The high demand for rapid wound healing has spurred the development of multifunctional and smart bioadhesives with strong bioadhesion, antibacterial effect, real-time sensing, wireless communication, and on-demand treatment capabilities. Bioadhesives with bio-inspired structures and chemicals have shown unprecedented adhesion strengths, as well as tunable optical, electrical, and bio-dissolvable properties. Accelerated wound healing has been achieved via directly released antibacterial and growth factors, material or drug-induced host immune responses, and delivery of curative cells. Most recently, the integration of biosensing and treatment modules with wireless units in a closed-loop system yielded smart bioadhesives, allowing real-time sensing of the physiological conditions (e.g., pH, temperature, uric acid, glucose, and cytokine) with iterative feedback for drastically enhanced, stage-specific wound healing by triggering drug delivery and treatment to avoid infection or prolonged inflammation. Despite rapid advances in the burgeoning field, challenges still exist in the design and fabrication of integrated systems, particularly for chronic wounds, presenting significant opportunities for the future development of next-generation smart materials and systems.
Bearing is one of the most important component of rotary machine, and its health state is directly related to the safety of industrial production. In this paper, health state assessment of bearing is investigated with feature enhancement and prediction error compensation. Specially, health state assessment consists of time-to-start prediction point detection and remaining useful life (RUL) prediction. In the first stage, variance feature based on Kalman filter is introduced to detect the time-to-start prediction point. Subsequently, complete ensemble empirical mode decomposition with adaptive noise is employed to reconstruct the degradation trend, and cumulative function is adopted to realize the feature enhancement, then efficient health indicator can be constructed. In the RUL prediction stage, degradation model and adaptive extended Kalman filter are fused to achieve the prediction, and bidirectional gated recurrent unit neural network is chosen to compensate the prediction error. Finally, experimental studies based on PRONOSTIA and XJTU-SY datasets are conducted to validate the effectiveness of the proposed method and its superiority over the traditional methods.
The generated power from multiple renewable energy sites can be collected and integrated into the conventional alternating current grid by using a multi-terminal high voltage direct current (MTDC) system based on the voltage source converter (VSC). Following the converter outage or large disturbance, this system with a fixed drooping control scheme suffers overloading of the power converter, resulting in the imbalance of active power-sharing and large deviation of direct current voltages. To improve control performance, this paper proposes a novel adaptive drooping control approach according to the variable droop constant to keep balance active power-sharing and manage the alternation of DC voltage in huge power disturbances. The designed scheme is implemented on the both rectifier side and inverter side converters. The variable droop constant is evaluated according to the converter headroom with DC voltage deviations. The performance of the planned control method is compared with the fixed and variable droop control techniques. A 400 kV four-terminal VSC-MTDC test network is developed to verify the effectiveness of the designed control method. The proposed control technique, based on the PSCAD simulation results, can achieve good performance in the rectifier and inverter converter outage and faults operating scenarios.
A dopant-free hole transport layer with high mobility and a low-temperature process is desired for optoelectronic devices. Here, we study a metal–organic framework material with high hole mobility and strong hole extraction capability as an ideal hole transport layer for perovskite solar cells. By utilizing lifting-up method, the thickness controllable floating film of Ni 3 (2,3,6,7,10,11-hexaiminotriphenylene) 2 at the gas–liquid interface is transferred onto ITO-coated glass substrate. The Ni 3 (2,3,6,7,10,11-hexaiminotriphenylene) 2 film demonstrates high compactness and uniformity. The root-mean-square roughness of the film is 5.5 nm. The ultraviolet photoelectron spectroscopy and the steady-state photoluminescence spectra exhibit the Ni 3 (HITP) 2 film can effectively transfer holes from perovskite film to anode. The perovskite solar cells based on Ni 3 (HITP) 2 as a dopant-free hole transport layer achieve a champion power conversion efficiency of 10.3%. This work broadens the application of metal–organic frameworks in the field of perovskite solar cells. Graphical Abstract
