https://bura.brunel.ac.uk/handle/2438/8631 Wed, 19 Aug 2026 05:37:15 GMT 2026-08-19T05:37:15Z https://bura.brunel.ac.uk/handle/2438/33691 Title: Grasping techniques: innovations in robotics and virtual reality Authors: Zhou, Mingzhao; Aburumman, Nadine Abstract: The act of grasping is a fundamental mode of interaction when manipulating objects, in both physical and virtual environments. Robotic grasping has been studied for more than three decades, leading to the development of sophisticated frameworks. In contrast, achieving believable grasping in virtual reality (VR) requires a complex interplay of graphics, physics, and perception, where realistic haptic feedback and high-quality rendering are crucial for user immersion. This paper reviews grasping techniques in both robotics and VR, analysing VR grasping from visual and haptic perspectives and identifying its current challenges. We then compare robotic and VR grasping, showing how the relatively mature evaluation metrics of robotics can inspire potential directions for improving VR grasping and support the development of solutions that are more stable, natural, and efficient. Beyond surveying existing methods, this paper provides an outlook on upcoming challenges and opportunities in grasping research across robotics and VR. We set out both a high-level vision towards natural, and reliable grasping in real and virtual domains and priorities, including benchmark creation, robust evaluation metrics, and improvements in haptic fidelity and latency. Description: Springer Nature is providing an unedited version [Article In Press] of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. Mon, 10 Aug 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33691 2026-08-10T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33687 Title: Binding Affinity Ranking at the Molecular Initiating Event (BARMIE): An open-source computational pipeline for the rapid screening of chemical interactions with steroid receptors from many species Authors: Calahorro, Fernando; Fouladi, Parsa; Pandini, Alessandro; Khushi, Matloob; Gaihre, Yogendra; Bury, Nic R Editors: Fernandez, Elias John Abstract: A challenge in ecological risk assessment is identifying the chemicals that pose the greatest threat and determining which species are most vulnerable to them. To help address this, this study has developed an in-silico open-source tool called BARMIE (Binding Affinity Ranking at the Molecular Initiating Event) to rapidly predict the chemical binding affinity of steroid receptor proteins to synthetic steroids to identify potentially vulnerable species and chemicals of concern. BARMIE was used to screen 163 teleost fish glucocorticoid receptors (GRs) for binding to the natural ligand cortisol and to 10 synthetic glucocorticoid drugs (GCs) designed to interact within the ligand-binding pocket (LBP) of GRs. BARMIE identified species from the superorder Protacanthopterygii with high-affinity GRs to synthetic GCs (e.g., vulnerable species).. BARMIE was also used to screen binding profiles of compounds in the Medicine for Malaria Venture Global Health Priority Box to rainbow trout GRs (rtGR1 and rtGR2). Of the 178 compounds, 24 and 36 bind within the LBP of rtGR1 and rtGR2, respectively. For 30 of these compounds, transactivation activity was assessed at 1µM in the presence or absence of 1µM cortisol and confirmed 2 compounds with agonistic properties (e.g., chemicals of concern) that would require further in vitro and/or in vivo studies to assess the environmental risk. BARMIE can rapidly generate predicted binding affinities for 100’s of species and chemicals as a first screen in environmental risk assessment to provide information on which substances to prioritise in downstream tests. Description: Data Availability: The code and installation instructions are available on Github https://github.com/ParsaFouladi/Barmie.; Supporting information is available online at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0353622#sec009 . Wed, 15 Jul 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33687 2026-07-15T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33685 Title: Spatiotemporal graph neural networks reveal conformational binding signature in protein dynamics Authors: Motta, Stefano; Santini, Gianluca; Mansoor, Samman; Nezhad, Ferdoos Hossein; Meli, Massimiliano; Pandini, Alessandro Abstract: Biomolecular function is often controlled by structural and dynamical adaptations to binding events. Although molecular dynamics (MD) simulations can capture these events at atomic resolution, separating functional signatures from stochastic noise remains challenging. Traditional methods often struggle to isolate mechanistically relevant differences across independent replicas. Here, we introduce an explainable deep learning approach that learns state-specific dynamic signatures directly from MD trajectories. By coupling a dynamic protein graph representation with group-aware contrastive learning across independent replicas, the model detects the signatures, filtering out trajectory-specific correlations. An explainable AI framework then maps the identified differences on individual residues. We demonstrate this approach by identifying “binding-ready” conformations in a T4-Lysozyme mutant, recovering the allosteric determinants of peptide recognition in the PDZ3 domain, and isolating a ligand-independent activation signature for the A2A receptor. Our GISTnet-MD method generalizes across unseen data during comparative MD analysis, translating raw trajectory differences into residue-level determinants of protein function. Description: Code availability: The code to train GISTnet-MD and perform the explainable AI pipeline is freely available on GitHub at https://github.com/MottaStefano/GISTnet-MD.; Acknowledgement: We acknowledge CINECA for the availability of high-performance computing resources as part of the agreement with the University of Milano-Bicocca and the award under the ISCRA initiative (project HP10BBT0CP). Thu, 21 May 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33685 2026-05-21T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33682 Title: Biosensing in the Internet of Medical Things: A System Architecture and Design-Oriented Survey Authors: Otoom, Mwaffaq; Alzubaidi, Mohammad A; Ghinea, Gheorghita; Al-Tahat, Shayma; Otoum, Nesreen Abstract: Diseases and healthcare concerns have become more prevalent in our society. As a result, more individuals are turning towards wearable technology as a way to keep track of their well-being. In addition, the introduction of the Internet of Medical Things will allow for continuous tracking of individuals’ health through a distributed network of devices.For engineers, the design of these networks must be done carefully in order to maximize their efficiency and effectiveness; however, engineers cannot focus solely on individual sensor technologies because they must consider all aspects of an IoMT ecosystem. This paper reviews biosensing systems used in the IoMT and provides an overview of how a wide variety of medical sensors can be integrated together for maximum effectiveness, illustrating how such systems can be organized into an entire ecosystem from the end-users’ perspective and how that broad Vision can be achieved through engineering design.This paper will help researchers and practitioners in the design and development of scalable, efficient and adaptable IoMT solutions. It will also help determine direction for future research into intelligent and sustainable biosensing systems. Tue, 12 May 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33682 2026-05-12T00:00:00Z