Showing papers by "University of Leicester published in 2022"

Changes in commuting behaviours in response to the COVID-19 pandemic in the UK

Abstract: The UK Government restrictions on non-essential work in response to the coronavirus disease 2019 (COVID-19) pandemic forced millions of working aged-adults into an unplanned lifestyle change. We present data on changes in commuting behaviour in response to COVID-19 and describe the facilitators and barriers to switching commuting behaviours, with a specific focus on cycling and walking.An online survey queried individuals' transport mode to/from work before and when becoming aware of COVID-19, when restrictions were in place and the transport mode they may use once restrictions are lifted. Free-form text responses were collected on why they may switch to a sustainable commute mode in the future and what would help/allow them to achieve this. Quantitative and qualitative data on those who commuted by car (single occupant) and public transport (bus/rail/park & ride) were analysed and presented separately.Overall, 725 car and public transport commuters responded; 72.4% were car commuters and 27.6% were public transport commuters before COVID-19. Of the car commuters, 81.9% may continue travelling by car once restrictions are lifted while 3.6% and 6.5% might change to walking and cycling, respectively. Of the public transport commuters, 49.0% might switch modes. From the free-form text responses three themes were identified: (a) perceived behavioural control towards cycling and walking (infrastructure and safety of roads, distance, weather) (b) key motivators to encourage a switch to cycling and walking (provision to support cycling, personal and environmental benefits); (c) the demands of current lifestyle (job requirements, family and lifestyle commitments).These UK data show how the COVID-19 pandemic has been an "external shock" causing some individuals to reassess their commuting mode. This provides an opportunity for theory-based behaviour change interventions tackling motivations, barriers and beliefs towards changing commute mode.

DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy

Abstract: (Aim) COVID-19 has caused more than 2.28 million deaths till 4/Feb/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 ...

A systematic survey of deep learning in breast cancer

Abstract: In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer‐aided (CAD) systems based on state‐of‐the‐art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers. Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

Histone H2B Deacylation Selectivity: Exploring Chromatin’s Dark Matter with an Engineered Sortase

Abstract: We describe a new method to produce histone H2B by semisynthesis with an engineered sortase transpeptidase. N-Terminal tail site-specifically modified acetylated, lactylated, and β-hydroxybutyrylated histone H2Bs were incorporated into nucleosomes and investigated as substrates of histone deacetylase (HDAC) complexes and sirtuins. A wide range of rates and site-specificities were observed by these enzyme forms suggesting distinct biological roles in regulating chromatin structure and epigenetics.

A Practical and Efficient Bidirectional Access Control Scheme for Cloud-Edge Data Sharing

Abstract: The cloud computing paradigm provides numerous tempting advantages, enabling users to store and share their data conveniently. However, users are naturally resistant to directly outsourcing their data to the cloud since the data often contain sensitive information. Although several fine-grained access control schemes for cloud-data sharing have been proposed, most of them focus on the access control of the encrypted data (e.g., restricting the decryption capabilities of the receivers). Distinct from the existing work, this article aims to address this challenging problem by developing a more practical bidirectional fine-grained access control scheme that can restrict the capabilities of both senders and receivers. To this end, we systematically investigate the access control for cloud data sharing. Inspired by the access control encryption (ACE), we propose a novel data sharing framework that combines the cloud side and the edge side. The edge server is located in the middle of all the communications, checking and preventing illegal communications according to the predefined access policy. Next, we develop an efficient access control algorithm by exploiting the attribute-based encryption and proxy re-encryption for the proposed framework. The experimental results show that our scheme exhibits superior performance in the encryption and decryption compared to the prior work.

