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Eye movements can support ongoing manipulative actions, but a class of so-called look ahead fixations (LAFs) are related to future tasks. We examined LAFs in a complex natural task-assembling a camping tent. Tent assembly is a relatively uncommon task and requires the completion of multiple subtasks in sequence over a 5- to 20-minute duration. Participants wore a head-mounted camera and eye tracker. Subtasks and LAFs were annotated. We document four novel aspects of LAFs. First, LAFs were not random and their frequency was biased to certain objects and subtasks. Pracinostat Second, latencies are larger than previously noted, with 35% of LAFs occurring within 10 seconds before motor manipulation and 75% within 100 seconds. Third, LAF behavior extends far into future subtasks, because only 47% of LAFs are made to objects relevant to the current subtask. Seventy-five percent of LAFs are to objects used within five upcoming steps. Last, LAFs are often directed repeatedly to the target before manipulation, suggesting memory volatility. LAFs with short fixation-action latencies have been hypothesized to benefit future visual search and/or motor manipulation. However, the diversity of LAFs suggest they may also reflect scene exploration and task relevance, as well as longer term problem solving and task planning.Aging is a strong risk factor and an independent prognostic factor for progressive human idiopathic pulmonary fibrosis (IPF). Aged mice develop nonresolving pulmonary fibrosis following lung injury. In this study, we found that mouse double minute 4 homolog (MDM4) is highly expressed in the fibrotic lesions of human IPF and experimental pulmonary fibrosis in aged mice. We identified MDM4 as a matrix stiffness-regulated endogenous inhibitor of p53. Reducing matrix stiffness down-regulates MDM4 expression, resulting in p53 activation in primary lung myofibroblasts isolated from IPF patients. Gain of p53 function activates a gene program that sensitizes lung myofibroblasts to apoptosis and promotes the clearance of apoptotic myofibroblasts by macrophages. Destiffening of the fibrotic lung matrix by targeting nonenzymatic cross-linking or genetic ablation of Mdm4 in lung (myo)fibroblasts activates the Mdm4-p53 pathway and promotes lung fibrosis resolution in aged mice. These findings suggest that mechanosensitive MDM4 is a molecular target with promising therapeutic potential against persistent lung fibrosis associated with aging.Although widely used for their potent anti-inflammatory and immunosuppressive properties, the prescription of glucocorticoid analogues (e.g., dexamethasone) has been associated with deleterious glucose metabolism, compromising their long-term therapeutic use. However, the molecular mechanism remains poorly understood. In the present study, through transcriptomic and epigenomic analysis of two mouse models, we identified a growth arrest and DNA damage-inducible β (Gadd45β)-dependent pathway that stimulates hepatic glucose production (HGP). Functional studies showed that overexpression of Gadd45β in vivo or in cultured hepatocytes activates gluconeogenesis and increases HGP. In contrast, liver-specific Gadd45β-knockout mice were resistant to high-fat diet- or steroid-induced hyperglycemia. Of pathophysiological significance, hepatic Gadd45β expression is up-regulated in several mouse models of obesity and diabetic patients. Mechanistically, Gadd45β promotes DNA demethylation of PGC-1α promoter in conjunction with TET1, thereby stimulating PGC-1α expression to promote gluconeogenesis and hyperglycemia. Collectively, these findings unveil an epigenomic signature involving Gadd45β/TET1/DNA demethylation in hepatic glucose metabolism, enabling the identification of pathogenic factors in diabetes.

Perceived stigma among patients with alopecia is associated with impaired quality of life; however, the magnitude of laypersons' stigma toward individuals with alopecia is unknown.

To determine the prevalence and magnitude of laypersons' stigma toward individuals with varying degrees of alopecia and whether stigma increases with increased severity of alopecia.

This was a cross-sectional study using an internet survey administered to a convenience sample of adult respondents in the US participating on the Amazon Mechanical Turk platform. Portrait images of 6 individuals without hair loss were created using artificial intelligence and stock images. Each portrait was edited to create 2 additional versions, 1 with scalp hair loss and 1 with complete hair loss, for a total of 18 images. On January 9 to 10, 2020, the survey presented each internet respondent with 1 randomly selected portrait to be used in answering a series of stigma-related questions from 3 domains stereotypes, social distance, and disease-rnt with disease-related myths, the absolute change on the myth scale decreased as alopecia severity increased, indicating decreased stigma (-0.7 to -1.2).

This cross-sectional survey study found that stigmatizing attitudes of laypersons toward patients with alopecia exist across a multitude of social and professional scenarios. Stigma prevalence and magnitude vary by alopecia severity and possibly by whether alopecia is believed to be a medical condition.

This cross-sectional survey study found that stigmatizing attitudes of laypersons toward patients with alopecia exist across a multitude of social and professional scenarios. Stigma prevalence and magnitude vary by alopecia severity and possibly by whether alopecia is believed to be a medical condition.

Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.

To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.

This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.

Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model.