A test of the ecological valence theory of color preference, the case of Arabic

Humans have systematic and reliable color preferences. The dominant account of color preference is that individuals like some colors more than others due to the valence of objects that they associate with colors (Ecological Valence Theory). In support of this theory, Palmer and Schloss show that the average valence of objects associated with a color, when weighted (the WAVE), explains up to 80% of the variation in color preference for adults from the United States (US). Here we investigate whether Ecological Valence Theory can account for the color preferences of female and male adults from Saudi Arabia to test how well the theory generalizes across cultures and how well it accounts for sex differences in color preference. We also extend the investigation of EVT by investigating whether abstract concept associations as well as object associations can account for preference. Saudi adults’ color preferences, color object and concept associations, and association valence ratings were collected, and the WAVE was computed and correlated with preference ratings. The WAVE accounted for no more than half of the variance in Saudi color preferences, although there was some degree of sex specificity in the relationship of the WAVE and color preference. Adding abstract concept associations did not account for more variance than object associations alone, but the number of abstract concept associations did account for a significant amount of the variance in color preference for females, but not males. The findings converge with other cross-cultural studies in suggesting that the success of EVT in accounting for color preference varies across cultures and indicates that additional factors other than color associations are likely also at play

Ensemble coding of color and luminance contrast

Ensemble coding has been demonstrated for many attributes including color, but the metrics on which this coding is based remain uncertain. We examined ensemble percepts for stimulus sets that varied in chromatic contrast between complementary hues, or that varied in luminance contrast between increments and decrements, in both cases focusing on the ensemble percepts for the neutral gray stimulus defining the category boundary. Each ensemble was composed of 16 circles with four contrast levels. Observers saw the display for 0.5 s and then judged whether a target contrast was a member of the set. False alarms were high for intermediate contrasts (within the range of the ensemble) and fell for higher or lower values. However, for ensembles with complementary hues, gray was less likely to be reported as a member, even when it represented the mean chromaticity of the set. When the settings were repeated for luminance contrast, false alarms for gray were higher and fell off more gradually for out-of-range contrasts. This difference implies that opposite luminance polarities represent a more continuous perceptual dimension than opponent-color variations, and that “gray” is a stronger category boundary for chromatic than luminance contrasts. For color, our results suggest that ensemble percepts reflect pooling within rather than between large hue differences, perhaps because the visual system represents hue differences more like qualitatively different categories than like quantitative differences within an underlying color “space.” The differences for luminance and color suggest more generally that ensemble coding for different visual attributes might depend on different processes that in turn depend on the format of the visual representation

Investigation of an Escherichia coli O145 outbreak in a child day-care centre - extensive sampling and characterization of eae- and stx1-positive E. coli yields epidemiological and socioeconomic insight

<p>Abstract</p> <p>Background</p> <p>On October 29^th2009 the health authorities in the city of Trondheim, Norway were alerted about a case of Shiga toxin-positive <it>E. coli </it>(STEC) O145 in a child with bloody diarrhoea attending a day-care centre. Symptomatic children in this day-care centre were sampled, thereby identifying three more cases. This initiated an outbreak investigation.</p> <p>Methods</p> <p>A case was defined as a child attending the day-care centre, in whom <it>eae- </it>and <it>stx</it>₁- but not <it>stx</it>₂-positive <it>E. coli </it>O145:H28 was diagnosed from a faecal sample, with multilocus variable number of tandem repeat analysis (MLVA) profile identical to the index isolate. All 61 children, a staff of 14 in the day-care centre, and 74 close contacts submitted faecal samples. Staff and parents were interviewed about cases' exposure to foods and animals. Faecal samples from 31 ewes from a sheep herd to which the children were exposed were analyzed for <it>E. coli </it>O145.</p> <p>Results</p> <p>Sixteen cases were identified, from which nine presented diarrhoea but not haemolytic uremic syndrome (HUS). The attack rate was 0.26, and varied between age groups (0.13-0.40) and between the three day-care centre departments (0.20-0.50), and was significantly higher amongst the youngest children. Median duration of shedding was 20 days (0-71 days). Children were excluded from the day-care centre during shedding, requiring parents to take compassionate leave, estimated to be a minimum total of 406 days for all cases. Atypical enteropathogenic <it>E. coli </it>(aEPEC) were detected among 14 children other than cases. These isolates were genotypically different from the outbreak strain. Children in the day-care centre were exposed to faecal pollution from a sheep herd, but <it>E. coli </it>O145 was not detected in the sheep.</p> <p>Conclusions</p> <p>We report an outbreak of <it>stx</it>₁- and <it>eae-</it>positive STEC O145:H28 infection with mild symptoms among children in a day-care centre. Extensive sampling showed occurrence of the outbreak strain as well as other STEC and aEPEC strains in the outbreak population. MLVA-typing of the STEC-isolates strongly indicates a common source of infection. The study describes epidemiological aspects and socioeconomic consequences of a non-O157 STEC outbreak, which are less commonly reported than O157 outbreaks.</p

