Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.     

Trading Speed and Accuracy by Coding Time: A Coupled-circuit Cortical Model

Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT) provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by ‘climbing’ activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network''s interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.     

Population Decoding in Rat Barrel Cortex: Optimizing the Linear Readout of Correlated Population Responses

Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation.     

A Life History Approach to Understanding Youth Time Preference

Following from life history and attachment theory, individuals are predicted to be sensitive to variation in environmental conditions such that risk and uncertainty are internalized by cognitive, affective, and psychobiological mechanisms. In turn, internalizing of environmental uncertainty is expected to be associated with attitudes toward risk behaviors and investments in education. Native American youth aged 10–19 years (n = 89) from reservation communities participated in a study examining this pathway. Measures included family environmental risk and uncertainty, present and future time perspective, adolescent attachment, attitudes toward risk, investments in education, and salivary cortisol. Results support the idea that environmental risk and uncertainty are internalized during development. In addition, internalizing mechanisms significantly predicted attitudes toward risk and education: (1) lower scores on future time perspective and higher cortisol predicted higher scores on risk attitudes, and (2) higher scores on future time perspective and lower scores on problems with attachment predicted higher self-reported school performance. Gender differences were seen, with males anticipating a shorter lifespan than females, which predicted higher scores on risk attitudes and lower school performance. Implications for research on adolescent problem behavior and academic achievement are discussed.     

A First-Passage Time Approach to Diffusion in Liquids and Superionic Conductors

Abstract

As a new tool to investigate single-particle motion in condensed matter, a first-passage time (FPT) approach to diffusion is developed and applied to the molecular dynamics simulations of simple liquids and superionic conductor CaF₂. It is shown that a continuous diffusion model reproduces the observed FPT distribution quite well for both liquids and CaF₂, which enables us to evaluate diffusion constants with good accuracy by our method. On a length scale as small as a lattice constant, however, the effect of hopping appears in the FPT distribution of F^? ions, which can not be described by a continuous diffusion model. A simple hopping diffusion model is proposed and examined from the FPT viewpoint.     

Genetic Demography of the Kuznetsk Basin Population: Changes in Marriage Assortativeness with Respect to Ethnicity and Age in the Belovo City Population with Time

A genetic demographic study has been performed in the city of Belovo with the use of the data on marriages contracted there in 1970 and 1994–1999. Marriage assortativeness with respect to age has been found to be the strongest and remain unchanged during the lifetime of one generation (r = 0.730 in 1970 and r = 0.801 in 1994–1999). Monoethnic marriages were substantially more frequent than interethnic ones in the Belovo population during the period studied, although the ethnic marriage assortativeness considerably decreased (K = 0.386 in 1970 and K = 0.141 in 1994–1999). Panmixia has been observed in the Russian population of Belovo. Other Eastern Slavs (Ukrainians and Belarussians) are characterized by negative marriage assortativeness and panmixia; positive marriage assortativeness has been found in other ethnic groups.__________Translated from Genetika, Vol. 41, No. 7, 2005, pp. 938–942.Original Russian Text Copyright © 2005 by Lavryashina, Ulyanova.     

Direct Reprogramming of Somatic Cells to Neurons: Pros and Cons of Chemical Approach

Neurochemical Research - Translating successful preclinical research in neurodegenerative diseases into clinical practice has been difficult. The preclinical disease models used for testing new...     

Defining the Study Population for an Observational Study to Ensure Sufficient Overlap: A Tree Approach

The internal validity of an observational study is enhanced by only comparing sets of treated and control subjects which have sufficient overlap in their covariate distributions. Methods have been developed for defining the study population using propensity scores to ensure sufficient overlap. However, a study population defined by propensity scores is difficult for other investigators to understand. We develop a method of defining a study population in terms of a tree which is easy to understand and display, and that has similar internal validity as that of the study population defined by propensity scores.     

