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1
Gildea, Daniel. "Grammar Factorization by Tree Decomposition." Computational Linguistics 37, no. 1 (2011): 231–48. http://dx.doi.org/10.1162/coli_a_00040.
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We describe the application of the graph-theoretic property known as treewidth to the problem of finding efficient parsing algorithms. This method, similar to the junction tree algorithm used in graphical models for machine learning, allows automatic discovery of efficient algorithms such as the O(n4) algorithm for bilexical grammars of Eisner and Satta. We examine the complexity of applying this method to parsing algorithms for general Linear Context-Free Rewriting Systems. We show that any polynomial-time algorithm for this problem would imply an improved approximation algorithm for the well-studied treewidth problem on general graphs.
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Wu, Peijie, Xianghai Meng, Li Song, and Wenze Zuo. "Crash Risk Evaluation and Crash Severity Pattern Analysis for Different Types of Urban Junctions: Fault Tree Analysis and Association Rules Approaches." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 1 (2019): 403–16. http://dx.doi.org/10.1177/0361198118822817.
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Urban junctions usually present significant safety concerns, and the majority of all crashes in urban areas occur in or near junctions. Factors contributing to crash severity at junctions have been explored, but crash risk levels and crash severity patterns of different junction types have hardly been investigated. In order to fill this gap, this study analyzed the safety performance of six junction types and the factors contributing to crash severity, in order to assist city transportation authorities to implement effective countermeasures. Fault tree analysis (FTA) was applied for the risk evaluation of urban junctions and association rules (AR) algorithm was employed for the crash severity pattern analysis based on data from the U.K. STATS19 database from 2012 to 2016. Overall, four types of urban junctions with high crash risk level and over 4,000 AR contributing to crash severity are identified in the present paper. The results show that: (a) roundabouts and mini-roundabouts have the lowest fatality and casualty rates while T-junctions or staggered junctions and crossroads have the highest crash risk levels; (b) FTA may produce inaccurate outcomes because of incorrect logic gates, but AR can generate real potential relationships between crash severity and risk factors; (c) crash severity patterns are quite complex and the interdependence between risk factors is different for each junction type; (d) risk factors such as male driver, no physical crossing facilities within 50 meters, and give way or uncontrolled junction are common in high-risk junctions at night.
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Madsen, Anders L., and Finn V. Jensen. "Lazy propagation: A junction tree inference algorithm based on lazy evaluation." Artificial Intelligence 113, no. 1-2 (1999): 203–45. http://dx.doi.org/10.1016/s0004-3702(99)00062-4.
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Parmentier, Axel, Victor Cohen, Vincent Leclère, Guillaume Obozinski, and Joseph Salmon. "Integer Programming on the Junction Tree Polytope for Influence Diagrams." INFORMS Journal on Optimization 2, no. 3 (2020): 209–28. http://dx.doi.org/10.1287/ijoo.2019.0036.
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Influence diagrams (ID) and limited memory influence diagrams (LIMID) are flexible tools to represent discrete stochastic optimization problems, with the Markov decision process (MDP) and partially observable MDP as standard examples. More precisely, given random variables considered as vertices of an acyclic digraph, a probabilistic graphical model defines a joint distribution via the conditional distributions of vertices given their parents. In an ID, the random variables are represented by a probabilistic graphical model whose vertices are partitioned into three types: chance, decision, and utility vertices. The user chooses the distribution of the decision vertices conditionally to their parents in order to maximize the expected utility. Leveraging the notion of rooted junction tree, we present a mixed integer linear formulation for solving an ID, as well as valid inequalities, which lead to a computationally efficient algorithm. We also show that the linear relaxation yields an optimal integer solution for instances that can be solved by the “single policy update,” the default algorithm for addressing IDs.
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SIRINANDA, K. G., M. BRAZIL, P. A. GROSSMAN, J. H. RUBINSTEIN, and D. A. THOMAS. "OPTIMAL LOCATION OF AN UNDERGROUND CONNECTOR USING DISCOUNTED STEINER TREE THEORY." ANZIAM Journal 62, no. 3 (2020): 334–51. http://dx.doi.org/10.1017/s1446181120000231.
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AbstractThe objective of this paper is to demonstrate that the gradient-constrained discounted Steiner point algorithm (GCDSPA) described in an earlier paper by the authors is applicable to a class of real mine planning problems, by using the algorithm to design a part of the underground access in the Rubicon gold mine near Kalgoorlie in Western Australia. The algorithm is used to design a decline connecting two ore bodies so as to maximize the net present value (NPV) associated with the connector. The connector is to break out from the access infrastructure of one ore body and extend to the other ore body. There is a junction on the connector where it splits in two near the second ore body. The GCDSPA is used to obtain the optimal location of the junction and the corresponding NPV. The result demonstrates that the GCDSPA can be used to solve certain problems in mine planning for which currently available methods cannot provide optimal solutions.
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Szántai, Tamás, and Edith Kovács. "Discovering a junction tree behind a Markov network by a greedy algorithm." Optimization and Engineering 14, no. 4 (2013): 503–18. http://dx.doi.org/10.1007/s11081-013-9232-8.
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Zhao, Yi, Jianxiao Ma, Linghong Shen, and Yong Qian. "Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination." Journal of Advanced Transportation 2020 (February 21, 2020): 1–11. http://dx.doi.org/10.1155/2020/6489027.
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This study develops three measures to optimize the junction-tree-based reinforcement learning (RL) algorithm, which will be used for network-wide signal coordination. The first measure is to optimize the frequency of running the junction-tree algorithm (JTA) and the intersection status division. The second one is to optimize the JTA information transmission mode. The third one is to optimize the operation of a single intersection. A test network and three test groups are built to analyze the optimization effect. Group 1 is the control group, group 2 adopts the optimizations for the basic parameters and the information transmission mode, and group 3 adopts optimizations for the operation of a single intersection. Environments with different congestion levels are also tested. Results show that optimizations of the basic parameters and the information transmission mode can improve the system efficiency and the flexibility of the green light, and optimizing the operation of a single intersection can improve the efficiency of both the system and the individual intersection. By applying the proposed optimizations to the existing JTA-based RL algorithm, network-wide signal coordination can perform better.
