The BRAzilian Seismographic Integrated Systems (BRASIS): infrastructure and data management

In geophysics and seismology, raw data need to be processed to generate useful information that can be turned into knowledge by researchers. The number of sensors that are acquiring raw data is increasing rapidly. Without good data management systems, more time can be spent in querying and preparing datasets for analyses than in acquiring raw data. Also, a lot of good quality data acquired at great effort can be lost forever if they are not correctly stored. Local and international cooperation will probably be reduced, and a lot of data will never become scientific knowledge. For this reason, the Seismological Laboratory of the Institute of Astronomy, Geophysics and Atmospheric Sciences at the University of Sao Paulo (IAG-USP) has concentrated fully on its data management system. This report describes the efforts of the IAG-USP to set up a seismology data management system to facilitate local and international cooperation.

Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature

Cloud-aerosol interaction is a key issue in the climate system, affecting the water cycle, the weather, and the total energy balance including the spatial and temporal distribution of latent heat release. Information on the vertical distribution of cloud droplet microphysics and thermodynamic phase as a function of temperature or height, can be correlated with details of the aerosol field to provide insight on how these particles are affecting cloud properties and their consequences to cloud lifetime, precipitation, water cycle, and general energy balance. Unfortunately, today's experimental methods still lack the observational tools that can characterize the true evolution of the cloud microphysical, spatial and temporal structure in the cloud droplet scale, and then link these characteristics to environmental factors and properties of the cloud condensation nuclei. Here we propose and demonstrate a new experimental approach (the cloud scanner instrument) that provides the microphysical information missed in current experiments and remote sensing options. Cloud scanner measurements can be performed from aircraft, ground, or satellite by scanning the side of the clouds from the base to the top, providing us with the unique opportunity of obtaining snapshots of the cloud droplet microphysical and thermodynamic states as a function of height and brightness temperature in clouds at several development stages. The brightness temperature profile of the cloud side can be directly associated with the thermodynamic phase of the droplets to provide information on the glaciation temperature as a function of different ambient conditions, aerosol concentration, and type. An aircraft prototype of the cloud scanner was built and flew in a field campaign in Brazil. The CLAIM-3D (3-Dimensional Cloud Aerosol Interaction Mission) satellite concept proposed here combines several techniques to simultaneously measure the vertical profile of cloud microphysics, thermodynamic phase, brightness temperature, and aerosol amount and type in the neighborhood of the clouds. The wide wavelength range, and the use of multi-angle polarization measurements proposed for this mission allow us to estimate the availability and characteristics of aerosol particles acting as cloud condensation nuclei, and their effects on the cloud microphysical structure. These results can provide unprecedented details on the response of cloud droplet microphysics to natural and anthropogenic aerosols in the size scale where the interaction really happens.

HER2 Testing in Breast Carcinoma Very Low Concordance Rate Between Reference and Local Laboratories in Brazil

Breast cancer accounts for approximately one quarter of all cancers in females. HER2 gene amplification or HER2 protein overexpression, detected in about 20% of breast carcinomas, predicts a more aggressive clinical course and determines eligibility for targeted therapy with trastuzumab. HER2 testing has become an essential part of the clinical evaluation of all breast carcinoma patients, and accurate HER2 results are critical in identifying patients who may be benefited from targeted therapy. This study investigated the concordance in the results of HER2 immunohistochemistry assays performed in 500 invasive breast carcinomas between a reference laboratory and 149 local laboratories from all geographic regions of Brazil. Our results showed an overall poor concordance (171 of 500 cases, 34.2%) regarding HER2 results between local and reference laboratories, which may be related to the low-volume load of HER2 assays, inexperience with HER2 scoring system, and/or technical issues related to immunohistochemistry in local laboratories. Standardization of HER2 testing with rigorous quality control measures by local laboratories is highly recommended to avoid erroneous treatment of breast cancer patients.

Systemic and Local Inflammation in Peritoneal Dialysis: Mechanisms, Biomarkers and Effects on Outcome

Thanks to the technological development in peritoneal dialysis (PD) during the last three decades, the most important problem nowadays for the nephrologists is the maintenance of the long-term function of the peritoneal membrane. Although PD may exert an early survival benefit as compared with hemodialysis (HD), long-term PD is often associated with histopathological alterations in the peritoneal membrane that are linked to peritoneal ultrafiltration deficit and increased mortality risk. These alterations are closely related to the presence of a chronic activated (local and systemic) inflammatory response. PD itself may have other factors associated that could further modulate the inflammatory response, such as the bioincompatibility of dialysis solutions, fluid overload and changes in the body composition. Understanding the pathophysiology of inflammation in PD is essential for the adoption of adequate strategies to improve both membrane and patient survival. Copyright (C) 2009 S. Karger AG, Basel

