50 resultados para Tatian, ca. 120-173.

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An environmentally benign, highly conductive, and mechanically strong binder system can overcome the dilemma of low conductivity and insufficient mechanical stability of the electrodes to achieve high performance lithium ion batteries (LIBs) at a low cost and in a sustainable way. In this work, the naturally occurring binder sodium alginate (SA) is functionalized with 3,4-propylenedioxythiophene-2,5-dicarboxylic acid (ProDOT) via a one-step esterification reaction in a cyclohexane/dodecyl benzenesulfonic acid (DBSA)/water microemulsion system, resulting in a multifunctional polymer binder, that is, SA-PProDOT. With the synergetic effects of the functional groups (e.g., carboxyl, hydroxyl, and ester groups), the resultant SA-PProDOT polymer not only maintains the outstanding binding capabilities of sodium alginate but also enhances the mechanical integrity and lithium ion diffusion coefficient in the LiFePO4 (LFP) electrode during the operation of the batteries. Because of the conjugated network of the PProDOT and the lithium doping under the battery environment, the SA-PProDOT becomes conductive and matches the conductivity needed for LiFePO4 LIBs. Without the need of conductive additives such as carbon black, the resultant batteries have achieved the theoretical specific capacity of LiFePO4 cathode (ca. 170 mAh/g) at C/10 and ca. 120 mAh/g at 1C for more than 400 cycles.

