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Olaquindox (OLA) and quinocetone (QCT) have been prohibited in aquatic products due to their significant toxicity and side effects. In this study, rapid and visual europium nanoparticle (EuNP)-based lateral flow strip biosensors (LFSBs) were developed for the simultaneous quantitative detection of OLA, QCT, and 3-methyl-quinoxaline-2-carboxylic acid (MQCA) in fish feed and tissue. The EuNP-LFSBs enabled sensitive detection for OLA, QCT, and MQCA with a limit of detection of 0.067, 0.017, and 0.099 ng/mL (R2 ≥ 0.9776) within 10 min. The average recovery of the EuNP-LFSBs was 95.13%, and relative standard deviations were below 9.38%. The method was verified by high-performance liquid chromatography (HPLC), and the test results were consistent. Therefore, the proposed LFSBs serve as a powerful tool to monitor quinoxalines in fish feeds and their residues in fish tissues.

The spline teeth fracture of separator plates in wet multi-plate clutches compromises driving safety and the vehicle’s lifespan. Tooth fracture is mainly caused by stress concentration at the tooth root and uneven circumferential load distribution. This paper considers parameters such as torque, teeth count, tooth profile, and misalignment errors, establishing the corresponding finite element (FE) model to analyze the impact of the above-mentioned parameters on the strength of the separator plates. Analysis under even and biased load circumstances demonstrated that an optimum tooth count and profile can significantly increase the strength of the separator plates, offering advice for the optimized design of wet multi-plate clutch separator plates.

The H5N1 avian influenza virus seriously affects the health of poultry and humans. Once infected, the mortality rate is very high. Therefore, accurate and timely detection of the H5N1 avian influenza virus is beneficial for controlling its spread. This article establishes a dual gene detection method based on dual RPA for simultaneously detecting the HA and M2 genes of H5N1 avian influenza virus, for the detection of H5N1 avian influenza virus. Design specific primers for the conserved regions of the HA and M2 genes. The sensitivity of the dual RT-RPA detection method for HA and M2 genes is 1 × 10−7 ng/μL. The optimal primer ratio is 1:1, the optimal reaction temperature is 40 °C, and the optimal reaction time is 20 min. Dual RT-RPA was used to detect 72 samples, and compared with RT-qPCR detection, the Kappa value was 1 (p value < 0.05), and the clinical sample detection sensitivity and specificity were both 100%. The dual RT-RPA method is used for the first time to simultaneously detect two genes of the H5N1 avian influenza virus. As an accurate and convenient diagnostic tool, it can be used to diagnose the H5N1 avian influenza virus.

Small molecules are significant risk factors for causing food safety issues, posing serious threats to human health. Sensitive screening for hazards is beneficial for enhancing public security. However, traditional detection methods are unable to meet the requirements for the field screening of small molecules. Therefore, it is necessary to develop applicable methods with high levels of sensitivity and specificity to identify the small molecules. Aptamers are short−chain nucleic acids that can specifically bind to small molecules. By utilizing aptamers to enhance the performance of recognition technology, it is possible to achieve high selectivity and sensitivity levels when detecting small molecules. There have been several varieties of aptamer target recognition techniques developed to improve the ability to detect small molecules in recent years. This review focuses on the principles of detection platforms, classifies the conjugating methods between small molecules and aptamers, summarizes advancements in aptamer−based conjugate recognition techniques for the detection of small molecules in food, and seeks to provide emerging powerful tools in the field of point−of−care diagnostics.

The reduction of friction-induced noise is a crucial research area for enhancing vehicle comfort, and this paper proposes a method based on circular pit texture to achieve this goal. We conducted a long-term sliding friction test using a pin-on-disc friction and a wear test bench to verify the validity of this method. To compare the friction noise of different surfaces, texture units with varying line densities were machined on the surface of friction disk samples. The resulting friction-wear and noise characteristics of the samples were analyzed in conjunction with the microscopic morphology of the worn surfaces. The results indicate that surfaces with textures can delay the onset of squeal noise, and the pattern of its development differs from that of smooth surfaces. The noise reduction effect is most evident due to the proper distribution of textures that can form furrow-like wear marks at the wear interface. The finite element results demonstrate that this morphology can improve pressure distribution at the leading point and reduce the tendency of system instability.

Cynomorium songaricum Rupr. (CSR) belongs to the family Cynomoriaceae. It is a perennial succulent parasitic herb with a reddish-brown coloration, predominantly submerged in sand and lacking chlorophyll. Traditionally, it has been used in ethnic medicine to treat various diseases, such as gastric ulcers, indigestion, bowel movements, and improving sexual function. To comprehensively collect CSR data, extensive literature searches were conducted using medical, ecological, and scientific databases such as Google Scholar, PubMed, Science Direct, Web of Science, and China National Knowledge Infrastructure (CNKI). This article summarizes and categorizes research on the uses, phytochemical characteristics, pharmacological activities, and toxicity of ethnic medicine, with the aim of establishing a solid foundation and proposing new avenues for exploring and developing potential applications of CSR. So far, a total of 98 compounds have been isolated and identified from CSR, including flavonoids, terpenes, steroids, and other compounds. It is worth noting that flavonoids and polysaccharides have significant antioxidant and anti-inflammatory properties. In addition, these compounds also show good application prospects in anti-tumor, antioxidant, anti-aging, anti-fatigue, anti-diabetes, and other aspects. Although extensive progress has been made in the basic research of CSR, further research is still needed to enhance the understanding of its mechanism of action and explore more unknown compounds. Our review indicates that CSR has broad prospects and deserves further research.

Avian influenza is caused by avian influenza virus infection; the H5N1 avian influenza virus is a highly pathogenic subtype, affecting poultry and human health. Since the discovery of the highly pathogenic subtype of the H5N1 avian influenza virus, it has caused enormous losses to the poultry farming industry. It was recently found that the H5N1 avian influenza virus tends to spread among mammals. Therefore, early rapid detection methods are highly significant for effectively preventing the spread of H5N1. This paper discusses the detection technologies used in the detection of the H5N1 avian influenza virus, including serological detection technology, immunological detection technology, molecular biology detection technology, genetic detection technology, and biosensors. Comparisons of these detection technologies were analyzed, aiming to provide some recommendations for the detection of the H5N1 avian influenza virus.

