A Thomason model structure on the category of small n-fold categories

We construct a cofibrantly generated Thomason model structure on the category of small n-fold categories and prove that it is Quillen equivalent to the standard model structure on the category of simplicial sets. An n-fold functor is a weak equivalence if and only if the diagonal of its n-fold nerve is a weak equivalence of simplicial sets. We introduce an n-fold Grothendieck construction for multisimplicial sets, and prove that it is a homotopy inverse to the n-fold nerve. As a consequence, the unit and counit of the adjunction between simplicial sets and n-fold categories are natural weak equivalences.

A model structure for coloured operads in symmetric spectra

We describe a model structure for coloured operads with values in the category of symmetric spectra (with the positive model structure), in which fibrations and weak equivalences are defined at the level of the underlying collections. This allows us to treat R-module spectra (where R is a cofibrant ring spectrum) as algebras over a cofibrant spectrum-valued operad with R as its first term. Using this model structure, we give sufficient conditions for homotopical localizations in the category of symmetric spectra to preserve module structures.

Model structure of human APOBEC3G.

BACKGROUND: APOBEC3G (apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like 3G) has antiretroviral activity associated with the hypermutation of viral DNA through cytosine deamination. APOBEC3G has two cytosine deaminase (CDA) domains; the catalytically inactive amino-terminal domain of APOBEC3G (N-CDA) carries the Vif interaction domain. There is no 3-D structure of APOBEC3G solved by X-ray or nuclear magnetic resonance. METHODOLOGY/PRINCIPAL FINDINGS: We predicted the structure of human APOBEC3G based on the crystal structure of APOBEC2. To assess the model structure, we evaluated 48 mutants of APOBEC3G N-CDA that identify novel variants altering DeltaVif HIV-1 infectivity and packaging of APOBEC3G. Results indicated that the key residue D128 is exposed at the surface of the model, with a negative local electrostatic potential. Mutation D128K changes the sign of that local potential. In addition, two novel functionally relevant residues that result in defective APOBEC3G encapsidation, R122 and W127, cluster at the surface. CONCLUSIONS/SIGNIFICANCE: The structure model identifies a cluster of residues important for packaging of APOBEC3G into virions, and may serve to guide functional analysis of APOBEC3G.

A semi-quantitative approach to deriving a model structure for the uptake of organic chemicals by vegetation

An expert elicitation exercise was undertaken to determine those components and processes that are most important for modeling plant uptake of organic chemicals. The state of our knowledge of these processes was also assessed. This semi-quantitative analysis allowed the construction of an idealized model with seven compartments; soil bulk, soil water, roots, stem, leaves, fruit, and air. Three main areas were identified further research: 1) the uptake of organic chemicals by fruit; 2) the internal transfer of organic chemicals between plant structures (e.g., stem and leaves); and 3) the transfer via the soil-air-plant pathway. Until new data becomes available to quantify these processes, it is proposed that an equilibrium partitioning approach is used between plant components other than fruit or that models consist of both an edible and inedible compartment.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

A semi-distributed ntegrated itrogen model for multiple source assessment in tchments (INCA): Part I — model structure and process equations

A new model has been developed for assessing multiple sources of nitrogen in catchments. The model (INCA) is process based and uses reaction kinetic equations to simulate the principal mechanisms operating. The model allows for plant uptake, surface and sub-surface pathways and can simulate up to six land uses simultaneously. The model can be applied to catchment as a semi-distributed simulation and has an inbuilt multi-reach structure for river systems. Sources of nitrogen can be from atmospheric deposition, from the terrestrial environment (e.g. agriculture, leakage from forest systems etc.), from urban areas or from direct discharges via sewage or intensive farm units. The model is a daily simulation model and can provide information in the form of time series at key sites, or as profiles down river systems or as statistical distributions. The process model is described and in a companion paper the model is applied to the River Tywi catchment in South Wales and the Great Ouse in Bedfordshire.

