2D, 3D and 4D active compound delivery in tissue engineering and regenerative medicine

Recent advances in tissue engineering and regenerative medicine have shown that controlling cells microenvironment during growth is a key element to the development of successful therapeutic system. To achieve such control, researchers have first proposed the use of polymeric scaffolds that were able to support cellular growth and, to a certain extent, favor cell organization and tissue structure. With nowadays availability of a large pool of stem cell lines, such approach has appeared to be rather limited since it does not offer the fine control of the cell micro-environment in space and time (4D). Therefore, researchers are currently focusing their efforts on developing strategies that include active compound delivery systems in order to add a fourth dimension to the design of 3D scaffolds. This review will focus on recent concepts and applications of 2D and 3D techniques that have been used to control the load and release of active compounds used to promote cell differentiation and proliferation in or out of a scaffold. We will first present recent advances in the design of 2D polymeric scaffolds and the different techniques that have been used to deposit molecular cues and cells in a controlled fashion. We will continue presenting the recent advances made in the design of 3D scaffolds based on hydrogels as well as polymeric fibers and we will finish by presenting some of the research avenues that are still to be explored.

Replicated nil associations of digit ratio (2D:4D) and absolute finger lengths with implicit and explicit measures of aggression. 'Replicaci??n de asociaciones nulas del ratio del segundo y cuarto dedo (2D:4D) y la longitud absoluta de los dedos con medidas de agresi??n impl??citas y expl??citas'

Resumen tomado de la publicaci??n

Thermal expansion of deuterated hopeite, Zn-3(PO4)(2)center dot 4D(2)O

The lattice parameters extracted from Lebail analysis of neutron powder diffraction data collected between 2 and 300 K have been used to calculate the temperature evolution of the thermal expansion tensor for hopeite, Zn-3(PO4)(2)center dot 2H(2)O, Pnma,Z=4with a= 10.6065(4) angstrom, b = 18.2977(4) angstrom, c= 5.0257(2) A at 275 K. The a lattice parameter shows a negative thermal expansion, the b lattice parameter appears to saturate at 275 K while the c lattice parameter has a more typical positive thermal expansion. At 275 K, the magnitudes of the thermal expansion coefficients are alpha(a) = -1. 1(4) x 10(-5) K-1, alpha(b) = 2.4(9) x 10(-6) K-1 and alpha(c) = 3.6(2) x 10(-1) K-1. Under the conditions of these experiments, hopeite begins to dehydrate to the dihydrate between 300 and 325 K, and between 480 and 500 K the monohydrate is formed. The thermal expansion of the dihydrate has been calculated between 335 and 480 and at 480 K the magnitudes of the thermal expansion coefficients are alpha(a) = 1(2) x 10(-5) K-1, alpha(b) = 4(l) x 10(-6) K-1, alpha(c) = 4(2) x 10(-5) K-1, alpha(beta) = 1 (1) x 10(-1) K-1, and alpha(v) = 2(2) x 10(-1) K-1. The thermal expansion of hopeite is described in terms of its crystal structure and possible dehydration mechanisms for the alpha and beta modifications of hopeite are discussed.

A singular vector perspective of 4D-Var: Filtering and interpolation

Four-dimensional variational data assimilation (4D-Var) combines the information from a time sequence of observations with the model dynamics and a background state to produce an analysis. In this paper, a new mathematical insight into the behaviour of 4D-Var is gained from an extension of concepts that are used to assess the qualitative information content of observations in satellite retrievals. It is shown that the 4D-Var analysis increments can be written as a linear combination of the singular vectors of a matrix which is a function of both the observational and the forecast model systems. This formulation is used to consider the filtering and interpolating aspects of 4D-Var using idealized case-studies based on a simple model of baroclinic instability. The results of the 4D-Var case-studies exhibit the reconstruction of the state in unobserved regions as a consequence of the interpolation of observations through time. The results also exhibit the filtering of components with small spatial scales that correspond to noise, and the filtering of structures in unobserved regions. The singular vector perspective gives a very clear view of this filtering and interpolating by the 4D-Var algorithm and shows that the appropriate specification of the a priori statistics is vital to extract the largest possible amount of useful information from the observations. Copyright © 2005 Royal Meteorological Society

Can 4D-Var use dynamical information from targeted observations of a baroclinic structure?

Targeted observations are generally taken in regions of high baroclinicity, but often show little impact. One plausible explanation is that important dynamical information, such as upshear tilt, is not extracted from the targeted observations by the data assimilation scheme and used to correct initial condition error. This is investigated by generating pseudo targeted observations which contain a singular vector (SV) structure that is not present in the background field or routine observations, i.e. assuming that the background has an initial condition error with tilted growing structure. Experiments were performed for a single case-study with varying numbers of pseudo targeted observations. These were assimilated by the Met Office four-dimensional variational (4D-Var) data assimilation scheme, which uses a 6 h window for observations and background-error covariances calculated using the National Meteorological Centre (NMC) method. The forecasts were run using the operational Met Office Unified Model on a 24 km grid. The results presented clearly demonstrate that a 6 h window 4D-Var system is capable of extracting baroclinic information from a limited set of observations and using it to correct initial condition error. To capture the SV structure well (projection of 0.72 in total energy), 50 sondes over an area of 1×106 km2 were required. When the SV was represented by only eight sondes along an example targeting flight track covering a smaller area, the projection onto the SV structure was lower; the resulting forecast perturbations showed an SV structure with increased tilt and reduced initial energy. The total energy contained in the perturbations decreased as the SV structure was less well described by the set of observations (i.e. as fewer pseudo observations were assimilated). The assimilated perturbation had lower energy than the SV unless the pseudo observations were assimilated with the dropsonde observation errors halved from operational values. Copyright © 2010 Royal Meteorological Society

Quasi-optimal assimilation of satellite retrievals for incremental 4D-Var

An interface between satellite retrievals and the incremental version of the four-dimensional variational assimilation scheme is developed, making full use of the information content of satellite measurements. In this paper, expressions for the function that calculates simulated observations from model states (called “observation operator”), together with its tangent linear version and adjoint, are derived. Results from our work can be used for implementing a quasi-optimal assimilation of satellite retrievals (e.g., of atmospheric trace gases) in operational meteorological centres.