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MADSEN, ANDERS L., and BRUCE D'AMBROSIO. "A FACTORIZED REPRESENTATION OF INDEPENDENCE OF CAUSAL INFLUENCE AND LAZY PROPAGATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 02 (2000): 151–65. http://dx.doi.org/10.1142/s0218488500000113.
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The efficiency of algorithms for probabilistic inference in Bayesian networks can be improved by exploiting independence of causal influence. The factorized representation of independence of causal influence offers a factorized decomposition of certain independence of causal influence models. We describe how LAZY propagation - a junction tree based inference algorithm - easily can be extended to take advantage of the decomposition offered by the factorized representation. We introduce two extensions to the factorized representation easing the knowledge acquisition task and reducing the space complexity of the representation exponentially in the state space size of the effect variable of an independence of causal influence model. We describe how the factorized representation can be used to solve tasks such as calculating the maximum a posteriori hypotheses, the maximum expected utility, and the most probable configuration. Finally, the results of an empirical evaluation indicate that considerable performance improvements can be obtained using LAZY propagation combined with the factorized representation compared to LAZY propagation performed in junction trees constructed after either parent divorcing or temporal transformation have been applied to the Bayesian network.
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Wang, Dan, Ying Tian, Tai Yong Wang, Shi Feng Ye, and Qiong Liu. "Fault Diagnosis of the Foundation Brake Rigging System Based on Fault Tree and Bayesian Network." Key Engineering Materials 693 (May 2016): 1734–40. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1734.
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Based on the analysis of the advantages and limits of the traditional fault tree and Bayesian network in fault diagnosis, the method that building the fault Bayesian network based on fault tree is proposed in this paper. The paper introduces the correspondences between elements of the fault tree and the fault Bayesian network, also describes the inference process of the junction tree algorithm in the fault Bayesian network. Then with the foundation brake rigging system of CRH380AL EMU as an example, we build up the fault tree, complete its transmission to the fault Bayesian network, proving the superiority of the fault Bayesian tree in fault analysis of the complex system at last.
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Ryu, Hyejeong. "Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*." Applied Sciences 10, no. 21 (2020): 7846. http://dx.doi.org/10.3390/app10217846.
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An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.
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Dai, Mengyuan, Hua Mu, Meiping Wu, and Zhiwen Xian. "Decentralized State Estimation Algorithm of Centralized Equivalent Precision for Formation Flying Spacecrafts Based on Junction Tree." International Journal of Aerospace Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/714302.
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As centralized state estimation algorithms for formation flying spacecraft would suffer from high computational burdens when the scale of the formation increases, it is necessary to develop decentralized algorithms. To the state of the art, most decentralized algorithms for formation flying are derived from centralized EKF by simplification and decoupling, rendering suboptimal estimations. In this paper, typical decentralized state estimation algorithms are reviewed, and a new scheme for decentralized algorithms is proposed. In the new solution, the system is modeled as a dynamic Bayesian network (DBN). A probabilistic graphical method named junction tree (JT) is used to analyze the hidden distributed structure of the DBNs. Inference on JT is a decentralized form of centralized Bayesian estimation (BE), which is a modularized three-step procedure of receiving messages, collecting evidences, and generating messages. As KF is a special case of BE, the new solution based on JT is equivalent in precision to centralized KF in theory. A cooperative navigation example of a three-satellite formation is used to test the decentralized algorithms. Simulation results indicate that JT has the best precision among all current decentralized algorithms.
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Lin, Wenshu, Weiwei Fan, Haoran Liu, Yongsheng Xu, and Jinzhuo Wu. "Classification of Handheld Laser Scanning Tree Point Cloud Based on Different KNN Algorithms and Random Forest Algorithm." Forests 12, no. 3 (2021): 292. http://dx.doi.org/10.3390/f12030292.
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Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training.
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Hao, Ping, and Bin Li Lu. "Research on Abnormal Data Processing Method in Intelligent Data Adapter Based on Bayesian Network." Advanced Materials Research 765-767 (September 2013): 1190–95. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1190.
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This paper put forward a bayesian network junction tree reasoning and rule reasoning hybrid algorithm to solve the adaptation problem in Public Data Center of heterogeneous system, based on the research in intelligent data processing method. Using adapters dynamic data monitoring function, first, pick out all abnormal data. then, apply the hybrid algorithm on the abnormal data. finally, recover the abnormal data and report abnormal processing result. This method has been applied to many domestic universitiess intelligent data exchange system in the Public Data Center. Through practice, this algorithm can effectively improve the reliability and integrity in heterogeneous data exchange system, and obtained good application effect.
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Dutta, S., D. Patra, H. Shankar, and P. Alok Verma. "Development of Gis Tool for the Solution of Minimum Spanning Tree Problem using Prim's Algorithm." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1105–14. http://dx.doi.org/10.5194/isprsarchives-xl-8-1105-2014.
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minimum spanning tree (MST) of a connected, undirected and weighted network is a tree of that network consisting of all its nodes and the sum of weights of all its edges is minimum among all such possible spanning trees of the same network. In this study, we have developed a new GIS tool using most commonly known rudimentary algorithm called Prim’s algorithm to construct the minimum spanning tree of a connected, undirected and weighted road network. This algorithm is based on the weight (adjacency) matrix of a weighted network and helps to solve complex network MST problem easily, efficiently and effectively. The selection of the appropriate algorithm is very essential otherwise it will be very hard to get an optimal result. In case of Road Transportation Network, it is very essential to find the optimal results by considering all the necessary points based on cost factor (time or distance). This paper is based on solving the Minimum Spanning Tree (MST) problem of a road network by finding it’s minimum span by considering all the important network junction point. GIS technology is usually used to solve the network related problems like the optimal path problem, travelling salesman problem, vehicle routing problems, location-allocation problems etc. Therefore, in this study we have developed a customized GIS tool using Python script in ArcGIS software for the solution of MST problem for a Road Transportation Network of Dehradun city by considering distance and time as the impedance (cost) factors. It has a number of advantages like the users do not need a greater knowledge of the subject as the tool is user-friendly and that allows to access information varied and adapted the needs of the users. This GIS tool for MST can be applied for a nationwide plan called Prime Minister Gram Sadak Yojana in India to provide optimal all weather road connectivity to unconnected villages (points). This tool is also useful for constructing highways or railways spanning several cities optimally or connecting all cities with minimum total road length.