Complex Population Response of Dorsal Putamen Neurons Predicts the Ability to Learn

Day-to-day variability in performance is a common experience. We investigated its neural correlate by studying learning behavior of monkeys in a two-alternative forced choice task, the two-armed bandit task. We found substantial session-to-session variability in the monkeys’ learning behavior. Recording the activity of single dorsal putamen neurons we uncovered a dual function of this structure. It has been previously shown that a population of neurons in the DLP exhibits firing activity sensitive to the reward value of chosen actions. Here, we identify putative medium spiny neurons in the dorsal putamen that are cue-selective and whose activity builds up with learning. Remarkably we show that session-to-session changes in the size of this population and in the intensity with which this population encodes cue-selectivity is correlated with session-to-session changes in the ability to learn the task. Moreover, at the population level, dorsal putamen activity in the very beginning of the session is correlated with the performance at the end of the session, thus predicting whether the monkey will have a "good" or "bad" learning day. These results provide important insights on the neural basis of inter-temporal performance variability.     

Modelling Population Dynamics in Realistic Landscapes with Linear Elements: A Mechanistic-Statistical Reaction-Diffusion Approach

We propose and develop a general approach based on reaction-diffusion equations for modelling a species dynamics in a realistic two-dimensional (2D) landscape crossed by linear one-dimensional (1D) corridors, such as roads, hedgerows or rivers. Our approach is based on a hybrid “2D/1D model”, i.e, a system of 2D and 1D reaction-diffusion equations with homogeneous coefficients, in which each equation describes the population dynamics in a given 2D or 1D element of the landscape. Using the example of the range expansion of the tiger mosquito Aedes albopictus in France and its main highways as 1D corridors, we show that the model can be fitted to realistic observation data. We develop a mechanistic-statistical approach, based on the coupling between a model of population dynamics and a probabilistic model of the observation process. This allows us to bridge the gap between the data (3 levels of infestation, at the scale of a French department) and the output of the model (population densities at each point of the landscape), and to estimate the model parameter values using a maximum-likelihood approach. Using classical model comparison criteria, we obtain a better fit and a better predictive power with the 2D/1D model than with a standard homogeneous reaction-diffusion model. This shows the potential importance of taking into account the effect of the corridors (highways in the present case) on species dynamics. With regard to the particular case of A. albopictus, the conclusion that highways played an important role in species range expansion in mainland France is consistent with recent findings from the literature.     

Olfactory Neurons Obtained through Nasal Biopsy Combined with Laser-Capture Microdissection: A Potential Approach to Study Treatment Response in Mental Disorders

Bipolar disorder (BD) is a severe neuropsychiatric disorder with poorly understood pathophysiology and typically treated with the mood stabilizer, lithium carbonate. Animal studies as well as human genetic studies indicate that lithium affects molecular targets that are involved in neuronal growth, survival and maturation, and notably molecules involved in Wnt signaling. Given the ethical challenge to obtaining brain biopsies for investigating dynamic molecular changes associated with lithium-response in the central nervous system (CNS), one may consider the use of neurons obtained from olfactory tissues to achieve this goal.The olfactory epithelium contains olfactory receptor neurons at different stages of development and glial-like supporting cells. This provides a unique opportunity to study dynamic changes in the CNS of patients with neuropsychiatric diseases, using olfactory tissue safely obtained from nasal biopsies. To overcome the drawback posed by substantial contamination of biopsied olfactory tissue with non-neuronal cells, a novel approach to obtain enriched neuronal cell populations was developed by combining nasal biopsies with laser-capture microdissection. In this study, a system for investigating treatment-associated dynamic molecular changes in neuronal tissue was developed and validated, using a small pilot sample of BD patients recruited for the study of the molecular mechanisms of lithium treatment response.     

A Principled Approach to Deriving Approximate Conditional Sampling Distributions in Population Genetics Models with Recombination

The multilocus conditional sampling distribution (CSD) describes the probability that an additionally sampled DNA sequence is of a certain type, given that a collection of sequences has already been observed. The CSD has a wide range of applications in both computational biology and population genomics analysis, including phasing genotype data into haplotype data, imputing missing data, estimating recombination rates, inferring local ancestry in admixed populations, and importance sampling of coalescent genealogies. Unfortunately, the true CSD under the coalescent with recombination is not known, so approximations, formulated as hidden Markov models, have been proposed in the past. These approximations have led to a number of useful statistical tools, but it is important to recognize that they were not derived from, though were certainly motivated by, principles underlying the coalescent process. The goal of this article is to develop a principled approach to derive improved CSDs directly from the underlying population genetics model. Our approach is based on the diffusion process approximation and the resulting mathematical expressions admit intuitive genealogical interpretations, which we utilize to introduce further approximations and make our method scalable in the number of loci. The general algorithm presented here applies to an arbitrary number of loci and an arbitrary finite-alleles recurrent mutation model. Empirical results are provided to demonstrate that our new CSDs are in general substantially more accurate than previously proposed approximations.THE probability of observing a sample of DNA sequences under a given population genetics model—which is referred to as the sampling probability or likelihood—plays an important role in a wide range of problems in a genetic variation study. When recombination is involved, however, obtaining an analytic formula for the sampling probability has hitherto remained a challenging open problem (see Jenkins and Song 2009, 2010 for recent progress on this problem). As such, much research (Griffiths and Marjoram 1996; Kuhner et al. 2000; Nielsen 2000; Stephens and Donnelly 2000; Fearnhead and Donnelly 2001; De Iorio and Griffiths 2004a,b; Fearnhead and Smith 2005; Griffiths et al. 2008; Wang and Rannala 2008) has focused on developing Monte Carlo methods on the basis of the coalescent with recombination (Griffiths 1981; Kingman 1982a,b; Hudson 1983), a well-established mathematical framework that models the genealogical history of sample chromosomes. These Monte Carlo-based full-likelihood methods mark an important development in population genetics analysis, but a well-known obstacle to their utility is that they tend to be computationally intensive. For a whole-genome variation study, approximations are often unavoidable, and it is therefore important to think of ways to minimize the trade-off between scalability and accuracy.A popular likelihood-based approximation method that has had a significant impact on population genetics analysis is the following approach introduced by Li and Stephens (2003): Given a set Φ of model parameters (e.g., mutation rate, recombination rate, etc.), the joint probability p(h₁, … , h_n | Φ) of observing a set {h₁, … , h_n} of haplotypes sampled from a population can be decomposed as a product of conditional sampling distributions (CSDs), denoted by π,(1)where π(h_k+1|h₁, …, h_k, Φ) is the probability of an additionally sampled haplotype being of type h_k+1, given a set of already observed haplotypes h₁, …, h_k. In the presence of recombination, the true CSD π is unknown, so Li and Stephens proposed using an approximate CSD in place of π, thus obtaining the following approximation of the joint probability:(2)Li and Stephens referred to this approximation as the product of approximate conditionals (PAC) model. In general, the closer is to the true CSD π, the more accurate the PAC model becomes. Notable applications and extensions of this framework include estimating crossover rates (Li and Stephens 2003; Crawford et al. 2004) and gene conversion parameters (Gay et al. 2007; Yin et al. 2009), phasing genotype data into haplotype data (Stephens and Scheet 2005; Scheet and Stephens 2006), imputing missing data to improve power in association mapping (Stephens and Scheet 2005; Li and Abecasis 2006; Marchini et al. 2007; Howie et al. 2009), inferring local ancestry in admixed populations (Price et al. 2009), inferring human colonization history (Hellenthal et al. 2008), inferring demography (Davison et al. 2009), and so on.Another problem in which the CSD plays a fundamental role is importance sampling of genealogies under the coalescent process (Stephens and Donnelly 2000; Fearnhead and Donnelly 2001; De Iorio and Griffiths 2004a,b; Fearnhead and Smith 2005; Griffiths et al. 2008). In this context, the optimal proposal distribution can be written in terms of the CSD π (Stephens and Donnelly 2000), and as in the PAC model, an approximate CSD may be used in place of π. The performance of an importance sampling scheme depends critically on the proposal distribution and therefore on the accuracy of the approximation . Often in conjunction with composite-likelihood frameworks (Hudson 2001; Fearnhead and Donnelly 2002), importance sampling has been used in estimating fine-scale recombination rates (McVean et al. 2004; Fearnhead and Smith 2005; Johnson and Slatkin 2009).So far, a significant scope of intuition has gone into choosing the approximate CSDs used in these problems (Marjoram and Tavaré 2006). In the case of completely linked loci, Stephens and Donnelly (2000) suggested constructing an approximation by assuming that the additional haplotype h_k+1 is an imperfect copy of one of the first k haplotypes, with copying errors corresponding to mutation. Fearnhead and Donnelly (2001) generalized this construction to include crossover recombination, assuming that the haplotype h_k+1 is an imperfect mosaic of the first k haplotypes (i.e., h_k+1 is obtained by copying segments from h₁, …, h_k, where crossover recombination can change the haplotype from which copying is performed). The associated CSD, which we denote by , can be interpreted as a hidden Markov model and so admits an efficient dynamic programming solution. Finally, Li and Stephens (2003) proposed a modification to Fearnhead and Donnelly''s model that limits the hidden state space, thereby providing a computational simplification; we denote the corresponding approximate CSD by .Although these approaches are computationally appealing, it is important to note that they are not derived from, though are certainly motivated by, principles underlying typical population genetics models, in particular the coalescent process (Griffiths 1981; Kingman 1982a,b; Hudson 1983). The main objective of this article is to develop a principled technique to derive an improved CSD directly from the underlying population genetics model. Rather than relying on intuition, we base our work on mathematical foundation. The theoretical framework we employ is the diffusion process. De Iorio and Griffiths (2004a,b) first introduced the diffusion-generator approximation technique to obtain an approximate CSD in the case of a single locus (i.e., no recombination). Griffiths et al. (2008) later extended the approach to two loci to include crossover recombination, assuming a parent-independent mutation model at each locus. In this article, we extend the framework to develop a general algorithm that applies to an arbitrary number of loci and an arbitrary finite-alleles recurrent mutation model.Our work can be summarized as follows. Using the diffusion-generator approximation technique, we derive a recursion relation satisfied by an approximate CSD. This recursion can be used to construct a closed system of coupled linear equations, in which the conditional sampling probability of interest appears as one of the unknown variables. The system of equations can be solved using standard numerical analysis techniques. However, the size of the system grows superexponentially with the number of loci and, consequently, so does the running time. To remedy this drawback, we introduce additional approximations to make our approach scalable in the number of loci. Specifically, the recursion admits an intuitive genealogical interpretation, and, on the basis of this interpretation, we propose modifications to the recursion, which then can be easily solved using dynamic programming. The computational complexity of the modified algorithm is polynomial in the number of loci, and, importantly, the resulting CSD has little loss of accuracy compared to that following from the full recursion.The accuracy of approximate CSDs has not been discussed much in the literature, except in the application-specific context for which they are being employed. In this article, we carry out an empirical study to explicitly test the accuracy of various CSDs and demonstrate that our new CSDs are in general substantially more accurate than previously proposed approximations. We also consider the PAC framework and show that our approximations also produce more accurate PAC-likelihood estimates. We note that for the maximum-likelihood estimation of recombination rates, the actual value of the likelihood may not be so important, as long as it is maximized near the true recombination rate. However, in many other applications—e.g., phasing genotype data into haplotype data, imputing missing data, importance sampling, and so on—the accuracy of the CSD and PAC-likelihood function over a wide range of parameter values may be important. Thus, we believe that the theoretical work presented here will have several practical implications; our method can be applied in a wide range of statistical tools that use CSDs, improving their accuracy.The remainder of this article is organized as follows. To provide intuition for the ensuing mathematics, we first describe a genealogical process that gives rise to our CSD. Using our genealogical interpretation, we consider two additional approximations and relate these to previously proposed CSDs. Then, in the following section, we derive our CSD using the diffusion-generator approach and provide mathematical statements for the additional approximations; some interesting limiting behavior is also described there. This section is self-contained and may be skipped by the reader uninterested in mathematical details. Finally, in the subsequent section, we carry out a simulation study to compare the accuracy of various approximate CSDs and demonstrate that ours are generally the most accurate.     

