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Paroxetine (PRX) is widely prescribed for treating psychiatric disorders. Emerging evidence suggests that PRX can act as an immunosuppressive agent, yet the molecular mechanisms underlying its effects are not fully understood. This study has investigated whether PRX influences phenotypic changes of monocytic cells and signaling pathways induced by immune oxysterol, like 27-hydroxycholesterol (27OHChol), that triggers an inflammatory response using THP-1 monocytic cells. Treatment with PRX impaired 27OHChol-induced transcription and production of the pro-inflammatory chemokine CCL2, which was associated with decreased migration of monocytic cells, and repressed the expression and activity of MMP-9. It reduced the expression of mature dendritic cell markers, like CD80, CD83, and CD88, and partially restored phagocytic function. PRX also impaired phosphorylation of Akt and the downstream targets of mTORC1, S6, and 4E-BP1. These results indicate that PRX suppresses 27OHChol-induced change of monocytic cells to a proinflammatory phenotype by influencing the Akt/mTORC1 pathway. We suggest that PRX exerts its anti-inflammatory effects by suppressing the activation of monocytic cells in response to immune oxysterol.

Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.

White blood cells (WBCs) act as mediators of inflammatory responses and are commonly measured in hospitals. Although several studies have reported a relation between WBC count and mortality, no systematic review or meta-analysis has been conducted. This study aimed to identify an association between WBC count and mortality. We conducted a systematic search on Embase using keywords such as “white blood cell” and “mortality.” We analyzed the hazard ratios (HRs) for WBC count of 1.0 × 109 cells/L regarding 2 criteria: the cause of mortality and the follow-up period. A total of 13 of 222 articles comprising a total of 62,904 participants were included in this study, meeting the criteria set. A positive association was observed between WBC count and mortality, as indicated by an HR of 1.10 (95% confidence interval [CI] 1.08 to 1.13). In additionally, WBC count emerged as a significant predictor of mortality in both groups, with an HR of 1.10 (95% CI 1.07 to 1.12) for patients with cardiovascular disease and an HR of 1.12 (95% CI 1.07 to 1.17) for the general population or patients with COVID-19. Furthermore, a higher WBC count demonstrated a significant association with long-term all-cause mortality (HR 1.09, 95% CI 1.07 to 1.12) and long-term cardiovascular mortality (HR 1.05, 95% CI 1.02 to 1.07). Similarly, a significant association was found between higher WBC count and short-term all-cause mortality (HR 1.12, 95% CI 1.09 to 1.16) and cardiovascular mortality (HR 1.12, 95% CI 1.07 to 1.17). Further research is necessary to explore the relation between WBC count and disease progression or death and to establish causality between elevated WBC count and disease progression.

This paper presents a study on optimal design to determine the installation position and link lengths of a robot within a designated workspace for welding, aiming to minimize singularities during the robot’s motion. Bi-objective functions are formulated to minimize singularities while maximizing the volumes of linear velocity manipulability ellipsoid and angular velocity manipulability ellipsoid, respectively, ensuring isotropy. We have constructed a simulation environment incorporating PID control to account for robot tracking errors. This environment was utilized as a simulator to derive a Bi-objective function set within a genetic algorithm. Through this, we optimized four robot link length variables and two installation position variables, selecting the optimal design variables on the Pareto Front. In the standard work object, the volume average of the linear velocity manipulability ellipsoid was confirmed to have improved by 72% compared to the initial level, and the isotropy of the angular velocity manipulability ellipsoid was confirmed to have improved by 23% compared to the initial level. Furthermore, correlation analysis between design parameters identified those with a high correlation with the objective functions, and the analysis results are discussed.

Simulation with a reasonable physical model is important to develop control algorithms for robots quickly, accurately, and safely without damaging the associated physical systems in various environments. However, it is difficult to choose the suitable tool for simulating a specific project. To help users in selecting the best tool when simulating a given project, we compare the performance of the four widely used physics engines, namely, ODE, Bullet, Vortex, and MoJoco, for various simple and complex industrial scenarios. We first summarize the technical algorithms implemented in each physics engine. We also designed four simulation scenarios ranging from simple scenarios for which analytic solution exists to complex industrial scenarios to compare the performance of each physics engine. We then present the simulation results in the default settings of all the physics engines, and analyze the behavior and contact force of the simulated objects.

