Direct Graph Research Articles

Contact Plan Design With Directional Space-Time Graph in Two-Layer Space Communication Networks

In the two-layer space communication network (TLSCN), communications can be performed to obtain higher throughput and lower latency by introducing various types of spatial nodes. However, the mobility of spatial nodes and the connectivity of spatial links result in the time-varying network topology, and intermittent link connection. This further leads to the lack of continuous contact, unreliable transmission, and high transmission cost. In this article, contact plan design (CPD) is employed to address the above problems by increasing the contact reliability while decreasing the contact cost. First, a directional space-time graph (DSTG) is constructed by considering the motion trajectory of spatial nodes and the time evolution nature of TLSCN. Afterwards, based on DSTG, we propose three CPD methods with greedy algorithm by considering the limited computing power of nodes. The three methods can optimize the objective functions of the total contact reliability, total contact cost, and invalid probability cost, respectively. The simulation results show that the proposed CPD methods can effectively improve the contact reliability, reduce the contact cost of TLSCN, and perform well with the increase of network density.

Efficient CGM-based parallel algorithms for the longest common subsequence problem with multiple substring-exclusion constraints

A variant of the Longest Common Subsequence (LCS) problem is the LCS problem with multiple substring-exclusion constraints (M-STR-EC-LCS), which has great importance in many fields especially in bioinformatics. This problem consists to compute the LCS of two strings X and Y of length n and m respectively that excluded a set of d constraints P={P1,P2,…,Pd} of total length r. Recently, Wang et al. proposed a sequential solution based on the dynamic programming technique that requires O(nmr) execution time and space. To the best of our knowledge, there is no parallel solutions for this problem. This paper describes new efficient parallel algorithms on Coarse Grained Multicomputer model (CGM) to solve this problem. Firstly, we propose a multi-level Direct Acyclic Graph (DAG) that determines the correct evaluation order of sub-problems in order to avoid redundancy due to overlap. Secondly, we propose two CGM parallel algorithms based on our DAG. The first algorithm is based on a regular partitioning of the DAG and requires O(nmrp) execution time with O(p) communication rounds where p is the number of processors used. Its main drawback is high idleness time of processors because due to the dependencies between the nodes in the DAG, over time it has many idle processors. The second algorithm uses an irregular partitioning of the DAG that minimizes this idleness time by allowing the processors to stay active as long as possible. It requires O(nmrp) execution time with O(kp) communication rounds. k is a constant integer allowing to setup the irregular partitioning. The both algorithms require O(r|Σ|p) preprocessing time where |Σ| is the length of the alphabet. The experimental results performed show a good agreement with theoretical predictions.

Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

BackgroundOutlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images.MethodsIn this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets.ResultsWith the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892.ConclusionThe CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours.

Networks of reinforced stochastic processes: Asymptotics for the empirical means

This work deals with systems of interacting reinforced stochastic processes, where each process $X^{j}=(X_{n,j})_{n}$ is located at a vertex $j$ of a finite weighted direct graph, and it can be interpreted as the sequence of “actions” adopted by an agent $j$ of the network. The interaction among the evolving dynamics of these processes depends on the weighted adjacency matrix $W$ associated to the underlying graph: indeed, the probability that an agent $j$ chooses a certain action depends on its personal “inclination” $Z_{n,j}$ and on the inclinations $Z_{n,h}$, with $h\neq j$, of the other agents according to the elements of $W$. Asymptotic results for the stochastic processes of the personal inclinations $Z^{j}=(Z_{n,j})_{n}$ have been subject of studies in recent papers (e.g., Aletti, Crimaldi and Ghiglietti [Ann. Appl. Probab. 27 (2017) 3787–3844], Crimaldi et al. [Synchronization and functional central limit theorems for interacting reinforced random walks (2019)]); while the asymptotic behavior of quantities based on the stochastic processes $X^{j}$ of the actions has never been studied yet. In this paper, we fill this gap by characterizing the asymptotic behavior of the empirical means $N_{n,j}=\sum_{k=1}^{n}X_{k,j}/n$, proving their almost sure synchronization and some central limit theorems in the sense of stable convergence. Moreover, we discuss some statistical applications of these convergence results concerning confidence intervals for the random limit toward which all the processes of the system almost surely converge and tools to make inference on the matrix $W$.

