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ObjectiveThe effect of moderate excess alcohol consumption is widely debated and has not been well defined in dilated cardiomyopathy (DCM). There is need for a greater evidence base to help advise patients. We sought to evaluate the effect of moderate excess alcohol consumption on cardiovascular structure, function and outcomes in DCM.MethodsProspective longitudinal observational cohort study. Patients with DCM (n=604) were evaluated for a history of moderate excess alcohol consumption (UK government guidelines; >14 units/week for women, >21 units/week for men) at cohort enrolment, had cardiovascular magnetic resonance and were followed up for the composite endpoint of cardiovascular death, heart failure and arrhythmic events. Patients meeting criteria for alcoholic cardiomyopathy were not recruited.ResultsDCM patients with a history of moderate excess alcohol consumption (n=98, 16%) had lower biventricular function and increased chamber dilatation of the left ventricle, right ventricle and left atrium, as well as increased left ventricular hypertrophy compared with patients without moderate alcohol consumption. They were more likely to be male (alcohol excess group: n=92, 94% vs n=306, 61%, p=ConclusionDCM patients with moderate excess alcohol consumption have adverse cardiac structure and function at presentation, but this is largely due to biological sex. Alcohol may contribute to sex-specific phenotypic differences in DCM. These findings help to inform lifestyle discussions for patients with DCM.

Aims: Children presenting with hypertrophic cardiomyopathy (HCM) in infancy are reported to have a poor prognosis, but this heterogeneous group has not been systematically characterized. This study aimed to describe the aetiology, phenotype, and outcomes of infantile HCM in a well-characterized multicentre European cohort. Methods and results: Of 301 children diagnosed with infantile HCM between 1987 and 2019 presenting to 17 European centres [male n=187 (62.1%)], underlying aetiology was non-syndromic (n=138, 45.6%), RASopathy (n=101, 33.6%), or inborn error of metabolism (IEM) (n=49, 16.3%). The most common reasons for presentation were symptoms (n=77, 29.3%), which were more prevalent in those with syndromic disease (n=62, 61.4%, P&nbsp

Personal growth and positive change are areas of great interest in psychology. This study examined the mediating influence of self-compassion on the relationship between two types of perfectionism and personal growth initiative in a sample of Filipino adults (N = 351). Findings suggest that those high in conscientious perfectionism, considered as adaptive perfectionism, are more likely to have self-compassion; and through high levels of self-compassion, are more likely to develop personal growth initiative. On the other hand, those high in self-evaluative perfectionism, considered maladaptive, are far less likely to have self-compassion; and through lower levels of self-compassion, are less likely to develop personal growth initiative. Implications for theory and practice, as well as suggestions for further research, are discussed.

Aims:\ud There is an important need for better biomarkers to predict left ventricular (LV) remodelling in dilated cardiomyopathy (DCM). We undertook a comprehensive assessment of cardiac structure and myocardial composition to determine predictors of remodelling.\ud \ud Methods and results:\ud Prospective study of patients with recent‐onset DCM with cardiovascular magnetic resonance (CMR) assessment of ventricular structure and function, extracellular volume (T1 mapping), myocardial strain, myocardial scar (late gadolinium enhancement) and contractile reserve (dobutamine stress). Regression analyses were used to evaluate predictors of change in LV ejection fraction (LVEF) over 12 months. We evaluated 56 participants (34 DCM patients, median LVEF 43%; 22 controls). Absolute LV contractile reserve predicted change in LVEF (1% increase associated with 0.4% increase in LVEF at 12 months, P = 0.02). Baseline myocardial strain (P = 0.39 global longitudinal strain), interstitial myocardial fibrosis (P = 0.41), replacement myocardial fibrosis (P = 0.25), and right ventricular contractile reserve (P = 0.17) were not associated with LV reverse remodelling. There was a poor correlation between contractile reserve and either LV extracellular volume fraction (r = −0.22, P = 0.23) or baseline LVEF (r = 0.07, P = 0.62). Men were more likely to experience adverse LV remodelling (P = 0.01) but age (P = 0.88) and disease‐modifying heart failure medication (beta‐blocker, P = 0.28; angiotensin‐converting enzyme inhibitor, P = 0.92) did not predict follow‐up LVEF.\ud \ud Conclusions:\ud Substantial recovery of LV function occurs within 12 months in most patients with recent‐onset DCM. Women had the greatest improvement in LVEF. A low LV contractile reserve measured by dobutamine stress CMR appears to identify patients whose LVEF is less likely to recover.

