An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. OBJECTIVE: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. METHODS: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. RESULTS: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). CONCLUSIONS: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Mechano/acousto-electric coupling between ReS2and surface acoustic wave.

Two-dimensional (2D) materials are promising candidates for developing next generation electronic/optoelectronic devices with programmable multi functions, due to their widely tunable properties by various physical stimuli. Mechanical strain is one of the most promising means to effectively modulate the physical properties of 2D materials. Nevertheless, few studies reported micro/nano scale controllable strain application platforms, limiting the development of novel mechano-electrical/optoelectrical devices based on 2D materials. This work proposes surface acoustic wave (SAW) device as a controllable strain modulation platform for 2D materials with sub-micro scale resolution. The platform uses the piezoelectric material (LiNbO3) as the substrate, which is deposited with interdigitated transducers (IDT) to generate SAW on the surface. The propagation of SAW causes surface deformation, which is then transferred to the 2D materials on the substrate. The period of the surface deformation/strain is related with that of SAW, which is determined by the period of IDT with nano meter scale. It is demonstrated that the photo luminescence spectrum of a 2D ReS2flake on this platform gradually shifts with the SAW excitation power, which reaches a shift of 3 nm as the SAW excitation power achieves 26 dBm, corresponding to a band gap increase of 5 meV. Meanwhile, the platform is also capable to provide acousto-electric coupling between SAW and 2D materials, which is demonstrated by the shift of the SAW resonant frequency due to the re-distribution of photo-generated carriers in ReS2upon light illumination.

Sudden respiratory and circulatory collapse after cesarean section: Amniotic fluid embolism or other reasons? - a case report.

BACKGROUND: For a healthy parturient, a cardiopulmonary collapse that suddenly occurs shortly after an uneventful caesarean section is a relatively rare event and presents a significant challenge for the anesthesia provider. CASE PRESENTATION: Amniotic fluid embolism (AFE) is characterized by acute and rapid collapse and is well known to the obstetric team. Our patient experienced sudden cardiovascular collapse, severe respiratory difficulty and hypoxia, in the absence of other explanations for these findings at the time, and thus AFE was immediately become the focus of the consideration. However, there is no quick, standard laboratory test for AFE, therefore the diagnosis is one of exclusion based on presenting symptoms and clinical course. After given symptomatic treatment, the patient made an uneventful initial recovery in a short period and developed a rash. We recognized that the postpartum shock was associated with delayed anaphylaxis of antibiotics. CONCLUSIONS: These observations have implications for understanding whenever administering drugs in surgery, which may affect the anesthesiologist's judgment regarding the complications of anesthesia. Even though serious complications of common perioperative drugs may rarely occur, anesthesia providers should be aware of the consideration. Early recognition and effective treatment are more important than prompt diagnosis.

Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks.

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.

Cadmium induced inflammation and apoptosis of porcine epididymis via activating RAF1/MEK/ERK and NF-κB pathways.

Cadmium (Cd) was a serious heavy metal pollutant. Cd exposure will cause damage to reproductive organs. It was largely unknown whether Cd exposure caused inflammation and apoptosis in epididymis. In this study, we established models of Cd exposure in swine, and the apoptotic level of epididymis was detected by in situ TUNEL fluorescence staining assay, the results showed that Cd exposure significantly increased TUNEL-apoptosis index. Furthermore, the results of qRT-PCR and Western blot showed that Cd activated the proto-oncogenic serine/threonine kinase-1 (RAF1)/mitogen-activated protein kinase (MEK)/extracellular signal-regulated kinase (ERK) signal pathway (RAF1/MEK/ERK) and led to the subsequent up-regulation of the nuclear factor-κB (NF-κB), tumor necrosis factor α (TNF-α), cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), interleukin-1ß (IL-1ß), interleukin-6 (IL-6), interleukin-8 (IL-8), caused inflammation in epididymis. NF-κB inflammation pathway also mediated the tumor protein P53 (P53) and indirectly activated the Cytochrome c (Cytc), B-cell lymphoma-2 (Bcl-2), Bcl-2-Associated X protein (Bax), Caspase 3, Caspase 9. In summary, we believed that the RAF1/MEK/ERK pathway came into play in the apoptosis of epididymal tissues exposed to Cd by activating the NF-κB Inflammation pathway, followed by activation of the mitochondrial apoptotic pathway. This study provides more abundant data for exploring the reproductive toxicity of Cd.

Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System.

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

Impact of Sevoflurane Versus Propofol Anesthesia on Post-Operative Cognitive Dysfunction in Elderly Cancer Patients: A Double-Blinded Randomized Controlled Trial.

BACKGROUND Research on the clinical outcomes of surgical patients anaesthetized with sevoflurane and the association of sevoflurane with post-operative cognitive dysfunction (POCD) is scarce. We evaluated whether sevoflurane-based anesthesia increased the incidence of POCD and worsened prognosis compared to propofol-based anesthesia in elderly cancer patients. MATERIAL AND METHODS This single-center, prospective, double-blind randomized controlled trial included 234 patients aged 65 to 86 years undergoing tumor resection who received sevoflurane-based (Group S) or propofol-based (Group P) anesthesia during surgery. A series of neuropsychological tests was performed to evaluate cognitive function before surgery and at 7 days and 3 months post-operation, and the results were compared to those of healthy controls. RESULTS At 7 days post-operation there were no significant differences in the incidence of POCD between patients who received sevoflurane-based or propofol-based anesthesia during surgery: Group S was at 29.1% (32 out of 110 patients) versus Group P at 27.3% (30 out of 110), P=0.764. At 3 months, Group S was at 11.3% (12 out of 106 patients) versus Group P at 9.2% (10 out of 109), P=0.604. During the first 2 days post-operation, the QoR-40 global score was significantly lower in Group S compared to Group P [POD 1: P=0.004; POD 2: P=0.001]. There were no significant differences in in-hospital post-operative complications, post-operative length of hospital stay, all-cause mortality at 30 days, and 3 months post-operation, or post-operative quality of life at 3 months between patients in Group S and Group P. CONCLUSIONS Sevoflurane-based anesthesia did not increase the incidence of POCD compared to propofol-based anesthesia at 7 days or 3 months post-operation or impact short-term post-operative prognosis.

Hydrogen sulfide upregulates miR-16-5p targeting PiK3R1 and RAF1 to inhibit neutrophil extracellular trap formation in chickens.

Hydrogen sulfide (H2S) is a toxic air pollutant that causes immune damage. Recent studies have found that neutrophil extracellular trap (NET) formation is one way in which neutrophils exert immune functions. In addition, the formation of NETs is also related to thrombosis and autoimmune diseases. Recent studies have shown that miRNAs are involved in the regulation of a variety of pathophysiological processes. Here, we investigated the role of H2S in regulating the formation of NETs by affecting miR-16-5p. Our study established an in vitro H2S exposure model for neutrophils using phorbol-myristate-acetate (PMA) to induce NET formation. We observed the morphological changes of cells with scanning electron microscopy and fluorescence microscopy. Then, the content of extracellular DNA and the expression of MPO and NE in each group were detected. The results showed that H2S inhibited the formation of NETs. The expression of miR-16-5p and its target genes PiK3R1 and RAF1 was then measured by qRT-PCR. H2S upregulated miR-16-5p and inhibited expression of the target genes PiK3R1 and RAF1, and it subsequently inhibited the Pi3K/AKT and ERK pathways and decreased respiratory burst levels. Furthermore, H2S attenuated inositol 1,4,5-trisphosphate receptor (IP3R)-mediated endoplasmic reticulum calcium outflow as well as autophagy caused by PMA. This study enriches H2S immunotoxicity research and provides a possible solution for the treatment of NET-related diseases.

Power Control and Clustering-Based Interference Management for UAV-Assisted Networks.

