Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies

Existing sequence alignment algorithms use heuristic scoring schemes which cannot be used as objective distance metrics. Therefore one relies on measures like the p- or log-det distances, or makes explicit, and often simplistic, assumptions about sequence evolution. Information theory provides an alternative, in the form of mutual information (MI) which is, in principle, an objective and model independent similarity measure. MI can be estimated by concatenating and zipping sequences, yielding thereby the "normalized compression distance". So far this has produced promising results, but with uncontrolled errors. We describe a simple approach to get robust estimates of MI from global pairwise alignments. Using standard alignment algorithms, this gives for animal mitochondrial DNA estimates that are strikingly close to estimates obtained from the alignment free methods mentioned above. Our main result uses algorithmic (Kolmogorov) information theory, but we show that similar results can also be obtained from Shannon theory. Due to the fact that it is not additive, normalized compression distance is not an optimal metric for phylogenetics, but we propose a simple modification that overcomes the issue of additivity. We test several versions of our MI based distance measures on a large number of randomly chosen quartets and demonstrate that they all perform better than traditional measures like the Kimura or log-det (resp. paralinear) distances. Even a simplified version based on single letter Shannon entropies, which can be easily incorporated in existing software packages, gave superior results throughout the entire animal kingdom. But we see the main virtue of our approach in a more general way. For example, it can also help to judge the relative merits of different alignment algorithms, by estimating the significance of specific alignments.Comment: 19 pages + 16 pages of supplementary materia

Microchannel-patterned and heparin micro-contact-printed biodegradable composite membranes for tissue-engineering applications

Microchannel-patterned starch–poly(capro-lactone)/hydydroxyapatite (SPCL–HA) and starch– poly(lactic acid) (SPLA) composite membranes were produced for use as a laminated tissueengineering scaffold that incorporates both physical and biochemical patterns. For this purpose, SPCL (30% starch) blended with inorganic hydroxyl apatite (50%) and SPLA (50% starch) membranes were made with compressive moulding. Consequently, the microchannel structures (width 102 μm, 174 μm intervals) were developed on the composite membranes by means of micro-patterned metal mould(s) and hydraulic pressing. An elastomer poly(dimetylsiloxane) stamp was used to transfer heparin as a biochemical cue over the microchannel surfaces by micro-contact printing (μCP). Toluidine blue staining of developed capillaries and heparin μCP-coated membranes showed that heparin was transferred predominantly over the microchannel surfaces. Fibroblast cell culture over the microchannel-formed and heparin μCP-modified SPCL–HA and SPLA membranes showed distinct growth patterns. In contrast to the uniform cell layer formed on unmodified microchannels, the cells were bridging across the grooves of heparin-printed microchannels. At extended culture periods, the heparin-printed microchannels were covered with a layer of fibroblast cells without cellular ingrowths inside. This study indicated that the topographical pattern could induce an organization of fibroblasts only with the biochemical cue and the cells’ functions can be controlled spatially over the microchannels by using both cues.E. T. Baran thanks the Portuguese Foundation for Science and Technology (FCT) for providing a post-doctoral scholarship (No. SFRH/BPD/30768/2006). This work was partially supported by the FCT through funds from the POCTI and/or FEDER programmes and also by the European Union-funded STREP Project HIPPOCRATES (Grant No. NMP3-CT-2003-505758)

Identification of irregularities and allocation suggestion of relative file system permissions

It is well established that file system permissions in large, multi-user environments can be audited to identify vulnerabilities with respect to what is regarded as standard practice. For example, identifying that a user has an elevated level of access to a system directory which is unnecessary and introduces a vulnerability. Similarly, the allocation of new file system permissions can be assigned following the same standard practices. On the contrary, and less well established, is the identification of potential vulnerabilities as well as the implementation of new permissions with respect to a system's current access control implementation. Such tasks are heavily reliant on expert interpretation. For example, the assigned relationship between users and groups, directories and their parents, and the allocation of permissions on file system resources all need to be carefully considered. This paper presents the novel use of statistical analysis to establish independence and homogeneity in allocated file system permissions. This independence can be interpreted as potential anomalies in a system's implementation of access control. The paper then presents the use of instance-based learning to suggest the allocation of new permissions conforming to a system's current implementation structure. Following this, both of the presented techniques are then included in a tool for interacting with Microsoft's New Technology File System (NTFS) permissions. This involves experimental analysis on six different NTFS directory structures within different organisations. The effectiveness of the developed technique is then established through analysing the true positive and true negative values. The presented results demonstrate the potential of the proposed techniques for overcoming complexities with real-world file system administratio

