Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction.

Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition speed and accuracy. Therefore, we proposed a lightweight multi-stream feature cross-fusion (L-MSFCF) model to recognize abnormal behaviors such as fighting, vicious kicking, climbing over the wall, et al.

Unsupervised Feature-Construction-Based Motor Fault Diagnosis.

Any bearing faults are a leading cause of motor damage and bring economic losses. Fast and accurate identification of bearing faults is valuable for preventing damaging the whole equipment and continuously running industrial processes without interruption. Vibration signals from a running motor can be utilized to diagnose a bearing health condition.

A Mood Semantic Awareness Model for Emotional Interactive Robots.

The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human-machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users' degree of interest are formulated.

EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism.

A variety of technologies that could enhance driving safety are being actively explored, with the aim of reducing traffic accidents by accurately recognizing the driver's state. In this field, three mainstream detection methods have been widely applied, namely visual monitoring, physiological indicator monitoring and vehicle behavior analysis. In order to achieve more accurate driver state recognition, we adopted a multi-sensor fusion approach.

CKD-516 potentiates the anti-cancer activity of docetaxel against epidermal growth factor receptor tyrosine kinase inhibitor-resistant lung cancer.

Lung cancer is the leading cause of cancer death. Although docetaxel has been used as a second- or third-line treatment for non-small cell lung cancer (NSCLC), the objective response rate is less than 10%. Hence, there is a need to improve the clinical efficacy of docetaxel monotherapy; combination therapy should be considered.

EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare.

Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal, which can improve the reliability of emotional judgments, and, furthermore, deeply mine the potential influence correlations between music and emotions.

A novel HDAC6 inhibitor, CKD-504, is effective in treating preclinical models of huntington's disease.

Huntington's disease (HD) is a neurodegenerative disorder, of which pathogenesis is caused by a polyglutamine expansion in the amino-terminus of huntingtin gene that resulted in the aggregation of mutant HTT proteins. HD is characterized by progressive motor dysfunction, cognitive impairment and neuropsychiatric disturbances. Histone deacetylase 6 (HDAC6), a microtubule-associated deacetylase, has been shown to induce transport- and release-defect phenotypes in HD models, whilst treatment with HDAC6 inhibitors ameliorates the phenotypic effects of HD by increasing the levels of α-tubulin acetylation, as well as decreasing the accumulation of mutant huntingtin (mHTT) aggregates, suggesting HDAC6 inhibitor as a HD therapeutics.

Novel Histone Deacetylase 6 Inhibitor Confers Anti-inflammatory Effects and Enhances Gut Barrier Function.

Background/aims: The purpose of the current study was to examine the anti-inflammatory effects of CKD-506, a novel histone deacetylase 6 inhibitor, on human peripheral blood mononuclear cells (PBMCs) and CD4+ T cells and to explore the relationship between CKD-506 and gut epithelial barrier function.

Methods: Lipopolysaccharide-stimulated human PBMCs from inflammatory bowel disease (IBD) patients were treated with CKD-506, and tumor necrosis factor (TNF)-α expression was measured using an enzyme-linked immunosorbent assay. The proliferation of CD4+ T cells from IBD patients was evaluated using flow cytometric analysis.

An Ensemble Semantic Textual Similarity Measure Based on Multiple Evidences for Biomedical Documents.

With the increasing volume of the published biomedical literature, the fast and effective retrieval of the literature on the sequence, structure, and function of biological entities is an essential task for the rapid development of biology and medicine. To capture the semantic information in biomedical literature more effectively when biomedical documents are clustered, we propose a new multi-evidence-based semantic text similarity calculation method. Two semantic similarities and one content similarity are used, in which two semantic similarities include MeSH-based semantic similarity and word embedding-based semantic similarity.

Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering.

The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover informative representations and define relevant articles from the rapidly growing biomedical literature due to the unsupervised nature of document clustering.

InfersentPPI: Prediction of Protein-Protein Interaction Using Protein Sentence Embedding With Gene Ontology Information.

Protein-protein interaction (PPI) prediction is meaningful work for deciphering cellular behaviors. Although many kinds of data and machine learning algorithms have been used in PPI prediction, the performance still needs to be improved. In this paper, we propose InferSentPPI, a sentence embedding based text mining method with gene ontology (GO) information for PPI prediction.

An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering.

Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data.

Comprehensive Evaluation of Differential Methylation Analysis Methods for Bisulfite Sequencing Data.

