Differential roles of cyclin–CDK1 complexes in cell migration and invasion

Data and resource availability: All relevant data can be found within the article and its supplementary information available online at: https://journals.biologists.com/jcs/article/138/13/jcs263697/368562/Differential-roles-of-cyclin-CDK1-complexes-in#supplementary-data .We have previously described a central role for CDK1 at the nexus of adhesion signalling and cell cycle progression, demonstrating that CDK1 has a non-canonical role in regulating integrin adhesion complexes and in the migration of cancer cells in 3D interstitial matrix. Here, we show that the CDK1-binding partners cyclinB1 and cyclinA2 also have roles in cell migration and invasion in both cancer and non-transformed cells. CyclinB1 plays a key role in RhoA activation to promote rear retraction in a membrane tension-dependent manner, whereas cyclinA2 has a general role in promoting motility. Knockdown of either cyclin significantly perturbs migration with contrasting phenotypes, whereas knockdown of both together has an additive effect, which arrests both migration and division. Our findings therefore describe how cyclin–CDK1 complexes orchestrate migration as well as division of cells, and that cyclinA2–CDK1 and cyclinB1–CDK1 complexes play distinct roles in motility.This work is supported by an Academy of Medical Sciences Springboard Award and an Action Bladder Cancer IOPP grant to M.C.J. J.H.R.H., M.H. and P.T.C. are supported by Cancer Research UK (DCRPGF\100002) and the Wellcome Trust (203128/A/16/Z and 226804/Z/22/Z). M.J.J. is supported by a Peninsula Medical School PhD Studentship award. Open Access funding provided by University of Manchester

Energy and Techno-Economic Assessment of Cooling Methods in Blue Hydrogen Production Processes

Data Availability Statement: Data have been made available in Brunel University of London’s repository via the Brunel Figshare database at 10.17633/rd.brunel.29479115.Supplementary Materials are available online at: https://www.mdpi.com/2227-9717/13/8/2638#app1-processes-13-02638 .Blue hydrogen is a promising low-carbon alternative to conventional fossil fuels. This technology has been garnering increasing attention with many technological advances in recent years, with a particular focus on the deployed materials and process configurations aimed at minimising the cost and CO2 emissions intensity of the process as well as maximising efficiency. However, less attention is given to the practical aspects of large-scale deployment, with the cooling requirements often being overlooked, especially across multiple locations. In particular, the literature tends to focus on CO2 emissions intensity of blue hydrogen production processes, with other environmental impacts such as water and electrical consumption mostly considered an afterthought. Notably, there is a gap to understand the impact of cooling methods on such environmental metrics, especially with technologies at a lower technology readiness level. Herein, two cooling methods (namely, air-cooling versus water-cooling) have been assessed and cross-compared in terms of their energy impact alongside techno-economics, considering deployment across two specific locations (United Kingdom and Saudi Arabia). A sorption-enhanced steam-methane reforming (SE-SMR) coupled with chemical-looping combustion (CLC) was used as the base process. Deployment of this process in the UK yielded a levelised cost of hydrogen (LCOH) of GBP 2.94/kg H2 with no significant difference between the prices when using air-cooling and water-cooling, despite the air-cooling approach having a higher electricity consumption. In Saudi Arabia, this process achieved a LCOH of GBP 0.70 and GBP 0.72 /kg H2 when using air- and water-cooling, respectively, highlighting that in particularly arid regions, air-cooling is a viable approach despite its increased electrical consumption. Furthermore, based on the economic and process performance of the SE-SMR-CLC process, the policy mechanisms and financial incentives that can be implemented have been discussed to further highlight what is required from key stakeholders to ensure effective deployment of blue hydrogen production.The research presented in this work has received financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) through the EPSRC Doctoral Training Partnerships (DTP) award, EP/T518116/1 (project reference: 2688399)

Intelligent Diagnosis of Closed-Loop Motor Drives Using Interior Control Signals Under Industrial Low Sampling Rate Conditions

