GM-CSF Priming Drives Bone Marrow-Derived Macrophages to a Pro-Inflammatory Pattern and Downmodulates PGE(2) in Response to TLR2 Ligands

In response to pathogen recognition by Toll-like receptors (TLRs) on their cell surface, macrophages release lipid mediators and cytokines that are widely distributed throughout the body and play essential roles in host responses. Granulocyte macrophage colony-stimulating factor (GM-CSF) is important for the immune response during infections to improve the clearance of microorganisms. In this study, we examined the release of mediators in response to TLR2 ligands by bone marrow-derived macrophages (BMDMs) primed with GM-CSF. We demonstrated that when stimulated with TLR2 ligands, non-primed BMDMs preferentially produced PGE(2) in greater amounts than LTB4. However, GM-CSF priming shifted the release of lipid mediators by BMDMs, resulting in a significant decrease of PGE(2) production in response to the same stimuli. The decrease of PGE(2) production from primed BMDMs was accompanied by a decrease in PGE-synthase mRNA expression and an increase in TNF-alpha and nitric oxide (NO) production. Moreover, some GM-CSF effects were potentiated by the addition of IFN-gamma. Using a variety of TLR2 ligands, we established that PGE(2) release by GM-CSF-primed BMDMs was dependent on TLR2 co-receptors (TLR1, TLR6), CD14, MyD88 and the nuclear translocation of NF kappa B but was not dependent on peroxisome proliferator-activated receptor-gamma (PPAR-gamma) activation. Indeed, GM-CSF priming enhanced TLR2, TLR4 and MyD88 mRNA expression and phospho-I kappa B alpha formation. These findings demonstrate that GM-CSF drives BMDMs to present a profile relevant to the host during infections.

Semantic Dementia Versus Nonfluent Progressive Aphasia Neuropsychological Characterization and Differentiation

Background: Early progressive nonfluent aphasia (PNFA) may be difficult to differentiate from semantic dementia (SD) in a nonspecialist setting. There are descriptions of the clinical and neuropsychological profiles of patients with PNFA and SD but few systematic comparisons. Method: We compared the performance of groups with SD (n = 27) and PNFA (n = 16) with comparable ages, education, disease duration, and severity of dementia as measured by the Clinical Dementia Rating Scale on a comprehensive neuropsychological battery. Principal components analysis and intergroup comparisons were used. Results: A 5-factor solution accounted for 78.4% of the total variance with good separation of neuropsychological variables. As expected, both groups were anomic with preserved visuospatial function and mental speed. Patients with SD had lower scores on comprehension-based semantic tests and better performance on verbal working memory and phonological processing tasks. The opposite pattern was found in the PNFA group. Conclusions: Neuropsychological tests that examine verbal and nonverbal semantic associations, verbal working memory, and phonological processing are the most helpful for distinguishing between PNFA and SD.

Semantic Equivalence of the Brazilian Portuguese version of the "Body Change Inventory"

With the increase in research on the components of Body Image, validated instruments are needed to evaluate its dimensions. The Body Change Inventory (BCI) assesses strategies used to alter body size among adolescents. The scope of this study was to describe the translation and evaluation for semantic equivalence of the BCI in the Portuguese language. The process involved the steps of (1) translation of the questionnaire to the Portuguese language; (2) back-translation to English; (3) evaluation of semantic equivalence; and (4) assessment of comprehension by professional experts and the target population. The six subscales of the instrument were translated into the Portuguese language. Language adaptations were made to render the instrument suitable for the Brazilian reality. The questions were interpreted as easily understandable by both experts and young people. The Body Change Inventory has been translated and adapted into Portuguese. Evaluation of the operational, measurement and functional equivalence are still needed.

Maternal LAMP/p55gagHIV-1 DNA Immunization Induces In Utero Priming and a Long-Lasting Immune Response in Vaccinated Neonates

Infants born to HIV-infected mothers are at high risk of becoming infected during gestation or the breastfeeding period. A search is thus warranted for vaccine formulations that will prevent mother-to-child HIV transmission. The LAMP/gag DNA chimeric vaccine encodes the HIV-1 p55gag fused to the lysosome-associated membrane protein-1 (LAMP-1) and has been shown to enhance anti-Gag antibody (Ab) and cellular immune responses in adult and neonatal mice; such a vaccine represents a new concept in antigen presentation. In this study, we evaluated the effect of LAMP/gag DNA immunization on neonates either before conception or during pregnancy. LAMP/gag immunization of BALB/c mice before conception by the intradermal route led to the transfer of anti-Gag IgG1 Ab through the placenta and via breastfeeding. Furthermore, there were an increased percentage of CD4+ CD25+ Foxp3+ T cells in the spleens of neonates. When offspring were immunized with LAMP/gag DNA, the anti-Gag Ab response and the Gag-specific IFN-gamma-secreting cells were decreased. Inhibition of anti-Gag Ab production and cellular responses were not observed six months after immunization, indicating that maternal immunization did not interfere with the long-lasting memory response in offspring. Injection of purified IgG in conjunction with LAMP/gag DNA immunization decreased humoral and cytotoxic T-cell responses. LAMP/gag DNA immunization by intradermal injection prior to conception promoted the transfer of Ab, leading to a diminished response to Gag without interfering with the development of anti-Gag T- and B-cell memory. Finally, we assessed responses after one intravenous injection of LAMP/gag DNA during the last five days of pregnancy. The intravenous injection led to in utero immunization. In conclusion, DNA vaccine enconding LAMP-1 with Gag and other HIV-1 antigens should be considered in the development of a protective vaccine for the maternal/fetal and newborn periods.