Background The impact of COVID-19 on physical and mental health and employment after hospitalisation with acute disease is not well understood. The aim of this study was to determine the effects of COVID-19-related hospitalisation on health and employment, to identify factors associated with recovery, and to describe recovery phenotypes. Methods The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a multicentre, long-term follow-up study of adults (aged ≥18 years) discharged from hospital in the UK with a clinical diagnosis of COVID-19, involving an assessment between 2 and 7 months after discharge, including detailed recording of symptoms, and physiological and biochemical testing. Multivariable logistic regression was done for the primary outcome of patient-perceived recovery, with age, sex, ethnicity, body-mass index, comorbidities, and severity of acute illness as covariates. A post-hoc cluster analysis of outcomes for breathlessness, fatigue, mental health, cognitive impairment, and physical performance was done using the clustering large applications k-medoids approach. The study is registered on the ISRCTN Registry (ISRCTN10980107). Findings We report findings for 1077 patients discharged from hospital between March 5 and Nov 30, 2020, who underwent assessment at a median of 5·9 months (IQR 4·9–6·5) after discharge. Participants had a mean age of 58 years (SD 13); 384 (36%) were female, 710 (69%) were of white ethnicity, 288 (27%) had received mechanical ventilation, and 540 (50%) had at least two comorbidities. At follow-up, only 239 (29%) of 830 participants felt fully recovered, 158 (20%) of 806 had a new disability (assessed by the Washington Group Short Set on Functioning), and 124 (19%) of 641 experienced a health-related change in occupation. Factors associated with not recovering were female sex, middle age (40–59 years), two or more comorbidities, and more severe acute illness. The magnitude of the persistent health burden was substantial but only weakly associated with the severity of acute illness. Four clusters were identified with different severities of mental and physical health impairment (n=767): very severe (131 patients, 17%), severe (159, 21%), moderate along with cognitive impairment (127, 17%), and mild (350, 46%). Of the outcomes used in the cluster analysis, all were closely related except for cognitive impairment. Three (3%) of 113 patients in the very severe cluster, nine (7%) of 129 in the severe cluster, 36 (36%) of 99 in the moderate cluster, and 114 (43%) of 267 in the mild cluster reported feeling fully recovered. Persistently elevated serum C-reactive protein was positively associated with cluster severity. Interpretation We identified factors related to not recovering after hospital admission with COVID-19 at 6 months after discharge (eg, female sex, middle age, two or more comorbidities, and more acute severe illness), and four different recovery phenotypes. The severity of physical and mental health impairments were closely related, whereas cognitive health impairments were independent. In clinical care, a proactive approach is needed across the acute severity spectrum, with interdisciplinary working, wide access to COVID-19 holistic clinical services, and the potential to stratify care. Funding UK Research and Innovation and National Institute for Health Research.

BackgroundTruncating variants in desmoplakin (DSPtv) are an important cause of arrhythmogenic cardiomyopathy (ACM), however the genetic architecture and genotype-specific risk factors are incompletely understood. We evaluated phenotype, risk factors for ventricular arrhythmias, and underlying genetics ofDSPtv cardiomyopathy.MethodsIndividuals withDSPtv and any cardiac phenotype, and their gene-positive family members were included from multiple international centers. Clinical data and family history information were collected. Event-free survival from ventricular arrhythmia was assessed. Variant location was compared between cases and controls, and literature review of reportedDSPtv performed.ResultsThere were 98 probands and 72 family members (mean age at diagnosis 43 ± 18 years, 59% female) with aDSPtv, of which 146 were considered clinically affected. Ventricular arrhythmia (sudden cardiac arrest, sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator therapy) occurred in 56 (33%) individuals.DSPtv location and proband status were independent risk factors for ventricular arrhythmia, while prior risk factors showed no association. Further, gene region was important with variants in cases (cohort n=98, Clinvar n=168) more likely to occur in the regions resulting in nonsense mediated decay of both majorDSPisoforms, compared to n=124 gnomAD control variants (148 [83.6%] versus 29 [16.4%], pConclusionsIn the largest series of individuals withDSPtv, we demonstrate variant location is a novel risk factor for ventricular arrhythmia, can inform variant interpretation, and provide critical insights to allow precision-based clinical management.

Background Prospective studies harnessing late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) afford the potential to non-invasively characterise the phenotypic substrate for sudden cardiac death (SCD) and simultaneously interrogate its mechanistic drivers. Purpose To assess the utility of infarct characterisation by CMR, including scar microstructure analysis, to predict SCD in prospectively investigated patients with coronary heart disease (CHD). Methods Patients with stable CHD were prospectively recruited into a registry between August 2009 and January 2016. The primary outcome for this study was SCD or aborted SCD. Patients with a secondary prevention implantable cardioverter defibrillator (ICD) indication were excluded. All patients had CMR with LGE imaging. Infarct quantification (core scar and peri-infarct zone [PIZ]) was performed by an independent level 3 CMR reader. Outcome events were adjudicated by a panel of cardiologists blinded to the CMR data. To investigate fibrosis microstructure, bespoke computational image processing algorithms were applied to the LGE images in order to extract specific morphological and texture related features. Results Of 437 patients (mean age 64, mean left ventricular ejection fraction [LVEF] 47%, 91% with LGE) followed for a median of 6.3 years, 49 patients (11.2%) experienced the primary outcome. Patients with higher PIZ mass had an increased risk of the primary outcome (10-year risk 0.7%, 24.0% and 37.8% for patients with PIZ mass Conclusions In this large prospective study of patients with stable CHD, both PIZ mass and core infarct mass independently predicted long-term SCD risk after adjusting for conventional predictors including LVEF. Reassuringly, minimal or absent LGE portended a comparatively low risk of SCD. Analysis of the scar microstructure identified several shape-based features that associated with SCD. These results highlight a potential avenue towards a more personalised approach to ICD implantation decisions. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Lung and Heart Institute, Imperial College London

Objectives The objective of this study was to determine the relationship between heart rate and relapse among patients in the TRED-HF (Therapy withdrawal in REcovered Dilated cardiomyopathy trial). Background Understanding markers and mechanisms of relapse among patients with recovered dilated cardiomyopathy (DCM) may enable personalized management. Methods The relationship between serial heart rate measurements and relapse was examined among patients in the TRED-HF trial, a randomized trial which examined the safety and feasibility of withdrawing heart failure therapy from 51 patients with recovered DCM over 6 months. In total, 25 patients were randomized to therapy withdrawal and 26 to continue therapy, of whom 25 subsequently began therapy withdrawal in a single arm crossover phase. Results The mean ± SD heart rate for those who had therapy withdrawn and did not relapse was 64.6 ± 10.7 beats/min at baseline and 74.7 ± 10.4 beats/min at follow-up, compared to 68.3 ± 11.3 beats/min at baseline and 86.1 ± 11.8 beats/min at follow-up for those who relapsed. After adjusting for differences in heart rate at baseline, patients who had therapy withdrawn and relapsed had a 10.4 beats/min (95% CI: 4.0–16.8) greater rise in heart rate than patients who had therapy withdrawn and did not relapse (P = 0.002). After data were adjusted for age, log N-terminal pro–B-type natriuretic peptide, and left ventricular ejection fraction (LVEF), heart rate (per 10 beats/min; hazard ratio [HR]: 1.65; 95% CI: 1.10-2.57; P = 0.01) and change in heart rate from baseline (per 10 beats/min; HR: 1.70; 95% CI: 1.12–2.57; p = 0.01) were associated with relapse. The results remained qualitatively the same after adjusting for beta-blocker dose. Conclusions For patients with DCM and improved LVEF, the rise in heart rate after treatment is withdrawn treatment identifies patients who are more likely to relapse. Whether the increase in heart rate is a marker or a mediator of relapse requires investigation. (Therapy withdrawal in REcovered Dilated cardiomyopathy trial [TRED]; NCT02859311), Central Illustration

