Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities.

Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.

3,4-dihydroxytoluene, a metabolite of rutin, suppresses the progression of nonalcoholic fatty liver disease in mice by inhibiting p300 histone acetyltransferase activity.

3,3',4',5,7-Pentahydroxyflavone-3-rhamnoglucoside (rutin) is a flavonoid with a wide range of pharmacological activities. Dietary rutin is hardly absorbed because the microflora in the large intestine metabolize rutin into a variety of compounds including quercetin and phenol derivatives such as 3,4-dihydroxyphenolacetic acid (DHPAA), 3,4-dihydroxytoluene (DHT), 3,4-hydroxyphenylacetic acid (HPAA) and homovanillic acid (HVA). We examined the potential of rutin and its metabolites as novel histone acetyltransferase (HAT) inhibitors. DHPAA, HPAA and DHT at the concentration of 25 µM significantly inhibited in vitro HAT activity with DHT having the strongest inhibitory activity. Furthermore, DHT was shown to be a highly efficient inhibitor of p300 HAT activity, which corresponded with its high degree of inhibition on intracellular lipid accumulation in HepG2 cells. Docking simulation revealed that DHT was bound to the p300 catalytic pocket, bromodomain. Drug affinity responsive target stability (DARTS) analysis further supported the possibility of direct binding between DHT and p300. In HepG2 cells, DHT concentration-dependently abrogated p300-histone binding and induced hypoacetylation of histone subunits H3K9, H3K36, H4K8 and H4K16, eventually leading to the downregulation of lipogenesis-related genes and attenuating lipid accumulation. In ob/ob mice, administration of DHT (10, 20 mg/kg, iv, every other day for 6 weeks) dose-dependently improved the NAFLD pathogenic features including body weight, liver mass, fat mass, lipid accumulation in the liver, and biochemical blood parameters, accompanied by the decreased mRNA expression of lipogenic genes in the liver. Our results demonstrate that DHT, a novel p300 histone acetyltransferase inhibitor, may be a potential preventive or therapeutic agent for NAFLD.

Nanometer-scale displacement measurement with high resolution using dual cavity Fabry-Pérot interferometer for biomimetic robots.

A sensor of a biomimetic robot has to measure very small environmental changes such as, nanometer scale strains or displacements. Fiber optic sensor can be also one of candidates for the biomimetic sensor because the sensor is like thread and the shape of the sensor is similar to muscle fiber. A fiber optic interferometer, which is an optical-based sensor, can measure displacement precisely, so such device has been widely studied for the measurement of displacement on a nanometer-scale. Especially, a Quadrature Phase-Shifted Fiber Fabry-Pérot interferometer (QPS-FFPI) uses phase-information for this measurement, allowing it to provide a precision result with high resolution. In theory, the QPS-FFPI generates two sinusoidal signals of which the phase difference should be 90 degrees for the exact measurement of the displacement. In order to guarantee the condition of the phase difference, the relative adjustment of the cavities of the optical fibers is required. However, with such precise adjustment it is very hard to fix the proper difference of the two cavities for quadrature-phase-shifting. In this paper, a dual-cavity FFPI is newly proposed to measure the displacement on a nanometer-scale with a specific type of signal processing. In the signal processing, a novel phase-compensation algorithm is applied to force the phase difference to be exactly 90 degrees without any physical adjustment. As a result, the paper shows that the phase-compensated dual-cavity FFPI can effectively measure nanometer-scale displacement with high resolution under dynamic conditions.

Bullying Involvement and Subtypes of Disabilities: Who is Likely to be Affected by What?

The current study uses latent class analysis (LCA) and binary logistic regression analysis to explore profiles of bullying and how they might be associated with the types of disabilities. LCA was used to determine a categorization of involvement in bullying among youth with various types of disabilities. Binary logistic regression analysis was conducted to explore how profiles of bullying involvement might be associated with types of disabilities. The study uses the 2016 National Survey of Children's Health, a large-scale survey completed on children's health, ages 0-17, in the United States. A total of 139,923 households were screened for eligibility. The study participants consisted of 50,212 caregivers of a child who completed the survey. Findings revealed that among caregivers of children without disabilities, 79.5% reported that their child was uninvolved, and 20.5% reported that their child was a victim of bullying. Children in the developmental disabilities, speech and/or language disorders, and learning disabilities groups, showed significant odds of being in the bullying victim group compared to those without any disabilities. The study did not find that children in any disability groups were likely to be in the perpetrator group.

