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Frequency and characteristics of axial involvement in psoriatic arthritis: results from the international multicentre AXIS study
No full textObjective: The Axial Involvement in Psoriatic Arthritis (AXIS) cohort aimed at evaluating the frequency of, and clinical and imaging features of axial involvement in psoriatic arthritis (PsA).Methods: AXIS (NCT04434885) is a prospective, multicenter, cross-sectional study conducted in 19 countries, by ASAS and GRAPPA. Participants with a diagnosis of PsA meeting CASPAR with musculoskeletal symptom duration ≤10 years and no prior exposure to biological or targeted synthetic DMARDs were consecutively included. Standardized clinical, laboratory, and imaging assessments (radiography and MRI of axial skeleton including sacroiliac joints-SIJ and spine) were performed. Imaging was reviewed locally and centrally to detect axial involvement. The presence of axial involvement was determined by local investigator judgment before and after central imaging review.Results: Among 409 participants, axial involvement was identified in 153 (37.4%) based on investigator’s initial assessment, was decreased to 112 (27.4%) in final evaluation after incorporating central imaging review. Participants with axial involvement were younger (45.2±13.8 vs. 47.6±12.6 years), more often male (56.3% vs. 51.5%), and had higher frequency of HLA-B*27 positivity (22.4% vs. 10.8%), inflammatory back pain (IBP) (74.7% vs. 43.4%), and elevated CRP (52.7% vs. 37.4%). Active inflammatory and structural imaging changes were highly discriminative between participants with and without axial involvement. The central review identified imaging signs of axial involvement (active inflammation or structural lesions) in 95 participants (23.2%).Conclusion: Axial involvement was identified in 27.4% of participants with PsA after final diagnostic assessment, with associated features including HLA-B*27 positivity, IBP, elevated CRP, and imaging changes in SIJ or spine
Youth social anxiety in the digital age: reconceptualising cognitive-behavioural processes
No full textDigital environments and social media have fundamentally transformed social interactions for young people, offering both opportunities and challenges for mental health. Individuals with social anxiety disorder (SAD), a condition characterised an intense fear of negative evaluation in social situations, may be particularly vulnerable to the dynamics of these digital environments, which now constitute a large part of adolescents’ social worlds. This paper examines the intersection of computer-mediated communication (CMC) and social anxiety in adolescence. We propose a framework that reconceptualises the Clark and Wells cognitive model of social anxiety in light of the distinctive affordances of social digital environments. Specifically, we integrate the model’s key components by considering how digital contexts influence (1) the interpretation of social cues, (2) processing the self as a social object, (3) the use of safety behaviours, and (4) pre- and post-event processing. We outline directions for future research and clinical implications
Regional variation of GDP per head within China, 1080-1850: implications for the Great Divergence debate
No full textWe examine regional variation in Chinese GDP per head for five benchmark years from the Song dynasty to the Qing. For the Ming and Qing dynasties, we provide a breakdown of regional GDP per head across seven macro regions, establishing that East Central China was the richest macro region. In addition, we provide data on the Yangzi Delta, the core of East Central China, widely seen as the richest part of China since 1400. Yangzi Delta GDP per head was 64 to 67 per cent higher than in China as a whole for three of the four Ming and Qing benchmarks, and 52 per cent higher during the late Ming. For the Northern Song dynasty, although it is not possible to derive a full regional breakdown, we provide data for Kaifeng Fu, the region containing the capital city as well as the Yangzi Delta. GDP per head in Kaifeng Fu was more than twice the level of China as a whole and higher than in the Yangzi Delta. Combined with aggregate data for GDP per head, these estimates suggest that China was the leading economy in the world during the Song dynasty and that the Great Divergence began around 1700 as the leading region of China fell decisively behind the leading region of Europe. They are also consistent with a shift in the economic centre of gravity from the north to the south between the Northern Song and Ming dynasties
People are STRANGE: material engagement and the creation of self-consciousness
Full text linkA groundbreaking exploration of self-consciousness through material engagement theory, redefining what it means to be human in a constantly changing world.The making of human consciousness and the question of self-becoming presents a remarkable complication along the continuum of sentient matter. Self-consciousness is an oddity that both unites humans with and differentiates them from other modes of conscious existence. Lambros Malafouris’s evocative proposal is that people are STRANGE, which stands for the process of Situated TRANsactional Genesis, by which self-becoming is realized at the intersection of mind and matter. This book breaks new ground by applying material engagement theory expertly to questions about self-location, the subject-object division, and the nature of self-boundaries.Malafouris argues that self-bounding (the process by which human ways of being are assembled, owned, or else bound to form what we call self or person) is rarely confined to a singular body. Our boundaries shift in response to the changing material environments and our modes of creative material engagement. Moreover, it is the bounding of consciousness that allows the unbounding of human thought and imagination. Self-bounding is the precondition for a borderless mind. Self-bound is thought-unbound. The theoretical upshot is that, rather than conceiving of self-consciousness as internal and ontological distinct from the material world, we must approach it as a continuous process fundamentally codependent with it
Ideological Cues, Partisanship, and Prejudice Against LGBTQ Judges
