641 research outputs found

    Three-Dimensional Triple-Resonance NMR of \u3csup\u3e13\u3c/sup\u3eC/\u3csup\u3e15\u3c/sup\u3eN-Enriched Proteins Using Constant-Time Evolution

    Get PDF
    Recently it has been convincingly demonstrated that 30 triple-resonance NMR provides a practical alternative for obtaining sequential resonance assignments in larger proteins ( 1, 2). This approach requires a set of five or six 30 NMR experiments that correlate the various protein backbone nuclei. Details regarding the mechanisms and technical implementations of these experiments have been described previously ( 3- 5). Two of the experiments used in this approach correlate backbone HÎą and CÎą resonances with either the intraresidue carbonyl resonance (CO) or the 15N resonance of the succeeding residue and are referred to as HCACO and HCA(CO)N, respectively. The present Communication describes a modification of these experiments which optimizes their sensitivity and removes the F1 antiphase character of correlations

    CYberinfrastructure for COmparative effectiveness REsearch (CYCORE): improving data from cancer clinical trials

    Get PDF
    Improved approaches and methodologies are needed to conduct comparative effectiveness research (CER) in oncology. While cancer therapies continue to emerge at a rapid pace, the review, synthesis, and dissemination of evidence-based interventions across clinical trials lag in comparison. Rigorous and systematic testing of competing therapies has been clouded by age-old problems: poor patient adherence, inability to objectively measure the environmental influences on health, lack of knowledge about patients’ lifestyle behaviors that may affect cancer’s progression and recurrence, and limited ability to compile and interpret the wide range of variables that must be considered in the cancer treatment. This lack of data integration limits the potential for patients and clinicians to engage in fully informed decision-making regarding cancer prevention, treatment, and survivorship care, and the translation of research results into mainstream medical care. Particularly important, as noted in a 2009 report on CER to the President and Congress, the limited focus on health behavior-change interventions was a major hindrance in this research landscape (DHHS 2009). This paper describes an initiative to improve CER for cancer by addressing several of these limitations. The Cyberinfrastructure for Comparative Effectiveness Research (CYCORE) project, informed by the National Science Foundation’s 2007 report “Cyberinfrastructure Vision for 21st Century Discovery” has, as its central aim, the creation of a prototype for a user-friendly, open-source cyberinfrastructure (CI) that supports acquisition, storage, visualization, analysis, and sharing of data important for cancer-related CER. Although still under development, the process of gathering requirements for CYCORE has revealed new ways in which CI design can significantly improve the collection and analysis of a wide variety of data types, and has resulted in new and important partnerships among cancer researchers engaged in advancing health-related CI

    Inspiratory muscle training reduces blood lactate concentration during volitional hyperpnoea

    Get PDF
    Although reduced blood lactate concentrations ([lac−]B) have been observed during whole-body exercise following inspiratory muscle training (IMT), it remains unknown whether the inspiratory muscles are the source of at least part of this reduction. To investigate this, we tested the hypothesis that IMT would attenuate the increase in [lac−]B caused by mimicking, at rest, the breathing pattern observed during high-intensity exercise. Twenty-two physically active males were matched for 85% maximal exercise minute ventilation (V˙Emax) and divided equally into an IMT or a control group. Prior to and following a 6 week intervention, participants performed 10 min of volitional hyperpnoea at the breathing pattern commensurate with 85% V˙Emax

    Analysis of motoneuron responses to composite synaptic volleys (computer simulation study)

    Get PDF
    This paper deals with the analysis of changes in motoneuron (MN) firing evoked by repetitively applied stimuli aimed toward extracting information about the underlying synaptic volleys. Spike trains were obtained from computer simulations based on a threshold-crossing model of tonically firing MN, subjected to stimulation producing postsynaptic potentials (PSPs) of various parameters. These trains were analyzed as experimental results, using the output measures that were previously shown to be most effective for this purpose: peristimulus time histogram, raster plot and peristimulus time intervalgram. The analysis started from the effects of single excitatory and inhibitory PSPs (EPSPs and IPSPs). The conclusions drawn from this analysis allowed the explanation of the results of more complex synaptic volleys, i.e., combinations of EPSPs and IPSPs, and the formulation of directions for decoding the results of human neurophysiological experiments in which the responses of tonically firing MNs to nerve stimulation are analyzed