When Internet of Things Meets Blockchain: Challenges in Distributed Consensus

Blockchain has been regarded as a promising technology for Internet of Things (IoT), since it provides significant solutions for decentralized network which can address trust and security concerns, high maintenance cost problem, etc. The decentralization provided by blockchain can be largely attributed to the use of consensus mechanism, which enables peer-to-peer trading in a distributed manner without the involvement of any third party. This article starts from introducing the basic concept of blockchain and illustrating why consensus mechanism plays an indispensable role in a blockchain enabled IoT system. Then, we discuss the main ideas of two famous consensus mechanisms including Proof of Work (PoW) and Proof of Stake (PoS), and list their limitations in IoT. Next, two mainstream Direct Acyclic Graph (DAG) based consensus mechanisms, i.e., the Tangle and Hashgraph, are reviewed to show why DAG consensus is more suitable for IoT system than PoW and PoS. Potential issues and challenges of DAG based consensus mechanism to be addressed in the future are discussed in the last.

Directed Acyclic Graph-based Distributed Ledgers – An Evolutionary Perspective

Blockchain platforms like Bitcoin and Ethereum have introduced a distributed and decentralized cryptocurrency system with no third-party intermediation required. These peer to peer network systems allows Internet users to directly transact with each other. However due to the heavy emphasis on decentralization, scalability has taken a back seat. It has also become a key issue in the wider adoption of these technologies. The change to the underlying data organizing structure to Direct Acyclic Graphs (DAG) of the distributed ledger, has significantly increased transaction scalability. In this paper, we analyse some of the Distributed Ledger Technologies that use DAGs and have shown marked improved in transaction performance without weakening security.

Autonomous Indoor Exploration Via Polygon Map Construction and Graph-Based SLAM Using Directional Endpoint Features

In this paper, a novel 2-D laser-based autonomous exploration approach for mobile robots is proposed, which is based on a novel polygon map construction approach and graph-based simultaneous localization and mapping (SLAM) with directional endpoint features. This approach is composed of three modules: graph-based SLAM using directional endpoint features, polygon map construction, and exploration. Different from existing approaches in the field of 2-D SLAM, the newly proposed 2-D graph-SLAM is based on 3-D “directional endpoint” features; on this basis, a well-known data structure “circular-doubly linked list” is applied to construct a novel polygon map for navigation. Note that it is efficient for circular-doubly linked list to initialize and update the polygon map. In addition, we propose a new information entropy calculation approach to quantify the entropy of the polygon map. Then for each candidate goal, we could obtain corresponding information gain and make next decision through collision detection. Comparative experimental results with respect to the well-known Gmapping and Karto SLAM are presented to show superior performance of the proposed graph-based SLAM. The autonomous exploration experiments in the office and hallway environments show the effectiveness of the proposed approach for robotic mapping and exploration tasks. Note to Practitioners —This paper is motivated by the challenges of autonomous exploration for mobile robots. We suggest a novel autonomous exploration approach through directional endpoint feature-based graph simultaneous localization and mapping (SLAM) and polygon map construction. This newly proposed approach does not require any artificial landmark or underlying occupancy grid map, which is easy to implement. The experiments are carried out in the office and hallway environments, including graph-based SLAM and autonomous exploration for mobile robots. The experimental results show the effectiveness of the proposed autonomous exploration framework. In the future research, we will address autonomous exploration in more challenging dynamic environments.

ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

Internet of Things (IoT) makes everything in the real world to get connected. The resource constrained characteristics and the different types of technology and protocols tend to the IoT be more vulnerable than the conventional networks. Intrusion Detection System (IDS) is a tool which monitors analyzes and detects the abnormalities in the network activities. Machine Learning techniques are implemented with the Intrusion detection systems to enhance the performance of IDS. Various studies on IoT reveals that Artificial Neural Network (ANN) provides better accuracy and detection rate than other approaches. In this paper, an Artificial Neural Network based IDS (ANNIDS) technique based on Multilayer Perceptron (MLP) is proposed to detect the attacks initiated by the Destination Oriented Direct Acyclic Graph Information Solicitation (DIS) attack and Version attack in IoT environment. Contiki O.S/Cooja Simulator 3.0 is used for the IoT simulation.

