On the relationship between unprompted thought and affective well-being: A systematic review and meta-analysis.

There is a growing recognition that thoughts often arise independently of external demands. These thoughts can span from reminiscing your last vacation to contemplating career goals to fantasizing about meeting your favorite musician. Often referred to as mind wandering, such frequently occurring unprompted thoughts have widespread impact on our daily functions, with the dominant narrative converging on a negative relationship between unprompted thought and affective well-being. In this systematic review of 76 studies, we implemented a meta-analysis and qualitative review to elucidate if and when unprompted thought is indeed negatively associated with affective well-being in adults. Using a multilevel mixed-model approach on 386 effect sizes from 23,168 participants across 64 studies, our meta-analyses indicated an overall relationship between unprompted thought and worse affective well-being (r¯ = -.18, 95% CI [-.23, -.14]); however, the magnitude and direction of this relationship changed when considering specific aspects of the phenomenon (including thought content and intentionality) and methodological approaches (including questionnaires vs. experience sampling). The qualitative review further contextualizes this relationship by revealing the nuances of how and when unprompted thought is associated with affective well-being. Taken together, our meta-analysis and qualitative review indicate that the commonly reported relationship between unprompted thought and affective well-being is contingent upon the content and conceptualization of unprompted thought, as well as the methodological and analytic approaches implemented. Based on these findings, we propose emerging directions for future empirical and theoretical work that highlight the importance of accounting for when, how, and for whom unprompted thought is associated with affective well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Webcam-based eye tracking to detect mind wandering and comprehension errors.

Recent advances in computer vision have opened the door for scalable eye tracking using only a webcam. Such solutions are particularly useful for online educational technologies, in which a goal is to respond adaptively to students' ongoing experiences. We used WebGazer, a webcam-based eye-tracker, to automatically detect covert cognitive states during an online reading-comprehension task related to task-unrelated thought and comprehension. We present data from two studies using different populations: (1) a relatively homogenous sample of university students (N = 105), and (2) a more diverse sample from Prolific (N = 173, with < 20% White participants). Across both studies, the webcam-based eye-tracker provided sufficiently accurate and precise gaze measurements to predict both task-unrelated thought and reading comprehension from a single calibration. We also present initial evidence of predictive validity, including a positive correlation between predicted rates of task-unrelated thought and comprehension scores. Finally, we present slicing analyses to determine how performance changed under certain conditions (lighting, glasses, etc.) and generalizability of the results across the two datasets (e.g., training on the data Study 1 and testing on data from Study 2, and vice versa). We conclude by discussing results in the context of remote research and learning technologies.

Sadness facilitates "deeper" reading comprehension: a behavioural and eye tracking study.

Reading is one of the most common everyday activities, yet research elucidating how affective influence reading processes and outcomes is sparse with inconsistent results. To investigate this question, we randomly assigned participants (N = 136) to happiness (positive affect), sadness (negative affect), and neutral video-induction conditions prior to engaging in self-paced reading of a long, complex science text. Participants completed assessments targeting multiple levels of comprehension (e.g. recognising factual information, integrating different textual components, and open-ended responses of concepts from memory) after reading and after a week-long delay. Results indicated that the Sadness (vs. Happiness) condition had higher comprehension scores, with the largest effects emerging for assessments targeting deeper levels comprehension immediately after reading. Eye-tracking analyses revealed that such benefits may be partly driven by sustained attentional focus over the 20-minute reading session. We discuss results with respect to theories on affect, cognition, and text comprehension.

A possible shared underlying mechanism among involuntary autobiographical memory and déjà vu.

We propose that IAM and déjà vu may not share a placement on the same gradient, per se, but the mechanism of cue familiarity detection, and a major differentiating factor between the two metacognitive experiences is whether the resulting inward directed search of memory yields retrieved content or not. Déjà vu may manifest when contentless familiarity detection is inexplicable by the experiencer.

Dons d'organes et de tissus après décès dans le cadre de l'aide médicale à mourir : mise à jour 2023 du document d'orientation pour l'élaboration de politiques.

