Memantine and graded motor imagery for complex regional pain syndrome (MEMOIR): Study protocol and statistical analysis plan for a decentralised, 2x2 factorial randomised trial

......National Health and Medical Research Council of Australia (NHMRC) grant number APP1163149

COMPARATIVE STUDY OF DEEP CO-AXIAL CLOSED LOOP AND U-SHAPED WELLBORE GEOTHERMAL SYSTEMS

Until recently, geothermal energy has been limited to regions with favorable subsurface conditions. Most installed geothermal systems are open, using two wells for fluid injection and extraction. Closed-loop systems have historically been used in low-depth installations as ground source heat pumps. However, climate changes and energy market volatility have catalyzed the development of deep-borehole heat exchangers (DBHE) - a potentially cost-competitive technology for direct heat applications and electricity generation. The closed-loop systems can be built in any area with a sustainable geothermal gradient, while millions of abandoned wells worldwide offer low-cost repurposing as closed-loop DBHEs, producing revenue without the large cost linked with drilling. This study provides a technical assessment of coaxial and U-shaped DBHE systems, with a primary focus on the effects of pipe insulation. Results indicate that the installation of proper insulation is a vital part of the system, with a particular emphasis on the return line insulation of U-shaped systems

The Effectiveness of Physical Activity and Nutrition Interventions for Children and Adolescents With Cerebral Palsy to Improve Physical Health and Cognitive Outcomes: A Systematic Review

Purpose: Using systematic review methodology, we set out to describe the evidence for physical activity and nutrition interventions for children and adolescents with cerebral palsy (CP) as compared with no intervention or exposure that reports physical health and cognitive outcomes. Method: Quantitative, primary studies that explored the effectiveness of these interventions, replicable in school and home contexts, in comparison to any other or no intervention or exposure in children and adolescents between the ages of 6–18 years old with a diagnosis of cerebral palsy were included (PROSPERO CRD42022322143). Risk of bias was assessed by Joanna Briggs Institute and QualSyst. Results: A total of 16 international heterogeneous studies (13 physical activity and 3 nutrition) with interventions ranging from a single exposure to 8 months, with quality 58% to 89% and effectiveness, D = 0.03 to 0.97, were included. Outcome measures were varied. Conclusion: The review brings together a number of high-quality studies on physical activity and nutrition interventions and promising findings of impact on cardiovascular, musculoskeletal, and cognitive outcomes. Evidence supports implementation of these interventions in community contexts. Future research would benefit from agreement on the use of core outcome measures for meta-synthesis

Optimality and solutions for conic robust multiobjective programs

The authors would like to thank the referees for valuable comments and suggestions. Research was supported by a research grant from Australian Research Council under Discovery Program Grant DP200101197. The main results of this paper were presented at the 5th IMA and OR Society Conference on Mathematics of Operational Research (Birmingham, United Kingdom, 2025), and the first author would like to acknowledge the support of the Mid and Early Career Academic Research Support Scheme (Brunel University of London, United Kingdom, 2024-2025), which made this possible.Mathematics Subject Classification: 65K10; 49K99; 90C46; 90C29.This paper presents a robust framework for handling a conic multiobjective linear optimization problem, where the objective and constraint functions are involving affinely parameterized data uncertainties. More precisely, we examine optimality conditions and calculate efficient solutions of the conic robust multiobjective linear problem. We provide necessary and sufficient linear conic criteria for efficiency of the underlying conic robust multiobjective linear program. It is shown that such optimality conditions can be expressed in terms of linear matrix inequalities and second-order conic conditions for a multiobjective semidefinite program and a multiobjective second order conic program, respectively. We show how efficient solutions of the conic robust multiobjective linear problem can be found via its conic programming reformulation problems including semidefinite programming and second-order cone programming problems. Numerical examples are also provided to illustrate that the proposed conic programming reformulation schemes can be employed to find efficient solutions for concrete problems including those arisen from practical applications.The first author would like to acknowledge the support of the Mid and Early Career Academic Research Support Scheme (Brunel University of London, United Kingdom, 2024-2025), which made this possible

