2,603 research outputs found

    Day at the Beach

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    Murine lymphoid procoagulant activity induced by bacterial lipopolysaccharide and immune complexes is a monocyte prothrombinase

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    Murine lymphoid cells respond rapidly to bacterial lipopolysaccharide or antigen-antibody complexes to initiate or accelerate the blood coagulation pathways. The monocyte or macrophage has been identified as the cellular source, although lymphocyte collaboration is required for the rapid induction of the procoagulant response. This procoagulant activity is identified in the present study as a direct prothrombin activator, i.e., a prothrombinase. Studies with plasmas deficient in single coagulation factors demonstrate that the induced murine procoagulant activity effector molecule does not require factors XII, VIII, VII, X, or V, but does require prothrombin to transform fibrinogen to fibrin. This enzyme(s) produces limited proteolysis of prothrombin to yield thrombin or thrombinlike products that are functionally capable of converting fibrinogen to fibrin. The prothrombinase is undetectable in freshly isolated Murine lymphoid cells respond rapidly to bacterial lipopolysaccharide or antigen-antibody complexes to initiate or accelerate the blood coagulation pathways. The monocyte or macrophage has been identified as the cellular source, although lymphocyte collaboration is required for the rapid induction of the procoagulant response. This procoagulant activity is identified in the present study as a direct prothrombin activator, i.e., a prothrombinase. Studies with plasmas deficient in single coagulation factors demonstrate that the induced murine procoagulant activity effector molecule does not require factors XII, VIII, VII, X, or V, but does require prothrombin to transform fibrinogen to fibrin. This enzyme(s) produces limited proteolysis of prothrombin to yield thrombin or thrombinlike products that are functionally capable of converting fibrinogen to fibrin. The prothrombinase is undetectable in freshly isolate

    Corticosteroid suppression of lipoxin A4 and leukotriene B4from alveolar macrophages in severe asthma

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    <p>Abstract</p> <p>Background</p> <p>An imbalance in the generation of pro-inflammatory leukotrienes, and counter-regulatory lipoxins is present in severe asthma. We measured leukotriene B<sub>4 </sub>(LTB<sub>4</sub>), and lipoxin A<sub>4 </sub>(LXA<sub>4</sub>) production by alveolar macrophages (AMs) and studied the impact of corticosteroids.</p> <p>Methods</p> <p>AMs obtained by fiberoptic bronchoscopy from 14 non-asthmatics, 12 non-severe and 11 severe asthmatics were stimulated with lipopolysaccharide (LPS,10 μg/ml) with or without dexamethasone (10<sup>-6</sup>M). LTB<sub>4 </sub>and LXA<sub>4 </sub>were measured by enzyme immunoassay.</p> <p>Results</p> <p>LXA<sub>4 </sub>biosynthesis was decreased from severe asthma AMs compared to non-severe (p < 0.05) and normal subjects (p < 0.001). LXA<sub>4 </sub>induced by LPS was highest in normal subjects and lowest in severe asthmatics (p < 0.01). Basal levels of LTB<sub>4 </sub>were decreased in severe asthmatics compared to normal subjects (p < 0.05), but not to non-severe asthma. LPS-induced LTB<sub>4 </sub>was increased in severe asthma compared to non-severe asthma (p < 0.05). Dexamethasone inhibited LPS-induced LTB<sub>4 </sub>and LXA<sub>4</sub>, with lesser suppression of LTB<sub>4 </sub>in severe asthma patients (p < 0.05). There was a significant correlation between LPS-induced LXA<sub>4 </sub>and FEV<sub>1 </sub>(% predicted) (r<sub>s </sub>= 0.60; p < 0.01).</p> <p>Conclusions</p> <p>Decreased LXA<sub>4 </sub>and increased LTB<sub>4 </sub>generation plus impaired corticosteroid sensitivity of LPS-induced LTB<sub>4 </sub>but not of LXA<sub>4 </sub>support a role for AMs in establishing a pro-inflammatory balance in severe asthma.</p