Graphene is considered a good lubricant additive. The lubricating properties of graphene lubricant at different concentrations and temperatures are studied via a four-ball friction and wear-testing machine. The results show that the coefficient of friction (COF) and wear scar diameter (WSD) of the steel ball with 0.035 wt% graphene lubricant decreased by 40.8% and 50.4%, respectively. Finally, through surface analysis, the following lubrication mechanism is proposed: as the added graphene particles can easily fill and cover the pores of the friction surface, the contact pressure of the rough peak is reduced, resulting in a lower COF and smoother surface. Although the COF increases with temperature, graphene lubricants still exhibit good lubrication effects.

To solve grid-scale problems and evaluation indicator selection in landscape ecological risk index (LERI) evaluation, this paper takes the Bailong River Basin in Gansu Province (BLRB) as an example. The LERI evaluation formulae and optimal grid scales were determined by screening landscape indices and area changes in the LERI at different grid scales. The evaluation indices were finally obtained according to the landscape characteristics and the correlation analysis of the landscape index value. Through the statistical analysis of the area of the LERI at the grid scale of 1–6 km, the optimal grid scale was determined to be 5 km. There was little change in land use patterns, with the most significant increases in artificial surfaces at 3.29% and 3.58%, respectively. Cultivated land was the only land use type to decrease by 184.3 km2. The LERI drops with the reduced cultivated land area; the landscape ecological medium risk area and cultivated land keep the same spatial distribution. Due to the limitation of the topography, cultivated land is generally distributed below 2500 m altitude, so 2500 m becomes the turning point in the spatial distribution of the LERI. The medium risk below 2500 m dominates the LERI type. Reduced cultivated land was the leading cause of reduced ecological risk according to an overlay analysis. The study of LERI evaluations provides a theoretical basis for sustainable and ecological environmental protection in the BLRB.

The recent pandemic of SARS-CoV-2 has underscored the critical need for rapid and precise viral detection technologies. Point-of-care (POC) technologies, which offer immediate and accurate testing at or near the site of patient care, have become a cornerstone of modern medicine. Prokaryotic Argonaute proteins (pAgo), proficient in recognizing target RNA or DNA with complementary sequences, have emerged as potential game-changers. pAgo present several advantages over the currently popular CRISPR/Cas systems-based POC diagnostics, including the absence of a PAM sequence requirement, the use of shorter nucleic acid molecules as guides, and a smaller protein size. This review provides a comprehensive overview of pAgo protein detection platforms and critically assesses their potential in the field of viral POC diagnostics. The objective is to catalyze further research and innovation in pAgo nucleic acid detection and diagnostics, ultimately facilitating the creation of enhanced diagnostic tools for clinic viral infections in POC settings.

Lanzhou is the only provincial capital city in Northwest China where the main stream of the Yellow River and its tributaries flow through the city. Due to its geographical location and the influence of various factors, it is difficult to evaluate and simulate the climatic, hydrological, and ecological processes of the main stream of the Yellow River and its tributaries in the region. In this study, the Wanchuan River basin, currently undergoing ecological restoration, was selected as the study area. Seasonal backscatter differences generated using Sentinel-1/2 (S1/S2) data and the CD algorithm were used to reduce the effects of surface roughness; vegetation indices, soils, and field measurements were used to jointly characterize the vegetation contribution and soil contribution. Then, SM maps with a grid spacing of 10 m × 10 m were generated in the Wanchuan River basin, covering an area of 1767.78 km2. To validate the results, optimal factors were selected, and a training set and validation set were constructed. The results indicated a high level of the coefficient of determination (R2) of 0.78 and the root mean square error (RMSE) of 0.08 for the comparison of measured and inverted water contents, indicating that the algorithm retrieved the SM values of the study area well. Furthermore, Box line plots with ERA5-Land and GLDAS confirmed that the algorithm is in good agreement with current SM products and feasibility for soil water content inversion work in the Wanchuan River basin.

Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.

Auricularia auricula is a traditional medicinal and edible mushroom with anti-aging effects. Many studies focused on polysaccharides and melanin. However, the anti-aging effects and mechanism of the nutritional supplementation of Auricularia auricula peptides (AAPs) were not elucidated. In this study, AAPs were prepared by enzymolysis of flavor protease and the protective effects on H2O2-induced senescence of HepG2 cells were explored for the first time. The potential mechanism was also investigated. AAPs were mostly composed of low molecular weights with less than 1000 Da accounting for about 79.17%, and contained comprehensive amino acids nutritionally, including seven essential amino acids, aromatic, acidic, and basic amino acids. AAPs nutritional supplementation could significantly decrease the levels of intracellular reactive oxygen species (ROS) and malondialdehyde (MDA), and increase the activities of antioxidant enzymes (SOD, CAT, and GSH-Px). In addition, the senescence-associated-β-galactosidase (SA-β-gal) activity was restrained, and the expression levels of senescence-associated secretory phenotype (SASP) (IL-6, IL-8, IL-1β, and CXCL2) were also decreased. Ribonucleic acid sequencing (RNA-Seq) was carried out to screen the differentially expressed genes (DEGs) between different groups. GO and KEGG enrichment analysis showed that the mechanism was related to the MAPK/NF-κB signaling pathways. Quantitative real-time PCR (qRT-PCR) analysis and Western blot were carried out to verify the key genes and proteins in the pathways, respectively. AAPs nutritional supplementation resulted a significant down-regulation in key the genes c-fos and c-jun and up-regulation in DUSP1 of the MAPK signaling pathway, and down-regulation in the key genes CXCL2 and IL-8 of the NF-κB signaling pathway. The results of Western blot demonstrate that AAPs nutritional supplementation could inhibit MAPK/NF-κB pathways by reducing the expression levels of IKK, IκB, P65, and phosphorylation of ERK, thus decreasing the inflammatory reaction and delaying cell senescence. It is the first time that AAPs nutritional supplementation was proved to have protective effects on H2O2-induced oxidative damage in HepG2 cells. These results implicate that dietary AAPs could be used as nutrients to reduce the development or severity of aging.