A model category structure on the category of simplicial multicategories

We establish a Quillen model structure on simplicial(symmetric) multicategories. It extends the model structure on simplicial categories due to J. Bergner [2]. We observe that our technique of proof enables us to prove a similar result for (symmetric) multicategories enriched over other monoidal model categories than simplicial sets. Examples include small categories, simplicial abelian groups and compactly generated Hausdorff spaces.

Calibration of structure in a distributed forecasting model for a semiarid flash flood: dynamic surface storage and channel roughness

Flash floods pose a significant danger for life and property. Unfortunately, in arid and semiarid environment the runoff generation shows a complex non-linear behavior with a strong spatial and temporal non-uniformity. As a result, the predictions made by physically-based simulations in semiarid areas are subject to great uncertainty, and a failure in the predictive behavior of existing models is common. Thus better descriptions of physical processes at the watershed scale need to be incorporated into the hydrological model structures. For example, terrain relief has been systematically considered static in flood modelling at the watershed scale. Here, we show that the integrated effect of small distributed relief variations originated through concurrent hydrological processes within a storm event was significant on the watershed scale hydrograph. We model these observations by introducing dynamic formulations of two relief-related parameters at diverse scales: maximum depression storage, and roughness coefficient in channels. In the final (a posteriori) model structure these parameters are allowed to be both time-constant or time-varying. The case under study is a convective storm in a semiarid Mediterranean watershed with ephemeral channels and high agricultural pressures (the Rambla del Albujón watershed; 556 km 2 ), which showed a complex multi-peak response. First, to obtain quasi-sensible simulations in the (a priori) model with time-constant relief-related parameters, a spatially distributed parameterization was strictly required. Second, a generalized likelihood uncertainty estimation (GLUE) inference applied to the improved model structure, and conditioned to observed nested hydrographs, showed that accounting for dynamic relief-related parameters led to improved simulations. The discussion is finally broadened by considering the use of the calibrated model both to analyze the sensitivity of the watershed to storm motion and to attempt the flood forecasting of a stratiform event with highly different behavior.

Development of a generic crop model template in the cropping system model APSIM

The Agricultural Production Systems slMulator, APSIM, is a cropping system modelling environment that simulates the dynamics of soil-plant-management interactions within a single crop or a cropping system. Adaptation of previously developed crop models has resulted in multiple crop modules in APSIM, which have low scientific transparency and code efficiency. A generic crop model template (GCROP) has been developed to capture unifying physiological principles across crops (plant types) and to provide modular and efficient code for crop modelling. It comprises a standard crop interface to the APSIM engine, a generic crop model structure, a crop process library, and well-structured crop parameter files. The process library contains the major science underpinning the crop models and incorporates generic routines based on physiological principles for growth and development processes that are common across crops. It allows APSIM to simulate different crops using the same set of computer code. The generic model structure and parameter files provide an easy way to test, modify, exchange and compare modelling approaches at process level without necessitating changes in the code. The standard interface generalises the model inputs and outputs, and utilises a standard protocol to communicate with other APSIM modules through the APSIM engine. The crop template serves as a convenient means to test new insights and compare approaches to component modelling, while maintaining a focus on predictive capability. This paper describes and discusses the scientific basis, the design, implementation and future development of the crop template in APSIM. On this basis, we argue that the combination of good software engineering with sound crop science can enhance the rate of advance in crop modelling. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Structural characterization of respiratory syncytial virus fusion inhibitor escape mutants: homology model of the F protein and a syncytium formation assay

Respiratory syncytial virus (RSV) is a ubiquitous human pathogen and the leading cause of lower respiratory tract infections in infants. Infection of cells and subsequent formation of syncytia occur through membrane fusion mediated by the RSV fusion protein (RSV-F). A novel in vitro assay of recombinant RSV-F function has been devised and used to characterize a number of escape mutants for three known inhibitors of RSV-F that have been isolated. Homology modeling of the RSV-F structure has been carried out on the basis of a chimera derived from the crystal structures of the RSV-F core and a fragment from the orthologous fusion protein from Newcastle disease virus (NDV). The structure correlates well with the appearance of RSV-F in electron micrographs, and the residues identified as contributing to specific binding sites for several monoclonal antibodies are arranged in appropriate solvent-accessible clusters. The positions of the characterized resistance mutants in the model structure identify two promising regions for the design of fusion inhibitors. (C) 2003 Elsevier Science (USA). All rights reserved.

