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Neighbourhood socioeconomic disadvantage from childhood to midlife and carotid atherosclerosis.

Olli Raitakari, Jaana Pentti, Juhani S. Koskinen, Juha Mykkänen, Suvi Rovio, Katja Pahkala, Markus Juonala, Terho Lehtimäki, Mika Kähönen, Ichiro Kawachi, Mika Kivimäki, Jorma Viikari, Jussi Vahtera

International Journal of Cardiology·2026

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Nanoparticles, thin films and temperature-dependent oxidation characteristics of metallic bismuth synthesized via solventless pyrolysis of bismuth(III) dithiolates: Polymorphism and photocatalytic dye degradation of bismuth sesquioxide

Xianglong Liu, Tingting Wang, Longhua Li, Junli Wang

Inorganic Chemistry Communications·2026

In this study, we report the preparation of Bi nanoparticles (NPs) and thin films via the solventless pyrolysis of bismuth(III) dithiolates (Bi2(S2CnH2n)3, n = 3, 4) at 160–220 °C, whose thermolysis features the absence of the precursor melting and finishes before the product (Bi metal) melting, and then we demonstrate their oxidation conversion to Bi2O3 NPs and thin films at different temperatures. The polymorphs and phase transitions of so-formed Bi2O3 are highly dependent on the oxidation temperature. Pure-phase β-Bi2O3 (tetragonal), formed by thermally oxidizing Bi NPs at 400 °C, will transform to α-Bi2O3 (monoclinic) by the traditionally known phase transition or β’-Bi2O3 by the c-oriented lattice thermal expansion as the oxidation temperature increases to 500 and 600 °C, which normally leads to an incomplete β → α transition and a mixed product of β’(β)/α phases. Compared to the β phase, the lattice-expanded variant β’-Bi2O3 is found to be stable at both high temperature (500–600 °C) and room temperature (RT) and to display a wide bandgap (∼3.0 eV), a narrow visible-light absorption range and thus relatively poor activity in RhB photodegradation. Moreover, Bi NPs are also found to be oxidized to bismuth oxycarbonate (Bi2O2CO3) at RT in the presence of H2O and CO2. This work opens a novel avenue to synthesize Bi and Bi2O3 NPs and thin films and presents new findings and insights regarding the phase regulation, polymorphism and photocatalytic performance of Bi2O3.

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Substance Use, Parental and Teacher Support, and Mental Health Outcomes Among Latinx Sexual and Gender Minority Youth

Roberto L. Abreu, Samuel J. Skidmore, Aldo M. Barrita, Jules P. Sostre, G. Tyler Lefevor, Ryan J. Watson

Journal of Homosexuality·2026·2 citations

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Selling masculinity – A qualitative analysis of gender representations in social media content about “low T”

Emma Grundtvig Gram, Barbara Mintzes, Tessa Copp, Ray Moynihan, Anthony Brown, Patti Shih, Brooke Nickel

Social Science & Medicine·2026

Testosterone has long been advertised through gendered messages that link masculinity with strength, sexual performance and vitality. In recent years, this marketing has moved onto social media, where platforms offer new ways to target audiences and shape ideas about men's health. This study examines how gender and masculinity are portrayed in social media content about testosterone testing and treatment on Instagram and TikTok. Using qualitative content analysis informed by performativity theories, we constructed four themes: (1) low testosterone as a crisis of masculinity and male sexual performance; (2) the rebranding of low testosterone from an “old man's problem” to an issue affecting younger men and their fitness; (3) self-optimisation tied to stereotypical masculine ideals; and (4) the construction of a binary opposition between being a “real man” and being feminine. These portrayals align with wider online communities, often referred to as the “manosphere”, which circulate narrow and exclusionary ideas of masculinity and regressive ideas and attitudes towards femininities. The analysed social media posts prey on men's insecurities about relationships and sexual performance and co-opt advocates' emancipatory language to sell testosterone products. Such portrayals of masculinity have medicalising implications for how men perceive themselves and their mental health, but also promote capitalistic practices like consumption of testosterone products for improving the masculine self without supporting evidence.