Development of a reduced diesel/PODEn mechanism for diesel engine application

Polyoxymethylene Dimethyl Ethers (PODEn) is a promising diesel alternative fuel, with a high cetane number and oxygen content, which can reduce the emissions of soot. In this study, a new simplified n-heptane–n-butylbenzene –PODEn–polycyclic aromatic hydrocarbon (PAH) mechanism for diesel engine application was developed. In this model, a mixture of n-heptane and n-butylbenzene is regarded as representative substance of diesel. First, the detailed mechanism of PODEn is simplified by using the methods of rate of production (ROP), direct relation graph with error propagation (DRGEP), and sensitivity analysis. The simplified PODEn sub-mechanism was coupled to the simplified multi-component diesel mechanism of n-heptane–n-butylbenzene–PAH to construct a simplified model of n-heptane–n-butylbenzene–PODEn–PAH containing 179 species and 769 reactions. Then the key dynamic parameters of the PODEn sub-model were optimized using the sensitivity analysis method. To confirm the rationality and reliability of the optimized diesel/PODEn reduced mechanism, the experimental values such as laminar flame velocity, component concentration and ignition delay were verified. The verification results show that there was little difference between the simulated values and the test values. Finally, the proposed mechanism was coupled to the computational fluid dynamic (CFD) software CONVERGE to predict the emission characteristics and combustion of an actual diesel engine under different mixing ratios of PODEn. The results for in-cylinder pressure and heat release rate (HRR), carbon monoxide (CO), nitrogen oxide (NOX) and soot emissions modeled by proposed mechanism are well in line with the test data, indicating that the newly developed diesel/PODEn reduced mechanism can simulate the combustion and emission of diesel engines.

Distributed Dynamic Averaging Tracking Without Rate Measurements

This article introduces a dynamic average tracking algorithm for multiagent systems over weight-balanced direct graphs. Toward the end, we propose a proportional-integral distributive observer for each system with exponential convergence. In particular, the proposed algorithm only requires mild initialization conditions, does not rely on the time derivatives of reference signals and can ensure average consensus tracking with bounded steady-state error. The simulation results verified the performance of the proposed scheme.

Development and validation of a reduced multi-component mechanism for diesel engine application

Aromatics and cycloalkanes which play important roles in soot formation, are two important components in diesel. This work constructed a reduced multi-component mechanism of n-heptane–n-butylbenzene (BBZ)– methylcyclohexane (MCH)–polycyclic aromatic hydrocarbon (PAH) with 183 species and 777 reactions for diesel engine emissions and combustion prediction. Based on the reduced mechanism of n-heptane–BBZ–PAH, this multi-component diesel mechanism was constructed by merging the reduced mechanism of MCH. First, the detailed mechanism of MCH was reduced based on the methods of direct relation graph with error propagation (DRGEP), sensitivity analysis, and rate of production (ROP) analysis. Next, some most important parameters of kinetics in the mechanism were optimized by sensitivity analysis method. Further, the simplified multi-component diesel mechanism was extensively validated using the experimental values of ignition delays, species concentrations, and laminar flame speeds. The developed mechanism provides favorable prediction results, indicating that it can be used for simulating the combustion of multiple components in diesel. Finally, the developed multi-component diesel mechanism was coupled with three-dimensional computational fluid dynamic (3D-CFD), and multidimensional numerical simulations were performed in a direct-injection compression ignition (DICI) engine at EGR = 0%, 13%, 27%, 37%. The prediction results well coincided with the experimental values of combustion characteristics and emissions of soot and NOx, indicating that this multi-component diesel mechanism could be applied for predicting practical engine simulations.

An Integrated Causal Path Identification Method

Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two variables X, Y. In statistics, path analysis is used to describe the direct dependence between a set of variables. But in fact, we usually do not know the causal order between variables. However, ignoring the direction of the causal path will prevent researchers from analyzing or using causal models. In this study, we propose a method for estimating causality based on observed data. First, observed variables are cleaned and valid variables are retained. Then, a direct linear non-Gaussian acyclic graph models (DirectLiNGAM) estimates the causal order K between variables. The third step is to estimate the adjacency matrix B of the causal relationship based on K. Next, since B is not convenient for model interpretation, we use adaptive lasso to prune the causal path and variables. Further, a causal path graph and a recursive model are established. Finally, we test and debug the recursive model, obtain a causal model with good fit, and estimate the direct, indirect and total effects between causal variables. This paper overcomes the randomness assigning causal order to variables. This study is different from the researcher’s understanding of his own model by generating some form of simulation data. The simplest and relatively unsmooth statistical learning method used in this study has obvious advantages in the field of interpretable machine learning.