Synthesis of prebiotic galactooligosaccharides from lactose using bifidobacterial β-galactosidase (BbgIV) immobilised on DEAE-Cellulose, Q-Sepharose and amino-ethyl agarose

The bifidobacterial β-galactosidase BbgIV was immobilised on DEAE-Cellulose and Q-Sepharose via ionic binding and on amino-ethyl- and glyoxal-agarose via covalent attachment, and was then used to catalyse the synthesis of galactooligosaccharides (GOS). The immobilisation yield exceeded 90 % using ionic binding, while it was low using aminoethyl agarose (25 – 28 %) and very low using glyoxal agarose (< 3 %). This was due to the mild conditions and absence of chemical reagents in ionic binding, compared to covalent attachment. The maximum GOS yield obtained using DEAE-Cellulose and Q-Sepharose was similar to that obtained using free BbgIV (49 – 53 %), indicating the absence of diffusion limitation and mass transfer issues. For amino-ethyl agarose, however, the GOS yield obtained was lower (42 – 44 %) compared to that obtained using free BbgIV. All the supports tried significantly (P < 0.05) increased the BbgIV operational stability and the GOS synthesis productivity up to 55 °C. Besides, six successive GOS synthesis batches were performed using BbgIV immobilised on Q-Sepharose; all resulted in similar GOS yields, indicating the possibility of developing a robust synthesis process. Overall, the GOS synthesis operation performance using BbgIV was improved by immobilising the enzyme onto solid supports, in particular on Q-Sepharos

The Hippo pathway member Yap plays a key role in influencing fate decisions in muscle satellite cells

Satellite cells are the resident stem cells of skeletal muscle. Mitotically quiescent in mature muscle, they can be activated to proliferate and generate myoblasts to supply further myonuclei to hypertrophying or regenerating muscle fibres, or self-renew to maintain the resident stem cell pool. Here, we identify the transcriptional co-factor Yap as a novel regulator of satellite cell fate decisions. Yap expression increases during satellite cell activation and Yap remains highly expressed until after the differentiation versus self-renewal decision is made. Constitutive expression of Yap maintains Pax7+ and MyoD+ satellite cells and satellite cell-derived myoblasts, promotes proliferation but prevents differentiation. In contrast, Yap knockdown reduces the proliferation of satellite cell-derived myoblasts by \u3c40%. Consistent with the cellular phenotype, microarrays show that Yap increases expression of genes associated with Yap inhibition, the cell cycle, ribosome biogenesis and that it represses several genes associated with angiotensin signalling. We also identify known regulators of satellite cell function such as BMP4, CD34 and Myf6 (Mrf4) as genes whose expression is dependent on Yap activity. Finally, we confirm in myoblasts that Yap binds to Tead transcription factors and co-activates MCAT elements which are enriched in the proximal promoters of Yap-responsive genes

YAP and TAZ regulate adherens junction dynamics and endothelial cell distribution during vascular development