Full text linkHow does the gender and sexual identity of a prospective judge shape public support for their nomination? We build upon recent scholarship on instrumental inclusivity and argue that, after accounting for nominee ideology, Americans of all partisan stripes will penalize LGBTQ nominees. Using a conjoint experiment, we randomly vary a prospective Biden US Supreme Court nominee’s gender and sexual identity. Crucially, we also randomize the nominee’s ideology, enabling us to disentangle LGBTQ identity from the ideological signal it sends and differentiate between genuine and instrumental support for LGBTQ nominees. Contrary to recent findings suggesting that Democrats reward minority judges, we find that respondents from both parties penalize LGBTQ nominees. The magnitude of these effects—roughly 14 percentage points for transgender nominees and 8 percentage points for gay or lesbian nominees—is considerable and second only to shared partisanship. Our study underscores that ideological alignment does not necessarily foster genuine inclusivity for LGBTQ individuals and highlights the persistent challenges of representation for marginalized groups in an era of polarized judicial nominations
AIEOU shared research agenda 2026
Full text linkThis report outlines the shared research agenda for AI in Education at Oxford University (AIEOU) in 2026. Through participatory research practices, it presents the research priorities agreed upon by the group
Cause-specific excess mortality in rural India during the COVID-19 pandemic 2020–2023: longitudinal analyses of deaths in 0.2 million rural health facilities
Full text linkObjectiveIndia had an estimated three to five million excess deaths from causes attributable to SARS-CoV-2 during 2020-2021, far exceeding official government statistics. Most deaths in India occur in rural areas, where medical certification of deaths is limited. Yet, the effects of the pandemic in rural settings remain largely undocumented. We estimated the cause-specific excess mortality in rural areas of selected states of India.DesignLongitudinal analyses of hospital mortality data.SettingsIndia's Health Management Information System (HMIS) reports the number of deaths by cause for adolescents or adults aged 10 years or more. We examined eight states with high coverage of the expected number of deaths in rural areas.ParticipantsWe analysed monthly death reports from the HMIS, which covered approximately 0.2 million health facilities during 2018-2023. We compared excess deaths during the peak COVID-19 months in rural health facilities to pre-COVID-19 and non-peak periods of 2021, and categorised reported causes by their probable association with COVID-19.Primary outcome measureExcesses of cause-specific and total mortality.ResultsDuring the April-June 2021 SARS-CoV-2 wave, predominantly driven by the Delta variant, monthly deaths in rural health facilities across India surged from approximately 200 000 to 500 000. In eight states with high-quality reporting, rural facility deaths increased by 270% (95% CI 267% to 272%) compared with the same months in 2018-2019, prior to the COVID-19 pandemic. Notably, this surge occurred despite a sharp decline in hospital admissions following the national lockdown in March 2020. The largest relative increase was for fever-related and respiratory diseases, and these deaths were markedly elevated even when compared to non-peak months of 2021. Generalising these findings from eight states to all of rural India yields an estimate of approximately 2.6 million excess rural deaths in April-June 2021. In contrast, there were few excess deaths during the Omicron viral waves in 2022-2023.ConclusionCOVID-19 substantially increased deaths in rural India during April-June 2021, but reassuringly, no significant excess mortality was observed in subsequent years. The HMIS provides an important opportunity to strengthen routine mortality surveillance in rural India
Topological Data Analysis for Unsupervised Feature Selection in Large Scale Spatial Omics Data Sets: Topological Data Analysis for Unsupervised Feature Selection.
Full text linkSpatial transcriptomics studies are becoming increasingly large and commonplace, necessitating simultaneous analysis of a large number of spatially resolved variables. Correspondingly, a diverse range of methodologies have been proposed to compare the spatial expression structure of genes. Here, we apply persistent homology, a method from topological data analysis, to produce a continuous quantification of spatial structure in a given gene’s expression, and show how this can be used for downstream tasks such as spatially variable gene identification. We explore the unique advantages of topology for this task, deriving biologically meaningful insights into kidney disease and myocardial infarction using public spatial transcriptomics data. We also show how the non-parametric nature of homology enables our methodology to extend naturally to other spatial omics modalities, demonstrating this on a spatial metabolomics sample. Our work showcases the advantages of using a continuous quantification of spatial structure over p-value based approaches to SVG identification, the potential for developing unified methods for the analysis of different spatial omics modalities, and the utility of persistent homology in big data applications
Background invariance testing according to semantic proximity
Full text linkIn many applications, machine-learned (ML) models are required to hold some invariance qualities, such as rotation, size, and intensity invariance. Among these, testing for background invariance presents a significant challenge due to the vast and complex data space it encompasses. To evaluate invariance qualities, we first use a visualization-based testing framework which allows human analysts to assess and make informed decisions about the invariance properties of ML models. We show that such informative testing framework is preferred as ML models with the same global statistics (e.g., accuracy scores) can behave differently and have different visualized testing patterns. However, such human analysts might not lead to consistent decisions without a systematic sampling approach to select representative testing suites. In this work, we present a technical solution for selecting background scenes according to their semantic proximity to a target image that contains a foreground object being tested. We construct an ontology for storing knowledge about relationships among different objects using association analysis. This ontology enables an efficient and meaningful search for background scenes of different semantic distances to a target image, enabling the selection of a test suite that is both diverse and reasonable. Compared with other testing techniques, e.g., random sampling, nearest neighbors, or other sampled test suites by visuallanguage models (VLMs), our method achieved a superior balance between diversity and consistency of human annotations, thereby enhancing the reliability and comprehensiveness of background invariance testing