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Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data

2022, John, Torsten, Martin, Lisandra L., Risselada, Herre Jelger, Abel, Bernd

Nanostructured surfaces are widespread in nature and are being further developed in materials science. This makes them highly relevant for biomolecules, such as peptides. In this data article, we present a curvature model and molecular dynamics (MD) simulation data on the influence of nanoparticle size on the stability of amyloid peptide fibrils related to our research article entitled “Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation” (John et al., 2022) [1]. We provide the code to perform MD simulations in GROMACS 4.5.7 software of arbitrarily chosen biomolecule oligomers adsorbed on a curved surface of chosen nanoparticle size. We also provide the simulation parameters and data for peptide oligomers of Aß40, NNFGAIL, GNNQQNY, and VQIYVK. The data provided allows researchers to further analyze our MD simulations and the curvature model allows for a better understanding of oligomeric structures on surfaces.

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Cell cycle-related genes associate with sensitivity to hydrogen peroxide-induced toxicity

2022, Bekeschus, Sander, Liebelt, Grit, Menz, Jonas, Singer, Debora, Wende, Kristian, Schmidt, Anke

Reactive oxygen species (ROS) such as hydrogen peroxide (H2O2) are well-described agents in physiology and pathology. Chronic inflammation causes incessant H2O2 generation associated with disease occurrences such as diabetes, autoimmunity, and cancer. In cancer, conditioning of the tumor microenvironment, e.g., hypoxia and ROS generation, has been associated with disease outcomes and therapeutic efficacy. Many reports have investigated the roles of the action of H2O2 across many cell lines and disease models. The genes predisposing tumor cell lines to H2O2-mediated demise are less deciphered, however. To this end, we performed in-house transcriptional profiling of 35 cell lines and simultaneously investigated each cell line's H2O2 inhibitory concentration (IC25) based on metabolic activity. More than 100-fold differences were observed between the most resistant and sensitive cell lines. Correlation and gene ontology pathway analysis identified a rigid association with genes intertwined in cell cycle progression and proliferation, as such functional categories dominated the top ten significant processes. The ten most substantially correlating genes (Spearman r > 0.70 or < -0.70) were validated using qPCR, showing complete congruency with microarray analysis findings. Western blotting confirmed the correlation of cell cycle-related proteins negatively correlating with H2O2 IC25. Top genes related to ROS production or antioxidant defense were only modest in correlation (Spearman r > 0.40 or < -0.40). In conclusion, our in-house transcriptomic correlation analysis revealed a set of cell cycle-associated genes associated with a priori resistance or sensitivity to H2O2-induced cellular demise with the detailed and causative roles of individual genes remaining unclear.

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Grain-size distribution dataset of supercritical flow sediments from a Gilbert-type delta that are associated with disaggregation bands

2022, Tanner, David C., Brandes, Christian, Winsemann, Jutta

This is a dataset of grain-size distribution in sub- and supercritical flow sediments of a Gilbert-type delta from an outcrop in North Germany. Thirteen samples of ca 2.5 kg were dried (at 105°C), and homogenised twice with a sample divider. A representative sample of 1-2 g was then analysed using laser diffraction. The grain-size distribution of the sand has a maximum between fine to medium sand, with a long fine fraction tail down to 0.06 µm and occasional coarse fractions (up to 1.5 mm) in some samples. Specific grain-size distributions correlate with the different sedimentary bedforms from which the samples were taken. This data is important for two reasons: Firstly, sedimentary structures formed by Froude supercritical flows are controlled by grain-size. However, few studies have provided grain-size datasets from the natural record, which often have a much wider grain-size distribution than experimentally-produced supercritical flow deposits. Secondly, the sands were deformed subsequently by disaggregation bands, a type of geological fault that only develops in porous granular materials, i.e. well-sorted, medium sand. The disaggregation bands are indicative of seismic or even aseismic, creeping movement of basement faults.

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Latent Class Cluster Analysis: Selecting the number of clusters

2022, Lezhnina, Olga, Kismihók, Gábor

Latent Class Cluster Analysis (LCCA) is an advanced model-based clustering method, which is increasingly used in social, psychological, and educational research. Selecting the number of clusters in LCCA is a challenging task involving inevitable subjectivity of analytical choices. Researchers often rely excessively on fit indices, as model fit is the main selection criterion in model-based clustering; it was shown, however, that a wider spectrum of criteria needs to be taken into account. In this paper, we suggest an extended analytical strategy for selecting the number of clusters in LCCA based on model fit, cluster separation, and stability of partitions. The suggested procedure is illustrated on simulated data and a real world dataset from the International Computer and Information Literacy Study (ICILS) 2018. For the latter, we provide an example of end-to-end LCCA including data preprocessing. The researcher can use our R script to conduct LCCA in a few easily reproducible steps, or implement the strategy with any other software suitable for clustering. We show that the extended strategy, in comparison to fit indices-based strategy, facilitates the selection of more stable and well-separated clusters in the data. • The suggested strategy aids researchers to select the number of clusters in LCCA • It is based on model fit, cluster separation, and stability of partitions • The strategy is useful for finding separable generalizable clusters in the data.