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Fingertip Force and Muscle Activation Patterns at Varying grasp Objects
Suji Park(Suji Park),Juhyun Park(Juhyun Park),Seyeon Oh(Seyeon Oh),Chaeyeon Heo(Chaeyeon Heo),Sieun Ho(Sieun Ho),Seonhong Hwang(Seonhong Hwang) 호서대학교 기초과학연구소 2022 기초과학연구 논문집 Vol.30 No.1
In this study, we tried to collect and analyze the kinetic and neurological information such as finger-tip forces and EMG for several representative (the most commonly used) grasp movements to explore their force and muscle activation patterns based on the newly defined grasp taxonomy. Ten able-bodied (five males, five females) volunteered to participate and they performed five different grasp tasks: holding a bottle (Bottle), turning a doorknob (Knob), cutting with a knife (Knife), brushing with a toothbrush (Toothbrush), holding a thick book (Book) after we attached five force sensitive resistor (FSR) sensors on the tip of fingers and four surface electromyogram (sEMG) electrodes on the lower arm of the subject’s dominant hand. Root Mean Square (RMS) and Mean Absolute Value (MAV) from the mean maximum values of sEMG(%) and fingertip force(kgf) of all ten subjects were extracted as features. The classification from the feature dataset using convolutional neural network (CNN) was applied and analyzed the results of accuracy and repeatability. The mean maximum values of EMG and fingertip forces during five different grasp tasks, and the MAV and RMS which were extracted features from the above were compared with task pairs. They showed significant differences in comparison of four pairs of tasks which were Bottle and Knife (p = 0.005 in both MAV and RMS), Bottle and Toothbrush (p = 0.005in both MAV and RMS), Bottle and Book (p = 0.013 in both MAV and RMS), Knob and Toothbrush (p = 0.047 in MAV and p = 0.028 in RMS). The classification accuracy of the Bottle grasp task was the largest at 60% (true positive predictive rate is 60% and false postive rate is 40%), while the other tasks showed an 30-40% of accuracy. Repeatability was 60% in the Bottle task and 50% in the Knob task, and those of the other tasks were ranged 30-40%. Overall, it is believed that the small number of samples in the study is the main reason of the low accuracy and repeatability of the classification. A total of nine variables (four sEMG and five forces) showed different significances in paired mean comparisons for five grasp tasks (graspping a bottle, turning a doorknob, cutting with a knife, brushing teeth with a toothbrush, holding a thick book). A comparison of the reduced variable from feature extraction also showed different classification accuracy for five grasp tasks.
Suji Han,Hyemi Shin,Jin-Ku Lee,Zhaoqi Liu,Raul Rabadan,Jeongwu Lee,Jihye Shin,Cheolju Lee,Heekyoung Yang,Donggeon Kim,Sung Heon Kim,Jooyeon Kim,Jeong-Woo Oh,Doo-Sik Kong,Jung-Il Le,Ho Jun Seol,Jung Wo 생화학분자생물학회 2019 Experimental and molecular medicine Vol.51 No.-
Glioblastoma (GBM) is the most lethal primary brain tumor with few treatment options. The survival of gliomainitiating cells (GICs) is one of the major factors contributing to treatment failure. GICs frequently produce and respond to their own growth factors that support cell proliferation and survival. In this study, we aimed to identify critical autocrine factors mediating GIC survival and to evaluate the anti-GBM effect of antagonizing these factors. Proteomic analysis was performed using conditioned media from two different patient-derived GBM tumor spheres under a growth factor-depleted status. Then, the antitumor effects of inhibiting an identified autocrine factor were evaluated by bioinformatic analysis and molecular validation. Proteins secreted by sphere-forming GICs promote cell proliferation/survival and detoxify reactive oxygen species (ROS). Among these proteins, we focused on midkine (MDK) as a clinically significant and pathologically relevant autocrine factor. Antagonizing MDK reduced the survival of GBM tumor spheres through the promotion of cell cycle arrest and the consequent apoptotic cell death caused by oxidative stress-induced DNA damage. We also identified PCBP4, a novel molecular predictor of resistance to anti-MDK treatment. Collectively, our results indicate that MDK inhibition is an important therapeutic option by suppressing GIC survival through the induction of ROS-mediated cell cycle arrest and apoptosis.
Suji Jeong,Borim An,Jung-Hyun Kim,Hyo-Won Han,Jung-Hyun Kim,Hye-Ryeon Heo,Kwon Soo Ha,한은택,Won Sun Park,홍석호 생화학분자생물학회 2020 Experimental and molecular medicine Vol.52 No.-
The efficient and reproducible derivation and maturation of multipotent hematopoietic progenitors from human pluripotent stem cells (hPSCs) requires the recapitulation of appropriate developmental stages and the microenvironment. Here, using serum-, xeno-, and feeder-free stepwise hematopoietic induction protocols, we showed that short-term and high-concentration treatment of hPSCs with bone morphogenetic protein 4 (BMP4) strongly promoted early mesoderm induction followed by increased hematopoietic commitment. This method reduced variations in hematopoietic differentiation among hPSC lines maintained under chemically defined Essential 8 medium compared to those maintained under less-defined mTeSR medium. We also found that perivascular niche cells (PVCs) significantly augmented the production of hematopoietic cells via paracrine signaling mechanisms only when they were present during the hematopoietic commitment phase. A protein array revealed 86 differentially expressed (>1.5-fold) secretion factors in PVC-conditioned medium compared with serum-free control medium, of which the transforming growth factor-β inducible gene H3 significantly increased the number of hematopoietic colony-forming colonies. Our data suggest that BMP4 and PVCs promote the hematopoietic differentiation of hPSCs in a differentiation stage-specific manner. This will increase our understanding of hematopoietic development and expedite the development of hPSC-derived blood products for therapeutic use.
Deep Recommender System Using Purchase History Data
Suji Kang,Changha Hwang 계명대학교 자연과학연구소 2017 Quantitative Bio-Science Vol.36 No.1
In this paper, we propose a deep recommender system that utilizes purchase history data of customers when ratings for products are not available at all. The purchase history data of these customers are obtained between May 2014 and July 2015 from nationwide branches of a particular supermarket chain. If the customer’s purchase cycle for a particular product is shorter than the average purchase cycle, then the preference for the product will be high. Using this idea, we generate rating data from these customers’ purchase history data. Among collaborative filtering recommender systems using rating data, we know that the restricted Boltzmann machine (RBM) shows the best performance in terms of prediction accuracy. Thus, for recommender system wepropose the use of conditional RBM, which utilizes additional information on whether or not to purchase. Through an experiment we show that conditional RBM works best in predicting the ratings for products. This recommender system is expected to be useful in future.