Myoelectric Human Computer Interaction Using Reliable Temporal Sequence-based Myoelectric Classification for Dynamic Hand Gestures

To put a computerized device under human control, various interface techniques have been commonly studied in the realm of Human Computer Interaction (HCI) design. What this dissertation focuses on is a myoelectric interface, which controls a device via neuromuscular electrical signals. Myoelectric interface has advanced by recognizing repeated patterns of the signal (pattern recognition-based myoelectric classification). However, when the myoelectric classification is used to extract multiple discrete states within limited muscle sites, there are robustness issues due to external conditions: limb position changes, electrode shifts, and skin condition changes. Examined in this dissertation is the robustness issue, or drop in the performance of the myoelectric classification when the limb position varies from the position where the system was trained. Two research goals outlined in this dissertation are to increase reliability of myoelectric system and to build a myoelectric HCI to manipulate a 6-DOF robot arm with a 1-DOF gripper. To tackle the robustness issue, the proposed method uses dynamic motions which change their poses and configuration over time. The method assumes that using dynamic motions is more reliable, vis-a-vis the robustness issues, than using static motions. The robustness of the method is evaluated by choosing the training sets and validation sets at different limb positions. Next, an HCI system manipulating a 6-DOF robot arm with a 1-DOF gripper is introduced. The HCI system includes an inertia measurement unit to measure the limb orientation, as well as EMG sensors to acquire muscle force and to classify dynamic motions. Muscle force and the orientation of a forearm are used to generate velocity commands. Classified dynamic motions are used to change the manipulation modes. The performance of the myoelectric interface is measured in terms of real-time classification accuracy, path efficiency, and time-related measures. In conclusion, this dissertation proposes a reliable myoelectric classification and develops a myoelectric interface using the proposed classification method for an HCI application. The robustness of the proposed myoelectric classification is verified as compared to previous myoelectric classification approaches. The usability of the developed myoelectric interface is compared to a well-known interface

A Parametric Study on the Immunomodulatory Effects of Electroacupuncture in DNP-KLH Immunized Mice

This study was conducted to compare the effects of low frequency electroacupuncture (EA) and high frequency EA at acupoint ST36 on the production of IgE and Th1/Th2 cytokines in BALB/c mice that had been immunized with 2,4-dinitrophenylated keyhole limpet protein (DNP-KLH), as well as to investigate the difference in the immunomodulatory effects exerted by EA stimulations at acupoint ST36 and at a non-acupoint (tail). Female BALB/c mice were divided into seven groups: normal (no treatments), IM (immunization only), ST36-PA (IM + plain acupuncture at ST36), ST36-LEA (IM + low frequency (1 Hz) EA at ST36), ST36-HEA (IM + high frequency (120 Hz) EA at ST36), NA-LEA (IM + low frequency (1 Hz) EA at non-acupoint) and NA-HEA (IM + high frequency (120 Hz) EA at non-acupoint). EA stimulation was performed daily for two weeks, and total IgE, DNP-KLH specific IgE, IL-4 and IFN-γ levels were measured at the end of the experiment. The results of this study showed that the IgE and IL-4 levels were significantly suppressed in the ST36-LEA and ST36-HEA groups, but not in the NA-LEA and NA-HEA groups. However, there was little difference in the immunomodulatory effects observed in the ST36-LEA and ST36-HEA groups. Taken together, these results suggest that EA stimulation-induced immunomodulation is not frequency dependent, but that it is acupoint specific

S100A8 and S100A9 in saliva, blood and gingival crevicular fluid for screening established periodontitis: a cross-sectional study

Background Periodontitis is one of major oral diseases, which has no consensus on early screening tool. This study aimed to compare the association and screening ability of S100A8 and S100A9 in saliva, blood and gingival crevicular fluid (GCF) for periodontitis status. Methods We recruited 149 community Korean adults, 50 no or initial periodontitis (NIPERIO) and 99 established periodontitis (PERIO). Using clinical attachment loss and a panoramic radiograph, stage II–IV of new classification of periodontitis proposed at 2018 was considered cases as PERIO. Enzyme linked immunosorbent assay kit was used to quantify S100A8 and S100A9. T-test, analysis of covariance, Mann–Whitney test and correlation analysis were applied to compare the relationship of S100A8 and S100A9 in saliva, blood, and GCF for periodontitis. Receiver operating characteristic curve was applied for screening ability. Results Among S100A8 and S100A9 in saliva, blood and GCF, S100A8 in saliva was significantly higher in PERIO than in NIPERIO (p < 0.05). However, S100A8 and S100A9 in GCF were higher in NIPERIO (p < 0.05). The screening ability of salivary S100A8 was 75% for PERIO, while that of GCF S100A8 was 74% for NIPERIO. Salivary S100A8 was positively correlated to blood S100A8 (p < 0.05). Conclusion Salivary S100A8 could be a potential diagnostic marker for established periodontitis and be useful for screening established periodontitis.This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT, Korea (NRF-2017M3A9B6062986). The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscrip

Hypotension in ICU Patients Receiving Vasopressor Therapy

Vasopressor infusion (VPI) is used to treat hypotension in an ICU. We studied compliance with blood pressure (BP) goals during VPI and whether a statistical model might be efficacious for advance warning of impending hypotension, compared with a basic hypotension threshold alert. Retrospective data were obtained from a public database. Studying adult ICU patients receiving VPI at submaximal dosages, we analyzed characteristics of sustained hypotension episodes (>15 min) and then developed a logistic regression model to predict hypotension episodes using input features related to BP trends. The model was then validated with prospective data. In the retrospective dataset, 102-of-215 ICU stays experienced >1 hypotension episode (median of 2.5 episodes per day in this subgroup). When trained with 75% of retrospective dataset, testing with the remaining 25% of the dataset showed that the model and the threshold alert detected 99.6% and 100% of the episodes, respectively, with median advance forecast times (AFT) of 12 and 0 min. In a second, prospective dataset, the model detected 100% of 26 episodes with a median AFT of 22 min. In conclusion, episodes of hypotension were common during VPI in the ICU. A logistic regression model using BP temporal trend features predicted the episodes before their onset

Hand Gesture Recognition Using EGaIn-Silicone Soft Sensors

Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%).https://doi.org/10.3390/s2109320

Effect of central PxxP motif in amphipathic alpha-helical peptides on antimicrobial activity and mode of action

Abstract Amphipathic α-helical peptides (AHPs) have shown potential as a therapeutic approach against multi-drug-resistant bacterial infections due to their broad-spectrum antimicrobial activity by disrupting bacterial membranes. However, their nonspecific interactions with membranes often result in cytotoxicity toward mammalian cells. Previous studies have shown that a PxxP motif near the middle of cathelicidin-derived antimicrobial peptides contributes to potent and selective antibacterial activity. In this study, we compared KL18 with KL-PxxP to examine the effects of the central PxxP motif in AHPs on their structure, antibiotic activity, and mode of action. In a membrane-mimetic environment, we observed that KL18 had a much higher helical content compared to KL-PxxP. In aqueous buffer, KL18 adopted a highly ordered α-helical conformation, while KL-PxxP exhibited a disordered conformation. We found that KL-PxxP exhibited 4–16 times higher antibacterial activity than KL18 and significantly reduced the hemolytic activity. These findings suggest that the dynamic conformational behaviors caused by the central PxxP motif conferred the antibacterial selectivity of AHPs. Additionally, KL-PxxP showed strong binding to anionic liposomes and weak binding to zwitterionic liposomes, explaining its selectivity for bacteria over mammalian cells. Despite having a low ability to dissipate the bacterial membrane potential, KL-PxxP translocated efficiently across lipid membranes. Therefore, we propose that the central PxxP motif in AHPs provides dynamic conformational behavior in aqueous and membrane-mimetic environments, enhances binding to anionic membranes, and facilitates translocation across lipid bilayers, resulting in improved antibacterial potency and selectivity. Understanding the unique structural characteristics and functional roles of the PxxP motif in the antimicrobial mechanism of action holds great potential for advancing the development of novel peptide antibiotics

Hand Gesture Recognition Using EGaIn-Silicone Soft Sensors

Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (&gt;90%)

Antibiotic susceptibility of Staphylococcus aureus with different degrees of biofilm formation

Abstract Staphylococcus aureus is one of the most common pathogens in biofilm-associated chronic infections. S. aureus living within biofilms evades the host immune response and is more resistant to antibiotics than planktonic bacteria. In this study, we generated S. aureus with low and high levels of biofilm formation using the rbf (regulator of biofilm formation) gene and performed a BioTimer assay to determine the minimum inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of various types of antibiotics. We showed that biofilm formation by S. aureus had a greater effect on MBC than MIC, probably due to the different growth modes between planktonic and biofilm bacteria. Importantly, we found that the MBC for biofilm S. aureus was much higher than that for planktonic cells, but there was little difference in MBC between low and high levels of biofilm formation. These results suggest that once the biofilm is formed, the bactericidal activity of antibiotics is significantly reduced, regardless of the degree of S. aureus biofilm formation. We propose that S. aureus strains with varying degrees of biofilm formation may be useful for evaluating the anti-biofilm activity of antimicrobial agents and understanding antibiotic resistance mechanisms by biofilm development