Submicrosecond pacemaker precision is behaviorally modulated: The gymnotiform electromotor pathway

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.

A trait-based approach for predicting species responses to environmental change from sparse data : how well might terrestrial mammals track climate change?

Funded by COST (European Cooperation in Science and Technology) CEH projects. Grant Numbers: NEC05264, NEC05100 Natural Environment Research Council UK. Grant Number: NE/J008001/1 © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Thermodynamic stability of wild-type and mutant p53 core domain

Some 50% of human cancers are associated with mutations in the core domain of the tumor suppressor p53. Many mutations are thought just to destabilize the protein. To assess this and the possibility of rescue, we have set up a system to analyze the stability of the core domain and its mutants. The use of differential scanning calorimetry or spectroscopy to measure its melting temperature leads to irreversible denaturation and aggregation and so is useful as only a qualitative guide to stability. There are excellent two-state denaturation curves on the addition of urea that may be analyzed quantitatively. One Zn2+ ion remains tightly bound in the holo-form of p53 throughout the denaturation curve. The stability of wild type is 6.0 kcal (1 kcal = 4.18 kJ)/mol at 25°C and 9.8 kcal/mol at 10°C. The oncogenic mutants R175H, C242S, R248Q, R249S, and R273H are destabilized by 3.0, 2.9, 1.9, 1.9, and 0.4 kcal/mol, respectively. Under certain denaturing conditions, the wild-type domain forms an aggregate that is relatively highly fluorescent at 340 nm on excitation at 280 nm. The destabilized mutants give this fluorescence under milder denaturation conditions.

Signals and signs in the nervous system: The dynamic anatomy of electrical activity is probably information-rich

The dichotomy between two groups of workers on neuroelectrical activity is retarding progress. To study the interrelations between neuronal unit spike activity and compound field potentials of cell populations is both unfashionable and technically challenging. Neither of the mutual disparagements is justified: that spikes are to higher functions as the alphabet is to Shakespeare and that slow field potentials are irrelevant epiphenomena. Spikes are not the basis of the neural code but of multiple codes that coexist with nonspike codes. Field potentials are mainly information-rich signs of underlying processes, but sometimes they are also signals for neighboring cells, that is, they exert influence. This paper concerns opportunities for new research with many channels of wide-band (spike and slow wave) recording. A wealth of structure in time and three-dimensional space is different at each scale—micro-, meso-, and macroactivity. The depth of our ignorance is emphasized to underline the opportunities for uncovering new principles. We cannot currently estimate the relative importance of spikes and synaptic communication vs. extrasynaptic graded signals. In spite of a preponderance of literature on the former, we must consider the latter as probably important. We are in a primitive stage of looking at the time series of wide-band voltages in the compound, local field, potentials and of choosing descriptors that discriminate appropriately among brain loci, states (functions), stages (ontogeny, senescence), and taxa (evolution). This is not surprising, since the brains in higher species are surely the most complex systems known. They must be the greatest reservoir of new discoveries in nature. The complexity should not deter us, but a dose of humility can stimulate the flow of imaginative juices.

Temporal fluctuations in coherence of brain waves.

