Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Mapping of Chagas disease research: analysis of publications in the period between 1940 and 2009

INTRODUCTION: Publications are often used as a measure of success in research work. Chagas disease occurs in Central and Southern America. However, during the past years, the disease has been occurring outside Latin America due to migration from endemic zones. This article describes a bibliometric review of the literature on Chagas disease research indexed in PubMed during a 70-year period. METHODS: Medline was used via the PubMed online service of the U.S. National Library of Medicine from 1940 to 2009. The search strategy was: Chagas disease [MeSH] OR Trypanosoma cruzi [MeSH]. RESULTS: A total of 13,989 references were retrieved. The number of publications increased steadily over time from 1,361 (1940-1969) to 5,430 (2000-2009) (coefficient of determination for linear fit, R²=0.910). Eight journals contained 25% of the Chagas disease literature. Of the publications, 64.2% came from endemic countries. Brazil was the predominant country (37%), followed by the United States (17.6%) and Argentina (14%). The ranking in production changed when the number of publications was normalized by estimated cases of Chagas disease (Panama and Uruguay), population (Argentina and Uruguay), and gross domestic product (Bolivia and Brazil). CONCLUSIONS: Several Latin American countries, where the prevalence of T. cruzi infection was not very high, were the main producers of the Chagas disease literature, after adjusting for economic and population indexes. The countries with more estimated cases of Chagas disease produced less research on Chagas disease than some developed countries.

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.

Soap bubble appearance in brain magnetic resonance imaging: cryptococcal meningoencephalitis

Although cryptococcal infections begin in the lungs, meningoencephalitis is the most frequently encountered manifestation of cryptococcosis among individuals with advanced immunosuppression. As the infection progresses along the Virchow-Robin spaces, these structures may become dilated with mucoid material produced by the capsule of the organism. We report a case of a 24-year-old man with cryptococcal meningoencephalitis in which magnetic resonance imaging showed clusters of gelatinous pseudocysts in the periventricular white matter, basal ganglia, mammillary bodies, midbrain peduncles and nucleus dentatus with a soap bubble appearance.

Brain schistosomiasis in mice experimentally infected with Schistosoma mansoni

Introduction Human neuroschistosomiasis has been reported in the literature, but the possibility of modeling neuroschistosomiasis in mice is controversial. Methods In two research laboratories in Brazil that maintain the Schistosoma mansoni life cycle in rodents, two mice developed signs of brain disease (hemiplegia and spinning), and both were autopsied. Results S. mansoni eggs, both with and without granuloma formation, were observed in the brain and meninges of both mice by optical microscopy. Conclusions This is the first description of eggs in the brains of symptomatic mice that were experimentally infected with S. mansoni. An investigation of experimental neuroschistosomiasis is now feasible.

Puerperal brain cryptococcoma in an HIV-negative woman successfully treated with fluconazole: a case report

Cryptococcus spp. cerebral abscesses are uncommon in immunocompetent subjects. The recommended induction treatment is the administration of amphotericin B plus flucytosine combined with resection for lesions ≥3cm. In this paper, we describe an HIV-negative woman diagnosed with a large cryptococcoma in the immediate postpartum period. The lesion was not resected, and due to amphotericin B intolerance, she received an extended course of fluconazole monotherapy. There was no disease recurrence during the 4 years of follow-up. The abrupt onset of her symptoms following delivery suggests that she developed a postpartum immune reconstitution syndrome. This case also demonstrates that in specific situations fluconazole monotherapy can be attempted in immunocompetent patients with cryptococcoma.

Chemotherapy versus best supportive care in stage IV non-small cell lung cancer, non metastatic to the brain

Stage IV non-small cell lung cancer is a fatal disease, with a median survival of 14 months. Systemic chemotherapy is the most common approach. However the impact in overall survival and quality of life still a controversy. OBJECTIVES: To determine differences in overall survival and quality of life among patients with stage IV non-small cell lung cancer non-metastatic to the brain treated with best supportive care versus systemic chemotherapy. PATIENTS: From February 1990 through December 1995, 78 eligible patients were admitted with the diagnosis of stage IV non-small cell lung cancer . Patients were divided in 2 groups: Group A (n=31 -- treated with best supportive care ), and Group B (n=47 -- treated with systemic chemotherapy). RESULTS: The median survival time was 23 weeks (range 5 -- 153 weeks) in Group A and 55 weeks (range 7.4 -- 213 weeks) in Group B (p=0.0018). In both groups, the incidence of admission for IV antibiotics and need of blood transfusions were similar. Patients receiving systemic chemotherapy were also stratified into those receiving mytomycin, vinblastin, and cisplatinum, n=25 and those receiving other combination regimens (platinum derivatives associated with other drugs, n=22). Patients receiving mytomycin, vinblastin, and cisplatinum, n=25 had a higher incidence of febrile neutropenia and had their cycles delayed for longer periods of time than the other group. These patients also had a shorter median survival time (51 versus 66 weeks, p=0.005). CONCLUSION: In patients with stage IV non-small cell lung cancer, non-metastatic to the brain, chemotherapy significantly increases survival compared with best supportive care.

