Development and evaluation of an inverse solution technique for studying helicopter maneuverability and agility

An inverse solution technique for determining the maximum maneuvering performance of a helicopter using smooth, pilotlike control inputs is presented. Also described is a pilot simulation experiment performed to investigate the accuracy of the solution resulting from this technique. The maneuverability and agility capability of the helicopter math model was varied by varying the pitch and roll damping, the maximum pitch and roll rate, and the maximum load-factor capability. Three maneuvers were investigated: a 180-deg turn, a longitudinal pop-up, and a lateral jink. The inverse solution technique yielded accurate predictions of pilot-in-the-loop maneuvering performance for two of the three maneuvers

A piloted simulation investigation of the normal load factor and longitudinal thrust required for air-to-air acquisition and tracking

A piloted simulation study was performed by the U.S. Army Aeroflighydynamics Directorate to develop insight into the maneuverability requirements for aggressive helicopter maneuvering tasks such as air-to-air combat. Both a conventional helicopter and a helicopter with auxiliary thrust were examined. The aircraft parameters of interest were the normal and longitudinal load factor envelopes. Of particular interest were the mission performance and handling qualities tradeoffs with the parameters of interest. Two air-to-air acquisition and tracking tasks and a return-to-cover task were performed to assess mission performance. Results indicate that without auxiliary thrust, the ownship normal load factor capability needs to match that of the adversary in order to provide satisfactory handling qualities. Auxiliary thrust provides significant handling qualities advantages and can be substituted to some extent for normal load factor capability. Auxiliary thrust levels as low as 0.2 thrust/weight can provide significant handling qualities advantages

Screening for atrial fibrillation – a cross-sectional survey of healthcare professionals in primary care

Introduction: Screening for atrial fibrillation (AF) in primary care has been recommended; however, the views of healthcare professionals (HCPs) are not known. This study aimed to determine the opinions of HCP about the feasibility of implementing screening within a primary care setting. Methods: A cross-sectional mixed methods census survey of 418 HCPs from 59 inner-city practices (Nottingham, UK) was conducted between October-December 2014. Postal and web-surveys ascertained data on existing methods, knowledge, skills, attitudes, barriers and facilitators to AF screening using Likert scale and open-ended questions. Responses, categorized according to HCP group, were summarized using proportions, adjusting for clustering by practice, with 95% C.Is and free-text responses using thematic analysis. Results: At least one General Practitioner (GP) responded from 48 (81%) practices. There were 212/418 (51%) respondents; 118/229 GPs, 67/129 nurses [50 practice nurses; 17 Nurse Practitioners (NPs)], 27/60 healthcare assistants (HCAs). 39/48 (81%) practices had an ECG machine and diagnosed AF in-house. Non-GP HCPs reported having less knowledge about ECG interpretation, diagnosing and treating AF than GPs. A greater proportion of non-GP HCPs reported they would benefit from ECG training specifically for AF diagnosis than GPs [proportion (95% CI) GPs: 11.9% (6.8–20.0); HCAs: 37.0% (21.7–55.5); nurses: 44.0% (30.0–59.0); NPs 41.2% (21.9–63.7)]. Barriers included time, workload and capacity to undertake screening activities, although training to diagnose and manage AF was a required facilitator. Conclusion: Inner-city general practices were found to have adequate access to resources for AF screening. There is enthusiasm by non-GP HCPs to up-skill in the diagnosis and management of AF and they may have a role in future AF screening. However, organisational barriers, such as lack of time, staff and capacity, should be overcome for AF screening to be feasibly implemented within primary care

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Coronavirus disease 2019 subphenotypes and differential treatment response to convalescent plasma in critically ill adults: secondary analyses of a randomized clinical trial