Genetic Polymorphism of Y Chromosome Haplogroup D-M174 in East Asian Populations.

Objective To explore the genetic polymorphism of Y chromosome D-M174 haplogroup and sub-haplogroups in East Asia. Methods The samples of 1 426 unrelated male individuals from East Asia were collected, and then 7 Y chromosome haplogroup D-M174 and the Y-SNP of its sub-haplogroups were detected with mini-sequencing. The 22 Y-STR genotypes were detected with DNA Typer™ Y26 kit. The haplogroup was analyzed using direct counting method, heatmap, phylogenetic cluster and network graph cluster, and then distribution of genetic polymorphism and the clustering relation between populations and samples of Y chromosome D haplogroup were discussed. Results Haplogroup D-M174 were distributed mostly among Tibetans （40.96%）and Japanese （35.71%）, while less or none were distributed among the surrounding areas of Tibet and other areas. Conclusion The geographical distribution of Y chromosome D-M174 haplogroup in East Asian populations has significant characteristics.

Intestinal Permeability and Inflammation Mediate Dietary Intake Associated Risks of Micronutrient Deficiencies at 15 Months: Results from the MAL-ED Study (OR07-04-19)

ObjectivesWe tested whether environmental enteric dysfunction (EED) is associated with risk of micronutrient deficiencies adjusting for diet and systemic inflammation, and whether it mediates the relation between intake and micronutrient status. MethodsData from 1283 children in the MAL-ED birth cohort were examined to evaluate risk of anemia, low plasma retinol, zinc, and ferritin, and high plasma transferrin receptor (TfR) at 15 months. We characterized gut inflammation and permeability by myeloperoxidase, neopterin, and alpha-1-antitrypsin concentrations from monthly asymptomatic fecal samples averaged from 9–15 months, and urinary recovery of lactulose (L) and mannitol (M) from LM tests at 9 and 15 months, converted to a ratio z score (LMZ) and averaged. Nutrient intakes from complementary foods were quantified monthly from 9–15 months and densities were averaged. Plasma alpha-1 acid glycoprotein at 15 months characterized systemic inflammation. Relationships among variables were modelled using a direct acyclic graph. ResultsGreater risk of anemia was associated with permeability [LMZ: 1.15 (95% confidence interval: 1.01–1.31)] and inflammation [myeloperoxidase: 1.16 (1.01–1.34)]. Greater risk of low ferritin was associated with alpha-1-antitrypsin [1.19 (1.03–1.37)] and neopterin [1.22 (1.04–1.43)]. Greater risk of low retinol was associated with LMZ [1.25 (1.07–1.45)]. However, myeloperoxidase was associated with lower risk of high TfR [0.86 (0.74–0.98)], neopterin with lower risk of low retinol [0.75 (0.62–0.89)], and alpha-1-antripysin with lower risk of low plasma zinc [0.83 (0.70–0.99)]. Greater nutrient densities of vitamin A, B6, calcium, protein and zinc were negatively associated with EED. Inverse associations between nutrient densities and micronutrient deficiency largely disappeared after adjustment for EED, suggesting that EED mediates associations between intake and status. ConclusionsEED is independently associated with increased risk of low ferritin, low retinol and anemia. Greater nutrient density from complementary foods may reduce EED, and control of micronutrient deficiency may require control of EED. Funding SourcesThe MAL-ED study was supported by the Bill & Melinda Gates Foundation, through awards to the Foundation for the NIH and NIH/FIC.

Mittag-Leffler stability for a new coupled system of fractional-order differential equations with impulses

This paper is devoted to investigation of the Mittag-Leffler stability problem for a new coupled system of fractional-order differential equations with impulses on networks. By using the direct graph theory, a new coupled model with two fractional-order impulsive equations on each vertex is constructed, and the related Lyapunov function is presented. By the Lyapunov direct method, sufficient conditions are derived to ensure the equilibrium point of the coupled fractional-order impulsive model is globally Mittag-Leffler stable. Our new results show a relation between the stability criteria and some topology property of the system. Finally, a numerical example is provided to illustrate the effectiveness of our results.

