The Experts below are selected from a list of 77925 Experts worldwide ranked by ideXlab platform
Tomoki Fukai - One of the best experts on this subject based on the ideXlab platform.
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interplay between a phase response curve and spike timing dependent plasticity leading to wireless clustering
Physical Review E, 2008Co-Authors: Hideyuki Câteau, Katsunori Kitano, Tomoki FukaiAbstract:A phase response curve (PRC) characterizes the signal transduction between oscillators such as neurons on a fixed network in a minimal manner, while spike-timing-dependent plasiticity (STDP) characterizes the way of rewiring networks in an activity-dependent manner. This paper demonstrates that these two key properties both related to the interaction times of oscillators work synergetically to carve functionally useful circuits. STDP working on neurons that prefer aSynchrony converts the initial asynchronous firing to clustered firing with Synchrony within a cluster. They get synchronized within a cluster despite their preference to aSynchrony because STDP selectively disrupts intracluster connections, which we call wireless clustering. Our PRC analysis reveals a triad mechanism: the network structure affects how the PRC is read out to determine the Synchrony tendency, the Synchrony tendency affects how the STDP works, and STDP affects the network structure, closing the loop.
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interactions between spike timing dependent plasticity and phase response curve lead to wireless clustering
International Conference on Neural Information Processing, 2007Co-Authors: Hideyuki Câteau, Katsunori Kitano, Tomoki FukaiAbstract:A phase response curve characterizes the signal transduction between neurons in a minimal manner,whereas spike-timing-dependent plasiticity (STDP) characterizes the way to rewire networks in an activity-dependent manner. The present paper demonstrates that these two key properties both related to spikes work synergetically to carve functionally useful circuits in the brain. STDP working on a population of neurons that prefer aSynchrony turns out to convert the initial asynchronous firing to clustered firing with Synchrony within a cluster. They get synchronized within a cluster despite their preference to aSynchrony because STDP selectively disrupts intra-cluster connections, which we call wireless eclustering.
Heather M Kharouba - One of the best experts on this subject based on the ideXlab platform.
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global shifts in the phenological Synchrony of species interactions over recent decades
Proceedings of the National Academy of Sciences of the United States of America, 2018Co-Authors: Heather M Kharouba, Johan Ehrlen, Andrew Gelman, Kjell Bolmgren, Jenica M Allen, Steve E Travers, Elizabeth M WolkovichAbstract:Phenological responses to climate change (e.g., earlier leaf-out or egg hatch date) are now well documented and clearly linked to rising temperatures in recent decades. Such shifts in the phenologies of interacting species may lead to shifts in their Synchrony, with cascading community and ecosystem consequences. To date, single-system studies have provided no clear picture, either finding Synchrony shifts may be extremely prevalent [Mayor SJ, et al. (2017) Sci Rep 7:1902] or relatively uncommon [Iler AM, et al. (2013) Glob Chang Biol 19:2348–2359], suggesting that shifts toward aSynchrony may be infrequent. A meta-analytic approach would provide insights into global trends and how they are linked to climate change. We compared phenological shifts among pairwise species interactions (e.g., predator–prey) using published long-term time-series data of phenological events from aquatic and terrestrial ecosystems across four continents since 1951 to determine whether recent climate change has led to overall shifts in Synchrony. We show that the relative timing of key life cycle events of interacting species has changed significantly over the past 35 years. Further, by comparing the period before major climate change (pre-1980s) and after, we show that estimated changes in phenology and Synchrony are greater in recent decades. However, there has been no consistent trend in the direction of these changes. Our findings show that there have been shifts in the timing of interacting species in recent decades; the next challenges are to improve our ability to predict the direction of change and understand the full consequences for communities and ecosystems.
Philippe Jolliet - One of the best experts on this subject based on the ideXlab platform.
