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With the advance of deep learning networks, their applications in the assessment of pavement conditions are gaining more attention. A convolutional neural network (CNN) is the most commonly used network in image classification. In terms of pavement assessment, most existing CNNs are designed to only distinguish between cracks and non-cracks. Few networks classify cracks in different levels of severity. Information on the severity of pavement cracks is critical for pavement repair services. In this study, the state-of-the-art CNN used in the detection of pavement cracks was improved to localize the cracks and identify their distress levels based on three categories (low, medium, and high). In addition, a fully convolutional network (FCN) was, for the first time, utilized in the detection of pavement cracks. These designed architectures were validated using the data acquired on four highways in Ontario, Canada, and compared with the ground truth that was provided by the Ministry of Transportation of Ontario (MTO). The results showed that with the improved CNN, the prediction precision on a series of test image patches were 72.9%, 73.9%, and 73.1% for cracks with the severity levels of low, medium, and high, respectively. The precision for the FCN was tested on whole pavement images, resulting in 62.8%, 63.3%, and 66.4%, respectively, for cracks with the severity levels of low, medium, and high. It is worth mentioning that the ground truth contained some uncertainties, which partially contributed to the relatively low precision.

Objectives Traditional moxibustion might be not safe due to the excessive heat stimulation or toxic chemical components involved. Electric moxibustion (EM), which has been recently developed as an alternative, offers adjustable and constant heat stimulation. This study aimed to investigate the psychophysical and psychophysiological responses to EM heat stimulation. Methods Twenty-seven healthy volunteers received two different levels of heat stimulation using EM. High-temperature (HT) and medium-temperature (MT) heat stimulations were randomly delivered at the TE5 acupoint on the left or right arm. Participants rated the intensity and the spatial information of the heat sensations immediately after each EM stimulation. Local blood flow around the acupoint was measured with Laser Doppler perfusion imaging before and after heat stimulation. Results Both HT-EM and MT-EM induced considerable heat sensations and enhanced local blood flow around the acupoints. HT-EM resulted in greater heat sensation compared to MT-EM. HT-EM induced a higher increase in local blood flow around the stimulation site compared to MT-EM. No remarkable adverse effects were noted. Conclusion Two different levels of EM heat stimulation induced two different levels of heat sensations and enhanced local blood flow. This preliminary study suggests that the newly developed EM can be further applied to examine the effectiveness of moxibustion in clinical trials.

Objective: Itch is an unpleasant sensation associated with an urge to scratch and is a major health care issue associated with atopic dermatitis (AD). Contagious itch, i.e., subjective feelings of itchiness induced by watching others’ scratching behavior, is common in patients with AD. Using electroencephalography, we examined alpha (8–13 Hz) oscillations in sensorimotor areas associated with the desire to scratch in patients with AD. Methods: Thirty-six patients with AD and 34 healthy controls (HCs) participated in this study. They evaluated their itch levels after watching short videos of a model scratching or tapping parts of his body. Neural oscillations were recorded from nine electrodes, including those placed over sensorimotor areas. Time–frequency analysis was used to compare mu rhythm suppression over the sensorimotor areas in response to these videos between patients with AD and HCs. Results: The behavioral test showed that the visual stimuli induced increased feelings of itchiness in patients with AD relative to HCs under the tapping and scratching conditions. The time–frequency analysis revealed that mu rhythm suppression in response to scratching images was significantly prominent in patients with AD, but not in HCs. Conclusion: Patients with AD exhibited increased susceptibility to contagious itch. This phenomenon might be related to enhanced mu rhythm suppression in sensorimotor areas of the brain in these patients. Our findings provide new insight into the neurophysiological basis of itch sensations in patients with AD.

The operant conditioning has been less studied than the classical conditioning as a mechanism of placebo-like effect, and two distinct learning mechanisms have never been compared to each other in terms of their neural activities. Twenty-one participants completed cue-learning based pain rating tasks while their brain responses were measured using functional magnetic resonance imaging. After choosing (instrumental) or viewing (classical) one of three predictive cues (low- and high-pain cues with different level of certainty), they received painful stimuli according to the selected cues. Participants completed the same task during the test session, except that they received only a high pain stimulus regardless of the selected cues to identify the effects of two learning paradigms. While receiving a high pain stimulation, low-pain cue significantly reduced pain ratings compared to high-pain cue, and the overall ratings were significantly lower under operant than under classical conditioning. Operant behavior activated the temporoparietal junction significantly more than the passive behavior did, and neural activity in the primary somatosensory cortex was significantly reduced during pain in instrumental as compared with classical conditioning trials. The results suggest that pain modulation can be induced by classical and operant conditioning, and mechanisms of attention and context change are involved in instrumental learning.

