49 research outputs found

    Mutations of RNA polymerase II activate key genes of the nucleoside triphosphate biosynthetic pathways

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    The yeast URA2 gene, encoding the rate-limiting enzyme of UTP biosynthesis, is transcriptionally activated by UTP shortage. In contrast to other genes of the UTP pathway, this activation is not governed by the Ppr1 activator. Moreover, it is not due to an increased recruitment of RNA polymerase II at the URA2 promoter, but to its much more effective progression beyond the URA2 mRNA start site(s). Regulatory mutants constitutively expressing URA2 resulted from cis-acting deletions upstream of the transcription initiator region, or from amino-acid replacements altering the RNA polymerase II Switch 1 loop domain, such as rpb1-L1397S. These two mutation classes allowed RNA polymerase to progress downstream of the URA2 mRNA start site(s). rpb1-L1397S had similar effects on IMD2 (IMP dehydrogenase) and URA8 (CTP synthase), and thus specifically activated the rate-limiting steps of UTP, GTP and CTP biosynthesis. These data suggest that the Switch 1 loop of RNA polymerase II, located at the downstream end of the transcription bubble, may operate as a specific sensor of the nucleoside triphosphates available for transcription

    A ROC analysis-based classification method for landslide susceptibility maps

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    [EN] A landslide susceptibility map is a crucial tool for landuse spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.Cantarino-Martí, I.; Carrión Carmona, MÁ.; Goerlich-Gisbert, F.; Martínez Ibáñez, V. (2018). A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 1-18. doi:10.1007/s10346-018-1063-4S118Armstrong MP, Xiao N, Bennett DA (2003) Using genetic algorithms to create multicriteria class intervals for choropleth maps. Ann Assoc Am Geogr 93(3):595–623. https://doi.org/10.1111/1467-8306.9303005Atkinson P, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385. https://doi.org/10.1016/S0098-3004(97)00117-9Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010Baeza C, Lantada N, Amorim S (2016) Statistical and spatial analysis of landslide susceptibility maps with different classification systems. Environ Earth Science 75:1318. https://doi.org/10.1007/s12665-016-6124-1Basofi A, Fariza A, Ahsan AS, Kamal IM (2015) A comparison between natural and head/tail breaks in LSI (landslide susceptibility index) classification for landslide susceptibility mapping: a case study in Ponorogo, East Java, Indonesia. 2015 International Conference on Science in Information Technology, pp 337–342Cantarino I (2013) Elaboración y validación de un modelo jerárquico derivado de SIOSE. Revista de Teledetección 39:5–21Carrara A, Crosta GB, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94(3–4):353–378. https://doi.org/10.1016/j.geomorph.2006.10.033Chacón J, Irigaray C, Fernández T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4):341–411Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472COPUT (1998) Lithology, exploitation of industrial rocks and landslide risk in the Valencian Community. Thematic Mapping Series. Department of Public Works of the Valencian Regional GovernmentDrummond C, Holte RC (2006) Cost curves: an improved method for visualizing classifier performance. Mach Learn 65(1):95–130Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51(2):241–256. https://doi.org/10.1007/s00254-006-0322-1Evans IS (1977) The selection of class intervals. Transactions of the Institute of British Geographers. Contemp Cartograph 2(1):98–124. https://doi.org/10.2307/622195Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions, Book Series: Wiley Series in Probability and Statistics. John Wiley & Sons. Print ISBN: 9780471526292. doi: https://doi.org/10.1002/0471445428Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens 70(5):627–633Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography: perspectives on spatial data analysis. SAGE Publications, Thousand Oaks 270 ppFrattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72. https://doi.org/10.1016/j.enggeo.2009.12.004Geisser S (1998) Comparing two tests used for diagnostic or screening processes. Stat Probability Lett 40:113–119Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45:23–41Günther A, Reichenbach P, Malet JP, van den Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2013) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides 10:529–546. https://doi.org/10.1007/s10346-012-0349-1Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85Gupta RP, Kanungo DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10(3):330–341. https://doi.org/10.1016/j.jag.2008.01.003Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184. https://doi.org/10.1016/j.geomorph.2006.04.007Hervás