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In modern precision medicine, it is an important research topic to predict cancer drug response. Due to incomplete chemical structures and complex gene features, however, it is an ongoing work to design efficient data-driven methods for predicting drug response. Moreover, since the clinical data cannot be easily obtained all at once, the data-driven methods may require relearning when new data are available, resulting in increased time consumption and cost. To address these issues, an incremental broad Transformer network (iBT-Net) is proposed for cancer drug response prediction. Different from the gene expression features learning from cancer cell lines, structural features are further extracted from drugs by Transformer. Broad learning system is then designed to integrate the learned gene features and structural features of drugs to predict the response. With the capability of incremental learning, the proposed method can further use new data to improve its prediction performance without retraining totally. Experiments and comparison studies demonstrate the effectiveness and superiority of iBT-Net under different experimental configurations and continuous data learning. [ABSTRACT FROM AUTHOR]

Spatial resolution is an important indicator that measures the quality of remote sensing images. Image texture has been successfully recovered by generative adversarial networks in deep learning super‐resolution (SR) methods. However, the existing methods are prone to image texture distortion. To solve the above problems, this paper proposes an improved generative adversarial network to enhance the super‐resolution reconstruction effect of medium‐ and low‐resolution (LR) remote sensing images. This network is based on the Super Resolution Generative Adversarial Network (SRGAN), which makes great improvements in the structure of the connection between the inside and outside of the residual block and the design of the model loss function. At the same time, the G1–G2–G3 structure between residuals effectively combines the image information of the three scales of small, medium and large. The model loss function can be designed based on the Charbonnier loss function to narrow the pixel distance between the reconstructed remote sensing image and the original image. Furthermore, targeted perceptual loss can direct the network to restore the texture details of the image according to the semantic category. The subjective and objective evaluation of the generated images and the ablation experiments prove that compared with SRGAN and other networks, our method can generate more realistic and reliable textures. Additionally, the indicators [peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), multiscale structural similarity (MS‐SSIM)] used to measure the quality of the reconstructed image obtain improved objective quantitative evaluation. [ABSTRACT FROM AUTHOR]

The core idea of active learning is to obtain higher model performance with less annotation cost. This paper proposes an independency‐enhancing adversarial active learning method. Independency‐enhancing adversarial active learning is different from the previous methods and pays more attention to sample independence. Specifically, it is believed that the informativeness of a group of samples is related to sample independence rather than the simple sum of the informativeness of each sample in the group. Therefore, an independent sample selection module based on hierarchical clustering is designed to ensure sample independence. An adversarial approach is used to learn the feature representation of a sample and use the predicted loss value to label the state of the sample. Finally, samples are selected according to the uncertainty of the samples, the diversity of the samples and the independence of the samples. The experimental results on four datasets (CIFAR‐100, Caltech‐101, Cityscapes and BDD100K) demonstrate the effectiveness and superiority of independency‐enhancing adversarial active learning. [ABSTRACT FROM AUTHOR]

Micro-nanobubbles (MNBs) have excellent stability and a long retention time in water; their properties vary greatly with different gas sources. In this study, the effects of MNBs with different gas sources, including CO 2 , N 2 , O 2 , and air, on growth and metabolism of the chemoautotrophic strain Sinomicrobium oceani WH-15, which fixes CO 2 via the Calvin-Benson-Bassham pathway, were evaluated. MNBs at low concentrations (5–20 %) could promote microbial growth with a maximum increase of 55 % (air MNBs) whereas MNBs at high concentrations (20–50 %) inhibited microbial growth with a maximum decrease of − 79 % (CO 2 MNBs). The influence of MNBs was achieved through indirect and direct roles. MNBs with different gas sources had different effects on physical and chemical factors (pH, dissolved oxygen, oxidation-reduction potential, and conductivity) of microbial culture medium. MNBs at high concentration led to an increased intracellular antioxidant capacity of microorganisms and a decrease in the intracellular reduced glutathione, which may be due to exogenous reactive oxygen species produced by MNBs. MNBs could bring reducing power to microorganisms and reduce their metabolic output of extracellular polysaccharides. Our results demonstrated that MNBs with different gas sources improved microbial CO 2 fixation through different mechanisms. [Display omitted] • Micro-nanobubbles (MNBs) improved CO 2 fixation by autotrophic microorganisms. • MNBs with different gas sources affected microorganisms through different mechanisms. • MNBs had both positive and negative effects on microorganisms. • Positive effects were predominant at low MNB concentrations, and vice versa. • Negative effects mainly came from reactive oxygen species produced by MNBs. [ABSTRACT FROM AUTHOR]

Movement intention inference for non-cooperative evasive targets in urban environments is difficult due to the lack of a priori knowledge of the possible target's movement intentions set. To solve this problem, this paper proposes a multi-level intention inference approach to construct and infer the movement intentions of a non-cooperative evasive target in urban environments. Firstly, to reasonably represent the possible movement intentions of the target, we decompose the target's movement intention into different regions at multiple levels according to the urban environment and the location of the target. At the same time, we divide the region at each level into several different sub-regions to represent the different movement intentions of the target at that level. Thus, the possible target's movement intentions are constructed as different regions at different levels where the target intends to go. Secondly, Convolutional Neural Network (CNN)-based intention inference models are developed, which can fuse the urban environment information and the observed target's trajectory to infer the target's movement intentions. Extensive simulation experiments results show that our proposed multi-level intention inference models can accurately and timely (with 79% accuracy and 84% timeliness) infer the region where the target intends to go when the possible destinations of the target are unknowable and maintain a robust inference performance when the target's behavior changes. [ABSTRACT FROM AUTHOR]

In order to solve the art decoration design of silicon oxide ceramic sculpture, a method for silicon oxide ceramic sculpture based on 3D printing technology is proposed. Firstly, the density of photosensitive resin premix before curing was measured by pycnometer method; the photosensitive resin was coated on tinplate and then put into a self-made UV curing box for curing. Secondly, the critical exposure and critical transmission depth of photosensitive resin without formula are calculated. Finally, the viscosity of ceramic slurry was measured by mixing S2 photosensitive resin formula with ceramic powder with different nano silica content. It is proved that the addition of nano silica powder can promote the decomposition of quartz glass and increase the sintering degree of the ceramic core. The primary sintering shrinkage of ceramic core gradually increases with the increase of nano silica content. In the length direction, the shrinkage increases from 3.33% to 5.61%; in the width direction, the shrinkage increases from 2.98% to 4.74%; and in the height direction, the shrinkage increases from 3.18% to 5.00%. [ABSTRACT FROM AUTHOR]

The person re-identification (ReID) method in a single-domain achieves appealing performance, but its reliance on label information greatly limits its extensibility. Therefore, the unsupervised cross-domain ReID method has received extensive attention. Its purpose is to optimize the model by using the labelled source domain and the unlabelled target domain and finally make the model well generalized in the target domain. We propose an unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC). Specifically, the measurement method used by MSC comprehensively considers the similarity between clusters, the number of samples in a cluster, and the combined similarity within a cluster. Different from the method based on triple loss, GDC can separate the distance distribution of positive and negative sample pairs in a global scope. In addition, considering that model performance is very sensitive to probability parameters when source domain memory is reconsolidated, we designed a dynamic memory reconsolidation (DMR) method to reduce the influence of parameters on performance. Extensive experiments on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of MSC-GDC over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Active learning aims to select the most valuable unlabelled samples for annotation. In this paper, we propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which selects samples based on their distribution, uncertainty, and redundancy. RRAAL includes a representation generator, state discriminator, and redundancy removal module (RRM). The purpose of the representation generator is to learn the feature representation of a sample, and the state discriminator predicts the state of the feature vector after concatenation. We added a sample discriminator to the representation generator to improve the representation learning ability of the generator and designed a norm online uncertainty indicator (Norm-OUI) to provide a more accurate uncertainty score for the state discriminator. In addition, we designed an RRM based on a greedy algorithm to reduce the number of redundant samples in the labelled pool. The experimental results on four datasets show that the state discriminator, Norm-OUI, and RRM can improve the performance of RRAAL, and RRAAL outperforms the previous state-of-the-art active learning methods. [ABSTRACT FROM AUTHOR]

Simple hydrothermal method and wet chemical method were both used to synthesize a novel SFO/BMO heterojunction, which was applied to the degradation of tetracycline (TC) in wastewater. Compared with the pure catalyst, the composite heterojunction catalyst can significantly improve the photodegradation efficiency. Under simulated sunlight, the degradation rate of TC by the composite with 30 wt% SFO mass could reach 77%, which was 20.8% higher than that of pure BMO and 50.4% higher than that of pure SFO. This is attributed to the low recombination rate of photogenerated electron-holes and the reduction in the band gap width caused by the synergy of BMO and SFO. At the same time, this phenomenon was confirmed by a series of characterizations, including XRD, SEM, XPS, FT-IR and UV–Vis, as well as analysis of the catalytic mechanism. The study provides a theoretical basis for the discovery of diverse photocatalysts. The heterojunction Bi2MoO6/SrFeO3-x is formed to reduce the recombination rate of photogenerated electron-holes, thereby improving the degradation efficiency of the original Bi2MoO6 to tetracycline wastewater. [ABSTRACT FROM AUTHOR]

The current unsupervised domain adaptation person re-identification (re-ID) method aims to solve the domain shift problem and applies prior knowledge learned from labelled data in the source domain to unlabelled data in the target domain for person re-ID. At present, the unsupervised domain adaptation person re-ID method based on pseudolabels has obtained state-of-the-art performance. This method obtains pseudolabels via a clustering algorithm and uses these pseudolabels to optimize a CNN model. Although it achieves optimal performance, the model cannot be further optimized due to the existence of noisy labels in the clustering process. In this paper, we propose a stable median centre clustering (SMCC) for the unsupervised domain adaptation person re-ID method. SMCC adaptively mines credible samples for optimization purposes and reduces the impact of label noise and outliers on training to improve the performance of the resulting model. In particular, we use the intracluster distance confidence measure of the sample and its K-reciprocal nearest neighbour cluster proportion in the clustering process to select credible samples and assign different weights according to the intracluster sample distance confidence of samples to measure the distances between different clusters, thereby making the clustering results more robust. The experiments show that our SMCC method can select credible and stable samples for training and improve performance of the unsupervised domain adaptation model. Our code is available at https://github.com/sunburst792/SMCC-method/tree/master. [ABSTRACT FROM AUTHOR]

In this paper, the back-calculated induction times of low-rank coal particles on the rising bubble with mobile surfaces were back-calculated from the micro-flotation rate constants. The back-calculated induction times slightly increased with the flotation recovery increase or the surfactant concentration decrease. It is because the drainage time accounting for most of the induction time is affected by the force exerted on the wetting film. Moreover, the force exerted on the wetting film is characterized by the Reynolds number. Furthermore, the Reynolds number increased with increasing bubble rising velocity due to an increase in bubble size as a result of decreasing surfactant concentration. Therefore, the back-calculated induction times could reflect the difference in the flotation recoveries at the same surfactant concentration. Meanwhile, it indicated that the hydrodynamic condition in the flotation process had a significant effect on the back-calculated results of induction times. From this investigation, it can be speculated that the back-calculated induction time of particles sliding on the rising bubble with mobile bubble surfaces is greatly influenced by the Reynolds number. [ABSTRACT FROM AUTHOR]

In this article, a minimum-fuel powered-descent optimal guidance algorithm that incorporates obstacle avoidance is presented. The approach is based on convex optimization that includes the obstacles using nonconvex functions. To convert these nonconvex obstacle constraints to convex ones, a simple linearization procedure is employed. It is proved that the optimal solution of the convex relaxation problem is also optimal for the original nonconvex one. The sensitivity of the multiobstacle avoidance method to the relaxation factor and its effectiveness under different conditions are also investigated through simulations. [ABSTRACT FROM AUTHOR]

