71 research outputs found

    Human Computer Interaction Meets Psychophysiology: A Critical Perspective

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    Human computer interaction (HCI) groups are more and more often exploring the utility of new, lower cost electroencephalography (EEG) interfaces for assessing user engagement and experience as well as for directly controlling computers. While the potential benefits of using EEG are considerable, we argue that research is easily driven by what we term naïve neurorealism. That is, data obtained with psychophysiological devices have poor reliability and uncertain validity, making inferences on mental states difficult. This means that unless sufficient care is taken to address the inherent shortcomings, the contributions of psychophysiological human computer interaction are limited to their novelty value rather than bringing scientific advance. Here, we outline the nature and severity of the reliability and validity problems and give practical suggestions for HCI researchers and reviewers on the way forward, and which obstacles to avoid. We hope that this critical perspective helps to promote good practice in the emerging field of psychophysiology in HCI

    Integrating ecology and evolutionary theory. A game changer for biodiversity conservation?

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    Currently, one of the central arguments in favour of biodiversity conservation is that it is essential for the maintenance of ecosystem services, that is, the benefits that people receive from ecosystems. However, the relationship between ecosystem services and biodiversity is contested and needs clarification. The goal of this chapter is to spell out the interaction and reciprocal influences between conservation science, evolutionary biology, and ecology, in order to understand whether a stronger integration of evolutionary and ecological studies might help clarify the interaction between biodiversity and ecosystem functioning as well as influence biodiversity conservation practices. To this end, the eco-evolutionary feedback theory proposed by David Post and Eric Palkovacs is analysed, arguing that it helps operationalise niche construction theory and develop a more sophisticated understanding of the relationship between ecosystem functioning and biodiversity. Finally, it is proposed that by deepening the integration of ecological and evolutionary factors in our understanding of ecosystem functioning, the eco-evolutionary feedback theory is supportive of an “evolutionary-enlightened management” of biodiversity within the ecosystem services approach.info:eu-repo/semantics/publishedVersio

    A Randomized Controlled Study of Parent-assisted Children’s Friendship Training with Children having Autism Spectrum Disorders

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    This study evaluated Children’s Friendship Training (CFT), a manualized parent-assisted intervention to improve social skills among second to fifth grade children with autism spectrum disorders. Comparison was made with a delayed treatment control group (DTC). Targeted skills included conversational skills, peer entry skills, developing friendship networks, good sportsmanship, good host behavior during play dates, and handling teasing. At post-testing, the CFT group was superior to the DTC group on parent measures of social skill and play date behavior, and child measures of popularity and loneliness, At 3-month follow-up, parent measures showed significant improvement from baseline. Post-hoc analysis indicated more than 87% of children receiving CFT showed reliable change on at least one measure at post-test and 66.7% after 3 months follow-up

    Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

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    Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

