Análise da distribuição espaço-temporal de dengue e da infestação larvária no município de Tupã, Estado de São Paulo

INTRODUÇÃO: O vírus da dengue é transmitido pela picada do mosquito Aedes aegypti e, o atual programa de controle não atinge o objetivo de impedir sua transmissão. Este trabalho objetivou analisar a relação entre a distribuição espaço-temporal de casos de dengue e os indicadores larvários no município de Tupã, de janeiro de 2004 a dezembro de 2007. MÉTODOS: Foram construídos indicadores larvários por quarteirão e totalidade do município. Utilizou-se o método cross-lagged correlation para avaliar a correlação entre casos de dengue e indicadores larvários. Foi utilizado estimador kernel para análise espacial. RESULTADOS: A correlação cruzada defasada entre casos de dengue e indicadores larvários foi significativa. Os mapas do estimador Kernel da positividade de recipientes indicam uma distribuição heterogênea, ao longo do período estudado. Nos dois anos de transmissão, a epidemia ocorreu em diferentes regiões. CONCLUSÕES: Não ficou evidenciada relação espacial entre infestação larvária e ocorrência de dengue. A incorporação de técnicas de geoprocessamento e análise espacial no programa, desde que utilizados imediatamente após a realização das atividades, podem contribuir com as ações de controle, indicando os aglomerados espaciais de maior incidência.

Arthropoda de solo em um ecossistema semi-árido da região neotropical: composição, variabilidade temporal e estratificação

Caatinga is an important laboratory for studies about arthropods adaptations and aclimatations because its precipitation is highly variable in time. We studied the effects of time variability over the composition of Arthropods in a caatinga area. The study was carried out at a preservation area on Almas Farm, São José dos Cordeiros, Paraíba. Samples were collected in two 100 m long parallel transects, separated for a 30 m distance, in a dense tree dominated caatinga area, between August 2007 and July 2008. Samples were collected in each transect every 10 m. Ten soil samples were taken from each transect, both at 0-5 cm (A) and 5-10 cm (B) depth, resulting in 40 samples each month. The Berlese funnel method was used for fauna extraction. We registered 26 orders and the arthropods density in the soil ranged from 3237 to 22774 individuals.m-2 from January 2007 to March 2008, respectively. There was no difference between layers A and B regarding orders abundance and richness. The groups recorded include groups with few records or that had no records in the Caatinga region yet as Pauropoda, Psocoptera, Thysanoptera, Protura and Araneae. Acari was the most abundant group, with 66,7% of the total number of individuals. Soil Arthropods presented a positive correlation with soil moisture, vegetal cover, precipitation and real evapotranspiration. Increases in fauna richness and abundance were registered in February, a month after the beginning of the rainy season. A periodic rain events in arid and semiarid ecosystems triggers physiological responses in edafic organisms, like arthropods. Edafic arthropods respond to time variability in the Caatinga biome. This fauna variation has to be considered in studies of this ecosystem, because the variation of Arthropods composition in soil can affect the dynamics of the food web through time

Temporal dynamics of the circadian heart rate following low and high volume exercise training in sedentary male subjects

Purpose: Increased risk of arrhythmic events occurs at certain times during the circadian cycle with the highest risk being in the second and fourth quarter of the day. Exercise improves treatment outcome in individuals with cardiovascular disease. How different exercise protocols affect the circadian rhythm and the associated decrease in adverse cardiovascular risk over the circadian cycle has not been shown. Methods: Fifty sedentary male participants were randomized into an 8-week high volume and moderate volume training and a control group. Heart rate was recorded using Polar Electronics and investigated with Cosinor analysis and by Poincaré plot derived features of SD1, SD2 and the complex correlation measure (CCM) at 1-h intervals over the 24-h period. Results: Moderate exercise significantly increased vagal modulation and the temporal dynamics of the heart rate in the second quarter of the circadian cycle (p = 0.004 and p = 0.007 respectively). High volume exercise had a similar effect on vagal output (p = 0.003) and temporal dynamics (p = 0.003). Cosinor analysis confirms that the circadian heart rate displays a shift in the acrophage following moderate and high volume exercise from before waking (1st quarter) to after waking (2nd quarter of day). Conclusions: Our results suggest that exercise shifts vagal influence and increases temporal dynamics of the heart rate to the 2nd quarter of the day and suggest that this may be the underlying physiological change leading to a decrease in adverse arrhythmic events during this otherwise high-risk period.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Quantitative electroencephalographic comodulation : an investigation of patterns in chronic fatigue syndrome

Introduction. This is a pilot study of quantitative electro-encephalographic (QEEG) comodulation analysis, which is used to assist in identifying regional brain differences in those people suffering from chronic fatigue syndrome (CFS) compared to a normative database. The QEEG comodulation analysis examines spatial-temporal cross-correlation of spectral estimates in the resting dominant frequency band. A pattern shown by Sterman and Kaiser (2001) and referred to as the anterior posterior dissociation (APD) discloses a significant reduction in shared functional modulation between frontal and centro-parietal areas of the cortex. This research attempts to examine whether this pattern is evident in CFS. Method. Eleven adult participants, diagnosed by a physician as having CFS, were involved in QEEG data collection. Nineteen-channel cap recordings were made in five conditions: eyes-closed baseline, eyes-open, reading task one, math computations task two, and a second eyes-closed baseline. Results. Four of the 11 participants showed an anterior posterior dissociation pattern for the eyes-closed resting dominant frequency. However, seven of the 11 participants did not show this pattern. Examination of the mean 8-12 Hz amplitudes across three cortical regions (frontal, central and parietal) indicated a trend of higher overall alpha levels in the parietal region in CFS patients who showed the APD pattern compared to those who did not have this pattern. All patients showing the pattern were free of medication, while 71% of those absent of the pattern were using antidepressant medications. Conclusions. Although the sample is small, it is suggested that this method of evaluating the disorder holds promise. The fact that this pattern was not consistently represented in the CFS sample could be explained by the possibility of subtypes of CFS, or perhaps co-morbid conditions. Further, the use of antidepressant medications may mask the pattern by altering the temporal characteristics of the EEG. The results of this pilot study indicate that further research is warranted to verify that the pattern holds across the wider population of CFS sufferers.