On the evaluation of firing densities for periodically driven neuron models

The leaky integrate-and-fire model for neuronal spiking events driven by a periodic stimulus is studied by using the Fokker-Planck formulation. To this purpose, an essential use is made of the asymptotic behavior of the first-passage-time probability density function of a time homogeneous diffusion process through an asymptotically periodic threshold. Numerical comparisons with some recently published results derived by a different approach are performed. Use of a new asymptotic approximation is then made in order to design a numerical algorithm of predictor-corrector type to solve the integral equation in the unknown first-passage-time probability density function. Such algorithm, characterized by a reduced (linear) computation time, is seen to provide a high computation accuracy. Finally, it is shown that such an approach yields excellent approximations to the firing probability density function for a wide range of parameters, including the case of high stimulus frequencies.     

Properties of synaptic noise in tonically active human motoneurons

The objective of these experiments was to determine the amount of synaptic noise on the cell membrane at various intervals after an action potential in a motoneuron firing at a specified frequency. Sources of noise such as variations in the level of voluntary drive were minimized by selecting only segments of the spike train in which the unit was running within prescribed frequency limits. The level of the membrane potential of the motoneuron during these intervals was determined using two test “pulses” (compound Ia excitatory postsynaptic potentials) of known amplitude. This enabled the probability of the membrane potential falling within a voltage “window” of known size at known times after the preceding spike to be determined. The probability density histograms showed that the fluctuations of membrane potential about a target interspike trajectory (i.e., the membrane noise) increased with time after the preceding spike. These fluctuations in the membrane potential can be accounted for by a one-dimensional “random walk” model of membrane noise. This model explains the salient features of the interval histograms, such as positive skewness at low target frequencies. A quantitative test of the model demonstrated its applicability to the motor pools of tibialis and masseter.     

Inferring recent historic abundance from current genetic diversity

Recent historic abundance is an elusive parameter of great importance for conserving endangered species and understanding the pre‐anthropogenic state of the biosphere. The number of studies that have used population genetic theory to estimate recent historic abundance from contemporary levels of genetic diversity has grown rapidly over the last two decades. Such assessments often yield unexpectedly large estimates of historic abundance. We review the underlying theory and common practices of estimating recent historic abundance from contemporary genetic diversity, and critically evaluate the potential issues at various estimation steps. A general issue of mismatched spatio‐temporal scales between the estimation itself and the objective of the estimation emerged from our assessment; genetic diversity–based estimates of recent historic abundance represent long‐term averages, whereas the objective typically is an estimate of recent abundance for a specific population. Currently, the most promising approach to estimate the difference between recent historic and contemporary abundance requires that genetic data be collected from samples of similar spatial and temporal duration. Novel genome‐enabled inference methods may be able to utilize additional information of dense genome‐wide distributions of markers, such as of identity‐by‐descent tracts, to infer recent historic abundance from contemporary samples only.     

Computing numerically the access resistance of a pore

The access resistance (AR) of a channel is an important component of the conductance of ion channels, particularly in wide and short channels, where it accounts for a substantial fraction of the total resistance to the movement of ions. The AR is usually calculated by using a classical and simple expression derived by Hall from electrostatics (J.E. Hall 1975 J. Gen. Phys. 66:531-532), though other expressions, both analytical and numerical, have been proposed. Here we report some numerical results for the AR of a channel obtained by solving the Poisson-Nernst-Planck equations at the entrance of a circular pore. Agreement is found between numerical calculations and analytical results from Hall's equation for uncharged pores in neutral membranes. However, for channels embedded in charged membranes, Hall's expression overestimates the AR, which is much lower and can even be neglected in some cases. The weak dependence of AR on the pore radius for charged membranes at low salt concentration can be exploited to separate the channel and the access contributions to the measured conductance.     

Depolarization-induced release of l-glutamic acid from isolated-resealed synaptic membrane vesicles

l-Glutamic acid actively loaded into resealed brain synaptic membrane vesicles was rapidly released into the incubation medium following the introduction of KCl and CaCl₂, or nigericin, or veratridine into the external medium. The KCl-induced release was enhanced by the presence of low (0.1 mM), extravesicular [Ca²⁺]. Neither the KCl-induced nor the veratridine-stimulated l-glutamate efflux were carrier-mediated processes. Finally, the KCl-stimulated l-glutamate efflux was dependent on the ratio of intra- to extravesicular [K⁺]. The observations described in this study were indicative of depolarization-induced l-glutamate release from isolated synaptic plasma membrane vesicles.     

