556 research outputs found

    Effects of local hypothermia-rewarming on physiology, metabolism and inflammation of acutely injured human spinal cord.

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    In five patients with acute, severe thoracic traumatic spinal cord injuries (TSCIs), American spinal injuries association Impairment Scale (AIS) grades A-C, we induced cord hypothermia (33 °C) then rewarming (37 °C). A pressure probe and a microdialysis catheter were placed intradurally at the injury site to monitor intraspinal pressure (ISP), spinal cord perfusion pressure (SCPP), tissue metabolism and inflammation. Cord hypothermia-rewarming, applied to awake patients, did not cause discomfort or neurological deterioration. Cooling did not affect cord physiology (ISP, SCPP), but markedly altered cord metabolism (increased glucose, lactate, lactate/pyruvate ratio (LPR), glutamate; decreased glycerol) and markedly reduced cord inflammation (reduced IL1β, IL8, MCP, MIP1α, MIP1β). Compared with pre-cooling baseline, rewarming was associated with significantly worse cord physiology (increased ICP, decreased SCPP), cord metabolism (increased lactate, LPR; decreased glucose, glycerol) and cord inflammation (increased IL1β, IL8, IL4, IL10, MCP, MIP1α). The study was terminated because three patients developed delayed wound infections. At 18-months, two patients improved and three stayed the same. We conclude that, after TSCI, hypothermia is potentially beneficial by reducing cord inflammation, though after rewarming these benefits are lost due to increases in cord swelling, ischemia and inflammation. We thus urge caution when using hypothermia-rewarming therapeutically in TSCI

    Spatial Division Multiplexed Microwave Signal processing by selective grating inscription in homogeneous multicore fibers