Whole-body center of gravity (CG) movements in relation to the center of pressure (COP) offer insights into the balance control strategies of the human body. Existing CG measurement methods using expensive measurement equipment fixed in a laboratory environment are not intended for continuous monitoring. The development of wireless sensing technology makes it possible to expand the measurement in daily life. The insole system is a wearable device that can evaluate human balance ability by measuring pressure distribution on the ground. In this study, a novel protocol (data preparation and model training) for estimating the 3-axis CG trajectory from vertical plantar pressures was proposed and its performance was evaluated. Input and target data were obtained through gait experiments conducted on 15 adult and 15 elderly males using a self-made insole prototype and optical motion capture system. One gait cycle was divided into four semantic phases. Features specified for each phase were extracted and the CG trajectory was predicted using a bi-directional long short-term memory (Bi-LSTM) network. The performance of the proposed CG prediction model was evaluated by a comparative study with four prediction models having no gait phase segmentation. The CG trajectory calculated with the optoelectronic system was used as a golden standard. The relative root mean square error of the proposed model on the 3-axis of anterior/posterior, medial/lateral, and proximal/distal showed the best prediction performance, with 2.12%, 12.97%, and 12.47%. Biomechanical analysis of two healthy male groups was conducted. A statistically significant difference between CG trajectories of the two groups was shown in the proposed model. Large CG sway of the medial/lateral axis trajectory and CG fall of the proximal/distal axis trajectory is shown in the old group. The protocol proposed in this study is a basic step to have gait analysis in daily life. It is expected to be utilized as a key element for clinical applications.

Accurate prediction of electric energy consumption is critical for both user convenience and supplier efficiency. This study introduces an ensemble approach that integrates clustering algorithms with machine learning (ML) models to enhance prediction accuracy by identifying consumption patterns within buildings. The research focused on residential apartments in the metropolitan area of Korea, utilizing four evaluation methods (Elbow-Method, Silhouette Score, Calinski-Harabasz Index, and Dunn Index) across five data collection intervals (10 min, 1 h, 1 day, 1 week, and 1 month). Five ML models (CatBoost, Decision Tree, LightGBM, Random Forest, XGBoost) were assessed for their prediction performance across clusters. Additionally, ML models that exhibited high performance within each cluster were amalgamated into an ensemble model to assess the predictive performance regarding total electric energy consumption at the research site. Optimal clustering resulted in two clusters (142 houses for C0, 206 houses for C1) using monthly resampled power data. CatBoost and LightGBM exhibited the highest average prediction performance. Based on the possible combinations of the two models applied to each cluster, four ensemble models were developed: CB-CB, CB-LGBM, LGBM-CB, and LGBM-LGBM. Statistical analysis confirmed that all ensemble models significantly outperformed the control group’s traditional ML approaches without clustering ( p  < 0.05 or 0.01). The proposed clustering-based ML ensemble model in this study can predict the energy consumed in buildings more accurately by accounting for the unique consumption pattern of each house. It is anticipated to contribute effectively to energy consumption reduction.

We propose a novel wearable haptic device that can provide kinesthetic haptic feedback for stiffness rendering of virtual objects in augmented reality (AR). Rendering stiffness of objects using haptic feedback is crucial for realistic finger-based object manipulation, yet challenging particularly in AR due to the co-presence of a real hand, haptic device, and rendered AR objects in the scenes. By adopting passive actuation with a tendon-based transmission mechanism, the proposed haptic device can generate kinesthetic feedback strong enough for immersive manipulation and prevention of inter-penetration in a small-form-factor, while maximizing the wearability and minimizing the occlusion in AR usage. A selective locking module is adopted in the device to allow for the rendering of the elasticity of objects. We perform an experimental study of two-finger grasping to verify the efficacy of the proposed haptic device for finger-based manipulation in AR. We also quantitatively compare/articulate the effects of different types of feedbacks across haptic and visual sense (i.e., kinesthetic haptic feedback, vibrotactile haptic feedback, and visuo-haptic feedback) for stiffness rendering of virtual objects in AR for the first time.

Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

In the present study, we investigated the cognitive improvement effects and its mechanisms of krill oil (KO) in Aβ25–35-induced Alzheimer’s disease (AD) mouse model. The Aβ25–35-injected AD mouse showed memory and cognitive impairment in the behavior tests. However, the administration of KO improved novel object recognition ability and passive avoidance ability compared with Aβ25–35-injected control mice in behavior tests. In addition, KO-administered mice showed shorter latency to find the hidden platform in a Morris water maze test, indicating that KO improved learning and memory abilities. To evaluate the cognitive improvement mechanisms of KO, we measured the oxidative stress-related biomarkers and apoptosis-related protein expressions in the brain. The administration of KO inhibited oxidative stress-related biomarkers such as reactive oxygen species, malondialdehyde, and nitric oxide compared with AD control mice induced by Aβ25–35. In addition, KO-administered mice showed down-regulation of Bax/Bcl-2 ratio in the brain. Therefore, this study indicated that KO-administered mice improved cognitive function against Aβ25–35 by attenuations of neuronal oxidative stress and neuronal apoptosis. It suggests that KO might be a potential agent for prevention and treatment of AD.