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Institutionalizing online platforms under the EU Digital Services Act: a panacea or a risk to innovation?
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Net zero carbon buildings: A review on recent advances, knowledge gaps and research directions
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Data will be made available on request.As the building sector is a significant contributor to global greenhouse gas emissions and energy consumption, achieving net zero carbon buildings (NZCBs) is vital for reducing environmental impact and meeting global climate goals. This review synthesised recent advances in minimising embodied carbon and operational carbon, identified key research gaps, and proposed future research for achieving NZCBs. It investigates the challenges and opportunities across legislative, financial, cultural, technological, and stakeholder domains. Then, best practices in the decarbonisation of buildings, such as implementation of energy efficiency measures, utilisation of renewable energy sources, and adoption of circular economy principles are examined. Additionally, innovations in new building materials, such as Cross-Laminated Timber (CLT), Cold-Formed Steel (CFS), and Highly Sulfated Calcium Silicate Cement (HSCSC), were found to have substantial potential for reducing embodied carbon. Moreover, technologies like Photovoltaic (PV) panels and modular construction contribute to reducing operational emissions. This study emphasises the importance of comprehensive policies, public education, and collaborative stakeholder engagement in driving the transition to NZCBs. Furthermore, a variety of future research on low-carbon materials, energy efficiency, policies, upfront costs and comparative studies on net zero emissions between developed and developing nations are crucial for scaling sustainable practices globally. The study aims to support global decarbonisation efforts in the built environment by examining best practices, technological innovations, and strategic approaches. These findings highlight the need for continued research and development in sustainable building technologies and the importance of implementing effective policies to achieve a net zero carbon future
Novel texture analysis method for optimising material property in extruded 6xxx alloys using artificial neural networks
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The data supporting this study are available upon request, subject to approval from the project's industrial sponsor, Constellium.This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.This study investigates the extruded texture of a 6xxx series high-strength aluminium alloy as a function of profile geometry using Electron Backscatter Diffraction (EBSD) and X-Ray diffraction pattern (XRD). A novel texture analysis method was designed to acquire and prepare reliable texture data for machine learning applications. The method categorizes textures into five distinct groups, with volume fractions calculated for each group. Furthermore, finite element analysis of the extrusion process revealed that axial tensile strain promotes a combination of 〈100〉 and 〈111〉 //ED texture components, while shear deformation induces 〈211〉 //ED texture components. The results were subsequently fed into an artificial neural network (ANN) model developed to link the texture to profile geometry, which governs the deformation modes experienced during the material flow. This approach represents a significant advancement towards real-time control of material properties during extrusion.Authors thankfully acknowledge financial support of the EPSRC Materials Made Smarter Centre (Reference EP/V061798/1)
Differential intestinal injury and unchanged systemic inflammatory responses to leg and whole-body passive hyperthermia in healthy humans
Get PDFData Availability Statement: The raw, unidentified data collected throughout this study will be made available via Brunel Figshare, an online data repository database.Acknowledgements: We sincerely thank all the participants for their commitment throughout the study and Tom Howes and Nuno Koch Esteves for their technical support. This multi-study investigation was conducted at the Centre for Human Performance, Exercise and Rehabilitation, Brunel University of London, between March 2017 and March 2018 and was partially supported by grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan (JSPS KAKENHI) grant numbers 19K20034 and 21K17582.Highlights:
• What is the central question of this study?
To what extent does prolonged lower-limb or whole-body passive hyperthermia cause intestinal injury and systemic inflammation?
• What is the main finding and its importance?
