High molar mass segmented macromolecular architectures by nitroxide mediated polymerisation

A straightforward synthetic pathway based on nitroxide mediated polymerisation (NMP) for the synthesis of a variety of high molar mass segmented copolymers comprising both polystyrene (PS) and polyether segments is reported. First, various precursors such as linear or star-shaped polyether macromonomers, containing either a-methylstyrene or styrene functions at one polymer terminus, as well as PS and polyether macroalkoxyamines bearing either 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) or N-tert-butyl-1-diethylphosphono-2,2-dimethylpropyl nitroxide (SG1) end-groups, were prepared. In a second step, these prepolymers were used to design different copolymer architectures such as block, graft, star-grafted, toothbrush and palm tree structures, in which PS constituted the backbone and polyether the side chains. Block copolymers were obtained by NMP of styrene initiated with polyether macroalkoxyamines. Copolymerisation of styrene with linear and star-shaped polyether macromonomers by NMP resulted in graft and star-grafted copolymers, respectively. A toothbrush copolymer was produced in a similar way with the exception of the initiator, which was a PS macroalkoxyamine. Likewise, palm tree architectures were obtained by homopolymerising polyether macromonomers initiated by PS macroinitiators. Advanced characterisation of the different polymer structures was performed, including 2D chromatography

Qualitative Methods Can Enrich Quantitative Research on Occupational Stress: An Example From One Occupational Group

The chapter examines the ways in which qualitative and quantitative methods support each other in research on occupational stress. Qualitative methods include eliciting from workers unconstrained descriptions of work experiences, careful first-hand observations of the workplace, and participant-observers describing ‘‘from the inside’’ a particular work experience. The chapter shows how qualitative research plays a role in (a) stimulating theory development, (b) generating hypotheses, (c) identifying heretofore researcher-neglected job stressors and coping responses, (d) explaining difficult-to-interpret quantitative findings, and (e) providing rich descriptions of stressful transactions. Extensive examples from research on job stress in teachers are used. The limitations of qualitative research, particularly in the area of verification, are also described

Optimization of Cutting Parameters for Hard Boring of AISI 4340 Steel Using Signal-to-Noise Ratio, Grey Relation Analysis and Analysis of Variance

Tool vibration in the boring process is the main concern because of the tool overhanging which leads to high tool wear, cutting force and cutting temperature. Interaction between machine dynamics and the metal cutting operation tool also results in tool vibration. The optimized cutting parameters will able to decrease tool vibration and in turn, increase the productivity in the manufacturing sector. In this study, statistical mathematical approaches to develop models for determining the impact of individual cutting parameters on cutting temperature, tool wear, cutting force, and tool vibration when hard boring AISI 4340 steels. During hard boring of AISI 4340 steel, the current investigation consisted of 27 run trials with three varying levels of cutting velocity, feed rate, and depth of cut and each of these variables was tested at three different levels. This work intends to simultaneous optimize statistical analysis such as Signal-to-Noise (S/N) ratio, Analysis of Variance (ANOVA) and Grey Relational Analysis (GRA). ANOVA and S/N ratio is used to identify the important cutting parameters on the single response optimization and GRA is used to optimize the multi-response optimization technique on cutting parameters. The results shows that both single and multi-response optimization technique shows the same optimized cutting parameter

Small RNA expression and strain specificity in the rat

<p>Abstract</p> <p>Background</p> <p>Digital gene expression (DGE) profiling has become an established tool to study RNA expression. Here, we provide an in-depth analysis of small RNA DGE profiles from two different rat strains (BN-Lx and SHR) from six different rat tissues (spleen, liver, brain, testis, heart, kidney). We describe the expression patterns of known and novel micro (mi)RNAs and <it>piwi</it>-interacting (pi)RNAs.</p> <p>Results</p> <p>We confirmed the expression of 588 known miRNAs (54 in antisense orientation) and identified 56 miRNAs homologous to known human or mouse miRNAs, as well as 45 new rat miRNAs. Furthermore, we confirmed specific A to I editing in brain for <it>mir-376a/b/c </it>and identified <it>mir-377 </it>as a novel editing target. In accordance with earlier findings, we observed a highly tissue-specific expression pattern for all tissues analyzed. The brain was found to express the highest number of tissue-specific miRNAs, followed by testis. Notably, our experiments also revealed robust strain-specific differential miRNA expression in the liver that is caused by genetic variation between the strains. Finally, we identified two types of germline-specific piRNAs in testis, mapping either to transposons or in strand-specific clusters.</p> <p>Conclusions</p> <p>Taken together, the small RNA compendium described here advances the annotation of small RNAs in the rat genome. Strain and tissue-specific expression patterns furthermore provide a strong basis for studying the role of small RNAs in regulatory networks as well as biological process like physiology and neurobiology that are extensively studied in this model system.</p

