Function of liver activation-regulated chemokine/CC chemokine ligand 20 is differently affected by cathepsin B and cathepsin D processing

Chemokine processing by proteases is emerging as an important regulatory mechanism of leukocyte functions and possibly also of cancer progression. We screened a large panel of chemokines for degradation by cathepsins B and D, two proteases involved in tumor progression. Among the few substrates processed by both proteases, we focused on CCL20, the unique chemokine ligand of CCR6 that is expressed on immature dendritic cells and subtypes of memory lymphocytes. Analysis of the cleavage sites demonstrate that cathepsin B specifically cleaves off four C-terminally located amino acids and generates a CCL20(1-66) isoform with full functional activity. By contrast, cathepsin D totally inactivates the chemotactic potency of CCL20 by generating CCL20(1-55), CCL20(1-52), and a 12-aa C-terminal peptide CCL20(59-70). Proteolytic cleavage of CCL20 occurs also with chemokine bound to glycosaminoglycans. In addition, we characterized human melanoma cells as a novel CCL20 source and as cathepsin producers. CCL20 production was up-regulated by IL-1alpha and TNF-alpha in all cell lines tested, and in human metastatic melanoma cells. Whereas cathepsin D is secreted in the extracellular milieu, cathepsin B activity is confined to cytosol and cellular membranes. Our studies suggest that CCL20 processing in the extracellular environment of melanoma cells is exclusively mediated by cathepsin D. Thus, we propose a model where cathepsin D inactivates CCL20 and possibly prevents the establishment of an effective antitumoral immune response in melanomas.

Dissociation between overt and unconscious face processing in fusiform face area

The precise role of the fusiform face area (FFA) in face processing remains controversial. In this study, we investigated to what degree FFA activation reflects additional functions beyond face perception. Seven volunteers underwent rapid event-related functional magnetic resonance imaging while they performed a face-encoding and a face-recognition task. During face encoding, activity in the FFA for individual faces predicted whether the individual face was subsequently remembered or forgotten. However, during face recognition, no difference in FFA activity between consciously remembered and forgotten faces was observed, but the activity of FFA differentiated if a face had been seen previously or not. This demonstrated a dissociation between overt recognition and unconscious discrimination of stimuli, suggesting that physiological processes of face recognition can take place, even if not all of its operations are made available to consciousness.

Unifying energy minimization and mutual information maximization for robust 2D/3D registration of X-ray and CT images

Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D/3D registration of X-ray fluoroscopy to CT images. Information theory has been used to derive similarity measure for image registration leading to the introduction of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. Previous attempt to incorporate spatial information into mutual information either requires computing the entropy of higher dimensional probability distributions, or is not robust to outliers. In this paper, we show how to incorporate spatial information into mutual information without suffering from these problems. Using a variational approximation derived from the Kullback-Leibler bound, spatial information can be effectively incorporated into mutual information via energy minimization. The resulting similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on datasets of two applications: (a) intra-operative patient pose estimation from a few (e.g. 2) calibrated fluoroscopic images, and (b) post-operative cup alignment estimation from single X-ray radiograph with gonadal shielding.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

[A mortality databank for international comparison and background information]

This article describes the structure and utilization of a computerized databank system for WHO mortality data. This system makes available "at finger-tips" data which previously were published by WHO in its blue volumes. The data can be handled much more flexible. At the moment the system provides information on age-standardized rates (direct standardization), total number of cases, as well as cover per age-group and year for about a hundred countries. The time period covered is 1950-1985, with exceptions for data which are not available to WHO.

Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: an Artificial Neural Network perspective

Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.

Robust Denoising using Feature and Color Information

We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising filters because features are highly correlated with rendered images. Filters based solely on features, however, are prone to blurring image details that are not well represented by the features. On the other hand, color buffers represent all details, but they may be less effective to determine filters because they are contaminated by the noise that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.