Gauging the strength of power frequency fields against membrane electrical noise

The possible physiological effect of power frequency fields (60 Hz in the US, 50 Hz in most other countries) is still a hotly debated issue. These relatively slow fields distribute themselves across cell membranes and a common approach has been to compare the strength of these fields to the strength of the electric noise that the membrane generates itself through Brownian motion. However, there has been disagreement among researchers on how to evaluate the membrane electric noise. In the first part of this article three major models are discussed. In the second part an ab initio modeling of membrane electric fields finds that different manifestations of Brownian noise lead to an electric noise intensity that is many times larger than what conventional estimates have yielded. Finally, the legitimacy of gauging a nonequilibrium external signal against internal equilibrium noise is questioned.     

Inferring macro‐ecological patterns from local presence/absence data

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.     

Excitability changes in the crustacean motor axon following activity

It has been shown experimentally that the crustacean motor axon is supernormally excitable following a train of action potentials (Zucker 1974). Such a phenomenon can lead to recruitment of terminals which are unexcited at low rates of stimulation. Although currents underlying the crustacean motor axon have been characterized (Connor et al. 1977), it is not known whether this membrane model accounts for a supernormal period, what might cause superexcitablity in this model, or how excitability might change during repetitive stimulation. In present study, it is demonstrated that the crustacean motor axon model does predict a supernormal period, that the supernormal period results from slow recovery from inactivation of the transient potassium, or A, current, and that supernormal excitability is enhanced by repetitive stimulation.     

Inferring responses to climate warming from latitudinal pattern of clonal hybridization

Climate warming may affect reproductive isolation between sympatric sister species by modifying reproductive phenology or mate choice. This is expected to result in a latitudinal progression of hybridization in response to the shifting of environmental conditions. The fish species northern redbelly dace (Chrosomus eos) and finescale dace (C. neogaeus) display a wide sympatric distribution in North America. The asexual reproduction of their hybrids allows determining where and when hybridization occurred. The aim of this study was twofold: first, to assess whether temperature affected reproductive isolation, and second, whether the effects of climate warming resulted in a latitudinal progression of hybridization. We performed a 500 km latitudinal survey (51 sites) in southeastern Quebec (Canada) and determined the distribution of clonal hybrid lineages. Results revealed a total of 78 hybrid lineages, including 70 which originated locally. We detected a significant difference between the southern and northern range of the survey in terms of the proportion of sites harboring local hybrids (20/23 vs. 8/28 sites, respectively) and hybrid diversity (57 vs. 13 lineages, respectively). This confirmed that there was more frequent interspecific mating in the warmest sites. In the southern range, diversity of lineages and simulations suggest that hybridization first took place (>7,000 years) in sites characterized by a longer growing season, followed by northerly adjacent sites (ca. 3,500–5,000 years). Moreover, evidence of hybridization occurring in present‐day time was detected. This suggests that the current warming episode is going beyond the limits of the previous warmest period of the Holocene.     

Proper synaptic vesicle formation and neuronal network activity critically rely on syndapin I

Synaptic transmission relies on effective and accurate compensatory endocytosis. F-BAR proteins may serve as membrane curvature sensors and/or inducers and thereby support membrane remodelling processes; yet, their in vivo functions urgently await disclosure. We demonstrate that the F-BAR protein syndapin I is crucial for proper brain function. Syndapin I knockout (KO) mice suffer from seizures, a phenotype consistent with excessive hippocampal network activity. Loss of syndapin I causes defects in presynaptic membrane trafficking processes, which are especially evident under high-capacity retrieval conditions, accumulation of endocytic intermediates, loss of synaptic vesicle (SV) size control, impaired activity-dependent SV retrieval and defective synaptic activity. Detailed molecular analyses demonstrate that syndapin I plays an important role in the recruitment of all dynamin isoforms, central players in vesicle fission reactions, to the membrane. Consistently, syndapin I KO mice share phenotypes with dynamin I KO mice, whereas their seizure phenotype is very reminiscent of fitful mice expressing a mutant dynamin. Thus, syndapin I acts as pivotal membrane anchoring factor for dynamins during regeneration of SVs.     