Mushroom bioactive peptides (MBPs) are bioactive peptides extracted directly or indirectly from edible mushrooms. MBPs are known to have antioxidant, anti-aging, antibacterial, anti-inflammatory and anti-hypertensive properties, and facilitate memory and cognitive improvement, antitumour and anti-diabetes activities, and cholesterol reduction. MBPs exert antioxidant and anti-inflammatory effects by regulating the MAPK, Keap1-Nrf2-ARE, NF-κB and TNF pathways. In addition, MBPs exert antibacterial, anti-tumour and anti-inflammatory effects by stimulating the proliferation of macrophages. The bioactivities of MBPs are closely related to their molecular weights, charge, amino acid compositions and amino acid sequences. Compared with animal-derived peptides, MBPs are ideal raw materials for healthy and functional products with the advantages of their abundance of resources, safety, low price, and easy-to-achieve large-scale production of valuable nutrients for health maintenance and disease prevention. In this review, the preparation, bioactivities, mechanisms and structure–activity relationships of MBPs were described. The main challenges and prospects of their application in functional products were also discussed. This review aimed to provide a comprehensive perspective of MBPs.

The wellbore of a sucker-rod pumping well experiences a multi-phase flow consisting of oil, gas, and water. The flow pattern and pump discharge pressure are greatly impacted by oil well production, which in turn significantly affects the simulation results of longitudinal vibration in the sucker-rod string. When calculating the discharge pressure in a hydrostatic column containing both oil and water (HC), the pressure is not affected by the oil well’s production. This thereby avoids interference between vibrations in the sucker-rod string’s longitudinal direction and the flow from the wellbore. Considering the coupling characteristics between the longitudinal vibration of the sucker-rod string and the wellbore flow, a mathematical model of the sucker-rod pumping system (CMSRS) and a mathematical model of the downhole energy efficiency parameters were established. In detail, the CMSRS comprises two parts: the discharge pressure mathematical models of multi-phase flow dynamics (MD) and the wave equation of the longitudinal vibration of the sucker-rod string. A numerical simulation model of the sucker-rod pumping system was constructed based on a mathematical model. We compared the experimental results, the simulation results of the CMSRS and the simulation results of the sucker-rod string based on the oil-water two–phase hydrostatic column (SMSRS) and found good agreement, indicating the feasibility of the CMSRS. The simulation details show the following: (1) The HC model’s discharge pressure exceeds that of the MD model by more than 33.52%. The polished rod load for the CMSRS is 18.01% lower than that of the SMSRS, and the pump input power for the CMSRS is 36.23% lower than that of the SMSRS. (2) The effective power simulation model based on the energy balance relationship is essentially the same as the effective power calculated by the model based on multi-phase flow effective power. This validates the accuracy of the multi-phase flow effective power model. (3) The limitations of the industry standard effective power model are that (i) the effective head is the net lift height of the fluid in the wellbore reduced to the oil and water phases rather than the effective lift height based on the energy balance relationship and (ii) the power of the gas phase delivered by the pumping pump is disregarded, and only the effective power of the pump delivering the oil-water mixture is considered. (4) The influence of the wellbore parameters on the wellbore efficiency and sub–efficiency is systematically analyzed. The analysis results have an important significance in the guidance of energy saving in pumping wells.

The five recognized zoonotic foodborne pathogens, namely, Listeria monocytogenes, Staphylococcus aureus, Streptococcus suis, Salmonella enterica and Escherichia coli O157:H7, pose a major threat to global health and social–economic development. These pathogenic bacteria can cause human and animal diseases through foodborne transmission and environmental contamination. Rapid and sensitive detection for pathogens is particularly important for the effective prevention of zoonotic infections. In this study, rapid and visual europium nanoparticle (EuNP)-based lateral flow strip biosensors (LFSBs) combined with recombinase polymerase amplification (RPA) were developed for the simultaneous quantitative detection of five foodborne pathogenic bacteria. Multiple T lines were designed in a single test strip for increasing the detection throughput. After optimizing the key parameters, the single-tube amplified reaction was completed within 15 min at 37 °C. The fluorescent strip reader recorded the intensity signals from the lateral flow strip and converted the data into a T/C value for quantification measurement. The sensitivity of the quintuple RPA-EuNP-LFSBs reached a level of 101 CFU/mL. It also exhibited good specificity and there was no cross-reaction with 20 non-target pathogens. In artificial contamination experiments, the recovery rate of the quintuple RPA-EuNP-LFSBs was 90.6–101.6%, and the results were consistent with those of the culture method. In summary, the ultrasensitive bacterial LFSBs described in this study have the potential for widespread application in resource-poor areas. The study also provides insights in respect to multiple detection in the field.

Porcine epidemic diarrhea virus (PEDV), porcine bocavirus (PBoV), and porcine rotavirus (PoRV) are associated with porcine viral diarrhea. In this study, triplex loop-mediated isothermal amplification (LAMP) combined with a lateral flow dipstick (LFD) was established for the simultaneous detection of PEDV, PoRV, and PBoV. The PEDV-gp6, PoRV-vp6, and PBoV-vp1 genes were selected to design LAMP primers. The amplification could be carried out at 64 °C using a miniature metal bath within 30 min. The triplex LAMP–LFD assay exhibited no cross-reactions with other porcine pathogens. The limits of detection (LODs) of PEDV, PoRV, and PBoV were 2.40 × 101 copies/μL, 2.89 × 101 copies/μL, and 2.52 × 101 copies/μL, respectively. The consistency between rt-qPCR and the triplex LAMP–LFD was over 99% in field samples testing. In general, the triplex LAMP–LFD assay was suitable for the rapid and simultaneous detection of the three viruses in the field.

