Using an optimized transformation protocol we have studied the possible interactions between transforming plasmid DNA and the Hansenula polymorpha genome. Plasmids consisting only of a pBR322 replicon, an antibiotic resistance marker for Escherichia coli and the Saccharomyces cerevisiae LEU2 gene were shown to replicate autonomously in the yeast at an approximate copy number of 6 (copies per genome equivalent). This autonomous behaviour is probably due to an H. polymorpha replicon-like sequence present on the S. cerevisiae LEU2 gene fragment. Plasmids replicated as multimers consisting of monomers connected in a head-to-tail configuration. Two out of nine transformants analysed appeared to contain plasmid multimers in which one of the monomers contained a deletion. Plasmids containing internal or flanking regions of the genomic alcohol oxidase gene were shown to integrate by homologous single or double cross-over recombination. Both single- and multi-copy (two or three) tandem integrations were observed. Targeted integration occurred in 1-22% of the cases and was only observed with plasmids linearized within the genomic sequences, indicating that homologous linear ends are recombinogenic in H. polymorpha. In the cases in which no targeted integration occurred, double-strand breaks were efficiently repaired in a homology-independent way. Repair of double-strand breaks was precise in 50-68% of the cases. Linearization within homologous as well as nonhomologous plasmid regions stimulated transformation frequencies up to 15-fold.
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This article investigates the transformative impacts of regular nature-based adventure activity engagement and its long-lasting effects on eudaimonic well-being (EWB), specifically mental health. Although extant research highlights a wide range of well-being and mental health benefits from participation in such pursuits, less is known about experienced outdoor adventure enthusiasts for whom adventure is a fundamental and transformational part of their lives. The study builds on an existing conceptual framework that synthesizes pertinent research concepts on nature-based activity engagement and subjective well-being benefits. It presents key findings from 40 semi-structured in-depth online interviews with respondents from the UK, Germany, and Serbia. Interview data were collated and analyzed using a thematic framework approach. The findings highlight the importance of outdoor adventure activity engagement for respondents’ mental and physical health and long-term well-being. Regular activity participation can be transformational in reducing feelings of ill-being and enhancing EWB. It can improve self-efficacy and identity development and promote the fulfilment of psychological needs, facilitated by key transformational catalyzers. Continually entering a liminal state, experiencing emotions, and overcoming challenges and risks during engagement are crucial to “successful” long-lasting transformation. Further research should continue to explore adventure’s transformational and EWB benefits to develop long-term data.
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Inaugural address of Frederike Praasterink, Professor Future Food Systems 22 February 2018. Three important principles contribute to the transformation of food systems: - Redesign food systems from ‘less bad’ to ‘net positive’ - Reconnect consumers, producers, youth - Revalue food through true cost accounting and new business models
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The aim was to study the needs, opportunities and effects of citizen engagement in the urban food system transition. This addresses the issue of - ‘how’, ‘in what ways’ and ‘through what methods’ - citizens can be engaged in the developments towards a more sustainable and healthy regional food system. The research project sought to investigate the roles citizen engagement can take in the transformation of the urban food environment towards healthier and sustainable food consumption patterns. The study covers desired food futures; food discourse; the message our bodies convey about our eating habits; the effect of Covid-19 on food pattern transformations; the term 'organic' in relation to food; mass media as a source of information about food.
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Some of the most valuable companies in the world accumulated their fortunes as a result of a business model innovation built upon matured technologies. Now the majority of them are investing and shifting their focus to the development of new technologies such as AI, blockchain and genetic editing. If an organization is to remain profitable, it needs to be able to quickly adjust its structure to the rapidly changing context. We contend that a way to do so is to build an organizational structure that is conductive to both generative and evaluative prototypes. We report on our action research with a leading European airline following the transformation of a team of four into a new department, through the lenses of continuous prototyping. We then propose an initial framework that conceptualises organizational prototypes and provides a rational and systematic way of approaching the devising of such. Finally, we outline several directions for further research. https://doi.org/10.33114/adim.2019.07.227 LinkedIn: https://www.linkedin.com/in/christine-de-lille-8039372/
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Ecocentrism is the broadest term for worldviews that recognize intrinsic value in all lifeforms and ecosystems themselves, including their abiotic components. Anthropocentrism, in contrast, values other lifeforms and ecosystems insofar as they are valuable for human well-being, preferences and interests. Herein, the authors examine the roots of ecocentrism and discuss its mixed history of international recognition. They argue that non-human nature has intrinsic value irrespective of human preferences or valuation, and they refute the claim that ecocentrism is misanthropic. They then summarize four key examples from the academic literature in which anthropocentrism fails to provide an ethic adequate for respecting and protecting planet Earth and its inhabitants. The authors conclude that ecocentrism is essential for solving our unprecedented environmental crisis, arguing its importance from four perspectives: ethical, evolutionary, spiritual and ecological. They contend that a social transformation towards ecocentrism is not only an ethical but a practical imperative, and they urge support for ecocentric understanding and practices. https://www.ecologicalcitizen.net/article.php?t=why-ecocentrism-key-pathway-sustainability https://www.linkedin.com/in/helenkopnina/
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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In dit abstract wordt de ontwikkeling van een online onderwijsmodule beschreven gericht op eHealth voor leefstijlverbetering
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Summary: Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths.
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