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It is unclear whether other animals require social learning to acquire adult-like breadths of information in the wild, including by key developmental milestones, or whether animals are capable of constructing their knowledge repertoires primarily through independent exploration. We investigated whether social learning mediates orangutans' diet-repertoire development, by translating an extensive dataset describing wild orangutans' behaviour into an empirically validated agent-based model. In this model, diets reliably developed to adult-like breadths only when simulated immatures benefited from multiple forms of social learning. Moreover, social learning was required for diets to reach adult-like breadths by the age immatures become independent from their mothers. This implies that orangutan diets constitute culturally dependent repertoires, with social learning enhancing the rate and outcomes of diet development past individual potentials. We discuss prospective avenues for researching the building of cultural repertoires in hominids and other species.31:T52b,Abstract Humans accumulate extensive repertoires of culturally transmitted information, reaching breadths exceeding any individual’s innovation capacity (culturally dependent repertoires). It is unclear whether other animals require social learning to acquire adult-like breadths of information in the wild, including by key developmental milestones, or whether animals are capable of constructing their knowledge repertoires primarily through independent exploration. We investigated whether social learning mediates orangutans’ diet-repertoire development, by translating an extensive dataset describing wild orangutans’ behaviour into an empirically validated agent-based model. In this model, diets reliably developed to adult-like breadths only when simulated immatures benefited from multiple forms of social learning. Moreover, social learning was required for diets to reach adult-like breadths by the age immatures become independent from their mothers. This implies that orangutan diets constitute culturally dependent repertoires, with social learning enhancing the rate and outcomes of diet development past individual potentials. We discuss prospective avenues for researching the building of cultural repertoires in hominids and other species.32:T518,Abstract This study used The Alcohol Policy model (TAP), a newly-developed proportional multi-state lifetable model, to estimate the avoidable alcohol-related disease, injury and healthcare cost burden in the Australian adult population (aged ≥ 15 years). The model base year was 2020. Changes in alcohol consumption were modelled to lead to changes in incident cases and mortality from alcohol-related diseases and injuries, long-term health outcomes summarized as health adjusted life years (HALYs), and healthcare costs. No discounting was applied. Over 25 years, elimination of alcohol consumption could prevent 25.9 million cases of disease and injury (23.1 million from acute causes) and avert 211,697 deaths, the majority of which would be from cancers (114,948). This translated to 5.1 (95% uncertainty interval [UI] 4.0 to 6.2) million HALYs gained and Australian dollars (AU$) 55 (95%UI 36 to 75) billion saved in healthcare costs. Over 60 years, the benefits were 17 (95%UI 14 to 21) million HALYs and AU$ 68 (95%UI 9.6 to 130) billion. These findings support investment in polices that reduce alcohol consumption. 33:Tc74,This research presents a novel, scientifically validated facial aging simulator. It integrates two published methodologies for personalized aging predictions, particularly concerning the impact of tobacco. This contrasts with widespread simulations on social network and gaming applications, often lacking scientific rigor and transparent methodology. This simulator combines elicited knowledge from 28 expert dermatologists with an AI-powered image generation system. Based on this knowledge, the model predicts 15-year facial aging signs according to various intrinsic and extrinsic factors, providing personalized probabilities of reaching specific aging stages (e.g., wrinkles, pigmentation). This work couples these 15-year predictions with a machine learning model (AMGAN) that generates personalized facial aging simulation images. The AMGAN was initially trained on a dataset of 600 individuals with sign scores averaged from 15 expert graders. We present predicted independent probabilities of reaching grades for different facial aging signs as a function of cumulative tobacco consumption. A simulation was performed using a 43-year-old subject's facial image to demonstrate the tool's capabilities in illustrating the impact of tobacco consumption. Confounding variables such as sun exposure, sunscreen use, and body mass index (BMI) were controlled, while cumulative smoking (less