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Abstract Background MutT homolog 1 (MTH1) sanitizes oxidized dNTP pools to promote the survival of cancer cells and its expression is frequently upregulated in cancers. Polyubiquitination stabilizes MTH1 to facilitate the proliferation of melanoma cells, suggesting the ubiquitin system controls the stability and function of MTH1. However, whether ubiquitination regulates MTH1 in gastric cancers has not been well defined. This study aims to investigate the interaction between MTH1 and a deubiquitinase, USP9X, in regulating the proliferation, survival, migration, and invasion of gastric cancer cells. Methods The interaction between USP9X and MTH1 was evaluated by co-immunoprecipitation (co-IP) in HGC-27 gastric cancer cells. siRNAs were used to interfere with USP9X expression in gastric cancer cell lines HGC-27 and MKN-45. MTT assays were carried out to examine the proliferation, propidium iodide (PI) and 7-AAD staining assays were performed to assess the cell cycle, Annexin V/PI staining assays were conducted to examine the apoptosis, and transwell assays were used to determine the migration and invasion of control, USP9X-deficient, and USP9X-deficient plus MTH1-overexpressing HGC-27 and MKN-45 gastric cancer cells. Results Co-IP data show that USP9X interacts with and deubiquitinates MTH1. Overexpression of USP9X elevates MTH1 protein level by downregulating its ubiquitination, while knockdown of USP9X has the opposite effect on MTH1. USP9X deficiency in HGC-27 and MKN-45 cells causes decreased proliferation, cell cycle arrest, extra apoptosis, and defective migration and invasion, which could be rescued by excessive MTH1. Conclusion USP9X interacts with and stabilizes MTH1 to promote the proliferation, survival, migration and invasion of gastric cancer cells.

Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading. CER-MADDPG emphasizes collaboration between UAVs and uses historical UAV experiences to classify and obtain optimal strategies. It enables collaboration among edge devices through the design of the ’critic’ network. Additionally, by defining good and bad experiences for UAVs, experiences are classified into two separate buffers, allowing UAVs to learn from them, seek benefits, avoid harm, and reduce system overhead. The performance of CER-MADDPG was verified through simulations in two aspects. First, the influence of key hyperparameters on performance was examined, and the optimal values were determined. Second, CER-MADDPG was compared with other baseline algorithms. The results show that compared with MADDPG and stochastic game-based resource allocation with prioritized experience replay, CER-MADDPG achieves the lowest system overhead and superior stability and scalability.

HIV causes intense polyclonal activation of B cells, resulting in increased numbers of spontaneously antibody-secreting cells in the circulation and hypergammaglobulinemia. It is accompanied by significant perturbations in various B cell subsets, such as increased frequencies of immature/transitional B cells, activated memory B cells, atypical memory B cells, short-lived plasmablasts and regulatory B cells, as well as by decreased frequencies of resting memory and resting naïve B cells. Furthermore, both memory and antigen-inexperienced naïve B cells show exhausted and immune-senescent phenotypes. HIV also drives the expansion and functional impairment of CD4+ T follicular helper cells, which provide help to B cells, crucial for the generation of germinal center reactions and production of long-lived plasma and memory B cells. By suppressing viral replication, anti-retroviral therapy reverses the virus-induced perturbations and functional defects, albeit inadequately. Due to HIV’s lingering impact on B cells, immune senescence and residual chronic inflammation, people with HIV (PWH), especially immune non-responders, are immunocompromised and mount suboptimal antibody responses to vaccination for SARS-CoV-2. Here, we review how functionally and phenotypically distinct B cell subsets are induced in response to a vaccine and an infection and how HIV infection and anti-retroviral therapy (ART) impact them. We also review the role played by HIV-induced defects and perturbations in B cells in the induction of humoral immune responses to currently used anti-SARS-CoV-2 vaccines in PWH on ART. We also outline different strategies that could potentially enhance the vaccine-induced antibody responses in PWH. The review will provide guidance and impetus for further research to improve the immunogenicity of these vaccines in this human population.

