Americans harbor much less favorable explicit sentiments toward young adults than toward older adults.

Public and academic discourse on ageism focuses primarily on prejudices targeting older adults, implicitly assuming that this age group experiences the most age bias. We test this assumption in a large, preregistered study surveying Americans' explicit sentiments toward young, middle-aged, and older adults. Contrary to certain expectations about the scope and nature of ageism, responses from two crowdsourced online samples matched to the US adult population (N = 1,820) revealed that older adults garner the most favorable sentiments and young adults, the least favorable ones. This pattern held across a wide range of participant demographics and outcome variables, in both samples. Signaling derogation of young adults more than benign liking of older adults, participants high on SDO (i.e., a key antecedent of group prejudice) expressed even less favorable sentiments toward young adults-and more favorable ones toward older adults. In two follow-up, preregistered, forecasting surveys, lay participants (N = 500) were generally quite accurate at predicting these results; in contrast, social scientists (N = 241) underestimated how unfavorably respondents viewed young adults and how favorably they viewed older adults. In fact, the more expertise in ageism scientists had, the more biased their forecasts. In a rapidly aging world with exacerbated concerns over older adults' welfare, young adults also face increasing economic, social, political, and ecological hardship. Our findings highlight the need for policymakers and social scientists to broaden their understanding of age biases and develop theory and policies that ponder discriminations targeting all age groups.

Acute Adenoviral Infection Elicits an Arrhythmogenic Substrate Prior to Myocarditis.

BACKGROUND: Viral cardiac infection represents a significant clinical challenge encompassing several etiological agents, disease stages, complex presentation, and a resulting lack of mechanistic understanding. Myocarditis is a major cause of sudden cardiac death in young adults, where current knowledge in the field is dominated by later disease phases and pathological immune responses. However, little is known regarding how infection can acutely induce an arrhythmogenic substrate before significant immune responses. Adenovirus is a leading cause of myocarditis, but due to species specificity, models of infection are lacking, and it is not understood how adenoviral infection may underlie sudden cardiac arrest. Mouse adenovirus type-3 was previously reported as cardiotropic, yet it has not been utilized to understand the mechanisms of cardiac infection and pathology. METHODS: We have developed mouse adenovirus type-3 infection as a model to investigate acute cardiac infection and molecular alterations to the infected heart before an appreciable immune response or gross cardiomyopathy. RESULTS: Optical mapping of infected hearts exposes decreases in conduction velocity concomitant with increased Cx43Ser368 phosphorylation, a residue known to regulate gap junction function. Hearts from animals harboring a phospho-null mutation at Cx43Ser368 are protected against mouse adenovirus type-3-induced conduction velocity slowing. Additional to gap junction alterations, patch clamping of mouse adenovirus type-3-infected adult mouse ventricular cardiomyocytes reveals prolonged action potential duration as a result of decreased IK1 and IKs current density. Turning to human systems, we find human adenovirus type-5 increases phosphorylation of Cx43Ser368 and disrupts synchrony in human induced pluripotent stem cell-derived cardiomyocytes, indicating common mechanisms with our mouse whole heart and adult cardiomyocyte data. CONCLUSIONS: Together, these findings demonstrate that adenoviral infection creates an arrhythmogenic substrate through direct targeting of gap junction and ion channel function in the heart. Such alterations are known to precipitate arrhythmias and likely contribute to sudden cardiac death in acutely infected patients.

Cold hardiness-informed budbreak reveals role of freezing temperatures and daily fluctuation in chill accumulation model.

Fundamental questions in bud dormancy remain, including what temperatures fulfill dormancy requirements (i.e., chill accumulation). Recent studies demonstrate freezing temperatures promote chill accumulation and cold hardiness influences time to budbreak - the phenotype used for dormancy evaluations. Here we evaluated bud cold hardiness (CH) and budbreak responses of grapevines (Vitis hybrids) throughout chill accumulation under three treatments: constant (5 °C), fluctuating (-3.5 to 6.5 °C daily), and field conditions (Madison, WI, USA). Chill treatments experiencing lower temperatures promoted greater gains in cold hardiness (CHfield>CHfluctuating>CHconstant). All treatments decreased observed time to budbreak with increased chill accumulation. However, perceived treatment effectiveness changed when time to budbreak was adjusted to remove cold acclimation effects. Among three classic chill models (North Carolina, Utah, and Dynamic), none were able to correctly describe adjusted time to budbreak responses to chill accumulation. Thus, a new model is proposed that expands the range of chill accumulation temperatures to include freezing temperatures and enhances chill accumulation under fluctuating temperature conditions. Most importantly, our analysis demonstrates adjustments for uneven acclimation change the perceived effectiveness of chill treatments. Therefore, future work in bud dormancy would benefit from simultaneously evaluating cold hardiness.

NYUS.2: an automated machine learning prediction model for the large-scale real-time simulation of grapevine freezing tolerance in North America.

Accurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data are limited due to the complexity of measurement. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. We combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automated machine learning engine. Feature importance was quantified by AutoGluon and SHAP (SHapley Additive exPlanations) value. The final model was evaluated and compared with previous models for its performance under different climate conditions. The final model achieved an overall 1.36°C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model has adequately extracted some biological mechanisms during training. The final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022-23 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time.