Drug abuse is becoming increasingly dangerous nowadays. Morphine (MOP), methamphetamine (MET) and ketamine (KET) are the most commonly abused drugs. The abuse of these drugs without supervision can cause serious harm to the human body and also endanger public safety. Developing a rapid and accurate method to screen drug suspects and thus control these drugs is essential to public safety. This paper presents a method for the simultaneous quantitative detection of these three drugs in hair by a europium nanoparticles-based fluorescence immunochromatographic assay (EuNPs−FIA). In our study, the test area of the nitrocellulose membrane was composed of three equally spaced detection lines and a quality control line. The test strip realized the quantitative analysis of the samples by detecting the fluorescence brightness of the europium nanoparticles captured on the test line within 15 min. For the triple test strip, the limits of detection of MOP, KET and MET were 0.219, 0.079 and 0.329 ng/mL, respectively. At the same time, it also showed strong specificity. The strip was stable and could be stored at room temperature for up to one year, and the average recovery rate was 85.98–115.92%. In addition, the EuNPs−FIA was validated by high-performance liquid chromatography (HPLC) analysis, and a satisfactory consistency was obtained. Compared to the current immunochromatographic methods used for detecting abused drugs in hair, this method not only increased the number of detection targets, but also ensured sensitivity, improving detection efficiency to a certain extent. The approach can also be used as an alternative to chromatography. It provides a rapid and accurate screening method for the detection of abused drugs in hair and has great application prospects in regard to public safety.

The compression molding performance of a substrate block has a significant effect on the quality and stability of mechanical transplanting. The physical experiment and DEM simulation were combined to evaluate the compression molding behavior of substrate block in this study. A calibration procedure of DEM parameters of peat particles was proposed at first. Then, the above parameters were brought into the contact model of the compression system–particles, and the effect of the loading speed on the compression behavior of the peat substrate block was investigated. The compressive force–displacement curves of the simulated and measured tests were all contained in the initial linear stage and non-linear stiffing stage. The particle number of central sections was higher than side section, and the variable coefficient was greater at higher loading speed. The substrate blocks all expanded after demolding. The higher the loading speed, the greater the expansion in the height’s direction, and the easier it was for cracks to be generated near the bottom. This study will provide a reference for the design of substrate block forming machines.

Conventional enzyme-based continuous glucose sensors in interstitial fluid usually rely on dissolved oxygen as the electron-transfer mediator to bring electrons from oxidase to electrode while generating hydrogen peroxide. This may lead to several problems. First, the sensor may provide biased detection results owing to fluctuation of oxygen in interstitial fluid. Second, the polymer coatings that regulate the glucose/oxygen ratio can affect the dynamic response of the sensor. Third, the glucose oxidation reaction continuously produces corrosive hydrogen peroxide, which may compromise the long-term stability of the sensor. Here, we introduce an oxygen-independent nonenzymatic glucose sensor based on water splitting-assisted electrocatalysis for continuous glucose monitoring. For the water splitting reaction (i.e., hydrogen evolution reaction), a negative pretreatment potential is applied to produce a localized alkaline condition at the surface of the working electrode for subsequent nonenzymatic electrocatalytic oxidation of glucose. The reaction process does not require the participation of oxygen; therefore, the problems caused by oxygen can be avoided. The nonenzymatic sensor exhibits acceptable sensitivity, reliability, and biocompatibility for continuous glucose monitoring in hypoxic environments, as shown by the in vitro and in vivo measurements. Therefore, we believe that it is a promising technique for continuous glucose monitoring, especially for clinically hypoxic patients.

The growth of field crops needs appropriate soil nutrients. As a basic fertilizer, liquid manure provides biological nutrients for crop growth and increases the content of organic matter in crops. However, improper spraying not only reduces soil fertility but also destroys soil structure. Therefore, the precise control of the amount of liquid manure is of great significance for agricultural production and weight loss. In this study, we first built the model of spraying control, then optimized the BP neural network algorithm through a genetic algorithm. The stability and efficiency of the optimized controller were compared with PID, fuzzy PID and BPNN-PID control. The simulation results show that the optimized algorithm has the shortest response time and lowest relative error. Finally, platform experiments were designed to verify the four control algorithms at four different vehicle speeds. The results show that, compared with other control algorithms, the control algorithm described here has good stability, short response time, small overshoot, and can achieve an accurate fertilizer application effect, providing an optimization scheme for research on the precise application of liquid manure.

Ferroptosis is a new form of iron-dependent cell death and plays an important role during the occurrence and development of various tumors. Increasingly, evidence shows a convincing interaction between ferroptosis and tumor immunity, which affects cancer patients’ prognoses. These two processes cooperatively regulate different developmental stages of tumors and could be considered important tumor therapeutic targets. However, reliable prognostic markers screened based on the combination of ferroptosis and tumor immune status have not been well characterized. Here, we chose the ssGSEA and ESTIMATE algorithms to evaluate the ferroptosis and immune status of a TCGA breast invasive ductal carcinoma (IDC) cohort, which revealed their correlation characteristics as well as patients’ prognoses. The WGCNA algorithm was used to identify genes related to both ferroptosis and immunity. Univariate COX, LASSO regression, and multivariate Cox regression models were used to screen prognostic-related genes and construct prognostic risk models. Based on the ferroptosis and immune scores, the cohort was divided into three groups: a high-ferroptosis/low-immune group, a low-ferroptosis/high-immune group, and a mixed group. These three groups exhibited distinctive survival characteristics, as well as unique clinical phenotypes, immune characteristics, and activated signaling pathways. Among them, low-ferroptosis and high-immune statuses were favorable factors for the survival rates of patients. A total of 34 differentially expressed genes related to ferroptosis-immunity were identified among the three groups. After univariate, Lasso regression, and multivariate stepwise screening, two key prognostic genes (GNAI2, PSME1) were identified. Meanwhile, a risk prognosis model was constructed, which can predict the overall survival rate in the validation set. Lastly, we verified the importance of model genes in three independent GEO cohorts. In short, we constructed a prognostic model that assists in patient risk stratification based on ferroptosis-immune-related genes in IDC. This model helps assess patients’ prognoses and guide individualized treatment, which also further eelucidatesthe molecular mechanisms of IDC.