Faces can be categorized in various ways, for example as male or female or as belonging to a specific biogeographic ancestry (race). Here we tested the importance of the main facial features for race perception. We exchanged inner facial features (eyes, mouth or nose), face contour (everything but those) or texture (surface information) between Asian and Caucasian faces. Features were exchanged one at a time, creating for each Asian/Caucasian face pair ten facial variations of the original face pair. German and Korean participants performed a race classification task on all faces presented in random order. The results show that eyes and texture are major determinants of perceived biogeographic ancestry for both groups of participants and for both face types. Inserting these features in a face of another race changed its perceived biogeographic ancestry. Contour, nose and mouth, in that order, had decreasing and much weaker influence on race perception for both participant groups. Exchanging those features did not induce a change of perceived biogeographic ancestry. In our study, all manipulated features were imbedded in natural looking faces, which were shown in an off-frontal view. Our findings confirm and extend previous studies investigating the importance of various facial features for race perception.

The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.

We investigated whether enhanced interoceptive bodily states of fear would facilitate recognition of the fearful faces. Participants performed an emotional judgment task after a bodily imagery task inside a functional magnetic resonance imaging scanner. In the bodily imagery task, participants were instructed to imagine feeling the bodily sensations of two specific somatotopic patterns: a fear-associated bodily sensation (FBS) or a disgust-associated bodily sensation (DBS). They were shown faces expressing various levels of fearfulness and disgust and instructed to classify the facial expression as fear or disgust. We found a stronger bias favoring the “fearful face” under the congruent FBS condition than under the incongruent DBS condition. The brain response to fearful versus intermediate faces increased in the fronto-insular-temporal network under the FBS condition, but not the DBS condition. The fearful face elicited activity in the anterior cingulate cortex and extrastriate body area under the FBS condition relative to the DBS condition. Furthermore, functional connectivity between the anterior cingulate cortex/extrastriate body area and the fronto-insular-temporal network was modulated according to the specific bodily sensation. Our findings suggest that somatotopic patterns of bodily sensation provide informative access to the collective visceral state in the fear processing via the fronto-insular-temporal network.

Background. Multivoxel pattern analysis has provided new evidence on somatotopic representation in the human brain. However, the effects of stimulus modality (e.g., penetrating needle versus non-penetrating touch) and level of classification (e.g., multiclass versus binary classification) on patterns of brain activity encoding spatial information of body parts have not yet been studied. We hypothesized that performance of brain-based prediction models may vary across the types of stimuli, and neural patterns of voxels in the SI and parietal cortex would significantly contribute to the prediction of stimulated locations. Objective. We aimed to (1) test whether brain responses to tactile stimuli could distinguish among stimulated locations on the body surface, (2) investigate whether the stimulus modality and number of classes affect classification performance, and (3) localize brain regions encoding the spatial information of somatosensory stimuli. Methods. Fifteen healthy participants completed two functional magnetic resonance imaging (MRI) scans and were stimulated via the insertion of acupuncture needles or by non-invasive touch stimuli (5.46-sized von Frey filament). Participants received the stimuli at four different locations on the upper and lower limbs (two sites each) for 5 min while blood-oxygen-level-dependent activity (BOLD) was measured using 3-Tesla MRI. We performed multivariate pattern analysis (MVPA) using parameter estimate images of each trial for each participant and the support vector classifier (SVC) function, and the prediction accuracy and other MVPA outcomes were evaluated using stratified five-fold cross validation. We estimated the significance of the classification accuracy using a permutation test with randomly labeled training data (n=10,000). Searchlight analysis was conducted to identify brain regions associated with significantly higher accuracy compared to predictions based on chance as obtained from a random classifier. Results. For the four-class classification (classifying four stimulated points on the body), SVC analysis of whole-brain beta values in response to acupuncture stimulation was able to discriminate among stimulated locations (mean accuracy, 0.31; q<0.01). The searchlight analysis found that values related to the right primary somatosensory cortex (SI) and intraparietal sulcus were significantly more accurate than those due to chance (p<0.01). On the other hand, the same classifier did not predict stimulated locations accurately for touch stimulation (mean accuracy, 0.25; q=0.66). For binary classification (discriminating between two stimulated body parts, i.e., the arm or leg), the SVC algorithm successfully predicted the stimulated body parts for both acupuncture (mean accuracy, 0.63; q<0.001) and touch stimulation (mean accuracy, 0.60; q<0.01). Searchlight analysis revealed that predictions based on the right SI, primary motor cortex (MI), paracentral gyrus, and superior frontal gyrus were significantly more accurate compared to predictions based on chance (p<0.05). Conclusion. Our findings suggest that the SI, as well as the MI, intraparietal sulcus, paracentral gyrus, and superior frontal gyrus, is responsible for the somatotopic representation of body parts stimulated by tactile stimuli. The MVPA approach for identifying neural patterns encoding spatial information of somatosensory stimuli may be affected by the stimulus type (penetrating needle versus non-invasive touch) and the number of classes (classification of four small points on the body versus two large body parts). Future studies with larger samples will identify stimulus-specific neural patterns representing stimulated locations, independent of subjective tactile perception and emotional responses. Identification of distinct neural patterns of body surfaces will help in improving neural biomarkers for pain and other sensory percepts in the future.