    Seroprevalence and distribution of arboviral infections among rural Kenyan adults: A cross-sectional study

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Arthorpod-borne viruses (arboviruses) cause wide-spread morbidity in sub-Saharan Africa, but little research has documented the burden and distribution of these pathogens.</p> <p>Methods</p> <p>Using a population-based, cross-sectional study design, we administered a detailed questionnaire and used ELISA to test the blood of 1,141 healthy Kenyan adults from three districts for the presence of anti-viral Immunoglobulin G (IgG) antibodies to the following viruses: dengue (DENV), West Nile (WNV), yellow fever (YFV), Chikungunya (CHIKV), and Rift Valley fever (RVFV).</p> <p>Results</p> <p>Of these, 14.4% were positive for DENV, 9.5% were WNV positive, 9.2% were YFV positive, 34.0% were positive for CHIKV and 0.7% were RVFV positive. In total, 46.6% had antibodies to at least one of these arboviruses.</p> <p>Conclusions</p> <p>For all arboviruses, district of residence was strongly associated with seropositivity. Seroprevalence to YFV, DENV and WNV increased with age, while there was no correlation between age and seropositivity for CHIKV, suggesting that much of the seropositivity to CHIKV is due to sporadic epidemics. Paradoxically, literacy was associated with increased seropositivity of CHIKV and DENV.</p

    NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR

    Get PDF
    Although the concepts of nonuniform sampling (NUS​​​​​​​) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge 4 decades ago (Bodenhausen and Ernst, 1981; Barna and Laue, 1987), it is only relatively recently that NUS has become more commonplace. Advantages of NUS include the ability to tailor experiments to reduce data collection time and to improve spectral quality, whether through detection of closely spaced peaks (i.e., “resolution”) or peaks of weak intensity (i.e., “sensitivity”). Wider adoption of these methods is the result of improvements in computational performance, a growing abundance and flexibility of software, support from NMR spectrometer vendors, and the increased data sampling demands imposed by higher magnetic fields. However, the identification of best practices still remains a significant and unmet challenge. Unlike the discrete Fourier transform, non-Fourier methods used to reconstruct spectra from NUS data are nonlinear, depend on the complexity and nature of the signals, and lack quantitative or formal theory describing their performance. Seemingly subtle algorithmic differences may lead to significant variabilities in spectral qualities and artifacts. A community-based critical assessment of NUS challenge problems has been initiated, called the “Nonuniform Sampling Contest” (NUScon), with the objective of determining best practices for processing and analyzing NUS experiments. We address this objective by constructing challenges from NMR experiments that we inject with synthetic signals, and we process these challenges using workflows submitted by the community. In the initial rounds of NUScon our aim is to establish objective criteria for evaluating the quality of spectral reconstructions. We present here a software package for performing the quantitative analyses, and we present the results from the first two rounds of NUScon. We discuss the challenges that remain and present a roadmap for continued community-driven development with the ultimate aim of providing best practices in this rapidly evolving field. The NUScon software package and all data from evaluating the challenge problems are hosted on the NMRbox platform

    NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR

    Get PDF
    Although the concepts of nonuniform sampling (NUS) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge 4 decades ago (Bodenhausen and Ernst, 1981; Barna and Laue, 1987), it is only relatively recently that NUS has become more commonplace. Advantages of NUS include the ability to tailor experiments to reduce data collection time and to improve spectral quality, whether through detection of closely spaced peaks (i.e., “resolution”) or peaks of weak intensity (i.e., “sensitivity”). Wider adoption of these methods is the result of improvements in computational performance, a growing abundance and flexibility of software, support from NMR spectrometer vendors, and the increased data sampling demands imposed by higher magnetic fields. However, the identification of best practices still remains a significant and unmet challenge. Unlike the discrete Fourier transform, non-Fourier methods used to reconstruct spectra from NUS data are nonlinear, depend on the complexity and nature of the signals, and lack quantitative or formal theory describing their performance. Seemingly subtle algorithmic differences may lead to significant variabilities in spectral qualities and artifacts. A community-based critical assessment of NUS challenge problems has been initiated, called the “Nonuniform Sampling Contest” (NUScon), with the objective of determining best practices for processing and analyzing NUS experiments. We address this objective by constructing challenges from NMR experiments that we inject with synthetic signals, and we process these challenges using workflows submitted by the community. In the initial rounds of NUScon our aim is to establish objective criteria for evaluating the quality of spectral reconstructions. We present here a software package for performing the quantitative analyses, and we present the results from the first two rounds of NUScon. We discuss the challenges that remain and present a roadmap for continued community-driven development with the ultimate aim of providing best practices in this rapidly evolving field. The NUScon software package and all data from evaluating the challenge problems are hosted on the NMRbox platform