Model-based groupware solution for distributed real-time collaborative 4D planning via teamwork

Construction planning plays a fundamental role in construction project management that requires team working among planners from a diverse range of disciplines and in geographically dispersed working situations. Model-based four-dimensional (4D) computer-aided design (CAD) groupware, though considered a possible approach to supporting collaborative planning, is still short of effective collaborative mechanisms for teamwork due to methodological, technological and social challenges. Targeting this problem, this paper proposes a model-based groupware solution to enable a group of multidisciplinary planners to perform real-time collaborative 4D planning across the Internet. In the light of the interactive definition method, and its computer-supported collaborative work (CSCW) design analysis, the paper discusses the realization of interactive collaborative mechanisms from software architecture, application mode, and data exchange protocol. These mechanisms have been integrated into a groupware solution, which was validated by a planning team in a truly geographically dispersed condition. Analysis of the validation results revealed that the proposed solution is feasible for real-time collaborative 4D planning to gain a robust construction plan through collaborative teamwork. The realization of this solution triggers further considerations about its enhancement for wider groupware applications.

Gaussian quadrature 4D-VAR

A new incremental four-dimensional variational (4D-Var) data assimilation algorithm is introduced. The algorithm does not require the computationally expensive integrations with the nonlinear model in the outer loops. Nonlinearity is accounted for by modifying the linearization trajectory of the observation operator based on integrations with the tangent linear (TL) model. This allows us to update the linearization trajectory of the observation operator in the inner loops at negligible computational cost. As a result the distinction between inner and outer loops is no longer necessary. The key idea on which the proposed 4D-Var method is based is that by using Gaussian quadrature it is possible to get an exact correspondence between the nonlinear time evolution of perturbations and the time evolution in the TL model. It is shown that J-point Gaussian quadrature can be used to derive the exact adjoint-based observation impact equations and furthermore that it is straightforward to account for the effect of multiple outer loops in these equations if the proposed 4D-Var method is used. The method is illustrated using a three-level quasi-geostrophic model and the Lorenz (1996) model.

Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model

Operational forecasting centres are currently developing data assimilation systems for coupled atmosphere-ocean models. Strongly coupled assimilation, in which a single assimilation system is applied to a coupled model, presents significant technical and scientific challenges. Hence weakly coupled assimilation systems are being developed as a first step, in which the coupled model is used to compare the current state estimate with observations, but corrections to the atmosphere and ocean initial conditions are then calculated independently. In this paper we provide a comprehensive description of the different coupled assimilation methodologies in the context of four dimensional variational assimilation (4D-Var) and use an idealised framework to assess the expected benefits of moving towards coupled data assimilation. We implement an incremental 4D-Var system within an idealised single column atmosphere-ocean model. The system has the capability to run both strongly and weakly coupled assimilations as well as uncoupled atmosphere or ocean only assimilations, thus allowing a systematic comparison of the different strategies for treating the coupled data assimilation problem. We present results from a series of identical twin experiments devised to investigate the behaviour and sensitivities of the different approaches. Overall, our study demonstrates the potential benefits that may be expected from coupled data assimilation. When compared to uncoupled initialisation, coupled assimilation is able to produce more balanced initial analysis fields, thus reducing initialisation shock and its impact on the subsequent forecast. Single observation experiments demonstrate how coupled assimilation systems are able to pass information between the atmosphere and ocean and therefore use near-surface data to greater effect. We show that much of this benefit may also be gained from a weakly coupled assimilation system, but that this can be sensitive to the parameters used in the assimilation.

An idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error

Atmosphere only and ocean only variational data assimilation (DA) schemes are able to use window lengths that are optimal for the error growth rate, non-linearity and observation density of the respective systems. Typical window lengths are 6-12 hours for the atmosphere and 2-10 days for the ocean. However, in the implementation of coupled DA schemes it has been necessary to match the window length of the ocean to that of the atmosphere, which may potentially sacrifice the accuracy of the ocean analysis in order to provide a more balanced coupled state. This paper investigates how extending the window length in the presence of model error affects both the analysis of the coupled state and the initialized forecast when using coupled DA with differing degrees of coupling. Results are illustrated using an idealized single column model of the coupled atmosphere-ocean system. It is found that the analysis error from an uncoupled DA scheme can be smaller than that from a coupled analysis at the initial time, due to faster error growth in the coupled system. However, this does not necessarily lead to a more accurate forecast due to imbalances in the coupled state. Instead coupled DA is more able to update the initial state to reduce the impact of the model error on the accuracy of the forecast. The effect of model error is potentially most detrimental in the weakly coupled formulation due to the inconsistency between the coupled model used in the outer loop and uncoupled models used in the inner loop.