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The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed.

Biodegradable films based on natural molecules are used for food production protection to extend shelf life. The current work aimed to fabricate and characterize bionanocomposite films. The biodegradable films were prepared from a mixture of tapioca starch/bovine gelatin (TB) with the loading of roselle calyx extract (RCE) and zinc oxide nanorod (ZnO-N). The incorporation of ZnO-N and RCE improved the mechanical and barrier properties of films. ZnO-N/RCE and RCE incorporated composite films indicated lower water vapor permeability (WVP) than neat film, 3.36 ×10-7, 3.96×10-7, and, 4.2×10-7 g/mPah respectively. Elongation at break, Young's modulus, and tensile strength of TB film, TB/RCE, and TB/RCE/ZnO-N film were measured as follows: 18.44%, 14.56%, and 10.69%; 0.79 MPa, 1.52 MPa, and 2.65 MPa; and 14.4 MPa, 21.85 MPa, and 28.22 MPa, respectively. The solubility of the active films decreased from 40.33% to 33.68% by the addition of RCE and ZnO-N into the composite matrix. This study indicated composite films based on starch/protein incorporated RCE and ZnO-N have the positive potential for applications in bionanocomposite packaging. [ABSTRACT FROM AUTHOR]

Mucosa-associated lymphoid tissue (MALT) lymphomas are slow-growing B-cell lymphomas mainly diagnosed in the stomach and termed gastric MALT lymphoma (G-MALT). Despite histological evaluation, immunostaining, and additional B-cell clonality analysis by fragment analysis, a clear-cut diagnosis is not feasible in all cases, especially for clinical follow-up of patients after treatment. We examined clonally rearranged immunoglobulin heavy- and light-chain gene sequences of 36 genomic DNA samples from six different patients obtained at different time points over the course of several years using the OncomineTM B-cell receptor pan-clonality next-generation sequencing (NGS) assay. Each case consisted of samples diagnosed with G-MALT and samples without evidence of lymphoma, based on histological examinations. We show a robust correlation (100%) of the results between the applied NGS method and histology-diagnosed G-MALT-positive patients. We also detected malignant clonotypes in samples where histology assessment failed to provide clear evidence of G-MALT (15 out of 19 samples). Furthermore, this method revealed malignant clonotypes much earlier in the disease course, with NGS of the immunoglobulin light chain being crucial in complementing immunoglobulin heavy-chain analysis. Hence, the value of NGS in routine lymphoma diagnostics is greatly significant and can be explored in order to provide better diagnoses and proffer the early detection of lymphoma relapse. [ABSTRACT FROM AUTHOR]

In today's United Kingdom, Islamophobia is as contested as it is real. Challenging this contestation, this article presents findings from ten small-scale, qualitative studies that seek to evidence and better understand the lived, tangible experience of Islamophobia in real-world spaces. To do so, this article briefly explores the development and incidence of Islamophobia in public and political spaces and how contestation has ensued. From here, the findings draw out how Muslims experience Islamophobia in their everyday lives, ranging from instances of verbal abuse through threats and intimidation to physical assault and violence. As part of this exploration, new insights are afforded into the role and impact of terrorist attacks on hate crimes, geopolitical and military conflicts, the content of Islamophobic abuse, and the rapidly changing nature of that, which shapes and informs tangible forms of Islamophobia. In doing so, this article concludes by contextualizing the realities of Islamophobia within the far from conducive public and political spaces of the UK. This article makes a timely contribution to and improves knowledge about Islamophobia in today's UK Keywords: Islamophobia, United Kingdom, Muslim Women, Muslim Men, Introduction Just over two decades ago, a groundbreaking report published by the anti-racism think-tank the Runnymede Trust stated that Islamophobia in the United Kingdom (UK) was becoming 'more explicit, more [...]

Accessible Summary: What is Known on the Subject?: Artificial intelligence (AI) is freely available, responds to very basic text input (such as a question) and can now create a wide range of outputs, communicating in many languages or art forms. AI platforms like OpenAI's ChatGPT can now create passages of text that could be used to create plans of care for people with mental health needs. As such, AI output can be difficult to distinguish from human‐output, and there is a risk that its use could go unnoticed. What this Paper Adds to Existing Knowledge?: Whilst it is known that AI can produce text or pass pre‐registration health‐profession exams, it is not known if AI can produce meaningful results for care delivery.We asked ChatGPT basic questions about a fictitious person who presents with self‐harm and then evaluated the quality of the output. We found that the output could look reasonable to laypersons but there were significant errors and ethical issues. There are potential harms to people in care if AI is used without an expert correcting or removing these errors. What are the Implications for Practice?: We suggest that there is a risk that AI use could cause harm if it was used in direct care delivery. There is a lack of policy and research to safeguard people receiving care ‐ and this needs to be in place before AI should be used in this way. Key aspects of the role of a mental health nurse are relational and AI use may diminish mental health nurses' ability to provide safe care in its current form.Many aspects of mental health recovery are linked to relationships and social engagement, however AI is not able to provide this and may push the people who are in most need of help further away from services that assist recovery. Background: Artificial intelligence (AI) is being increasingly used and discussed in care contexts. ChatGPT has gained significant attention in popular and scientific literature although how ChatGPT can be used in care‐delivery is not yet known. Aims: To use artificial intelligence (ChatGPT) to create a mental health nursing care plan and evaluate the quality of the output against the authors' clinical experience and existing guidance. Materials & Methods: Basic text commands were input into ChatGPT about a fictitious person called 'Emily' who presents with self‐injurious behaviour. The output from ChatGPT was then evaluated against the authors' clinical experience and current (national) care guidance. Results: ChatGPT was able to provide a care plan that incorporated some principles of dialectical behaviour therapy, but the output had significant errors and limitations and thus there is a reasonable likelihood of harm if used in this way. Discussion: AI use is increasing in direct‐care contexts through the use of chatbots or other means. However, AI can inhibit clinician to care‐recipient engagement, 'recycle' existing stigma, and introduce error, which may thus diminish the ability for care to uphold personhood and therefore lead to significant avoidable harms. Conclusion: Use of AI in this context should be avoided until a point where policy and guidance can safeguard the wellbeing of care recipients and the sophistication of AI output has increased. Given ChatGPT's ability to provide superficially reasonable outputs there is a risk that errors may go unnoticed and thus increase the likelihood of patient harms. Further research evaluating AI output is needed to consider how AI may be used safely in care delivery. [ABSTRACT FROM AUTHOR]