With the continuous development of deep learning, the performance of the intelligent diagnosis system for ocular fundus diseases has been significantly improved, but during the system training process, problems like lack of fundus samples and uneven sample distribution (the number of disease samples is much smaller than the number of normal samples) have become increasingly prominent. In view of the previous issues, this paper proposes a method for generating fundus images based on "Combined GAN" (Com-GAN), which can generate both normal fundus images and fundus images with hard exudates, so that the sample distribution can be more even, while the fundus data are expanded. First, this paper uses existing images to train a Com-GAN, which consists of two subnetworks: im-WGAN and im-CGAN; then, it uses the trained model to generate fundus images, then performs qualitative and quantitative evaluation on the generated images, and adds the images to the original image set to expand the datasets; finally, based on this expanded training set, it trains the hard exudate detection system. The expanded datasets effectively improve the generalization ability of the system on the public datasets DIARETDB1 and e-ophtha EX, thereby verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Short-term load forecasting has been playing an increasingly important role in electric power systems. Effective forecasting of the future electricity demand, however, is difficult in view of the complicated effects on load by a variety of factors. A hybrid method based support vector regression (SVR) with meteorological factors and electricity price is proposed to address such problem. First, the input used in this paper, are specific ratio value combinations of each characteristic parameter affecting the hourly electricity load. Second, SVR is used to analyze and develop a load forecasting model. Third, an improved adaptive genetic algorithm (IAGA) is utilized to optimize the specific ratio value combinations of each characteristic parameter, penalty factor C and Gaussian kernel function σ to accurately establish a forecasting model. The experimental results show that the proposed method can obtain better forecasting performance in comparison with other standard and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to verify the correctness and feasibility of simultaneous localization and mapping (SLAM) algorithm based on red-green-blue depth (RGB-D) camera in high precision navigation and localization of celestial exploration rover. Design/methodology/approach: First, a positioning algorithm based on depth camera is proposed. Second, the realization method is described from the five aspects of feature detection method, feature point matching, point cloud mapping, motion estimation and high precision optimization. Feature detection: taking the precision, real-time and motion basics as the comprehensive consideration, the ORB (oriented FAST and rotated BRIEF) features extraction method is adopted; feature point matching: solves the similarity measure of the feature descriptor vector and how to remove the mismatch point; point cloud mapping: the two-dimensional information on RGB and the depth information on D corresponding; motion estimation: the iterative closest point algorithm is used to solve point set registration; and high precision optimization: optimized by using the graph optimization method. Findings: The proposed high-precision SLAM algorithm is very effective for solving high precision navigation and positioning of celestial exploration rover. Research limitations/implications: In this paper, the simulation validation is based on an open source data set for testing; the physical verification is based on the existing unmanned vehicle platform to simulate the celestial exploration rover. Practical implications: This paper presents a RGB-D camera-based navigation algorithm, which can be obtained by simulation experiment and physical verification. The real-time and accuracy of the algorithm are well behaved and have strong applicability, which can support the tests and experiments on hardware platform and have a better environmental adaptability. Originality/value: The proposed SLAM algorithm can deal with the high precision navigation and positioning of celestial exploration rover effectively. Taking into account the current wide application prospect of computer vision, the method in this paper can provide a study foundation for the deep space probe. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to verify the feasibility of lunar capture braking through three methods based on particle swarm optimization (PSO) and compare the advantages and disadvantages of the three strategies by analyzing the results of the simulation. Design/methodology/approach: The paper proposes three methods to verify capture braking based on PSO. The constraints of the method are the final lunar orbit eccentricity and the height of the final orbit around the Moon. At the same time, fuel consumption is used as a performance indicator. Then, the PSO algorithm is used to optimize the track of the capture process and simulate the entire capture braking process. Findings: The three proposed braking strategies under the framework of PSO algorithm are very effective for solving the problem of lunar capture braking. The simulation results show that the orbit in the opposite direction of the trajectory has the most serious attenuation at perilune, and it should consume the least amount of fuel in theoretical analysis. The methods based on the fixed thrust direction braking and thrust uniform rotation braking can better ensure the final perilune control accuracy and fuel consumption. As for practice, the fixed thrust direction braking method is better realized among the three strategies. Research limitations/implications: The process of lunar capture is a complicated process, involving effective coordination between multiple subsystems. In this article, the main focus is on the correctness of the algorithm, and a simplified dynamic model is adopted. At the same time, because the capture time is short, the lunar curvature can be omitted. Furthermore, to better compare the pros and cons of different braking modes, some influence factors and perturbative forces are not considered, such as the Earth's flatness, light pressure and system noise and errors. Practical implications: This paper presents three braking strategies that can satisfy all the constraints well and optimize the fuel consumption to make the lunar capture more effective. The results of comparative analysis demonstrate that the three strategies have their own superiority, and the fixed thrust direction braking is beneficial to engineering realization and has certain engineering practicability, which can also provide reference for lunar exploration orbit design. Originality/value: The proposed capture braking strategies based on PSO enable effective capture of the lunar module. During the lunar exploration, the capture braking phase determines whether the mission will be successful or not, and it is essential to control fuel consumption on the premise of accuracy. The three methods in this paper can be used to provide a study reference for the optimization of lunar capture braking. [ABSTRACT FROM AUTHOR]

Abstract A novel Z -scheme g-C 3 N 4 /LaCoO 3 photocatalyst was prepared by a facial mixing-heating and precipitation method. Doping g-C 3 N 4 on LaCoO 3 , the heterojunction exhibits excellent photocatalytic performance and stability compared with bare LaCoO 3 in the degradation of tetracycline hydrochloride (TC). The enhanced photocatalytic activity are attributed to the high separation efficiency of electron-hole pairs by the formation of Z -scheme heterojunction structure. The photocatalytic degradation rate of TC is up to 92% and the kinetic rate constant (k) of the optimal g-C 3 N 4 /LaCoO 3 composite is about 14.5 times and 2.3 times of that of bare LaCoO 3 and g-C 3 N 4 , respectively. Graphical abstract Unlabelled Image Highlights • A novel Z-scheme g-C 3 N 4 /LaCoO 3 heterojunction photocatalyst was prepared. • High efficiency in tetracycline hydrochloride photocatalytic degradation. • Higher degradation kinetic constant (14.5 times than pure LaCoO 3) of the composite. • The photocatalytic mechanism of Z-scheme g-C 3 N 4 /LaCoO 3 heterojunction was proposed. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. The typical symptomatology of PD includes motor symptoms; however, a range of nonmotor symptoms, such as intestinal issues, usually occur before the motor symptoms. Various microorganisms inhabiting the gastrointestinal tract can profoundly influence the physiopathology of the central nervous system through neurological, endocrine, and immune system pathways involved in the microbiota–gut–brain axis. In addition, extensive evidence suggests that the gut microbiota is strongly associated with PD. This review summarizes the latest findings on microbial changes in PD and their clinical relevance, describes the underlying mechanisms through which intestinal bacteria may mediate PD, and discusses the correlations between gut microbes and anti-PD drugs. In addition, this review outlines the status of research on microbial therapies for PD and the future directions of PD–gut microbiota research. [ABSTRACT FROM AUTHOR]

In this paper, terminal diameter and expansion velocity of the three-phase contact (TPC) line on the low-rank coal surfaces with different surface roughness were investigated. To eliminate the heterogeneity effect on the hydrophilic surfaces of low-rank coal particles, experimental samples with low ash content were adopted. Moreover, the analysis result of XPS indicated that there were few hydrophilic mineral particles on the low-rank coal surface. Furthermore, from the analyses of SEM and AFM measurements there are many crevices (pillars, protrusions) of different sizes (height and width) in different local areas of low-rank coal surfaces. Therefore, the three-phase contact lines on the low-rank coal surfaces presented different wetting behaviors. It demonstrated that the wetting film diameter became smaller and the expansion velocity of the three-phase contact line was faster while the surface roughness of low-rank coal samples increased. [ABSTRACT FROM AUTHOR]