than 10 or greater than 20 pack-years) was varied. This tool effectively visualizes the known effects of smoking on aging and provides personalized quantification of its impact on specific facial wrinkles. Recognizing that individuals prioritize different facial signs, the simulator allows users to focus on areas of personal concern. By focusing on preserving skin longevity and preventing premature aging, the simulator, which can quantify the impact of smoking on individual aging trajectories, effectively motivates positive lifestyle changes. This personalized approach offers a promising preventative messaging strategy, particularly for audiences resistant to traditional methods, and strengthens the scientific rationale regarding the impact of tobacco on specific aging signs. This work introduces a novel, scientifically robust facial aging simulator, addressing the lack of scientific and dermatological validation in most existing aging simulators, especially those used in social networks and gaming applications. Integrating two published research and leveraging dermatologist knowledge, the simulator generates personalized aging predictions (visualized as images and probabilities). This personalized, quantified, and visual approach, focused on the impact of tobacco, empowers informed decisions through probability comparisons of individual facial signs, motivating behavior change. Consequently, by focusing on preserving skin longevity and preventing premature aging, it offers a novel preventative messaging tool, particularly effective for those unresponsive to traditional campaigns, by promoting healthier behaviors. The synergy between expert dermatological knowledge and AI-generated imagery reinforces the scientific basis for the impact of smoking on facial aging.25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"Modeling Study: study design Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on Modeling Study (study design). Clinician-written summaries and actionable practice insights. 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Accessed Oct 2023.","article-title":null,"journal-title":null,"doi-asserted-by":null}],"short-title":[],"update-policy":"https://doi.org/10.1007/springer_crossmark_policy","alternative-id":["1545"],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"original-title":[],"container-title":["International Journal of Mental Health and Addiction"],"reference-count":58,"published-online":{"date-time":null,"timestamp":null,"date-parts":[[2025,11,13]]},"references-count":58,"short-container-title":["Int J Ment Health Addiction"],"is-referenced-by-count":0},"unpaywall":{"doi":"10.1007/s11469-025-01545-8","is_oa":true,"title":"The Avoidable Health Burden and Healthcare Costs Related to Alcohol Consumption in Australia: Multistate Life Table Modelling","updated":"2025-11-15T00:17:51+00:00","published_date":"2025-11-13","best_oa_location":{"url":"https://link.springer.com/content/pdf/10.1007/s11469-025-01545-8.pdf","pmh_id":null,"url_for_pdf":"https://link.springer.com/content/pdf/10.1007/s11469-025-01545-8.pdf","url_for_landing_page":"https://doi.org/10.1007/s11469-025-01545-8"}}},"created_at":"2025-12-04T03:42:26.941Z","updated_at":"2025-12-05T07:03:31.304Z","tags":[{"paper_doi":"10.1007/s11469-025-01545-8","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1007/s11469-025-01545-8","tag_type":"category","value":"Psychology, Clinical","value_numeric":null},{"paper_doi":"10.1007/s11469-025-01545-8","tag_type":"condition","value":"Alcohol Use","value_numeric":null},{"paper_doi":"10.1007/s11469-025-01545-8","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.1007/s11469-025-01545-8","tag_type":"study_design","value":"Modeling Study","value_numeric":null},{"paper_doi":"10.1007/s11469-025-01545-8","tag_type":"warning","value":"Findings are projections that depend on model assumptions and no discounting.","value_numeric":null},{"paper_doi":"10.1007/s11469-025-01545-8","tag_type":"warning","value":"Study design label approximated from a proportional multi‑state lifetable model.","value_numeric":null}],"summaries":{"what_studied":"A proportional multi‑state lifetable model (The Alcohol Policy model, base year 2020) estimated the avoidable disease, injury, deaths, health‑adjusted life years (HALYs), and healthcare costs in Australians aged ≥15 years if alcohol consumption were eliminated over 25 and 60 years, with no discounting.","key_result":"Over 25 years, elimination of alcohol could prevent 25.9 million cases (23.1 million acute), avert 211,697 deaths (114,948 from cancers), yield 5.1 million HALYs (95% UI 4.0–6.2), and save AU$55 billion (95% UI 36–75) in healthcare costs; over 60 years, gains were 17 million HALYs (95% UI 14–21) and AU$68 billion (95% UI 9.6–130).","limitations":"Model-based projections using a newly developed proportional multi‑state lifetable; analyses applied no discounting; single-country scope (Australia, base year 2020).","practice_takeaway":"Large health and cost benefits are projected from reducing alcohol consumption in Australia, supporting investment in alcohol‑reduction policies."}},{"doi":"10.1007/s13555-025-01570-4","pmid":"41134452","title":"A Combined Approach to Predict Tobacco-Induced Facial Aging Using Dermatologist Knowledge Elicitation and Generative Models","journal_title":"Dermatology and Therapy","journal_issn":"2193-8210","published_at":"2025-10-24T00:00:00.000Z","url":"https://doi.org/10.1007/s13555-025-01570-4","source_score":7,"is_retracted":false,"is_open_access":true,"raw_metadata":{"ooir":{"day":"2025-10-24","doi":"10.1007/s13555-025-01570-4","days":"7","issn":"2193-8210","rank":"7","field":"Clinical Medicine","score":"7","category":"Dermatology","categories":["Dermatology"],"_categories":["Dermatology"]},"pubmed":{"doi":"10.1007/s13555-025-01570-4","title":"A Combined Approach to Predict Tobacco-Induced Facial Aging Using Dermatologist Knowledge Elicitation and Generative Models.","journal":"Dermatology and therapy","methods":"","results":"We present predicted independent probabilities of reaching grades for different facial aging signs as a function of cumulative tobacco consumption. A simulation was performed using a 43-year-old subject's facial image to demonstrate the tool's capabilities in illustrating the impact of tobacco consumption. Confounding variables such as sun exposure, sunscreen use, and body mass index (BMI) were controlled, while cumulative smoking (less than 10 or greater than 20 pack-years) was varied. This tool effectively visualizes the known effects of smoking on aging and provides personalized quantification of its impact on specific facial wrinkles. Recognizing that individuals prioritize different facial signs, the simulator allows users to focus on areas of personal concern.","abstract":"$33","keywords":["AI","Artificial intelligence","Elicitation","Longevity","Predictive model","Skin aging","Smoking","Tobacco"],"pubmed_id":"41134452","conclusions":"By focusing on preserving skin longevity and preventing premature aging, the simulator, which can quantify the impact of smoking on individual aging trajectories, effectively motivates positive lifestyle changes. This personalized approach offers a promising preventative messaging strategy, particularly for audiences resistant to traditional methods, and strengthens the scientific rationale regarding the impact of tobacco on specific aging signs.","publication_date":"2025-10-24T00:00:00"},"crossref":{"DOI":"10.1007/s13555-025-01570-4","URL":"https://doi.org/10.1007/s13555-025-01570-4","ISSN":["2193-8210","2190-9172"],"link":[{"URL":"https://link.springer.com/content/pdf/10.1007/s13555-025-01570-4.pdf","content-type":"application/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https://link.springer.com/article/10.1007/s13555-025-01570-4/fulltext.html","content-type":"text/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https://link.springer.com/content/pdf/10.1007/s13555-025-01570-4.pdf","content-type":"application/pdf","content-version":"vor","intended-application":"similarity-checking"}],"page":null,"type":"journal-article","issue":null,"score":1,"title":["A Combined Approach to Predict Tobacco-Induced Facial Aging Using Dermatologist Knowledge Elicitation and Generative Models"],"author":[{"given":"Edouard","family":"Raynaud","sequence":"first","affiliation":[]},{"given":"Laudine","family":"Bertrand","sequence":"additional","affiliation":[]},{"given":"Frederic","family":"Flament","sequence":"additional","affiliation":[]},{"given":"Julien","family":"Despois","sequence":"additional","affiliation":[]},{"given":"Sileye","family":"Ba","sequence":"additional","affiliation":[]},{"given":"Emmanuelle","family":"Tancrède-Bohin","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hussein","family":"Jouni","sequence":"additional","affiliation":[]}],"issued":{"date-parts":[[2025,10,24]]},"member":"297","prefix":"10.1007","source":"Crossref","volume":null,"created":{"date-time":"2025-10-24T14:19:18Z","timestamp":1761315558000,"date-parts":[[2025,10,24]]},"indexed":{"date-time":"2025-10-24T14:25:57Z","timestamp":1761315957671,"date-parts":[[2025,10,24]]},"license":[{"URL":"https://creativecommons.org/licenses/by-nc/4.0","start":{"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000,"date-parts":[[2025,10,24]]},"delay-in-days":0,"content-version":"tdm"},{"URL":"https://creativecommons.org/licenses/by-nc/4.0","start":{"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000,"date-parts":[[2025,10,24]]},"delay-in-days":0,"content-version":"vor"}],"subject":[],"abstract":null,"language":"en","relation":{},"resource":{"primary":{"URL":"https://link.springer.com/10.1007/s13555-025-01570-4"}},"subtitle":[],"assertion":[{"name":"received","group":{"name":"ArticleHistory","label":"Article History"},"label":"Received","order":1,"value":"4 August 2025"},{"name":"accepted","group":{"name":"ArticleHistory","label":"Article History"},"label":"Accepted","order":2,"value":"8 October 2025"},{"name":"first_online","group":{"name":"ArticleHistory","label":"Article History"},"label":"First Online","order":3,"value":"24 October 2025"},{"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"},"order":1},{"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"},"order":2,"value":"Hussein Jouni, Laudine Bertrand, Frederic Flament, Sileye Ba, Emmanuelle Tancrede-Bohin, Tao Li are employees of L’Oréal. Julien Despois was employee of L’Oréal. Edouard Raynaud works as a consultant dermatologist for L’Oréal."