Morphodynamic analysis of cerebral aneurysm pulsation from time-resolved rotational angiography

This paper presents a technique to estimate and model patient-specific pulsatility of cerebral aneurysms over onecardiac cycle, using 3D rotational X-ray angiography (3DRA) acquisitions. Aneurysm pulsation is modeled as a time varying-spline tensor field representing the deformation applied to a reference volume image, thus producing the instantaneousmorphology at each time point in the cardiac cycle. The estimated deformation is obtained by matching multiple simulated projections of the deforming volume to their corresponding original projections. A weighting scheme is introduced to account for the relevance of each original projection for the selected time point. The wide coverage of the projections, together with the weighting scheme, ensures motion consistency in all directions. The technique has been tested on digital and physical phantoms that are realistic and clinically relevant in terms of geometry, pulsation and imaging conditions. Results from digital phantomexperiments demonstrate that the proposed technique is able to recover subvoxel pulsation with an error lower than 10% of the maximum pulsation in most cases. The experiments with the physical phantom allowed demonstrating the feasibility of pulsation estimation as well as identifying different pulsation regions under clinical conditions.

Multiuser detection in a dynamic environment — Part II: Joint user identification and parameter estimation

The problem of jointly estimating the number, the identities, and the data of active users in a time-varying multiuser environment was examined in a companion paper (IEEE Trans. Information Theory, vol. 53, no. 9, September 2007), at whose core was the use of the theory of finite random sets on countable spaces. Here we extend that theory to encompass the more general problem of estimating unknown continuous parameters of the active-user signals. This problem is solved here by applying the theory of random finite sets constructed on hybrid spaces. We doso deriving Bayesian recursions that describe the evolution withtime of a posteriori densities of the unknown parameters and data.Unlike in the above cited paper, wherein one could evaluate theexact multiuser set posterior density, here the continuous-parameter Bayesian recursions do not admit closed-form expressions. To circumvent this difficulty, we develop numerical approximationsfor the receivers that are based on Sequential Monte Carlo (SMC)methods (“particle filtering”). Simulation results, referring to acode-divisin multiple-access (CDMA) system, are presented toillustrate the theory.

Sequential estimation of multipath MIMO-OFDM channels

Wireless “MIMO” systems, employing multiple transmit and receive antennas, promise a significant increase of channel capacity, while orthogonal frequency-division multiplexing (OFDM) is attracting a good deal of attention due to its robustness to multipath fading. Thus, the combination of both techniques is an attractive proposition for radio transmission. The goal of this paper is the description and analysis of a new and novel pilot-aided estimator of multipath block-fading channels. Typical models leading to estimation algorithms assume the number of multipath components and delays to be constant (and often known), while their amplitudes are allowed to vary with time. Our estimator is focused instead on the more realistic assumption that the number of channel taps is also unknown and varies with time following a known probabilistic model. The estimation problem arising from these assumptions is solved using Random-Set Theory (RST), whereby one regards the multipath-channel response as a single set-valued random entity.Within this framework, Bayesian recursive equations determine the evolution with time of the channel estimator. Due to the lack of a closed form for the solution of Bayesian equations, a (Rao–Blackwellized) particle filter (RBPF) implementation ofthe channel estimator is advocated. Since the resulting estimator exhibits a complexity which grows exponentially with the number of multipath components, a simplified version is also introduced. Simulation results describing the performance of our channel estimator demonstrate its effectiveness.

Automatic low baseline stereo in urban areas

In this work we propose a new automatic methodology for computing accurate digital elevation models (DEMs) in urban environments from low baseline stereo pairs that shall be available in the future from a new kind of earth observation satellite. This setting makes both views of the scene similarly, thus avoiding occlusions and illumination changes, which are the main disadvantages of the commonly accepted large-baseline configuration. There still remain two crucial technological challenges: (i) precisely estimating DEMs with strong discontinuities and (ii) providing a statistically proven result, automatically. The first one is solved here by a piecewise affine representation that is well adapted to man-made landscapes, whereas the application of computational Gestalt theory introduces reliability and automation. In fact this theory allows us to reduce the number of parameters to be adjusted, and tocontrol the number of false detections. This leads to the selection of a suitable segmentation into affine regions (whenever possible) by a novel and completely automatic perceptual grouping method. It also allows us to discriminate e.g. vegetation-dominated regions, where such an affine model does not apply anda more classical correlation technique should be preferred. In addition we propose here an extension of the classical ”quantized” Gestalt theory to continuous measurements, thus combining its reliability with the precision of variational robust estimation and fine interpolation methods that are necessary in the low baseline case. Such an extension is very general and will be useful for many other applications as well.

Sequential estimation of time-varying multipath channel for MIMO-OFDM systems

In this paper, we introduce a pilot-aided multipath channel estimator for Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. Typical estimation algorithms assume the number of multipath components and delays to be known and constant, while theiramplitudes may vary in time. In this work, we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The estimation problem arising from this assumption is solved using Random Set Theory (RST), which is a probability theory of finite sets. Due to the lack of a closed form of the optimal filter, a Rao-Blackwellized Particle Filter (RBPF) implementation of the channel estimator is derived. Simulation results demonstrate the estimator effectiveness.

Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns.