Trade-offs between different defense traits exist commonly in plants. However, no evidence suggests that symbiotic microbes can drive a trade-off between plant pathogen and herbivore defense. The present study aims to investigate whether the mixture of epiphytic Fusarium oxysporum and Fusarium fujikuroi can drive the trade-off between the two defense traits in invasive Ipomoea cairica. Surface-sterilized I. cairica cuttings pre-inoculated with the epiphytic fungal mixture served as an epiphyte-inoculated (E+) group, while cuttings sprayed with sterile PDB served as an epiphyte-free (E−) group. After 3 days of incubation, E+ and E− cuttings were subjected to the challenge from a fungal pathogen and an insect herbivore, respectively. The results suggested that E+ cuttings had less rotted and yellowed leaf rates per plant than E− cuttings after Colletotrichum gloeosporioides infection. On the contrary, E+ cuttings had higher absolute and relative fresh weight losses per leaf than E− cuttings after Taiwania circumdata introduction. In the absence of challenges from the two natural enemies, salicylic acid and H2O2 accumulation occurred in E+ cuttings, which activated their SA-dependent pathogen defense and resulted in an increase in chitinase and β-1,3-glucanase activities. Although jasmonic acid accumulation also occurred in E+ cuttings, their JA-dependent herbivore defense responses were antagonized by SA signaling, leading to a decrease in total phenol content and phenylalanine ammonia-lyase activity. The activity of generalized defense enzymes, including superoxide dismutase, peroxidase, and catalase, did not differ between E+ and E− cuttings. Together, our findings indicate that a trade-off between pathogen and herbivore defense in I. cairica had already been driven by the epiphytic fungal mixture before the challenge by the two natural enemies. This study provides a novel insight into biocontrol strategies for I. cairica.

Zhaotong pig (ZTP) is a Chinese indigenous pig breed in Yunnan Province, known for its unique body shape and appearance, good meat quality, strong foraging ability, and adaptability. However, there is still a lack of research on its genome. In order to investigate the genetic diversity, population structure, and selection signatures of the breed, we conducted a comprehensive analysis by resequencing on 30 ZTPs and comparing them with genomic data from 10 Asian wild boars (AWBs). A total of 45,514,452 autosomal SNPs were detected in the 40 pigs, and 23,649,650 SNPs were retained for further analysis after filtering. The HE, HO, PN, MAF, π, and Fis values were calculated to evaluate the genetic diversity, and the results showed that ZTPs had higher genetic diversity and lower inbreeding coefficient compared with AWBs. Population structure was analyzed using NJ tree, PCA, ADMIXTURE, and LD methods. It was found that ZTPs were population independent of AWBs and had a lower LD decay compared to AWBs. Moreover, the results of the IBS genetic distance and G matrix showed that most of the individuals had large genetic distances and distant genetic relationships in ZTPs. Selection signatures were detected between ZTPs and AWBs by using two methods, FST and π ratio. Totals of 1104 selected regions and 275 candidate genes were identified. Finally, functional enrichment analysis identified some annotated genes that might affect fat deposition (NPY1R, NPY5R, and NMU), reproduction (COL3A1, COL5A2, GLRB, TAC3, and MAP3K12), growth (STAT6 and SQOR), tooth development (AMBN, ENAM, and ODAM), and immune response (MBL2, IL1A, and DNAJA3). Our results will provide a valuable basis for the future effective protection, breeding, and utilization of ZTPs.

The newly filled soil in reclamation areas shows a significant consolidation effect, resulting in severe ground settlements and inducing extremely adverse impacts on the stability of foundations on site. Based on the continuous boundary conditions, this paper establishes a one-dimensional filled soil-native seabed consolidation theory with consideration of the self-weight of the filled soil and the bedding characteristics of the seabed simultaneously, and derives an analytical solutions to the response of the excess pore water pressure and the consolidation degree through the eigenfunction method. The proposed solution is subsequently verified through the degradation of the boundary conditions and mathematical models. By virtue of the proposed solution, a parametric study is conducted to investigate the influence of the spatio-temporal impact factors (including the self-weight of the filled soil, moduli of the filled soil, time factor, and additional load at ground) on the consolidation. The main conclusions can be drawn as follows: ①The self-weight of the filled soil drives the consolidation of the newly filled ground and should not be ignored in practical engineering. ②The effect of the permeability coefficient on the dissipation of the excess pore water pressure is relatively complicated. In case of change in the penetrability of a certain soil layer, it will have opposite influences on the excess pore water pressure of the overlying and underlying soil layers. ③The volume compressibility of the soil has a significant influence on the excess water pressure. And the influence of the volume compressibility of the deeply buried soil is more significant. ④The additional load caused by, for instance, the stacking at the ground, will slow down the consolidation of the site.

Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.

BackgroundRecent research has suggested that patients with metastatic non-small cell lung cancer (mNSCLC) can achieve ongoing response after discontinuation of immune checkpoint inhibitor (ICI), but the best time to discontinue and the factors influencing efficacy remain unknown.MethodA systematic search was performed for prospective clinical trials in patients with mNSCLC treated with ICIs published up to July 10, 2022. Eligible studies reported treatment-free survival (TFS) after discontinuation of ICI in partial objective responders. We calculated objective response rate (ORR) and TFS using random-effects models with respective 95% confidence intervals (Cis), and performed subgroup analyses to discuss the specific associations between ORR and TFS and the associated influencing factors.ResultsAcross the 26 cohorts (3833 patients) included, the weighted mean ORR for all patients was 29.30% (95% CI 24.28% to 34.57%), with ICI plus chemotherapy (48.83%, 95% CI 44.36% to 53.30%) significantly higher than monotherapy (23.40%, 95% CI 18.53% to 28.62%). 395 patients were all patients who were complete or partial responders in the study, 194 discontinued ICI treatment, and nearly 35.5% achieved a durable response. No significant differences in TFS were found between subgroups according to the ICI regimen classification. Four cohorts of patients who completed 35 courses of treatment showed high levels of pooled TFS at 6 (80.18%, 95% CI 53.03% to 97.87%) and 12 months (66.98%, 95% CI 46.90% to 84.47%). Three cohorts of patients discontinued ICI treatment due to treatment-related adverse events (TRAEs) with the TFS rates at 6 (76.98%, 95% CI 65.79% to 86.65%) and 12 months (64.79%, 95% CI 50.20% to 78.19%).ConclusionPatients with mNSCLC were able to achieve ongoing responses after discontinuation of ICI. In conclusion, the results of this meta-analysis indicate that different treatment regimens, different drugs or different treatment durations may have an impact on TFS.

Abstract Background A growing body of research suggests that methylated genes can be used as early diagnostic markers for cancer. Some studies on methylated Syndecan 2 (SDC2) have shown that it has a great diagnostic ability in colorectal cancer. This meta-analysis was aimed to estimate the diagnostic performance of methylated SDC2 as a potential novel biomarker to screen for the colorectal cancer. Methods Two independent researchers conducted a comprehensive literature search to identify all relevant studies on SDC2 methylation for the diagnosis of colorectal cancer from inception to March 1, 2021. By using STATA and Revman software, the data were analyzed using a Bivariate mixed model. The quality of each study was also evaluated. Results A total of 12 studies comprised of 1574 colorectal cancer patients and 1945 healthy people were included in our meta-analysis. Bivariate analysis showed a pooled sensitivity of 0.81 [95% confidence interval (CI) 0.74–0.86], specificity of 0.95 (95% CI 0.93–0.96), positive likelihood ratio of 15.29 (95% CI 10.83–21.60), and negative likelihood ratio of 0.21 (95% CI 0.15–0.27). The diagnostic odds ratio and the area under the summary ROC curve for diagnosing colorectal cancer were 74.42 (95% CI45.44–121.89) and 0.96 (95% CI 0.94–0.97), respectively. For adenomas, the pooled sensitivity and specificity were 0.47 (95% CI 0.34–0.61) and 0.95 (95% CI 0.92–0.97), respectively. Conclusions Our analysis revealed that methylated SDC2 could be considered as a potential novel biomarker to screen for colorectal cancer.

Abstract Location‐based services have attracted significant attention since the concept of Industry 4.0 was proposed and the ‘Internet +’ era began owing to its social and commercial value. Many scholars have attempted to introduce machine learning in indoor fingerprint positioning to improve indoor positioning accuracy, enhance system robustness, reduce costs, and improve the performance of indoor positioning methods. A comprehensive overview of indoor positioning technology, its methods, and classifications is provided. Furthermore, a detailed review of the application of WiFi fingerprinting and machine learning methods in indoor positioning is presented, and the advantages and disadvantages of these methods when applied to indoor positioning are analyzed. This study summarizes the difficulties and challenges encountered by indoor positioning and suggests development directions.

Abstract Objective High-grade serous ovarian cancer (HGSOC) is an aggressive gynaecological malignancy and associated with poor prognosis. Here we examined the effects of miR-625-3p on proliferation, treatment, migration and invasion in HGSOC. Methods The proliferation of HGSOC cells was evaluated by MTT assay. Transwell assay was performed to examine migration and matrigel assay were used to assess invasion. The effect of miR-625-3p on cisplatin-induced apoptosis was investigated by Caspase-Glo3/7 assay. The dual-luciferase reporter assay was carried out to confirm the potential binding site. Results Overexpression of miR-625-3p promoted proliferation, and increased migration and invasion in HGSOC cells. MiR-625-3p significantly inhibited cisplatin sensitivity in HGSOC cells. Meanwhile, miR-625-3p decreased cisplatin-induced apoptosis by regulation of BAX and Bcl-2 expression. Furthermore, aberrant expression of miR-625-3p changed PTEN expression by directly binding to 3’UTR of PTEN. Further study showed miR-625-3p expression was higher in human HGSOC tissue than normal ovarian tissues and associated with higher clinical stage. Conclusions miR-625-3p promotes HGSOC growth, involves chemotherapy resistance and might serve as a potential biomarker to predict chemotherapy response and prognosis in HGSOC.

Abstract This meta-analysis was aimed to estimate the diagnostic performance of volatile organic compounds (VOCs) as a potential novel tool to screen for the neoplasm of the digestive system. An integrated literature search was performed by two independent investigators to identify all relevant studies investigating VOCs in diagnosing neoplasm of the digestive system from inception to 7th December 2020. STATA and Revman software were used for data analysis. The methodological quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate mixed model was used and meta-regression and subgroup analysis were performed to identify possible sources of heterogeneity. A total of 36 studies comprised of 1712 cases of neoplasm and 3215 controls were included in our meta-analysis. Bivariate analysis showed a pooled sensitivity of 0.87 (95% confidence interval (CI) 0.83–0.90), specificity of 0.86 (95% CI 0.82–0.89), a positive likelihood ratio of 6.18 (95% CI 4.68–8.17), and a negative likelihood ratio of 0.15 (95% CI 0.12–0.20). The diagnostic odds ratio and the area under the summary ROC curve for diagnosing neoplasm of the digestive system were 40.61 (95% CI 24.77–66.57) and 0.93 (95% CI 0.90–0.95), respectively. Our analyses revealed that VOCs analysis could be considered as a potential novel tool to screen for malignant diseases of the digestive system.

In order to study the working characteristics of energy pile groups, based on the Abaqus finite element simulation, assigns the average temperature of the heat transfer stable stage to the pile body for steady-state thermo-mechanical coupling calculation, and proposes a simplified analysis method for the bearing characteristics of energy pile groups.The reliability of this research method is verified through the comparative analysis with field data.Combined with an example, this method is used to analyze the bearing characteristics of energy group piles under the action of pure mechanical load and thermal coupling.The results show that the non-uniform settlement of the distributed symmetrical arrangement of energy piles in the pile group foundation is significantly less than that of the centralized arrangement, and the layout has a great influence on the response characteristics of the pile foundation structure.Under the condition of equal stiffness of pile foundation, the control effect of pile group tilt is better by increasing pile diameter and decreasing pile spacing.The research results of this paper can provide some reference value for the engineering application of energy pile group.

Due to changes in light intensity, varying degrees of aphid aggregation, and small scales in the climate chamber environment, accurately identifying and counting aphids remains a challenge. In this paper, an improved YOLOv5 aphid detection model based on CNN is proposed to address aphid recognition and counting. First, to reduce the overfitting problem of insufficient data, the proposed YOLOv5 model uses an image enhancement method combining Mosaic and GridMask to expand the aphid dataset. Second, a convolutional block attention mechanism (CBAM) is proposed in the backbone layer to improve the recognition accuracy of aphid small targets. Subsequently, the feature fusion method of bi-directional feature pyramid network (BiFPN) is employed to enhance the YOLOv5 neck, further improving the recognition accuracy and speed of aphids; in addition, a Transformer structure is introduced in front of the detection head to investigate the impact of aphid aggregation and light intensity on recognition accuracy. Experiments have shown that, through the fusion of the proposed methods, the model recognition accuracy and recall rate can reach 99.1%, the value mAP@0.5 can reach 99.3%, and the inference time can reach 9.4 ms, which is significantly better than other YOLO series networks. Moreover, it has strong robustness in actual recognition tasks and can provide a reference for pest prevention and control in climate chambers.

Indoor 3D positioning is useful in multistory buildings, such as shopping malls, libraries, and airports. This study focuses on indoor 3D positioning using wireless access points (AP) in an environment without adding additional hardware facilities in large-scale complex places. The integration of a deep learning algorithm into indoor 3D positioning is studied, and a 3D dynamic positioning model based on temporal fingerprints is proposed. In contrast to the traditional positioning models with a single input, the proposed method uses a sliding time window to build a temporal fingerprint chip as the input of the positioning model to provide abundant information for positioning. Temporal information can be used to distinguish locations with similar fingerprint vectors and to improve the accuracy and robustness of positioning. Moreover, deep learning has been applied for the automatic extraction of spatiotemporal features. A temporal convolutional network (TCN) feature extractor is proposed in this paper, which adopts a causal convolution mechanism, dilated convolution mechanism, and residual connection mechanism and is not limited by the size of the convolution kernel. It is capable of learning hidden information and spatiotemporal relationships from the input features and the extracted spatiotemporal features are connected with a deep neural network (DNN) regressor to fit the complex nonlinear mapping relationship between the features and position coordinates to estimate the 3D position coordinates of the target. Finally, an open-source public dataset was used to verify the performance of the localization algorithm. Experimental results demonstrated the effectiveness of the proposed positioning model and a comparison between the proposed model and existing models proved that the proposed model can provide more accurate three-dimensional position coordinates.

High-throughput antibody sequencing allows in-depth insights into human antibody repertoires. To investigate the characteristics of antibody repertoires in patients with chronic HBV infection, we performed Illumina sequencing and IMGT/HighV-QUEST analysis of B lymphocytes from healthy adults and the HBV carriers with high or low level of viral replication. The comparative study revealed high levels of similarity between the IgM and IgG repertoires of the HBV carriers and the healthy adults, including the somatic mutations in V regions, the average CDR3 length, and the occurrence of junctional modifications. Nevertheless, the diversity of the unique clones decreased and some clusters of unique clones expanded in the IgM repertoire of chronic HBV carriers (CHB) compared with healthy adults (HH) and inactive HBV carriers (IHB). Such difference in clone diversity and expansion was not observed in the IgG repertoires of the three populations. More shared antibody clones were found between the IgM repertoires of IHB and HH than that found between CHB and HH (7079 clones vs. 2304 clones). Besides, the biased used IGHD genes were IGHD2-2 and IGHD3-3 in CHB library but were IGHD3-10 and IGHD3-22 in IHB and HH library. In contrast, for IgG repertories, the preferred used VDJ genes were similar in all the three populations. These results indicated that low level of serum HBV might not induce significant changes in BCR repertoires, and high level of HBV replication could have more impacts on IgM repertories than IgG repertoires. Taken together, our findings provide a better understanding of the antibody repertoires of HBV chronically infected individuals.

