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Endothelial dysfunction and inflammatory immune response trigger dedifferentiation of vascular smooth muscle cells (SMCs) from contractile to synthetic phenotype and initiate arterial occlusion. However, the complex vascular remodeling process playing roles in arterial occlusion initiation is largely unknown. We performed bulk sequencing of small and messenger RNAs in a rodent arterial injury model. Bioinformatic data analyses reveal that six miRNAs are overexpressed in injured rat carotids as well as synthetic-type human vascular SMCs. In vitro cell-based assays show that four miRNAs (miR-130b-5p, miR-132-3p, miR-370-3p, and miR-410-3p) distinctly regulate the proliferation of and monocyte adhesion to the vascular SMCs. Individual inhibition of the four selected miRNAs strongly prevents the neointimal hyperplasia in the injured rat carotid arteries. Mechanistically, miR-132-3p and miR-370-3p direct the cell cycle progression, triggering SMC proliferation. Gene ontology analysis of mRNA sequencing data consistently reveal that the miRNA targets include gene clusters that direct proliferation, differentiation, and inflammation. Notably, bone morphogenic protein (BMP)-7 is a prominent target gene of miR-370-3p, and it regulates vascular SMC proliferation in cellular and animal models. Overall, this study first reports that the miR-370-3p/BMP-7 axis determines the vascular SMC phenotype in both rodent and human systems.

Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task that requires the modeling of complex nonlinear relationships among the HVAC load, indoor temperature, and outdoor environment. This article proposes a new strategy for optimal operation of an HVAC system in a commercial building. The system for indoor temperature control is divided into three sub-systems, each of which is modeled using an artificial neural network (ANN). The ANNs are then interconnected and integrated into an optimization problem for temperature set-point scheduling. The problem is reformulated to determine the optimal set-points using a deterministic search algorithm. After the optimal scheduling has been initiated, the ANNs undergo online learning repeatedly, mitigating overfitting. Case studies are conducted to analyze the performance of the proposed strategy, compared to strategies with a pre-determined temperature set-point, an ideal physics-based building model, and other types of machine learning-based modeling and scheduling methods. The case study results confirm that the proposed strategy is effective in terms of the HVAC energy cost, practical applicability, and training data requirements.

Background Ginseng (Panax ginseng Meyer) is one of the world's most valuable medicinal plants with numerous pharmacological effects. Ginseng has been cultivated from wild mountain ginseng collections for a few hundred years. However, the genetic diversity of cultivated and wild ginseng populations is not fully understood. Methods We developed 92 polymorphic microsatellite markers based on whole-genome sequence data. We selected five markers that represent clear allele diversity for each of their corresponding loci to elucidate genetic diversity. These markers were applied to 147 individual plants, including cultivars, breeding lines, and wild populations in Korea and neighboring countries. Results Most of the 92 markers displayed multiple-band patterns, resulting from genome duplication, which causes confusion in interpretation of their target locus. The five high-resolution markers revealed 3 to 8 alleles from each single locus. The proportion of heterozygosity (He) ranged from 0.027 to 0.190, with an average of 0.132, which is notably lower than that of previous studies. Polymorphism information content of the markers ranged from 0.199 to 0.701, with an average of 0.454. There was no statistically significant difference in genetic diversity between cultivated and wild ginseng groups, and they showed intermingled positioning in the phylogenetic relationship. Conclusion Ginseng has a relatively high level of genetic diversity, and cultivated and wild groups have similar levels of genetic diversity. Collectively, our data demonstrate that current breeding populations have abundant genetic diversity for breeding of elite ginseng cultivars.

