the future. Keywords: oral squamous cell carcinoma, co-expression, WGCNA, hub gene

Oral tissue-derived mesenchymal stem cells, such as periodontal ligament stem cells (PDLSCs) and gingival mesenchymal stem cells (GMSCs), possess different biological characteristics, but the molecular mechanism remains unclear, which restricts their application in tissue engineering. Long noncoding RNAs (lncRNAs) are known to be significant regulators of gene expression, but our knowledge about their roles in the regulation of stem cell biological properties is still limited. This study compared the lncRNA and mRNA expression profiles between PDLSCs and GMSCs through microarray analysis, and applied bioinformatics methods to analyze and predict the function and connection of differentially expressed genes, aiming to screen potential key regulators of diverse biological characteristics in PDLSCs and GMSCs. Microarray analysis showed that 2162 lncRNAs and 1347 mRNAs were significantly differentially expressed between PDLSCs and GMSCs. Gene ontology (GO) analysis and pathway analysis indicated that these differentially expressed genes were involved in diverse biological processes and signaling pathways. The gene signal network and pathway relation network predicted some potentially important regulators. The coding-noncoding gene coexpression network (CNC network) revealed many potential lncRNA-mRNA connection pairs that participated in the regulation of biological behaviors. These results stressed the roles of lncRNAs in controlling stem cell biological behaviors and provided guides for molecular mechanistic study of different biological characteristics in PDLSCs and GMSCs.

In this paper, an EEG-based brain-computer-interface (BCI) system used for automatic sleep stage classification was proposed. After wavelet de-noising, thirteen features, including spectral and statistical analysis of single-channel EEG signal, were extracted for each epoch. Support Vector Machine was employed to classify four states (Awake/ Light Sleep/ Slow Wave Sleep/ Rapid Eye Movement). Two experiments involving ten recordings from Sleep-EDF Database and the other three healthy subjects in our laboratory were conducted to validate our method and BCI system. For the former, the overall accuracy and kappa coefficient has achieved 86.82% and 0.83 of Fpz-Cz channel, respectively (86.01% and 0.81 for Pz-Oz channel, respectively). For the latter, the overall accuracy and kappa coefficient has achieved 81.30% and 0.73, respectively. The experimental outcomes validate the superior of the proposed method in accuracy.

For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses.

// Jialiang Yang 1 , Jing Qiu 2 , Kejing Wang 2 , Lijuan Zhu 2 , Jingjing Fan 2 , Deyin Zheng 3 , Xiaodi Meng 4 , Jiasheng Yang 5 , Lihong Peng 1 , Yu Fu 2 , Dahan Zhang 6 , Shouneng Peng 7 , Haiyun Huang 2 and Yi Zhang 2 1 College of Information Engineering, Changsha Medical University, Changsha 410219, P. R. China 2 Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China 3 Department of Mathematics, Hangzhou Normal University, Hangzhou 311121, P. R. China 4 Department of Food Science, Fujian Agriculture and Forestry University, Fuzhou 35002, P. R. China 5 Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore 6 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, P. R. China 7 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA Correspondence to: Yi Zhang, email: zhaqi1972@163.com Haiyun Huang, email: jialiang.yang@mssm.edu Keywords: bioinformatics, human obesity, obesity-related diseases, protein interaction network, gene expression Received: May 03, 2017 Accepted: June 05, 2017 Published: July 22, 2017 ABSTRACT Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example, TGFB1 and VEGFA are inferred to be the top two key genes mediating obesity-breast cancer connection in modules associated with brain development. Finally, the top modules identified by OBNet in breast cancer significantly overlap with modules identified from TCGA breast cancer gene expression study, revealing the power of OBNet in identifying biological processes involved in the disease.

The aim of this study is to determine the potential prognostic value of using Brain-computer Interface (BCI) to identify patients with disorder of consciousness (DOC), who show potential for recovery. A retrospective study involved 51 patients with DOC were conducted. Each patient conducted in a BCI experiment to detect awareness and received a 3-months follow-up. The BCI accuracies were correlated to patient outcomes according to the Coma Recovery Scale Revised (CRS-R). The statistical tests showed that patients with significant accuracies higher than the chance level showed greater improvement in CRS-R scores after 3 months compared to patients without significant accuracies. The sensitivity and specificity of the BCI accuracies in determining individual's consciousness recovery after 3 months are 67.7% and 90% respectively. A highly significant relationship between BCI accuracies and subsequent recovery was thus found. The BCI is suggested as an important tool to assess information-processing capacities that can predict the likelihood of recovery in patients with disorder of consciousness.

