LEADER 05204nam 22006254a 450 001 9910458074503321 005 20200520144314.0 010 $a1-281-00554-1 010 $a9786611005542 010 $a0-08-049208-8 035 $a(CKB)1000000000363770 035 $a(EBL)294651 035 $a(OCoLC)437181623 035 $a(SSID)ssj0000222302 035 $a(PQKBManifestationID)11172890 035 $a(PQKBTitleCode)TC0000222302 035 $a(PQKBWorkID)10168821 035 $a(PQKB)10856451 035 $a(MiAaPQ)EBC294651 035 $a(Au-PeEL)EBL294651 035 $a(CaPaEBR)ebr10186381 035 $a(CaONFJC)MIL100554 035 $a(EXLCZ)991000000000363770 100 $a20030425d2004 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aPlant cell death processes$b[electronic resource] /$fedited by Larry D. Noode?n 210 $aAmsterdam $cBoston $cElsevier Academic Press$dc2004 215 $a1 online resource (419 p.) 300 $aIncludes bibliographical references and index. 311 $a0-12-520915-0 327 $aFront Cover; Plant Cell Death Processes; Copyright Page; Contents; Contributors; Foreword-Aging and Death; Preface; Chapter 1. Introduction; I. What this Book Covers; II. The Processes-Senescence, Aging, Programmed Cell Death, Apoptosis, etc.-Evolving Concepts; III. Apoptosis in Animals; IV. Apoptosis in Plants; V. The Senescence Syndrome; VI. Hormonal Controls; VII. Evolution; References; Chapter 2. Plant Cell Death and Cell Differentiation; I. Introduction; II. The Scope of PCD in Plants; III. Prereproductive Cell Death; IV. Reproductive Cell Death; V. Conclusions; References 327 $aChapter 3. Cell Death in Plant Disease: Mechanisms and Molecular Markers I. Introduction; II. Role of Cell Death during Plant-Pathogen Interactions; III. Structural and Biochemical Changes Accompanying Cell Death during Plant Disease; IV. Definition of Steps Involved in the Signaling Process of Cell Death Induction during Plant-Pathogen Interactions; V. Molecular Components for Cell Death Control during Plant-Pathogen Interactions; VI. Global Analyses of Markers for Cell Death Induction by Plant Pathogens; References; Chapter 4. Changes in Gene Expression during Senescence; I. Introduction 327 $aII. Changes in Patterns of Nucleic Acids and Proteins during Senescence III. Similarities between Senescing and Ripening Tissues; IV. Identification and Classification of Senescence-related Genes; V. Senescence-related Genes; VI. Function of SR Genes in Senescence; VII. Summary; References; Chapter 5. Genes that Alter Senescence; I. Introduction; II. Senescence as a Genetically Programmed Process; III. Genes Involved in Execution of Senescence; IV. Genes Affecting Senescence through Action on the Hormonal Controls; V. Genes that Alter Senescence in Response to Environmental Factors 327 $aVI. Genes Controlling Vegetative Growth (Regeneration) and Monocarpic Senescence VII. Regulatory Genes and Intracellular Signaling; VIII. Conclusions; References; Chapter 6. Senescence and Genetic Engineering; I. Introduction; II. The Relationship of Cytokinins and Senescence; III. The Relationship of Ethylene and Senescence; IV. Concluding Remarks; References; Chapter 7. Proteolysis; I. Introduction; II. Selective Hydrolysis of Peptide Bonds; III. Proteolytic Activities in Plants; IV. Proteolysis in Relation to Cell Death; V. Regulation of Protein Catabolism; VI. Conclusions; References 327 $aChapter 8. Ethylene Signaling in Plant Cell Death I. Introduction; II. Ethylene Biosynthesis Pathways; III. Temporal and Spatial Regulation of Ethylene Biosynthesis; IV. Ethylene Signal Transduction Pathway; V. Ethylene Cross Talk with Other Plant Hormones; VI. Protease Involvement and Ethylene Biosynthesis in PCD; VII. Hormonal Regulation of Plant PCD; VIII. Perspective; References; Chapter 9. Jasmonates - Biosynthesis and Role in Stress Responses and Developmental Processes; I. Introduction; II. Jasmonates and Related Compounds 327 $aIII. LOX-derived Compounds and the Biosynthesis of Octadecanoids and Jasmonates 330 $aProgrammed cell death is a common pattern of growth and development in both animals and plants. However, programmed cell death and related processes are not as generally recognized as central to plant growth. This is changing fast and is becoming more of a focus of intensive research. This edited work will bring under one cover recent reviews of programmed cell death, apoptosis and senescence.Summaries of the myriad aspects of cell death in plants Discussion of the broadest implications of these disparate results A unification of fields where there has been no cross talk 606 $aPlant physiology 606 $aCell death 608 $aElectronic books. 615 0$aPlant physiology. 615 0$aCell death. 676 $a571.9/36 701 $aNoode?n$b Larry D$0974843 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910458074503321 996 $aPlant cell death processes$92219776 997 $aUNINA LEADER 08451nam 22008535 450 001 996465421703316 005 20201108053901.0 010 $a3-540-28646-2 024 7 $a10.1007/b99492 035 $a(CKB)1000000000212494 035 $a(DE-He213)978-3-540-28646-2 035 $a(SSID)ssj0000103977 035 $a(PQKBManifestationID)11108658 035 $a(PQKBTitleCode)TC0000103977 035 $a(PQKBWorkID)10072132 035 $a(PQKB)10503453 035 $a(MiAaPQ)EBC3088325 035 $a(PPN)155235125 035 $a(EXLCZ)991000000000212494 100 $a20121227d2004 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnt Colony Optimization and Swarm Intelligence$b[electronic resource] $e4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004, Proceeding /$fedited by Marco Dorigo, Mauro Birattari, Christian Blum, Luca M. Gambardella, Francesco Mondada, Thomas Stützle 205 $a1st ed. 2004. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2004. 215 $a1 online resource (XIV, 438 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v3172 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-22672-9 320 $aIncludes bibliographical references and index. 