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Although zinc oxide nanoparticles (ZnO NPs) have demonstrated promising anticancer activity, their therapeutic performances are frequently influenced by the synthesis routes and associated distinct physicochemical characteristics. Herein, ZnO NPs were synthesized through a cost-effective flame spray pyrolysis-assisted approach and their selective anticancer effects and underlying intrinsic apoptosis signaling pathway in human gastric carcinoma cell line (AGS) were examined. Transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and dynamic light scattering (DLS) investigations were used to characterize the synthesized ZnO NPs. Afterwards, AGS and normal fibroblast cells (L-929) were treated with various concentrations of ZnO NPs, and cell proliferation was examined by the MTT assay to determine the IC50 concentration of NPs. Furthermore, the quantification of ROS and apoptosis was done by fluorometer and flow cytometry assays. Additionally, superoxide dismutase (SOD) and catalase (CAT) activity assays were performed to evaluate the oxidative stress-induced cytotoxicity mediated by ZnO NPs against AGS cells. Moreover, caspase-3/-8/-9 activity and mitochondrial membrane potential (MMP) assays were done. Finally, the expression levels of p53, Bax, Bcl-2, caspase-9/-8/-3, and cytochrome c were assessed by quantitative real time PCR and western blot analyses to explore the probable mitochondrial apoptosis signaling pathway induced by ZnO NPs in AGS cells. The results showed that ZnO NPs with an absorption maximum at 350 nm, an average size of 70 nm, a hydrodynamic radius of 92.89 nm, and a zeta potential of −43.13 mV potentially and selectively inhibited the proliferation of AGS cells in comparison with L-929 cells through a remarkable increase in the production of ROS, induction of apoptosis, reduction of SOD and CAT activity, activation of caspase-9/-3, and MMP disruption. Moreover, ZnO NPs triggered up-regulation of p53, Bax, caspase-9/-3, cytochrome c and down-regulation of Bcl-2 at mRNA and protein levels. This study suggests that synthesized ZnO NPs through flame spray pyrolysis can induce selective anticancer effects against gastric cancer cells via intrinsic apoptosis signaling pathway.

This study employs gyrokinetic simulations to investigate ion temperature gradient (ITG) turbulence in realistic fusion reactor plasmas featuring reversed magnetic shear. The weakly negative magnetic shear is observed to be more stable for the ITG instability than strongly positive shear in this equilibrium configuration, primarily stemming from the scarcity of mode rational surfaces induced by the weak negative shear. This superiority in suppression for the negative shear persists in nonlinear turbulence with zonal flow artificially eliminated, where the emergence of turbulence solitons is observed and found associated with locally dense mode rational surfaces. However, the difference in transport levels among different magnetic shears diminishes in the presence of self-consistently generated zonal flow, accompanied by the disappearance of turbulence solitons. The nonlinear generation of zonal flow is found to be significantly affected by the magnetic shear. The study reveals a remarkable phenomenon that the Dimits shift no longer exists for negative magnetic shear, which is attributed to the weakness of the zonal flow generation near the ITG marginal stability.

Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis.

Abstract Background Constant emerging sites infested with Oncomelania hupensis (O. hupensis) impede the goal realization of eliminating schistosomiasis. The study assessed the spatial and temporal distributions of new Oncomelania snail habitats in Hunan Province from 1949 to 2016. Methods We used the data from annual snail surveys throughout Hunan Province for the period from 1949 to 2016. Global Moran’s I, Anselin local Moran’s I statistics (LISA) and a retrospective space-time permutation model were applied to determine the spatial and temporal distributions of emerging snail-infested sites. Results There were newly discovered snail-infested sites almost every year in 1949–2016, except for the years of 1993, 2009 and 2012. The number of emerging sites varied significantly in the five time periods (1949–1954, 1955–1976, 1977–1986, 1986–2003 and 2004–2016) (H = 25.35, p

How to save the energy of unmanned aerial vehicles (UAVs) and then enable long-distance transport is a very real and difficult task. However, for UAVs, the classic object detection algorithm, such as the deep convolutional neural network–based object detection algorithm and the classic flight control algorithm, such as the PID-based position control algorithm, require significant energy, which limits the application scenarios of the UAV system. In view of this problem, this paper proposes a lightweight object detection network and a linear active disturbance rejection controller (LADRC) for the quadrotor with the cable-suspended payload (QCSP) system to improve energy efficiency. The system uses a YOLOV3 network and embeds it into the Jesson NX mobile platform to accurately detect the target position. Furthermore, a nonlinear velocity controller with a cable-suspended structure to control the velocity of the payload, a LADRC algorithm is adopted to achieve fast and accurate control of the payload position. Simulations and real flight experiments show that the proposed object detection algorithm and the LADRC control strategy can save the energy of drone effectively.

Visual Simultaneous Localization and Mapping (SLAM) system is mainly used in real-time localization and mapping tasks of robots in various complex environments, while traditional monocular vision algorithms are struggling to cope with weak texture and dynamic scenes. To solve these problems, this work presents an object detection and clustering assisted SLAM algorithm (OC-SLAM), which adopts a faster object detection algorithm to add semantic information to the image and conducts geometrical constraint on the dynamic keypoints in the prediction box to optimize the camera pose. It also uses RGB-D camera to perform dense point cloud reconstruction with the dynamic objects rejected, and facilitates European clustering of dense point clouds to jointly eliminate dynamic features combining with object detection algorithm. Experiments in the TUM dataset indicate that OC-SLAM enhances the localization accuracy of the SLAM system in the dynamic environments compared with original algorithm and it has shown impressive performance in the localizition and can build a more precise dense point cloud map in dynamic scenes.

