Yanjian Liao

Research Progress on Microfluidic Chip of Cell Separation Based on Dielectrophoresis

Abstract Cell separation technology is an important means for cell sorting and cell-population purification. It is the current international hotspot in biochemical analysis which is widely applied in many fields such as biology, medicine, agriculture, and environment. This review introduces the development status of cell separation using microfluidic chip based on dielectrophoresis (DEP). It expounds the working principle of DEP and the key factors that impact the DEP of cell separation such as cellular size, electrode shapes and signals. Finally, it forecasts the future development trend of cell separation using microfluidic chip based on DEP.

Microfluidic pool structure for cell docking and rapid mixing

A microfluidic pool structure for cell docking and rapid mixing is described. The pool structure is defined as a microchamber on one structural layer of a bilayer chip and connects with two or more individual microchannels on the other structural layer. In contrast to the turbulent flow in a macroscale pool, laminar streams enter and exit this microfluidic pool structure with definite and controllable direction that may be influenced by the location and geometry of the pool. A simple microfluidic model was used to validate this hypothesis. In this model, a microscale pool structure was made on the lower layer of a chip and connected with three parallel microchannels in the upper layer. Simulation and experimental results indicated that the flow profile within the pool structure was determined by its geometry and location. This could be used as a flow control method and it was simpler than designs based on microvalve, hydraulic pressure, or electrokinetic force, and has some important applications. For example, controllable streams within this structure were used to immobilize biological cells along the microchannel walls. When different solution streams flowed through the pool, rapid diffusion of analytes occurred for short diffusion distance between vertical flow laminas. Furthermore, desired dilution (mixing) ratio could be obtained by controlling the geometry of the microfluidic pool.

Characterization of lipid films by an angle-interrogation surface plasmon resonance imaging device

Surface topographies of lipid films have an important significance in the analysis of the preparation of giant unilamellar vesicles (GUVs). In order to achieve accurately high-throughput and rapidly analysis of surface topographies of lipid films, a homemade SPR imaging device is constructed based on the classical Kretschmann configuration and an angle interrogation manner. A mathematical model is developed to accurately describe the shift including the light path in different conditions and the change of the illumination point on the CCD camera, and thus a SPR curve for each sampling point can also be achieved, based on this calculation method. The experiment results show that the topographies of lipid films formed in distinct experimental conditions can be accurately characterized, and the measuring resolution of the thickness lipid film may reach 0.05 nm. Compared with existing SPRi devices, which realize detection by monitoring the change of the reflective-light intensity, this new SPRi system can achieve the change of the resonance angle on the entire sensing surface. Thus, it has higher detection accuracy as the traditional angle-interrogation SPR sensor, with much wider detectable range of refractive index.

The impact of sEMG feature weight on the recognition of similar grasping gesture

This paper is aimed to study the impact of sEMG feature weight on the recognition of similar grasping gesture, of which the classification performance is hindered by their alike underlying muscle activation pattern. The sEMG were collected from six forearm hand muscles (EPB, EPI, FDS, PL, MB, ED) when subjects conducted a 4-second different grasping gestures. Then empirical mode decomposition (EMD) was employed to represent the sEMG as IMF components, of which the time-domain features (ZC and WAMP) were obtained. Then PCA was used to reduce the dimension of feature set. The optimal training set of similar grasping gestures was determined by adjusting the weight of sEMG feature in the feature space. SVM classifier and Genetic-Algorithm (GA) were used to classify the patterns of the gestures. Experimental results showed that the classification rate of a gesture was improved with the increasing proportion of its sEMG feature in training set, and different gesture exhibited different sensitivity for gesture recognition. The present work suggested that the proportion of candidate gesture should not be equal, and the contribution of different gesture should be considered to optimize the training set.

Preparation of giant lipid vesicles with controllable sizes by a modified hydrophilic polydimethylsiloxane microarray chip

This paper presents an accessible method to prepare giant lipid vesicles (GLVs) with controllable sizes based on the quick formation of patterned lipid films. Lipid solutions naturally penetrate into arrayed micro-apertures on a modified hydrophilic Polydimethylsiloxane (PDMS) chip, and excess lipid films on the surface are removed by a glass slide. Three main factors, the depth and diameter of the micro-apertures and concentration of the lipid solution, were investigated to obtain an optimal preparation condition. Based on this condition, the formed GLVs have a controllable size and narrow size distribution (the standard deviation < 5 μm). By controlling the diameter of the micro-aperture and concentration of the lipid solution, GLVs with various sizes (23, 48, 66 and 82 μm) can be formed.

Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities

So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gestures dominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 ± 1.2 years) were recruited to perform similar grasping tasks (fist, spherical, and cylindrical grasping) and similar pinch tasks (finger, key, and tape pinch). Each task was sustained for 4 s and followed by a 5-s rest interval to avoid fatigue, and the procedure was repeated 60 times for every task. sEMG were recorded from six forearm hand muscles during grasping or pinch tasks, and 4-s sEMG from each channel was segmented for empirical mode decomposition analysis trial by trial. The muscle activity was quantified with zero crossing (ZC) and Wilson amplitude (WAMP) of the first four resulting intrinsic mode function. Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with support vector machine and genetic algorithm was used for hand gesture recognition. The sample number for each hand gesture was designed to be rearranged according to different sample proportion in training set, and corresponding recognition rate was calculated to evaluate the effect of sample assignment change on gesture classification. Either for similar grasping or pinch tasks, the sample assignment change in training set affected the overall recognition rate of candidate hand gesture. Compare to conventional results with uniformly assigned training samples, the recognition rate of similar pinch gestures was significantly improved when the sample of finger-, key-, and tape-pinch gesture were assigned as 60, 20, and 20%, respectively. Similarly, the recognition rate of similar grasping gestures also rose when the sample proportion of fist, spherical, and cylindrical grasping was 40, 30, and 30%, respectively. Our results suggested that the recognition rate of hand gestures can be regulated by change sample arrangement in training set, which can be potentially used to improve fine-gesture recognition for myoelectric robotic hand exoskeleton control.