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[ Objective ] VQ proteins are plant-specific proteins with the conserved VQ motifs ( Fxxh- VQxhTG ), which play an important role in plant growth and development as well as in response to abiotic stresses. The role of the VQ gene family in litchi ( Litchi chinensis ) had not been systematically studied. We identified the LcVQ gene family members as well as their expression patterns in different tissues of ly- chee and their responses to abiotic stresses. This laid a foundation for studying its stress-resistant mecha- nism. [ Methods ] The sequences of LcVQ genes were obtained from the L . chinensis genome using bioin- formatics methods , and their physicochemical properties , subcellular localizations , gene structures , and conserved motifs were analyzed ; a phylogenetic tree was constructed using MEGA 6.0 to analyze the phy- logenetic relationships of the VQ proteins in L . chinensis , Arabidopsis , and Oryza sativa ; the responses to various abiotic stresses were verified by the quantitative real-time PCR ( qRT-PCR ) . [ Results ] A total of 18 LcVQ genes were identified from L . chinensis genome , named as LcVQ1 - 18 , which were clustered into 9 subgroups. They were widely distributed on 11 of 15 chromosomes of lychee. The amino acid num- bers of LcVQ proteins were ranged from 111 to 427 , and molecular weights from 12.48 kD to 45.49 kD. Subcellular localizations showed that except for LcVQ15 and LcVQ17 were localized at the cytoplasm , all other LcVQ proteins were localized at the nucleus. The LcVQ promoters contained a large number of growth and development , plant hormone , and stress-responsiveness related response elements. The LcVQ genes expression was varied at different tissues , which was divided into universal expression and specific expression. Under the treatment of low temperature , high temperature , drought , and salt stress , we found that 4 , 3 , 3 , and 4 LcVQ genes were significantly up-regulated at 3 hours of treatment , respective- ly , indicating that these genes could respond to the abiotic stresses. [ Conclusion ] There are 18 VQ mem- bers in the L . chinensis genome with typical VQ binding domains and participate in different stress re- sponses.

In this paper, the influence of the multiple injection strategy on premixed charge compression ignition (PCCI) combustion of a heavy-duty diesel engine in low load speed extension is studied experimentally and numerically, and the general optimization direction of low-load spray combustion of a heavy-duty diesel engine is obtained. The injection timing should match the shape of the combustion chamber, so that the oil, gas, and chamber are combined to make full use of the advantage of the shape of the combustion chamber. As the speed increases, the multi-injection strategy can overcome the defects such as the decrease of injection rate and the decrease of oil-gas mixing degree caused by the extension of injection duration. For single injection at a low speed, through optimizing the injection strategy, the NOx emission is reduced by 38%, the soot emission is decreased by one order of magnitude, and the indicated thermal efficiency is increased by 8.66%. For single injection at a medium speed, the indicated thermal efficiency remains unchanged, the NOx emission is reduced by 59.3%, and the soot emission is reduced by 70%. For single injection at a high speed, the thermal efficiency and the NOx emission are increased slightly while the soot emission is decreased significantly. In addition, it is found that with the increase of speed, the influence of the multiple injection strategy on indicated thermal efficiency and emissions is gradually increased, and the optimization effect of the multiple injection strategy is more obvious than that of low speed.

Objective To explore the potential active components and anti-inflammation mechanism of a Chinese herb from Dong ethnic minority, Madeng'ai (MDA). Methods UPLC-Q-TOF-MS was taken to analyze and identify the major components of MDA. The potential active targets of MDA were retrieved in the Swiss Target Prediction database, and the disease-related targets were searched in the Omim, Disgene and Genecards databases. The common targets of active components and inflammation were obtained for the potential targets of MDA in the treatment against inflammation. The "drug-disease-target" network and protein-protein interaction (PPI) network were separately constructed by Cytoscape 3.9.1 and String database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the overlapping targets were performed through David database. Finally, RT-qPCR and Western blotting were conducted to determine the mRNA levels of several inflammation-related factors and the possible signaling pathways in RAW264.7 cells after LPS treatment. Results There were 29 compounds identified by UPLC-Q-TOF/MS, and the top 5 were tormentic acid, ellagic acid, procyanidin C1, epicatechin and procyanidin B1. There were 104 core targets of MDA docking inflammation through database retrieval and after duplicate removal, and TNF-α, IL-6, AKT1 and ALB might be the core targets. A total of 761 GO items were enriched, including 600 items concerning biological processes, 52 items on cell components, and 109 items involving molecular functions. KEGG analysis screened out 160 signal pathways, mainly focusing on NF-κB, MAPK, PI3K-AKT and so forth signaling pathways. Compared with the normal control group, the mRNA levels of TNF-α, IL-6, IL-1β and CCL2 were increased significantly in the model group (LPS treatment group)(P < 0.05), and the phosphorylation levels of p38, ERK and JNK were obviously up-regulated (P < 0.05). While, MDA treatment notably declined the mRNA levels of TNF-α, IL-6, IL-1β and CCL2 (P < 0.05), and down-regulated the phosphorylation levels of p38, ERK and JNK when compared with the model group (P < 0.05). Conclusion Our study forecasts that the water extract of MDA, a Chinese traditional herb from Dong minority, has the potential active ingredients in treatment of inflammation, and shows anti-inflammatory effects through multiple pathways, such as MAPK pathway.