Formation of functional synaptic connections between cultured cortical neurons from agrin‐deficient mice

Numerous studies suggest that the extracellular matrix protein agrin directs the formation of the postsynaptic apparatus at the neuromuscular junction (NMJ). Strong support for this hypothesis comes from the observation that the high density of acetylcholine receptors (AChR) normally present at the neuromuscular junction fails to form in muscle of embryonic agrin mutant mice. Agrin is expressed by many populations of neurons in the central nervous system (CNS), suggesting that this molecule may also play a role in neuron–neuron synapse formation. To test this hypothesis, we examined synapse formation between cultured cortical neurons isolated from agrin‐deficient mouse embryos. Our data show that glutamate receptors accumulate at synaptic sites on agrin‐deficient neurons. Moreover, electrophysiological analysis demonstrates that functional glutamatergic and gamma‐aminobutyric acid (GABA)ergic synapses form between mutant neurons. The frequency and amplitude of miniature postsynaptic glutamatergic and GABAergic currents are similar in mutant and age‐matched wild‐type neurons during the first 3 weeks in culture. These results demonstrate that neuron‐specific agrin is not required for formation and early development of functional synaptic contacts between CNS neurons, and suggest that mechanisms of interneuronal synaptogenesis are distinct from those regulating synapse formation at the neuromuscular junction. © 1999 John Wiley & Sons, Inc. J Neurobiol 39: 547–557, 1999     

Inferring the microscopic surface energy of protein–protein interfaces from mutation data

Mutations at protein–protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical values, yielding mean energies of ?48 cal mol^?1 Å^?2 for interactions between hydrophobic surfaces, ?51 to ?80 cal mol^?1 Å^?2 for surfaces of complementary charge, and 66–83 cal mol^?1 Å^?2 for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at ?24 cal mol^?1 Å^?2. Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid‐body interactions (r = 0.66). When used to rerank docking poses, it can place near‐native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50–95% of the cohesive energy. The model is available open‐source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/ . Proteins 2015; 83:640–650. © 2015 Wiley Periodicals, Inc.     

Synthesis, characteristics and biological activity of pentacoordinated spirophosphoranes derived from amino acids

Summary. The reactions of phosphorus trichloride with various amino acids afford the pentacoordinated spirophosphoranes. The reaction procedures were traced by ³¹P NMR spectra techniques. A new crystal structure of alanine derivative was characterized, which is a slightly distorted TBP structure. Besides, this kind of spirophosphoranes are potent inhibitors to tyrosinase.     

Inferring patterns of influenza transmission in swine from multiple streams of surveillance data

Swine populations are known to be an important source of new human strains of influenza A, including those responsible for global pandemics. Yet our knowledge of the epidemiology of influenza in swine is dismayingly poor, as highlighted by the emergence of the 2009 pandemic strain and the paucity of data describing its origins. Here, we analyse a unique dataset arising from surveillance of swine influenza at a Hong Kong abattoir from 1998 to 2010. We introduce a state–space model that estimates disease exposure histories by joint inference from multiple modes of surveillance, integrating both virological and serological data. We find that an observed decrease in virus isolation rates is not due to a reduction in the regional prevalence of influenza. Instead, a more likely explanation is increased infection of swine in production farms, creating greater immunity to disease early in life. Consistent with this, we find that the weekly risk of exposure on farms equals or exceeds the exposure risk during transport to slaughter. We discuss potential causes for these patterns, including competition between influenza strains and shifts in the Chinese pork industry, and suggest opportunities to improve knowledge and reduce prevalence of influenza in the region.     

Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies

Dynamical behavior of a biological neuronal network depends significantly on the spatial pattern of synaptic connections among neurons. While neuronal network dynamics has extensively been studied with simple wiring patterns, such as all-to-all or random synaptic connections, not much is known about the activity of networks with more complicated wiring topologies. Here, we examined how different wiring topologies may influence the response properties of neuronal networks, paying attention to irregular spike firing, which is known as a characteristic of in vivo cortical neurons, and spike synchronicity. We constructed a recurrent network model of realistic neurons and systematically rewired the recurrent synapses to change the network topology, from a localized regular and a “small-world” network topology to a distributed random network topology. Regular and small-world wiring patterns greatly increased the irregularity or the coefficient of variation (Cv) of output spike trains, whereas such an increase was small in random connectivity patterns. For given strength of recurrent synapses, the firing irregularity exhibited monotonous decreases from the regular to the random network topology. By contrast, the spike coherence between an arbitrary neuron pair exhibited a non-monotonous dependence on the topological wiring pattern. More precisely, the wiring pattern to maximize the spike coherence varied with the strength of recurrent synapses. In a certain range of the synaptic strength, the spike coherence was maximal in the small-world network topology, and the long-range connections introduced in this wiring changed the dependence of spike synchrony on the synaptic strength moderately. However, the effects of this network topology were not really special in other properties of network activity. Action Editor: Xiao-Jing Wang     