Neuroanatomical normative modelling in frontotemporal lobar degeneration: higher heterogeneity in the behavioural variant

Data availability: Data used in preparation of this article were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) and the 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) databases (https://4rtni-ftldni.ini.usc.edu/ and https://ida.loni.usc.edu/login.jsp). The investigators at FTLDNI and 4RTNI contributed to the design and implementation of FTLDNI and 4RTNI and/or provided data, but did not participate in analysis or writing of this report.Supplementary Information is available online at: https://link.springer.com/article/10.1007/s00415-025-13378-5#Sec26 (DOCX 36194 KB).Introduction: Frontotemporal lobar degeneration (FTLD) includes heterogenous diseases: behavioural variant frontotemporal dementia (bvFTD), primary progressive aphasias (PPA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). We applied neuroanatomical normative modelling to quantify individual atrophy patterns and heterogeneity within and between FTLD forms. Methods: We included 160 participants across FTLDNI and 4RTNI studies: controls (n = 15), bvFTD (n = 22), nfvPPA (n = 14), svPPA (n = 21), CBS (n = 43) and PSP (n = 45). Using cortical thickness and subcortical volumes from 3T MRIs, we applied normative modelling with a large healthy reference dataset (n = 58,836), further accounting for age, sex, and scanner. Outlier regions (z < – 1.96) were used to compute total outlier counts (tOC) and Hamming distances, capturing individual atrophy patterns and inter-subject dissimilarity. Results: bvFTD, svPPA, CBS and PSP showed significantly higher cortical tOC than controls, with all groups showing higher subcortical tOC than controls, especially svPPA and PSP. bvFTD, svPPA, CBS and PSP had significantly higher cortical Hamming distance scores than controls, with higher scores in bvFTD and svPPA than nfvPPA and PSP. svPPA and PSP had significantly higher subcortical scores than controls and CBS. Greater disease severity (measured using the Clinical Dementia Rating—CDR for PSP and CBS, and the CDR® plus NACC-FTLD global scores for FTD variants) was associated with increased tOC and dissimilarity, highlighting the link between clinical progression and neuroanatomical heterogeneity. Conclusions: The pronounced heterogeneity within and between FTLD subtypes (particularly in bvFTD) increases with disease progression and may reflect distinct underlying pathologies. This supports the development of subtype-specific biomarkers and emphasize the need for personalized diagnostic and therapeutic strategies.This work was primarily funded by the BRUNEL RESEARCH INITIATIVE & ENTERPRISE FUND (BRIEF) 2023/24 (12796115). M.B. was also supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517) and a grant from Alzheimer’s Research UK (ARUK-PPG2023B-013). A.V. acknowledges the support by funding obtained under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3—Call for tender No. 341 of 15/03/2022 of the Italian Ministry of University and Research funded by the European Union-NextGenerationEU, Project code PE0000006, Concession Decree No. 1553 of 11/10/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000930002, “A multiscale integrated approach to the study of the nervous system in health and disease” (MNESYS). Data collection and sharing for this project were funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health Grant R01 AG032306) and by the 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) (National Institutes of Health Grant R01 AG038791) and through generous contributions from the Tau Research Consortium. FTLDNI and 4RTNI studies are coordinated through the University of California, San Francisco, Memory and Aging Center. FTLDNI and 4RTNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Applying Digital Spatial Profiling of the Transcriptome to Elucidate Disease Mechanisms of Psychosis in Alzheimer’s disease