Background: Spinocerebellar ataxia 17 (SCA17) belongs to the family of neurodegenerative diseases caused by polyglutamine (polyQ) expansion. In SCA17, polyQ expansion occurs in the TATA box binding protein (TBP) and leads to the misfolding of TBP and the preferential degeneration in the cerebellar Purkinje neurons. Currently there is no effective treatment for SCA17. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is a recently identified neurotrophic factor, and increasing MANF expression ameliorated SCA17 neuropathology in TBP-105Q knock-in (KI) mouse model, indicating that MANF could be a therapeutic target for treating SCA17. Methods: In this study, we screened a collection of 2000 FDA-approved chemicals using a stable cell line expressing luciferase reporter, which is driven by MANF promoter. We identified several potential candidates that can induce the expression of MANF. Of these inducers, piperine is an agent that potently induces the luciferase expression or MANF expression. Results: Addition of piperine in both cellular and mouse models of SCA17 alleviated toxicity caused by mutant TBP. Although mutant TBP is primarily localized in the nuclei, the polyQ expansion in TBP is able to induce ER stress, suggesting that nuclear misfolded proteins can also elicit ER stress as cytoplasmic misfolded proteins do. Moreover, piperine plays its protective role by reducing toxicity caused by the ER stress. Conclusion: Our study established piperine as a MANF-based therapeutic agent for ER stress-related neuropathology in SCA17. [ABSTRACT FROM AUTHOR]

Purpose In the future, large space truss structures will be likely to require on-orbit assembly. One of the several proposed methods includes cooperative assembly performed by pressure-suited astronauts during extravehicular activity (EVA) and space robots. An intelligent planning method was presented to generate optimal assembly tasks.Design/methodology/approach Firstly, the inherent hierarchical nature of truss structures allows assembly sequences to be considered from strut level and structural volume element (SVE) level. Then, a serial assembly strategy in human-robot environment was applied. Furthermore, a two-level planning algorithm was presented. At the first-level planning, one ant colony algorithm for assembly sequence planning was improved to adopt assembly direction and time as heuristic information and did not consider assembly tasks. And, at the second-level planning, another novel colony algorithm for assembly task planning mainly considered results of the first-level planning, human-robot interactive information, serial assembly strategy and assembly task distributions.Findings The proposed two-level planning algorithm is very effective for solving the human and robot cooperative assembly of large space truss structures.Research limitations/implications In this paper, the case study is based on the following assumptions: each tetrahedron is assembled by two astronauts; each pentahedron is assembled by three astronauts.Practical implications A case illustrates the results of the two-level planning. From this case study, because of geometrical symmetry nature of large space truss structures, the optimal assembly sequences are not only one.Originality/value The improved ant colony algorithm can deal with the assembly sequence and task planning in human-robot environment more effectively. [ABSTRACT FROM AUTHOR]

Shock train hysteresis was investigated through wind tunnel experiments and numerical simulations of the internal flow in a supersonic isolator under background waves generated by a 14° wedge. An adjustable Laval nozzle was developed so that the throat area could be continuously changed and the incoming Mach number was varied between 1.8 and 2.4. Under the continuous decrease of the incoming Mach number, the impingement points of the background waves are observed to move uniformly upstream and the pressure of the relaxing boundary layer that is not swept by the impingement point decreases linearly. The pressure in the region that is swept by the impingement point first undergoes an adverse pressure gradient that results from the presence of the background wave, and then undergoes the favorable pressure gradient in the relaxing boundary layer. Hysteresis occurs when the shock train moves near the impingement point of the background wave, which is an inherent property of a bistable system. The triggering factor of the bistable system is that the adverse pressure gradient region that results from the background wave/boundary layer interaction cannot resist the pressure rise generated by the shock train leading edge, and the shock train can only stabilize upstream or downstream of the impingement point of the background wave. The maintenance factor of the bistable system is that the two positions of the shock train upstream and downstream of the impingement point of the background wave can match the same backpressure. • Experiments with continuous incoming Mach number variation are conducted. • Evolution of background wave under continuous incoming Mach number variation is discussed. • Shock train hysteresis under continuous incoming Mach number variation is found. • Mechanism of shock train hysteresis under the interaction of background wave is revealed. [ABSTRACT FROM AUTHOR]

TiO2, molecularly imprinted TiO2(TiO2/OTC) and a novel Si doped molecularly imprinted TiO2(TiO2/SiO2/OTC) nano photocatalytic materials were prepared by liquid phase deposition method. The products were characterized by XRD and SEM. The photocatalytic activities of the samples were evaluated by the photocatalytic degradation of oxytetracycline wastewater under a xenon lamp irradiation. The results showed that TiO2/SiO2/OTC and TiO2/OTC could improve both the photocatalytic activity and the molecular recognition ability, while the photocatalytic activities of TiO2/SiO2/OTC sample was more effective than TiO2/OTC sample with 5 times reuse. The photocatalytic activity of TiO2/SiO2/OTC photocatalyst sample had a greater improvement and its photocatalytic degradation of oxytetracycline wastewater reached 80.79% in 120 minute reaction. [ABSTRACT FROM PUBLISHER]

Mutations in PTEN-induced kinase 1 (PINK1) gene cause PARK6 familial Parkinsonism, and loss of the stability of PINK1 may also contribute to sporadic Parkinson's disease (PD). Degradation of PINK1 occurs predominantly through the ubiquitin proteasome system (UPS), however, to date, few of the proteins have been found to regulate the degradation of PINK1. Using the yeast two-hybrid system and pull-down methods, we identified bcl-2-associated athanogene 5 (BAG5), a BAG family member, directly interacted with PINK1. We showed that BAG5 stabilized PINK1 by decreasing the ubiquitination of PINK1. Interestingly, BAG5 rescued MPP+- and rotenone-induced mitochondria dysfunction by up-regulating PINK1 in vitro. In PINK1-null mice and MPTP-treated mice, BAG5 significantly increased in the substantia nigra pars compacta (SNpc) although PINK1 was decreased. Our findings indicated that BAG5, as a key protein to stabilize PINK1, is a promising therapeutic tool for preventing mitochondrial dysfunction following oxidative stress. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to design a solar sail heliocentric transfer orbit which can meet the requirements of control system and capture orbit, and to provide the change of angles for attitude control system. Design/methodology/approach – Aiming at the problem of solar sail heliocentric transfer orbits design, this paper addresses the derivation of analytical optimal control law. The control laws can realize the combination of the control of each orbit element, but they can only give local optimal solution to meet the practical needs of mission. In order to solve this problem and meet the capture orbit and the attitude control system requirements, the modified genetic algorithm based on the analytical control law is introduced. Findings – The algorithm addressed by this paper includes results closer to the global optimization, and also can meet the engineering constraints. Practical implications – The analytical optimal control law can be applied to the future onboard sail control systems. The blending optimal algorithm is demonstrated to be suitable as a method of preliminary design for solar sail deep space exploration mission. Originality/value – A blending optimal algorithm combining the analytical control law and genetic algorithm is proposed; the algorithm can search for global optimization based on the local optimal results of analytical control law. [ABSTRACT FROM AUTHOR]