Model structures on the category of small double categories

In this paper we obtain several model structures on DblCat, the category of small double categories. Our model structures have three sources. We first transfer across a categorification-nerve adjunction. Secondly, we view double categories as internal categories in Cat and take as our weak equivalences various internal equivalences defined via Grothendieck topologies. Thirdly, DblCat inherits a model structure as a category of algebras over a 2-monad. Some of these model structures coincide and the different points of view give us further results about cofibrant replacements and cofi brant objects. As part of this program we give explicit descriptions and discuss properties of free double categories, quotient double categories, colimits of double categories, and several nerves and categorifications.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Comprehensive evaluation of the WaterGAP3 model across climatic, physiographic, and anthropogenic gradients

In den letzten Jahrzehnten haben sich makroskalige hydrologische Modelle als wichtige Werkzeuge etabliert um den Zustand der globalen erneuerbaren Süßwasserressourcen flächendeckend bewerten können. Sie werden heutzutage eingesetzt um eine große Bandbreite wissenschaftlicher Fragestellungen zu beantworten, insbesondere hinsichtlich der Auswirkungen anthropogener Einflüsse auf das natürliche Abflussregime oder der Auswirkungen des globalen Wandels und Klimawandels auf die Ressource Wasser. Diese Auswirkungen lassen sich durch verschiedenste wasserbezogene Kenngrößen abschätzen, wie z.B. erneuerbare (Grund-)Wasserressourcen, Hochwasserrisiko, Dürren, Wasserstress und Wasserknappheit. Die Weiterentwicklung makroskaliger hydrologischer Modelle wurde insbesondere durch stetig steigende Rechenkapazitäten begünstigt, aber auch durch die zunehmende Verfügbarkeit von Fernerkundungsdaten und abgeleiteten Datenprodukten, die genutzt werden können, um die Modelle anzutreiben und zu verbessern. Wie alle makro- bis globalskaligen Modellierungsansätze unterliegen makroskalige hydrologische Simulationen erheblichen Unsicherheiten, die (i) auf räumliche Eingabedatensätze, wie z.B. meteorologische Größen oder Landoberflächenparameter, und (ii) im Besonderen auf die (oftmals) vereinfachte Abbildung physikalischer Prozesse im Modell zurückzuführen sind. Angesichts dieser Unsicherheiten ist es unabdingbar, die tatsächliche Anwendbarkeit und Prognosefähigkeit der Modelle unter diversen klimatischen und physiographischen Bedingungen zu überprüfen. Bisher wurden die meisten Evaluierungsstudien jedoch lediglich in wenigen, großen Flusseinzugsgebieten durchgeführt oder fokussierten auf kontinentalen Wasserflüssen. Dies steht im Kontrast zu vielen Anwendungsstudien, deren Analysen und Aussagen auf simulierten Zustandsgrößen und Flüssen in deutlich feinerer räumlicher Auflösung (Gridzelle) basieren. Den Kern der Dissertation bildet eine umfangreiche Evaluierung der generellen Anwendbarkeit des globalen hydrologischen Modells WaterGAP3 für die Simulation von monatlichen Abflussregimen und Niedrig- und Hochwasserabflüssen auf Basis von mehr als 2400 Durchflussmessreihen für den Zeitraum 1958-2010. Die betrachteten Flusseinzugsgebiete repräsentieren ein breites Spektrum klimatischer und physiographischer Bedingungen, die Einzugsgebietsgröße reicht von 3000 bis zu mehreren Millionen Quadratkilometern. Die Modellevaluierung hat dabei zwei Zielsetzungen: Erstens soll die erzielte Modellgüte als Bezugswert dienen gegen den jegliche weiteren Modellverbesserungen verglichen werden können. Zweitens soll eine