Qualitative and Quantitative Methods in Occupational-Stress Research

The paper examined the ways in which qualitative and quantitative methods support each other in research on occupational stress. Qualitative methods include (a) eliciting from unconstrained descriptions of work experiences, (b) careful first-hand observations at the workplace, and (c) participant‑observers describing “from the inside” a particular work experience. The paper shows how qualitative research stimulates theory development, hypothesis generation, and the identification of job stressors and coping responses. The limitations of qualitative research, particularly in the area of verification, are also described

Scoring epidemiological forecasts on transformed scales

Forecast evaluation is essential for the development of predictive epidemic models and can inform their use for public health decision-making. Common scores to evaluate epidemiological forecasts are the Continuous Ranked Probability Score (CRPS) and the Weighted Interval Score (WIS), which can be seen as measures of the absolute distance between the forecast distribution and the observation. However, applying these scores directly to predicted and observed incidence counts may not be the most appropriate due to the exponential nature of epidemic processes and the varying magnitudes of observed values across space and time. In this paper, we argue that transforming counts before applying scores such as the CRPS or WIS can effectively mitigate these difficulties and yield epidemiologically meaningful and easily interpretable results. Using the CRPS on log-transformed values as an example, we list three attractive properties: Firstly, it can be interpreted as a probabilistic version of a relative error. Secondly, it reflects how well models predicted the time-varying epidemic growth rate. And lastly, using arguments on variance-stabilizing transformations, it can be shown that under the assumption of a quadratic mean-variance relationship, the logarithmic transformation leads to expected CRPS values which are independent of the order of magnitude of the predicted quantity. Applying a transformation of log(x + 1) to data and forecasts from the European COVID-19 Forecast Hub, we find that it changes model rankings regardless of stratification by forecast date, location or target types. Situations in which models missed the beginning of upward swings are more strongly emphasised while failing to predict a downturn following a peak is less severely penalised when scoring transformed forecasts as opposed to untransformed ones. We conclude that appropriate transformations, of which the natural logarithm is only one particularly attractive option, should be considered when assessing the performance of different models in the context of infectious disease incidence

Human judgement forecasting of COVID-19 in the UK

Background In the past, two studies found ensembles of human judgement forecasts of COVID-19 to show predictive performance comparable to ensembles of computational models, at least when predicting case incidences. We present a follow-up to a study conducted in Germany and Poland and investigate a novel joint approach to combine human judgement and epidemiological modelling. Methods From May 24th to August 16th 2021, we elicited weekly one to four week ahead forecasts of cases and deaths from COVID-19 in the UK from a crowd of human forecasters. A median ensemble of all forecasts was submitted to the European Forecast Hub. Participants could use two distinct interfaces: in one, forecasters submitted a predictive distribution directly, in the other forecasters instead submitted a forecast of the effective reproduction number Rt . This was then used to forecast cases and deaths using simulation methods from the EpiNow2 R package. Forecasts were scored using the weighted interval score on the original forecasts, as well as after applying the natural logarithm to both forecasts and observations. Results The ensemble of human forecasters overall performed comparably to the official European Forecast Hub ensemble on both cases and deaths, although results were sensitive to changes in details of the evaluation. Rt forecasts performed comparably to direct forecasts on cases, but worse on deaths. Self-identified “experts” tended to be better calibrated than “non-experts” for cases, but not for deaths. Conclusions Human judgement forecasts and computational models can produce forecasts of similar quality for infectious disease such as COVID-19. The results of forecast evaluations can change depending on what metrics are chosen and judgement on what does or doesn\u27t constitute a "good" forecast is dependent on the forecast consumer. Combinations of human and computational forecasts hold potential but present real-world challenges that need to be solved