Fusion genes represent an important class of biomarkers and therapeutic targets in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with extensive manual annotations (ChimerKB). In this update, we present all three modules substantially enhanced by incorporating the recent flood of deep sequencing data and related publications. ChimerSeq now covers all 10 565 patients in the TCGA project, with compilation of computational results from two reliable programs of STAR-Fusion and FusionScan with several public resources. In sum, ChimerSeq includes 65 945 fusion candidates, 21 106 of which were predicted by multiple programs (ChimerSeq-Plus). ChimerPub has been upgraded by applying a deep learning method for text mining followed by extensive manual curation, which yielded 1257 fusion genes including 777 cases with experimental supports (ChimerPub-Plus). ChimerKB includes 1597 fusion genes with publication support, experimental evidences and breakpoint information. Importantly, we implemented several new features to aid estimation of functional significance, including the fusion structure viewer with domain information, gene expression plot of fusion positive versus negative patients and a STRING network viewer. The user interface also was greatly enhanced by applying responsive web design. ChimerDB 4.0 is available at http://www.kobic.re.kr/chimerdb/.

Objective Neuroimaging in headache patients identifies clinically significant neurological abnormalities and plays an important role in excluding secondary headache diagnoses. We performed a systematic review and meta-analysis of the existing guidelines and studies surrounding neuroimaging in headache patients. Methods The research question involved determining the prevalence of detecting clinically significant neurological abnormalities using neuroimaging in patients suspected of primary headache. Searches of the PubMed and Embase databases were conducted on English-language studies published from 1991 to 2016, and the reference lists of the retrieved articles were also checked manually. All headache subtypes and patients aged ≥15 years were included in the analysis. Results Ten studies met the selection criteria. The pooled prevalence of detecting clinically significant abnormalities in the neuroimaging of headache patients was 8.86% (95% confidence interval: 5.12-15.33%). Subsequently, diverse subgroup analyses were performed based on the detection method, headache type, study type, study region, age group, and disease type. Conclusion The present findings indicate that limited neuroimaging methods should be carefully considered for headache diagnostic purposes when there are red flag symptoms. Limitations and suggested directions for future studies on neuroimaging in headache patients are described.

The present study explored how neurocognitive function correlated with the clinical symptoms of somatic symptom disorder (SSD) by evaluating changes in cognitive abilities according to differences in relevant factors.A total of 44 patients with SSD and 30 healthy controls completed tests assessing various neurocognitive domains, including verbal memory, psychomotor speed, executive function, working memory, and sustained and divided attention. They also completed questionnaires for psychological assessment. The same tests and questionnaires were completed by 26 SSD patients 6 months later.The SSD patients had significantly lower scores on the attentional and verbal memory tests than did the healthy controls. Performance on the attentional test was significantly associated with the level of somatic symptoms and anxiety. The follow-up assessment results of the SSD patients revealed improved performance on the verbal learning and fluency tests as well as improvements in somatic symptoms, anxiety, and depression. It was also observed that changes in verbal learning and attentional functions were significantly associated with improvements in somatic symptoms.The present study suggests that neurocognitive dysfunctions are subtle and not specific to SSD, but certain cognitive functions may be related to the clinical symptoms and improvements of patients with SSD.

This study was performed to investigate altered regional gray matter volume (rGMV) and structural covariance related to somatic symptom disorder (SSD) and its phenotypic subtypes. Additionally, this study examined the relationships of structural alteration with cognitive functioning and clinical symptoms. Forty-three unmedicated patients with SSD and thirty normal controls completed psychological questionnaires and neurocognitive tests, as well as brain magnetic resonance imaging. Voxel-based morphometry and structural covariances were compared between groups and between subgroups within the SSD group. Patients with SSD exhibited attenuated structural covariances in the pallidal-cerebellar circuit, as well as regions in the default mode and sensorimotor network, compared to normal controls. The cerebellar rGMVs were negatively correlated with the severity of somatic symptoms. With respect to cognitive functioning, the rGMVs of the right pallidum, left medial orbitofrontal gyrus, and bilateral supramarginal gyrus were correlated with attentional capacity in SSD patients. In subgroup analyses, patients with somatic pain showed denser structural covariances between the bilateral superior temporal pole and left angular gyrus, the left middle temporal pole and left angular gyrus, and the left amygdala and right inferior orbitofrontal gyrus, while patients with headache and dizziness had greater structural covariance between the right inferior temporal gyrus and right cerebellum. The findings revealed the structural alteration involved in SSD; they also suggested that specific brain regions and networks may contribute to different subtypes of SSD.

The endothelial inflammatory response plays an important role in atherogenesis by inducing nuclear factor (NF)κB-dependent cell adhesion molecule expression and monocyte recruitment. Here, we screened for natural ligands and investigated the ability of shinjulactone A to inhibit interleukin-1β (IL-1β)-induced endothelial inflammatory signaling.The natural compound library included 880 single compounds isolated from medicinal plants by the Korean Medicinal Material Bank. Primary endothelial cells were pretreated with single compounds before stimulation with IL-1β to induce endothelial inflammation. Endothelial inflammation was measured by assaying NFκB activation and monocyte adhesion. The endothelial-mesenchymal transition (EndMT) was evaluated using cell type-specific marker protein expression and morphology.Shinjulactone A was identified as an efficient blocker of IL-1β -induced NFκB activation, with a half-maximal inhibitory concentration of approximately 1 µM, and monocyte recruitment in endothelial cells. However, it did not affect lipopolysaccharide-induced NFκB activation in macrophages. Compared to Bay 11-782, a well-known NFκB inhibitor that shows considerable cytotoxicity during long-term treatment, shinjulactone A did not affect endothelial cell viability. Furthermore, it also significantly inhibited the EndMT, which is known to promote atherosclerosis and plaque instability.We suggest that shinjulactone A may be an effective and safe drug candidate for atherosclerosis because it targets and inhibits both endothelial inflammation and the EndMT, without impairing NFκB-dependent innate immunity in macrophages.

Objective The quantitative electroencephalography (qEEG) of patients with somatic symptom disorder (SSD) was not yet thoroughly studied. This study aimed to investigate qEEG of SSD patients compared with those of normal controls (NCs), and changes therein after treatment. Methods SSD patients currently without treatment and age- and sex-matched NCs were recruited. Spectral analysis of 64-channel EEG recording was performed and somatization, anxiety, and depression were evaluated via self-rating scales at baseline. After six months of treatment as usual, SSD patients were longitudinally followed up for assessments. Results At baseline, the SSD group (n = 44) had higher alpha (p = 0.047) and lower beta 2 (p = 0.027) and gamma power (p = 0.001) compared with NCs (n = 29). After 6-month treatment, SSD patients showed improvement in symptoms, as well as increased beta 1 (p = 0.032), beta 2 (p = 0.012), and gamma power (p = 0.009) compared with baseline. A significant correlation was observed between the change in somatization score and temporal gamma power (r = −0.424, p = 0.031), and between the change in anxiety score and beta 2 power in the frontal (r = −0.420, p = 0.033) and central (r = −0.484, p = 0.012) regions. Conclusions EEG findings in this study may provide neurophysiological features of SSD. The alpha enhancement and reduced fast wave activity may reflect attentional dysfunction in patients with SSD. Decreased fast wave activity is reversible and may serve as a state marker of SSD.