As a measure of dynamical structure, short-term fluctuations of coherence between 0.3 and 100 Hz in the electroencephalogram (EEG) of humans were studied from recordings made by chronic subdural macroelectrodes 5-10 mm apart, on temporal, frontal, and parietal lobes, and from intracranial probes deep in the temporal lobe, including the hippocampus, during sleep, alert, and seizure states. The time series of coherence between adjacent sites calculated every second or less often varies widely in stability over time; sometimes it is stable for half a minute or more. Within 2-min samples, coherence commonly fluctuates by a factor up to 2-3, in all bands, within the time scale of seconds to tens of seconds. The power spectrum of the time series of these fluctuations is broad, extending to 0.02 Hz or slower, and is weighted toward the slower frequencies; little power is faster than 0.5 Hz. Some records show conspicuous swings with a preferred duration of 5-15s, either irregularly or quasirhythmically with a broad peak around 0.1 Hz. Periodicity is not statistically significant in most records. In our sampling, we have not found a consistent difference between lobes of the brain, subdural and depth electrodes, or sleeping and waking states. Seizures generally raise the mean coherence in all frequencies and may reduce the fluctuations by a ceiling effect. The coherence time series of different bands is positively correlated (0.45 overall); significant nonindependence extends for at least two octaves. Coherence fluctuations are quite local; the time series of adjacent electrodes is correlated with that of the nearest neighbor pairs (10 mm) to a coefficient averaging approximately 0.4, falling to approximately 0.2 for neighbors-but-one (20 mm) and to < 0.1 for neighbors-but-two (30 mm). The evidence indicates fine structure in time and space, a dynamic and local determination of this measure of cooperativity. Widely separated frequencies tending to fluctuate together exclude independent oscillators as the general or usual basis of the EEG, although a few rhythms are well known under special conditions. Broad-band events may be the more usual generators. Loci only a few millimeters apart can fluctuate widely in seconds, either in parallel or independently. Scalp EEG coherence cannot be predicted from subdural or deep recordings, or vice versa, and intracortical microelectrodes show still greater coherence fluctuation in space and time. Widely used computations of chaos and dimensionality made upon data from scalp or even subdural or depth electrodes, even when reproducible in successive samples, cannot be considered representative of the brain or the given structure or brain state but only of the scale or view (receptive field) of the electrodes used. Relevant to the evolution of more complex brains, which is an outstanding fact of animal evolution, we believe that measures of cooperativity are likely to be among the dynamic features by which major evolutionary grades of brains differ.

1990 University of Michigan Football Team

University of Michigan Men's Basketball Team, 1996

Back Row: asst. coach Jay Smith, Jerod Ward, Maceo Baston, Robert Traylor, Maurice Taylor, Willie Mitchell, asst. coach Scott Perry

Front Row: coach Steve Fisher, Louis Bullock, Neal Morton, Dugan Fife, Albert White, Travis Conlan, asst. coach Brian Dutcher

University of Michigan Men's Basketball Team, 1997

Back Row: asst. coach Scott Perry, asst. coach Brian Dutcher, academic advisor Bob Clifford, Albert White, Jerod Ward, Peter Vignier, Maurice Taylor, Robert Traylor, Maceo Baston, ?, coach Steve Fisher

Front Row: ?, Ron Oliver, Ryan De Kuiper, Travis Conlan, Louis Bullock, Brandon Hughes, equip. mngr. Bob Bland

University of Michigan Men's Basketball Team, 1998

Back Row: ?, ?, Travis Conlan, Brandon Smith, Darius Taylor, Robbie Reid, ?,

Front Row: Ron Oliver, Jerod Ward, Josh Asselin, Robert Traylor, Peter Vignier, Erik Szyndlar, Louis Bullock

University of Michigan Men's Basketball Team, 1998

L-R: Ron Oliver (25), Darius Taylor (10), Jerod Ward (32) Robbie Reid (3), Peter Vignier (55), Robert Traylor (54), Josh Asselin (25), Travis Conlan (21), Maceo Baston (30), Brandun Smith (34), Louis Bullock (11)

University of Michigan Men's Basketball Team, 1999

Back Row: asst. coach Scott Trost, asst. coach Kurtis Townsend, Leon Jones, Brandon Smith, Chris Young, Josh Asselin, Peter Vignier, Erik Szyndlar, Darius Taylor, coach Brian Ellerbe

Front Row: equip. mngr. Bob Bland, asst. coach Lorenzo Neely, Donte Scott, Robbie Reid, Louis Bullock, Ron Oliver, admin. assoc. Tom Sorboro, trainer Steve Stricker

Virtual weigh station.

Indiana Department of Transportation, Indianapolis

FDA Medical Library serials holdings list /

Mode of access: Internet.