As a group of typical endocrine disrupters, phthalates are simultaneously present in a variety of environmental media and enter human body through multiple exposure pathways. In this study, field monitoring data were used to characterize the skin‐air (Klg), dust‐air (Kd), and PM2.5‐air (Kp) partition coefficients of DiBP, DnBP, and DEHP. The median values of log(Klg) in the summer and winter were 7.654 and 7.932, 7.265 and 7.902, 9.419 and 9.015 for DiBP, DnBP, and DEHP, respectively, and Klg was significantly higher in the winter. The median Kd (m3/mg) in the summer (0.036–0.151 for DiBP, 0.021–0.036 for DnBP and 1.479–4.069 for DEHP) were significantly higher than the counterparts in the winter (0.027–0.065 for DiBP, 0.022–0.245 for DnBP, and 0.140–3.250 for DEHP). In addition, Kd was associated with material of surface and residence time of dust. The Kp values (m3/μg) of DiBP, DnBP, and DEHP in the summer (0.053, 0.015, and 0.021) were also significantly higher than the counterparts in the winter (0.011, 0.004, and 0.025). The partition of phthalates was influenced by built environment, such as temperature, humidity, ventilation, indoor chemistry, smoking, and building age. Except Klg, there was substantial discrepancy between the estimates of K with empirical equations and the values of K based on field monitoring data in our study. [ABSTRACT FROM AUTHOR]

Abstract: The PLA2G6 gene encodes a group VIA calcium-independent phospholipase A2, and has been suggested as the causative gene for autosomal recessive dystonia-parkinsonism. We conducted a case–control study using 531 mainland Chinese Parkinson''s disease (PD) patients and 561 healthy controls, and genotyped 4 tag single nucleotide polymorphisms (SNPs) of the PLA2G6 gene: rs4375, rs2267369, rs132985, and rs2284063. Logistic regression analysis revealed no difference in genotype or allele frequencies for any of the SNPs between the sporadic PD group and control group. Similarly, comparison of SNPs in patients with either early-onset (EOPD, ≤50 years) or late-onset (>50 years) PD revealed no statistical differences from controls. We detected no significant association of the 4 SNPs with PD at the genotypic level, after adjustment for age. The rs132985 genotype frequency showed a difference in male patients but not in female patients, but the P value did not survive Bonferroni correction (Pcorr = 0.068). We found that the rs132985 A–rs2284063 C haplotype is marginally associated with increased risk of developing PD (P = 0.048) after 10,000 permutations. These findings suggest that PLA2G6 is not a susceptibility gene for PD in our population. However, a broader examination and a replication of this study in other populations are needed. [Copyright &y& Elsevier]

Abstract: A mixture of Ti/Si/TiC/diamond powders was employed to fabricate the Ti3SiC2 bonded diamond composite using the spark plasma sintering-reactive synthesis method. The addition of diamond does not inhibit the synthesis of Ti3SiC2 in the sintered product. In the matrix Ti3SiC2 grains developed lamellar morphology with an average length size of 5–10μm. Ti3SiC2 matrix displays good pullout strength with diamond, and the Ti3SiC2 bonded diamond material exhibits good wear resistance. [Copyright &y& Elsevier]

Abstract: Early-onset familial Alzheimer''s disease (EOFAD) has been associated with mutations in three genes, of which presenilin 1 (PSEN1) mutations are the most frequent. Here we report a novel PSEN1 mutation in a Chinese family with autosomal dominant Alzheimer''s disease with an onset age in the early 40s. Molecular genetic analysis showed a 507-509delATC mutation at codon 169, leading to the deletion of serine in residue 169 (Ser169del). The amnestic presentation and absence of other features contrast with the other two mutations at codon 169 which have been associated with myoclonic jerks and seizures. [Copyright &y& Elsevier]

Abstract: Bathing wastewater was treated by a pilot-scale submerged membrane bioreactor (MBR) for more than 60 days. The results showed that the removal rates of main pollutants of wastewater such as CODCr, LAS, –N and total nitrogen (TN) were above 93%, 99%, 99%, and 90%, respectively. The results of denaturing gel gradient electrophoresis (DGGE) and fluorescent in situ hybridization (FISH) indicated that the bacteria were stable. The abundant nitrobacteria intercepted by the membrane led to the high removal rate of ammonia and TN. FISH and 16S rDNA gene sequence analysis revealed that some specific phylogenetic group of bacteria, the Pseudomonas sp. Ochrobactrum anthropi sp. and Enterobacter sp. probably played a major role in the development of the mature biofilms, which led to the severe irreversible membrane biofouling. [Copyright &y& Elsevier]

This paper presents a novel ensemble method of forecasting the residential electricity demand. Firstly, the time-series of the original input variables is filtered by unscented kalman filter (UKF), and then the incremental percentages of current and previous sample points are taken as new input features of the proposed method. Secondly, an improved coupled generative adversarial stacked auto-encoder (ICoGASA) consisting of three generative adversarial networks (GAN) is developed to generate more similar errors in weather forecast and lifestyles of different residents, with less noise. All of the three GANs are composed of two deep belief networks (DBNs), which serve as generator and discriminator, respectively. The three generators of GANs are used to simulate the samples with positive error, negative error and mixed error, respectively. Then the output of the three discriminators is integrated by memristor array (MA), and the integrated output of each ICoGASA are integrated by self-organizing map (SOM). Thirdly, the input weights of SOM are optimized by MA and a new weight updated strategy (WUS). Compared with other state-of-the-art ensemble methods, the scopes of the root mean square error (RMSE) are reduced by [8.295, 16.221] %, [15.507, 28.066] %, [20.494, 36.969] %, respectively. • Filtering input data by unscented kalman filter. • Sample simulation according to an improved deep learning model. • Integrated strategy by self-organizing map and memristor array. • Weight updated strategy based on chaotic map sequence and memristor array. [ABSTRACT FROM AUTHOR]

In this paper, a novel ensemble method is proposed to forecast the hourly consumption of residential electricity. Firstly, variational mode decomposition (VMD) is applied to decompose weather conditions (relative humidity and temperature, etc.), residential building data (manually operated appliances relevant to residents' lifestyle, dishwasher, heating heat-pump, and television, etc.), and electricity price into several band-limited intrinsic mode functions (BLIMFs). Then the incremental kernel principal component analysis (IKPCA) is applied to extract the incremental kernel principal components (IKPCs) from the BLIMFs. Next, IKPCs are encoded as images by the Gramian Angular Fields (GAFs). Secondly, a novel ensemble method based on conditional generative adversarial networks (CGANs), is applied to simulate the variability in people's electrical behavior and weather forecast errors. Moreover, the elitist search strategy of the multi-population genetic algorithm (MPGA) is introduced to realize the communication among each sub-CGAN. And then all sub-CGANs are integrated by the Huffman coding (HC). Thirdly, an improved dragonfly algorithm (IDA) is developed to optimize the weights of HC. The experimental results show that the forecasting results of the proposed ensemble method are obviously better than those of other standard and state-of-the-art methods tested in this paper. • Encoding original time series into images. • Removing redundant and excessive information. • Sample simulation according to input data. • Integrated strategy by Huffman coding. • Elitist search strategy of the multi-population genetic algorithm. [ABSTRACT FROM AUTHOR]

