114612 research outputs found
Sort by
Transporting comparative effectiveness evidence between countries : considerations for health technology assessments
Get PDFInternal validity is often the primary concern for health technology assessment agencies when assessing comparative effectiveness evidence. However, the increasing use of real-world data from countries other than a health technology assessment agency's target population in effectiveness research has increased concerns over the external validity, or "transportability", of this evidence, and has led to a preference for local data. Methods have been developed to enable a lack of transportability to be addressed, for example by accounting for cross-country differences in disease characteristics, but their consideration in health technology assessments is limited. This may be because of limited knowledge of the methods and/or uncertainties in how best to utilise them within existing health technology assessment frameworks. This article aims to provide an introduction to transportability, including a summary of its assumptions and the methods available for identifying and adjusting for a lack of transportability, before discussing important considerations relating to their use in health technology assessment settings, including guidance on the identification of effect modifiers, guidance on the choice of target population, estimand, study sample and methods, and how evaluations of transportability can be integrated into health technology assessment submission and decision processes. [Abstract copyright: © 2023. The Author(s).
P and Fe doping, a strategy to develop light and magnetic responsive multifunctional materials : the case of LiMn2O4
No full textThe current work reports an unprecedented multifunctional material with optical activity and a modified magnetic response by a unique combination of doping with P and Fe into the spinel LiMn2O4. Through inductively coupled plasma – optical emission spectroscopy, X-ray absorption near-edge spectroscopy, X-ray diffraction and scanning transmission electron microscopy, the chemical composition, oxidation state and the crystalline structure are determined. Solid-state UV-Vis spectroscopy, magnetic susceptibility and electronic conductivity reveal the critical importance of the interaction between iron and phosphorus when simultaneously doping the crystalline structure of LiMn2O4. The presence of Fe and P considerably increases charge carrier concentration as a mechanism for enhancing electronic conductivity. Fe and P doping also creates Fe-Fe spin interactions that allow double electron optical excitations. This opens a pathway to create multifunctional materials for light-assisted charging lithium-ion batteries. P doping also induces the formation of magnetic clusters arising from the Fe-O-Fe, Fe-O-Mn and Mn-O-Mn spin exchange interactions. The magnetic response of the materials is strongly influenced by the relative amount of Fe in octahedral or tetrahedral sites of the spinel structure. Such ferrimagnetic behaviour has not been reported before LiMn2O4 doped with Fe or P separately. The potential applicability of this newly identified magnetic feature was demonstrated by a significant capacity gain when a lithium-ion cell is exposed to a static external magnetic field
Catalysis on the move
No full textRecent years have witnessed an expansion in the use of catalytic processes beyond chemical labs, together with the emergence of improved catalysts, methodologies and reactors for more standard applications. To share and discuss these advances, the 2nd International Conference on Unconventional Catalysis, Reactors & Applications (UCRA-2), held in Leamington Spa, UK in September 2022, brought together the very best research and innovation in catalysis, chemical engineering, and their applications. Surrounded by beautiful views of the Warwickshire countryside, old friends and colleagues enjoyed a rich scientific programme that covered from the synthesis of unconventional catalysts and manufacturing methods, to photo/electro/plasma-activated catalysts and processes, and catalysis in unconventional environments such as living human cells
Quantum convolution neural network for multi-nutrient detection and stress identification in plant leaves
No full textNutrient stress can impose significant metabolic strain on plants, resulting in declining agricultural productivity. Nitrogen, phosphorus, and potassium are essential growth-limiting nutrients and are the primary building elements for amino acids, nucleic acids, proteins, and chlorophyll. The absence of these nutrients has observable effects on various plant characteristics, such as leaf size, color, and plant height. However, recent technological advances in imaging have given birth to computer vision-based plant phenomics, which holds great promise for plant research and management. The non-destructive, quick, automated assessments made possible by these imaging techniques are transforming the field of plant nutrient stress research and monitoring. This paper presents a hybrid quantum–classical model (HQCM) for identifying multi-nutrient stress and nutrient stress level quantification (NSLQ) for plant stress level identification by analyzing plant leaf images, utilizing the combined capabilities of classical and quantum computing systems. The HQCM model uses groundnut multi-nutrient stress (private), rice plant nutrient stress (public), and plant village (public) datasets for experimentation. The HQCM model exhibited impressive levels of accuracy, achieving rates of 97.79%, 97.37%, and 98.75% on the groundnut, rice, and plant village datasets, respectively. This performance surpasses conventional models, including Xception, DECM, DWC, and ResNet50V2. The proposed model showed significant advancements in accuracy, reaching the performance of state-of-art algorithms by 3.24%, 4.07%, and 6.73%, thus emphasizing its better performance
A novel score-based LiDAR point cloud degradation analysis method