Background Although alopecia affects the quality of life, its pathogenesis is unknown, because cellular interactions in the hair follicle are complex. Several authors have suggested using herbal medicine to treat alopecia, and bioinformatics and network pharmacology may constitute a new research strategy in this regard because herbal medicines contain various chemical components. This study used association rule mining (ARM) and network analysis to analyze the combinations of medicinal herbs used to treat alopecia. Methods We searched Chinese, Korean, and English databases for literature about alopecia treatment, extracting the names of each herbal prescription and herb. The meridian tropism and classification category of each herb were also investigated. Using ARM, we identified frequently combined two-herb and three-herb sets. Using network analysis, we divided the herbs into several modules according to prescription pattern. Results Fifty-six articles and 489 herbal medicines were included—312 internal and 177 external medicines. Among the 312 medicinal herbs used in internal medicine group, the most frequently combined two-herb set was Polygonum multiflorum Thunb. (何首烏) and Angelica sinensis (Oliv.) Dlels (當歸). The most frequently used three-herb combination was Polygonum multiflorum Thunb., Angelica sinensis (Oliv.) Dlels, and Ligusticum chuanxiong Hort. (川芎). In network analysis, three modules were identified. The herbs of Module 1 were related to the liver and kidney meridians, and those of Module 3 were related to the Stomach meridian. Conclusions We identified the frequency, characteristics, and functional modules of herb combinations frequently used in alopecia treatment. We confirmed the value of classical medicinal herb theory. This finding will prompt further bioinformatics and network pharmacology research on alopecia. Electronic supplementary material The online version of this article (10.1186/s12906-018-2269-7) contains supplementary material, which is available to authorized users.

The discovery that pleasant touch is coded by C-tactile fibers has generated considerable research interest and increased understanding of the skin as a channel for social information via cutaneous senses. However, no study has differentiated between the pleasant response to visual and tactile non-human stimulations. Our study investigated pleasant touch in which the visual and haptic touch information was obtained from an affective, but non-social experience, by a custom-built non-human device. Participants (n=19) received soft brush strokes on their lower left arm delivered by a rotary tactile stimulator (physical session) or watched a video of an arm being stroked by a rotary tactile stimulator (visual session). The brush strokes were delivered at the same velocities (0.3, 1, 3, 10, 30cm/s) and force (0.4N) in both sessions. After each trial, participants rated the pleasantness of the touch. Analysis of variance was used to assess the effects of velocity and modality (visual touch vs. physical touch) on the pleasantness rating. Participants rated strokes between 1 and 10cm/s as most pleasant under both conditions. The pleasantness rating patterns differed significantly among velocities; however, no significant differences were found between modalities. Visual and physical (without human-to-human interaction) touch elicited similar behavioral responses, including an inverted U-shaped perception of pleasantness. These findings suggest that the pleasantness of touch is influenced by the velocity of the strokes in both visual and physical touch with a non-human stimulation.

