Constitutive mutants of the GM-CSF receptor reveal multiple pathways leading to myeloid cell survival, proliferation, and granulocyte-macrophage differentiation

Activation of the granulocyte-macrophage colony-stimulating factor (GM-CSF) family of receptors promotes the survival, proliferation, and differentiation of cells of the myeloid compartment. Several signaling pathways are activated downstream of the receptor, however it is not clear how these induce specific biologic outcomes. We have previously identified 2 classes of constitutively active mutants of the shared signaling subunit, human (h) betac, of the human GM-CSF/interieukin-3 (IL-3)/IL-5 receptors that exhibit different modes of signaling. In a factor-dependent bipotential myeloid cell line, FDB1, an activated mutant containing a substitution in the transmembrane domain (V449E) induces factor-independent proliferation and survival, while mutants in the extracellular domain induce factor-independent granulocyte-macrophage differentiation. Here we have used further mutational analysis to demonstrate that there are nonredundant functions for several regions of the cytoplasmic domain with regard to mediating proliferation, viability, and differentiation, which have not been revealed by previous studies with the wild-type GM-CSF receptor. This unique lack of redundancy has revealed an association of a conserved membrane-proximal region with viability signaling and a critical but distinct role for tyrosine 577 in the activities of each class of mutant.

Measuring macromolecular diffusion using heteronuclear multiple-quantum pulsed-field-gradient NMR

We have previously shown that H-1 pulsed-field-gradient (PFG) NMR spectroscopy provides a facile method for monitoring protein self-association and can be used, albeit with some caveats, to measure the apparent molecular mass of the diffusant [Dingley et al. (1995) J. Biomol. NMR, 6, 321-328]. In this paper we show that, for N-15-labelled proteins, selection of H-1-N-15 multiple-quantum (MQ) coherences in PFG diffusion experiments provides several advantages over monitoring H-1 single-quantum (SQ) magnetization. First, the use of a gradient-selected MQ filter provides a convenient means of suppressing resonances from both the solvent and unlabelled solutes. Second, H-1-N-15 zero-quantum coherence dephases more rapidly than H-1 SQ coherence under the influence of a PFG. This allows the diffusion coefficients of larger proteins to be measured more readily. Alternatively, the gradient length and/or the diffusion delay may be decreased, thereby reducing signal losses from relaxation. In order to extend the size of macromolecules to which these experiments can be applied, we have developed a new MQ PFG diffusion experiment in which the magnetization is stored as longitudinal two-spin order for most of the diffusion period, thus minimizing sensitivity losses due to transverse relaxation and J-coupling evolution.

Novel gene containing multiple epidermal growth factor-like motifs transiently expressed in the papillae of the ascidian tadpole larvae

We have investigated molecular mechanisms of the embryonic development of an ascidian, a primitive chordate which shares features of both invertebrates and vertebrates, with a view to identifying genes involved in development and metamorphosis, We isolated 12 partial cDNA sequences which were expressed in a stage-specific manner using differential display, We report here the isolation of a full-length cDNA sequence for one of these genes which was specifically expressed during the tailbud and larval stages of ascidian development, This cDNA, 1213 bp in length, is predicted to encode a protein of 337 amino acids containing four epidermal growth factor (EGF)-like repeats and three novel cysteine-rich repeats, Characterization of its spatial expression pattern by in situ hybridisation in late tailbud and larval embryos demonstrated strong expression localised throughout the papillae and anteriormost trunk and weaker expression in the epidermis of the remainder of the embryo, As recent evidence indicates that the signal for metamorphosis originates in the anterior trunk region, these results suggest that this gene may have a role in signalling the initiation of metamorphosis. (C) 1997 Wiley-Liss, Inc.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Identification of genetic alterations by multiple ligation-dependent probe amplification (MLPA) analysis in oligodendrogliomas

Concurrent deletion at 1p/19q is a common signature of oligodendrogliomas, and it may, be identified in low-grade tumours (grade II) suggesting it represents an early event in the development of these brain neoplasms. Additional non-random changes primarily involve CDKN2A, PTEN and EGFR. Identification of all of these genetic changes has become an additional parameter in the evaluation of the clinical patients` prognosis, including good response to conventional chemotherapy. Multiple ligation-dependent probe amplification (MLPA) analysis is a new methodology that allows an easy identification of the oligodendrogliomas` abnormalities in a single step. No need of the respective constitutional DNA from each patient is another advantage of this method. We used MLPA kits P088 and P105 to determine the molecular characteristics of a series of 40 oligodendrogliomas. Deletions at I p and 19q were identified in 45% and 65% of cases, respectively. Alterations of EGFR, CDKN2A, ERBB2, PTEN and TP53 were also identified in variable frequencies among 7% to 35% of tumours. These findings demonstrate that MLPA is a reliable technique to the detection of molecular genetic changes in oligodendrogliomas.