Radioresistance prevails as one of the largest obstacles in the clinical treatment of nasopharyngeal carcinoma (NPC). Meanwhile, tumor-derived extracellular vesicles (TEVs) possess the ability to manipulate radioresistance in NPC. However, its mechanism remains to be further explored. Therefore, the current study set out to explore the mechanism of microRNA (miR)-142-5p delivered by TEVs in regard to the radiosensitivity of NPC. Firstly, peripheral blood samples were collected from patients with radioresistance and radiosensitivity, followed by RT-qPCR detection of miR-142-5p expression. A dual-luciferase reporter assay was carried out to elucidate the targeting relationship of miR-142-5p with HGF and EGF. In addition, radiotherapy-resistant NPC cell models were established by screening NPC cells with gradient increasing radiation exposure, and co-incubated with EVs isolated from miR-142-5p mimic-transfected NPC cells, followed by overexpression of HGF and EGF. Moreover, cell viability was detected by means of MTS, cell proliferation with a colony formation assay, cell apoptosis with flow cytometry, and expression patterns of related genes with the help of Western blot analysis. NPC xenotransplantation models in nude mice were also established by subcutaneous injection of 5-8FR cells to determine apoptosis, tumorigenicity, and radiosensitivity in nude mice. It was found that miR-142-5p was poorly expressed in peripheral blood from NPC patients with radioresistance. Mechanistic experimentation illustrated that miR-142-5p inversely targeted HGF and EGF to inactivate the HGF/c-Met and EGF/EGFR pathways, respectively. NPC cell apoptosis was observed to be augmented, while their radioresistance and proliferation were restricted by EVs-miR-142-5p or HGF silencing, or EGF silencing. Furthermore, EVs-miR-142-5p inhibited growth and radioresistance and accelerated the apoptosis of radiotherapy-resistant NPC cells in nude mice by inhibiting the HGF/c-Met and EGF/EGFR pathways. Collectively, our findings indicated that TEVs might inhibit the HGF/c-Met and EGF/EGFR pathways by delivering miR-142-5p into radiotherapy-resistant NPC cells to enhance radiosensitivity in NPC.
The kernel method, especially the kernel-fusion method, is widely used in social networks, computer vision, bioinformatics, and other applications. It deals effectively with nonlinear classification problems, which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation, enabling the use of kernel methods to predict the structure and function of sequences. Therefore, the kernel method is significant in the solution of bioinformatics problems. Various kernels applied in bioinformatics are explained clearly, which can help readers to select proper kernels to distinguish tasks. Mass biological sequence data occur in practical applications. Research of the use of machine learning methods to obtain knowledge, and how to explore the structure and function of biological methods for theoretical prediction, have always been emphasized in bioinformatics. The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction. This review focuses on the requirements of classification tasks of biological sequence data. It studies kernel methods and optimization algorithms, including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.
Circulating tumor cells (CTCs) have tremendous potential to indicate disease progression and monitor therapeutic response using minimally invasive approaches. Considering the limitations of affinity strategies based on their cost, effectiveness, and simplicity, size-based enrichment methods that involve low-cost, label-free, and relatively simple protocols have been further promoted. Nevertheless, the key challenges of these methods are clogging issues and cell aggregation, which reduce the recovery rates and purity. Inspired by the natural phenomenon that the airflow around a windmill is disturbed, in this study, a windmill-like hole array on the SU-8 membrane was designed to perturb the fluid such that cells in a fluid would be able to self-mix and that the pressure acting on cells or the membrane would be dispersed to allow a greater velocity. In addition, based on the advantages of fluid coatings, a lipid coating was used to modify the membrane surface to prevent cell aggregation and clogging of the holes. Under the optimal conditions, recovery rates of 93% and 90% were found for A549 and HeLa cells in a clinical simulation test of our platform with a CTC concentration of 20–100 cells per milliliter of blood. The white blood cell (WBC) depletion rate was 98.7% ( n = 15), and the CTC detection limit was less than 10 cells per milliliter of blood ( n = 6). Moreover, compared with conventional membrane filtration, the advantages of the proposed device for the rapid (2 mL/min) and efficient enrichment of CTCs without clogging were shown both experimentally and theoretically. Due to its advantages in the efficient, rapid, uniform, and clog-free enrichment of CTCs, our platform offers great potential for metastatic detection and therapy analyses.