An analysis of accommodation needs of adults with an intellectual disability in Toowoomba and surrounding areas

Introduction The objective of this study was to analyse the accommodation needs of people with intellectual disability over the age of 18 years in Toowoomba and contiguous shires. In 2004, a group of carers established Toowoomba Intellectual Disability Support Association (TIDSA) to address the issue of the lack of supported accommodation for people with intellectual disability over the age of 18 and the concerns of ageing carers. The Centre for Rural and Remote Area Health (CRRAH) was engaged by TIDSA to ascertain this need and undertook a research project funded by the Queensland Gambling Community Benefit Fund. While data specifically relating to people with intellectual disability and their carers are difficult to obtain, the Australian Bureau of Statistics report that carers of people with a disability are more likely to be female and at least 65 years of age. Projections by the National Centre for Social and Economic Modelling (NATSEM) show that disability rates are increasing and carer rates are decreasing. Thus the problem of appropriate support to the increasing number of ageing carers and those who they care for will be a major challenge to policy makers and is an issue of immediate concern. In general, what was once the norm of accommodating people with intellectual disability in large institutions is now changing to accommodating into community-based residences (Annison, 2000; Young, Ashman, Sigafoos, & Grevell, 2001). However, in Toowoomba and contiguous shires, TIDSA have noted that the availability of suitable accommodation for people with intellectual disability over the age of 18 years is declining with no new options available in an environment of increasing demand. Most effort seemed to be directed towards crisis provision. Method This study employed two phases of data gathering, the first being the distribution of a questionnaire through local service providers and upon individual request to the carers of people with intellectual disability over the age of 18. The questionnaire comprised of Likert-type items intended to measure various aspects of current and future accommodation issues. Most questions were followed with space for free-response comments to provide the opportunity for carers to further clarify and expand on their responses. The second phase comprised semi-structured interviews conducted with ten carers and ten people with intellectual disability who had participated in the Phase One questionnaire. Interviews were transcribed verbatim and subjected to content analysis where major themes were explored. Results Age and gender Carer participants in this study totalled 150. The mean age of these carers was 61.5 years and ranged from 40 – 91 years. Females comprised 78% of the sample (mean age = 61.49; range from 40-91) and 22% were male (mean age = 61.7 range from 43-81). The mean age of people with intellectual disability in our study was 37.2 years ranging from 18 – 79 years with 40% female (mean age = 39.5; range from 19-79) and 60% male (mean age = 35.6; range from 18-59). The average age of carers caring for a person over the age of 18 who is living at home is 61 years. The average age of the carer who cares for a person who is living away from home is 62 years. The overall age range of both these groups of carers is between 40 and 81 years. The oldest group of carers (mean age = 70 years) were those where the person with intellectual disability lives away from home in a large residential facility. Almost one quarter of people with an intellectual disability who currently live at home is cared for by one primary carer and this is almost exclusively a parent.

Image quality assessment and medical physics evaluation of different portable dental X-ray units

Introduction: Recently developed portable dental X-ray units increase the mobility of the forensic odontologists and allow more efficient X-ray work in a disaster field, especially when used in combination with digital sensors. This type of machines might also have potential for application in remote areas, military and humanitarian missions, dental care of patients with mobility limitation, as well as imaging in operating rooms. Objective: To evaluate radiographic image quality acquired by three portable X-ray devices in combination with four image receptors and to evaluate their medical physics parameters. Materials and methods: Images of five samples consisting of four teeth and one formalin-fixed mandible were acquired by one conventional wall-mounted X-ray unit, MinRay (R) 60/70 kVp, used as a clinical standard, and three portable dental X-ray devices: AnyRay (R) 60 kVp, Nomad (R) 60 kVp and Rextar (R) 70 kVp, in combination with a phosphor image plate (PSP), a CCD, or a CMOS sensor. Three observers evaluated images for standard image quality besides forensic diagnostic quality on a 4-point rating scale. Furthermore, all machines underwent tests for occupational as well as patient dosimetry. Results: Statistical analysis showed good quality imaging for all system, with the combination of Nomad (R) and PSP yielding the best score. A significant difference in image quality between the combination of the four X-ray devices and four sensors was established (p < 0.05). For patient safety, the exposure rate was determined and exit dose rates for MinRay (R) at 60 kVp, MinRay (R) at 70 kVp, AnyRay (R), Nomad (R) and Rextar (R) were 3.4 mGy/s, 4.5 mGy/s, 13.5 mGy/s, 3.8 mGy/s and 2.6 mGy/s respectively. The kVp of the AnyRay (R) system was the most stable, with a ripple of 3.7%. Short-term variations in the tube output of all the devices were less than 10%. AnyRay (R) presented higher estimated effective dose than other machines. Occupational dosimetry showed doses at the operator`s hand being lowest with protective shielding (Nomad (R): 0.1 mu Gy). It was also low while using remote control (distance > 1 m: Rextar (R) < 0.2 mu Gy, MinRay (R) < 0.1 mu Gy). Conclusions: The present study demonstrated the feasibility of three portable X-ray systems to be used for specific indications, based on acceptable image quality and sufficient accuracy of the machines and following the standard guidelines for radiation hygiene. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Integrating high and moderate spatial resolution image data to estimate forest age structure

