784 research outputs found
diseño de investigación experimental y anova aplicados a la identificación del material absorbente acústico más eficiente de un sistema masa-muelle-masa para la prevención de enfermedades ocupacionales provocadas por el ruido.
Describe la metodología para elaborar un diseño experimental y un análisis de varianza con el fin de identificar qué tipo de material absorbente acústico es el más eficiente en los sistemas masa-muelle-masa, y con ello lograr la prevención de las enfermedades ocupacionales provocadas por la exposición al ruido
The Future of Destination Marketing Organizations in the Insight Era
There has been a growing interest in examining the implementation of insight-era technologies (e.g., AI, social media) and big data for sustainable tourism development. However, actionable guidelines to promote a holistic adaptation and the effective functioning of destination marketing/management organizations (DMOs) in the increasingly data-infused world are still needed. This perspective paper posits a research-based framework that DMOs can use to become more responsive and efficient in their marketing and planning efforts in the current AI-infused world. Four propositions are presented to support DMOs\u27 transition to the insight-era: (a) DMOs\u27 organizational adaptations and workforce development and training, (b) active engagement with destinations\u27 stakeholders and data sharing, (c) leverage user-generated data and emergent technologies for destination marketing, and (d) DMOs\u27 data-driven decision making
Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks
[EN] Hippocampal firing is organized in theta sequences controlled by internal memory processes and by external sensory cues, but how these computations are coordinated is not fully understood. Although theta activity is commonly studied as a unique coherent oscillation, it is the result of complex interactions between different rhythm generators. Here, by separating hippocampal theta activity in three different current generators, we found epochs with variable theta frequency and phase coupling, suggesting flexible interactions between theta generators. We found that epochs of highly synchronized theta rhythmicity preferentially occurred during behavioral tasks requiring coordination between internal memory representations and incoming sensory information. In addition, we found that gamma oscillations were associated with specific theta generators and the strength of theta-gamma coupling predicted the synchronization between theta generators. We propose a mechanism for segregating or integrating hippocampal computations based on the flexible coordination of different theta frameworks to accommodate the cognitive needs.European Regional Development Fund BFU2015-64380-C2-1-R Santiago Canals
European Regional Development Fund BFU2015-64380-C2-2-R David Moratal
European Regional Development Fund PGC2018-101055-B-I00 Santiago Canals
Horizon 2020 Framework Programme 668863 (SyBil-AA) Santiago Canals
Agencia Estatal de Investigacion SEV-2017-0723 Santiago Canals
Ministerio de Economia y Competitividad TEC2016-80063-C3-3-R Claudio R Mirasso
Ministerio de Economia y Competitividad TEC2016-80063-C3-2-R Ernesto Pereda
Agencia Estatal de Investigacion MDM-2017-0711 Claudio R Mirasso
Ministerio de Economi ' a y Competitividad SAF2016-80100-R Oscar Herreras
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.López-Madrona, VJ.; Pérez-Montoyo, E.; Alvarez-Salvado, E.; Moratal, D.; Herreras, O.; Pereda, E.; Mirasso, CR.... (2020). Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks. eLife. 9:1-35. https://doi.org/10.7554/eLife.57313S1359Ahmed, O. J., & Mehta, M. R. (2012). Running Speed Alters the Frequency of Hippocampal Gamma Oscillations. Journal of Neuroscience, 32(21), 7373-7383. doi:10.1523/jneurosci.5110-11.2012Ainge, J. A., van der Meer, M. A. A., Langston, R. F., & Wood, E. R. (2007). Exploring the role of context-dependent hippocampal activity in spatial alternation behavior. Hippocampus, 17(10), 988-1002. doi:10.1002/hipo.20301Alonso, A., & García-Austt, E. (1987). Neuronal sources of theta rhythm in the entorhinal cortex of the rat. Experimental Brain Research, 67(3), 502-509. doi:10.1007/bf00247283Álvarez-Salvado, E., Pallarés, V., Moreno, A., & Canals, S. (2014). Functional MRI of long-term potentiation: imaging network plasticity. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1633), 20130152. doi:10.1098/rstb.2013.0152Amzica, F., & Steriade, M. (1998). Electrophysiological correlates of sleep delta waves. Electroencephalography and Clinical Neurophysiology, 107(2), 69-83. doi:10.1016/s0013-4694(98)00051-0Andersen, P., Holmqvist, B., & Voorhoeve, P. E. (1966). Entorhinal Activation of Dentate Granule Cells. Acta Physiologica Scandinavica, 66(4), 448-460. doi:10.1111/j.1748-1716.1966.tb03223.xBarnett, L., & Seth, A. K. (2011). Behaviour of Granger causality under filtering: Theoretical invariance and practical application. Journal of Neuroscience Methods, 201(2), 404-419. doi:10.1016/j.jneumeth.2011.08.010Barth, A. M., Domonkos, A., Fernandez-Ruiz, A., Freund, T. F., & Varga, V. (2018). Hippocampal Network Dynamics during Rearing Episodes. Cell Reports, 23(6), 1706-1715. doi:10.1016/j.celrep.2018.04.021Bell, A. J., & Sejnowski, T. J. (1995). An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7(6), 1129-1159. doi:10.1162/neco.1995.7.6.1129Belluscio, M. A., Mizuseki, K., Schmidt, R., Kempter, R., & Buzsaki, G. (2012). Cross-Frequency Phase-Phase Coupling between Theta and Gamma Oscillations in the Hippocampus. Journal of Neuroscience, 32(2), 423-435. doi:10.1523/jneurosci.4122-11.2012Benito, N., Fernández-Ruiz, A., Makarov, V. A., Makarova, J., Korovaichuk, A., & Herreras, O. (2013). Spatial Modules of Coherent Activity in Pathway-Specific LFPs in the Hippocampus Reflect Topology and Different Modes of Presynaptic Synchronization. Cerebral Cortex, 24(7), 1738-1752. doi:10.1093/cercor/bht022Bland, B. H., & Whishaw, I. Q. (1976). Generators and topography of hippocampal Theta (RSA) in the anaesthetized and freely moving rat. Brain Research, 118(2), 259-280. doi:10.1016/0006-8993(76)90711-3Bragin, A., Jando, G., Nadasdy, Z., Hetke, J., Wise, K., & Buzsaki, G. (1995). Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat. The Journal of Neuroscience, 15(1), 47-60. doi:10.1523/jneurosci.15-01-00047.1995Bruns, A., & Eckhorn, R. (2004). Task-related coupling from high- to low-frequency signals among visual cortical areas in human subdural recordings. International Journal of Psychophysiology, 51(2), 97-116. doi:10.1016/j.ijpsycho.2003.07.001Buzsáki, G. (2002). Theta Oscillations in the Hippocampus. Neuron, 33(3), 325-340. doi:10.1016/s0896-6273(02)00586-xBuzsáki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13(6), 407-420. doi:10.1038/nrn3241Buzsáki, G., & Draguhn, A. (2004). Neuronal Oscillations in Cortical Networks. Science, 304(5679), 1926-1929. doi:10.1126/science.1099745Buzsáki, G., & Moser, E. I. (2013). Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nature Neuroscience, 16(2), 130-138. doi:10.1038/nn.3304Cabral, H. O., Vinck, M., Fouquet, C., Pennartz, C. M. A., Rondi-Reig, L., & Battaglia, F. P. (2014). Oscillatory Dynamics and Place Field Maps Reflect Hippocampal Ensemble Processing of Sequence and Place Memory under NMDA Receptor Control. Neuron, 81(2), 402-415. doi:10.1016/j.neuron.2013.11.010Canals, S., Beyerlein, M., Merkle, H., & Logothetis, N. K. (2009). Functional MRI Evidence for LTP-Induced Neural Network Reorganization. Current Biology, 19(5), 398-403. doi:10.1016/j.cub.2009.01.037Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., … Knight, R. T. (2006). High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex. Science, 313(5793), 1626-1628. doi:10.1126/science.1128115Canolty, R. T., & Knight, R. T. (2010). The functional role of cross-frequency coupling. Trends in Cognitive Sciences, 14(11), 506-515. doi:10.1016/j.tics.2010.09.001Cardin, J. A., Carlén, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., … Moore, C. I. (2009). Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature, 459(7247), 663-667. doi:10.1038/nature08002Castellanos, N. P., & Makarov, V. A. (2006). Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods, 158(2), 300-312. doi:10.1016/j.jneumeth.2006.05.033Charpak, S., Paré, D., & Llinás, R. (1995). The Entorhinal Cortex Entrains Fast CA1 Hippocampal Oscillations in the Anaesthetized Guinea-pig: Role of the Monosynaptic Component of the Perforant Path. European Journal of Neuroscience, 7(7), 1548-1557. doi:10.1111/j.1460-9568.1995.tb01150.xChen A. 2006. Fast kernel density independent component analysis. Independent Component Analysis and Blind Signal Separation, Lecture Notes in Computer Science.Cohen, M. X. (2014). Analyzing Neural Time Series Data. doi:10.7551/mitpress/9609.001.0001Cole, S. R., & Voytek, B. (2017). Brain Oscillations and the Importance of Waveform Shape. Trends in Cognitive Sciences, 21(2), 137-149. doi:10.1016/j.tics.2016.12.008Cole, S., & Voytek, B. (2018). Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns. doi:10.1101/452987Cole, S., & Voytek, B. (2019). Cycle-by-cycle analysis of neural oscillations. Journal of Neurophysiology, 122(2), 849-861. doi:10.1152/jn.00273.2019Colgin, L. L., Denninger, T., Fyhn, M., Hafting, T., Bonnevie, T., Jensen, O., … Moser, E. I. (2009). Frequency of gamma oscillations routes flow of information in the hippocampus. Nature, 462(7271), 353-357. doi:10.1038/nature08573Colgin, L. L. (2013). Mechanisms and Functions of Theta Rhythms. Annual Review of Neuroscience, 36(1), 295-312. doi:10.1146/annurev-neuro-062012-170330Colgin, L. L. (2015). Theta–gamma coupling in the entorhinal–hippocampal system. Current Opinion in Neurobiology, 31, 45-50. doi:10.1016/j.conb.2014.08.001Colgin, L. L. (2016). Rhythms of the hippocampal network. Nature Reviews Neuroscience, 17(4), 239-249. doi:10.1038/nrn.2016.21Csicsvari, J., Hirase, H., Czurkó, A., Mamiya, A., & Buzsáki, G. (1999). Oscillatory Coupling of Hippocampal Pyramidal Cells and Interneurons in the Behaving Rat. The Journal of Neuroscience, 19(1), 274-287. doi:10.1523/jneurosci.19-01-00274.1999DeCoteau, W. E., Thorn, C., Gibson, D. J., Courtemanche, R., Mitra, P., Kubota, Y., & Graybiel, A. M. (2007). Learning-related coordination of striatal and hippocampal theta rhythms during acquisition of a procedural maze task. Proceedings of the National Academy of Sciences, 104(13), 5644-5649. doi:10.1073/pnas.0700818104Douchamps, V., Jeewajee, A., Blundell, P., Burgess, N., & Lever, C. (2013). Evidence for Encoding versus Retrieval Scheduling in the Hippocampus by Theta Phase and Acetylcholine. Journal of Neuroscience, 33(20), 8689-8704. doi:10.1523/jneurosci.4483-12.2013Dudai, Y., & Morris, R. G. M. (2013). Memorable Trends. Neuron, 80(3), 742-750. doi:10.1016/j.neuron.2013.09.039Dvorak, D., Radwan, B., Sparks, F. T., Talbot, Z. N., & Fenton, A. A. (2018). Control of recollection by slow gamma dominating mid-frequency gamma in hippocampus CA1. PLOS Biology, 16(1), e2003354. doi:10.1371/journal.pbio.2003354Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: Oscillations and synchrony in top–down processing. Nature Reviews Neuroscience, 2(10), 704-716. doi:10.1038/35094565Fell, J., & Axmacher, N. (2011). The role of phase synchronization in memory processes. Nature Reviews Neuroscience, 12(2), 105-118. doi:10.1038/nrn2979Fernandez-Ruiz, A., Makarov, V. A., Benito, N., & Herreras, O. (2012). Schaffer-Specific Local Field Potentials Reflect Discrete Excitatory Events at Gamma Frequency That May Fire Postsynaptic Hippocampal CA1 Units. Journal of Neuroscience, 32(15), 5165-5176. doi:10.1523/jneurosci.4499-11.2012Fernández-Ruiz, A., Makarov, V. A., & Herreras, O. (2012). Sustained increase of spontaneous input and spike transfer in the CA3-CA1 pathway following long-term potentiation in vivo. Frontiers in Neural Circuits, 6. doi:10.3389/fncir.2012.00071Fernández-Ruiz, A., Oliva, A., Nagy, G. A., Maurer, A. P., Berényi, A., & Buzsáki, G. (2017). Entorhinal-CA3 