An In-Line photonic biosensor for monitoring of glucose concentrations

This paper presents two PDMS photonic biosensor designs that can be used for continuous monitoring of glucose concentrations. The first design, the internally immobilized sensor, consists of a reactor chamber, micro-lenses and self-alignment structures for fiber optics positioning. This sensor design allows optical detection of glucose concentrations under continuous glucose flow conditions of 33 μL/h based on internal co-immobilization of glucose oxidase (GOX) and horseradish peroxidase (HRP) on the internal PDMS surface of the reactor chamber. For this design, two co-immobilization methods, the simple adsorption and the covalent binding (PEG) methods were tested. Experiments showed successful results when using the covalent binding (PEG) method, where glucose concentrations up to 5 mM with a coefficient of determination (R2) of 0.99 and a limit of detection of 0.26 mM are detectable. The second design is a modified version of the internally immobilized sensor, where a microbead chamber and a beads filling channel are integrated into the sensor. This modification enabled external co-immobilization of enzymes covalently onto functionalized silica microbeads and allows binding a huge amount of HRP and GOX enzymes on the microbeads surfaces which increases the interaction area between immobilized enzymes and the analyte. This has a positive effect on the amount and rate of chemical reactions taking place inside the chamber. The sensor was tested under continuous glucose flow conditions and was found to be able to detect glucose concentrations up to 10 mM with R2 of 0.98 and a limit of detection of 0.7 mM. Such results are very promising for the application in photonic LOC systems used for online analysis © 2014 by the authors; licensee MDPI, Basel, Switzerland.This work has been funded by the German Research Foundation (DFG) within the framework of the Research Unit 856 Microsystems for Particulate Life-Science Products. One of the authors (S.B.) gratefully acknowledges the financial support of the Volkswagen Foundation. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)Peer Reviewe

Intelligent SDN Traffic Classification Using Deep Learning: Deep-SDN

Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area

211th ENMC International Workshop: Development of diagnostic criteria and management strategies for McArdle Disease and related rare glycogenolytic disorders to improve standards of care. 17-19 April 2015, Naarden, The Netherlands

Twenty participants, including one patient representative, from 9 countries (UK, Spain, France, Sweden, Finland, Denmark, Italy, Germany and USA) attended the workshop, the aims of which were to agree on the best practice strategies for diagnosis and management of McArdle disease (GSDV) and related rare glycolytic disorders of skeletal muscle

From exercise intolerance to functional improvement: The second wind phenomenon in the identification of McArdle disease

McArdle disease is the most common of the glycogen storage diseases. Onset of symptoms is usually in childhood with muscle pain and restricted exercise capacity. Signs and symptoms are often ignored in children or put down to 'growing pains' and thus diagnosis is often delayed. Misdiagnosis is not uncommon because several other conditions such as muscular dystrophy and muscle channelopathies can manifest with similar symptoms. A simple exercise test performed in the clinic can however help to identify patients by revealing the second wind phenomenon which is pathognomonic of the condition. Here a patient is reported illustrating the value of using a simple 12 minute walk test.RSS is funded by Ciências sem Fronteiras/CAPES Foundation. The authors would like to thank the Association for Glycogen Storage Disease (UK), the EUROMAC Registry funded by the European Union, the Muscular Dystrophy Campaign, the NHS National Specialist Commissioning Group and the Myositis Support Group for funding

Deep Learning Towards Intelligent Vehicle Fault Diagnosis

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Optimization of decrementing evoked potential mapping for functional substrate identification in ischaemic ventricular tachycardia ablation

Ventricular tachycardia (VT) ablation approaches based on high-density mapping, which enable the rapid acquisition of thousands of mapping points in order to delineate slow conduction zones, have been widely adopted.1 The identification of functionally relevant substrates has been advanced by the identification of potentials participating in the initiation and/or maintenance of scar-dependent VT. During right ventricular apical (RVA) pacing with an extra-stimulus (S2), these potentials display delayed conduction (decremental) behaviour (DeEP).2 This methodology has been shown to be more specific in identifying the critical isthmus of re-entrant VT.3 An important factor accounting for decrement is conduction velocity (CV) restitution.2 With a short-coupled S2, CV will decrease, and further delay occurs in the near-field signal with respect to the far-field signal, creating DeEPs. Conventionally, the S2 has been delivered at ventricular effective refractory period (VERP) + 20 ms to elicit decrement.3–5 However data are lacking on justifying the delivery of the S2 at VERP + 20 ms, which may result in areas defined as DeEP due to intrinsic CV restitution properties, thus creating larger-than-required ablation target areas. We hypothesized that DeEPs are better identified with longer S2 coupling intervals. The second hypothesis was to consider the definition of a DeEP as the range of decrement beyond 10 ms has not been previously explored and to identify the best combination of these parameters

Comparison of Asian Aquaculture Products by Use of Statistically Supported Life Cycle Assessment

We investigated aquaculture production of Asian tiger shrimp, whiteleg shrimp, giant river prawn, tilapia, and pangasius catfish in Bangladesh, China, Thailand, and Vietnam by using life cycle assessments (LCAs), with the purpose of evaluating the comparative eco-efficiency of producing different aquatic food products. Our starting hypothesis was that different production systems are associated with significantly different environmental impacts, as the production of these aquatic species differs in intensity and management practices. In order to test this hypothesis, we estimated each system's global warming, eutrophication, and freshwater ecotoxicity impacts. The contribution to these impacts and the overall dispersions relative to results were propagated by Monte Carlo simulations and dependent sampling. Paired testing showed significant (p < 0.05) differences between the median impacts of most production systems in the intraspecies comparisons, even after a Bonferroni correction. For the full distributions instead of only the median, only for Asian tiger shrimp did more than 95% of the propagated Monte Carlo results favor certain farming systems. The major environmental hot-spots driving the differences in environmental performance among systems were fishmeal from mixed fisheries for global warming, pond runoff and sediment discards for eutrophication, and agricultural pesticides, metals, benzalkonium chloride, and other chlorine-releasing compounds for freshwater ecotoxicity. The Asian aquaculture industry should therefore strive toward farming systems relying upon pelleted species-specific feeds, where the fishmeal inclusion is limited and sourced sustainably. Also, excessive nutrients should be recycled in integrated organic agriculture together with efficient aeration solutions powered by renewable energy sources. © 2015 American Chemical Society.Additional coauthors: M. Mahfujul Haque Froukje Kruijssen, Geert R. de Snoo, Reinout Heijungs, Peter M. van Bodegom, and Jeroen B. Guiné

Photo(geno)toxicity changes associated with hydroxylation of the aromatic chromophores during diclofenac metabolism

[EN] Diclofenac (DCF) can cause adverse reactions such as gastrointestinal, renal and cardiovascular disorders; therefore, topical administration may be an attractive alternative to the management of local pain in order to avoid these side effects. However, previous studies have shown that DCF, in combination with sunlight, displays capability to induce photosensitivity disorders. In humans, DCF is biotransformed into hydroxylated metabolites at positions 4¿ and 5 (4¿OH-DCF and 5OH-DCF), and this chemical change produces non negligible alterations of the drug chromophore, resulting in a significant modification of its light-absorbing properties. In the present work, 5OH-DCF exhibited higher photo(geno)toxic potential than the parent drug, as shown by several in vitro assays (3T3 NRU phototoxicity, DNA ssb gel electrophoresis and COMET), whereas 4¿OH-DCF did not display significant photo(geno)toxicity. This could be associated, at least partially with their more efficient UV-light absorption by 5OH-DCF metabolite and with a higher photoreactivity. Interestingly, most of the cellular DNA damage photosensitized by DCF and 5OH-DCF was repaired by the cells after several hours, although this effect was not complete in the case of 5OH-DCF.This work was supported by the Carlos III Institute of Health (Grants: RD16/0006/0030, PI16/01877), by the MINECO (Grants: CTQ2013-47872, CTQ2016-78875), and by the Generalitat Valenciana (Prometeo 2017/075).García -Laínez, G.; Ana M Marínez-Reig; Limones Herrero, D.; Jiménez Molero, MC.; Miranda Alonso, MÁ.; Andreu Ros, MI. (2018). Photo(geno)toxicity changes associated with hydroxylation of the aromatic chromophores during diclofenac metabolism. Toxicology and Applied Pharmacology. 341:51-55. https://doi.org/10.1016/j.taap.2018.01.005S515534