Methode zur diagnostischen Modellevaluierung entwickelt und getestet werden, die eindeutige Ansatzpunkte zur Modellverbesserung aufzeigen soll, falls die Modellgüte unzureichend ist. Hierzu werden komplementäre Modellgütemaße mit neun Gebietsparametern verknüpft, welche die klimatischen und physiographischen Bedingungen sowie den Grad anthropogener Beeinflussung in den einzelnen Einzugsgebieten quantifizieren. WaterGAP3 erzielt eine mittlere bis hohe Modellgüte für die Simulation von sowohl monatlichen Abflussregimen als auch Niedrig- und Hochwasserabflüssen, jedoch sind für alle betrachteten Modellgütemaße deutliche räumliche Muster erkennbar. Von den neun betrachteten Gebietseigenschaften weisen insbesondere der Ariditätsgrad und die mittlere Gebietsneigung einen starken Einfluss auf die Modellgüte auf. Das Modell tendiert zur Überschätzung des jährlichen Abflussvolumens mit steigender Aridität. Dieses Verhalten ist charakteristisch für makroskalige hydrologische Modelle und ist auf die unzureichende Abbildung von Prozessen der Abflussbildung und –konzentration in wasserlimitierten Gebieten zurückzuführen. In steilen Einzugsgebieten wird eine geringe Modellgüte hinsichtlich der Abbildung von monatlicher Abflussvariabilität und zeitlicher Dynamik festgestellt, die sich auch in der Güte der Niedrig- und Hochwassersimulation widerspiegelt. Diese Beobachtung weist auf notwendige Modellverbesserungen in Bezug auf (i) die Aufteilung des Gesamtabflusses in schnelle und verzögerte Abflusskomponente und (ii) die Berechnung der Fließgeschwindigkeit im Gerinne hin. Die im Rahmen der Dissertation entwickelte Methode zur diagnostischen Modellevaluierung durch Verknüpfung von komplementären Modellgütemaßen und Einzugsgebietseigenschaften wurde exemplarisch am Beispiel des WaterGAP3 Modells erprobt. Die Methode hat sich als effizientes Werkzeug erwiesen, um räumliche Muster in der Modellgüte zu erklären und Defizite in der Modellstruktur zu identifizieren. Die entwickelte Methode ist generell für jedes hydrologische Modell anwendbar. Sie ist jedoch insbesondere für makroskalige Modelle und multi-basin Studien relevant, da sie das Fehlen von feldspezifischen Kenntnissen und gezielten Messkampagnen, auf die üblicherweise in der Einzugsgebietsmodellierung zurückgegriffen wird, teilweise ausgleichen kann.

Calibration and sensitivity analysis of a river water quality model under unsteady flow conditions

Water quality models generally require a relatively large number of parameters to define their functional relationships, and since prior information on parameter values is limited, these are commonly defined by fitting the model to observed data. In this paper, the identifiability of water quality parameters and the associated uncertainty in model simulations are investigated. A modification to the water quality model `Quality Simulation Along River Systems' is presented in which an improved flow component is used within the existing water quality model framework. The performance of the model is evaluated in an application to the Bedford Ouse river, UK, using a Monte-Carlo analysis toolbox. The essential framework of the model proved to be sound, and calibration and validation performance was generally good. However some supposedly important water quality parameters associated with algal activity were found to be completely insensitive, and hence non-identifiable, within the model structure, while others (nitrification and sedimentation) had optimum values at or close to zero, indicating that those processes were not detectable from the data set examined. (C) 2003 Elsevier Science B.V. All rights reserved.