The role of MMP-9 and its fibronectin-like domain in tumor growth & invasion : certainties, controversies & discrepancies

Tumors are often compared to wounds that do not heal, where the crosstalk between tumor cells and their surrounding stroma is crucial at all stages of development, from the initial primary growth to metastasis. Similar to wound healing, fibroblasts in the tumor stroma differentiate into myofibroblasts, also referred to as "cancer-associated fibroblasts" (CAFs), primarily, but not exclusively, in response to transforming growth factor-ß (TGF-ß). Myofibroblasts in turn enhance tumor progression by remodeling the stroma. Among molecules implicated in stroma remodeling, matrix metalloproteinases (MMPs), and MMP-g in particular, play a prominent role. However, the mechanisms that regulate MMP-g activation and function remain poorly understood. Recent evidence indicates that tumor cell surface association of MMP-g is an important event in its activation, and more generally in tumor growth and invasion. In the present work we address the potential association of MMP-g activity with cell-surface recruitment to human fibroblasts. We show for the first time that recruitment of MMP-g to the MRC-5 fibroblast cell surface occurs through the fibronectin-like (FN) domain, shared only by MMP-g and MMP-2 among all the MMPs. Functional assays suggest that both the pro- and active form of MMP-g trigger a-smooth muscle actin (aSMA) expression in resting fibroblasts that reflects myofibroblast differentiation, possibly through TGF-ß activation. Moreover, the FN domain of MMP-g inhibits both MMP-g-induced TGF-ß activation and aSMA expression by sequestering MMP-g. Xenograft experiments in NOD/SCID mice using HT1080 fibrosarcoma or MDA-MD231 breast adenocarcinoma cells stably expressing the FN domain of MMP-g revealed no changes in primary tumor growth. However, in the context of metastasis, expression of the FN domain by these same tumor cells dramatically increased their metastatic proclivity whereas expression of wt MMP-g either promoted no change or actually reduced the number of metastases. We observed a decrease of an active form of MMP-g in MDA-MB231 cells overexpressing the FN domain suggesting that the FN domain may inhibit MMP-g activity in Tumors are often compared to wounds that do not heal, where the crosstalk between tumor cells and their surrounding stroma is crucial at all stages of development, from the initial primary growth to metastasis. Similar to wound healing, fibroblasts in the tumor stroma differentiate into myofibroblasts, also referred to as "cancer-associated fibroblasts" (CAFs), primarily, but not exclusively, in response to transforming growth factor-ß (TGF-ß). Myofibroblasts in turn enhance tumor progression by remodeling the stroma. Among molecules implicated in stroma remodeling, matrix metalloproteinases (MMPs), and MMP-g in particular, play a prominent role. However, the mechanisms that regulate MMP-g activation and function remain poorly understood. Recent evidence indicates that tumor cell surface association of MMP-g is an important event in its activation, and more generally in tumor growth and invasion. In the present work we address the potential association of MMP-g activity with cell-surface recruitment to human fibroblasts. We show for the first time that recruitment of MMP-g to the MRC-5 fibroblast cell surface occurs through the fibronectin-like (FN) domain, shared only by MMP-g and MMP-2 among all the MMPs. Functional assays suggest that both the pro- and active form of MMP-g trigger a-smooth muscle actin (aSMA) expression in resting fibroblasts that reflects myofibroblast differentiation, possibly through TGF-ß activation. Moreover, the FN domain of MMP-g inhibits both MMP-g-induced TGF-ß activation and aSMA expression by sequestering MMP-g. Xenograft experiments in NOD/SCID mice using HT1080 fibrosarcoma or MDA-MD231 breast adenocarcinoma cells stably expressing the FN domain of MMP-9 revealed no changes in primary tumor growth. However, in the context of metastasis, expression of the FN domain by these same tumor cells dramatically increased their metastatic proclivity whereas expression of wt MMP-g either promoted no change or actually reduced the number of metastases. We observed a decrease of an active form of MMP-9 in MDA-MB231 cells overexpressing the FN domain suggesting that the FN domain may inhibit MMP-9 activity in those cells and therefore prevent MMP-9-induced activation of TGF-b, which results in increased invasion. Curiously, xenografts of SW480 colorectal adenocarcinoma cells stably expressing the FN domain of MMP-9 displayed reduced growth at both the primary (subcutaneous) injection site and the lungs of NOD/SCID mice, in experimental metastasis assays, whilst the same cells overexpressing wt MMP-9 showed enhanced growth and dissemination. Gelatin zymography of conditioned medium revealed that these effects may be due to the FN domain, which displaces MMP-9 from SW480 cell surface. These observations suggest a dual role of MMP-9 and its FN domain in primary tumor growth and metastasis, underscoring the notion that the effect of MMP-9 on tumor cells may depend on the cell type and highlighting possible protective effects of MMPs in tumor progression.

