Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

International audienceStochastic watershed is an image segmentation technique based on mathematical morphology which produces a probability density function of image contours. Estimated probabilities depend mainly on local distances between pixels. This paper introduces a variant of stochastic watershed where the probabilities of contours are computed from a Gaussian model of image regions. In this framework, the basic ingredient is the distance between pairs of regions, hence a distance between normal distributions. Hence several alternatives of statistical distances for normal distributions are compared, namely Bhattacharyya distance, Hellinger metric distance and Wasserstein metric distance

Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation

Transcriptome analyses of the Giardia lamblia life cycle

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Molecular and Biochemical Parasitology 174 (2010): 62-65, doi:10.1016/j.molbiopara.2010.05.010.We quantified mRNA abundance from 10 stages in the Giardia lamblia life cycle in vitro using Serial Analysis of Gene Expression (SAGE). 163 abundant transcripts were expressed constitutively. 71 transcripts were upregulated specifically during excystation and 42 during encystation. Nonetheless, the transcriptomes of cysts and trophozoites showed major differences. SAGE detected co-expressed clusters of 284 transcripts differentially expressed in cysts and excyzoites and 287 transcripts in vegetative trophozoites and encysting cells. All clusters included known genes and pathways as well as proteins unique to Giardia or diplomonads. SAGE analysis of the Giardia life cycle identified a number of kinases, phosphatases, and DNA replication proteins involved in excystation and encystation, which could be important for examining the roles of cell signaling in giardial differentiation. Overall, these data pave the way for directed gene discovery and a better understanding of the biology of Giardia lamblia.BJD, DSR, and FDG were supported by NIH grants AI42488, GM61896, DK35108, and AI051687. DP and SGS were supported by grants from the Swedish Natural Science Research Council, the Swedish Medical Research Council, and the Karolinska Institutet. AGM, SRB, SPP, and MJC were supported by NIH grant AI51089 and by the Marine Biological Laboratory’s Program in Global Infectious Diseases, funded by the Ellison Medical Foundation

Crenarchaeal CdvA Forms Double-Helical Filaments Containing DNA and Interacts with ESCRT-III-Like CdvB

International audienceBACKGROUND: The phylum Crenarchaeota lacks the FtsZ cell division hallmark of bacteria and employs instead Cdv proteins. While CdvB and CdvC are homologues of the eukaryotic ESCRT-III and Vps4 proteins, implicated in membrane fission processes during multivesicular body biogenesis, cytokinesis and budding of some enveloped viruses, little is known about the structure and function of CdvA. Here, we report the biochemical and biophysical characterization of the three Cdv proteins from the hyperthermophilic archaeon Metallospherae sedula. METHODOLOGY/PRINCIPAL FINDINGS: Using sucrose density gradient ultracentrifugation and negative staining electron microscopy, we evidenced for the first time that CdvA forms polymers in association with DNA, similar to known bacterial DNA partitioning proteins. We also observed that, in contrast to full-lengh CdvB that was purified as a monodisperse protein, the C-terminally deleted CdvB construct forms filamentous polymers, a phenomenon previously observed with eukaryotic ESCRT-III proteins. Based on size exclusion chromatography data combined with detection by multi-angle laser light scattering analysis, we demonstrated that CdvC assembles, in a nucleotide-independent way, as homopolymers resembling dodecamers and endowed with ATPase activity in vitro. The interactions between these putative cell division partners were further explored. Thus, besides confirming the previous observations that CdvB interacts with both CdvA and CdvC, our data demonstrate that CdvA/CdvB and CdvC/CdvB interactions are not mutually exclusive. CONCLUSIONS/SIGNIFICANCE: Our data reinforce the concept that Cdv proteins are closely related to the eukaryotic ESCRT-III counterparts and suggest that the organization of the ESCRT-III machinery at the Crenarchaeal cell division septum is organized by CdvA an ancient cytoskeleton protein that might help to coordinate genome segregation

Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process

Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical tractability. They have been widely applied to gain understanding of the underlying mechanisms for spike timing in neurons, and have served as building blocks for more elaborate models. Especially the Ornstein–Uhlenbeck process is popular to describe the stochastic fluctuations in the membrane potential of a neuron, but also other models like the square-root model or models with a non-linear drift are sometimes applied. Data that can be described by such models have to be stationary and thus, the simple models can only be applied over short time windows. However, experimental data show varying time constants, state dependent noise, a graded firing threshold and time-inhomogeneous input. In the present study we build a jump diffusion model that incorporates these features, and introduce a firing mechanism with a state dependent intensity. In addition, we suggest statistical methods to estimate all unknown quantities and apply these to analyze turtle motoneuron membrane potentials. Finally, simulated and real data are compared and discussed. We find that a square-root diffusion describes the data much better than an Ornstein–Uhlenbeck process with constant diffusion coefficient. Further, the membrane time constant decreases with increasing depolarization, as expected from the increase in synaptic conductance. The network activity, which the neuron is exposed to, can be reasonably estimated to be a threshold version of the nerve output from the network. Moreover, the spiking characteristics are well described by a Poisson spike train with an intensity depending exponentially on the membrane potential

Selective Condensation Drives Partitioning and Sequential Secretion of Cyst Wall Proteins in Differentiating Giardia lamblia

Controlled secretion of a protective extracellular matrix is required for transmission of the infective stage of a large number of protozoan and metazoan parasites. Differentiating trophozoites of the highly minimized protozoan parasite Giardia lamblia secrete the proteinaceous portion of the cyst wall material (CWM) consisting of three paralogous cyst wall proteins (CWP1–3) via organelles termed encystation-specific vesicles (ESVs). Phylogenetic and molecular data indicate that Diplomonads have lost a classical Golgi during reductive evolution. However, neogenesis of ESVs in encysting Giardia trophozoites transiently provides basic Golgi functions by accumulating presorted CWM exported from the ER for maturation. Based on this “minimal Golgi” hypothesis we predicted maturation of ESVs to a trans Golgi-like stage, which would manifest as a sorting event before regulated secretion of the CWM. Here we show that proteolytic processing of pro-CWP2 in maturing ESVs coincides with partitioning of CWM into two fractions, which are sorted and secreted sequentially with different kinetics. This novel sorting function leads to rapid assembly of a structurally defined outer cyst wall, followed by slow secretion of the remaining components. Using live cell microscopy we find direct evidence for condensed core formation in maturing ESVs. Core formation suggests that a mechanism controlled by phase transitions of the CWM from fluid to condensed and back likely drives CWM partitioning and makes sorting and sequential secretion possible. Blocking of CWP2 processing by a protease inhibitor leads to mis-sorting of a CWP2 reporter. Nevertheless, partitioning and sequential secretion of two portions of the CWM are unaffected in these cells. Although these cysts have a normal appearance they are not water resistant and therefore not infective. Our findings suggest that sequential assembly is a basic architectural principle of protective wall formation and requires minimal Golgi sorting functions

Spike Timing and Reliability in Cortical Pyramidal Neurons: Effects of EPSC Kinetics, Input Synchronization and Background Noise on Spike Timing

In vivo studies have shown that neurons in the neocortex can generate action potentials at high temporal precision. The mechanisms controlling timing and reliability of action potential generation in neocortical neurons, however, are still poorly understood. Here we investigated the temporal precision and reliability of spike firing in cortical layer V pyramidal cells at near-threshold membrane potentials. Timing and reliability of spike responses were a function of EPSC kinetics, temporal jitter of population excitatory inputs, and of background synaptic noise. We used somatic current injection to mimic population synaptic input events and measured spike probability and spike time precision (STP), the latter defined as the time window (Δt) holding 80% of response spikes. EPSC rise and decay times were varied over the known physiological spectrum. At spike threshold level, EPSC decay time had a stronger influence on STP than rise time. Generally, STP was highest (≤2.45 ms) in response to synchronous compounds of EPSCs with fast rise and decay kinetics. Compounds with slow EPSC kinetics (decay time constants>6 ms) triggered spikes at lower temporal precision (≥6.58 ms). We found an overall linear relationship between STP and spike delay. The difference in STP between fast and slow compound EPSCs could be reduced by incrementing the amplitude of slow compound EPSCs. The introduction of a temporal jitter to compound EPSCs had a comparatively small effect on STP, with a tenfold increase in jitter resulting in only a five fold decrease in STP. In the presence of simulated synaptic background activity, precisely timed spikes could still be induced by fast EPSCs, but not by slow EPSCs

Time-Warp–Invariant Neuronal Processing

A biophysical mechanism acting in auditory neurons allows the brain to process the high variability of speaking rates in natural speech in a time-warp-invariant manner

Synaptic Transmission and Plasticity in an Active Cortical Network

BACKGROUND: The cerebral cortex is permanently active during both awake and sleep states. This ongoing cortical activity has an impact on synaptic transmission and short-term plasticity. An activity pattern generated by the cortical network is a slow rhythmic activity that alternates up (active) and down (silent) states, a pattern occurring during slow wave sleep, anesthesia and even in vitro. Here we have studied 1) how network activity affects short term synaptic plasticity and, 2) how synaptic transmission varies in up versus down states. METHODOLOGY/PRINCIPAL FINDINGS: Intracellular recordings obtained from cortex in vitro and in vivo were used to record synaptic potentials, while presynaptic activation was achieved either with electrical or natural stimulation. Repetitive activation of layer 4 to layer 2/3 synaptic connections from ferret visual cortex slices displayed synaptic augmentation that was larger and longer lasting in active than in silent slices. Paired-pulse facilitation was also significantly larger in an active network and it persisted for longer intervals (up to 200 ms) than in silent slices. Intracortical synaptic potentials occurring during up states in vitro increased their amplitude while paired-pulse facilitation disappeared. Both intracortical and thalamocortical synaptic potentials were also significantly larger in up than in down states in the cat visual cortex in vivo. These enhanced synaptic potentials did not further facilitate when pairs of stimuli were given, thus paired-pulse facilitation during up states in vivo was virtually absent. Visually induced synaptic responses displayed larger amplitudes when occurring during up versus down states. This was further tested in rat barrel cortex, where a sensory activated synaptic potential was also larger in up states. CONCLUSIONS/SIGNIFICANCE: These results imply that synaptic transmission in an active cortical network is more secure and efficient due to larger amplitude of synaptic potentials and lesser short term plasticity

Intense Synaptic Activity Enhances Temporal Resolution in Spinal Motoneurons

In neurons, spike timing is determined by integration of synaptic potentials in delicate concert with intrinsic properties. Although the integration time is functionally crucial, it remains elusive during network activity. While mechanisms of rapid processing are well documented in sensory systems, agility in motor systems has received little attention. Here we analyze how intense synaptic activity affects integration time in spinal motoneurons during functional motor activity and report a 10-fold decrease. As a result, action potentials can only be predicted from the membrane potential within 10 ms of their occurrence and detected for less than 10 ms after their occurrence. Being shorter than the average inter-spike interval, the AHP has little effect on integration time and spike timing, which instead is entirely determined by fluctuations in membrane potential caused by the barrage of inhibitory and excitatory synaptic activity. By shortening the effective integration time, this intense synaptic input may serve to facilitate the generation of rapid changes in movements