Flexible Pressure Sensors in Human-Machine Interface Applications.

Flexible sensors are highly flexible, malleable, and capable of adapting todifferent shapes, surfaces, and environments, which opens a wide range ofpotential applications in the field of human-machine interface (HMI). Inparticular, flexible pressure sensors as a crucial member of the flexiblesensor family, are widely used in wearable devices, health monitoringinstruments, robots and other fields because they can achieve accuratemeasurement and convert the pressure into electrical signals. The mostintuitive feeling that flexible sensors bring to people is the change ofhuman-machine interface interaction, from the previous rigid interaction suchas keyboard and mouse to flexible interaction such as smart gloves, more inline with people's natural control habits. Many advanced flexible pressuresensors have emerged through extensive research and development, and to adaptto various fields of application. Researchers have been seeking to enhanceperformance of flexible pressure sensors through improving materials, sensingmechanisms, fabrication methods, and microstructures. This paper reviews the flexible pressure sensors in HMI in recent years, mainlyincluding the following aspects: current cutting-edge flexible pressuresensors; sensing mechanisms, substrate materials and active materials; sensorfabrication, performances, and their optimization methods; the flexiblepressure sensors for various HMI applications and their prospects.

Stretchable, Self-Healing, and Bioactive Hydrogel with High-Functionality N,N'-bis(acryloyl)cystamine Dynamically Bonded Ag@polydopamine Crosslinkers for Wearable Sensors.

Hydrogels present attractive opportunities as flexible sensors due to their soft nature and tunable physicochemical properties. Despite significant advances, practical application of hydrogel-based sensor is limited by the lack of general routes to fabricate materials with combination of mechanical, conductive, and biological properties. Here, a multi-functional hydrogel sensor is reported by in situ polymerizing of acrylamide (AM) with N,N'-bis(acryloyl)cystamine (BA) dynamic crosslinked silver-modified polydopamine (PDA) nanoparticles, namely PAM/BA-Ag@PDA. Compared with traditional polyacrylamide (PAM) hydrogel, the BA-Ag@PDA nanoparticles provide both high-functionality crosslinks and multiple interactions within PAM networks, thereby endowing the optimized PAM/BA-Ag@PDA hydrogel with significantly enhanced tensile/compressive strength (349.80 kPa at 383.57% tensile strain, 263.08 kPa at 90% compressive strain), lower hysteresis (5.2%), improved conductivity (2.51 S m-1) and excellent near-infrared (NIR) light-triggered self-healing ability. As a strain sensor, the PAM/BA-Ag@PDA hydrogel shows a good sensitivity (gauge factor of 1.86), rapid response time (138 ms), and high stability. Owing to abundant reactive groups in PDA, the PAM/BA-Ag@PDA hydrogel exhibits inherent tissue adhesiveness and antioxidant, along with a synergistic antibacterial effect by PDA and Ag. Toward practical applications, the PAM/BA-Ag@PDA hydrogel can conformally adhere to skin and monitor subtle activities and large-scale movements with excellent reliability, demonstrating its promising applications as wearable sensors for healthcare.

Hierarchical Echo State Network With Sparse Learning: A Method for Multidimensional Chaotic Time Series Prediction.

Echo state network (ESN), a type of special recurrent neural network with a large-scale randomly fixed hidden layer (called a reservoir) and an adaptable linear output layer, has been widely employed in the field of time series analysis and modeling. However, when tackling the problem of multidimensional chaotic time series prediction, due to the randomly generated rules for input and reservoir weights, not only the representation of valuable variables is enriched but also redundant and irrelevant information is accumulated inevitably. To remove the redundant components, reduce the approximate collinearity among echo-state information, and improve the generalization and stability, a new method called hierarchical ESN with sparse learning (HESN-SL) is proposed. The HESN-SL mines and captures the latent evolution patterns hidden from the dynamic system by means of layer-by-layer processing in stacked reservoirs, and leverage monotone accelerated proximal gradient algorithm to train a sparse output layer with variable selection capability. Meanwhile, we further prove that the HESN-SL satisfies the echo state property, which guarantees the stability and convergence of the proposed model when applied to time series prediction. Experimental results on two synthetic chaotic systems and a real-world meteorological dataset illustrate the proposed HESN-SL outperforms both original ESN and existing hierarchical ESN-based models for multidimensional chaotic time series prediction.

Trefoil factor 3 silencing can inhibit the proliferation and apoptosis of lung cancer cells.

PURPOSE: At present, the global incidence of lung cancer is still high. Exploring its effective treatment is still a crucial research direction. Trefoil factor 3 (TFF3) was found to be related to the proliferation and apoptosis of many tumor cells. Therefore, this article focuses on the effect of TFF3 on SPC-A1 lung cancer cells. METHODS: The tissue samples of lung cancer patients were collected, and the expression level of TFF3 was detected by Western blot (WB) and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) techniques. After transfection technique was used to silence the expression of TFF3 in SPC-A1 cells, the proliferation activity of SPC-A1 cells was detected by CCK-8 assay and EdU staining, and the cell cycle and related factor expression levels were also detected. The apoptosis rate of SPC-A1 cells was detected by Tunel staining and flow cytometry, and the expression levels of apoptosis-related factors were also detected. RESULTS: TFF3 in lung cancer tissues was obviously higher than that in para-carcinoma tissue. At the same time, similar results were found in SPC-A1 lung cancer cells. CCK-8 assay and EdU staining found that silencing TFF3 gene expression can effectively inhibit the proliferation of SPC-A1 cells. Flow cytometry detection of SPC-A1 cell cycle showed that cells were blocked in G0/G1 phase, and the number of cells in S+G2/M phase was obviously reduced. Cyclin D1 expression was also obviously reduced. At the same time, silencing TFF3 gene expression can promote the increase of Bax expression and inhibit the expression of Bcl-2, thereby increasing the apoptosis rate of SPC-A1 cells. Furthermore, silencing the TFF3 gene can effectively inhibit the excessive activation of the Wnt/ß-catenin pathway in SPC-A1 cells. CONCLUSIONS: Our results show that the expression of TFF3 in lung cancer was obviously increased. Silencing TFF3 in SPC-A1 cells can inhibit the cell proliferation and promote cell apoptosis. At the same time, we confirmed that silencing TFF3 gene can inhibit the abnormal activation of Wnt/ß-catenin signaling pathway in SPC-A1 cells.