Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals.

Background and Objective: Exoskeleton robot control should ideally be based on human voluntary movement intention. The readiness potential (RP) component of the motion-related cortical potential is observed before movement in the electroencephalogram and can be used for intention prediction. However, its single-trial features are weak and highly variable, and existing methods cannot achieve high cross-temporal and cross-subject accuracies in practical online applications. Therefore, this work aimed to combine a deep convolutional neural network (CNN) framework with a transfer learning (TL) strategy to predict the lower limb voluntary movement intention, thereby improving the accuracy while enhancing the model generalization capability; this would also provide sufficient processing time for the response of the exoskeleton robotic system and help realize robot control based on the intention of the human body. Methods: The signal characteristics of the RP for lower limb movement were analyzed, and a parameter TL strategy based on CNN was proposed to predict the intention of voluntary lower limb movements. We recruited 10 subjects for offline and online experiments. Multivariate empirical-mode decomposition was used to remove the artifacts, and the moment of onset of voluntary movement was labeled using lower limb electromyography signals during network training. Results: The RP features can be observed from multiple data overlays before the onset of voluntary lower limb movements, and these features have long latency periods. The offline experimental results showed that the average movement intention prediction accuracy was 95.23% ± 1.25% for the right leg and 91.21% ± 1.48% for the left leg, which showed good cross-temporal and cross-subject generalization while greatly reducing the training time. Online movement intention prediction can predict results about 483.9 ± 11.9 ms before movement onset with an average accuracy of 82.75%. Conclusion: The proposed method has a higher prediction accuracy with a lower training time, has good generalization performance for cross-temporal and cross-subject aspects, and is well-prioritized in terms of the temporal responses; these features are expected to lay the foundation for further investigations on exoskeleton robot control.

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer.

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods' intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods.

Enhanced reduction of sulfate by iron-carbon microelectrolysis: interaction mechanism between microelectrolysis and microorganisms.

Sulfate wastewater has a wide range of sources and greatly harms water, soil, and plants. Iron-carbon microelectrolysis (IC-ME) is a potentially sustainable strategy to improve the treatment of sulfate (SO42-) wastewater by sulfate-reducing bacteria (SRB). In this study, an iron-carbon mixed micro-electrolysis bioreactor (R1), iron-carbon layered bioreactor (R2), activated carbon bioreactor (R3), and scrap iron filing bioreactor (R4) were constructed by up-flow column experimental device. The performance and mechanism of removing high-concentration sulfate wastewater under different sulfate concentrations, hydraulic retention times (HRT), and chemical oxygen demand (COD)/SO42- were discussed. The results show that the iron-carbon microelectrolysis-enhanced SRB technology can remove high-concentration sulfate wastewater, and the system can still operate normally at low pH. In the high hydraulic loading stage (HRT = 12 h, COD/SO42- = 1.4), the SO42- removal rate of the R1 reactor reached 98.08%, and the ORP value was stable between - 350 and - 450 mV, providing a good ORP environment for SRB. When HRT = 12 h and influent COD/SO42- = 1.4, the R1 reactor sulfate removal rate reached 96.7%. When the influent COD/SO42- = 0.7, the sulfate removal rate was 52.9%, higher than the control group. Biological community analysis showed that the abundance of SRB in the R1 reactor was higher than that in the other three groups, indicating that the IC-ME bioreactor could promote the enrichment of SRB and improve its population competitive advantage. It can be seen that the synergistic effect between IC-ME and biology plays a vital role in the treatment of high-concentration sulfate wastewater and improves the biodegradability of sulfate. It is a promising process for treating high-concentration sulfate wastewater.

Preparation of lignite-loaded nano-FeS and its performance for treating acid Cr(VI)-containing wastewater.

In this study, lignite-loaded nano-FeS (nFeS@Lignite) was successfully prepared by ultrasonic precipitation, and its potential for treating acid Cr(VI)-containing wastewater was explored. The results showed that the 40--80-nm rod-shaped nFeS was successfully loaded onto lignite particles, and the maximum adsorption capacity of Cr(VI) by nFeS@Lignite reached 33.08 mg∙g-1 (reaction time = 120 min, pH = 4, temperature = 298.15 K). The adsorption process of Cr(VI) by nFeS@Lignite fitted the pseudo-second-order model and the Langmuir isotherm model, and thermodynamic results showed that the adsorption process was an endothermic process with an adsorption enthalpy of 28.0958 kJ·mol-1. The inhibition intensity of coexisting anions on Cr(VI) removal was in the order of PO43- > NO3- > SO42- > Cl-, and the increase of ionic strength resulted in more pronounced inhibition. Electrostatic adsorption, reduction, and precipitation were synergistically engaged in the adsorption of Cr(VI) by nFeS@Lignite, among which reduction played a major role. The characterization results showed that Fe2+, S2-, and Cr(VI) were converted to FeOOH, S8, SO42-, Fe2O3, Cr2O3, and Fe(III)-Cr(III) complexes. This research demonstrates that nFeS@Lignite is a good adsorbent with promising potential for application in the remediation of heavy metal-contaminated wastewater.

