The physiological consequences of crib-biting in horses in response to an ACTH challenge test

Stereotypies are repetitive and relatively invariant patterns of behavior, which are observed in a wide range of species in captivity. Stereotypic behavior occurs when environmental demands produce a physiological response that, if sustained for an extended period, exceeds the natural physiological regulatory capacity of the organism, particularly in situations that include unpredictability and uncontrollability. One hypothesis is that stereotypic behavior functions to cope with stressful environments, but the existing evidence is contradictory. To address the coping hypothesis of stereotypies, we triggered physiological reactions in 22 horses affected by stereotypic behavior (crib-biters) and 21 non-crib-biters (controls), using an ACTH challenge test. Following administration of an ACTH injection, we measured saliva cortisol every 30min and heart rate (HR) continuously for a period of 3h. We did not find any differences in HR or HR variability between the two groups, but crib-biters (Group CB) had significantly higher cortisol responses than controls (Group C; mean±SD: CB, 5.84±2.62ng/ml, C, 4.76±3.04ng/ml). Moreover, crib-biters that did not perform the stereotypic behavior during the 3-hour test period (Group B) had significantly higher cortisol levels than controls, which was not the case of crib-biters showing stereotypic behavior (Group A) (B, 6.44±2.38ng/ml A, 5.58±2.69ng/ml). Our results suggest that crib-biting is a coping strategy that helps stereotypic individuals to reduce cortisol levels caused by stressful situations. We conclude that preventing stereotypic horses from crib-biting could be an inappropriate strategy to control this abnormal behavior, as it prevents individuals from coping with situations that they perceive as stressful.

Response profiles of murine spiral ganglion neurons on multi-electrode arrays.

OBJECTIVE Cochlear implants (CIs) have become the gold standard treatment for deafness. These neuroprosthetic devices feature a linear electrode array, surgically inserted into the cochlea, and function by directly stimulating the auditory neurons located within the spiral ganglion, bypassing lost or not-functioning hair cells. Despite their success, some limitations still remain, including poor frequency resolution and high-energy consumption. In both cases, the anatomical gap between the electrode array and the spiral ganglion neurons (SGNs) is believed to be an important limiting factor. The final goal of the study is to characterize response profiles of SGNs growing in intimate contact with an electrode array, in view of designing novel CI devices and stimulation protocols, featuring a gapless interface with auditory neurons. APPROACH We have characterized SGN responses to extracellular stimulation using multi-electrode arrays (MEAs). This setup allows, in our view, to optimize in vitro many of the limiting interface aspects between CIs and SGNs. MAIN RESULTS Early postnatal mouse SGN explants were analyzed after 6-18 days in culture. Different stimulation protocols were compared with the aim to lower the stimulation threshold and the energy needed to elicit a response. In the best case, a four-fold reduction of the energy was obtained by lengthening the biphasic stimulus from 40 μs to 160 μs. Similarly, quasi monophasic pulses were more effective than biphasic pulses and the insertion of an interphase gap moderately improved efficiency. Finally, the stimulation with an external electrode mounted on a micromanipulator showed that the energy needed to elicit a response could be reduced by a factor of five with decreasing its distance from 40 μm to 0 μm from the auditory neurons. SIGNIFICANCE This study is the first to show electrical activity of SGNs on MEAs. Our findings may help to improve stimulation by and to reduce energy consumption of CIs and thereby contribute to the development of fully implantable devices with better auditory resolution in the future.

Multiple functions of gingival and mucoperiosteal fibroblasts in oral wound healing and repair

Fibroblasts are cells of mesenchymal origin. They are responsible for the production of most extracellular matrix in connective tissues and are essential for wound healing and repair. In recent years, it has become clear that fibroblasts from different tissues have various distinct traits. Moreover, wounds in the oral cavity heal under very special environmental conditions compared with skin wounds. Here, we reviewed the current literature on the various interconnected functions of gingival and mucoperiosteal fibroblasts during the repair of oral wounds. The MEDLINE database was searched with the following terms: (gingival OR mucoperiosteal) AND fibroblast AND (wound healing OR repair). The data gathered were used to compare oral fibroblasts with fibroblasts from other tissues in terms of their regulation and function during wound healing. Specifically, we sought answers to the following questions: (i) what is the role of oral fibroblasts in the inflammatory response in acute wounds; (ii) how do growth factors control the function of oral fibroblasts during wound healing; (iii) how do oral fibroblasts produce, remodel and interact with extracellular matrix in healing wounds; (iv) how do oral fibroblasts respond to mechanical stress; and (v) how does aging affect the fetal-like responses and functions of oral fibroblasts? The current state of research indicates that oral fibroblasts possess unique characteristics and tightly controlled specific functions in wound healing and repair. This information is essential for developing new strategies to control the intraoral wound-healing processes of the individual patient.

