There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

Background: About a third of home-dwelling older people fall each year, and institutionalized older people even report a two- or threefold higher rate for falling

Sensitivity and false alarm rate of a fall sensor in long-term fall detection in the elderly

Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

/pdf/activity-recognition-using-cell-phone-accelerometers-3k6h9fhibm.pdf

Activity recognition using cell phone accelerometers

The support of a failing heart with pump devices has been an essential element in cardiac health care for several decades It is therefore important to understand the left ventricular response to the pumping action of these devices when connected to the native heart Furthermore, monitoring of aortic valve opening and closure is important in avoiding valve stenosis and thrombogenesis during pump support This paper reports the first steps in simulating the effects of outlet pump pressure on aortic valve closure of the heart assisted by an implantable blood pump A two-dimensional fluid structure interaction aortic valve model is presented with blood flow in left ventricular chamber using the Arbitrary Lagrangian-Eulerian Finite Element Method formulation to predict the AV closure during outflow of blood from the left ventricle into the left ventricular assist device (LVAD)

Modeling aortic valve closure under the action of a ventricular assist device

Computational Modelling of Transcatheter Mitral Valve Replacement to Predict Post–Procedural Haemodynamics

Noncoding RNAs (ncRNAs) comprise a significant proportion of the mammalian genome, but their biological significance in neurodevelopment and in diseases is poorly understood. In this study, we have performed a genome-wide investigation of human noncoding RNAs for cell regulatory functions in brain tissue. By analysing ENCODE regulatory features, associations with FANTOM5 tissue-specific enhancers, as well as tissue-specific expression profiles, we have identified 17,743 noncoding RNAs comprising at least one nervous system-related expression Quantitative Trait Loci (eQTL) polymorphism that is associated with protein coding genes. Of these, 908 brain-enriched noncoding RNAs (comprising 907 long noncoding RNAs and 1 pseudogene) also overlap with chromatin states characterised as enhancers. Based on these criteria, we referred to such noncoding RNAs with putative enhancer activity as brain "enhancer-ncRNAs". To investigate their impact in neurodevelopmental disorders, we integrated GWAS SNPs and Copy Number Variation (CNV) data and found that 265 enhancer-ncRNAs were either mutated (CNV deletion or duplication) or contain at least one GWAS SNPs in the context of such conditions. Of these, the eQTL-associated gene for 82 enhancer-ncRNAs did not overlap with either GWAS SNPs or CNVs. However, in 23 of these 82 enhancer-ncRNAs, eQTL interaction was explained solely by the presence of each of these noncoding RNAs, suggesting in such contexts that mutations to neurodevelopment gene enhancers disrupt ncRNA interaction. We also cross-referenced our data with the DECIPHER database of clinical phenotypes to find that mutations to 34 of the 82 enhancer-ncRNAs are significantly associated with phenotypes including behavioural abnormality, and cognitive impairment. Taken together, we provide evidence for a distinct set of brain-enriched ncRNAs that influence genomic enhancers during neurodevelopment, suggesting enhancer mutations may be relevant to the functions for such ncRNAs in neurodevelopmental disorders.

/pdf/evidence-for-enhancer-noncoding-rnas-enhancer-ncrnas-with-41ur2kfanm.pdf

Evidence for enhancer noncoding RNAs (enhancer-ncRNAs) with gene regulatory functions relevant to neurodevelopmental disorders

Traditional methods of energy expenditure estimation, in the free-living environment, attempted using accelerometry operate without knowledge of the slope of the terrain which is being traversed. The ability to recognise the gradient of the walking surface will most likely improve upon these simplistic energy estimates. This paper expands upon previous work in this area, and investigates the benefit of step-by-step segmentation of the accelerometry signal in classifying the various gradients. Tri-axial accelerometry signals from 12 subjects, performing 30 s of walking on 4 different gradients (up and down paved ramps with gradients of 4.8% and 17.2%), were collected. A feature subset selection search procedure was applied to find the optimal subset of 65 extracted features which maximise the classification accuracy, performed with a Gaussian Mixture Model (GMM) classifier, as estimated using six-fold cross-validation. An overall classification accuracy of 94.83% was achieved using 13 features, for the four-class problem. There was an improvement of 4.1% upon the same classification task, without knowledge of the start/end times of individual steps, indicating that segmentation of the accelerometry signals at a step-by-step resolution is important for the automated classification of terrain gradient during walking. (6 pages)

Nigel H. Lovell

Papers

Modeling aortic valve closure under the action of a ventricular assist device

Computational Modelling of Transcatheter Mitral Valve Replacement to Predict Post–Procedural Haemodynamics

Evidence for enhancer noncoding RNAs (enhancer-ncRNAs) with gene regulatory functions relevant to neurodevelopmental disorders

An investigation of the impact of gait segmentation on accelerometry-based inclined terrain classification

Non-invasive activity-based control of an implantable rotary blood pump: Software simulation study