Prepregnancy Dietary Patterns and Their Association with Perinatal Outcomes: A Prospective Cohort Study

BackgroundDietary intake may be associated with neonatal outcomes, yet little is known about the influence of prepregnancy dietary pattern (DP). ObjectivesTo evaluate the association between prepregnancy DPs and perinatal outcomes. DesignProspective cohort study during pregnancy (baseline between 5 and 13 gestational week and three follow-up visits: 20 to 26 gestational weeks, 30 to 36 gestational weeks, and 30 to 40 days postpartum, respectively). Diet was assessed in the first trimester using a food frequency questionnaire and having prepregnancy as the time frame. Participants/settingTwo hundred fifty-three pregnant women met the following eligibility criteria (20 to 40 years of age, 5 to 13 weeks of gestation at baseline, free of chronic [except obesity] or infectious diseases, and with a singleton pregnancy). The final sample was composed of 193 pregnant women attending a public health care center in Rio de Janeiro, Brazil, from 2009 to 2012. Main outcome measuresType of delivery, large for gestational age (LGA), birth length (BL)>90th percentile, Apgar score<7 at 1 minute, and preterm birth. Statistical analysesReduced rank regression was used to identify prepregnancy DPs that explain the following response variables: fiber density (daily dietary fiber intake in grams, divided by total daily energy intake in kilocalories), dietary energy density, and percent energy from saturated fat. Statistical analyses included multiple logistic regression models. The following covariates were defined as confounders based on a unique Direct Acyclic Graph for each outcome: maternal age, current smoker, alcohol consumption, years of education, and first-trimester leisure physical activity. ResultsThe prevalence of normal delivery was 56.7%. LGA occurred in 16%, BL>90th percentile in 24.3%, Apgar score<7 at 1 minute in 14.2%, and preterm birth in 9.5% of the study population. Three DPs were identified: “fast food and candies” was associated with higher odds of LGA (odds ratio [OR]=4.38, 95% CI: 1.32 to 14.48) and BL>90th percentile (OR=4.81, 95% CI: 1.77 to 13.07); “beans, bread, and fat” was inversely associated with Apgar score<7 at 1 minute (OR=0.14, 95% CI: 0.03 to 0.70); and “vegetables and dairy” was inversely associated with preterm birth (OR=0.24, 95% CI: 0.06 to 0.97). There was no association between adherence to DPs and type of delivery. ConclusionsHigher adherence to fast food and candies prepregnancy DP increased the odds of LGA birth, while a higher adherence to vegetables and dairy DP decreased the odds of preterm birth.

On the Optimal Design of a Bipartite Matching Queueing System

We consider a multi-class multi-server queueing system and study the problem of designing an optimal matching topology (or service compatibility structure) between customer classes and servers under a FCFS-ALIS service discipline. Specifically, we are interested in finding matching topologies that optimize --in a Pareto efficiency-- sense the trade-off between two competing objectives: (i) minimizing customers' waiting time delays and (ii) maximizing matching rewards generated by pairing customers and servers. Our analysis of the problem is divided in three main parts. First, under heavy-traffic conditions, we show that any bipartite matching system can be partitioned into a collection of complete resource pooling (CRP) subsystems, which are interconnected by means of a direct acyclic graph (DAG). We show that this DAG together with the aggregate service capacity on each CRP component fully determine the vector of steady-state waiting times. In particular, we show that the average (scaled) steady-state delay across all customer classes is asymptotically equal to the number of CRP components divided by the total system capacity. Second, since computing matching rewards under a FCFS-ALIS service discipline is computationally infeasible as the number of customer classes and servers grow large, we propose a quadratic programming (QP) formulation to approximate matching rewards. We show that the QP formulation is exact for a number of instances of the problem and provides a very good approximation in general. Extensive numerical experiments show that in over 98% of problem instances the relative error between the exact rewards and the QP approximate rewards is less than 2%. Lastly, combining our characterization of average delays in terms of the number of CRP components and the quadratic programming formulation to compute matching rewards, we propose a mixed-integer linear program (MILP) that can be used to find the set of matching topologies that define the Pareto frontier of reward-delay pairs.

Development of a reduced four-component (toluene/n-heptane/iso-octane/ethanol) gasoline surrogate model

The prospect of blending gasoline fuel with ethanol is being investigated as a potential way to improve the knock residence of the base gasoline. However, one of the drawbacks is a lack of proper understanding of the reason for the non-linear response of blending ethanol and gasoline. This non-linearity could be better understood by an improved knowledge of the interactions of these fuel components at a molecular level. This study proposed a highly reduced four-component (toluene/n-heptane/iso-octane/ethanol) gasoline surrogate model containing 59 species and 270 reactions. The model was reduced using the direct relation graph with expert knowledge (DRG-X) (Lu and Law, 20015; Lu et al., 2011) and isomer lumping method. The computational singular perturbation (CSP) analysis were performed to reduce the potential stiffness issues by accordingly adjusting the Arrhenius coefficients of the proper reactions. The model has been comprehensively validated against wide range of ignition delay times (IDT) and flame speed (FS) measurement data as well as compared against two representative literature models from Liu et al. (2013) and Wang et al. (2015). Overall, good agreements were observed between model predictions and experimental data across the entire research octane number (RON), equivalence ratio, pressure and temperature range. In addition, the model has also been coupled with the computational fluid dynamic (CFD) models to simulate the experimental data of constant volume reacting spray of a low-octane gasoline (Haltermann straight-run naphtha), and in-cylinder pressures and temperatures of a high-octane gasoline (Haltermann Gasoline) combustion in a heavy duty compression ignition engine. The coupled model can qualitatively predict the experimentally obtained data with an improved performance for PRF, TPRF, and TPRF-ethanol surrogates.

Evaluating transit network resilience through graph theory and demand-elastic measures: Case study of the Toronto transit system

The reliability of public transit networks is of critical importance the world over. As many transit systems are increasingly exposed to various causes of service disruptions, there exists a need to quantitatively measure the operational resilience of a transit network. This paper presents an approach for transit resilience measurement that combines several metrics from the existing literature. As a case study, the paper examines and quantifies the resilience of the public transit network in Toronto, Canada to operational disruptions. The approach adopted in this work is a combination of quantitative methods founded in Graph Theory, where the public transit network is represented as a directional graph, and demand-elastic methods using transportation network simulation models to complement the network science approaches. The research findings revealed the critical stations in Toronto's subway network, which if disrupted, would create major negative impacts on passenger trip times. The reasons for their inherent critical nature are discussed and analyzed. This work was also able to spatially quantify transit resilience by identifying low-risk and at-risk areas within Toronto. Although the results are specific to Toronto, making it the first study to analyze transit resilience elaborately in this city, the techniques employed can be applied to any sufficiently detailed transit network.

The probing behaviour of the planthopperDelphacodes kuscheli(Hemiptera: Delphacidae) on two alternating hosts, maize and oat

AbstractDelphacodes kuscheliis the most important natural vector of Mal de Río Cuarto virus (MRCV) in Argentina, aFijivirus(Reoviridae) that causes important economic losses on maize production. Although this planthopper does not breed successfully on maize, virus transmission occurs when adults migrate from oat, a winter host and a reservoir of the virus, to maize. Probing behaviour on both hosts was recorded and analysed using the direct current electrical penetration graph (DC‐EPG) system. Eight main waveforms were identified during probing behaviour byD. kuscheli. These waveforms were interpreted with reference to that described for delphacids and were related to putative probing activities: np (non‐penetration or non‐probe); N1, N2 and N3 (stylet pathway: penetration, salivation, stylet movement and extracellular activities near the phloem region); N4‐a (sieve element salivation); N4‐b (phloem sap ingestion); N5 (xylem activity); and N6 (derailed stylet mechanics). The EPG variables showed that on maize,D. kuscheliexhibited longer periods of non‐probe and longer time from first probe to first phloem activity, with a higher percentage of probing spent in stylet pathway and xylem phase than on oat. Furthermore, on maize, fewer insects showed phloem phase activities (N4‐a and N4‐b) and sustained phloem ingestion than on oat, and the time on phloem ingestion was shorter than on oat. The constrained behaviour ofD. kuschelion maize suggests that plant resistance factors may be involved.