Objective numeracy exacerbates framing effects from decision-making under political risk.

While Prospect Theory helps to explain decision-making under risk, studies often base frames on hypothetical events and fail to acknowledge that many individuals lack the ability and motivation to engage in complex thinking. We use an original survey of US adults (N = 2813) to test Prospect Theory in the context of the May 2023 debt ceiling negotiations in the US Congress and assess whether objective numeracy moderates framing effects. We hypothesize and find evidence to suggest that most respondents are risk-averse to potential gains and risk-accepting to potential losses; however, high numerates are more risk-averse and risk-accepting to gains and losses, respectively, than low numerates. We also find that need for cognition interacts with numeracy to moderate framing effects for prospective losses, such that higher need for cognition attenuates risk-acceptance among low numerates and exacerbates risk-acceptance among high numerates. Our results are robust to a range of other covariates and in models accounting for the interaction between political knowledge and need for cognition, indicating joint moderating effects from two knowledge domains similarly conditioned by the desire to engage in effortful thinking. Our findings demonstrate that those who can understand and use objective information may remain subjectively persuaded by certain policy frames.

Single-Cell Untargeted Lipidomics Using Liquid Chromatography and Data-Dependent Acquisition after Live Cell Selection.

We report the development and validation of an untargeted single-cell lipidomics method based on microflow chromatography coupled to a data-dependent mass spectrometry method for fragmentation-based identification of lipids. Given the absence of single-cell lipid standards, we show how the methodology should be optimized and validated using a dilute cell extract. The methodology is applied to dilute pancreatic cancer and macrophage cell extracts and standards to demonstrate the sensitivity requirements for confident assignment of lipids and classification of the cell type at the single-cell level. The method is then coupled to a system that can provide automated sampling of live, single cells into capillaries under microscope observation. This workflow retains the spatial information and morphology of cells during sampling and highlights the heterogeneity in lipid profiles observed at the single-cell level. The workflow is applied to show changes in single-cell lipid profiles as a response to oxidative stress, coinciding with expanded lipid droplets. This demonstrates that the workflow is sufficiently sensitive to observing changes in lipid profiles in response to a biological stimulus. Understanding how lipids vary in single cells will inform future research into a multitude of biological processes as lipids play important roles in structural, biophysical, energy storage, and signaling functions.

Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells.

In this work, we demonstrate the development and first application of nanocapillary sampling followed by analytical flow liquid chromatography-mass spectrometry for single-cell lipidomics. Around 260 lipids were tentatively identified in a single cell, demonstrating remarkable sensitivity. Human pancreatic ductal adenocarcinoma cells (PANC-1) treated with the chemotherapeutic drug gemcitabine can be distinguished from controls solely on the basis of their single-cell lipid profiles. Notably, the relative abundance of LPC(0:0/16:0) was significantly affected in gemcitabine-treated cells, in agreement with previous work in bulk. This work serves as a proof of concept that live cells can be sampled selectively and then characterized using automated and widely available analytical workflows, providing biologically relevant outputs.

Spatial single cell metabolomics: Current challenges and future developments.

Single cell metabolomics is a rapidly advancing field of bio-analytical chemistry which aims to observe cellular biology with the greatest detail possible. Mass spectrometry imaging and selective cell sampling (e.g. using nanocapillaries) are two common approaches within the field. Recent achievements such as observation of cell-cell interactions, lipids determining cell states and rapid phenotypic identification demonstrate the efficacy of these approaches and the momentum of the field. However, single cell metabolomics can only continue with the same impetus if the universal challenges to the field are met, such as the lack of strategies for standardisation and quantification, and lack of specificity/sensitivity. Mass spectrometry imaging and selective cell sampling come with unique advantages and challenges which, in many cases are complementary to each other. We propose here that the challenges specific to each approach could be ameliorated with collaboration between the two communities driving these approaches.

Nanocapillary sampling coupled to liquid chromatography mass spectrometry delivers single cell drug measurement and lipid fingerprints.

This work describes the development of a new approach to measure drug levels and lipid fingerprints in single living mammalian cells. Nanocapillary sampling is an approach that enables the selection and isolation of single living cells under microscope observation. Here, live single cell nanocapillary sampling is coupled to liquid chromatography for the first time. This allows molecular species to be separated prior to ionisation and improves measurement precision of drug analytes. The efficiency of transferring analytes from the sampling capillary into a vial was optimised in this work. The analysis was carried out using standard flow liquid chromatography coupled to widely available mass spectrometry instrumentation, highlighting opportunities for widespread adoption. The method was applied to 30 living cells, revealing cell-to-cell heterogeneity in the uptake of different drug molecules. Using this system, we detected 14-158 lipid features per single cell, revealing the association between bedaquiline uptake and lipid fingerprints.

Are Republicans and Conservatives More Likely to Believe Conspiracy Theories?

A sizable literature tracing back to Richard Hofstadter's The Paranoid Style (1964) argues that Republicans and conservatives are more likely to believe conspiracy theories than Democrats and liberals. However, the evidence for this proposition is mixed. Since conspiracy theory beliefs are associated with dangerous orientations and behaviors, it is imperative that social scientists better understand the connection between conspiracy theories and political orientations. Employing 20 surveys of Americans from 2012 to 2021 (total n = 37,776), as well as surveys of 20 additional countries spanning six continents (total n = 26,416), we undertake an expansive investigation of the asymmetry thesis. First, we examine the relationship between beliefs in 52 conspiracy theories and both partisanship and ideology in the U.S.; this analysis is buttressed by an examination of beliefs in 11 conspiracy theories across 20 more countries. In our second test, we hold constant the content of the conspiracy theories investigated-manipulating only the partisanship of the theorized villains-to decipher whether those on the left or right are more likely to accuse political out-groups of conspiring. Finally, we inspect correlations between political orientations and the general predisposition to believe in conspiracy theories over the span of a decade. In no instance do we observe systematic evidence of a political asymmetry. Instead, the strength and direction of the relationship between political orientations and conspiricism is dependent on the characteristics of the specific conspiracy beliefs employed by researchers and the socio-political context in which those ideas are considered. Supplementary Information: The online version contains supplementary material available at 10.1007/s11109-022-09812-3.