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Moving in the Anthropocene
(2018)
Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.
Rabbit associated genotype 3 hepatitis E virus (HEV) strains were detected in feral, pet and farm rabbits in different parts of the world since 2009 and recently also in human patients. Here, we report a serological and molecular survey on 72 feral rabbits, collected along a rural-urban transect in and next to Frankfurt am Main, Central Germany. ELISA investigations revealed in 25 of 72 (34.7%) animals HEV-specific antibodies. HEV derived RNA was detected in 18 of 72 (25%) animals by reverse transcription-polymerase chain reaction assay. The complete genomes from two rabbitHEV-strains, one from a rural site and the other from an inner-city area, were generated by a combination of high-throughput sequencing, a primer walking approach and 5′- and 3′- rapid amplification of cDNA ends. Phylogenetic analysis of open reading frame (ORF)1-derived partial and complete ORF1/ORF2 concatenated coding sequences indicated their similarity to rabbit-associated HEV strains. The partial sequences revealed one cluster of closely-related rabbitHEV sequences from the urban trapping sites that is well separated from several clusters representing rabbitHEV sequences from rural trapping sites. The complete genome sequences of the two novel strains indicated similarities of 75.6–86.4% to the other 17 rabbitHEV sequences; the amino acid sequence identity of the concatenated ORF1/ORF2-encoded proteins reached 89.0–93.1%. The detection of rabbitHEV in an inner-city area with a high human population density suggests a high risk of potential human infection with the zoonotic rabbitHEV, either by direct or indirect contact with infected animals. Therefore, future investigations on the occurrence and frequency of human infections with rabbitHEV are warranted in populations with different contact to rabbits.
Interindividual variability in the regulation of the human stress system accounts for a part of the individual's liability to stress-related diseases. These differences are influenced by environmental and genetic factors. Early childhood adversity is a well-studied environmental factor affecting an individual's stress response which has been shown to be modulated by gene environment interaction (GxE). Neuropeptide Y (NPY) plays a role in stress regulation and genetic variation in NPY may influence stress responses. In this study, we analyzed the association of a common variant in the NPY gene promoter, rs16147, with cortisol and ACTH responses to acute psychosocial stress in young adults from the Mannheim Study of Children at Risk (MARS), an ongoing epidemiological cohort study following the outcome of early adversity from birth into adulthood. We found evidence of a GxE interaction between rs16147 and early adversity significantly affecting HPA axis responses to acute psychosocial stress. These findings suggest that the neurobiological mechanisms linking early adverse experience and later neuroendocrine stress regulation are modulated by a gene variant whose functional relevance is documented by increasing convergent evidence from in vitro, animal and human studies.
Proceedings of KogWis 2010 : 10th Biannual Meeting of the German Society for Cognitive Science
(2010)
As the latest biannual meeting of the German Society for Cognitive Science (Gesellschaft für Kognitionswissenschaft, GK), KogWis 2010 at Potsdam University reflects the current trends in a fascinating domain of research concerned with human and artificial cognition and the interaction of mind and brain. The Plenary talks provide a venue for questions of the numerical capacities and human arithmetic (Brian Butterworth), of the theoretical development of cognitive architectures and intelligent virtual agents (Pat Langley), of categorizations induced by linguistic constructions (Claudia Maienborn), and of a cross-level account of the “Self as a complex system“ (Paul Thagard). KogWis 2010 integrates a wealth of experimental research, cognitive modelling, and conceptual analysis in 5 invited symposia, over 150 individual talks, 6 symposia, and more than 40 poster contributions. Some of the invited symposia reflect local and regional strenghts of research in the Berlin-Brandenburg area: the two largests research fields of the university Cognitive Sciences Area of Excellence in Potsdam are represented by an invited symposium on “Information Structure” by the Special Research Area 632 (“Sonderforschungsbereich”, SFB) of the same name, of Potsdam University and Humboldt-University Berlin, and by a satellite conference of the research group “Mind and Brain Dynamics”. The Berlin School of Mind and Brain at Humboldt-University Berlin takes part with an invited symposium on “Decision Making” from a perspective of cognitive neuroscience and philosophy and the DFG Cluster of Excellence “Languages of Emotion” of Free University presents interdisciplinary research results in an invited symposium on “Symbolising Emotions”.
Value-added (VA) modeling can be used to quantify teacher and school effectiveness by estimating the effect of pedagogical actions on students’ achievement. It is gaining increasing importance in educational evaluation, teacher accountability, and high-stakes decisions. We analyzed 370 empirical studies on VA modeling, focusing on modeling and methodological issues to identify key factors for improvement. The studies stemmed from 26 countries (68% from the USA). Most studies applied linear regression or multilevel models. Most studies (i.e., 85%) included prior achievement as a covariate, but only 2% included noncognitive predictors of achievement (e.g., personality or affective student variables). Fifty-five percent of the studies did not apply statistical adjustments (e.g., shrinkage) to increase precision in effectiveness estimates, and 88% included no model diagnostics. We conclude that research on VA modeling can be significantly enhanced regarding the inclusion of covariates, model adjustment and diagnostics, and the clarity and transparency of reporting.
What is the added value from attending a certain school or being taught by a certain teacher? To answer this question, the value-added (VA) model was developed. In this model, the actual achievement attained by students attending a certain school or being taught by a certain teacher is juxtaposed with the achievement that is expected for students with the same background characteristics (e.g., pretest scores). To this end, the VA model can be used to compute a VA score for each school or teacher, respectively. If actual achievement is better than expected achievement, there is a positive effect (i.e., a positive VA score) of attending a certain school or being taught by a certain teacher. In other words, VA models have been developed to “make fair comparisons of the academic progress of pupils in different settings” (Tymms 1999, p. 27). Their aim is to operationalize teacher or school effectiveness objectively. Specifically, VA models are often used for accountability purposes and high-stakes decisions (e.g., to allocate financial or personal resources to schools or even to decide which teachers should be promoted or discharged). Consequently, VA modeling is a highly political topic, especially in the USA, where many states have implemented VA or VA-based models for teacher evaluation (Amrein-Beardsley and Holloway 2017; Kurtz 2018). However, this use for high-stakes decisions is highly controversial and researchers seem to disagree concerning the question if VA scores should be used for decision-making (Goldhaber 2015). For a more exhaustive discussion of the use of VA models for accountability reasons, see, for example, Scherrer (2011).
Given the far-reaching impact of VA scores, it is surprising that there is scarcity of systematic reviews of how VA scores are computed, evaluated, and how this research is reported. To this end, we review 370 empirical studies from 26 countries to rigorously examine several key issues in VA modeling, involving (a) the statistical model (e.g., linear regression, multilevel model) that is used, (b) model diagnostics and reported statistical parameters that are used to evaluate the quality of the VA model, (c) the statistical adjustments that are made to overcome methodological challenges (e.g., measurement error of the outcome variables), and (d) the covariates (e.g., pretest scores, students’ sociodemographic background) that are used when estimating expected achievement.
All this information is critical for meeting the transparency standards defined by the American Educational Research Association (AERA 2006). Transparency is vital for educational research in general and especially for highly consequential research, such as VA modeling. First, transparency is highly relevant for researchers. The clearer the description of the model, the easier it is to build upon the knowledge of previous research and to safeguard the potential for replicating previous results. Second, because decisions that are based on VA scores affect teachers’ lives and schools’ futures, not only educational agents but also the general public should be able to comprehend how these scores are calculated to allow for public scrutiny. Specifically, given that VA scores can have devastating consequences on teachers’ lives and on the students they teach, transparency is particularly important to evaluate the chosen methodology to compute VA models for a certain purpose. Such evaluations are essential to answer the question to what extent the quality of VA scores allows to base far-reaching decisions on these scores for accountability purposes.
Aus dem Inhalt dieser Ausgabe: BEITRÄGE: Elmar Henrich: Die Luccheser Bergmilizen: Bauernmobilisierung und Bauernmilitanz in einer Renaissancerepublik des späten 16. und frühen 17. Jahrhunderts, Jutta Nowosadtko: Der „Vampyrus Serviensis“ und sein Habitat: Impressionen von der österreichischen Militärgrenze, PROJEKTE: Thomas Fuchs, Ulrich Kandolf: Die Wehrbereichsbibliothek II (Hannover) in der Niedersächsischen Landesbibliothek, Sascha Möbius: „... Der blutdürstige Degen drung ihnen die Feder so gar aus der Hand.“ : Unruhe und Krieg in der Chronik des Lübecker Schreiners Heinrich Christian Schulze (1728-1734) (Dissertationsprojekt), Ernst Riegg: Die Erinnerungskultur der Stadt vom Spätmittelalter bis zum 18. Jahrhundert : ihre Erforschung anhand der städtischen Chronistik (DFG-Projekt, Potsdam), BERICHTE: Gregor Maier: „Krieg und Umbruch um 1800“ (12. bis 13. März 2004 Tübingen), Olaf Jessen: „Zur Geschichte des militärischen Denkens vom späten Mittelalter bis zum 20. Jahrhundert“ (29. bis 30. April 2004 Stuttgart), Cornel Zwierlein: „Militär und Gesellschaft im Europa der Neuzeit“ (13. bis 17. September 2004 Trient), Sonja Neubauer: „Christentum und Krieg in der Moderne“ (26. bis 29. September 2004 Weingarten), Protokoll der Mitgliederversammlung vom 29.10.2004, REZENSIONEN: Elke Anna Werner: Peter Burke, Augenzeugenschaft : Bilder als historische Quellen, Berlin 2003, Andrea Pühringer: Steffen Martus, Marina Münkler, Werner Röcke (Hrsg.), Schlachtfelder : Codierung von Gewalt im medialen Wandel, Berlin 2003, Arbeitskreis