    Study protocol: evaluation of a parenting and stress management programme: a randomised controlled trial of Triple P discussion groups and stress control

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    &lt;br&gt;Background: Children displaying psychosocial problems are at an increased risk of negative developmental outcomes. Parenting practices are closely linked with child development and behaviour, and parenting programmes have been recommended in the treatment of child psychosocial problems. However, parental mental health also needs to be addressed when delivering parenting programmes as it is linked with parenting practices, child outcomes, and treatment outcomes of parenting programmes. This paper describes the protocol of a study examining the effects of a combined intervention of a parenting programme and a cognitive behavioural intervention for mental health problems.&lt;/br&gt; &lt;br&gt;Methods: The effects of a combined intervention of Triple P Discussion Groups and Stress Control will be examined using a randomised controlled trial design. Parents with a child aged 3?8?years will be recruited to take part in the study. After obtaining informed consent and pre-intervention measures, participants will be randomly assigned to either an intervention or a waitlist condition. The two primary outcomes for this study are change in dysfunctional/ineffective parenting practices and change in symptoms of depression, anxiety, and stress. Secondary outcomes are child behaviour problems, parenting experiences, parental self-efficacy, family relationships, and positive parental mental health. Demographic information, participant satisfaction with the intervention, and treatment fidelity data will also be collected. Data will be collected at pre-intervention, mid-intervention, post-intervention, and 3-month follow-up.&lt;/br&gt; &lt;br&gt;Discussion: The aim of this paper is to describe the study protocol of a randomised controlled trial evaluating the effects of a combined intervention of Triple P Discussion Groups and Stress Control in comparison to a waitlist condition. This study is important because it will provide evidence about the effects of this combined intervention for parents with 3?8?year old children. The results of the study could be used to inform policy about parenting support and support for parents with mental health problems. Trial registration ClinicalTrial.gov: NCT01777724, UTN: U1111-1137-1053.&lt;/br&gt

    Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke.

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    Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; &lt;4 Hz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation

    Broadband random optoelectronic oscillator

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    [EN] Random scattering of light in transmission media has attracted a great deal of attention in the field of photonics over the past few decades. An optoelectronic oscillator (OEO) is a microwave photonic system offering unbeatable features for the generation of microwave oscillations with ultra-low phase noise. Here, we combine the unique features of random scattering and OEO technologies by proposing an OEO structure based on random distributed feedback. Thanks to the random distribution of Rayleigh scattering caused by inhomogeneities within the glass structure of the fiber, we demonstrate the generation of ultra-wideband (up to 40¿GHz from DC) random microwave signals in an open cavity OEO. The generated signals enjoy random characteristics, and their frequencies are not limited by a fixed cavity length figure. The proposed device has potential in many fields such as random bit generation, radar systems, electronic interference and countermeasures, and telecommunications.Thanks N. Shi and Y. Yang for comments and discussion. This work was supported by the National Key Research and Development Program of China under 2018YFB2201902 and the National Natural Science Foundation of China under 61925505. This work was also partly supported by the National Key Research and Development Program of China under 2018YFB2201901, 2018YFB2201903, and the National Natural Science Foundation of China under 61535012 and 61705217.Ge, Z.; Hao, T.; Capmany Francoy, J.; Li, W.; Zhu, N.; Li, M. (2020). Broadband random optoelectronic oscillator. Nature Communications. 11(1):1-8. https://doi.org/10.1038/s41467-020-19596-xS18111Feng, S., Kane, C., Lee, P. A. & Stone, A. D. Correlations and fluctuations of coherent wave transmission through disordered media. Phys. Rev. Lett. 61, 834 (1988).Wiersma, D. S. & Cavalieri, S. Light emission: a temperature-tunable random laser. Nature 414, 708 (2001).Wiersma, D. S. The physics and applications of random lasers. Nat. Phys. 4, 359 (2008).Turitsyn, S. K. et al. Random distributed feedback fibre laser. Nat. Photonics 4, 231–235 (2010).Babin, S. A., El-Taher, A. E., Harper, P., Podivilov, E. V. & Turitsyn, S. K. Tunable random fiber laser. Phys. Rev. A 84, 021805 (2011).Turitsyn, S. K. et al. Random distributed feedback fibre lasers. Phys. Rep. 542, 133–193 (2014).Barnoski, M., Rourke, M., Jensen, S. M. & Melville, R. T. Optical time domain reflectometer. Appl. Opt. 16, 2375–2379 (1977).Yao, X. S. & Maleki, L. Optoelectronic microwave oscillator. JOSA B 13, 1725–1735 (1996).Maleki, L. Sources: the optoelectronic oscillator. Nat. Photonics 5, 728 (2011).Yao, X. S. & Maleki, L. Multiloop optoelectronic oscillator. IEEE J. Quantum Electron 36, 79–84 (2000).Hao, T. et al. Breaking the limitation of mode building time in an optoelectronic oscillator. Nat. Commun. 9, 1839 (2018).Zhang, W. & Yao, J. Silicon photonic integrated optoelectronic oscillator for frequency-tunable microwave generation. J. Lightwave Technol. 36, 4655–4663 (2018).Hao, T. et al. Toward Monolithic Integration of OEOs: from systems to chips. J. Lightwave Technol. 36, 4565–4582 (2018).Zhang, J. & Yao, J. Parity-time–symmetric optoelectronic oscillator. Sci. Adv. 4, eaar6782 (2018).Liu, Y. et al. Observation of parity-time symmetry in microwave photonics. Light Sci. Appl. 7, 38 (2018).Nakazawa, M. Rayleigh backscattering theory for single-mode optical fibers. JOSA 73, 1175–1180 (1983).Hartog, A. & Gold, M. On the theory of backscattering in single-mode optical fibers. J. Lightwave Technol. 2, 76–82 (1984).Eickhoff, W., & Ulrich, R. Statistics of backscattering in single-mode fiber. In Optical Fiber Communication Conference. Optical Society of America (1981).Alekseev, A. E., Tezadov, Y. A. & Potapov, V. T. Statistical properties of backscattered semiconductor laser radiation with different degrees of coherence. Quantum Electron 42, 76–81 (2012).Gysel, P. & Staubli, R. K. Statistical properties of Rayleigh backscattering in single-mode fibers. J. Lightwave Technol. 8, 561–567 (1990).Staubli, R. K. & Gysel, P. Statistical properties of single-mode fiber rayleigh backscattered intensity and resulting detector current. IEEE Trans. Commun. 40, 1091–1097 (1992).Levy, E. C., Horowitz, M. & Menyuk, C. R. Modeling optoelectronic oscillators. JOSA B 26, 148–159 (2009).Yariv, A. Introduction to Optical Electronics 2nd edn. (Holt, Rinehart and Winston, New York, 1976).Aoki, Y., Tajima, K. & Mito, I. Input power limits of single-mode optical fibers due to stimulated Brillouin scattering in optical communication systems. J. Lightwave Technol. 6, 710–719 (1988).Song, H. J., Shimizu, N., Kukutsu, N., Nagatsuma, T. & Kado, Y. Microwave photonic noise source from microwave to sub-terahertz wave bands and its applications to noise characterization. IEEE Trans. Microw. Theory Tech. 56, 2989–2997 (2008).Chembo, Y. K., et al. Optoelectronic oscillators with time-delayed feedback. Rev. Mod. Phys. 91, 035006 (2019).Callan, K. E. et al. Broadband chaos generated by an optoelectronic oscillator. Phys. Rev. Lett. 104, 113901 (2010).Lavrov, R. et al. Electro-optic delay oscillator with nonlocal nonlinearity: Optical phase dynamics, chaos, and synchronization. Phys. Rev. E. 80, 026207 (2009).Wolf, A., Swift, J. B., Swinney, H. L. & Vastano, J. A. Determining Lyapunov exponents from a time series. Phys. D. 16, 285–317 (1985).Grassberger, P. & Procaccia, I. Characterization of strange attractors. Phys. Rev. Lett. 50, 346 (1983).Grassberger, P. & Procaccia, I. Measuring the strangeness of strange attractors. Phys. D. 9, 189–208 (1983).Romeira, B. et al. Broadband chaotic signals and breather oscillations in an optoelectronic oscillator incorporating a microwave photonic filter. J. Lightwave Technol. 32, 3933–3942 (2014)