327 $aA Comparison Between ACO Algorithms for the Set Covering Problem -- A Comparison Between ACO Algorithms for the Set Covering Problem -- A VLSI Multiplication-and-Add Scheme Based on Swarm Intelligence Approaches -- ACO for Continuous and Mixed-Variable Optimization -- An Ant Approach to Membership Overlay Design -- An Ant Colony Optimisation Algorithm for the Set Packing Problem -- An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP -- An External Memory Implementation in Ant Colony Optimization -- BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior -- Competition Controlled Pheromone Update for Ant Colony Optimization -- Cooperative Transport of Objects of Different Shapes and Sizes -- Deception in Ant Colony Optimization -- Evolution of Direct Communication for a Swarm-bot Performing Hole Avoidance -- Gathering Multiple Robotic A(ge)nts with Limited Sensing Capabilities -- Improvements on Ant Routing for Sensor Networks -- Integrating ACO and Constraint Propagation -- Logistic Constraints on 3D Termite Construction -- Modeling Ant Behavior Under a Variable Environment -- Multi-type Ant Colony: The Edge Disjoint Paths Problem -- On the Design of ACO for the Biobjective Quadratic Assignment Problem -- Reasons of ACO?s Success in TSP -- S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty -- Time-Scattered Heuristic for the Hardware Implementation of Population-Based ACO -- Short Papers -- Ad Hoc Networking with Swarm Intelligence -- An Ant Colony Heuristic for the Design of Two-Edge Connected Flow Networks -- An Experimental Analysis of Loop-Free Algorithms for Scale-Free Networks -- An Experimental Study of the Ant Colony System for the Period Vehicle Routing Problem -- An Extension of Ant Colony System to Continuous Optimization Problems -- Ant Algorithms for Urban Waste Collection Routing -- Ants Can Play Music -- Backtracking Ant System for the Traveling Salesman Problem -- Colored Ants for Distributed Simulations -- Dynamic Routing in Mobile Wireless Networks Using ABC-AdHoc -- Fuzzy Ant Based Clustering -- How to Use Ants for Hierarchical Clustering -- Inversing Mechanical Parameters of Concrete Gravity Dams Using Ant Colony Optimization -- Large Pheromones: A Case Study with Multi-agent Physical A* -- Near Parameter Free Ant Colony Optimisation -- Particle Swarm Optimization Algorithm for Permutation Flowshop Sequencing Problem -- Search Bias in Constructive Metaheuristics and Implications for Ant Colony Optimisation -- Task Oriented Functional Self-organization of Mobile Agents Team: Memory Optimization Based on Correlation Feature -- Towards a Real Micro Robotic Swarm -- Posters -- A Hybrid Ant Colony System Approach for the Capacitated Vehicle Routing Problem -- A Swarm-Based Approach for Selection of Signal Plans in Urban Scenarios -- Ant Colony Behaviour as Routing Mechanism to Provide Quality of Service -- Applying Ant Colony Optimization to the Capacitated Arc Routing Problem -- Dynamic Optimization Through Continuous Interacting Ant Colony -- Dynamic Routing in Traffic Networks Using AntNet -- First Competitive Ant Colony Scheme for the CARP -- Hypothesis Corroboration in Semantic Spaces with Swarming Agents -- Mesh-Partitioning with the Multiple Ant-Colony Algorithm. 330 $a1 With its fourth edition, the ANTS series of workshops has changed its name. The original?ANTS?From Ant Colonies to Artificial Ants: International Workshop on Ant Algorithms? has become ?ANTS ? International Workshop on Ant Colony Optimization and Swarm Intelligence?. This change is mainly due to the following reasons. First, the term ?ant algorithms? was slower in spreading in the research community than the term ?swarm intelligence?, while at the same time research inso-called swarm robotics was the subject of increasing activity: it was therefore an obvious choice to substitute the term ant algorithms with the more accepted and used term swarm intelligence. Second, although swarm intelligence research has undoubtedly produced a 2 number of interesting and promising research directions , we think it is fair to say that its most successful strand is the one known as ?ant colony optimization?.Ant colony optimization, first introduced in the early 1990s as a novel tool for the approximate solution of discrete optimization problems,has recently seen an explosion in the number of its applications, both to academic and real-world problems, and is currently being extended to the realm of continuous optimization (a few papers on this subject being published in these proceedings). It is therefore a reasonable choice to have the term ant colony optimization as part of the workshop name. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v3172 606 $aMathematical optimization 606 $aComputer science 606 $aAlgorithms 606 $aComputers 606 $aNumerical analysis 606 $aComputer science?Mathematics 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aNumeric Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I1701X 606 $aDiscrete Mathematics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17028 615 0$aMathematical optimization. 615 0$aComputer science. 615 0$aAlgorithms. 615 0$aComputers. 615 0$aNumerical analysis. 615 0$aComputer science?Mathematics. 615 14$aOptimization. 615 24$aComputer Science, general. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aComputation by Abstract Devices. 615 24$aNumeric Computing. 615 24$aDiscrete Mathematics in Computer Science. 676 $a519.6 702 $aDorigo$b Marco$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBirattari$b Mauro$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBlum$b Christian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGambardella$b Luca M$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMondada$b Francesco$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aStützle$b Thomas$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465421703316 996 $aAnt Colony Optimization and Swarm Intelligence$9772767 997 $aUNISA