    The transcriptomics of an experimentally evolved plant-virus interaction

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    [EN] Models of plant-virus interaction assume that the ability of a virus to infect a host genotype depends on the matching between virulence and resistance genes. Recently, we evolved tobacco etch potyvirus (TEV) lineages on different ecotypes of Arabidopsis thaliana, and found that some ecotypes selected for specialist viruses whereas others selected for generalists. Here we sought to evaluate the transcriptomic basis of such relationships. We have characterized the transcriptomic responses of five ecotypes infected with the ancestral and evolved viruses. Genes and functional categories differentially expressed by plants infected with local TEV isolates were identified, showing heterogeneous responses among ecotypes, although significant parallelism existed among lineages evolved in the same ecotype. Although genes involved in immune responses were altered upon infection, other functional groups were also pervasively over-represented, suggesting that plant resistance genes were not the only drivers of viral adaptation. Finally, the transcriptomic consequences of infection with the generalist and specialist lineages were compared. Whilst the generalist induced very similar perturbations in the transcriptomes of the different ecotypes, the perturbations induced by the specialist were divergent. Plant defense mechanisms were activated when the infecting virus was specialist but they were down-regulated when infecting with generalist.We thank Francisca de la Iglesia and Paula Agudo for excellent technical assistance and our labmates for useful discussions and suggestions. This work was supported by grants BFU2012-30805 from the Spanish Ministry of Economy and Competitiveness (MINECO), PROMETEOII/2014/021 from Generalitat Valenciana and EvoEvo (ICT610427) from the European Commission 7th Framework Program to SFE, and grant PROMETEOII/2014/025 to JD. JMC was supported by a JAE-doc postdoctoral contract from CSIC. JH was recipient of a predoctoral contract from MINECO.Hillung, J.; García-García, F.; Dopazo, J.; Cuevas Torrijos, JM.; Elena Fito, SF. (2016). The transcriptomics of an experimentally evolved plant-virus interaction. Scientific Reports. 6:1-19. https://doi.org/10.1038/srep24901S1196Duffy, S., Shackelton, L. A. & Holmes, E. C. Rates of evolutionary change in viruses: patterns and determinants. Nat. Rev. Genet. 9, 267–276 (2008).Parrish, C. R. et al. Cross-species virus transmission and the emergence of new epidemic diseases. Microbiol. Mol. Biol. Rev. 72, 457–470 (2008).Holmes, E. C. The comparative genomics of viral emergence. Proc. Natl. Acad. Sci. USA 107, 1742–1746 (2010).Sanjuán, R., Nebot, M. R., Chirico, N., Mansky, L. M. & Belshaw, R. Viral mutation rates. J. Virol. 84, 9733–9748 (2010).Elena, S. F. et al. The evolutionary genetics of emerging plant RNA viruses. Mol. Plant-Microbe Interact. 24, 287–293 (2011).Holmes, E. C. The evolutionary genetics of emerging viruses. Annu. Rev. Ecol. Evol. Syst. 40, 353–372 (2009).Domingo, E. Mechanisms of viral emergence. Vet. Res. 41, 38 (2010).King, K. C. & Lively, C. M. Does genetic diversity limit disease spread in natural host populations? Heredity 109, 199–203 (2012).Kearney, C. M., Thomson, M. J. & Roland, K. E. Genome evolution of Tobacco mosaic virus populations during long-term passaging in a diverse range of hosts. Arch. Virol. 144, 1513–1526 (1999).Tan, Z. et al. Mutations in Turnip mosaic virus genomes that have adapted to Raphanus sativus . J. Gen. Virol. 88, 501–510 (2005).Rico, P., Ivars, P., Elena, S. F. & Hernández, C. Insights into the selective pressures restricting Pelargonium flower break virus genome variability: evidence for host adaptation. J. Virol. 80, 8124–8132 (2006).Wallis, C. M. et al. Adaptation of Plum pox virus to a herbaceous host (Pisum sativum) following serial passages. J. Gen. Virol. 88, 2839–2845 (2007).Agudelo-Romero, P., de la Iglesia, F. & Elena, S. F. The pleiotropic cost of host-specialization in tobacco etch potyvirus. Infect. Genet. Evol. 8, 806–814 (2008).Bedhomme, S., Lafforgue, G. & Elena, S. F. Multihost experimental evolution of a plant RNA virus reveals local adaptation and host-specific mutations. Mol. Biol. Evol. 29, 1481–1492 (2012).García-Arenal, F. & Fraile A. Trade-offs in host range evolution of plant viruses. Plant Pathol. 62, S2–S9. (2013).Calvo, M., Malinowski, T. & García, J. A. Single amino acid changes in the 6K1-CI region can promote the alternative adaptation of Prunus- and Nicotiana- propagated Plum pox virus C isolates to either host. Mol. Plant-Microbe Interact. 27, 136–149 (2014).Cuevas, J. M., Willemsen, A., Hillung, J., Zwart, M. P. & Elena, S. F. Temporal dynamics of intra-host molecular evolution for a plant RNA virus. Mol. Biol. Evol. 32, 1132–1147 (2015).Minicka, J., Rymelska, N., Elena, S. F., Czerwoniec, A. & Hasiów-Jaroszewska, B. Molecular evolution of Pepino mosaic virus during long-term passaging in different hosts and its impact on virus virulence. Ann. Appl. Biol. 166, 389–401 (2015).Agudelo-Romero, P., Carbonell, P., Pérez-Amador, M. A. & Elena, S. F. Virus adaptation by manipulation of host's gene expression. PLos ONE 3, e2397 (2008).Weigel, D. Natural variation in arabidopsis: from molecular genetics to ecological genomics. Plant Physiol. 