Syndrome differentiation (SD) results in a diagnostic conclusion based on a cluster of concurrent symptoms and signs, including pulse form and tongue color. In Korea, there is a strong interest in the standardization of Traditional Medicine (TM). In order to standardize TM treatment, standardization of SD should be given priority. The aim of this study was to explore the SD, or symptom clusters, of patients with atopic dermatitis (AD) using non-negative factorization methods and k-means clustering analysis. We screened 80 patients and enrolled 73 eligible patients. One TM dermatologist evaluated the symptoms/signs using an existing clinical dataset from patients with AD. This dataset was designed to collect 15 dermatologic and 18 systemic symptoms/signs associated with AD. Non-negative matrix factorization was used to decompose the original data into a matrix with three features and a weight matrix. The point of intersection of the three coordinates from each patient was placed in three-dimensional space. With five clusters, the silhouette score reached 0.484, and this was the best silhouette score obtained from two to nine clusters. Patients were clustered according to the varying severity of concurrent symptoms/signs. Through the distribution of the null hypothesis generated by 10,000 permutation tests, we found significant cluster-specific symptoms/signs from the confidence intervals in the upper and lower 2.5% of the distribution. Patients in each cluster showed differences in symptoms/signs and severity. In a clinical situation, SD and treatment are based on the practitioners' observations and clinical experience. SD, identified through informatics, can contribute to development of standardized, objective, and consistent SD for each disease.

Additional file 1: Table S1. Additional tables.

Objective: The optimal acupoints for a particular disease can be determined by analysis of diagnosis patterns. The objective of this study was to reveal the association between such patterns and the acupoints prescribed in clinical practice using medical data extracted from case reports. Methods: This study evaluated online virtual diagnoses made by currently practicing Korean medical doctors (N = 80). The doctors were presented with 10 case reports published in Korean medical journals and were asked to diagnose the patients and prescribe acupoints accordingly. A network analysis and the term frequency-inverse document frequency (tf-idf) method were used to analyse and quantify the relationship between diagnosis patterns and prescribed acupoints. Results: The network analysis showed that ST36, LI4, LR3, and SP6 were the most frequently used acupoints across all diagnoses. The tf-idf values showed the acupoints used for specific diseases, such as BL40 for bladder disease and LU9 for lung disease. Conclusions: The associations between diagnosis patterns and prescribed acupoints were identified using an online virtual diagnosis modality. Network and text mining analyses revealed commonly applied and disease-specific acupoints in both qualitative and quantitative terms.

The present study applied multivoxel pattern analysis to decode spatial discrimination in pain perception to acupuncture needle from brain functional magnetic resonance image. Fourteen participants were stimulated by acupuncture needles at two adjacent body parts on their left forearm (PC6 vs. HT7). We trained support vector machines on the spatial information from the whole-brain functional magnetic resonance imaging data and projected the support vector machine weight to the brain image space to represent the effect of each voxel on the classifier output. Using region-of-interest masks in individual brains, we trained and tested a linear support vector machine classifier on the accuracy of spatial discrimination in trial-wise functional magnetic resonance imaging data. A classical univariate general linear model analysis testing for differences between the two different locations did not reveal any significant differences. Multivoxel pattern analysis revealed that the brain regions for the prediction of sensory discrimination in pain perceptions to two different points were in the primary somatosensory cortex, primary motor cortex, and supramarginal gyrus, anterior and posterior insula, anterior and posterior cingulate cortex, ventromedial prefrontal cortex, and inferior parietal lobule. Our findings suggest that spatial localizations of pain perceptions to acupuncture needle can be predicted by the neural response patterns in the somatosensory areas and the frontoparietal areas.

The experience of pain relief arises from physiological and psychological factors, and attributes such as the commercial features of analgesic treatments have been shown to influence placebo analgesia by affecting treatment expectations. Therefore, treatment valuation from price information should influence the placebo analgesic effect. This hypothesis was tested in a functional magnetic resonance imaging study in which healthy subjects were enrolled in a 2-day experiment. On day 1, the participants (n = 19) had treatment experiences with 2 different placebo creams during a conditioning session without receiving information on treatment price. On day 2, placebo analgesia was tested after providing price information (high vs low) while functional magnetic resonance imaging was performed. The results showed that the higher priced placebo treatment leads to enhanced pain relief. Placebo analgesia in response to the higher priced treatment was associated with activity in the ventral striatum, ventromedial prefrontal cortex, and ventral tegmental area. The behavioral results indicate that the experience of pain was influenced by treatment valuation from price. Our findings reveal that the context of values in pain control is associated with activity in expectation- and reward-related circuitry. Perspective Treatment with higher price was associated with enhanced placebo analgesia, and this effect was influenced by activities in expectation and reward processing brain areas. The context of value such as medical cost influences cognitive evaluation processes to modulate pain. Our study may help evaluate a patient's preference toward high-priced drugs.

