Role of Oxidants in Interstitial Lung Diseases: Pneumoconioses, Constrictive Bronchiolitis, and Chronic Tropical Pulmonary Eosinophilia

Oxidants such as superoxide anion, hydrogen peroxide, and myeloperoxidase from activated inflammatory cells in the lower respiratory tract contribute to inflammation and injury. Etiologic agents include inorganic particulates such as asbestos, silica, or coal mine dust or mixtures of inorganic dust and combustion materials found in World Trade Center dust and smoke. These etiologic agents are phagocytosed by alveolar macrophages or bronchial epithelial cells and release chemotactic factors that recruit inflammatory cells to the lung. Chemotactic factors attract and activate neutrophils, eosinophils, mast cells, and lymphocytes and further activate macrophages to release more oxidants. Inorganic dusts target alveolar macrophages, World Trade Center dust targets bronchial epithelial cells, and eosinophils characterize tropical pulmonary eosinophilia (TPE) caused by filarial organisms. The technique of bronchoalveolar lavage in humans has recovered alveolar macrophages (AMs) in dust diseases and eosinophils in TPE that release increased amounts of oxidants in vitro. Interestingly, TPE has massively increased eosinophils in the acute form and after treatment can still have ongoing eosinophilic inflammation. A course of prednisone for one week can reduce the oxidant burden and attendant inflammation and may be a strategy to prevent chronic TPE and interstitial lung disease

Parabolic resonances and instabilities in near-integrable two degrees of freedom Hamiltonian flows

When an integrable two-degrees-of-freedom Hamiltonian system possessing a circle of parabolic fixed points is perturbed, a parabolic resonance occurs. It is proved that its occurrence is generic for one parameter families (co-dimension one phenomenon) of near-integrable, t.d.o. systems. Numerical experiments indicate that the motion near a parabolic resonance exhibits new type of chaotic behavior which includes instabilities in some directions and long trapping times in others. Moreover, in a degenerate case, near a {\it flat parabolic resonance}, large scale instabilities appear. A model arising from an atmospherical study is shown to exhibit flat parabolic resonance. This supplies a simple mechanism for the transport of particles with {\it small} (i.e. atmospherically relevant) initial velocities from the vicinity of the equator to high latitudes. A modification of the model which allows the development of atmospherical jets unfolds the degeneracy, yet traces of the flat instabilities are clearly observed

Workers' Compensation

Apresentação do tema em debate sobre a "qualidade da vida"

Setting Parameters for Biological Models With ANIMO

ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions between biological entities in form of a graph, while the parameters determine the speed of occurrence of such interactions. When a mismatch is observed between the behavior of an ANIMO model and experimental data, we want to update the model so that it explains the new data. In general, the topology of a model can be expanded with new (known or hypothetical) nodes, and enables it to match experimental data. However, the unrestrained addition of new parts to a model causes two problems: models can become too complex too fast, to the point of being intractable, and too many parts marked as "hypothetical" or "not known" make a model unrealistic. Even if changing the topology is normally the easier task, these problems push us to try a better parameter fit as a first step, and resort to modifying the model topology only as a last resource. In this paper we show the support added in ANIMO to ease the task of expanding the knowledge on biological networks, concentrating in particular on the parameter settings

Symmetry breaking perturbations and strange attractors

The asymmetrically forced, damped Duffing oscillator is introduced as a prototype model for analyzing the homoclinic tangle of symmetric dissipative systems with \textit{symmetry breaking} disturbances. Even a slight fixed asymmetry in the perturbation may cause a substantial change in the asymptotic behavior of the system, e.g. transitions from two sided to one sided strange attractors as the other parameters are varied. Moreover, slight asymmetries may cause substantial asymmetries in the relative size of the basins of attraction of the unforced nearly symmetric attracting regions. These changes seems to be associated with homoclinic bifurcations. Numerical evidence indicates that \textit{strange attractors} appear near curves corresponding to specific secondary homoclinic bifurcations. These curves are found using analytical perturbational tools

