mHealth system for the early detection of infectious diseases using biomedical signals

Latin American Congress on Automation and Robotics LACAR 2019, 30/10/2019-01/11/2019, Cali, Colombia.Detection at an early stage of an infection is a major clinical challenge. An infection that is not diagnosed in time can not only seriously affect the health of the infected patient, but also spread and initiate a contagious approach towards other people. This paper deals with mHealth system for medical care and pre-diagnosis. The developed mHealth system use an Android App that collects physiological signals from the patients with a portable and easy-to-use sensors kit. The focus of the work is put on being able to build a low-cost system that using a very small amounts of data (one set record per patient and day). The processed data are uploaded to an online database to train a clinical decision support system to automatically diagnose infections. The mHealth system may be operated by the same personnel on site not requiring to be medical or computational skilled at all. The implementation takes five kinds of measures simultaneously (Electrodermal Activity, Body Temperature, Blood Pressure, Heart Beat Rate and Oxygen Saturation (SPO2)). A real implementation has been tested and results confirm that the sampling process can be done very fast and steadily Finally, the App usability was tested, showing a fast learning curve and no significant differences are observable in learning time by people with different skills or age. These usability factors are key for the mHealth system success

Disentangling astroglial physiology with a realistic cell model in silico

Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. Our simulations suggest that currents generated by glutamate transporters or K+ channels have negligible distant effects on membrane voltage and that individual astrocytes can successfully handle extracellular K+ hotspots. We show how intracellular Ca2+ buffers affect Ca2+ waves and why the classical Ca2+ sparks-and-puffs mechanism is theoretically compatible with common readouts of astroglial Ca2+ imaging

Effectiveness of a self-management intervention with personalised genetic and lifestyle-related risk information on coronary heart disease and diabetes-related risk in type 2 diabetes (CoRDia): study protocol for a randomised controlled trial

Background Many patients with type 2 diabetes fail to achieve good glycaemic control. Poor control is associated with complications including coronary heart disease (CHD). Effective self-management and engagement in health behaviours can reduce risks of complications. However, patients often struggle to adopt and maintain these behaviours. Self-management interventions have been found to be effective in improving glycaemic control. Recent developments in the field of genetics mean that patients can be given personalised information about genetic- and lifestyle-associated risk of developing CHD. Such information may increase patients’ motivation to engage in self-management. The Coronary Risk in Diabetes (CoRDia) trial will compare the effectiveness of a self-management intervention, with and without provision of personalised genetic- and lifestyle-associated risk information, with usual care, on clinical and behavioural outcomes, the cognitive predictors of behaviour, and psychological wellbeing. Methods/Design Participants will be adults aged 25–74 years registered with general practices in the East of England, diagnosed with type 2 diabetes, with no history of heart disease, and with a glycated haemoglobin level of ≥6.45 % (47 mmol/mol). Consenting participants will be randomised to one of three arms: usual care control, group self-management only, group self-management plus personalised genetic- and lifestyle-associated risk information. The self-management groups will receive four weekly 2-hour group sessions, focusing on knowledge and information sharing, problem solving, goal setting and action planning to promote medication adherence, healthy eating, and physical activity. Primary outcomes are glycaemic control and CHD risk. Clinical data will be collected from GP records, including HbA1c, weight, body mass index, blood pressure, and HDL and total cholesterol. Self-reported health behaviours, including medication adherence, healthy eating and physical activity, and cognitive outcomes will be assessed by questionnaire. Measures will be taken at baseline, 3 months (questionnaire only), 6 months and 12 months post-baseline. Discussion This study will determine whether the addition of personalised genetic- and lifestyle-associated CHD risk information to a group self-management intervention improves diabetes control and CHD risk compared with group self-management and usual care. Effectiveness of the combined intervention on health behaviours cognitions theorised to predict them, and psychological outcomes will also be investigated. Trial registration This study has been registered at ClinicalTrials.gov; registration identifier NCT01891786, registered 28 June 2013

Bayesian analysis of Jolly-Seber type models; Incorporating heterogeneity in arrival and departure

We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We demonstrate this approach using a data set of migrating semipalmated sandpipers (Calidris pussila) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. We demonstrate the use of reversible jump MCMC methods to derive posterior distributions for the model parameters and the models, simultaneously. The algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes

Redrawing the Map of Great Britain from a Network of Human Interactions

Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.National Science Foundation (U.S.)AT & TAudi AGUnited States. Dept. of Defense (National Defense Science and Engineering Fellowship Program

Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

<p>Abstract</p> <p>Background</p> <p>Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.</p> <p>Results</p> <p>In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent <it>hypotheses </it>that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.</p> <p>Conclusion</p> <p>Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.</p

An examination of factors influencing the choice of therapy for patients with coronary artery disease

BACKGROUND: A diverse range of factors influence clinicians' decisions regarding the allocation of patients to different treatments for coronary artery disease in routine cardiology clinics. These include demographic measures, risk factors, co-morbidities, measures of objective cardiac disease, symptom reports and functional limitations. This study examined which of these factors differentiated patients receiving angioplasty from medication; bypass surgery from medication; and bypass surgery from angioplasty. METHODS: Univariate and multivariate logistic regression analyses were conducted on patient data from 214 coronary artery disease patients who at the time of recruitment had been received a clinical assessment and were reviewed by their cardiologist in order to determine the form of treatment they were to undergo: 70 would receive/continue medication, 71 were to undergo angioplasty and 73 were to undergo bypass surgery. RESULTS: Analyses differentiating patients receiving angioplasty from medication produced 9 significant univariate predictors, of which 5 were also multivariately significant (left anterior descending artery disease, previous coronary interventions, age, hypertension and frequency of angina). The analyses differentiating patients receiving surgery from angioplasty produced 12 significant univariate predictors, of which 4 were multivariately significant (limitations in mobility range, circumflex artery disease, previous coronary interventions and educational level). The analyses differentiating patients receiving surgery from medication produced 14 significant univariate predictors, of which 4 were multivariately significant (left anterior descending artery disease, previous cerebral events, limitations in mobility range and circumflex artery disease). CONCLUSION: Variables emphasised in clinical guidelines are clearly involved in coronary artery disease treatment decisions. However, variables beyond these may also be important factors when therapy decisions are undertaken thus their roles require further investigation

Unified representation of fixtures: Clamping, locating and supporting elements in CNC manufacture

International audienceA CNC machining operation is the outcome of the application of the integrated capabilities of various resources within the CNC machining centre. Part fixtures, clamping and other location mechanisms are essential subsets of CNC machining resources. Today, various fixturing techniques and attachments available on the market allow manufacturers to enhance their production capability without buying expensive machine tools. This technology rich fixturing domain is detached while representing and exchanging machine tool resource information for making manufacturing decisions. The research work in this paper utilizes the STEP-NC compliant Unified Manufacturing Resource Model (UMRM) for representing fixtures in conjunction with the parent CNC machining centre. Thus UMRM is enhanced in this context to represent various fixtures such as universal vises, chucks, pallets, auxiliary rotary tables among others. The major contribution of this paper is the application of the extension of the UMRM approach for representing fixturing domain, which allows generic modelling of fixtures and loading devices in addition to machine workpiece and process modelling. This would enable the stage of automated process planning and manufacturing. The universal approach in representing resource information allows the data to be utilised for making a wide variety of manufacturing decisions

Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools

Scale-adjusted metrics (SAMs) are a significant achievement of the urban scaling hypothesis. SAMs remove the inherent biases of per capita measures computed in the absence of isometric allometries. However, this approach is limited to urban areas, while a large portion of the world’s population still lives outside cities and rural areas dominate land use worldwide. Here, we extend the concept of SAMs to population density scale-adjusted metrics (DSAMs) to reveal relationships among different types of crime and property metrics. Our approach allows all human environments to be considered, avoids problems in the definition of urban areas, and accounts for the heterogeneity of population distributions within urban regions. By combining DSAMs, cross-correlation, and complex network analysis, we find that crime and property types have intricate and hierarchically organized relationships leading to some striking conclusions. Drugs and burglary had uncorrelated DSAMs and, to the extent property transaction values are indicators of affluence, twelve out of fourteen crime metrics showed no evidence of specifically targeting affluence. Burglary and robbery were the most connected in our network analysis and the modular structures suggest an alternative to "zero-tolerance" policies by unveiling the crime and/or property types most likely to affect each other