With the increasing research on the aqueous two-phase systems (ATPS), the ATPS containing ionic liquid (IL) and salt have been widely used in the extraction and purification process. In this work, the partitioning of 2-chlorophenol in novel ATPS composed of n -butylpyridinium trifluoromethanesulfonate and salts (ammonium sulfate, sodium dihydrogen phosphate) is evaluated. Firstly, the cloud point titration method was used to determine the binodal curves data experimentally at four temperatures and atmospheric pressure. And Merchuk equation was used to fit the binodal curves. Then, the gravimetric method was used to initially determine the tie-lines data and the concentration of all the ions in the top and bottom phases were determined by ion chromatography method to study the ion exchange in the ATPS. The reliability of tie-lines was evaluated by Othmer-Tobias and Bancroft equations. Finally, the influence of types of salts and temperature on the phase diagrams were studied carefully. Besides, in order to analyze the application of ATPS in the extraction, the partition coefficient of 2-chlorophenol was calculated at the same total composition while at different temperature. The ATPS formed by n -butylpyridinium trifluoromethanesulfonate, ammonium sulfate, and water at 298.15 K was found to have the maximum amount of partition coefficient and extraction efficiency. [ABSTRACT FROM AUTHOR]

The estimation of cement compressive strength is of great significance in the quality inspections, technological designs, and engineering applications for cement. Compared to destructive methods, the nondestructive estimation approaches save the cost in the manpower and material. However, the existing nondestructive methods have the large error because the used influence factors are difficult to control and the used two-dimensional microstructure images can not reflect the specific spatial structure of the entire cement. In this paper, a novel model is proposed to estimate the cement compressive strength using three-dimensional microstructure images and deep belief network. To reduce the computation consumption induced by three-dimensional images with abundant information, this method extracts image features that reflect the cement hydration state to estimate cement compressive strength. Deep belief network is applied to build the estimation model. Its unique training pattern and flexibility of parameters improve the ability to learn nonlinear relationships between microstructure images and cement compressive strength. Furthermore, the training processes are accelerated on the graphics processing units. The experimental results prove that the proposed method estimates cement compressive strength nondestructively and improves the efficiency. [ABSTRACT FROM AUTHOR]

Accurate classification and identification of the detected terrain is the basis for the long-distance patrol mission of the planetary rover. But terrain measurement based on vision and radar is subject to conditions such as light changes and dust storms. In this paper, under the premise of not increasing the sensor load of the existing rover, a terrain classification and recognition method based on vibration is proposed. Firstly, the time-frequency domain transformation of vibration information is realized by fast Fourier transform (FFT), and the characteristic representation of vibration information is given. Secondly, a deep neural network based on multi-layer perception is designed to realize classification of different terrains. Finally, combined with the Jackal unmanned vehicle platform, the XQ unmanned vehicle platform, and the vibration sensor, the terrain classification comparison test based on five different terrains was completed. The results show that the proposed algorithm has higher classification accuracy, and different platforms and running speeds have certain influence on the terrain classification at the same time, which provides support for subsequent practical applications. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a diabetic complication affecting the eyes, which is the main cause of blindness in young and middle-aged people. In order to speed up the diagnosis of DR, a mass of deep learning methods have been used for the detection of this disease, but they failed to attain excellent results due to unbalanced training data, i.e., the lack of DR fundus images. To address the problem of data imbalance, this paper proposes a method dubbed retinal fundus images generative adversarial networks (RF-GANs), which is based on generative adversarial network, to synthesize retinal fundus images. RF-GANs is composed of two generation models, RF-GAN1 and RF-GAN2. Firstly, RF-GAN1 is employed to translate retinal fundus images from source domain (the domain of semantic segmentation datasets) to target domain (the domain of EyePACS dataset connected to Kaggle (EyePACS)). Then, we train the semantic segmentation models with the translated images, and employ the trained models to extract the structural and lesion masks (hereafter, we refer to it as Masks) of EyePACS. Finally, we employ RF-GAN2 to synthesize retinal fundus images using the Masks and DR grading labels. This paper verifies the effectiveness of the method: RF-GAN1 can narrow down the domain gap between different datasets to improve the performance of the segmentation models. RF-GAN2 can synthesize realistic retinal fundus images. Adopting the synthesized images for data augmentation, the accuracy and quadratic weighted kappa of the state-of-the-art DR grading model on the testing set of EyePACS increase by 1.53% and 1.70%, respectively. [ABSTRACT FROM AUTHOR]

• AD onset and progression were accompanied with an unappreciated serum autoantibody response. • Seven candidate serum AD-specific autoantibodies were identified. • A combinatory signature of the seven autoantibodies showed high accuracy in distinguishing AD. • The autoantibody alternation provide an opportunity to identify patients with MCI. Autoimmunity plays a key role in the pathogenesis of Alzheimer's disease (AD). However, whether autoantibodies in peripheral blood can be used as biomarkers for AD has been elusive. Serum samples were obtained from 1,686 participants, including 767 with AD, 146 with mild cognitive impairment (MCI), 255 with other neurodegenerative diseases, and 518 healthy controls. Specific autoantibodies were measured using a custom-made immunoassay. Multivariate support vector machine models were employed to investigate the correlation between serum autoantibody levels and disease states. As a result, seven candidate AD-specific autoantibodies were identified, including MAPT, DNAJC8, KDM4D, SERF1A, CDKN1A, AGER, and ASXL1. A classification model with high accuracy (area under the curve (AUC) = 0.94) was established. Importantly, these autoantibodies could distinguish AD from other neurodegenerative diseases and out-performed amyloid and tau protein concentrations in cerebrospinal fluid in predicting cognitive decline (P < 0.001). This study indicated that AD onset and progression are possibly accompanied by an unappreciated serum autoantibody response. Therefore, future studies could optimize its application as a convenient biomarker for the early detection of AD. [ABSTRACT FROM AUTHOR]

Introduction: Recently, chloride channel CLIC‐like 1 (CLCC1) was reported to be a novel ALS‐related gene. We aimed to screen CLCC1 variants in our ALS cohort and further explore the genotype–phenotype correlation of CLCC1‐related ALS. Methods: We screened rare damaging variants in CLCC1 from our cohorts of 1005 ALS patients and 1224 healthy controls with whole‐exome sequencing in Central South China. Fisher's exact test was conducted for association analysis at the entire gene level and single variant level. Results: In total, four heterozygous missense variants in CLCC1 were identified from four unrelated sporadic ALS patients and predicted to be putative pathogenic by in silico tools and protein model prediction, accounting for 0.40% of all patients (4/1005). The four variants were c.A275C (p.Q92P), c.G1139A (p.R380K), c.C1244T (p.T415M), and c.G1328A (p.R443Q), respectively, which had not been reported in ALS patients previously. Three of four variants were located in exon 10. Patients harboring CLCC1 variants seemed to share a group of similar clinical features, including earlier age at onset, rapid progression, spinal onset, and vulnerable cognitive status. Statistically, we did not find CLCC1 to be associated with the risk of ALS at the entire gene level or single variant level. Conclusion: Our findings further expanded the genetic and clinical spectrum of CLCC1‐related ALS and provided more genetic evidence for anion channel involvement in the pathogenesis of ALS, but further investigations are needed to verify our findings. [ABSTRACT FROM AUTHOR]

Accurate perception of the detected terrain is a precondition for the planetary rover to perform its own mission. However, terrain measurement based on vision and LIDAR is subject to environmental changes such as strong illumination and dust storms. In this paper, considering the influence of uncertainty in the detection process, a vibration/gyro coupled terrain estimation method based on multipoint ranging information is proposed. The terrain update model is derived by analyzing the measurement uncertainty and motion uncertainty. Combined with Clearpath Jackal unmanned vehicle—the terrain mapping accuracy test based on ROS (Robot Operating System) simulation environment—indoor Optitrack auxiliary environment and outdoor soil environment was completed. The results show that the proposed algorithm has high reconstruction ability for a given scale terrain. The reconstruction accuracy in the above test environments is within 1 cm, 2 cm, and 6 cm, respectively. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) signals, has become a popular research topic and attracted wide attention. However, how to extract effective features from EEG signals and accurately recognize them by classifiers have also become an increasingly important task. Therefore, in this paper, we propose an emotion recognition method of EEG signals based on the ensemble learning method, AdaBoost. First, we consider the time domain, time-frequency domain, and nonlinear features related to emotion, extract them from the preprocessed EEG signals, and fuse the features into an eigenvector matrix. Then, the linear discriminant analysis feature selection method is used to reduce the dimensionality of the features. Next, we use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. Finally, the proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. The proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension. Compared with other existing methods, the performance of the proposed method is significantly improved. [ABSTRACT FROM AUTHOR]

In this work, a novel in-situ synthetic TA-APTES NPs multifunctional imprinted membrane was fabricated by tanning-inspired coating for selective adsorption of tetrabromobisphenol a (TBBPA) and oil-water emulsion separation. Tannic acid (TA) from natural plants can be cross-linked with 3-aminopropyltriethoxysilane (APTES) by a simple green method to form nanosphere structures with secondary reaction sites. With TA-APTES coating, the membrane can be quickly converted from hydrophobic to super-hydrophilic. TEOS is not only used as a cross-linking agent in imprinting process，but also can be used for TA-APTES hybridization on the membrane surface to introduce stable silicone hydroxyl groups to the membrane surface. The modified membranes showed good hydrophilicity with a water flux of 27,272Lm−2 h−1 at 0.1Mpa, which was 18 times higher than that of pure PVDF membranes. In addition, the separation efficiency of four different types of oil-water emulsions (n-hexane, petroleum ether, dichloromethane and kerosene) reached 99.6%, 98.3%, 95.6%, and 98.7%, respectively. Among them, the oil water emulsion flux of dichloromethane could reach 1000.1. what's more, the prepared membranes also maintained relatively high selective separation efficiencies of TBBPA in oil-water mixtures. To better explain the mechanism of the TBBPA reaction process, the active sites in the TBBPA reaction process were calculated and predicted by density generalization theory (DFT). The Br and phenolic hydroxyl groups on the benzene ring of TBBPA showed higher activity and might be the main reaction sites. In addition, the imprinted composite membranes can be easily regenerated and reused without performance loss through a simple cleaning process. The coating process inspired by natural polyphenol chemistry has the advantage of being green, cost-effective and low energy, showing real potential in multifunctional membrane applications. [Display omitted] • Preparation of a novel multifunctional composite membrane for oil-water separation and selective adsorption of TBBPA. • TA-APTES coating replaces traditional PDA coatings and demonstrates amazing adhesion and hydrophilicity. • TA-APTES coating is used to provide a secondary reaction platform for imprinted polymerization. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder. An increasing number of researchers have found extra motor features in ALS, which are also called ALS-plus syndromes. Besides, a great majority of ALS patients also have cognitive impairment. However, clinical surveys of the frequency and genetic background of ALS-plus syndromes are rare, especially in China. Methods: We investigated a large cohort of 1015 patients with ALS, classifying them into six groups according to different extramotor symptoms and documenting their clinical manifestations. Meanwhile, based on their cognitive function, we divided these patients into two groups and compared demographic characteristics. Genetic screening for rare damage variants (RDVs) was also performed on 847 patients. Results: As a result, 16.75% of patients were identified with ALS-plus syndrome, and 49.5% of patients suffered cognitive impairment. ALS-plus group had lower ALSFRS-R scores, longer diagnostic delay time, and longer survival times, compared to ALS pure group. RDVs occurred less frequently in ALS-plus patients than in ALS-pure patients (P = 0.042) but showed no difference between ALS-cognitive impairment patients and ALS-cognitive normal patients. Besides, ALS-cognitive impairment group tends to harbour more ALS-plus symptoms than ALS-cognitive normal group (P = 0.001). Conclusion: In summary, ALS-plus patients in China are not rare and show multiple differences from ALS-pure patients in clinical and genetic features. Besides, ALS-cognitive impairment group tends to harbour more ALS-plus syndrome than ALS-cognitive normal group. Our observations correspond with the theory that ALS involves several diseases with different mechanisms and provide clinical validation. [ABSTRACT FROM AUTHOR]