    Residential end-uses disaggregation and demand response evaluation using integral transforms

    Full text link
    [EN] Demand response is a basic tool used to develop modern power systems and electricity markets. Residential and commercial segments account for 40%-50% of the overall electricity demand. These segments need to overcome major obstacles before they can be included in a demand response portfolio. The objective of this paper is to tackle some of the technical barriers and explain how the potential of enabling technology (smart meters) can be harnessed, to evaluate the potential of customers for demand response (end-uses and their behaviors) and, moreover, to validate customers' effective response to market prices or system events by means of non-intrusive methods. A tool based on the Hilbert transform is improved herein to identify and characterize the most suitable loads for the aforesaid purpose, whereby important characteristics such as cycling frequency, power level and pulse width are identified. The proposed methodology allows the filtering of aggregated load according to the amplitudes of elemental loads, independently of the frequency of their behaviors that could be altered by internal or external inputs such as weather or demand response. In this way, the assessment and verification of customer response can be improved by solving the problem of load aggregation with the help of integral transforms.This work has been supported by Spanish Government (Ministerio de Economia, Industria y Competitividad) and EU FEDER fund (No. ENE2013-48574-C2-2-P&1-P, No. ENE2015-70032-REDT).Gabaldón Marín, A.; Molina, R.; Marin-Parra, A.; Valero, S.; Álvarez, C. (2017). Residential end-uses disaggregation and demand response evaluation using integral transforms. Journal of Modern Power Systems and Clean Energy. 5(1):91-104. https://doi.org/10.1007/s40565-016-0258-8S9110451Chardon A, Almén O, Lewis PE (2009) Demand response: a decisive breakthrough for Europe. Capgemini, Enerdata. https://www.capgemini.com/resources/demand_response_a_decisive_breakthrough_for_europeFaruqui A, Harris D, Hledick R (2010) Unlocking the €53 billion savings from smart meters in the EU: how increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy 38(10):6222–6231Federal Energy Regulatory Commission (FERC) (2015) Assessment of demand response and advanced metering, staff report. http://www.ferc.gov/legal/staff-reports/2015/demand-response.pdfFERC Order 745 (December 2011) & 719 (December 2009). http://www.ferc.gov/legal/maj-ord-reg.aspEuropean Commission (2011) Energy efficiency plan 2011, COM (2011) 109 final. http://eur-lex.europa.eu/LexUriServJoitn Research Center (JRC), European Commission (2010) Energy Service Companies market in Europe, status report 2010. http://publications.jrc.ec.europa.eu/repository/handle/111111111/15108European Commission (2011) Impact assessment accompanying document “Energy Efficiency Plan 2011”, SEC (2011) 277 final. http://www.eurosfaire.prd.fr/7pc/bibliotheque/consulter.php?id=2357European Environment Agency (EEA) (2012) Final energy consumption by sector and fuel. http://www.eea.europa.eu/data-and-maps/indicators/final-energy-consumption-by-sector-8/assessment-2Piette MA, Watson D, Motegi N et al (2007) Automated critical peak pricing field tests: 2006 pilot program description and results. California Energy Commission, PIER Energy Systems Integration Research Program: CEC-500-03-026Gomatom K, Holmes C, Kresta D (2013) Non-intrusive load monitoring. https://www.e3tnw.orgDimetrosky S, Parkinson K, Lieb N (2013) Residential lighting evaluation protocol, Report NREl/SR-7A30-53827. National Renewable Energy LaboratoryZeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84Liang J, Simon K, Kendall G et al (2010) Load signature study part I: basic concept, structure and methodology. IEEE Trans Power Deliv 25(2):551–560Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891Powers J, Margossian