Ecosystem Scale Declines in Elk Recruitment and Population Growth with Wolf Colonization: A Before-After-Control-Impact Approach

The reintroduction of wolves (Canis lupus) to Yellowstone provided the unusual opportunity for a quasi-experimental test of the effects of wolf predation on their primary prey (elk – Cervus elaphus) in a system where top-down, bottom-up, and abiotic forces on prey population dynamics were closely and consistently monitored before and after reintroduction. Here, we examined data from 33 years for 12 elk population segments spread across southwestern Montana and northwestern Wyoming in a large scale before-after-control-impact analysis of the effects of wolves on elk recruitment and population dynamics. Recruitment, as measured by the midwinter juvenile∶female ratio, was a strong determinant of elk dynamics, and declined by 35% in elk herds colonized by wolves as annual population growth shifted from increasing to decreasing. Negative effects of population density and winter severity on recruitment, long recognized as important for elk dynamics, were detected in uncolonized elk herds and in wolf-colonized elk herds prior to wolf colonization, but not after wolf colonization. Growing season precipitation and harvest had no detectable effect on recruitment in either wolf treatment or colonization period, although harvest rates of juveniles∶females declined by 37% in wolf-colonized herds. Even if it is assumed that mortality due to predation is completely additive, liberal estimates of wolf predation rates on juvenile elk could explain no more than 52% of the total decline in juvenile∶female ratios in wolf-colonized herds, after accounting for the effects of other limiting factors. Collectively, these long-term, large-scale patterns align well with prior studies that have reported substantial decrease in elk numbers immediately after wolf recolonization, relatively weak additive effects of direct wolf predation on elk survival, and decreased reproduction and recruitment with exposure to predation risk from wolves.     

A Population Genetics-Based and Phylogenetic Approach to Understanding the Evolution of Virulence in the Genus Listeria