Interior control signals derived from motor controllers have gained increasing attention in closed-loop motor drive systems for interturn short-circuit fault diagnosis. Mainstream diagnosis methods generally rely on the extraction of control signals within experimental settings featuring high sampling rates, such as 10 kHz or 40 kHz. However, in practical engineering, the industrial sampling rate of control signals typically reaches only 1 kHz or even lower. This limitation makes it challenging for control signals to intuitively distinguish between healthy and faulty states. To address this practical constraint, an intelligent diagnosis method, termed the prior knowledge integrated contrastive diagnosis model (PK-CDM), is proposed. First, space voltage vectors of interior control signals are extracted as inputs of the PK-CDM to detect the interturn short circuit in a closed-loop motor drive system. Second, the physical variation regularity of space voltage vectors is formulated as the prior diagnostic knowledge to compensate for the lack of information under low sampling rate conditions. Finally, a contrastive pretraining strategy is employed to facilitate the construction of the PK-CDM at an industrially low sampling rate. Experimental results demonstrated that the proposed PK-CDM solves the issue of information loss under industrial low sampling rate conditions by integration of prior diagnostic knowledge with a contrastive learning strategy, thereby yielding superior diagnostic accuracy compared to other state-of-the-art (SOTA) methods.This work was supported in part by the National Key R&D Program of China under Grant 2022YFB3402100, in part by the Key Program of the National Natural Science Foundation of China under Grant 52435003, in part by the National Science Fund for Distinguished Young Scholars of China under Grant 52025056, in part by Shaanxi Science and Technology Innovation Team under Grant 2023-CX-TD-15, in part by the Sanqin Scholar Innovation Team and in part by the Fundamental Research Funds for the Central Universities

The Effect of Social Media and Artificial Intelligence (AI) on Fear of Missing Out (FoMo) within Kuwait’s Context

This study examines the impact of Artificial Intelligence (AI) and Social Media (SM) on the Fear of Missing Out (FoMO) among general social media users in Kuwait and employees of the Ministry of Interior (MOI) of Kuwait. Employing a mixed-methods approach, data was gathered from a diverse sample including social media experts, academics, Kuwaiti users, and MOI personnel. In-depth interviews were conducted and analysed using NVivo software. Qualitative findings highlight significant ethical concerns, particularly regarding privacy and transparency in the integration of AI within social media platforms. Participants emphasized both individual and collective responsibility in curbing the overuse of AI-driven features. Quantitative results indicate that FoMO is widely perceived as an addictive behaviour, and many respondents find it ethically acceptable to utilize FoMO in marketing strategies. This study contributes to a deeper understanding of how AI-driven algorithms influence user behaviour on social media. It underscores the responsibility of platforms to strike a balance between personalized content delivery and ethical considerations. The findings also call for stronger policy interventions to address privacy violations and the lack of transparency surrounding AI technologies

Innover pour l’Anthropocène dans des écoles rurales du Lesotho

Presented at: 2025 Conference: Educating in an Uncertain World: A Global Challenge, July 2-4, 2025, Workshop 4. Educating in the Time of the Anthropocene = Colloque 2025 : Éduquer dans un monde incertain : un enjeu mondial, 2-4 juillet 2025, Atelier 4. Éduquer au temps de l’AnthropocèneSince Western-style education was introduced to Africa in the 19th century, it has played a central role in producing the Anthropocene, shaping the attributes and aspirations of young people, and facilitating their incorporation into the global bioeconomy. In rural areas of the contemporary Global South, however, many young people find themselves surplus to the needs of today’s global economy, but also alienated from rural life – both by schooling and by an increasingly hostile climate. In this paper, we describe the outcomes of an intervention in which 34 student teachers conducted action research in rural Lesotho schools, seeking to make education more meaningful to rural learners. While the student teachers found ways to engage learners in the curriculum, their interventions tended to reinforce the conventional role of schooling, rather than offering a more transformative pathway.Depuis qu’elle a été introduite en Afrique au xixe siècle, l’éducation de type occidental a joué un rôle fondamental dans l’avènement de l’Anthropocène, façonnant les caractères et les aspirations des jeunes et facilitant leur incorporation dans la bioéconomie mondiale. Dans les zones rurales du Sud global, cependant, un grand nombre de jeunes se trouvent actuellement en situation de ne représenter qu’un surplus inutile par rapport aux besoins de l’économie mondiale contemporaine, mais également aliénés de la vie rurale du fait de l’enseignement scolaire et de conditions climatiques de plus en plus hostiles. Dans cet article, nous décrivons les résultats d’une intervention au cours de laquelle 34 étudiants-enseignants ont mené une recherche-action dans des écoles rurales au Lesotho, cherchant à accroître le sens de l’enseignement pour les apprenants ruraux. Si ces futurs enseignants ont trouvé des solutions pour impliquer leurs élèves dans le curriculum, leurs interventions ont eu tendance à renforcer le rôle conventionnel de la scolarité plutôt qu’à suggérer des pistes de travail plus transformatrices.Economic & Social Research Council, grant ref: ES/V001116/1, Title: Equipping Lesotho's primary school teachers for educating and motivating rural children

Can Immersive Training Complement On-Road Cycle Training for Children? Two Intervention Studies in Urban and Rural UK Communities