},{"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"},"order":3,"value":"The authors did not consider it necessary to submit this study to an ethics committee. All volunteers who participated in the generation of images transformed by the AMGAN simulator signed an image rights consent form authorizing both scientific publication and educational presentations."}],"deposited":{"date-time":"2025-10-24T14:19:19Z","timestamp":1761315559000,"date-parts":[[2025,10,24]]},"issn-type":[{"type":"print","value":"2193-8210"},{"type":"electronic","value":"2190-9172"}],"published":{"date-parts":[[2025,10,24]]},"publisher":"Springer Science and Business Media LLC","reference":[{"DOI":"10.1016/j.jid.2020.12.011","key":"1570_CR1","year":"2021","issue":null,"author":"J Krutmann","volume":"141","first-page":"1096","unstructured":"Krutmann J, Schikowski T, Morita A, Berneburg M. Environmentally-induced (extrinsic) skin aging: exposomal factors and underlying mechanisms. 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Ann Intern Med. 1991;114(10):840–4.","article-title":null,"journal-title":"Ann Intern Med","doi-asserted-by":"publisher"},{"DOI":"10.1056/NEJM199711133372011","key":"1570_CR4","year":"1997","issue":"20","author":"J Uitto","volume":"337","first-page":"1463","unstructured":"Uitto J. Understanding premature skin aging. N Engl J Med. 1997;337(20):1463–5.","article-title":null,"journal-title":"N Engl J Med","doi-asserted-by":"publisher"},{"DOI":"10.1111/srt.13602","key":"1570_CR5","year":"2024","issue":null,"author":"H Jouni","volume":null,"first-page":null,"unstructured":"Jouni H, Jouffe L, Tancrede-Bohin E, et al. Predicting the evolution of clinical skin aging in a multi-ethnic population: developing causal Bayesian networks using dermatological expertise. Skin Res Technol. 2024. https://doi.org/10.1111/srt.13602.","article-title":null,"journal-title":"Skin Res Technol","doi-asserted-by":"publisher"},{"DOI":"10.1111/ijd.17599","key":"1570_CR6","year":"2025","issue":"3","author":"E Raynaud","volume":"64","first-page":"575","unstructured":"Raynaud E, Vashi N, Harvey V, et al. Quantifying expert knowledge to leverage new data: example of multi-ethnic skin aging prediction with elicitation. Int J Dermatol. 2025;64(3):575–7.","article-title":null,"journal-title":"Int J Dermatol","doi-asserted-by":"publisher"},{"DOI":null,"key":"1570_CR7","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Despois J, Flament F, Perrot M, AgingMapGAN (AMGAN). High-resolution controllable face aging with spatially-aware conditional gans. computer vision and pattern recognition (cs.CV). arXiv:2008.10960","article-title":null,"journal-title":null,"doi-asserted-by":null},{"DOI":null,"key":"1570_CR8","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Bazin R, Doublet E. Skin Aging Atlas, vol. 1. Caucasian type. Paris: Editions Med’Com; 2007.","article-title":null,"journal-title":null,"doi-asserted-by":null},{"DOI":null,"key":"1570_CR9","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Bazin R, Flament F. Skin Aging Atlas, vol. 2. Asian type. Paris: Editions Med’Com; 2010.","article-title":null,"journal-title":null,"doi-asserted-by":null},{"DOI":null,"key":"1570_CR10","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Bazin R, Flament F, Giron F. Skin Aging Atlas, vol. 3. Afro-American type. Paris: Editions Med’Com; 2012.","article-title":null,"journal-title":null,"doi-asserted-by":null},{"DOI":"10.2471/BLT.09.069575","key":"1570_CR11","year":"2009","issue":"8","author":"GT Fong","volume":"87","first-page":"640","unstructured":"Fong GT, Hammond D, Hitchman SC. The impact of pictures on the effectiveness of tobacco warnings. Bull World Health Organ. 2009;87(8):640–3.","article-title":null,"journal-title":"Bull World Health Organ","doi-asserted-by":"publisher"},{"DOI":"10.1111/ics.12512","key":"1570_CR12","year":"2019","issue":"1","author":"R Jiang","volume":"41","first-page":"67","unstructured":"Jiang R, Kezele I, Levinshtein A, et al. A new procedure, free from human assessment that automatically grades some facial skin structural signs. Comparison with assessments by experts, using referential atlases of skin ageing. 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Papers