Background: Psychosis occurs in 30-40% of individuals with AD. New insights into disease mechanisms may lead to novel pharmacological targets and treatments. Previous studies have focused on bulk tissue analysis with limited results. Digital spatial profiling (DSP) is a new technique for spatial analysis of RNA or proteins in fixed tissue. It allows quantitative profiling with spatial complexity to be collected from samples in a non-destructive manner. In this pilot study we used DSP to compare whole transcriptome data in amyloid beta and non-amyloid beta regions in participants with and without psychosis (AD+P; AD-P). Method: Six post-mortem brain samples from prefrontal cortex were provided by the Kings College London Brains for Dementia Research (BDR) brain bank. Frozen and formalin fixed, paraffin embedded (FFPE) sections were supplied in order to test the platform on each type. Psychosis positive and negative groups were selected based on Neuropsychiatric Inventory (NPI) assessments. Samples were hematoxylin and eosin (H&E) stained as well as stained with fluorescent antibodies for AT8, NeuN, SYTO13 and Aβ. Regions of interest (ROIs) are selected based on morphology markers and tissue morphology (see Figure 1 for Amyloid ROI selection). Result: H&E staining revealed the frozen samples to be too badly degraded so the analysis was conducted on FFPE sections. AT8 staining showed widespread tau pathology to the extent that it was not possible to confidently select non-tau ROIs. Analysis of Aβ plaque containing and Aβ plaque free regions, comparing AD+P and AD-P groups, found 314 differentially expressed genes in plaque free regions, and 172 differentially expressed genes in plaque containing regions (Figure 2). Of these 172 genes, 28 were not differentially expressed in plaque free regions, forming a plaque-specific signature of genes differentially expressed in AD+P. Conclusion: This pilot study demonstrates the potential of the NanoString GeoMx™ DSP platform as an innovative spatial transcriptomics methodology for investigating AD+P with the potential to uncover differentially expressed genes that may be missed by bulk RNA sequencing studies. FFPE sections appear to be optimal. Analysing earlier stage disease and more sections per subject may help with better differentiation of tau and non-tau ROIs

Implicit bias in referrals to relational psychological therapies: review and recommendations for mental health services

Data availability statement: The dataset supporting this study is publicly available on Brunel University's Figshare repository. It can be accessed at the following link: https://doi.org/10.17633/rd.brunel.27332307.v2.Introduction: Timely and appropriate psychological treatment is an essential element required to address the growing burden of mental health issues, which has significant implications for individuals, society, and healthcare systems. However, research indicates that implicit biases among mental health professionals may influence referral decisions, potentially leading to disparities in access to relational psychological therapies. This study investigates bias in referral practices within mental health services, identifying key themes in referral procedures and proposing recommendations to mitigate bias and promote equitable access. Methods: A systematic review of literature published between 2002 and 2022 was conducted, focusing on biases, referral practices, and relational psychological therapies. The search strategy involved full-text screening of studies meeting inclusion criteria, specifically those examining professional and organizational implicit bias in mental health referrals. Thematic synthesis was employed to analyze and categorize bias within these domains, providing a structured framework for understanding its impact on referral decision making processes. Results: The search yielded 2,964 relevant papers, of which 77 underwent full-text screening. Ultimately, eight studies met the inclusion criteria and were incorporated into the review. The analysis revealed that bias development mechanisms in referral decisions occurred across five key domains: resource allocation, organizational procedures, clinical roles, decision-making, and referral preferences. These domains highlight organizational and practitioner-level factors contributing to disparities in access to psychological therapies. Discussion: Findings suggest that implicit biases within referral processes can limit equitable access to psychological therapies, particularly relational therapies that emphasize therapeutic alliance and patient-centered care. This study provides recommendations to address these biases, including standardized referral guidelines, enhanced professional training on implicit bias, and improved oversight mechanisms within mental health services.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. CNWL NHS Foundation Trust provided a grant to Brunel University of London

Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke Within the UK Biobank

Data Availability Statement: The data used in this study is available on request from the UK Biobank.Acknowledgments: This research was conducted using the UK Biobank under Application Number 60549 (www.ukbiobank.ac.uk (accessed on 5 February 2021)). The UK Biobank is generously supported by its founding funders, the Wellcome Trust and the UK Medical Research Council, as well as by the British Heart Foundation, Cancer Research UK, the Department of Health, the Northwest Regional Development Agency, and the Scottish Government. The MEGASTROKE project received funding from sources specified at https://megastroke.org/acknowledgements.html (accessed on 13 September 2022).Supplementary Materials are available online at: https://www.mdpi.com/2227-9032/13/9/1003#app1-healthcare-13-01003 .Background and Objective: Stroke is one of the leading causes of mortality and long-term disability in adults over 18 years of age globally, and its increasing incidence has become a global public health concern. Accurate stroke prediction is highly valuable for early intervention and treatment. There is a scarcity of studies evaluating the prediction value of genetic liability in the prediction of the risk of stroke. Materials and Methods: Our study involved 243,339 participants of European ancestry from the UK Biobank. We created stroke genetic liability using data from MEGASTROKE genome-wide association studies (GWASs). In our study, we built four predictive models with and without stroke genetic liability in the training set, namely a Cox proportional hazard (Coxph) model, gradient boosting model (GBM), decision tree (DT), and random forest (RF), to estimate time-to-event risk for stroke. We then assessed their performances in the testing set. Results: Each unit (standard deviation) increase in genetic liability increases the risk of incident stroke by 7% (HR = 1.07, 95% CI = 1.02, 1.12, p-value = 0.0030). The risk of stroke was greater in the higher genetic liability group, demonstrated by a 14% increased risk (HR = 1.14, 95% CI = 1.02, 1.27, p-value = 0.02) compared with the low genetic liability group. The Coxph model including genetic liability was the best-performing model for stroke prediction achieving an AUC of 69.54 (95% CI = 67.40, 71.68), NRI of 0.202 (95% CI = 0.12, 0.28; p-value = 0.000) and IDI of 1.0 × 10−4 (95% CI = 0.000, 3.0 × 10−4; p-value = 0.13) compared with the Cox model without genetic liability. Conclusions: Incorporating genetic liability in prediction models slightly improved prediction models of stroke beyond conventional risk factors.This research received no external funding

An Impulsive Approach to State Estimation for Multirate Singularly Perturbed Complex Networks Under Bit Rate Constraints

In this article, the problem of ultimately bounded state estimation is investigated for discrete-time multirate singularly perturbed complex networks under the bit rate constraints, where the sensor sampling period is allowed to differ from the updating period of the networks. The facilitation of communication between sensors and the remote estimator through wireless networks, which are subject to bit rate constraints, involves the use of a coding-decoding mechanism. For efficient estimation in the presence of periodic measurements, a specialized impulsive estimation method is developed, which aims to carry out impulsive corrections precisely at the instants when the measurement signal is received by the estimator. By employing the iteration analysis method under the impulsive mechanism, a sufficient condition is established that ensures the exponential boundedness of the estimation error dynamics. Furthermore, an optimization algorithm is introduced for addressing the challenges related to bit rate allocation and the design of desired estimator gains. Within the presented theoretical framework, the correlation between estimation performance and bit rate allocation is elucidated. Finally, a simulation example is provided to demonstrate the validity of the proposed estimation approach.10.13039/501100019033-Key Area Research and Development Program of Guangdong Province (Grant Number: 2021B0101410005); 10.13039/501100003453-Natural Science Foundation of Guangdong Province of China (Grant Number: 2021A1515011634 and 2021B1515420008); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U22A2044 and 62206063); Local Innovative and Research Teams Project of Guangdong Special Support Program of China (Grant Number: 2019BT02X353); 10.13039/501100004543-China Scholarship Council (Grant Number: 202208440312)

SharkNet Networks Applications in Smart Manufacturing Using IoT and Machine Learning

Data Availability Statement: 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