Resume Objectifs La chirurgie des tumeurs cutanees est courante et bien codifiee. Il existe une discordance entre des recommandations sur les marges macroscopiques a appliquer et les decisions therapeutiques prises sur des marges histologiques. Cette etude a pour vocation d’etudier la retraction cutanee qui survient lors de l’exerese puis dans le formol, pour comprendre la discordance anatomoclinique souvent retrouvee. Patients et methodes Il s’agissait d’une etude prospective, d’un mois, incluant les patients beneficiant d’une chirurgie cutanee. Pour chaque tumeur, le chirurgien effectuait 4 mesures de marges avant puis apres l’exerese ; marges mesurees a nouveau en histologie. Le critere de jugement etait la difference de mesure de marges en preoperatoire, postoperatoire et histologie. Ces donnees etaient ponderees en fonction de facteurs lies au patient et a la tumeur. Resultats Soixante-dix-neuf tumeurs pour 61 patients ont ete etudiees. L’etude a montre une retraction significative entre les mesures preoperatoires et postoperatoires, de 0,4 a 0,6 mm. Elle n’est correlee a aucun des autres facteurs testes. Une retraction significative entre 0,4 et 0,5 mm a egalement ete montree entre les mesures preoperatoires et histologiques. En revanche, il existe une augmentation significative entre les mesures postoperatoires et histologiques. Conclusion Ce dernier resultat pourrait etre lie a la zone peri-lesionnelle inflammatoire que le chirurgien considere comme etant tumoral donc exclut de sa marge alors que l’histologie peut montrer une zone saine. Devant ces resultats, un comite d’expert menant une etude plus importante pourrait inclure des recommandations sur les marges histologiques aux recommandations cliniques actuelles.

Eva Turk and colleagues believe that there is much to learn from the experiences of low and middle income countries in co-producing knowledge and working with communities to find feasible and acceptable solutions to healthcare concerns...

Background: Acute myocarditis is a complex inflammatory disease increasingly associated with dilated cardiomyopathy (DCM) and arrhythmogenic ventricular cardiomyopathy (ACM), but its underlying genetic basis is unknown. We sought to determine if there is a genetic overlap between myocarditis and inherited forms of cardiomyopathy. Methods: Population-based cohort of 336 consecutive patients with acute myocarditis enrolled in London and Maastricht. All participants underwent targeted DNA-sequencing for well-characterised cardiomyopathy-associated genes with comparison to healthy controls (n=1053). Case ascertainment was assessed against national admission data. Findings: Variants of known or likely pathogenicity were identified in 6% of cases compared to

Aims: To characterize adverse ventricular remodelling after withdrawing therapy in recovered dilated cardiomyopathy (DCM). Methods and results: TRED-HF was a randomized controlled trial with a follow-on single-arm cross-over phase that examined the safety and feasibility of therapy withdrawal in patients with recovered DCM over 6 months. The primary endpoint was relapse of heart failure defined by (i) a reduction in left ventricular (LV) ejection fraction >10% and to 10% increase in LV end-diastolic volume and to above the normal range, (iii) a twofold rise in N-terminal pro-B-type natriuretic peptide and to >400 ng/L, or (iv) evidence of heart failure. LV mass, LV and right ventricular (RV) global longitudinal strain (GLS) and extracellular volume were measured using cardiovascular magnetic resonance at baseline and follow-up (6 months or relapse) for 48 patients. LV cell and extracellular matrix masses were derived. The effect of withdrawing therapy, stratified by relapse and genotype, was investigated in the randomized and follow-on phases. In the randomized comparison, withdrawing therapy led to an increase in mean LV mass [5.4 g/m 2; 95% confidence interval (CI) 1.3–9.5] and cell mass (4.2 g/m 2; 95% CI 0.5–8.0) and a reduction in LV (3.5; 95% CI 1.6–5.5) and RV (2.4; 95% CI 0.1–4.7) GLS. In a non-randomized comparison of all patients (n = 47) who had therapy withdrawn in either phase, there was an increase in LV mass (6.2 g/m 2; 95% CI 3.6–8.9; P = 0.0001), cell mass (4.0 g/m 2; 95% CI 1.8–6.2; P = 0.0007) and matrix mass (1.7 g/m 2; 95% CI 0.7–2.6; P = 0.001) and a reduction in LV GLS (2.7; 95% CI 1.5–4.0; P = 0.0001). Amongst those who had therapy withdrawn and did not relapse, similar changes were observed (n = 28; LV mass: 5.1 g/m 2, 95% CI 1.5–8.8, P = 0.007; cell mass: 3.7 g/m 2, 95% CI 0.3–7.0, P = 0.03; matrix mass: 1.7 g/m 2, 95% CI 0.4–3.0, P = 0.02; LV GLS: 1.7, 95% CI 0.1–3.2, P = 0.04). Patients with TTN variants (n = 10) who had therapy withdrawn had a greater increase in LV matrix mass (mean effect of TTN: 2.6 g/m 2; 95% CI 0.4–4.8; P = 0.02). Conclusion: In TRED-HF, withdrawing therapy caused rapid remodelling, with early tissue and functional changes, even amongst patients who did not relapse.

