102 research outputs found

    Influence of HAART on Alternative Reading Frame Immune Responses over the Course of HIV-1 Infection

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    Background: Translational errors can result in bypassing of the main viral protein reading frames and the production of alternate reading frame (ARF) or cryptic peptides. Within HIV, there are many such ARFs in both sense and the antisense directions of transcription. These ARFs have the potential to generate immunogenic peptides called cryptic epitopes (CE). Both antiretroviral drug therapy and the immune system exert a mutational pressure on HIV-1. Immune pressure exerted by ARF CD8(+) T cells on the virus has already been observed in vitro. HAART has also been described to select HIV-1 variants for drug escape mutations. Since the mutational pressure exerted on one location of the HIV-1 genome can potentially affect the 3 reading frames, we hypothesized that ARF responses would be affected by this drug pressure in vivo. Methodology/Principal findings: In this study we identified new ARFs derived from sense and antisense transcription of HIV-1. Many of these ARFs are detectable in circulating viral proteins. They are predominantly found in the HIV-1 env nucleotide region. We measured T cell responses to 199 HIV-1 CE encoded within 13 sense and 34 antisense HIV-1 ARFs. We were able to observe that these ARF responses are more frequent and of greater magnitude in chronically infected individuals compared to acutely infected patients, and in patients on HAART, the breadth of ARF responses increased. Conclusions/Significance: These results have implications for vaccine design and unveil the existence of potential new epitopes that could be included as vaccine targets.International AIDS Vaccine Initiative (IAVI

    Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae

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    A mechanistic, dynamic model was developed to predict infection of loquat fruit by conidia of Fusicladium eriobotryae, the causal agent of loquat scab. The model simulates scab infection periods and their severity through the sub-processes of spore dispersal, infection, and latency (i.e., the state variables); change from one state to the following one depends on environmental conditions and on processes described by mathematical equations. Equations were developed using published data on F. eriobotryae mycelium growth, conidial germination, infection, and conidial dispersion pattern. The model was then validated by comparing model output with three independent data sets. The model accurately predicts the occurrence and severity of infection periods as well as the progress of loquat scab incidence on fruit (with concordance correlation coefficients .0.95). Model output agreed with expert assessment of the disease severity in seven loquatgrowing seasons. Use of the model for scheduling fungicide applications in loquat orchards may help optimise scab management and reduce fungicide applications.This work was funded by Cooperativa Agricola de Callosa d'En Sarria (Alicante, Spain). Three months' stay of E. Gonzalez-Dominguez at the Universita Cattolica del Sacro Cuore (Piacenza, Italy) was supported by the Programa de Apoyo a la Investigacion y Desarrollo (PAID-00-12) de la Universidad Politecnica de Valencia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.González Domínguez, E.; Armengol Fortí, J.; Rossi, V. (2014). Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae. PLoS ONE. 9(9):1-12. https://doi.org/10.1371/journal.pone.0107547S11299Sánchez-Torres, P., Hinarejos, R., & Tuset, J. J. (2009). Characterization and Pathogenicity ofFusicladium eriobotryae, the Fungal Pathogen Responsible for Loquat Scab. Plant Disease, 93(11), 1151-1157. doi:10.1094/pdis-93-11-1151Gladieux, P., Caffier, V., Devaux, M., & Le Cam, B. (2010). Host-specific differentiation among populations of Venturia inaequalis causing scab on apple, pyracantha and loquat. Fungal Genetics and Biology, 47(6), 511-521. doi:10.1016/j.fgb.2009.12.007González-Domínguez, E., Rossi, V., Armengol, J., & García-Jiménez, J. (2013). Effect of Environmental Factors on Mycelial Growth and Conidial Germination ofFusicladium eriobotryae, and the Infection of Loquat Leaves. Plant Disease, 97(10), 1331-1338. doi:10.1094/pdis-02-13-0131-reGonzález-Domínguez, E., Rossi, V., Michereff, S. J., García-Jiménez, J., & Armengol, J. (2014). Dispersal of conidia of Fusicladium eriobotryae and spatial patterns of scab in loquat orchards in Spain. European Journal of Plant Pathology, 139(4), 849-861. doi:10.1007/s10658-014-0439-0Becker, C. M. (1994). Discontinuous Wetting and Survival of Conidia ofVenturia inaequalison Apple Leaves. Phytopathology, 84(4), 372. doi:10.1094/phyto-84-372Hartman, J. R., Parisi, L., & Bautrais, P. (1999). Effect of Leaf Wetness Duration, Temperature, and Conidial Inoculum Dose on Apple Scab Infections. Plant Disease, 83(6), 531-534. doi:10.1094/pdis.1999.83.6.531Holb, I. J., Heijne, B., Withagen, J. C. M., & Jeger, M. J. (2004). Dispersal of Venturia inaequalis Ascospores and Disease Gradients from a Defined Inoculum Source. Journal of Phytopathology, 152(11-12), 639-646. doi:10.1111/j.1439-0434.2004.00910.xRossi, V., Giosue, S., & Bugiani, R. (2003). Influence of Air Temperature on the Release of Ascospores of Venturia inaequalis. Journal of Phytopathology, 151(1), 50-58. doi:10.1046/j.1439-0434.2003.00680.xStensvand, A., Gadoury, D. M., Amundsen, T., Semb, L., & Seem, R. C. (1997). Ascospore Release and Infection of Apple Leaves by Conidia and Ascospores ofVenturia inaequalisat Low Temperatures. Phytopathology, 87(10), 1046-1053. doi:10.1094/phyto.1997.87.10.1046Machardy WE (1996) Apple scab. Biology, epidemiology and management. St. Paul: APS Press. 545.James, J. R. (1982). Environmental Factors Influencing Pseudothecial Development and Ascospore Maturation ofVenturia inaequalis. Phytopathology, 72(8), 1073. doi:10.1094/phyto-72-1073Li, B., Zhao, H., Li, B., & Xu, X.-M. (2003). Effects of temperature, relative humidity and duration of wetness period on germination and infection by conidia of the pear scab pathogen (Venturia nashicola). Plant Pathology, 52(5), 546-552. doi:10.1046/j.1365-3059.2003.00887.xLi, B.-H., Xu, X.-M., Li, J.-T., & Li, B.