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Zhu, Feng, H. M. Abdul Aziz, Xinwu Qian, and Satish V. Ukkusuri. "A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework." Transportation Research Part C: Emerging Technologies 58 (September 2015): 487–501. http://dx.doi.org/10.1016/j.trc.2014.12.009.
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Hwang, Soochan, Sang-Young Cho, Taehyung Wang, and Phillip C. Y. Sheu. "A Fast 3-D Visualization Methodology Using Characteristic Views of Objects." International Journal of Software Engineering and Knowledge Engineering 08, no. 01 (1998): 97–114. http://dx.doi.org/10.1142/s021819409800008x.
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This paper describes a 3-D visualization method based on the concept of characteristic views (CVs). The idea of characteristic views was derived based on the observation that the infinite possible views of a 3-D object can be grouped into a finite number of equivalence classes so that within each class all the views are isomorphic in the sense that they have the same line-junction graphs. To visualize the changes of scenes in real time, the BSP tree algorithm is known to be efficient in a static environment in which the viewpoint can be changed easily. However, if a scene consists of many objects and each object consists of many polygons, the time complexity involved in traversing a BSP tree increases rapidly so that the original BSP tree algorithm may not be efficient. The method proposed in this paper is object-oriented in the sense that, for all viewpoints, at the preprocessing stage the ordering for displaying the objects is determined. At run time, the objects are displayed based on a pre-calculated ordering according to the viewpoint. In addition, a CV is used as a basic 2-D projected image of a 3-D object.
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Muthukumar, S., R. A. Srivardhan, and P. Subhash Chandra Bose. "System Reliability Estimation of Divert Attitude Control System of a Launch Vehicle using Bayesian Networks." Defence Science Journal 70, no. 1 (2020): 90–94. http://dx.doi.org/10.14429/dsj.70.13708.
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Divert attitude and control system (DACS) is a one-shot system and provides attitude correction and translation of the Launch vehicle. DACS consists of many flight critical sub systems which are arranged in a series configuration. The traditional Reliability block diagram and Fault tree diagram methods are unsuitable for reliability modelling, when considering uncertainty among the components and system. Bayesian network is the natural choice to model dependencies among the components and system. DACS being one shot system, it is very expensive and time consuming to test more number of systems during the design and development. Hence the data is drawn from component level, subsystem level and expert opinion is used for reliability estimation. In this paper, Bayesian network modelling of DAC system was carried out for estimating the reliability using multi-level data. An algorithm is developed for computation of Conditional probabilities in Bayesian network. Posterior probability distribution of components is calculated using Markov Chain Monte Carlo (MCMC) simulations and results are compared with Junction tree based exact inference algorithm. MATLAB code is developed to estimate the reliability of DAC system.
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Syahrani, Iswaya Maalik. "ANALISIS PEMBANDINGAN TEKNIK ENSEMBLE SECARA BOOSTING(XGBOOST) DAN BAGGING (RANDOMFOREST) PADA KLASIFIKASI KATEGORI SAMBATAN SEKUENS DNA." Jurnal Penelitian Pos dan Informatika 9, no. 1 (2019): 27. http://dx.doi.org/10.17933/jppi.2019.090103.
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<p class="JGI-AbstractIsi">Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.</p>
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Di, Ruohai, Xiaoguang Gao, Zhigao Guo, and Kaifang Wan. "A Threat Assessment Method for Unmanned Aerial Vehicle Based on Bayesian Networks under the Condition of Small Data Sets." Mathematical Problems in Engineering 2018 (June 13, 2018): 1–17. http://dx.doi.org/10.1155/2018/8484358.
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The autonomous decision-making of a UAV is based on rapid and accurate threat assessment of the target. Accordingly, modeling of threat assessment under the condition of a small data set is studied in this paper. First, the operational scenario of a manned/unmanned aerial vehicle is constructed, and feature selection and data preprocessing are performed. Second, to obtain the structure, a modeling method for threat assessment is proposed based on an improved BIC score. Finally, the obtained model is applied to compute the threat probability using the junction tree algorithm. The experimental results show that the method proposed in this paper is an available method for threat assessment under the condition of small data sets.
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Wenhui, Luo, Cai Fengtian, Wu Chuna, and Meng Xingkai. "Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks." Advances in Civil Engineering 2021 (March 4, 2021): 1–11. http://dx.doi.org/10.1155/2021/6624579.
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Accurate inference of knowledge about highway transportation safety risks forms a crucial aspect of building a knowledge graph. Based on the data related to highway transportation accidents, this study has developed a Bayesian network model. The initial identification of the network nodes is through expert scoring. The network structure is then constructed by utilizing the prior expert knowledge and K2 greedy search algorithm. Later, the network parameters are trained via the expectation-maximization (EM) algorithm. Finally, knowledge about highway transportation safety risks is inferred using the junction tree algorithm. A comparison is made between the trained conditional and actual probabilities during the network parameter training to verify the validity of the proposed model that accords with expert experience, thereby proving the model validity. Further, its main “causal chain” is inferred to be an improper emergency response-human failure-accident occurrence, where the probability of driver failure is 82%, and the probability of accident occurrence is 68% by taking “a certain road traffic accident” as an example. There is consistency between the inference results and the actual accident sequence that suggests the effectiveness of the proposed knowledge inference method.
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Pakzad, Payam, and Venkat Anantharam. "Estimation and Marginalization Using the Kikuchi Approximation Methods." Neural Computation 17, no. 8 (2005): 1836–73. http://dx.doi.org/10.1162/0899766054026693.
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In this letter, we examine a general method of approximation, known as the Kikuchi approximation method, for finding the marginals of a product distribution, as well as the corresponding partition function. The Kikuchi approximation method defines a certain constrained optimization problem, called the Kikuchi problem, and treats its stationary points as approximations to the desired marginals. We show how to associate a graph to any Kikuchi problem and describe a class of local message-passing algorithms along the edges of any such graph, which attempt to find the solutions to the problem. Implementation of these algorithms on graphs with fewer edges requires fewer operations in each iteration. We therefore characterize minimal graphs for a Kikuchi problem, which are those with the minimum number of edges. We show with empirical results that these simpler algorithms often offer significant savings in computational complexity, without suffering a loss in the convergence rate. We give conditions for the convexity of a given Kikuchi problem and the exactness of the approximations in terms of the loops of the minimal graph. More precisely, we show that if the minimal graph is cycle free, then the Kikuchi approximation method is exact, and the converse is also true generically. Together with the fact that in the cycle-free case, the iterative algorithms are equivalent to the well-known belief propagation algorithm, our results imply that, generically, the Kikuchi approximation method can be exact if and only if traditional junction tree methods could also solve the problem exactly.