158, 2–22 (2012).Mahajan, S. K., Chisholm, S. T., Whitham, S. A. & Carrington, J. C. Identification and characterization of a locus (RTM1) that restricts long-distance movement of Tobacco etch virus in Arabidopsis thaliana . Plant J. 14, 177–186 (1998).Whitham, S. A., Yamamoto, M. L. & Carrington, J. C. Selectable viruses and altered susceptibility mutants in Arabidopsis thaliana . Proc. Natl. Acad. Sci. USA 96, 772–777 (1999).Whitham, S. A., Anderberg, R. J., Chisholm, S. T. & Carrington, J. C. Arabidopsis RTM2 gene is necessary for specific restriction of Tobacco etch virus and encodes an unusual small heat shock-like protein. Plant Cell 12, 569–582 (2000).Chisholm, S. T., Mahajan, S. K., Whitham, S. A., Yamamoto, M. L. & Carrington, J. C. Cloning of the Arabidopsis RTM1 gene, which controls restriction of long-distance movement of Tobacco etch virus . Proc. Natl. Acad. Sci. USA 97, 489–494 (2000).Chisholm, S. T., Parra, M. A., Anderberg, R. J. & Carrington, J. C. Arabidopsis RTM1 and RTM2 genes function in phloem to restrict long-distance movement of Tobacco etch virus . Plant Physiol. 127, 1667–1675 (2001).Cosson, P. et al. RTM3, which controls long-distance movement of potyviruses, is a member of a new plant gene family encoding a MEPRIN and TRAF homology domain-containing protein. Plant Physiol. 154, 222–232 (2010).Cosson, P., Sofer, L., Schurdi-Levraud, V. & Revers, F. A member of a new plant gene family encoding a MEPRIN and TRAF homology (MATH) domain-containing protein is involved in restriction of long distance movement of plant viruses. Plant Signal. Behav. 5, 1321–1323 (2010).Agudelo-Romero P. et al. Changes in gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus . Virol. J. 5, 92 (2008).Lalić, J., Agudelo-Romero, P., Carrasco, P. & Elena, S. F. Adaptation of tobacco etch potyvirus to a susceptible ecotype of Arabidopsis thaliana capacitates it for systemic infection of resistant ecotypes. Phil. Trans. R. Soc. B 65, 1997–2008 (2010).Hillung, J., Cuevas, J. M. & Elena, S. F. Transcript profiling of different Arabidopsis thaliana ecotypes in response to tobacco etch potyvirus infection. Front. Microbiol. 3, 229 (2012).Hillung, J., Cuevas, J. M. & Elena, S. F. Evaluating the within-host fitness effects of mutations fixed during virus adaptation to different ecotypes of a new host. Phil. Trans. R. Soc. B 370, 20140292 (2015).Hillung, J., Cuevas, J. M., Valverde, S. & Elena, S. F. Experimental evolution of an emerging plant virus in host genotypes that differ in their susceptibility to infection. Evolution 68, 2467–2480 (2014).Sartor, M. A., Leikauf, G. D. & Medvedovic, M. LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics 25, 211–217 (2009).Montaner, D. & Dopazo, J. Multidimensional gene set analysis of genomic data. PLos ONE 5, e10348 (2010).Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLos ONE 6, e21800 (2011).Grennan, A. K. Regulation of starch metabolism in Arabidopsis leaves. Plant Physiol. 142, 1343–1345 (2006).Johnson, P. R. & Ecker, J. R. The ethylene gas signal transduction pathway: a molecular perspective. Annu. Rev. Genet. 32, 227–254 (1998).Wang, K. L., Li, H. & Ecker, J. R. Ethylene biosynthesis and signaling networks. Plant Cell 14, S131–S151 (2002).Binns, D. et al. QuickGO: a web-based tool for gene ontology searching. Bioinformatics 25, 3045–3046 (2009).Stintzi, A., Weber, H., Reymond, P., Browse, J. & Farmer, E. E. Plant defense in the absence of jasmonic acid: the role of cyclopentenones. Proc. Natl. Acad. Sci. USA 98, 12837–12842 (2001).Luna, E. et al. Callose deposition: a multifaceted plant defense response. Mol. Plant-Microbe Interact. 24, 183–193 (2011).Ghoshroy, S., Freedman, K., Lartey, R. & Citovsky, V. Inhibition of plant viral systemic infection by non-toxic concentrations of cadmium. Plant J. 13, 591–602 (1998).Hayashi, N. et al. Nef of HIV-1 interacts directly with calcium-bound calmodulin. Protein Sci. 11, 529–537 (2002).Zacharias, D. A., Violin, J. D., Newton, A. C. & Tsien, R. Y. Partitioning of lipic-modified monomeric GFPs into membrane microdomains of live cells. Science 296, 913–916 (2002).Rojas, M. R. et al. Functional analysis of proteins involved in movement of the monopartite begomovirus, Tomato yellow leaf curl virus. Virology 291, 110–125 (2001).Padmanabhan, M. S., Goregaoker, S. P., Golem, S., Shiferaw, H. & Culver, J. N. Interaction of the Tobacco mosaic virus replicase protein with the Aux/IAA protein PAP1/IAA26 is associated with disease development. J. Virol. 79, 2549–2558 (2005).Lurin, C. et al. Genome-wide analysis of Arabidopsis pentatricopeptide repeat proteins reveals their essential role in organelle biogenesis. Plant Cell 16, 2089–2103 (2004).Takenaka, M., Verbitskiy, D., Zehrmann, A. & Brennicke, A. Reverse genetic screening identifies five E-class PPR proteins involved in RNA editing in mitochondria of Arabidopsis thaliana . J. Biol. Chem. 285, 27122–27129 (2010).Gillissen, B. et al. A new family of high-affinity transporters for adenine, cytosine, and purine derivatives in Arabidopsis . Plant Cell 12, 291–300 (2000).Li, S., Fu, Q., Chen, L., Huang, W. & Yu, D. Arabidopsis thaliana WRKY25, WRKY26, and WRKY33 coordinate induction of plant thermotolerance. Planta 233, 1237–1252 (2011).Divol, F. et al. Involvement of the xyloglucan endotransglycosylase/hydrolases encoded by celery XTH1 and Arabidopsis XTH33 in the phloem response to aphids. Plant Cell. Environ. 