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Neurally adjusted ventilatory assist (NAVA) improves patient–ventilator interaction during non-invasive ventilation delivered by face mask
Intensive Care Medicine, 2012Co-Authors: Lise Piquilloud, Philippe Jolliet, Emilie Bialais, Jean Roeseler, Thierry Sottiaux, Pierre-françois Laterre, Didier Tassaux, Bernard Lambermont, Jean-pierre RevellyAbstract:Purpose To determine if, compared to pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces patient–ventilator aSynchrony in intensive care patients undergoing noninvasive ventilation with an oronasal face mask. Methods In this prospective interventional study we compared patient–ventilator Synchrony between PS (with ventilator settings determined by the clinician) and NAVA (with the level set so as to obtain the same maximal airway pressure as in PS). Two 20-min recordings of airway pressure, flow and electrical activity of the diaphragm during PS and NAVA were acquired in a randomized order. Trigger delay ( T _d), the patient’s neural inspiratory time ( T _in), ventilator pressurization duration ( T _iv), inspiratory time in excess ( T _iex), number of aSynchrony events per minute and aSynchrony index (AI) were determined. Results The study included 13 patients, six with COPD, and two with mixed pulmonary disease. T _d was reduced with NAVA: median 35 ms (IQR 31–53 ms) versus 181 ms (122–208 ms); p = 0.0002. NAVA reduced both premature and delayed cyclings in the majority of patients, but not the median T _iex value. The total number of aSynchrony events tended to be reduced with NAVA: 1.0 events/min (0.5–3.1 events/min) versus 4.4 events/min (0.9–12.1 events/min); p = 0.08. AI was lower with NAVA: 4.9 % (2.5–10.5 %) versus 15.8 % (5.5–49.6 %); p = 0.03. During NAVA, there were no ineffective efforts, or late or premature cyclings. PaO_2 and PaCO_2 were not different between ventilatory modes. Conclusion Compared to PS, NAVA improved patient ventilator Synchrony during noninvasive ventilation by reducing T _d and AI. Moreover, with NAVA, ineffective efforts, and late and premature cyclings were absent.
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patient ventilator aSynchrony during noninvasive ventilation a bench and clinical study
Chest, 2012Co-Authors: Guillaume Carteaux, Laurent Brochard, Aissam Lyazidi, Ana Cordobaizquierdo, Laurence Vignaux, Philippe Jolliet, Arnaud W Thille, Jeanchristophe M RichardAbstract:Background Different kinds of ventilators are available to perform noninvasive ventilation (NIV) in ICUs. Which type allows the best patient-ventilator Synchrony is unknown. The objective was to compare patient-ventilator Synchrony during NIV between ICU, transport—both with and without the NIV algorithm engaged—and dedicated NIV ventilators. Methods First, a bench model simulating spontaneous breathing efforts was used to assess the respective impact of inspiratory and expiratory leaks on cycling and triggering functions in 19 ventilators. Second, a clinical study evaluated the incidence of patient-ventilator asynchronies in 15 patients during three randomized, consecutive, 20-min periods of NIV using an ICU ventilator with and without its NIV algorithm engaged and a dedicated NIV ventilator. Patient-ventilator aSynchrony was assessed using flow, airway pressure, and respiratory muscles surface electromyogram recordings. Results On the bench, frequent auto-triggering and delayed cycling occurred in the presence of leaks using ICU and transport ventilators. NIV algorithms unevenly minimized these asynchronies, whereas no aSynchrony was observed with the dedicated NIV ventilators in all except one. These results were reproduced during the clinical study: The aSynchrony index was significantly lower with a dedicated NIV ventilator than with ICU ventilators without or with their NIV algorithm engaged (0.5% [0.4%-1.2%] vs 3.7% [1.4%-10.3%] and 2.0% [1.5%-6.6%], P Conclusions Dedicated NIV ventilators allow better patient-ventilator Synchrony than ICU and transport ventilators, even with their NIV algorithm. However, the NIV algorithm improves, at least slightly and with a wide variation among ventilators, triggering and/or cycling off synchronization.