Emotional perception can be shaped by inferences about bodily states. Here, we investigated whether exteroceptive inferences about bodily sensations in the chest area influence the perception of fearful faces. Twenty-two participants received pseudo-electrical acupuncture stimulation at three different acupoints: CV17 (chest), CV23 (chin), and PC6 (left forearm). All stimuli were delivered with corresponding visual cues, and the control condition included visual cues that did not match the stimulated body sites. After the stimulation, the participants were shown images with one of five morphed facial expressions, ranging from 100% fear to 100% disgust, and asked to classify them as fearful or disgusted. Brain activity was measured using functional magnetic resonance imaging during the facial expression classification task. When the participants expected that they would receive stimulation of the chest (CV17), the ratio of fearful to non-fearful classifications decreased compared to the control condition, and brain activities within the periaqueductal gray and the default mode network decreased when they viewed fearful faces. Our findings suggest that bodily sensations around the chest, but not the other tested body parts, were selectively associated with fear perception and that altering external inferences inhibited the perception of fearful faces.

Objective. Attempts to understand the emotion have evolved from the perspective of an independent cognitive system of the mind to that of an interactive response involving the body. This study aimed to quantify and visualize relationships between different emotions and bodily organ systems from the perspective of East Asian medicine. Methods. Term frequency-inverse document frequency (tf-idf) method was used to quantify the significance of Five Viscera and the gallbladder relative to seven different emotions through the classical medical text of DongUiBoGam. Bodily organs that corresponded to different emotions were visualized using a body template. Results. The emotions had superior tf-idf values with the following bodily organs: anger with the liver, happiness with the heart, thoughtfulness with the heart and spleen, sadness with the heart and lungs, fear with the kidneys and the heart, surprise with the heart and the gallbladder, and anxiety with the heart and the lungs. Specific patterns between the emotions and corresponding bodily organ systems were identified. Conclusion. The present findings will further the current understanding of the relationship between the mind and body from the perspective of East Asian medicine. Western medicine characterizes emotional disorders using “neural” language while East Asian medicine uses “somatic” language.

Background Korean cosmetics are widely exported throughout Asia. Cosmetics exploiting traditional Korean medicine lead this trend; thus, the traditional medicinal literature has been invaluable in terms of cosmetic development. We sought candidate medicinal herbs for skincare. Methods We used data mining to investigate associations between medicinal herbs and skin-related keywords (SRKs) in a classical text. We selected 26 SRKs used in the Donguibogam text; these referred to 626 medicinal herbs. Using a term frequency-inverse document frequency approach, we extracted data on herbal characteristics by assessing the co-occurrence frequencies of 52 medicinal herbs and the 26 SRKs. Results We extracted the characteristics of the 52 herbs, each of which exhibited a distinct skin-related action profile. For example Ginseng Radix was associated at a high-level with tonification and anti-aging, but Rehmanniae Radix exhibited a stronger association with anti-aging. Of the 52 herbs, 46 had been subjected to at least one modern study on skincare-related efficacy. Conclusions We made a comprehensive list of candidate medicinal herbs for skincare via data mining a classical medical text. This enhances our understanding of such herbs and will help with discovering new candidate herbs.