30, 187–201 (2007).Vissenberg, K., Fry, S. C., Pauly, M., Höfte, H. & Verbelen, J. P. XTH acts at the microfibril-matrix interface during cell elongation. J. Exp. Bot. 56, 673–683 (2005).Ham, B. K., Li, G., Kang, B. H., Zeng, F. & Lucas, W. J. Overexpression of Arabidopsis plasmodesmata germin-like proteins disrupts root growth and development. Plant Cell 24, 3630–3648 (2012).Bae, M. S., Cho, E. J., Choi, E. Y. & Park, O. K. Analysis of the Arabidopsis nuclear proteome and its response to cold stress. Plant J. 36, 652–663 (2003).Zargar, S. M. et al. Correlation analysis of proteins responsive to Zn, Mn, or Fe deficiency in Arabidopsis roots based on iTRAQ analysis. Plant Cell Rep. 34, 157–166 (2015).Kleffmann, T. et al. The Arabidopsis thaliana chloroplast proteome reveals pathway abundance and novel protein functions. Curr. Biol. 14, 354–362 (2004).Zybailov, B. et al. Sorting signals, N-terminal modifications and abundance of the chloroplast proteome. PLos ONE 3, e1994 (2008).Wu, P. et al. Phosphate starvation triggers distinct alterations of gene expression in Arabidopsis roots and leaves. Plant Physiol. 132, 1260–1271 (2003).Oh, S. A., Lee, S. Y., Chung, I. K., Lee, C. H. & Nam H. G. A senescence-associated gene of Arabidopsis thaliana is distinctively regulated during natural and artificially induced leaf senescence. Plant Mol. Biol. 30, 739–754 (1996).Schenk, P. M., Kazan, K., Rusu, A. G., Manners, J. M. & Maclean, D. J. The SEN1 gene of Arabidopsis is regulated by signals that link plant defence responses and senescence. Plant Physiol. Biochem. 43, 997–1005 (2005).Fernández-Calvino, L. et al. Activation of senescence-associated dark-inducible (DIN) genes during infection contributes to enhanced susceptibility to plant viruses. Mol. Plant Pathol. 17, 3–15 (2016).Vierstra, R. D. Proteolysis in plants: mechanisms and functions. Plant Mol. Biol. 32, 275–302 (1996).Bögre, L., Okrész, L., Henriques, R. & Anthony, R. G. Growth signalling pathways in Arabidopsis and the AGC protein kinases. Trends Plant Sci. 8, 424–431 (2003).An, L. et al. A zinc finger protein gene ZFP5 integrates phytohormone signalling to control root hair development in Arabidopsis . Plant J. 72, 474–490 (2012).Zhou, Z., An, L., Sun, L. & Gan, Y. ZFP5 encodes a functionally equivalent GIS protein to control trichome initiation. Plant Signal. Behav. 7, 28–30 (2012).Zhou, Z. et al. Zinc finger protein 5 is required for the control of trichome initiation by acting upstream of zinc finger protein 8 in Arabidopsis . Plant Physiol. 157, 673–682 (2011).Lee, D. J. et al. Genome-wide expression profiling of ARABIDOPSIS RESPONSE REGULATOR 7 (ARR7) overexpression in cytokinin response. Mol. Genet. Genomics 277, 115–137 (2007).Theologis, A. et al. Sequence and analysis of chromosome 1 of the plant Arabidopsis thaliana . Nature 408, 816–820 (2000).Heyndrickx, K. S. & Vandepoele, K. Systematic identification of functional plant modules through the integration of complementary data sources. Plant Physiol. 159, 884–901 (2012).Martinoia, E. et al. Multifunctionality of plant ABC transporter - more than just detoxifiers. Planta 214, 345–355 (2002).Kaneda, M. et al. ABC transporters coordinately expressed during lignification of Arabidopsis stems include a set of ABCBs associated with auxin transport. J. Exp. Bot. 62, 2063–2077 (2011).Alejandro, S. et al. AtABCG29 is a monolignol transporter involved in lignin biosynthesis. Curr. Biol. 22, 1207–1212 (2012).Riechmann, J. L. et al. Arabidopsis transcription factors: genome-wide comparative analysis among eukaryotes. Science 290, 2105–2110 (2000).Ohashi-Ito, K. & Bergmann, D. C. Regulation of the Arabidopsis root vascular initial population by LONESOME HIGHWAY . Development 134, 2959–2968 (2007).Averyanov, A. Oxidative burst and plant disease resistance. Front. Biosci. 1, 142–152 (2009).Flury, P., Klauser, D., Schulze, B., Boller, T. & Bartels, S. The anticipation of danger: microbe-associated molecular pattern perception enhances AtPep-triggered oxidative burst. Plant Physiol. 161, 2023–2035 (2013).Tanaka, K., Nguyen, C. T., Liang, Y., Cao, Y. & Stacey, G. Role of LysM receptors in chitin-triggered plant innate immunity. Plant Signal. Behav. 8, e22598 (2013).Nakamura, K. & Matsuoka, K. Protein targeting to the vacuole in plant cells. Plant Physiol. 101, 1–5 (1993).Elena, S. F., Agudelo-Romero, P. & Lalić, J. The evolution of viruses in multi-host fitness landscapes. Open Virol. J. 3, 1–6 (2009).Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 3 (2004).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).Benjamini,Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29, 1165–1188 (2001).Sneath, P. & Sokal, R. Numerical Taxonomy. ( W.H. Freeman, 1973).D'Haeseler, P. How does gene expression clustering work? Nat. Biotech. 23, 1499–1501 (2005).Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006)