In vitro evolution predicts emerging SARS-CoV-2 mutations with high affinity for ACE2 and cross-species binding

Abstract: Emerging SARS-CoV-2 variants are creating major challenges in the ongoing COVID-19 pandemic. Being able to predict mutations that could arise in SARS-CoV-2 leading to increased transmissibility or immune evasion would be extremely valuable in development of broad-acting therapeutics and vaccines, and prioritising viral monitoring and containment. Here we use in vitro evolution to seek mutations in SARS-CoV-2 receptor binding domain (RBD) that would substantially increase binding to ACE2. We find a double mutation, S477N and Q498H, that increases affinity of RBD for ACE2 by 6.5-fold. This affinity gain is largely driven by the Q498H mutation. We determine the structure of the mutant-RBD:ACE2 complex by cryo-electron microscopy to reveal the mechanism for increased affinity. Addition of Q498H to SARS-CoV-2 RBD variants is found to boost binding affinity of the variants for human ACE2 and confer a new ability to bind rat ACE2 with high affinity. Surprisingly however, in the presence of the common N501Y mutation, Q498H inhibits binding, due to a clash between H498 and Y501 side chains. To achieve an intermolecular bonding network, affinity gain and cross-species binding similar to Q498H alone, RBD variants with the N501Y mutation must acquire instead the related Q498R mutation. Thus, SARS-CoV-2 RBD can access large affinity gains and cross-species binding via two alternative mutational routes involving Q498, with route selection determined by whether a variant already has the N501Y mutation. These mutations are now appearing in emerging SARS-CoV-2 variants where they have the potential to influence human-to-human and cross-species transmission.

A Hybrid Framework for Lung Cancer Classification

Abstract: Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84%±0.156% (accuracy), 99.84%±0.153% (precision), 99.84%±0.156% (sensitivity), and 99.84%±0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods.

Type 2 Diabetes and Impaired Physical Function: A Growing Problem

Abstract: The focus in diabetes care has traditionally been around the optimisation of the glycaemic control and prevention of complications. However, the prevention of frailty and improvement in physical function have now emerged as new targets of diabetes management. This is mainly driven by the significant adverse impact that early onset frailty and decline in physical function have on health outcomes, functional independence, and quality of life in people with type 2 diabetes (T2D). There is an increasing emphasis in the expert consensus and management algorithms to improve physical function in people with T2D, predominantly through lifestyle interventions, including exercise and the control of modifiable risk factors. Trials of novel glucose-lowering therapies (GLTs) also now regularly assess the impact of these novel agents on measures of physical function within their secondary outcomes to understand the impact that these agents have on physical function. However, challenges remain as there is no consensus on the best method of assessing physical function in clinical practice, and the recognition of impaired physical function remains low. In this review, we present the burden of a reduced physical function in people with T2D, outline methods of assessment used in healthcare and research settings, and discuss strategies for improvement in physical function in people with T2D.

Online representations of non-canonical sentences are more than good-enough.

Abstract: Proponents of good-enough processing suggest that readers often (mis)interpret certain sentences using fast-and-frugal heuristics, such that for non-canonical sentences (e.g., The dog was bitten by the man) people confuse the thematic roles of the nouns. We tested this theory by examining the effect of sentence canonicality on the reading of a follow-up sentence. In a self-paced reading study, 60 young and 60 older adults read an implausible sentence in either canonical (e.g., It was the peasant that executed the king) or non-canonical form (e.g., It was the king that was executed by the peasant), followed by a sentence that was implausible given a good-enough misinterpretation of the first sentence (e.g., Afterwards, the peasant rode back to the countryside) or a sentence that was implausible given a correct interpretation of the first sentence (e.g., Afterwards, the king rode back to his castle). We hypothesised that if non-canonical sentences are systematically misinterpreted, then sentence canonicality would differentially affect the reading of the two different follow-up types. Our data suggested that participants derived the same interpretations for canonical and non-canonical sentences, with no modulating effect of age group. Our findings suggest that readers do not derive an incorrect interpretation of non-canonical sentences during initial parsing, consistent with theories of misinterpretation effects that instead attribute these effects to post-interpretative processes.