J (2017) El inventario de movimientos de ladera de España ALISSA: Metodología y análisis preliminar. In: Alonso E, Corominas J, Hürlimann M (Eds.), Taludes 2017. Proc. IX Simposio Nacional sobre Taludes y Laderas Inestables, Santander, 27–30 June 2017. CIMNE, Barcelona, pp. 629–639Jaedicke C, Van Den Eeckhaut M, Nadim F et al (2014) Identification of landslide hazard and risk ‘hotspots’ in Europe. Bull Eng Geol Environ 73:325. https://doi.org/10.1007/s10064-013-0541-0Jenks GF (1967) The data model concept in statistical mapping. Int Yearbook Cartograph 7:186–190Jiang B (2013) Head/tail breaks: a new classification scheme for data with a heavy-tailed distribution. Prof Geogr 65(3):482–494. https://doi.org/10.1080/00330124.2012.700499Kiang MY (2003) A comparative assessment of classification methods. Decis Support Syst 35(4):441–454. https://doi.org/10.1016/S0167-9236(02)00110-0Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174Langping L, Hengxing L, Changbao G, Yongshuang Z, Quanwen L, Yuming W (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14:727–741. https://doi.org/10.1007/s10346-016-0771-xLee S (2007) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Process Landforms 32:2133–2148. https://doi.org/10.1002/esp.1517Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107(4):606–616. https://doi.org/10.1016/j.rse.2006.10.010López-Ratón M, Rodríguez-Álvarez MX, Cadarso-Suárez C, Gude-Sampedro F (2014) Optimal cutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw 61(8):4Malet JP, Puissant A, Mathieu A, Van Den Eeckhaut M, Fressard M (2013) Integrating spatial multi-criteria evaluation and expert knowledge for country-scale landslide susceptibility analysis: application to France. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin. https://doi.org/10.1007/978-3-642-31325-7_40McGee S (2002) Simplifying likelihood ratios. J Gen Intern Med 17:647–650Metz C (1978) Basic principles of ROC analysis. Semin Nucl Med VIII(4):183–198Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173. https://doi.org/10.1007/s10346-006-0036-1Ohlmacher G, Davis J (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343. https://doi.org/10.1016/S0013-7952(03)00069-3Powell RL, Matzke N, de Souza C Jr, Clark M, Numata I, Hess LL, Roberts DA (2004) Sources of error accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sens Environ 90(2):221–234. https://doi.org/10.1016/j.rse.2003.12.007Saaty T (1980) The analytic hierarchy process. McGraw Hill, New YorkSmits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. Int J Remote Sens 20:1461–1486Stehman SV, Czaplewski RL (1998) Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sens Environ 64:331–344Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293Van Den Eeckhaut M, Hervás J, Jaedicke C, Malet J-P, Montanarella L, Nadim F (2012) Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides 8:357–369Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. Natural hazards. UNESCO, ParisZhu X (2016) GIS for environmental applications. Routledge, Abingdon, p 490Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–57

    Dissection of Pol II Trigger Loop Function and Pol II Activity–Dependent Control of Start Site Selection In Vivo

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    Structural and biochemical studies have revealed the importance of a conserved, mobile domain of RNA Polymerase II (Pol II), the Trigger Loop (TL), in substrate selection and catalysis. The relative contributions of different residues within the TL to Pol II function and how Pol II activity defects correlate with gene expression alteration in vivo are unknown. Using Saccharomyces cerevisiae Pol II as a model, we uncover complex genetic relationships between mutated TL residues by combinatorial analysis of multiply substituted TL variants. We show that in vitro biochemical activity is highly predictive of in vivo transcription phenotypes, suggesting direct relationships between phenotypes and Pol II activity. Interestingly, while multiple TL residues function together to promote proper transcription, individual residues can be separated into distinct functional classes likely relevant to the TL mechanism. In vivo, Pol II activity defects disrupt regulation of the GTP-sensitive IMD2 gene, explaining sensitivities to GTP-production inhibitors, but contrasting with commonly cited models for this sensitivity in the literature. Our data provide support for an existing model whereby Pol II transcriptional activity provides a proxy for direct sensing of NTP levels in vivo leading to IMD2 activation. Finally, we connect Pol II activity to transcription start site selection in vivo, implicating the Pol II active site and transcription itself as a driver for start site scanning, contravening current models for this process

    Transcriptome-Wide Binding Sites for Components of the Saccharomyces cerevisiae Non-Poly(A) Termination Pathway: Nrd1, Nab3, and Sen1

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    RNA polymerase II synthesizes a diverse set of transcripts including both protein-coding and non-coding RNAs. One major difference between these two classes of transcripts is the mechanism of termination. Messenger RNA transcripts terminate downstream of the coding region in a process that is coupled to cleavage and polyadenylation reactions. Non-coding transcripts like Saccharomyces cerevisiae snoRNAs terminate in a process that requires the RNA–binding proteins Nrd1, Nab3, and Sen1. We report here the transcriptome-wide distribution of these termination factors. These data sets derived from in vivo protein–RNA cross-linking provide high-resolution definition of non-poly(A) terminators, identify novel genes regulated by attenuation of nascent transcripts close to the promoter, and demonstrate the widespread occurrence of Nrd1-bound 3′ antisense transcripts on genes that are poorly expressed. In addition, we show that Sen1 does not cross-link efficiently to many expected non-coding RNAs but does cross-link to the 3′ end of most pre–mRNA transcripts, suggesting an extensive role in mRNA 3′ end formation and/or termination

    Epithelial-immune cell interplay in primary Sjogren syndrome salivary gland pathogenesis

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    In primary Sjogren syndrome (pSS), the function of the salivary glands is often considerably reduced. Multiple innate immune pathways are likely dysregulated in the salivary gland epithelium in pSS, including the nuclear factor-kappa B pathway, the inflammasome and interferon signalling. The ductal cells of the salivary gland in pSS are characteristically surrounded by a CD4(+) T cell-rich and B cell-rich infiltrate, implying a degree of communication between epithelial cells and immune cells. B cell infiltrates within the ducts can initiate the development of lymphoepithelial lesions, including basal ductal cell hyperplasia. Vice versa, the epithelium provides chronic activation signals to the glandular B cell fraction. This continuous stimulation might ultimately drive the development of mucosa-associated lymphoid tissue lymphoma. This Review discusses changes in the cells of the salivary gland epithelium in pSS (including acinar, ductal and progenitor cells), and the proposed interplay of these cells with environmental stimuli and the immune system. Current therapeutic options are insufficient to address both lymphocytic infiltration and salivary gland dysfunction. Successful rescue of salivary gland function in pSS will probably demand a multimodal therapeutic approach and an appreciation of the complicity of the salivary gland epithelium in the development of pSS. Salivary gland dysfunction is an important characteristic of primary Sjogren syndrome (pSS). In this Review, the authors discuss various epithelial abnormalities in pSS and the mechanisms by which epithelial cell-immune cell interactions contribute to disease development and progression

    Computer-mediated communication and conversation analysis

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    An increasing number of researchers use conversation analysis (CA) methodology to investigate interactional dimensions of computer-mediated communication (CMC) and their impact on language and learning. While there is a significant body of CA research focusing on naturally occurring telephone and face-to-face conversation, researchers’ attention since the late 1990s has shifted to new contexts where communication between human beings is mediated by computers. This chapter is focused on CA research in the educational sphere, where participants are using an additional or a foreign language. CA research on human interaction developed robust analytical tools to identify and understand the unique interactional resources which are available to users in technologically mediated contexts. In particular, researchers are able to draw on previous CA research on face-to-face and telephone interaction to explore affordances and constraints of new technologies for learning, and how users use language to adapt to new and evolving interactional contexts. This chapter will therefore provide a brief overview of early CMC and CA research on technologically mediated interaction. Following this overview, major contributions where CA is systematically applied to computer-mediated talk will be presented, focusing specifically on findings related to language and interaction in L2 educational settings
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