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Hereditary hearing loss is one of the most common human neurosensory disorders, and there is a great need for early intervention methods such as genetically screening newborns. Single nucleotide polymorphisms (SNPs) are the major genetic targets for hearing-loss screening. In this study, a fully integrated SNP genotyping system was constructed to identify hereditary hearing loss-related genetic markers from human whole blood. The entire detection process, including blood cell lysis, nucleic acid extraction, the reaction mixture distribution, the chambers sealing and the two-colour multiplex competitive allele-specific polymerase chain reaction (KASP), can be automatically conducted in a self-contained cassette within 3 hours. To critically evaluate the performance of the system, its specificity, sensitivity and stability were assessed. Then, 13 clinical samples were genotyped with this fluidic cassette system to detect seven hotspot deafness-associated mutations in three genes (

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 is profoundly influencing the global healthcare system and people's daily lives. The high resource consumption of coronavirus disease 2019 (COVID-19) is resulting in insufficient surveillance of coinfection or resurgence of other critical respiratory epidemics, which is of public concern. To facilitate evaluation of the current coinfection situation, a microfluidic system (MAPnavi) is developed for the rapid (40 min) and sensitive diagnosis of multiple respiratory viruses from swab samples in a fully sealed and automated manner, in which a nested-recombinase polymerase amplification and the CRISPR-based amplification system is first proposed to ensure the sensitivity and specificity. This novel system has a remarkably low limit of detection (50-200 copies mL

Protein biomarkers are widely used for disease diagnosis, but the current detection methods utilized in centralized laboratories are mainly based on enzyme-linked immunosorbent assay (ELISA)-derived sandwich-type immunoassays such as chemiluminescent or electrochemiluminescent immunoassays, which suffer from long detection times and cumbersome instruments. For the point-of-care (POC) detection of protein biomarkers, various test strips for lateral flow immunoassay (LFIA) have been manufactured, but their detection sensitivities and capabilities for raw samples are limited. In this study, an enhanced centrifugation-assisted lateral flow immunoassay (ECLFIA) was established to rapidly detect protein biomarkers in whole blood with a higher sensitivity than LFIA. By inserting a nitrocellulose membrane into a centrifugal disc, fully automated operations, including sample preparation, active lateral flow actuation, washing, and signal amplification, which could hardly be performed in conventional LFIA, were enabled on the centrifugal platform for ECLFIA. The entire process for detecting human prostate specific antigen (PSA) in a drop of blood (20 μL) could be completed in 15 min. The limit of detection for our ECLFIA system was 0.028 ng mL-1, showing a 21.4-fold improvement compared to that of LFIA. Moreover, this system was utilized to detect PSA in 34 clinical samples. The results were compared to those measured using a commercial instrument used in the hospital, and a good correlation coefficient of 0.986 was obtained, demonstrating the practicality of this ECLFIA system. In summary, the ECLFIA system established in this study can be an efficient tool for the POC detection of protein biomarkers with comprehensive advantages in sensitivity, simplicity and speed.

Long Short-Term Memory (LSTM) neural network has been widely used in many applications, but its application in classification of vehicle movement patterns is still limited. In this paper, LSTM is applied to the vehicle behavior recognition problem to identify the left turn, right turn and straight behavior of the vehicle at the intersection. On the basis of the traditional LSTM classification model, this paper transversely merges the input features and then inputs into a LSTM cell to get an improved model. The improved model can make full use of the input information and reduce unnecessary calculations, and the output of a single LSTM cell model can filter out interference information and retain important information, so it has better classification effect and faster training speed. The experimental results show that the proposed improved LSTM network classification model in this paper has a significant improvement in recognition accuracy and training speed compared with the improved model, the accuracy is increased by 1.6%, and the training time is reduced by 3.96 s. In addition, this paper also applies the improved model to regression problems, emotion classification and handwritten digit recognition and all of them have a good improvement effect, which improves the applicability and stability of LSTM in classification problems and provides a new way to deal with classification problems.