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Chen, Zhaobo, Gangzhu Qiao, and Jianchao Zeng. "Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model." Sustainability 11, no. 18 (2019): 5021. http://dx.doi.org/10.3390/su11185021.
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Unsafe behaviours, such as violations of rules and procedures, are commonly identified as important causal factors in coal mine accidents. Meanwhile, a recurring conclusion of accident investigations is that worker states, such as mental fatigue, illness, physiological fatigue, etc., are important contributory factors to unsafe behaviour. In this article, we seek to provide a quantitative analysis on the relationship between the worker state and unsafe behaviours in coal mine accidents, based on a case study drawn from Chinese practice. Using Bayesian networks (BN), a graphical structure of the network was designed with the help of three experts from a coal mine safety bureau. In particular, we propose a verbal versus numerical fuzzy probability assessment method to elicit the conditional probability of the Bayesian network. The junction tree algorithm is further employed to accomplish this analysis. According to the BN established by expert knowledge, the results show that when the worker is in a poor state, the most vulnerable unsafe behaviour is violation, followed by decision-making error. Furthermore, insufficient experience may be the most significant contributory factor to unsafe behaviour, and poor fitness for duty may be the principal state that causes unsafe behaviours.
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PAN, HEPING, and LIN LIU. "FUZZY BAYESIAN NETWORKS — A GENERAL FORMALISM FOR REPRESENTATION, INFERENCE AND LEARNING WITH HYBRID BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 07 (2000): 941–62. http://dx.doi.org/10.1142/s021800140000060x.
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This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.
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Wang, Borui, and Geoffrey Gordon. "Learning General Latent-Variable Graphical Models with Predictive Belief Propagation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6118–26. http://dx.doi.org/10.1609/aaai.v34i04.6076.
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Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence. All previous methods, including the EM algorithm and the spectral algorithms, face severe limitations that largely restrict their applicability and affect their performance. In order to overcome these limitations, in this paper we introduce a novel formulation of message-passing inference over junction trees named predictive belief propagation, and propose a new learning and inference algorithm for general latent-variable graphical models based on this formulation. Our proposed algorithm reduces the hard parameter learning problem into a sequence of supervised learning problems, and unifies the learning of different kinds of latent graphical models into a single learning framework, which is local-optima-free and statistically consistent. We then give a proof of the correctness of our algorithm and show in experiments on both synthetic and real datasets that our algorithm significantly outperforms both the EM algorithm and the spectral algorithm while also being orders of magnitude faster to compute.
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Yershov, S. V., and F. V. Kostukevich. "Modeling technology based on fuzzy object-oriented Bayesian belief networks." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 179–87. http://dx.doi.org/10.15407/pp2016.02-03.179.
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The basic components of information technology inductive modeling causation under uncertainty based on fuzzy object-oriented Bayesian networks is proposed. The technology is based on a combination of transformation algorithms Bayesian network in the junction tree. New more efficient algorithms for Bayesian network transformation are resulted from modifications known algorithms; algorithms based on the use of more information on the graphical representation of the network are considered. Structurally functional model are described, it is designed to implement the transformation of fuzzy object-oriented Bayesian networks.
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Cai, Heng. "Spatial Modeling of Disaster Resilience in the Lower Mississippi River Basin." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-35-2019.
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<p><strong>Abstract.</strong> The Lower Mississippi River Basin (LMRB) in southeastern Louisiana is one of the most impacted and vulnerable coasts in the continental USA. This area has been facing recurring threats from coastal hazards in a long run. These hazardous events have negatively impacted the communities in various degrees. Therefore, identifying the places that are resilient to disasters and understanding the dynamic interactions among indicators are critical topics for pre-disaster preparation, post-disaster recovery, and establishment of mitigation plans.</p><p> Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addresses the natural&amp;ndash;human system as a whole. This study employed a BN to study the interdependencies of ten resilience variables and population change in the Lower Mississippi River Basin (LMRB) at the census block group scale. A genetic algorithm was used to identify an optimal BN where population change, a cumulative resilience indicator, was the target variable. The results uncovered a set of important resilience variables that could account for the spatial variation of population changes in a region vulnerable to coastal hazards, also identify and quantify the interactions among these variables in a probabilistic form, the results of which can then be used for future scenario modeling and planning for resilience.</p><p> Specifically, an optimal Bayesian network model was developed to explain the population change dynamics using data at block group scale. The study first extracted 10 variables from a group of 35 to derive the network. Through a genetic algorithm and after 906 generations, the resultant optimized Bayesian network was achieved with a cross-validation accuracy of 67%. The expectation-maximization (EM) method was used to learn the conditional probability tables, and the junction tree (JT) algorithm was applied to compute the posterior probabilities. Six variables were found to have direct impacts on population change, including hazard exposure, hazard damages, distance to coastline, employment rate, percent housing units built before 1970, and percent households with female householder. The remaining four variables are indirect variables, including percent agriculture land, percent flood zone area, percent owner-occupied housing units, and population density. Each variable has a conditional probability table so that its impacts on the probability of population change can be evaluated as it propagates through the network. These probabilities could be used for scenario modeling in the subsequent studies to help inform policies to reduce vulnerability and enhance resilience.</p><p> We further used the developed BN model to simulate the probability of percent of population change under different scenarios by varying the probability distributions of some input variables. The simulation process was carried out through computing the posterior probability of the target variable. Findings from the scenarios simulation offer useful information on how the factors interact that lead to population change, which can be used to inform decision making on disaster resilience and adaptation strategies.</p><p> For example, the scenarios of lowering either the hazard exposure or damages (Scenarios 1 and 2) reveal a spatial pattern of which population-change state 4 (&amp;minus;5.5% to 34.4%) becomes dominant, whereas extreme increase and decrease are less probable. Findings from Scenario 3 show that without the hazard exposure and damages the spatial pattern shows that no extreme population increase or decrease would possibly happen under the current socioeconomic and environmental condition. Results also show that reducing the percentage of housing units built before 1970 would be more effective than increasing the employment rate in terms of reducing population loss.</p>
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Yuval, Omer, Yael Iosilevskii, Anna Meledin, Benjamin Podbilewicz, and Tom Shemesh. "Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. elegans." PLOS Computational Biology 17, no. 7 (2021): e1009185. http://dx.doi.org/10.1371/journal.pcbi.1009185.