With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methylated regions in bisulfite sequencing data, and more are being developed continuously. Here, we focused on a comprehensive evaluation of commonly used differential methylation analysis methods and describe the potential strengths and limitations of each method.

Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification.

Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques.

XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction.

Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, numerous studies have been proposed using artificial intelligence techniques to predict the risk of developing SiNCDs. However, determining the most significant features and developing interpretable models are rather challenging in such systems.

GEN2VCF: a converter for human genome imputation output format to VCF format.

Background: For a genome-wide association study in humans, genotype imputation is an essential analysis tool for improving association mapping power. When IMPUTE software is used for imputation analysis, an imputation output (GEN format) should be converted to variant call format (VCF) with imputed genotype dosage for association analysis. However, the conversion requires multiple software packages in a pipeline with a large amount of processing time.

Phase I and pharmacokinetic study of the vascular-disrupting agent CKD-516 (NOV120401) in patients with refractory solid tumors.

We report a phase I pharmacological study of an oral formulation of CKD-516, a vascular-disrupting agent, in patients with refractory solid tumors. Twenty-seven patients (16 in the dose-escalation cohort and 11 in the expansion cohort) received a single daily dose (5-25 mg) of CKD-516 five days per week. Nausea (67%) and diarrhea (63%) were the most common treatment-related adverse events.

Reconstruction error based deep neural networks for coronary heart disease risk prediction.

Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by predicting high-risk people who are to suffer from CHD, it is significant to reduce the risks of developing CHD.

Acetylation changes tau interactome to degrade tau in Alzheimer's disease animal and organoid models.

Alzheimer's disease (AD) is an age-related neurodegenerative disease. The most common pathological hallmarks are amyloid plaques and neurofibrillary tangles in the brain. In the brains of patients with AD, pathological tau is abnormally accumulated causing neuronal loss, synaptic dysfunction, and cognitive decline.

Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach.

Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events.

ZNF492 and GPR149 methylation patterns as prognostic markers for clear cell renal cell carcinoma: Array‑based DNA methylation profiling.

The present study aimed to identify novel methylation markers of clear cell renal cell carcinoma (ccRCC) using microarray methylation analysis and evaluate their prognostic relevance in patient samples. To identify cancer‑specific methylated biomarkers, microarray profiling of ccRCC samples from our institute (n=12) and The Cancer Genome Atlas (TCGA) database (n=160) were utilized, and the prognostic relevance of candidate genes were investigated in another TCGA dataset (n=153). For validation, pyrosequencing analyses with ccRCC samples from our institute (n=164) and another (n=117) were performed and the potential clinical application of selected biomarkers was examined.

Risk Scoring System for Prognosis Estimation of Multivessel Disease Among Patients with ST-Segment Elevation Myocardial Infarction.

Multivessel disease (MVD) is an independent risk factor for poor prognosis in acute myocardial infarction patients. Although several global risk scoring systems (RSS) are in use in clinical practice, there is no dedicated RSS for MVD in ST-segment elevation myocardial infarction (STEMI). The primary objective of this study is to develop a novel RSS to estimate the prognosis of patients with MVD in STEMI.

Methylation Signature for Prediction of Progression Free Survival in Surgically Treated Clear Cell Renal Cell Carcinoma.

Background: Little is known about epigenetic silencing of genes by promoter hypermethylation in renal cell carcinoma (RCC). The aim of this study was to identify prognostic methylation markers in surgically treated clear cell RCC (ccRCC).

Methods: Methylation patterns were assayed using the Infinium HumanMethylation450 BeadChip array on pairs of ccRCC and normal tissue from 12 patients.

Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry.

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry.

Positive Effect of Baby-Friendly Hospital Initiatives on Improving Mothers' Intention for Successful Breastfeeding in Korea.

Background: In Korea, the breastfeeding (BF) rate of infants aged 6 months or more is drastically decreasing, and this phenomenon is particularly worrisome for the future health of the population. The present study aimed to identify an antenatal strategy for initiation and continuation of human BF, and to identify how Baby-Friendly Hospitals (BFHs) may positively influence the intention to breastfeed.

Methods: A total of 414 pregnant Korean antenatal women were surveyed using questionnaires to determine current knowledge of the benefits of human breast milk, whether they planned to breastfeed after delivery, to continue BF after reinstatement in the workforce, are willing to abide by rooming-in care for infants, and plan to give birth at BFHs.