BACKGROUND: Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. METHODS: We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay. RESULTS: Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. CONCLUSIONS: IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

Technical note: Preliminary estimation of rockfall runout zones

Rockfall propagation areas can be determined using a simple geometric rule known as shadow angle or energy line method based on a simple Coulomb frictional model implemented in the CONEFALL computer program. Runout zones are estimated from a digital terrain model (DTM) and a grid file containing the cells representing rockfall potential source areas. The cells of the DTM that are lowest in altitude and located within a cone centered on a rockfall source cell belong to the potential propagation area associated with that grid cell. In addition, the CONEFALL method allows estimation of mean and maximum velocities and energies of blocks in the rockfall propagation areas. Previous studies indicate that the slope angle cone ranges from 27° to 37° depending on the assumptions made, i.e. slope morphology, probability of reaching a point, maximum run-out, field observations. Different solutions based on previous work and an example of an actual rockfall event are presented here.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Parameter estimation for the distribution of single cell lag times.

In Quantitative Microbial Risk Assessment, it is vital to understand how lag times of individual cells are distributed over a bacterial population. Such identified distributions can be used to predict the time by which, in a growth-supporting environment, a few pathogenic cells can multiply to a poisoning concentration level. We model the lag time of a single cell, inoculated into a new environment, by the delay of the growth function characterizing the generated subpopulation. We introduce an easy-to-implement procedure, based on the method of moments, to estimate the parameters of the distribution of single cell lag times. The advantage of the method is especially apparent for cases where the initial number of cells is small and random, and the culture is detectable only in the exponential growth phase.

Panel index VAR models: Specification, Estimation, Testing and Leading Indicators

This paper proposes a method to conduct inference in panel VAR models with cross unit interdependencies and time variations in the coefficients. The approach can be used to obtain multi-unit forecasts and leading indicators and to conduct policy analysis in a multiunit setups. The framework of analysis is Bayesian and MCMC methods are used to estimate the posterior distribution of the features of interest. The model is reparametrized to resemble an observable index model and specification searches are discussed. As an example, we construct leading indicators for inflation and GDP growth in the Euro area using G-7 information.

Virus-like particles induce robust human T-helper cell responses.

Among synthetic vaccines, virus-like particles (VLPs) are used for their ability to induce strong humoral responses. Very little is reported on VLP-based-vaccine-induced CD4(+) T-cell responses, despite the requirement of helper T cells for antibody isotype switching. Further knowledge on helper T cells is also needed for optimization of CD8(+) T-cell vaccination. Here, we analysed human CD4(+) T-cell responses to vaccination with MelQbG10, which is a Qβ-VLP covalently linked to a long peptide derived from the melanoma self-antigen Melan-A. In all analysed patients, we found strong antibody responses of mainly IgG1 and IgG3 isotypes, and concomitant Th1-biased CD4(+) T-cell responses specific for Qβ. Although less strong, comparable B- and CD4(+) T-cell responses were also found specific for the Melan-A cargo peptide. Further optimization is required to shift the response more towards the cargo peptide. Nevertheless, the data demonstrate the high potential of VLPs for inducing humoral and cellular immune responses by mounting powerful CD4(+) T-cell help.

Large-scale evolutionary trends of Acrochordiceratidae Arthaber, 1911 (Ammonoidea, Middle Triassic) and Cope's rule

Directed evolution of life through millions of years, such as increasing adult body size, is one of the most intriguing patterns displayed by fossil lineages. Processes and causes of such evolutionary trends are still poorly understood. Ammonoids (externally shelled marine cephalopods) are well known to have experienced repetitive morphological evolutionary trends of their adult size, shell geometry and ornamentation. This study analyses the evolutionary trends of the family Acrochordiceratidae Arthaber, 1911 from the Early to Middle Triassic (251228 Ma). Exceptionally large and bed-rock-controlled collections of this ammonoid family were obtained from strata of Anisian age (Middle Triassic) in north-west Nevada and north-east British Columbia. They enable quantitative and statistical analyses of its morphological evolutionary trends. This study demonstrates that the monophyletic clade Acrochordiceratidae underwent the classical evolute to involute evolutionary trend (i.e. increasing coiling of the shell), an increase in its shell adult size (conch diameter) and an increase in the indentation of its shell suture shape. These evolutionary trends are statistically robust and seem more or less gradual. Furthermore, they are nonrandom with the sustained shift in the mean, the minimum and the maximum of studied shell characters. These results can be classically interpreted as being constrained by the persistence and common selection pressure on this mostly anagenetic lineage characterized by relatively moderate evolutionary rates. Increasing involution of ammonites is traditionally interpreted by increasing adaptation mostly in terms of improved hydrodynamics. However, this trend in ammonoid geometry can also be explained as a case of Copes rule (increasing adult body size) instead of functional explanation of coiling, because both shell diameter and shell involution are two possible paths for ammonoids to accommodate size increase.

Toward a Statistically Robust Assessment of Social and Solidarity Economy Actors. Conceptual Development and Empirical Validation

Many definitions and debates exist about the core characteristics of social and solidarity economy (SSE) and its actors. Among others, legal forms, profit, geographical scope, and size as criteria for identifying SSE actors often reveal dissents among SSE scholars. Instead of using a dichotomous, either-in-or-out definition of SSE actors, this paper presents an assessment tool that takes into account multiple dimensions to offer a more comprehensive and nuanced view of the field. We first define the core dimensions of the assessment tool by synthesizing the multiple indicators found in the literature. We then empirically test these dimensions and their interrelatedness and seek to identify potential clusters of actors. Finally we discuss the practical implications of our model.