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Complex dendritic trees are a distinctive feature of neurons. Alterations to dendritic morphology are associated with developmental, behavioral and neurodegenerative changes. The highly-arborized PVD neuron of C. elegans serves as a model to study dendritic patterning; however, quantitative, objective and automated analyses of PVD morphology are missing. Here, we present a method for neuronal feature extraction, based on deep-learning and fitting algorithms. The extracted neuronal architecture is represented by a database of structural elements for abstracted analysis. We obtain excellent automatic tracing of PVD trees and uncover that dendritic junctions are unevenly distributed. Surprisingly, these junctions are three-way-symmetrical on average, while dendritic processes are arranged orthogonally. We quantify the effect of mutation in git-1, a regulator of dendritic spine formation, on PVD morphology and discover a localized reduction in junctions. Our findings shed new light on PVD architecture, demonstrating the effectiveness of our objective analyses of dendritic morphology and suggest molecular control mechanisms.
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BHATTACHARYA, UJJWAL, TANMOY KANTI DAS, AMITAVA DATTA, SWAPAN KUMAR PARUI, and BIDYUT BARAN CHAUDHURI. "A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (2002): 845–64. http://dx.doi.org/10.1142/s0218001402002027.
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This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.
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Veerapathran, Anandharaman, Micheal Schell, Francisca Beato, et al. "CD4 Treg and CD4 Tcon Utilize Distinct TCR Vbeta Repertoires in Response to Alloantigen." Blood 126, no. 23 (2015): 4286. http://dx.doi.org/10.1182/blood.v126.23.4286.4286.
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Abstract Background: Regulatory CD4 T cells (Treg) are potent to suppress the responses of conventional CD4+ (Tcon) and CD8+ T cells to alloantigens and prevent graft-vs.-host diseases (GVHD). Since the mechanisms for thymic selection of Treg and Tcon are distinct, we hypothesized that the two cells types use distinct TCR repertoires in the response to same alloantigen. We have used high throughput deep sequencing to study the T cell receptor (TCR) Vbeta CDR3 repertoire of CD4 Treg and CD4 Tcon at baseline and again after expansion with alloantigen. Methods: CD4+CD25+CD127- Treg and CD4+CD25-CD127- Tcon were FACS-sorted from healthy donor PBMCs or umblical cord blood mononuclear cells. Aliquots of sorted Treg and Tcon were expanded separately by DC from the same Major Histocompatibility (MHC)-mismatched donor. Treg were cultured with DCs, IL-2, IL-15 and rapamycin, while Tcon were cultured with DCs and IL-2. Genomic DNA from baseline or expanded purified Treg and Tcon was sequenced, at least 10^6 deep, by Adaptive Biotechnologies multiplex kit to detect unique T cell receptor (TCR) Vbeta CDR3 sequences. Data was analyzed by the algorithm established for the Adaptive Biotechnologies software and characterized according to the IMGT (International ImmunoGeneTics information system) nomenclature. Results: The TCR Vbeta CDR3 sequences of baseline natural Treg and Tcon T cell have minimal overlap, indicating that the thymus shapes distinct TCR repertoires in these two cell types. However, the CDR3 length and the frequency of nucleotide deletion or insertion at the Vbeta-Dbeta and Dbeta-Jbeta junction were similar. By employing the "robust regression model", we identified expanded TCR Vbeta CDR3 sequences among both Treg and Tcon after in vitro culture with the same alloantigens. These expanded Treg and Tcon used unique TCR Vbeta CDR3 sequences that are not shared by the other cell type. Expanded Treg and Tcon displayed fewer nucleotide deletions and insertions at the Vbeta-Dbeta and Dbeta-Jbeta junction than at baseline. The frequency of the expanded sequences, insertions and deletions were of the same magnitude in Treg and Tcon suggesting that both undergo similar processes of antigen-driven TCR selection and magnitude of cell expansion in vitro. Conclusion: CD4 Treg and Tcon utilize largely distinct TCR Vbeta CDR3 repertoires at baseline and after expansion against the same alloantigens. Questions remain whether Treg and Tcon recognize the same or distinct peptides from the same antigen, and whether they bind peptide with different avidity. TCR Vbeta CDR3 deep sequencing ought to be used to track single Treg and Tcon after adoptive cell therapy. Figure 1. CDR3 nucleotide sequences of natural Treg and Tcon do not overlap before (Baseline) and after (End) expansion against alloantigen Figure 1. CDR3 nucleotide sequences of natural Treg and Tcon do not overlap before (Baseline) and after (End) expansion against alloantigen Disclosures No relevant conflicts of interest to declare.
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Nicholls-Mindlin, Jessica, Angus McLellan, David Gee, Lauren Fuzi, Matthew Taylor, and Digby Quested. "Demystifying the pathway of assessment and treatment for bipolar disorder – utilising co-production and algorithms to personalise the approach." BJPsych Open 7, S1 (2021): S149—S150. http://dx.doi.org/10.1192/bjo.2021.420.
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AimsTo develop an evidence based, patient centred treatment pathway for people experiencing symptoms of bipolar disorder (BD), modifiable to include local resources.MethodThis project was developed in line with current approaches to service development such as coproduction, with patient and public involvement (PPI) and enhancing personalisation of treatment in medicine. As part of a local initiative, a multi-disciplinary team was brought together to understand and analyse the current local pathway for those affected by BD. It was found that the approach to assessment and management was not consistent between locality teams. Two experts by experience who have a diagnosis of BD were invited to become involved with the development of the pathway. Meetings were set up to enable coproduction and elicit information from those with the diagnosis. The responses provided insight into the effectiveness of different approaches used nationally to inform the methods and resources that are most helpful and appropriate to comprehensively support those with the illness.NICE guideline evidence was used to create two algorithms to streamline the care of those with BD in both primary and secondary care. These algorithms include pharmacological, psychological and social approaches. It also considers the junctions at which referrals should be made and the criteria on which decisions are based.ResultOne algorithm was designed for use in primary care and will be distributed to local GPs to clarify the initial steps for assessment and management of BD and the criteria for referral. A second decision tree will be made available to all doctors working in mental health services with detailed medication options, when they are appropriate and whether additional psychological intervention should be considered e.g. post-discharge groups. Other specialist options such as Early Intervention for Psychosis and Perinatal Mental Health Services were also included. An information pack was created to be offered to all those with a diagnosis or possible diagnosis of BD. This contains useful resources such as skills and exercises that patients may find of benefit, external resources and websites regarding additional support and further information on BD, its nature and management.ConclusionThe approach and resources collated here will help to streamline the management of those with bipolar disorder whilst also ensuring a more consistent approach. The involvement of experts by experience and the incorporation of NICE guidelines ensures a well-rounded and comprehensive set of documents that will be helpful to both clinicians and patients.