Introduction In TRED-HF, 40% of patients with recovered dilated cardiomyopathy (DCM) relapsed in the short-term during phased withdrawal of drug therapy. Non-invasive markers of relapse may be used to monitor patients who wish a trial of therapy withdrawal and provide insights into the pathophysiological drivers of relapse. Purpose To investigate the relationship between changes in heart rate (HR) and relapse amongst patients with recovered DCM undergoing therapy withdrawal in TRED-HF. Methods Patients with recovered DCM were randomised to phased withdrawal of therapy or to continue therapy for 6 months. After 6 months of continued therapy, those in the control arm underwent withdrawal of therapy in a single arm crossover phase. HR was measured at each study visit. Mean HR and 95% confidence intervals (CI) were calculated at baseline, 45 days after baseline, 45 days prior to the end of the study or relapse and at the end of the study or relapse. Patients were stratified by treatment arm and the occurrence of the primary relapse end-point. Heart rate at follow-up was compared amongst patients who had therapy withdrawn and relapsed versus those who had therapy withdrawn and did not. ANCOVA was used to adjust for differences in HR at baseline between the two groups. Results Of 51 patients randomised, 26 were assigned to continue therapy and 25 to withdraw therapy. In the randomised and cross-over phases, 20 patients met the primary relapse end-point; one patient withdrew from the study and one patient completed follow-up in the control arm but did not enter the cross-over phase. Mean HR (standard deviation) at baseline and follow-up for (i) patients in the control arm was 69.9 (9.8) & 65.9 (9.1) respectively; (ii) for those who had therapy withdrawn and did not relapse was 64.6 (10.7) & 74.7 (10.4) respectively; and (iii) for those who had therapy withdrawn and relapsed was 68.3 (11.3) & 86.1 (11.8) respectively [all beats per minute]. The mean change in HR between the penultimate visit and the final visit for those who had therapy withdrawn and did not relapse was −2.4 (9.7) compared to 3.1 (15.5) for those who relapsed. After adjusting for differences in HR at baseline, the mean difference in HR measured at follow-up between patients who underwent therapy withdrawal and did, and did not relapse was 10.4bpm (95% CI 4.0–16.8; p=0.002) (Figure 1 & Table 1). Conclusion(s) A larger increase in HR may be a simple and effective marker of relapse for patients with recovered DCM who have insisted on a trial of therapy withdrawal. Whether HR control is crucial to the maintenance of remission amongst patients with improved cardiac function, or is simply a marker of deteriorating cardiac function, warrants further investigation. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): British Heart Foundation

Sensor-based human activity recognition has become one of the challenging and emerging research areas. Several machine learning algorithm with appropriate feature extraction has been used to solve human activity recognition task. However, recent research mainly focused on various deep learning algorithms, our focus of this study is measuring the performance of traditional machine learning algorithms with the incorporation of frequency-domain features. Because deep learning methods require a high computational cost. In this paper, we used Naive Bayes, K-Nearest Neighbour, SVM, Random Forest and Multilayer Perceptron with necessary feature extraction for our experimentation. We achieved best performance for K-Nearest Neighbour. Our experiment was a part of "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data" followed by the team MoonShot_BD. We concluded that with proper feature extraction, machine learning techniques may be useful to solve activity recognition with a low computational cost.

Human activity recognition on sensor data plays a vital role in health monitoring and elderly care service monitoring. Although tremendous progress has been noticed to the use of sensor technology to collect activity recognition data, recognition still remains challenging due to the pervasive nature of the activities. In this paper, we present a Convolution Neural Network (CNN) model by our team DataDrivers_BD in "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data" which is quite challenging because of the similarity among the tasks. On the other hand, the dissimilarity among the users patterns of working for a particular task. Since CNN can retrieve informative features automatically, it has become one of the most prominent methods in activity recognition. Our extensive experiment on nurse care activity recognition challenge dataset also achieved significant accuracy of 91.59% outperforming the existing state of the art algorithms.

Online monitoring of electroencephalogram (EEG) signals is challenging due to the high volume of data and power requirements. Compressed sensing (CS) may be employed to address these issues. Compressed sensing using a sparse binary matrix, owing to its low power features, and reconstruction/decompression using spatiotemporal sparse Bayesian learning have been shown to constitute a robust framework for fast, energy efficient and accurate multichannel bio-signal monitoring. EEG signal, however, does not show a strong temporal correlation. Therefore, the use of sparsifying dictionaries has been proposed to exploit the sparsity in a transformed domain instead. Assuming sparsification adds values, a challenge, therefore, in employing this CS framework for the EEG signal, is to identify the suitable dictionary. Using real multichannel EEG data from 15 subjects, in this paper, we systematically evaluate the performance of the framework when using various wavelet bases while considering their key attributes namely number of vanishing moments and coherence with sensing matrix. We identified Beylkin as the wavelet dictionary leading to the best performance. Using the same dataset, we then compared the performance of Beylkin with the discrete cosine basis, often used in the literature, and the alternative of not using a sparsifying dictionary. We further demonstrate that using dictionaries (Beylkin and Discrete Cosine Transform (DCT)) may improve performance tangibly only for a high compression ratio (CR) of 80% and with smaller block sizes, as compared to using no dictionaries.