Understanding the spatial distribution of schistosome infection is critical for tailoring preventive measures to control and eliminate schistosomiasis. This study used spatial analysis to determine risk factors that may impact Schistosoma japonicum infection and predict risk in Hunan and Jiangxi Provinces in the People’s Republic of China. The study employed survey data collected in Hunan and Jiangxi in 2016. Independent variable data were obtained from publicly available sources. Bayesian-based geostatistics was used to build models with covariate fixed effects and spatial random effects to identify factors associated with the spatial distribution of infection. Prevalence of schistosomiasis was higher in Hunan (12.8%) than Jiangxi (2.6%). Spatial distribution of schistosomiasis varied at pixel level (0.1 × 0.1 km), and was significantly associated with distance to nearest waterbody (km, β = −1.158; 95% credible interval [CrI]: −2.104, −0.116) in Hunan and temperature (°C, β = −4.359; 95% CrI: −9.641, −0.055) in Jiangxi. The spatial distribution of schistosomiasis in Hunan and Jiangxi varied substantially and was significantly associated with distance to nearest waterbody. Prevalence of schistosomiasis decreased with increasing distance to nearest waterbody in Hunan, indicating that schistosomiasis control should target individuals in close proximity to open water sources as they are at highest risk of infection.

This paper presents a versatile multi-sensor fusion method and decision-making algorithm for ambulatory and continuous patient monitoring purposes via a body sensor network (BSN). Gait features including spatio-temporal parameters, gait asymmetry, and regularity were identified and estimated from individual patients data collected from clinical trials. Hence, a continuous assessment and diagnosis of the improvement or the deterioration of the lower limb rehabilitation process is ensured. The experimental results from 10-m free walking trials indicated that the proposed method has a good consistency with the clinically used observational method. The gait assessment results were comparable with previous studies. Gait segmentation succeed even when the pace deviates significantly from the healthy subjects' reference value, which provides proof of objectivity and effectiveness of this preliminary research, namely, using wearable inertial measurement unit (IMUs) as an indicator to detect gait abnormality in subjects with neurological disorders. The hypothesis of gait quality-related clinical trials were designed and validated via both machine learning approach and feature layer data fusion. With further validations, the proposed inertial sensor-based gait assessment approach has the potential to be applied both routinely in clinical practice and for tele-health scenes such as fall detection of the elder at home.

For clusters of UAVs, the scale and density of the cluster determine its ability to solve the task. With the increasing density of aerial vehicles, effectively planning a reasonable flight path and avoiding conflicts among flight paths have become key problems for UAV clusters. The traditional control method is to detect potential conflicts through radar monitoring or location reporting in the air and to then change the flight path, including the height, heading, and speed, through manual instruction. To solve the problem of path conflicts for UAV clusters, a method for calculating the collision probabilities of UAVs is established under the constraints of mission space and the number of UAVs. In cluster flight mode, automatic tracking and prediction of UAV cluster tracks are implemented to avoid path conflicts in clusters. In addition, to address the inconsistency problem because of noise caused by the state information of multi UAV communication under a dynamic environment, a state estimation method is proposed based on the Kalman algorithm. To achieve aircraft track planning, cluster state prediction and collision probability are eventually calculated to avoid the clusters of formation UAVs conflicting on paths during flight. Finally, the simulation results verify the validity and effectiveness of the proposed method in multi UAV formation flight planning.

The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to meet the requirements of small size, low cost, and quick response that robots require. In order to address these issues, a novel electronic nose with active perception was designed and an ensemble learning method was proposed to distinguish the smell of different objects. An array of three MQ303 semiconductor gas sensors and an electrochemical sensor DART-2-Fe5 were used to construct the novel electronic nose, and the proposed ensemble learning method with four algorithms realized the active odor perception function. The experiment results verified that the accuracy of the active odor perception can reach more than 90%, even though it used 30% training data. The novel electronic nose with active perception based on the ensemble learning method can improve the efficiency and accuracy of odor data collection and olfactory perception.

Harvesting bio-mechanical energy is a promising route to powering wearable electronics, however design obstacles remain. Here the authors report on a triboelectric nanogenerator with optimized materials and design that can sustainably power an electronic watch and fitness tracker solely by human motion.

Self-charging systems based on the connection of a nanogenerator and an energy storage unit through a rectifier can have low energy storage efficiencies. Here, the authors design the charging cycle to maximize the energy storage efficiency of a triboelectric nanogenerator by introducing a motion-induced switch.

Abstract Evidence for the treatment of patients with mild-to-moderate chronic obstructive pulmonary disease (COPD) is limited. The efficacy of N-acetylcysteine (an antioxidant and mucolytic agent) for patients with mild-to-moderate COPD is uncertain. In this multicentre, randomised, double-blind, placebo-controlled trial, we randomly assigned 968 patients with mild-to-moderate COPD to treatment with N-acetylcysteine (600 mg, twice daily) or matched placebo for two years. Eligible participants were 40-80 years of age and had mild-to-moderate COPD (forced expiratory volume in 1 second [FEV1] to forced vital capacity ratio