Coronavirus disease 2019 (COVID-19) has emerged, rapidly spread and caused significant morbidity and mortality worldwide. There is an urgent public health need for rapid, sensitive, specific, and on-site diagnostic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In this study, a fully integrated and portable analyzer was developed to detect SARS-CoV-2 from swab samples based on solid-phase nucleic acid extraction and reverse transcription loop-mediated isothermal amplification (RT-LAMP). The swab can be directly inserted into a cassette for multiplexed detection of respiratory pathogens without pre-preparation. The overall detection process, including swab rinsing, magnetic bead-based nucleic acid extraction, and 8-plex real-time RT-LAMP, can be automatically performed in the cassette within 80 min. The functionality of the cassette was validated by detecting the presence of a SARS-CoV-2 pseudovirus and three other respiratory pathogens, i.e., Klebsiella pneumoniae, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia. The limit of detection (LoD) for the SARS-CoV-2 pseudovirus was 2.5 copies/μL with both primer sets (N gene and ORF1ab gene), and the three bacterial species were successfully detected with an LoD of 2.5 colony-forming units (CFU)/μL in 800 μL of swab rinse. Thus, the analyzer developed in this study has the potential to rapidly detect SARS-CoV-2 and other respiratory pathogens on site in a “raw-sample-in and answer-out” manner.

Intellectualisation is one of the three reforming technologies in automotive industry, which is now changing the mobility mode and human society. High safety and intelligence are the pre-requisites for putting self-driving vehicles into markets. This study presents the hardware and software architecture for intelligent vehicles, as well as their road verification in typical traffic scenarios. The hardware system includes environmental sensors, computing platforms, vehicle actuators, and vehicle platforms, which is able to provide redundant protection against the main controller failure. The software system includes environmental perception module, scene cognition module, decision and control module, human–computer interaction module and public service support module. To evaluate the performance of the developed architecture, the road tests of automated driving system were carried out in two typical traffic scenarios, including: (i) closed road test in Yuanboyuan region; (ii) open road test on Beijing-Tianjin highway. The real road test shows that the designed hardware and software systems for intelligent vehicles have desirable robustness, which can realise accurate and reliable environment perception, decision-making and motion control.

The lab-on-a-disc is a powerful microfluidic platform that skillfully takes advantage of centrifugal force to controllably drive liquids with the assistance of passive or active valves. However, the passive valves are mainly triggered by the rotation speed and can be easily influenced by the surface chemistry of the channel, while the active valves usually require a complicated fabrication or actuation procedure. In this study, a novel active valve that can be easily triggered by an electromagnet was proposed and applied on the centrifugation platform. This valve, named the electromagnet-triggered pillar (ETP) valve, consisted of a metal pin and pressure sensitive adhesive (PSA) tape, and is closed until the pin is lifted up by an electromagnet to partially separate the PSA tape from the substrate. As a typical application, this valve is utilized to construct a centrifugal chip for mycotoxin detection. With four ETP valves in a unit, the sample and liquid reagents can be sequentially released into the reaction chamber that was spotted with mycotoxin conjugates to accomplish the whole immunoassay. Four mycotoxins (aflatoxin B1, ochratoxin A, T-2 toxin, and zearalenone) were simultaneously detected on this chip with limits of detection lower than the permissible limits set by the regulatory agencies of China, demonstrating the practicability of this easy-to-use active valve.

Extracellular vesicles (EVs) are cell-derived nanoscale vesicles that provide promising biomarkers for the non-invasive diagnosis of cancer because they carry important cancer-related DNA, RNA and protein biomarkers. However, the clinical application of EVs is limited by tedious and non-standardized isolation methods that require bulky instrumentation. Here, we propose an easy-to-operate, simple dielectrophoretic (DEP) method for EV isolation with higher recovery efficiency (>83%) and higher purity than ultracentrifugation (UC). The DEP chip reduces the isolation procedure from 8 h to 30 min. To facilitate subsequent analysis, our DEP chip achieved integration of EV isolation and in situ lysis of EVs for the first time. Our chip also achieved on-chip siRNA delivery to EVs isolated by DEP. We found that EVs isolated from the plasma of lung cancer patients contained higher levels of miR-21, miR-191 and miR-192 compared to those from healthy people. With on-chip detection, EGFR in EVs could distinguish lung cancer patients from healthy people. Overall, this study provides an efficient and practical approach to the isolation and detection of EVs, which could be used for the early diagnosis of lung cancer.

Various types of valving techniques, especially passive valves, have been demonstrated on centrifugal microfluidic devices, which are simple to fabricate and easy to implement. In this study, a novel interruptible siphon valving technique is proposed to enhance the flexibility for the flow control on passive centrifugal microfluidic platforms. Differs from traditional siphon valves, the main feature of the interruptible valve is having an additional air vent at the crest of the siphon channel. Using this design, a primed siphon can discharge liquids in a proper range of spin speed, or be cut off by further increasing the rotation frequency. An analytical model of the interruptible siphon valving is proposed and verified to estimate the cut-off spin speed, which can be a guidance for the device design. To demonstrate the potential of this valve for flow control, five typical applications based on interruptible siphon valving were proposed and evaluated, including band-pass siphon valving, repeated washing, inward pumping, programmable flow control and flow sequencing. Our methods are well balanced in simplicity and flexibility, and have significant potential to be applied in multistep biochemical analysis, immunoassays and nucleic acid tests.