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Nicholls-Mindlin, Jessica, Digby Quested, Matthew Taylor, Lauren Fuzi, and David Gee. "Demystifying the pathway of assessment and treatment for bipolar disorder – utilising co-production and algorithms to personalise the approach." BJPsych Open 7, S1 (2021): S150. http://dx.doi.org/10.1192/bjo.2021.421.
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AimsTo develop an evidence based, patient centred treatment pathway for people experiencing symptoms of bipolar disorder (BD), modifiable to include local resources.MethodThis project was developed in line with current approaches to service development such as coproduction, with patient and public involvement (PPI) and enhancing personalisation of treatment in medicine. As part of a local initiative, a multi-disciplinary team was brought together to understand and analyse the current local pathway for those affected by BD. It was found that the approach to assessment and management was not consistent between locality teams. Two experts by experience who have a diagnosis of BD were invited to become involved with the development of the pathway. Meetings were set up to enable coproduction and elicit information from those with the diagnosis. The responses provided insight into the effectiveness of different approaches used nationally to inform the methods and resources that are most helpful and appropriate to comprehensively support those with the illness.NICE guideline evidence was used to create two algorithms to streamline the care of those with BD in both primary and secondary care. These algorithms include pharmacological, psychological and social approaches. It also considers the junctions at which referrals should be made and the criteria on which decisions are based.ResultOne algorithm was designed for use in primary care and will be distributed to local GPs to clarify the initial steps for assessment and management of BD and the criteria for referral. A second decision tree will be made available to all doctors working in mental health services with detailed medication options, when they are appropriate and whether additional psychological intervention should be considered e.g. post-discharge groups. Other specialist options such as Early Intervention for Psychosis and Perinatal Mental Health Services were also included. An information pack was created to be offered to all those with a diagnosis or possible diagnosis of BD. This contains useful resources such as skills and exercises that patients may find of benefit, external resources and websites regarding additional support and further information on BD, its nature and management.ConclusionThe approach and resources collated here will help to streamline the management of those with bipolar disorder whilst also ensuring a more consistent approach. The involvement of experts by experience and the incorporation of NICE guidelines ensures a well-rounded and comprehensive set of documents that will be helpful to both clinicians and patients.
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Scherler, Dirk, and Wolfgang Schwanghart. "Drainage divide networks – Part 1: Identification and ordering in digital elevation models." Earth Surface Dynamics 8, no. 2 (2020): 245–59. http://dx.doi.org/10.5194/esurf-8-245-2020.
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Abstract. We propose a novel way to measure and analyze networks of drainage divides from digital elevation models. We developed an algorithm that extracts drainage divides based on the drainage basin boundaries defined by a stream network. In contrast to streams, there is no straightforward approach to order and classify divides, although it is intuitive that some divides are more important than others. A meaningful way of ordering divides is the average distance one would have to travel down on either side of a divide to reach a common stream location. However, because measuring these distances is computationally expensive and prone to edge effects, we instead sort divide segments based on their tree-like network structure, starting from endpoints at river confluences. The sorted nature of the network allows for assigning distances to points along the divides, which can be shown to scale with the average distance downslope to the common stream location. Furthermore, because divide segments tend to have characteristic lengths, an ordering scheme in which divide orders increase by 1 at junctions mimics these distances. We applied our new algorithm to the Big Tujunga catchment in the San Gabriel Mountains of southern California and studied the morphology of the drainage divide network. Our results show that topographic metrics, like the downstream flow distance to a stream and hillslope relief, attain characteristic values that depend on the drainage area threshold used to derive the stream network. Portions along the divide network that have lower than average relief or are closer than average to streams are often distinctly asymmetric in shape, suggesting that these divides are unstable. Our new and automated approach thus helps to objectively extract and analyze divide networks from digital elevation models.
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Bartel, Seth-Emil T., Susan E. Haywood, Jason C. Woods, et al. "Role of collateral paths in long-range diffusion in lungs." Journal of Applied Physiology 104, no. 5 (2008): 1495–503. http://dx.doi.org/10.1152/japplphysiol.01005.2007.
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The long-range apparent diffusion coefficient (LRADC) of 3He gas in lungs, measured over times of several seconds and distances of 1–3 cm, probes the connections between the airways. Previous work has shown the LRADC to be small in health and substantially elevated in emphysema, reflecting tissue destruction, which is known to create collateral pathways. To better understand what controls LRADC, we report computer simulations and measurements of 3He gas diffusion in healthy lungs. The lung is generated with a random algorithm using well-defined rules, yielding a three-dimensional set of nodes or junctions, each connected by airways to one parent node and two daughters; airway dimensions are taken from published values. Spin magnetization in the simulated lung is modulated sinusoidally, and the diffusion equation is solved to 1,000 s. The modulated magnetization decays with a time constant corresponding to an LRADC of ∼0.001 cm2/s, which is smaller by a factor of ∼20 than the values in healthy lungs measured here and previously in vivo and in explanted lungs. It appears that collateral gas pathways, not present in the simulations, are functional in healthy lungs; they provide additional and more direct routes for long-range motion than the canonical airway tree. This is surprising, inasmuch as collateral ventilation is believed to be physiologically insignificant in healthy lungs. We discuss the effect on LRADC of small collateral connections through airway walls and rule out other possible mechanisms. The role of collateral paths is supported by measurements of smaller LRADC in pigs, where collateral ventilation is known to be smaller.