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Many municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

A spatial multicriteria model for urban accessibility mapping

Accessibility is nowadays an important issue for the development of cities. It is seen as a priority in order toguarantee equal access to fundamental rights, to improve the quality of life of citizens and to ensure that everyone, regardless of age, mobility or ability, have equal access to all the resources and benefits cities have to offer. Consequently, factors closely related to the accessibility have gained a higher relevance for identifying and assessing the location of urban facilities. The main goal of the paper is to present an accessibility evaluation model applied in Santarém, in Brazil, a city located midway between the larger cities of Belem and Manaus. The research instruments, sampling method and data analysis proposed for mapping urban accessibility are described. Daily activities were used to identify and group key destinations. The model was implemented within a geographic information system and integrates the individualâ s perspective through the definition of each key destination weight, reflecting their significance for daily activities in the urban area. Accessibility to key destinations was mapped over 24 districts of the city of Santarém. The results of this model application can support city administration decision-making for new investments in order to improve urban quality of life.

Mapping numbers onto space in preschool children and adults

Tese de Doutoramento em Psicologia - Especialidade em Psicologia Experimental e Ciências Cognitivas

From the periphery to the brain: Lipocalin-2, a friend or foe?

Lipocalin-2 (LCN2) is an acute-phase protein that, by binding to iron-loaded siderophores, acts as a potent bacteriostatic agent in the iron-depletion strategy of the immune system to control pathogens. The recent identification of a mammalian siderophore also suggests a physiological role for LCN2 in iron homeostasis, specifically in iron delivery to cells via a transferrin-independent mechanism. LCN2 participates, as well, in a variety of cellular processes, including cell proliferation, cell differentiation and apoptosis, and has been mostly found up-regulated in various tissues and under inflammatory states, being its expression regulated by several inducers. In the central nervous system less is known about the processes involving LCN2, namely by which cells it is produced/secreted, and its impact on cell proliferation and death, or in neuronal plasticity and behaviour. Importantly, LCN2 recently emerged as a potential clinical biomarker in multiple sclerosis and in ageing-related cognitive decline. Still, there are conflicting views on the role of LCN2 in pathophysiological processes, with some studies pointing to its neurodeleterious effects, while others indicate neuroprotection. Herein, these various perspectives are reviewed and a comprehensive and cohesive view of the general function of LCN2, particularly in the brain, is provided.

Assessing the application of polypropylene multifilament yarns in brain phantoms

The currently available clinical imaging methods do not provide highly detailed information about location and severity of axonal injury or the expected recovery time of patients with traumatic brain injury [1]. High-Definition Fiber Tractography (HDFT) is a novel imaging modality that allows visualizing and quantifying, directly, the degree of axons damage, predicting functional deficits due to traumatic axonal injury and loss of cortical projections. This imaging modality is based on diffusion technology [2]. The inexistence of a phantom able to mimic properly the human brain hinders the possibility of testing, calibrating and validating these medical imaging techniques. Most research done in this area fails in key points, such as the size limit reproduced of the brain fibers and the quick and easy reproducibility of phantoms [3]. For that reason, it is necessary to develop similar structures matching the micron scale of axon tubes. Flexible textiles can play an important role since they allow producing controlled packing densities and crossing structures that match closely the human crossing patterns of the brain. To build a brain phantom, several parameters must be taken into account in what concerns to the materials selection, like hydrophobicity, density and fiber diameter, since these factors influence directly the values of fractional anisotropy. Fiber cross-section shape is other important parameter. Earlier studies showed that synthetic fibrous materials are a good choice for building a brain phantom [4]. The present work is integrated in a broader project that aims to develop a brain phantom made by fibrous materials to validate and calibrate HDFT. Due to the similarity between thousands of hollow multifilaments in a fibrous arrangement, like a yarn, and the axons, low twist polypropylene multifilament yarns were selected for this development. In this sense, extruded hollow filaments were analysed in scanning electron microscope to characterize their main dimensions and shape. In order to approximate the dimensional scale to human axons, five types of polypropylene yarns with different linear density (denier) were used, aiming to understand the effect of linear density on the filament inner and outer areas. Moreover, in order to achieve the required dimensions, the polypropylene filaments cross-section was diminished in a drawing stage of a filament extrusion line. Subsequently, tensile tests were performed to characterize the mechanical behaviour of hollow filaments and to evaluate the differences between stretched and non-stretched filaments. In general, an increase of the linear density causes the increase in the size of the filament cross section. With the increase of structure orientation of filaments, induced by stretching, breaking tenacity increases and elongation at break decreases. The production of hollow fibers, with the required characteristics, is one of the key steps to create a brain phantom that properly mimics the human brain that may be used for the validation and calibration of HDFT, an imaging approach that is expected to contribute significantly to the areas of brain related research.