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Symmetry in language statistics shapes the geometry of model representations

Dhruva Karkada, Daniel J. Korchinski, Andres Nava, Matthieu Wyart, Yasaman Bahri

arXiv·2026

Although learned representations underlie neural networks' success, their fundamental properties remain poorly understood. A striking example is the emergence of simple geometric structures in LLM representations: for example, calendar months organize into a circle, years form a smooth one-dimensional manifold, and cities' latitudes and longitudes can be decoded by a linear probe. We show that the statistics of language exhibit a translation symmetry -- e.g., the co-occurrence probability of two months depends only on the time interval between them -- and we prove that the latter governs the aforementioned geometric structures in high-dimensional word embedding models. Moreover, we find that these structures persist even when the co-occurrence statistics are strongly perturbed (for example, by removing all sentences in which two months appear together) and at moderate embedding dimension. We show that this robustness naturally emerges if the co-occurrence statistics are collectively controlled by an underlying continuous latent variable. We empirically validate this theoretical framework in word embedding models, text embedding models, and large language models.

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The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale

Aral, Sinan, Li, Haiwen, Zuo, Rui

arXiv·2026

We executed 24,000 search queries in 243 countries, generating 2.8 million AI and traditional search results in 2024 and 2025. We found a rapid global expansion of AI search and key trends that reflect important, previously hidden, policy decisions by AI companies that impact human exposure to AI search worldwide. From 2024 to 2025, overall exposure to Google AI Overviews (AIO) expanded from 7 to 229 countries, with surprising exclusions like France, Turkey, China and Cuba, which do not receive AI search results, even today. While only 1% of Covid search queries were answered by AI in 2024, over 66% of Covid queries were answered by AI in 2025 -- a 5600% increase signaling a clear policy shift on this critical health topic. Our results also show AI search surfaces significantly fewer long tail information sources, lower response variety, and significantly more low credibility and right- and center-leaning information sources, compared to traditional search, impacting the economic incentives to produce new information, market concentration in information production, and human judgment and decision-making at scale. The social and economic implications of these rapid changes in our information ecosystem necessitate a global debate about corporate and governmental policy related to AI search.

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Why do we do astrophysics?

David W. Hogg

arXiv·2026

At time of writing, large language models (LLMs) are beginning to obtain the ability to design, execute, write up, and referee scientific projects on the data-science side of astrophysics. What implications does this have for our profession? In this white paper, I list - and argue for - a set of facts or "points of agreement" about what astrophysics is, or should be; these include considerations of novelty, people-centrism, trust, and (the lack of) clinical value. I then list and discuss every possible benefit that astrophysics can be seen as bringing to us, and to science, and to universities, and to the world; these include considerations of love, weaponry, and personal (and personnel) development. I conclude with a discussion of two possible (extreme and bad) policy recommendations related to the use of LLMs in astrophysics, dubbed "let-them-cook" and "ban-and-punish." I argue strongly against both of these; it is not going to be easy to develop or adopt good moderate policies.

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Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability

Aaditya Vikram Prasad, Connor Watts, Jack Merullo, Dhruvil Gala, Owen Lewis, Thomas McGrath, Ekdeep Singh Lubana

arXiv·2026

Language models trained on large-scale datasets have been shown to learn features that encode abstract concepts such as factuality or intent. Such features are traditionally used for test-time monitoring or steering. We present an alternative affordance: features as scalable supervision for open-ended tasks. We consider the case of hallucination-reduction as a desirable, yet open-ended behavior and design a reinforcement learning (RL) pipeline, titled RLFR (Reinforcement Learning from Feature Rewards), that uses features as reward functions. Grounded in a novel probing framework that identifies candidate hallucinated claims, our pipeline teaches a model to intervene and correct its completions when it is uncertain of their factuality. Furthermore, the pipeline enables scalable test-time compute, guided once more by our reward features. This end-to-end process operationalized on Gemma-3-12B-IT results in a policy that is 58% less likely to hallucinate compared to the original model (when run in tandem with our probing harness), while preserving performance on standard benchmarks. Taken together, by grounding supervision in the language of features, this paper introduces a novel paradigm in the use of interpretability for learning open-ended tasks.

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Neighbourhood socioeconomic disadvantage from childhood to midlife and carotid atherosclerosis.

Nanoparticles, thin films and temperature-dependent oxidation characteristics of metallic bismuth synthesized via solventless pyrolysis of bismuth(III) dithiolates: Polymorphism and photocatalytic dye degradation of bismuth sesquioxide

Substance Use, Parental and Teacher Support, and Mental Health Outcomes Among Latinx Sexual and Gender Minority Youth

Selling masculinity – A qualitative analysis of gender representations in social media content about “low T”

Namwali Serpell on Approaching Toni Morrison’s Work As a Reader and a Critic

how white fear killed the romantic comedy.

Symmetry in language statistics shapes the geometry of model representations

How to wow a popular-science writer with your research expertise

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The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale

Why do we do astrophysics?

Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability