21827 research outputs found
Sort by
The human ribosome modulates multidomain protein biogenesis by delaying cotranslational domain docking
Get PDFProteins with multiple domains are intrinsically prone to misfold, yet fold efficiently during their synthesis on the ribosome. This is especially important in eukaryotes, where multidomain proteins predominate. Here we sought to understand how multidomain protein folding is modulated by the eukaryotic ribosome. We used hydrogen–deuterium exchange mass spectrometry and cryo-electron microscopy to characterize the structure and dynamics of partially synthesized intermediates of a model multidomain protein. We find that nascent subdomains fold progressively during synthesis on the human ribosome, templated by interactions across domain interfaces. The conformational ensemble of the nascent chain is tuned by its unstructured C-terminal segments, which keep interfaces between folded domains in dynamic equilibrium until translation termination. This contrasts with the bacterial ribosome, on which domain interfaces form early and remain stable during synthesis. Delayed domain docking may avoid interdomain misfolding to promote the maturation of multidomain proteins in eukaryotes
From ethnic ties to marketing assets: strategies for developing customer relationships in migrant-owned microbusinesses
Get PDFPurpose This study aims to investigate the strategies employed by migrant-owned microbusinesses (MOMBs) to enhance the mobilisability of their ethnic diaspora ties with co-ethnic customers in order to build long-term customer commitment. Design/methodology/approach We conducted a 15-month ethnographic study of MOMBs in the UK. Our study involved observations of 16 businesses and migrant online communities and interviews with owners and customers. This methodology allowed us to closely examine how these businesses initiate, nurture and maintain relationships with co-ethnic customers and to identify effective strategies. Findings Our research identifies six strategic components of MOMBs for transforming ethnic ties into marketing-oriented ties within the diaspora context. We show how their effectiveness depends on customers’ attitudes toward ethnic identity, the social context and the social location of both parties. Finally, we outline how these ties uniquely contribute to customer commitment. Originality/value By developing an ethnicity-informed commitment framework based on ethnic studies and relationship management literature, we unravel (1) The strategies for transforming ethnic ties into marketing-oriented ethnic ties within the diaspora context and (2) The mechanisms through which ethnic ties contribute to customers’ long-term affective, continuance and normative commitment
Coming Clean and Avoiding Bubble Trouble–Using Detergents Wisely in the Purification of Membrane Proteins for Cryo-EM Studies
Get PDFDetergent solubilisation remains the most commonly used but potentially problematic method to extract membrane proteins from lipid bilayers for Cryo-EM studies. Although recent advances have introduced excellent alternatives—such as amphipols, nanodiscs and SMALPs—the use of detergents is often necessary for intermediate steps. In this paper, we share our experiences working with detergent-solubilised samples within the modern Cryo-EM structural pipeline from the perspective of an EM specialist. Our aim is to inform novice users about potential challenges they may encounter. Drawing on specific examples from a variety of biological membrane systems, including Magnesium channels, lipopolysaccharide biosynthesis, and the human major facilitator superfamily transporters, we describe how the intrinsic properties of detergent-extracted samples can affect protein purification, Cryo-EM grid preparation (including the formation of vitreous ice) and the reconstitution of proteins into micelles. We also discuss how these unique characteristics can impact different stages of structural analysis and lead to complications in single-particle averaging software analysis. For each case, we present our insights into the underlying causes and suggest possible mitigations or alternative approaches
Explainable AI-Assisted Triage in Emergency Departments Using Multi-Modal Clinical Data
No full textOvercrowding in emergency departments (EDs) increases waiting times and triage errors, straining healthcare systems and compromising patient safety. Existing AI-assisted triage systems primarily rely on structured data (e.g., vital signs, demographics), while neglecting unstructured information such as clinical text and images. To address this gap, we propose an explainable AI-assisted decision support system that integrates multi-modal clinical data. Using the Korean Triage and Acuity Scale (KTAS) dataset, we combine structured features with unstructured patient assessment text in a multimodal framework. Nurse-assigned scores are included to capture clinical judgement, with expert-reviewed triage levels as ground truth. After preprocessing, multiple classifiers were evaluated; Random Forest and AdaBoost achieved the highest performance(F1-scores: 89% and 87%; AUCs: 95.3% and 95.6%). RandomForest was selected as the final model and deployed via an explainable AI-powered web-based interface (Flask, HTML/CSS/JS,Docker). Findings demonstrate that explainable machine learning can improve ED triage accuracy, support clinical decision-making,and enhance transparency
