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Background: Apical ground-glass opacification (GGO) identified on CT angiography (CTA) performed for suspected acute stroke was developed in 2020 as a coronavirus-disease-2019 (COVID-19) diagnostic and prognostic biomarker in a retrospective study during the first wave of COVID-19. Objective: To prospectively validate whether GGO on CTA performed for suspected acute stroke is a reliable COVID-19 diagnostic and prognostic biomarker and whether it is reliable for COVID-19 vaccinated patients. Methods: In this prospective, pragmatic, national, multi-center validation study performed at 13 sites, we captured study data consecutively in patients undergoing CTA for suspected acute stroke from January-March 2021. Demographic and clinical features associated with stroke and COVID-19 were incorporated. The primary outcome was the likelihood of reverse-transcriptase-polymerase-chain-reaction swab-test-confirmed COVID-19 using the GGO biomarker. Secondary outcomes investigated were functional status at discharge and survival analyses at 30 and 90 days. Univariate and multivariable statistical analyses were employed. Results: CTAs from 1,111 patients were analyzed, with apical GGO identified in 8.5 % during a period of high COVID-19 prevalence. GGO showed good inter-rater reliability (Fleiss κ = 0.77); and high COVID-19 specificity (93.7 %, 91.8–95.2) and negative predictive value (NPV; 97.8 %, 96.5–98.6). In subgroup analysis of vaccinated patients, GGO remained a good diagnostic biomarker (specificity 93.1 %, 89.8–95.5; NPV 99.7 %, 98.3–100.0). Patients with COVID-19 were more likely to have higher stroke score (NIHSS (mean +/- SD) 6.9 +/- 6.9, COVID-19 negative, 9.7 +/- 9.0, COVID-19 positive; p = 0.01), carotid occlusions (6.2 % negative, 14.9 % positive; p = 0.02), and larger infarcts on presentation CT (ASPECTS 9.4 +/- 1.5, COVID-19 negative, 8.6 +/- 2.4, COVID-19 positive; p = 0.00). After multivariable logistic regression, GGO (odds ratio 15.7, 6.2–40.1), myalgia (8.9, 2.1–38.2) and higher core body temperature (1.9, 1.1–3.2) were independent COVID-19 predictors. GGO was associated with worse functional outcome on discharge and worse survival after univariate analysis. However, after adjustment for factors including stroke severity, GGO was not independently predictive of functional outcome or mortality. Conclusion: Apical GGO on CTA performed for patients with suspected acute stroke is a reliable diagnostic biomarker for COVID-19, which in combination with clinical features may be useful in COVID-19 triage.

Timed feeding drives adipose browning, although the integrative mechanisms for the same remain unclear. Here, we show that twice-a-night (TAN) feeding generates biphasic oscillations of circulating insulin and leptin, representing their entrainment by timed feeding. Insulin and leptin surges lead to marked cellular, functional, and metabolic remodeling of subcutaneous white adipose tissue (sWAT), resulting in increased energy expenditure. Single-cell RNA-sequencing (scRNA-seq) analyses and flow cytometry demonstrate a role for insulin and leptin surges in innate lymphoid type 2 (ILC2) cell recruitment and sWAT browning, since sWAT depot denervation or loss of leptin or insulin receptor signaling or ILC2 recruitment each dampens TAN feeding-induced sWAT remodeling and energy expenditure. Consistently, recreating insulin and leptin oscillations via once-a-day timed co-injections is sufficient to favorably remodel innervated sWAT. Innervation is necessary for sWAT remodeling, since denervation of sWAT, but not brown adipose tissue (BAT), blocks TAN-induced sWAT remodeling and resolution of inflammation. In sum, reorganization of nutrient-sensitive pathways remodels sWAT and drives the metabolic benefits of timed feeding.

This article aims to present learning strategies that can be applied to OpenWHO online courses to primarily increase learning retention and nudge behavior change where applicable. This paper draws on existing research on learning strategies and takes an innovative approach to recommend how these strategies could be applied to learning design to support online learners. Thus, by examining recent cognitive science literature, this critical reflection paper presents approaches to design effective online courses that improve learning retention or nudge changes in behavior where necessary.

The retina is exquisitely patterned, with neuronal somata positioned at regular intervals to completely sample the visual field. Here, we show that phosphatase and tensin homolog (Pten) controls starburst amacrine cell spacing by modulating vesicular trafficking of cell adhesion molecules and Wnt proteins. Single-cell transcriptomics and double-mutant analyses revealed that Pten and Down syndrome cell adhesion molecule Dscam) are co-expressed and function additively to pattern starburst amacrine cell mosaics. Mechanistically, Pten loss accelerates the endocytic trafficking of DSCAM, FAT3, and MEGF10 off the cell membrane and into endocytic vesicles in amacrine cells. Accordingly, the vesicular proteome, a molecular signature of the cell of origin, is enriched in exocytosis, vesicle-mediated transport, and receptor internalization proteins in Pten conditional knockout (PtencKO) retinas. Wnt signaling molecules are also enriched in PtencKO retinal vesicles, and the genetic or pharmacological disruption of Wnt signaling phenocopies amacrine cell patterning defects. Pten thus controls vesicular trafficking of cell adhesion and signaling molecules to establish retinal amacrine cell mosaics.

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.

Humans preferentially recall items that are presented in close temporal proximity together -- a phenomenon known as the "temporal contiguity effect". In this study, we investigate whether this phenomenon emerges naturally when training a recurrent neural network with episodic memory on free recall tasks. The model learns to recall items in the order they were presented, consistent with the human contiguity effect. The strength of this effect predicts the performance of individual networks, mirroring experimental findings in humans where stronger contiguity effects predict higher recall performance. The contiguity effect in the model is supported by a neural representation of item index, resembling the `method of loci'. This differs from prominent computational models of human memory, which use a slow decay of past information to guide sequential retrieval. Our findings provide insights into the mechanisms underlying episodic memory and pave the way for future studies of its interactions with other cognitive processes.

