Head, face and neck injury patterns for electric scooter accidents identified on computed tomography scanning: Does legislative change enforcing safer riding practices have an impact on morbidity for significant head, face and neck trauma?

INTRODUCTION: The purpose of this study is to review whether legislative change enforcing safer riding conditions for Electric Scooters (E-Scooter), regardless of other factors, had an impact on reducing significant head, facial and neck trauma. Additionally, to identify the radiological injury patterns for head, face and neck injuries identified on CT imaging for a patient's initial presentation to the emergency department (ED) resulting from an E-Scooter accident. METHODS: A retrospective single-centre observational study at a metropolitan tertiary ED of patients presenting after an E-Scooter accident comparing 6 months before and after legislative change. RESULTS: Four hundred and forty-three patients presented following an E-Scooter accident: 191 patients 6 months before and 252 patients 6 months after legislative change. One hundred and sixty-two patients pre- and 217 patients post-legislative change had negative CT studies. Twenty-nine patients pre- and 35 patients post-legislative change had CT studies demonstrating significant head, face or neck trauma. The most common type of intracranial bleeding was subarachnoid haemorrhage followed by subdural haemorrhage with a significant proportion (41%) presenting with multi-factorial intracranial bleeding. There was no specific injury pattern involving the cranial vault or cervical spine. Of the patients presenting with a significant injury, facial bones were the most common injury site (84% (n = 54)). The most common site of facial fractures was the nasal bones followed by dental trauma and maxillary fractures. CONCLUSION: This single-centre, retrospective observational study has shown no reduction in serious head, neck and facial injuries. Large-scale, multicentre studies will need to be undertaken to understand the true impact of legislative change.

A silicon-based functional self-assembled aptasensor for the detection of aflatoxin B1 by SERS sensing.

One of the most harmful contaminants found in corn and its products is aflatoxin B1 (AFB1) and thus developing reliable detection methods is of great significance to consumers and the food industry. In this research, AuMBA@Ag nanoparticles (NPs) and AgNPs deposited on a silicon wafer (Si@AgNPs) were functionalized with an aptamer and its complementary strand, respectively, and self-assembled into a SERS aptasensor, which generated strong SERS signals. AFB1 bound to the aptamer prior to the complementary chain, causing AuMBA@Ag NPs to detach from Si@AgNPs. The complex dissociated, leading to a decrease in signal intensity from the solid-phase substrate. Under optimal conditions, the linear detection range was 0.05-20.0 ng mL-1, and the detection limit was 0.039 ng mL-1. Notably, the aptasensor demonstrated a recovery rate between 92.77% and 110.13% when utilized for the detection of AFB1 in corn flour and oil, indicating its good potential for detecting AFB1 in real sample matrices. In conclusion, a quantitative and reliable specific SERS detection system for AFB1 was developed in this study with significant applicability to food safety.

Rethinking children's right to leisure during school holidays.

Leisure and health are human rights that apply to both children and adults. Leisure can enhance health and enable people to participate fully in leisure activities. One of children's main opportunities for leisure is during school holidays. Little previous research has focused on this time in children's lives. This paper presents a review of the literature surrounding school holidays, providing a critique of educational and public health approaches that focus narrowly on children's future outcomes that may be associated with how they spend their time during these leisure periods. It argues that a more sociological understanding, rooted within child-centred approaches to leisure, provides the opportunity for children's agency, participation and citizenship to be investigated more fully.

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms.

Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.

Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network.