Firearm Possession Rates in Home Countries and Firearm Suicide Rates Among US- and Foreign-Born Suicide Decedents in the United States: Analysis of Combined Data from the National Violent Death Reporting System and the Small Arms Survey.

BACKGROUND: Suicide by firearms is a serious public health issue in the United States. However, little research has been conducted on the relationship between cultural backgrounds and suicide by firearms, specifically in those born and raised in the United States compared to those who have immigrated to the United States. OBJECTIVE: To better understand the relationship between cultural backgrounds and suicide, this study aimed to examine firearm suicide rates among US- and foreign-born suicide decedents based on the firearm possession rate in the decedent's home country. METHODS: Multivariate logistic regression was performed to analyze data of 28,895 suicide decedents from 37 states obtained from the 2017 National Violent Death Reporting System data set. The firearm possession rate in the home countries of foreign-born suicide decedents was obtained from the 2017 Small Arms Survey. RESULTS: The firearm suicide rate was about twice as high among US-born suicide decedents compared to their foreign-born counterparts. Meanwhile, suicide by hanging was about 75% higher among foreign-born compared to US-born suicide decedents. Those from countries with a low-to-medium firearm possession rate were significantly less likely to use firearms compared to US-born suicide decedents (adjusted odds ratio [AOR]=0.45, 95% CI 0.31-0.65, and AOR=0.46, 95% CI 0.39-0.53, respectively). Meanwhile, firearm suicide rates were not different between US- and foreign-born suicide decedents from countries with a similarly high firearm possession rate. CONCLUSIONS: The results suggest that there is an association between using firearms as a means of suicide and the firearm possession rate in the decedent's home country. Suicide by firearms in the United States needs to be understood in the sociocultural context related to firearm possession.

Characteristics and risk factors of suicide among people who attempted self-harm in South Korea: A longitudinal National Cohort Study in South Korea.

Although self-harm is known as a significant risk factor for suicide, there are insufficient studies on the characteristics of people who self-harmed and the factors affecting suicide using a national dataset in Asia. This study aimed to identify demographic, clinical, and socioeconomic factors of individuals who attempted self-harm concerning suicide mortality. By analyzing the Korean National Health Insurance Service data from 2002 to 2020, we compared the people who attempted self-harm to the general population and explored factors affecting suicide by using the Cox proportional hazards model. Older age, female sex, lower socioeconomic status, and psychiatric conditions were associated with higher self-harm attempts. Suicide was more prevalent among males with mild disabilities, using fatal self-harm methods, and higher Charlson Comorbidity Index (CCI) scores. Socioeconomic factors that were significantly related to self-harm attempt were relatively less significant in the suicide survival analysis, while male gender, older age, fatal self-harm methods, high CCI scores, psychiatric diagnosis, and drinking habits were significantly associated with lower suicide survival rates. These results showed that demographic, clinical and socioeconomic factors affecting self-harm differ from those affecting actual suicidal death after self-harm. These insights may assist in developing targeted prevention strategies for specific populations.

Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much?

BACKGROUND: Multiple Imputation (MI) is known as an effective method for handling missing data in public health research. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a variable. METHODS: Using data from "Predictive Study of Coronary Heart Disease" study, this study examined the effectiveness of multiple imputation in data with 20% missing to 80% missing observations using absolute bias (|bias|) and Root Mean Square Error (RMSE) of MI measured under Missing Completely at Random (MCAR), Missing at Random (MAR), and Not Missing at Random (NMAR) assumptions. RESULTS: The |bias| and RMSE of MI was much smaller than of the results of CCA under all missing mechanisms, especially with a high percentage of missing. In addition, the |bias| and RMSE of MI were consistent regardless of increasing imputation numbers from M=10 to M=50. Moreover, when comparing imputation mechanisms, MCMC method had universally smaller |bias| and RMSE than those of Regression method and Predictive Mean Matching method under all missing mechanisms. CONCLUSION: As missing percentages become higher, using MI is recommended, because MI produced less biased estimates under all missing mechanisms. However, when large proportions of data are missing, other things need to be considered such as the number of imputations, imputation mechanisms, and missing data mechanisms for proper imputation.