Evaluating Perceived Realism of Micro-Movement Strategies in Artificial Social Agents
Get PDFThis study empirically evaluates the perceived realism of six micro-movement strategies implemented in two agent embodiments (Humanoid and Dummy). Thirty participants (ages 18–55) recruited via the Gorilla platform viewed randomised 5-second video clips (six micro-movement strategies × two embodiments). After each clip, participants rated five dimensions—Resemblance, Daily-Life Likelihood, Efficiency, Movement Realism, and Overall Agent Realism—on a continuous 0–100 scale. Descriptive statistics for a representative movement (TurnBackward) revealed that the Dummy variant scored lower in Overall Agent Realism than the Humanoid, despite similar Movement Realism ratings. Two-way ANOVAs showed significant main effects of Movement Type on Resemblance, Day-to-day, Efficiency, and Movement Realism but no significant Movement × Embodiment interactions. Agent type significantly influenced only Overall Agent Realism. Mixed Linear Models corroborated that CurveForward and Forward movements positively impacted Resemblance and Day-to-day, while Backward and Strafe had negative effects. Correlation analysis revealed strong positive associations among Resemblance, Day-to-day, Efficiency, and Movement Realism, but weaker links to Overall Agent Realism. These findings validate the proposed micro-movement taxonomy and indicate that movement kinematics drive perceived realism more than agent appearance, guiding future ASA controller design
Neuromonitoring and neuroprotection during neonatal aortic arch surgery:A United Kingdom and Ireland survey
Get PDFIntroduction Neonatal aortic arch surgery is associated with neurological morbidity of varying severity which is detected and potentially limited through neuroprotective strategies. We conducted a survey of healthcare professionals at all neonatal cardiac surgery centres in the United Kingdom and Ireland to determine current intraoperative neuromonitoring and neuroprotection practice. Methods An online cross-sectional survey was sent to congenital cardiac surgeons, cardiac anaesthetists, clinical perfusion scientists, and clinical neurophysiology professionals in all 12 level 1 paediatric cardiac surgical centres. Information was sought on their current clinical practice in neonates undergoing aortic arch surgery, including pharmacological management, cardiopulmonary bypass, acid-base and blood pressure management, neuromonitoring, and hypothermic circulatory arrest, and the feasibility and willingness to participate in a future clinical trial of neuroprotective strategies in these patients. Results We received 55 (34%) responses, including representatives of all four clinical disciplines in 9 (75%) centres. Cooling to a nasopharyngeal temperature of 18°C before hypothermic circulatory arrest, selective antegrade cerebral perfusion, and near-infrared spectroscopy (NIRS) monitoring are common practice, whereas pharmacology, acid-base management, blood pressure and flow parameters, and NIRS-based interventions vary. In 7 (58%) centres, respondents from all four disciplines were willing to consider participation in a future clinical trial on neuroprotection. Conclusions Aspects of intraoperative neuroprotection and neuromonitoring are common across centres, although key areas of practice differ between practitioners and institutions. Most respondents were willing to participate in a future multi-centre clinical trial, which suggests clinical equipoise in the optimal strategy to protect the neonatal brain during aortic arch surgery
AI-Driven Reasoning Mechanism for Enhanced Detection of Illicit Money Flows
No full textLaw enforcement agencies face substantial difficulties tracking illicit money flow activities because these operations have become more complex and difficult to detect. Conventional detection methods often struggle to reveal advancing criminal financial networks effectively. To address this gap, this study proposes an innovative AI-driven reasoning mechanism that leverages advanced natural language processing, deep learning, machine learning, and human crowd intelligence. The suggested methodology exclusively incorporates automated reasoning capabilities with insights from expert human input, creating a forceful framework capable of discovering subtle patterns indicative of money laundering. By using innovative artificial intelligence tools and stylometric analysis, the reasoning mechanism increases the transparency, interpretability, and reliability of investigative processes. This research contributes to anti-money laundering investigations by supporting law enforcement agencies with a sophisticated analytical system that can detect complex money laundering activities while staying efficient and scalable
Artificial Intelligence in Physical Therapy for Neurological Rehabilitation: A Systematic Mapping Study
Get PDFNeurological diseases represent a major global health burden, with stroke alone ranking as the second leading cause of mortality and disability. Physical rehabilitation is essential for minimizing impairments and improving quality of life for neurological patients. However, traditional rehabilitation faces significant challenges including high costs, and limited access to specialized staff. Information Technology (IT) systems, particularly those incorporating Artificial Intelligence (AI), have emerged as promising solutions to address these rehabilitation challenges. We present a Systematic Mapping Study (SMS) that analyses studies addressing the challenges of physical rehabilitation for neurological diseases through AI applications. There have been similar SMSs analysing AI on physical rehabilitation, but none were focused on neurological diseases, which require special attention due to their socioeconomic impact. 53 primary studies from the literature were included and analysed in our study. The results indicate that AI has been used to effectively support the rehabilitation of neurological diseases. Machine Learning (ML) techniques, and in particular Convolutional Neural Networks (CNNs), are the most frequently employed approaches. We also identify that most studies lack disease-specific adaptations, representing a major opportunity for improvement. Additionally, we applied the knowledge acquired in this study to our own line of research on the topic, which uses fuzzy logic to adjust rehabilitation routines automatically
Exemplifying practice-based research: the influence of age on myopia progression
Get PDFClinical relevance: The electronic storage of patient records and modern-day search engines present private practitioners with a unique opportunity to extract valuable data for investigative research purposes. However, practitioners seldom harness this resource and consequently a vast repository of clinical data remains largely unexplored. Background: This study, based on real-world data from an optometric practice, stands as an example of how clinicians can actively contribute to research. In doing so it underscores the role played by age in determining the rate of natural myopia progression. Methods: A retrospective data analysis of the refractive status, age and optical correction type of participants, was conducted over six years. Forty-four participants were recruited (25 contact lens and 19 spectacle wearers), with a presenting age varying from 5 to 20 years (median, 11 years). Non-cycloplegic, monocular foveal refractions were completed using a ShinNippon open-field autorefractor, corroborated with subjective refraction. The mean spherical equivalent refractive error was calculated for the participants’ initial visit (baseline measure) and for a six-year follow-up visit (progression measure), with myopia progression defined as the difference between these measures. Statistical analyses were computed using Decision Tree Analysis, with a significance level set at 95%. Results: The participant age at first visit exerted a significant influence on natural myopia progression over the assessment period (F 1,42 = 17.11, p 10 years (mean, −1.13 D). Neither degree of myopia at the initial visit nor optical correction type had a significant effect on progression (p > 0.05). Conclusions: Utilizing the advantage of small real-world data samples, the benefit of research by private practitioners was demonstrated, providing evidence that the age at which a child first presents for an eye examination is highly influential in determining their rate of myopia progression
Leveraging ensemble clustering for privacy-preserving data fusion:Analysis of big social-media data in tourism
Get PDFDiscovering knowledge from social media becomes a trend in many domains such as tourism, where users' feedback and rating are the basis of recommendation systems. In this context, cluster analysis has been a major tool to disclose user groups by which the process of collaborative filtering can better determine a personalised suggestion. Matching this to the curse of big data is a challenge with previous studies either implementing conventional techniques on a distributed system or making use of data sampling. Specific to ensemble clustering, only a few aim to obtain both scalability and privacy preserving that are significant to handling social data. This paper presents a new bi-level framework of ensemble clustering in which an instance-segment based analysis is adopted to ensure data privacy and reduce the complexity of clustering the whole dataset. Unlike existing studies, instead of drawing a single clustering from each segment, multiple clusterings are selected to better represent instances therein. Based on published tourism datasets and different experimental settings, the new approach usually outperforms its baselines whilst being competitive to related methods found in the literature. Additional case studies on simulated big datasets and noisy variations are reported and discussed in addition to the analysis of algorithmic parameters
Data sources:
Access Repository Dashboard
Do you manage Aston Publications Explorer? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!