Objectives This study aims to investigate the prognostic significance of late gadolinium enhancement (LGE) in patients without coronary artery disease and with normal range left ventricular (LV) volumes and ejection fraction. Background Nonischemic patterns of LGE with normal LV volumes and ejection fraction are increasingly detected on cardiovascular magnetic resonance, but their prognostic significance, and consequently management, is uncertain. Methods Patients with midwall/subepicardial LGE and normal LV volumes, wall thickness, and ejection fraction on cardiovascular magnetic resonance were enrolled and compared to a control group without LGE. The primary outcome was actual or aborted sudden cardiac death (SCD). Results Of 748 patients enrolled, 401 had LGE and 347 did not. The median age was 50 years (interquartile range: 38-61 years), LV ejection fraction 66% (interquartile range: 62%-70%), and 287 (38%) were women. Scan indications included chest pain (40%), palpitation (33%) and breathlessness (13%). No patient experienced SCD and only 1 LGE+ patient (0.13%) had an aborted SCD in the 11th follow-up year. Over a median of 4.3 years, 30 patients (4.0%) died. All-cause mortality was similar for LGE+/- patients (3.7% vs 4.3%; P = 0.71) and was associated with age (HR: 2.04 per 10 years; 95% CI: 1.46-2.79; P < 0.001). Twenty-one LGE+ and 4 LGE- patients had an unplanned cardiovascular hospital admission (HR: 7.22; 95% CI: 4.26-21.17; P < 0.0001). Conclusions There was a low SCD risk during long-term follow-up in patients with LGE but otherwise normal LV volumes and ejection fraction. Mortality was driven by age and not LGE presence, location, or extent, although the latter was associated with greater cardiovascular hospitalization for suspected myocarditis and symptomatic ventricular tachycardia., Central Illustration

Ecosystem service assessments are increasingly used to support natural resource management, but there is a bias in their application towards terrestrial systems and higher income countries. Tropical marine applications are particularly scarce, especially in SE Asia. Given the growing coastal population and expansion in blue economy sectors in SE Asia, evidence to support effective marine planning, such as ecosystem service assessments, is urgently needed. Data deficiencies for marine systems, especially (but not only) in lower income countries is a significant obstacle for ecosystem service assessments. To overcome this, we develop an ecosystem service potential matrix which combines evidence taken from an extensive literature review together with expert opinion. The matrix includes both natural and modified habitats as the service providing units. The ecosystem service potential for habitats are scored at the macro level (e.g. mangrove) due to insufficient evidence to score micro-habitats (e.g. fringe, basin or riverine mangroves). The majority of evidence is available for biogenic habitats (mangroves, coral reefs and seagrass meadows) with comparatively little for sedimentary habitats. While provisioning, regulating and cultural services are scored, published evidence is more readily available for provisioning and regulating services. Confidence scores, indicating the uncertainty in the ecosystem service potential scores are included in the matrix. To our knowledge this is the first attempt to systematically capture the provision of ecosystem services from tropical marine habitats. Although initially developed for four marine biosphere reserves and protected areas in SE Asia, the generic nature of the evidence included suggests that the matrix constitutes a valuable baseline for marine ecosystem service assessments within SE Asia and provides a robust foundation for development in future work.

Importance The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen Main Outcomes and Measures Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

Combining the advantages of lithium-ion batteries and supercapacitors is an interesting solution to high-energy devices with the maintenance of high power output. Herein we report on the performance of the lithium-ion capacitors (LICs), exploiting graphene-based materials and activated carbon as negative and positive electrodes, respectively. The electrochemical properties of pre-lithiated reduced graphite oxides (reduced thermally—TRGO, or chemically—CRGO) and pristine graphite are compared based on measurements conducted in two- and three-electrode cells. Chemically reduced graphite oxide (CRGO) displays excellent performance at current densities up to 8 A g−1. The assembled hybrid capacitor delivers the energy density around 80 Wh kg−1 along a wide range of power densities. Promising results show that even at power value of 24.8 kW kg−1, the device retains energy density over 35 Wh kg−1. The cycle performance also shows good energy retention comparing with a graphite anode. However, better energy retention is observable for the TRGO-negative electrode material. After the 2000 of cycles, the AC/TRGO(Li) system loaded with the current density of 1 A g−1 provides energy of 58 Wh kg−1.

Cardiovascular magnetic resonance (CMR) is a versatile imaging modality that enables aetiological assessment and provides additional information to that of standard echocardiography in a significant proportion of patients with heart failure. In addition to highly accurate and reproducible assessment of ventricular volumes and replacement fibrosis, multiparametric mapping techniques have rapidly evolved to further expand the diagnostic and prognostic applications in various conditions ranging from acute inflammatory and ischaemic cardiomyopathy, to cardiac involvement in systemic diseases such as sarcoidosis and iron overload cardiomyopathy. In this review, we discuss the established role of T2* imaging and rapidly evolving clinical applications of myocardial T2 mapping as quantitative adjuncts to established qualitative imaging techniques.

Negotiations on the liberalization of environmental goods (EGs) and services within the WTO Doha Round (mandated in November 2001) are facing specific challenges. Conflicting interests and differing perceptions of the benefits of increased trade in EGs were reflected in different approaches proposed for determining EGs. Using import data of 34 Organisation for Economic Co-operation and Development (OECD) member countries and from a sample of 167 countries, from 1995 to 2012, we discuss the trade effect of reducing barriers on EGs. We analyze the lists of EGs proposed by the Asia-Pacific Economic Cooperation and OECD using a Translog gravity model. We found that removing tariff barriers for EGs will have a modest impact because for the biggest importers and exporters, elasticities of trade costs are very low while for most trading relationships they are very high, making it difficult for exporters to maintain their markets. Overall, our results suggest that, because of their substantial effect on international trade, future negotiations on EGs should also address the issues of standards and nontariff barriers.

