Graph Analasys

Early psychiatry investigated dreams to understand psychopathologies. Contemporary psychiatry, which neglects dreams, has been criticized for lack of objectivity. In search of quantitative insight into the structure of psychotic speech, we investigated speech graph attributes (SGA) in patients with schizophrenia, bipolar disorder type I, and non-psychotic controls as they reported waking and dream contents. Schizophrenic subjects spoke with reduced connectivity, in tight correlation with negative and cognitive symptoms measured by standard psychometric scales. Bipolar and control subjects were undistinguishable by waking reports, but in dream reports bipolar subjects showed significantly less connectivity. Dream-related SGA outperformed psychometric scores or waking-related data for group sorting. Altogether, the results indicate that online and offline processing, the two most fundamental modes of brain operation, produce nearly opposite effects on recollections: While dreaming exposes differences in the mnemonic records across individuals, waking dampens distinctions. The results also demonstrate the feasibility of the differential diagnosis of psychosis based on the analysis of dream graphs, pointing to a fast, low-cost and language-invariant tool for psychiatric diagnosis and the objective search for biomarkers. The Freudian notion that ‘‘dreams are the royal road to the unconscious’’ is clinically useful, after all.

Graph analysis of dream reports is especially informative about psychosis

Early psychiatry investigated dreams to understand psychopathologies. Contemporary psychiatry, which neglects dreams, has been criticized for lack of objectivity. In search of quantitative insight into the structure of psychotic speech, we investigated speech graph attributes (SGA) in patients with schizophrenia, bipolar disorder type I, and non-psychotic controls as they reported waking and dream contents. Schizophrenic subjects spoke with reduced connectivity, in tight correlation with negative and cognitive symptoms measured by standard psychometric scales. Bipolar and control subjects were undistinguishable by waking reports, but in dream reports bipolar subjects showed significantly less connectivity. Dream-related SGA outperformed psychometric scores or waking-related data for group sorting. Altogether, the results indicate that online and offline processing, the two most fundamental modes of brain operation, produce nearly opposite effects on recollections: While dreaming exposes differences in the mnemonic records across individuals, waking dampens distinctions. The results also demonstrate the feasibility of the differential diagnosis of psychosis based on the analysis of dream graphs, pointing to a fast, low-cost and language-invariant tool for psychiatric diagnosis and the objective search for biomarkers. The Freudian notion that ‘‘dreams are the royal road to the unconscious’’ is clinically useful, after all.

Efficient robust optimization based on polynomial chaos and tabu search algorithm

The study of robust design methodologies and techniques has become a new topical area in design optimizations in nearly all engineering and applied science disciplines in the last 10 years due to inevitable and unavoidable imprecision or uncertainty which is existed in real word design problems. To develop a fast optimizer for robust designs, a methodology based on polynomial chaos and tabu search algorithm is proposed. In the methodology, the polynomial chaos is employed as a stochastic response surface model of the objective function to efficiently evaluate the robust performance parameter while a mechanism to assign expected fitness only to promising solutions is introduced in tabu search algorithm to minimize the requirement for determining robust metrics of intermediate solutions. The proposed methodology is applied to the robust design of a practical inverse problem with satisfactory results.

A fish-based biotic integrity index for assessment of lowland streams in southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A multimetric index based on fish fauna for the evaluation of the biotic integrity of streams at a mesohabitat scale

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

DEVELOPMENT OF A CHEMICALLY MODIFIED ELECTRODE BASED ON CARBON PASTE AND FUNCTIONALIZED SILICA-GEL FOR PRECONCENTRATION AND VOLTAMMETRIC DETERMINATION OF MERCURY(II)

A mercury-sensitive chemically modified electrode (CME) based on modified silica gel-containing carbon paste was developed. The functional group attached to the silica gel surface was 3-(2-thiobenzimidazolyl)propyl, which is able to complex mercury ions. This electrode was applied to the determination of mercury(II) ions in aqueous solution. The mercury was chemically preconcentrated on the CME prior to voltammetric determination by anodic stripping in the differential-pulse mode. A calibration graph covering the concentration range from 0.08 to 2 mg l-1 was constructed. The precision for six determinations of 0.122 and 0.312 mg l-1 Hg(II) was 3.2 and 2.9% (relative standard deviation), respectively. The detection limit for a 5-min preconcentration period was 0.013 mg l-1. A study for foreign ions was also made.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

A Framework to Optimize Biodiversity Restoration Efforts Based on Habitat Amount and Landscape Connectivity

The effectiveness of ecological restoration actions toward biodiversity conservation depends on both local and landscape constraints. Extensive information on local constraints is already available, but few studies consider the landscape context when planning restoration actions. We propose a multiscale framework based on the landscape attributes of habitat amount and connectivity to infer landscape resilience and to set priority areas for restoration. Landscapes with intermediate habitat amount and where connectivity remains sufficiently high to favor recolonization were considered to be intermediately resilient, with high possibilities of restoration effectiveness and thus were designated as priority areas for restoration actions. The proposed method consists of three steps: (1) quantifying habitat amount and connectivity; (2) using landscape ecology theory to identify intermediate resilience landscapes based on habitat amount, percolation theory, and landscape connectivity; and (3) ranking landscapes according to their importance as corridors or bottlenecks for biological flows on a broader scale, based on a graph theory approach. We present a case study for the Brazilian Atlantic Forest (approximately 150 million hectares) in order to demonstrate the proposed method. For the Atlantic Forest, landscapes that present high restoration effectiveness represent only 10% of the region, but contain approximately 15 million hectares that could be targeted for restoration actions (an area similar to today's remaining forest extent). The proposed method represents a practical way to both plan restoration actions and optimize biodiversity conservation efforts by focusing on landscapes that would result in greater conservation benefits. © 2013 Society for Ecological Restoration.