Historische Bildforschung (Hrsg.): Der Krieg im Bild – Bilder vom Krieg : Hamburger Beiträge zur Historischen Bildforschung, Frankfurt/Main 2003, Maren Lorenz: Johann Carl Wilhelm Moehsen, Betrachtungen über die Berlinischen Selbstmörder unter den Soldaten : nach dem Manuskript aus den Materialien der Berliner Mittwochsgesellschaft, hrsg. von Hans-Uwe Lammel, Hannover 2004, Thomas Wollschläger: Michael Römling, Ein Heer ist ein großes gefräßiges Tier : Soldaten in spanischen und kaiserlichen Diensten und die Bevölkerung der vom Krieg betroffenen Gebiete in Italien zwischen 1509 und 1530, Göttingen 2002, Jörg Muth: Jürgen Kloosterhuis (Bearb.), Legendäre „lange Kerls“. Quellen zur Regimentskultur der Königsgrenadiere Friedrich Wilhelms I., 1713-1740, Berlin 2003, Martin Winter: Alfred Messerli, Adolf Muschg (Hrsg.), Schreibsucht. Autobiographische Schriften des Pietisten Ulrich Bräker (1735-1798), Göttingen 2004, Michael Kleinen, Sascha Möbius: Stig Förster, Markus Pöhlmann, Dierk Walter (Hrsg.), Schlachten der Weltgeschichte : von Salamis bis Sinai, München 2001, ANKÜNDIGUNGEN: 11. Forschungskolloquium: Neuere Forschungen zur Militärgeschichte, Universität Potsdam, Winersemester 2004/05, AMG-Tagung: „Krieg, Militär und Migration in der Frühen Neuzeit“ (17. bis 19. November 2005 Tübingen), Aufruf: Werner-Hahlweg-Preis 2006 für Militärgeschichte und Wehrwissenschaften, Michael Hochedlinger: Aufruf: Habsburgs Generale. Die kaiserliche und kaiserlich-königliche Generalität 1618-1815 : ein biographisches Lexikon
There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these "imported" techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed.
The 2-D distinct element method (DEM) code (PFC2D_V5) is used here to simulate the evolution of subsidence-related karst landforms, such as single and clustered sinkholes, and associated larger-scale depressions. Subsurface material in the DEM model is removed progressively to produce an array of cavities; this simulates a network of subsurface groundwater conduits growing by chemical/mechanical erosion. The growth of the cavity array is coupled mechanically to the gravitationally loaded surroundings, such that cavities can grow also in part by material failure at their margins, which in the limit can produce individual collapse sinkholes. Two end-member growth scenarios of the cavity array and their impact on surface subsidence were examined in the models: (1) cavity growth at the same depth level and growth rate; (2) cavity growth at progressively deepening levels with varying growth rates. These growth scenarios are characterised by differing stress patterns across the cavity array and its overburden, which are in turn an important factor for the formation of sinkholes and uvalalike depressions. For growth scenario (1), a stable compression arch is established around the entire cavity array, hindering sinkhole collapse into individual cavities and favouring block-wise, relatively even subsidence across the whole cavity array. In contrast, for growth scenario (2), the stress system is more heterogeneous, such that local stress concentrations exist around individual cavities, leading to stress interactions and local wall/overburden fractures. Consequently, sinkhole collapses occur in individual cavities, which results in uneven, differential subsidence within a larger-scale depression. Depending on material properties of the cavity-hosting material and the overburden, the larger-scale depression forms either by sinkhole coalescence or by widespread subsidence linked geometrically to the entire cavity array. The results from models with growth scenario (2) are in close agreement with surface morphological and subsurface geophysical observations from an evaporite karst area on the eastern shore of the Dead Sea.
Between-School Variation in Students' Achievement, Motivation, Affect, and Learning Strategies
(2017)
To plan group-randomized trials where treatment conditions are assigned to schools, researchers need design parameters that provide information about between-school differences in outcomes as well as the amount of variance that can be explained by covariates at the student (L1) and school (L2) levels. Most previous research has offered these parameters for U.S. samples and for achievement as the outcome. This paper and the online supplementary materials provide design parameters for 81 countries in three broad outcome categories (achievement, affect and motivation, and learning strategies) for domain-general and domain-specific (mathematics, reading, and science) measures. Sociodemographic characteristics were used as covariates. Data from representative samples of 15-year-old students stemmed from five cycles of the Programme for International Student Assessment (PISA; total number of students/schools: 1,905,147/70,098). Between-school differences as well as the amount of variance explained at L1 and L2 varied widely across countries and educational outcomes, demonstrating the limited generalizability of design parameters across these dimensions. The use of the design parameters to plan group-randomized trials is illustrated.