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The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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Toth, Spatial relationship between neuronal activity and bold functional mri, NeuroImage , 21 (3), 2004, 876--885. doi:10.1016/j.neuroimage.2003.10.018 A. Connelly, G. D. Jackson, R. S. Frackowiak, J. W. Belliveau, F. Vargha-Khadem and D. G. Gadian, Functional mapping of activated human primary cortex with a clinical mr imaging system, Radiology , 188 (1), 1993, 125--130. L. Allison, Hidden Markov Models, Technical Report , School of Computer and Software Engineering, Monash University, 2000. R. J. Elliott, L. Aggoun and J.B. Moore, Hidden Markov Models: Estimation and Control, Appl. Math.-Czech. , 2004. B. Bhavnagri, Discontinuities of plane functions projected from a surface with methods for finding these , Technical Report, 2009. B. Bhavnagri, Computer Vision using Shape Spaces , Technical Report,1996, University of Adelaide. B. Bhavnagri, A method for representing shape based on an equivalence relation on polygons, Pattern Recogn. , 27 (2), 1994, 247--260. doi:10.1016/0031-3203(94)90057-4 D. F. Abbott, A. B. Waites, A. S. Harvey and G. D. Jackson, Exploring epileptic seizure onset with fmri, NeuroImage , 36(S1) (344TH-PM), 2007. M. C. Mackey and L. Glass, Oscillation and chaos in physiological control systems, Science , 197 , 1977, 287--289. S. H. Strogatz, SYNC - The Emerging Science of Spontaneous Order , Theia, New York, 2003. J. W. Kim, J. A. Roberts and P. A. Robinson, Dynamics of epileptic seizures: Evolution, spreading, and suppression, J. Theor. Biol. , 257 (4), 2009, 527--532. doi:10.1016/j.jtbi.2008.12.009 Y. Kuramoto, T. Aoyagi, I. Nishikawa, T. Chawanya T and K. Okuda, Neural network model carrying phase information with application to collective dynamics, J. Theor. Phys. , 87 (5), 1992, 1119--1126. V. B. Mountcastle, The columnar organization of the neocortex, Brain , 120 (4), 1997, 701. doi:10.1093/brain/120.4.701 F. L. Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski and D. N. Velis, Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity, Epilepsia , 44 (12), 2003, 72--83. F. H. Lopes da Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski and D. N. Velis, Dynamical diseases of brain systems: different routes to epileptic seizures, ieee T. Bio-Med. Eng. , 50 (5), 2003, 540. L.D. Iasemidis, Epileptic seizure prediction and control, ieee T. Bio-Med. Eng. , 50 (5), 2003, 549--558. L. D. Iasemidis, D. S. Shiau, W. Chaovalitwongse, J. C. Sackellares, P. M. Pardalos, J. C. Principe, P. R. Carney, A. Prasad, B. Veeramani, and K. Tsakalis, Adaptive epileptic seizure prediction system, ieee T. Bio-Med. Eng. , 50 (5), 2003, 616--627. K. Lehnertz, F. Mormann, T. Kreuz, R.G. Andrzejak, C. Rieke, P. David and C. E. Elger, Seizure prediction by nonlinear eeg analysis, ieee Eng. Med. Biol. , 22 (1), 2003, 57--63. doi:10.1109/MEMB.2003.1191451 K. Lehnertz, R. G. Andrzejak, J. Arnhold, T. Kreuz, F. Mormann, C. Rieke, G. Widman and C. E. Elger, Nonlinear eeg analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation, and prevention, J. Clin. Neurophysiol. , 18 (3), 2001, 209. B. Litt and K. Lehnertz, Seizure prediction and the preseizure period, Curr. Opin. Neurol. , 15 (2), 2002, 173. doi:10.1097/00019052-200204000-00008 B. Litt and J. Echauz, Prediction of epileptic seizures, Lancet Neurol. , 1 (1), 2002, 22--30. doi:10.1016/S1474-4422(02)00003-0 M. M{a}kiranta, J. Ruohonen, K Suominen, J. Niinim{a}ki, E. Sonkaj{a}rvi, V. Kiviniemi, T. Sepp{a}nen, S. Alahuhta, V. J{a}ntti and O. Tervonen, {bold} signal increase preceeds eeg spike activity--a dynamic penicillin induced focal epilepsy in deep anesthesia, NeuroImage , 27 (4), 2005, 715--724. doi:10.1016/j.neuroimage.2005.05.025 K. Lehnertz, F. Mormann, H. Osterhage, A. M{u}ller, J. Prusseit, A. Chernihovskyi, M. Staniek, D. Krug, S. Bialonski and C. E. Elger, State-of-the-art of seizure prediction, J. Clin. Neurophysiol. , 24 (2), 2007, 147. doi:10.1097/WNP.0b013e3180336f16 F. Mormann, T. Kreuz, C. Rieke, R. G. Andrzejak, A. Kraskov, P. David, C. E. Elger and K. Lehnertz, On the predictability of epileptic seizures, Clin. Neurophysiol. , 116 (3), 2005, 569--587. doi:10.1016/j.clinph.2004.08.025 F. Mormann, R. G. Andrzejak, C. E. Elger and K. Lehnertz, Seizure prediction: the long and winding road, Brain , 130 (2), 2007, 314--333. doi:10.1093/brain/awl241 Z. Rogowski, I. Gath and E. Bental, On the prediction of epileptic seizures, Biol. Cybern. , 42 (1), 1981, 9--15. Y. Salant, I. Gath, O. 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S Zukin, Electrical coupling and neuronal synchronization in the mammalian brain, Neuron , 41 (4), 2004, 495 --511. doi:10.1016/S0896-6273(04)00043-1 L.D. Iasemidis, J. Chris Sackellares, H. P. Zaveri and W. J. Williams, Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures, Brain Topogr. , 2 (3), 1990, 187--201. doi:10.1007/BF01140588 M. Le Van Quyen, J. Martinerie, V. Navarro, M. Baulac and F. J. Varela, Characterizing neurodynamic changes before seizures, J. Clin. Neurophysiol. , 18 (3), 2001, 191. J. Martinerie, C. Adam, M. Le Van Quyen, M. Baulac, S. Clemenceau, B. Renault and F. J. Varela, Epileptic seizures can be anticipated by non-linear analysis, Nat. Med. , 4 (10), 1998, 1173--1176. doi:10.1038/2667 A. Pikovsky, M. Rosenblum, J. Kurths and R. C. Hilborn, Synchronization: A universal concept in nonlinear science, Amer. J. Phys. , 70 , 2002, 655. H. R. Wilson and J. D. 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Schmitz, Axonal gap junc ions between principal neurons: A novel source of network oscillations, and perhaps epileptogenesis., Rev. Neuroscience , 13 (1), 2002, 1. doi:10.1146/annurev.neuro.27.070203.144303 M. Scheffer, J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter, V. Dakos, H. Held, E. H. van Nes, M. Rietkerk and G. Sugihara, Early-warning signals for critical transitions, Nature , 461 (7260), 2009, 53--59. doi:10.1038/nature08227 K. Murphy, A Brief Introduction to Graphical Models and Bayesian Networks , 2008, http://www.cs.ubc.ca/murphyk/Bayes/bnintro.html . R. C. Bradley, An elementary

