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Macrophage elastase (ME) was originally named when metal-dependent elastolytic activity was detected in conditioned media of murine macrophages. Subsequent cDNA cloning of the mouse and human enzyme demonstrated that ME is a distinct member of the matrix metalloproteinase family. To date, the catalytic parameters that describe the hydrolysis of elastin by ME have not been quantified and its activity against other matrix proteins have not been described. In this report, we have examined the action of purified recombinant human ME (rHME), produced in Escherichia coli, on elastin and other extracellular matrix proteins. On a molar basis, rHME is approximately 30% as active as human leukocyte elastase in solubilizing elastin. rHME also efficiently degrades ␣ 1 -antitrypsin (␣ 1 -AT), the primary physiological inhibitor of human leukocyte elastase. In addition, rHME efficiently degrades fibronectin, laminin, entactin, type IV collagen, chondroitan sulfate, and heparan sulfate. These results suggest that HME may be required for macrophages to penetrate basement membranes and remodel injured tissue during inflammation. Moreover, abnormal expression of HME may contribute to destructive processes such as pulmonary emphysema and vascular aneurysm formation. To further understand the specificity of HME, the initial cleavage sites in ␣ 1 -AT have been determined. In addition, the hydrolysis of a series of synthetic peptides with different P 1 residues has been determined. rHME can accept large and small amino acids at the P 1 site, but has a preference for leucine.Macrophage elastase (MMP-12) shares many properties with other members of the matrix metalloproteinase (MMP) 1 gene family, yet it is unique in several ways. Like other MMPs, metalloelastase requires zinc for catalytic activity, is inhibited by the tissue inhibitors of metalloproteinases (TIMPs), and has common structural domains with other MMPs (1, 2). Human macrophage elastase (HME) is most closely related to collagenase-1 (MMP-1) and stromelysin-1 (MMP-3), being 49% identical to each at the amino acid level (3). Moreover, the gene for macrophage elastase, composed of a common 10-exon, 9-intron structure, is on human chromosome 11q22.2/22.3 with at least six other MMPs (4). Despite these similarities, HME possesses certain distinct biological and biochemical properties. Expression appears to be largely restricted to tissue macrophages (4). Upon activation, it not only cleaves its 8-kDa N-terminal domain, but also has a unique propensity to autolytically release its 23-kDa C-terminal domain resulting in a mature active 22-kDa proteinase (3)(4)(5).Macrophage elastase shares its elastolytic activity (6, 7) with only a few MMPs, including the gelatinases (MMP-2 and MMP-9) and, to a lesser extent, matrilysin (8, 9). However, despite this characteristic activity, the relative capacity of metalloelastase (human or mouse) to degrade elastin has never been quantified. In addition, the catalytic capacity of metalloelastase against other extracellular matrix components has...

The German TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission (TDM) collects two global data sets of very high resolution (VHR) synthetic aperture radar (SAR) images between 2011 and 2013. Such imagery provides a unique information source for the identification of built-up areas in a so far unique spatial detail. This letter presents the novel implementation of a fully automated processing system for the delineation of human settlements worldwide based on the SAR data acquired in the context of the TDM. The proposed Urban Footprint Processor (UFP) includes three main processing stages dedicated to: i) the extraction of texture information suitable for highlighting regions characterized by highly structured and heterogeneous built-up areas; ii) the generation of a binary settlement layer (built-up, non-built-up) based on an unsupervised classification scheme accounting for both the original backscattering amplitude and the extracted texture; and iii) a final post-editing and mosaicking phase aimed at providing the final Urban Footprint (UF) product for arbitrary geographical regions. Experimental results assess the high potential of the TDM data and the proposed UFP to provide highly accurate geo-data for an improved global mapping of human settlements.

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Automatic segmentation of anatomical structures in medical images is a valuable tool for efficient computer-aided radiotherapy and surgery planning and an enabling technology for dynamic adaptive radiotherapy. This paper presents the design, algorithms and validation of new software for the automatic segmentation of CT images used for radiotherapy treatment planning. A coarse to fine approach is followed that consists of presegmentation, anatomic orientation and structure segmentation. No user input or a priori information about the image content is required. In presegmentation, the body outline, the bones and lung equivalent tissue are detected. Anatomic orientation recognizes the patient's position, orientation and gender and creates an elastic mapping of the slice positions to a reference scale. Structure segmentation is divided into localization, outlining and refinement, performed by procedures with implicit anatomic knowledge using standard image processing operations. The presented version of algorithms automatically segments the body outline and bones in any gender and patient position, the prostate, bladder and femoral heads for male pelvis in supine position, and the spinal canal, lungs, heart and trachea in supine position. The software was developed and tested on a collection of over 600 clinical radiotherapy planning CT stacks. In a qualitative validation on this test collection, anatomic orientation correctly detected gender, patient position and body region in 98% of the cases, a correct mapping was produced for 89% of thorax and 94% of pelvis cases. The average processing time for the entire segmentation of a CT stack was less than 1 min on a standard personal computer. Two independent retrospective studies were carried out for clinical validation. Study I was performed on 66 cases (30 pelvis, 36 thorax) with dosimetrists, study II on 52 cases (39 pelvis, 13 thorax) with radio-oncologists as experts. The experts rated the automatically produced structures on the scale 1-excellent (no corrections necessary, maximum time saving), 2-good (corrections necessary for up to 1/3 of slices), 3-acceptable (major corrections necessary, but still time saving), 4-not acceptable (manual redrawing more efficient, no time saving). A rating

Abstract. In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.