Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.

Review of combinations of experimental and computational techniques to identify and understand genes involved in innate immunity and effector-triggered defence.

The innate immune system includes a first layer of defence that recognises conserved pathogen-associated molecular patterns that are essential for microbial fitness. Resistance (R) gene-based recognition of pathogen effectors, which function in modulation or avoidance of host immunity, activates a second layer of plant defence. In this review, experimental and computational techniques are considered to improve understanding of the plant immune system. Biocomputation contributes to discovery of the molecular genetic basis of host resistance against pathogens. Sequenced genomes have been used to identify R genes in plants. Resistance gene enrichment sequencing based on conserved protein domains has increased the number of R genes with nucleotide-binding site and leucine-rich repeat domains. Network analysis will contribute to an improved understanding of the innate immune system and identify novel genes for partial disease resistance. Machine learning algorithms are expected to become important in defining aspects of the immune system that are less well characterised, including identification of R genes that lack conserved protein domains.

Cerebellar output controls generalized spike-and-wave discharge occurrence.

OBJECTIVE: Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike-and-wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizures. METHODS: Two unrelated mouse models of generalized absence seizures were used: the natural mutant tottering, which is characterized by a missense mutation in Cacna1a, and inbred C3H/HeOuJ. While simultaneously recording single CN neuron activity and electrocorticogram in awake animals, we investigated to what extent pharmacologically increased or decreased CN neuron activity could modulate GSWD occurrence as well as short-lasting, on-demand CN stimulation could disrupt epileptic seizures. RESULTS: We found that a subset of CN neurons show phase-locked oscillatory firing during GSWDs and that manipulating this activity modulates GSWD occurrence. Inhibiting CN neuron action potential firing by local application of the γ-aminobutyric acid type A (GABA-A) agonist muscimol increased GSWD occurrence up to 37-fold, whereas increasing the frequency and regularity of CN neuron firing with the use of GABA-A antagonist gabazine decimated its occurrence. A single short-lasting (30-300 milliseconds) optogenetic stimulation of CN neuron activity abruptly stopped GSWDs, even when applied unilaterally. Using a closed-loop system, GSWDs were detected and stopped within 500 milliseconds. INTERPRETATION: CN neurons are potent modulators of pathological oscillations in thalamocortical network activity during absence seizures, and their potential therapeutic benefit for controlling other types of generalized epilepsies should be evaluated.

Dendritic morphology predicts pattern recognition performance in multi-compartmental model neurons with and without active conductances.

In this paper we examine how a neuron's dendritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that generates all possible dendritic trees with 22 terminal points, and one that creates representative samples of trees with 128 terminal points. Based on these trees, we construct multi-compartmental models. To assess the performance of the resulting neuronal models, we quantify their ability to discriminate learnt and novel input patterns. We find that the dendritic morphology does have a considerable effect on pattern recognition performance and that the neuronal performance is inversely correlated with the mean depth of the dendritic tree. The results also reveal that the asymmetry index of the dendritic tree does not correlate with the performance for the full range of tree morphologies. The performance of neurons with dendritic tapering is best predicted by the mean and variance of the electrotonic distance of their synapses to the soma. All relationships found for passive neuron models also hold, even in more accentuated form, for neurons with active membranes.

STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron.

Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can affect motor behaviour. We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. For high convergence ratios, the irregularity induced spike rate acceleration depends on short-term depression (STD) at the Purkinje cell synapses. At low convergence ratios, or for synchronised Purkinje cell input, the firing rate increase is independent of STD. The transformation of input irregularity into output spike rate occurs in response to artificial input spike trains as well as to spike trains recorded from Purkinje cells in tottering mice, which show highly irregular spiking patterns. Our results suggest that STD may contribute to the accelerated CN spike rate in tottering mice and they raise the possibility that the deficits in motor control in these mutants partly result as a pathological consequence of this natural form of plasticity.

Development of an illustrated index of tooth appearance-perception-based quantification of tooth discolouration and surface defects.

BACKGROUND: Existing indices to quantify tooth discolouration are mostly aetiology-specific. An index of tooth appearance (IOTA), derived from all types of tooth discolouration and surface defects, would allow the quantification of attractiveness for psychological assessment and treatment planning OBJECTIVE: To develop a perception based IOTA for quantification of all forms of tooth discolouration and surface defects. METHODS: One hundred images of discoloured teeth were twice ranked by a panel of judges according to perceived attractiveness. Mean image score was then used to arrange the images into a continuum of attractiveness and from these, ten images were selected, to constitute the illustrated IOTA. A second panel of judges assessed 35 clinical pictures using the IOTA, on two occasions. RESULTS: The first 100 images were assessed with a correlation of 0.79-0.81 between the two ranking sessions and with intra-group reproducibility of 0.8-0.94. The second panel of judges used the developed IOTA quickly, with an intra-judge correlation of 0.87 and inter-judge reliability of 0.72 and 0.74 for two sessions. CONCLUSIONS: The IOTA could be used by clinicians as a tool for quantifying disfigurement in teeth, irrespective of aetiology or histology.

The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability.

OBJECTIVES: The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (Texp ) and choice of diffusion cell on model quality and performance. METHODS: Data were collated from the literature. Static and flow-through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of Texp was assessed by comparing a range of datasets where Texp either remained constant or was varied from 22 to 45 °C. KEY FINDINGS: Using data from flow-through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow-through cell experiments reduces overall model quality. Consideration of Texp improves model quality when the dataset used exhibits a wide range of experimental temperatures. CONCLUSIONS: This study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz-type, experiments should be used to construct the model and Texp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.

Development of a Gaussian Process - feature selection model to characterise (poly)dimethylsiloxane (Silastic® ) membrane permeation.

OBJECTIVES: The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation. METHODS: A total of 2942 descriptors were calculated for a data set of 77 chemicals. Data were processed to remove redundancy, single values, imbalanced and highly correlated data, yielding 1363 relevant descriptors. For four independent test sets, feature selection methods were applied and modelled via a variety of Machine Learning methods. KEY FINDINGS: Two sets of molecular descriptors which can provide improved predictions, compared to existing models, have been identified. Best permeation predictions were found with Gaussian Process methods. The molecular descriptors describe lipophilicity, partial charge and hydrogen bonding as key determinants of PDMS permeation. CONCLUSIONS: This study highlights important considerations in the development of relevant models and in the construction and use of the data sets used in such studies, particularly that highly correlated descriptors should be removed from data sets. Predictive models are improved by the methodology adopted in this study, notably the systematic evaluation of descriptors, rather than simply using any and all available descriptors, often based empirically on in vitro experiments. Such findings also have clear relevance to a number of other fields.

The application of Gaussian processes in the prediction of percutaneous absorption.

OBJECTIVES: The aim was to assess mathematically the nature of a skin permeability dataset and to determine the utility of Gaussian processes in developing a predictive model for skin permeability, comparing it with existing methods for deriving predictive models. METHODS: Principal component analysis was carried out in order to determine the nature of the dataset. MatLab software was used to assess the performance of Gaussian process, single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs) using a range of statistical measures. KEY FINDINGS: Principal component analysis showed that the dataset is inherently non-linear. The Gaussian process model yielded a predictive model that provides a significantly more accurate estimate of skin absorption than previous models, particularly QSPRs (which were consistently worse than Gaussian process or SLN models), and does so across a wider range of molecular properties. Gaussian process models appear particularly capable of providing excellent predictions where previous studies have shown QSPRs to fail, such as where penetrants have high log P and high molecular weight. CONCLUSIONS: A non-linear approach was more appropriate than QSPRs or SLNs for the analysis of the dataset employed herein, as the prediction and confidence values in the prediction given by the Gaussian process are better than with other methods examined. Gaussian process provides a novel way of analysing skin absorption data that is substantially more accurate, statistically robust and reflective of our empirical understanding of skin absorption than the QSPR methods so far applied to skin absorption.

Combining experts in order to identify binding sites in yeast and mouse genomic data.

The identification of cis-regulatory binding sites in DNA is a difficult problem in computational biology. To obtain a full understanding of the complex machinery embodied in genetic regulatory networks it is necessary to know both the identity of the regulatory transcription factors and the location of their binding sites in the genome. We show that using an SVM together with data sampling to classify the combination of the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms. The resulting classifier produces fewer false positive predictions and so reduces the expensive experimental procedure of verifying the predictions.

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression.

OBJECTIVES: The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. METHODS: Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality. KEY FINDINGS: The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure-permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. CONCLUSIONS: The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets.

Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise.

Many forms of synaptic plasticity require the local production of volatile or rapidly diffusing substances such as nitric oxide. The nonspecific plasticity these neuromodulators may induce at neighboring non-active synapses is thought to be detrimental for the specificity of memory storage. We show here that memory retrieval may benefit from this non-specific plasticity when the applied sparse binary input patterns are degraded by local noise. Simulations of a biophysically realistic model of a cerebellar Purkinje cell in a pattern recognition task show that, in the absence of noise, leakage of plasticity to adjacent synapses degrades the recognition of sparse static patterns. However, above a local noise level of 20%, the model with nonspecific plasticity outperforms the standard, specific model. The gain in performance is greatest when the spatial distribution of noise in the input matches the range of diffusion-induced plasticity. Hence non-specific plasticity may offer a benefit in noisy environments or when the pressure to generalize is strong.

Mayo, Myriad, America Invents Act and BPCIA: how has the United States biopharmaceutical market been affected?