    The molecular characterisation of Escherichia coli K1 isolated from neonatal nasogastric feeding tubes

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    Background: The most common cause of Gram-negative bacterial neonatal meningitis is E. coli K1. It has a mortality rate of 10–15%, and neurological sequelae in 30– 50% of cases. Infections can be attributable to nosocomial sources, however the pre-colonisation of enteral feeding tubes has not been considered as a specific risk factor. Methods: Thirty E. coli strains, which had been isolated in an earlier study, from the residual lumen liquid and biofilms of neonatal nasogastric feeding tubes were genotyped using pulsed-field gel electrophoresis, and 7-loci multilocus sequence typing. Potential pathogenicity and biofilm associated traits were determined using specific PCR probes, genome analysis, and in vitro tissue culture assays. Results: The E. coli strains clustered into five pulsotypes, which were genotyped as sequence types (ST) 95, 73, 127, 394 and 2076 (Achman scheme). The extra-intestinal pathogenic E. coli (ExPEC) phylogenetic group B2 ST95 serotype O1:K1:NM strains had been isolated over a 2 week period from 11 neonates who were on different feeding regimes. The E. coli K1 ST95 strains encoded for various virulence traits associated with neonatal meningitis and extracellular matrix formation. These strains attached and invaded intestinal, and both human and rat brain cell lines, and persisted for 48 h in U937 macrophages. E. coli STs 73, 394 and 2076 also persisted in macrophages and invaded Caco-2 and human brain cells, but only ST394 invaded rat brain cells. E. coli ST127 was notable as it did not invade any cell lines. Conclusions: Routes by which E. coli K1 can be disseminated within a neonatal intensive care unit are uncertain, however the colonisation of neonatal enteral feeding tubes may be one reservoir source which could constitute a serious health risk to neonates following ingestion

    Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression

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    BACKGROUND: Gene expression is regulated mainly by transcription factors (TFs) that interact with regulatory cis-elements on DNA sequences. To identify functional regulatory elements, computer searching can predict TF binding sites (TFBS) using position weight matrices (PWMs) that represent positional base frequencies of collected experimentally determined TFBS. A disadvantage of this approach is the large output of results for genomic DNA. One strategy to identify genuine TFBS is to utilize local concentrations of predicted TFBS. It is unclear whether there is a general tendency for TFBS to cluster at promoter regions, although this is the case for certain TFBS. Also unclear is the identification of TFs that have TFBS concentrated in promoters and to what level this occurs. This study hopes to answer some of these questions. RESULTS: We developed the cluster score measure to evaluate the correlation between predicted TFBS clusters and promoter sequences for each PWM. Non-promoter sequences were used as a control. Using the cluster score, we identified a PWM group called PWM-PCP, in which TFBS clusters positively correlate with promoters, and another PWM group called PWM-NCP, in which TFBS clusters negatively correlate with promoters. The PWM-PCP group comprises 47% of the 199 vertebrate PWMs, while the PWM-NCP group occupied 11 percent. After reducing the effect of CpG islands (CGI) against the clusters using partial correlation coefficients among three properties (promoter, CGI and predicted TFBS cluster), we identified two PWM groups including those strongly correlated with CGI and those not correlated with CGI. CONCLUSION: Not all PWMs predict TFBS correlated with human promoter sequences. Two main PWM groups were identified: (1) those that show TFBS clustered in promoters associated with CGI, and (2) those that show TFBS clustered in promoters independent of CGI. Assessment of PWM matches will allow more positive interpretation of TFBS in regulatory regions
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