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study.

Introduction: Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG. Methods: Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia. Results: Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention. Discussion: The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength.

This study aimed to highlight the demand for upper limb compound motion decoding to provide a more diversified and flexible operation for the electromyographic hand. In total, 60 compound motions were selected, which were combined with four gestures, five wrist angles, and three strength levels. Both deep learning methods and machine learning classifiers were compared to analyze the decoding performance. For deep learning, three structures and two ways of label encoding were assessed for their training processes and accuracies; for machine learning, 24 classifiers, seven features, and a combination of classifier chains were analyzed. Results show that for this relatively small sample multi-target surface electromyography (sEMG) classification, feature combination (mean absolute value, root mean square, variance, 4th-autoregressive coefficient, wavelength, zero crossings, and slope signal change) with Support Vector Machine (quadric kernel) outstood because of its high accuracy, short training process, less computation cost, and stability (p < 0.05). The decoding result achieved an average test accuracy of 98.42 ± 1.71% with 150 ms sEMG. The average accuracy for separate gestures, wrist angles, and strength levels were 99.35 ± 0.67%, 99.34 ± 0.88%, and 99.04 ± 1.16%. Among all 60 motions, 58 showed a test accuracy greater than 95%, and one part was equal to 100%.

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model.

The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human-robot interaction, but the internal relationship of EEG and sEMG signals is not clear, so their fusion still has some shortcomings. A precise fusion method of EEG and sEMG using the CNN-LSTM model was investigated to detect lower limb voluntary movement in this study. At first, the EEG and sEMG signal processing of each stage was analyzed so that the response time difference between EEG and sEMG can be estimated to detect lower limb voluntary movement, and it can be calculated by the symbolic transfer entropy. Second, the data fusion and feature of EEG and sEMG were both used for obtaining a data matrix of the model, and a hybrid CNN-LSTM model was established for the EEG and sEMG-based decoding model of lower limb voluntary movement so that the estimated value of time difference was about 24 ∼ 26 ms, and the calculated value was between 25 and 45 ms. Finally, the offline experimental results showed that the accuracy of data fusion was significantly higher than feature fusion-based accuracy in 5-fold cross-validation, and the average accuracy of EEG and sEMG data fusion was more than 95%; the improved average accuracy for eliminating the response time difference between EEG and sEMG was about 0.7 ± 0.26% in data fusion. In the meantime, the online average accuracy of data fusion-based CNN-LSTM was more than 87% in all subjects. These results demonstrated that the time difference had an influence on the EEG and sEMG fusion to detect lower limb voluntary movement, and the proposed CNN-LSTM model can achieve high performance. This work provides a stable and reliable basis for human-robot interaction of the lower limb exoskeleton.

An asynchronous artifact-enhanced electroencephalogram based control paradigm assisted by slight facial expression.

In this study, an asynchronous artifact-enhanced electroencephalogram (EEG)-based control paradigm assisted by slight-facial expressions (sFE-paradigm) was developed. The brain connectivity analysis was conducted to reveal the dynamic directional interactions among brain regions under sFE-paradigm. The component analysis was applied to estimate the dominant components of sFE-EEG and guide the signal processing. Enhanced by the artifact within the detected electroencephalogram (EEG), the sFE-paradigm focused on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm contained four steps, including "obvious non-sFE-EEGs exclusion," "interface 'ON' detection," "sFE-EEGs real-time decoding," and "validity judgment." It provided the asynchronous function, decoded eight instructions from the latest 100 ms signal, and greatly reduced the frequent misoperation. In the offline assessment, the sFE-paradigm achieved 96.46% ± 1.07 accuracy for interface "ON" detection and 92.68% ± 1.21 for sFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This sFE-paradigm was applied to two online manipulations for evaluating stability and agility. In "object-moving with a robotic arm," the averaged intersection-over-union was 60.03 ± 11.53%. In "water-pouring with a prosthetic hand," the average water volume was 202.5 ± 7.0 ml. During online, the sFE-paradigm performed no significant difference (P = 0.6521 and P = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of sFE-paradigm, enabling a novel solution to the non-invasive EEG-based control in real-world challenges.

Dynamic experiments of acid mine drainage with Rhodopseudomonas spheroides activated lignite immobilized sulfate-reducing bacteria particles treatment.

Aiming at the problem that the treatment of acid mine drainage (AMD) by sulfate-reducing bacteria (SRB) biological method is susceptible to pH, metal ions, sulfate and carbon source. Lignite immobilized SRB particles (SRB-LP) and Rhodopseudomonas spheroides (R. spheroides) activated lignite immobilized SRB particles (R-SRB-LP) were prepared using microbial immobilization technology with SRB, R. spheroides and lignite as the main substrates. The dynamic experimental columns 1# and 2# were constructed with SRB-LP and R-SRB-LP as fillers, respectively, to investigate the dynamic repair effect of SRB-LP and R-SRB-LP on AMD. The mechanism of AMD treated with R-L-SRB particles was analyzed by scanning electron microscopy (SEM), fourier transform infrared (FTIR) spectrometer and low-temperature nitrogen adsorption. The result showed that the combination of R. spheroides and lignite could continuously provide carbon source for SRB, so that the highest removal rates of SO42-, Cu2+ and Zn2+ in AMD by R-SRB-LP were 93.97%, 98.52% and 94.42%, respectively, and the highest pH value was 7.60. The dynamic repair effect of R-SRB-LP on AMD was significantly better than that of SRB-LP. The characterization results indicated that after R-SRB-LP reaction, the functional groups of -OH and large benzene ring structure in lignite were broken, the lignite structure was destroyed, and the specific surface area was 1.58 times larger than before reaction. It illustrated that R. spheroides provided carbon source for SRB by degrading lignite. The strong SRB activity in R-SRB-LP, SRB can co-treat AMD with lignite, so that the dynamic treatment effect of R-SRB-LP on AMD is significantly better than that of SRB-LP.

Adsorption behaviors and mechanisms of Cu2+, Zn2+ and Pb2+ by magnetically modified lignite.

The study aims to solve the problems of limited capacity and difficult recovery of lignite to adsort Cu2+, Zn2+ and Pb2+ in acid mine wastewater (AMD). Magnetically modified lignite (MML) was prepared by the chemical co-precipitation method. Static beaker experiments and dynamic continuous column experiments were set up to explore the adsorption properties of Cu2+, Zn2+ and Pb2+ by lignite and MML. Lignite and MML before and after the adsorption of heavy metal ions were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectrometer (FTIR). Meanwhile, the adsorption mechanisms of Cu2+, Zn2+ and Pb2+ by lignite and MML were revealed by combining the adsorption isotherm model and the adsorption kinetics model. The results showed that the pH, adsorbent dosage, temperature, initial concentration of heavy metal ions, and contact time had an influence on the adsorption of Cu2+, Zn2+ and Pb2+ by lignite and MML, and the adsorption processes were more in line with the Langmuir model. The adsorption kinetics experiments showed that the adsorption processes were jointly controlled by multiple adsorption stages. The adsorption of heavy metal ions by lignite obeyed the Quasi first-order kinetic model, while the adsorption of MML was chemisorption that obeyed the Quasi second-order kinetic model. The negative ΔG and positive ΔH of Cu2+ and Zn2+ indicated the spontaneous and endothermic nature reaction, while the negative ΔH of Pb2+ indicated the exothermic nature reaction. The dynamic continuous column experiments showed that the average removal rates of Cu2+, Zn2+ and Pb2+ by lignite were 78.00, 76.97 and 78.65%, respectively, and those of heavy metal ions by MML were 82.83, 81.57 and 83.50%, respectively. Compared with lignite, the adsorption effect of MML was better. As shown by SEM, XRD and FTIR tests, Fe3O4 was successfully loaded on the surface of lignite during the magnetic modification, which made the surface morphology of lignite coarser. Lignite and MML removed Cu2+, Zn2+ and Pb2+ from AMD in different forms. In addition, the adsorption process of MML is related to the O-H stretching vibration of carboxylic acid ions and the Fe-O stretching vibration of Fe3O4 particles.

Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter.

Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.

Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention.