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Kim, Taehyung, Joon Ho Moon, Jae-Sook Ahn, et al. "RNA Sequencing-Based Measurement of Fusion-Transcript for Minimal Residual Disease (MRD) Monitoring in Core-Binding Factor Acute Myeloid Leukemia (CBF-AML)." Blood 132, Supplement 1 (2018): 2669. http://dx.doi.org/10.1182/blood-2018-99-112037.
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Abstract Introduction Recent studies utilizing NGS demonstrated that residual allelic burden at complete remission (CR) is associated with worse overall survival (OS) and relapse incidence in AML. Quantitative PCR (qPCR) based disease monitoring is current practice in CBF-AML. However, qPCR requires standardization and the result for the same sample may vary depending on several factors. Also, other known prognostic factors such as cKIT mutation require an additional test. As RNA-seq can detect gene rearrangement as well as somatic mutations, we hypothesized that RNA-seq on samples taken at diagnosis and at remission can be used to monitor these genetic alterations simultaneously and can be utilized for minimal residual disease (MRD) monitoring in CBF-AML. Patients and Methods This study included 42 CBF-AML patients (23 RUNX1-RUNX1T1 and 19 CBFB-MYH11 AML). Overall, 84 bone marrow samples (42 diagnosis-CR pairs) were subjected to targeted RNA-seq using Illumina TruSight Pan-Cancer panel. After read mapping, gene count was measured using HTSeq followed by DEseq2 for gene expression quantification. Average number of sequenced reads was 3.5M reads with 87% overall mapping rate. Gene fusions in diagnostic samples were detected using EricScript. All 84 samples as well as 42 samples from T-cell fraction (CD3+, as a control) were also subjected to DNA sequencing, targeting a panel of 84 genes (Agilent SureSelect custom gene panel). Average on-target coverage was 1,606x. All other computational analyses were done using R and python. Results In diagnostic samples, class-defining gene fusion events were detected in all 42 patients. In CR samples, we tracked identical junctions identified in corresponding diagnostic samples. As expected, both CBFB-MYH11 and RUNX1-RUNX1T1 showed significant reduction in all CR samples compared to their corresponding diagnostic samples (p < 2.2e-13 and p < 6.3e-05, Fig A and B). CBFB-MYH11 was detectable in 6/19 CR samples (32%) and RUNX1-RUNX1T1 was detectable in 15/23 CR samples (65%). Reduction level of RUNX1-RUNX1T1 measured by RNA-seq showed positive correlation with the reduction level measured by qPCR (Pearson's Rho = 0.74, p < 5.4e-05, Fig C). As per mutational profile at diagnosis, we detected 74 mutations in 38 samples (n=38/42, 90%). NRAS (36%), KIT (36%), KRAS (17%) and, ASXL2 (17%) were commonly mutated. Survival analyses on each gene and each protein locus identified cKIT-D816 mutation as an adverse prognostic factor (HR = 3.57, [1.15 - 11.11], p = 0.028). We were able to detect all cKIT-D816 mutations in RNA-seq. Using information from NGS, we built a prognostic model for RUNX1-RUNX1T1 AML (n = 23). Decision tree analysis identified three distinct subgroups of RUNX1-RUNX1T1 AML on the basis of reduction level of RUNX1-RUNX1T1 and mutation profile (Fig D). Consistent with previous studies, 3-log or deeper reduction of RUNX1-RUNX1T1 transcript level was the most significant prognostic factor (low risk group). The algorithm further divided the patients who failed to achieve 3-log reduction according to the presence of cKIT-D816 mutation at diagnosis (intermediate and high risk group). For three defined groups, 2-year OS rates were 87%, 74%, and 33% (p = 0.08, Fig E) and 2-year relapse incidence rates were 13%, 42%, and 67% (p = 0.048, Fig F). Conclusion RNA-seq can be utilized to quantify RUNX1-RUNX1T1 and CBFB-MYH11 transcripts on diagnostic and CR samples in CBF-AML. We also showed that RNA-seq can stratify RUNX1-RUNX1T1 AML patients into three risk groups according to their long-term prognosis. Figure. Figure. Disclosures No relevant conflicts of interest to declare.
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Keelan, Jonathan, and James P. Hague. "The role of vascular complexity on optimal junction exponents." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-84432-1.
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AbstractWe examine the role of complexity on arterial tree structures, determining globally optimal vessel arrangements using the Simulated AnneaLing Vascular Optimization algorithm, a computational method which we have previously used to reproduce features of cardiac and cerebral vasculatures. In order to progress computational methods for growing arterial networks, deeper understanding of the stability of computational arterial growth algorithms to complexity, variations in physiological parameters (such as metabolic costs for maintaining and pumping blood), and underlying assumptions regarding the value of junction exponents is needed. We determine the globally optimal structure of two-dimensional arterial trees; analysing how physiological parameters affect tree morphology and optimal bifurcation exponent. We find that considering the full complexity of arterial trees is essential for determining the fundamental properties of vasculatures. We conclude that optimisation-based arterial growth algorithms are stable against uncertainties in physiological parameters, while optimal bifurcation exponents (a key parameter for many arterial growth algorithms) are affected by the complexity of vascular networks and the boundary conditions dictated by organs.
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Suresh, S., and K. Sridharan. "Gradually Varied Flow Computation in Structured Irrigation Networks." Frontiers in Earth Science 9 (August 18, 2021). http://dx.doi.org/10.3389/feart.2021.663193.
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In this paper an algorithm for analysis and design of a structured irrigation network and a traditional irrigation network are developed on the basis of the gradually varied flow analysis of the flow in the entire network. The model developed brings advantages of the structured irrigation network concept and also allows the user to analyses and design the flow distribution in the entire network. Model built around the gradually varied flow analysis model with a tree network is divided into a group based on network consisting of an initial value problem and also a boundary value problem with junctions as the reconciled boundaries. The essence of the model is that it can handle several types of structures such as head regulators, duckbill weirs in the main canal, an open flume, a pipe semi module, a pipe outlet with and without a sleeve, proportional distributors and tail clusters in the offtakes. These structures are very important from the standpoint of analysis and design for successful implementation of a structured irrigation network on an existing system and also for analysis and design of a traditional irrigation system consisting of many gates and other structures. The focus of this paper is the efficacy of the water management in ensuring equitable distribution of flow at different discharges released at the head works.
