Charge redistribution at Pd surfaces: ab initio grounds for tight-binding interatomic potentials

A simplified tight-binding description of the electronic structure is often necessary for complex studies of surfaces of transition metal compounds. This requires a self-consistent parametrization of the charge redistribution, which is not obvious for late transition series elements (such as Pd, Cu, Au), for which not only d but also s-p electrons have to be taken into account. We show here, with the help of an ab initio FP-LMTO approach, that for these elements the electronic charge is unchanged from bulk to the surface, not only per site but also per orbital. This implies different level shifts for each orbital in order to achieve this orbital neutrality rule. Our results invalidate any neutrality rule which would allow charge redistribution between orbitals to ensure a common rigid shift for all of them. Moreover, in the case of Pd, the power law which governs the variation of band energy with respect to coordination number, is found to differ significantly from the usual tight-binding square root.Comment: 6 pages, 2 figures, Latex; Phys.Rev. B 56 (1997

Agricultural land-use change and ash (Fraxinus excelsior L.) colonization in Pyrenean landscapes: an interdisciplinary case study

 ONLINE FIRSTInternational audienceChanges in agricultural land use are responsible for significant modifications in mountain landscapes. This study is part of an interdisciplinary research on the processes and consequences of spontaneous afforestation of Pyrenean landscapes by ash, and the possibilities for its management. We address the relationships between vegetation dynamics and land-use change from the combination of an agricultural study of change in farm management and an ecological study of grassland colonization by ash. In the framework of a village case study, we characterized parcels management and land-use histories, and analyzed the dynamics of the composition of grassland vegetation communities. From a joint analysis of the results obtained in each discipline, we discuss the limitations and comple-mentarities of the two approaches for the interdisciplinary assessment of the afforestation process

The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths

We present the Planck Sky Model (PSM), a parametric model for the generation of all-sky, few arcminute resolution maps of sky emission at submillimetre to centimetre wavelengths, in both intensity and polarisation. Several options are implemented to model the cosmic microwave background, Galactic diffuse emission (synchrotron, free-free, thermal and spinning dust, CO lines), Galactic H-II regions, extragalactic radio sources, dusty galaxies, and thermal and kinetic Sunyaev-Zeldovich signals from clusters of galaxies. Each component is simulated by means of educated interpolations/extrapolations of data sets available at the time of the launch of the Planck mission, complemented by state-of-the-art models of the emission. Distinctive features of the simulations are: spatially varying spectral properties of synchrotron and dust; different spectral parameters for each point source; modeling of the clustering properties of extragalactic sources and of the power spectrum of fluctuations in the cosmic infrared background. The PSM enables the production of random realizations of the sky emission, constrained to match observational data within their uncertainties, and is implemented in a software package that is regularly updated with incoming information from observations. The model is expected to serve as a useful tool for optimizing planned microwave and sub-millimetre surveys and to test data processing and analysis pipelines. It is, in particular, used for the development and validation of data analysis pipelines within the planck collaboration. A version of the software that can be used for simulating the observations for a variety of experiments is made available on a dedicated website.Comment: 35 pages, 31 figure

Wild-type ALK and activating ALK-R1275Q and ALK-F1174L mutations upregulate Myc and initiate tumor formation in murine neural crest progenitor cells.

The anaplastic lymphoma kinase (ALK) gene is overexpressed, mutated or amplified in most neuroblastoma (NB), a pediatric neural crest-derived embryonal tumor. The two most frequent mutations, ALK-F1174L and ALK-R1275Q, contribute to NB tumorigenesis in mouse models, and cooperate with MYCN in the oncogenic process. However, the precise role of activating ALK mutations or ALK-wt overexpression in NB tumor initiation needs further clarification. Human ALK-wt, ALK-F1174L, or ALK-R1275Q were stably expressed in murine neural crest progenitor cells (NCPC), MONC-1 or JoMa1, immortalized with v-Myc or Tamoxifen-inducible Myc-ERT, respectively. While orthotopic implantations of MONC-1 parental cells in nude mice generated various tumor types, such as NB, osteo/chondrosarcoma, and undifferentiated tumors, due to v-Myc oncogenic activity, MONC-1-ALK-F1174L cells only produced undifferentiated tumors. Furthermore, our data represent the first demonstration of ALK-wt transforming capacity, as ALK-wt expression in JoMa1 cells, likewise ALK-F1174L, or ALK-R1275Q, in absence of exogenous Myc-ERT activity, was sufficient to induce the formation of aggressive and undifferentiated neural crest cell-derived tumors, but not to drive NB development. Interestingly, JoMa1-ALK tumors and their derived cell lines upregulated Myc endogenous expression, resulting from ALK activation, and both ALK and Myc activities were necessary to confer tumorigenic properties on tumor-derived JoMa1 cells in vitro

Effects of anharmonic strain on phase stability of epitaxial films and superlattices: applications to noble metals

Epitaxial strain energies of epitaxial films and bulk superlattices are studied via first-principles total energy calculations using the local-density approximation. Anharmonic effects due to large lattice mismatch, beyond the reach of the harmonic elasticity theory, are found to be very important in Cu/Au (lattice mismatch 12%), Cu/Ag (12%) and Ni/Au (15%). We find that is the elastically soft direction for biaxial expansion of Cu and Ni, but it is for large biaxial compression of Cu, Ag, and Au. The stability of superlattices is discussed in terms of the coherency strain and interfacial energies. We find that in phase-separating systems such as Cu-Ag the superlattice formation energies decrease with superlattice period, and the interfacial energy is positive. Superlattices are formed easiest on (001) and hardest on (111) substrates. For ordering systems, such as Cu-Au and Ag-Au, the formation energy of superlattices increases with period, and interfacial energies are negative. These superlattices are formed easiest on (001) or (110) and hardest on (111) substrates. For Ni-Au we find a hybrid behavior: superlattices along and like in phase-separating systems, while for they behave like in ordering systems. Finally, recent experimental results on epitaxial stabilization of disordered Ni-Au and Cu-Ag alloys, immiscible in the bulk form, are explained in terms of destabilization of the phase separated state due to lattice mismatch between the substrate and constituents.Comment: RevTeX galley format, 16 pages, includes 9 EPS figures, to appear in Physical Review

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

Natural reforestation of European mountain landscapes raises major environmental and societal issues. With local stakeholders in the Pyrenees National Park area (France), we studied agricultural landscape colonisation by ash (Fraxinus excelsior) to enlighten its impacts on biodiversity and other landscape functions of importance for the valley socio-economics. The study comprised an integrated assessment of land-use and land-cover change (LUCC) since the 1950s, and a scenario analysis of alternative future policy. We combined knowledge and methods from landscape ecology, land change and agricultural sciences, and a set of coordinated field studies to capture interactions and feedback in the local landscape/land-use system. Our results elicited the hierarchically-nested relationships between social and ecological processes. Agricultural change played a preeminent role in the spatial and temporal patterns of LUCC. Landscape colonisation by ash at the parcel level of organisation was merely controlled by grassland management, and in fact depended on the farmer's land management at the whole-farm level. LUCC patterns at the landscape level depended to a great extent on interactions between farm household behaviours and the spatial arrangement of landholdings within the landscape mosaic. Our results stressed the need to represent the local SES function at a fine scale to adequately capture scenarios of change in landscape functions. These findings orientated our modelling choices in the building an agent-based model for LUCC simulation (SMASH - Spatialized Multi-Agent System of landscape colonization by ASH). We discuss our method and results with reference to topical issues in interdisciplinary research into the sustainability of multifunctional landscapes

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics