On the Unequal Burden of Obesity: Obesity's Adverse Consequences Are Contingent on Regional Obesity Prevalence.

Obesity has adverse consequences for those affected. We tested whether the association between obesity and its adverse consequences is reduced in regions in which obesity is prevalent and whether lower weight bias in high-obese regions can account for this reduction. Studies 1 and 2 used data from the United States (N = 2,846,132 adults across 2,546 counties) and United Kingdom (N = 180,615 adults across 380 districts) that assessed obesity's adverse consequences in diverse domains: close relationships, economic outcomes, and health. Both studies revealed that the association between obesity and its adverse consequences is reduced (or absent) in high-obese regions. Study 3 used another large-scale data set (N = 409,837 across 2,928 U.S. counties) and revealed that lower weight bias in high-obese regions seems to account for (i.e., mediate) the reduction in obesity's adverse consequences. Overall, our findings suggest that obesity's adverse consequences are partly social and, thus, not inevitable.

Racial Minorities Face Discrimination From Across the Political Spectrum When Seeking to Form Ties on Social Media: Evidence From a Field Experiment.

We conducted a preregistered field experiment examining racial discrimination in tie formation on social media. We randomly assigned research accounts varying on race (Black, White) and politics (liberal/Democrat, conservative/Republican, neutral) to follow a politically balanced sample of Twitter (i.e., X) users (N = 5,951) who were unaware they were in a research study. We examined three predictions from the social and political psychology literatures: i) individuals favor White over Black targets, ii) this tendency is stronger for conservatives/Republicans than for liberals/Democrats, and iii) greater discrimination by conservatives/Republicans is explained by the assumption that racial minorities are liberal/Democrat. We found evidence that individuals were less likely to reciprocate social ties with Black accounts than White accounts. However, this tendency was not moderated by individuals' political orientation, shared partisanship, or partisan mismatch. In sum, this work provides field experimental evidence for racial discrimination in tie formation on social media by individuals across political backgrounds.

A Case for the Use of Open Data as a Tool to Incorporate Socioscientific Topics into Neuroscience Education.

Education scholars have called for an increased focus on developing curricula based on culturally relevant pedagogy (Ladson-Billings, 1995). A key tenet of Ladson-Billings' (1995; 2014) theory of culturally relevant pedagogy is the development of students' sociopolitical consciousness, whereby students feel empowered and encouraged to evaluate and solve real-world interdisciplinary problems. Here, we propose that open science datasets could serve as a valuable tool for neuroscience educators to foster their students' sociopolitical consciousness. Using the open data available through the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) as a case study, this article will explore how open science can be leveraged as a tool to encourage socioscientific thinking amongst neuroscience students. We overview a collection of lessons created by the Allen Institute's Education & Engagement team that provides a scaffolded exploration of an open science resource through a socioscientific lens. We supplement our discussion of the lessons with feedback from students who completed the lessons during a day-long workshop hosted at the Allen Institute in Seattle, WA. We conclude by reflecting on the future role this type of interdisciplinary, open science-based approach to curricula could have across neuroscience education more broadly.

From Fragmentation to Open Access: Building an Open Data Portal for Health Data Dissemination.

Open data is defined as data that can be used and redistributed by anyone with minimal or no restrictions. A design science research methodology was applied to the development of an open data portal for theMinistry of Health Sri Lanka (MoH) to share national datasets. Following requirement gathering and literature review, the open data portal was developed using open-source software and implemented at the MoH Sri Lanka. Fifty datasets obtained from the MoH were categorised and published in the open data portal. However, several barriers cast doubt on the long-term feasibility of the open data portal project.

Testosterone rapidly increases men's emotion-based dehumanization of a conservatively dressed woman.

Past research has found that sexualized women are often dehumanized (i.e., attributed reduced human qualities). However, the mechanisms contributing to such dehumanization remain poorly understood. In this pre-registered experiment involving a within-subject, placebo-controlled, cross-over design, we tested whether testosterone contributes to men's (N = 120, age range: 18-38 years) dehumanization of women. After administration of intranasal testosterone or placebo gel, men watched a video of a woman wearing either modest (i.e., conservative) or revealing (i.e., sexualized) clothing (between-subjects factor) and then completed three subtle dehumanization tasks, measuring emotion-based, personality-based, and perceptual dehumanization. We hypothesized that testosterone would increase dehumanization, especially for men who watched the "sexualized-clothing" video. Instead, we found that, while men engaged in emotion-based dehumanization toward the sexualized woman both when they had testosterone and placebo, testosterone increased emotion-based dehumanization toward the conservatively dressed woman. Other forms of dehumanization were not affected by testosterone. We also explored whether personality (e.g., dominance) and biological (e.g., CAG repeat polymorphism) traits that have been found to moderate the effects of testosterone on some social behaviors also moderated the effects examined here, but we did not find any significant moderations. Overall, this experiment revealed a novel physiological mechanism affecting emotion-based dehumanization.

Enhancing the design of voting advice applications with BERT language model.

The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.

