Prolactin induces a hyperpolarising current in rat paraventricular oxytocinergic neurones.

Prolactin and oxytocin are important reproductive hormones implicated in several common adaptive functions during pregnancy, pseudopregnancy and lactation. Recently, extracellular recordings of supraoptic neurones have shown that prolactin may modulate the electrical activity of oxytocinergic neurones. However, no study has been conducted aiming to establish whether prolactin directly influences this activity in oxytocinergic paraventricular neurones. In the present study, we addressed this question by studying the effects of prolactin on the electrical activity and voltage-current relationship of identified paraventricular neurones in rat brain slices. Whole-cell recordings were obtained and neurones were classified on the basis of their morphological and electrophysiological fingerprint (i.e. magnocellular or parvicellular) and neuropeptide phenotype (i.e. oxytocinergic or non-oxytocinergic). We report that prolactin elicited a hyperpolarising current in 37% of the neurones in this nucleus, of which the majority (67%) were identified as putative magnocellular oxytocin neurones and the reminder (33%) were regarded as oxytocin-negative, parvicellular neuroendocrine neurones. Our results suggest that, in addition to the well-established negative feedback loop between prolactin-secreting lactotrophs and dopaminergic neurones in the arcuate nucleus, an inhibitory feedback loop also exists between lactotrophs and oxytocinergic paraventricular neurones.

Image adaptive point-spread function estimation and deconvolution for in vivo confocal microscopy.

Visualizing deep inside the tissue of a thick biological sample often poses severe constraints on image conditions. Standard restoration techniques (denoising and deconvolution) can then be very useful, allowing one to increase the signal-to-noise ratio and the resolution of the images. In this paper, we consider the problem of obtaining a good determination of the point-spread function (PSF) of a confocal microscope, a prerequisite for applying deconvolution to three-dimensional image stacks acquired with this system. Because of scattering and optical distortion induced by the sample, the PSF has to be acquired anew for each experiment. To tackle this problem, we used a screening approach to estimate the PSF adaptively and automatically from the images. Small PSF-like structures were detected in the images, and a theoretical PSF model reshaped to match the geometric characteristics of these structures. We used numerical experiments to quantify the sensitivity of our detection method, and we demonstrated its usefulness by deconvolving images of the hearing organ acquired in vitro and in vivo.

Wavelet-based image restoration for compact X-ray microscopy.

Compact water-window X-ray microscopy with short exposure times will always be limited on photons owing to sources of limited power in combination with low-efficency X-ray optics. Thus, it is important to investigate methods for improving the signal-to-noise ratio in the images. We show that a wavelet-based denoising procedure significantly improves the quality and contrast in compact X-ray microscopy images. A non-decimated, discrete wavelet transform (DWT) is applied to original, noisy images. After applying a thresholding procedure to the finest scales of the DWT, by setting to zero all wavelet coefficients of magnitude below a prescribed value, the inverse DWT to the thresholded DWT produces denoised images. It is concluded that the denoising procedure has potential to reduce the exposure time by a factor of 2 without loss of relevant image information.

Image restoration for confocal microscopy: improving the limits of deconvolution, with application to the visualization of the mammalian hearing organ.

Deconvolution algorithms have proven very effective in conventional (wide-field) fluorescence microscopy. Their application to confocal microscopy is hampered, in biological experiments, by the presence of important levels of noise in the images and by the lack of a precise knowledge of the point spread function (PSF) of the system. We investigate the application of wavelet-based processing tools to deal with these problems, in particular wavelet denoising methods, which turn out to be very effective in application to three-dimensional confocal images. When used in combination with more classical deconvolution algorithms, these methods provide a robust and efficient restoration scheme allowing one to deal with difficult imaging conditions. To make our approach applicable in practical situations, we measured the PSF of a Biorad-MRC1024 confocal microscope under a large set of imaging conditions, including in situ acquisitions. As a specific biological application, we present several examples of restorations of three-dimensional confocal images acquired inside an intact preparation of the hearing organ. We also provide a quantitative assessment of the gain in quality achieved by wavelet-aided restorations over classical deconvolution schemes, based on a set of numerical experiments that we performed with test images.

Memory capacity in large idiotypic networks.

Many models of immune networks have been proposed since the original work of Jerne [1974, Ann. Immun. (Inst. Pasteur)125C, 373-389]. Recently, a limited class of models (Weisbuch et al., 1990, J. theor. Biol 146, 483-499) have been shown to maintain immunological memory by idiotypic network interactions. We examine generalizations of these models when the networks are both large and highly connected to study their memory capacity, i.e., their ability to account for immunization to a large number of random antigens. Our calculations show that in these minimal models, random connectivities with continuously distributed affinities reduce the memory capacity to essentially nil.