98 resultados para Terrence Malick


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What are the limits and modulators of neural precision? We address this question in the most regular biological oscillator known, the electric organ command nucleus in the brainstem of wave-type gymnotiform fish. These fish produce an oscillating electric field, the electric organ discharge (EOD), used in electrolocation and communication. We show here that the EOD precision, measured by the coefficient of variation (CV = SD/mean period) is as low as 2 × 10−4 in five species representing three families that range widely in species and individual mean EOD frequencies (70–1,250 Hz). Intracellular recording in the pacemaker nucleus (Pn), which commands the EOD cycle by cycle, revealed that individual Pn neurons of the same species also display an extremely low CV (CV = 6 × 10−4, 0.8 μs SD). Although the EOD CV can remain at its minimum for hours, it varies with novel environmental conditions, during communication, and spontaneously. Spontaneous changes occur as abrupt steps (250 ms), oscillations (3–5 Hz), or slow ramps (10–30 s). Several findings suggest that these changes are under active control and depend on behavioral state: mean EOD frequency and CV can change independently; CV often decreases in response to behavioral stimuli; and lesions of one of the two inputs to the Pn had more influence on CV than lesions of the other input.

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Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

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Running increases neurogenesis in the dentate gyrus of the hippocampus, a brain structure that is important for memory function. Consequently, spatial learning and long-term potentiation (LTP) were tested in groups of mice housed either with a running wheel (runners) or under standard conditions (controls). Mice were injected with bromodeoxyuridine to label dividing cells and trained in the Morris water maze. LTP was studied in the dentate gyrus and area CA1 in hippocampal slices from these mice. Running improved water maze performance, increased bromodeoxyuridine-positive cell numbers, and selectively enhanced dentate gyrus LTP. Our results indicate that physical activity can regulate hippocampal neurogenesis, synaptic plasticity, and learning.

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Neocortex, a new and rapidly evolving brain structure in mammals, has a similar layered architecture in species over a wide range of brain sizes. Larger brains require longer fibers to communicate between distant cortical areas; the volume of the white matter that contains long axons increases disproportionally faster than the volume of the gray matter that contains cell bodies, dendrites, and axons for local information processing, according to a power law. The theoretical analysis presented here shows how this remarkable anatomical regularity might arise naturally as a consequence of the local uniformity of the cortex and the requirement for compact arrangement of long axonal fibers. The predicted power law with an exponent of 4/3 minus a small correction for the thickness of the cortex accurately accounts for empirical data spanning several orders of magnitude in brain sizes for various mammalian species, including human and nonhuman primates.

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We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

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A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a “map”) and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.

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Melanin-concentrating hormone (MCH) is a 19-aa cyclic neuropeptide originally isolated from chum salmon pituitaries. Besides its effects on the aggregation of melanophores in fish several lines of evidence suggest that in mammals MCH functions as a regulator of energy homeostasis. Recently, several groups reported the identification of an orphan G protein-coupled receptor as a receptor for MCH (MCH-1R). We hereby report the identification of a second human MCH receptor termed MCH-2R, which shares about 38% amino acid identity with MCH-1R. MCH-2R displayed high-affinity MCH binding, resulting in inositol phosphate turnover and release of intracellular calcium in mammalian cells. In contrast to MCH-1R, MCH-2R signaling is not sensitive to pertussis toxin and MCH-2R cannot reduce forskolin-stimulated cAMP production, suggesting an exclusive Gαq coupling of the MCH-2R in cell-based systems. Northern blot and in situ hybridization analysis of human and monkey tissue shows that expression of MCH-2R mRNA is restricted to several regions of the brain, including the arcuate nucleus and the ventral medial hypothalamus, areas implicated in regulation of body weight. In addition, the human MCH-2R gene was mapped to the long arm of chromosome 6 at band 6q16.2–16.3, a region reported to be associated with cytogenetic abnormalities of obese patients. The characterization of a second mammalian G protein-coupled receptor for MCH potentially indicates that the control of energy homeostasis in mammals by the MCH neuropeptide system may be more complex than initially anticipated.