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Neurally adjusted ventilatory assist improves patient–ventilator interaction
Intensive Care Medicine, 2011Co-Authors: Lise Piquilloud, Laurence Vignaux, Philippe Jolliet, Emilie Bialais, Jean Roeseler, Thierry Sottiaux, Pierre-françois Laterre, Didier TassauxAbstract:Purpose To determine if, compared with pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces trigger delay, inspiratory time in excess, and the number of patient–ventilator asynchronies in intubated patients. Methods Prospective interventional study in spontaneously breathing patients intubated for acute respiratory failure. Three consecutive periods of ventilation were applied: (1) PS1, (2) NAVA, (3) PS2. Airway pressure, flow, and transesophageal diaphragmatic electromyography were continuously recorded. Results All results are reported as median (interquartile range, IQR). Twenty-two patients were included, 36.4% (8/22) having obstructive pulmonary disease. NAVA reduced trigger delay (ms): NAVA, 69 (57–85); PS1, 178 (139–245); PS2, 199 (135–256). NAVA improved expiratory Synchrony: inspiratory time in excess (ms): NAVA, 126 (111–136); PS1, 204 (117–345); PS2, 220 (127–366). Total aSynchrony events were reduced with NAVA (events/min): NAVA, 1.21 (0.54–3.36); PS1, 3.15 (1.18–6.40); PS2, 3.04 (1.22–5.31). The number of patients with aSynchrony index (AI) >10% was reduced by 50% with NAVA. In contrast to PS, no ineffective effort or late cycling was observed with NAVA. There was less premature cycling with NAVA (events/min): NAVA, 0.00 (0.00–0.00); PS1, 0.14 (0.00–0.41); PS2, 0.00 (0.00–0.48). More double triggering was seen with NAVA, 0.78 (0.46–2.42); PS1, 0.00 (0.00–0.04); PS2, 0.00 (0.00–0.00). Conclusions Compared with standard PS, NAVA can improve patient–ventilator Synchrony in intubated spontaneously breathing intensive care patients. Further studies should aim to determine the clinical impact of this improved Synchrony.
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neurally adjusted ventilatory assist improves patient ventilator interaction
Intensive Care Medicine, 2011Co-Authors: Lise Piquilloud, Laurence Vignaux, Philippe Jolliet, Emilie Bialais, Jean Roeseler, Thierry Sottiaux, Pierre-françois Laterre, Didier TassauxAbstract:To determine if, compared with pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces trigger delay, inspiratory time in excess, and the number of patient–ventilator asynchronies in intubated patients. Prospective interventional study in spontaneously breathing patients intubated for acute respiratory failure. Three consecutive periods of ventilation were applied: (1) PS1, (2) NAVA, (3) PS2. Airway pressure, flow, and transesophageal diaphragmatic electromyography were continuously recorded. All results are reported as median (interquartile range, IQR). Twenty-two patients were included, 36.4% (8/22) having obstructive pulmonary disease. NAVA reduced trigger delay (ms): NAVA, 69 (57–85); PS1, 178 (139–245); PS2, 199 (135–256). NAVA improved expiratory Synchrony: inspiratory time in excess (ms): NAVA, 126 (111–136); PS1, 204 (117–345); PS2, 220 (127–366). Total aSynchrony events were reduced with NAVA (events/min): NAVA, 1.21 (0.54–3.36); PS1, 3.15 (1.18–6.40); PS2, 3.04 (1.22–5.31). The number of patients with aSynchrony index (AI) >10% was reduced by 50% with NAVA. In contrast to PS, no ineffective effort or late cycling was observed with NAVA. There was less premature cycling with NAVA (events/min): NAVA, 0.00 (0.00–0.00); PS1, 0.14 (0.00–0.41); PS2, 0.00 (0.00–0.48). More double triggering was seen with NAVA, 0.78 (0.46–2.42); PS1, 0.00 (0.00–0.04); PS2, 0.00 (0.00–0.00). Compared with standard PS, NAVA can improve patient–ventilator Synchrony in intubated spontaneously breathing intensive care patients. Further studies should aim to determine the clinical impact of this improved Synchrony.