Open Data-Driven Decision Making: Observations from COVID-19 Minimal Essential Data, Canton Zürich, Switzerland.

This research delves into the relationship between open data and decision making, drawing insights from the utilization of Minimal Essential Data (MED) during the COVID-19 pandemic in Canton Zürich, Switzerland. It examines observations and lessons learned, emphasizing the pivotal role of open data in response to an emergency. Through a case study approach, this article sheds light on the practical implications and observed strategies in publishing open data from digital public health surveillance software. While it is arguable that open data has the potential to enhance data quality in the context of a public health emergency, our findings indicate that it is firstly essential that a Minimal Essential Dataset (MED) is defined and publicly published, an open data publication platform and procedure are secondly in place (ideally following FAIR data principles), and thirdly, that regular data quality reviews are conducted.

A method to promote safe cycling powered by large language models and AI agents.

This paper presents a novel information generation methodology to support safer cycling patterns in urban environments, leveraging for that Large Language Models (LLMs), AI-based agents, and open geospatial data. By processing multiple files containing previously computed urban risk levels and existing mobility infrastructure, which are generated by exploiting open data sources, our method exploits multi-layer data preprocessing procedures and prompt engineering to create easy-to-use, user-friendly assistive systems that are able to provide useful information concerning cycling safety. Through a well-defined processing pipeline based on Data Ingestion and Preparation, Agents Orchestration, and Decision Execution methodological steps, our method shows how to integrate open-source tools and datasets, ensuring reproducibility and accessibility for urban planners and cyclists. Moreover, an AI agent is also provided, which fully implements our method and acts as a proof-of-concept implementation. This paper demonstrates the effectiveness of our method in enhancing cycling safety and urban mobility planning.•A novel method that combines LLMs and AI agents is defined to enhance the processing of multi-domain open geospatial data, potentially promoting cycling safety.•It integrates urban risk data and cycling infrastructure for a more comprehensive understanding of cycling resources, which become accessible by textual or audio prompts.

Does Physiological Arousal Increase Social Transmission of Information? Two Replications of Berger (2011).

People share information for many reasons. For example, Berger (2011, N = 40) found that undergraduate participants manipulated to have higher physiological arousal were more likely to share a news article with others via email than people who had low arousal. Berger's research is widely cited as evidence of the causal role of arousal in sharing information and has been used to explain why information that induces high-arousal emotions is shared more than information that induces low-arousal emotions. We conducted two replications (N = 111, N = 160) of Berger's study, using the same arousal manipulation but updating the sharing measure to reflect the rise of information sharing through social media. Both studies failed to find an impact of incidental physiological arousal on undergraduate participants' willingness to share news articles on social media. Our studies cast doubt on the idea that incidental physiological arousal-in the absence of other factors-impacts people's decisions to share information on social networking sites.

Not All Powerful People Are Created Equal: An Examination of Gender and Pathways to Social Hierarchy Through the Lens of Social Cognition.

Across four studies (N = 816 U.S. adults), we uncovered a gender stereotype about dual pathways to social hierarchy: Men were associated with power, and women were associated with status. We detected this pattern both explicitly and implicitly in perceptions of individuals drawn from Forbes magazine's powerful people lists in undergraduate and online samples. We examined social-cognitive implications, including prominent people's degree of recognition by individuals and society, and the formation of men's and women's self-concepts. We found that power (status) ratings predicted greater recognition of men (women) and lesser recognition of women (men). In terms of the self-concept, we found that women internalized the stereotype associating women with status more than power implicitly and explicitly. Although men explicitly reported having less status and more power than women, men implicitly associated the self with status as much as power. No gender differences emerged in the desires for power and status.

Why Twitter Sometimes Rewards What Most People Disapprove of: The Case of Cross-Party Political Relations.

Recent evidence has shown that social-media platforms like Twitter (now X) reward politically divisive content, even though most people disapprove of interparty conflict and negativity. We document this discrepancy and provide the first evidence explaining it, using tweets by U.S. Senators and American adults' responses to them. Studies 1a and 1b examined 6,135 such tweets, finding that dismissing tweets received more Likes and Retweets than tweets that engaged constructively with opponents. In contrast, Studies 2a and 2b (N = 856; 1,968 observations) revealed that the broader public, if anything, prefers politicians' engaging tweets. Studies 3 (N = 323; 4,571 observations) and 4 (N = 261; 2,610 observations) supported two distinct explanations for this disconnect. First, users who frequently react to politicians' tweets are an influential yet unrepresentative minority, rewarding dismissing posts because, unlike most people, they prefer them. Second, the silent majority admit that they too would reward dismissing posts more, despite disapproving of them. These findings help explain why popular online content sometimes distorts true public opinion.

The Language of (Non)Replicable Social Science.

