ObjectivesGambling stigma is an important issue which influences, for example, the low rates of help-seeking observed internationally. Harm prevention advertisements aim to increase awareness regarding gambling harms among the population, although little empirical evaluation has been performed to see if current campaigns reduce or might alternatively contribute to gambling stigma. We therefore designed an exploratory mixed methods study to explore the effects of five UK-based harm prevention advertisements.Study designOnline experiment.MethodsParticipants (N = 498) completed two blocks containing the Problem Gambling Severity Index (PGSI), and the five advertisements shown in random order. Three Likert items were summed to measure each advertisement for stigmatization (e.g., “This advertisement makes me think that people who gamble heavily are at fault for whatever may happen to them”), and participants also wrote perspectives via a text box. The Likert items were analyzed via mixed models, and the text perspectives subjected to a thematic analysis.ResultsQuantitative results showed that one advertisement (called “chasing losses” here) was associated with a mean stigmatization score indicating agreement that it could contribute to gambling stigma. This was not the case for the remaining advertisements. Furthermore, participants with higher PGSI scores tended to give higher stigmatization scores for all advertisements. The thematic analysis supported these findings, with participants also suggesting how the adverts could be improved.ConclusionsThe designers of harm prevention advertisements should consider their potential contribution to stigma. The higher stigmatization scores from those with higher PGSI scores underscores the need for multiple gambling-related harm interventions to help people experiencing gambling harms.

Behavioural ecologists aim to understand the causes of animal social structure. Connecting theoretical models of social structure with empirical observations remains, however, a formidable challenge. While most of the current statistical methods for animal social network analysis rely on data that are aggregated over time and summarised as one behavioural dimension ( e.g. , an adjacency-matrix), common behavioural sampling techniques ( e.g. , focal-animal sampling) produce data in continuous time, and involve different behaviours. Furthermore, empiricists in the field are generally interested in causal inference, but lack a framework to rigorously analyse focal-animal sampling data in light of transparent causal assumptions. As a consequence, common methods are often inappropriate, and can lead to wrong biological conclusions. Here, we introduce a causal Bayesian modelling framework to empirically study the causes of social network structure from focal-animal sampling data. We start by outlining a generative model that encodes how biological and measurement processes jointly produce social network data in continuous time; namely, as a temporal sequence of dyadic behavioural states ( e.g. , no body contact, social resting, grooming). Building upon the generative model, we develop a statistical model : a multilevel, multiplex Bayesian model that takes raw focal observations as input, and produces a posterior probability distribution for the generative parameters as output. After validating the statistical model's performance with sparse data—common in real-world settings—we illustrate its application with an empirical data set collected in wild Assamese macaques. We notably showcase how researchers can compute probabilistic estimates for well-defined causal hypotheses about the drivers of social structure. With this work, we not only contribute novel theoretical and statistical tools to the field, but also illustrate a workflow that allows researchers to iteratively translate their domain expertise into a formal analytical strategy—bridging theoretical and empirical research in behavioural ecology.

自1609年阿姆斯特丹银行成立以来，各国银行部门逐步垄断了货币发行权，通过买入外汇、黄金、股票和信贷扩张等方式发行货币，使大量的社会经济产出被转化为外汇储备、黄金储备、银行持股、银行债权等金融资本。这一垄断制度已经持续数百年，具有高度隐蔽性，其理论依据是“货币是银行的负债凭证”——掩盖了货币的真实来源。本文深入贯彻习近平经济思想，通过逻辑推演发现：货币是劳动力商品的表现形式，本质是劳动力，是人民生育孩子的果实；人民的生育劳动构成了货币的价值基准和最终支持，是货币的真实来源。这一发现揭示了“生育铸币”是客观存在的事实，表明货币主权应归属于人民。进一步实证研究发现：货币发行实际是国家经济的初次分配，匹配的是国家经济增长；现行货币发行制度使人民的生育劳动未能参与社会分配、得不到应有的经济补偿，造成“银行印钞买黄金、妈妈缺钱买奶粉”的根本性矛盾对立，导致经济陷入债务高企、需求不足、就业困难、内卷严重和新生儿锐减的系统性困境。然后，本文进行了制度创新设计，研究发现：若建立“生育铸币制度”——即只通过生育补助的方式发行增量货币，就能解除矛盾，走出困境，做强国内大循环，建成消费强国，实现高质量发展。

This study explores how economic theories, particularly CEPAL structuralism, intersect with Lou Andreas-Salomé’s reflections on the human condition and chronic dissatisfaction. Moving beyond the reductionist figure of homo economicus, it examines how socio-economic and cultural structures shape both historical inequality and the subjective experience of incompleteness. The analysis argues that the relationship between objective structures and individual existence is marked by tensions and constraints, but also by creative and transformative potential. In this way, the study seeks to connect economic and existential dimensions in order to reflect on the paradox of human beings who, while objectifying themselves, endlessly pursue wholeness.

Importance: Women are consistently reported to experience steeper rates after an Alzheimers disease (AD) diagnosis. We tested if this could be due to a sex-specific cognitive reserve based in verbal memory. Objective: To determine whether the divergence in verbal memory trajectories from cognitively normal (CN) to AD varies by sex. Design: We employed a longitudinal cohort design (ADNI, PREVENT-AD) to model cognitive change over time in Aβ- CN participants versus Aβ+ participants that progressed to AD. Setting: The study was conducted in two longitudinal research cohorts. ADNI is multi-site initiative recruiting through clinical referrals and digital outreach, while PREVENT-AD, based at the Douglas Hospital, recruits individuals with a familial history of AD. Participants: Both ADNI and PREVENT-AD aim to characterize AD. At baseline, ADNI participants may have normal cognition (CN), mild cognitive impairment or dementia, while PREVENT-AD participants were all CN. For this study, we harmonized CN participants from both cohorts as controls and defined the AD trajectory group as Aβ+ ADNI participants diagnosed with AD at baseline or during follow-up. Main Outcome(s) and Measure(s): The main measure used in this study was the RAVLT Immediate Recall sub-scores: Total Learning (trials 1–5), Early Learning (average of trials 1–2), and Late Learning (average of trials 4–5). All results are summarized using Bayesian Highest Density Intervals (HDI) to provide estimates of parameter uncertainty. Results: The final sample 987 participants (440 AD) with an average of 3.9 ± 2.0 follow-up visits. Females maintained normal verbal memory longer than males, with delayed decline onset of 2.7 years (Total Learning), 3.8 years (Early Learning), and 1.4 years (Late Learning). Rates of decline differed significantly by sex: females decline by 0.5/75 points per year (Total Learning), 0.1/15 points per year (Early Learning) and 0.09/15 points per year (Late Learning). Conclusions and Relevance: Female may mask early signs of AD through compensatory verbal mechanisms, delaying detection but contributing to sharper decline once symptoms emerge. Current testing paradigms need to explore assessments that are more sensitive to early cognitive change in females that move beyond reliance on verbal memory.

Background Respiratory syncytial virus (RSV) is a major cause of acute lower respiratory infections worldwide, with high morbidity among infants and older adults. Recent approvals of long-acting monoclonal antibodies (mAbs) and vaccines offer new prevention opportunities, but their impact in Asian settings remains uncertain. Methods We developed age-structured SEIR transmission models for Hong Kong, Beijing, and Thailand, calibrated to region-specific surveillance and seroprevalence data from 2014-2019. Using Bayesian inference, we estimated RSV transmission dynamics and simulated intervention scenarios involving long-acting mAbs for high-risk infants, maternal vaccination, and older adult vaccination. Findings RSV showed distinct seasonality: year-round in Hong Kong, winter peaks in Beijing, and rainy-season peaks in Thailand. Estimated annual infection attack rates among infants <1 year were 51.1% in Hong Kong and 22.5% in Beijing, and 75.8% and 70.1% among children aged 1-4 years, compared with 27.9% among 0-4 years in Thailand. Simulations suggest long-acting mAbs and maternal vaccination (coverage 38.5%) could avert 15.6-19.5% and 18.0-25.2% of severe infant outcomes, respectively. Vaccination of older adults (coverage 30-40%) reduced RSV-associated outcomes by 21.7-27.7% in Hong Kong, 33.9-39.7% in Beijing and 34.8-49.8% in Thailand. Combined interventions achieved reductions of 39.2% (27.5-55.8), 49.4% (42.0-59.7), and 53.8% (44.1-64.8) in severe outcomes among infants <1 year, 19.3% (18.9-21.7), 27.7% (26.9-31.5) and 31.5% (31.3-33.9) among 60-74 years, 26.5% (26.2-28.7), 52.3% (51.5-54.8) and 37.8% (37.6-40.0) among 75 years or above, in Hong Kong, Beijing, and Thailand, respectively. Interpretation Our modelling framework provides a novel approach to evaluate RSV prevention strategies in Asian populations with diverse seasonality. As real-world effectiveness data emerge, future research should refine estimates and optimise intervention combinations for maximum public health impact.

The exponential growth of misinformation has disrupted democratic discourse, public health communication, and social cohesion. AI-based tools have emerged as essential mechanisms for identifying and mitigating fake news. Situated within broader social, ethical, and psychological contexts, this paper presents the Fake News Detector, a machine learning-based text classification system. While leveraging natural language processing and supervised learning algorithms, this study underlines human cognitive biases, ethical dilemmas in algorithmic decision-making, and societal consequences of automated truth arbitration. Irrespective of technical performance, responsible AI deployment calls for fairness, interpretability, transparency, and media literacy-oriented education. The Fake News Detector serves both as a technical prototype and a philosophical case study in human-machine collaboration against information disorder.

Although collision-like causes are fundamental in philosophical and psychological theories of causation, humans conceptualize many events as causes that lack direct contact. We argue that how people think and talk about different causes is deeply connected and investigate how children learn this mapping. If Andy hits Suzy with his bike, Suzy falls into a fence and it breaks, Andy caused the fence to break but Suzy broke it. If Suzy forgets sunscreen and gets sunburned, the absence of sunscreen caused Suzy’s sunburn, but the sun burned her skin. We tested 691 children and 150 adults. Four-year-old children mapped “caused” to distal causes and “broke” to proximal causes (Experiment 1). Though four-year-old children didn’t map “caused” to absences until later (Experiment 2), they already referred to absences when asked “why” an outcome occurred (Experiment 3). Our findings highlight the role of semantics and pragmatics in developing these mappings.