DEVELOPMENT AND APPLICATION OF MINIATURIZED SAMPLE PREPARATION PROCEDURES FOR ENVIRONMENTAL WATER CONTAMINANTS

Ph.DDOCTOR OF PHILOSOPH

Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree

Legged robots can pass through complex field environments by selecting gaits and discrete footholds carefully. Traditional methods plan gait and foothold separately and treat them as the single-step optimal process. However, such processing causes its poor passability in a sparse foothold environment. This paper novelly proposes a coordinative planning method for hexapod robots that regards the planning of gait and foothold as a sequence optimization problem with the consideration of dealing with the harshness of the environment as leg fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve some defeats of the standard MCTS applicating in the field of legged robot planning. The proposed planning algorithm combines the fault-tolerant gait method to improve the passability of the algorithm. Finally, compared with other planning methods, experiments on terrains with different densities of footholds and artificially-designed challenging terrain are carried out to verify our methods. All results show that the proposed method dramatically improves the hexapod robot's ability to pass through sparse footholds environment

Early Screening of Children With Autism Spectrum Disorder Based on Electroencephalogram Signal Feature Selection With L1-Norm Regularization

Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of children with ASD, and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state, and the extracted EEG features have some disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy

Grasping force prediction based on sEMG signals

In order to realize the force control, when the prosthetic hand grasps the object, the forearm electromyography signal is collected by the multi-channel surface electromyography (sEMG) acquisition system. The grasping force information of the human hand is recorded by the six-dimensional force sensor. The root mean square (RMS) of the electromyography signal steady state is selected, which is an effective feature. The gene expression programming algorithm (GEP) and BP neural network are used to construct the prediction model and predict the grasping force. The force prediction accuracy of GEP algorithm and BP neural network algorithm are discussed under different grasping power levels and different grasping modes. The performance of the two algorithm models are evaluated by two measures of root mean square error (RMSE) and correlation coefficient (CC). The results show that the RMS eigenvalue extracted from the sEMG signal can better characterize the grasping force. The prediction model with GEP algorithm has smaller relative error and higher prediction effect

Untargeted metabolomics of the cochleae from two laryngeally echolocating bats

High-frequency hearing is regarded as one of the most functionally important traits in laryngeally echolocating bats. Abundant candidate hearing-related genes have been identified to be the important genetic bases underlying high-frequency hearing for laryngeally echolocating bats, however, extensive metabolites presented in the cochleae have not been studied. In this study, we identified 4,717 annotated metabolites in the cochleae of two typical laryngeally echolocating bats using the liquid chromatography–mass spectroscopy technology, metabolites classified as amino acids, peptides, and fatty acid esters were identified as the most abundant in the cochleae of these two echolocating bat species, Rhinolophus sinicus and Vespertilio sinensis. Furthermore, 357 metabolites were identified as significant differentially accumulated (adjusted p-value <0.05) in the cochleae of these two bat species with distinct echolocating dominant frequencies. Downstream KEGG enrichment analyses indicated that multiple biological processes, including signaling pathways, nervous system, and metabolic process, were putatively different in the cochleae of R. sinicus and V. sinensis. For the first time, this study investigated the extensive metabolites and associated biological pathways in the cochleae of two laryngeal echolocating bats and expanded our knowledge of the metabolic molecular bases underlying high-frequency hearing in the cochleae of echolocating bats

Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration

Motor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans

Association of Glomerular Filtration Rate with High-Sensitivity Cardiac Troponin T in a Community-Based Population Study in Beijing

BACKGROUND: Reduced renal function is an independent risk factor for cardiovascular disease mortality, and persistently elevated cardiac troponin T (cTnT) is frequently observed in patients with end-stage renal disease. In the general population the relationship between renal function and cTnT levels may not be clear because of the low sensitivity of the assay. In this study, we investigated the level of cTnT using a highly sensitive assay (hs-cTnT) and evaluated the association of estimated glomerular filtration rate (eGFR) with detectable hs-cTnT levels in a community-based population. METHODS: The serum hs-cTnT levels were measured in 1365 community dwelling population aged ≥45 years in Beijing, China. eGFR was determined by the Chinese modifying modification of diet in renal disease (C-MDRD) equation. RESULTS: With the highly sensitive assay, cTnT levels were detectable (≥3pg/mL) in 744 subjects (54.5%). The result showed that eGFR was associated with Log hs-cTnT (r = -0.14, P<0.001). After adjustment for the high predicted Framingham Coronary Heart Disease (CHD) risk (10-year risk >20%) and other prognostic indicators, moderate to severe reduced eGFR was independently associated with detectable hs-cTnT, whereas normal to mildly reduced eGFR was not independently associated with detectable hs-cTnT. In addition, after adjustment for other risk factors, the high predicted Framingham CHD risk was associated with detectable hs-cTnT in the subjects with different quartile levels of eGFR. CONCLUSION: The levels of hs-cTnT are detectable in a community-based Chinese population and low eGFR is associated with detectable hs-cTnT. Moreover, eGFR and high predicted Framingham CHD risk are associated with detectable hs-cTnT in subjects with moderate-to-severe reduced renal function