ObjectivePost-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.MethodsFive-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.ResultsThe DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.ConclusionWe developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

Abstract Background Malignant tumor is one of the most serious diseases endangering human health. Circular RNAs play an important role in the tumorigenesis and progression of various malignant tumors. Although various studies have investigated the biological function of circular RNA circSMARCA5 in malignant tumors, the prognostic value of circSMARCA5 in malignant tumor patients has not been systematically analyzed. Methods Relevant studies were obtained from the PubMed and Web of Science database. The quality of the enrolled studies was evaluated using the Newcastle-Ottawa Scale quality assessment system. Survival features and clinicopathological features were assessed using pooled hazard ratios and odds ratios with 95% confidence intervals, respectively. Results Overall, 7 relevant publications were enrolled in the meta-analysis. CircSMARCA5 expression was significantly correlated with better OS (HR = 0.51, 95%CI 0.41–0.65) or DFS/RFS/PFS (HR = 0.56, 95%CI 0.43–0.73) in malignant tumors. In the pooled analyses of clinicopathological characteristics, malignant tumors with higher circSMARCA5 were better differentiated (OR = 0.41, 95%CI 0.19–0.88). CircSMARCA5 expression was correlated with less advanced TNM stage (OR = 0.33, 95%CI 0.19–0.55). Moreover, malignant tumors with higher circSMARCA5 expression have less advanced lymph node metastasis (OR = 0.26, 95%CI 0.08–0.79). Conclusion These results indicated that circSMARCA5 was a promising biomarker in malignant tumors, which may potentially facilitate clinical decisions in the future.

Most existing risk assessments of Android applications directly assign weights to factors according to experience, and calculate security risks by counting the frequency statistics of few factors.A new method for risk assessment of Android applications is proposed, which can provide both quantitative and qualitative assessment.This method integrates multiple risk factors such as system permissions, API calls, the action properties of Intent Filter, and data flow.The risks of factors are assigned based on their risk classification and addition, and the weights of factor subsets are distributed based on hierarchical clustering.Experiments show that the assessment results can effectively reflect the real security risks of Android applications.

Fake news spreads rapidly on social networks; the aim of this study is to compare the characteristics of the social relationship networks (SRNs) of refuters and non-refuters to provide a scientific basis for developing effective strategies for debunking fake news. First, based on six types of fake news published on Sina Weibo (a Chinese microblogging website) during 2015–2019 in China, a deep learning method was used to build text classifiers for identifying debunked posts (DPs) and non-debunked posts (NDPs). Refuters and non-refuters were filtered out, and their follower–followee relationships on social media were obtained. Second, the differences between DPs and NDPs were compared in terms of the volume and growth rate of the posts across various types of fake news. The SRNs of refuters and non-refuters and the k-core decompositions of these SRNs were constructed, and the differences in the growth rates between DPs and NDPs were explored. Business-related fake news was revealed to be debunked better; society-related fake news, the most widely spread in China, was debunked poorly; and science- and politics-related fake news was debunked the worst. Additionally, more celebrity accounts, larger node sizes with follower-followee relationships in the SRNs, and more weakly connected components were found to lead to a faster growth rate in the dissemination of posts, regardless of whether the posts were DPs or NDPs. This study can help practitioners develop more effective strategies for debunking fake news on social media in China.

