Microfluidic platform for circulating tumor cells isolation and detection

Circulating tumor cells(CTCs)are essential biomarkers for liquid biopsies,which are important in the early screening,prognosis,and real-time monitoring of cancer.However,CTCs are less abundant in the peripheral blood of patients,therefore,their isolation is necessary.Recently,the use of microfluidics for CTC sorting has become a research hotspot owing to its low cost,ease of integration,low sample consumption,and unique advantages in the manipulation of micron-sized particles.Herein,we review the latest research on microfluidics-based CTC sorting.Specifically,we consider active sorting using external fields(electric,magnetic,acoustic,and optical tweezers)and passive sorting using the flow effects of cells in specific channel structures(microfiltration sorting,deterministic lateral displacement sorting,and inertial sorting).The advantages and limitations of each method and their recent applications are summarized here.To conclude,a forward-looking perspective is presented on future research on the microfluidic sorting of CTCs.

Electrokinetic flow in the U-shaped micro-nanochannels

U-shaped micro-nanochannels can generate significant flow disturbance as well as locally amplified electric field, which gives itself potential to be microfluidic mixers, electrokinetic pumps,and even cell lysis process. Numerical simulation is utilized in this work to study the hidden characteristics of the U-shaped micro-nanochannel system, and the effects of key controlling parameters(the external voltage and pressure) on the device output metrics(current, maximum values of electric field, shear stress and flow velocity) were evaluated. A large portion of current flowing through the whole system goes through the nanochannels, rather than the middle part of the microchannel, with its value increasing linearly with the increase of voltage. Due to the local ion depletion near micro-nanofluidic junction, significantly enhanced electric field(as much as 15 fold at V=1 V and P_0=0) as well as strong shear stress(leading to electrokinetic flow) is generated.With increasing external pressure, both electric field and shear stress can be increased initially(due to shortening of depletion region length), but are suppressed eventually at higher pressure due to the destruction of ion depletion layer. Insights gained from this study could be useful for designing nonlinear electrokinetic pumps and other systems.

A fast automatic identification method for seismic belts based on distance correlation and its earthquake case

Earthquake prediction practice and a large number of earthquake cases show that anomalous images of small earthquake belts may appear near the epicenter before strong earthquakes.Through the research of earthquake cases,researchers have a relatively consistent method to determine the clarity of an identified seismic belt,but there is still a lack of method on seismic belt identification from the distribution of scattered points.Due to the complexity of exhaustive algorithm,the rapid automatic identification technique of seismic belts has been progressing slowly.Visual recognition is still the basic method of seismic belt identification.Based on the algorithm of distance correlation,this paper presents a fast automatic identification method of seismic belts.The effectiveness of this method was proved by 100 random earthquakes and an example of seismic belts of magnitude 4.0 before the 2005 Jiujiang M5.7 earthquake.The results show that:①the automatic identification of seismic belts should first identify the"relational earthquake",then identify the"suspected seismic belt",and finally use the criterion of seismic belt clarity to determine;②random earthquakes and real earthquakes identification results show that the distance correlation method can realize the fast automatic identification of seismic belts by computer.

Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges

Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.

A transferable energy management strategy for hybrid electric vehicles via dueling deep deterministic policy gradient

Due to the high mileage and heavy load capabilities of hybrid electric vehicles(HEVs),energy management becomes crucial in improving energy efficiency.To avoid the over-dependence on the hard-crafted models,deep reinforcement learning(DRL)is utilized to learn more precise energy management strategies(EMSs),but cannot generalize well to different driving situations in most cases.When driving cycles are changed,the neural network needs to be retrained,which is a time-consuming and laborious task.A more efficient transferable way is to combine DRL algorithms with transfer learning,which can utilize the knowledge of the driving cycles in other new driving situations,leading to better initial performance and a faster training process to convergence.In this paper,we propose a novel transferable EMS by incorporating the DRL method and dueling network architecture for HEVs.Simulation results indicate that the proposed method can generalize well to new driving cycles,with comparably initial performance and faster convergence in the training process.