Didier Tassaux - One of the best experts on this subject based on the ideXlab platform.
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Neurally adjusted ventilatory assist (NAVA) improves patient–ventilator interaction during non-invasive ventilation delivered by face mask
Intensive Care Medicine, 2012Co-Authors: Lise Piquilloud, Philippe Jolliet, Emilie Bialais, Jean Roeseler, Thierry Sottiaux, Pierre-françois Laterre, Didier Tassaux, Bernard Lambermont, Jean-pierre RevellyAbstract:Purpose To determine if, compared to pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces patient–ventilator aSynchrony in intensive care patients undergoing noninvasive ventilation with an oronasal face mask. Methods In this prospective interventional study we compared patient–ventilator Synchrony between PS (with ventilator settings determined by the clinician) and NAVA (with the level set so as to obtain the same maximal airway pressure as in PS). Two 20-min recordings of airway pressure, flow and electrical activity of the diaphragm during PS and NAVA were acquired in a randomized order. Trigger delay ( T _d), the patient’s neural inspiratory time ( T _in), ventilator pressurization duration ( T _iv), inspiratory time in excess ( T _iex), number of aSynchrony events per minute and aSynchrony index (AI) were determined. Results The study included 13 patients, six with COPD, and two with mixed pulmonary disease. T _d was reduced with NAVA: median 35 ms (IQR 31–53 ms) versus 181 ms (122–208 ms); p = 0.0002. NAVA reduced both premature and delayed cyclings in the majority of patients, but not the median T _iex value. The total number of aSynchrony events tended to be reduced with NAVA: 1.0 events/min (0.5–3.1 events/min) versus 4.4 events/min (0.9–12.1 events/min); p = 0.08. AI was lower with NAVA: 4.9 % (2.5–10.5 %) versus 15.8 % (5.5–49.6 %); p = 0.03. During NAVA, there were no ineffective efforts, or late or premature cyclings. PaO_2 and PaCO_2 were not different between ventilatory modes. Conclusion Compared to PS, NAVA improved patient ventilator Synchrony during noninvasive ventilation by reducing T _d and AI. Moreover, with NAVA, ineffective efforts, and late and premature cyclings were absent.
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Neurally adjusted ventilatory assist improves patient–ventilator interaction
Intensive Care Medicine, 2011Co-Authors: Lise Piquilloud, Laurence Vignaux, Philippe Jolliet, Emilie Bialais, Jean Roeseler, Thierry Sottiaux, Pierre-françois Laterre, Didier TassauxAbstract:Purpose To determine if, compared with pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces trigger delay, inspiratory time in excess, and the number of patient–ventilator asynchronies in intubated patients. Methods Prospective interventional study in spontaneously breathing patients intubated for acute respiratory failure. Three consecutive periods of ventilation were applied: (1) PS1, (2) NAVA, (3) PS2. Airway pressure, flow, and transesophageal diaphragmatic electromyography were continuously recorded. Results All results are reported as median (interquartile range, IQR). Twenty-two patients were included, 36.4% (8/22) having obstructive pulmonary disease. NAVA reduced trigger delay (ms): NAVA, 69 (57–85); PS1, 178 (139–245); PS2, 199 (135–256). NAVA improved expiratory Synchrony: inspiratory time in excess (ms): NAVA, 126 (111–136); PS1, 204 (117–345); PS2, 220 (127–366). Total aSynchrony events were reduced with NAVA (events/min): NAVA, 1.21 (0.54–3.36); PS1, 3.15 (1.18–6.40); PS2, 3.04 (1.22–5.31). The number of patients with aSynchrony index (AI) >10% was reduced by 50% with NAVA. In contrast to PS, no ineffective effort or late cycling was observed with NAVA. There was less premature cycling with NAVA (events/min): NAVA, 0.00 (0.00–0.00); PS1, 0.14 (0.00–0.41); PS2, 0.00 (0.00–0.48). More double triggering was seen with NAVA, 0.78 (0.46–2.42); PS1, 0.00 (0.00–0.04); PS2, 0.00 (0.00–0.00). Conclusions Compared with standard PS, NAVA can improve patient–ventilator Synchrony in intubated spontaneously breathing intensive care patients. Further studies should aim to determine the clinical impact of this improved Synchrony.