© Copyright Neto et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.Formation of blood vessel networks by sprouting angiogenesis is critical for tissue growth, homeostasis and regeneration. How endothelial cells arise in adequate numbers and arrange suitably to shape functional vascular networks is poorly understood. Here we show that YAP/TAZ promote stretch-induced proliferation and rearrangements of endothelial cells whilst preventing bleeding in developing vessels. Mechanistically, YAP/TAZ increase the turnover of VE-Cadherin and the formation of junction associated intermediate lamellipodia, promoting both cell migration and barrier function maintenance. This is achieved in part by lowering BMP signalling. Consequently, the loss of YAP/TAZ in the mouse leads to stunted sprouting with local aggregation as well as scarcity of endothelial cells, branching irregularities and junction defects. Forced nuclear activity of TAZ instead drives hypersprouting and vascular hyperplasia. We propose a new model in which YAP/TAZ integrate mechanical signals with BMP signaling to maintain junctional compliance and integrity whilst balancing endothelial cell rearrangements in angiogenic vessels.FN was financially supported by the Fundação para a Ciência e a Tecnologia (FCT), CRUK-CRICK and the MDC. ACV, AKB and EBK were supported by the DZHK (German Centre for Cardiovascular Research), AS was supported by the EMBO (European Molecular Biology Organization), JRC was supported by the FCT. CAF is supported by the FCT, EC-ERC Starting Grant, Portugal2020 program. MP is supported by the Max Planck Society, the ERC Starting Grant ANGIOMET, the Deutsche Forschungsgemeinschaft, the Excellence Cluster Cardiopulmonary System, the LOEWE grant Ub-Net, the DZHK, the Stiftung Charité and the EMBO Young Investigator Program. HG is supported by the DZHK and ERC Consolidator Grant Reshape 311719.info:eu-repo/semantics/publishedVersio

A Novel Conserved Isoform of the Ubiquitin Ligase UFD2a/UBE4B Is Expressed Exclusively in Mature Striated Muscle Cells

Yeast Ufd2p was the first identified E4 multiubiquitin chain assembly factor. Its vertebrate homologues later referred to as UFD2a, UBE4B or E4B were also shown to have E3 ubiquitin ligase activity. UFD2a function in the brain has been well established in vivo, and in vitro studies have shown that its activity is essential for proper condensation and segregation of chromosomes during mitosis. Here we show that 2 alternative splice forms of UFD2a, UFD2a-7 and -7/7a, are expressed sequentially during myoblast differentiation of C2C12 cell cultures and during cardiotoxin-induced regeneration of skeletal muscle in mice. UFD2a-7 contains an alternate exon 7, and UFD2a-7/7a, the larger of the 2 isoforms, contains an additional novel exon 7a. Analysis of protein or mRNA expression in mice and zebrafish revealed that a similar pattern of isoform switching occurs during developmental myogenesis of cardiac and skeletal muscle. In vertebrates (humans, rodents, zebrafish), UFD2a-7/7a is expressed only in mature striated muscle. This unique tissue specificity is further validated by the conserved presence of 2 muscle-specific splicing regulatory motifs located in the 3′ introns of exons 7 and 7a. UFD2a interacts with VCP/p97, an AAA-type ATPase implicated in processes whose functions appear to be regulated, in part, through their interaction with one or more of 15 previously identified cofactors. UFD2a-7/7a did not interact with VCP/p97 in yeast 2-hybrid experiments, which may allow the ATPase to bind cofactors that facilitate its muscle-specific functions. We conclude that the regulated expression of these UFD2a isoforms most likely imparts divergent functions that are important for myogenisis

The RVDM: modelling impacts, evolution and competition processes to determine riparian vegetation dynamics