BACKGROUND: Equal, diverse, and inclusive teams lead to higher productivity, creativity, and greater problem-solving ability resulting in more impactful research. However, there is a gap between equality, diversity, and inclusion (EDI) research and practices to create an inclusive research culture. Research networks are vital to the research ecosystem, creating valuable opportunities for researchers to develop their partnerships with both academics and industrialists, progress their careers, and enable new areas of scientific discovery. A feature of a network is the provision of funding to support feasibility studies - an opportunity to develop new concepts or ideas, as well as to 'fail fast' in a supportive environment. The work of networks can address inequalities through equitable allocation of funding and proactive consideration of inclusion in all of their activities. METHODS: This study proposes a strategy to embed EDI within research network activities and funding review processes. This paper evaluates 21 planned mitigations introduced to address known inequalities within research events and how funding is awarded. EDI data were collected from researchers engaging in a digital manufacturing network activities and funding calls to measure the impact of the proposed method. RESULTS: Quantitative analysis indicates that the network's approach was successful in creating a more ethnically diverse network, engaging with early career researchers, and supporting researchers with care responsibilities. However, more work is required to create a gender balance across the network activities and ensure the representation of academics who declare a disability. Preliminary findings suggest the network's anonymous funding review process has helped address inequalities in funding award rates for women and those with care responsibilities, more data are required to validate these observations and understand the impact of different interventions individually and in combination. CONCLUSIONS: In summary, this study offers compelling evidence regarding the efficacy of a research network's approach in advancing EDI within research and funding. The network hopes that these findings will inform broader efforts to promote EDI in research and funding and that researchers, funders, and other stakeholders will be encouraged to adopt evidence-based strategies for advancing this important goal.

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.

The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20-82.66%) and semi-supervised learning (81.39-85.26%), active learning models were able to achieve higher accuracy (82.85-85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.

'Do they ever think about people like us?': The experiences of people with learning disabilities in England and Scotland during the COVID-19 pandemic.

People with learning disabilities in England and Scotland have experienced an increased risk of illness and death during the COVID-19 pandemic. Drawing on data of a longitudinal qualitative study with 71 disabled people and 31 disability organisations, this article examines the experiences of 24 people with learning disabilities in England and Scotland during the pandemic, reflecting on what rendered them vulnerable and placed them at risk. Qualitative interviews were conducted with participants and key informants at two timepoints; June-August 2020 and February-April 2021. Findings emerged across four key themes: failure to plan for the needs of people with learning disabilities; the suspension and removal of social care; the impact of the pandemic on people's everyday routines; and lack of vaccine prioritisation. The inequalities experienced by people with learning disabilities in this study are not particular to the pandemic. We explore the findings in the context of theoretical frameworks of vulnerability, including Fineman's conceptualisation of a 'vulnerability paradigm'. We conclude that the structured marginalisation of people with disabilities, entrenched by government action and inaction, have created and exacerbated their vulnerability. Structures, policies and action must change.

Epistemic sabotage: The production and disqualification of evidence in disability benefit claims.

Disability benefits function by demarcating categories of need (the administrative category of disability) and determine eligibility using assessments of functioning. In the UK, these assessments are the Work Capability Assessment and PIP assessment. Inherently technical and abstruse processes, these assessments have been opportune sites for welfare reform in recent years. Disability benefits have also been a central point of contention between disability studies and sociology. Sociology has traditionally favoured an 'incomes approach' and called for more adequate financial support from the state. Early figures in the disabled people's movement rejected this position, and aligned with an oppression paradigm, argued for a more radical economic and social inclusion. We contend that this divide, set out in the Fundamental Principles of Disability, remains relevant for researching welfare reform today. This article treats benefits assessments as epistemic practices-interactional processes wherein claimants, their personal health professionals and commercial assessment providers come together in the production of knowledge about disability. Data include 50 in-depth interviews with benefit claimants and a discourse analysis of official texts directed at claimants, personal health professionals and commercial assessment providers. We outline a phenomenon we term 'epistemic sabotage', whereby the knowledge claims of claimants and their health professionals are systemically disqualified.

Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy.

The addition of incorrect agri-food powders to a production line due to human error is a large safety concern in food and drink manufacturing, owing to incorporation of allergens in the final product. This work combines near-infrared spectroscopy with machine-learning models for early detection of this problem. Specifically, domain adaptation is used to transfer models from spectra acquired under stationary conditions to moving samples, thereby minimizing the volume of labelled data required to collect on a production line. Two deep-learning domain-adaptation methodologies are used: domain-adversarial neural networks and semisupervised generative adversarial neural networks. Overall, accuracy of up to 96.0% was achieved using no labelled data from the target domain moving spectra, and up to 99.68% was achieved when incorporating a single labelled data instance for each material into model training. Using both domain-adaptation methodologies together achieved the highest prediction accuracies on average, as did combining measurements from two near-infrared spectroscopy sensors with different wavelength ranges. Ensemble methods were used to further increase model accuracy and provide quantification of model uncertainty, and a feature-permutation method was used for global interpretability of the models.

Mechanism of Visible Light-Mediated Alkene Aminoarylation with Arylsulfonylacetamides.

Alkene aminoarylation with arylsulfonylacetamides via a visible-light mediated radical Smiles-Truce rearrangement represents a convenient approach to the privileged arylethylamine pharmacaphore traditionally generated by circuitous, multi-step sequences. Herein, we report detailed synthetic, spectroscopic, kinetic, and computational studies designed to interrogate the proposed mechanism, including the key aryl transfer event. The data are consistent with a rate-limiting 1,4-aryl migration occurring either via a stepwise process involving a radical Meisenheimer-like intermediate or in a concerted fashion dependent on both arene electronics and alkene sterics. Our efforts to probe the mechanism have significantly expanded the substrate scope of the transformation with respect to the migrating aryl group and provide further credence to the synthetic potential of radical aryl migrations.

A review of ultrasonic sensing and machine learning methods to monitor industrial processes.

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved generalisation, reduced development time, ability for continuous retraining, and the correlation of sensor data to important process information. However, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, select the type of machine learning algorithm to use, and find a suitable set of model hyperparameters. The aim of this article is to facilitate implementation of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. The article first reviews the use of ultrasonic sensors for monitoring processes, before reviewing the combination of ultrasonic measurements and machine learning. We include literature from other sectors such as structural health monitoring. This review covers feature extraction, feature selection, algorithm choice, hyperparameter selection, data augmentation, domain adaptation, semi-supervised learning and machine learning interpretability. Finally, recommendations for applying machine learning to the reviewed processes are made.

The Best Services Trial (BeST?): a cluster randomised controlled trial comparing the clinical and cost-effectiveness of New Orleans Intervention Model with services as usual (SAU) for infants and young children entering care.

BACKGROUND: Abused and neglected children are at increased risk of health problems throughout life, but negative effects may be ameliorated by nurturing family care. It is not known whether it is better to place these children permanently with substitute (foster or adoptive) families or to attempt to reform their birth families. Previously, we conducted a feasibility randomised controlled trial (RCT) of the New Orleans Intervention Model (NIM) for children aged 0-60 months coming into foster care in Glasgow. NIM is delivered by a multidisciplinary health and social care team and offers families, whose child has been taken into foster care, a structured assessment of family relationships followed by a trial of treatment aiming to improve family functioning. A recommendation is then made for the child to return home or for adoption. In the feasibility RCT, families were willing to be randomised to NIM or optimised social work services as usual and equipoise was maintained. Here we present the protocol of a substantive RCT of NIM including a new London site. METHODS: The study is a multi-site, pragmatic, single-blind, parallel group, cluster randomised controlled superiority trial with an allocation ratio of 1:1. We plan to recruit approximately 390 families across the sites, including those recruited in our feasibility RCT. They will be randomly allocated to NIM or optimised services as usual and followed up to 2.5 years post-randomisation. The principal outcome measure will be child mental health, and secondary outcomes will be child quality of life, the time taken for the child to be placed in permanent care (rehabilitation home or adoption) and the quality of the relationship with the primary caregiver. DISCUSSION: The study is novel in that infant mental health professionals rarely have a role in judicial decisions about children's care placements, and RCTs are rare in the judicial context. The trial will allow us to determine whether NIM is clinically and cost-effective in the UK and findings may have important implications for the use of mental health assessment and treatment as part of the decision-making about children in the care system.

Disabled people in Britain and the impact of the COVID-19 pandemic.