Improve protective efficacy of a TB DNA-HSP65 vaccine by BCG priming

Vaccines are considered by many to be one of the most successful medical interventions against infectious diseases. But many significant obstacles remain, such as optimizing DNA vaccines for use in humans or large animals. The amount of doses, route and easiness of administration are also important points to consider in the design of new DNA vaccines. Heterologous prime-boost regimens probably represent the best hope for an improved DNA vaccine strategy. In this study, we have shown that heterologous prime-boost vaccination against tuberculosis (TB) using intranasal BCG priming/DNA-HSP65 boosting (BCGin/DNA) provided significantly greater protection than that afforded by a single subcutaneous or intranasal dose of BCG. In addition, BCGin/DNA immunization was also more efficient in controlling bacterial loads than were the other prime-boost schedules evaluated or three doses of DNA-HSP65 as a naked DNA. The single dose of DNA-HSP65 booster enhanced the immunogenicity of a single subcutaneous BCG vaccination, as evidenced by the significantly higher serum levels of anti-Hsp65 IgG2a Th1-induced antibodies, as well as by the significantly greater production of IFN-γ by antigen-specific spleen cells. The BCG prime/DNA-HSP65 booster was also associated with better preservation of lung parenchyma.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

IFN-γ-induced priming maintains long-term strain-transcending immunity against blood-stage Plasmodium chabaudi malaria

The mechanism by which protective immunity to Plasmodium is lost in the absence of continued exposure to this parasite has yet to be fully elucidated. It has been recently shown that IFN-γ produced during human and murine acute malaria primes the immune response to TLR agonists. In this study, we investigated whether IFN-γ-induced priming is important to maintain long-term protective immunity against Plasmodium chabaudi AS malaria. On day 60 postinfection, C57BL/6 mice still had chronic parasitemia and efficiently controlled homologous and heterologous (AJ strain) challenge. The spleens of chronic mice showed augmented numbers of effector/effector memory (TEM) CD4(+) cells, which is associated with increased levels of IFN-γ-induced priming (i.e., high expression of IFN-inducible genes and TLR hyperresponsiveness). After parasite elimination, IFN-γ-induced priming was no longer detected and protective immunity to heterologous challenge was mostly lost with >70% mortality. Spontaneously cured mice had high serum levels of parasite-specific IgG, but effector T/TEM cell numbers, parasite-driven CD4(+) T cell proliferation, and IFN-γ production were similar to noninfected controls. Remarkably, the priming of cured mice with low doses of IFN-γ rescued TLR hyperresponsiveness and the capacity to control heterologous challenge, increasing the TEM cell population and restoring the CD4(+) T cell responses to parasites. Contribution of TLR signaling to the CD4(+) T cell responses in chronic mice was supported by data obtained in mice lacking the MyD88 adaptor. These results indicate that IFN-γ-induced priming is required to maintain protective immunity against P. chabaudi and aid in establishing the molecular basis of strain-transcending immunity in human malaria.

A Fuzzy Ontology-Driven Approach to Semantic Interoperability in e-Government Big Data

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society.

People semantic description and re-identification from point cloud geometry

The automatic extraction of biometric descriptors of anonymous people is a challenging scenario in camera networks. This task is typically accomplished making use of visual information. Calibrated RGBD sensors make possible the extraction of point cloud information. We present a novel approach for people semantic description and re-identification using the individual point cloud information. The proposal combines the use of simple geometric features with point cloud features based on surface normals.

Semantic-based middleware solutions to support context-aware service provisioning in pervasive environments