IntroductionThe Institute of Medicine recommended the advance of innovation and entrepreneurship training programs within the Clinical & Translational Science Award (CTSA) program; however, there remains a gap in adoption by CTSA institutes. The University of Michigan’s Michigan Institute for Clinical & Health Research and Fast Forward Medical Innovation (FFMI) partnered to develop a pilot program designed to teach CTSA hubs how to implement innovation and entrepreneurship programs at their home institutions.Materials and methodsThe program provided a 2-day onsite training experience combined with observation of an ongoing course focused on providing biomedical innovation, commercialization and entrepreneurial training to a medical academician audience (FFMI fastPACE).ResultsAll 9 participating CTSA institutes reported a greater connection to biomedical research commercialization resources. Six launched their own version of the FFMI fastPACE course or modified existing programs. Two reported greater collaboration with their technology transfer offices.ConclusionThe FFMI fastPACE course and training program may be suitable for CTSA hubs looking to enhance innovation and entrepreneurship within their institutions and across their innovation ecosystems.

Human activity recognition is a challenging task due to complexity and variations of human movements while performing activities by different subjects. Extracting features to model the temporal evolution of different movements plays an important role in this task. In this paper, we present the approach followed by our team, Dark_Shadow, to recognize complex nurse activities in the "Nurse Care Activity Recognition Challenge" [1]. We present a deep learning method to capture the movements of essential body parts from time series of human activity data collected by sensors and then classify them. Deep learning approaches have provided satisfactory results in various human activity recognition tasks. In this work, we propose a Gated Recurrent Unit (GRU) model with attention mechanism to recognize the nurse activities. We obtain approximately 66.43% accuracy for person-wise one leave out cross validation.

Introduction: Chronic pain is one of the most prevalent causes of disability worldwide, and digital interventions may be one of the ways to meet this need. Randomised controlled trials have demonstrated that digital interventions can be effective in treating chronic pain. This study aimed to establish the clinical effectiveness of a web-based pain management programme (PMP), specifically whether it would lead to improved clinical outcomes and reduced health care costs in a real-world clinical setting. Methods: Of 738 participants, 438 engaged with the programme and 300 did not. Two analyses were conducted: a within-subjects pre–post comparison of clinical outcomes for participants who completed the programme and a between-groups comparison of health care usage for those who engaged and those who did not. Results: Participants who completed the programme made significant improvements with regard to their perceived health status, level of disability, mood, confidence managing pain, problems in life due to pain and level of pain. Around one-third of participants made reliable changes in their levels of disability, depression and anxiety. There was no relationship between gender or age and engagement with the programme. Those who engaged with the programme demonstrated reduced health care costs in the year following referral, whereas health care costs of non-engagers increased. Limitations of the study include a high drop-out rate and a non-randomised comparison group. Results must therefore be interpreted with some caution. Conclusion: A web-based pain management programme can be clinically effective and may be a useful addition to the treatments offered by pain management services.

Hemorrhagic myocarditis is a potentially fatal complication of excessive levels of systemic inflammation. It has been reported in viral infection, but is also possible in systemic autoimmunity. Epicutaneous treatment of mice with the Toll-like receptor 7 (TLR-7) agonist Resiquimod induces auto-antibodies and systemic tissue damage, including in the heart, and is used as an inducible mouse model of systemic lupus erythematosus (SLE). Here, we show that overactivation of the TLR-7 pathway of viral recognition by Resiquimod treatment of CFN mice induces severe thrombocytopenia and internal bleeding, which manifests most prominently as hemorrhagic myocarditis. We optimized a cardiac magnetic resonance (CMR) tissue mapping approach for the in vivo detection of diffuse infiltration, fibrosis and hemorrhages using a combination of T1, T2 and T2* relaxation times, and compared results with ex vivo histopathology of cardiac sections corresponding to CMR tissue maps. This allowed detailed correlation between in vivo CMR parameters and ex vivo histopathology, and confirmed the need to include T2* measurements to detect tissue iron for accurate interpretation of pathology associated with CMR parameter changes. In summary, we provide detailed histological and in vivo imaging-based characterization of acute hemorrhagic myocarditis as an acute cardiac complication in the mouse model of Resiquimod-induced SLE, and a refined CMR protocol to allow non-invasive longitudinal in vivo studies of heart involvement in acute inflammation. We propose that adding T2* mapping to CMR protocols for myocarditis diagnosis improves diagnostic sensitivity and interpretation of disease mechanisms. This article has an associated First Person interview with the first author of the paper.

BACKGROUNDHemorrhagic myocarditis is a potentially fatal complication of excessive levels of systemic inflammation. It has been reported in viral infection, but is also possible in systemic autoimmunity. Cardiac magnetic resonance (CMR) imaging is the current gold standard for non-invasive detection of suspected inflammatory damage to the heart and changes in T1 and T2 relaxation times are commonly used to detect edema associated with immune cell infiltration and fibrosis. These measurements also form the basis of the Lake Louise Criteria, which define a framework for the CMR-based diagnosis of myocarditis. However, they do not take into account the possibility of hemorrhage leading to tissue iron deposition which strongly influences T1 and T2 measurements and may complicate interpretation based on these two parameters only.METHODSSystemic inflammation was induced in CFN mice by application of the TLR-7 agonist Resiquimod. Histopathology was performed on heart sections to assess immune cell infiltration (Hematoxylin & Eosin), fibrosis (PicoSirius Red) and tissue iron deposition (Perl’s Prussian Blue). A multi-parametric cardiac MRI tissue mapping approach measuring T1, T2, and T2* relaxation times was established to non-invasively identify these parameters in this small rodent model.RESULTSResiquimod-treated mice developed severe thrombocytopenia and hemorrhagic myocarditis. We identified patches of cardiac hemorrhage based on the presence of two major MRI phenotypes. Increased T2 with normal T1 and T2* values correlated with infiltration/edema only, while decreased T1, T2, and T2* values identify areas with infiltration/edema in the presence of iron indicating hemorrhagic myocarditis.CONCLUSIONWe show that over-activation of the TLR-7 pathway by Resiquimod treatment of CFN mice induces an early immune response reminiscent of excessive systemic inflammation due to viral infection. This causes internal bleeding which manifests most prominently as severe hemorrhagic myocarditis. We optimized a comprehensive noninvasive in vivo MRI approach based on quantitative measurement of T1, T2 and T2* to demonstrate the presence of diffuse myocardial edema, infiltration and iron deposition without the need of contrast agent administration. We propose that adding quantitative T2* mapping to CMR protocols for detection of myocarditis will improve diagnostic sensitivity and interpretation of disease mechanisms.

Background Increased nitric oxide (NO) and its generating enzyme, inducible nitric oxide synthase (iNOS), are observed in experimental models of idiopathic pulmonary fibrosis (IPF). Asymmetric dimethylarginine (ADMA) competitively inhibits iNOS and is hydrolysed by dimethylarginine dimethylaminohydrolase (DDAH) 1 and 2. Modulation of NO bioavailability via pharmacological/genetic knock-out of DDAH1 attenuates bleomycin-induced fibrosis in vivo. Using a candidate gene approach with an a priori hypothesis, we investigated the impact of DDAH1 variants associated with lower ADMA (thus indicative of DDAH1 over-activity) on lung function decline and survival in IPF. Methods Consecutive patients with a new diagnosis IPF were recruited from a tertiary UK ILD centre (n=70). Only Caucasian individuals were included, patients on anti-fibrotic agents were excluded. Clinical end points were: time to first FVC decline (≥10% relative change in FVC from baseline) and survival (months survived following presentation). DDAH1 tSNPs were identified in Caucasian individuals genotyped in the International HapMap Project. Those with minor allele frequency >40% in the IPF cohort and an association with lower ADMA levels in a normal cohort were selected – rs530006 and rs6576765. Survival curves and stepwise multivariate Cox proportional hazards analyses were performed. Results Baseline pulmonary function did not vary between genotypes. FVC decline differed between rs530006 genotypes (p Conclusion Minor homozygote carriage of DDAH1 variant rs530006 was associated with accelerated disease progression and mortality in IPF. These findings require validation in a larger, replicate cohort of IPF patients. Identification of aggressive disease phenotypes may allow risk stratification and earlier therapeutic intervention in IPF.

Natural disasters, such as earthquakes, can cause severe destruction and create havoc in the society. Buildings and other structures may collapse during disaster incidents causing injuries and deaths to victims trapped under debris and rubble. Immediately after a natural disaster incident, it becomes extremely difficult for first responders and rescuers to find and save trapped victims. Often searches are carried out blindly in random locations, which delay the rescue of the victims. This paper introduces a Smartphone Assisted Disaster Recovery (SmartDR) method for post-disaster communication using Smartphones. SmartDR utilizes the device-to-device (D2D) communication technology in Fifth Generation (5G) networks, which enables direct communication between proximate devices without the need of relaying through a network infrastructure, such as mobile access points or mobile base stations. We examine a scenario of multi-hop D2D communication where smartphones carried by trapped victims and other people in disaster affected areas can self-detect the occurrence of a disaster incident by monitoring the radio environment and then can self-switch to a disaster mode to transmit emergency help messages with their location coordinates to other nearby smartphones. To locate other nearby smartphones also operating in the disaster mode and in the same channel, each smartphone runs a rendezvous process. The emergency messages are thus relayed to the functional base station or rescue centre. To facilitate routing of the emergency messages, we propose a path selection algorithm, which considers both delay and the leftover energy of a device (a smartphone in this case). Thus, the SmartDR method includes: (i) a multi-channel channel hopping rendezvous protocol to improve the victim localization or neighbour discovery, and (ii) an energy-aware multi-path routing (Energy-aware ad-hoc on-demand distance vector or E-AODV) protocol to overcome the higher energy depletion rate at devices associated with single shortest path routing. The SmartDR method can guide search and rescue operations and increase the possibility of saving lives immediately aftermath a disaster incident. A simulation-based performance study is conducted to evaluate the protocol performance in post-disaster scenario. Simulation results show that a significant performance gain is achievable when a device utilizes the channel information for the rendezvous process and the leftover energy for routing path selection. Our results show that peer discovery in multi-channel D2D environment can be significantly improved when channel quality information is considered in CH sequence design. Moreover, selecting a routing path based on LoE of a device and the standard deviation of residual energy of a path, can not only enhance the network lifetime, but also reduce the chance of network being partitioned.

This paper studies the impact of growth, and trade and investment openness on the environment at the regional level. We find support for the environmental Kuznet Curve hypothesis for CO2 emissions in Africa, Asia and OECD countries. We show that Foreign Direct Investment (FDI) has detrimental effects on CO2 emissions in Africa, the Middle East and North Africa, the former United Socialist Soviet Republic and Eastern Europe, and South America, but not in Asia where it seems to have beneficial effects. FDI raises SO2 emissions in South America, although it does lower them in Africa. Local investment is contributing significantly to both CO2 and SO2 emission increases in most regions while trade openness reduces (increases) CO2 in OECD countries (South America).

