Estimation of Joint Angle Based on Surface Electromyogram Signals Recorded at Different Load Levels

To control upper-limb exoskeletons and prostheses, surface electromyogram (sEMG) is widely used for estimation of joint angles. However, the variations in the load carried by the user can substantially change the recorded sEMG and consequently degrade the accuracy of joint angle estimation. In this paper, we aim to deal with this problem by training classification models using a pool of sEMG data recorded from all different loads. The classification models are trained as either subject-specific or subject-independent, and their results are compared with the performance of classification models that have information about the carried load. To evaluate the proposed system, the sEMG signals are recorded during elbow flexion and extension from three participants at four different loads (i.e. 1, 2, 4 and 6 Kg) and six different angles (i.e. 0, 30, 60, 90, 120, 150 degrees). The results show while the loads were assumed unknown and the applied training data was relatively small, the proposed joint angle estimation model performed significantly above the chance level in both the subject-specific and subject-independent models. However, transferring from known to unknown load in the subject-specific classifiers leads to 20% to 32% loss in the average accuracy

A phase Ib study combining the second-generation DNA hypomethylating agent (DHA) guadecitabine (SGI-110) and ipilimumab in patients with metastatic melanoma: The NIBIT-M4 Study

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Guardian: Hypervisor as Security Foothold for Personal Computers

Abstract. Personal computers lack of a security foothold to allow the end-users to protect their systems or to mitigate the damage. Existing candidates either rely on a large Trusted Computing Base (TCB) or are too costly to widely deploy for commodity use. To fill this gap, we propose a hypervisor-based security foothold, named as Guardian, for commodity personal computers. We innovate a bootup and shutdown mechanism to achieve both integrity and availability of Guardian. We also propose two security utilities based on Guardian. One is a device mon-itor which detects malicious manipulation on camera and network adaptors. The other is hyper-firewall whereby Guardian expects incoming and outgoing network packets based on policies specified by the user. We have implemented Guardian ( ≈ 25K SLOC) and the two utilities ( ≈ 2.1K SLOC) on a PC with an Intel pro-cessor. Our experiments show that Guardian is practical and incurs insignificant overhead to the system.

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population

Prognostic indicators and outcomes of hospitalised COVID-19 patients with neurological disease: An individual patient data meta-analysis

Background Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. Methods We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. Results We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67–82]), than encephalopathy (54% [42–65]). Intensive care use was high (38% [35–41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27–32]. The hazard of death was comparatively lower for patients in the WHO European region. Interpretation Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Integrating GIS, remote sensing, and mathematical modelling for surface water quality management in irrigated watersheds; Dissertation, UNESCO-IHE Institute for Water Education, Delft.

The intensive uses of limited water resources, the growing population rates and the various increasing human activities put high and continuous stresses on these resources. Major problems affecting the water quality of streams and lakes may arise from inadequately treated sewage, poor land use practices, inadequate controls on the discharges of industrial waste waters, uncontrolled poor agricultural practices, excessive use of fertilizers, and a lack of integrated watershed management. This book focuses on the management of surface water quality in physically complex watersheds and data scarce environments. It presents a generic framework for a Water Quality Management Information System, integrating the physically based hydrodynamic and water quality models with the spatial and temporal capabilities of remote sensing. The developed framework is applied and tested on the Edko drainage catchment and shallow lake system in the western part of the Nile Delta, Egypt. The framework includes a hierarchy of models: 1D-2D basic hydrodynamic model for a combined shallow lake-drainage system, 2D hydrodynamic model of the shallow lake, 2D water quality and eutrophication screening models of the lake. The lake models succeeded to simulate the main water quality parameters including the oxygen compounds, nutrients, temperature, salinity, total suspended matter (TSM) and chlorophyll-a. Additionally, the results of this research showed that the integration of remote sensing data for the calibration of water quality and eutrophication models proved to be a reliable and successful methodology that could be applied on similar physical and environmental conditions

Integrating GIS, remote sensing and mathematical modelling for surface water quality management in irrigated watersheds

The intensive uses of limited water resources, the growing population rates and the various increasing human activities put high and continuous stresses on these resources. Major problems affecting the water quality of rivers, streams and lakes may arise from inadequately treated sewage, poor land use practices, inadequate controls on the discharges of industrial waste waters, uncontrolled poor agricultural practices, excessive use of fertilizers, and a lack of integrated watershed management. This study explores the impact of these pollution problems and the water quality degradation of Irrigated agricultural watersheds When the watersheds have a complex physical basis of interacting water bodies such as canals, drains and coastal lagoons as in the case of irrigated watersheds in coastal river Deltas, and when these environments are ‘data scarce environments’, the problems of managing water quality becomes more obvious and the need for reliable solutions becomes an urgent requirement. This study focused on the management of surface water quality problems in such watersheds and the importance of taking into consideration all the watershed components and the effects of pollution from the upstream canals on the downstream coastal lakes. In this study a generic framework for a (Water Quality Management Information System) is developed depending on the integration of physically based hydrodynamic and water quality models with GIS capabilities and the spatial and temporal capabilities of remote sensing in water quality modeling. The application is developed and tested for the Edko drainage catchment and shallow lake system in the western part of the Nile Delta, Egypt. The developed framework includes a hierarchy of modeling tools: a 1D-2D basic hydrodynamic model for a combined shallow lake-drainage system, a detailed 2D hydrodynamic model of the shallow lake, and a 2D water quality and eutrophication screening models for the lake system. The basic water quality model for the lake system simulates the main water quality parameters including the oxygen compounds, nutrients compounds, temperature, salinity and the total suspended matter (TSM). The complexity of the physical and ecological properties of the lake system implied the use of different methodologies for models calibration using remote sensing. The combination of remote sensing with mathematical modelling, for the calibration and verification of TSM and chlorophyll-concentrations in the shallow lake system showed reliable and successful results.Hydraulic EngineeringCivil Engineering and Geoscience

Design and development of a low cost prosthetic arm control system based on sEMG signal

The aim of this paper is to design and develop a low-cost prosthetic arm based on surface electromyography (sEMG) signal activities of the biceps muscle during upper-limb movement. Different methods are described in the literature, but many problems are encountered in dealing with the online processing of raw EMG (rEMG) signals, such as signal sampling and memory requirements. In this paper, the enveloped EMG (eEMG) signal is used as a control signal that reduces signal sampling rate and memory requirements. The relationship between elbow motion and the activity level of the biceps muscle is characterized using relevant extracted features (root mean square (RMS)). Validation of the proposed low-cost system is conducted using comparison with a professional biomedical system (Bioback MP150). In addition, the estimated equation of movements of each subject is estimated based on the recorded data. From this equation, the angle of motion is calculated as the control of the movement of the robotic arm. Finally, the system proposed in this paper considers the eEMG signal rather than the rEMG signal and deals with the signal based on a sample of 1 KHz rather than 10 KHz. This system reduces our target cost (reduction in hardware requirements and processing time) with acceptable accuracy. The experimental results illustrate that the eEMG signal has the same featuresprint as that of the rEMG signal, and the eEMG signal can generate the control signal required to move the prosthetic arm