The genus Listeria includes (i) the opportunistic pathogens L. monocytogenes and L. ivanovii, (ii) the saprotrophs L. innocua, L. marthii, and L. welshimeri, and (iii) L. seeligeri, an apparent saprotroph that nevertheless typically contains the prfA virulence gene cluster. A novel 10-loci multilocus sequence typing scheme was developed and used to characterize 67 isolates representing six Listeria spp. (excluding L. grayi) in order to (i) provide an improved understanding of the phylogeny and evolution of the genus Listeria and (ii) use Listeria as a model to study the evolution of pathogenicity in opportunistic environmental pathogens. Phylogenetic analyses identified six well-supported Listeria species that group into two main subdivisions, with each subdivision containing strains with and without the prfA virulence gene cluster. Stochastic character mapping and phylogenetic analysis of hly, a gene in the prfA cluster, suggest that the common ancestor of the genus Listeria contained the prfA virulence gene cluster and that this cluster was lost at least five times during the evolution of Listeria, yielding multiple distinct saprotrophic clades. L. welshimeri, which appears to represent the most ancient clade that arose from an ancestor with a prfA cluster deletion, shows a considerably lower average sequence divergence than other Listeria species, suggesting a population bottleneck and a putatively different ecology than other saprotrophic Listeria species. Overall, our data suggest that, for some pathogens, loss of virulence genes may represent a selective advantage, possibly by facilitating adaptation to a specific ecological niche.Population genetics-based and phylogenetic studies have greatly contributed to the understanding of the evolutionary history and ecology of bacterial pathogens. In particular, multilocus sequence analyses (MLSA) and single-nucleotide polymorphism (SNP)-based population genetics research have revealed the microevolutionary patterns of species complexes like the Bacillus cereus complex (12) or the microevolution of well-known pathogens like Yersinia pestis (2), Salmonella enterica serovar Typhi (57), and Mycobacterium tuberculosis (18). One of the common findings of these studies is that obligate pathogens generally have a genetically clonal population structure as inferred by MLSA (1), while the population structure of free-living facultative pathogenic bacteria is characterized by relatively high genetic variability (12, 70). It has been hypothesized that these differences in population structure are related to the fact that some obligate pathogens represent epidemic clones (38), i.e., clonal lineages whose members have an epidemiological advantage compared to other lineages and are therefore able to quickly spread within the population. Because this dispersal of the members of an epidemic clone occurs rapidly, there is not enough time to accumulate mutations.In this paper we present a phylogenetic and population genetics study of the genus Listeria. This genus consists of six closely related pathogenic (L. monocytogenes and L. ivanovii) and nonpathogenic (L. innocua, L. welshimeri, L. seeligeri, and a newly described species, L. marthii) species as well as a distantly related species, L. grayi (22). Another new species, L. rocourtiae, has been recently reported (33), but isolates were not available for inclusion in the study reported here. Because of the distant phylogenetic relatedness of L. grayi to the other Listeria species, it has been suggested that this species should be put in a separate genus, Murraya (63); L. grayi was thus not included in our study reported here. L. monocytogenes and L. ivanovii are facultative pathogens of warm-blooded animals and are the causative agents of a severe infectious disease, listeriosis (67). While L. monocytogenes has a wide host range, including humans, the host range of L. ivanovii seems to be largely restricted to ruminants, in particular sheep (13), even though some human listeriosis cases caused by L. ivanovii have been reported (34).Key virulence genes in Listeria include (i) six genes (prfA, plcA, hly, mpl, actA, and plcB) clustered in a genomic element, designated the prfA virulence cluster or the Listeria pathogenicity island (LiPI), and (ii) members of the internalin family (61). Genes in the prfA cluster encode functions that that are necessary for inter- and intracellular motility and intracellular survival in the host cell. While some internalin genes encode proteins essential for host cell invasion (e.g., inlA and inlB) (3), inlC has recently been shown to encode a protein critical for cell-to-cell spread (52), and the functions of a number of other internalin proteins still remain to be elucidated (40). A number of internalin genes are also organized in clusters, including the inlAB operon, the inlGHE operon (which can also be present as an inlGC2DE or as an inlC2DE operon), which is found in L. monocytogenes and an L. ivanovii species-specific pathogenicity island encoding sphingomyelinase and numerous internalins (13). Importantly, the presence or absence of the prfA cluster and virulence characteristics can also be used to classify Listeria species and clades into three groups, including (i) species that do contain the prfA virulence cluster and are known pathogens, like L. monocytogenes and L. ivanovii, (ii) species that lack the prfA virulence cluster and are nonpathogenic (L. marthii and L. welshimeri), and (iii) species in which the presence of the prfA virulence cluster varies by strain. The last group contains L. seeligeri, which is nonpathogenic, although the majority of strains in the population contain the prfA virulence cluster (69), and L. innocua, which is also nonpathogenic, and although most strains lack the prfA virulence cluster, a small proportion of strains do carry this cluster (31, 68). The facts that the genus Listeria contains closely related nonpathogenic and pathogenic species and that strains with and without the prfA cluster within the same species make this genus an interesting model system for studies on the evolution of pathogenicity in opportunistic environmental pathogens. In addition, an improved understanding of the phylogeny and evolution of pathogenic and nonpathogenic Listeria spp. will also help in the development of appropriate assays for the specific detection and identification of human and animal pathogenic Listeria strains as well as regulations and intervention strategies that specifically target pathogenic species and strains.     

Decoding with the A:I wobble pair is inefficient.

tRNAs with inosine (I) in the first position read three codons ending in U, C and A. However, A-ending codons read with I are rarely used. In Escherichia coli, CGA/U/C are all read solely by tRNAICGArg. CGU and CGC are very common codons, but CGA is very rare. Three independent in vivo assays show that translation of CGA is relatively inefficient. In the first, nine tandem CGA cause a strong rho-mediated polar effect on expression of a lacZ reporter gene. The inhibition is made more extreme by a mutation in ribosomal protein S12 (rpsL), which indicates that ribosomal binding by tRNAICGArg is slow and/or unstable in the CGA cluster. The second assay, in which codons are substituted for the regulatory UGA of the RF2 frameshift, confirms that aa-tRNA selection is slow and/or unstable at CGA. In the third assay, CGA is found to be a poor 5' context for amber suppression, which suggests that an A:I base pair in the P site can interfere with translation of a codon in the A site. Two possible errors, frameshifting and premature termination by RF2, are not significant causes for inefficiency at CGA. It is concluded that the A:I pair destabilizes codon:anticodon complexes during two successive ribosomal cycles, and it is suggested that these properties contribute to the rare usage of codons read with the A:I base pair.     