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Raman spectra of natrouranospinite complemented with infrared spectra were studied and related to the structure of the mineral. Observed bands were assigned to the stretching and bending vibrations of (UO2)2+ and (AsO4)3- units and of water molecules. U-O bond lengths in uranyl and O-H…O hydrogen bond lengths were calculated from the Raman and infrared spectra.

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Raman spectra of metauranospinite Ca[(UO2)(AsO4)]2.8H2O complemented with infrared spectra were studied. Observed bands were assigned to the stretching and bending vibrations of (UO2)2+ and (AsO4)3- units and of water molecules. U-O bond lengths in uranyl and O-H…O hydrogen bond lengths were calculated from the Raman and infrared spectra.

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BACKGROUND: Although we know much about the molecular makeup of the sinus node (SN) in small mammals, little is known about it in humans. The aims of the present study were to investigate the expression of ion channels in the human SN and to use the data to predict electrical activity. METHODS AND RESULTS: Quantitative polymerase chain reaction, in situ hybridization, and immunofluorescence were used to analyze 6 human tissue samples. Messenger RNA (mRNA) for 120 ion channels (and some related proteins) was measured in the SN, a novel paranodal area, and the right atrium (RA). The results showed, for example, that in the SN compared with the RA, there was a lower expression of Na(v)1.5, K(v)4.3, K(v)1.5, ERG, K(ir)2.1, K(ir)6.2, RyR2, SERCA2a, Cx40, and Cx43 mRNAs but a higher expression of Ca(v)1.3, Ca(v)3.1, HCN1, and HCN4 mRNAs. The expression pattern of many ion channels in the paranodal area was intermediate between that of the SN and RA; however, compared with the SN and RA, the paranodal area showed greater expression of K(v)4.2, K(ir)6.1, TASK1, SK2, and MiRP2. Expression of ion channel proteins was in agreement with expression of the corresponding mRNAs. The levels of mRNA in the SN, as a percentage of those in the RA, were used to estimate conductances of key ionic currents as a percentage of those in a mathematical model of human atrial action potential. The resulting SN model successfully produced pacemaking. CONCLUSIONS: Ion channels show a complex and heterogeneous pattern of expression in the SN, paranodal area, and RA in humans, and the expression pattern is appropriate to explain pacemaking.

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The mineral lewisite, (Ca,Fe,Na)2(Sb,Ti)2O6(O,OH)7 an antimony bearing mineral has been studied by Raman spectroscopy. A comparison is made with the Raman spectra of other minerals including bindheimite, stibiconite and roméite. The mineral lewisite is characterised by an intense sharp band at 517 cm-1 with a shoulder at 507 cm-1 assigned to SbO stretching modes. Raman bands of medium intensity for lewisite are observed at 300, 356 and 400 cm-1. These bands are attributed to OSbO bending vibrations. Raman bands in the OH stretching region are observed at 3200, 3328, 3471 cm-1 with a distinct shoulder at 3542 cm-1. The latter is assigned to the stretching vibration of OH units. The first three bands are attributed to water stretching vibrations. The observation of bands in the 3200 to 3500 cm-1 region suggests that water is involved in the lewisite structure. If this is the case then the formula may be better written as Ca, Fe2+, Na)2(Sb, Ti)2(O,OH)7 •xH2O.