ObjectiveProstate cancer (PCa) is one of the most common malignant tumors, accounting for 20% of total tumors ranked first in males. PCa is usually asymptomatic at the early stage and the specificity of the current biomarkers for the detection of PCa is low. The present study evaluates circulating tumor DNA (ctDNA) in blood or urine, which can be used as biomarkers of PCa and the combination of these markers may increase the sensitivity and specificity of the detection of PCa.MethodsTissue, blood, and urine samples were collected from patients with PCa. All prostate tissue specimens underwent pathological examination. A hybrid-capture-based next-generation sequencing assay was used for plasma and urinary ctDNA profiling. Sequencing data were analyzed by an in-house pipeline for mutation calling. Mutational profiles of PCa and BPH were compared in both plasma and urine samples. Associations of detected mutations and clinical characteristics were statistically analyzed.ResultsA significant association of mutation allele frequencies (MAFs) in the blood samples with patients with metastatic PCa rather than patients with primary PCa, and MAFs are changed after treatment in patients with PCa. Further, the number of mutations in urine is not associated with clinical characteristics of PCa patients, but the frequencies of mutation alleles in the urine are associated with patient age. Comparison of cfDNA aberration profiles between urine and blood reveals more alterations in urine than in blood, including TP53, AR, ATM, MYC, and SPOP mutations.ConclusionThis work provides the potential clinical application of urine, in addition to blood, as a powerful and convenient non-invasive approach in personalized medicine for patients with PCa.

Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.

In traditional wireless sensor networks (WSN), data is transmitted to base stations through multiple-hop. However, multi-hop transmission will cause problems such as energy holes, uneven energy consumption, and unreliable data transmission. Aiming at the defects of traditional networks, this paper proposes a scheme for collaborative data collection using multiple mobile nodes (MN) as sink nodes. In this scenario, an important issue is how to reasonably plan the data collection path of each mobile node under a series of constraints such as energy. This paper studies routing strategy and path planning. Firstly, a dynamic clustering algorithm is used to cluster the randomly arranged sensor nodes, and then a sensor node with higher energy is manually arranged at the virtual cluster center generated by the clustering algorithm as a cluster head node to establish a data collection cluster. Then the monitoring area is divided into several parts according to the number of MN, so that the MN traverses the cluster head nodes in the respective monitoring areas for data collection. At the same time, in order to enable the MN to complete data collection within the energy limit and improve the path balance, a path-based path equalization algorithm (PEABR) is proposed to adjust the path of the MN, further reduce and equalize the path length of the MN to satisfy the constraint conditions, and optimize the path planning scheme. Finally, simulation was carried out by using Matlab simulator, and the simulation experiments which were performed in a laboratory environment verified the feasibility and effectiveness of the algorithm.

Aiming at preventing the network performance deterioration caused by fixed link topology connections in optically switched networks, this paper proposes and validates an interconnection and control mechanism that can automatically optimize and reconfigure network topology according to the network traffic volume. This mechanism combines deep reinforcement learning with software defined network technology. Based on the real-time network performance parameters collected by the software-defined network control plane and the performance feedback of the network simulation system, the continuous training of the topology structure can be realized in the model of deep reinforcement learning. The automatic network topology reconfiguration is achieved with improved network performance. The experimental results show that for a given traffic intensity, the model trained by deep reinforcement learning can learn the topology reconstruction strategy and realize automatic selection. The optimal network topology structure can be achieved in one step, with reduced average network delay and average packet loss.

Light sheet fluorescence microscope with single light sheet illumination enables rapid 3D imaging of living cells. In this paper we show the design, fabrication and characterization of a diffractive optical element producing several light sheets along a 45° inclined tube. The element, which is based on a multi-focal diffractive lens and a linear grating, generates five thin light sheets with equal intensities when combined with a refractive cylindrical lens. The generated uniform light sheets can be applied for the scanning of samples in tubes enabling flow-driven 3-dimensional imaging.

Owing to the proliferation of rumors on social media, it is necessary to disseminate debunking information to minimize the harm caused by them. Using content analysis, sentiment analysis, and regression analysis, this study examined the mediating role of follower count in the relationship between the debunker’s identity and sharing behavior, and it explored the relationship between the text sentiment of debunking information and sharing behavior based on data on the spread of three rumors that circulated extensively on social media. Using an ordinary account as a reference, we found that the mediating or suppression effect (i.e., when direct and indirect effects are significant and opposite) of follower count in the relationship between debunker’s identity (celebrity, media, or government) and sharing behavior was significant. The three test identities (celebrity, media, and government) had more followers than the ordinary account, which resulted in a significant positive effect on the number of reposts. The debunker’s identity did not have a positive effect on the sharing of debunking information when controlling for mediating variables. Debunking information with emotional overtones (positive or negative) was shared more widely compared with information with neutral emotions, and the dominant emotional polarity was different in the three different rumors. These findings can contribute to the generation of debunking information content, which can aid in the development of effective communication strategies and improvement in the efficiency of crisis management.

