Extremity in horizontal and vertical Likert scale format responses. Some evidence on how visual distance between response categories influences extreme responding

In four survey experiments we show that people generally answer more extremely to survey items presented in vertical versus horizontal Likert formats. Our findings suggest that this effect may be at least partly driven by differences in the visual range spanned by the response scale (i.e. the visual distance between endpoint response categories is larger in horizontal than in a vertical format). In addition, compared to traditional horizontal Likert data, vertical Likert data contain more variance, which is mainly non-substantive. As a result, data obtained with scale formats that have different distances between response categories (as is typically the case for vertical vs. horizontal formats) may lead to differences in measurement model parameter estimates like residual terms, and in some cases factor loadings and construct correlations. Based on these results, we provide recommendations on the use of response scale formats in online surveys, bearing in mind that several online survey tool providers promote the use of vertical Likert formats and even automatically change traditional horizontal formats of Likert-type items to vertical Likert formats when viewed on small screens (e.g., on mobile phones)

MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API). MolGraph also implements a chemistry module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. To validate the GNNs, they were benchmarked against the datasets of MoleculeNet, as well as three chromatographic retention time datasets. The results on these benchmarks illustrate that the GNNs performed as expected. Additionally, the GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data. MolGraph is available at https://github.com/akensert/molgraph. Installation, tutorials and implementation details can be found at https://molgraph.readthedocs.io/en/latest/.Comment: 14 pages, 4 figures, 4 table

Development of liquid chromatography methods coupled to mass spectrometry for the analysis of substances with a wide variety of polarity in meconium.

International audienceMeconium is the first fecal excretion of newborns. This complex accumulative matrix allows assessing the exposure of the fetus to xenobiotics during the last 6 months of pregnancy. To determine the eventual effect of fetal exposure to micropollutants in this matrix, robust and sensitive analytical methods must be developed. This article describes the method development of liquid chromatography methods coupled to triple quadrupole mass spectrometry for relevant pollutants. The 28 selected target compounds had different physico-chemical properties from very polar (glyphosate) to non-polar molecules (pyrethroids). Tests were performed with three different types of columns: reversed phase, ion exchange and HILIC. As a unique method could not be determined for the simultaneous analysis of all compounds, three columns were selected and suitable chromatographic methods were optimized. Similar results were noticed for the separation of the target compounds dissolved in either meconium extract or solvent for reversed phase and ion exchange columns. However, for HILIC, the matrix had a significant influence on the peak shape and robustness of the method. Finally, the analytical methods were applied to “real” meconium samples

Ultra-rapid separation of an angiotensin mixture in nanochannels using shear-driven chromatography

The present paper reports on the separation of a mixture of fluorescein isothiocyanate-labeled angiotensin I and II peptides in a shear-driven nanochannel with a C-18-coating and using an eluent consisting of 5% acetonitrile in 0.02 M aqueous phosphate buffer at pH 6.5. The flat-rectangular nanochannel in fused silica consisted of an etched structure in combination with a flat moving wall. The very fast separation kinetics that can be achieved in a nanochannel allowed to separate the angiotensin peptides in less then 0.2 s in a distance of only 1.8 mm. Plate heights as small as 0.4 mu m were calculated after substraction of the injection effect. (c) 2006 Elsevier B.V. All rights reserved. \u