Review of current GPS methodologies for producing accurate time series and their error sources

The Global Positioning System (GPS) is an important tool to observe and model geodynamic processes such as plate tectonics and post-glacial rebound. In the last three decades, GPS has seen tremendous advances in the precision of the measurements, which allow researchers to study geophysical signals through a careful analysis of daily time series of GPS receiver coordinates. However, the GPS observations contain errors and the time series can be described as the sum of a real signal and noise. The signal itself can again be divided into station displacements due to geophysical causes and to disturbing factors. Examples of the latter are errors in the realization and stability of the reference frame and corrections due to ionospheric and tropospheric delays and GPS satellite orbit errors. There is an increasing demand on detecting millimeter to sub-millimeter level ground displacement signals in order to further understand regional scale geodetic phenomena hence requiring further improvements in the sensitivity of the GPS solutions. This paper provides a review spanning over 25 years of advances in processing strategies, error mitigation methods and noise modeling for the processing and analysis of GPS daily position time series. The processing of the observations is described step-by-step and mainly with three different strategies in order to explain the weaknesses and strengths of the existing methodologies. In particular, we focus on the choice of the stochastic model in the GPS time series, which directly affects the estimation of the functional model including, for example, tectonic rates, seasonal signals and co-seismic offsets. Moreover, the geodetic community continues to develop computational methods to fully automatize all phases from analysis of GPS time series. This idea is greatly motivated by the large number of GPS receivers installed around the world for diverse applications ranging from surveying small deformations of civil engineering structures (e.g., subsidence of the highway bridge) to the detection of particular geophysical signals. (C) 2017 Elsevier Ltd. All rights reserved.

Does eye tremor provide the hyperacuity phenomenon?

Cédric Duchene

This paper is devoted to a study of the role of the fluctuations that the eye is subject to, from the point of view of noise-enhanced processing. To this end, a basic model of the retina is considered, namely a regular sampler subject to space and time fluctuations that model the random sampling and the involuntary eye tremor respectively. The filtering that can be done by the photoreceptor is also taken into account and the study focuses on a stochastic model of a natural scene. To quantify the effect of the noise, a coefficient of correlation between the signal acquired by a given photoreceptor and a given point of the scene that the eye is looking at is considered. It is shown both for academic examples and for a more realistic case that the fluctuations which affect the retina can induce noise-enhanced processing effects. The observed effect is then interpreted as a stochastic control of the retina via the random tremor.

2009

Optimal Design of Signaling Modules

Andrijana Radivojevic

One of the basic characteristics of every living system is the ability to respond to extracellular signals. This is carried out through a limited number of protein-based signaling networks, whose function is not based only on simple transmission of the received signals, but incorporates the processing, encoding and integration of both external and internal signals. The results than lead to different changes in gene expression and regulate cell growth, mitogenesis, differentiation, embryo development, and stress responses in mammalian cells, whereas the malfunction is in correlation with diseases such as cancer, asthma and diabetes. In signaling networks, the basic units are covalent modification cycles, which comprise the activation and deactivation of proteins by other proteins. Protein modification in cell signaling – typically a phosphorylation and dephosphorylation – is a general mechanism responsible for the transfer of a wide variety of chemical signals in biological systems. Although the concept does not seem to be complex from a biochemical point of view, these simple systems can nevertheless provide a large diapason of dynamical responses and are therefore ubiquitous building blocks of signaling pathways. These cycles are often linked, forming multiple layers of cycles, the so-called cascades. Commonly observed instance of signal transduction through a series of protein kinase reactions are the kinases of the mitogen-activated protein kinase (MAPK) cascades. These pathways, which are found in almost all eukaryotes, play an important role in controlling different cellular processes, including fundamental functions. The activation of the cellular response by MAPK pathways typically involves at least three phosphorylation steps. In order to better understand the nature of this regulation and to gain greater insight into the mechanisms that determine the function of cells, signaling modules have been intensively studied using mathematical modeling and computational simulations, through the fast growing field of systems biology and its disciplines. The primary aim is to faithfully describe the system and to be able to predict the system behavior. Synergistically with experimental analysis, the reported observations have allowed one to identify properties of these pathways, such as fast signal propagation, large amplification, short signal duration and noise resistance. Since biochemical parameters in signaling pathways are not easily accessible experimentally, it is necessary to use advanced mathematical tools for their correct estimation. Using the paradigm of man-made optimal signal transduction systems, we chose to take the research path for discovering optimal design of cellular signaling modules. To approach the main thesis objective, we first identified the key system parameters through global sensitivity analysis. Comparative analysis of differences and similarities within different system architectures revealed some insights for initial parameter classification and starting point for optimal system design. In order to be able to interpret a broader range of phenotypes, we take into account both steady-state and dynamic properties simultaneously. Furthermore, we investigated the trade-offs between optimal characteristics. As a result, we found the biochemical and biophysical parameters that determine these trade-offs and we analyzed if there exist conditions under which we can simultaneously achieve optimal steady-state and dynamic performance. We first analyze what are the design principles that lead the system to have the minimal signaling times, subject to a certain level of amplification gain. In this setup, we bring out our main research question: are there any trade-offs and interplay between different steady-state and dynamic properties? Furthermore, we include the property of ultrasensitivity and eventually solve multi-objective optimization problems. A particularly insightful finding of this work is that, upon judicious selection of the kinetic parameters, a simple covalent modification cycle is able to meet multiple objectives simultaneously. In particular, this analysis may help explain why signaling cycles are so ubiquitous in cell signaling. The enhancement of ultrasensitivity and faster signal propagation in the multicyclic systems clearly show the advantages of the natural choice of designing signaling pathways in the form of signaling cascades. The thesis concludes with the potential research steps that could be taken along the same path, and that would gather more quantitative knowledge about signaling pathways.