This paper reports on in-depth qualitative interviews conducted with 69 disabled people in England and Scotland, and with 28 key informants from infrastructure organisations in the voluntary and statutory sectors, about the impact of COVID-19, and measures taken to control it. Participants were recruited through voluntary organisations. As with everyone, the Pandemic has had a huge impact: we discuss the dislocations it has caused in everyday life; the failures of social care; the use of new technologies; and participants' view on leadership and communication. We conclude with suggestions for urgent short term and medium term responses, so that the United Kingdom and other countries can respond better to this and other pandemics, and build a more inclusive world.

Can categorised values of maximal oxygen uptake discriminate patterns of exercise dysfunction in pectus excavatum: a prospective cohort study?

Cohort studies of patients with pectus excavatum have inadequately characterised exercise dysfunction experienced. Cardiopulmonary exercise test data were delineated by maximal oxygen uptake values >80%, which was tested to examine whether patterns of exercise physiology were distinguished. METHODS: Seventy-two patients considered for surgical treatment underwent assessment of pulmonary function and exercise physiology with pulmonary function tests and cardiopulmonary exercise test between 2006 and 2019. Seventy who achieved a threshold respiratory gas exchange ratio of >1.1 were delineated by maximal oxygen uptake >80%, (group A, n=33) and <80% (group B, n=37) and comparison of constituent physiological parameters performed. RESULTS: The cohort was 20.8 (±SD 6.6) years of age, 60 men, with a Haller's Index of 4.1 (±SD 1.4). Groups A and B exhibited similar demography, pulmonary function test results and Haller's index values. Exercise test parameters of group B were lower than group A; work 79.2% (±SD 11.3) versus 97.7 (±SD 10.1), anaerobic threshold 38.1% (±SD 7.8) versus 49.7% (±SD 9.1) and O2 pulse 77.4% (±SD 9.8) versus 101.8% (±SD 11.7), but breathing reserve was higher, 54.9% (±SD 13.1) versus 44.2% (±SD 10.8), p<0.001 for each. Both groups exhibited similar incidences of carbon dioxide retention at peak exercise. A total of 65 (93%) exhibited abnormal values of at least one of four exercise test measures. CONCLUSION: This study showed that patients with pectus excavatum exhibited multiple physiological characteristics of compromised exercise function. It is the first study that defines differing patterns of exercise dysfunction and provides evidence that patients with symptomatic pectus excavatum should be considered for surgical treatment.

Transfer learning for process monitoring using reflection-mode ultrasonic sensing.

The fourth industrial revolution is set to integrate entire manufacturing processes using industrial digital technologies such as the Internet of Things, Cloud Computing, and machine learning to improve process productivity, efficiency, and sustainability. Sensors collect the real-time data required to optimise manufacturing processes and are therefore a key technology in this transformation. Ultrasonic sensors have benefits of being low-cost, in-line, non-invasive, and able to operate in opaque systems. Supervised machine learning models can correlate ultrasonic sensor data to useful information about the manufacturing materials and processes. However, this requires a reference measurement of the process material to label each data point for model training. Labelled data is often difficult to obtain in factory environments, and so a method of training models without this is desirable. This work compares two domain adaptation methods to transfer models across processes, so that no labelled data is required to accurately monitor a target process. The two method compared are a Single Feature transfer learning approach and Transfer Component Analysis using three features. Ultrasonic waveforms are unique to the sensor used, attachment procedure, and contact pressure. Therefore, only a small number of transferable features are investigated. Two industrially relevant processes were used as case studies: mixing and cleaning of fouling in pipes. A reflection-mode ultrasonic sensing technique was used, which monitors the sound wave reflected from the interface between the vessel wall and process material. Overall, the Single Feature method produced the highest prediction accuracies: up to 96.0% and 98.4% to classify the completion of mixing and cleaning, respectively; and R2 values of up to 0.947 and 0.999 to predict the time remaining until completion. These results highlight the potential of combining ultrasonic measurements with transfer learning techniques to monitor industrial processes. Although, further work is required to study various effects such as changing sensor location between source and target domains.

Performance of the AusSCORE II and STS Score for Coronary Artery Bypass Grafting in a New Zealand Population.

