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Resumen Palpita flegia (Cramer 1777) (Lepidoptera, Crambidae), es un insecto plaga que se alimenta del follaje de Cascabela thevetia (L.) Lippold., originario de México. Para el manejo integrado de la especie se requieren conocimientos sobre su biología y comportamiento. El objetivo del trabajo fue determinar el número de estadios larvales y duración de fases biológicas de P. flegia en condiciones de laboratorio, de acuerdo con los requerimientos calóricos expresados en grados días. P. flegia posee un desarrollo larval de seis estadios. El periodo larval duró 25 días, el pupal 16 y la sobrevivencia del adulto cinco días. El ciclo biológico a especie requiere 403.52 grados días. Los incrementos poblaciones se producen durante otoños con temperaturas promedio de 15 °C y humedad relativa de 60 a 75 %. La presencia del parasitoide de pupas Brachymeria flegiae (Hymenoptera, Chalcididae) se verifica. La poda sanitaria y la conservación de enemigos naturales se indican como medidas para el control de la plaga.(AU)

Abstract Palpita flegia (Cramer 1777) (Lepidoptera, Crambidae, is a pest insect that feeds on the foliage of Cascabela thevetia (L.) Lippold. The integrated management of the species a full knowledge about its biology and behavior. The objective of this study was to determine the number of larval stages and duration of biological phases in P. flegia under laboratory conditions, to determine the caloric requirements expressed in degrees days and to describe the ethological aspects of the species. Palpita flegia has six larval development stages. The larval phase lasted 25 days, the pupal phase 16 days, and adult survival was five days. This species requires 403.52 degree days for the development of the biological cycle. Population increases during fall with average temperatures of 15 °C and relative humidity of 60 to 75 %. Presence of the pupal parasitoid Brachymeria flegiae Burks, 1960 (Hymenoptera, Chalcididae) was verified. Sanitary pruning and conservation of natural enemies are indicated as measures for pest control.(AU)

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OBJECTIVE: The interictal epileptic discharges (IEDs) occurring in stereotactic EEG (SEEG) recordings are in general abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. A framework is developed to model these network interactions. METHODS: To identify the synchronized neuronal activity underlying the IEDs, the variation in correlation over time of the SEEG signals is related to the occurrence of IEDs using the general linear model. The interdependency is assessed of the brain areas that reflect highly synchronized neural activity by applying independent component analysis, followed by cluster analysis of the spatial distributions of the independent components. The spatiotemporal interactions of the spike clusters reveal the leading or lagging of brain areas. RESULTS: The analysis framework was evaluated for five successfully operated patients, showing that the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone. The additional value of the framework was demonstrated for two more patients, who were MRI-negative and for whom surgery was not successful. CONCLUSIONS: A network approach is promising in case of complex epilepsies. SIGNIFICANCE: Analysis of IEDs is considered a valuable addition to routine review of SEEG recordings, with the potential to increase the success rate of epilepsy surgery.

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The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.

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The question whether our brain pathways adhere to a geometric grid structure has been a popular topic of debate in the diffusion imaging and neuroscience societies. Wedeen et al. (2012a, b) proposed that the brain's white matter is organized like parallel sheets of interwoven pathways. Catani et al. (2012) concluded that this grid pattern is most likely an artifact, resulting from methodological biases that cause the tractography pathways to cross in orthogonal angles. To date, ambiguities in the mathematical conditions for a sheet structure to exist (e.g. its relation to orthogonal angles) combined with the lack of extensive quantitative evidence have prevented wide acceptance of the hypothesis. In this work, we formalize the relevant terminology and recapitulate the condition for a sheet structure to exist. Note that this condition is not related to the presence or absence of orthogonal crossing fibers, and that sheet structure is defined formally as a surface formed by two sets of interwoven pathways intersecting at arbitrary angles within the surface. To quantify the existence of sheet structure, we present a novel framework to compute the sheet probability index (SPI), which reflects the presence of sheet structure in discrete orientation data (e.g. fiber peaks derived from diffusion MRI). With simulation experiments we investigate the effect of spatial resolution, curvature of the fiber pathways, and measurement noise on the ability to detect sheet structure. In real diffusion MRI data experiments we can identify various regions where the data supports sheet structure (high SPI values), but also areas where the data does not support sheet structure (low SPI values) or where no reliable conclusion can be drawn. Several areas with high SPI values were found to be consistent across subjects, across multiple data sets obtained with different scanners, resolutions, and degrees of diffusion weighting, and across various modeling techniques. Under the strong assumption that the diffusion MRI peaks reflect true axons, our results would therefore indicate that pathways do not form sheet structures at every crossing fiber region but instead at well-defined locations in the brain. With this framework, sheet structure location, extent, and orientation could potentially serve as new structural features of brain tissue. The proposed method can be extended to quantify sheet structure in directional data obtained with techniques other than diffusion MRI, which is essential for further validation.

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We develop a new formulation, mathematically elegant, to detect critical points of 3D scalar images. It is based on a topological number, which is the generalization to three dimensions of the 2D winding number. We illustrate our method by considering three different biomedical applications, namely, detection and counting of ovarian follicles and neuronal cells and estimation of cardiac motion from tagged MR images. Qualitative and quantitative evaluation emphasizes the reliability of the results.