-D. (2005). Effects of temperature and continuous and interrupted wetness on the infection of pear leaves by conidia of Venturia nashicola. Plant Pathology, 54(3), 357-363. doi:10.1111/j.1365-3059.2005.01207.xUMEMOTO, S. (1990). Dispersion of ascospores and conidia of causal fungus of Japanese pear scab, Venturia nashicola. Japanese Journal of Phytopathology, 56(4), 468-473. doi:10.3186/jjphytopath.56.468Rossi, V., Salinari, F., Pattori, E., Giosuè,, S., & Bugiani, R. (2009). Predicting the Dynamics of Ascospore Maturation ofVenturia pirinaBased on Environmental Factors. Phytopathology, 99(4), 453-461. doi:10.1094/phyto-99-4-0453Spotts, R. A. (1991). Effect of Temperature and Wetness on Infection of Pear byVenturia pirinaand the Relationship Between Preharvest Inoculation and Storage Scab. Plant Disease, 75(12), 1204. doi:10.1094/pd-75-1204Spotts, R. A. (1994). Factors Affecting Maturation and Release of Ascospores ofVenturia pirinain Oregon. Phytopathology, 84(3), 260. doi:10.1094/phyto-84-260Villalta, O., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Australasian Plant Pathology, 29(4), 255. doi:10.1071/ap00048Villalta, O. N., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Effects of temperature and leaf wetness duration on infection of pear leaves by Venturia pirina. Australian Journal of Agricultural Research, 51(1), 97. doi:10.1071/ar99068Lan, Z., & Scherm, H. (2003). Moisture Sources in Relation to Conidial Dissemination and Infection byCladosporium carpophilumWithin Peach Canopies. Phytopathology, 93(12), 1581-1586. doi:10.1094/phyto.2003.93.12.1581Lawrence, Jr., E. G. (1982). Environmental Effects on the Development and Dissemination ofCladosporium carpophilumon Peach. Phytopathology, 72(7), 773. doi:10.1094/phyto-72-773Gottwald, T. R. (1985). Influence of Temperature, Leaf Wetness Period, Leaf Age, and Spore Concentration on Infection of Pecan Leaves by Conidia ofCladosporium caryigenum. Phytopathology, 75(2), 190. doi:10.1094/phyto-75-190Latham, A. J. (1982). Effects of Some Weather Factors andFusicladium effusumConidium Dispersal on Pecan Scab Occurrence. Phytopathology, 72(10), 1339. doi:10.1094/phyto-72-1339MARZO, L., FRISULLO, S., LOPS, F., & ROSSI, V. (1993). Possible dissemination of Spilocaea oleagina conidia by insects (Ectopsocus briggsi). EPPO Bulletin, 23(3), 389-391. doi:10.1111/j.1365-2338.1993.tb01341.xLOPS, F., FRISULLO, S., & ROSSI, V. (1993). Studies on the spread of the olive scab pathogen, Spilocaea oleagina. EPPO Bulletin, 23(3), 385-387. doi:10.1111/j.1365-2338.1993.tb01340.xObanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2007). Effect of temperature, relative humidity, leaf wetness and leaf age on Spilocaea oleagina conidium germination on olive leaves. European Journal of Plant Pathology, 120(3), 211-222. doi:10.1007/s10658-007-9209-6Obanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2010). Effects of temperature, inoculum concentration, leaf age, and continuous and interrupted wetness on infection of olive plants by Spilocaea oleagina. Plant Pathology, 60(2), 190-199. doi:10.1111/j.1365-3059.2010.02370.xViruega, J. R., Moral, J., Roca, L. F., Navarro, N., & Trapero, A. (2013). Spilocaea oleaginain Olive Groves of Southern Spain: Survival, Inoculum Production, and Dispersal. Plant Disease, 97(12), 1549-1556. doi:10.1094/pdis-12-12-1206-reViruega, J. R., Roca, L. F., Moral, J., & Trapero, A. (2011). Factors Affecting Infection and Disease Development on Olive Leaves Inoculated withFusicladium oleagineum. Plant Disease, 95(9), 1139-1146. doi:10.1094/pdis-02-11-0126Eikemo, H., Gadoury, D. M., Spotts, R. A., Villalta, O., Creemers, P., Seem, R. C., & Stensvand, A. (2011). Evaluation of Six Models to Estimate Ascospore Maturation in Venturia pyrina. Plant Disease, 95(3), 279-284. doi:10.1094/pdis-02-10-0125Li, B.-H., Yang, J.-R., Dong, X.-L., Li, B.-D., & Xu, X.-M. (2007). A dynamic model forecasting infection of pear leaves by conidia of Venturia nashicola and its evaluation in unsprayed orchards. European Journal of Plant Pathology, 118(3), 227-238. doi:10.1007/s10658-007-9138-4Rossi, V., Giosuè, S., & Bugiani, R. (2007). A-scab (Apple-scab), a simulation model for estimating risk of Venturia inaequalis primary infections. EPPO Bulletin, 37(2), 300-308. doi:10.1111/j.1365-2338.2007.01125.xXU, X.-M., BUTT, D. J., & SANTEN, G. (1995). A dynamic model simulating infection of apple leaves by Venturia inaequalis. Plant Pathology, 44(5), 865-876. doi:10.1111/j.1365-3059.1995.tb02746.xRoubal, C., Regis, S., & Nicot, P. C. (2012). Field models for the prediction of leaf infection and latent period ofFusicladium oleagineumon olive based on rain, temperature and relative humidity. Plant Pathology, 62(3), 657-666. doi:10.1111/j.1365-3059.2012.02666.xPayne, A. F., & Smith, D. L. (2012). Development and Evaluation of Two Pecan Scab Prediction Models. Plant Disease, 96(9), 1358-1364. doi:10.1094/pdis-03-11-0202-reTrapman M, Jansonius PJ (2008) Disease management in organic apple orchards is more than applying the right product at the correct time. Ecofruit-13th International Conference on Cultivation Technique and Phytopathological Problems in Organic Fruit-Growing: Proceedings to the Conference from 18th February to 20th February 2008 at Weinsberg/Germany. 