Modification of the number of GABA A receptors (GABA A Rs) clustered at inhibitory synapses can regulate inhibitory synapse strength with important implications for information processing and nervous system plasticity and pathology. Currently, however, the mechanisms that regulate the number of GABA A Rs at synapses remain poorly understood. By imaging superecliptic pHluorin tagged GABA A R subunits we show that synaptic GABA A R clusters are normally stable, but that increased neuronal activity upon glutamate receptor (GluR) activation results in their rapid and reversible dispersal. This dispersal correlates with increases in the mobility of single GABA A Rs within the clusters as determined using singleparticle tracking of GABA A Rs labeled with quantum dots. GluRdependent dispersal of GABA A R clusters requires Ca 2+ influx via NMDA receptors (NMDARs) and activation of the phosphatase calcineurin. Moreover, the dispersal of GABA A R clusters and increased mobility of individual GABA A Rs are dependent on serine 327 within the intracellular loop of the GABA A R γ2 subunit. Thus, NMDAR signaling, via calcineurin and a key GABA A R phosphorylation site, controls the stability of synaptic GABA A Rs, with important implications for activitydependent control of synaptic inhibition and neuronal plasticity.ion channels | plasticity | trafficking | diffusion | calcineurin S ynaptic inhibition plays a critical role in regulating neuronal excitability and information processing in the brain. The number of GABA A receptors (GABA A Rs) in the surface membrane and at synaptic sites is an important determinant of inhibitory synapse strength (1), but the mechanisms that rapidly control synaptic GABA A R number and stability remain poorly understood. Activation of Ca 2+ -permeable ionotropic glutamate receptors (GluRs) during plasticity and in pathology can result in down-modulation of inhibitory synapse strength and GABA A R function (2-5) but the molecular and cellular mechanisms underlying GluR-dependent changes in the strength of GABAergic inhibition remain unclear.A major mechanism for modulating GABA A R activity is the direct phosphorylation of residues within the intracellular loops of GABA A R subunits, which can regulate synaptic inhibition, GABA A R channel kinetics, and trafficking (6-9). The rapid movement of neurotransmitter receptors (including GABA A Rs) (10-12) into and out of synapses has also recently emerged as an important mechanism for regulating synaptic strength (13). However, whether GABA A R phosphorylation can directly regulate the synaptic stability of GABA A Rs and their lateral diffusion and movement into and out of synapses is unknown.Here, by live cell imaging of surface GABA A R clusters with pHsensitive superecliptic pHluorin (SEP) and single GABA A Rs with quantum dots (QDs), we investigate the mechanisms that regulate activity-dependent control of the lateral diffusion, clustering, and stability of GABA A Rs at inhibitory synapses. We find that Ca 2+ entry through NMDA receptors (NMDARs) l...

Diffusion tensor magnetic resonance imaging (DT-MRI) is unique in providing information about both the structural integrity and the orientation of white matter fibers in vivo and, through "tractography", revealing the trajectories of white matter tracts. DT-MRI is therefore a promising technique for detecting differences in white matter architecture between different subject populations. However, while studies involving analyses of group averages of scalar quantities derived from DT-MRI data have been performed, as yet there have been no similar studies involving the whole tensor. Here we present the first step towards realizing such a study, i.e., the spatial normalization of whole tensor data sets. The approach is illustrated by spatial normalization of 10 DT-MRI data sets to a standard anatomical template. Both qualitative and quantitative approaches are described for assessing the results of spatial normalization. Techniques are then described for combining the spatially normalized data sets according to three definitions of average, i.e., the mean, median, and mode of a distribution of tensors. The current absence of, and hence need for, appropriate statistical tests for comparison of results derived from group-averaged DT-MRI data sets is then discussed. Finally, the feasibility of performing tractography on the group-averaged DT-MRI data set is investigated and the possibility and implications of generating a generic map of brain connectivity from a group of subjects is considered. © 2002 Elsevier Science (USA)

Endocytosis via clathrin-coated pits is a well-understood process; however, clathrin also assembles into large, flat clathrin lattices (FCLs), which remain poorly described. Quantitative electron, superresolution, and live-cell microscopy reveal that FCLs provide stable platforms for the recruitment of endocytic cargo.

Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1-11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases.

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc.

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Polarized light imaging (PLI) is a method to image fiber orientation in gross histological brain sections based on the birefringent properties of the myelin sheaths. The method uses the transmission of polarized light to quantitatively estimate the fiber orientation and inclination angles at every point of the imaged section. Multiple sections can be assembled into a 3D volume, from which the 3D extent of fiber tracts can be extracted. This article describes the physical principles of PLI and describes two major applications of the method: the imaging of white matter orientation of the rat brain and the generation of fiber orientation maps of the human brain in white and gray matter. The strengths and weaknesses of the method are set out.

Deep neural networks have been shown to suffer from a surprising weakness: their classification outputs can be changed by small, non-random perturbations of their inputs. This adversarial example phenomenon has been explained as originating from deep networks being "too linear" (Goodfellow et al., 2014). We show here that the linear explanation of adversarial examples presents a number of limitations: the formal argument is not convincing; linear classifiers do not always suffer from the phenomenon, and when they do their adversarial examples are different from the ones affecting deep networks.We propose a new perspective on the phenomenon. We argue that adversarial examples exist when the classification boundary lies close to the submanifold of sampled data, and present a mathematical analysis of this new perspective in the linear case. We define the notion of adversarial strength and show that it can be reduced to the deviation angle between the classifier considered and the nearest centroid classifier. Then, we show that the adversarial strength can be made arbitrarily high independently of the classification performance due to a mechanism that we call boundary tilting. This result leads us to defining a new taxonomy of adversarial examples. Finally, we show that the adversarial strength observed in practice is directly dependent on the level of regularisation used and the strongest adversarial examples, symptomatic of overfitting, can be avoided by using a proper level of regularisation.