Safe and effective vaccines for Corona Virus Disease 2019 (COVID-19) can prevent the virus from infecting human populations and treat patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we discuss the inhibitory abilities of primary and booster vaccine-induced antibodies inhibitory ability toward the SARS-CoV-2 wild-type strain, as well as B.1.1.7, B.1.351, P.1, B.1.617.2, and B.1.1.529. We confirmed these antibodies had the strongest inhibitory effects on the wild-type strain and cross-inhibition activities against other mutant strains after two inactivated vaccine doses. However, the B.1.351, B.1.617.2 and B.1.1.529 mutants exhibit antibody resistance in the vaccine serum. Antibodies induced by homologous inactivated vaccines (n = 92) presented more effective inhibition against tested SARS-CoV-2 strains (p < 0.0001), especially B.1.351, B.1.617.2, and B.1.1.529 mutant strains, which had strong immune escape characteristics. In addition, a heterologous booster vaccination (n = 50) of a protein subunit vaccine ZifiVax (ZF2001) significantly restored humoral immune responses and even showed an increasing response against wild-type, B.1.351, B.1.617.2, and B.1.1.529 than homologous inactivated vaccines. Our analysis of the humoral immune response elicited by the different vaccine regimens, including inhibiting antibodies, indicated that a booster, whether homologous or heterologous, could be essential for achieving greater efficacy against SARS-CoV-2.

Conductive and stretchable fibers are the cornerstone of intelligent textiles and imperceptible electronics. Among existing fiber conductors, gallium-based liquid metals (LMs) featuring high conductivity, fluidity, and self-healing are excellent candidates for highly stretchable fibers with sensing, actuation, power generation, and interconnection functionalities. However, current LM fibers fabricated by direct injection or surface coating have a limitation in shape programmability. This hinders their applications in functional fibers with tunable electromechanical response and miniaturization. Here, we reported a simple and efficient method to create shape-programmable LM fibers using the phase transition of gallium. Gallium metal wires in the solid state can be easily shaped into a 3D helical structure, and the structure can be preserved after coating the wire with polyurethane and liquifying the metal. The 3D helical LM fiber offered enhanced stretchability with a high breaking strain of 1273% and showed invariable conductance over 283% strain. Moreover, we can reduce the fiber diameter by stretching the fiber during the solidification of polyurethane. We also demonstrated applications of the programmed fibers in self-powered strain sensing, heart rate monitoring, airflow, and humidity sensing. This work provided simple and facile ways toward functional LM fibers, which may facilitate the broad applications of LM fibers in e-skins, wearable computation, soft robots, and smart fabrics.

As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.

To intercept a maneuvering target in a two-dimensional plane, the inverse optimal guidance law based on zero effort miss (ZEM) and disturbance observer (DO) is studied in this paper. Firstly, the relative kinematics equation is simplified to obtain the missile-target ZEM and its dynamics. In order to enhance the robustness of the inverse optimal guidance law, the integral of the ZEM is introduced as a new state to form an augmented system with the original system based on the idea of proportional integral (PI) control. Then, the target maneuver acceleration is assumed as the unknown external disturbance of the guidance augmented system, which is estimated by the DO. Based on the estimated value of DO and the backstepping method, the inverse optimal guidance law is designed to reduce the adverse effect of the disturbance on the guidance system. Finally, simulations are designed to verify the effectiveness of the inverse optimal guidance method based on DO.

The plant-specific SHI-related sequence (SRS) family of transcription factors plays a vital role in growth regulation, plant development, phytohormone biosynthesis, and stress response. However, the genome-wide identification and role in the abiotic stress-related functions of the SRS gene family were not reported in white sweet clover (Melilotus albus). In this study, nine M. albus SRS genes (named MaSRS01-MaSRS09) were identified via a genome-wide search method. All nine genes were located on six out of eight chromosomes in the genome of M. albus and duplication analysis indicated eight segmentally duplicated genes in the MaSRS family. These MaSRS genes were classified into six groups based on their phylogenetic relationships. The gene structure and motif composition results indicated that MaSRS members in the same group contained analogous intron/exon and motif organizations. Further, promoter region analysis of MaSRS genes uncovered various growth, development, and stress-responsive cis-acting elements. Protein interaction networks showed that each gene has both functions of interacting with other genes and members within the family. Moreover, real-time quantitative PCR was also performed to verify the expression patterns of nine MaSRS genes in the leaves of M. albus. The results showed that nine MaSRSs were up- and down-regulated at different time points after various stress treatments, such as salinity, low-temperature, salicylic acid (SA), and methyl jasmonate (MeJA). This is the first systematic study of the M. albus SRS gene family, and it can serve as a strong foundation for further elucidation of the stress response and physiological improvement of the growth functions in M. albus.

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.

Aiming to solve the problems of easy clogging and high energy consumption of multi-way fertilization devices for liquid manure, a crushing and impurity removal device for liquid manure was designed by combining the physical characteristics of liquid manure and impurities, and building the corresponding test bench. The proposed device could crush flexible impurities such as straw and filoplume and intercept hard impurities with high density. The main structural parameters of the device were determined according to the survey analysis and the theoretical design. The influences of cutter head shape, cutter edge angle, cutter shaft speed, and cutting clearance on the disqualification rate and energy consumption of straw crushing were obtained by a single-factor experiment. Furthermore, the Box–Behnken central composite design method of the response surface was employed to investigate the effects of the cutter shaft speed, cutting clearance, and cutter edge angle on the disqualification rate and energy consumption of straw crushing. In addition, the working parameters of the device were optimized by employing the response surface method. On this basis, the mathematical relationship model among the disqualification rate, energy consumption, and all influencing factors was established. The results show that the optimal combination of working parameters includes a cutter shaft speed of 312 r/min, a cutting clearance of 1.4 mm, and a cutter edge angle of 45°. From the prediction model, the predicted failure rate was 4.15%, and the predicted energy consumption was 47.53 J. The verification experiment was then performed under the optimal combination of working parameters. The obtained disqualification rate was 4.08% and the energy consumption was 47.56 J, which met the design and work requirements.