In order to make an accurate prediction of vehicle trajectory in a dynamic environment, a Unidirectional and Bidirectional LSTM (UB-LSTM) vehicle trajectory prediction model combined with behavior recognition is proposed, and then an acceleration trajectory optimization algorithm is proposed. Firstly, the interactive information with the surrounding vehicles is obtained by calculation, then the vehicle behavior recognition model is established by using LSTM, and the vehicle information is input into the behavior recognition model to identify vehicle behavior. Then, the trajectory prediction model is established based on Unidirectional and Bidirectional LSTM, and the identified vehicle behavior and the input information of the behavior recognition model are input into the trajectory prediction model to predict the horizontal and vertical speed and coordinates of the vehicle in the next 3 seconds. Experiments are carried out with NGSIM data sets, and the experimental results show that the mean square error (MSE) between the predicted trajectory and the actual trajectory obtained by this method is 0.124, which is 97.2% lower than that of the method that does not consider vehicle behavior and directly predicts the trajectory. The test loss is 0.000497, which is 95.68% lower than that without considering vehicle behavior. The predicted trajectory is obviously optimized, closer to the actual trajectory, and the performance is more stable.

The gold standard for diagnosing infectious diseases is culture-based identification of bacterial pathogens, which is time-consuming and labour-intensive. Current advances in molecular diagnostics and microfluidic technologies have made the rapid detection of bacteria or viruses in clinical specimens possible. However, the need for rapid, sensitive and multiplex detection of pathogens in a "sample-in and answer-out" manner has not been fully satisfied. In this study, a self-contained and fully integrated fluidic cassette and its supporting analyser were constructed for multiplex detection of bacteria to accelerate the diagnosis of urinary tract infections (UTIs). The fully integrated cassette contains all the necessary components and reagents for bacterial analysis. All of the bacterial analysis processes, including bacterial lysis, magnetic silica bead-based DNA extraction, DNA elution and multiplex loop-mediated amplification (LAMP), are automatically conducted in the cassette. This cassette was successfully applied for the detection of four major pathogenic bacteria in UTIs, i.e., Escherichia coli, Proteus mirabilis, Salmonella typhimurium and Staphylococcus aureus. The first three were successfully detected with a limit of detection (LoD) of 1 colony-forming unit (CFU) μL-1 and the last was with a LoD of 10 CFU μL-1 in urine samples, demonstrating that the cassette has similar sensitivity compared to that of the manual protocol, which is lower than that required by UTIs. The turnaround time for this cassette-based sample-to-answer system was approximately 100 minutes, and the detection is sensitive, fully automated, and accurate, demonstrating the potential to be a useful diagnostic tool for UTIs.

To prevent the propagation of pathogens and minimize the damage to humans, an ultrasensitive and colorimetric dual detection method for pathogens was developed and validated based on the Multiplex Super Polymerase Chain Reaction (MS-PCR) and Asymmetric Tailing Hybridization Chain Reaction (AT-HCR). In the 5-min MS-PCR, dual pathogens were recognized and amplified simultaneously, generating plenty of dsDNA products with protruding ssDNA toeholds. Since a blocker of oxyethyleneglycol bridge was exquisitely inserted into two-set forward primers, which hindered the polymerase extension. Then, two-set ssDNA toeholds initiated two-set AT-HCR in 20-min and 60-min visual analysis depending on G-quadruplex DNAzyme was induced by Terminal deoxynucleotidyl Transferase (TdT) at the tails of AT-HCR at 3′-OH. During the process, the first magnetic separation was conducted after MS-PCR based on our home-made XP beads, to remove primer-dimers and other impurities in 30-min. The second magnetic separation was performed before TdT catalysis based on dynabeads, to eliminate unreacted AT-HCR hairpins in 15-min. To challenge the practical application capability of this strategy, the detection of analyte in fat-free milk was also tested and demonstrated similar detection capability to visualize single cell. Therefore, the proposed sensor exhibited a high selectivity and sensitivity and is thus applicable for visual single cell detection of dual-pathogens of Salmonella spp. and Staphylococcus aureus simultaneously in 130-min. Generally, it provides a visual, ultrasensitive, rapid and universal detective method for dual-target analysis.