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    [EN] The use of Spatial Division Multiplexing for Microwave Photonics signal processing is proposed and experimentally demonstrated, for the first time to our knowledge, based on the selective inscription of Bragg gratings in homogeneous multicore fibers. The fabricated devices behave as sampled true time delay elements for radiofrequency signals offering a wide range of operation possibilities within the same optical fiber. The key to processing flexibility comes from the implementation of novel multicavity configurations by inscribing a variety of different fiber Bragg gratings along the different cores of a 7-core fiber. This entails the development of the first fabrication method to inscribe high-quality gratings characterized by arbitrary frequency spectra and located in arbitrary longitudinal positions along the individual cores of a multicore fiber. Our work opens the way towards the development of unique compact fiber-based solutions that enable the implementation of a wide variety of 2D (spatial and wavelength diversity) signal processing functionalities that will be key in future fiber-wireless communications scenarios. We envisage that Microwave Photonics systems and networks will benefit from this technology in terms of compactness, operation versatility and performance stability.We thank Prof. Jose Capmany for the thoughtful discussions and recommendations that greatly contribute to this work. This research was supported by the Spanish MINECO Projects TEC2014-60378-C2-1-R and TEC2015-62520-ERC, the Valencian Research Excellency Award Program GVA PROMETEO 2013/012, the Spanish MECD FPU Scholarship (FPU13/04675) for J. Hervas, and the Spanish MINECO Ramon y Cajal Program (RYC-2014-16247) for I. Gasulla.Gasulla Mestre, I.; Barrera Vilar, D.; Hervás-Peralta, J.; Sales Maicas, S. (2017). Spatial Division Multiplexed Microwave Signal processing by selective grating inscription in homogeneous multicore fibers. Scientific Reports. 7(41727):1-10. https://doi.org/10.1038/srep41727S110741727Samsung Electronics Co, “5G Vision”, available at http://www.samsung.com/global/business-images/insights/2015/Samsung-5G-Vision-0.pdf (2015).Technology Focus on Microwave Photonics. Nat. Photonics 5, 723 (2011).J. Capmany, J. Mora, I. Gasulla, J. Sancho, J. Lloret & S. Sales . Microwave photonic signal processing. IEEE J. Lightw. Technol. 31, 571–586 (2013).Y. Long & J. Wang . Ultra-high peak rejection notch microwave photonic filter using a single silicon microring resonator. Opt. Express 23, 17739–17750 (2015).Y. Long & J. Wang . All-optical tuning of a nonlinear silicon microring assisted microwave photonic filter: theory and experiment. Opt. Express 23, 17758–17771 (2015).Y. Long, L. Zhou & J. Wang . Photonic-assisted microwave signal multiplication and modulation using a silicon Mach–Zehnder modulator. Sci. Reports 6, 20215 (2016).J. Sancho, J. Bourderionnet, J. Lloret, S. Combrié, I. Gasulla, S. Xavier, S. Sales, P. Colman, G. Lehoucq, D. Dolfi, J. Capmany & A. De Rossi . Integrable microwave filter based on a photonic crystal delay line. Nat. Commun. 3, 1075 (2012).F. Ohman, K. Yvind & J. Mørk . Slow Light in a Semiconductor Waveguide for True-Time Delay Applications in Microwave Photonics. IEEE Photon. Technol. Lett. 19, 1145–1157 (2007).P. A. Morton & J. B. Khurgin. Microwave photonic delay line with separate tuning of the optical carrier. IEEE Photon. Technol. Lett. 21, 1686–1688 (2009).D. Marpaung, C. Roeloffzen, R. Heideman, A. Leinse, S. Sales & J. Capmany . Integrated microwave photonics. Lasers Photon. Rev. 7, 506–538 (2013).I. Gasulla & J. Capmany . Microwave photonics applications of multicore fibers. Photonics J. 4, 877–888 (2012).S. Garcia & I. Gasulla . Design of Heterogeneous Multicore fibers as sampled True Time Delay Lines. Opt. Lett. 40, 621–624 (2015).F. Zeng & J. Yao . All-optical microwave filters using uniform fiber Bragg gratings with identical reflectivities. IEEE J. Lightw. Technol. 23, 1410 (2005).C. Wang & J. Yao . Fiber Bragg gratings for microwave photonics subsystems. Opt. Express 21, 22868–22884 (2013).I. Gasulla, D. Barrera & S. Sales . Microwave photonic devices based on multicore fibers. 16th International Conference on Transparent Optical Networks (ICTON), Graz, Austria, 2014.I. Gasulla, D. Barrera, J. Hervás, S. García & S. Sales . Multi-cavity Microwave Photonics devices built upon multicore fibres. 18th International Conference on Transparent Optical Networks (ICTON), Trento (Italy), pp. 1–4, 2016.K. O. Hill & G. Meltz . Fiber Bragg grating technology fundamentals and overview. IEEE J. Lightw. Technol. 15, 1263–1276 (1997).T. Erdogan . Fiber grating spectra. IEEE J. Lightw. Technol. 15, 1277–1294 (1997).D. Barrera, I. Gasulla & S. Sales . Multipoint two-dimensional curvature optical fiber sensor based on a non-twisted homogeneous four-core fiber. IEEE J. Lightw. Technol. 33, 2445–2450 (2015).T. Birks, B. Mangan, A. Diez, J. Cruz, S. Leon-Saval, J. Bland-Hawthorn & D. Murphy . Multicore optical fibres for astrophotonics. In CLEO/Europe and EQEC 2011 Conference Digest, OSA Technical Digest (CD)d (Optical Society of America, 2011), paper JSIII2_1.C. Wang, Z. Yan, Q. Sun, Z. Sun, C. Mou, J. Zhang, A. Badmos & L. Zhang . Fibre Bragg gratings fabrication in four core fibres. Proc. SPIE 9886, Micro-Structured and Specialty Optical Fibres IV, 98860H (2016).M. J. Cole, W. H. Loh, R. I. Laming, M. N. Zervas & S. Barcelos . Moving fibre/phase mask-scanning beam technique for enhanced flexibility in producing fibre gratings with uniform phase mask. Electron. Lett. 31, 1488–1490 (1995).M. Gallagher & U. Österberg . Time resolved 3.10 eV luminescence in germanium-doped silica glass. Appl. Phys. Lett. 63, 2987–2988 (1993).T. Komukai & M. Nakazawa . Fabrication of high-quality long fiber Bragg grating by monitoring 3.1 eV radiation (400 nm) from GeO defects. IEEE Photon. Tech. Lett. 8, 1495–1497 (1996).R. R. Thomson, H. T. Bookey, N. D. Psaila, A. Fender, S. Campbell, W. N. MacPherson, J. S. Barton, D. T. Reid & A. K. Kar . Ultrafast-laser inscription of a three dimensional fan-out device for multicore fiber coupling applications. Opt. Express 15, 11691–11697 (2007)

    Beyond Embodiment: From Internal Representation of Action to Symbolic Processes

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    In sensorimotor integration, representation involves an anticipatory model of the action to be performed. This model integrates efferent signals (motor commands), its reafferent consequences (sensory consequences of an organism’s own motor action), and other afferences (sensory signals) originated by stimuli independent of the action performed. Representation, a form of internal modeling, is invoked to explain the fact that behavior oriented to the achievement of future goals is relatively independent from the immediate environment. Internal modeling explains how a cognitive system achieves its goals despite variations in the environment with insufficient and noisy sensory–perceptual data. In a self that acts intentionally on the environment, knowledge is dependent upon the necessity to guide actions directed toward an aim. The self-inner model, a representation of internal and external environments (including reafferent and afferent messages) and also of the behavior plans and desirable future states (aims) and efferent intentions (motor planning and motor command messages), is intrinsically linked to a thinking capacity, which is supposed to emerge from the binding of multiple influences. Thinking emerges when higher behavior strategies are considered possible and capable of leading to aims or the fulfillment of intentions. In this model, symbolization processes are projective and anticipatory and, in this way, beyond present referents. Symbolization occurs linked to action planning, command, and regulation in mental simulation. Meaning is related to an inner sense of a self that acts over the environment