[Clinical factors associated with response to biofeedback therapy for patients with chronic constipation].

BACKGROUND/AIMS: Biofeedback therapy has been widely used for the treatment of constipated patients. However, there are only a few reports about the clinical factors that can predict the effectiveness of biofeedback therapy. The aim of this study was to evaluate prognostic factors before the initiation of biofeedback treatment in constipated patients. METHODS: Biofeedback treatment was performed in 114 patients with constipation. After classifying the patients into two groups, responder and non-responder by subjective and objective parameters, univariate and multivariate analysis were performed to evaluate the factors associated with effectiveness of biofeedback therapy. RESULTS: Eighty-five patients (74.6%) responded to biofeedback therapy. Pre-treatment balloon expulsion test, paradoxical contraction on manometry, defecation index and anal residual pressure during straining were the factors that influenced the results of biofeedback treatment. On multivariate analysis, defecation index (odds ratio=67.5, p<0.05) and paradoxical contraction on manometry (odds ratio=0.053, p<0.05) were the factors that showed significant difference between the responders and non-responders. CONCLUSIONS: This study suggests that several pre-treatment prognostic factors are associated with response to biofeedback for the constipated patients. Using prognostic factors, we may be able to evaluate the patterns of pelvic floor dysfunction and responsiveness of biofeedback therapy for the patients with constipation.

Caspase and mitogen activated protein kinase pathways are involved in Solanum lyratum herba induced apoptosis.

AIM OF STUDY: Solanum lyratum herba (SLH) has been traditionally used for the treatment of febrifuge, diarrhea, eye disease and cancer with little scientific evidences. Thus, in the present study, to elucidate the antitumor mechanism of SLH: in vitro and in vivo experiments were performed with hexane fraction of Solanum lyratum herba (HSLH). MATERIALS AND METHODS: Cytotoxicity assay, 4'-6-diamidino-2-phenylindole (DAPI) staining, flow cytometric analysis for sub-G1 peaks, Western blot analysis were used with the antibodies of apoptosis related proteins in vitro. In addition, the effect of HSLH on in vivo tumor growth was evaluated in Lewis lung carcinoma (LLC) tumor model and immunohistochemistry also was performed with terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining in tumor section. RESULTS: HSLH exhibited cytotoxicity against LLC cells most effectively among its solvent fractions. Ladder like DNA fragmentation and apoptotic features such as chromatin condensation and apoptotic bodies were observed in HSLH treated LLC cells by 4'-6-diamidino-2-phenylindole staining. HSLH also significantly increased sub-G(1) peaks, activated caspase-8, -9 and -3 proteins and cleaved poly(ADP-ribose) polymerase (PARP). Furthermore, HSLH increased the phosphorylation of extracellular signal-regulated kinase (ERK), transiently activated phospho-JNK (c-jun N-terminal kinase) and downregulated phospho-p38 MAPK. In addition, we have found for the first time HSLH treatment effectively suppressed the in vivo growth of LLC to up to approximately 30% of untreated control at 50mg/kg and significantly increased apoptotic expression in tumor section by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining. Taken together, these findings strongly demonstrate that hexane fraction of Solanum lyratum herba exerts antitumor activity via caspase activation and MAPK regulation and can be effectively applied to lung cancer as a cancer chemopreventive agent.

A case of neurotoxicity following 5-fluorouracil-based chemotherapy.

5-Fluorouracil (5-FU) is a commonly used chemotherapeutic agent. However, its neurotoxicity is rare and not well recognized. We report a case of 5-FU neurotoxicity with organic brain syndrome and progression to multifocal leukoencephalopathy in a 44-year-old male patient having malignant gastrointestinal stromal tumor. 5-FU-induced neurotoxicity should, therefore, be considered as an important differential diagnosis in cancer patients with neurological abnormality and history of chemotherapy.