BACKGROUND: The updated Australian System for Cardiac Operative Risk Evaluation (AusSCORE II) and the Society of Thoracic Surgeons (STS) Score are well-established tools in cardiac surgery for estimating operative mortality risk. No validation analysis of both risk models has been undertaken for a contemporary New Zealand population undergoing isolated coronary bypass surgery. We therefore aimed to assess the efficacy of these models in predicting mortality for New Zealand patients receiving isolated coronary artery bypass grafting (CABG). MATERIAL AND METHODS: A prospective database was maintained of patients undergoing isolated CABG at a major tertiary referral centre in New Zealand between September 2014 and September 2017. This database collected the patients' demographic, clinical, biochemical, operative and mortality data. The primary outcome measure was the correlation between the predicted AusSCORE II and STS Score mortality risks and the observed 30-day mortality events for all patients in the database using discrimination and calibration statistics. Discrimination and calibration were assessed using receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow test respectively. RESULTS: A total of 933 patients underwent isolated CABG during the 3-year study period. There were seven deaths in the study cohort occurring within 30 days of surgery. Discrimination analysis demonstrated the area under the ROC curve (AUC) of the AusSCORE II and STS Score as 88.2% (95% CI: 85.9-90.2, p<0.0001) and 92.1% (95% CI: 90.2-93.7, p<0.0001) respectively. Calibration analysis revealed Hosmer-Lemeshow test p-values for the AusSCORE II and STS Score as 0.696 and 0.294 respectively. DISCUSSION: ROC curve analysis produced very high and statistically significant AUC values for the AusSCORE II and STS Score. Hosmer-Lemeshow test analysis revealed that both risk scoring tools are well calibrated for our study cohort. Therefore, the AusSCORE II and STS Score are both strongly predictive of 30-day mortality for isolated coronary artery bypass grafting surgery in our New Zealand patient population. Both risk models have performed with excellent discrimination and calibration. There is, however, a need to consider the performance of these risk stratification models in other cardiac surgical procedures outside isolated coronary bypass surgery where appropriate.

Hypomorphic mTOR Downregulates CDK6 and Delays Thymic Pre-T LBL Tumorigenesis.

PI3K/AKT/mTOR pathway hyperactivation is frequent in T-cell acute lymphoblastic leukemia/lymphoma (T-ALL/LBL). To model inhibition of mTOR, pre-T-cell lymphoblastic leukemia/lymphoma (pre-T LBL) tumor development was monitored in mice with T lymphocyte-specific, constitutively active AKT (Lck-MyrAkt2) that were either crossed to mTOR knockdown (KD) mice or treated with the mTOR inhibitor everolimus. Lck-MyrAkt2;mTOR KD mice lived significantly longer than Lck-MyrAkt2;mTOR wild-type (WT) mice, although both groups ultimately developed thymic pre-T LBL. An increase in survival was also observed when Lck-MyrAkt2;mTOR WT mice were treated for 8 weeks with everolimus. The transcriptional profiles of WT and KD thymic lymphomas were compared, and Ingenuity Pathway Upstream Regulator Analysis of differentially expressed genes in tumors from mTOR WT versus KD mice identified let-7 and miR-21 as potential regulatory genes. mTOR KD mice had higher levels of let-7a and miR-21 than mTOR WT mice, and rapamycin induced their expression in mTOR WT cells. CDK6 was one of the most downregulated targets of both let-7 and miR21 in mTOR KD tumors. CDK6 overexpression and decreased expression of let-7 in mTOR KD cells rescued a G1 arrest phenotype. Combined mTOR (rapamycin) and CDK4/6 (palbociclib) inhibition decreased tumor size and proliferation in tumor flank transplants, increased survival in an intravenous transplant model of disseminated leukemia compared with single agent treatment, and cooperatively decreased cell viability in human T-ALL/LBL cell lines. Thus, mTOR KD mice provide a model to explore drug combinations synergizing with mTOR inhibitors and can be used to identify downstream targets of inhibition.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression.

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning.

Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches.