Impact Of Host Plant Xylem Fluid On Xylella Fastidiosa Multiplication ...

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TREATMENT THRESHOLDS FOR THE GLASSY-WINGED SHARPSHOOTER BASED ON THE LOCAL EPIDEMIOLOGY OF PIERCE’S DISEASE SPREAD (A STAGE-STRUCTURED EPIDEMIC MODEL) Project Leader: Thomas M. Perring Dept. of Entomology University of California Riverside, CA 92521 Cooperators: Jon C. Allen Dept. of Ecolog Evolution and Marine Biology University of California Santa Barbara, CA 9316 Yong-Lak Park Dept. of Entomology University of California Riverside, CA 92521 Charles A. Farrar Dept. of Entomology University of California Riverside, CA 92521 Rayda K. Krell Dept. of Entomology University of California Riverside, CA 92521 Reporting period: The results reported here are from work conducted from July 1, 2004 to October 8, 2004. ABSTRACT The conditions for the successful invasion of a vineyard by Pierce’s disease (PD) are not well understood. To help integrate what knowledge we do have and indicate areas where research is needed we are developing a more biologically detailed model than has been previously available. Fortunately there is a large ensemble of literature from epidemiology regarding this problem, and in addition, much has been done toward solving the kinds of equations that arise in this work in terms of both mathematics and software. Here we outline very briefly our progress to date, and the ways in which these sorts of models can help us to better manage and understand the PD system. Here we describe a system of delay equations for modeling the dynamics of PD vectored by the glassy-winged sharpshooter (GWSS). We will analyze and study this system to derive threshold conditions for the invasion of a vineyard by PD and GWSS. Thresholds for disease outbreaks are common among epidemiological systems and a large literature exists on this subject. In addition new software (not commercially released yet) has been made available to us for solving these kinds of systems. We will attempt to use our model system to bring this methodology to the PD/GWSS problem and find new ways of controlling this disease. INTRODUCTION Last year we presented a model to evaluate how the threshold might change in relation to various biological and ecological factors (Perring et al. 2003). It was designed to determine the number of GWSS required to cause a single PD infection in grape. The primary model parameters were the proportion of GWSS carrying PD, GWSS transmission efficiency of PD, proportion of GWSS that will move from citrus to grape, the number of grapevines that a single GWSS will visit, grape varietal susceptibility, and the probability of an infection event resulting in disease. Our recent work, reported in this progress report, is an extension of the previous efforts and is more biologically detailed, allowing us to address more complicated biological processes affecting the epidemiology of PD in grapes. Over eighty years of research in epidemiology has shown that epidemics tend to be triggered when the generation reproductive factor of the pathogen becomes greater than 1.0 (Kermack and McKendrick 1927, Anderson 1978, Diekmann and Heesterbeek 2000, van den Driessche and Watmough 2002, Wonham et al. 2003). This fortunate result is useful in management since it provides us with a target threshold that will trigger a PD epidemic in grapes. More than just a threshold, this approach will provide a function for the basic generation factor of increase of the pathogen, R 0 , as a parameter function from the model. The pathogen will grow into an epidemic or decline to zero according to whether R 0 is greater or less than 1.0. It is particularly helpful that this threshold indicator is a function of all of the model parameters, since this indicates what parameters the threshold is most sensitive to and therefore how management can be most effectively focused. Some of the things that we intuitively expect to be important are density of GWSS, pathogen titer of the insects, and their dispersal rate and feeding rate. OBJECTIVES 1. Develop a model to describe the epidemiology of GWSS transmission of PD to provide a framework for organizing data and examining relationships between data from different research projects. 2. Use the model to develop field-specific treatment thresholds to prevent GWSS transmission of PD. RESULTS AND CONCLUSIONS Our results consist of a model system of state equations describing the progress of PD in a vineyard vectored by GWSS. Here we develop our basic model as set of four balance equations, two equations for the GWSS and two equations for grapes. - 269 -
The state variables, process functions and parameters are defined in Table 1. We emphasize that this model is in an early development stage, and undoubtedly will evolve and improve as we develop it further. We used the delay-differential equation (DDE) formalism developed by Murdoch et al. (1987) and Murdoch et al. (2003) for stage structured insects, and to their formulation we will add time dependence (temperature forcing) of the developmental delays (although for simplicity we will not elaborate on this here). The time dependence in the delays can be incorporated according to the mathematical recipes developed by Nisbet and Gurney (1983), Gurney et al. (1983), Gurney and Nisbet (1998) and Nisbet (1998). Methods for setting up the initial history for starting the models are outlined well in Gurney et al. (1983). We will solve our set of equations using a new delay differential equation (DDE) solver, ddesd.m, (with time and system varying delays) developed for The Mathworks (Matlab) by L. F. Shampine (Shampine & Thompson 2001, Shampine 2004). The solver is not yet a part of Matlab itself, but a version is available on the Web at: http://faculty.smu.edu/lshampin/current.html . Our model system. The state balance equations are written as a set delay-differential equations (DDEs) with functions for recruitment, infection and death rates as: dAS () t Susceptible Adults: = Rt ( −TJ) SJ − X( t) + X( t−T1) S1−DA( t) dt dAI () t Infectious Adults: = Xt ( ) −Xt ( −TS 1) 1−DA( t) 1.1 dt dS() t Susceptible Vines: = DV () t −Y() t dt dI() t Infectious Vines: = Yt ( −T2 ) −DV ( t) dt We adopted and slightly modified the notation of Murdoch et al. (2003) by using R(t), X(t), Y(t) and D(t) to represent recruitment (R), infection of GWSS (X) infection of vines (Y) and death rate (D) functions for each stage, and we then define each of these for our case. These equations indicated that the rate of change of a stage is simply the input to that stage minus output from that stage. The interpretations for each equation are outlined below. Susceptible adult equation. The first equation says that susceptible adults have input from reproduction, one juvenile delay period (T J ) in the past times survival going through the juvenile stage, R(t - T J )S J . Another input to susceptible adults is (possible) recovery from an infectious adult class with a time delay, X(t - T 1 )S 1 where T 1 is the time that the disease persists in an infected adult, and S 1 is the survival during the infectious period. Outputs from susceptible adults are infection by feeding on an infectious vine, X(t), and death, D A (t). Infectious adult equation. The second equation says that infectious adults have input from the infection process, X(t), (which was output from the susceptible class) and output to (possible) recovery from infection, X(t - T 1 )S 1 , and death, D A (t). Susceptible vine equation. The third equation says that susceptible vines have input equal to death rate of infectious vines, D V (t), that is, we assume that dead vines are replaced at the death rate. Output from susceptible vines is infection by infectious sharpshooters, Y(t). Infectious vine equation. The last equation says that infectious vines have input from the infection process with a latent period time lag, Y(t - T 2 ), where T 2 is the latent period of the disease in vines after becoming infected. We assume that all vines survive the latent period. Output from the infectious vine equation is by death of infected vines, D V (t). Our model system of equations will allow us to simulate the introduction and progress of PD into a vineyard under different conditions and management strategies. What we would like is to see the disease die-out and not invade the vineyard effectively. What we do not understand at this point is how all of the factors influence this scenario and determine its progress and to which factors spread is most sensitive. By studying the dynamic behavior of this model system we can learn how different management options are likely to affect the disease progress in a vineyard, giving us new ideas and methods about how to best control and prevent disease outbreaks. - 270 -
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IMPACT OF HOST PLANT XYLEM FLUID ON
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0.18 600 Optical density 0.16 0.14
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OPTIMIZING MARKER-ASSISTED SELECTIO
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esistant and susceptible genotypes
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MAP BASED IDENTIFICATION AND POSITI
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esistant genotypes are in process a
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BREEDING PIERCE’S DISEASE RESISTA
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Progeny from crosses of field resis
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a = (1=low, 4= high); b = (1=green,
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- 78 -
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v.8) for differences due to the pla
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SHARPSHOOTER FEEDING BEHAVIOR IN RE
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correlated with all other findings
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EFFECTS OF FEEDING SUBSTRATE ON RET
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REFERENCES Bextine, B. and T. Mille
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will also provide insights into the
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OBJECTIVES 1. Determine glassy-wing
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GWSS per 30 s sweep (seasonal avera
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ELISA for the presence of GWSS egg
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Project Leader: Thomas P. Freeman E
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Freeman, T. P., R. A. Leopold, D. R
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pathogen, when they move into viney
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A NOVEL IMMUNOLOGICAL APPROACH FOR
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1.6 GWSS Adults That Fed on Protein
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Hagler, J.R. & S.E. Naranjo. 2004.
