High frequency of functionally active Melan-a-specific T cells in a patient with progressive immunoproteasome-deficient melanoma.

Tumor-reactive T cells play an important role in cancer immunosurveillance. Applying the multimer technology, we report here an unexpected high frequency of Melan-A-specific CTLs in a melanoma patient with progressive lymph node metastases, consisting of 18 and 12.8% of total peripheral blood and tumor-infiltrating CD8+ T cells, respectively. Melan-A-specific CTLs revealed a high cytolytic activity against allogeneic Melan-A-expressing target cells but failed to kill the autologous tumor cells. Loading of the tumor cells with Melan-A peptide reversed the resistance to killing, suggesting impaired function of the MHC class I antigen processing and presentation pathway. Mutations of the coding region of the HLA-A2 binding Melan-A26-35 peptide or down-regulation of the MHC class I heavy chain, the antigenic peptide TAP, and tapasin could be excluded. However, PCR and immunohistochemical analysis revealed a deficiency of the immunoproteasomes low molecular weight protein 2 and low molecular weight protein 7 in the primary tumor cells, which affects the quantity and quality of generated T-cell epitopes and might explain the resistance to killing. This is supported by our data, demonstrating that the resistance to killing can be partially reversed by pre-exposure of the tumor cells to IFN-gamma, which is known to induce the immunoproteasomes. Overall, this is the first report of an extremely high frequency of tumor-specific CTLs that exhibit competent T-cell-effector functions but fail to lyse the autologous tumor cells. Immunotherapeutic approaches should not only focus on the induction of a robust antitumor immune response, but should also have to target tumor immune escape mechanisms.

Nanomedicines for active targeting: physico-chemical characterization of paclitaxel-loaded anti-HER2 immunonanoparticles and in vitro functional studies on target cells.

Paclitaxel (Tx)-loaded anti-HER2 immunonanoparticles (NPs-Tx-HER) were prepared by the covalent coupling of humanized monoclonal anti-HER2 antibodies (trastuzumab, Herceptin) to Tx-loaded poly (dl-lactic acid) nanoparticles (NPs-Tx) for the active targeting of tumor cells that overexpress HER2 receptors. The physico-chemical properties of NPs-Tx-HER were compared to unloaded immunonanoparticles (NPs-HER) to assess the influence of the drug on anti-HER2 coupling to the NP surface. The immunoreactivity of sulfo-MBS activated anti-HER2 mAbs and the in vitro efficacy of NPs-Tx-HER were tested on SKOV-3 ovarian cancer cells that overexpress HER2 antigens. Tx-loaded nanoparticles (NPs-Tx) obtained by a salting-out method had a size of 171+/-22 nm (P.I.=0.1) and an encapsulation efficiency of about of 78+/-10%, which corresponded to a drug loading of 7.8+/-0.8% (w/w). NPs-Tx were then thiolated and conjugated to activated anti-HER2 mAbs to obtain immunonanoparticles of 237+/-43 nm (P.I.=0.2). The influence of the activation step on the immunoreactivity of the mAbs was tested on SKOV-3 cells using 125I-radiolabeled mAbs, and the activity of the anti-HER2 mAbs was minimally affected after sulfo-MBS functionalization. Approximately 270 molecules of anti-HER2 mAbs were bound per nanoparticle. NPs-Tx-HER exhibited a zeta potential of 0.2+/-0.1 mV. The physico-chemical properties of the Tx-loaded immunonanoparticles were very similar to unloaded immunonanoparticles, suggesting that the encapsulation of the drug did not influence the coupling of the mAbs to the NPs. No drug loss was observed during the preparation process. DSC analysis showed that encapsulated Tx is in an amorphous or disordered-crystalline phase. These results suggest that Tx is entrapped in the polymeric matrix and not adsorbed to the surface of the NPs. In vitro studies on SKOV-3 ovarian cancer cells demonstrated the greater cytotoxic effect of NPs-Tx-HER compared to other Tx formulations. The results showed that at 1 ng Tx/ml, the viability of cells incubated with drug encapsulated in NP-Tx-HER was lower (77.32+/-5.48%) than the viability of cells incubated in NPs-Tx (97.4+/-12%), immunonanoparticles coated with Mabthera, as irrelevant mAb (NPs-Tx-RIT) (93.8+/-12%) or free drug (92.3+/-9.3%).

Modelling of iron-filled magneto-active polymers with a dispersed chain-like microstructure

Magneto-active polymers are a class of smart materials commonly manufactured by mixing micron-sized iron particles in a rubber-like matrix. When cured in the presence of an externally applied magnetic field, the iron particles arrange themselves into chain-like structures that lend an overall anisotropy to the material. It has been observed through electron micrographs and X-ray tomographs that these chains are not always perfect in structure, and may have dispersion due to the conditions present during manufacturing or some undesirable material properties. We model the response of these materials to coupled magneto-mechanical loading in this paper using a probability based structure tensor that accounts for this imperfect anisotropy. The response of the matrix material is decoupled from the chain phase, though still being connected through kinematic constraints. The latter is based on the definition of a 'chain deformation gradient' and a 'chain magnetic field'. We conclude with numerical examples that demonstrate the effect of chain dispersion on the response of the material to magnetoelastic loading.

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.

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.

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.