EPFL2012

Theoretical and Experimental Studies of Dendritic Metacommunities

Francesco Carrara

The present thesis deals with the understanding of the origins and the mechanisms of maintenance of biodiversity in natural landscapes, in particular by identifying key processes that define large-scale patterns of abundance and diversity. Biological communities often occur in spatially structured habitats where connectivity directly affects dispersal and metacommunity processes. Recent theoretical work suggests that dispersal constrained by the connectivity of specific habitat structures affects diversity patterns and species interactions. This is particularly relevant in dendritic networks epitomized by fluvial ecological corridors. This thesis addresses whether connectivity alone can explain observed features of biodiversity and selectively promote different components of community composition in river-like landscapes, such as local species richness or the among-community similarity. The relevance of this thesis lies in the major ecological challenges posed by the topic, and its fundamental importance to conservation biology. The studies pursued herein are also deemed relevant because of the influence of the spatial connectivity and dispersal on population dynamics and of the relevance of biodiversity to ecosystem functioning. Mechanisms of species coexistence were investigated with a blend of theoretical tools (broadly related to statistical mechanics and the theory of stochastic processes) and experimental work using laboratory microbial communities. The research tools ranged from aspects of modern coexistence theory in a local perspective to the recent concept of the metacommunity in spatial ecology, within a unified framework. The study of biodiversity in riverine ecosystems guided by observational data has been addressed by combining theoretical metacommunity models with laboratory experiments. The results are diverse. First, they show experimentally that connectivity per se shapes key components of biodiversity in metacommunities. Local dispersal in isotropic lattice landscapes homogenizes local species richness and leads to pronounced spatial persistence. By contrast, dispersal along dendritic landscapes leads to higher variability in local diversity and among-community composition. Although headwaters exhibit relatively lower species richness, they are crucial for the maintenance of regional biodiversity. By suitably arranging patch sizes within river-like networks the effect of local habitat capacity (i.e., the patch size) and dendritic connectivity on biodiversity can be experimentally disentangled in aquatic microcosm metacommunities. It is shown in this thesis that species coexistence and community assembly depend on intricate, non-trivial interactions of local community capacity and network positioning. Furthermore, an interaction of spatial arrangement of habitat capacity and dispersal along river-like networks also affects a key ecosystem descriptor, namely regional evenness. High regional evenness in community composition is found only in landscapes preserving geomorphological scaling properties of patch sizes. In riverine environments some of the rarer species sustained regionally more abundant populations and were better able to track their own niche requirements compared to landscapes with homogeneous patch size or landscapes with spatially uncorrelated patch size. All the experimental results were supported and extended by a theoretical analysis where the above mechanisms have been generalized. This thesis provides the first direct experimental evidence that spatially constrained dendritic connectivity is a key factor for community composition and population persistence in riverine landscapes. As such, this thesis assesses a longstanding issue in spatial community ecology. It offers unique insights into the ecological forces structuring natural communities in a key ecosytem, and demonstrates principles that can be further tested in theoretical metacommunity models possibly to be extended to real riverine ecosystems. Taken together, the analyses show how the structure of ecological networks interacts with the spatial environmental matrix in determining biodiversity patterns and the functioning of biological communities. The analyses also suggest that altering the natural linkage between dendritic connectivity and patch size strongly affects community properties at multiple scales. The first part of this thesis (chapters 2 and 3) addresses key aspects of biodiversity-ecosystem functioning research where the combination of theory-guided experiments and theoretical investigations shows how a stochastic implementation of population dynamics proves fundamental for key community properties such as species persistence and community stability. The diversity-productivity and diversity-stability relationships are explored. Both experimental findings and the results of a stochastic model fitted to the experimental interaction matrix, suggest the emergence of strong stabilizing forces when species from different functional groups interact in the same environment, increasing species coexistence and community biomass production. The last part (chapter 6) provides a synthesis of this thesis work, in that it aims at unifying aspects from niche-theory, usually adopted in spatially implicit models, with those characteristic of a spatially explicit context from a typical real-life mountainous regions. It is dedicated to the possible explanation for a macroecological pattern routinely observed from organisms in different domains of life, that is, the mid-elevational peak in local species richness. Guided by empirical observations on diversity of macroinvertebrates in Swiss river basins, a theoretical ansatz is provided which is deemed to capture the essential geomorphological drivers and controls relating species-fitness to altitude. A set of overarching conclusions and perspectives for future research are discussed in the concluding chapter.