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RESULTS: Oviposition Survey Wild gr
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Host specificity testing: No-choice
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currently employed morphological ch
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increase our present understanding
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BIOLOGY AND MORPHOMETRIC ANALYSIS O
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Table 1. Mean a developmental durat
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EFFECTS OF USING CONSTANT AND CYCLI
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REFERENCES Gautam, R. D. 1986. Effe
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PARASITISM OF THE GLASSY-WINGED SHA
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Table 1. Parasitism by G.ashmeadi o
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Project Leader: Robert F. Luck Dept
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A more interesting analysis using t
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MYCOPATHOGENS AND THEIR EXOTOXINS I
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POPULATION DYNAMICS AND INTERACTION
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No egg masses were recorded on olea
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EXPLORATION FOR FACULTATIVE ENDOSYM
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Although Baumannia and Wolbachia we
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EFFECTS OF SUBLETHAL DOSES OF IMIDA
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Table 2. Mortality and flight perfo
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A NOVEL METHOD TO INDUCE OVIPOSITIO
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REFERENCES Brodbeck, B. V., P. C. A
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Cohorts H. coagulata neonates were
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REFERENCES Blua, M. J. and D. J. W.
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Figure 2 shows the average numbers
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REPRODUCTIVE BIOLOGY AND PHYSIOLOGY
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REFERENCES Blua, M. J., Phillips, P
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RESULTS Some plant families had no
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Table 5. Winter, spring and summer
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16S rDNA is by far the most sequenc
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DNA MICROARRAY AND MUTATIONAL ANALY
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Inhibition of biofilm is BSA concen
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CULTURE-INDEPENDENT ANALYSIS OF END
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Borneman, J., M. Chrobak, G. Della
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P (CFU P OBJECTIVE 1. Determine the
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Purcell, AH and SR Saunders. 1999a.
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Figure 1: Southern blot of tolC::ap
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Project Leader: David Gilchrist Dep
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III. Production of transgenic plant
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RESULTS Construction of cDNA Librar
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CONCLUSIONS Genetic resistance and
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Mutagenesis of Xylella The EZ::TN T
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ISOLATION OF BACTERIOPHAGES SPECIFI
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Project Leader: Michele M. Igo Sect
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outer membrane fractions using two-
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1995; Purcell and Saunders, 1999).