BackgroundThis study aimed to explore the diagnostic value of alpha-l-fucosidase (AFU) in prostate cancer (PCa) patients with “gray-zone PSA” and to investigate the correlation between AFU expression and clinicopathological characteristics of PCa patients.MethodsThe level of AFU and other necessary clinicopathological variables of patients were retrieved from electronic medical records. The transcriptome profiling and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database. The protein level of AFU in tissue was assessed by immunohistochemistry (IHC). All the data were processed by appropriate analysis methods. The p-value of

Background: The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media.Methods: A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index (DEI) was developed to measure the effectiveness of different debunking methods.Results: The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of Car rumor and political category of Russia rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method.Conclusion: The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.

Prostate cancer is one of the most common malignant tumors that threaten the health of men. It is urgent to explore new molecular targets and develop new drugs for the treatment of prostate cancer. Circular RNAs (circRNAs) are aberrantly expressed in various malignant tumors. The dysregulated circRNAs are involved in the metastasis, tumor growth, drug resistance, and immunosuppression of malignant tumors. The present review systematically summarized publications concerning the biological implications of circRNAs in prostate cancer. The PubMed and Web of Science databases were used to retrieve publications concerning circRNAs and prostate cancer until June 16, 2021. The following keywords were used in the literature search: (circRNA OR circular RNA) AND prostate cancer. 73 publications were enrolled in the present systematic review to summarize the role of circRNAs in prostate cancer. The dysregulated and functional circRNAs were involved in the cell cycle, proliferation, migration, invasion, metastasis, drug resistance and radiosensitivity of prostate cancer. In addition, circRNAs could function through EVs and serve as prognostic and diagnostic biomarkers. Certain circRNAs were correlated with clinicopathological features of prostate cancer. A comprehensive review of the molecular mechanism of the tumorigenesis and progression of prostate cancer may contribute to the development of new therapies of prostate cancer in the future.

In order to solve the problem of the poor robustness for extracting the multi-lane marking, a multi-lane detection algorithm based on deep convolutional neural network is proposed. Since the results of the feature extraction of lane boundary were affected by various factors, a deep convolutional neural network based on FCN network is used for lane boundary feature extraction, and the neural network can be used to classify the lane images at the pixel level. Then the network parameters are trained on the public Tusimple data set and evaluated at Caltech Lanes data set. The last part combines a linear model with a curved model to realize the establishment of the lane marking equation, Hough transform is used to determine the fit interval and the least square method is used to fit lane marking. The experimental results show that the average accuracy of the algorithm for identifying lane on the Tusimple data set is 98.74%, and the accuracy rate on the Caltech Lanes data set is 96.29%.

Abstract Background Metastatic prostate cancer is a fatal disease despite multiple new approvals in recent years. Recent studies revealed that circular RNAs (circRNAs) can be involved in cancer metastasis. Defining the role of circRNAs in prostate cancer metastasis and discovering therapeutic targets that block cancer metastasis is of great significance for the treatment of prostate cancer. Methods The circSOBP levels in prostate cancer (PCa) were determined by qRT‐PCR. We evaluated the function of circSOBP using a transwell assay and nude mice lung metastasis models. Immunofluorescence assay and electron microscopic assay were applied to determine the phenotypes of prostate cancer cells’ migration. We used fluorescence in situ hybridization assay to determine the localization of RNAs. Dual luciferase and rescue assays were applied to verify the interactions between circSOBP, miR‐141‐3p, MYPT1, and phosphomyosin light chain (p‐MLC2). Results We observed that circSOBP level was significantly lower in PCa specimens compared with adjacent noncancerous prostate specimens, and was correlated with the grade group of PCa. Overexpression of circSOBP suppressed PCa migration and invasion in vitro and metastasis in vivo. CircSOBP depletion increased migration and invasion and induced amoeboid migration of PCa cells. Mechanistically, circSOBP bound miR‐141‐3p and regulated the MYPT1/p‐MLC2 axis. Moreover, the depletion of MYPT1 reversed the inhibitory effect of circSOBP on the migration and invasion of PCa cells. Complementary intronic Alu elements induced but were not necessary for the formation of circSOBP. The nuclear export of circSOBP was mediated by URH49. Conclusion Our results suggest that circSOBP suppresses amoeboid migration of PCa cells and inhibits migration and invasion through sponging miR‐141‐3p and regulating the MYPT1/p‐MLC2 axis.