Get PDFAssisted and automated driving systems critically depend on high-quality sensor data to build accurate situational awareness. A key aspect of maintaining this quality is the ability to quantify the perception sensor degradation through detecting dissimilarities in sensor data. Amongst various perception sensors, LiDAR technology has gained traction, due to a significant reduction of its cost and the benefits of providing a detailed 3D understanding of the environment (point cloud). However, measuring the dissimilarity between LiDAR point clouds, especially in the context of data degradation due to noise factors, has been underexplored in the literature. A comprehensive point cloud dissimilarity score metric is essential for detecting severe sensor degradation, which could lead to hazardous events due to the compromised performance of perception tasks. Additionally, this score metric plays a central role in the use of virtual sensor models, where a thorough validation of sensor models is required for accuracy and reliability. To address this gap, this paper introduces a novel framework that evaluates point clouds dissimilarity based on high-level geometries. Contrasting with traditional methods like the computationally expensive Hausdorff metric which involves correspondence-search algorithms, our framework uses a tailored downsampling method to ensure efficiency. This is followed by condensing point clouds into shape signatures which results in efficient comparison. In addition to controlled simulations, our framework demonstrated repeatability, robustness, and consistency, in highly noisy real-world scenarios, surpassing traditional methods
Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer
Get PDFColorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%–4% and 7–8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models’ performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations
Assessing the impact of first-life lithium-ion battery degradation on second-life performance
Get PDFThe driving and charging behaviours of Electric Vehicle (EV) users exhibit considerable variation, which substantially impacts the battery degradation rate and its root causes. EV battery packs undergo second-life application after first-life retirement, with SoH measurements taken before redeployment. However, the impact of the root cause of degradation on second-life performance remains unknown. Hence, the question remains whether it is necessary to have more than a simple measure of state of health (SoH) before redeployment. This article presents experimental data to investigate this. As part of the experiment, a group of cells at around 80% SoH, representing retired EV batteries, were cycled using a representative second-life duty cycle. Cells with a similar root cause of degradation in the first life (100–80% SoH) exhibited the same degradation rate in second life after being cycled with the same duty cycle during the second life. When the root cause of degradation in the first life is different, the degradation rate in the second life may not be the same. These findings suggest that the root cause of a cell’s first-life degradation impacts how it degrades in its second life. Postmortem analysis (photographic and SEM images) reveals the similar physical condition of negative electrodes which have similar degradation rates in their second life cycle. This demonstrates that cells with a similar first life SoH and root cause of degradation indeed experience a similar life during their second life. The experimental results, along with the subsequent postmortem analysis, suggest that relying solely on SoH assessment is insufficient. It is crucial to take into account the root causes of cell degradation before redeployment
Reflection behaviour of SH0 from small defects in thin sheets, with application to EMAT inspection of titanium
No full textDefects can arise within the weld during the laser welding process of thin titanium sheets, and it is critical that these defects are detected. Shear horizontal (SH) waves offer good potential for defect detection, but require an understanding of how the ultrasonic waves interact with defects, in order to develop an optimised inspection setup, increasing the probability of defect detection. The geometry of a defect strongly affects the reflected signal magnitude, with finite element simulations showing that defect width and length determine the maximum SH0 wave reflection. Experiments confirm this behaviour. The defect width is shown to affect the reflection behaviour due to wave interference. Phase differences between the front face reflection and back face reflection cause a shift in the peak position. In addition, the experimental work presented in this paper also shows the potential for electromagnetic acoustic transducers to be used for the inspection of titanium components
The connectivity of the human frontal pole cortex, and a theory of its involvement in exploit versus explore
No full textThe frontal pole is implicated in humans in whether to exploit resources versus explore alternatives. Effective connectivity, functional connectivity, and tractography were measured between six human frontal pole regions and for comparison 13 dorsolateral and dorsal prefrontal cortex regions, and the 360 cortical regions in the Human Connectome Project Multi-modal-parcellation atlas in 171 HCP participants. The frontal pole regions have effective connectivity with Dorsolateral Prefrontal Cortex regions, the Dorsal Prefrontal Cortex, both implicated in working memory; and with the orbitofrontal and anterior cingulate cortex reward/non-reward system. There is also connectivity with temporal lobe, inferior parietal, and posterior cingulate regions. Given this new connectivity evidence, and evidence from activations and damage, it is proposed that the frontal pole cortex contains autoassociation attractor networks that are normally stable in a short-term memory state, and maintain stability in the other prefrontal networks during stable exploitation of goals and strategies. However, if an input from the orbitofrontal or anterior cingulate cortex that expected reward, non-reward, or punishment is received, this destabilizes the frontal pole and thereby other prefrontal networks to enable exploration of competing alternative goals and strategies. The frontal pole connectivity with reward systems may be key in exploit versus explore
CAMANet : class activation map guided attention network for radiology report generation
No full textRadiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists. Recent advancements in Radiology Report Generation (RRG) are largely driven by improving a model's capabilities in encoding single-modal feature representations, while few studies explicitly explore the cross-modal alignment between image regions and words. Radiologists typically focus first on abnormal image regions before composing the corresponding text descriptions, thus cross-modal alignment is of great importance to learn a RRG model which is aware of abnormalities in the image. Motivated by this, we propose a C lass A ctivation M ap guided A ttention Net work (CAMANet) which explicitly promotes cross-modal alignment by employing aggregated class activation maps to supervise cross-modal attention learning, and simultaneously enrich the discriminative information. Experimental results demonstrate that CAMANet outperforms previous SOTA methods on two commonly used RRG benchmarks