Introduction Herbal prescriptions are frequently sought as complementary medicine treatment for inflammatory bowel disease (IBD). However, variability in pattern identification of Traditional Medicine has been criticised. Using patient data on clinical remission, we aimed to develop pattern identification (PI) algorithms refined by identified pattern and key symptoms which practitioners can easily differentiate. Methods Symptoms of IBD patients with remission were divided into Large intestine, Water-dampness, Respiratory, Upper gastrointestinal (GI) tract, and Coldness patterns. Using the term frequency-inverse document frequency (TF-IDF) method, symptoms described as indications of the herbal prescriptions and 5 patterns were matched. Decision tree modeling was used for prediction of relevant patterns from symptoms. Five-fold cross validation and sensitivity analysis was conducted to validate the model. Results Incomplete feeling of bowel emptying for Large intestine pattern, water dampness for Water-dampness pattern, chronic rhinitis for Respiratory pattern, gastric stuffiness for Upper GI tract pattern, and abdominal pain for Coldness pattern were selected by TF-IDF analysis. Overall accuracy of the decision tree was 64.4 %. In the treatment algorithm by the decision tree modeling, associations emerged between presence of incomplete feeling of bowel emptying and Large intestine type (100 %), chronic rhinitis and Respiratory type (100 %), water dampness symptoms and Water-dampness type (78.4 %), and gastric stuffiness and Upper GI tract type (58.3 %). Conclusions The PI algorithm we suggest can help clinicians determine patterns of IBD. Future studies with a large sample could yield an improved algorithm that predicts not only patterns but also corresponding herbal medicine prescriptions.

Radiotherapy devices using magnetic resonance (MR) imaging are being developed because MR is a safe imaging protocol that provides clear anatomical details. However, the application of MR-based radiotherapy to the aging population is limited because of its high cost and the increased use of metal implants such as cardiac pacemakers and artificial joints. To improve the accuracy of computed tomography (CT)-based radiotherapy planning, we propose a synthetic approach that translates a CT image into an MR image using adversarial cycle-consistent networks with paired data learning. The networks were trained to transform 2D brain CT image slices into 2D brain MR image slices, combining adversarial loss, cycle-consistent loss, voxel-wise loss, and gradient difference loss. The experiments were analyzed using the CT and MR images of 20 subjects, and an ablation study was conducted to show the strength of the proposed objective function. The experimental results show that the proposed method is accurate and robust for predicting MR images from CT images and also outperforms two state-of-the-art methods.

Objectives : Pulse diagnosis is considered particularly important among several methods of diagnosis in DongUiBoGam. In spite of its importance, numerous and various pulse descriptions made it difficult to learn and practice pulse diagnosis. In this article, we tried to analyze properties of the twenty-seven pulses from pulse diagnosis cases from DongUiBoGam to enable the practical understanding of pulse diagnosis. Methods : We constituted the four axis according to the eight important pulses. And we analyzed properties of the twenty-seven pulses through the relationship between the four pairs of important pulses and the twenty-seven pulses. To quantify the relevances of important pulses to the twenty-seven pulses, we used the term frequency-inverse document frequency(TF-IDF) method. Results : We could elicit properties of the twenty-seven pulses according to the four axis. Also, we reexamined the categorization of the seven exterior pulses / the eight interior pulses and the similar pulses from DongUiBoGam with the analysis results. Conclusions : We could understand properties of the twenty-seven pulses more specifically with the eight important pulses. And we also could see the relationship among the twenty-seven pulses on each axis. However, the limitation arising from insufficient number of pulse diagnosis cases in this research requires further research with more sources such as other traditional medical records or clinical records in the present time.

Background : Saam acupuncture were composed by applying the elemental concepts from the Five Phase theory - the relationships between the cycles such as Saeng(Sheng, ‘nourishing’ or ‘creating’) and Geuk(Ke, ‘suppressing’ or ‘controlling’) - onto the Five Phase points and 12 channels to compensate for the imbalance in each of the 12 main energy traits. Objective : The present study is aimed to find out the characteristics of Five Phase points pattern in Saam acupuncture. Methods : We analysed the characteristics of five elements of the Five Phase points in Korean medical texts such as Saamdoinchimguyogyeol, Dongeuibogam and Chimgugyeongheombang in mid Chosun Dynasty. Using non-negative factorization(NNMF) methods, we extracted the feature matrix of five elements of Five Phase points in each classic medical text. Results : In Saam acupuncture, two characteristics were most prominent: (1) "Self" component of Five elements, (2) "Mother" and "Grandmother" component of Five elements. Conclusions : Saam acupuncture used the combination of Five-Shu acupoint based on ZangFu pattern identification. Our findings suggest that grasping the characteristics of Five Phase points combinations can improve the understanding the selection of the relevant acupoints based on the ZangFu pattern identifications.