Unmanned Aerial Vehicle (UAV) has been widely used in various applications of wireless network. A system of UAVs has the function of collecting data, offloading traffic for ground Base Stations (BSs) and illuminating coverage holes. However, inter-UAV interference is easily introduced because of the huge number of LoS paths in the air-to-ground channel. In this paper, we propose an interference management framework for UAV-assisted networks, consisting of two main modules: power control and UAV clustering. The power control is executed first to adjust the power levels of UAVs. We model the problem of power control for UAV networks as a non-cooperative game which is proved to be an exact potential game and the Nash equilibrium is reached. Next, to further improve system user rate, coordinated multi-point (CoMP) technique is implemented. The cooperative UAV sets are established to serve users and thus transforming the interfering links into useful links. Affinity propagation is applied to build clusters of UAVs based on the interference strength. Simulation results show that the proposed algorithm integrating power control with CoMP can effectively reduce the interference and improve system sum-rate, compared to Non-CoMP scenario. The law of cluster formation is also obtained where the average cluster size and the number of clusters are affected by inter-UAV distance.

Fault-tolerant prescribed performance control of nonlinear systems with process faults and actuator failures.

This paper investigates the fault-tolerant prescribed performance control problem for a class of multiple-input single-output unknown nonlinear systems subject to process faults and actuator failures. In contrast to the related works, we consider a general class of nonlinear systems with both multiplicative nonlinearities and additive nonlinearities corrupted by the process faults; only the boundedness of the process faults and the continuity of the nonlinear functions are required, without the explicit or fixed structures of the fault functions. To conquer this problem, a less-demanding and low-complexity fault-tolerant prescribed performance control approach is proposed. The controller is independent of the specific information of faults or the system model and does not invoke fault diagnosis or neural/fuzzy approximation to acquire such knowledge. It achieves the reference tracking with the predefined rate and accuracy. A comparative simulation on a single-link robot is conducted to illustrate the effectiveness and superiority of the proposed approach.

Robust Prescribed Performance Control of Nonlinear Systems With Unknown Odd Powers.

This article is concerned with the problem of reference tracking for the lower-triangular nonlinear systems with a chain of odd powers. Contrary to most of the related studies, this work is focused on the case where neither the odd powers nor their bounds are known. This renders the majority of the existing methods for stability analysis and control design for the odd-power systems infeasible. To surmount this challenge, a robust prescribed performance control strategy together with a constraint analysis by contradiction is put forward. Instead of the well-established adding one power integrator technique, a group of barrier functions are employed to combat the tracking error and the intermediate errors. In lieu of the Lyapunov stability theory, a constraint analysis by contradiction is carried out, which discloses the inherent robustness of the control system against the nonparametric uncertainties, the unmatched disturbances and the unknown odd powers. It is guaranteed that the tracking error enters into a preassigned neighborhood of zero after a given time, with a predefined bound on the overshoot. In addition, the proposed control exhibits a striking simplicity. Despite the severe model uncertainties and the recursive control design, no effort needs to be paid for parameter identification, function approximation, disturbance estimation, or derivative calculation. The above theoretical findings are substantiated by the comparative simulation results.

Manipulation of Charge Transport in MoS2/MoTe2 Field Effect Transistors and Heterostructure by Propagating the Surface Acoustic Wave.

Two-dimensional (2D) materials with atomic-scale thickness are promising candidates to develop next-generation electronic and optoelectronic devices with multiple functions due to their widely tunable physical properties by various stimuli. The surface acoustic wave (SAW) produced at the surface of the piezoelectrical substrate can generate electrical and strain fields simultaneously with micro/nanometer resolution during propagation. It provides a stable and wireless platform to manipulate the rich and fascinating properties of 2D materials. However, the interaction mechanisms between the SAW and 2D materials remain unclear, preventing further development and potential applications of SAW-integrated 2D devices. This work studied the acoustoelectric (AE) charge transport mechanism in 2D materials thoroughly by characterizing the performances of the n-type MoS2 and p-type MoTe2 field effect transistors (FETs) and the MoS2/MoTe2 p-n junction driven by the SAW. As compared to the case driven by the static electrical field alone, the SAW drove the electron and hole transport along the same direction as its propagation, and the generated AE current always had the opposite direction to the AE voltage. In the device level, the 2D FETs showed a significantly reduced subthreshold swing up to around 67% when the SAW was used to drive the channel carriers, indicating that the SAW enhanced the on/off switching speed. Moreover, the MoTe2/MoS2 p-n junction showed a tunable photoresponsivity by the power and propagation direction of the SAW. These findings provide a solid foundation to promote future research and potential applications of SAW-driven multifunctional devices based on 2D materials.

