Alexandre Steyer

X-ray high-resolution vascular network imaging

This paper presents the first application of high‐resolution X‐ray synchrotron tomography to the imaging of large microvascular networks in biological tissue samples. This technique offers the opportunity of analysing the full three‐dimensional vascular network from the micrometre to the millimetre scale. This paper presents the specific sample preparation method and the X‐ray imaging procedure. Either barium or iron was injected as contrast agent in the vascular network. The impact of the composition and concentration of the injected solution on the X‐ray synchrotron tomography images has been studied. Two imaging modes, attenuation and phase contrast, are compared. Synchrotron high‐resolution computed tomography offers new prospects in the three‐dimensional imaging of in situ biological vascular networks.

From homogeneous to fractal normal and tumorous microvascular networks in the brain.

We studied normal and tumorous three-dimensional (3D) microvascular networks in primate and rat brain. Tissues were prepared following a new preparation technique intended for high-resolution synchrotron tomography of microvascular networks. The resulting 3D images with a spatial resolution of less than the minimum capillary diameter permit a complete description of the entire vascular network for volumes as large as tens of cubic millimeters. The structural properties of the vascular networks were investigated by several multiscale methods such as fractal and power-spectrum analysis. These investigations gave a new coherent picture of normal and pathological complex vascular structures. They showed that normal cortical vascular networks have scale-invariant fractal properties on a small scale from 1.4 μm up to 40 to 65 μm. Above this threshold, vascular networks can be considered as homogeneous. Tumor vascular networks show similar characteristics, but the validity range of the fractal regime extend to much larger spatial dimensions. These 3D results shed new light on previous two dimensional analyses giving for the first time a direct measurement of vascular modules associated with vessel-tissue surface exchange.

Online Discussion Groups as Socal Networks

ABSTRACT While consumer behavior researchers have long studied word-of-mouth and diffusion of information among reference groups, the emergence of the internet has recently provided the means to empirically establish just how this process takes place, as well as who contributes to it. In this study, the way information was shared among consumers in relation to two product categories was described as following a power law. Implications for marketing and research are provided.

On the Frontier: Structural Effects in a Diffusion Model based on Influence Matrixes

The aim of this paper is to set the foundation of an approach of the diffusion-adoption problem of an innovation or a technological standard, based on the building of influence matrixes. This means that agents are to be considered as participating in social networks that provide the support and the framework of the adoption-diffusion process.

Revisiting Individual Choices in Group Settings: The Long and Winding (Less Traveled) Road?

This study revisits Ariely and Levav’s previous findings in relation to consumers’ need for variety when ordering (food or beverages) in a group setting. We examine how group opinion and unanimity can explain consumers’ individual choice in a group setting. We hypothesize that the relationship between individual choice and group opinion is nonmonotonic as it is moderated by the degree of unanimity around an alternative. We demonstrate this effect in two empirical studies. We show that choice patterns are curvilinear, with previous findings accurately reflecting only specific sections of the overall pattern of individual choices in a group setting.

Géométrie de l'interaction sociale : Le modèle de diffusion en avalanches spatiales

Un nouveau modèle de diffusion des innovations est construit au confluent des travaux de Bass (1969) sur la diffusion aléatoire et de ceux de Hägerstrand (1952) sur la diffusion spatiale. Le modèle repose sur une structure particulière du réseau social au travers duquel se propage le mimétisme. Cette structure est à la fois aléatoire et régulière. Le modèle obtenu est validé empiriquement en ajustement et en prévision à partir de données empruntées à Hägerstrand (1967), Bass (1969) et Rogers (1995).

Modeling the Social Structure of Online Discussion Groups: Implications for Controlling Viral Marketing

This research examines the process by which information diffuses within newsgroups on the Internet. Our results empirically demonstrate that these newsgroups are scale-free networks where the potential for information dissemination is important, albeit somewhat unpredictable. This leads us to reconsider the econometric foundations of forecasting methods typically used by marketers.

Self Organised Criticality in Economic and Social Networks

Diverse approaches of innovation diffusion, in the presence of increasing returns, have been outlined or explored in the recent literature. We propose, four ourselves, to take into account the idea that agents, in the situation to adopt or not an innovation or a new technological standard, are “situated” within a social network, that is the support of influence effects. Our approach aim is here to explore the role of learning processes into the propagation dynamics within a network structure. In a recent model, formally represented by a neural network, we have introduced a relational learning that constitutes a way to set up an endogenous network evolution. We prove the existence of a self organised criticality phenomenon, where some agents acquire key-positions within the network that bring them a strong structural capacity of influence over the whole population of potential adopters. In this paper, we study the way how network auto-organisation can lead, under given conditions, to a critical state characterised by macroscopic effects generated from microscopic impulses at the level of the individual agent. It is the peculiar structure of those critical networks that allow macroscopic “avalanches” to take place, on which the diffusion process is likely to lean. We analyse the way learning leads endogenously to such a critical state and how it strikes against the finite size of the network.