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Liu, Dongli, Jing Li, Saisai Zhang, Xiangjing Wang, Wensheng Xiang, and ji zhang. "Leaf spot disease of Orychophragmus violaceus caused by Alternaria tenuissima in China." Plant Disease, April 5, 2021. http://dx.doi.org/10.1094/pdis-10-20-2287-pdn.
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Orychophragmus violaceus (L.) O. E. Schulz, also called February orchid or Chinese violet cress, belongs to the Brassicaceae family and is widely cultivated as a green manure and garden plant in China. During the prolonged rainy period in August 2020, leaf spot disease of O. violaceus was observed in the garden of Northeast Agricultural University, Harbin, Heilongjiang province. One week after the rainy days, the disease became more serious and the disease incidence ultimately reached approximately 80%. The disease symptoms began as small brown spots on the leaves, and gradually expanded to irregular or circular spots. As the disease progressed, spots became withered with grayish-white centers and surrounded by dark brown margins. Later on, the centers collapsed into holes. For severely affected plants, the spots coalesced into large necrotic areas and resulted in premature defoliation. No conidiophores or hyphae were present, and disease symptoms were not observed on other tissues of O. violaceus. To isolate the pathogen, ten leaves with typical symptoms were collected from different individual plants. Small square leaf pieces (5×5 mm) were excised from the junction of diseased and healthy tissues, disinfected in 75% ethanol solution for 1 min, rinsed in sterile distilled water, and then transferred to Petri dishes (9 cm in diameter) containing potato dextrose agar (PDA). After 3 days of incubation at 25 oC in darkness, newly grown-out mycelia were transferred onto fresh PDA and purified by single-spore isolation. Nine fungal isolates (NEAU-1 ~ NEAU-9) showing similar morphological characteristics were obtained and no other fungi were isolated. The isolation frequency from the leaves was almost 90%. On PDA plates, all colonies were grey-white with loose and cottony aerial hyphae, and then turned olive-green and eventually brown with grey-white margins. The fungus formed pale brown conidiophores with sparsely branched chains on potato carrot agar (PCA) plates after incubation at 25 oC in darkness for 7 days. Conidia were ellipsoidal or ovoid, light brown, and ranged from 18.4 to 59.1 × 9.2 to 22.3 µm in size, with zero to two longitudinal septa and one to five transverse septa and with a cylindrical light brown beak (n = 50). Based on the cultural and morphological characteristics, the fungus was tentatively identified as Alternaria tenuissima (Simmons 2007). Genomic DNA was extracted from the mycelia of five selected isolates (NEAU-1 ~ NEAU-5). The internal transcribed spacer region (ITS) was amplified and sequenced using primers ITS1/ITS4 (White et al., 1990). Blast analysis demonstrated that these five isolates had the same ITS sequence, and the ITS sequence of representative strain NEAU-5 (GenBank accession No. MW139354) showed 100% identity with the type strains of Alternaria alternata CBS916.96 and Alternaria tenuissima CBS918.96. Furthermore, the translation elongation factor 1-α gene (TEF), RNA polymerase II second largest subunit (RPB2), and glyceraldehyde 3-phosphate dehydrogenase (GPD) of representative strain NEAU-5 were amplified and sequenced using primers EF1-728F/EF1-986R (Carbone and Kohn 1999), RPB2-5F2/RPB2-5R (Sung et al., 2007), and Gpd1/Gpd2 (Berbee et al., 1999), respectively. The sequences of RPB2, GPD, and TEF of strain NEAU-5 were submitted to GenBank with accession numbers of MW401634, MW165223, and MW165221, respectively. Phylogenetic trees based on ITS, RPB2, GPD, and TEF were constructed with the neighbour-joining and maximum-likelihood algorithms using MEGA software version 7.0. The results demonstrated that strain NEAU-5 formed a robust clade with A. tenuissima CBS918.96 (supported by 99% and 96% bootstrap values) in the neighbour-joining and maximum-likelihood trees. As mentioned above, strain NEAU-5 produced seldomly branched conidial chains on PCA plates. The pattern is consistent with that of A. tenuissima (Kunze) Wiltshire, but distinct from that of A. alternata which could produce abundant secondary ramification (Simmons 2007). Thus, strain NEAU-5 was identified as A. tenuissima based on its morphology and phylogeny. Pathogenicity tests were carried out by inoculating five unwounded leaves with a conidial suspension of strain NEAU-5 (approximately 106 conidia/ml) on five different healthy plants cultivated in garden, and an equal number of leaves on the same plants inoculated with sterilized ddH2O served as negative controls. Inoculated and control leaves were covered with clear plastic bags for 3 days. After 6 days, small brown and irregular or circular spots were observed on all leaves inoculated with conidial suspension, while no such symptoms were observed in the control. The tests were repeated three times. Furthermore, the pathogenicity tests were also performed using 2-month-old potted plants in a growth chamber (28 oC, 90% relative humidity, 12 h/12 h light/dark) with two repetitions. Five healthy plants were inoculated by spraying 20 ml of a conidial suspension of strain NEAU-5 (approximately 106 conidia/ml) onto unwounded leaves. Five other healthy plants were inoculated with sterilized ddH2O as controls. After 7 days, similar symptoms were observed on leaves inoculated with strain NEAU-5, whereas no symptoms were observed in the control. The pathogen was reisolated from the inoculated leaves and identified as A. tenuissima by morphological and molecular methods. In all pathogenicity tests, A. tenuissima could successfully infect unwounded leaves of O. violaceus, indicating a direct interaction between leaves and A. tenuissima. It is known that high humidity and fairly high temperatures can favor the epidemics of Alternaria leaf spot (Yang et al., 2018), and this may explain why severe leaf spot disease of O. violaceus was observed after prolonged rain. Previously, it has been reported that Alternaria brassicicola and Alternaria japonica could cause leaf blight and spot disease on O. violaceus in Hebei and Jiangsu Provinces, China, respectively (Guo et al., 2019; Sein et al., 2020). Although these pathogens could lead to similar disease symptoms on the leaves of O. violaceus, it is easy to distinguish them by the morphological characteristics of conidiophores and ITS gene sequences. To our knowledge, this is the first report of A. tenuissima causing leaf spot disease of O. violaceus in China.