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Raman spectra of mineral peretaite Ca(SbO)4(OH)2(SO4)2•2H2O were studied, and related to the structure of the mineral. Raman bands observed at 978 and 980 cm-1 and a series of overlapping bands observed at 1060, 1092, 1115, 1142 and 1152 cm-1 are assigned to the SO42- ν1 symmetric and ν3 antisymmetric stretching modes. Raman bands at 589 and 595 cm-1 are attributed to the SbO symmetric stretching vibrations. The low intensity Raman bands at 650 and 710 cm-1 may be attributed to SbO antisymmetric stretching modes. Raman bands at 610 cm-1 and at 417, 434 and 482 cm-1 are assigned to the SO42- 4 and 2 bending modes, respectively. Raman bands at 337 and 373 cm-1 are assigned to O-Sb-O bending modes. Multiple Raman bands for both SO42- and SbO stretching vibrations support the concept of the non-equivalence of these units in the coquandite structure.

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Raman spectroscopy has been used to study the arsenate minerals haidingerite Ca(AsO3OH)•H2O and brassite Mg(AsO3OH)•4H2O. Intense Raman bands in haidingerite spectrum observed at 745 and 855 cm-1 are assigned to the (AsO3OH)2- ν3 antisymmetric stretching and ν1 symmetric stretching vibrational modes. For brassite two similarly assigned intense bands are found at 809 and 862 cm-1. The observation of multiple Raman bands in the (AsO3OH)2- stretching and bending regions suggests that the arsenate tetrahedrons in the crystal structures of both minerals studied are strongly distorted. Broad Raman bands observed at 2842 cm-1 for haidingerite and 3035 cm-1 for brassite indicate strong hydrogen bonding of water molecules in the structure of these minerals. OH…O hydrogen bond lengths were calculated from the Raman spectra based on empiric relations.

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The removal of arsenate anions from aqueous media, sediments and wasted soils is of environmental significance. The reaction of gypsum with the arsenate anion results in pharmacolite mineral formation, together with related minerals. Raman and infrared spectroscopy have been used to study the mineral pharmacolite Ca(HAsO4)•2H2O. The mineral is characterised by an intense Raman band at 865 cm-1 assigned to the (AsO4)3- symmetric stretching mode. The equivalent infrared band is found at 864 cm-1. The low intensity Raman band at 886 cm-1 provides evidence for (AsO3OH)2-. A series of overlapping bands in the 300 to 450 cm-1 are attributed to ν2 and ν4 bending modes. Prominent Raman bands at around 3187 cm-1 are assigned to water OH stretching vibrations and the two sharp bands at 3425 and 3526 cm-1 to the OH stretching vibrations of (HOAsO3) units.

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Raman spectra of the uranyl titanate mineral betafite were obtained and related to the mineral structure. A comparison is made with the spectra of uranyl oxyhydroxide hydrates. Observed bands are attributed to the (UO2)2+ stretching and bending vibrations, U-OH bending vibrations, H2O and (OH)- stretching, bending and libration modes. U-O bond lengths in uranyls and O-H…O bond lengths are calculated from the wavenumbers assigned to the stretching vibrations. Raman bands of betafite are comparable with those of the uranyl oxyhydroxides. The mineral betafite is metamict as is evidenced by the intensity of the UO stretching and bending modes being of lower intensity than expected and with bands that are significantly broader.

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Raman spectra of the uranyl titanate mineral brannerite were analysed and related to the mineral structure. A comparison is made with the Raman spectra of uranyl oxyhydroxide hydrates. Observed bands are attributed to the TiO and (UO2)2+ stretching and bending vibrations, U-OH bending vibrations, H2O and (OH)- stretching, bending and libration modes. U-O bond lengths in uranyls and O-H…O bond lengths are calculated from the wavenumbers assigned to the stretching vibrations. Raman bands of brannerite are in harmony with those of the uranyl oxyhydroxides. The mineral brannerite is metamict as is evidenced by the intensity of the UO stretching and bending modes being of lower intensity than expected and with bands that are significantly broader.

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Raman spectra of the uranyl titanate mineral euxenite were analyzed and related to the mineral structure. A comparison is made with the Raman spectra of uranyl oxyhydroxide hydrates. The obsd. bands are attributed to the Ti[n.63743]O and (UO2)2+ stretching and bending vibrations, as well as lattice vibrations of rare-earth ions. The Raman bands of euxenite are in harmony with those of the uranyl oxyhydroxides. The mineral euxenite is metamict as is evidenced by the intensity of the U[n.63743]O stretching and bending modes, which are of lower intensity than expected, and with bands that are significantly broader.