To investigate the risk factors for REM sleep behavior disorder (RBD) in a case-control study. Participants with probable RBD (pRBD) were defined using the RBD Questionnaire–Hong Kong (RBDQ-HK). Controls were collected by matching age and sex. Demographic information, lifestyle, comorbidity, prodromal symptoms of Parkinson's disease (PD), and blood biomarkers were assessed. The associations between these factors and pRBD were investigated by logistic regression. Partial correlation analysis was used to assess the association between the severity of RBD and depression. A total of 278 pRBD participants (age = 58.31 ± 15.82 years) and 556 controls (age = 58.16 ± 15.84 years) were enrolled in this study. Patients with pRBD were more likely to be current alcohol drinkers (OR 1.50, 95% CI 1.0–2.32). Participants with pRBD had a higher Hamilton Depression Rating Scale (HAMD-17) score (OR 1.17, 95% CI 1.11–1.22) than controls and were more likely to report arthritis (OR 1.53, 95% CI 1.08–2.16), constipation (OR 1.93, 95% CI 1.31–2.86), hyposmia (OR 1.71, 95% CI 1.10–2.67), and depression (OR 3.15, 95% CI 2.17–4.58). Higher levels of total cholesterol (OR 1.15, 95% CI 0.99–1.33) and low-density lipoprotein (OR 1.21, 95% CI 0.99–1.47) had borderline associations with pRBD. Additionally, the severity of pRBD was positively related to depression (r = 0.31, P < 0.01). We determined several risk factors for pRBD in this case-control study. Future studies are needed to understand the mechanism underlying the association between these factors and pRBD. • Patients with pRBD were more likely to be current alcohol drinkers. • Participants with pRBD had a higher HAMD-17 score than controls and were more likely to report arthritis, constipation, hyposmia, and depression. • Higher levels of total cholesterol and low-density lipoprotein had borderline associations with pRBD. [ABSTRACT FROM AUTHOR]

This paper proposes a promising cost-effective phase current detection scheme for different multiphase switched reluctance motors (SRMs) by employing the multiplexed current sensors. According to the phase current characteristics for the odd-numbered multiphase SRM with m phases and even-numbered multiphase SRM with n phases, the phase windings are specially divided into (m + 1)/2 and n/2 groups, respectively, and each group shares one Hall-effect current sensor. By building the matrix functions to express the relationship between phase currents and sensor currents, the phase currents can be calculated in real time with a simple algorithm and fewer current sensors. Compared to existing phase current detection schemes, the developed method has high universality and can achieve the intact phase current detection without losing any excitation, freewheeling, and demagnetization currents. Moreover, the presented method is easy to implement without any converter change and pulse injection, which shows great potential for massive industrial application to avoid the voltage penalty, current distortion, higher switching loss, and further electromagnetic interference issues. The simulation and experiments are carried out to validate the feasibility and effectiveness of the developed phase current detection scheme. [ABSTRACT FROM AUTHOR]

Aims: To study the brain metabolic signature in Chinese amyotrophic lateral sclerosis (ALS) patients and compare the difference in brain metabolic patterns between ALS with and without genetic variants. Methods: We included 146 patients with ALS and 128 healthy controls (HCs). All patients with ALS underwent genetic testing to screen for ALS related genetic variants and were then divided into genetic (n = 22) and nongenetic ALS (n = 93) subgroups. All participants underwent brain 18F‐FDG‐PET scans. Group comparisons were performed using the two‐sample t‐test model of SPM12. Results: We identified a large of hypometabolic clusters in ALS patients as compared with HCs, especially in the bilateral basal ganglia, midbrain, and cerebellum. Moreover, hypometabolism in the bilateral temporal lobe, precentral gyrus and hypermetabolism in the left anterior cingulate, occipital lobe, and bilateral frontal lobe were also found in ALS patients as compared with HCs. Compared with nongenetic ALS patients, genetic ALS patients showed hypometabolism in the right postcentral gyrus, precuneus, and middle occipital gyrus. The incidence of sensory disturbance in patients with genetic ALS was higher than that in patients with nongenetic ALS (5 of 22 [22.72%] vs. 7 of 93 [7.52%], p = 0.036). Conclusions: Our investigation provided unprecedented evidence of relative hypometabolism in the midbrain and cerebellum in ALS patients. Genetic ALS patients showed a specific signature of brain metabolism and a higher incidence of sensory disturbance, indicating that genetic factors may be an underlying cause affecting the brain metabolism and increasing the risk of sensory disturbance in ALS. [ABSTRACT FROM AUTHOR]

Objectives: Hereditary spastic paraplegia (HSP) is a genetically heterogeneous disease caused by over 70 genes, with a significant number of patients still genetically unsolved. In this study, we recruited a suspected HSP family characterized by spasticity, developmental delay, ataxia and hypomyelination, and intended to reveal its molecular etiology by whole exome sequencing (WES) and long‐read sequencing (LRS) analyses. Methods: WES was performed on 13 individuals of the family to identify the causative mutations, including analyses of SNVs (single‐nucleotide variants) and CNVs (copy number variants). Accurate circular consensus (CCS) long‐read sequencing (LRS) was used to verify the findings of CNV analysis from WES. Results: SNVs analysis identified a missense variant c.195G>T (p.E65D) of MORF4L2 at Xq22.2 co‐segregating in this family from WES data. Further CNVs analysis revealed a microdeletion, which was adjacent to the MORF4L2 gene, also co‐segregating in this family. LRS verified this microdeletion and confirmed the deletion range (chrX: 103,690,507–103,715,018, hg38) with high resolution at nucleotide level accuracy. Interpretations: In this study, we identified an Xq22.2 microdeletion (about 24.5 kb), which contains distal enhancers of the PLP1 gene, as a likely cause of SPG2 in this family. The lack of distal enhancers may result in transcriptional repression of PLP1 in oligodendrocytes, potentially affecting its role in the maintenance of myelin, and causing SPG2 phenotype. This study has highlighted the importance of noncoding genomic alterations in the genetic etiology of SPG2. [ABSTRACT FROM AUTHOR]

Funayama et al. recently identified mutations in the CHCHD2 gene in Japanese families with autosomal dominant Parkinson's disease, increasing our knowledge about the monogenic cause of this disorder. However, there is no report regarding the association between CHCHD2 and Parkinson's disease (PD) in the Chinese Han population. The aim of this study was to obtain the prevalence of CHCHD2 mutations in Chinese familial PD. Genetic analysis of mutations in CHCHD2 gene was conducted in a cohort of 92 families with autosomal dominant Parkinson's disease from mainland China. No mutations in CHCHD2 gene were identified, suggesting that CHCHD2 mutations might not be a common cause of PD in Chinese familial cases. [ABSTRACT FROM AUTHOR]

This investigation reports the exploration of the mechanism for coal desulfurization utilizing ultrasonic (US) with peroxyacetic acid. Changes in surface and pore diameter for coal samples were determined before and after US treatment. To study its chemical effect, production regularities of hydroxyl radical under US field was analyzed by the iodine release method. Experimental results showed that the specific surface area, total pore volume and average pore size of treated samples were improved compared to untreated coal. In addition, the production rule of hydroxyl radical was consistent with that of desulfurization rate. XPS analysis results indicated that the removals of pyrite, mercaptan, and sulfur ether from coal were more than 60%, while thiophene, sulfoxide and sulfone were difficult to remove from coal. The further oxidation of sulfur-containing groups in coal was obtained due to the physical and chemical effects of the ultrasonic. These findings confirmed that the synergistic action of physical and chemical effects of the US played an important role in this desulfurization process, which could serve as a reference for further optimizing the coal desulfurization process. [ABSTRACT FROM AUTHOR]