This paper discusses how the United States biopharmaceutical market has been affected by recent changes in patent law resulting from United States legislations (Biologics Price Competition and Innovation Act and the Leahy-Smith America Invents Act) and Supreme Court precedents (Mayo Collaborative Services v. Prometheus Laboratories, Inc. and Molecular Pathology v. Myriad Genetics). The authors interviewed eight key opinion leaders from the United States knowledgeable in biopharmaceuticals, including industry veterans, patent counsel, senior scientists and jurists. This paper summarizes the opinions of the key opinion leaders. This paper explains the impact of these Supreme Court decisions - i.e., broadening the exceptions to patent eligibility for law of nature and natural phenomenon - on biopharmaceutical innovations and provides future perspectives.

Support vector regression to estimate the permeability enhancement of potential transdermal enhancers.

OBJECTIVES: Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations. METHODS: The aim of this study was to apply SVR methods with two different kernels in order to estimate the enhancement ratio of chemical enhancers of permeability. KEY FINDINGS: A statistically significant regression SVR model was developed. It was found that SVR with a nonlinear kernel provided the best estimate of the enhancement ratio for a chemical enhancer. CONCLUSIONS: Support vector regression is a viable method to develop predictive models of biological processes, demonstrating improvements over other methods. In addition, the results of this study suggest that a global approach to modelling a biological process may not necessarily be the best method and that a 'mixed-methods' approach may be best in optimising predictive models.

The role of global and feature based information in gender classification of faces: a comparison of human performance and computational models.

Most computational models for gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here, we use a global and feature based representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone. We also present results of human subjects performance on gender classification task and evaluate how the different dimensionality reduction techniques compare with human subjects performance. The results support the psychological plausibility of the global and feature based representation.

Interfacing of Science, Medicine and Law: The Stem Cell Patent Controversy in the United States and the European Union.

The patent eligibility of stem cells-particularly those derived from human embryos-has long been under debate in both the scientific and legal communities. On the basis of moral grounds, the European Patent Office (EPO) has refrained from granting patents for stem cells obtained through the destruction of human embryos. On the contrary, the United States Patent and Trademark Office (USPTO) has historically granted patents regarding the isolation and use of human embryonic and other stem cells. To date, these US patents remain valid despite an increasing onslaught of challenges in court. However, recent precedents established in US courts significantly narrow the scope of patent eligibility within biotechnology. This article compares the implications of recent legal changes on stem cell patent eligibility between the EU and US.

Clustering predicts memory performance in networks of spiking and non-spiking neurons.

The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong negative linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes.

OBJECTIVES: Predicting the rate of percutaneous absorption of a drug is an important issue with the increasing use of the skin as a means of moderating and controlling drug delivery. One key feature of this problem domain is that human skin permeability (as K(p)) has been shown to be inherently non-linear when mathematically related to the physicochemical parameters of penetrants. As such, the aims of this study were to apply and evaluate Gaussian process (GP) regression methods to datasets for membranes other than human skin, and to explore how the nature of the dataset may influence its analysis. METHODS: Permeability data for absorption across rodent and pig skin, and artificial membranes (polydimethylsiloxane, PDMS, i.e. Silastic) membranes was collected from the literature. Two quantitative structure-permeability relationship (QSPR) models were used to compare with the GP models. Further performance metrics were computed in terms of all predictions, and a range of covariance functions were examined: the squared exponential (SE), neural network (NNone) and rational quadratic (QR) covariance functions, along with two simple cases of Matern covariance function (Matern3 and Matern5) where the polynomial order is set to 1 and 2, respectively. As measures of performance, the correlation coefficient (CORR), negative log estimated predictive density (NLL, or negative log loss) and mean squared error (MSE) were employed. KEY FINDINGS: The results demonstrated that GP models with different covariance functions outperform QSPR models for human, pig and rodent datasets. For the artificial membranes, GPs perform better in one instance, and give similar results in other experiments (where different covariance parameters produce similar results). In some cases, the GP predictions for some of the artificial membrane dataset are poorly correlated, suggesting that the physicochemical parameters employed in this study might not be appropriate for developing models that represent this membrane. CONCLUSIONS: While the results of this study indicate that permeation across rodent (mouse and rat) and pig skin is, in a statistical sense, similar, and that the artificial membranes are poor replacements of human or animal skin, the overriding issue raised in this study is the nature of the dataset and how it can influence the results, and subsequent interpretation, of any model produced for particular membranes. The size of the datasets, in both absolute and comparative senses, appears to influence model quality. Ideally, to generate viable cross-comparisons the datasets for different mammalian membranes should, wherever possible, exhibit as much commonality as possible.

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

OBJECTIVES: The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. METHODS: Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. KEY FINDINGS: The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. CONCLUSIONS: The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.