B, Smith B (1991) Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data. IEEE Comput Appl Power 4(2):42–47Farinaccio L, Zmeureanu R (1999) Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy Build 30(3):245–259Marceau ML, Zmeureanu R (2000) Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Convers Manag 41(13):1389–1403Baransju M, Voss J (2004) Genetic algorithm for pattern detection in NIALM systems. In: IEEE international conference on systems, man and cybernetics, 10–13 Oct, pp 3462–3468Baranski H, Voss J (2004) Detecting patterns of appliances from total load data using a dynamic programming approach. In: Fourth IEEE international conference on data mining (ICDM’04), 1–4 Nov, Brighton, UK, pp 327–330Sankara A (2015) Energy disaggregation in NIALM using hidden Markov models. Masters Theses, Missouri University of Science and Technology, Paper 7414Kolter J, Jaakkola T (2012) Approximate inference in additive factorial HMMs with application to energy disaggregation. J Mach Learn 22:1472–1482Leeb SB, Shaw SR, Kirtley SL (1995) Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Trans Power Deliv 10(3):1200–1210Patel SN, Robertson T, Kientz JA et al (2007) At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In: Conference on ubiquitous computing, pp 271–288Bonglifi R, Squartini S, Fagiani M et al (2015) Unsupervised algorithms for non-intrusive load monitoring: an up-to-date overview. In: EEEIC conference. doi: 10.1109/EEEIC.2015.7165334Browne TJ, Vittal V, Heydt GT et al (2008) A comparative assessment of two techniques for modal identification from power system measurements. IEEE Trans Power Syst 23(3):1408–1415Senroy N, Suryanarayanan S, Ribeiro PF (2007) An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Trans Power Syst 22(4):1843–1850Gabaldon A, Ortiz M, Molina R et al (2014) Disaggregation of electric loads of small customers through the application of the Hilbert transform. Energ Effic 7(4):711–728. doi: 10.1007/s12053-013-9250-6Kolter JZ, Johnson MJ (2011) REDD: a public data set for energy disaggregation research. http://101.96.10.59/people.csail.mit.edu/mattjj/papers/kddsust2011.pdfFibaro wall switches. http://www.fibaro.com/en/the-fibaro-system/IP-Symcon: innovative centre for the entire building automation. https://www.symcon.de/Energy Information Administration (2001) End-use consumption electricity 2001. http://www.eia.gov/emeu/recs/recs2001/enduse2001/enduse2001.htmlPoularikas AD (1999) The Hilbert transform, handbook of formulas and tables for signal processing. CRC Press LLC, Boca RatonHuang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proced R Soc Lond 454(1971):903–995Deering R, Kaiser JF (2005) The use of a masking signal to improve empirical mode decomposition. In: Proceedings IEEE international conference acoustic, speech, and signal processing, pp 485–488Liang J, Simon K, Kendall G, Cheng J et al (2010) Load signature study part II: disaggregation framework, simulation, and applications. IEEE Trans Power Deliv 25(2):561–569Load participation in ancillary services. http://www1.eere.energy.gov/analysis/pdfsGabaldón A, Guillamón A, Ruiz MC et al (2010) Development of a methodology for clustering electricity-price series to improve customer response initiatives. IET Gener Transm Distrib 4(6):706–71

    Genetic structure of sigmodontine rodents (Cricetidae) along an altitudinal gradient of the Atlantic Rain Forest in southern Brazil

    Get PDF
    The population genetic structure of two sympatric species of sigmodontine rodents (Oligoryzomys nigripes and Euryoryzomys russatus) was examined for mitochondrial DNA (mtDNA) sequence haplotypes of the control region. Samples were taken from three localities in the Atlantic Rain Forest in southern Brazil, along an altitudinal gradient with different types of habitat. In both species there was no genetic structure throughout their distribution, although levels of genetic variability and gene flow were high
    • …
    corecore