The Effect of Increasing Intracranial Pressure on Ocular Vestibular-Evoked Myogenic Potential Frequency Tuning.

OBJECTIVE Ocular vestibular-evoked myogenic potentials (oVEMPs) represent extraocular muscle activity in response to vestibular stimulation. The authors sought to investigate whether posture-induced increase of the intracranial pressure (ICP) modulated oVEMP frequency tuning, that is, the amplitude ratio between 500-Hz and 1000-Hz stimuli. DESIGN Ten healthy subjects were enrolled in this study. The subjects were positioned in the horizontal plane (0 degree) and in a 30-degree head-downwards position to elevate the ICP. In both positions, oVEMPs were recorded using 500-Hz and 1000-Hz air-conducted tone bursts. RESULTS When tilting the subject from the horizontal plane to the 30-degree head-down position, oVEMP amplitudes in response to 500-Hz tone bursts distinctly decreased (3.40 μV versus 2.06 μV; p < 0.001), whereas amplitudes to 1000 Hz were only slightly diminished (2.74 μV versus 2.48 μV; p = 0.251). Correspondingly, the 500/1000-Hz amplitude ratio significantly decreased when tilting the subjects from 0- to 30-degree inclination (1.59 versus 1.05; p = 0.029). Latencies were not modulated by head-down position. CONCLUSIONS Increasing ICP systematically alters oVEMPs in terms of absolute amplitudes and frequency tuning characteristics. oVEMPs are therefore in principle suited for noninvasive ICP monitoring.

Frequency modulation of ERK activation dynamics rewires cell fate.

Transient versus sustained ERK MAP kinase (MAPK) activation dynamics induce proliferation versus differentiation in response to epidermal (EGF) or nerve (NGF) growth factors in PC-12 cells. Duration of ERK activation has therefore been proposed to specify cell fate decisions. Using a biosensor to measure ERK activation dynamics in single living cells reveals that sustained EGF/NGF application leads to a heterogeneous mix of transient and sustained ERK activation dynamics in distinct cells of the population, different than the population average. EGF biases toward transient, while NGF biases toward sustained ERK activation responses. In contrast, pulsed growth factor application can repeatedly and homogeneously trigger ERK activity transients across the cell population. These datasets enable mathematical modeling to reveal salient features inherent to the MAPK network. Ultimately, this predicts pulsed growth factor stimulation regimes that can bypass the typical feedback activation to rewire the system toward cell differentiation irrespective of growth factor identity.

Ethanolic fermentation: new functions for an old pathway

Ethanolic fermentation is an ancient metabolic pathway. In plants, it is a major route of {ATP} production under anaerobic conditions. In addition, recent developments suggest that the pathway has important functions in the presence of oxygen. Both of the enzymes required for the production of acetaldehyde and ethanol, pyruvate decarboxylase and alcohol dehydrogenase, are highly abundant in pollen, resulting in fermentation in fully oxygenated cells. Acetaldehyde toxicity is an inevitable side effect of aerobic fermentation. Could acetaldehyde be the elusive pollen factor that contributes to male sterility in cmsT maize? The versatility of this ancient pathway is also illustrated by the induction of aerobic fermentation by environmental stress and activation of a defense response by overexpression of pyruvate decarboxylase.

Analysis of the Heat-Shock Response Displayed by Two Chaetomium Species Originating from Different Thermal Environments

Three features of the heat shock response, reorganization of protein expression, intracellular accumulation of trehalose, and alteration in unsaturation degree of fatty acids were investigated in the thermophilic fungus Chaetomium thermophile and compared to the response displayed by a closely related mesophilic species, C. brasiliense. Thermophilic heat shock response paralleled the mesophilic response in many respects like (i) the temperature difference observed between normothermia and the upper limit of translational activity, (ii) the transient nature of the heat shock response at the level of protein expression including both the induction of heat shock proteins (HSPs) as well as the repression of housekeeping proteins, (iii) the presence of representatives of high-molecular-weight {HSPs} families, (iv) intracellular accumulation of trehalose, and finally (v) modifications in fatty acid composition. On the other hand, a great variability between the two organisms was observed for the proteins expressed during stress, in particular a protein of the {HSP60} family that was only observed in C. thermophile. This peptide was also present constitutively at normal temperature and may thus fulfil thermophilic functions. It is shown that accumulation of trehalose does not play a part in thermophily but is only a stress response. C. thermophile contains less polyunsaturated fatty acids at normal temperature than C. brasiliense, a fact that can be directly related to thermophily. When subjected to heat stress, both organisms tended to accumulate shorter and less unsaturated fatty acids.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().