Development of simple multiplex real-time pcr assays for foodborne pathogens detection and identification on lightcycler

Most acute intestinal diseases are caused by food-borne pathogens. A fast and simple real-time PCR-based procedure for simultaneous detection of food contamination by any of the five food-borne pathogens: Campylobacter jejuni, Mycobacterium bovis, Enterobacter sakazaki, Shigella boydii, Clostridium perfrigens using multiplex EvaGreen real-time PCR for LightCycler was developed and evaluated. Real-time qPCR showed excellent sensitivity. Tm calling and Melting Curve Genotyping (MCG) were used for analysis of PCR product melting curves. The Melting Curve Genotyping option showed good performance for discrimination of positive samples containing DNA of single pathogen or pathogen mixtures from negative samples

Paper Withdrawn Before the Issue Release

This paper presents the architecture and implementation of a set of novel sensor nodes designed to measure ammonium, nitrate and chloride in real time, sending the data, by means of a network, to the base station in order to control the pollution in a lake. The results obtained being compared with those provided by the corresponding reference methods. Recovery analyses with ion selective electrodes and standard methods, study of interferences, and evaluation of major sensor features have also been carried out. The use of a wireless system for monitoring purposes will not only reduce the overall monitoring system cost in term of facilities setup and labor cost, but will also provide flexibility in terms of distance. The major advantages of the proposed in-line analysis compared with the classical off-line procedures are the elimination of contaminants due to sample handling, the minimization of the overall cost of data acquisition, the possibility of real-time analysis, allowing the rapid detection of pollutants, the ability to obtain detailed spatial and temporal data sets of complete environments, obtaining the spatial distribution of the analyzed parameters, as well as its variation with the passing of time, and finally the possibility of performing measurements in locations which are difficult to access (in this case a deep lake)

A new application of Internet of Things and Cloud Services in Analytical Chemistry: Determination of bicarbonate in water

[EN] In a constantly evolving world, new technologies such as Internet of Things (IoT) and cloud-based services offer great opportunities in many fields. In this paper we propose a new approach to the development of smart sensors using IoT and cloud computing, which open new interesting possibilities in analytical chemistry. According to IoT philosophy, these new sensors are able to integrate the generated data on the existing IoT platforms, so that information may be used whenever needed. Furthermore, the utilization of these technologies permits one to obtain sensors with significantly enhanced features using the information available in the cloud. To validate our new approach, a bicarbonate IoT-based smart sensor has been developed. A classical CO2 ion selective electrode (ISE) utilizes the pH information retrieved from the cloud and then provides an indirect measurement of bicarbonate concentration, which is offered to the cloud. The experimental data obtained are compared to those yielded by three other classical ISEs, with satisfactory results being achieved in most instances. Additionally, this methodology leads to lower-consumption, low-cost bicarbonate sensors capable of being employed within an IoT application, for instance in the continuous monitoring of HCO3- in rivers. Most importantly, this innovative application field of IoT and cloud approaches can be clearly perceived as an indicator for future developments over the short-term.This research was funded by the Spanish Ministerio de Economia y Competitividad, grant number DPI2016-80303-C2-1-P.Capella Hernández, JV.; Bonastre Pina, AM.; Ors Carot, R.; Peris Tortajada, M. (2019). A new application of Internet of Things and Cloud Services in Analytical Chemistry: Determination of bicarbonate in water. Sensors. 19(24):1-13. https://doi.org/10.3390/s19245528S1131924Perry, C. T., Salter, M. A., Harborne, A. R., Crowley, S. F., Jelks, H. L., & Wilson, R. W. (2011). Fish as major carbonate mud producers and missing components of the tropical carbonate factory. Proceedings of the National Academy of Sciences, 108(10), 3865-3869. doi:10.1073/pnas.1015895108Pandolfi, J. M., Connolly, S. R., Marshall, D. J., & Cohen, A. L. (2011). Projecting Coral Reef Futures Under Global Warming and Ocean Acidification. Science, 333(6041), 418-422. doi:10.1126/science.1204794Jaquet, J.-M., Nirel, P., & Martignier, A. (2013). Preliminary investigations on picoplankton-related precipitation of alkaline-earth metal carbonates in meso-oligotrophic lake Geneva (Switzerland). Journal of Limnology, 72(3), 50. doi:10.4081/jlimnol.2013.e50Lewis, C. N., Brown, K. A., Edwards, L. A., Cooper, G., & Findlay, H. S. (2013). Sensitivity to ocean acidification parallels natural pCO2 gradients experienced by Arctic copepods under winter sea ice. Proceedings of the National Academy of Sciences, 110(51), E4960-E4967. doi:10.1073/pnas.1315162110Kaloo, M. A., Sunder Raman, R., & Sankar, J. (2016). Novel structurally tuned DAMN receptor for «in situ» diagnosis of bicarbonate in environmental waters. The Analyst, 141(8), 2367-2370. doi:10.1039/c6an00218hBotta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Capella, J. V., Bonastre, A., Ors, R., & Peris, M. (2014). A step forward in the in-line river monitoring of nitrate by means of a wireless sensor network. Sensors and Actuators B: Chemical, 195, 396-403. doi:10.1016/j.snb.2014.01.039Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2019). A Survey on Internet of Things and Cloud Computing for Healthcare. Electronics, 8(7), 768. doi:10.3390/electronics8070768Lopez-Barbosa, N., Gamarra, J. D., & Osma, J. F. (2016). The future point-of-care detection of disease and its data capture and handling. Analytical and Bioanalytical Chemistry, 408(11), 2827-2837. doi:10.1007/s00216-015-9249-2Kassal, P., Steinberg, I. M., & Steinberg, M. D. (2013). Wireless smart tag with potentiometric input for ultra low-power chemical sensing. Sensors and Actuators B: Chemical, 184, 254-259. doi:10.1016/j.snb.2013.04.049Piyare, R., & Lee, S. R. (2013). Towards Internet of Things (IOTS): Integration of Wireless Sensor Network to Cloud Services for Data Collection and Sharing. International journal of Computer Networks & Communications, 5(5), 59-72. doi:10.5121/ijcnc.2013.5505Carminati, M., Mezzera, L., Ferrari, G., Sampietro, M., Turolla, A., Di Mauro, M., & Antonelli, M. (2018). A Smart Sensing Node for Pervasive Water Quality Monitoring with Anti-Fouling Self-Diagnostics. 2018 IEEE International Symposium on Circuits and Systems (ISCAS). doi:10.1109/iscas.2018.8351833Borrego, C., Ginja, J., Coutinho, M., Ribeiro, C., Karatzas, K., Sioumis, T., … Penza, M. (2018). Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II. Atmospheric Environment, 193, 127-142. doi:10.1016/j.atmosenv.2018.08.028Gervasi, O., Murgante, B., Misra, S., Gavrilova, M. L., Rocha, A. M. A. C., Torre, C., … Apduhan, B. O. (Eds.). (2015). Computational Science and Its Applications -- ICCSA 2015. Lecture Notes in Computer Science. doi:10.1007/978-3-319-21407-8LIU, Y., LIANG, Y., XUE, L., LIU, R., TAO, J., ZHOU, D., … HU, W. (2019). Polystyrene-coated Interdigitated Microelectrode Array to Detect Free Chlorine towards IoT Applications. Analytical Sciences, 35(5), 505-509. doi:10.2116/analsci.18p460Ping, H., Wang, J., Ma, Z., & Du, Y. (2018). Mini-review of application of IoT technology in monitoring agricultural products quality and safety. International Journal of Agricultural and Biological Engineering, 11(5), 35-45. doi:10.25165/j.ijabe.20181105.3092Alreshaid, A. T., Hester, J. G., Su, W., Fang, Y., & Tentzeris, M. M. (2018). Review—Ink-Jet Printed Wireless Liquid and Gas Sensors for IoT, SmartAg and Smart City Applications. Journal of The Electrochemical Society, 165(10), B407-B413. doi:10.1149/2.0341810jesDjelouat, H., Amira, A., & Bensaali, F. (2018). Compressive Sensing-Based IoT Applications: A Review. Journal of Sensor and Actuator Networks, 7(4), 45. doi:10.3390/jsan7040045Kassal, P., Steinberg, M. D., & Steinberg, I. M. (2018). Wireless chemical sensors and biosensors: A review. Sensors and Actuators B: Chemical, 266, 228-245. doi:10.1016/j.snb.2018.03.074Alahi, M. E. E., Xie, L., Mukhopadhyay, S., & Burkitt, L. (2017). A Temperature Compensated Smart Nitrate-Sensor for Agricultural Industry. IEEE Transactions on Industrial Electronics, 64(9), 7333-7341. doi:10.1109/tie.2017.2696508FIWARE Foundationhttps://www.fiware.org/Xie, X., & Bakker, E. (2013). Non-Severinghaus Potentiometric Dissolved CO2 Sensor with Improved Characteristics. Analytical Chemistry, 85(3), 1332-1336. doi:10.1021/ac303534

Historical Building Monitoring Using an Energy-Efficient Scalable Wireless Sensor Network Architecture

We present a set of novel low power wireless sensor nodes designed for monitoring wooden masterpieces and historical buildings, in order to perform an early detection of pests. Although our previous star-based system configuration has been in operation for more than 13 years, it does not scale well for sensorization of large buildings or when deploying hundreds of nodes. In this paper we demonstrate the feasibility of a cluster-based dynamic-tree hierarchical Wireless Sensor Network (WSN) architecture where realistic assumptions of radio frequency data transmission are applied to cluster construction, and a mix of heterogeneous nodes are used to minimize economic cost of the whole system and maximize power saving of the leaf nodes. Simulation results show that the specialization of a fraction of the nodes by providing better antennas and some energy harvesting techniques can dramatically extend the life of the entire WSN and reduce the cost of the whole system. A demonstration of the proposed architecture with a new routing protocol and applied to termite pest detection has been implemented on a set of new nodes and should last for about 10 years, but it provides better scalability, reliability and deployment properties

Survival of hematological patients after discharge from the intensive care unit: a prospective observational study

INTRODUCTION: Although the survival rates of hematological patients admitted to the ICU are improving, little is known about the long-term outcome. Our objective was to identify factors related to long-term outcome in hematological patients after ICU discharge. METHODS: A prospective, observational study was carried out in seven centers in Spain. From an initial sample of 161 hematological patients admitted to one of the participating ICUs during the study period, 62 were discharged alive and followed for a median time of 23 (1 to 54) months. Univariate and multivariate analysis were performed to identify the factors related to long term-survival. Finally, variables that influence the continuation of the scheduled therapy for the hematological disease were studied. RESULTS: Mortality after ICU discharge was 61%, with a median survival of 18 (1 to 54) months. In the multivariate analysis, an Eastern Cooperative Oncology Group score (ECOG) >2 at ICU discharge (Hazard ratio 11.15 (4.626 to 26.872)), relapse of the hematological disease (Hazard ratio 9.738 (3.804 to 24.93)) and discontinuation of the planned treatment for the hematological disease (Hazard ratio 4.349 (1.286 to 14.705)) were independently related to mortality. Absence of stem cell transplantation, high ECOG and high Acute Physiology and Chronic Health Evaluation II (APACHE II) scores decreased the probability of receiving the planned therapy for the hematological malignancy. CONCLUSIONS: Both ICU care and post-ICU management determine the long-term outcome of hematological patients who are discharged alive from the ICU

Fernando de Castro and the discovery of the arterial chemoreceptors

Producción CientíficaWhen de Castro entered the carotid body(CB)field,the organ was considered to be a small autonomic ganglion,a gland,a glomusorglomerulus,or a paraganglion. In his 1928 paper,de Castro concluded:“Insum,the Glomuscarotic umisinnervated by centripetal fibers,whose trophic center sare located in thesensory ganglia of the glossopharyngeal, and not by centrifugal[efferent] or secret o motor fibers a sisthe case for glands ; these are precisely the facts which lead to suppose that the Glomuscaroticumisa sensory organ.”A few pages down,de Castro wrote:“The Glomus represents an organ with multiplereceptors furnished with specialized receptor cells like those of the sensory organs [tastebuds?]...As aplausible hypothesis we propos et hattheGlomuscaroti cum represents a sensory organ, at present the only one in its kind, dedicated to capture certain qualitative variations in the composition of blood, a function that,possibly by are flex mechanism would have an effect on the functionalactivity of other organs... Therefore, thesensory fiber would not be directly stimulated by blood, but via the intermediation of the epithelial cell soft he organ, which, as their structures suggests, possess a secretory function which would participate in the stimulation of the centripetal fibers.”In our article we will recreat et he experiments that allowed Fernando de Castrotoreach this first conclusion. Also, we will scrutinize the natural endowment sand the scientific knowledge that drove de Castrotomaket the triple hypotheses : the CBaschemoreceptor (variationsinbloodcomposition),as a secondary sensory receptor which functioning involves a chemical synapse, and as a center, origin of systemicreflexes. After a brief account of the systemic reflex effects resulting from the CB stimulation, we wil lcomplete our article with a general view of the cellular-molecular mechanisms currently thought to be involved in the functionin go fthis arterial chemoreceptor

The I4U Mega Fusion and Collaboration for NIST Speaker Recognition Evaluation 2016

The 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4 continents. The joint submission and several of its 32 sub-systems were among top-performing systems. A lot of efforts have been devoted to two major challenges, namely, unlabeled training data and dataset shift from Switchboard-Mixer to the new Call My Net dataset. This paper summarizes the lessons learned, presents our shared view from the sixteen research groups on recent advances, major paradigm shift, and common tool chain used in speaker recognition as we have witnessed in SRE'16. More importantly, we look into the intriguing question of fusing a large ensemble of sub-systems and the potential benefit of large-scale collaboration.Peer reviewe

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

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