The Influence of Sidedness in Unilateral Cleft Lip and Palate on Mid Facial Growth at Five Years of Age.

ObjectiveTo determine whether facial growth at five years is different for children with a left versus right sided cleft lip and palate.DesignRetrospective cohort study.SettingSeven UK regional cleft centres.

Manchester Intermittent Diet in Gestational Diabetes Acceptability Study (MIDDAS-GDM): a two-arm randomised feasibility protocol trial of an intermittent low-energy diet (ILED) in women with gestational diabetes and obesity in Greater Manchester.

Introduction: The prevalence of gestational diabetes mellitus (GDM) is rising in the UK and is associated with maternal and neonatal complications. National Institute for Health and Care Excellence guidance advises first-line management with healthy eating and physical activity which is only moderately effective for achieving glycaemic targets. Approximately 30% of women require medication with metformin and/or insulin.

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics.

Location estimation based on feature mode matching with deep network models.

Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements.

Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy.

Using Smartphone TechnolOGy to Support an EffecTive Home ExeRcise Intervention to Prevent Falls amongst Community-Dwelling Older Adults: The TOGETHER Feasibility RCT.

Introduction: Falls have major implications for quality of life, independence, and cost of health services. Strength and balance training has been found to be effective in reducing the rate/risk of falls, as long as there is adequate fidelity to the evidence-based programme. The aims of this study were to (1) assess the feasibility of using the "Motivate Me" and "My Activity Programme" interventions to support falls rehabilitation when delivered in practice and (2) assess study design and trial procedures for the evaluation of the intervention.

Regional variation in longitudinal trajectories of primary care opioids prescribing across Health Boards in Scotland: a population-based study.

Background: This study aims to describe the longitudinal trajectory of opioid prescribing at the practice level and assess associated factors, including Health Boards and socioeconomic status.

Research Design And Methods: This drug utilization research used practice-level dispensing data from 2016 to 2018. Practice-level prescription opioids dispensed were quantified by the defined daily doses (DDDs) per 1000 registrants.

Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting.

Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend.

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them.

Investigating the prescribing trajectory and geographical drug utilisation patterns of gabapentinoids in primary care in England: An ecological study.

Aims: This study aimed to investigate the prescribing trajectory, geographical variation and population factors, including socioeconomic status (SES), related to prescribing gabapentinoids in primary care in England.

Methods: This ecological study applied practice-level dispensing data and statistics from the UK National Health Service Digital and Office for National Statistics from 2013 to 2019. The prescribing of gabapentinoids (in defined daily doses [DDDs]/1000 people) was measured annually and quarterly.

Imaging modalities to inform the detection and diagnosis of early caries.

Background: The detection and diagnosis of caries at the earliest opportunity is fundamental to the preservation of tooth tissue and maintenance of oral health. Radiographs have traditionally been used to supplement the conventional visual-tactile clinical examination. Accurate, timely detection and diagnosis of early signs of disease could afford patients the opportunity of less invasive treatment with less destruction of tooth tissue, reduce the need for treatment with aerosol-generating procedures, and potentially result in a reduced cost of care to the patient and to healthcare services.

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance.

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.

Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components.

Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network.

The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of "Circumjacent Monitoring-Blind Area Inference".

Can smartphone technology be used to support an effective home exercise intervention to prevent falls amongst community dwelling older adults?: the TOGETHER feasibility RCT study protocol.

Introduction: Falls have major implications for quality of life, independence and cost to the health service. Strength and balance training has been found to be effective in reducing the rate/risk of falls, as long as there is adequate fidelity to the evidence-based programme. Health services are often unable to deliver the evidence-based dose of exercise and older adults do not always sufficiently adhere to their programme to gain full outcomes.

An evaluation of the bootstrap for model validation in mixture models.

Bootstrapping has been used as a diagnostic tool for validating model results for a wide array of statistical models. Here we evaluate the use of the non-parametric bootstrap for model validation in mixture models. We show that the bootstrap is problematic for validating the results of class enumeration and demonstrating the stability of parameter estimates in both finite mixture and regression mixture models.

Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications.

In this paper, a novel semi-supervised segmentation framework based on a spot-divergence supervoxelization of multi-sensor fusion data is proposed for autonomous forest machine (AFMs) applications in complex environments. Given the multi-sensor measuring system, our framework addresses three successive steps: firstly, the relationship of multi-sensor coordinates is jointly calibrated to form higher-dimensional fusion data. Then, spot-divergence supervoxels representing the size-change property are given to produce feature vectors covering comprehensive information of multi-sensors at a time.

Herbst appliance with skeletal anchorage versus dental anchorage in adolescents with Class II malocclusion: study protocol for a randomised controlled trial.

Background: The Herbst appliance is an orthodontic appliance that is used for the correction of class II malocclusion with skeletal discrepancies. Research has shown that this is effective. However, a potential harm is excessive protrusion of the lower front teeth.

Online Denoising Based on the Second-Order Adaptive Statistics Model.

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter.

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information.

Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches.

Understanding clinical prediction models as 'innovations': a mixed methods study in UK family practice.

Background: Well-designed clinical prediction models (CPMs) often out-perform clinicians at estimating probabilities of clinical outcomes, though their adoption by family physicians is variable. How family physicians interact with CPMs is poorly understood, therefore a better understanding and framing within a context-sensitive theoretical framework may improve CPM development and implementation. The aim of this study was to investigate why family physicians do or do not use CPMs, interpreting these findings within a theoretical framework to provide recommendations for the development and implementation of future CPMs.

A review of statistical updating methods for clinical prediction models.

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations.

Facial Aesthetic Outcomes of Cleft Surgery: Assessment of Discrete Lip and Nose Images Compared with Digital Symmetry Analysis.

Background: High-quality aesthetic outcomes are of paramount importance to children growing up after cleft lip and palate surgery. Establishing a validated and reliable assessment tool for cleft professionals and families will facilitate cleft units, surgeons, techniques, and protocols to be audited and compared with greater confidence. This study used exemplar images across a five-point aesthetic scale, identified in a pilot project, to score lips and noses as separate units and compared these human scores with computer-based SymNose symmetry scores.