Intestinal fatty acid binding protein, a marker of intestinal injury, increased only during whole-body hyperthermia, with augmented concentrations observed in 50% of participants after 2 h. This outcome indicates a potential responder/non-responder paradigm. Despite this evidence of intestinal injury, concentrations of circulatory cytokines, chemokines and growth factors were unaltered in all passive hyperthermia conditions. These findings expand our knowledge of the gastrointestinal and systemic inflammatory responses to passive hyperthermia and provide insight into the safety of lower-limb and whole-body thermal therapy interventions.Hyperthermia can cause intestinal injury, facilitating endotoxin translocation and an inflammatory response that has been associated with heat illness. However, the potential occurrence of these responses has been incompletely reported during passive hyperthermia, and the independent effect of hyperthermia is equivocal. Furthermore, passive hyperthermia is a feature of heat therapy interventions, with mechanistic understanding developing. This experiment quantified the changes in intestinal fatty acid binding protein (iFABP), a marker of intestinal injury, and cytokine, chemokine and growth factor responses during three different prolonged passive hyperthermia protocols. Eight healthy males visited the laboratory on four counterbalanced occasions to undertake 2.5 h of rest (CON), one-leg heating (OLH), two-leg heating (TLH) and whole-body heating (WBH) via a garment circulating water at 50°C. Plasma concentrations of iFABP and 38 cytokines, chemokines and growth factors were quantified periodically, and core temperature (Tcore) was measured continuously. The Tcore increased from baseline in OLH, TLH and WBH (+0.4°C ± 0.2°C, +0.7°C ± 0.2°C and +2.3°C ± 0.4°C, respectively; P < 0.05) but remained unchanged in CON. iFABP increased from baseline in WBH only (∆587 ± 651 pg ml−1) and was different from CON and OLH in WBH after 2 h (P < 0.05). Increased iFABP (∆1085 ± 572 pg ml−1) was observed in 50% of participants at the end of WBH, with the other 50% demonstrating no change (∆89 ± 19 pg ml−1). All chemokines, cytokines and growth factors were unchanged in all protocols. These data indicate that passive whole-body hyperthermia, but not lower-limb hyperthermia, can cause intestinal injury in some individuals without a systemic inflammatory response.Ministry of Education, Culture, Sports, Science and Technology of Japan (JSPS KAKENHI). Grant Numbers: 19K20034, 21K1758
Lightweight Facial Attractiveness Prediction Using Dual Label Distribution.
Get PDFFacial attractiveness prediction (FAP) aims to assess facial attractiveness automatically based on human aesthetic perception. Previous methods using deep convolutional neural networks have improved the performance, but their large-scale models have led to a deficiency in efficiency. In addition, most methods fail to take full advantage of the dataset. In this paper, we present a novel end-to-end FAP approach that integrates dual label distribution and lightweight design. The manual ratings, attractiveness score, and standard deviation are aggregated explicitly to construct a dual-label distribution to make the best use of the dataset, including the attractiveness distribution and the rating distribution. Such distributions, as well as the attractiveness score, are optimized under a joint learning framework based on the label distribution learning (LDL) paradigm. The data processing is simplified to a minimum for a lightweight design, and MobileNetV2 is selected as our backbone. Extensive experiments are conducted on two benchmark datasets, where our approach achieves promising results and succeeds in balancing performance and efficiency. Ablation studies demonstrate that our delicately designed learning modules are indispensable and correlated. Additionally, the visualization indicates that our approach can perceive facial attractiveness and capture attractive facial regions to facilitate semantic predictions. The code is available at https://github.com/enquan/2D_FAP
SharkNet Networks Applications in Smart Manufacturing Using IoT and Machine Learning
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The necessary research data have been presented in the article.With the advancement of Industry 4.0, 3D printing has become a critical technology in smart manufacturing; however, challenges remain in the integrated management, quality control, and remote monitoring of multiple 3D printers. This study proposes an intelligent cloud monitoring system based on the SharkNet dynamic network, IoT, and artificial neural networks (ANNs). The system utilizes a SharkNet dynamic network to integrate low-cost sensors for environmental monitoring to enable low-latency data transmission and deploys ANN models on the cloud for print quality prediction and process parameter optimization. Next, we experimentally validated the system using the Taguchi design and ANN-based analysis, focusing on optimizing printing process parameters and improving surface quality. The main results show that the designed system has a communication delay of 40–50 ms and 99.8% transmission reliability under moderate load, and the system reduces the surface roughness prediction error to less than 17.2%. In addition, the ANN model outperforms conventional methods in capturing the nonlinear relationships of the variables, and the system can be based on the model to improve print quality and productivity by enabling real-time parameter adjustments. The system retains a high degree of scalability in terms of real-time monitoring and parallel or complex control of multiple devices, which demonstrates its potential for applications in smart manufacturing.This research was funded by the Graduate Student Innovation Program of Shanxi Province, Grant No. 2023SJ214. It was also partly funded by Brunel University London