Planning the design and discussing the results are routine tasks carried out daily by researchers all over the world. This study is focused on making these stages of research time- and cost-efficient without affecting the reliability of the data. Following the principles of the Taguchi method brings such a result. This approach ensures the quality of research by optimisation of the experimental procedure. In the presented work, the Taguchi method solves an exemplary research problem of the corrosion process of aluminium current collector in lithium-ion batteries. Operating factors selected for the optimisation include temperature (25, 35, 45 °C), the volume ratio of ethylene carbonate to diethyl carbonate in the solvent (1:1, 1:2, 1:4), and type of lithium salt (LiPF6, LiTFSI, LiBOB). Using three operating factors with three levels of variability requires data assembly in an orthogonal array L9 with nine experimental runs in total. In this study, the Taguchi approach is validated by performing a full-factorial procedure of twenty-seven individual experiments. The response of tested systems is analysed by cyclic voltammetry and electrochemical impedance spectroscopy, which enables the evaluation of corrosion parameters in each experimental run. The assessed parameters indicate the intensity of the aluminium corrosion depending on the operating conditions. These conditions cause unwanted variance, which accelerates corrosion and lowers the life expectancy of lithium-ion batteries. The Taguchi signal-to-noise-ratio analysis reveals that the most significant variation is caused by the lithium salt in the electrolyte (S/N LiBOB ≥ LiPF6 > LiTFSI). Temperature and alkyl carbonates play a secondary role. The Taguchi prediction method is also consistent with the full-factorial approach, which warrants reliability.

Predicting whether patients with interstitial lung disease (ILD) require supplemental oxygen during air travel is difficult. Hypoxic challenge testing (HCT) is recommended to determine the need for in-flight oxygen but is only available in specialised centres. We previously identified a formula combining resting PaO2 and the composite physiological index (CPI) [(1.1 x PaO2 on air) - (0.07 x CPI), Lota et al, ERJ, 2012 40: P690] with a score of >9 predictive of fitness-to-fly. Our objective was to validate this formula in 134 patients who underwent HCT between 2016-2017. HCT was performed by inhalation of a gas mixture containing an FiO2 of 0.15 for 20 minutes. Patients were defined as fit-to-fly if SpO2>85% and/or PaO2>6.6kPa (BTS, 2011). In the study cohort, mean (±SD) age was 63±12.6 years, 52% female, 33% had a history of smoking and 30% had a diagnosis of idiopathic pulmonary fibrosis. The mean TLCO, KCO, FEV1 and FVC was 33.9±12.8%, 65.8±16.9%, 66.9±19.6% and 68.3±18.9%, respectively. Mean CPI was 55.5±12.5. 51/134 patients (38%) were deemed fit-to-fly according to BTS guidelines. A predictive score >9 was observed in 16/134 patients (12%). Of these, all were fit-to-fly on HCT when SpO2>85% was used as the indicator, with only 2/16 having a borderline PaO2 just below 6.6kPa. The negative predictive value of the score was 87%. This patient cohort was characterised by severe ILD. Although our formula identified a relatively small proportion of patients, it was highly specific in predicting fitness to fly, and may be usefully applied to patients with less severe ILD, if confirmed prospectively, to identify which patients to refer on to formal hypoxic challenge testing.

The major burden of sudden cardiac death (SCD) in patients with heart disease occurs in those with a left ventricular ejection fraction > 40%. Although the annual risk of SCD may be lower in these patients compared to those with lower LVEF, their lifetime cumulative risk of SCD may be greater due to a better overall prognosis. It is plausible that those with LVEF > 40% who are at highest risk of life-threatening arrhythmia will benefit from implantable cardioverter defibrillators. Features that identify patients with a LVEF > 40% at high risk of SCD are urgently needed. We review existing studies examining SCD markers in this sub-group and discuss gaps in the current evidence base.

Welcome to the 2017 Special Issue on the IEEE Circuits and Systems Society (CASS) Regional Flagship Conferences, a collection of selected papers presented at the 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS 2016), held in Jeju, South Korea, October 25–28, 2016, the 23rd IEEE International Conference on Electronics Circuits and Systems (ICECS 2016), held in Monte Carlo, Monaco, December 11–14, 2016, and the 8th IEEE Latin American Symposium on Circuits and Systems (LASCAS 2017), held in Bariloche, Argentina, February 20–23, 2017. In APCCAS 2016, 212 papers were presented, selected from 310 papers submitted; in ICECS 2016, 191 papers were presented, selected from 353 papers submitted; in LASCAS 2017, 70 papers were presented, selected from 172 papers submitted.

Virtualization of physical network devices is a relatively new technology, that allows to improve the network organization and gives new possibilities for Software Defined Networking (SDN). Network virtualization is also commonly used for testing and debugging environments, before implementing new designs in production networks. Important aspect of network virtualization is selecting virtual platform and technology, that offer maximal performance with minimal physical resource utilization. This article presents a comparative analysis of performance of the virtual network created by the virtual CSR1000v and virtual machines running Windows 8.1 on two different virtual private cloud platforms: VMware vSphere 5.5 and Microsoft Hyper-V Server 2012 R2. In such prepared testbed we study the response time (delay) and throughput of virtual network devices.

This multicenter case-controlled pilot study evaluated myocardial inflammatory burden (IB) and phenotype in endomyocardial biopsies (EMBs) with and without pathologic antibody-mediated rejection (pAMR). Sixty-five EMBs from five European heart transplant centers were centrally reviewed as positive (grade 2, n = 28), suspicious (grade 1, n = 7) or negative (n = 30) for pAMR. Absolute counts of total, intravascular (IV) and extravascular (EV) immunophenotyped mononuclear cells were correlated with pAMR grade, capillary C4d deposition, donor specific antibody (DSA) status and acute cellular rejection (ACR). In pAMR+ biopsies, equivalent number of IV CD3+ T lymphocytes (23 ± 4/0.225 mm(2) ) and CD68+ macrophages (21 ± 4/0.225 mm(2) ) were seen. IB and cell phenotype correlated with pAMR grade, C4d positivity and DSA positivity (p < 0.0001). High numbers of IV T lymphocytes were associated with low grade ACR (p = 0.002). In late-occurring AMR EV plasma cells occurring in 34% of pAMR+ EMBs were associated with higher IB. The IB in AMR correlated with pAMR+, C4d positivity and DSA positivity. In pAMR+ equivalent numbers of IV T lymphocytes and macrophages were found. The presence of plasma cells was associated with a higher IB and occurrence of pAMR late after transplantation.