Hye Youn Park,1 Ye Eun Jang,1 Sunghee Oh,1 Pyung Bok Lee2 1Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea; 2Pain Center, Seoul National University Bundang Hospital, Seongnam, KoreaCorrespondence: Pyung Bok LeePain Center, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-Gil, Bungdang-gu, Seongnam, Gyeonggi-do, KoreaTel +82-31-787-7495Fax +82-31-787-4058Email painfree@snubh.orgObjective: This study investigated psychological characteristics of patients with chronic complex regional pain syndrome (CRPS) and examined relationships between psychosocial factors and pain severity.Methods: In total, 76 patients with CRPS, 95 patients with other types of chronic pain, 171 healthy controls, and 66 patients with major depressive disorder (MDD) were included. Minnesota Multiphasic Personality Inventory (MMPI-2) profiles and scores on the Beck Depression Inventory and State–Trait Anxiety Inventory were calculated. Pain intensity was measured using a visual analog scale (VAS).Results: Patients with CRPS scored higher on the Hypochondriasis (Hs), Depression (D), Hysteria (Hy), Paranoia (Pa), and Psychasthenia (Pt) scales of the MMPI-2 compared to healthy controls. The CRPS group scored lower on the D, Psychopathic deviate (Pd), Pa, Pt, Schizophrenia (Sc), and Social introversion (Si) scales compared to the MDD group. Although CRPS patients reported higher levels of pain than patients with other types of pain, the MMPI profiles of the two pain groups did not differ significantly. Linear regression analyses revealed that pain severity was significantly associated with depression and scores on the Masculinity–Femininity (Mf) scale.Conclusion: This is the first comparative study of the psychological characteristics of chronic CRPS patients, healthy controls, and patients with MDD. The neurotic profiles of CRPS patients were more psychologically adaptable than were those of patients with MDD; however, this profile was shared by both pain groups. The present findings further showed that, although pain severity was not a major contributor to depression, patients with CRPS should be evaluated for depressive symptoms.Keywords: pain, anxiety, depression, complex regional pain syndrome, major depressive disorder, Minnesota Multiphasic Personality Inventory-2

The purpose of this study is to classify the bid price volatility level with machine learning and parameters from bid documents as risk factors. To this end, we studied project-oriented risk factors affecting the bid price and pre-bid clarification document as the uncertainty of bid documents through preliminary research. The authors collected Caltrans’s bid summary and pre-bid clarification document from 2011–2018 as data samples. To train the classification model, the data were preprocessed to create a final dataset of 269 projects consisting of input and output parameters. The projects in which the bid inquiries were not resolved in the pre-bid clarification had higher bid averages and bid ranges than the risk-resolved projects. Besides this, regarding the two classification models with neural network (NN) algorithms, Model 2, which included the uncertainty in the bid documents as a parameter, predicted the bid average risk and bid range risk more accurately (52.5% and 72.5%, respectively) than Model 1 (26.4% and 23.3%, respectively). The accuracy of Model 2 was verified with 40 verification test datasets.

This paper analysed construction bidding information in order to define risk factors that can be appeared in the bid documents of construction projects. For this purpose, text analysis was conducted on biddersO inquiry information (Pre-bid RFI), which inquires uncertain information and omissions in the bid documents in order for pre-bid clarification. From the results of the analysis, what types of risk factors exist in the bid documents and what parts of the bid documents can be pre-reviewed to proactively respond to the uncertain ownerOs requirements. The results are expected to be used as important information for pre-bid clarification of bid documents. Moreover, this study can be meaningful in that it provides a comprehensive way to grasp a large amount of 1,054 documents without analysing the contents of individual documents directly through analysis of bidding information of construction projects using text mining.

We report proteogenomic analysis of diffuse gastric cancers (GCs) in young populations. Phosphoproteome data elucidated signaling pathways associated with somatic mutations based on mutation-phosphorylation correlations. Moreover, correlations between mRNA and protein abundances provided potential oncogenes and tumor suppressors associated with patient survival. Furthermore, integrated clustering of mRNA, protein, phosphorylation, and N-glycosylation data identified four subtypes of diffuse GCs. Distinguishing these subtypes was possible by proteomic data. Four subtypes were associated with proliferation, immune response, metabolism, and invasion, respectively; and associations of the subtypes with immune- and invasion-related pathways were identified mainly by phosphorylation and N-glycosylation data. Therefore, our proteogenomic analysis provides additional information beyond genomic analyses, which can improve understanding of cancer biology and patient stratification in diffuse GCs.

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/.