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules. © 2012 Rodrigo et al.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) grants BFU2009-06993 (S. F. E.) and BIO2006-13107 (C. L.) and by Generalitat Valenciana grant PROMETEO2010/016 (S. F. E.). G. R. is supported by a graduate fellowship from the Generalitat Valenciana (BFPI2007-160) and J.C. by a contract from MICINN grant TIN2006-12860. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Carrera Montesinos, J.; Ruiz-Ferrer, V.; Del Toro, F.; Llave, C.; Voinnet, O.; Elena Fito, SF. (2012). A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS ONE. 7(7):40526-40526. https://doi.org/10.1371/journal.pone.0040526S405264052677Peng, X., Chan, E. Y., Li, Y., Diamond, D. L., Korth, M. J., & Katze, M. G. (2009). Virus–host interactions: from systems biology to translational research. Current Opinion in Microbiology, 12(4), 432-438. doi:10.1016/j.mib.2009.06.003Dodds, P. N., & Rathjen, J. P. (2010). Plant immunity: towards an integrated view of plant–pathogen interactions. Nature Reviews Genetics, 11(8), 539-548. doi:10.1038/nrg2812Maule, A., Leh, V., & Lederer, C. (2002). The dialogue between viruses and hosts in compatible interactions. Current Opinion in Plant Biology, 5(4), 279-284. doi:10.1016/s1369-5266(02)00272-8Whitham, S. A., Quan, S., Chang, H.-S., Cooper, B., Estes, B., Zhu, T., … Hou, Y.-M. (2003). Diverse RNA viruses elicit the expression of common sets of genes in susceptibleArabidopsis thalianaplants. The Plant Journal, 33(2), 271-283. doi:10.1046/j.1365-313x.2003.01625.xBailer, S., & Haas, J. (2009). Connecting viral with cellular interactomes. Current Opinion in Microbiology, 12(4), 453-459. doi:10.1016/j.mib.2009.06.004Whitham, S. A., Yang, C., & Goodin, M. M. (2006). Global Impact: Elucidating Plant Responses to Viral Infection. Molecular Plant-Microbe Interactions, 19(11), 1207-1215. doi:10.1094/mpmi-19-1207MacPherson, J. I., Dickerson, J. E., Pinney, J. W., & Robertson, D. L. (2010). Patterns of HIV-1 Protein Interaction Identify Perturbed Host-Cellular Subsystems. PLoS Computational Biology, 6(7), e1000863. doi:10.1371/journal.pcbi.1000863Jenner, R. G., & Young, R. A. (2005). Insights into host responses against pathogens from transcriptional profiling. Nature Reviews Microbiology, 3(4), 281-294. doi:10.1038/nrmicro1126Andeweg, A. C., Haagmans, B. L., & Osterhaus, A. D. (2008). Virogenomics: the virus–host interaction revisited. Current Opinion in Microbiology, 11(5), 461-466. doi:10.1016/j.mib.2008.09.010Elena, S. F., Carrera, J., & Rodrigo, G. (2011). A systems biology approach to the evolution of plant–virus interactions. Current Opinion in Plant Biology, 14(4), 372-377. doi:10.1016/j.pbi.2011.03.013Tan, S.-L., Ganji, G., Paeper, B., Proll, S., & Katze, M. G. (2007). Systems biology and the host response to viral infection. Nature Biotechnology, 25(12), 1383-1389. doi:10.1038/nbt1207-1383De la Fuente, A. (2010). From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases. Trends in Genetics, 26(7), 326-333. doi:10.1016/j.tig.2010.05.001Albert, R. (2005). Scale-free networks in cell biology. Journal of Cell Science, 118(21), 4947-4957. doi:10.1242/jcs.02714Yu, H., Braun, P., Yildirim, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., … Vidal, M. (2008). High-Quality Binary Protein Interaction Map of the Yeast Interactome Network. Science, 322(5898), 104-110. doi:10.1126/science.1158684Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: understanding the cell’s functional organization. Nature Reviews Genetics, 5(2), 101-113. doi:10.1038/nrg1272Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378-382. doi:10.1038/35019019Mukhtar, M. S., Carvunis, A.