MicroRNAs (miRNAs) are endogenous molecules with regulatory functions. The purification and enrichment of miRNA are essential for its precise and sensitive detection. miRNA isolated using commercial kits contains abundant interfering RNAs, and the concentration of miRNA may not be adequate for detection. Herein, we prepared a reduced graphene oxide (rGO)-based magnetic solid-phase extraction material for the enrichment and ultrasensitive detection of miRNA from intricate nucleic acid solutions. In situ reverse transcription (RT) was developed as the most efficient approach to desorb miRNA from rGO among the methods that are compatible for the subsequent amplification reported thus far. Additionally, rolling circle amplification and qPCR were used to detect let-7a with a decrease of the limit of detection by 24.7- and 31.3-fold, respectively. This material was also successfully used to extract and detect miRNA from total RNA isolated from human plasma. Our results show that the material prepared in this study has the potential for cancer biopsy in clinics and the discovery of new miRNAs in scientific research.

Centrifugal microfluidics has proven to be successful in biomedical diagnostics, biological analysis and environmental monitoring. However, the automation of multi-step sample processing, reaction and detection remains a great challenge. In this study, a conditional siphon priming technique is introduced for multi-step liquid addition or selective routing. Since the siphon channel is locally modified or venting is blocked by liquid in another chamber, siphon priming can be triggered by liquid addition or venting at low frequency. Using this technique, sequential release of liquids and selective routing in multiple manners were successfully achieved. As a proof of concept, a centrifugal microfluidic platform was designed for on-site ammonium analysis in water samples. The linear range of ammonium concentrations is extended by integration of a dilution process. This novel valving technique provides new solutions for integration of complex liquid handling processes on centrifugal platforms.

Urine examination is a basic program in routine check-ups that can reveal multiple parameters about our health and thus has significant meaning. However, instruments for urine analysis in hospitals are usually bulky and expensive. In this study, we have constructed a pocket-sized and CMOS imaging-based analytical device for urine diagnosis at home or on the spot. The shape of traditional dipsticks was transformed into matrix arrays and pasted on a reaction unit for colorimetric arrays. Chromaticity values were used to establish the functional relationship between the red-green-blue intensities of images and the concentrations of different indices. The size of the entire system was reduced to 30 × 30 × 45 mm, and once charged this device can continuously work for >4 h accommodating more than 1000 tests. To validate the performance of our device, more than 200 real human urine samples were tested and the results generated by our portable device and a commercial instrument show a great consistency (the consistency for most results is >90%) between the two systems.

Microfluidic electric impedance flow cytometry (IFC) chips have strong advantages over the traditional flow cytometry system because they are self-contained, disposable, economic in reagent consumption, and easier to operate. However, the throughput, sensitivity, and simplicity of the microfluidic IFC chips are inversely related to one another, and their reported impedance-based cell differentiation capability is in general limited. In this paper, we designed a sheath-less microfluidic IFC chip with a constriction structure between the detection electrodes to enhance the particle sensing performance, and built the entire sensing system around it, including the sensing circuit and data processing software. The measurement of the volume, limit of detection (∼3 μm), coefficient variation (6.83%) and other characteristics of the device was performed using the standard polymer beads. This sheath-less polydimethylsiloxane microfluidic device had a simple structure, which could maintain a single-cell sequence flow at the detection area, and displayed high signal-to-noise ratio (23.5–32.6 dB) and low coincidence ratio signals for further analysis. The throughput of the chip for single-cell screening can reach up to 172 cells per s, and thus, 10 000 cells could be analyzed in a few minutes for statistical analysis. Moreover, the electrical conductance and susceptance were found to be good at differentiating the bead/cell sizes and membrane/surface characteristics of cells/beads, respectively. These parameters were used to classify the population of a large amount of drug-treated cells (>10 000 cells per sample), displaying good performance in distinguishing apoptotic/necrotic cells from live cells. The ratios of apoptotic, necrotic, and live cells analyzed using our system were consistent with the traditional flow cytometry results (R2 = 0.9796). Along with the miniaturization of the electric sensing circuit, our system can be applicable for novel, compact and easy operative (single) cell analysis systems in the future.