The clutch temperature rise characteristics in successive shifting conditions are crucial to its thermal stability and thermal safety. In the present paper, a comprehensive numerical model is proposed to investigate the temperature change of separator discs during successive shifting with the consideration of convection heat transfer in disengaged friction pair gaps, which is validated by repeated shifting experiments on the SAE#2 test bench. Since the second separator disc near the piston has the widest disengaged gaps and double-sided heat input, its temperature rise and temperature drop are the highest. The temperature rise gradually equals the temperature drop with the increasing working cycle, then the maximum clutch temperature no longer increases. The longer the shifting interval, the better the heat dissipation is, thus the lower the accumulated temperature rise. Moreover, the increasing lubrication oil temperature reduces the convection heat transfer and increases the temperature rise in an engaging process, but the accumulated temperature rise does not increase due to the widened friction pair gaps. This paper can obtain the temperature rise characteristics of a wet multi-disc clutch concerning its disengaged gaps during successive shifting, which is a promising candidate for investigating its overall performance.

In recent years, foodborne disease outbreaks have caused huge losses to the economy and have had severe impacts on public health. The accuracy and variety of detection techniques is crucial to controlling the outbreak and spread of foodborne diseases. The need for instruments increases the difficulty of field detection, while manually-handled samples are subject to user error and subjective interpretation. Here, we use a mini automatic nucleic acid extractor combined with recombinant polymerase amplification (RPA) and lateral flow immunoassay (LFIA) for simultaneous quantitative detection of five major foodborne pathogens. The pre-treatment device using the magnetic bead method allows for nucleic acid extraction of the reagent tank without manual operation, which is highly efficient and stable for preventing aerosol contamination. The nuc gene of Staphylococcus aureus, the toxR gene of Vibrio parahaemolyticus, the rfbE gene of Escherichia coli O157:H7, the hlyA gene of Listeria monocytogenes, and the fimY gene of Salmonella enterica were used as target fragments. The labeled antibody concentration is optimized on the LFIA to find the equilibrium point for the binding capacity of the five chemical markers and to efficiently and accurately visualize the bands. The RPA assay shows an optimal performance at 37 °C for 15 min. The optimized RPA-LFIA detection limit can reach 101 CFU/mL. There was no cross-reactivity among forty-eight strains. Furthermore, the average recoveries in spiked food samples were 90.5–104.5%. In summary, the RPA-LFIA established in this study can detect five pathogenic bacteria simultaneously with little dependence on laboratory equipment, and it has promising prospects for screening in low-resource areas.

Background: The pathogenesis of chronic pancreatitis is still unclear. Trypsinogen activation is an active factor in acute pancreatitis that has not been studied in the occurrence of chronic pancreatitis. Methods: Immunofluorescence was used to detect the location and expression of trypsinogen in chronic pancreatitis and normal tissues. Microarray and single-cell RNA-seq (scRNA-seq) were used to screen core genes and pathways in pancreatic stellate cells (PSCs). Western blotting and immunofluorescence were used to verify trypsinogen expression in PSCs after silencing Rabep1. Immunofluorescence and flow cytometry were used to validate trypsinogen activation and PSC activation after intervening in the endocytosis pathway. Results: Endocytosed trypsinogen was found in PSCs in CP clinical samples. Bioinformatic analysis showed that Rabep1 is a core gene that regulates trypsinogen endocytosis through the endocytosis pathway, verified by Western blot and immunofluorescence. Immunofluorescence and flow cytometry analyses confirmed the activation of trypsinogen and PSCs through the endocytosis pathway in PSCs. Conclusion: This study discovered a new mechanism by which trypsinogen affects the activation of PSCs and the occurrence and development of CP. Through communication between pancreatic acinar cells and PSCs, trypsinogen can be endocytosed by PSCs and activated by the Rabep1 gene.

This paper investigates the effects of disc deformation on the tilting-induced vibration of a splined spinning disc with axial-fixed boundaries.The purpose is to provide an intuitive interpretation of the vibration variance of the wet clutch system with different deformed discs. First, tilting models of flat and deformed discs are derived by introducing distinctive shape functions. Additionally, the inner spline interface is chosen as the friction boundary. Then, an impact model between friction pairs and the rigid boundary is established by adopting Hertz’s contact theory. Finally, the dynamic equations are solved via numerical methods, and the responses are analyzed in both time and frequency domains. The deformation can increase the nonlinearity of the dynamic response of the spinning disc. Moreover, the effects of increasing the impulse force and reducing the boundary distance are quite similar; they both increase the motion intensity.

To detect two types of β2-agonist residues at the same time, we coupled two haptens of clenbuterol (CLE) and ractopamine (RAC) to the same carrier protein through diazotization to prepare dimeric artificial antigen, and a fluorescent lateral flow immunoassay method based on europium nanoparticles (EuNP-FLFIA) was established by combining polyclonal antibodies with europium nanoparticles to form probes. Under optimized conditions, the EuNP-FLFIA could simultaneously detect eight aniline-type and one phenol-type β2-agonists, and the limits of detection (LOD) were 0.11–0.19 ng/mL and 0.12 ng/mL, respectively. The recovery rate of this method was 84.00–114.00%. This method was verified by liquid chromatography–tandem mass spectrometry (LC-MS/MS), and the test results were consistent (R2 > 0.98). Therefore, the method established in this study could be used as a high-throughput screening for the efficient and sensitive detection of β2-agonists in food.