-R., Dreze, M., Epple, P., Steinbrenner, J., … Moore, J. (2011). Independently Evolved Virulence Effectors Converge onto Hubs in a Plant Immune System Network. Science, 333(6042), 596-601. doi:10.1126/science.1203659Calderwood, M. A., Venkatesan, K., Xing, L., Chase, M. R., Vazquez, A., Holthaus, A. M., … Johannsen, E. (2007). Epstein-Barr virus and virus human protein interaction maps. Proceedings of the National Academy of Sciences, 104(18), 7606-7611. doi:10.1073/pnas.0702332104De Chassey, B., Navratil, V., Tafforeau, L., Hiet, M. S., Aublin‐Gex, A., Agaugué, S., … Lotteau, V. (2008). Hepatitis C virus infection protein network. Molecular Systems Biology, 4(1), 230. doi:10.1038/msb.2008.66Shapira, S. D., Gat-Viks, I., Shum, B. O. V., Dricot, A., de Grace, M. M., Wu, L., … Hacohen, N. (2009). A Physical and Regulatory Map of Host-Influenza Interactions Reveals Pathways in H1N1 Infection. Cell, 139(7), 1255-1267. doi:10.1016/j.cell.2009.12.018Dyer, M. D., Murali, T. M., & Sobral, B. W. (2008). The Landscape of Human Proteins Interacting with Viruses and Other Pathogens. PLoS Pathogens, 4(2), e32. doi:10.1371/journal.ppat.0040032Golem, S., & Culver, J. N. (2003). Tobacco mosaic virusInduced Alterations in the Gene Expression Profile ofArabidopsis thaliana. Molecular Plant-Microbe Interactions, 16(8), 681-688. doi:10.1094/mpmi.2003.16.8.681Espinoza, C., Medina, C., Somerville, S., & Arce-Johnson, P. (2007). Senescence-associated genes induced during compatible viral interactions with grapevine and Arabidopsis. Journal of Experimental Botany, 58(12), 3197-3212. doi:10.1093/jxb/erm165Yang, C., Guo, R., Jie, F., Nettleton, D., Peng, J., Carr, T., … Whitham, S. A. (2007). Spatial Analysis ofArabidopsis thalianaGene Expression in Response toTurnip mosaic virusInfection. Molecular Plant-Microbe Interactions, 20(4), 358-370. doi:10.1094/mpmi-20-4-0358Agudelo-Romero, P., Carbonell, P., de la Iglesia, F., Carrera, J., Rodrigo, G., Jaramillo, A., … Elena, S. F. (2008). Changes in the gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus. Virology Journal, 5(1), 92. doi:10.1186/1743-422x-5-92Agudelo-Romero, P., Carbonell, P., Perez-Amador, M. A., & Elena, S. F. (2008). Virus Adaptation by Manipulation of Host’s Gene Expression. PLoS ONE, 3(6), e2397. doi:10.1371/journal.pone.0002397Ascencio-Ibáñez, J. T., Sozzani, R., Lee, T.-J., Chu, T.-M., Wolfinger, R. D., Cella, R., & Hanley-Bowdoin, L. (2008). Global Analysis of Arabidopsis Gene Expression Uncovers a Complex Array of Changes Impacting Pathogen Response and Cell Cycle during Geminivirus Infection. Plant Physiology, 148(1), 436-454. doi:10.1104/pp.108.121038Babu, M., Griffiths, J. S., Huang, T.-S., & Wang, A. (2008). Altered gene expression changes in Arabidopsis leaf tissues and protoplasts in response to Plum pox virus infection. BMC Genomics, 9(1), 325. doi:10.1186/1471-2164-9-325De Vienne, D. M., Giraud, T., & Martin, O. C. (2007). A congruence index for testing topological similarity between trees. Bioinformatics, 23(23), 3119-3124. doi:10.1093/bioinformatics/btm500Wise, R. P., Moscou, M. J., Bogdanove, A. J., & Whitham, S. A. (2007). Transcript Profiling in Host–Pathogen Interactions. Annual Review of Phytopathology, 45(1), 329-369. doi:10.1146/annurev.phyto.45.011107.143944Handford, M. G., & Carr, J. P. (2007). A defect in carbohydrate metabolism ameliorates symptom severity in virus-infected Arabidopsis thaliana. Journal of General Virology, 88(1), 337-341. doi:10.1099/vir.0.82376-0Hou, B., Lim, E.-K., Higgins, G. S., & Bowles, D. J. (2004). N-Glucosylation of Cytokinins by Glycosyltransferases ofArabidopsis thaliana. Journal of Biological Chemistry, 279(46), 47822-47832. doi:10.1074/jbc.m409569200Schwender, J., Goffman, F., Ohlrogge, J. B., & Shachar-Hill, Y. (2004). Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds. Nature, 432(7018), 779-782. doi:10.1038/nature03145Pagán, I., Alonso-Blanco, C., & García-Arenal, F. (2008). Host Responses in Life-History Traits and Tolerance to Virus Infection in Arabidopsis thaliana. PLoS Pathogens, 4(8), e1000124. doi:10.1371/journal.ppat.1000124Carrera, J., Rodrigo, G., Jaramillo, A., & Elena, S. F. (2009). Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biology, 10(9), R96. doi:10.1186/gb-2009-10-9-r96Geisler-Lee, J., O’Toole, N., Ammar, R., Provart, N. J., Millar, A. H., & Geisler, M. (2007). A Predicted Interactome for Arabidopsis. Plant Physiology, 145(2), 317-329. doi:10.1104/pp.107.103465Ma, S., Gong, Q., & Bohnert, H. J. (2007). An Arabidopsis gene network based on the graphical Gaussian model. Genome Research, 17(11), 1614-1625. doi:10.1101/gr.6911207Yamada, T., & Bork, P. (2009). Evolution of biomolecular networks — lessons from metabolic and protein interactions. Nature Reviews Molecular Cell Biology, 10(11), 791-803. doi:10.1038/nrm2787Humphries, M. D., & Gurney, K. (2008). Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence. PLoS ONE, 3(4), e0002051. doi:10.1371/journal.pone.0002051Stumpf, M. P. H., & Ingram, P. J. (2005). Probability models for degree distributions of protein interaction networks. Europhysics Letters (EPL), 71(1), 152-158. doi:10.1209/epl/i2004-10531-8Khanin, R., & Wit, E. (2006). How Scale-Free Are Biological Networks. Journal of Computational Biology, 13(3), 810-818. doi:10.1089/cmb.2006.13.810Daudin, J.-J., Picard, F., & Robin, S. (2007). A mixture model for random graphs. Statistics and Computing, 18(2), 173-183. doi:10.1007/s11222-007-9046-7Uetz, P. (2006). Herpesviral Protein Networks and Their Interaction with the Human Proteome. Science, 311(5758), 239-242. doi:10.1126/science.1116804Choi, I.-R., Stenger, D. C., & French, R. (2000). Multiple Interactions among Proteins Encoded by the Mite-Transmitted Wheat Streak Mosaic Tritimovirus. Virology, 267(2), 185-198. doi:10.1006/viro.1999.0117Guo, D., Saarma, M., Rajamäki, M.-L., & Valkonen, J. P. T. (2001). Towards a protein interaction map of potyviruses: protein interaction matrixes of two potyviruses based on the yeast two-hybrid system. Journal of General Virology, 82(4), 935-939. doi:10.1099/0022-1317-82-4-935Lin, L., Shi, Y., Luo, Z., Lu, Y., Zheng, H., Yan, F., … Wu, Y. (2009). Protein–protein interactions in two potyviruses using the yeast two-hybrid system. Virus Research, 142(1-2), 36-40. doi:10.1016/j.virusres.2009.01.006Shen, W., Wang, M., Yan, P., Gao, L., & Zhou, P. (2010). Protein interaction matrix of Papaya ringspot virus type P based on a yeast two-hybrid system. Acta Virologica, 54(1), 49-54. doi:10.4149/av_2010_01_49Redner, S. (2008). Teasing out the missing links. Nature, 453(7191), 47-48. doi:10.1038/453047aIrizarry, R. A. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2), 249-264. doi:10.1093/biostatistics/4.2.249Smyth, G. K. (2004). Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), 1-25. doi:10.2202/1544-6115.1027Allemeersch, J., Durinck, S., Vanderhaeghen, R., Alard, P., Maes, R., Seeuws, K., … Kuiper, M. T. R. (2005). Benchmarking the CATMA Microarray. A Novel Tool forArabidopsis Transcriptome Analysis. Plant Physiology, 137(2), 588-601. doi:10.1104/pp.104.051300Cleveland, W. S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), 829-836. doi:10.1080/01621459.1979.10481038Tarraga, J., Medina, I., Carbonell, J., Huerta-Cepas, J., Minguez, P., Alloza, E., … Dopazo, J. (2008). GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Research, 36(Web Server), W308-W314. doi:10.1093/nar/gkn303Al-Shahrour, F., Minguez, P., Vaquerizas, J. M., Conde, L., & Dopazo, J. (2005). BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Research, 33(Web Server), W460-W464. doi:10.1093/nar/gki456Al-Shahrour, F., Minguez, P., Tárraga, J., Medina, I., Alloza, E., Montaner, D., & Dopazo, J. (2007). FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Research, 35(suppl_2), W91-W96. doi:10.1093/nar/gkm260Mueller, L. A., Zhang, P., & Rhee, S. Y. (2003). AraCyc: A Biochemical Pathway Database for Arabidopsis. Plant Physiology, 132(2), 453-460. doi:10.1104/pp.102.017236Navratil, V., de Chassey, B., Combe, C., & Lotteau, V. (2011). When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases. BMC Systems Biology, 5(1), 13. doi:10.1186/1752-0509-5-13Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.

Worldwide comparison of survival from childhood leukaemia for 1995–2009, by subtype, age, and sex (CONCORD-2): a population-based study of individual data for 89 828 children from 198 registries in 53 countries

Background Global inequalities in access to health care are reflected in differences in cancer survival. The CONCORD programme was designed to assess worldwide differences and trends in population-based cancer survival. In this population-based study, we aimed to estimate survival inequalities globally for several subtypes of childhood leukaemia. Methods Cancer registries participating in CONCORD were asked to submit tumour registrations for all children aged 0-14 years who were diagnosed with leukaemia between Jan 1, 1995, and Dec 31, 2009, and followed up until Dec 31, 2009. Haematological malignancies were defined by morphology codes in the International Classification of Diseases for Oncology, third revision. We excluded data from registries from which the data were judged to be less reliable, or included only lymphomas, and data from countries in which data for fewer than ten children were available for analysis. We also excluded records because of a missing date of birth, diagnosis, or last known vital status. We estimated 5-year net survival (ie, the probability of surviving at least 5 years after diagnosis, after controlling for deaths from other causes [background mortality]) for children by calendar period of diagnosis (1995-99, 2000-04, and 2005-09), sex, and age at diagnosis (< 1, 1-4, 5-9, and 10-14 years, inclusive) using appropriate life tables. We estimated age-standardised net survival for international comparison of survival trends for precursor-cell acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML). Findings We analysed data from 89 828 children from 198 registries in 53 countries. During 1995-99, 5-year agestandardised net survival for all lymphoid leukaemias combined ranged from 10.6% (95% CI 3.1-18.2) in the Chinese registries to 86.8% (81.6-92.0) in Austria. International differences in 5-year survival for childhood leukaemia were still large as recently as 2005-09, when age-standardised survival for lymphoid leukaemias ranged from 52.4% (95% CI 42.8-61.9) in Cali, Colombia, to 91.6% (89.5-93.6) in the German registries, and for AML ranged from 33.3% (18.9-47.7) in Bulgaria to 78.2% (72.0-84.3) in German registries. Survival from precursor-cell ALL was very close to that of all lymphoid leukaemias combined, with similar variation. In most countries, survival from AML improved more than survival from ALL between 2000-04 and 2005-09. Survival for each type of leukaemia varied markedly with age: survival was highest for children aged 1-4 and 5-9 years, and lowest for infants (younger than 1 year). There was no systematic difference in survival between boys and girls. Interpretation Global inequalities in survival from childhood leukaemia have narrowed with time but remain very wide for both ALL and AML. These results provide useful information for health policy makers on the effectiveness of health-care systems and for cancer policy makers to reduce inequalities in childhood survival

ITALIAN CANCER FIGURES - REPORT 2015: The burden of rare cancers in Italy = I TUMORI IN ITALIA - RAPPORTO 2015: I tumori rari in Italia

OBJECTIVES: This collaborative study, based on data collected by the network of Italian Cancer Registries (AIRTUM), describes the burden of rare cancers in Italy. Estimated number of new rare cancer cases yearly diagnosed (incidence), proportion of patients alive after diagnosis (survival), and estimated number of people still alive after a new cancer diagnosis (prevalence) are provided for about 200 different cancer entities. MATERIALS AND METHODS: Data herein presented were provided by AIRTUM population- based cancer registries (CRs), covering nowadays 52% of the Italian population. This monograph uses the AIRTUM database (January 2015), which includes all malignant cancer cases diagnosed between 1976 and 2010. All cases are coded according to the International Classification of Diseases for Oncology (ICD-O-3). Data underwent standard quality checks (described in the AIRTUM data management protocol) and were checked against rare-cancer specific quality indicators proposed and published by RARECARE and HAEMACARE (www.rarecarenet.eu; www.haemacare.eu). The definition and list of rare cancers proposed by the RARECAREnet "Information Network on Rare Cancers" project were adopted: rare cancers are entities (defined as a combination of topographical and morphological codes of the ICD-O-3) having an incidence rate of less than 6 per 100,000 per year in the European population. This monograph presents 198 rare cancers grouped in 14 major groups. Crude incidence rates were estimated as the number of all new cancers occurring in 2000-2010 divided by the overall population at risk, for males and females (also for gender-specific tumours).The proportion of rare cancers out of the total cancers (rare and common) by site was also calculated. Incidence rates by sex and age are reported. The expected number of new cases in 2015 in Italy was estimated assuming the incidence in Italy to be the same as in the AIRTUM area. One- and 5-year relative survival estimates of cases aged 0-99 years diagnosed between 2000 and 2008 in the AIRTUM database, and followed up to 31 December 2009, were calculated using complete cohort survival analysis. To estimate the observed prevalence in Italy, incidence and follow-up data from 11 CRs for the period 1992-2006 were used, with a prevalence index date of 1 January 2007. Observed prevalence in the general population was disentangled by time prior to the reference date (≤2 years, 2-5 years, ≤15 years). To calculate the complete prevalence proportion at 1 January 2007 in Italy, the 15-year observed prevalence was corrected by the completeness index, in order to account for those cancer survivors diagnosed before the cancer registry activity started. The completeness index by cancer and age was obtained by means of statistical regression models, using incidence and survival data available in the European RARECAREnet data. RESULTS: In total, 339,403 tumours were included in the incidence analysis. The annual incidence rate (IR) of all 198 rare cancers in the period 2000-2010 was 147 per 100,000 per year, corresponding to about 89,000 new diagnoses in Italy each year, accounting for 25% of all cancer. Five cancers, rare at European level, were not rare in Italy because their IR was higher than 6 per 100,000; these tumours were: diffuse large B-cell lymphoma and squamous cell carcinoma of larynx (whose IRs in Italy were 7 per 100,000), multiple myeloma (IR: 8 per 100,000), hepatocellular carcinoma (IR: 9 per 100,000) and carcinoma of thyroid gland (IR: 14 per 100,000). Among the remaining 193 rare cancers, more than two thirds (No. 139) had an annual IR &lt;0.5 per 100,000, accounting for about 7,100 new cancers cases; for 25 cancer types, the IR ranged between 0.5 and 1 per 100,000, accounting for about 10,000 new diagnoses; while for 29 cancer types the IR was between 1 and 6 per 100,000, accounting for about 41,000 new cancer cases. Among all rare cancers diagnosed in Italy, 7% were rare haematological diseases (IR: 41 per 100,000), 18% were solid rare cancers. Among the latter, the rare epithelial tumours of the digestive system were the most common (23%, IR: 26 per 100,000), followed by epithelial tumours of head and neck (17%, IR: 19) and rare cancers of the female genital system (17%, IR: 17), endocrine tumours (13% including thyroid carcinomas and less than 1% with an IR of 0.4 excluding thyroid carcinomas), sarcomas (8%, IR: 9 per 100,000), central nervous system tumours and rare epithelial tumours of the thoracic cavity (5%with an IR equal to 6 and 5 per 100,000, respectively). The remaining (rare male genital tumours, IR: 4 per 100,000; tumours of eye, IR: 0.7 per 100,000; neuroendocrine tumours, IR: 4 per 100,000; embryonal tumours, IR: 0.4 per 100,000; rare skin tumours and malignant melanoma of mucosae, IR: 0.8 per 100,000) each constituted &lt;4% of all solid rare cancers. Patients with rare cancers were on average younger than those with common cancers. Essentially, all childhood cancers were rare, while after age 40 years, the common cancers (breast, prostate, colon, rectum, and lung) became increasingly more frequent. For 254,821 rare cancers diagnosed in 2000-2008, 5-year RS was on average 55%, lower than the corresponding figures for patients with common cancers (68%). RS was lower for rare cancers than for common cancers at 1 year and continued to diverge up to 3 years, while the gap remained constant from 3 to 5 years after diagnosis. For rare and common cancers, survival decreased with increasing age. Five-year RS was similar and high for both rare and common cancers up to 54 years; it decreased with age, especially after 54 years, with the elderly (75+ years) having a 37% and 20% lower survival than those aged 55-64 years for rare and common cancers, respectively. We estimated that about 900,000 people were alive in Italy with a previous diagnosis of a rare cancer in 2010 (prevalence). The highest prevalence was observed for rare haematological diseases (278 per 100,000) and rare tumours of the female genital system (265 per 100,000). Very low prevalence (&lt;10 prt 100,000) was observed for rare epithelial skin cancers, for rare epithelial tumours of the digestive system and rare epithelial tumours of the thoracic cavity. COMMENTS: One in four cancers cases diagnosed in Italy is a rare cancer, in agreement with estimates of 24% calculated in Europe overall. In Italy, the group of all rare cancers combined, include 5 cancer types with an IR&gt;6 per 100,000 in Italy, in particular thyroid cancer (IR: 14 per 100,000).The exclusion of thyroid carcinoma from rare cancers reduces the proportion of them in Italy in 2010 to 22%. Differences in incidence across population can be due to the different distribution of risk factors (whether environmental, lifestyle, occupational, or genetic), heterogeneous diagnostic intensity activity, as well as different diagnostic capacity; moreover heterogeneity in accuracy of registration may determine some minor differences in the account of rare cancers. Rare cancers had worse prognosis than common cancers at 1, 3, and 5 years from diagnosis. Differences between rare and common cancers were small 1 year after diagnosis, but survival for rare cancers declined more markedly thereafter, consistent with the idea that treatments for rare cancers are less effective than those for common cancers. However, differences in stage at diagnosis could not be excluded, as 1- and 3-year RS for rare cancers was lower than the corresponding figures for common cancers. Moreover, rare cancers include many cancer entities with a bad prognosis (5-year RS &lt;50%): cancer of head and neck, oesophagus, small intestine, ovary, brain, biliary tract, liver, pleura, multiple myeloma, acute myeloid and lymphatic leukaemia; in contrast, most common cancer cases are breast, prostate, and colorectal cancers, which have a good prognosis. The high prevalence observed for rare haematological diseases and rare tumours of the female genital system is due to their high incidence (the majority of haematological diseases are rare and gynaecological cancers added up to fairly high incidence rates) and relatively good prognosis. The low prevalence of rare epithelial tumours of the digestive system was due to the low survival rates of the majority of tumours included in this group (oesophagus, stomach, small intestine, pancreas, and liver), regardless of the high incidence rate of rare epithelial cancers of these sites. This AIRTUM study confirms that rare cancers are a major public health problem in Italy and provides quantitative estimations, for the first time in Italy, to a problem long known to exist. This monograph provides detailed epidemiologic indicators for almost 200 rare cancers, the majority of which (72%) are very rare (IR&lt;0.5 per 100,000). These data are of major interest for different stakeholders. Health care planners can find useful information herein to properly plan and think of how to reorganise health care services. Researchers now have numbers to design clinical trials considering alternative study designs and statistical approaches. Population-based cancer registries with good quality data are the best source of information to describe the rare cancer burden in a population

The histology of ovarian cancer: worldwide distribution and implications for international survival comparisons (CONCORD-2)

Objective Ovarian cancers comprise several histologically distinct tumour groups with widely different prognosis. We aimed to describe the worldwide distribution of ovarian cancer histology and to understand what role this may play in international variation in survival. Methods The CONCORD programme is the largest population-based study of global trends in cancer survival. Data on 681,759 women diagnosed during 1995â\u80\u932009 with cancer of the ovary, fallopian tube, peritoneum and retroperitonum in 51 countries were included. We categorised ovarian tumours into six histological groups, and explored the worldwide distribution of histology. Results During 2005â\u80\u932009, type II epithelial tumours were the most common. The proportion was much higher in Oceania (73.1%), North America (73.0%) and Europe (72.6%) than in Central and South America (65.7%) and Asia (56.1%). By contrast, type I epithelial tumours were more common in Asia (32.5%), compared with only 19.4% in North America. From 1995 to 2009, the proportion of type II epithelial tumours increased from 68.6% to 71.1%, while the proportion of type I epithelial tumours fell from 23.8% to 21.2%. The proportions of germ cell tumours, sex cord-stromal tumours, other specific non-epithelial tumours and tumours of non-specific morphology all remained stable over time. Conclusions The distribution of ovarian cancer histology varies widely worldwide. Type I epithelial, germ cell and sex cord-stromal tumours are generally associated with higher survival than type II tumours, so the proportion of these tumours may influence survival estimates for all ovarian cancers combined. The distribution of histological groups should be considered when comparing survival between countries and regions

Lancet

BACKGROUND: In 2015, the second cycle of the CONCORD programme established global surveillance of cancer survival as a metric of the effectiveness of health systems and to inform global policy on cancer control. CONCORD-3 updates the worldwide surveillance of cancer survival to 2014. METHODS: CONCORD-3 includes individual records for 37.5 million patients diagnosed with cancer during the 15-year period 2000-14. Data were provided by 322 population-based cancer registries in 71 countries and territories, 47 of which provided data with 100% population coverage. The study includes 18 cancers or groups of cancers: oesophagus, stomach, colon, rectum, liver, pancreas, lung, breast (women), cervix, ovary, prostate, and melanoma of the skin in adults, and brain tumours, leukaemias, and lymphomas in both adults and children. Standardised quality control procedures were applied; errors were rectified by the registry concerned. We estimated 5-year net survival. Estimates were age-standardised with the International Cancer Survival Standard weights. FINDINGS: For most cancers, 5-year net survival remains among the highest in the world in the USA and Canada, in Australia and New Zealand, and in Finland, Iceland, Norway, and Sweden. For many cancers, Denmark is closing the survival gap with the other Nordic countries. Survival trends are generally increasing, even for some of the more lethal cancers: in some countries, survival has increased by up to 5% for cancers of the liver, pancreas, and lung. For women diagnosed during 2010-14, 5-year survival for breast cancer is now 89.5% in Australia and 90.2% in the USA, but international differences remain very wide, with levels as low as 66.1% in India. For gastrointestinal cancers, the highest levels of 5-year survival are seen in southeast Asia: in South Korea for cancers of the stomach (68.9%), colon (71.8%), and rectum (71.1%); in Japan for oesophageal cancer (36.0%); and in Taiwan for liver cancer (27.9%). By contrast, in the same world region, survival is generally lower than elsewhere for melanoma of the skin (59.9% in South Korea, 52.1% in Taiwan, and 49.6% in China), and for both lymphoid malignancies (52.5%, 50.5%, and 38.3%) and myeloid malignancies (45.9%, 33.4%, and 24.8%). For children diagnosed during 2010-14, 5-year survival for acute lymphoblastic leukaemia ranged from 49.8% in Ecuador to 95.2% in Finland. 5-year survival from brain tumours in children is higher than for adults but the global range is very wide (from 28.9% in Brazil to nearly 80% in Sweden and Denmark). INTERPRETATION: The CONCORD programme enables timely comparisons of the overall effectiveness of health systems in providing care for 18 cancers that collectively represent 75% of all cancers diagnosed worldwide every year. It contributes to the evidence base for global policy on cancer control. Since 2017, the Organisation for Economic Co-operation and Development has used findings from the CONCORD programme as the official benchmark of cancer survival, among their indicators of the quality of health care in 48 countries worldwide. Governments must recognise population-based cancer registries as key policy tools that can be used to evaluate both the impact of cancer prevention strategies and the effectiveness of health systems for all patients diagnosed with cancer. FUNDING: American Cancer Society; Centers for Disease Control and Prevention; Swiss Re; Swiss Cancer Research foundation; Swiss Cancer League; Institut National du Cancer; La Ligue Contre le Cancer; Rossy Family Foundation; US National Cancer Institute; and the Susan G Komen Foundation

Recent Advances on the Multiplex Molecular Detection of Plant Viruses and Viroids

[EN] Plant viruses are still one of the main contributors to economic losses in agriculture. It has been estimated that plant viruses can cause as much as 50 billion euros loss worldwide, per year. This situation may be worsened by recent climate change events and the associated changes in disease epidemiology. Reliable and early detection methods are still one of the main and most effective actions to develop control strategies for plant viral diseases. During the last years, considerable progress has been made to develop tools with high specificity and low detection limits for use in the detection of these plant pathogens. Time and cost reductions have been some of the main objectives pursued during the last few years as these increase their feasibility for routine use. Among other strategies, these objectives can be achieved by the simultaneous detection and (or) identification of several viruses in a single assay. Nucleic acid-based detection techniques are especially suitable for this purpose. Polyvalent detection has allowed the detection of multiple plant viruses at the genus level. Multiplexing RT polymerase chain reaction (PCR) has been optimized for the simultaneous detection of more than 10 plant viruses/viroids. In this short review, we provide an update on the progress made during the last decade on techniques such as multiplex PCR, polyvalent PCR, non-isotopic molecular hybridization techniques, real-time PCR, and array technologies to allow simultaneous detection of multiple plant viruses. Also, the potential and benefits of the powerful new technique of deep sequencing/next-generation sequencing are described.This work was funded by grant BIO2017-88321-R from the Spanish Direccion General de Investigacion Cientifica y Tecnica (DGICYT) and the Prometeo Program GV2014/010 from the Generalitat Valenciana.Pallás Benet, V.; Sanchez Navarro, JÁ.; James, D. (2018). Recent Advances on the Multiplex Molecular Detection of Plant Viruses and Viroids. Frontiers in Microbiology. 9. https://doi.org/10.3389/fmicb.2018.02087S