Modeling Quantitative Value of Habitats for Marine and Estuarine Populations

Coastal habitats (e.g., seagrass beds, shallow mud and sand flats) strongly influence survival, growth, and reproduction of exploited marine fish and invertebrate species. Many of these species have declined over the past decades, coincident with widespread degradation of coastal habitats, such that an urgent need exists to model the quantitative value of coastal habitats to their population dynamics. Demand for habitat considerations will increase as fisheries management contends with habitat issues in stock assessments and management in general moves towards a more ecosystem-based approach. The modeling of habitat function to support fishery species has, to date, been done on a case-by-case basis involving diverse approaches and types of population models, which has made it difficult to generalize about methods for incorporating habitat into population models. In this review, we offer guiding concepts for how habitat effects can be incorporated in population models commonly used to simulate the population dynamics of exploited fish and invertebrate species. We categorize population models based on whether they are static or dynamic representations of population status, and for dynamic, further into unstructured, age/size class structured, and individual-based. We then use examples to illustrate how habitat has been incorporated, implicitly (correlative) and explicitly (mechanistically), into each of these categories. We describe the methods used and provide details on their implementation and utility to facilitate adaptation of the approaches for other species and systems. We anticipate that our review can serve as a stimulus for more widespread use of population models to quantify the value of coastal habitats for exploited species, so that their importance can be accurately realized and to facilitate cross-species and cross-system comparisons. Quantitative evaluation of habitat effects in population dynamics will increasingly be needed for traditional stock assessments, ecosystem-based fisheries management, conservation of at-risk habitats, and recovery of overexploited stocks that rely on critical coastal habitats during their life cycle

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Contribuci?n de las alianzas de colaboraci?n universidad-empresa de investigaci?n, desarrollo e innovaci?n para desarrollar proyectos de innovaci?n tecnol?gica en producto en pa?ses emergentes

El prop?sito del presente estudio es profundizar los factores claves organizacionales m?s influyentes en las alianzas de colaboraci?n universidad-empresa en I+D+i para desarrollar proyectos exitosos de innovaci?n tecnol?gica en producto en pa?ses emergentes. La literatura evidencia que los estudios se han enfocado en identificar una serie de factores internos y externos que influyen a la formaci?n y consolidaci?n de estas alianzas pero muy limitado en profundizar los factores claves organizacionales para desarrollar proyectos exitosos de innovaci?n tecnol?gica una vez formada la relaci?n. Finalmente este estudio propone un modelo de investigaci?n basado en 5 factores organizacionales claves siendo estos la participaci?n de la empresa en la definici?n del producto final de innovaci?n, la similitud en la percepci?n de la innovaci?n, la participaci?n de expertos externos en innovaci?n, la participaci?n de gestores externos en proyectos de innovaci?n y los incentivos que motivan la participaci?n de expertos en innovaci?n externos. Como variable dependiente se tiene al proyecto exitoso de innovaci?n tecnol?gica en producto y como variables controladoras se tienen al tipo de universidad y al tipo de negocio

Contribuci?n de las alianzas de colaboraci?n universidad-empresa de investigaci?n, desarrollo e innovaci?n para desarrollar proyectos de innovaci?n tecnol?gica en producto en pa?ses emergentes

El prop?sito del presente estudio es profundizar los factores claves organizacionales m?s influyentes en las alianzas de colaboraci?n universidad-empresa en I+D+i para desarrollar proyectos exitosos de innovaci?n tecnol?gica en producto en pa?ses emergentes. La literatura evidencia que los estudios se han enfocado en identificar una serie de factores internos y externos que influyen a la formaci?n y consolidaci?n de estas alianzas pero muy limitado en profundizar los factores claves organizacionales para desarrollar proyectos exitosos de innovaci?n tecnol?gica una vez formada la relaci?n. Finalmente este estudio propone un modelo de investigaci?n basado en 5 factores organizacionales claves siendo estos la participaci?n de la empresa en la definici?n del producto final de innovaci?n, la similitud en la percepci?n de la innovaci?n, la participaci?n de expertos externos en innovaci?n, la participaci?n de gestores externos en proyectos de innovaci?n y los incentivos que motivan la participaci?n de expertos en innovaci?n externos. Como variable dependiente se tiene al proyecto exitoso de innovaci?n tecnol?gica en producto y como variables controladoras se tienen al tipo de universidad y al tipo de negocio