Antibiotic residues and illegal additives are among the most common contaminants in milk and other dairy products, and they have become essential public health concerns. To ensure the safety of milk, rapid and convenient screening methods are highly desired. Here, we integrated microarray technology into a microfluidic device to achieve rapid, sensitive, and fully automated detection of chloramphenicol, tetracyclines, enrofloxacin, cephalexin, sulfonamides, and melamine in milk on a centrifugal microfluidic platform with two rotation axes. All the liquid reagent for the immunoassay was prestored in the reagent chambers of the microdevice and can be released on demand. The whole detection can be automatically accomplished within 17 min, and the limits of detection were defined as 0.92, 1.01, 1.83, 1.14, 1.96, and 7.80 μg/kg for chloramphenicol, tetracycline (a typical drug of tetracyclines), enrofloxacin, cephalexin, sulfadiazine (a typical drug of sulfonamides), and melamine, respectively, satisfying the national standards for maximum residue limits in China. Raw milk samples were used to test the performance of the current immunoassay system, and the recovery rates in the repeatability tests ranged from 80 to 111%, showing a good performance. In summary, the immunoassay system established in this study can simultaneously detect six contaminants of four samples in a fully automated, cost-effective, and easy-to-use manner and thus has great promise as a screening tool for food safety testing.

The genotyping of SNPs (single nucleotide polymorphisms) is a prerequisite for the analysis of many genetic diseases, including hereditary hearing-loss. However, the existing methods for SNP detection suffer from a long detection period, tedious operation, and a high risk of carryover contamination. To address these challenges, a microfluidic chip is constructed for rapid and efficient SNP genotyping by dividing the sample into many independent chambers for Kompetitive Allele Specific PCR in this study. Using this strategy, multiple detection can be easily accomplished and the challenge for the establishment of multiplex PCR is fundamentally overcome. The entire detection can be finished within 2 h in a fully sealed manner with this method, which is quite simple compared to SNaPshot and MassArray. After assessment of the basic performance, this chip was applied to screen 15 mutations, including SNPs and InDels (insertion-deletion markers), that can cover more than 80% of cases of hereditary hearing-loss in China. Over 40 clinical samples were analyzed with this microfluidic chip for SNP genotyping, and the results are consistent with that obtained by Sanger sequencing, demonstrating its practicability and potential in the application of genetic disease detection.

Lateral flow immunoassay (LFIA) is widely used but is limited by its sensitivity. In this study, a novel centrifugation-assisted lateral flow immunoassay (CLFIA) was proposed that had enhanced sensitivity compared to traditional LFIA based on test strips. For CLFIA, a vaulted piece of nitrocellulose membrane was prepared and inserted into a centrifugal disc. Powered by the centrifugal force, the sample volume on the disc was not limited and the flow rate of the reaction fluid was steady and adjustable at different rotation speeds. It was found that lower rotation speeds and larger sample volumes resulted in greater signal intensity in the nitrocellulose membrane as well as higher sensitivity, indicating that the actively controlled flow on the disc allowed for sensitivity enhancement of CLFIA. To operate CLFIA on the centrifugal disc, a portable and cost-effective operating device was constructed to rotate the disc with a stepper motor and collect the results with a smartphone. The proposed method was successfully applied to detect prostate specific antigen (PSA) in human serum. Standard curves were established for CLFIA and LFIA, and both had correlation coefficients of up to 0.99. Under optimal conditions (1500 rpm rotation speed, 120 μL sample volume), the detection limit of CLFIA reached 0.067 ng/mL, showing a 6.2-fold improvement in sensitivity compared to that of LFIA. With clinical serum samples, a good correlation was observed between PSA concentrations measured by CLFIA and by a bulky commercial instrument in hospital. In summary, this portable, cost-effective, and easy-to-use system holds great promise for biomarker detection with enhanced sensitivity compared to traditional LFIA.

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.

The ability to perform long-range pedestrian detection is essential for autonomous vehicles. However, for 3-D LIDAR, an object's point cloud becomes sparse when it is away, directly affecting its detection as a result. In this paper, a novel density enhancement method is proposed to improve the quality of a sparse point cloud. The input of the method is an object's raw point cloud; first, a high-quality local coordinate system of the point cloud is built using a new evaluation metric, and then radial basis function (RBF)-based interpolation is performed based on the local coordinate system. Finally, a resampling algorithm is used to generate a new point cloud that not only meets a density requirement but also fits the object's geometric shape. Novel features of our method are its evaluation metric of a local coordinate system and method to choose a good shape parameter and kernel in RBF-based interpolation step. The effectiveness of this method is demonstrated using naturalistic data and three experiments.