Dual-Input Control of Spike Timing in the Hippocampus by Theta-Gamma Coupling. Neuron, 93(5), 1213-1226.e5. doi:10.1016/j.neuron.2017.02.017Fernández-Ruiz, A., & Herreras, O. (2013). Identifying the synaptic origin of ongoing neuronal oscillations through spatial discrimination of electric fields. Frontiers in Computational Neuroscience, 7. doi:10.3389/fncom.2013.00005Freeman, J. A., & Nicholson, C. (1975). Experimental optimization of current source-density technique for anuran cerebellum. Journal of Neurophysiology, 38(2), 369-382. doi:10.1152/jn.1975.38.2.369Fries, P. (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences, 9(10), 474-480. doi:10.1016/j.tics.2005.08.011Fries, P. (2015). Rhythms for Cognition: Communication through Coherence. Neuron, 88(1), 220-235. doi:10.1016/j.neuron.2015.09.034Goutagny, R., Gu, N., Cavanagh, C., Jackson, J., Chabot, J.-G., Quirion, R., … Williams, S. (2013). Alterations in hippocampal network oscillations and theta-gamma coupling arise before Aβ overproduction in a mouse model of Alzheimer’s disease. European Journal of Neuroscience, 37(12), 1896-1902. doi:10.1111/ejn.12233Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424. doi:10.2307/1912791Green, K. F., & Rawlins, J. N. P. (1979). Hippocampal theta in rats under urethane: Generators and phase relations. Electroencephalography and Clinical Neurophysiology, 47(4), 420-429. doi:10.1016/0013-4694(79)90158-5Hasselmo, M. E., Bodelón, C., & Wyble, B. P. (2002). A Proposed Function for Hippocampal Theta Rhythm: Separate Phases of Encoding and Retrieval Enhance Reversal of Prior Learning. Neural Computation, 14(4), 793-817. doi:10.1162/089976602317318965Helfrich, R. F., Mander, B. A., Jagust, W. J., Knight, R. T., & Walker, M. P. (2018). Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting. Neuron, 97(1), 221-230.e4. doi:10.1016/j.neuron.2017.11.020Helfrich, R. F., Lendner, J. D., Mander, B. A., Guillen, H., Paff, M., Mnatsakanyan, L., … Knight, R. T. (2019). Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans. Nature Communications, 10(1). doi:10.1038/s41467-019-11444-xHerreras, O. (1990). Propagating dendritic action potential mediates synaptic transmission in CA1 pyramidal cells in situ. Journal of Neurophysiology, 64(5), 1429-1441. doi:10.1152/jn.1990.64.5.1429Herreras, O., Makarova, J., & Makarov, V. A. (2015). New uses of LFPs: Pathway-specific threads obtained through spatial discrimination. Neuroscience, 310, 486-503. doi:10.1016/j.neuroscience.2015.09.054Herreras, O. (2016). Local Field Potentials: Myths and Misunderstandings. Frontiers in Neural Circuits, 10. doi:10.3389/fncir.2016.00101Holsheimer, J. (1987). Electrical conductivity of the hippocampal CA1 layers and application to current-source-density analysis. Experimental Brain Research, 67(2). doi:10.1007/bf00248560Iaccarino, H. F., Singer, A. C., Martorell, A. J., Rudenko, A., Gao, F., Gillingham, T. Z., … Tsai, L.-H. (2016). Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature, 540(7632), 230-235. doi:10.1038/nature20587Igarashi, K. M., Lu, L., Colgin, L. L., Moser, M.-B., & Moser, E. I. (2014). Coordination of entorhinal–hippocampal ensemble activity during associative learning. Nature, 510(7503), 143-147. doi:10.1038/nature13162Jackson, J. C., Johnson, A., & Redish, A. D. (2006). Hippocampal Sharp Waves and Reactivation during Awake States Depend on Repeated Sequential Experience. Journal of Neuroscience, 26(48), 12415-12426. doi:10.1523/jneurosci.4118-06.2006Jiang, H., Bahramisharif, A., van Gerven, M. A. J., & Jensen, O. (2015). Measuring directionality between neuronal oscillations of different frequencies. NeuroImage, 118, 359-367. doi:10.1016/j.neuroimage.2015.05.044Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., … Vicente, R. (2015). Untangling cross-frequency coupling in neuroscience. Current Opinion in Neurobiology, 31, 51-61. doi:10.1016/j.conb.2014.08.002Klausberger, T., Magill, P. J., Márton, L. F., Roberts, J. D. B., Cobden, P. M., Buzsáki, G., & Somogyi, P. (2003). Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo. Nature, 421(6925), 844-848. doi:10.1038/nature01374Klausberger, T., & Somogyi, P. (2008). Neuronal Diversity and Temporal Dynamics: The Unity of Hippocampal Circuit Operations. Science, 321(5885), 53-57. doi:10.1126/science.1149381Kocsis, B., Bragin, A., & Buzsáki, G. (1999). Interdependence of Multiple Theta Generators in the Hippocampus: a Partial Coherence Analysis. The Journal of Neuroscience, 19(14), 6200-6212. doi:10.1523/jneurosci.19-14-06200.1999Korovaichuk, A., Makarova, J., Makarov, V. A., Benito, N., & Herreras, O. (2010). Minor Contribution of Principal Excitatory Pathways to Hippocampal LFPs in the Anesthetized Rat: A Combined Independent Component and Current Source Density Study. Journal of Neurophysiology, 104(1), 484-497. doi:10.1152/jn.00297.2010Kramer, M. A., Tort, A. B. L., & Kopell, N. J. (2008). Sharp edge artifacts and spurious coupling in EEG frequency comodulation measures. Journal of Neuroscience Methods, 170(2), 352-357. doi:10.1016/j.jneumeth.2008.01.020Kramis, R., Vanderwolf, C. H., & Bland, B. H. (1975). Two types of hippocampal rhythmical slow activity in both the rabbit and the rat: Relations to behavior and effects of atropine, diethyl ether, urethane, and pentobarbital. Experimental Neurology, 49(1), 58-85. doi:10.1016/0014-4886(75)90195-8Lakatos, P., Shah, A. S., Knuth, K. H., Ulbert, I., Karmos, G., & Schroeder, C. E. (2005). An Oscillatory Hierarchy Controlling Neuronal Excitability and Stimulus Processing in the Auditory Cortex. Journal of Neurophysiology, 94(3), 1904-1911. doi:10.1152/jn.00263.2005Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., & Schroeder, C. E. (2008). Entrainment of Neuronal Oscillations as a Mechanism of Attentional Selection. Science, 320(5872), 110-113. doi:10.1126/science.1154735Lasztóczi, B., & Klausberger, T. (2014). Layer-Specific GABAergic Control of Distinct Gamma Oscillations in the CA1 Hippocampus. Neuron, 81(5), 1126-1139. doi:10.1016/j.neuron.2014.01.021Lasztóczi, B., & Klausberger, T. (2016). Hippocampal Place Cells Couple to Three Different Gamma Oscillations during Place Field Traversal. Neuron, 91(1), 34-40. doi:10.1016/j.neuron.2016.05.036Łęski, S., Kublik, E., Świejkowski, D. A., Wróbel, A., & Wójcik, D. K. (2009). Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density. Journal of Computational Neuroscience, 29(3), 459-473. doi:10.1007/s10827-009-0203-1Lever, C., Burton, S., & Ο’Keefe, J. (2006). Rearing on Hind Legs, Environmental Novelty, and the Hippocampal Formation. Reviews in the Neurosciences, 17(1-2). doi:10.1515/revneuro.2006.17.1-2.111Lisman, J. E., & Idiart, M. A. P. (1995). Storage of 7 ± 2 Short-Term Memories in Oscillatory Subcycles. Science, 267(5203), 1512-1515. doi:10.1126/science.7878473Lisman, J. E., & Jensen, O. (2013). The Theta-Gamma Neural Code. Neuron, 77(6), 1002-1016. doi:10.1016/j.neuron.2013.03.007Lopes-dos-Santos, V., van de Ven, G. M., Morley, A., Trouche, S., Campo-Urriza, N., & Dupret, D. (2018). Parsing Hippocampal Theta Oscillations by Nested Spectral Components during Spatial Exploration and Memory-Guided Behavior. Neuron, 100(4), 940-952.e7. doi:10.1016/j.neuron.2018.09.031López-Aguado, L., Ibarz, J. ., & Herreras, O. (2001). Activity-dependent changes of tissue resistivity in the CA1 region in vivo are layer-specific: modulation of evoked potentials. Neuroscience, 108(2), 249-262. doi:10.1016/s0306-4522(01)00417-1Lozano-Soldevilla, D., ter Huurne, N., & Oostenveld, R. (2016). Neuronal Oscillations with Non-sinusoidal Morphology Produce Spurious Phase-to-Amplitude Coupling and Directionality. Frontiers in Computational Neuroscience, 10. doi:10.3389/fncom.2016.00087Makarov, V. A., Makarova, J., & Herreras, O. (2010). Disentanglement of local field potential sources by independent component analysis. Journal of Computational Neuroscience, 29(3), 445-457. doi:10.1007/s10827-009-0206-yMakarova, J. (2011). Parallel readout of pathway-specific inputs to laminated brain structures. Frontiers in Systems Neuroscience, 5. doi:10.3389/fnsys.2011.00077Martín-Vázquez, G., Makarova, J., Makarov, V. A., & Herreras, O. (2013). Determining the True Polarity and Amplitude of Synaptic Currents Underlying Gamma Oscillations of Local Field Potentials. PLoS ONE, 8(9), e75499. doi:10.1371/journal.pone.0075499Martín-Vázquez, G., Benito, N., Makarov, V. A., Herreras, O., & Makarova, J. (2015). Diversity of LFPs Activated in Different Target Regions by a Common CA3 Input. Cerebral Cortex, 26(10), 4082-4100. doi:10.1093/cercor/bhv211McNaughton, B. L., Barnes, C. A., & O’Keefe, J. (1983). The contributions of position, direction, and velocity to single unit activity in the hippocampus of freely-moving rats. Experimental Brain Research, 52(1). doi:10.1007/bf00237147Mitzdorf, U. (1985). Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiological Reviews, 65(1), 37-100. doi:10.1152/physrev.1985.65.1.37Mizuseki, K., Sirota, A., Pastalkova, E., & Buzsáki, G. (2009). Theta Oscillations Provide Temporal Windows for Local Circuit Computation in the Entorhinal-Hippocampal Loop. Neuron, 64(2), 267-280. doi:10.1016/j.neuron.2009.08.037Mizuseki, K., & Buzsaki, G. (2014). Theta oscillations decrease spike synchrony in the hippocampus and entorhinal cortex. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1635), 20120530. doi:10.1098/rstb.2012.0530Montgomery, S. M., Betancur, M. I., & Buzsaki, G. (2009). Behavior-Dependent Coordination of Multiple Theta Dipoles in the Hippocampus. Journal of Neuroscience, 29(5), 1381-1394. doi:10.1523/jneurosci.4339-08.2009Montgomery, S. M., & Buzsaki, G. (2007). Gamma oscillations dynamically couple hippocampal CA3 and CA1 regions during memory task performance. Proceedings of the National Academy of Sciences, 104(36), 14495-14500. doi:10.1073/pnas.0701826104Moreno, A., Morris, R. G. M., & Canals, S. (2015). Frequency-Dependent Gating of Hippocampal–Neocortical Interactions. Cerebral Cortex, 26(5), 2105-2114. doi:10.1093/cercor/bhv033Mormann, F., Fell, J., Axmacher, N., Weber, B., Lehnertz, K., Elger, C. E., & Fernández, G. (2005). Phase/amplitude reset and theta-gamma interaction in the human medial temporal lobe during a continuous word recognition memory task. Hippocampus, 15(7), 890-900. doi:10.1002/hipo.20117Neym
Magnetic Gearboxes for Aerospace Applications
Magnetic gearboxes are contactless mechanisms for torque-speed conversion. They present no wear, no friction and no fatigue. They need no lubricant and can be customized for other mechanical properties as stiffness or damping. Additionally, they can protect structures and mechanisms against overloads, limitting the transmitted torque. In this work, spur, planetary and "magdrive" or "harmonic drive" configurations are compared considering their use in aerospace applications. The most recent test data are summarized to provide some useful help for the design engineer
Performance of Magnetic-Superconductor Non-Contact Harmonic Drive for Cryogenic Space Applications
Harmonic drives are profusely used in aerospace mainly because of their compactness and large reduction ratio. However, their use in cryogenic environments is still a challenge. Lubrication and fatigue are non-trivial issues under these conditions. The objective of the Magnetic-Superconductor Cryogenic Non-contact Harmonic Drive (MAGDRIVE) project, funded by the EU Space FP7, is to design, build, and test a new concept of MAGDRIVE. Non-contact interactions among magnets, soft magnetic materials, and superconductors are efficiently used to provide a high reduction ratio gear that smoothly and naturally operates at cryogenic environments. The limiting elements of conventional harmonic drives (teeth, flexspline, and ball bearings) are substituted by contactless mechanical components (magnetic gear and superconducting magnetic bearings). The absence of contact between moving parts prevents wear, lubricants are no longer required, and the operational lifetime is greatly increased. This is the first mechanical reducer in mechanical engineering history without any contact between moving parts. In this paper, the test results of a −1:20 inverse reduction ratio MAGDRIVE prototype are reported. In these tests, successful operation at 40 K and 10−3 Pa was demonstrated for more than 1.5 million input cycles. A maximum torque of 3 N·m and an efficiency of 80% were demonstrated. The maximum tested input speed was 3000 rpm, six times the previous existing record for harmonic drives at cryogenic temperature
Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study
: The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135-15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359-5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138-5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184-5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598-9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090-6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286-5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p < 0.001; 95% CI 1.912-7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138-0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143-0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990)
Multidifferential study of identified charged hadron distributions in -tagged jets in proton-proton collisions at 13 TeV
Jet fragmentation functions are measured for the first time in proton-proton
collisions for charged pions, kaons, and protons within jets recoiling against
a boson. The charged-hadron distributions are studied longitudinally and
transversely to the jet direction for jets with transverse momentum 20 GeV and in the pseudorapidity range . The
data sample was collected with the LHCb experiment at a center-of-mass energy
of 13 TeV, corresponding to an integrated luminosity of 1.64 fb. Triple
differential distributions as a function of the hadron longitudinal momentum
fraction, hadron transverse momentum, and jet transverse momentum are also
measured for the first time. This helps constrain transverse-momentum-dependent
fragmentation functions. Differences in the shapes and magnitudes of the
measured distributions for the different hadron species provide insights into
the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb
public pages
Study of the decay
The decay is studied
in proton-proton collisions at a center-of-mass energy of TeV
using data corresponding to an integrated luminosity of 5
collected by the LHCb experiment. In the system, the
state observed at the BaBar and Belle experiments is
resolved into two narrower states, and ,
whose masses and widths are measured to be where the first uncertainties are statistical and the second
systematic. The results are consistent with a previous LHCb measurement using a
prompt sample. Evidence of a new
state is found with a local significance of , whose mass and width
are measured to be and , respectively. In addition, evidence of a new decay mode
is found with a significance of
. The relative branching fraction of with respect to the
decay is measured to be , where the first
uncertainty is statistical, the second systematic and the third originates from
the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb
public pages
Measurement of the ratios of branching fractions and
The ratios of branching fractions
and are measured, assuming isospin symmetry, using a
sample of proton-proton collision data corresponding to 3.0 fb of
integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The
tau lepton is identified in the decay mode
. The measured values are
and
, where the first uncertainty is
statistical and the second is systematic. The correlation between these
measurements is . Results are consistent with the current average
of these quantities and are at a combined 1.9 standard deviations from the
predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb
public pages
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