Depression symptom-specific genetic associations in clinically diagnosed and proxy case Alzheimer’s disease
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All GWAS summary statistics generated in the process of conducting this study have been deposited on Zenodo at https://doi.org/10.5281/zenodo.13828101 (ref. 121). Individual-level data from UK Biobank, GLAD and PROTECT are subject to restrictions. Data are available on reasonable request from UK Biobank (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/contact-us) through application to the NIHR BioResource for GLAD (https://bioresource.nihr.ac.uk/using-our-bioresource/academic-and-clinical-researchers/apply-for-bioresource-data/) and through the PROTECT data team (https://medicine.exeter.ac.uk/clinical-biomedical/research/protect/). The Alzheimer’s disease GWAS summary statistics used in this study are publically available through the GWAS catalog (https://www.ebi.ac.uk/gwas/efotraits/MONDO_0004975). GWAS summary statistics for the Wightman et al. GWAS excluding the UK Biobank are available at https://vu.data.surfsara.nl/index.php/s/LGjeIk6phQ6zw8I. For clinical and broad depression, summary statistics are available through the Psychiatric Genomic Consortium (https://pgc.unc.edu). eQTL summary datasets used in SMR analysis from Lloyd-Jones et al.100 and PsychENCODE101 can be obtained from the website of the Yang laboratory (https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata). This study has been pre-registered on the Open Science Framework (https://osf.io/94q35/?view_only=e77f72d4100d47eea7f3ef07dfa9c059).Code availability;
Code for performing these analyses has been deposited on GitHub (https://github.com/lpgilchrist/PHQ-9_AD_genetic_overlap_project). This study made use of the following publicly available analysis software: CAUSE (https://jean997.github.io/cause/index.html); coloc (https://chr1swallace.github.io/coloc/); COLOC-reporter (https://github.com/ThomasPSpargo/COLOC-reporter); FUMA GWAS (https://fuma.ctglab.nl); HDL (https://github.com/zhenin/HDL); LAVA (https://github.com/josefin-werme/LAVA); LDSC (https://github.com/bulik/ldsc); MegaPRS (https://dougspeed.com/megaprs/); METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation); MTAG (https://github.com/JonJala/mtag); MungeSumstats (https://github.com/Al-Murphy/MungeSumstats); REGENIE (https://rgcgithub.github.io/regenie/); SMR (https://yanglab.westlake.edu.cn/software/smr/); susieR (https://stephenslab.github.io/susieR/index.html); TwoSampleMR (https://mrcieu.github.io/TwoSampleMR/).Supplementary information is available online at: https://www.nature.com/articles/s44220-024-00369-0#Sec33 .For the purposes of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any accepted author manuscript version arising from this submission.Depression is a risk factor for the later development of Alzheimer’s disease (AD), but evidence for the genetic relationship is mixed. Assessing depression symptom-specific genetic associations may better clarify this relationship. To address this, we conducted genome-wide meta-analysis (a genome-wide association study, GWAS) of the nine depression symptom items, plus their sum score, on the Patient Health Questionnaire (PHQ-9) (GWAS-equivalent N: 224,535–308,421) using data from UK Biobank, the GLAD study and PROTECT, identifying 37 genomic risk loci. Using six AD GWASs with varying proportions of clinical and proxy (family history) case ascertainment, we identified 20 significant genetic correlations with depression/depression symptoms. However, only one of these was identified with a clinical AD GWAS. Local genetic correlations were detected in 14 regions. No statistical colocalization was identified in these regions. However, the region of the transmembrane protein 106B gene (TMEM106B) showed colocalization between multiple depression phenotypes and both clinical-only and clinical + proxy AD. Mendelian randomization and polygenic risk score analyses did not yield significant results after multiple testing correction in either direction. Our findings do not demonstrate a causal role of depression/depression symptoms on AD and suggest that previous evidence of genetic overlap between depression and AD may be driven by the inclusion of family history-based proxy cases/controls. However, colocalization at TMEM106B warrants further investigation.L.G. is funded by the King’s College London DRIVE-Health Centre for Doctoral Training and the Perron Institute for Neurological and Translational Science. P.P. is funded by Alzheimer’s Research UK. S.K. is funded by MSWA and the Perron Institute. H.L.D. acknowledges funding from the Economic and Social Research Council (ESRC). D.M.H. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (ref. 213674/Z/18/Z). B.N.A. acknowledges funding from an NIHR pre-doctoral fellowship (NIHR301067). A.I. is funded by the Motor Neurone Disease Association (MNDA), MND Scotland, Darby Rimmer MND Foundation, Rosetrees Trust, Alzheimer’s Research UK, Spastic Paraplegia Foundation, LifeArc and The NIHR Maudsley Biomedical Research Centre. T.P.S. acknowledges funding from the MNDA. This Article represents independent research that was part funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This research was conducted using the UK Biobank Resource under application no. 18177. We thank the UK Biobank Team for collecting the data and making it available. We also thank the UK Biobank participants. We thank the GLAD Study volunteers for their participation, and gratefully acknowledge the NIHR BioResource centers, NHS Trusts and staff for their contribution. We thank the National Institute for Health Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. This study presents independent research funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Further information can be found at https://www.maudsleybrc.nihr.ac.uk/facilities/bioresource/. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the HSC R&D Division, King’s College London or the Department of Health and Social Care. The PROTECT study was funded/supported by the National Institute of Health and Care Research Exeter Biomedical Research Centre. PROTECT genetic data were funded in part by the University of Exeter through the MRC Proximity to Discovery: Industry Engagement Fund (External Collaboration, Innovation and Entrepreneurism: Translational Medicine in Exeter 2 (EXCITEME2) ref. MC_PC_17189). Genotyping was performed at deCODE Genetics.
As data used in this study was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the ADNI investigators contributed to the conception of the sample and acquisition of the data, but were not participants in other parts of this study, such as conceptualization, data analysis or writing. A full acknowledgment list of ADNI investigators is available at https://adni.loni.usc.edu/wp-content/uploads/2024/07/ADNI-Acknowledgement-List_July2024.pdf. Details on ADNI data access can be found at https://adni.loni.usc.edu/data-samples/adni-data/#AccessData. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health grant U01 AG024904) and DOD ADNI (Department of Defense award no. W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research are providing funds to support ADNI clinical sites in Canada. Private-sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Similarly, data were obtained from the GERAD1 Consortium, and GERAD1 investigators contributed to the conception of the cohort and acquisition of the data, but did not participate in the conceptualization, analysis or writing of this study. A full list of GERAD1 collaborators can be found in Supplementary Section 3. For GERAD1 data, Cardiff University was supported by the Wellcome Trust, MRC, ARUK and the Welsh Assembly Government. Cambridge University and King’s College London acknowledge support from the MRC. ARUK supported sample collections at the South West Dementia Bank and the Universities of Nottingham, Manchester and Belfast. The Belfast group acknowledges support from the Alzheimer’s Society, Ulster Garden Villages, Northern Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The MRC and Mercer’s Institute for Research on Ageing supported the Trinity College group. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer’s and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by National Institutes of Health (NIH) grants, the Barnes Jewish Foundation and the Charles and Joanne Knight Alzheimer’s Research Initiative. Patient recruitment for the MRC Prion Unit/University College London(UCL) Department of Neurodegenerative Disease collection was supported by the UCL Hospitals/UCL Biomedical Centre and NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by Lundbeck SA. The Bonn group was supported by the German Federal Ministry of Education and Research, Competence Network Dementia and Competence Network Degenerative Dementia, and Alfried Krupp von Bohlen und Halbach-Stiftung. The Genetic and Environmental Risk for Alzheimer’s Disease (GERAD1) Consortium also used samples ascertained by the National Institute of Mental Health Alzheimer’s Disease Genetics Initiative. The i-Select chip was funded by the French National Foundation on Alzheimer’s disease and related disorders. The European Alzheimer’s Disease Initiative was supported by a LABEX (Laboratory of Excellence Program Investment for the Future) DISTALZ grant, the Institut National de la Santé et de la Recherche Médicale, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. The GERAD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium was supported by the MRC (grant no. 503480), ARUK (grant no. 503176), the Wellcome Trust (grant no. 082604/2/07/Z) and the German Federal Ministry of Education and Research (Competence Network Dementia grant nos. 01GI0102, 01GI0711 and 01GI0420). The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was partly supported by NIH/NIA grant no. R01 AG033193; NIA grant no. AG081220; AGES contract no. N01-AG-12100; National Heart, Lung and Blood Institute grant no. R01 HL105756; the Icelandic Heart Association; and the Erasmus Medical Center and Erasmus University. The Alzheimer’s Disease Genetics Consortium was supported by NIH/NIA grant nos. U01 AG032984, U24 AG021886 and U01 AG016976; and Alzheimer’s Association grant no. ADGC-10-196728.
