Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches

In patients diagnosed with pharmaco-resistant epilepsy, cerebral areas responsible for seizure generation can be defined by performing implantation of intracranial electrodes. The identification of the epileptogenic zone (EZ) is based on visual inspection of the intracranial electroencephalogram (IEEG) performed by highly qualified neurophysiologists. New computer-based quantitative EEG analyses have been developed in collaboration with the signal analysis community to expedite EZ detection. The aim of the present report is to compare different signal analysis approaches developed in four different European laboratories working in close collaboration with four European Epilepsy Centers. Computer-based signal analysis methods were retrospectively applied to IEEG recordings performed in four patients undergoing pre-surgical exploration of pharmaco-resistant epilepsy. The four methods elaborated by the different teams to identify the EZ are based either on frequency analysis, on nonlinear signal analysis, on connectivity measures or on statistical parametric mapping of epileptogenicity indices. All methods converge on the identification of EZ in patients that present with fast activity at seizure onset. When traditional visual inspection was not successful in detecting EZ on IEEG, the different signal analysis methods produced highly discordant results. Quantitative analysis of IEEG recordings complement clinical evaluation by contributing to the study of epileptogenic networks during seizures. We demonstrate that the degree of sensitivity of different computer-based methods to detect the EZ in respect to visual EEG inspection depends on the specific seizure pattern.

Die Rolle des Benefit-Finding für die Qualität der Anpassung an das Leben nach dem Spitzensport

Die Lebensereignisforschung postuliert, dass die Anpassung an eine durch ein kritisches Ereignis veränderte Situation durch Benefit-Finding gefördert wird, indem Menschen Gewinnbringendes für ihr Leben erkennen (Filipp & Aymanns, 2010). Während in der frühen Forschung zum oft als kritisches Lebensereignis beschriebenen Karriereende im Spitzensport Benefit-Finding mitbedacht wurde, wird es in der aktuellen Forschung nur punktuell berücksichtigt (z.B. Curtis & Ennis, 1988, Wippert, 2011). Basierend auf dem Konzept Kritisches Lebensereignis (Filipp, 1995) untersucht die vorliegende Studie die Rolle des Benefit-Finding für die kurz-, mittel- und langfristige Qualität der Anpassung an das Karriereende. Methods: 290 Schweizer Spitzenathleten (Frauenanteil: 32.8%) aus 64 Sportarten wurden etwa 7.46 Jahre nach ihrem Karriereende mittels Fragebogen zum Benefit-Finding, Erleben des Karriereendes, zur Dauer und subjektiven Qualität der Anpassung an das Karriereende sowie zum psychischen Wohlbefinden befragt. Die Datenauswertung erfolgte mittels Strukturgleichungsmodellierung. Results: Das Modell zur Vorhersage der langfristigen Anpassungsqualität (psychische Wohlbefinden) an das Karriereende mit einer Varianzaufklärung von R2 = .26 passt recht gut zu den Daten (χ2 = 114.764, p ≤ .001, df = 56, CFI = .93, SRMR = .06, RMSEA = .06; AGFI = .91). Wie postuliert, hat das Ausmass von Benefit-Finding einen – über die kurz- und mittelfristige Anpassungsqualität (positive Emotionen, Anpassungsdauer und subjektive Anpassungsqualität) – vermittelten Effekt auf das psychische Wohlbefinden im Leben nach dem Spitzensport. Discussion/Conclusion: Das Konzept Kritisches Lebensereignis kristallisierte sich als zielführender Ansatz für die Analyse von zusammenwirkenden Faktoren hinsichtlich Qualität der Anpassung an das Leben nach dem Spitzensport heraus. Die Befunde indizieren, dass sportpsychologische Interventionen mit Fokus auf Benefit-Finding, zusammen mit anderen Elementen der gängigen Career-Assistance-Programme, kurzfristig für eine gelingende Transition und langfristig ein günstiges psychisches Wohlbefinden sinnvoll sind. References: Curtis, J. & Ennis, R. (1988). Negative consequences of leaving competitive sport? Comparative findings for former elite-level hockey players. Sociology of Sport Journal, 5, 87-106. Filipp, S.-H. (Hrsg.) (1995). Kritische Lebensereignisse (3. Aufl.). Weinheim: Beltz. Filipp, S.-H. & Aymanns, P. (2010). Kritische Lebensereignisse und Lebenskrisen. Vom Umgang mit den Schattenseiten des Lebens. Stuttgart: Kohlhammer. Wippert, P.-M. (2011). Kritische Lebensereignisse in Hochleistungsbiografien. Untersuchungen an Spitzensportlern, Tänzern und Musikern. Lengerich: Pabst.

