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Classical statistical analysis of data can be complemented or replaced with data analysis based on machine learning. However, in certain disciplines, such as education research, studies are frequently limited to small datasets, which raises several questions regarding biases and coincidentally positive results. In this study, we present a refined approach for evaluating the performance of a binary classification based on machine learning for small datasets. The approach includes a non-parametric permutation test as a method to quantify the probability of the results generalising to new data. Furthermore, we found that a repeated nested cross-validation is almost free of biases and yields reliable results that are only slightly dependent on chance. Considering the advantages of several evaluation metrics, we suggest a combination of more than one metric to train and evaluate machine learning classifiers. In the specific case that both classes are equally important, the Matthews correlation coefficient exhibits the lowest bias and chance for coincidentally good results. The results indicate that it is essential to avoid several biases when analysing small datasets using machine learning. [ABSTRACT FROM AUTHOR]

Research has documented robust associations between greater disgust sensitivity and (1) concerns about disease, and (2) political conservatism. However, the COVID-19 disease pandemic raised challenging questions about these associations. In particular, why have conservatives—despite their greater disgust sensitivity—exhibited less concern about the pandemic? Here, we investigate this "conservatism-disgust paradox" and address several outstanding theoretical questions regarding the interrelations among disgust sensitivity, ideology, and pandemic response. In four studies (N = 1,764), we identify several methodological and conceptual factors—in particular, an overreliance on self-report measures—that may have inflated the apparent associations among these constructs. Using non-self-report measures, we find evidence that disgust sensitivity may be a less potent predictor of disease avoidance than is typically assumed, and that ideological differences in disgust sensitivity may be amplified by self-report measures. These findings suggest that the true pattern of interrelations among these factors may be less "paradoxical" than is typically believed. [ABSTRACT FROM AUTHOR]

Background: The uptake and intracellular trafficking of sphingolipids, which self-associate into plasma membrane microdomains, is associated with many pathological conditions, including viral and toxin infection, lipid storage disease, and neurodegenerative disease. However, the means available to label the trafficking pathways of sphingolipids in live cells are extremely limited. In order to address this problem, we have developed an exogenous, non-toxic probe consisting of a 25-amino acid sphingolipid binding domain, the SBD, derived from the amyloid peptide Aβ, and conjugated by a neutral linker with an organic fluorophore. The current work presents the characterization of the sphingolipid binding and live cell trafficking of this novel probe, the SBD peptide. SBD was the name given to a motif originally recognized by Fantini et al [1] in a number of glycolipid-associated proteins, and was proposed to interact with sphingolipids in membrane microdomains. Methodology/Principal Findings: In accordance with Fantini's model, optimal SBD binding to membranes depends on the presence of sphingolipids and cholesterol. In synthetic membrane binding assays, SBD interacts preferentially with raft-like lipid mixtures containing sphingomyelin, cholesterol, and complex gangliosides in a pH-dependent manner, but is less glycolipid-specific than Cholera toxin B (CtxB). Using quantitative time-course colocalization in live cells, we show that the uptake and intracellular trafficking route of SBD is unlike that of either the non-raft marker Transferrin or the raft markers CtxB and Flotillin2-GFP. However, SBD traverses an endolysosomal route that partially intersects with raft-associated pathways, with a major portion being diverted at a late time point to rab11-positive recycling endosomes. Trafficking of SBD to acidified compartments is strongly disrupted by cholesterol perturbations, consistent with the regulation of sphingolipid trafficking by cholesterol. Conclusions/Significance: The current work presents the characterization and trafficking behavior of a novel sphingolipid-binding fluorescent probe, the SBD peptide. We show that SBD binding to membranes is dependent on the presence of cholesterol, sphingomyelin, and complex glycolipids. In addition, SBD targeting through the endolysosomal pathway in neurons is highly sensitive to cholesterol perturbations, making it a potentially useful tool for the analysis of sphingolipid trafficking in disease models that involve changes in cholesterol metabolism and storage. [ABSTRACT FROM AUTHOR]