Number of expressed cancer/testis antigens identifies focal adhesion pathway genes as possible targets for multiple myeloma therapy

Considering that the importance of cancer/testis (CT) antigens in multiple myeloma (MM) biology is still under investigation, the present study aimed to: (1) identify genes differentially expressed in MM using microarray analysis of plasma cell samples, separated according to the number of expressed CTs; (2) examine possible pathways related to MM pathogenesis; (3) validate the expression of candidate genes by quantitative real-time PCR (RQ-PCR). Three samples predominantly positive (>6 expressed), including the U266 cell line, and three samples predominantly negative (0 or 1 expressed CT for the 13 analyzed CT antigens), were submitted for microarray analysis. Validation by RQ-PCR from 24 MM samples showed that the ITGAS gene was downregulated in predominantly positive (>6 expressed CTs, p = 0.0030) and in tumor versus normal plasma cells (p = 0.0182). The RhoD gene was overexpressed in tumor plasma cells when compared to normal plasma cells (p = 0.0339). Results of the microarray analysis corroborate the hypothesis that MM could be separated into predominantly positive and predominantly negative expression. The differential expression of ITGA5 and RhoD suggests disruption of the focal adhesion pathway in MM and offers a new target field to be explored in this disease.

Three-dimensional space-borne synthetic aperture radar (SAR) imaging with multiple pass processing

Three-dimensional (3D) synthetic aperture radar (SAR) imaging via multiple-pass processing is an extension of interferometric SAR imaging. It exploits more than two flight passes to achieve a desired resolution in elevation. In this paper, a novel approach is developed to reconstruct a 3D space-borne SAR image with multiple-pass processing. It involves image registration, phase correction and elevational imaging. An image model matching is developed for multiple image registration, an eigenvector method is proposed for the phase correction and the elevational imaging is conducted using a Fourier transform or a super-resolution method for enhancement of elevational resolution. 3D SAR images are obtained by processing simulated data and real data from the first European Remote Sensing satellite (ERS-1) with the proposed approaches.

The mouse secretome: functional classification of the proteins secreted into the extracellular environment

We have developed a computational strategy to identify the set of soluble proteins secreted into the extracellular environment of a cell. Within the protein sequences predominantly derived from the RIKEN representative transcript and protein set, we identified 2033 unique soluble proteins that are potentially secreted from the cell. These proteins contain a signal peptide required for entry into the secretory pathway and lack any transmembrane domains or intracellular localization signals. This class of proteins, which we have termed the mouse secretome, included >500 novel proteins and 92 proteins

Multiple centroid method to evaluate the adaptability of alfalfa genotypes

This study aimed to evaluate the efficiency of multiple centroids to study the adaptability of alfalfa genotypes (Medicago sativa L.). In this method, the genotypes are compared with ideotypes defined by the bissegmented regression model, according to the researcher's interest. Thus, genotype classification is carried out as determined by the objective of the researcher and the proposed recommendation strategy. Despite the great potential of the method, it needs to be evaluated under the biological context (with real data). In this context, we used data on the evaluation of dry matter production of 92 alfalfa cultivars, with 20 cuttings, from an experiment in randomized blocks with two repetitions carried out from November 2004 to June 2006. The multiple centroid method proved efficient for classifying alfalfa genotypes. Moreover, it showed no unambiguous indications and provided that ideotypes were defined according to the researcher's interest, facilitating data interpretation.

Multi-attribute classification of housing conservation status in urban regeneration actions

Urban regeneration is more and more a “universal issue” and a crucial factor in the new trends of urban planning. It is no longer only an area of study and research; it became part of new urban and housing policies. Urban regeneration involves complex decisions as a consequence of the multiple dimensions of the problems that include special technical requirements, safety concerns, socio-economic, environmental, aesthetic, and political impacts, among others. This multi-dimensional nature of urban regeneration projects and their large capital investments justify the development and use of state-of-the-art decision support methodologies to assist decision makers. This research focuses on the development of a multi-attribute approach for the evaluation of building conservation status in urban regeneration projects, thus supporting decision makers in their analysis of the problem and in the definition of strategies and priorities of intervention. The methods presented can be embedded into a Geographical Information System for visualization of results. A real-world case study was used to test the methodology, whose results are also presented.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Augmented LDPC Graph for Distributed Video Coding with Multiple Side Information

The advances made in channel-capacity codes, such as turbo codes and low-density parity-check (LDPC) codes, have played a major role in the emerging distributed source coding paradigm. LDPC codes can be easily adapted to new source coding strategies due to their natural representation as bipartite graphs and the use of quasi-optimal decoding algorithms, such as belief propagation. This paper tackles a relevant scenario in distributedvideo coding: lossy source coding when multiple side information (SI) hypotheses are available at the decoder, each one correlated with the source according to different correlation noise channels. Thus, it is proposed to exploit multiple SI hypotheses through an efficient joint decoding technique withmultiple LDPC syndrome decoders that exchange information to obtain coding efficiency improvements. At the decoder side, the multiple SI hypotheses are created with motion compensated frame interpolation and fused together in a novel iterative LDPC based Slepian-Wolf decoding algorithm. With the creation of multiple SI hypotheses and the proposed decoding algorithm, bitrate savings up to 8.0% are obtained for similar decoded quality.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Classificação de atividades físicas através do uso do acelerómetro do smartphone

Dissertação de natureza científica realizada para a obtenção do grau de Mestre em Engenharia de redes de comunicação e Multimédia