Data availability: We have provided the Mendeley Data doi, and we have uploaded additional files with the manuscript.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S2214140525000684?via%3Dihub#appsec1 .Introduction: Cyclists are frequent casualties in road traffic collisions; failure to look is a contributory factor. Recent research shows that immersive training may improve children's performance, including their observational skills, when cycling on roads. However, robust data in this regard are scarce. Methods: In two related studies, we collected data from 95 children aged 9–11 years across two different UK locations – a cycling-supportive city and a rural town – to ascertain the effects of immersive cycle training on their cycling attitudes and confidence, their situation awareness, and on-road performance. In the urban study we employed a traditional control group design (immersive intervention vs. control); in the rural study, we compared two immersive interventions (with verbal prompts vs. without). At pre-intervention, post-intervention, and 4–6 weeks later (retention), the children reported their attitudes and confidence and completed video-based situation awareness tests (SATs) and on-road cycling assessments (ORCAs). Changes in parental confidence and attitudes were also recorded. Findings: In both studies, ORCA performance improved pre-to-post-intervention, irrespective of group. SATs scores did not improve but were somewhat correlated with ORCA performance. Although the children's cycling attitudes did not change, their confidence increased post-intervention. Parents' confidence in their child's ability to cycle increased significantly from pre-intervention to follow-up, after watching POV footage recorded during their child's retention phase ORCA. Conclusions: The contribution of immersive training to young children's on-road cycling ability is indeterminate. We tentatively suggest that a combination of independent on-road, immersive, and video-based cycling experiences may improve this ability and consequently increase parental confidence.These studies were part-funded by The Road Safety Trust via a Strategic Priority Grant (grant number 302_0_23), for which a related report is available here: https://www.roadsafetytrust.org.uk/small-grants-awarded/bikeability-trust

Social Entropy Informer: A Multi-Scale Model-Data Dual-Driven Approach for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is fundamental in various applications, such as autonomous driving, intelligent surveillance, and traffic management. Existing methods generally fall into two categories: model-driven approaches and data-driven approaches. However, both approaches have inherent limitations when applied to real-world scenarios, particularly in capturing the complex interactions between pedestrians and modeling the stochastic nature of human motion. Notably, there is a lack of research on integrating the strengths of model-driven and data-driven paradigms, which can better address these challenges. This paper aims to fill these limitations by proposing a novel model-data dual-driven approach, called Social Entropy Informer (SEI), for pedestrian trajectory prediction. SEI simultaneously models local and global pedestrian interactions while incorporating information entropy to capture human motion’s inherent randomness and uncertainty quantitatively, which provides a robust framework for predicting pedestrian trajectories. Furthermore, we propose a new loss function derived from information theory, which accounts for the stochasticity of pedestrian movement and enhances the model’s ability to generalize across diverse scenarios. The SEI framework integrates feature extraction, entropy-based stochastic modeling, and the new loss function, improving prediction accuracy and model interpretability. Experimental results demonstrate that SEI outperforms other benchmark methods in prediction accuracy.10.13039/501100013088-Qinglan Project of Jiangsu Province of China; Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant Number: 23KJB520038); Research Enhancement Fund of Xi’an Jiaotong-Liverpool University (XJTLU) (Grant Number: REF-23-01-008); Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia (Grant Number: GPIP: 108-135-2024)

Using GIFscapes to examine young people's affective experiences of urban spaces

Data availability: The authors do not have permission to share data.This article proposes Graphics Interchange Format (GIF) as a novel methodology to investigate the affective experiences of young people in urban spaces, a group often excluded in urban planning processes. Drawing on children’s geographies, non-representational theory, and digital geographies, we introduce the concept of GIFscapes to evoke the relational, sensory, and emotional dynamics shaping our interactions with urban environments often overlooked by more traditional research methods. The paper is based on research for a community consultation in Uxbridge, London. It developed a participatory methodology with young people who chose GIFs to express their perceptions of Uxbridge’s town centre. We show how GIF’s affective capacity elicits insights into the youth’s affective experiences of urban environments as well as provides an engaging platform for dialogue to discuss their uses, perceptions and needs from urban environments. Our discussion reveals how important mundane urban atmospheres, such as rhythms, maintenance, and uses, are in shaping young people’s perceptions and attachments to place and highlights their views on neglected urban infrastructures, insufficient inclusive spaces, and social marginalisation. By demonstrating how GIFs enable accessing nuanced affective experiences of urban atmospheres, this research advances the field of children’s urban geographies and affective geographies. The article emphasises the value of embracing digital methods in urban studies to create cities that are physically functional and emotionally and experientially responsive.The Spanish Government and NextGenerationEU supported this work

Circular economy and taxation: The implications for tax policy

Magazine articleWe consider how a move towards a circular economy can bridge the gap for sustainable growth - and how this would impact tax policy

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