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

Clutch disengaging dynamic characteristics, including the disengaging duration and the variations of friction pair gaps and friction torque, are crucial to the shifting control of an automatic transmission. In the present paper, the influence of lubrication oil (ATF) temperature on disengaging dynamic characteristics is investigated through a comprehensive numerical model for the clutch disengaging process, which considers the hydrodynamic lubrication, the asperity contact, the heat transfer, the spline resistance, and the impact between the piston and clutch hub. Moreover, the non-uniformity coefficient (NUC) is proposed to characterize the disengaging uniformity of friction pairs. As the ATF temperature increases from 60 °C to 140 °C, the clutch disengaging duration shortens remarkably (shortened by 55.1%); besides, the NUC sees a decreasing trend before a slight increase. When the ATF temperature is 80 °C, the distribution of friction pair gaps is most uniform. During the disengaging process, the increase of ATF temperature not only accelerates the change of the lubrication status between friction pairs but also contributes to the decrease of contact torque and hydrodynamic torque. This research demonstrates for the first time, evidence for clutch disengaging dynamic characteristics with the consideration of ATF temperature.

The constitutive model is the crucial part for the finite element analyses. To study the elasto-plastic properties of unbound granular materials (UGMs) under repeated vehicular loads, an elasto-plastic constitutive model called revised spatially mobilized plane (SMP) was proposed and validated. In this study, the revised SMP model was used for the plastic strain analyses of a typical three-layer pavement structure. To make comparisons, the Mohr-Coulomb and Druck-Prager models were employed for the numerical computation. The results show that plastic tensile and compressive strains in the horizontal and vertical directions appear on the top surface of UGM using the revised SMP model, but no plastic strains are produced by the Mohr-Coulomb and Druck-Prager models. The distribution of plastic strains in the revised SMP model had a good relationship with the actual loading areas under the vehicular loading, which related to the rutting. With the Mohr-Coulomb and Druck-Prage models, a great plastic strain was produced during the first several loading cycles and hardly increased in the following loading cycles, while the plastic strain in the revised SMP model presented an obvious increasing tendency with increased loading cycles. The predicted permanent deformations of the revised SMP, Mohr-Coulomb and Druck-Prage models were 0.557 mm, 0.78 mm and 0.155 mm, respectively. Our work reveals that the Mohr-Coulomb model may over-predict and Druck-Prage model may under-predict the rutting of pavement in early loading stage and the results proved that the revised SMP model had advantages in the description of the plastic strain of UMG under repeated loads.

Background: CXCs are important genes that regulate inflammation and tumor metastasis. However, the expression level, prognosis value, and immune infiltration of CXCs in cancers are not clear. Methods: Multiple online datasets were used to analyze the expression, prognosis, and immune regulation of CXCs in this study. Network analysis of the Amadis database and GEO dataset was used to analyze the regulation of intestinal flora on the expression of CXCs. A mouse model was used to verify the fact that intestinal bacterial dysregulation can affect the expression of CXCs. Results: In the three cancers, multiple datasets verified the fact that the mRNA expression of this family was significantly different; the mRNA levels of CXCL3, 8, 9, 10, 14, and 17 were significantly correlated with the prognosis of three cancers. CXCs were correlated with six types of immuno-infiltrating cells in three cancers. Immunohistochemistry of clinical samples confirmed that the expression of CXCL8 and 10 was higher in three cancer tissues. Animal experiments have shown that intestinal flora dysregulation can affect CXCL8 and 10 expressions. Conclusion: Our results further elucidate the function of CXCs in cancers and provide new insights into the prognosis and immune infiltration of breast, colon, and pancreatic cancers, and they suggest that intestinal flora may influence disease progression through CXCs.

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

Heavy metals in food packaging materials have been indicated to release into the environment at slow rates. Heavy metal contamination, especially that of cadmium (Cd), is widely acknowledged as a global environment threat that leads to continuous growing pollution levels in the environment. Traditionally, the detection of the concentration of Cd relies on expensive precision instruments, such as inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma-atomic emission spectrometry (ICP-AES). In this study, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) based on a specific monoclonal antibody was proposed to rapidly detect Cd. The half-inhibitory concentration and detection sensitivity of the anti-cadmium monoclonal antibody of the ic-ELISA were 5.53 ng mL−1 and 0.35 ng mL−1, respectively. The anti-Cd monoclonal antibody possessed high specificity while diagnosising other heavy metal ions, including Al (III), Ca (II), Cu (II), Fe (III), Hg (II), Mg (II), Mn (II), Pb (II), Zn (II), Cr (III) and Ni (II). The average recovery rates of Cd ranged from 89.03–95.81% in the spiked samples of packing materials, with intra- and inter-board variation coefficients of 7.20% and 6.74%, respectively. The ic-ELISA for Cd detection was applied on 72 food packaging samples that consisted of three material categories—ceramic, glass and paper. Comparison of the detection results with ICP-AES verified the accuracy of the ic-ELISA. The correlation coefficient between the ic-ELISA and the ICP-AES methods was 0.9634, demonstrating that the proposed ic-ELISA approach could be a useful and effective tool for the rapid detection of Cd in food packaging materials.

Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.

Under strong winds and at low temperatures, heat loss of hot-mix asphalt mixtures is likely to occur, which leads to temperature segregation. Temperature segregation affects the forming quality and the performance of asphalt pavements. In this study, a phase-change thermal-insulation agent (PCTIA) was prepared for reducing the temperature dissipation. A cooling simulation experiment was performed to test the temperature-dissipation process for an ordinary asphalt mixture and the asphalt mixture mixed with PCTIA (AM-PCTIA). The thermal-insulation effect was analyzed according to the temperature difference and the thermal-insulation extension time. Moreover, two indices—the thermal-insulation accumulated time difference value (IATDV) and thermal-insulation accumulated time difference index (IATDI)—were proposed for evaluating the thermal-insulation ability and efficiency. The results indicated that the temperature at the center of the AM-PATIA was 4 °C higher than that for the ordinary asphalt mixture. The insulation time was prolonged by 29.8 min at the ambient temperature of 15 °C. As the ambient temperature increased, the thermal-insulation effect of the PCTIA improved.

Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape.