Fast assessment of structural models of ion channels based on their predicted current–voltage characteristics

Computational prediction of protein structures is a difficult task, which involves fast and accurate evaluation of candidate model structures. We propose to enhance single‐model quality assessment with a functionality evaluation phase for proteins whose quantitative functional characteristics are known. In particular, this idea can be applied to evaluation of structural models of ion channels, whose main function ‐ conducting ions ‐ can be quantitatively measured with the patch‐clamp technique providing the current–voltage characteristics. The study was performed on a set of KcsA channel models obtained from complete and incomplete contact maps. A fast continuous electrodiffusion model was used for calculating the current–voltage characteristics of structural models. We found that the computed charge selectivity and total current were sensitive to structural and electrostatic quality of models. In practical terms, we show that evaluating predicted conductance values is an appropriate method to eliminate models with an occluded pore or with multiple erroneously created pores. Moreover, filtering models on the basis of their predicted charge selectivity results in a substantial enrichment of the candidate set in highly accurate models. Tests on three other ion channels indicate that, in addition to being a proof of the concept, our function‐oriented single‐model quality assessment method can be directly applied to evaluation of structural models of some classes of protein channels. Finally, our work raises an important question whether a computational validation of functionality should be included in the evaluation process of structural models, whenever possible. Proteins 2016; 84:217–231. © 2015 Wiley Periodicals, Inc.     

Predicting protein complex geometries with a neural network

A major challenge of the protein docking problem is to define scoring functions that can distinguish near‐native protein complex geometries from a large number of non‐native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom‐pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near‐native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge‐based energy functions for scoring. We show that a distance‐dependent atom pair potential performs much better than a simple atom‐pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge‐based scoring functions such as ZDOCK 3.0, ZRANK, ITScore‐PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network‐based scoring function achieves a reasonable performance in rigid‐body unbound docking of proteins. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

A before–after control–impact assessment to understand the potential impacts of highway construction noise and activity on an endangered songbird

Anthropogenic noise associated with highway construction and operation can have individual‐ and population‐level consequences for wildlife (e.g., reduced densities, decreased reproductive success, behavioral changes). We used a before–after control–impact study design to examine the potential impacts of highway construction and traffic noise on endangered golden‐cheeked warblers (Setophaga chrysoparia; hereafter warbler) in urban Texas. We mapped and monitored warbler territories before (2009–2011), during (2012–2013), and after (2014) highway construction at three study sites: a treatment site exposed to highway construction and traffic noise, a control site exposed only to traffic noise, and a second control site exposed to neither highway construction or traffic noise. We measured noise levels at varying distances from the highway at sites exposed to construction and traffic noise. We examined how highway construction and traffic noise influenced warbler territory density, territory placement, productivity, and song characteristics. In addition, we conducted a playback experiment within study sites to evaluate acute behavioral responses to highway construction noises. Noise decreased with increasing distance from the highways. However, noise did not differ between the construction and traffic noise sites or across time. Warbler territory density increased over time at all study sites, and we found no differences in warbler territory placement, productivity, behavior, or song characteristics that we can attribute to highway construction or traffic noise. As such, we found no evidence to suggest that highway construction or traffic noise had a negative effect on warblers during our study. Because human population growth will require recurring improvements to transportation infrastructure, understanding wildlife responses to anthropogenic noise associated with the construction and operation of roads is essential for effective management and recovery of prioritized species.     

Testing for top‐down cascading effects in a biomass‐driven ecological network of soil invertebrates

1. To investigate the structural changes of a food‐web architecture, we considered real data coming from a soil food web in one abandoned pasture with former low‐pressure agriculture management and we reproduced the corresponding ecological network within a multi‐agent fully programmable modeling environment in order to simulate dynamically the cascading effects due to the removal of entire functional guilds.
2. We performed several simulations differing from each other for the functional implications. At the first trophic level, we simulated a removal of the prey, that is, herbivores and microbivores, while at the second trophic level, we simulated a removal of the predators, that is, omnivores and carnivores. The five main guilds were removed either separately or in combination.
3. The alteration in the food‐web architecture induced by the removal of entire functional guilds was the highest when the entire second trophic level was removed, while the removal of all microbivores caused an alteration in the food‐web structure of less than 5% of the total changes due to the removal of opportunistic and predatory species.
4. Omnivores alone account for the highest shifts in time of the numerical abundances of the remaining species, providing computational evidence of the importance of the degree of omnivory in the stabilization of soil biota.