Using publicly available data from 299 preregistered replications from the social sciences, we found that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the article characteristics, study design and results, author information, and replication effort. To understand why, we analyzed the textual differences between replicable and nonreplicable studies. Our findings suggest that the language in replicable studies is transparent and confident, written in a detailed and complex manner, and generally exhibits markers of truthful communication, possibly demonstrating the researchers' confidence in the study. Nonreplicable studies, however, are vaguely written and have markers of persuasion techniques, such as the use of positivity and clout. Thus, our findings allude to the possibility that authors of nonreplicable studies are more likely to make an effort, through their writing, to persuade readers of their (possibly weaker) results.

Code sharing in ecology and evolution increases citation rates but remains uncommon.

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low-impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open-access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation.

Impact of new antifungal medications on onychomycosis prescriptions and costs in Japan: A nationwide claims database study.

Onychomycosis, a fungal nail infection, is a common dermatological condition in Japan, with a prevalence of approximately 5%-10%. Despite the introduction of new antifungal medications and updated treatment guidelines published in 2019, data on real-world prescription trends and the associated medical costs are limited. This study aimed to investigate the prescription patterns and medical costs of topical and oral antifungal medications for onychomycosis in Japan from fiscal years 2014 to 2021 using the National Database of Health Insurance Claims and Specific Health Checkups of Japan Open Data. We analyzed the annual prescription volumes and medical costs of four antifungal medications: efinaconazole, luliconazole, fosravuconazole, and terbinafine. The prescription volume of efinaconazole, a topical medication launched in 2014, rapidly increased and dominated the market share. Fosravuconazole, an oral medication introduced in 2018, showed an increasing trend, coinciding with a decline in efinaconazole prescriptions. Terbinafine, a well-established oral medication, experienced a substantial decrease in prescription volume. The sex- and age-adjusted prescription volume per 100 000 population was higher among older adults, particularly for efinaconazole. The total medical costs for onychomycosis treatment more than doubled in fiscal year 2015 compared with that for 2014, mainly driven by efinaconazole prescriptions, and exceeded 30 billion Japanese yen in fiscal years 2019-2021. The costs slightly decreased in fiscal years 2020 and 2021, possibly due to the introduction of fosravuconazole. The predominance of topical prescriptions, especially in older adults, raises concerns regarding adherence to the Japanese guidelines that recommend oral antifungals as the first-line treatment for onychomycosis. The substantial increase in medical costs also highlights the economic burden of onychomycosis and the need for cost-effective treatment strategies. This study provides valuable insights into the real-world prescription trends and medical costs of onychomycosis treatment in Japan, suggesting an opportunity to assess potential gaps between guideline recommendations and clinical practice.

Children Sustain Cooperation in a Threshold Public-Goods Game Even When Seeing Others' Outcomes.

Many societal challenges are threshold dilemmas requiring people to cooperate to reach a threshold before group benefits can be reaped. Yet receiving feedback about others' outcomes relative to one's own (relative feedback) can undermine cooperation by focusing group members' attention on outperforming each other. We investigated the impact of relative feedback compared to individual feedback (only seeing one's own outcome) on cooperation in children from Germany and India (6- to 10-year-olds, N = 240). Using a threshold public-goods game with real water as a resource, we show that, although feedback had an effect, most groups sustained cooperation at high levels in both feedback conditions until the end of the game. Analyses of children's communication (14,374 codable utterances) revealed more references to social comparisons and more verbal efforts to coordinate in the relative-feedback condition. Thresholds can mitigate the most adverse effects of social comparisons by focusing attention on a common goal.

Exploration, Distributed Attention, and Development of Category Learning.

Category learning is a crucial aspect of cognition that involves organizing entities into equivalence classes. Whereas adults tend to focus on category-relevant features, young children often distribute attention between relevant and irrelevant ones. The reasons for children's distributed attention are not fully understood. In two category-learning experiments with adults and with children aged 4, 5, and 6 (N = 201), we examined potential drivers of distributed attention, including (a) immature filtering of distractors and (b) the general tendency for exploration or broad information sampling. By eliminating distractor competition, we reduced filtering demands. Despite identifying the features critical for accurate categorization, children, regardless of their categorization performance, continued sampling more information than was necessary. These results indicate that the tendency to sample information extensively contributes to distributed attention in young children. We identify candidate drivers of this tendency that need to be examined in future research.

Electroencephalogram Decoding Reveals Distinct Processes for Directing Spatial Attention and Encoding Into Working Memory.

Past work reveals a tight relationship between spatial attention and storage in visual working memory. But is spatially attending an item tantamount to working memory encoding? Here, we tracked electroencephalography (EEG) signatures of spatial attention and working memory encoding while independently manipulating the number of memory items and the spatial extent of attention in two studies of adults (N = 39; N = 33). Neural measures of spatial attention tracked the position and size of the attended area independent of the number of individuated items encoded into working memory. At the same time, multivariate decoding of the number of items stored in working memory was insensitive to variations in the breadth and position of spatial attention. Finally, representational similarity analyses provided converging evidence for a pure load signal that is insensitive to the spatial extent of the stored items. Thus, although spatial attention is a persistent partner of visual working memory, it is functionally dissociable from the selection and maintenance of individuated representations in working memory.