Testing for knowledge: Application of machine learning techniques for prediction of flashover in a 1/5 scale ISO 13784‐1 enclosure

A machine learning algorithm was applied to predict the onset of flashover in archival experiments in a 1/5 scale ISO 13784‐1 enclosure constructed with sandwich panels. The experiments were performed to assess whether a small‐scale model could provide a better full‐scale correlation than the single burning item test. To predict the binary output, a regularized logistic regression model was chosen as ML environment, for which lasso‐regression significantly reduced the amount of variance at a negligible increase in bias. With the regularized model, it was possible to discern the predictive variables and determine the decision boundary. In addition, a methodology was put forward on how to use the to update the learning algorithm iteratively. As a result, it was shown how a learning algorithm can be used to facilitate ongoing experimentation. At first as a crude guideline, and in later stages, as an accurate prediction algorithm. It is foreseen that, by iteratively updating the algorithm, by compiling existing and new experiments in databases, and by applying fire safety knowledge, the final learned algorithm will be able to make accurate predictions for unseen samples and test conditions

A global experiment on motivating social distancing during the COVID-19 pandemic

Significance Communicating in ways that motivate engagement in social distancing remains a critical global public health priority during the COVID-19 pandemic. This study tested motivational qualities of messages about social distancing (those that promoted choice and agency vs. those that were forceful and shaming) in 25,718 people in 89 countries. The autonomy-supportive message decreased feelings of defying social distancing recommendations relative to the controlling message, and the controlling message increased controlled motivation, a less effective form of motivation, relative to no message. Message type did not impact intentions to socially distance, but people’s existing motivations were related to intentions. Findings were generalizable across a geographically diverse sample and may inform public health communication strategies in this and future global health emergencies. Abstract Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

SPINE high-throughput crystallization, crystal imaging and recognition techniques: current state, performance analysis, new technologies and future aspects.

This paper reviews the developments in high-throughput and nanolitre-scale protein crystallography technologies within the remit of workpackage 4 of the Structural Proteomics In Europe (SPINE) project since the project's inception in October 2002. By surveying the uptake, use and experience of new technologies by SPINE partners across Europe, a picture emerges of highly successful adoption of novel working methods revolutionizing this area of structural biology. Finally, a forward view is taken of how crystallization methodologies may develop in the future

First steps towards effective methods in exploiting high-throughput technologies for the determination of human protein structures of high biomedical value

The EC 'Structural Proteomics In Europe' contract is aimed specifically at the atomic resolution structure determination of human protein targets closely linked to health, with a focus on cancer ( kinesins, kinases, proteins from the ubiquitin pathway), neurological development and neurodegenerative diseases and immune recognition. Despite the challenging nature of the analysis of such targets, similar to 170 structures have been determined to date. Here, the impact of high- throughput technologies, such as parallel expression of multiple constructs, the use of standardized refolding protocols and optimized crystallization screens or the use of mass spectrometry to assist sample preparation, on the structural biology of mammalian protein targets is illustrated through selected examples

CRYSTALLOGRAPHIC ANALYSIS OF THE CATALYTIC MECHANISM OF HALOALKANE DEHALOGENASE

Crystal structures of haloalkane dehalogenase were determined in the presence of the substrate 1,2-dichloroethane. At pH 5 and 4-degrees-C, substrate is bound in the active site without being converted; warming to room temperature causes the substrate's carbon-chlorine bond to be broken, producing a chloride ion with concomitant alkylation of the active-site residue Asp124. At pH 6 and room temperature the alkylated enzyme is hydrolysed by a water molecule activated by the His289-Asp260 pair in the active site. These results show that catalysis by the dehalogenase proceeds by a two-step mechanism involving an ester intermediate covalently bound at Asp124