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neurally adjusted ventilatory assist improves patient ventilator interaction
Intensive Care Medicine, 2011Co-Authors: Lise Piquilloud, Laurence Vignaux, Philippe Jolliet, Emilie Bialais, Jean Roeseler, Thierry Sottiaux, Pierre-françois Laterre, Didier TassauxAbstract:To determine if, compared with pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces trigger delay, inspiratory time in excess, and the number of patient–ventilator asynchronies in intubated patients. Prospective interventional study in spontaneously breathing patients intubated for acute respiratory failure. Three consecutive periods of ventilation were applied: (1) PS1, (2) NAVA, (3) PS2. Airway pressure, flow, and transesophageal diaphragmatic electromyography were continuously recorded. All results are reported as median (interquartile range, IQR). Twenty-two patients were included, 36.4% (8/22) having obstructive pulmonary disease. NAVA reduced trigger delay (ms): NAVA, 69 (57–85); PS1, 178 (139–245); PS2, 199 (135–256). NAVA improved expiratory Synchrony: inspiratory time in excess (ms): NAVA, 126 (111–136); PS1, 204 (117–345); PS2, 220 (127–366). Total aSynchrony events were reduced with NAVA (events/min): NAVA, 1.21 (0.54–3.36); PS1, 3.15 (1.18–6.40); PS2, 3.04 (1.22–5.31). The number of patients with aSynchrony index (AI) >10% was reduced by 50% with NAVA. In contrast to PS, no ineffective effort or late cycling was observed with NAVA. There was less premature cycling with NAVA (events/min): NAVA, 0.00 (0.00–0.00); PS1, 0.14 (0.00–0.41); PS2, 0.00 (0.00–0.48). More double triggering was seen with NAVA, 0.78 (0.46–2.42); PS1, 0.00 (0.00–0.04); PS2, 0.00 (0.00–0.00). Compared with standard PS, NAVA can improve patient–ventilator Synchrony in intubated spontaneously breathing intensive care patients. Further studies should aim to determine the clinical impact of this improved Synchrony.
Sally A Power - One of the best experts on this subject based on the ideXlab platform.
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effects of within tree flowering aSynchrony on the dynamics of seed and wasp production in an australian fig species
Journal of Biogeography, 1996Co-Authors: James M Cook, Sally A PowerAbstract:Within-tree flowering aSynchrony in figs, which may allow pollinating wasps to avoid the risks of dispersal in inclement conditions, has been predicted as a trait to be favoured in highly seasonal environments. Comparisons of such asynchronous figs with better-known species that exhibit within-tree Synchrony might also be expected to reveal differences in the outcome of the conflict between pollinator wasp and fig seed production, and the dynamics of non-pollinating wasps. This paper presents data on wasp and seed production in Ficus rubiginosa Desf. ex Vent., an asynchronous species that occurs in the highly seasonal environment of south-eastern Australia. In contrast to recent studies of figs showing within-tree flowering Synchrony, syconium size was the main determinant of wasp and seed production in F rubiginosa. Non-pollinating wasps were highly prevalent but occurred in low numbers and appeared to have relatively little impact on pollinator wasp or fig seed production. Data on flower positions revealed that non-pollinating wasps occurred almost exclusively in the outer layer of flowers, while pollinators were more abundant in the inner flower layer, which may represent an area of enemy-free space. The ratio of seeds to female pollinator wasps, an index of fig sex allocation, was more seed-biased than in several New World fig species that exhibit within-tree Synchrony. This last result supports the idea that within-tree fruiting aSynchrony permits a degree of self-pollination in F rubiginosa.
