4,148 research outputs found
Shorter, warmer winters may inhibit production of ephyrae in a population of the moon jellyfish Aurelia aurita
No full textScyphozoan jellyfish blooms display high interannual variability in terms of timing of appearance and size of the bloom. To understand the causes of this variability, the conditions experienced by the polyps prior to the production of ephyrae in the spring were examined. Polyps reared from planula larvae of Aurelia aurita medusae collected from southern England (50°49′58.8; − 1°05′36.9) were incubated under orthogonal combinations of temperature (4, 7, 10 °C) and duration (2, 4, 6, 8 weeks), representing the range of winter conditions in that region, before experiencing an increase to 13 °C. Timing and success of strobilation were recorded. No significant production of ephyrae was observed in any of the 2- and 4-week incubations, or in any 10 °C incubation. Time to first ephyra release decreased with longer winter incubations, and more ephyrae were produced following longer and colder winter simulations. This experiment indicates that A. aurita requires a minimum period of cooler temperatures to strobilate, and contradicts claims that jellyfish populations will be more prevalent in warming oceans, specifically in the context of warmer winter conditions. Such investigations on population-specific ontogeny highlights the need to examine each life stage separately as well as in the context of its environment
Extreme changes in salinity drive population dynamics of Catostylus mosaicus medusae in a modified estuary
No full textModifications to estuaries through the construction of barrages alter the natural dynamics of inhabitant species by controlling freshwater inputs into those systems. To understand the effects of modified freshwater flows on a native scyphozoan jellyfish, Catostylus mosaicus, and to identify the environmental drivers of medusa occurrence, we analysed a 20-year observational dataset composed of 11 environmental variables and medusa presence/absence from 15 sampling stations located below the Fitzroy Barrage, in the Fitzroy River, Queensland. Major decreases in salinity (minimum salinity 0) occurred approximately 16 times during the 20-year period and medusae disappeared from the estuary following every major freshwater flow event. Salinity was identified as the most influential variable contributing to variation in the number of upper estuary sites reporting jellyfish. We then ran two laboratory experiments to test the following hypotheses: (i) prolonged decreases in salinity impair survival, pulsation, and respiration rates of C. mosaicus medusae; and (ii) transient decreases temporarily impair pulsation and respiration but medusae recover when salinity returns to normal levels. Medusae were unable to survive extended periods at extreme low salinities, such that they would experience when a barrage opens fully, but had significantly higher survival and recovery rates following smaller, transient changes to salinity that might occur following a moderate rainfall event. This demonstrates for the first time that modification of freshwater flow by a barrage regulates the population dynamics of an estuarine jellyfish, and highlights the need for robust, long term datasets, and to firmly embed experimental approaches in realistic ecological contexts
Widening (linguistic) participation and pedagogy for inclusion: ways forward
No full textHow can practitioners with an interest in language enhance teaching in Higher Education so that it is more inclusive for students from diverse socio-cultural backgrounds, who may be bilingual, multi-lingual or speak a variety of English? This panel focuses on a number of case studies which consider the student perspective and explore approaches which empower students to access and participate more effectively in their academic and professional communities
Author, Philosopher Alexandra Stoddard to Speak March 2 at Williams Library
No full textOXFORD, Miss. – Contemporary philosopher, author, interior designer and speaker Alexandra Stoddard gives an inspirational lecture and reading March 2 at the University of Mississippi
Stages for the More Sustainable Farm
No full textCurrently, agricultural farm units are faced with a double and most times contradictory challenge, in order to be successful: on the one hand the invested capital has to be profitable and the economic performance has to be maximised. On the other hand, given the socio-environmental situation, it is necessary to preserve and to protect the environment and natural resources. Given the potential conflict of the two aims, since the satisfaction of one implies the underperformance of the other (and vice versa), the question then is: which is the solution to choose? We intend, in this work, to formulate a farm plan with the purpose of reconciling the criteria of environmental sustainability with that of economic competitiveness. For this achievement we proceed to the comparative study of sustainability of different groups of farms identified in the study area (first evaluation cycle) through MESMIS (“Marco para la Evaluación de Sistemas de Manejo de Recursos Naturales Mediante Indicadores de Sustentabilidad” - Framework for Evaluation of Natural-Resource Systems Handling through Sustainability Indicators) methodology, that allowed to select the more sustainable group of farms. Based on the found potentialities and weakness on these production systems, we stepped to the planning of a production unit of bovine meat, which obeys simultaneously to economic and environmental objectives, using Multicriteria Decision. We finished the work with the sustainability evaluation between groups of farms identified previously and the planned farms (second evaluation cycle), based, again, in the MESMIS methodology, to confirm (or not) the greatest sustainability of the last ones. Analyses of the results allow us to confirm the greatest relative sustainability of the planned farm, for the diverse traced scenarios.Decision taking, planning, sustainability, Environmental Economics and Policy, Farm Management,
Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data
No full textThe high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance
Exhibiting Fashion Symposium: Dr. Alexandra Palmer “Fashion Exhibitions: The Good, the Bad, and the Pointless”
No full textThe Museum at FIT presented Exhibiting Fashion, its twenty-first academic symposium on Friday, March 8, 2019. This symposium explored the history of fashion curating, the different ways fashion is displayed in museum settings, and how national and regional identities influence fashion exhibitions. The symposium was organized in conjunction with Exhibitionism: 50 Years of The Museum at FIT, which commemorated the rich history of the museum, the site of more than 200 exhibitions since the 1970s.Dr. Alexandra Palmer is the Nora E. Vaughan Senior Curator at the Royal Ontario Museum. She has curated numerous exhibitions including Christian Dior, and she is the author of the book Christian Dior: History and Modernity, 1947–1957
Reescrita de si pelo outro: identidade portuguesa e paródia em Deus-dará, de Alexandra Lucas Coelho / Rewriting oneself through the other: Portuguese identity and parody in Deus-dará, by Alexandra Lucas Coelho
No full textResumo: O artigo aponta o modo como o romance Deus-dará de Alexandra Lucas Coelho, escritora portuguesa contemporânea, pode ser compreendido como um exercício de renegociação da identidade portuguesa em relação a questões referentes à colonização no Brasil. Mais do que isso, problematiza-se como, por meio da estratégia da paródia no texto ficcional, a autora consegue expressar uma necessidade e possibilidade de se redefinir pelo outro em um movimento contrário ao do discurso colonial – o que também ocorre em suas entrevistas e em suas narrativas de viagens, tais como em Vai, Brasil e Cinco Voltas na Bahia e um beijo para Caetano Veloso. Palavras-chave: identidade portuguesa; paródia; pós-modernismo; escrita portuguesa contemporânea; Alexandra Lucas Coelho. Abstract: The article observes how the novel Deus-dará, by Alexandra Lucas Coelho, a Portuguese contemporary writer consists in an exercise of renegotiation for the Portuguese identity in relation to issues that refer to the colonization process in Brazil. Moreover, this text seeks to show how parody as a fictional literary strategy helps the author in expressing a necessity and a possibility of redefining oneself through the other, in a direction that goes in the opposite way of the colonial speech. This necessity and this possibility also appear in the author’s interviews and travel books, such as Vai, Brasil and Cinco Voltas na Bahia e um beijo para Caetano Veloso, which will also be mentioned in this article.Keywords: Portuguese identity; parody; post-modernism; Portuguese contemporary writing; Alexandra Lucas Coelho
Author Rights Workshop
No full textLearning material associated with Alexandra Kohn's presentation as a part of the ABC Copyright 2020 Fall Speaker Series, hosted by the University of Alberta Copyright Office
What Drives Jellyfish Population Cycles? Influence of Climate and Environment on the Complex Life Histories of Scyphozoans
No full textJellyfish population cycles and bloom events occur at global, regional, and local scales. Understanding what causes these cycles now and in the future is a major question in jellyfish bloom research, because of the potential impacts on ecosystem function and services. Most bloom forming scyphozoan jellyfish have complex life histories involving a long-lived asexually reproducing benthic polyp and a sexually reproducing pelagic medusae. Environmental and climate factors affect each life stage, but we do not fully understand how these variables drive life stage transition, or how demographic differences in survival, growth and fecundity translate into visible jellyfish outbreaks. We undertook a comprehensive laboratory and field-based study of the physicochemical conditions that control survival, fecundity and phase transition of the different life stages of scyphozoan jellyfish. Through this research, we examine the effects of environmental drivers on jellyfish population cycles and life stage transition. Modifications to estuaries through the construction of barrages alter the natural dynamics of inhabitant species by controlling freshwater inputs into those systems, driving the presence and absence of medusae from estuaries. As well as this, we explore how environmental conditions translate into reproductive success or failure in temperate populations from the medusa to the polyp life stage, demonstrating that early polyp growth rates are strongly linked to their thermal environment and highlighting a potential marine heatwave event. We examine not only the effects of temperature and other climate drivers on scyphozoan jellyfish growth, survival and reproduction, but also whether epigenetic transgenerational effects can drive acclimation to warmer summer temperatures in the short term in the context of a warming ocean. No parental effects were observed in the first or second generation, and in the third generation the transgenerational effects of temperature were subtle and appeared most strongly in cooling scenarios. Finally, within the setting of anthropogenically-driven climate change, we demonstrate for the first time that A. aurita polyps require a minimum period of cooler temperatures to strobilate, contradicting claims that jellyfish populations will be more prevalent in warming oceans, specifically in the context of warmer winter conditions. To answer these questions, we chose the common, or moon jellyfish Aurelia aurita as our primary experimental organism. However, we expanded our research to other species to demonstrate how they may vary in both environment and response to forcing factors as compared to a ‘typical’ model species. This thesis highlights the importance of examining each population within the context of their environment, and advances our understanding of how the climate and environment affect jellyfish life stage transition
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