    Marginalization of end-use technologies in energy innovation for climate protection

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    Mitigating climate change requires directed innovation efforts to develop and deploy energy technologies. Innovation activities are directed towards the outcome of climate protection by public institutions, policies and resources that in turn shape market behaviour. We analyse diverse indicators of activity throughout the innovation system to assess these efforts. We find efficient end-use technologies contribute large potential emission reductions and provide higher social returns on investment than energy-supply technologies. Yet public institutions, policies and financial resources pervasively privilege energy-supply technologies. Directed innovation efforts are strikingly misaligned with the needs of an emissions-constrained world. Significantly greater effort is needed to develop the full potential of efficient end-use technologies

    Atmospheric Ice‐Nucleating Particles in the Dusty Tropical Atlantic

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    Desert dust is one of the most important atmospheric ice-nucleating aerosol species around the globe. However, there have been very few measurements of ice-nucleating particle (INP) concentrations in dusty air close to desert sources. In this study we report the concentration of INPs in dust laden air over the tropical Atlantic within a few days' transport of one of the world's most important atmospheric sources of desert dust, the Sahara. These measurements were performed as part of the Ice in Clouds Experiment-Dust campaign based in Cape Verde, during August 2015. INP concentrations active in the immersion mode, determined using a droplet-on-filter technique, ranged from around 10² m⁻³ at -12°C to around 10⁵ m⁻³ at -23°C. There is about 2 orders of magnitude variability in INP concentration for a particular temperature, which is determined largely by the variability in atmospheric dust loading. These measurements were made at altitudes from 30 to 3,500 m in air containing a range of dust loadings. The ice active site density (n s ) for desert dust dominated aerosol derived from our measurements agrees with several laboratory-based parameterizations for ice nucleation by desert dust within 1 to 2 orders of magnitude. The small variability in n s values determined from our measurements (within about 1 order of magnitude) is striking given that the back trajectory analysis suggests that the sources of dust were geographically diverse. This is consistent with previous work, which indicates that desert dust's ice-nucleating activity is only weakly dependent on source

    Prevalence of Obesity and the Relationship between the Body Mass Index and Body Fat: Cross-Sectional, Population-Based Data

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    Background: Anthropometric measures such as the body mass index (BMI) and waist circumference are widely used as convenient indices of adiposity, yet there are limitations in their estimates of body fat. We aimed to determine the prevalence of obesity using criteria based on the BMI and waist circumference, and to examine the relationship between the BMI and body fat.Methodology/Principal Findings: This population-based, cross-sectional study was conducted as part of the Geelong Osteoporosis Study. A random sample of 1,467 men and 1,076 women aged 20&ndash;96 years was assessed 2001&ndash;2008. Overweight and obesity were identified according to BMI (overweight 25.0&ndash;29.9 kg/m2; obesity 30.0 kg/m2) and waist circumference (overweight men 94.0–101.9 cm; women 80.0–87.9 cm; obesity men 102.0 cm, women $88.0 cm); body fat mass was assessed using dual energy X-ray absorptiometry; height and weight were measured and lifestyle factors documented by self-report. According to the BMI, 45.1% (95%CI 42.4&ndash;47.9) of men and 30.2% (95%CI 27.4&ndash;33.0) of women were overweight and a further 20.2% (95%CI 18.0&ndash;22.4) of men and 28.6% (95%CI 25.8&ndash;31.3) of women were obese. Using waist circumference, 27.5% (95%CI 25.1&ndash;30.0) of men and 23.3% (95%CI 20.8&ndash;25.9) of women were overweight, and 29.3% (95%CI 26.9&ndash;31.7) of men and 44.1% (95%CI 41.2&ndash;47.1) of women, obese. Both criteria indicate that approximately 60% of the population exceeded recommended thresholds for healthy body habitus. There was no consistent pattern apparent between BMI and energy intake. Compared with women, BMI overestimated adiposity in men, whose excess weight was largely attributable to muscular body builds and greater bone mass. BMI also underestimated adiposity in the elderly. Regression models including gender, age and BMI explained 0.825 of the variance in percent body fat.Conclusions/Significance: As the BMI does not account for differences in body composition, we suggest that gender- and age-specific thresholds should be considered when the BMI is used to indicate adiposity.<br /

    TRY plant trait database - enhanced coverage and open access

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    Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives
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