Search

Psychopathy is traditionally linked to higher-level cognitive processes, but its impact on low-level cognitive functions has remained largely unexplored. This study investigates the relationship between the fundamental cognitive function of physical distance estimation and our social predisposition towards others, as captured by psychopathic traits, grouped into the three facets of Fearless Dominance, Self-Centered Impulsivity and Coldheartedness. Using an innovative experimental design, participants estimated the distance of an approaching avatar agent. We demonstrate that social processing, as reflected in the scale of Psychopathy in the general population, significantly distorts egocentric space perception. Strikingly, this distortion is absent with non-anthropomorphic agents. The study also reveals that agent emotional expressions do not affect distance estimation, when they are task-irrelevant, suggesting a disocciation between the effect of psychopathic traits on social processing and emotion processing. These results uncover a direct top-down effect of psychopathic traits on basic spatial cognition, in the presence of others. These new insights into the basic cognitive mechanisms underlying social interactions have broader implications for any field involving interpersonal spatial behaviour., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Various spectrometric methods can be used to conduct metabolomics studies. Nuclear magnetic resonance (NMR) or mass spectrometry (MS) coupled with separation methods, such as liquid or gas chromatography (LC and GC, respectively), are the most commonly used techniques. Once the raw data have been obtained, the real challenge lies in the bioinformatics required to conduct: (i) data processing (including preprocessing, normalization, and quality control); (ii) statistical analysis for comparative studies (such as univariate and multivariate analyses, including PCA or PLS-DA/OPLS-DA); (iii) annotation of the metabolites of interest; and (iv) interpretation of the relationships between key metabolites and the relevant phenotypes or scientific questions to be addressed. Here, we will introduce and detail a stepwise protocol for use of the Workflow4Metabolomics platform (W4M), which provides user-friendly access to workflows for processing of LC-MS, GC-MS, and NMR data. Those modular and extensible workflows are composed of existing standalone components (e.g., XCMS and CAMERA packages) as well as a suite of complementary W4M-implemented modules. This tool suite is accessible worldwide through a web interface and is hosted on UseGalaxy France. The extensible Virtual Research Environment (VRE) provided offers pre-configured workflows for metabolomics communities (platforms, end users, etc.), as well as possibilities for sharing among users. By providing a consistent ecosystem of tools and workflows through Galaxy, W4M makes it possible to process MS and NMR data from hundreds of samples using an ordinary personal computer, after step-by-step workflow optimization. © 2025 Wiley Periodicals LLC. Basic Protocol 1: W4M account creation, working history preparation, and data upload Support Protocol 1: How to prepare an NMR zip file Support Protocol 2: How to convert MS data from proprietary format to open format Support Protocol 3: How to get help with W4M (IFB forum) and how to report a problem on the GitHub repository Basic Protocol 2: LC-MS data processing Alternate Protocol 1: GC-MS data processing Alternate Protocol 2: NMR data processing Basic Protocol 3: Statistical analysis Basic Protocol 4: Annotation of metabolites from LC-MS data Alternate Protocol 3: Annotation of metabolites from NMR data., (© 2025 Wiley Periodicals LLC.)

Purpose: Compare demographic, care provision, and health-related characteristics of individuals fulfilling multiple illness-related caregiving roles (i.e. multicaregiving) versus singular (cancer only) caregiving and investigate factors associated with caregivers' mental and physical functioning., Design: Cross-sectional national survey., Participants: Family caregivers who self-reported illness-related caregiving for cancer survivors only (singular caregivers: n  = 465) or for one or more family members with illnesses in addition to the cancer survivors (multicaregivers: n = 109)., Methods: Descriptive, logistic, and linear regression analysis., Findings: Singular caregivers and multicaregivers were similar on demographic, care provision, and health characteristics. Caregiving group was not associated with caregivers' mental or physical functioning. Several caregiver and care recipient characteristics were associated with mental and physical functioning for singular caregivers; however, only age was associated with multicaregivers' mental functioning., Conclusions: Uncovering correlates of multicaregiving can help describe who may serve in these roles and how concurrent care responsibilities may influence caregivers' well-being.