CONTEXTE: Depuis la parution du document d'orientation de 2019 de la Société canadienne du sang (SCS) pour les dons d'organes et de tissus après décès dans le cadre de l'aide médicale à mourir (AMM), le gouvernement fédéral a apporté des amendements à sa loi sur l'AMM. La présente mise à jour s'adresse aux médecins, aux organismes de don d'organes, aux spécialistes des soins de fin de vie et de l'AMM et aux responsables des orientations politiques; on y explique les répercussions de ces amendements. MÉTHODES: La Société canadienne du sang a réuni un groupe de 63 spécialistes de divers domaines (soins intensifs, dons d'organes et de tissus, administration des services de santé, AMM, bioéthique, droit et recherche) pour faire le point sur ces changements à l'occasion de son Forum d'orientation sur le don d'organes et de tissus après décès dans le cadre de l'aide médicale à mourir. Deux personnes admissibles à l'AMM et 2 proches de personnes ayant fait un don d'organe après l'AMM y ont aussi participé. Dans le cadre de 3 réunions consécutives tenues en ligne entre juin 2021 et avril 2022, les personnes participant au Forum ont abordé divers sujets en grands et petits groupes. Les discussions ont été guidées par une revue documentaire publiée selon la méthodologie JBI. Nous avons utilisé une technique du groupe nominal adaptée pour rédiger les recommandations qui ont fait consensus. La gestion des intérêts concurrents s'est faite selon les principes du Guidelines International Network, un réseau international qui se consacre aux lignes directrices de pratique clinique. RECOMMANDATIONS: Même si bon nombre des recommandations du document d'orientation de 2019 demeurent pertinentes, la présente mise à jour a permis d'en actualiser 2 et d'en formuler 8 nouvelles dans les domaines suivants : aiguillage vers un organisme de don d'organes, consentement, dons dirigés et conditionnels, procédure d'AMM, détermination du décès, équipes soignantes et préparation de rapports. INTERPRÉTATION: Les politiques et les pratiques en matière de don d'organes et de tissus après l'AMM au Canada doivent être conformes à la législation canadienne actuelle. La mise à jour de ce document d'orientation aidera les médecins à s'y retrouver parmi les enjeux médicaux, juridiques et éthiques inhérents à leur travail auprès de personnes qui souhaitent faire un don d'organes après l'AMM.

Erratum: Paired evaluation of machine-learning models characterizes effects of confounders and outliers.

[This corrects the article DOI: 10.1016/j.patter.2023.100791.].

Paired evaluation of machine-learning models characterizes effects of confounders and outliers.

The true accuracy of a machine-learning model is a population-level statistic that cannot be observed directly. In practice, predictor performance is estimated against one or more test datasets, and the accuracy of this estimate strongly depends on how well the test sets represent all possible unseen datasets. Here we describe paired evaluation as a simple, robust approach for evaluating performance of machine-learning models in small-sample biological and clinical studies. We use the method to evaluate predictors of drug response in breast cancer cell lines and of disease severity in patients with Alzheimer's disease, demonstrating that the choice of test data can cause estimates of performance to vary by as much as 20%. We show that paired evaluation makes it possible to identify outliers, improve the accuracy of performance estimates in the presence of known confounders, and assign statistical significance when comparing machine-learning models.

Proteomic profiling across breast cancer cell lines and models.

We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.

Deceased organ and tissue donation after medical assistance in dying: 2023 updated guidance for policy.

BACKGROUND: Since Canadian Blood Services (CBS) developed policy guidance in 2019 for organ and tissue donation after medical assistance in dying (MAiD), the federal government has made changes to legislation related to MAiD. This document provides updated guidance for clinicians, organ donation organizations, end-of-life care experts, MAiD providers and policy-makers on the impact of these changes. METHODS: Canadian Blood Services assembled a group of 63 experts from critical care, organ and tissue donation, health care administration, MAiD, bioethics, law and research to review the legislative changes in the Organ and Tissue Donation After Medical Assistance in Dying - Guidance for Policy forum. Two patients who had requested and been found eligible for MAiD and 2 family members of patients who had donated organs after MAiD were also included as participants. In a series of 3 online meetings from June 2021 to April 2022, forum participants addressed a variety of topics in small and large groups. These discussions were informed by a comprehensive scoping review using JBI methodology. We used an adapted form of nominal group technique to develop the recommendations, which were approved by consensus of the participants. Management of competing interests was in accordance with Guideline International Network principles. RECOMMENDATIONS: Although many of the recommendations from the guidance developed in 2019 are still relevant, this guidance provides 2 updated recommendations and 8 new recommendations in the following areas: referral to an organ donation organization, consent, directed and conditional donation, MAiD procedures, determination of death, health care professionals and reporting. INTERPRETATION: Policies and practices for organ and tissue donation after MAiD in Canada should align with current Canadian legislation. This updated guidance will help clinicians navigate the medical, legal and ethical challenges that arise when they support patients pursuing donation after MAiD.

What Flips Attention?

A central feature of our waking mental experience is that our attention naturally toggles back and forth between "external" and "internal" stimuli. In the midst of an externally demanding task, attention can involuntarily shift internally with no clear reason how or why thoughts momentarily shifted inward. In the case of external attention, we are typically exploring and encoding aspects of our external world, whereas internal attention often involves searching for and retrieving potentially relevant information from our memory networks. Cognitive science has traditionally focused on understanding forms of internal and external attention separately, leaving a mystery about what sparks the seemingly automatic shifts between the two. Specifically, what shifts attentional focus from being outward-directed to being inward-directed? We present a candidate mechanism: Familiarity-detection.

PEACOCK: a machine learning approach to assess the validity of cell type-specific enhancer-gene regulatory relationships.

The vast majority of disease-associated variants identified in genome-wide association studies map to enhancers, powerful regulatory elements which orchestrate the recruitment of transcriptional complexes to their target genes' promoters to upregulate transcription in a cell type- and timing-dependent manner. These variants have implicated thousands of enhancers in many common genetic diseases, including nearly all cancers. However, the etiology of most of these diseases remains unknown because the regulatory target genes of the vast majority of enhancers are unknown. Thus, identifying the target genes of as many enhancers as possible is crucial for learning how enhancer regulatory activities function and contribute to disease. Based on experimental results curated from scientific publications coupled with machine learning methods, we developed a cell type-specific score predictive of an enhancer targeting a gene. We computed the score genome-wide for every possible cis enhancer-gene pair and validated its predictive ability in four widely used cell lines. Using a pooled final model trained across multiple cell types, all possible gene-enhancer regulatory links in cis (~17 M) were scored and added to the publicly available PEREGRINE database ( www.peregrineproj.org ). These scores provide a quantitative framework for the enhancer-gene regulatory prediction that can be incorporated into downstream statistical analyses.

Investigating the appraisal structure of spontaneous thoughts: evidence for differences among unexpected thought, involuntary autobiographical memories, and ruminative thought.

Involuntary thinking occurs when mental states arise without intention. Such thoughts can take different forms, such as involuntary autobiographical memories (IAM), ruminative thoughts, and unexpected thoughts-all of which are popular areas of study, albeit in somewhat disparate literatures. Despite these mental states sharing a common thread of feeling involuntary in nature, it is nevertheless unclear what separates them phenomenologically. We conducted a set of exploratory and confirmatory experiments to elucidate the appraisal dimensions behind these forms of involuntary thought, with a particular interest in understanding the phenomenology behind unexpected thoughts that are predicted to violate expectations of both timing and content. Across two experiments, we found that unexpected thoughts had unique appraisal structures compared to the other two forms of involuntary thought: they were less identifiably cued, more surprising in content and timing, and offered new information (i.e., insight). We discuss how these distinctions support recent theories regarding the nature of unexpected thought and its relation to other forms of involuntary thinking, namely IAM and ruminative thought, which are the more commonly studied forms of involuntary thinking.

Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences.