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Ce projet de recherche explore un nouveau mécanisme de régulation de l’activité du domaine HECT de la ligase Itch. Ce domaine est responsable de la polyubiquitylation des protéines impliquant le plus souvent leur dégradation par le protéasome. Itch est une ligase de l’ubiquitine de la famille CWH contenant un domaine HECT catalytique en C-terminal, quatre domaines WW, et un domaine C2 N-terminal qui est important pour sa localisation cellulaire. Les ligases CWH interagissent par leur domaine WW avec leurs ligands. Un mécanisme proposé pour ces ligases est que la première molécule d’ubiquitine liée au substrat active le domaine HECT de manière à former une chaine d’ubiquitine sur le substrat. Itch a une particularité dans la famille CWH, car elle possède un domaine riche en proline qui lui permet d’interagir avec plusieurs protéines à domaine SH3. Dans cette étude, nous avons déterminé l’effet de l’ubiquitylation initiale des protéines SH3 sur l’activité du domaine HECT de la ligase Itch, et sur la régulation de ces substrats.

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Ce projet de recherche explore un nouveau mécanisme de régulation de l’activité du domaine HECT de la ligase Itch. Ce domaine est responsable de la polyubiquitylation des protéines impliquant le plus souvent leur dégradation par le protéasome. Itch est une ligase de l’ubiquitine de la famille CWH contenant un domaine HECT catalytique en C-terminal, quatre domaines WW, et un domaine C2 N-terminal qui est important pour sa localisation cellulaire. Les ligases CWH interagissent par leur domaine WW avec leurs ligands. Un mécanisme proposé pour ces ligases est que la première molécule d’ubiquitine liée au substrat active le domaine HECT de manière à former une chaine d’ubiquitine sur le substrat. Itch a une particularité dans la famille CWH, car elle possède un domaine riche en proline qui lui permet d’interagir avec plusieurs protéines à domaine SH3. Dans cette étude, nous avons déterminé l’effet de l’ubiquitylation initiale des protéines SH3 sur l’activité du domaine HECT de la ligase Itch, et sur la régulation de ces substrats.

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The Integrated Ocean Drilling Program (IODP) Expedition 310 recovered drill cores from the drowned reefs around the island of Tahiti (17°40'S, 149°30'W), many of which contained samples of massive corals from the genus Porites. Herein we report on one well-preserved fossil coral sample: a 13.6 cm long Porites sp. dated by uranium series techniques at 9523 ± 33 years. Monthly delta18O and Sr/Ca determinations reveal nine clear and robust annual cycles. Coral delta18O and Sr/Ca determinations estimate a mean temperature of ca. 24.3°C (ca. 3.2°C colder than modern) for Tahiti at 9.5 ka; however, this estimate is viewed with caution since potential sources of cold bias in coral geochemistry remain to be resolved. The interannual variability in coral delta18O is similar between the 9.5 ka coral record and a modern record from nearby Moorea. The seasonal cycle in coral Sr/Ca is approximately the same or greater in the 9.5 ka coral record than in modern coral records from Tahiti. Paired analysis of coral delta18O and Sr/Ca indicates cold/wet (warm/dry) interannual anomalies, opposite from those observed in the modern instrumental record.

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Back Row: Todd Jager, Paul Schmidt, Bill Shinavier, Mike Gittleson, Kit Cartwright, Greg Mattison, Fred Jackson, Les Miles, Lloyd Carr, Bill Harris, Bobby Morrison, Mike DeBord, Jim Herrmann, John McNulty, John Milligan, Jon Falk, Phil Bromley, Steve Connelly

8th Row: Brian Letscher, Brian Townsend, Ed O'Dowd, Jason Cole, Jim Plocki, Andy Riegler, Chris Singletary, Jon Jansen, Jeff Holtry, Marcus Ray, Anthony Williams, Tim Laws, Bob Bland, Brian Hagens, Gordon Grace, Shemy Schembechler, Scott Draper

7th Row: Darren Petterson, Ed Kiser, Jay Feely, Noah Parker, Juaquin Feazell, Clint Copenhaver, Tyrone Butterfield, Kraig Baker, Todd Brooks, Mark Campbell, Scott Dreisbach, Chris Floyd, Chris Howard, Matt Sygo, Nate Miller, Terrence Quinn, Clarence Thompson

6th Row: Andre Weathers, Sam Sword, Josh Cockrell, Thomas Mondry, Jace Morgan, Sean Parini, Nate DeLong, Brent Blackwell, Brian Griese, Jeff Springer, Lance Sanders, Colby Keefer, Matt DeYoung, Rasheed Simmons, Jerame Tuman, Earnest Sanders

