Synthesis of a radioiodinated photoreactive MAGE-1 peptide derivative and photoaffinity labeling of cell-associated human leukocyte antigen-A1 molecules.

The synthesis of a photoreactive derivative of the human leukocyte antigen-A1 (HLA-A1)-restricted MAGE-1 peptide 161-169 (EADPTGHSY) is described. Using conventional automated solid-phase peptide synthesis, a photoreactive derivative of this peptide was synthesized by replacing histidine-167 with photo-reactive N-beta-4-azidosalicyloyl-L-2,3-diaminopropionic acid. The C-terminal tyrosine was incorporated as phosphotyrosine. This peptide derivative was radioiodinated in the presence of chloramine T. This iodination took place selectively at the photoreactive group, because the phosphate ester prevented tyrosine iodination. Following dephosphorylation with alkaline phosphatase and chromatographic purification, the radiolabeled peptide derivative was incubated with cells expressing HLA-A1 or other HLA molecules. Photoactivation resulted in efficient photoaffinity labeling of HLA-A1. Other HLA molecules or other cellular components were not detectably labeled. This labeling was inhibited by HLA-A1 but not by HLA-A2-binding peptides. This synthesis is generally applicable and can also be adapted to the synthesis of well-defined radiolabeled nonphotoreactive peptide derivatives.

Preferential synthesis of prostaglandin D2 by neurons and prostaglandin E2 by fibroblasts and nonneuronal cells in chick dorsal root ganglia.

To determine the type and the relative amount of prostaglandins (PGs) synthesized by various neural tissues, homogenates of meninges, dorsal root ganglia (DRG) capsules, decapsulated DRG, and unsheathed sciatic nerves were incubated with [1-14C]arachidonic acid. Homogenates of cultured cells (meningeal cells, fibroblasts, and nonneuronal or neuronal DRG cells) were used to specify the cells producing particular PGs. The highest synthetic capacity was found in fibroblast-rich tissues (meninges and DRG capsules) and in cultures of meningeal cells or fibroblasts. Two major cyclooxygenase products were formed: [14C]PGE2 and an unusual 14C-labeled compound, Y. The accumulation of compound Y, corresponding probably to 15-hydroperoxy PGE2, was completely impaired by addition of exogenous GSH, which conversely enhanced the synthesis of [14C]PGE2 and promoted the formation of [14C]PGD2. In contrast, decapsulated DRG or unsheathed sciatic nerves displayed a 10-20 times lower capacity to synthesize PGs than fibroblast-rich tissues and produced mainly [14C]PGE2 and [14C]PGD2. In this case, [14C]PGE2 or [14C]PGD2 synthesis was neither enhanced nor promoted by addition of exogenous GSH. Neuron-enriched DRG cell cultures allowed us to specify that [14C]PGD2 is the major prostanoid produced by primary sensory neurons as compared with nonneuronal DRG cells. Because PGD2 synthesis in DRG and more specifically in DRG neurons does not depend on exogenous GSH and differs from PGD2 synthesis in fibroblast-rich tissues, it is concluded that at least two distinct enzymatic processes contribute to PGD2 formation in the nervous system.(ABSTRACT TRUNCATED AT 250 WORDS)

Physiological state and rate protein synthesis of phytoplankton off the coast of Peru as measured by sulfur-35 incorporation

Analiza el estado de la fisiología del fitoplancton de las aguas costeras cercanas a Perú

Parallelizing remote sensing image geometric correction

Remote sensing spatial, spectral, and temporal resolutions of images, acquired over a reasonably sized image extent, result in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is very attractive for monitoring, management, and scienti c activities. With Moore's Law alive and well, more and more parallelism is introduced into all computing platforms, at all levels of integration and programming to achieve higher performance and energy e ciency. Being the geometric calibration process one of the most time consuming processes when using remote sensing images, the aim of this work is to accelerate this process by taking advantage of new computing architectures and technologies, specially focusing in exploiting computation over shared memory multi-threading hardware. A parallel implementation of the most time consuming process in the remote sensing geometric correction has been implemented using OpenMP directives. This work compares the performance of the original serial binary versus the parallelized implementation, using several multi-threaded modern CPU architectures, discussing about the approach to nd the optimum hardware for a cost-e ective execution.

Reliability of the image evaluation system PGMI

Purpose: Many countries used the PGMI (P=perfect, G=good, M=moderate, I=inadequate) classification system for assessing the quality of mammograms. Limits inherent to the subjectivity of this classification have been shown. Prior to introducing this system in Switzerland, we wanted to better understand the origin of this subjectivity in order to minimize it. Our study aimed at identifying the main determinants of the variability of the PGMI system and which criteria are the most subjected to subjectivity. Methods and Materials: A focus group composed of 2 experienced radiographers and 2 radiologists specified each PGMI criterion. Ten raters (6 radiographers and 4 radiologists) evaluated twice a panel of 40 randomly selected mammograms (20 analogic and 20 digital) according to these specified PGMI criteria. The PGMI classification was assessed and the intra- and inter-rater reliability was tested for each professional group (radiographer vs radiologist), image technology (analogic vs digital) and PGMI criterion. Results: Some 3,200 images were assessed. The intra-rater reliability appears to be weak, particularly in respect to inter-rater variability. Subjectivity appears to be largely independent of the professional group and image technology. Aspects of the PGMI classification criteria most subjected to variability were identified. Conclusion: Post-test discussions enabled to specify more precisely some criteria. This should reduce subjectivity when applying the PGMI classification system. A concomitant, important effort in training radiographers is also necessary.

Comparison of organ doses and image quality between CT and flat panel XperCT scans in wrist and inner ear examinations.

The aim of this study was to evaluate and compare organ doses delivered to patients in wrist and petrous bone examinations using a multislice spiral computed tomography (CT) and a C-arm cone-beam CT equipped with a flat-panel detector (XperCT). For this purpose, doses to the target organ, i.e. wrist or petrous bone, together with those to the most radiosensitive nearby organs, i.e. thyroid and eye lens, were measured and compared. Furthermore, image quality was compared for both imaging systems and different acquisition modes using a Catphan phantom. Results show that both systems guarantee adequate accuracy for diagnostic purposes for wrist and petrous bone examinations. Compared with the CT scanner, the XperCT system slightly reduces the dose to target organs and shortens the overall duration of the wrist examination. In addition, using the XperCT enables a reduction of the dose to the eye lens during head scans (skull base and ear examinations).

Habitudes alimentaires et image corporelle chez des étudiants de deuxième année de gymnase à Lausanne [Food habits and body image of students in Lausanne]

During adolescence, nutrition needs are high; however the literature shows that few adolescents are following standardized nutritional requirements. A few weeks before an intervention about nutrition to high school adolescents in Lausanne, they were invited to fill in a self-reported questionnaire about their nutrition modes and habits, and their self-image satisfaction (N = 198). Results show that only 5% of youth are eating 5 fruits and vegetables per day and only 29% 3 to 5 dairy products. 21% of female and 6% of boys are not satisfied about their self-image, and those exhibiting a poor self-image tend to adopt health compromising eating patterns in a higher proportion. During adolescence it is important not only to investigate the nutritional habits but also one's self image.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.