The collection of spatial information to quantify changes to the state and condition of the environment is a fundamental component of conservation or sustainable utilization of tropical and subtropical forests, Age is an important structural attribute of old-growth forests influencing biological diversity in Australia eucalypt forests. Aerial photograph interpretation has traditionally been used for mapping the age and structure of forest stands. However this method is subjective and is not able to accurately capture fine to landscape scale variation necessary for ecological studies. Identification and mapping of fine to landscape scale vegetative structural attributes will allow the compilation of information associated with Montreal Process indicators lb and ld, which seek to determine linkages between age structure and the diversity and abundance of forest fauna populations. This project integrated measurements of structural attributes derived from a canopy-height elevation model with results from a geometrical-optical/spectral mixture analysis model to map forest age structure at a landscape scale. The availability of multiple-scale data allows the transfer of high-resolution attributes to landscape scale monitoring. Multispectral image data were obtained from a DMSV (Digital Multi-Spectral Video) sensor over St Mary's State Forest in Southeast Queensland, Australia. Local scene variance levels for different forest tapes calculated from the DMSV data were used to optimize the tree density and canopy size output in a geometric-optical model applied to a Landsat Thematic Mapper (TU) data set. Airborne laser scanner data obtained over the project area were used to calibrate a digital filter to extract tree heights from a digital elevation model that was derived from scanned colour stereopairs. The modelled estimates of tree height, crown size, and tree density were used to produce a decision-tree classification of forest successional stage at a landscape scale. The results obtained (72% accuracy), were limited in validation, but demonstrate potential for using the multi-scale methodology to provide spatial information for forestry policy objectives (ie., monitoring forest age structure).

Monitoring the composition and form of urban environments based on the vegetation-impervious surface-soil (VIS) model by sub-pixel analysis techniques

The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Systeme Pour I'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Several approaches were evaluated for mapping the urban composition of Brisbane city (south-cast Queensland, Australia) using Landsat 5 Thematic Mapper data and 1:5000 aerial photographs. The methods evaluated were: image classification; interpretation of aerial photographs; and constrained linear mixture analysis. Over 900 reference sample points on four transects were extracted from the aerial photographs and used as a basis to check output of the classification and mixture analysis. Distinctive zonations of VIS related to urban composition were found in the per-pixel classification and aggregated air-photo interpretation; however, significant spectral confusion also resulted between classes. In contrast, the VIS fraction images produced from the mixture analysis enabled distinctive densities of commercial, industrial and residential zones within the city to be clearly defined, based on their relative amount of vegetation cover. The soil fraction image served as an index for areas being (re)developed. The logical match of a low (L)-resolution, spectral mixture analysis approach with the moderate spatial resolution image data, ensured the processing model matched the spectrally heterogeneous nature of the urban environments at the scale of Landsat Thematic Mapper data.

Automation & control remote laboratory: evaluating a cooperative methodology

This paper presents a study carried out in order to evaluate the students' perception in the development and use of remote Control and Automation education kits developed by two Universities. Three projects, based on real world environments, were implemented, being local and remotely operated. Students implemented the kits using the theoretical and practical knowledge, being the teachers a catalyst in the learning process. When kits were operational, end-user students got acquainted to the kits in the course curricula units. It is the author's believe that successful results were achieved not only in the learning progress on the Automation and Control fields (hard skills) but also on the development of the students soft skills, leading to encouraging and rewarding goals, motivating their future decisions and promoting synergies in their work. The design of learning experimental kits by students, under teacher supervision, for future use in course curricula by enduser students is an advantageous and rewarding experience.

A Logic and tool for local reasoning about security protocols

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.