Biodiversity and Ecosystem Function in the Gulf of Maine: Pattern and Role of Zooplankton and Pelagic Nekton

This paper forms part of a broader overview of biodiversity of marine life in the Gulf of Maine area (GoMA), facilitated by the GoMA Census of Marine Life program. It synthesizes current data on species diversity of zooplankton and pelagic nekton, including compilation of observed species and descriptions of seasonal, regional and cross-shelf diversity patterns. Zooplankton diversity in the GoMA is characterized by spatial differences in community composition among the neritic environment, the coastal shelf, and deep offshore waters. Copepod diversity increased with depth on the Scotian Shelf. On the coastal shelf of the western Gulf of Maine, the number of higher-level taxonomic groups declined with distance from shore, reflecting more nearshore meroplankton. Copepod diversity increased in late summer, and interdecadal diversity shifts were observed, including a period of higher diversity in the 1990s. Changes in species diversity were greatest on interannual scales, intermediate on seasonal scales, and smallest across regions, in contrast to abundance patterns, suggesting that zooplankton diversity may be a more sensitive indicator of ecosystem response to interannual climate variation than zooplankton abundance. Local factors such as bathymetry, proximity of the coast, and advection probably drive zooplankton and pelagic nekton diversity patterns in the GoMA, while ocean-basin-scale diversity patterns probably contribute to the increase in diversity at the Scotian Shelf break, a zone of mixing between the cold-temperate community of the shelf and the warm-water community offshore. Pressing research needs include establishment of a comprehensive system for observing change in zooplankton and pelagic nekton diversity, enhanced observations of "underknown'' but important functional components of the ecosystem, population and metapopulation studies, and development of analytical modeling tools to enhance understanding of diversity patterns and drivers. Ultimately, sustained observations and modeling analysis of biodiversity must be effectively communicated to managers and incorporated into ecosystem approaches for management of GoMA living marine resources.

Die verschiedenen Geruchsarten der Rosen

von Herrn Henry Curtis

AVIAN ROTAVIRUSES

The prevalence of antirotavirus antibodies in chickens and turkeys in the Gonzales, Texas and Llano, Texas areas was studied. Caged layer chicken flocks were found to have a prevalence of 64% when samples were taken randomly. This compares to 45% in chicken broiler breeder flocks and 92% in turkey breeding flocks. The natural occurrence of turkey rotavirus infection in two separate field studies showed an increase in mortality varying from 9% to 45% above expected death losses. Clinically, pasted vents, lacitude, and general malaise were noted in affected poults. Lesions noted on post mortem examination were; slight ballooning of the small intestine, excessively large ceca, and mild hyperemia of the small and large intestines.^ The use of maternal antibody from simian rotavirus immunized chickens' eggs for preventing murine rotavirus infection in infant mice was investigated. There was a reduction from 91% to 15% incidence when infant mice were treated twice daily with egg yolk immunoglobulin.^ The need for a convenient, easily grown and rapidly reproducing model for avian and mammalian rotaviruses led to the use of coturnix chicks. The turkey rotavirus was adapted to the quail chicks be serial passage. Transmission and scanning electron microscopy as well as micropathological methods were used in the study of the pathogenesis of rotavirus infection in quail and infant mice. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Targeting Special Populations for Family Preservation: The Influence of Worker Competence and Organizational Culture

Today there are approximately 581,000 children in the United States foster care system. Children of color, one special population group, are disproportionately represented in the foster care system. Family preservation, a program that aims to improve family functioning and thus decrease the need for foster care, has been examined closely. Some researchers believe that family preservation programs have failed partly due to practitioners' inability to target appropriate families (Feldman, 1990; Schuerman, Rzepnicki & Littell, 1994). Additionally, research confirms that children of color are not the target of family preservation services (Denby, Curtis, & Alford, 1998). Improvements in the effectiveness of family preservation will require many types of reform both internal and external to the program. Among the types of internal reform needed is accurate "targeting of services. " Given the overrepresentation of children of color in the foster care system, this group must be among those who are targeted for services. The results of a national survey of 254 family preservation workers reveal a "profile" of the worker who is likely to target special populations, including children of color, for family preservation services. A case is made for service improvements and training to facilitate the "profiled" workers' competencies.

Family Preservation Journal, 2003, Volume 7. (Entire issue)

Entire issue (large pdf file) Articles include: Social Workers' Perceptions of Family Preservation Programs. Elaine M Maccio, David Skiba, Howard J Doueck, Karen A. Randolph, Elisabeth A. Weston, and Lorie E. Anderson Targeting Special Populations for Family Preservation: The Influence of Worker Competence and Organizational Culture. Ramona W: Denby, Keith A. Alford, and Carla M Curtis Understanding and Fostering Family Resilience. Robert G. Blair Walking Our Talk in the Neighborhoods: Building Professional/Natural Helper Partnerships. Jill Kinney and Margaret Trent Intersystem Collaboration: A Statewide Initiative to Support Families. Elizabeth M Tracy, David E. Biegel, Ann C. Rebeck, and Jeffrey A. Johnsen