In the field of lower limb exoskeletons, besides its electromechanical system design and control, attention has been paid to realizing the linkage of exoskeleton robots to humans via electroencephalography (EEG) and electromyography (EMG). However, even the state of the art performance of lower limb voluntary movement intention decoding still faces many obstacles. In the following work, focusing on the perspective of the inner mechanism, a homology characteristic of EEG and EMG for lower limb voluntary movement intention was conducted. A mathematical model of EEG and EMG was built based on its mechanism, which consists of a neural mass model (NMM), neuromuscular junction model, EMG generation model, decoding model, and musculoskeletal biomechanical model. The mechanism analysis and simulation results demonstrated that EEG and EMG signals were both excited by the same movement intention with a response time difference. To assess the efficiency of the proposed model, a synchronous acquisition system for EEG and EMG was constructed to analyze the homology and response time difference from EEG and EMG signals in the limb movement intention. An effective method of wavelet coherence was used to analyze the internal correlation between EEG and EMG signals in the same limb movement intention. To further prove the effectiveness of the hypothesis in this paper, six subjects were involved in the experiments. The experimental results demonstrated that there was a strong EEG-EMG coherence at 1 Hz around movement onset, and the phase of EEG was leading the EMG. Both the simulation and experimental results revealed that EEG and EMG are homologous, and the response time of the EEG signals are earlier than EMG signals during the limb movement intention. This work can provide a theoretical basis for the feasibility of EEG-based pre-perception and fusion perception of EEG and EMG in human movement detection.

Novel approach for electromyography-controlled prostheses based on facial action.

Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications. Graphical abstract Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications.

Processing Surface EMG Signals for Exoskeleton Motion Control.

The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.

An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials.

Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject's intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach. To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ±â€¯2.64 dB, versus 3.38 ±â€¯0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ±â€¯7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ±â€¯3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach.

Endurance exercise prevents high-fat-diet induced heart and mobility premature aging and dsir2 expression decline in aging Drosophila.

High-Fat-Diet (HFD)-induced obesity is a major contributor to heart and mobility premature aging and mortality in both Drosophila and humans. The dSir2 genes are closely related to aging, but there are few directed reports showing that whether HFD could inhibit the expression dSir2 genes. Endurance exercise can prevent fat accumulation and reverse HFD-induced cardiac dysfunction. Endurance also delays age-relate functional decline. It is unclear whether lifetime endurance exercise can combat lifetime HFD-induced heart and mobility premature aging, and relieve the harmful HFD-induced influence on the dSir2 gene and lifespan yet. In this study, flies are fed a HFD and trained from when they are 1 week old until they are 5 weeks old. Then, triacylglycerol levels, climbing index, cardiac function, lifespan, and dSir2 mRNA expressions are measured. We show that endurance exercise improves climbing capacity, cardiac contraction, and dSir2 expression, and it reduces body and heart triacylglycerol levels, heart fibrillation, and mortality in both HFD and aging flies. So, lifelong endurance exercise delays HFD-induced accelerated age-related locomotor impairment, cardiac dysfunction, death, and dSir2 expression decline, and prevents HFD-induced premature aging in Drosophila.

WITHDRAWN: Regular exercise protects aging Drosophila from high-fat-diet-induced locomotor impairment, cardiac dysfunction, lifespan shortening, and Nmnat and dSir2 expression decline.

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An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm.

One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control.

A Canopy Density Model for Planar Orchard Target Detection Based on Ultrasonic Sensors.

Orchard target-oriented variable rate spraying is an effective method to reduce pesticide drift and excessive residues. To accomplish this task, the orchard targets' characteristic information is needed to control liquid flow rate and airflow rate. One of the most important characteristics is the canopy density. In order to establish the canopy density model for a planar orchard target which is indispensable for canopy density calculation, a target density detection testing system was developed based on an ultrasonic sensor. A time-domain energy analysis method was employed to analyze the ultrasonic signal. Orthogonal regression central composite experiments were designed and conducted using man-made canopies of known density with three or four layers of leaves. Two model equations were obtained, of which the model for the canopies with four layers was found to be the most reliable. A verification test was conducted with different layers at the same density values and detecting distances. The test results showed that the relative errors of model density values and actual values of five, four, three and two layers of leaves were acceptable, while the maximum relative errors were 17.68%, 25.64%, 21.33% and 29.92%, respectively. It also suggested the model equation with four layers had a good applicability with different layers which increased with adjacent layers.