Low-Complexity Tracking Control of Unknown Strict-Feedback Systems With Quantitative Performance Guarantees.

This article is concerned with the prescribed performance tracking control problem for the strict-feedback systems with unknown nonlinearities and unmatched disturbances. The challenge lies in the realization of a complete performance specification for trajectory tracking in the sense of quantitatively regulating the peak value, overshoot, settling time, and accuracy while ensuring that the initial condition holds naturally. To this end, an error transformation, equipped with a shifting function, is introduced and incorporated with a new-type barrier function. Then, a class of performance functions is exploited to quantify the settling times and steady-state bounds of the intermediate errors. Moreover, to improve the flexibility of formulating performance specifications for the tracking error, a pair of asymmetric performance boundaries are further designed. With their combination, a novel robust prescribed performance control (PPC) approach is proposed in this article. It not only achieves the quantitative performance guarantees but also preserves the unique simplicity of PPC, evading the needs for function approximation, parameter identification, disturbance estimation, derivative calculation, or command filtering. The above theoretical findings are confirmed via three simulation studies.

The role of construction industry and construction policy on sustainable rural development in China.

Recently, rural development has depended on the construction industry's success due to the high employment rate in the construction industry and its development role in the rural areas, and this phenomenon needs research focus. Hence, the current article examines the impact of the construction industry (construction industry revenue and growth) and construction policy (construction industry subsidies) on sustainable rural development in China. The study also used the control variable of gross domestic product (GDP) and industrialization. The article has collected secondary data from the Ministry of Housing and Urban-Rural Development and World Development Indicators (WDI) from 1991 to 2020. The article has applied the Augmented Dickey-Fuller (ADF) test to examine stationarity and quantile autoregressive distributed lag (QARDL) model to investigate the association among variables. The results revealed that the construction industry revenue, growth, construction policy GDP, and industrialization positively link sustainable rural development in China. Thus, the findings exposed that if the country's construction industry improved, rural development also increased accordingly. This study guides the policy development authorities to develop effective policies related to improvement in the construction industry that will enhance sustainable rural development.

Roles of phase change materials on the morphological, physical, rheological and temperature regulating performances of high-viscosity modified asphalt.

The composite of phase change material (PCM) and high-viscosity modified asphalt (HVMA) is expected as a new material regulating the temperature of high-performance pavements, thereby ameliorating the urban heat island effect. This study focused on evaluating the roles of two kinds of PCMs, i.e. paraffin/expanded graphite/high-density polyethylene composite material (PHDP) and polyethylene glycol (PEG), on a series of performances of HVMA. Fluorescence microscopy observations, physical rheological properties tests and indoor temperature regulating tests were conducted to determine the morphological, physical, rheological and temperature regulating performances of PHDP/HVMA or PEG/HVMA composites with various PCM contents prepared by fusion blending. Fluorescence microscopy test results revealed that the PHDP and PEG could be uniformly distributed in HVMA, but their distribution size and morphology were obviously different. Physical test results showed an increase in the penetration values of both PHDP/HVMA and PEG/HVMA compared to the HVMA without PCM. Their softening points did not change significantly with increasing PCM content due to the presence of a high-content of polymeric spatial reticulation. Ductility test reflected that the low-temperature properties of PHDP/HVMA were improved. However, the ductility of PEG/HVMA was much reduced due to the presence of large size PEG particles especially at 15 % PEG content. Rheological results from the recovery percent and non-recoverable creep compliance at 64 °C confirmed that the PHDP/HVMA and PEG/HVMA had excellent high-temperature rutting resistance regardless of PCM contents. Notably, the phase angle results reflected that the PHDP/HVMA was more viscous at 5-30 °C and more elastic at 30-60 °C. By contrast, PEG/HVMA was more elastic at the whole temperature range of 5-60 °C. Lastly but not least, compared to HVMA without PCM, the temperature regulating effect was 4 °C during heating of PHDP/HVMA containing 4 % PHDP and PEG/HVMA containing 15 % PEG, and their delay time were 456 s and 1240 s, respectively.

The supply and demand of water-related ecosystem services in the Asian water tower and its downstream area.