One of the most efficient nondestructive methods for pipeline in-line inspection is magnetic flux leakage (MFL) inspection. Estimating the size of the defect from MFL signal is one of the key problems of MFL inspection. As the inspection signal is usually contaminated by noise, sizing the defect is an ill-posed inverse problem, especially when sizing the depth as a complex shape. An actor-critic structure-based algorithm is proposed in this paper for sizing complex depth profiles. By learning with more information from the depth profile without knowing the corresponding MFL signal, the algorithm proposed saves computational costs and is robust. A pinning strategy is embedded in the reconstruction process, which highly reduces the dimension of action space. The pinning actor-critic structure (PACS) helps to make the reward for critic network more efficient when reconstructing the depth profiles with high degrees of freedom. A nonlinear FEM model is used to test the effectiveness of algorithm proposed under 20 dB noise. The results show that the algorithm reconstructs the depth profile of defects with good accuracy and is robust against noise.

Tracing food products along the entire supply chain is important for achieving better management of food products. Traditionally, centralized traceability systems have been developed for such purposes. One major drawback of this approach is that different users of the supply chain have their own systems with their own complexities and distinct features; thus, the interaction among them creates challenges when implementing a single centralized system. Therefore, a decentralized traceability system is favorable for tracing food products along the supply chain. In this study, we develop a supply chain traceability system framework based on blockchain and radio frequency identification (RFID) technology. The system consists of a decentralized blockchain-enabled data storage platform for data management and an RFID system at the packaging level for data collection and storage. We applied a consortium blockchain to the application. Fabric 2.0 in Hyperledger was chosen as the development platform. The proposed blockchain-enabled platform can provide decentralized data management and its underlying algorithm can guarantee data security. The system includes a creatively designed blockchain-enabled data structure in the RFID tag. When people scan the tag, the relevant information is written in the tag as a block linked to the previous blocks; simultaneously, the information is transmitted to the blockchain platform and recorded on the platform. No battery is required and the system works when there is an RFID reader nearby. The usage conditions included shipment, stocking, and storage. The RFID tag can be directly attached to paper packaging. This approach embeds the blockchain technique into the RFID tag and develops a corresponding system. The new traceability system has the potential to simplify the tracking of products and can be scaled for industrial use.

The vertical vibration of a viscoelastic pile immersed in arbitrarily layered soil is investigated by taking the interaction among pile, pile surrounding soil (PSS) and pile end soil (PES) into account. Firstly, considering both the stratification and stress wave effect of soil, a mathematical model of the pile–soil system is established based on the fictitious soil pile (FSP) model. Then, utilizing the impedance function transfer method and Laplace transform technique, the analytical solutions of the vertical dynamic impedance of pile are derived in the frequency domain. The analytical solutions are validated by comparing them with other existing solutions. Finally, a parametric study is put forward to investigate the properties of PES on the vertical dynamic impedance of pile. The results reveal that the properties of PES have a significant effect on the vertical dynamic impedance of pile, but there is a critical influence thickness for this effect. For the cases of the PES thickness exceeding the critical influence thickness, further increase of PES thickness will not affect the dynamic behavior of the pile–soil system.

At present, electric vehicles (EVs) are attracting increasing attention and have great potential for replacing fossil-fueled vehicles, especially for logistics applications. However, energy management for EVs is essential for them to be advantageous owing to their limitations with regard to battery capacity and recharging times. Therefore, inefficiencies can be expected for EV-based logistical operations without an energy management plan, which is not necessarily considered in traditional routing exercises. In this study, for the logistics application of EVs to manage energy and schedule the vehicle route, a system is proposed. The system comprises two parts: (1) a case-based reasoning subsystem to forecast the energy consumption and travel time for each route section, and (2) a genetic algorithm to optimize vehicle routing with an energy consumption situation as a new constraint. A dynamic adjustment algorithm is also adopted to achieve a rapid response to accidents in which the vehicles might be involved. Finally, a simulation is performed to test the system by adjusting the data from the vehicle routing problem with time windows. Solomon benchmarks are used for the validations. The analysis results show that the proposed vehicle management system is more economical than the traditional method.