ANM data are accessible via Synapse (https://www.synapse.org/Synapse:syn22252881). The AddNeuroMed study was supported by InnoMed (Innovative Medicines in Europe)—an Integrated Project funded by the European Union of the Sixth Framework program priority FP6-2004-LIFESCIHEALTH-5, Life Sciences, Genomics and Biotechnology for Health.
Compensation was not provided for participants in any of the above studies.
We also thank and acknowledge the contribution and use of the CREATE high-performance computing cluster at King’s College London (King’s Computational Research, Engineering and Technology Environment (CREATE); retrieved 23 May 2023 from https://doi.org/10.18742/rnvf-m076).
The analysis flowchart in Fig. 1 was created and licensed in BioRender (https://www.BioRender.com/z76j516).
The ethics committee/IRB of King’s College London gave ethical approval for this work. Ethical approval for the UK Biobank study was granted by the National Information Governance Board for Health and Social Care and the NHS North West Multicentre Research Ethics Committee (11/NW/0382). Data access permission was granted under UK Biobank application 18177. Written informed consent was obtained from all participants by UK Biobank. The GLAD Study was approved by the London–Fulham Research Ethics Committee on 21 August 2018 (REC ref. 18/LO/1218) following a full review by the committee. The NIHR BioResource has been approved as a Research Tissue Bank by the East of England–Cambridge Central Committee (REC ref. 17/EE/0025). The PROTECT study received ethical approval from the UK London Bridge National Research Ethics Committee (ref. 13/LO/1578). Data were obtained from ADNI, AddNeuroMed and GERAD1 following formal request to each consortia. Permission was granted for the use of data
Development and learning at the operational level in the British and Indian armies during the second world war
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContextualised with an examination of pre-war doctrine and military thought within both the British and Indian Armies this thesis examines doctrine, training, and selection of personnel to understand how the British and Indian Armies developed more effective ways of conducting warfare at the operational level between 1939-1945. It argues that the War Office failed to settle key doctrinal and organisational questions before the war, and subsequently failed to centralise authority over doctrine and training during the war. This meant that there was limited uniformity in doctrine and that this negatively affected the ability of the British and Indian Armies to learn and adapt together. Ultimately the War Office preferred to influence military conduct through the Military Secretary’s branch which instituted increasingly centralised control throughout the war. This thesis therefore emphasises the agency of national armies, local theatre headquarters and individual commanders in controlling their own individual courses of development over the central control of the War Office.
This thesis offers a new perspective on the extent to which the armies of the British Empire worked and learned as an integrated ‘Imperial Army’ during the Second World War. Recent research has emphasised the integrated, and Imperial nature of the British, Indian and Commonwealth Armies. This especially emphasises their ability to work together, it is claimed, because of shared doctrine and staff procedures. To date, research has focused upon pre-war War Office doctrine and examination of military activity at the tactical level. This thesis has a different scope and conducts a comparative examination of the development and conduct of the operational level within the British and Indian Armies. This, therefore, studies the development of the British and Indian Armies and their doctrine at the operational level from across the world, not just that which was produced by the War Office
Clinical Manifestations
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When assessed in the Mild Behavioral Impairment (MBI) framework, late-life onset psychotic like symptoms (MBI-psychosis) are associated with incident cognitive decline and dementia. One approach to examining the genetic basis of this association, is to use Polygenic Risk Scores (PRS) to determine whether genetic propensity for late-life onset psychosis is shared with other traits. We aimed to elucidate the shared genetic liability between Educational Attainment, Intelligence, Reasoning, Memory, Neuroticism, Alzheimer’s Disease, Major Depression, Schizophrenia and Bipolar Disorder and Mild Behavioral Impairment (MBI)-Psychosis in later life.