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing “in action and in context”. This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.     

A Subsequent Fit of Time Series and Amplitude Histogram of Patch-Clamp Records Reveals Rate Constants up to 1 per Microsecond

Fast gating in time series of patch-clamp current demands powerful tools to reveal the rate constants of the adequate Hidden Markov model. Here, two approaches are presented to improve the temporal resolution of the direct fit of the time series. First, the prediction algorithm is extended to include intermediate currents between the nominal levels as caused by the anti-aliasing filter. This approach can reveal rate constants that are about 4 times higher than the corner frequency of the anti-aliasing filter. However, this approach is restricted to time series with very low noise. Second, the direct fit of the time series is combined with a beta fit, i.e., a fit of the deviations of the amplitude histogram from the Gaussian distribution. Since the “theoretical” amplitude histograms for higher-order Bessel filters cannot be calculated by analytical tools, they are generated from simulated time series. In a first approach, a simultaneous fit of the time series and of the Beta fit is tested. This simultaneous fit, however, inherits the drawbacks of both approaches, not the benefits. More successful is a subsequent fit: The fit of the time series yields a set of rate constants. The subsequent Beta fit uses the slow rate constants of the fit of the time series as fixed parameters and the optimization algorithm is restricted to the fast ones. The efficiency of this approach is illustrated by means of time series obtained from simulation and from the dominant K⁺ channel in Chara. This shows that temporal resolution can reach the microsecond range.     

生态系统的人口承载力理论初探

人口膨涨是当今人类面临的五大问题之一,也是我国目前所面临的最棘手的一个问题。近年来,人口学的发展很快,但是目前人口学研究的主要问题是人口统计、人口预测、人口控制和人口经济等方面,其中对于人口控制的目标,即定量研究理想人口数量的工作则刚开始。从生态学角度讨论人口承载力问题的更少。生态系统人口承载力的研究不仅可以给出一个生态系统(如全球生态系统或一个流域或一个地区)在保证系统结构和功能不受破坏条件下所能供养的最大人口数,而且可以给出该生态系统的人口控制目标,使人口控制的目标选择具有科学的基础。     

A Population Accounting Approach to Assess Tourism Contributions to Conservation of IUCN-Redlisted Mammal Species

Over 1,000 mammal species are red-listed by IUCN, as Critically Endangered, Endangered or Vulnerable. Conservation of many threatened mammal species, even inside protected areas, depends on costly active day-to-day defence against poaching, bushmeat hunting, invasive species and habitat encroachment. Many parks agencies worldwide now rely heavily on tourism for routine operational funding: >50% in some cases. This puts rare mammals at a new risk, from downturns in tourism driven by external socioeconomic factors. Using the survival of individual animals as a metric or currency of successful conservation, we calculate here what proportions of remaining populations of IUCN-redlisted mammal species are currently supported by funds from tourism. This proportion is ≥5% for over half of the species where relevant data exist, ≥15% for one fifth, and up to 66% in a few cases. Many of these species, especially the most endangered, survive only in one single remaining subpopulation. These proportions are not correlated either with global population sizes or recognition as wildlife tourism icons. Most of the more heavily tourism-dependent species, however, are medium sized (>7.5 kg) or larger. Historically, biological concern over the growth of tourism in protected areas has centered on direct disturbance to wildlife. These results show that conservation of threatened mammal species has become reliant on revenue from tourism to a previously unsuspected degree. On the one hand, this provides new opportunities for conservation funding; but on the other, dependence on such an uncertain source of funding is a new, large and growing threat to red-listed species.