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DEVELOPMENT OF SSR MARKERS FOR GENO
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Strain Name Host of Origin County o
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ROLE OF ATTACHMENT OF XYLELLA FASTI
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wild-type cells was not conclusive
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DETERMINATION OF GENES CONFERRING H
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S1 S2 S3 S4 Nb123456789bb123456789b
Page 159 and 160: MULTILOCUS SEQUENCE TYPING TO IDENT
Page 161 and 162: GENOME-WIDE IDENTIFICATION OF RAPID
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Page 165 and 166: EFFECTS OF CHEMICAL MILIEU ON ATTAC
Page 167 and 168: CONCLUSIONS Our overall objective i
Page 169 and 170: RESULTS Objective 1. We conducted t
Page 171 and 172: Redak, R. A., Purcell, A. H., Lopes
Page 173 and 174: In parallel, an “activating trans
Page 175 and 176: PATTERNS OF XYLELLA FASTIDIOSA INFE
Page 177 and 178: % of vessels 100 80 60 40 20 Mugwor
Page 179 and 180: DOCUMENTATION AND CHARACTERIZATION
Page 181 and 182: Magnolia002 showed more identity (9
Page 183 and 184: PLASMID ADDICTION AS A NOVEL APPROA
Page 185 and 186: GENETIC VARIABILITY OF XYLELLA FAST
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Page 189 and 190: probing activities). In preliminary
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Page 193 and 194: 12. Hopkins DL (1977) Diseases caus
Page 195 and 196: The almost complete absence of info
Page 197 and 198: QUANTIFYING LANDSCAPE-SCALE MOVEMEN
Page 199 and 200: Table 1. The mean (±SD) ELISA read
Page 201 and 202: EPIDEMIOLOGICAL ASSESSMENTS OF PIER
Page 203 and 204: multiply to relatively high (easily
Page 205 and 206: SPATIAL DATABASE CREATION AND MAINT
Page 207 and 208: Project Leader: Thomas M. Perring D
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Page 213 and 214: DEVELOPMENT OF A FIELD SAMPLING PLA
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Page 221 and 222: OBJECTIVES 1. Track the movement of
Page 223 and 224: REFERENCES 1. Beard, C.B., Cordon-R
Page 225 and 226: cases, positive phloem samples were
Page 227 and 228: EXPLOITING XYLELLA FASTIDIOSA PROTE
Page 229 and 230: Figure 2. Polymer disk accumulation
Page 231 and 232: CHARACTERIZATION OF NEONICOTINOIDS
Page 233 and 234: EVALUATION OF RESISTANCE POTENTIAL
Page 235 and 236: Project Leader: Doug Cook Dept. of
Page 237 and 238: Based on the in silico analysis, de
Page 239 and 240: CONTROL OF PIERCE’S DISEASE THROU
Page 241 and 242: Figure 1. Oleander ‘White’ afte
Page 243 and 244: RESULTS During the reporting period
Page 245 and 246: DEVELOPMENT OF AN ARTIFICIAL DIET A
Page 247 and 248: DESIGN OF CHIMERIC ANTI-MICROBIAL P
Page 249 and 250: Neutrophil elastase Cecropin B Chim
Page 251 and 252: and WTXb is very low (0.00-0.40%) a
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Page 257 and 258: Table 1. Single-populations descrip
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These novel observations strongly s
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SEQUENCE DIVERGENCE IN TWO MITOCHON
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Table 3. Pairwise sequence distance
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DEVELOPMENT OF MOLECULAR DIAGNOSTIC
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A. B. Relative Density of SCAR Band
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THE ALIMENTARY TRACK OF GLASSY-WING
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We have dissected and identified al
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REALIZED LIFETIME PARASITISM AND TH
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REPRODUCTIVE AND DEVELOPMENTAL BIOL
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Mean adult longevity (days) 25 20 1
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the proposed exploratory work; thes
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SEARCHING FOR AND COLLECTING EGG PA
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REFERENCES Hoddle, M. S. and S. V.
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some Gram(+) bacteria, but are inac
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7. Hultmark, D., et al., Insect Imm
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Isolation of Fungal Pathogens Soil
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IDENTIFICATION OF MECHANISMS MEDIAT
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concentrations of 1.5 x 10 7 bacter
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anchor it in the outer membrane (sh
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SYMBIOTIC CONTROL OF PIERCE’S DIS
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MANAGEMENT OF PIERCE’S DISEASE OF
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pfF mutants are taken up by insects
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Simpson, A. J. G., F. C. Reinach, P
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Al-Wahaibi, A.K., Owen, and J. G. M
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patterns differed among the lines,
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Punja, Z.K. 2001. Genetic engineeri
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mind, we conducted an additional st
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REFERENCES Toscano, N., Byrne, F.,
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RESULTS AND CONCLUSIONS The program
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COMPATIBILITY OF INSECTICIDES WITH
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More tests are in progress to addre
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Impact Of Host Plant Xylem Fluid On Xylella Fastidiosa Multiplication ...

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