Method:
A total of 7,307 older adults without dementia were included in the analytical sample. MBI-Psychosis status (present or absent) was determined by the Mild Behavioral Impairment Checklist (MBI-C) rated by participants and study partners (that is ‘self’ and ‘informant’ ratings). Each PRS was tested in a logistic regression model with MBI-Psychosis status as the dependent variable, and with age, sex and ancestry as covariates.
Result:
Higher PRS for Major Depression, Schizophrenia and Neuroticism were all associated with an higher odds of MBI-Psychosis. PRS for schizophrenia was only associated with self-reported MBI-psychosis, not informant reported MBI-psychosis (see Figure 1). Higher PRS for Educational Attainment and Intelligence were both associated with lower odds of MBI-Psychosis. In analysis stratified by self-reported education level, the relationship between higher PRS for educational attainment and lower odds of MBI-psychosis was only present in those we left school at 16.
Conclusion:
In early life, psychosis is known to overall with cognitive, psychiatric and personality traits. These data extend this observation to later-life psychosis. The significance of the differences between self and informant reported symptoms are yet to be determined but may be a mix of measurement error and the different respondents having a propensity to report symptoms which reflect different etiologies. We also show that established protective factors against cognitive decline, like educational attainment, in later life may also extend to late life neuropsychiatric syndrome
Some new evidence using fractional integration about trends, breaks and persistence in polar amplification
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The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.Electronic supplementary material is available online at: https://www.nature.com/articles/s41598-025-92990-x#Sec8 .A Correction to this article was published on 09 September 2025: https://doi.org/10.1038/s41598-025-18734-z . Correction to: Scientific Reports https://doi.org/10.1038/s41598-025-92990-x, published online 11 March 2025
In the original version of this Article, the Acknowledgements section was incomplete.
It now reads
“Comments from the Editor and four anonymous reviewers are gratefully acknowledged. Prof. Luis A. Gil-Alana also gratefully acknowledges financial support from the MINEIC-AEI-FEDER PID2020-113691RB-I00 project from ‘Ministerio de Economía, Industria y Competitividad’ (MINEIC), ‘Agencia Estatal de Investigación’ (AEI) Spain and ‘Fondo Europeo de Desarrollo Regional’ (FEDER), and from Internal Projects of the Universidad Francisco de Vitoria. He is also grateful for the continuous support of Izaro Gil Cabo.”
The original Article has been corrected.This paper uses fractional integration methods to obtain new evidence on polar amplification. The adopted modelling framework is very general since it allows the differencing parameter to take any real value, including fractional ones, and provides useful information on both the short and the long run. The analysis is carried out using monthly temperature anomaly data for both the Arctic and the Antarctic, as well as the Northern and Southern Hemisphere, which have been obtained from the NOAA (National Center for Environmental Information) archive. The main findings can be summarised as follows. There is evidence of Arctic amplification, since the upward trend in the Arctic data is more pronounced compared to that in the Northern Hemisphere series, but not of Antarctic amplification, where the opposite holds. Also, the effects of forcings are more long-lived in the Arctic/Northern hemisphere than in the other pole/hemisphere. These results are robust to whether or not seasonality is explicitly modelled. In addition, temperature changes in the poles have bigger effects on those in the corresponding hemisphere if they occur in the Antarctic rather than in the Arctic.Prof. Luis A. Gil-Alana also gratefully acknowledges financial support from the MINEIC-AEI-FEDER PID2020-113691RB-I00 project from ‘Ministerio de Economía, Industria y Competitividad’ (MINEIC), ‘Agencia Estatal de Investigación’ (AEI) Spain and ‘Fondo Europeo de Desarrollo Regional’ (FEDER), and from Internal Projects of the Universidad Francisco de Vitoria
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