Lateral organ boundaries (LOB) domain (LBD) genes, a gene family encoding plant-specific transcription factors, play important roles in plant growth and development. At present, though there have been a number of genome-wide analyses on LBD gene families and functional studies on individual LBD proteins, the diverse functions of LBD family members still confuse researchers and an effective strategy is required to summarize their functional diversity. To further integrate and improve our understanding of the phylogenetic classification, functional characteristics and regulatory mechanisms of LBD proteins, we review and discuss the functional characteristics of LBD proteins according to their classifications under a phylogenetic framework. It is proved that this strategy is effective in the anatomy of diverse functions of LBD family members. Additionally, by phylogenetic analysis, one monocot-specific and one eudicot-specific subclade of LBD proteins were found and their biological significance in monocot and eudicot development were also discussed separately. The review will help us better understand the functional diversity of LBD proteins and facilitate further studies on this plant-specific transcription factor family.

Foodborne pathogens can cause foodborne illness. In reality, one food sample may carry more than one pathogen. A rapid, sensitive, and multiple target method for bacteria detection is crucial in food safety. For the simultaneous detection of Staphylococcus aureus, Vibrio parahaemolyticus, and Salmonella Enteritidis, multi-objective recombinase polymerase amplification (RPA) combined with a lateral flow dipstick (LFD) was developed in this study. The whole process, including amplification and reading, can be completed in 15 min at 37 °C. The detection limits were 2.6 × 101 CFU/mL for Staphylococcus aureus, 7.6 × 101 CFU/mL for Vibrio parahaemolyticus, and 1.29 × 101 CFU/mL for Salmonella Enteritidis. Moreover, colored signal intensities on test lines were measured by a test strip reader to achieve quantitative detection for Staphylococcus aureus (R2 = 0.9903), Vibrio parahaemolyticus (R2 = 0.9928), and Salmonella Enteritidis (R2 = 0.9945). In addition, the method demonstrated good recoveries (92.00%−107.95%) in the testing of spiked food samples. Therefore, the multiplex LFD-RPA assay is a feasible method for the rapid, sensitive, and quantitative detection of bacterial pathogens in seafood.

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 × 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder−decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

Salmonella can cause serious foodborne diseases. We have developed a lateral flow immunoassay combined with recombinase polymerase amplification (LFD-RPA) for detection of Salmonella in food. The conserved fragment (fimY) was selected as the target gene. Under an optimal condition (37 °C, 10 min), the sensitivity was 12 colony-forming units (CFU)/mL in a pure culture. Testing with 16 non-Salmonella strains as controls revealed that LFD-RPA was specific to the fimY gene of Salmonella. The established assay could detect Salmonella at concentrations as low as 1.29 × 102 CFU/mL in artificially contaminated samples. This detection was at a slightly higher level than that for a pure bacterial culture. Combined with the test strip reader, the LFD-RPA is a feasible method for quantitative detection of Salmonella based on the test line intensity, which was the ratio for the test line and control line of the reflected light. The method could be a potential point-of-care test in limited resource areas and provides a new approach and technical support for the diagnosis of food safety.

A fluorescent immunochromatographic test strip (FICTS) based on the use of europium nanoparticles (EuNPs) was developed and applied to detect citrinin (CIT) in Monascus fermented food. The sensitivity of the immunoassay to detect CIT was greatly improved by the use of a specific monoclonal antibody to attach EuNPs to form a probe. Under optimum conditions, the visual detection limit was 2.5 ng/mL, and the detection limit of the instrument was 0.05 ng/mL. According to the results, the IC50 was 0.4 ng/mL. Matrix interference from various Monascus fermented foods was investigated in food sample detection. The immunosensor also demonstrated high recoveries (86.8−113.0%) and low relative standard deviations (RSDs) (1.8−15.3%) when testing spiked Monascus fermented food. The detection results of this method showed a good correlation (R2 > 0.98) with high-performance liquid chromatography (HPLC). The results showed that the FICTS method could be used as a rapid, sensitive method to detect CIT in Monascus fermented food.

Copper-based friction material (CFM) and paper-based friction material (PFM) are the two most commonly used clutch friction materials. The friction and wear characteristics of these two kinds of friction materials under dry conditions were investigated by the pin-on-disc test over a broad range of applied loads, rotating speeds and ambient temperatures. Before experiments, the running-in test was conducted to stabilize the coefficient of friction (COF) and wear amount of the test samples. After experiments, the metallographic micrographs of the tested samples were presented to investigate the wear mechanisms. Experimental results showed that both the COF and wear depth of the CFM are much greater than these of PFM. The COF of CFM decreases with the increase of applied load, and changes slightly with the variation of rotating speed, whereas it increases first and then decreases with the increase of ambient temperature. However, the COF of PFM decreases dramatically with the increase of the rotating speed and ambient temperature, while it remains stable at first and then decreases slowly as the applied load increases. Additionally, under such three working conditions, the wear depth of CFM changes linearly, while the wear depth of PFM varies greatly. This study can be used as a guide for selecting friction materials for clutches with different applications.

The major low molecular inhibitors showed inhibition in the hydration of clay in the laboratory for water-based drilling fluids, according to the principle of intercalation adsorption. However, inhibitors have failed and caused serious engineering accidents in drilling oil and natural gas. This paper investigated the transmission of several of drilling fluids to indicate whether low molecular inhibitor for drilling can effectively inhibit the wellbore hydration. The inhibition of drilling fluid with the plugging of mud cakes, was significantly weakened based on the hydration expansion of cores and cutting recoveries. The residual contents of inhibitors were determined with the precolumn derivation of high-performance liquid chromatography (HPLC) analysis and were chartered with Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) analysis in the structure of the derivative. The clogging behavior of the mud cake was described by environmental scanning electron microscopy (ESEM). Experiments show that 40 wt% to 90 wt% by weight of the corrosion inhibitor cannot pass through the mud cake in the dynamic filtration of the drilling fluid. The mud cake can be further divided into a nanostructure layer, a homogeneous layer and an anisotropic layer with different permeability. Most inhibitors should be limited to the nanostructure layer and the homogeneous layer.