The brain actively interprets sensory inputs by integrating top-down and bottom-up information. Humans can make inferences on somatosensation based on prior experiences and expectations even without the actual stimulation. We used functional magnetic resonance imaging to investigate the neural substrates of the expectations of the sensory experience of cutaneous electrical stimulation on acupoint without actual stimuli. This study included 22 participants who wore sticker-type electrodes attached on three different acupoints on different body regions: CV17 (chest), CV23 (chin), and left PC6 (arm). Participants evaluated de qi sensations after they expected electrical stimulation on those points in random order without actual stimulation. All stimuli were presented with corresponding visual information of the stimulation sites. The control condition included the same visual information but outside the body. The expectations of cutaneous electrical stimuli without actual stimulation on three acupoints resulted in greater de qi sensation compared to the control condition. Cognitive components of cutaneous electrical stimulation exhibited greater brain activation in the anterior insula, pre-supplementary motor area, and secondary somatosensory area. The expectations of acupuncture stimulation exhibited a distinct experience of somatosensation as well as brain activations in insula and pre-supplementary motor area. Our findings suggest that the sensory experience of the pseudo-cutaneous stimulation may be derived from the predictive role of the salience network in monitoring internal and external body states., Highlights • The pseudo-cutaneous stimulation can elicit remarkable somatic sensations. • Insula and pre-SMA are involved in the expectations of sensory experience. • Somatic sensation from pseudo-stimulation is influenced by top-down information.

The brain can only attend to a fraction of all the information that is entering the visual system at any given moment. One way of overcoming the so-called bottleneck of selective attention (e.g., J. M. Wolfe, Vo, Evans, & Greene, 2011) is to make use of redundant visual information and extract summarized statistical information of the whole visual scene. Such ensemble representation occurs for low-level features of textures or simple objects, but it has also been reported for complex high-level properties. While the visual system has, for example, been shown to compute summary representations of facial expression, gender, or identity, it is less clear whether perceptual input from all parts of the visual field contributes equally to the ensemble percept. Here we extend the line of ensemble-representation research into the realm of race and look at the possibility that ensemble perception relies on weighting visual information differently depending on its origin from either the fovea or the visual periphery. We find that observers can judge the mean race of a set of faces, similar to judgments of mean emotion from faces and ensemble representations in low-level domains of visual processing. We also find that while peripheral faces seem to be taken into account for the ensemble percept, far more weight is given to stimuli presented foveally than peripherally. Whether this precision weighting of information stems from differences in the accuracy with which the visual system processes information across the visual field or from statistical inferences about the world needs to be determined by further research.

Ye-Seul Lee,1 Won-Mo Jung,1 Hyunchul Jang,2 Sanghyun Kim,2 Sun-Yong Chung,3 Younbyoung Chae1 1Acupuncture and Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, Seoul, 2Mibyeong Research Center, Korean Institute of Oriental Medicine, Daejeon, 3Department of Neuropsychiatry, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea Objectives: Recently, there has been increasing interest in preventing and managing diseases both inside and outside medical institutions, and these concerns have supported the development of the individual Personal Health Record (PHR). Thus, the current study created a mobile platform called “Mind Mirror” to evaluate psychological and physical conditions and investigated whether PHRs would be a useful tool for assessment of the dynamic relationship between the emotional and physical conditions of an individual.Methods: Mind Mirror was used to collect 30days of observational data about emotional valence and the physical states of pain and fatigue from 20 healthy participants, and these data were used to analyze the dynamic relationship between emotional and physical conditions. Additionally, based on the cross-correlations between these three parameters, a multilevel multivariate regression model (mixed linear model [MLM]) was implemented.Results: The strongest cross-correlation between emotional and physical conditions was at lag 0, which implies that emotion and body condition changed concurrently. In the MLM, emotional valence was negatively associated with fatigue (β =-0.233, P

Results : In our pilot experiment, half of the participants showed significantly improved manipulation skills in complex lifting/thrusting techniques after training with AMES which is developed in this study, while the other half of the participants did not show significant improvements. Conclusions : The AMES could be useful in acupuncture-manipulation training for students. Our findings suggest that novice can improve sophisticated hand movement for acupuncture manipulation with sensorimotor learning using visual feedback.

Background In clinical practice, acupuncture manipulation is highly individualised for each practitioner. Before we establish a standard for acupuncture manipulation, it is important to understand completely the manifestations of acupuncture manipulation in the actual clinic. To examine motion patterns during acupuncture manipulation, we generated a fitted model of practitioners’ motion patterns and evaluated their consistencies in acupuncture manipulation. Methods Using a motion sensor, we obtained real-time motion data from eight experienced practitioners while they conducted acupuncture manipulation using their own techniques. We calculated the average amplitude and duration of a sampled motion unit for each practitioner and, after normalisation, we generated a true regression curve of motion patterns for each practitioner using a generalised additive mixed modelling (GAMM). Results We observed significant differences in rotation amplitude and duration in motion samples among practitioners. GAMM showed marked variations in average regression curves of motion patterns among practitioners but there was strong consistency in motion parameters for individual practitioners. The fitted regression model showed that the true regression curve accounted for an average of 50.2% of variance in the motion pattern for each practitioner. Conclusions Our findings suggest that there is great inter-individual variability between practitioners, but remarkable intra-individual consistency within each practitioner.

Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach.

Objectives : DongUiBoGam is a representative medical literature in Korea. This research intends to structurally grasp how DongUiBoGam understands the human body and review the methods of acupuncture and moxibustion in the NaeGyeong chapter of it using text mining. Methods : The structure of DongUiBoGam was analyzed with specific parts of the book that described contents, major premises of understanding the human body, and processes of treatment. We analyzed characteristics of each acupoints in a relationship with causes of diseases & symptoms in the NaeGyeong chapter using a Term Frequency - Inverse Document Frequency(TFIDF). Results : Three different categories of pattern identification(PI) were formed after structural analysis of DongUiBoGam. Every causes of diseases & symptoms were transformed according to the three categories of PI. After analyzing the relationship between acupoints and causes of diseases & symptoms, 114 acupoints were visualized with TFIDF values of three PI categories. Conclusions : The selection of acupoints in NaeGyeong chapter of DongUiBoGam were linked to causes of diseases & symptoms based on the three PI categories. Through visualization of bipartite relationships between acupoints and causes of diseases & symptoms, we could easily understand characteristics of each acupoint.

Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.

Nowadays a lot of trials for collecting electronic medical records (EMRs) exist. However, structuring data format for EMR is an especially labour-intensive task for practitioners. Here we propose a new mark-up language for medical record charting (called Charting Language), which borrows useful properties from programming languages. Thus, with Charting Language, the text data described in dynamic situation can be easily used to extract information.

We investigated commonalities and differences in brain responses to enhanced bodily attention around acupuncture points with and without stimulation. Fourteen participants received acupuncture needles at both PC6 and HT7 acupoints in the left hand. To enhance bodily attention to acupoints, participants responded to the locations of stimulations in a two-alternative forced choice task. Two fMRI scans were taken in a block design: session 1 labeled with manual stimulation (genuine stimulation) and session 2 labeled with electro-acupuncture (pseudo-stimulation). To compare cortical activation patterns, data were analyzed using the Freesurfer software package. Both genuine-and pseudo-stimulation resulted in brain activations in the insula, anterior cingulate cortex, secondary somatosensory cortex, superior parietal cortex and brain deactivation in the medial prefrontal cortex, posterior cingulate cortex, inferior parietal cortex and the parahippocampus. Genuine acupuncture stimulation exhibited greater brain activation in the posterior insula, posterior operculum and the caudal part of the anterior cingulate cortex, compared with pseudo-stimulation. We demonstrated that enhanced bodily attention triggered by genuine acupuncture stimulation can activate the salience network and deactivate the default mode network regardless of the type of stimulation. The component of enhanced attention to a certain part of the body is significant in the brain response to acupuncture stimulation.

The indications of acupoints are thought to be highly associated with the lines of the meridian systems. The present study used data mining methods to analyze the characteristics of the indications of each acupoint and to visualize the relationships between the acupoints and disease sites in the classic Korean medical textChimgoogyeongheombang. Using a term frequency-inverse document frequency (tf-idf) scheme, the present study extracted valuable data regarding the indications of each acupoint according to the frequency of the cooccurrences of eight Source points and eighteen disease sites. Furthermore, the spatial patterns of the indications of each acupoint on a body map were visualized according to the tf-idf values. Each acupoint along the different meridians exhibited different constellation patterns at various disease sites. Additionally, the spatial patterns of the indications of each acupoint were highly associated with the route of the corresponding meridian. The present findings demonstrate that the indications of each acupoint were primarily associated with the corresponding meridian system. Furthermore, these findings suggest that the routes of the meridians may have clinical implications in terms of identifying the constellations of the indications of acupoints.