16–22.HOLB, I. J., JONG, P. F., & HEIJNE, B. (2003). Efficacy and phytotoxicity of lime sulphur in organic apple production. Annals of Applied Biology, 142(2), 225-233. doi:10.1111/j.1744-7348.2003.tb00245.xGent, D. H., Mahaffee, W. F., McRoberts, N., & Pfender, W. F. (2013). The Use and Role of Predictive Systems in Disease Management. Annual Review of Phytopathology, 51(1), 267-289. doi:10.1146/annurev-phyto-082712-102356Alavanja, M. C. R., Hoppin, J. A., & Kamel, F. (2004). Health Effects of Chronic Pesticide Exposure: Cancer and Neurotoxicity. Annual Review of Public Health, 25(1), 155-197. doi:10.1146/annurev.publhealth.25.101802.123020Brent KJ, Hollomon DW (2007) Fungicide resistance in crop pathogens: How can it be managed? FRAC Monog 2. Fungicide Resistance Action Committee.Shtienberg, D. (2013). Will Decision-Support Systems Be Widely Used for the Management of Plant Diseases? Annual Review of Phytopathology, 51(1), 1-16. doi:10.1146/annurev-phyto-082712-102244Leffelaar P (1993) On Systems Analysis and Simulation of Ecological Processes. Kluwer. London.Rossi V, Giosuè S, Caffi T (2010) Modelling plant diseases for decision making in crop protection. In: Oerke E-C, Gerhards R, Menz G, Sikora RA, editors. Precision Crop Protection-the Challenge and Use of Heterogeneity.Hui, C. (2006). Carrying capacity, population equilibrium, and environment’s maximal load. Ecological Modelling, 192(1-2), 317-320. doi:10.1016/j.ecolmodel.2005.07.001Townsend C, Begon M, Harper J (2008) Essentials of ecology. John Wiley and Sons. New York. 510.Zadoks J, Schein R (1979) Epidemiology and plant disease management. Oxford University Press, New York. 427.Bennett, J. C., Diggle, A., Evans, F., & Renton, M. (2013). Assessing eradication strategies for rain-splashed and wind-dispersed crop diseases. Pest Management Science, 69(8), 955-963. doi:10.1002/ps.3459Ghanbarnia, K., Dilantha Fernando, W. G., & Crow, G. (2009). Developing Rainfall- and Temperature-Based Models to Describe Infection of Canola Under Field Conditions Caused by Pycnidiospores of Leptosphaeria maculans. Phytopathology, 99(7), 879-886. doi:10.1094/phyto-99-7-0879Gilligan, C. A., & van den Bosch, F. (2008). Epidemiological Models for Invasion and Persistence of Pathogens. Annual Review of Phytopathology, 46(1), 385-418. doi:10.1146/annurev.phyto.45.062806.094357Buck, A. L. (1981). New Equations for Computing Vapor Pressure and Enhancement Factor. Journal of Applied Meteorology, 20(12), 1527-1532. doi:10.1175/1520-0450(1981)0202.0.co;2Madden L V, Hughes G, van den Bosch F (2007) The study of plant disease epidemics. APS press. St. Paul. 421.González-Domínguez E, Rodríguez-Reina J, García-Jiménez J, Armengol J (2014) Evaluation of fungicides to control loquat scab caused by Fusicladium eriobotryae. Plant Heal Prog Accepted.De Wolf, E. D., & Isard, S. A. (2007). Disease Cycle Approach to Plant Disease Prediction. Annual Review of Phytopathology, 45(1), 203-220. doi:10.1146/annurev.phyto.44.070505.143329Krause, R. A., & Massie, L. B. (1975). Predictive Systems: Modern Approaches to Disease Control. Annual Review of Phytopathology, 13(1), 31-47. doi:10.1146/annurev.py.13.090175.000335Fourie, P., Schutte, T., Serfontein, S., & Swart, F. (2013). Modeling the Effect of Temperature and Wetness on Guignardia Pseudothecium Maturation and Ascospore Release in Citrus Orchards. Phytopathology, 103(3), 281-292. doi:10.1094/phyto-07-11-0194Gadoury, D. M. (1982). A Model to Estimate the Maturity of Ascospores ofVenturia inaequalis. Phytopathology, 72(7), 901. doi:10.1094/phyto-72-901Holtslag, Q. A., Remphrey, W. R., Fernando, W. G. D., St-Pierre, R. G., & Ash, G. H. B. (2004). The development of a dynamic diseaseforecasting model to controlEntomosporium mespilionAmelanchier alnifolia. Canadian Journal of Plant Pathology, 26(3), 304-313. doi:10.1080/07060660409507148Legler SEE, Caffi T, Rossi V (2013) A Model for the development of Erysiphe necator chasmothecia in vineyards. Plant Pathol. DOI:10.1111/ppa.12145.Luo, Y., & Michailides, T. J. (2001). Risk Analysis for Latent Infection of Prune by Monilinia fructicola in California. Phytopathology, 91(12), 1197-1208. doi:10.1094/phyto.2001.91.12.1197Gadoury, D. M. (1986). Forecasting Ascospore Dose of Venturia inaequalis in Commercial Apple Orchards. Phytopathology, 76(1), 112. doi:10.1094/phyto-76-112Gent, D. H., De Wolf, E., & Pethybridge, S. J. (2011). Perceptions of Risk, Risk Aversion, and Barriers to Adoption of Decision Support Systems and Integrated Pest Management: An Introduction. Phytopathology, 101(6), 640-643. doi:10.1094/phyto-04-10-0124Schut, M., Rodenburg, J., Klerkx, L., van Ast, A., & Bastiaans, L. (2014). Systems approaches to innovation in crop protection. A systematic literature review. Crop Protection, 56, 98-108. doi:10.1016/j.cropro.2013.11.017Mills W, Laplante A (1954) Diseases and insect in the orchard. Cornell Ext Bull 711.GVA (2013) Octubre-Noviembre 2013. Butlletí d’avisos 13.MacHardy, W. E. (1989). A Revision of Mills’s Criteria for Predicting Apple Scab Infection Periods. Phytopathology, 79(3), 304. doi:10.1094/phyto-79-30