Climate change and human activities can have an impact on the supply and demand of water-related ecosystem services (WRESs) in the Asian water tower (AWT) and its downstream area, which is closely related to the production and livelihoods of billions of people. However, few studies have taken the AWT and its downstream area as a whole to assess the supply-demand relationship of WRESs. This study aims to assess the future trends of the supply-demand relationship of WRESs in the AWT and its downstream area. Here, the supply-demand relationship of WRESs in 2019 was assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and socio-economic data. Then, future scenarios were selected under the framework of the Scenario Model Intercomparison Project (ScenarioMIP). Finally, trends in the supply-demand of WRESs were analysed at multiple scales from 2020 to 2050. The study found that the supply-demand imbalance of WRESs in the AWT and its downstream area will continue to intensify. The area with imbalance intensification was 2.38 × 106 km2 (61.7 %). The supply-demand ratio of WRESs will decline significantly under different scenarios (p < 0.05). The main reason for the imbalance intensification in WRESs is the constant growth of human activities, with a relative contribution of 62.8 %. Our findings suggest that in addition to the pursuit of climate mitigation and adaptation, attention should also be paid to the impact of rapid human activity growth on the supply-demand imbalance of WRESs.

Water Quality Degradation Due to Heavy Metal Contamination: Health Impacts and Eco-Friendly Approaches for Heavy Metal Remediation.

Water quality depends on its physicochemical and biological parameters. Changes in parameters such as pH, temperature, and essential and non-essential trace metals in water can render it unfit for human use. Moreover, the characteristics of the local environment, geological processes, geochemistry, and hydrological properties of water sources also affect water quality. Generally, groundwater is utilized for drinking purposes all over the globe. The surface is also utilized for human use and industrial purposes. There are several natural and anthropogenic activities responsible for the heavy metal contamination of water. Industrial sources, including coal washery, steel industry, food processing industry, plastic processing, metallic work, leather tanning, etc., are responsible for heavy metal contamination in water. Domestic and agricultural waste is also responsible for hazardous metallic contamination in water. Contaminated water with heavy metal ions like Cr (VI), Cd (II), Pb (II), As (V and III), Hg (II), Ni (II), and Cu (II) is responsible for several health issues in humans, like liver failure, kidney damage, gastric and skin cancer, mental disorders and harmful effects on the reproductive system. Hence, the evaluation of heavy metal contamination in water and its removal is needed. There are several physicochemical methods that are available for the removal of heavy metals from water, but these methods are expensive and generate large amounts of secondary pollutants. Biological methods are considered cost-effective and eco-friendly methods for the remediation of metallic contaminants from water. In this review, we focused on water contamination with toxic heavy metals and their toxicity and eco-friendly bioremediation approaches.

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion. Methods: A total of 100 subjects aged 18-30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland-Altman analysis. Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland-Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed. Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis.

Background: Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets. Method: We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software "limma" package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines. Result: CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A. Conclusion: In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.

Identification of five hub immune genes and characterization of two immune subtypes of osteoarthritis.

Background: Osteoarthritis (OA) is one of the most prevalent chronic diseases, leading to degeneration of joints, chronic pain, and disability in the elderly. Little is known about the role of immune-related genes (IRGs) and immune cells in OA. Method: Hub IRGs of OA were identified by differential expression analysis and filtered by three machine learning strategies, including random forest (RF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM). A diagnostic nomogram model was then constructed by using these hub IRGs, with receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) estimating its performance and clinical impact. Hierarchical clustering analysis was then conducted by setting the hub IRGs as input information. Differences in immune cell infiltration and activities of immune pathways were revealed between different immune subtypes. Result: Five hub IRGs of OA were identified, including TNFSF11, SCD1, PGF, EDNRB, and IL1R1. Of them, TNFSF11 and SCD1 contributed the most to the diagnostic nomogram model with area under the curve (AUC) values of 0.904 and 0.864, respectively. Two immune subtypes were characterized. The immune over-activated subtype showed excessively activated cellular immunity with a higher proportion of activated B cells and activated CD8 T cells. The two phenotypes were also seen in two validation cohorts. Conclusion: The present study comprehensively investigated the role of immune genes and immune cells in OA. Five hub IRGs and two immune subtypes were identified. These findings will provide novel insights into the diagnosis and treatment of OA.