The Dutch Apollo 11 Goodwill display contains genuine Moon rocks

Data supporting the publication of the article titled "The Dutch Apollo 11 Goodwill display contains genuine Moon rocks" (https://doi.org/10.1038/s43247-024-01961-z) in Nature Communications Earth and Environment.MoonMatExp: Matlab data for hyperspectralScan D: tomosynthesis scanner databaseClusterTA: raw spectral datasetMR A11_tomo-A: low resolution tomography projectionsMR A11_tomo-B_NORM_REAL: high-resolution tomography projections (pre-processed

Effective Components of Collaborative Care for Depression in Primary Care:An Individual Participant Data Meta-Analysis

IMPORTANCE: Collaborative care is a multicomponent intervention for patients with chronic disease in primary care. Previous meta-analyses have proven the effectiveness of collaborative care for depression; however, individual participant data (IPD) are needed to identify which components of the intervention are the principal drivers of this effect.OBJECTIVE: To assess which components of collaborative care are the biggest drivers of its effectiveness in reducing symptoms of depression in primary care.DATA SOURCES: Data were obtained from MEDLINE, Embase, Cochrane Library, PubMed, and PsycInfo as well as references of relevant systematic reviews. Searches were conducted in December 2023, and eligible data were collected until March 14, 2024.STUDY SELECTION: Two reviewers assessed for eligibility. Randomized clinical trials comparing the effect of collaborative care and usual care among adult patients with depression in primary care were included.DATA EXTRACTION AND SYNTHESIS: The study was conducted according to the IPD guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. IPD were collected for demographic characteristics and depression outcomes measured at baseline and follow-ups from the authors of all eligible trials. Using IPD, linear mixed models with random nested effects were calculated.MAIN OUTCOMES AND MEASURES: Continuous measure of depression severity was assessed via validated self-report instruments at 4 to 6 months and was standardized using the instrument's cutoff value for mild depression.RESULTS: A total of 35 datasets with 38 comparisons were analyzed (N = 20 046 participants [57.3% of all eligible, with minimal differences in baseline characteristics compared with nonretrieved data]; 13 709 [68.4%] female; mean [SD] age, 50.8 [16.5] years). A significant interaction effect with the largest effect size was found between the depression outcome and the collaborative care component therapeutic treatment strategy (-0.07; P &lt; .001). This indicates that this component, including its key elements manual-based psychotherapy and family involvement, was the most effective component of the intervention. Significant interactions were found for all other components, but with smaller effect sizes.CONCLUSIONS AND RELEVANCE: Components of collaborative care most associated with improved effectiveness in reducing depressive symptoms were identified. To optimize treatment effectiveness and resource allocation, a therapeutic treatment strategy, such as manual-based psychotherapy or family integration, may be prioritized when implementing a collaborative care intervention.</p

It’s Inevitable That Open Access Will Simply Expand: Key Considerations for the Growth of Open Access Hubs: Interview Data, 2024

Open access (OA) hubs have become pivotal in democratising academic knowledge and fostering global accessibility to research. These digital platforms allow unrestricted sharing of scholarly work, bridging gaps in access to academic resources. Despite their transformative potential, the adoption and sustainability of OA hubs face significant challenges, including legal complexities, ethical dilemmas, technical barriers, and disciplinary disparities. This study explores these multifaceted issues through qualitative interviews with 11 experts involved in OA initiatives, employing an expert sampling design. A total of 45 individuals were invited to participate, with 11 ultimately taking part. The interviews had an approximate average length of 90 minutes. To ensure confidentiality, all identifying information was removed from the interview transcripts. Additionally, one participant requested that all his information be excluded from the final transcripts, and this request was honoured. The findings reveal strategies to address copyright concerns, enhance usability, and foster inclusivity. They underscore the critical importance of embedding equity, diversity, and inclusion (EDI) principles within OA practices while advocating for robust funding models to ensure long-term sustainability. By addressing these challenges, OA hubs can evolve to support a more equitable and accessible academic publishing landscape. By prioritising sustainability and inclusivity, OA platforms can fulfil their potential to revolutionise scholarly communication, paving the way for a more equitable and accessible academic landscape. Open access hubs, including platforms like ArXiv, REPEC, SocArxiv, CrimRxiv, and institutional repositories, provide free and unrestricted access to preprint and postprint versions of academic publications. By offering an alternative to traditional peer reviewed journals, often owned by commercial publishers, through light-touch moderation these platforms accelerate the dissemination of academic work and enhance global accessibility. However, open access hubs are not yet widely adopted in many fields, particularly in the Humanities and Social Sciences. This project investigates the legal, ethical, and technical challenges of establishing and maintaining online repositories for open access to academic literature. Using semi-structured qualitative interviews with experts—such as open access hub founders, academic librarians, and leaders in the open science movement—the project explores: - The legal challenges faced by open access platforms. - Key ethical considerations involved in their operation. - Technical barriers to wider usability and adoption. The study identifies strategies to address these challenges and aims to inspire the development and growth of open access hubs within the social sciences. By drawing on lessons from existing platforms, the project contributes to a more inclusive, accessible, and efficient academic publishing landscape

Circulating CC16, immune response to Mycoplasma pneumoniae and lung function: a population-based, multi-cohort study

Background: Sufficient levels of club cell secretory protein (CC16) are essential to protect against lung function impairments. Experimental studies show that CC16 modulates inflammatory responses and protects against airway hyperresponsiveness following Mycoplasma pneumoniae (Mp) infection. Individuals with asthma have low circulating CC16 levels and increased susceptibility to Mp infection. We sought to determine whether low CC16 and Mp seropositivity have combined effects on lung function deficits predisposing to airflow limitation, particularly among individuals with asthma.Methods: Serum levels of CC16 and IgG antibodies against Mp (MpIgG) were measured in adult participants from the population-based cohorts BAMSE, MAAS, LSC, and TESAOD, and were the basis for stratification into four groups: normal CC16/MpIgG-, normal CC16/MpIgG+, low CC16/MpIgG-, low CC16/MpIgG+. Associations between these groups and lung function (FEV1 and FEV1/FVC) were assessed by linear regression, adjusting for covariates, and meta-analyzed estimates were calculated.Results: Low CC16 levels were associated with decreased lung function in the total population, with no evidence of combined effects between low CC16 and MpIgG positivity. Among asthmatic participants, the low CC16/MpIgG+ group had remarkably lower levels of FEV1/FVC z-scores (-0.84 (CI: -1.29, -0.38)) compared to the reference group, and Mp seropositivity was associated with significant deficits in FEV1/FVC z-scores among those with low CC16 (-0.60, CI: -1.08, -0.12), but not among those with normal CC16 (-0.10, CI: -0.56, 0.36). Conclusion: This suggests that individuals with asthma with low levels of CC16 combined with a history of Mp infection may be more susceptible to deficits in FEV1/FVC, the hallmark of airflow limitation, emphasizing the need for prospective studies designed to test this hypothesis.<br/

Does promising facilitate children’s delay of gratification in interdependent contexts?

For cooperation to succeed, individuals must often “delay gratification” and forego an immediate reward for a larger delayed reward that is co-produced through the cooperative act. This experiment asked whether a promise to wait increased children’s propensity to coordinate with their partner by waiting to eat their own treat. In this first cooperative marshmallow test conducted online, 5- to 6-year-old UK-based children (N=66) interacted from their homes via video call with a confederate child who either promised to not eat his treat (promise condition) or expressed the possibility that he might eat his treat (social risk condition). Across the full dataset and a reduced dataset in which participants were not accidentally interrupted during the task (N=48), children in the promise condition waited longer to eat their treat than children in the social risk condition. Younger children, but not older children, also successfully delayed gratification more often in the promise condition than in the social risk condition. Thus, even when communication is one-sided in an interdependent marshmallow task, explicit promises can support children’s motivation to delay gratification relative to explicit uncertainty

The Price of Knowledge:Universities and Slavery in Anglo-American Perspective

This article centers on the different ways that English and American universities have uncovered, publicized, and responded to their historical connections to transatlantic slavery. It argues that these differences reflect how national publics engage with their institutions of higher learning, and English campuses lack of the physical traces of enslavement that exist at some American universities, allowing the former’s students and faculty to view unfree labor as something that existed elsewhere. The author uses her home institution, the University of Manchester, as a case study of the challenges and prospects for this sort of enquiry in the UK context

Creating an Inclusive School Environment to Support International New Arrivals

This workshop session took place at the ITE/ECT 2025 Annual PGCE Conference Inclusion &amp; Inspiration - Education for Social Justice. 17 January 2025. University of Manchester. In recent years there has been a rise in international new arrivals in schools in England. Depending on the location, some schools have welcomed a much larger proportion of these children, which has quickly changed the demographics of the school. In this session, Choen looked at a case study of a primary school in Greater Manchester which had welcomed many international new arrivals in recent years, with the majority of these children coming from Hong Kong. The session discussed both the challenges and opportunities these new arrivals brought to the school. It also highlighted the role of teachers in creating a more inclusive school environment to support these new arrivals

Non-Unique Machine Learning Mapping in Data-Driven Reynolds Averaged Turbulence Models

Recent growing interest in using machine learning for turbulence modelling has led to many proposed data-driven turbulence models in the literature. However, most of these models have not been developed with overcoming non-unique mapping (NUM) in mind, which is a significant source of training and prediction error. Only NUM caused by one-dimensional channel flow data has been well studied in the literature, despite most data-driven models having been trained on two-dimensional flow data. The present work aims to be the first detailed investigation on NUM caused by two-dimensional flows. A method for quantifying NUM is proposed and demonstrated on data from a flow over periodic hills, and an impinging jet. The former is a wall-bounded separated flow, and the latter is a shear flow containing stagnation and recirculation. This work confirms that data from two-dimensional flows can cause NUM in data-driven turbulence models with the commonly used invariant inputs. This finding was verified with both cases, which contain different flow phenomena, hence showing that NUM is not limited to specific flow physics. Furthermore, the proposed method revealed that regions containing low strain and rotation or near pure shear cause the majority of NUM in both cases - approximately 76% and 89% in the flow over periodic hills and impinging jet, respectively. These results led to viscosity ratio being selected as a supplementary input variable (SIV), demonstrating that SIVs can reduce NUM caused by data from two-dimensional flows

Building energy consumption prediction for campus accommodation buildings based on spatial temporal graph convolution networks

The Net Zero Building (NZB) strategy has been regarded as the fundamental pathway to achieve sustainable cities and communities and to amend climate change. This necessitates an accurate understanding of the energy consumption of buildings which serves as a baseline reference for any energy planning or building retrofit. Artificial Intelligence (AI) approaches have been well documented in predicting energy consumption of buildings with credits of accuracy and efficiency. However, most of the studies focused on a single building which ignored or overlooked the interdependencies of buildings, especially for those buildings with the same group of users such as educational campuses where students and university staffs usually share the facilities and infrastructures across buildings. Predicting energy consumption independently significantly affects the accuracy of AI models. To fill this research gap, this study proposes a spatial-temporal graph convolutional network (STGCN) algorithm to predict the hourly energy consumption of campus buildings in northern England. To evaluate the feasibility of the STGCN algorithm, several popular AI algorithms were also employed for comparison. The results indicated that STGCN can significantly improve the prediction performance that conventional machine learning algorithms