Epithelial-mesenchymal transition (EMT) is known to play an important role in cancer progression, metastasis and drug resistance. Although there are controversies surrounding the causal relationship ...between EMT and cancer metastasis, the role of EMT in cancer drug resistance has been increasingly recognized. Numerous EMT-related signaling pathways are involved in drug resistance in cancer cells. Cells undergoing EMT show a feature similar to cancer stem cells (CSCs), such as an increase in drug efflux pumps and anti-apoptotic effects. Therefore, targeting EMT has been considered a novel opportunity to overcome cancer drug resistance. This review describes the mechanism by which EMT contributes to drug resistance in cancer cells and summarizes new advances in research in EMT-associated drug resistance.
Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and transferring from station to station. An increasing number of deep learning algorithms are ...being utilized to forecast metro ridership due to the development of computational intelligence. However, limited efforts have been exerted to consider spatiotemporal features, which are important in forecasting ridership through deep learning methods, in large-scale metro networks. To fill this gap, this paper proposes a parallel architecture comprising convolutional neural network (CNN) and bi-directional long short-term memory network (BLSTM) to extract spatial and temporal features, respectively. Metro ridership data are transformed into ridership images and time series. Spatial features can be learned from ridership image data by using CNN, which demonstrates favorable performance in video detection. Time series data are input into the BLSTM which considers the historical and future impacts of ridership in temporal feature extraction. The two networks are concatenated in parallel and prevented from interfering with each other. Joint spatiotemporal features are fed into a fully connected network for metro ridership prediction. The Beijing metro network is used to demonstrate the efficiency of the proposed algorithm. The proposed model outperforms traditional statistical models, deep learning architectures, and sequential structures, and is suitable for ridership prediction in large-scale metro networks. Metro authorities can thus effectively allocate limited resources to overcrowded areas for service improvement.
Achieving strong coupling between plasmonic oscillators can significantly modulate their intrinsic optical properties. Here, we report the direct observation of ultrafast plasmonic hot electron ...transfer from an Au grating array to an MoS
monolayer in the strong coupling regime between localized surface plasmons (LSPs) and surface plasmon polaritons (SPPs). By means of femtosecond pump-probe spectroscopy, the measured hot electron transfer time is approximately 40 fs with a maximum external quantum yield of 1.65%. Our results suggest that strong coupling between LSPs and SPPs has synergetic effects on the generation of plasmonic hot carriers, where SPPs with a unique nonradiative feature can act as an 'energy recycle bin' to reuse the radiative energy of LSPs and contribute to hot carrier generation. Coherent energy exchange between plasmonic modes in the strong coupling regime can further enhance the vertical electric field and promote the transfer of hot electrons between the Au grating and the MoS
monolayer. Our proposed plasmonic strong coupling configuration overcomes the challenge associated with utilizing hot carriers and is instructive in terms of improving the performance of plasmonic opto-electronic devices.
Abstract
Human ATP-binding cassette (ABC) subfamily A (ABCA) transporters mediate the transport of various lipid compounds across the membrane. Mutations in human ABCA transporters have been ...described to cause severe hereditary disorders associated with impaired lipid transport. However, little is known about the mechanistic details of substrate recognition and translocation by ABCA transporters. Here, we present three cryo-EM structures of human ABCA4, a retina-specific ABCA transporter, in distinct functional states at resolutions of 3.3–3.4 Å. In the nucleotide-free state, the two transmembrane domains (TMDs) exhibit a lateral-opening conformation, allowing the lateral entry of substrate from the lipid bilayer. The N-retinylidene-phosphatidylethanolamine (NRPE), the physiological lipid substrate of ABCA4, is sandwiched between the two TMDs in the luminal leaflet and is further stabilized by an extended loop from extracellular domain 1. In the ATP-bound state, the two TMDs display a closed conformation, which precludes the substrate binding. Our study provides a molecular basis to understand the mechanism of ABCA4-mediated NRPE recognition and translocation, and suggests a common ‘lateral access and extrusion’ mechanism for ABCA-mediated lipid transport.
The wind‐turbine wake growth is crucial for wake assessment. At present, it can only be determined empirically, which is the primary source of prediction errors in the analytical wake model, and a ...physically‐based method is urgently needed. Recently, the plume model is proposed for wake width prediction in the neutral boundary layer based on Taylor's diffusion theory. However, this model is not applicable for high‐roughness neutral and strongly convective conditions, which is mainly related to the fact that the specified far wake point in the plume model is too close to the virtual wake origin. In this condition, the wake width prediction has evident convex function characteristics, which is inconsistent with the actual linear expansion of wake width. To this end, we propose a coupled model of the plume model and the traditional wake model (CPT model) to calculate the wake growth rate iteratively, thereby obtaining the wake width and velocity deficits in the far‐wake region. The average wake width prediction error decreases from 11.75% to 3.1% in these conditions. Since the wind‐turbine‐induced‐turbulence contribution is dominant in the wake recovery in the very stable boundary layer, both models have low but engineering acceptable prediction accuracy. Except for the above conditions, both the plume and CPT models can predict the wake width well, and their average wake width prediction errors are 2.5% and 1.9%, respectively. This implies that the proposed CPT model has higher prediction accuracy and a broader application range.