    Cloaked websites: propaganda, cyber-racism and epistemology in the digital era

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    This article analyzes cloaked websites, which are sites published by individuals or groups who conceal authorship in order to disguise deliberately a hidden political agenda. Drawing on the insights of critical theory and the Frankfurt School, this article examines the way in which cloaked websites conceal a variety of political agendas from a range of perspectives. Of particular interest here are cloaked white supremacist sites that disguise cyber-racism. The use of cloaked websites to further political ends raises important questions about knowledge production and epistemology in the digital era. These cloaked sites emerge within a social and political context in which it is increasingly difficult to parse fact from propaganda, and this is a particularly pernicious feature when it comes to the cyber-racism of cloaked white supremacist sites. The article concludes by calling for the importance of critical, situated political thinking in the evaluation of cloaked websites

    Shared genetic influences do not explain the association between parent-offspring relationship quality and offspring internalizing problems:results from a Children-of-Twins study

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    Background: Associations between parenting and child outcomes are often interpreted as reflecting causal, social influences. However, such associations may be confounded by genes common to children and their biological parents. To the extent that these shared genes influence behaviours in both generations, a passive genetic mechanism may explain links between them. Here we aim to quantify the relative importance of passive genetic v. social mechanisms in the intergenerational association between parent–offspring relationship quality and offspring internalizing problems in adolescence. Methods: We used a Children-of-Twins (CoT) design with data from the parent-based Twin and Offspring Study of Sweden (TOSS) sample [909 adult twin pairs and their offspring; offspring mean age 15.75 (2.42) years], and the child-based Swedish Twin Study of CHild and Adolescent Development (TCHAD) sample [1120 adolescent twin pairs; mean age 13.67 (0.47) years]. A composite of parent-report measures (closeness, conflict, disagreements, expressions of affection) indexed parent–offspring relationship quality in TOSS, and offspring self-reported internalizing symptoms were assessed using the Child Behavior Checklist (CBCL) in both samples. Results: A social transmission mechanism explained the intergenerational association [r = 0.21 (0.16–0.25)] in our best-fitting model. A passive genetic transmission pathway was not found to be significant, indicating that parental genetic influences on parent–offspring relationship quality and offspring genetic influences on their internalizing problems were non-overlapping. Conclusion: These results indicate that this intergenerational association is a product of social interactions between children and parents, within which bidirectional effects are highly plausible. Results from genetically informative studies of parenting-related effects should be used to help refine early parenting interventions aimed at reducing risk for psychopathology

    The relationship between parental depressive symptoms and offspring psychopathology: Evidence from a children-of-twins study and an adoption study

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    BACKGROUND: Parental depressive symptoms are associated with emotional and behavioural problems in offspring. However, genetically informative studies are needed to distinguish potential causal effects from genetic confounds, and longitudinal studies are required to distinguish parent-to-child effects from child-to-parent effects. METHOD: We conducted cross-sectional analyses on a sample of Swedish twins and their adolescent offspring (n = 876 twin families), and longitudinal analyses on a US sample of children adopted at birth, their adoptive parents, and their birth mothers (n = 361 adoptive families). Depressive symptoms were measured in parents, and externalizing and internalizing problems measured in offspring. Structural equation models were fitted to the data. RESULTS: Results of model fitting suggest that associations between parental depressive symptoms and offspring internalizing and externalizing problems remain after accounting for genes shared between parent and child. Genetic transmission was not evident in the twin study but was evident in the adoption study. In the longitudinal adoption study child-to-parent effects were evident. CONCLUSIONS: We interpret the results as demonstrating that associations between parental depressive symptoms and offspring emotional and behavioural problems are not solely attributable to shared genes, and that bidirectional effects may be present in intergenerational associations
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