Purpose Benefit from convalescent plasma therapy for coronavirus disease 2019 (COVID-19) has been inconsistent in randomized clinical trials (RCTs) involving critically ill patients. As COVID-19 patients are immunologically heterogeneous, we hypothesized that immunologically similar COVID-19 subphenotypes may differ in their treatment responses to convalescent plasma and explain inconsistent findings between RCTs . Methods We tested this hypothesis in a substudy involving 1239 patients, by measuring 26 biomarkers (cytokines, chemokines, endothelial biomarkers) within the randomized, embedded, multifactorial, adaptive platform trial for community-acquired pneumonia (REMAP-CAP) that assigned 2097 critically ill COVID-19 patients to either high-titer convalescent plasma or usual care. Primary outcome was organ support free days at 21 days (OSFD-21) . Results Unsupervised analyses identified three subphenotypes/endotypes. In contrast to the more homogeneous subphenotype-2 (N = 128 patients, 10.3%; with elevated type i and type ii effector immune responses) and subphenotype-3 (N = 241, 19.5%; with exaggerated inflammation), the subphenotype-1 had variable biomarker patterns (N = 870 patients, 70.2%). Subphenotypes-2, and -3 had worse outcomes, and subphenotype-1 had better outcomes with convalescent plasma therapy compared with usual care (median (IQR). OSFD-21 in convalescent plasma vs usual care was 0 (− 1, 21) vs 10 (− 1, to 21) in subphenotype-2; 1.5 (− 1, 21) vs 12 (− 1, to 21) in suphenotype-3, and 0 (− 1, 21) vs 0 (− 1, to 21) in subphenotype-1 (test for between-subphenotype differences in treatment effects p = 0.008). Conclusions We reported three COVID-19 subphenotypes, among critically ill adults, with differential treatment effects to ABO-compatible convalescent plasma therapy. Differences in subphenotype prevalence between RCT populations probably explain inconsistent results with COVID-19 immunotherapies

A qualitative study of the impact of severe asthma and its treatment showing that treatment burden is neglected in existing asthma assessment scales

Background People with severe asthma experience significant respiratory symptoms and suffer adverse effects of oral corticosteroids (OCS), including disturbed mood and physical symptoms. OCS impacts on health-related quality of life (HRQoL) have not been quantified. Asthma HRQoL scales are valid as outcome measures for patients requiring OCS only if they assess the deficits imposed by OCS. Aims The aim of this study was to compare the burden of disease and treatment in patients with severe asthma with items in eight asthma-specific HRQoL scales. Methods Twenty-three patients with severe asthma recruited from a severe asthma clinic were interviewed about the impact of their respiratory symptoms and the burden of their treatment. The domains from a thematic analysis of these interviews were compared with the items of eight asthma-specific HRQoL scales. Results In addition to the burden caused by symptoms, ten domains of OCS impact on HRQoL were identified: depression, irritability, sleep, hunger, weight, skin, gastric, pain, disease anxiety, and medication anxiety. Some patients experienced substantial HRQoL deficits attributed to OCS. Although all HRQoL scales include some OCS-relevant items, all eight scales fail to adequately assess the several types of burden experienced by some patients while on OCS. Conclusion The burden of OCS in severe asthma is neglected in policy and practice because it is not assessed in outcome studies. Existing asthma HRQoL scales provide an overly positive estimation of HRQoL in patients with frequent exposure to OCS and underestimate the benefit of interventions that reduce OCS exposure. Changes to existing measurement procedures are needed

A single, one-off measure of depression and anxiety predicts future symptoms, higher healthcare costs, and lower quality of life in coronary heart disease patients: Analysis from a multi-wave, primary care cohort study

To determine whether a one-off, baseline measure of depression and anxiety in a primary care, coronary heart disease (CHD) population predicts ongoing symptoms, costs, and quality of life across a 3-year follow-up.Longitudinal cohort study.16 General Practice surgeries across South-East London.803 adults (70% male, mean age 71 years) contributing up to 7 follow-up points.Ongoing reporting of symptoms, health care costs, and quality of life.At baseline, 27% of the sample screened positive for symptoms of depression and anxiety, as measured by the Hospital Anxiety and Depression Scale (HADS). The probability of scoring above the cut-off throughout the follow-up was 71.5% (p<0.001) for those screening positive at baseline, and for those screening negative, the probability of scoring below the cut-off throughout the follow-up was 97.6% (p<0.001). Total health care costs were 39% higher during follow-up for those screening positive (p<0.05). Quality of life as measured by the SF-12 was lower on the mental component during follow-up for those screening positive (-0.75, CI -1.53 to 0.03, p = 0.059), and significantly lower on the physical component (-4.99, CI -6.23 to -.376, p<0.001).A one-off measure for depression and anxiety symptoms in CHD predicts future symptoms, costs, and quality of life over the subsequent three-years. These findings suggest symptoms of depression and anxiety in CHD persist throughout long periods and are detrimental to a patient's quality of life, whilst incurring higher health care costs for primary and secondary care services. Screening for these symptoms at the primary care level is important to identify and manage patients at risk of the negative effects of this comorbidity. Implementation of screening, and possible collaborative care strategies and interventions that help mitigate this risk should be the ongoing focus of researchers and policy-makers

Emergence of a Small-World Functional Network in Cultured Neurons

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project