    Keratan sulphate in the tumour environment

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    Keratan sulphate (KS) is a bioactive glycosaminoglycan (GAG) of some complexity composed of the repeat disaccharide D-galactose β1→4 glycosidically linked to N-acetyl glucosamine. During the biosynthesis of KS, a family of glycosyltransferase and sulphotransferase enzymes act sequentially and in a coordinated fashion to add D-galactose (D-Gal) then N-acetyl glucosamine (GlcNAc) to a GlcNAc acceptor residue at the reducing terminus of a nascent KS chain to effect chain elongation. D-Gal and GlcNAc can both undergo sulphation at C6 but this occurs more frequently on GlcNAc than D-Gal. Sulphation along the developing KS chain is not uniform and contains regions of variable length where no sulphation occurs, regions which are monosulphated mainly on GlcNAc and further regions of high sulphation where both of the repeat disaccharides are sulphated. Each of these respective regions in the KS chain can be of variable length leading to KS complexity in terms of chain length and charge localization along the KS chain. Like other GAGs, it is these variably sulphated regions in KS which define its interactive properties with ligands such as growth factors, morphogens and cytokines and which determine the functional properties of tissues containing KS. Further adding to KS complexity is the identification of three different linkage structures in KS to asparagine (N-linked) or to threonine or serine residues (O-linked) in proteoglycan core proteins which has allowed the categorization of KS into three types, namely KS-I (corneal KS, N-linked), KS-II (skeletal KS, O-linked) or KS-III (brain KS, O-linked). KS-I to -III are also subject to variable addition of L-fucose and sialic acid groups. Furthermore, the GlcNAc residues of some members of the mucin-like glycoprotein family can also act as acceptor molecules for the addition of D-Gal and GlcNAc residues which can also be sulphated leading to small low sulphation glycoforms of KS. These differ from the more heavily sulphated KS chains found on proteoglycans. Like other GAGs, KS has evolved molecular recognition and information transfer properties over hundreds of millions of years of vertebrate and invertebrate evolution which equips them with cell mediatory properties in normal cellular processes and in aberrant pathological situations such as in tumourogenesis. Two KS-proteoglycans in particular, podocalyxin and lumican, are cell membrane, intracellular or stromal tissue–associated components with roles in the promotion or regulation of tumour development, mucin-like KS glycoproteins may also contribute to tumourogenesis. A greater understanding of the biology of KS may allow better methodology to be developed to more effectively combat tumourogenic processes
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