Detrended Fluctuation Analysis (DFA) is the most popular fractal analytical technique used to evaluate the strength of long-range correlations in empirical time series in terms of the Hurst exponent, H. Specifically, DFA quantifies the linear regression slope in log-log coordinates representing the relationship between the time series' variability and the number of timescales over which this variability is computed. We compared the performance of two methods of fractal analysis-the current gold standard, DFA, and a Bayesian method that is not currently well-known in behavioral sciences: the Hurst-Kolmogorov (HK) method-in estimating the Hurst exponent of synthetic and empirical time series. Simulations demonstrate that the HK method consistently outperforms DFA in three important ways. The HK method: (i) accurately assesses long-range correlations when the measurement time series is short, (ii) shows minimal dispersion about the central tendency, and (iii) yields a point estimate that does not depend on the length of the measurement time series or its underlying Hurst exponent. Comparing the two methods using empirical time series from multiple settings further supports these findings. We conclude that applying DFA to synthetic time series and empirical time series during brief trials is unreliable and encourage the systematic application of the HK method to assess the Hurst exponent of empirical time series in behavioral sciences.

Targeting TBK1 to overcome resistance to cancer immunotherapy.

Despite the success of PD-1 blockade in melanoma and other cancers, effective treatment strategies to overcome resistance to cancer immunotherapy are lacking1,2. Here we identify the innate immune kinase TANK-binding kinase 1 (TBK1)3 as a candidate immune-evasion gene in a pooled genetic screen4. Using a suite of genetic and pharmacological tools across multiple experimental model systems, we confirm a role for TBK1 as an immune-evasion gene. Targeting TBK1 enhances responses to PD-1 blockade by decreasing the cytotoxicity threshold to effector cytokines (TNF and IFNγ). TBK1 inhibition in combination with PD-1 blockade also demonstrated efficacy using patient-derived tumour models, with concordant findings in matched patient-derived organotypic tumour spheroids and matched patient-derived organoids. Tumour cells lacking TBK1 are primed to undergo RIPK- and caspase-dependent cell death in response to TNF and IFNγ in a JAK-STAT-dependent manner. Taken together, our results demonstrate that targeting TBK1 is an effective strategy to overcome resistance to cancer immunotherapy.

Perceived Group Identity Alters Task-Unrelated Thought and Attentional Divergence During Conversations.

Task-unrelated thought (TUT) occurs frequently in our daily lives and across a range of tasks, but we know little about how this phenomenon arises during and influences the way we communicate. Conversations also provide a novel opportunity to assess the alignment (or divergence) in TUT during dyadic interactions. We conducted a study to determine: (a) the frequency of TUT during conversation as well as how partners align/diverge in their rates of TUT, (b) the subjective and behavioral correlates of TUT and TUT divergence during conversation, and (c) if perceived social group identity impacts TUT and TUT divergence during conversation. We used a minimal groups induction procedure to assign participants (N = 126) to either an ingroup, outgroup, or control condition. We then asked them to converse with one another via a computer-mediated text chat application for 10 min while self-reporting TUTs. On average, participants reported TUT about once every 2 min; however, this rate was lower for participants in the ingroup condition, compared to the control condition. Conversational pairs in the ingroup condition were also aligned more in their rates of TUT compared to the outgroup condition. Finally, we discuss subjective and behavioral correlates of TUT and TUT divergence in conversations, such as valence, turn-taking ratios, and topic shifts.

Virtual nature experiences and mindfulness practices while working from home during COVID-19: Effects on stress, focus, and creativity.

In this study, we focus on the impact of daily virtual nature experiences combined with mindfulness practices on remote workers' creativity, stress, and focus over an extended period (9 weeks) during the COVID-19 pandemic. Our results show a positive effect of virtual reality (VR) nature experience on increasing focus and reducing stress. When VR nature and mindfulness practices were combined, we also found an increase in convergent thinking task performance. Our findings demonstrate that 10-minute daily exposure to VR nature and mindfulness practices could compensate for some of the adverse effects of working remotely by improving some aspects of workers' well-being and creativity.

Automatically detecting task-unrelated thoughts during conversations using keystroke analysis.

Task-unrelated thought (TUT), commonly referred to as mind wandering, is a mental state where a person's attention moves away from the task-at-hand. This state is extremely common, yet not much is known about how to measure it, especially during dyadic interactions. We thus built a model to detect when a person experiences TUTs while talking to another person through a computer-mediated conversation, using their keystroke patterns. The best model was able to differentiate between task-unrelated thoughts and task-related thoughts with a kappa of 0.363, using features extracted from a 15 second window. We also present a feature analysis to provide additional insights into how various typing behaviors can be linked to our ongoing mental states.

Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop.

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo®). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.