Semi-supervised remote sensing image classification with cluster kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

Micromorfologia e gênese de luvissolos e planossolos desenvolvidos de rochas metamórficas no semi-árido brasileiro

Apesar da ocorrência comum de Luvissolos e Planossolos nas áreas de rochas do Pré-Cambriano na região semi-árida do Brasil, há poucas informações disponíveis sobre a micromorfologia e a gênese desses solos. Objetivou-se com este trabalho realizar a caracterização micromorfológica de perfis de solos dispostos em toposseqüências nas quais os Luvissolos são os principais componentes e, com isso, gerar informações para subsidiar a interpretação da sua gênese. Doze perfis de solos, sendo 10 de Luvissolos e dois de Planossolos, distribuídos em quatro toposseqüências, foram selecionados em áreas semi-áridas dos Estados de Pernambuco e da Paraíba. Amostras indeformadas foram coletadas de horizontes selecionados, impregnadas, cortadas e polidas para confecção das seções delgadas, que foram analisadas sob microscópio petrográfico. A descrição das amostras indicou que não há evidências micromorfológicas que sustentem que a argiluviação tenha sido um processo efetivo na formação do gradiente textural dos solos estudados. A remoção preferencial (erosão diferencial) da argila dos horizontes superficiais é apontada como o principal processo atuante na formação do gradiente textural encontrado na maioria dos solos estudados. As principais pedofeições observadas estão relacionadas ao intemperismo dos minerais primários, notadamente da biotita, à dinâmica de formação e dissolução dos compostos de Fe e à reorganização da massa do solo em função das mudanças de umidade do solo decorrentes das alternâncias entre períodos secos e chuvosos.

Semantic domains and supersens tagging for domain-specific ontology learning

In this paper we propose a novel unsupervised approach to learning domain-specific ontologies from large open-domain text collections. The method is based on the joint exploitation of Semantic Domains and Super Sense Tagging for Information Retrieval tasks. Our approach is able to retrieve domain specific terms and concepts while associating them with a set of high level ontological types, named supersenses, providing flat ontologies characterized by very high accuracy and pertinence to the domain.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

Impedance field and transition from thermal to shot noise in Cd1-xZnxTe semi-insulating Ohmic detectors

Within a drift-diffusion model we investigate the role of the self-consistent electric field in determining the impedance field of a macroscopic Ohmic (linear) resistor made by a compensated semi-insulating semiconductor at arbitrary values of the applied voltage. The presence of long-range Coulomb correlations is found to be responsible for a reshaping of the spatial profile of the impedance field. This reshaping gives a null contribution to the macroscopic impedance but modifies essentially the transition from thermal to shot noise of a macroscopic linear resistor. Theoretical calculations explain a set of noise experiments carried out in semi-insulating CdZnTe detectors.

A time-domain method for the analysis of thermal impedance response preserving the convolution form

The study of the thermal behavior of complex packages as multichip modules (MCM¿s) is usually carried out by measuring the so-called thermal impedance response, that is: the transient temperature after a power step. From the analysis of this signal, the thermal frequency response can be estimated, and consequently, compact thermal models may be extracted. We present a method to obtain an estimate of the time constant distribution underlying the observed transient. The method is based on an iterative deconvolution that produces an approximation to the time constant spectrum while preserving a convenient convolution form. This method is applied to the obtained thermal response of a microstructure as analyzed by finite element method as well as to the measured thermal response of a transistor array integrated circuit (IC) in a SMD package.

c-Jun N-terminal kinase binding domain-dependent phosphorylation of mitogen-activated protein kinase kinase 4 and mitogen-activated protein kinase kinase 7 and balancing cross-talk between c-Jun N-terminal kinase and extracellular signal-regulated kinase pathways in cortical neurons

The c-Jun N-terminal kinase (JNK) is a mitogen-activated protein kinase (MAPK) activated by stress-signals and involved in many different diseases. Previous results proved the powerful effect of the cell permeable peptide inhibitor d-JNKI1 (d-retro-inverso form of c-Jun N-terminal kinase-inhibitor) against neuronal death in CNS diseases, but the precise features of this neuroprotection remain unclear. We here performed cell-free and in vitro experiments for a deeper characterization of d-JNKI1 features in physiological conditions. This peptide works by preventing JNK interaction with its c-Jun N-terminal kinase-binding domain (JBD) dependent targets. We here focused on the two JNK upstream MAPKKs, mitogen-activated protein kinase kinase 4 (MKK4) and mitogen-activated protein kinase kinase 7 (MKK7), because they contain a JBD homology domain. We proved that d-JNKI1 prevents MKK4 and MKK7 activity in cell-free and in vitro experiments: these MAPKK could be considered not only activators but also substrates of JNK. This means that d-JNKI1 can interrupt downstream but also upstream events along the JNK cascade, highlighting a new remarkable feature of this peptide. We also showed the lack of any direct effect of the peptide on p38, MEK1, and extracellular signal-regulated kinase (ERK) in cell free, while in rat primary cortical neurons JNK inhibition activates the MEK1-ERK-Ets1/c-Fos cascade. JNK inhibition induces a compensatory effect and leads to ERK activation via MEK1, resulting in an activation of the survival pathway-(MEK1/ERK) as a consequence of the death pathway-(JNK) inhibition. This study should hold as an important step to clarify the strong neuroprotective effect of d-JNKI1.

