Advancing teleoperation for legged manipulation with wearable motion capture.

The sanctity of human life mandates the replacement of individuals with robotic systems in the execution of hazardous tasks. Explosive Ordnance Disposal (EOD), a field fraught with mortal danger, stands at the forefront of this transition. In this study, we explore the potential of robotic telepresence as a safeguard for human operatives, drawing on the robust capabilities demonstrated by legged manipulators in diverse operational contexts.

PlutoNet: An efficient polyp segmentation network with modified partial decoder and decoder consistency training.

Deep learning models are used to minimize the number of polyps that goes unnoticed by the experts and to accurately segment the detected polyps during interventions. Although state-of-the-art models are proposed, it remains a challenge to define representations that are able to generalize well and that mediate between capturing low-level features and higher-level semantic details without being redundant. Another challenge with these models is that they are computation and memory intensive, which can pose a problem with real-time applications.

Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy.

Colonoscopy screening is the gold standard procedure for assessing abnormalities in the colon and rectum, such as ulcers and cancerous polyps. Measuring the abnormal mucosal area and its 3D reconstruction can help quantify the surveyed area and objectively evaluate disease burden. However, due to the complex topology of these organs and variable physical conditions, for example, lighting, large homogeneous texture, and image modality estimating distance from the camera (aka depth) is highly challenging.

Hypoxia-Associated Gene Signatures Are Not Prognostic in High-Risk Localized Prostate Cancers Undergoing Androgen Deprivation Therapy With Radiation Therapy.

Purpose: Men with high-risk prostate cancer (PCa) are treated with androgen deprivation therapy (ADT) and radiation therapy, but the disease reoccurs in 30% of patients. Biochemical recurrence of PCa after treatment is influenced by tumor hypoxia. Tumors with high levels of hypoxia are aggressive, resistant to treatment, and have increased metastatic capacity.

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges.

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care.

Application of computational fluid dynamics to investigate pathophysiological mechanisms in exercise-induced laryngeal obstruction.

The underlying pathophysiological mechanisms of exercise-induced laryngeal obstruction (EILO) remain to be fully established. It is hypothesized that high inspiratory flow rates can exert a force on laryngeal airway walls that contribute to its inward collapse causing obstruction. Computational fluid dynamics (CFD) presents an opportunity to explore the distribution of forces in a patient-specific upper airway geometry.

ERABiLNet: enhanced residual attention with bidirectional long short-term memory.

Alzheimer's Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI).

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.

Introduction: Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity.

Decoding dynamic visual scenes across the brain hierarchy.

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes.

Multi-centre benchmarking of deep learning models for COVID-19 detection in chest x-rays.

Introduction: This study is a retrospective evaluation of the performance of deep learning models that were developed for the detection of COVID-19 from chest x-rays, undertaken with the goal of assessing the suitability of such systems as clinical decision support tools.

Methods: Models were trained on the National COVID-19 Chest Imaging Database (NCCID), a UK-wide multi-centre dataset from 26 different NHS hospitals and evaluated on independent multi-national clinical datasets. The evaluation considers clinical and technical contributors to model error and potential model bias.

A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available.

MDA5-autoimmunity and interstitial pneumonitis contemporaneous with the COVID-19 pandemic (MIP-C).

Background: Anti-MDA5 (Melanoma differentiation-associated protein-5) positive dermatomyositis (MDA5-DM) is characterised by rapidly progressive interstitial lung disease (ILD) and high mortality. MDA5 is an RNA sensor and a key pattern recognition receptor for the SARS-CoV-2 virus.

Methods: This is a retrospective observational study of a surge in MDA5 autoimmunity, as determined using a 15 muscle-specific autoantibodies (MSAs) panel, between Janurary 2018 and December 2022 in Yorkshire, UK.

Toward quantitative super-resolution methods for cryo-CLEM.

Cryogenic ultrastructural imaging techniques such as cryo-electron tomography have produced a revolution in how the structure of biological systems is investigated by enabling the determination of structures of protein complexes immersed in a complex biological matrix within vitrified cell and model organisms. However, so far, the portfolio of successes has been mostly limited to highly abundant complexes or to structures that are relatively unambiguous and easy to identify through electron microscopy. In order to realize the full potential of this revolution, researchers would have to be able to pinpoint lower abundance species and obtain functional annotations on the state of objects of interest which would then be correlated to ultrastructural information to build a complete picture of the structure-function relationships underpinning biological processes.

The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised.

Computational modelling of articular joints with biphasic cartilage: recent advances, challenges and opportunities.

Biphasic models have been widely used to simulate the time-dependent biomechanical response of soft tissues. Modelling techniques of joints with biphasic weight-bearing soft tissues have been markedly improved over the last decade, enhancing our understanding of the function, degenerative mechanism and outcomes of interventions of joints. This paper reviews the recent advances, challenges and opportunities in computational models of joints with biphasic weight-bearing soft tissues.

Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort.

Purpose: Periodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone, can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration (AMD), in individuals with no eye disease.

Design: Cross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank.

Terahertz imaging of human skin: Toward detection of abnormal skin pathologies.

Terahertz (THz) imaging has long held promise for skin cancer detection but has been hampered by the lack of practical technological implementation. In this article, we introduce a technique for discriminating several skin pathologies using a coherent THz confocal system based on a THz quantum cascade laser. High resolution THz images (with diffraction limited to the order of 100 m) of several different lesion types were acquired and compared against one another using the amplitude and phase values.

, , or ? Understanding the perception of audio quality in music by hearing aid users.

Introduction: Previous work on audio quality evaluation has demonstrated a developing convergence of the key perceptual attributes underlying judgments of quality, such as timbral, spatial and technical attributes. However, across existing research there remains a limited understanding of the crucial perceptual attributes that inform audio quality evaluation for people with hearing loss, and those who use hearing aids. This is especially the case with music, given the unique problems it presents in contrast to human speech.