Cognitive decline presents individual and societal challenges, underscoring the importance of identifying risk factors to inform interventions for older adults. This cross-sectional study examined how physical activity (PA), perceived stress, and social support were related to cognitive function, with mild behavioral impairment (MBI) as a mediator, among 410 Thai older adults. Structural equation model revealed that perceived stress was associated with increased MBI (p < .001), which was directly related to cognitive function (p < .05). Higher social support was associated with lower MBI (p < .05), while moderate-to-vigorous PA was related to reduced MBI (p < .001) and improved cognitive function (p < .001). MBI mediated the associations of perceived stress (indirect effect = −0.244) and moderate-to-vigorous PA (indirect effect = 0.08) with cognitive function. Perceived stress–MBI relationship was stronger in participants with chronic diseases (∆χ2 = 4.236; p = 0.025). Findings support developing interventions to mitigate MBI, with input from health care professionals.

Forecasting of armed conflicts is a critical area of research with the potential to save lives and mitigate suffering. While existing forecasting models offer valuable point predictions, they often lack individual-level uncertainty estimates, limiting their usefulness for decision-making. Several approaches exist to estimate uncertainty, such as parametric and Bayesian prediction intervals, bootstrapping, quantile regression, but these methods often rely on restrictive assumptions, struggle to provide well-calibrated intervals across the full range of outcomes, or are computationally intensive. Conformal prediction offers a model-agnostic alternative that guarantees a user-specified level of coverage but typically provides only marginal coverage, potentially resulting in non-uniform coverage across different regions of the outcome space. In this article, we introduce a novel extension called bin-conditional conformal prediction (BCCP), which enhances standard conformal prediction (SCP) by ensuring consistent coverage rates across user-defined subsets (bins) of the outcome variable. We apply BCCP to simulated data as well as the forecasting of fatalities from armed conflicts, and demonstrate that it provides well-calibrated uncertainty estimates across various ranges of the outcome. Compared to SCP, BCCP offers improved local coverage, though this comes at the cost of slightly wider prediction intervals.

Protein-losing enteropathy associated with failing Fontan circulation remains a significant challenge in long-term, single-ventricle circulation management. Evidence is very limited regarding medical therapies associated with a reduction in disease severity and morbidity outside of cardiac transplantation. We report a novel experience with remission of recalcitrant protein-losing enteropathy after treatment of Cryptosporidium gastroenteritis with a combination of antimicrobials including nitazoxanide, paromomycin, and azithromycin.

Current political developments worldwide illustrate that research on democratic backsliding is as important as ever. A recent exchange in Political Science & Politics (February 2024) highlighted again that the measurement of democracy remains a challenge. With many democracy indicators consisting of subjective assessments rather than factual observations, trends in democracy over time could be due to human biases in the coding of these indicators rather than empirical facts. This article leverages two cutting-edge Large Language Models (LLMs) for the coding of democracy indicators from the V-Dem project. With access to huge amounts of information, these models may be able to rate the many “soft” characteristics of regimes at substantially lower costs. Whereas LLM-generated codings largely align with expert coders for many countries, we show that when these models deviate from human assessments, they do so in different but consistent ways. Some LLMs are too pessimistic and others consistently overestimate the democratic quality of these countries. Although the combination of the two LLM codings can alleviate this concern, we conclude that it is difficult to replace human coders with LLMs because the extent and direction of these attitudes is not known a priori.

In the 1880s, Sievers proposed that in Old English words such as *feorhes, the loss of the post-consonantal *h caused compensatory lengthening of the vowel: fēores. Since there are no unambiguous traces of this sound change in later English, widespread analogical restitution of the short vowels was assumed (e.g. from feorh). The evidence for this lengthening is largely metrical. I argue that while Sievers is correct that words like <feores> often need to scan with a heavy initial syllable, this need not be explained by a general lengthening in the language at large. Indeed, the distribution of where heavy scansions are required in verse is typical for metrical archaisms: late prehistoric metrical values of words preserved for poetic convenience. Just as wundor ‘marvel’ can continue to be scanned as monosyllabic *wundr, or contracted hēan can scan as disyllabic *hēahan, so can light-syllabled feores continue to scan as heavy *feorhes. The same sets of poems that prefer non-epenthesized or non-contracted forms also prefer the heavy scansions of feores-type words. If heavy scansions of feores-words are seen as a matter of poetic convention, then the hypothesis of compensatory lengthening in the language generally is left without evidence and should be rejected.

Student debt heavily shapes the life decisions and outlook of those living with it. This article examines the relationship between undergraduates’ views of President Joe Biden’s actions on student debt and their support for him in the 2024 election prior to his dropping out of the race. Using a representative survey from a large private university in the Northeast, we assess how student views of Biden’s handling of student loan relief correlated with voting intentions among registered student voters. Our analysis reveals that students who believed the Biden administration adequately addressed student debt were significantly more likely to support him compared to those who believed he had done too little. Additionally, our findings suggest that whereas increased student debt relief could have bolstered Biden’s support among liberal and very liberal students, its impact on moderate and conservative students was more limited. This study highlights the electoral implications of student debt relief policies, particularly in shaping young voters’ preferences, and it underscores the potential for targeted economic benefits to influence voter behavior in a highly polarized political environment.