Analysis of fish assemblages in sectors along a salinity gradient based on species, families and functional groups

Neste trabalho foi testado o efeito do gradiente de salinidade do eixo-leste oeste do sistema subtropical Complexo Estuarino da Baía de Paranaguá na estrutura dos peixes de águas rasas, determinado de acordo com as métricas taxonômica (famílias e espécies) e de composição funcional. Um total de 152 espécies foi registrado. As famílias com maior número de espécies foram Sciaenidae, Carangidae, Haemulidae e Gobiidae. As espécies mais abundantes foram A. brasiliensis, H. clupeola, A. januaria e A. tricolor. Os visitantes marinhos dominaram em número de espécies, seguidos pelos migrantes marinhos e estuarinos. A maioria das espécies são zoobentívoras, seguidas pelas piscívoras e zooplanctívoras. As famílias e espécies mais relacionadas com condições estuarinas dominaram no setor mesohalino e aquelas mais relacionadas com condições marinhas dominaram no setor euhalino. A métrica taxonômica foi mais eficiente na caracterização das assembleias de peixes ao longo do gradiente estuarino de salinidade do que a funcional. Isso ocorreu principalmente porque indivíduos de todos os grupos funcionais estiveram presentes ao longo de todos os setores de salinidade, invalidando o emprego dessa métrica na diferenciação das assembleias nos diversos setores. Nosso resultado foi diferente do encontrado em outros estuários tropicais e subtropicais, que enfatizaram a importância dos grupos funcionais na estruturação das assembleias de peixes ao longo de um gradiente de salinidade.

Leakage detection in water pipe networks using electromechanical impedance (EMI) based technique

The electromechanical impedance (EMI) technique has been successfully used in structural health monitoring (SHM) systems on a wide variety of structures. The basic concept of this technique is to monitor the structural integrity by exciting and sensing a piezoelectric transducer, usually a lead zirconate titanate (PZT) wafer bonded to the structure to be monitored and excited in a suitable frequency range. Because of the piezoelectric effect, there is a relationship between the mechanical impedance of the host structure, which is directly related to its integrity, and the electrical impedance of the PZT transducer, obtained by a ratio between the excitation and the sensing signals.This work presents a study on damage (leaks) detection using EMI based method. Tests were carried out in a rig water system built in a Hydraulic Laboratory for different leaks conditions in a metallic pipeline. Also, it was evaluated the influence of the PZT position bonded to the pipeline. The results show that leaks can effectively be detected using common metrics for damage detection such as RMSD and CCDM. Further, it was observed that the position of the PZT bonded to the pipes is an important variable and has to be controlled.

An improved interolog mapping-based computational prediction of protein protein interactions with increased network coverage

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Should phylogenetic and functional diversity metrics compose macroinvertebrate multimetric indices for stream biomonitoring?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Systematic Evaluation of Extraction Methods for Multiplatform-Based Metabotyping: Application to the Fasciola hepatica Metabolome

Combining data from multiple analytical platforms is essential for comprehensive study of the molecular phenotype (metabotype) of a given biological sample. The metabolite profiles generated are intrinsically dependent on the analytical platforms, each requiring optimization of instrumental parameters, separation conditions, and sample extraction to deliver maximal biological information. An in-depth evaluation of extraction protocols for characterizing the metabolome of the hepatobiliary fluke Fasciola hepatica, using ultra performance liquid chromatography and capillary electrophoresis coupled with mass spectroscopy is presented. The spectrometric methods were characterized by performance, and metrics of merit were established, including precision, mass accuracy, selectivity, sensitivity, and platform stability. Although a core group of molecules was common to all methods, each platform contributed a unique set, whereby 142 metabolites out of 14,724 features were identified. A mixture design revealed that the chloroform:methanol:water proportion of 15:59:26 was globally the best composition for metabolite extraction across UPLC-MS and CE-MS platforms accommodating different columns and ionization modes. Despite the general assumption of the necessity of platform-adapted protocols for achieving effective metabotype characterization, we show that an appropriately designed single extraction procedure is able to fit the requirements of all technologies. This may constitute a paradigm shift in developing efficient protocols for high-throughput metabolite profiling with more-general analytical applicability.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking

This paper presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the image foresting transform with a never-exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods such as live wire and graph cuts. Several experiments show that riverbed can significantly reduce the number of user interactions (anchor points), as compared to live wire for objects with complex shapes. This paper also includes a discussion about how to combine different methods in order to take advantage of their complementary strengths.