Poor indoor air quality indicated by elevated indoor CO2 concentrations has been linked with impaired cognitive function, yet current findings of the cognitive impact of CO2 are inconsistent. This ...review summarizes the results from 37 experimental studies that conducted objective cognitive tests with manipulated CO2 concentrations, either through adding pure CO2 or adjusting ventilation rates (the latter also affects other indoor pollutants). Studies with varied designs suggested that both approaches can affect multiple cognitive functions. In a subset of studies that meet objective criteria for strength and consistency, pure CO2 at a concentration common in indoor environments was only found to affect high‐level decision‐making measured by the Strategic Management Simulation battery in non‐specialized populations, while lower ventilation and accumulation of indoor pollutants, including CO2, could reduce the speed of various functions but leave accuracy unaffected. Major confounding factors include variations in cognitive assessment methods, study designs, individual and populational differences in subjects, and uncertainties in exposure doses. Accordingly, future research is suggested to adopt direct air delivery for precise control of CO2 inhalation, include brain imaging techniques to better understand the underlying mechanisms that link CO2 and cognitive function, and explore the potential interaction between CO2 and other environmental stimuli.
Traffic accidents usually lead to severe human casualties and huge economic losses in real-world scenarios. Timely accurate prediction of traffic accidents has great potential to protect public ...safety and reduce economic losses. However, it is challenging to predict traffic accidents due to the complex causality of traffic accidents with multiple factors, including spatial correlations, temporal dynamic interactions and external influences in traffic-relevant heterogeneous data. To overcome the above issues, this paper proposes a novel Deep Spatio-Temporal Graph Convolutional Network, namely DSTGCN, to predict traffic accidents. The proposed model is composed of three components: the first component is the spatial learning layer which performs graph convolutional operations on spatial information to learn the correlations in space. The second component is the spatio-temporal learning layer which utilizes graph and standard convolutions to capture the dynamic variations in both spatial and temporal perspective. The third component is the embedding layer which aims to obtain meaningful and semantic representations of external information. To evaluate the proposed model, we collect large-scale real-world data, including accident records, citi-wide vehicle speeds, road networks, meteorological conditions, and Point-of-Interest distributions. Experimental results on real-world datasets demonstrate that DSTGCN outperforms both classical and state-of-the-art methods.
Ultraviolet (UV) photodetectors with high responsivity and fast response are crucial for practical applications. Double perovskite Cs2AgBiBr6 has emerged as a promising optoelectronic material due to ...its excellent physics and photoelectric properties. However, no work is reported based on its film for photodetector applications. Herein, an ITO/SnO2/Cs2AgBiBr6/Au hole‐transport layer free planar heterojunction device is fabricated for photodetector application. The device is self‐powered with two responsivity peaks at 350 and 435 nm, which is suitable for ultraviolet‐A (320–400 nm) and deep‐blue light detecting. A high responsivity of 0.11 A W−1 at 350 nm and a quick response time of less than 3 ms are obtained, which is significantly higher than other semiconductor oxide heterojunction‐based UV detectors. More importantly, the stability is significantly better than most of the hybrid perovskite photodetectors reported so far. Its photocurrent shows no obvious degradation after more than 6 months storage in ambient conditions without any encapsulation. Consequently, the utilization of Cs2AgBiBr6 film is a practical approach for high performance, large‐area lead‐free perovskite photodetector applications. For the mechanism, it is found that photogenerated carriers in Cs2AgBiBr6 film are separated at the Cs2AgBiBr6/SnO2 heterojunction interface by its built‐in field. The low toxicity and high stability of this double perovskite active layer make it very promising for practical applications.
A high‐quality lead‐free double perovskite Cs2AgBiBr6 film based self‐powered photo‐detector is successfully demonstrated. The device with Cs2AgBiBr6/SnO2 heterojunction shows good wavelength selectivity for UV‐A and blue light. As an UV detector, its performance is much higher than other semiconductor oxide heterojunction‐based devices. In addition, this device has excellent stability in ambient conditions. It is believed that the double perovskite Cs2AgBiBr6 film based photodetector can be commercialized due to its low toxicity and excellent intrinsic stability.
Seismic clustering is a vital technique in seismology for identifying patterns in seismic events and providing insights into geological processes. However, its application to ongoing ...landslide-induced signals and impact of outliers have not received much research attention. This letter presents a novel consensus clustering strategy with outlier removal for landslide-induced seismic signals. The proposed approach incorporates a parameter setting method to improve clustering accuracy and robustness. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art clustering methods.
An increasing number of reports have shown that diverse microRNAs are involved in tumorigenesis and tumor progression. We performed this study to identify novel miRNAs that may be involved in lung ...cancer and study on their functions. We tested the expression of 450 miRNAs in lung tumor tissues and adjacent non-cancerous tissues. We found that miRNA-545 was less abundant in cancerous lung tissues than in adjacent non-cancerous tissues. Our further studies showed that miR-545 suppressed cell proliferation in vitro and in vivo. We also found that miR-545 caused cell cycle arrest at the G0/G1 phase and induced cell apoptosis in lung cancer cells by targeting cyclin D1 and CDK4 genes. The effects of cyclin D1 and CDK4 down-regulated by miR-545 were similar to those caused by siRNAs of cyclin D1 and CDK4, and overexpression of cyclin D1 and CDK4 could abolish the miR-545-induced inhibition of cell proliferation. In conclusion, miR-545 suppressed cell proliferation by inhibiting the expression of cyclin D1 and CDK4. Our findings provide new knowledge regarding the role of miR-545 in the development of lung cancer and indicate the potential application of miR-545 in cancer therapy.
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