Tracking operating trains and identifying illegal intruders are two important and critical issues in subway safety management. One challenge is to find a reliable methodology that would enable these two needs to be addressed with high sensitivity and spatial resolution over a long-distance range. This paper proposes a novel monitoring approach based on distributed vibration, which is suitable for both train tracking and incursion detection. For an actual subway system, ultra-weak fiber Bragg grating (FBG) sensing technology was applied to collect the distributed vibration responses from moving trains and intruders. The monitoring data from the subway operation stage were directly utilized to evaluate the feasibility of the proposed method for tracking trains. Moreover, a field simulation experiment was performed to validate the possibility of detecting human intrusion. The results showed that the diagonal signal pattern in the distributed vibration response can be used to reveal the location and speed of the moving loads (e.g., train and intruders). Other train parameters, such as length and the number of compartments, can also be obtained from the vibration responses through cross-correlation and envelope processing. Experimental results in the time and frequency domains within the selected intrusion range indicated that the proposed method can distinguish designed intrusion cases in terms of strength and mode.

Biological and pharmaceutical (B&P) products are essential in modern life. Currently, the demand and requirements of these products are dramatically growing; product quality and integrity then become crucial. As a key factor for ensuring quality and integrity, the current product status monitoring system needs to be enhanced in a more sophisticated and comprehensive way. This research describes the design of a wireless sensor network (WSN) monitoring system for B&P products supply chain management. In this study, the system architecture is presented based on the proposed framework. The framework consists of three processes: the first one is to generate a WSN with full coverage, the second one is to determine the optimal position of the sink, and the last process is to add additional relay nodes for providing fault tolerance to the network. Finally, a feasibility test on B&P product storage in a hospital is conducted. The proposed system provides a systematic approach for the deployment of sensor nodes and relay nodes. It also provides a relatively simple and rapid method for industries in general to follow and implement when designing a WSN system.

In this work, uniformly-dispersed platinum nanoparticles (PtNPs) were synthesized by a simple chemical reduction method, in which citric acid and sodium borohydride acted as a stabilizer and reducer, respectively. An ultrasensitive colorimetric sensor for the facile and rapid detection of Ag+ ions was constructed based on the peroxidase mimetic activities of the obtained PtNPs, which can catalyze the oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) by H2O2 to produce colored products. The introduced Ag+ would be reduced to Ag0 by the capped citric acid, and the deposition of Ag0 on the PtNPs surface, can effectively inhibit the peroxidase-mimetic activity of PtNPs. Through measuring the maximum absorption signal of oxidized TMB at 652 nm, ultra-low detection limits (7.8 pM) of Ag+ can be reached. In addition to such high sensitivity, the colorimetric assay also displays excellent selectivity for other ions of interest and shows great potential for the detection of Ag+ in real water samples.

Immune disorder is considered the main pathogenesis of autoimmune diseases, such as rheumatoid arthritis (RA). The balance of the two special subsets of CD4+T cells, T helper cell 17 (Th17), and Regulator T cell (Treg) is the key factor of maintaining a normal immune response. Dendritic cells (DCs), which are the most powerful antigen-presenting cells, play an important role in regulating the balance of Th17 and Treg. The combination of disease modifying antirheumatic drugs (DMARDs) is an important strategy of RA therapy. In this study, we investigated the effect of MTX and CTX on DC maturation in ovalbumin (OVA) immunized mice. Th17 inflammatory response is stronger, while the level of DCs maturity is higher. In contrast, the immunosuppression of Treg is stronger. We found that MTX combined with CTX significantly inhibited the DCs maturity and downregulated the antigen presenting capacity of DCs. As a result, it reestablished a balance of Th17 and Treg. Our study adds a novel mechanism and therapeutic target of MTX combined with CTX for autoimmune disease treatment.

A governing equation for large-strain consolidation of PVD-improved soil is developed based on the equivalent elliptical cylindrical model, which accounts for depth-dependent initial void ratio and initial effective stress induced by soil self-weight, well resistance with variable discharge capacity, time-dependent load, and nonlinear variations of compressibility and permeability. Then, a numerical solution is obtained by means of COMSOL Multiphysics and is validated through comparisons with existing solutions and measured data. The parametric studies show that the difference between large-strain consolidation and small-strain consolidation reduces with the decreases of the compression index and applied load. Although the soil self-weight has little effect on the degree of consolidation in some cases, the soil settlement will be significantly overestimated if the self-weight is ignored. The development of consolidation slowed as the ratio of compressibility over horizontal permeability increased. [ABSTRACT FROM AUTHOR]