Option valuation as an expectation in the complex domain: The Black-Scholes case

It is very well known that the first succesful valuation of a stock option was done by solving a deterministic partial differential equation (PDE) of the parabolic type with some complementary conditions specific for the option. In this approach, the randomness in the option value process is eliminated through a no-arbitrage argument. An alternative approach is to construct a replicating portfolio for the option. From this viewpoint the payoff function for the option is a random process which, under a new probabilistic measure, turns out to be of a special type, a martingale. Accordingly, the value of the replicating portfolio (equivalently, of the option) is calculated as an expectation, with respect to this new measure, of the discounted value of the payoff function. Since the expectation is, by definition, an integral, its calculation can be made simpler by resorting to powerful methods already available in the theory of analytic functions. In this paper we use precisely two of those techniques to find the well-known value of a European call

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

The prognostic value of semi-quantitative i-123 mibg scintigraphy at diagnosis in high risk neuroblastoma: validation of the siopen score method

Purpose: SIOPEN scoring of 123I mIBG imaging has been shown to predict response to induction chemotherapy and outcome at diagnosis in children with HRN.Method: Patterns of skeletal 123I mIBG uptake were assigned numerical scores (Mscore) ranging from 0 (no metastasis) to 72 (diffuse metastases) within 12 body areas as described previously. 271 anonymised, paired image data sets acquired at diagnosis and on completion of Rapid COJEC induction chemotherapy were reviewed, constituting a representative sample of 1602 children treated prospectively within the HR-NBL1/SIOPEN trial. Pre-and post-treatment Mscores were compared with bone marrow cytology (BM) and 3 year event free survival (EFS).Results: Results 224/271 patients showed skeletal MIBG-uptake at diagnosis and were evaluable forMIBG-response. Complete response (CR) on MIBG to Rapid COJEC induction was achieved by 66%, 34% and 15% of patients who had pre-treatment Mscores of <18 (n¼65, 29%), 18-44 (n¼95,42%) and Y ´ 45 (n¼64, 28.5%) respectively (chi squared test p<.0001). Mscore at diagnosis and on completion of Rapid COJEC correlated strongly with BM involvement (p<0.0001). The correlation of pre score with post scores and response was highly significant (p<0.001). Most importantly, the 3 year EFS in 47 children with Mscore 0 at diagnosis was 0.68 (A ` 0.07), by comparison with 0.42 (A` 0.06), 0.35 (A` 0.05) and 0.25 (A` 0.06) for patients in pre-treatment score groups <18, 18-44 and Y ´ 45, respectively (p<0.001). AnMscore threshold ofY ´ 45 at diagnosis was associated with significantly worse outcome by comparison with all other Mscore groups (p¼0.029). The 3 year EFS of 0.53 (A` 0.07) of patients in metastatic CR (mIBG and BM) after Rapid Cojec (33%) is clearly superior to patients not achieving metastatic CR (0.24 (A ` 0.04), p¼0.005).Conclusion: SIOPEN scoring of 123I mIBG imaging has been shown to predict response to induction chemotherapy and outcome at diagnosis in children with HRN.

Emission of nitrous oxide and carbon dioxide from semi-arid tropical soils in Chiapas México

The semi-arid region of Chiapas is dominated by N2 -fixing shrubs, e.g., Acacia angustissima. Urea-fertilized soil samples under maize were collected from areas covered and uncovered by A. angustissima in different seasons and N2O and CO2 emissions were monitored. The objective of this study was to determine the effects of urea and of the rainy and dry season on gas emissions from semi-arid soil under laboratory conditions. Urea and soil use had no effect on CO2 production. Nitrons oxide emission from soil was three times higher in the dry than in the rainy season, while urea fertilization doubled emissions. Emissions were twice as high from soil sampled under A. angustissima canopy than from arable land, but 1.2 lower than from soil sampled outside the canopy, and five times higher from soil incubated at 40 % of the water-holding capacity (WHC) than at soil moisture content, but 15 times lower than from soil incubated at 100 WHC. It was found that the soil sampling time and water content had a significant effect on N2O emissions, while N fertilizer and sampling location were less influent.