Tumor necrosis factor-alpha (TNF-α) inhibits osteogenic differentiation of murine bone marrow stromal cells, and transcription factor Runx2 serves as an essential regulation target in the process. The underlying mechanism may involve the regulation of Runx2 expression and the Runx2 activity in downstream gene transcription, which has not been fully elucidated. In this study, ST2 murine bone marrow-derived stromal cells were treated with bone morphogenetic protein-2 (BMP-2) and/or TNF-α in osteogenic medium, and the expression of Runx2 was estimated. Cells were transfected with Runx2, p65, inhibitor of κBα (IκBα), 9.0 kb bone sialoprotein (BSP) promoter-luciferase or osteoblast-specific cis-acting element 2 (OSE2)-luciferase reporter vectors, and then real time-PCR and dual luciferase analysis were used to investigate the effect of TNF-α on Runx2-activated osteogenic gene transcription and the molecular mechanism. We found that TNF-α inhibited BMP-2-induced osteogenic marker expression and both the spontaneous and BMP-2-induced Runx2 expression. TNF-α stimulation or overexpression of nuclear factor-kappa B (NF-κB) p65 subunit repressed the Runx2-activated BSP and osteocalcin (OC) transcriptions. The Runx2-induced 9.0 kb BSP promoter activity was attenuated by TNF-α or p65, while the OSE2 activity was not affected. Besides, blockage of NF-κB by IκBα overexpression eliminated these inhibitory effects of TNF-α on Runx2 signaling. These results suggest that in murine bone marrow stromal cells undergoing osteogenic differentiation, TNF-α and it activated NF-κB pathway inhibit the expression of Runx2 gene, and suppress the Runx2-mediated osteogenic gene transcription via the 9.0 kb BSP promoter.

With the development of new technologies in transcriptome and epigenetics, RNAs have been identified to play more and more important roles in life processes. Consequently, various methods have been proposed to assess the biological functions of RNAs and thus classify them functionally, among which comparative study of RNA structures is perhaps the most important one. To measure the structural similarity of RNAs and classify them, we propose a novel three dimensional (3D) graphical representation of RNA secondary structure, in which an RNA secondary structure is first transformed into a characteristic sequence based on chemical property of nucleic acids; a dynamic 3D graph is then constructed for the characteristic sequence; and lastly a numerical characterization of the 3D graph is used to represent the RNA secondary structure. We tested our algorithm on three datasets: (1) Dataset I consisting of nine RNA secondary structures of viruses, (2) Dataset II consisting of complex RNA secondary structures including pseudo-knots, and (3) Dataset III consisting of 18 non-coding RNA families. We also compare our method with other nine existing methods using Dataset II and III. The results demonstrate that our method is better than other methods in similarity measurement and classification of RNA secondary structures.

Alcohol septal ablation (ASA) has been used widely to treat patients with hypertrophic obstructive cardiomyopathy (HOCM). During the routine ASA procedure, it is difficult to detect the septal injury in real-time. The aim of the present study is to assess myocardial injury during ASA by recording intracoronary electrocardiogram (IC-ECG). From 2012 to 2015, 31 HOCM patients were treated with ASA, and IC-ECG was recorded in 21 patients successfully before and after ethanol injection. The elevation of ST-segment on IC-ECG after ethanol injection was expressed as its ratio to the level before injection or the absolute increasing value. Blood samples were collected before and after ASA for measuring changes in cardiac biomarkers. The ratio value of ST-segment elevation was positively correlated with both the amount of ethanol injected (r = 0.645, P = 0.001) and the myocardial injury size (creatine kinase-MB area under the curve (AUC) of CK-MB) (r = 0.466, P = 0.017). The absolute increment of ST-segment was also positively associated with both the amount of ethanol (r = 0.665, P = 0.001) and AUC of CK-MB (0.685, P = 0.001). However, there was no statistical correlation between the reduction of left ventricular outflow tract gradient and ST-segment elevation. Additionally no severe ASA procedure-related complications were observed in our patients. In conclusion, myocardial injury induced by ethanol injection can be assessed immediately by ST-segment elevation on IC-ECG. This study is the first to show that IC-ECG is a useful method for predicting myocardial injury during ASA in real-time.