Our choices often require us to prioritize some features of our rich sensory experience over others. Past work suggests that humans solve this problem by focusing on relevant information while discarding that which is irrelevant. Yet learning which features to prioritize requires extensive experience. Moreover, features that are irrelevant now may become relevant in the future. One way to address these issues is by sampling individual richly encoded experiences from episodic memory. Here we hypothesize that episodic memory is used to guide decisions based on multiple features of past events. We test this hypothesis using an experiment in which participants made choices about the value of features that were present in multiple past experiences. We find evidence suggesting that participants used episodic memories to flexibly access features of past events during decision making. Overall, these results suggest that episodic memory promotes adaptive decisions when knowledge of multiple features is necessary.

Working memory is widely assumed to underlie multi-step planning, where representations of possible future actions and rewards are iteratively updated before determining a choice. But most working memory research focuses on a context where stimuli are presented simultaneously and the value of encoding each stimulus is independent of others. It is unclear how working memory functions in planning scenarios where the rewards of future actions unfold over time, are retained in working memory, and must be integrated for plan selection. To bridge this gap, we adapted a version of the "mouselab task" in which participants sequentially observe the reward at each node in a decision tree before selecting a plan that maximizes cumulative rewards. We specified a theoretical model to characterize the optimal encoding and maintenance strategy for this task given the working memory constraints, which trades off the cost of storing information with the potential benefit of informing later choices. The model encoded rewards in choice-relevant plans more often, in particular, rewards on the best and (to a lesser extent) worst plans. We then tested this hypotheses on human participants, who showed the same pattern in the accuracy of their explicit recall. Our study thus establishes an empirical and theoretical foundation for models of how people encode and maintain information during planning.

A major goal of cognitive science is to characterize how an individual's past experiences guide their present decisions in a sequential task. Various empirical evidence support a process of incremental learning, well-characterized by the framework of reinforcement learning, whereby repeated exposures to similar situations shape decisions. However, in a complex world with sparse data a more sample-efficient process is needed. Prior work has suggested that episodic memory supports decision-making in such settings. Here, we provide novel behavioral evidence that episodic memory supports decision-making in temporally extended settings. We propose that value-based decision-making and episodic memory share common mechanisms to encode and retrieve past events, which in turn shape option evaluation and ultimately choice. In two experiments, we empirically test hypotheses that relate classic dynamics of sequential episodic memory retrieval to response patterns in novel evaluation and decision tasks. We find subjects' reported value estimates are subject to biases analogous to classic episodic memory biases (Experiment 1), and their choices are best captured by an episodic recall-based model (Experiment 2). These results suggest a novel link between value-based decision-making and episodic memory, which could reflect a psychologically plausible mechanism for computing decision variables by Monte Carlo sampling.

It has been suggested that humans mentally simulate the outcomes of their actions when making decisions. However, this process can be challenging in real-world decision-making, which typically involves temporally extended decision trees with numerous potential outcomes. Here, we demonstrate with a computational model that temporally extended decisions can be achieved with just a few forward simulations, formalized as rollouts. We also show that, under resource constraints, performing many partial (shallow) rollouts can yield more favorable outcomes than performing fewer full (deep) rollouts. Additionally, our model captures behaviors traditionally attributed to pruning or satisficing strategies without the need for explicit heuristics, providing an alternative explanation for these phenomena. Finally, we show that the dynamics of value estimation over successive rollouts closely resemble evidence accumulation models. Our framework offers a plausible mechanism for temporally extended decision-making and provides insights into the neural underpinnings underlying this process.

Most recent research on human planning attempts to adjudicate between a small set of hypothesized models based on their ability to predict participants' choices, using carefully designed experiments and/or model comparison. Here, we propose an alternative approach. We designed a task in which gaze is highly indicative of participants' planning operations, allowing us to discover properties of human planning from eye-tracking data in a data-driven way. Our results reveal ways that people's planning strategies have both similarities and differences with classical planning algorithms like best-first search and Monte Carlo tree search. They also provide a more nuanced perspective on previously proposed properties of human planning like pruning and depth limits. We conclude that planning research would benefit greatly from an increased use of rich sources of data that provide more direct evidence about the internal processes underlying sequential decision-making.

Fasting triggers diverse physiological adaptations including increases in circulating fatty acids and mitochondrial respiration to facilitate organismal survival. The mechanisms driving mitochondrial adaptations and respiratory sufficiency during fasting remain incompletely understood. Here we show that fasting or lipid availability stimulates mTORC2 activity. Activation of mTORC2 and phosphorylation of its downstream target NDRG1 at serine 336 sustains mitochondrial fission and respiratory sufficiency. Time-lapse imaging shows that NDRG1, but not the phosphorylation-deficient NDRG1Ser336Ala mutant, engages with mitochondria to facilitate fission in control cells, as well as in those lacking DRP1. Using proteomics, a small interfering RNA screen, and epistasis experiments, we show that mTORC2-phosphorylated NDRG1 cooperates with small GTPase CDC42 and effectors and regulators of CDC42 to orchestrate fission. Accordingly, RictorKO, NDRG1Ser336Ala mutants and Cdc42-deficient cells each display mitochondrial phenotypes reminiscent of fission failure. During nutrient surplus, mTOR complexes perform anabolic functions; however, paradoxical reactivation of mTORC2 during fasting unexpectedly drives mitochondrial fission and respiration.