Quantitative biodistribution and pharmacokinetics of multimodal gadolinium-based nanoparticles for lungs using ultrashort TE MRI

Objective

To study the biodistribution and lung pharmacokinetics of tracheally administered gadolinium-based contrast agents [gadoteric acid and multimodal ultra-small rigid platforms (USRPs)], to validate their pharmacokinetics against optical imaging of fluorescent USRPs, and to test their short-term toxicity.

Materials and methods

Ultrashort echo-time (UTE) lung proton magnetic resonance imaging (MRI) was performed at 4.7-Tesla (T) after the intratracheal instillation of different concentrations of contrast agent solutions in mice. Pharmacokinetic models were implemented on the absolute concentration calculated from the MRI signal enhancement measurements. Fluorescent USRPs were used to obtain optical images with the same protocol. Bronchoalveolar lavage inflammatory cell count and serum creatinine measurement were performed on four groups of instilled mice (sham, saline, USRPs, lipopolysaccharide).

Results

MR and optical imaging showed similar kinetics of the USRPs, passing from the airways to the lung tissue and to the kidneys, with negligible hepatic clearance. No significant increase of lung and renal inflammation markers were observed in USRP-instilled animals.

Conclusion

A T ₁-weighted radial UTE sequence was found to be valuable in quantitatively monitoring the biodistribution and pharmacokinetics of nanoparticles in the lungs of mice. The observed favorable pharmacokinetics, which was validated by fluorescence imaging, ensures the negligible toxicity of the nanoprobes, making the USRPs and the developed protocol good candidates for applications on selected lung diseases.     

Standardized MR protocol for the evaluation of MRA sequences and/or contrast agents effects in high-degree arterial stenosis analysis

Purpose To investigate the relative role of high resolution (spatial or temporal) magnetic resonance angiography (MRA) sequence and of contrast agent properties in the evaluation of high-degree arterial stenosis. Methods We qualitatively and quantitatively studied both 50 and 95% (300 μm diameter) stenosis of a 6 mm arterial phantom with two contrast agents (CA), Gd-DOTA (r₁ =2.9 mM⁻¹ s⁻¹) versus P760 (r₁ =25 mM⁻¹ s⁻¹) at several CA concentrations, including arterial peak concentration after injection of either a single or double dose of CA, using either a high temporal (booster) or high spatial (HR) resolution 3D MRA sequences. Experimental data were then compared to theoretical data. Results With the 3D HR sequence, both visual and quantitative analysis were significantly better compared to the 3D booster sequence, at each phantom diameter. Quantitative analysis was significantly improved by injection of a double versus a single dose of each CA (Gd-DOTA or P760), primarily in high degree stenosis. Conclusion Combined MRA spatial resolution and high CA efficiency are mandatory to correctly evaluate high degree stenosis.     

A fair method for centralized optimization of multi-TSO power systems

This paper addresses the problem of centralized optimization of an interconnected power system partitioned into several regions controlled by different transmission system operators (TSOs). It is assumed that those utilities have agreed to transferring some of their competencies to a centralized control center, which is in charge of setting the control variables in the entire system to satisfy every utility’s individual objective. This paper proposes an objective method for centralized optimization of such multi-TSO power systems, which relies on the assumption that each TSO has a real-valued optimization function focusing on its control area only. This method is illustrated on the IEEE 118 bus system partitioned into three TSOs. It is applied to the optimal reactive power dispatch problem, where the control variables are the voltage settings for generators and compensators. After showing that the method has some properties of fairness, namely freedom from envy, efficiency, accountability, and altruism, we emphasize its robustness with respect to certain biased behavior of the different TSOs.     

Ultraviolet photodetectors and readout based on a-IGZO semiconductor technology

In this work, real-time ultraviolet photodetectors are realized through metal–semiconductor–metal (MSM) structures. Amorphous indium gallium zinc oxide (a-IGZO) is used as semiconductor material and gold as metal electrodes. The readout of an individual sensor is implemented by a transimpedance amplifier (TIA) consisting of an all-enhancement a-IGZO thin-film transistor (TFT) operational amplifier and a switched capacitor (SC) as feedback resistance. The photosensor and the transimpedance amplifier are both manufactured on glass substrates. The measured photosensor possesses a high responsivity R , a low response time t _{R E S} , and a good noise equivalent power value NEP .     