What matters most in acute care: an interview study with older people living with frailty

Abstract: Healthcare outcome goals are central to person-centred acute care, however evidence among older people is scarce. Older people who are living with frailty have distinct requirements for healthcare delivery and have distinct risk for adverse outcomes from healthcare. There is insufficient evidence for whether those living with frailty also have distinct healthcare outcome goals. This study explored the nature of acute care outcome goals in people living with frailty.Healthcare outcome goals were explored using semi-structured patient interviews. Participants aged over 65 with Clinical Frailty Score 5-8 (mild to very severe frailty) were recruited during their first 72 hours in a UK hospital. Purposive, maximum variation sampling was guided by lay partners from a Patient and Public Involvement Forum specialising in ageing-related research. Qualitative analysis used a blended approach based on framework and constant comparative methodologies for the identification of themes. Findings were validated through triangulation with participant, lay partner, and technical expert review.The 22 participants were aged 71 to 98 and had mild to very severe frailty. One quarter were living with dementia. Most participants had reflected on their situation and considered their outcome goals. Theme categories (and corresponding sub-categories) were 'Autonomy' (information, control, and security) and 'Functioning' (physical, psychosocial, and relief). A novel 'security' theme was identified, whereby participants sought to feel safe in their usual living place and with their health problems. Those living with milder frailty were concerned to maintain ability to support loved ones, while those living with most severe frailty were concerned about burdening others.Outcome goals for acute care among older participants living with frailty were influenced by the insecurity of their situation and fear of deterioration. Patients may be supported to feel safe and in control through appropriate information provision and functional support.

Shaping Success through Creative Failure: A Historical Sensemaking Analysis of the Computerisation of the UK Financial Market

Abstract: This paper draws on the concept of sensemaking and sensegiving to examine how the failure of a project, TAURUS, influenced the successful development of an innovative security settlement system, CREST, which has shaped the computerisation of the wholesale UK financial industry. We use a historiographic interpretative approach to analyse publicly available documents, via three theoretical constructs that have emerged from combining business history and organisational studies literature. First, we define historical sensegiving as the ability to shape contextually the way others make sense of complex historical situations. Second, we establish the sensemaking of failure, which is the ability to make sense of failure in a historical context. Finally, we find that historical enactment supports the creation of structures and events by bracketing them in a historical context. We coin the term ‘creative failure’ to characterise how failure can be reimagined as a route to creative success through a sensemaking process.

Exploring compact binary merger host galaxies and environments with <tt>zELDA</tt>

Abstract: ABSTRACT Compact binaries such as double neutron stars or a neutron star paired with a black hole, are strong sources of gravitational waves during coalescence and also the likely progenitors of various electromagnetic phenomena, notably short-duration gamma-ray bursts (SGRBs), and kilonovae. In this work, we generate populations of synthetic binaries and place them in galaxies from the large-scale hydrodynamical galaxy evolution simulation, eagle. With our zELDA code, binaries are seeded in proportion to star formation rate, and we follow their evolution to merger using both the bpass and cosmic binary stellar evolution codes. We track their dynamical evolution within their host galaxy potential, to estimate the galactocentric distance at the time of the merger. Finally, we apply observational selection criteria to allow comparison of this model population with the legacy sample of SGRBs. We find a reasonable agreement with the redshift distribution (peaking at 0.5 &lt; z &lt; 1), host morphologies, and projected galactocentric offsets (modal impact parameter ≲10 kpc). Depending on the binary simulation used, we predict $\sim 16\!-\!35{{\ \rm per\ cent}}$ of SGRB events would appear ‘host-less’, i.e. sources that merge with high impact parameters or have hosts fainter than the detection limit (H &gt; 26).

A multi-focus color image fusion algorithm based on low vision image reconstruction and focused feature extraction

Abstract: Multi-focus image fusion is a process of generating fused images by merging multiple images with different degrees of focus in the same scene. In multi-focus image fusion, the accuracy of the detected focus area is critical for improving the quality of the fused image. Combining the structural gradient, we propose a multi-focus color image fusion algorithm based on low vision image reconstruction and focus feature extraction. First, the source images are input into the deep residual network (ResNet) to conduct the low vision image reconstructed by the super-resolution method. Next, an end-to-end restoration model is used to improve the image details and maintain the edges of the image through rolling guidance filter. What is more, the difference image is obtained from the reconstructed image and the source image. Then, the fusion decision map is generated based on the focus area detection method based on structural gradient. Finally, the source image and the fusion decision map are used for weighted fusion to generate a fusion image. Experimental results show that our algorithm is quite accurate in detecting the edge of the focus area. Compared with other algorithms, the proposed algorithm improves the recognition accuracy of decision focus and defocused areas. It can well retain the detailed texture features and edge structure of the source image.

Systematic review with meta-analysis of diagnostic test accuracy for COVID-19 by mass spectrometry.

Abstract: Background The global COVID-19 pandemic has led to extensive development in many fields, including the diagnosis of COVID-19 infection by mass spectrometry. The aim of this systematic review and meta-analysis was to assess the accuracy of mass spectrometry diagnostic tests developed so far, across a wide range of biological matrices, and additionally to assess risks of bias and applicability in studies published to date. Method 23 retrospective observational cohort studies were included in the systematic review using the PRISMA-DTA framework, with a total of 2858 COVID-19 positive participants and 2544 controls. Risks of bias and applicability were assessed via a QUADAS-2 questionnaire. A meta-analysis was also performed focusing on sensitivity, specificity, diagnostic accuracy and Youden's Index, in addition to assessing heterogeneity. Findings Sensitivity averaged 0.87 in the studies reviewed herein (interquartile range 0.81–0.96) and specificity 0.88 (interquartile range 0.82–0.98), with an area under the receiver operating characteristic summary curve of 0.93. By subgroup, the best diagnostic results were achieved by viral proteomic analyses of nasopharyngeal swabs and metabolomic analyses of plasma and serum. The performance of other sampling matrices (breath, sebum, saliva) was less good, indicating that these protocols are currently insufficiently mature for clinical application. Conclusions This systematic review and meta-analysis demonstrates the potential for mass spectrometry and ‘omics in achieving accurate test results for COVID-19 diagnosis, but also highlights the need for further work to optimize and harmonize practice across laboratories before these methods can be translated to clinical applications.

Immortal time bias for life-long conditions in retrospective observational studies using electronic health records

Abstract: Immortal time bias is common in observational studies but is typically described for pharmacoepidemiology studies where there is a delay between cohort entry and treatment initiation.This study used the Clinical Practice Research Datalink (CPRD) and linked national mortality data in England from 2000 to 2019 to investigate immortal time bias for a specific life-long condition, intellectual disability. Life expectancy (Chiang's abridged life table approach) was compared for 33,867 exposed and 980,586 unexposed individuals aged 10+ years using five methods: (1) treating immortal time as observation time; (2) excluding time before date of first exposure diagnosis; (3) matching cohort entry to first exposure diagnosis; (4) excluding time before proxy date of inputting first exposure diagnosis (by the physician); and (5) treating exposure as a time-dependent measure.When not considered in the design or analysis (Method 1), immortal time bias led to disproportionately high life expectancy for the exposed population during the first calendar period (additional years expected to live: 2000-2004: 65.6 [95% CI: 63.6,67.6]) compared to the later calendar periods (2005-2009: 59.9 [58.8,60.9]; 2010-2014: 58.0 [57.1,58.9]; 2015-2019: 58.2 [56.8,59.7]). Date of entry of diagnosis (Method 4) was unreliable in this CPRD cohort. The final methods (Method 2, 3 and 5) appeared to solve the main theoretical problem but residual bias may have remained.We conclude that immortal time bias is a significant issue for studies of life-long conditions that use electronic health record data and requires careful consideration of how clinical diagnoses are entered onto electronic health record systems.