5th Row: Head Coach Gary Moeller, Scott Loeffler, George Howell, Will Carr, Rob Swett, Jon Ritchie, Tim Biakabutuka, Zach Adami, Damon Denson, Pierre Cooper, Trevor Pryce, Mike Elston, Ben Huff, Seth Smith, Dr. Gerald O'Connor, Dr. Edward Wojtys

4th Row: Joe Ries, Mike Hynes, Rod Payne, Julian Norment, Tyrone Noble, Amani Toomer, Kerwin Waldroup, Remy Hamilton, Bryan Williams, Jared Lancer, Paul Peristeris, Glen Steele, Paul Berry

3rd Row: John Partchenko, Woody Hankins, Jean-Agnus Charles, Jarrett Irons, Eric Wendt, Steve Evans, Thomas Guynes, Jon Runyan, Mark Bolach, Harold Goodwin, Ty Law, Steve King, Mike Vanderbeek

2nd Row: Mercury Hayes, Chuck Winters, Todd Richards, Chad Petterson, Ante Skorput, Joe Marinaro, Trent Zenkewicz, Jason Horn, Trezelle Jenkins, Mike Sullivan, Rob Vander Leest, Erik Lovell, Deollo Anderson

Front Row: Bobby Powers, Deon Johnson, Tony Henderson, Walter Smith, Steve Morrison, Todd Collins, Matt Dyson, Tyrone Wheatley, Jay Riemersma, Eddie Davis, Jason Carr, Che' Foster

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Front row: Lloyd Carr, William Carr, Chuck Winters, Remy Hamilton, Steve King, Rod Payne, Jarrett Irons, Damon Denson, Thomas Guynes, Mark Bolach, Mike Vanderbeek, Clarence Thompson, Woodrow Hankins.

2nd row: Paul Peristeris, Mike Hynes, Jeff Springer, Brent Blackwell, Brian Griese, Mike Elston, Glen Steele, Zach Adami, John Partchenko, Ben Huff, Earnest Sanders, Rob Swett, Matt DeYoung, Bryan Williams

3rd row: Todd Brooks, Colby Keefer, Joe Ries, Scott Dreisbach, Jerome Tuman, Mark Campbell, Jon Jansen, Juaquin Feezell, Seen Parini, Chris Floyd, Chris Howard, Anthony Williams, Tyrone Butterfield.

4th row: Eric Mayes, Andre Weathers, Marcus Ray, Kraig Baker, Chris Singletary, Rasheed Simmons, Clint Copenhaver, Noah Parker, Nate Miller, Sam Sword, Bove Crispin, Jay Feely.

5th row: Darren Petterson, Terrence Quinn, Clarence Williams, Daydrion Taylor, DiAllo Johnson, James Hall, Aaron Shea, Chris Ziemann, Jeff Potts, Steve Frazier, Josh Williams, David Bowens, Charles Woodson, Scott Parachek.

6th row: Tate Schanski, Kenneth Jackson, Jeff Smokevitch, Jason Vinson, Tai Streets, Tom Brady, Chad Carpenter, Pat Kratus, Rob Renes, Brent Washington, Kevin Bryant, J.R. Ford, Jeff Del Verne, Russell Shaw.

7th row: LeAundre Brown, Jason Foster, Dhani Jones, Tom Hendricks, David Brandt Paul Tannous, Grady Brooks, Jason Kapsner, Jeff Backus, Steve Hutchinson, Eric Wilson, Corey Sargent, Jerry Johnson, John Anes, Marcus Knight.

8th row: Chris Kurpeikis, Jason Cole, Jason Carr, Eric Dean, Patrick Bolger, Rick Turner, Jason Cummings, Chad Henman, Ryan Parini, Ian Gold, Aaron Wright, Eric Warner, Andy Sachler, Chris Roth, Dan Williams, Bob Bland, Brian Hagens, Bill Priestap, Matt Hamilton.

9th row: Todd Jager, Derek Stebbins, Paul Schmidt, Mike Gittleson, Paul Barry, Vance Bedford, Brady Hoke, Jim Herrmann, Greg Mattison, Mike DeBord, Fred Jackson, Bobby Morrison, Stan Parrish, Erik Campbell, Scott Draper, Jon Falk, Phil Bromley, Steve Connelly, Harold Goodwin, Scot Loeffler.