Anthropogenic aerosol emissions mapping and characterization by imaging spectroscopy – application to a metallurgical industry and a petrochemical complex

This paper is focused on the retrieval of industrial aerosol optical thickness (AOT) and microphysical properties by means of airborne imaging spectroscopy. Industrial emissions generally lead to optically thin plumes requiring an adapted detection method taking into account the weak proportion of particles sought in the atmosphere. To this end, a semi-analytical model combined with the Cluster-Tuned Matched Filter (CTMF) algorithm is presented to characterize those plumes, requiring the knowledge of the soil under the plume. The model allows the direct computation of the at-sensor radiance when a plume is included in the radiative transfer. When applied to industrial aerosol classes as defined in this paper, simulated spectral radiances can be compared to ‘real’ MODTRAN (Moderate Resolution Atmospheric Transmission) radiances using the Spectral Angle Mapper (SAM). On the range from 0.4 to 0.7 µm, for three grounds (water, vegetation, and bright one), SAM scores are lower than 0.043 in the worst case (a both absorbing and scattering particle over a bright ground), and usually lower than 0.025. The darker the ground reflectance is, the more accurate the results are (typically for reflectance lower than 0.3). Concerning AOT retrieval capabilities, with a pre-calculated model for a reference optical thickness of 0.25, we are able to retrieve plume AOT at 550 nm in the range 0.0 to 0.4 with an error usually ranging between 9% and 13%. The first test case is a CASI (Compact Airborne Spectrographic Imager) image acquired over the metallurgical industry of Fos-sur-Mer (France). First results of the use of the model coupled with CTMF algorithm reveal a scattering aerosol plume with particle sizes increasing with the distance from the stack (from detection score of 54% near the stack for particles with a diameter of 0.1 µm, to 69% away from it for 1.0 µm particles). A refinement is made then to estimate more precisely aerosol plume properties, using a multimodal distribution based on the previous results. It leads to find a mixture of sulfate and brown carbon particles with a plume AOT ranging between 0.2 and 0.5. The second test case is an AHS (Airborne Hyperspectral Scanner) image acquired over the petrochemical site of Antwerp (Belgium). The first CTMF application results in detecting a brown carbon aerosol of 0.1 µm mode (detection score is 51%). Refined results show the evolution of the AOT decreasing from 0.15 to 0.05 along the plume for a mixture of brown carbon fine mode and 0.3 µm radius of sulfate aerosol.     

Abnormality detection using low-level co-occurring events

We propose in this paper a method for behavior modeling and abnormal events detection which uses low-level features. In conventional object-based approaches, objects are identified, classified, and tracked to locate those with suspicious behavior. We proceed directly with event characterization and behavior modeling using low-level features. We first learn statistics about co-occurring events in a spatio-temporal volume in order to build the normal behavior model, called the co-occurrence matrix. The notion of co-occurring events is defined using mutual information between motion labels sequences. Then, in the second phase, the co-occurrence matrix is used as a potential function in a Markov random field framework to describe, as the video streams in, the probability of observing new volumes of activity. The co-occurrence matrix is thus used for detecting moving objects whose behavior differs from the ones observed during the training phase. Interestingly, the Markov random field distribution implicitly accounts for speed, direction, as well as the average size of the objects without any higher-level intervention. Furthermore, when the spatio-temporal volume is sufficiently large, the co-occurrence distribution contains the average normal path followed by moving objects. Our method has been tested on various indoor and outdoor videos representing various challenges.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

Generalized Eigenvalue Decomposition Applied to Estimation of Spatial rPPG Distribution of Skin

Journal of Mathematical Imaging and Vision - Remote photoplethysmography (rPPG) has been at the forefront recently, thanks to its capacity in estimating non-contact physiological parameters such as...     

Validation and automatic test generation on UML models: the AGATHA approach

The related economic goals of test generation are quite important for software industry. Manufacturers ever seeking to increase their productivity need to avoid malfunctions at the time of system specification: the later the defaults are detected, the greater the cost is. Consequently, the development of techniques and tools able to efficiently support engineers who are in charge of elaborating the specification constitutes a major challenge whose fallout concerns not only sectors of critical applications but also all those where poor conception could be extremely harmful to the brand image of a product.This article describes the design and implementation of a set of tools allowing software developers to validate UML (the Unified Modeling Language) specifications. This toolset belongs to the AGATHA environment, which is an automated test generator, developed at CEA/LIST.The AGATHA toolset is designed to validate specifications of communicating concurrent units described using an EIOLTS formalism (Extended Input Output Labeled Transition System). The goal of the work described in this paper is to provide an interface between UML and an EIOLTS formalism giving the possibility to use AGATHA on UML specifications.In this paper we describe first the translation of UML models into the EIOLTS formalism, and the translation of the results of the behavior analysis, provided by AGATHA, back into UML. Then we present the AGATHA toolset; we particularly focus on how AGATHA overcomes several problems of combinatorial explosion. We expose the concept of symbolic calculus and detection of redundant paths, which are the main principles of AGATHA's kernel. This kernel properly computes all the symbolic behaviors of a system specified in EIOLTS and automatically generates tests by way of constraint solving. Eventually we apply our method to an example and explain the different results that are computed.     

d-Dimensional parametric models for dynamic animation of deformable objects

This paper introduces an accurate, efficient, and unified engine dedicated to dynamic animation of d-dimensional deformable objects. The objects are modelled as d-dimensional manifolds defined as functional combinations of a mesh of 3D control points, weighted by parametric blending functions. This model ensures that, at each time step, the object shape conforms to its manifold definitions. The object motion is deduced from the control points dynamic animation. In fact, control points should be viewed as the degrees of freedom of the continuous object. The chosen dynamic equations (Lagrangian formalism) reflect this generic modelling scheme and yield an exact and computationally efficient linear system.