Origami has become an optimal methodological choice for creating complex three-dimensional (3D) structures and soft robots. The simple and low-cost origami-inspired folding assembly provides a new method for developing 3D soft robots, which is ideal for future intelligent robotic systems. Here, we present a series of materials, structural designs, and fabrication methods for developing independent, electrically controlled origami 3D soft robots for walking and soft manipulators. The 3D soft robots are based on soft actuators, which are multilayer structures with a dielectric elastomer (DE) film as the deformation layer and a laser-cut PET film as the supporting flexible frame. The triangular and rectangular design of the soft actuators allows them to be easily assembled into crawling soft robots and pyramidal- and square-shaped 3D structures. The crawling robot exhibits very stable crawling behaviors and can carry loads while walking. Inspired by origami folding, the pyramidal and square-shaped 3D soft robots exhibit programmable out-of-plane deformations and easy switching between two-dimensional (2D) and 3D structures. The electrically controllable origami deformation allows the 3D soft robots to be used as soft manipulators for grasping and precisely locking 3D objects. This work proves that origami-inspired fold-based assembly of DE actuators is a good reference for the development of soft actuators and future intelligent multifunctional soft robots.
A novel snapback-free RC-LIGBT with integrated self-biased N-MOSFET is proposed and investigated by simulation. The device features an integrated self-biased N-MOSFET(ISM) on the anode active region. One side of the ISM is shorted to the P + anode electrode of RC-LIGBT and the other side is connected to the N + anode via a floating ohmic contact. The adaptively turn-on/off of the ISM contributes to improve the static and dynamic performance of the ISM RC-LIGBT. In the forward-state, due to the off-state of the ISM, the snapback could be effectively suppressed without requiring extra device area compared to the SSA (separated shorted anode) and STA (segmented trenches in the anode) LIGBTs. In the reverse conduction, the ISM is turned on and the parasitic NPN in the ISM is punched through, which provides a current path for the reverse current. Meanwhile, during the turn-off and reverse recovery states, the ISM turns on, providing a rapid electron extraction path. Thus, a superior tradeoff between the on-state voltage drop ( V on ) and turnoff loss ( E off ) as well as an improved reverse recovery characteristic can be obtained. Compared to the STA device, the proposed ISM RC-LIGBT reduces E off by 21.5% without snapback. Its reverse recovery charge is reduced by 53.7%/58.6% compared to that of the SSA LIGBT with L b = 40/60 μm at the same V on . Due to the prominent static and dynamic characteristic, the power loss of ISM RC-LIGBT in a completed switching cycle is reduced.
Long-period waveguide grating based filters have attracted attention due to their flexible fabrication, a variety of materials and structures, low back reflection, low insertion loss, and excellent performance in the tuning range and temperature sensitivity. To our knowledge, for the first time, a two-segment polymer long-period waveguide grating was cascaded to implement a filter with a narrower bandwidth. Experimental results showed that the device had a maximum extinction ratio of 24 dB at 1 577 nm, and the 12 dB bandwidth was 10 nm. The temperature sensitivity of the fabricated device was 1.79 nm/°C.
The two-dimensional (2D) lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the environment. We propose a robust 2D lidar simultaneous localization and mapping (SLAM) algorithm working in ambiguous environments. To improve the front-end scan-matching module’s accuracy and robustness, we propose performing degeneration analysis, line landmark tracking, and environment coverage analysis. The max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous environment. Moreover, we propose a tightly coupled framework that integrates lidar, wheel odometer, and inertial measurement unit (IMU). The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM problem. The experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8% of the existing method. And it can run stably in ambiguous environments where the existing method will be failed. Moreover, it ccan successfully construct a map with an area of more than 250 000 square meters.
Background Interleukin-6 (IL-6) has been reported to be critical in oral squamous cell carcinoma (OSCC). However, the set of pathways that IL-6 might activate in OSCC are not fully understood. Methods IL-6 and Sox4 expressions were first determined with RT-qPCR, ELISA, Western blot, or immunohistochemistry in OSCC tissues, and correlations between IL-6 and Sox4 expression and patient pathological characteristics were examined, and Kaplan–Meier approach was employed for evaluating the prognostic utility in OSCC patients. CCK-8, EdU stain and colony formation assays were utilized to test cell proliferation in vitro. Mechanistically, downstream regulatory proteins of IL-6 were verified through chromatin immunoprecipitation, luciferase reporter, pull-down, and the rescued experiments. Western blot was used for detecting protein expression. A nude mouse tumorigenicity assay was used to confirm the role of IL-6 and Sox4 in vivo. Results IL-6 was upregulated in OSCC tissues, and Sox4 expression was positively correlated with IL-6 expression. High IL-6 and Sox4 expression was closely related to tumor size, TNM stage, and a poorer overall survival. Besides, IL-6 could accelerate OSCC cell proliferation by activating inflammasome via JAK2/STAT3/Sox4/NLRP3 pathways in vitro and in vivo. Furthermore, STAT3 played as a transcription factor which positively regulated Sox4, and IL-6 promotes Sox4 expression by activating JAK2/STAT3 pathway. Moreover, through the rescue experiments, we further confirmed that IL-6 could promote proliferation and NLRP3 inflammasome activation via JAK2/STAT3/Sox4 pathway in OSCC cells. Finally, knockdown of Sox4 suppressed OSCC growth in vivo, and antagonized the acceleration of IL-6 on tumor growth. Conclusions We confirmed that IL-6 plays an oncogenic role in OSCC progression by activating JAK2/STAT3/Sox4/NLRP3 pathway, which might be the therapeutic targets for OSCC remedy.
Wearable electronics, as essential components of the Internet of Things (IoT), have attracted widespread attention, and the trend is to configure attractive wearable smart microsystems by integrating sensing, powering, and other functions. Herein, we developed an elastic hybrid triboelectric–electromagnetic microenergy harvester (named EHTE) to realize hybrid sensing and microenergy simultaneously. This EHTE is a highly integrated triboelectric nanogenerator (TENG) and electromagnetic nanogenerator (EMG). Based on the triboelectric–electromagnetic hybrid mechanism, an enhanced electrical output of the EHTE was achieved successfully, which demonstrates the feasibility of the EHTE for microelectronics powering. Moreover, with the merits of the EMG, the developed hybrid microenergy harvester integrated both active frequency sensing and passive inductive sensing capabilities. Specifically, the almost linear correlation of the electromagnetic outputs to the frequencies of the external stimulus endowed the proposed EHTE with an outstanding active frequency sensing ability. In addition, due to the unique structural configuration of the EMG (i.e., a conductive permanent magnet (PM), hybrid deformation layer, and flexible printed circuit board (FPCB) coil), an opportunity was provided for the developed EHTE to serve as a passive inductive sensor based on the eddy current effect (i.e., a form of electromagnetic induction). Therefore, the developed EHTE successfully achieved the integration of hybrid sensing (i.e., active frequency sensing and passive inductive sensing) and microenergy (i.e., the combination of electromagnetic effect and triboelectric effect) within a single device, which demonstrates the potential of this newly developed EHTE for wearable electronic applications, especially in applications of compact active microsystems.
A length-matched micro Fabry-Perot (FP) interferometer is proposed for strain measurement under irradiation environment. Theoretical simulation shows that a well length-matched FP sensor can achieve a very low drift of the cavity length and strain sensitivity in irradiation environment. In experiment, such an FP cavity is realized by laser micromachining. It shows a low cavity length drift of −0.037 µm and a strain sensitivity deviation of 0.52%, respectively, under gamma irradiation. Meanwhile, the intensity of interference fringes is also stable. As a result, such a length-matched FP cavity is a very promising candidate for strain sensing in radiative environments.
A tactile sensor system enables natural interaction between humans and machines; this interaction is crucial for dexterous robotic hands, interactive entertainment, and other smart scenarios. However, the lack of sliding friction detection significantly limits the accuracy and scope of interactions due to the absence of sophisticated information, such as slippage, material and roughness of held objects. Here, inspired by the stick-slip phenomena in the sliding process, we have developed a multifunctional biomimetic tactile system based on the stick-slip sensing strategy, which is a universal method to detect slippage and estimate the surface properties of objects by sliding. This system consists of a flexible fingertip-inspired tactile sensor, a read-out circuit and a machine-learning module. Based on the stick-slip sensing strategy, our system was endowed with high recognition rates for slippage detection (100.0%), material classification (93.3%) and roughness discrimination (92.8%). Moreover, robotic hand manipulation, interactive games and object classification are demonstrated with this multifunctional system for comprehensive and promising human–machine interactions.
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