Surveillance active du cancer de la prostate [Active surveillance in prostate cancer].

Active surveillance in prostate cancer The spread of PSA in the screening of prostate cancer has almost doubled the incidence of this disease in the last twenty years. An improved understanding of the natural history of this cancer allows for risk stratification of the disease and to better predict insignificant prostate cancer. Active surveillance has recently been proposed as a new option to delay or avoid a radical treatment for patients with low-risk disease. The principle, results and future perspectives of this treatment modality are discussed in this review.

Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

Small ubiquitin-like modifier conjugating enzyme with active site mutation acts as dominant negative inhibitor of SUMO conjugation in arabidopsis(F).

Small ubiquitin-like modifier (SUMO) conjugation affects a broad range of processes in plants, including growth, flower initiation, pathogen defense, and responses to abiotic stress. Here, we investigate in vivo and in vitro a SUMO conjugating enzyme with a Cys to Ser change in the active site, and show that it has a dominant negative effect. In planta expression significantly perturbs normal development, leading to growth retardation, early flowering and gene expression changes. We suggest that the mutant protein can serve as a probe to investigate sumoylation, also in plants for which poor genetic infrastructure precludes analysis via loss-of-function mutants.

Kaposi sarcoma herpes virus antibody response and viremia following highly active antiretroviral therapy in the Swiss HIV Cohort study.

OBJECTIVE: To describe the effect of HAART on Kaposi sarcoma herpes virus (KSHV) antibody response and viremia among HIV-positive MSM. DESIGN: A follow-up study of 272 HIV-positive MSM (including 22 with Kaposi sarcoma) who first initiated HAART between January 1996 and July 2004 in the Swiss HIV Cohort Study. METHODS: For each individual, two serum samples, one at HAART initiation and another 24 months later, were tested for latent and lytic KSHV antibodies using immunofluorescence assays, and for KSHV viremia using PCR. Factors associated with changes in KSHV antibody titers and viremia were evaluated. RESULTS: At HAART initiation, 69.1 and 75.0% of patients were seropositive to latent and lytic KSHV antibodies, respectively. Seropositivity was associated with the presence of Kaposi sarcoma, older age, lower CD8 cell count and higher CD4/CD8 ratio. Prevalence of KSHV viremia at HAART initiation was 6.4%, being significantly higher among patients with Kaposi sarcoma (35.0%), and those with HIV viral loads 100 000 copies/ml (11.7%) or higher. At 24-month follow-up, geometric mean titers (GMTs) among KSHV seropositive patients increased and antibody seroprevalence was higher. Having Kaposi sarcoma and/or CD4 cell counts less than 50 cells/microl at HAART initiation was associated both with higher probability for antibody titers to increase (including seroconversion) and larger increases in GMTs. Only one of 17 viremic patients at HAART initiation had viremia at 24-month follow-up. CONCLUSION: HAART increases KSHV-specific humoral immune response and clearance of viremia among HIV-infected MSM, consistent with the dramatic protection offered by HAART against Kaposi sarcoma.

Sparse regularization for fiber ODF reconstruction: from the suboptimality of ℓ2 and ℓ1 priors to ℓ0.

Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a large variety of methods have been recently proposed to shorten the acquisition times. Among them, spherical deconvolution approaches have gained a lot of interest for their ability to reliably recover the intra-voxel fiber configuration with a relatively small number of data samples. To overcome the intrinsic instabilities of deconvolution, these methods use regularization schemes generally based on the assumption that the fiber orientation distribution (FOD) to be recovered in each voxel is sparse. The well known Constrained Spherical Deconvolution (CSD) approach resorts to Tikhonov regularization, based on an ℓ(2)-norm prior, which promotes a weak version of sparsity. Also, in the last few years compressed sensing has been advocated to further accelerate the acquisitions and ℓ(1)-norm minimization is generally employed as a means to promote sparsity in the recovered FODs. In this paper, we provide evidence that the use of an ℓ(1)-norm prior to regularize this class of problems is somewhat inconsistent with the fact that the fiber compartments all sum up to unity. To overcome this ℓ(1) inconsistency while simultaneously exploiting sparsity more optimally than through an ℓ(2) prior, we reformulate the reconstruction problem as a constrained formulation between a data term and a sparsity prior consisting in an explicit bound on the ℓ(0)norm of the FOD, i.e. on the number of fibers. The method has been tested both on synthetic and real data. Experimental results show that the proposed ℓ(0) formulation significantly reduces modeling errors compared to the state-of-the-art ℓ(2) and ℓ(1) regularization approaches.

HCF-1 is cleaved in the active site of O-GlcNAc transferase.

Host cell factor-1 (HCF-1), a transcriptional co-regulator of human cell-cycle progression, undergoes proteolytic maturation in which any of six repeated sequences is cleaved by the nutrient-responsive glycosyltransferase, O-linked N-acetylglucosamine (O-GlcNAc) transferase (OGT). We report that the tetratricopeptide-repeat domain of O-GlcNAc transferase binds the carboxyl-terminal portion of an HCF-1 proteolytic repeat such that the cleavage region lies in the glycosyltransferase active site above uridine diphosphate-GlcNAc. The conformation is similar to that of a glycosylation-competent peptide substrate. Cleavage occurs between cysteine and glutamate residues and results in a pyroglutamate product. Conversion of the cleavage site glutamate into serine converts an HCF-1 proteolytic repeat into a glycosylation substrate. Thus, protein glycosylation and HCF-1 cleavage occur in the same active site.

Design and evaluation of a solid sampler for the monitoring of airborne 1,6-hexamethylene diisocyanate (HDI) and its prepolymers in 2-component spray painting

An active, solvent-free solid sampler was developed for the collection of 1,6-hexamethylene diisocyanate (HDI) aerosol and prepolymers. The sampler was made of a filter impregnated with 1-(2-methoxyphenyl)piperazine contained in a filter holder. Interferences with HDI were observed when a set of cellulose acetate filters and a polystyrene filter holder were used; a glass fiber filter and polypropylene filter cassette gave better results. The applicability of the sampling and analytical procedure was validated with a test chamber, constructed for the dynamic generation of HDI aerosol and prepolymers in commercial two-component spray paints (Desmodur(R) N75) used in car refinishing. The particle size distribution, temporal stability, and spatial uniformity of the simulated aerosol were established in order to test the sample. The monitoring of aerosol concentrations was conducted with the solid sampler paired to the reference impinger technique (impinger flasks contained 10 mL of 0.5 mg/mL 1-(2-methoxyphenyl)piperazine in toluene) under a controlled atmosphere in the test chamber. Analyses of derivatized HDI and prepolymers were carried out by using high-performance liquid chromatography and ultraviolet detection. The correlation between the solvent-free and the impinger techniques appeared fairly good (Y = 0.979X - 0.161; R = 0.978), when the tests were conducted in the range of 0.1 to 10 times the threshold limit value (TLV) for HDI monomer and up to 60-mu-g/m3 (3 U.K. TLVs) for total -N = C = O groups.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI.

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.