Filtrage EPSF pour l'amélioration d'image

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Filtrage adaptatif par diffusion anisotropique multiechelle base sur le principe du maximum d'entropie

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The French national prospective cohort of patients co-infected with HIV and HCV (ANRS CO13 HEPAVIH): Early findings, 2006-2010

<p>Abstract</p> <p>Background</p> <p>In France, it is estimated that 24% of HIV-infected patients are also infected with HCV. Longitudinal studies addressing clinical and public health questions related to HIV-HCV co-infection (HIV-HCV clinical progression and its determinants including genetic dimension, patients' experience with these two diseases and their treatments) are limited. The ANRS CO 13 HEPAVIH cohort was set up to explore these critical questions.</p> <p>To describe the cohort aims and organization, monitoring and data collection procedures, baseline characteristics, as well as follow-up findings to date.</p> <p>Methods</p> <p>Inclusion criteria in the cohort were: age > 18 years, HIV-1 infection, chronic hepatitis C virus (HCV) infection or sustained response to HCV treatment. A standardized medical questionnaire collecting socio-demographic, clinical, biological, therapeutic, histological, ultrasound and endoscopic data is administered at enrolment, then every six months for cirrhotic patients or yearly for non-cirrhotic patients. Also, a self-administered questionnaire documenting socio-behavioral data and adherence to HIV and/or HCV treatments is administered at enrolment and yearly thereafter.</p> <p>Results</p> <p>A total of 1,175 patients were included from January 2006 to December 2008. Their median age at enrolment was 45 years and 70.2% were male. The median CD4 cell count was 442 (IQR: 304-633) cells/μl and HIV RNA plasma viral load was undetectable in 68.8%. Most participants (71.6%) were on HAART. Among the 1,048 HIV-HCV chronically co-infected patients, HCV genotype 1 was predominant (56%) and cirrhosis was present in 25%. As of January, 2010, after a median follow-up of 16.7 months (IQR: 11.3-25.3), 13 new cases of decompensated cirrhosis, nine hepatocellular carcinomas and 20 HCV-related deaths were reported, resulting in a cumulative HCV-related severe event rate of 1.9/100 person-years (95% CI: 1.3-2.5). The rate of HCV-related severe events was higher in cirrhotic patients and those with a low CD4 cells count, but did not differ according to sex, age, alcohol consumption, CDC clinical stage or HCV status.</p> <p>Conclusion</p> <p>The ANRS CO 13 HEPAVIH is a nation-wide cohort using a large network of HIV treatment, infectious diseases and internal medicine clinics in France, and thus is highly representative of the French population living with these two viruses and in care.</p

HCV genome-wide genetic analyses in context of disease progression and hepatocellular carcinoma

<div><p>Hepatitis C virus (HCV) is a major cause of hepatitis and hepatocellular carcinoma (HCC) world-wide. Most HCV patients have relatively stable disease, but approximately 25% have progressive disease that often terminates in liver failure or HCC. HCV is highly variable genetically, with seven genotypes and multiple subtypes per genotype. This variation affects HCV’s sensitivity to antiviral therapy and has been implicated to contribute to differences in disease. We sequenced the complete viral coding capacity for 107 HCV genotype 1 isolates to determine whether genetic variation between independent HCV isolates is associated with the rate of disease progression or development of HCC. Consensus sequences were determined by sequencing RT-PCR products from serum or plasma. Positions of amino acid conservation, amino acid diversity patterns, selection pressures, and genome-wide patterns of amino acid covariance were assessed in context of the clinical phenotypes. A few positions were found where the amino acid distributions or degree of positive selection differed between in the HCC and cirrhotic sequences. All other assessments of viral genetic variation and HCC failed to yield significant associations. Sequences from patients with slow disease progression were under a greater degree of positive selection than sequences from rapid progressors, but all other analyses comparing HCV from rapid and slow disease progressors were statistically insignificant. The failure to observe distinct sequence differences associated with disease progression or HCC employing methods that previously revealed strong associations with the outcome of interferon α-based therapy implies that variable ability of HCV to modulate interferon responses is not a dominant cause for differential pathology among HCV patients. This lack of significant associations also implies that host and/or environmental factors are the major causes of differential disease presentation in HCV patients.</p></div

Hyperspectral remote sensing of shallow waters: Considering environmental noise and bottom intra-class variability for modeling and inversion of water reflectance

International audienceHyperspectral remote sensing is now an established tool to determine shallow water properties over large areas, usually by inverting a semi-analytical model of water reflectance. However, various sources of error may make the observed subsurface remote-sensing reflectance deviate from the model, resulting in an increased retrieval error when inverting the model based on classical least-squares fitting. In this paper, we propose a probabilistic forward model of shallow water reflectance variability that describes two of the main sources of error, namely, (1) the environmental noise that includes every source of above-water variability (e.g., sensor noise and rough water surface), and (2) the potentially complex inherent spectral variability of each benthic class through their associated spectral covariance matrix. Based on this probabilistic model, we derive two inversion approaches, namely, MILE (MaxImum Likelihood estimation including Environmental noise) and MILEBI (MaxImum Likelihood estimation including Environmental noise and Bottom Intra-class variability) that utilize the information contained in the proposed covariance matrices to further constrain the inversion while allowing the observation to differ from the model in the less reliable wavebands. In this paper, MILE and MILEBI are compared with the widely used least-squares (LS) criterion in terms of depth, water clarity and benthic cover retrievals. For these three approaches, we also assess the influence of constraining bottom mixture coefficients to sum to one on estimation results.The results show that the proposed probabilistic model is a valuable tool to investigate the influence of bottom intra-class variability on subsurface reflectance, e.g., as a function of optical depth or environmental noise. As expected, this influence is critical in very optically shallow waters, and decreases with increasing optical depth. The inversion results obtained from synthetic and airborne data of Quiberon Peninsula, France, show that MILE and MILEBI generally provide better performances than LS. For example, in the case of airborne data with depth ranging from 0.44 to 12.00 m, the bathymetry estimation error decreases by about 32% when using MILE and MILEBI instead of LS. Estimated maps of bottom cover are also more consistent when derived using sum-to-one constrained versions of MILE and MILEBI. MILE is shown to be a simple but powerful method to map simple benthic habitats with negligible influence of intra-class variability. Alternatively, MILEBI is to be preferred if this variability cannot be neglected, since taking bottom covariance matrices into account concurrently with mean reflectance spectra may help the bottom discrimination, e.g., in the presence of overlapping classes. This study thus shows that taking potential sources of error into account through appropriate parameterizations of spectral covariance may be critical to improve the remote sensing of shallow waters, hence making MILE and MILEBI interesting alternatives to LS

Predicting minimum uncertainties in the inversion of ocean color geophysical parameters based on Cramer-Rao bounds

International audienceWe present an analytical approach based on Cramer-Rao Bounds (CRBs) to investigate the uncertainties in estimated ocean color parameters resulting from the propagation of uncertainties in the bio-optical reflectance modeling through the inversion process. Based on given bio-optical and noise probabilistic models, CRBs can be computed efficiently for any set of ocean color parameters and any sensor configuration, directly providing the minimum estimation variance that can be possibly attained by any unbiased estimator of any targeted parameter. Here, CRBs are explicitly developed using (1) two water reflectance models corresponding to deep and shallow waters, resp., and (2) four probabilistic models describing the environmental noises observed within four Sentinel-2 MSI, HICO, Sentinel-3 OLCI and MODIS images, resp. For both deep and shallow waters, CRBs are shown to be consistent with the experimental estimation variances obtained using two published remote-sensing methods, while not requiring one to perform any inversion. CRBs are also used to investigate to what extent perfect a priori knowledge on one or several geophysical parameters can improve the estimation of remaining unknown parameters. For example, using pre-existing knowledge of bathymetry (e.g., derived from LiDAR) within the inversion is shown to greatly improve the retrieval of bottom cover for shallow waters. Finally, CRBs are shown to provide valuable information on the best estimation performances that may be achieved with the MSI, HICO, OLCI and MODIS configurations for a variety of oceanic, coastal and inland waters. CRBs are thus demonstrated to be an informative and efficient tool to characterize minimum uncertainties in inverted ocean color geophysical parameters

Analysis and quantification of seabed adjacency effects in the subsurface upward radiance in shallow waters

International audienceThe estimation of the bathymetry and the detection of targets located on the seabed of shallow waters using remote sensing techniques is of great interest for many environmental applications in coastal areas such as benthic habitat mapping, monitoring of seabed aquatic plants and the subsequent management of littoral zones. For that purpose, knowledge of the optical effects induced by the neighborhood of a given seabed target and by the water column itself is required to better interpret the subsurface upward radiance measured by satellite or shipborne radiometers. In this paper, the various sources of photons that contribute to the subsurface upward radiance are analyzed. In particular, the adjacency effects caused by the neighborhood of a given seabed target are quantified for three water turbidity conditions, namely clear, moderately turbid and turbid waters. Firstly, an analytical expression of the subsurface radiance is proposed in order to make explicit the radiance terms corresponding to these effects. Secondly, a sensitivity study is performed using radiative transfer modeling to determine the influence of the seabed adjacency effects on the upward signal with respect to various parameters such as the bathymetry or the bottom brightness. The results show that the highest contributions of the adjacency effects induced by the neighborhood of a seabed target to the subsurface radiance could reach 26%, 18% and 9% for clear, moderately turbid and turbid water conditions respectively. Therefore, the detection of a seabed target could be significantly biased if the seabed adjacency effects are ignored in the analysis of remote sensing measurements. Our results could be further used to improve the performance of inverse algorithms dedicated to the retrieval of bottom composition, water optical properties and/or bathymetry

Analysis and quantification of seabed adjacency effects in the subsurface upward radiance in shallow waters

International audienceThe estimation of the bathymetry and the detection of targets located on the seabed of shallow waters using remote sensing techniques is of great interest for many environmental applications in coastal areas such as benthic habitat mapping, monitoring of seabed aquatic plants and the subsequent management of littoral zones. For that purpose, knowledge of the optical effects induced by the neighborhood of a given seabed target and by the water column itself is required to better interpret the subsurface upward radiance measured by satellite or shipborne radiometers. In this paper, the various sources of photons that contribute to the subsurface upward radiance are analyzed. In particular, the adjacency effects caused by the neighborhood of a given seabed target are quantified for three water turbidity conditions, namely clear, moderately turbid and turbid waters. Firstly, an analytical expression of the subsurface radiance is proposed in order to make explicit the radiance terms corresponding to these effects. Secondly, a sensitivity study is performed using radiative transfer modeling to determine the influence of the seabed adjacency effects on the upward signal with respect to various parameters such as the bathymetry or the bottom brightness. The results show that the highest contributions of the adjacency effects induced by the neighborhood of a seabed target to the subsurface radiance could reach 26%, 18% and 9% for clear, moderately turbid and turbid water conditions respectively. Therefore, the detection of a seabed target could be significantly biased if the seabed adjacency effects are ignored in the analysis of remote sensing measurements. Our results could be further used to improve the performance of inverse algorithms dedicated to the retrieval of bottom composition, water optical properties and/or bathymetry

Mapping Benthic Habitats by Extending Non-Negative Matrix Factorization to Address the Water Column and Seabed Adjacency Effects

International audienceMonitoring of coastal areas by remote sensing is an important issue. The interest of using an unmixing method to determine the seabed composition from hyperspectral aerial images of coastal areas is investigated. Unmixing provides both seabed abundances and endmember reflectances. A sub-surface mixing model is presented, based on a recently proposed oceanic radiative transfer model that accounts for seabed adjacency effects in the water column. Two original non-negative matrix factorization (N MF)-based unmixing algorithms, referred to as WADJU M (Water ADJacency UnMixing) and WU M (Water UnMixing, no adjacency effects) are developed, assuming as known the water column bio-optical properties. Simulations show that WADJU M algorithm achieves performance close to that of the N MF-based unmixing of the seabed without any water column, up to 10 m depth. WU M performance is lower and decreases with the depth. The robustness of the algorithms when using erroneous information about the water column bio-optical properties is evaluated. The results show that the abundance estimation is more reliable using WADJU M approach. WADJU M is applied to real data acquired along the French coast; the derived abundance maps of the benthic habitats are discussed and compared to the maps obtained using a fixed spectral library and a least-square (LS) estimation of the seabed mixing coefficients. The results show the relevance of the WADJU M algorithm for the local analysis of the benthic habitats