The dynamicity and heterogeneity that characterize pervasive environments raise new challenges in the design of mobile middleware. Pervasive environments are characterized by a significant degree of heterogeneity, variability, and dynamicity that conventional middleware solutions are not able to adequately manage. Originally designed for use in a relatively static context, such middleware systems tend to hide low-level details to provide applications with a transparent view on the underlying execution platform. In mobile environments, however, the context is extremely dynamic and cannot be managed by a priori assumptions. Novel middleware should therefore support mobile computing applications in the task of adapting their behavior to frequent changes in the execution context, that is, it should become context-aware. In particular, this thesis has identified the following key requirements for novel context-aware middleware that existing solutions do not fulfil yet. (i) Middleware solutions should support interoperability between possibly unknown entities by providing expressive representation models that allow to describe interacting entities, their operating conditions and the surrounding world, i.e., their context, according to an unambiguous semantics. (ii) Middleware solutions should support distributed applications in the task of reconfiguring and adapting their behavior/results to ongoing context changes. (iii) Context-aware middleware support should be deployed on heterogeneous devices under variable operating conditions, such as different user needs, application requirements, available connectivity and device computational capabilities, as well as changing environmental conditions. Our main claim is that the adoption of semantic metadata to represent context information and context-dependent adaptation strategies allows to build context-aware middleware suitable for all dynamically available portable devices. Semantic metadata provide powerful knowledge representation means to model even complex context information, and allow to perform automated reasoning to infer additional and/or more complex knowledge from available context data. In addition, we suggest that, by adopting proper configuration and deployment strategies, semantic support features can be provided to differentiated users and devices according to their specific needs and current context. This thesis has investigated novel design guidelines and implementation options for semantic-based context-aware middleware solutions targeted to pervasive environments. These guidelines have been applied to different application areas within pervasive computing that would particularly benefit from the exploitation of context. Common to all applications is the key role of context in enabling mobile users to personalize applications based on their needs and current situation. The main contributions of this thesis are (i) the definition of a metadata model to represent and reason about context, (ii) the definition of a model for the design and development of context-aware middleware based on semantic metadata, (iii) the design of three novel middleware architectures and the development of a prototypal implementation for each of these architectures, and (iv) the proposal of a viable approach to portability issues raised by the adoption of semantic support services in pervasive applications.

Semantic coordination tuple centres for eHealth Systems

In questa tesi partendo dai limiti sintattici dello scambio di Electronic Patient Records (EHRs), si arriva alla creazione di un framework che supporti lo scambio di informazioni semantiche. Il framework creato si chiama Semantic TuCSoN ed è una estensione di TuCSoN (Tuple Centres Spread over the Network). Semantic TuCSoN viene modellato per il contesto eHealth definendo gli agenti e le politiche di coordinamento atte allo scambio di EHR. Questo framework vine infine testa per verificarne le performance allo scopo di valutare un suo ulteriore utilizzo.

Semantic coordination through programmable Tuple spaces

Two of the main features of today complex software systems like pervasive computing systems and Internet-based applications are distribution and openness. Distribution revolves around three orthogonal dimensions: (i) distribution of control|systems are characterised by several independent computational entities and devices, each representing an autonomous and proactive locus of control; (ii) spatial distribution|entities and devices are physically distributed and connected in a global (such as the Internet) or local network; and (iii) temporal distribution|interacting system components come and go over time, and are not required to be available for interaction at the same time. Openness deals with the heterogeneity and dynamism of system components: complex computational systems are open to the integration of diverse components, heterogeneous in terms of architecture and technology, and are dynamic since they allow components to be updated, added, or removed while the system is running. The engineering of open and distributed computational systems mandates for the adoption of a software infrastructure whose underlying model and technology could provide the required level of uncoupling among system components. This is the main motivation behind current research trends in the area of coordination middleware to exploit tuple-based coordination models in the engineering of complex software systems, since they intrinsically provide coordinated components with communication uncoupling and further details in the references therein. An additional daunting challenge for tuple-based models comes from knowledge-intensive application scenarios, namely, scenarios where most of the activities are based on knowledge in some form|and where knowledge becomes the prominent means by which systems get coordinated. Handling knowledge in tuple-based systems induces problems in terms of syntax - e.g., two tuples containing the same data may not match due to differences in the tuple structure - and (mostly) of semantics|e.g., two tuples representing the same information may not match based on a dierent syntax adopted. Till now, the problem has been faced by exploiting tuple-based coordination within a middleware for knowledge intensive environments: e.g., experiments with tuple-based coordination within a Semantic Web middleware (surveys analogous approaches). However, they appear to be designed to tackle the design of coordination for specic application contexts like Semantic Web and Semantic Web Services, and they result in a rather involved extension of the tuple space model. The main goal of this thesis was to conceive a more general approach to semantic coordination. In particular, it was developed the model and technology of semantic tuple centres. It is adopted the tuple centre model as main coordination abstraction to manage system interactions. A tuple centre can be seen as a programmable tuple space, i.e. an extension of a Linda tuple space, where the behaviour of the tuple space can be programmed so as to react to interaction events. By encapsulating coordination laws within coordination media, tuple centres promote coordination uncoupling among coordinated components. Then, the tuple centre model was semantically enriched: a main design choice in this work was to try not to completely redesign the existing syntactic tuple space model, but rather provide a smooth extension that { although supporting semantic reasoning { keep the simplicity of tuple and tuple matching as easier as possible. By encapsulating the semantic representation of the domain of discourse within coordination media, semantic tuple centres promote semantic uncoupling among coordinated components. The main contributions of the thesis are: (i) the design of the semantic tuple centre model; (ii) the implementation and evaluation of the model based on an existent coordination infrastructure; (iii) a view of the application scenarios in which semantic tuple centres seem to be suitable as coordination media.