An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Three-dimensional (3D) scaffolds with chemical diversity are significant to direct cell adhesion onto targeted surfaces, which provides solutions to further control over cell fates and even tissue formation. However, the site-specific modification of specific biomolecules to realize selective cell adhesion has been a challenge with the current methods when building 3D scaffolds. Conventional methods of immersing as-prepared structures in solutions of biomolecules lead to nonselective adsorption; recent printing methods have to address the problem of switching multiple nozzles containing different biomolecules. The recently developed concept of macroscopic supramolecular assembly (MSA) based on the idea of "modular assembly" is promising to fabricate such 3D scaffolds with advantages of flexible design and combination of diverse modules with different surface chemistry. Herein we report an MSA method to fabricate 3D ordered structures with internal chemical diversity for site-selective cell adhesion. The 3D structure is prepared via 3D alignment of polydimethylsiloxane (PDMS) building blocks with magnetic pick-and-place operation and subsequent interfacial bindings between PDMS based on host/guest molecular recognition. The site-specific cell affinity is realized by distributing targeted building blocks that are modified with polylysine molecules of opposite chiralities: PDMS modified with films containing poly-l-lysine (PLL) show higher cell density than those with poly-d-lysine (PDL). This principle of selective cell adhesion directed simply by spatial distribution of chiral molecules has been proven effective for five different cell lines. This facile MSA strategy holds promise to build complex 3D microenvironment with on-demand chemical/biological diversities, which is meaningful to study cell/material interactions and even tissue formation.
Currently, electromyography pattern-recognition (EMG-PR) based myoelectric prosthesis is widely used in many laboratories worldwide. In the EMG-PR based method, EMG features would be extracted from the EMG signals and used to predict the user's motion intent. However, in clinical use, many interferences such as muscle fatigue, electrode shift and so on, were usually introduced to degrade the feature quality, which would decay the performance of a trained EMG-PR classifier in identifying motion intentions. In this study, a novel preprocessing strategy, feature filtering, was proposed to improve the performance of EMG-PR based classifier in motion classification. Three feature filtering methods of mean filter (MF), Median filter (MDF), and Weighted Average filter (WAF) were designed to investigate the effectiveness of this strategy. By analyzing the results of six able-bodied subjects, it demonstrated that the motion classification performance could be improved by using the feature filtering strategy, achieving the increments of 4.4%, 2.8%, and 3.5% for MF, MDF and WAF, respectively. These preliminary results suggest that using the feature filtering strategy may enhance the robustness of EMG-based myoelectric control.
Considering the ice shedding phenomena on ice-covered outdoor insulators, this paper conducted the artificial experiments by using a five-unit suspension ceramic insulator string covered with wet-grown ice to investigate the effects of ice shedding on the icing discharge characteristics. According to IEEE Standard 1783/2009, the minimum flashover voltage (VMF), propagation of discharges to flashover and related leakage current (LC) were measured. It was found that VMF after ice shedding can be increased by about 17% as compared with that before ice shedding. The initiation and formation of discharge arcs across ice-free regions caused by ice shedding become difficult, showing an unstable propagating path, indeterminate arc shape and longer arc column. Although VMF under ice shedding conditions is higher than that without ice shedding, the fundamental component, and harmonics show lower amplitudes for the latter case. The ratios of harmonics to the fundamental are well in accordance with discharge characteristics during the flashover for which the ratios show a relatively stable varying tendency in the absence of ice shedding, but show changeable and indeterminate variation under ice shedding conditions. The obtained results are helpful to understand the icing state and its influence on surface discharges for preventing the icing flashovers.
The preconcentration of trace gallium, germanium, molybdenum and indium by trapping with precipitation of phenylfluorone (PF), and the determination of the elements by GFAAS were developed. The effects such as those of acidity, amounts of PF, aging time, volume of test solution, and the coexistent ions on the preconcentration of the trace elements were examined in detail. The optimum conditions of preconcentration for Ga(III) were pH approximately 2 test solution 500 mL with added 10.00 mg x mL(-1) PF (2.00 mL) and aging for 4 h, those for Ge(IV) were pH approximately 2 test solution 500 mL with added 10.00 mg x mL(-1) PF (4.00 mL) and aging for 10 h, those for Mo(V) were pH approximately 3 test solution 1 000 mL with added 10.00 mg x mL(-1) PF (3.00 mL) and aging for 6 h, and those for In(III) were pH approximately 5 test solution 100 mL with added 10.00 mg x mL(-1) PF (3.00 mL) and aging for 10 h. The experiment results showed that the main contribution to trapping trace gallium, germanium, molybdenum and indium with PF precipitation was post-precipitation instead of coprecipitation. The detection limits (3s) were 0.12 ng x mL(-1) for gallium, 0.30 ng x mL(-1) for germanium, 0.046 ng x mL(-1) for molybdenum and 2.7 ng x mL(-1) for indium. The developed methods were successfully applied to the determination of trace amount of the elements in water samples, geological standard reference materials, and zinc concentrate samples by graphite furnace atomic absorption spectrometry.
Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Recently, an EEG-based robotic arm control has offered a key solution to the problem of high level amputation or severe neuromuscular damage. Several attempt to use EMG-based pattern recognition (PR) failed due to insufficient myoelectric signals from the residual limb to perform control functions. The EEG activity recorded from human scalp is used to control the movement of a robotic arm. This can either be achieved when the arm is attached to or separated from the amputee stump by interfacing the brain directly to the robotic arm through the brain-machine interface (BMI). To build an intelligent robotic (or prosthetics) system that will manipulate object seamlessly with multiple degrees of freedom (DoF), it is required that a robust learning algorithm which is able to control the prosthetic arm while interacting with the environment should be implemented. However, the conventional machine learning approach of using handcrafted features to design a robot controller that can perform multiple task is not a feasible option. Hence, we propose a robust learning control which is based on unsupervised learning algorithm of deep autoencoder. We applied stacked autoencoder to generate our features, and softmax layer is then used to classify five different motor imagery tasks. The proposed method produced an overall accuracy of 98.9% across the four amputees recruited for the experiments. Our algorithm shows a better performance when compared with the state-of-the art classifiers. Thus, the proposed results demonstrates the possibility of providing better control performance for EEG-based prosthesis.
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