[EN] The riparian vegetation dynamic model (RVDM) is an ecohydrological model aimed to study the vegetation dynamics in riparian areas that represents an upgrade with respect to previous tools in the way of understanding the riparian dynamics. Important novelties are proposed by this tool, including a high temporal resolution (daily time step), a proposal of a new plant classification approach useful for research and management (successional plant functional types), good representation of the key processes that determine the vegetation dynamics in riparian areas (drought and flood impacts, recruitment, growth, succession and competition), an easy implementation and feasible inclusion of river morphodynamics in the model implementation (including different daily elevation and soil maps in the inputs). The model implementation in a Mediterranean semi-arid study site resulted satisfactorily (cell by cell calibration accuracy >= 65% and cell by cell validation accuracy between 40% and 60%), demonstrating the great potential of this approach for future research and management applications. Although 36 parameters are included in the model conceptualization, the global sensitivity analysis demonstrated that only eight types of parameters are actually influent. These parameters are as follows: minimum time since mixed for transition to terrestrial, root depths, transpiration factors, critical shear stress of early stages, minimum biomass required to allow succession, germination minimum capillary water content in the upper soil, effective depth considered for evaporation from bare soil and coverage of pioneers. Riparian vegetation dynamic model will be a useful tool for gaining a better understanding of the riparian plants behaviour under different ecohydrological conditions. Copyright (C) 2015 John Wiley & Sons, Ltd.This research has been developed within the research project SCARCE (Consolider-Ingenio 2010 CSD2009-00065) supported by the Spanish Ministry of Economy and Competitiveness. The hydrological data, the aerial photographs and the meteorological data have been supplied by the Hydrological Studies Centre (CEH-CEDEX), the Jucar River Basin Authority and the Spanish National Meteorological Agency (AEMET), respectively.García-Arias, A.; Francés, F. (2016). The RVDM: modelling impacts, evolution and competition processes to determine riparian vegetation dynamics. Ecohydrology. 9(3):438-459. https://doi.org/10.1002/eco.1648S43845993Baird, K. J., & Maddock, T. (2005). Simulating riparian evapotranspiration: a new methodology and application for groundwater models. Journal of Hydrology, 312(1-4), 176-190. doi:10.1016/j.jhydrol.2005.02.014Benjankar, R., Egger, G., Jorde, K., Goodwin, P., & Glenn, N. F. (2011). Dynamic floodplain vegetation model development for the Kootenai River, USA. Journal of Environmental Management, 92(12), 3058-3070. doi:10.1016/j.jenvman.2011.07.017Benjankar, R., Burke, M., Yager, E., Tonina, D., Egger, G., Rood, S. B., & Merz, N. (2014). Development of a spatially-distributed hydroecological model to simulate cottonwood seedling recruitment along rivers. Journal of Environmental Management, 145, 277-288. doi:10.1016/j.jenvman.2014.06.027BOEDELTJE, G., BAKKER, J. P., TEN BRINKE, A., VAN GROENENDAEL, J. M., & SOESBERGEN, M. (2004). Dispersal phenology of hydrochorous plants in relation to discharge, seed release time and buoyancy of seeds: the flood pulse concept supported. Journal of Ecology, 92(5), 786-796. doi:10.1111/j.0022-0477.2004.00906.xBrinson, M. M., & Verhoeven, J. (1999). Riparian forests. Maintaining Biodiversity in Forest Ecosystems, 265-299. doi:10.1017/cbo9780511613029.010CAMPBELL, G. S. (1974). A SIMPLE METHOD FOR DETERMINING UNSATURATED CONDUCTIVITY FROM MOISTURE RETENTION DATA. Soil Science, 117(6), 311-314. doi:10.1097/00010694-197406000-00001Camporeale, C., & Ridolfi, L. (2006). Riparian vegetation distribution induced by river flow variability: A stochastic approach. Water Resources Research, 42(10). doi:10.1029/2006wr004933Camporeale, C., Perucca, E., Ridolfi, L., & Gurnell, A. M. (2013). MODELING THE INTERACTIONS BETWEEN RIVER MORPHODYNAMICS AND RIPARIAN VEGETATION. Reviews of Geophysics, 51(3), 379-414. doi:10.1002/rog.20014Canadell, J., Jackson, R. B., Ehleringer, J. B., Mooney, H. A., Sala, O. E., & Schulze, E.