Tradition wireless sensor networks (WSNs) transmit data by single or multiple hops. However, some sensor nodes (SNs) close to a static base station forward data more frequently than others, which results in the problem of energy holes and makes networks fragile. One promising solution is to use a mobile node as a mobile sink (MS), which is especially useful in energy-constrained networks. In these applications, the tour planning of MS is a key to guarantee the network performance. In this paper, a novel strategy is proposed to reduce the latency of mobile data gathering in a WSN while the routing strategies and tour planning of MS are jointly optimized. First, the issue of network coverage is discussed before the appropriate number of clusters being calculated. A dynamic clustering scheme is then developed where a virtual cluster center is defined as the MS sojourn for data collection. Afterwards, a tour planning of MS based on prediction is proposed subject to minimizing the traveling distance to collect data. The proposed method is simulated in a MATLAB platform to show the overall performance of the developed system. Furthermore, the physical tests on a test rig are also carried out where a small WSN based on an unmanned aerial vehicle (UAV) is developed in our laboratory. The test results validate the feasibility and effectiveness of the method proposed.

Light-sheet fluorescence microscopy (LSFM) with single light-sheet illumination enables rapid 3Dimaging of living cells. In this paper we show the design, fabrication and characterization of a diffractive optical element producing several light sheets along an inclined tube for applications in flow-driven imaging. The element, which is based on a multi-focal Fresnel zone plate and a linear grating, generates in combination with a refractive cylindrical lens five thin light sheets of equal intensity.

The relationship between obesity and renal cell carcinoma (RCC) has been widely investigated. However, the effect of estrogen on this relationship in female RCC patients has not been evaluated. We conducted a case-control study to investigate the role of estrogen as a potential modifier of the association between obesity and RCC risk in Chinese women.A total of 497 consecutive female patients with pathologically confirmed RCC, including 364 clear cell RCC (ccRCC), were enrolled. Age-matched controls were selected from cancer-free females seeking physical examination in our institution. Estrogen receptor-β (ER-β) and insulin-like growth factor (IGF)-1 receptor (IGF-1R) expression levels were detected in RCC tissues. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated by logistic regression models.We observed a positive association between overweight and RCC risk in pre-menopausal but not post-menopausal women. Similar association was also observed between overweight and ccRCC risk. Overweight pre-menopausal women had an increased risk of RCC (OR: 1.67, 95%CI: 1.01–2.76), as well as an increased risk of ccRCC (OR: 1.73, 95%CI: 1.02–2.99), after adjusting for potential confounders. IGF-1R expression levels were higher in pre-menopausal compared with post-menopausal cases (P = 0.015).These results suggest that estrogen plays an important role in RCC etiology and may modify the association between obesity and RCC risk in women. We hypothesize that estrogen may up-regulate IGF-1R and potentiate the deleterious effects of obesity-related elevations of insulin and IGFs. Keywords: Renal cell carcinoma, Estrogen, Obesity, Insulin-like growth factor receptor, Gender

In the water area monitoring of the traditional wireless sensor networks (WSNs), the monitoring data are mostly transmitted to the base station through multihop. However, there are many problems in multihop transmission in traditional wireless sensor networks, such as energy hole, uneven energy consumption, unreliable data transmission, and so on. Based on the high maneuverability of unmanned aerial vehicles (UAVs), a mobile data collection scheme is proposed, which uses UAV as a mobile sink node in WSN water monitoring and transmits data wirelessly to collect monitoring node data efficiently and flexibly. In order to further reduce the energy consumption of UAV, the terminal nodes are grouped according to the dynamic clustering algorithm and the nodes with high residual energy in the cluster are selected as cluster head nodes. Then, according to the characteristics of sensor nodes with a certain range of wireless signal coverage, the angular bisection method is introduced on the basis of the traditional ant colony algorithm to plan the path of UAV, which further shortens the length of the mobile path. Finally, the effectiveness and correctness of the method are proved by simulation and experimental tests.

The key of determining trace elements in plant by Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) is to eliminate the organic matrix effect. In this study, the improvement of acid digesting system, digestion method and sample weight has solved the matrix effect. Digestion results are evaluated by digesting Certified Reference Material (GBW07603) with HNO3-H2O2, HNO3-HF and HNO3-HF-H2O2. Different sample weights, including 50 mg and 100 mg, and digestion methods including twice-sealed high pressure digestion, once-sealed high pressure digestion and microwave-assisted digestion are evaluated. Trace elements were analyzed by ICP-MS with In as the internal standard element. Experimental results show that HNO3-HF-H2O2 is the best digestion reagent for plants and 50 mg sample has the least matrix effect. The trace element contents for microwave-assisted digestion are obviously lower than the real values. Twice-sealed high pressure digestion was an efficient method for digesting plants.