-D. (1996). Maximum rooting depth of vegetation types at the global scale. Oecologia, 108(4), 583-595. doi:10.1007/bf00329030Cannell, M. G. R., Milne, R., Sheppard, L. J., & Unsworth, M. H. (1987). Radiation Interception and Productivity of Willow. The Journal of Applied Ecology, 24(1), 261. doi:10.2307/2403803Cao, X., Jia, J. B., Li, H., Li, M. C., Luo, J., Liang, Z. S., … Luo, Z. B. (2011). Photosynthesis, water use efficiency and stable carbon isotope composition are associated with anatomical properties of leaf and xylem in six poplar species. Plant Biology, 14(4), 612-620. doi:10.1111/j.1438-8677.2011.00531.xCerrillo, T., Rodríguez, M. E., Achinelli, F., Doffo, G., & Luquez, V. M. (2013). Do greenhouse experiments predict willow responses tolong term flooding events in the field? Bosque (Valdivia), 34(1), 17-18. doi:10.4067/s0717-92002013000100009Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37-46. doi:10.1177/001316446002000104Collalti, A., Perugini, L., Santini, M., Chiti, T., Nolè, A., Matteucci, G., & Valentini, R. (2014). A process-based model to simulate growth in forests with complex structure: Evaluation and use of 3D-CMCC Forest Ecosystem Model in a deciduous forest in Central Italy. Ecological Modelling, 272, 362-378. doi:10.1016/j.ecolmodel.2013.09.016Corenblit, D., Tabacchi, E., Steiger, J., & Gurnell, A. M. (2007). Reciprocal interactions and adjustments between fluvial landforms and vegetation dynamics in river corridors: A review of complementary approaches. Earth-Science Reviews, 84(1-2), 56-86. doi:10.1016/j.earscirev.2007.05.004Corenblit, D., Steiger, J., Tabacchi, E., González, E., & Planty-Tabacchi, A.-M. (2012). ECOSYSTEM ENGINEERS MODULATE EXOTIC INVASIONS IN RIPARIAN PLANT COMMUNITIES BY MODIFYING HYDROGEOMORPHIC CONNECTIVITY. River Research and Applications, 30(1), 45-59. doi:10.1002/rra.2618Coulthard, T. J., Hicks, D. M., & Van De Wiel, M. J. (2007). Cellular modelling of river catchments and reaches: Advantages, limitations and prospects. Geomorphology, 90(3-4), 192-207. doi:10.1016/j.geomorph.2006.10.030Douma, J. C., de Haan, M. W. A., Aerts, R., Witte, J.-P. M., & van Bodegom, P. M. (2011). Succession-induced trait shifts across a wide range of NW European ecosystems are driven by light and modulated by initial abiotic conditions. Journal of Ecology, 100(2), 366-380. doi:10.1111/j.1365-2745.2011.01932.xFormann, E., Habersack, H. M., & Schober, S. (2007). Morphodynamic river processes and techniques for assessment of channel evolution in Alpine gravel bed rivers. Geomorphology, 90(3-4), 340-355. doi:10.1016/j.geomorph.2006.10.029García-Arias, A., Francés, F., Ferreira, T., Egger, G., Martínez-Capel, F., Garófano-Gómez, V., … Rodríguez-González, P. M. (2012). Implementing a dynamic riparian vegetation model in three European river systems. Ecohydrology, 6(4), 635-651. doi:10.1002/eco.1331García-Arias, A., Francés, F., Morales-de la Cruz, M., Real, J., Vallés-Morán, F., Garófano-Gómez, V., & Martínez-Capel, F. (2013). Riparian evapotranspiration modelling: model description and implementation for predicting vegetation spatial distribution in semi-arid environments. Ecohydrology, 7(2), 659-677. doi:10.1002/eco.1387Glenn, E., Huete, A., Nagler, P., & Nelson, S. (2008). Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape. Sensors, 8(4), 2136-2160. doi:10.3390/s8042136González, E., Comín, F. A., & Muller, E. (2009). Seed dispersal, germination and early seedling establishment of Populus alba L. under simulated water table declines in different substrates. Trees, 24(1), 151-163. doi:10.1007/s00468-009-0388-yGREET, J., ANGUS WEBB, J., & COUSENS, R. D. (2011). The importance of seasonal flow timing for riparian vegetation dynamics: a systematic review using causal criteria analysis. Freshwater Biology, 56(7), 1231-1247. doi:10.1111/j.1365-2427.2011.02564.xGuilloy-Froget, H., Muller, E., Barsoum, N., & Hughes, F. M. M. (2002). Dispersal, germination, and survival of Populus nigra L. (Salicaceae) in changing hydrologic conditions. Wetlands, 22(3), 478-488. doi:10.1672/0277-5212(2002)022[0478:dgasop]2.0.co;2Gurnell, A., Thompson, K., Goodson, J., & Moggridge, H. (2008). Propagule deposition along river margins: linking hydrology and ecology. Journal of Ecology, 96(3), 553-565. doi:10.1111/j.1365-2745.2008.01358.xHood, W. G., & Naiman, R. J. (2000). Plant Ecology, 148(1), 105-114. doi:10.1023/a:1009800327334Hooke, J. M., Brookes, C. J., Duane, W., & Mant, J. M. (2005). A simulation model of morphological, vegetation and sediment changes in ephemeral streams. Earth Surface Processes and Landforms, 30(7), 845-866. doi:10.1002/esp.1195Hornberger, G. (1980). Eutrophication in peel inlet—I. The problem-defining behavior and a mathematical model for the phosphorus scenario. Water Research, 14(1), 29-42. doi:10.1016/0043-1354(80)90039-1Kozlowski, T. T. (2002). Physiological-ecological impacts of flooding on riparian forest ecosystems. Wetlands, 22(3), 550-561. doi:10.1672/0277-5212(2002)022[0550:peiofo]2.0.co;2Laio, F., Porporato, A., Fernandez-Illescas, C. ., & Rodriguez-Iturbe, I. (2001). Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress. Advances in Water Resources, 24(7), 745-762. doi:10.1016/s0309-1708(01)00007-0Lite, S. J., Bagstad, K. J., & Stromberg, J. C. (2005). Riparian plant species richness along lateral and longitudinal gradients of water stress and flood disturbance, San Pedro River, Arizona, USA. Journal of Arid Environments, 63(4), 785-813. doi:10.1016/j.jaridenv.2005.03.026Lytle, D. A., & Poff, N. L. (2004). Adaptation to natural flow regimes. Trends in Ecology & Evolution, 19(2), 94-100. doi:10.1016/j.tree.2003.10.002Maddock T III Baird KJ 2003 A riparian evapotranspiration package for Modflow-96 and Modflow-2000 University of Arizona 60Maddock T III Baird KJ Hanson RT Schmid W Hoori A 2012 RIP-ET: a riparian evapotranspiration package for MODFLOW-2005 76Mahoney, J. M., & Rood, S. B. (1998). Streamflow requirements for cottonwood seedling recruitment—An integrative model. Wetlands, 18(4), 634-645. doi:10.1007/bf03161678McCree, K. J. (1971). The action spectrum, absorptance and quantum yield of photosynthesis in crop plants. Agricultural Meteorology, 9, 191-216. doi:10.1016/0002-1571(71)90022-7Medici, C., Wade, A. J., & Francés, F. (2012). Does increased hydrochemical model complexity decrease robustness? Journal of Hydrology, 440-441, 1-13. doi:10.1016/j.jhydrol.2012.02.047MERRITT, D. M., SCOTT, M. L., LeROY POFF, N., AUBLE, G. T., & LYTLE, D. A. (2010). Theory, methods and tools for determining environmental flows for riparian vegetation: riparian vegetation-flow response guilds. Freshwater Biology, 55(1), 206-225. doi:10.1111/j.1365-2427.2009.02206.xMontaldo, N., Rondena, R., Albertson, J. D., & Mancini, M. (2005). Parsimonious modeling of vegetation dynamics for ecohydrologic studies of water-limited ecosystems. Water Resources Research, 41(10). doi:10.1029/2005wr004094Mouton, A. M., De Baets, B., & Goethals, P. L. M. (2010). Ecological relevance of performance criteria for species distribution models. Ecological Modelling, 221(16), 1995-2002. doi:10.1016/j.ecolmodel.2010.04.017NAGLER, P. (2004). Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River. Agricultural and Forest Meteorology, 125(1-2), 1-17. doi:10.1016/j.agrformet.2004.03.008Naiman, R. J., Latterell, J. J., Pettit, N. E., & Olden, J. D. (2008). Flow variability and the biophysical vitality of river systems. Comptes Rendus Geoscience, 340(9-10), 629-643. doi:10.1016/j.crte.2008.01.002Naumburg, E., Mata-gonzalez, R., Hunter, R. G., Mclendon, T., & Martin, D. W. (2005). Phreatophytic Vegetation and Groundwater Fluctuations: A Review of Current Research and Application of Ecosystem Response Modeling with an Emphasis on Great Basin Vegetation. Environmental Management, 35(6), 726-740. doi:10.1007/s00267-004-0194-7Neff, K. P., & Baldwin, A. H. (2005). Seed dispersal into wetlands: Techniques and results for a restored tidal freshwater marsh. Wetlands, 25(2), 392-404. doi:10.1672/14PADILLA, F. M., & PUGNAIRE, F. I. (2007). Rooting depth and soil moisture control Mediterranean woody seedling survival during drought. Functional Ecology, 21(3), 489-495. doi:10.1111/j.1365-2435.2007.01267.xPasquato, M., Medici, C., Friend, A. D., & Francés, F. (2014). Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data. Ecohydrology, 8(6), 1024-1036. doi:10.1002/eco.1559Perona, P., Camporeale, C., Perucca, E., Savina, M., Molnar, P., Burlando, P., & Ridolfi, L. (2009). Modelling river and riparian vegetation interactions and related importance for sustainable ecosystem management. Aquatic Sciences, 71(3), 266-278. doi:10.1007/s00027-009-9215-1Perucca, E., Camporeale, C., & Ridolfi, L. (2006). Influence of river meandering dynamics on riparian vegetation pattern formation. Journal of Geophysical Research, 111(G1). doi:10.1029/2005jg000073Polley, H. W., Phillips, R. L., Frank, A. B., Bradford, J. A., Sims, P. L., Morgan, J. A., & Kiniry, J. R. (2010). Variability in Light-Use Efficiency for Gross Primary Productivity on Great Plains Grasslands. Ecosystems, 14(1), 15-27. doi:10.1007/s10021-010-9389-3Porporato, A., Laio, F., Ridolfi, L., & Rodriguez-Iturbe, I. (2001). Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress. Advances in Water Resources, 24(7), 725-744. doi:10.1016/s0309-1708(01)00006-9Quevedo, D. I., & Francés, F. (2008). A conceptual dynamic vegetation-soil model for arid and semiarid zones. Hydrology and Earth System Sciences, 12(5), 1175-1187. doi:10.5194/hess-12-1175-2008Rodriguez-Iturbe, I., Porporato, A., Laio, F., & Ridolfi, L. (2001). Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress. Advances in Water Resources, 24(7), 695-705. doi:10.1016/s0309-1708(01)00004-5Rood, S. B., Braatne, J. H., & Hughes, F. M. R. (2003). Ecophysiology of riparian cottonwoods: stream flow dependency, water relations and restoration. Tree Physiology, 23(16), 1113-1124. doi:10.1093/treephys/23.16.1113Ryel, R., Caldwell, M., Yoder, C., Or, D., & Leffler, A. (2002). Hydraulic redistribution in a stand of Artemisia tridentata: evaluation of benefits to transpiration assessed with a simulation model. Oecologia, 130(2), 173-184. doi:10.1007/s004420100794Schenk, H. J., & Jackson, R. B. (2002). Rooting depths, lateral root spreads and below-ground/above-ground allometries of plants in water-limited ecosystems. Journal of Ecology, 90(3), 480-494. doi:10.1046/j.1365-2745.2002.00682.xScott RL Goodrich DC Levick LR 2003 A GIS-based management tool to quantify riparian vegetation groundwater use 222 227Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., … Venevsky, S. (2003). Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9(2), 161-185. doi:10.1046/j.1365-2486.2003.00569.xSoetaert, K., Hoffmann, M., Meire, P., Starink, M., Oevelen, D. van, Regenmortel, S. V., & Cox, T. (2004). Modeling growth and carbon allocation in two reed beds (Phragmites australis) in the Scheldt estuary. Aquatic Botany, 79(3), 211-234. doi:10.1016/j.aquabot.2004.02.001Stevens LE Waring LG 1985 The effects of prolonged flooding on the riparian plant community in Grand CanyonTabacchi, E., Correll, D. L., Hauer, R., Pinay, G., Planty‐Tabacchi, A., & Wissmar, R. C. (1998). Development, maintenance and role of riparian vegetation in the river landscape. Freshwater Biology, 40(3), 497-516. doi:10.1046/j.1365-2427.1998.00381.xTabacchi, E., Planty-Tabacchi, A.-M., Roques, L., & Nadal, E. (2005). Seed inputs in riparian zones: implications for plant invasion. River Research and Applications, 21(2-3), 299-313. doi:10.1002/rra.848TURNER, D. P., URBANSKI, S., BREMER, D., WOFSY, S. C., MEYERS, T., GOWER, S. T., & GREGORY, M. (2003). A cross-biome comparison of daily light use efficiency for gross primary production. Global Change Biology, 9(3), 383-395. doi:10.1046/j.1365-2486.2003.00573.xWade, A. J., Hornberger, G. M., Whitehead, P. G., Jarvie, H. P., & Flynn, N. (2001). On modeling the mechanisms that control in-stream phosphorus, macrophyte, and epiphyte dynamics: An assessment of a new model using general sensitivity analysis. Water Resources Research, 37(11), 2777-2792. doi:10.1029/2000wr000115Webb, R. H., & Leake, S. A. (2006). Ground-water surface-water interactions and long-term change in riverine riparian vegetation in the southwestern United States. Journal of Hydrology, 320(3-4), 302-323. doi:10.1016/j.jhydrol.2005.07.022Woodward, F. I., & Cramer, W. (1996). Plant functional types and climatic change: Introduction. Journal of Vegetation Science, 7(3), 306-308. doi:10.1111/j.1654-1103.1996.tb00489.xYe, F., Chen, Q., Blanckaert, K., & Ma, J. (2012). Riparian vegetation dynamics: insight provided by a process-based model, a statistical model and field data. Ecohydrology, 6(4), 567-585. doi:10.1002/eco.1348Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D., … Wofsy, S. C. (2007). Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agricultural and Forest Meteorology, 143(3-4), 189-207. doi:10.1016/j.agrformet.2006.12.001Zheng, Z., & Wang, G. (2007). Modeling the dynamic root water uptake and its hydrological impact at the Reserva Jaru site in Amazonia. Journal of Geophysical Research: Biogeosciences, 112(G4), n/a-n/a. doi:10.1029/2007jg00041

Intangible resources of competitive advantage: Analysis of 49 Asian airlines across three business models

Without sustainable competitive advantage firms have limited economic reasons to exist and will decline. Competitive advantage concerns the factors which provide competitive strength. This paper is based upon the resource-based view which considers firm resources to be heterogeneous and which believes that firms only have a small bundle of core resources irrespective of their overall performance. This research establishes the role of 36 intangible resources for 49 Asian airlines across three business models: network airlines; low-cost subsidiaries from network airlines; and low-cost carriers. It uses the VRIN framework which examines whether resources are valuable, rare, inimitable, and non-substitutable. Research participants distribute points between their chosen seven resources according to their perceived role in firm performance. Resources which meet all four requirements of VRIN are considered core competences and sources of sustained advantage. Across all 49 Asian airlines, the top-three most important resources of advantage are slots, brand, and product/service reputation. While these core resources are predictable, they have not previously been proven within the context of airlines, let alone geographically and by airline model. Results show that the core bundle of resources vary for each model, which helps to explain the difference in performance across models, and that some resources which were expected to be high-ranking, such as organisational culture and customer focus, were not.Full Tex