Role of Small Oligomers on the Amyloidogenic Aggregation Free-Energy Landscape

We combine atomic-force-microscopy particle-size-distribution measurements with earlier measurements on 1-anilino-8-naphthalene sulfonate, thioflavin T, and dynamic light scattering to develop a quantitative kinetic model for the aggregation of β-lactoglobulin into amyloid. We directly compare our simulations to the population distributions provided by dynamic light scattering and atomic force microscopy. We combine species in the simulation according to structural type for comparison with fluorescence fingerprint results. The kinetic model of amyloidogenesis leads to an aggregation free-energy landscape. We define the roles of and propose a classification scheme for different oligomeric species based on their location in the aggregation free-energy landscape. We relate the different types of oligomers to the amyloid cascade hypothesis and the toxic oligomer hypothesis for amyloid-related diseases. We discuss existing kinetic mechanisms in terms of the different types of oligomers. We provide a possible resolution to the toxic oligomer-amyloid coincidence.     

A model for the origin and properties of flicker-induced geometric phosphenes

We present a model for flicker phosphenes, the spontaneous appearance of geometric patterns in the visual field when a subject is exposed to diffuse flickering light. We suggest that the phenomenon results from interaction of cortical lateral inhibition with resonant periodic stimuli. We find that the best temporal frequency for eliciting phosphenes is a multiple of intrinsic (damped) oscillatory rhythms in the cortex. We show how both the quantitative and qualitative aspects of the patterns change with frequency of stimulation and provide an explanation for these differences. We use Floquet theory combined with the theory of pattern formation to derive the parameter regimes where the phosphenes occur. We use symmetric bifurcation theory to show why low frequency flicker should produce hexagonal patterns while high frequency produces pinwheels, targets, and spirals.     

A Bayesian Method for Deriving Species-Sensitivity Distributions: Selecting the Best-Fit Tolerance Distributions of Taxonomic Groups

We present a Bayesian method for deriving species-sensitivity distributions (SSDs). We employed four Bayesian statistical models to consider differences in tolerance to toxic substances among different taxonomic groups. We first used a Malkov chain Monte Carlo simulation based on these models to estimate the SSD parameters. We then computed deviance information criterion values of the models and compared them in order to select the model with the best predictive ability. We applied this approach to seven substances (zinc, lead, hexavalent chromium, cadmium, nickel, short-chain chloride paraffin, and chloroform) as case examples, and then compared the derived SSDs from the selected models and a model that assumed no tolerance differences among taxonomic groups. We discuss the advantages and limitations of our approach on the basis of our results.     

The canid genome: behavioral geneticists' best friend?

We review a range of studies on the genetic contribution to behavior in canid species. We begin by identifying factors that make canids a promising model in behavioral genetics and proceed to review research over the last decade that has used canids to identify genetic contributions to behavior. We first review studies that have selectively bred dogs to identify genetic contributions to behavior and then review studies that estimate heritability from populations of non‐laboratory bred dogs. We subsequently review studies that used molecular genetics to identify gene–behavior associations and note associations that have been uncovered. We then note challenges in canid behavioral genetics research that require further consideration. We finish by suggesting alternative phenotyping methods and identify areas in which canids may have as yet unexploited advantages, such as in gene–environment interaction studies where genetic factors are found to moderate the effects of environmental variables.     

Statistical prediction and molecular dynamics simulation

We describe a statistical approach to the validation and improvement of molecular dynamics simulations of macromolecules. We emphasize the use of molecular dynamics simulations to calculate thermodynamic quantities that may be compared to experimental measurements, and the use of a common set of energetic parameters across multiple distinct molecules. We briefly review relevant results from the theory of stochastic processes and discuss the monitoring of convergence to equilibrium, the obtaining of confidence intervals for summary statistics corresponding to measured quantities, and an approach to validation and improvement of simulations based on out-of-sample prediction. We apply these methods to replica exchange molecular dynamics simulations of a set of eight helical peptides under the AMBER potential using implicit solvent. We evaluate the ability of these simulations to quantitatively reproduce experimental helicity measurements obtained by circular dichroism. In addition, we introduce notions of statistical predictive estimation for force-field parameter refinement. We perform a sensitivity analysis to identify key parameters of the potential, and introduce Bayesian updating of these parameters. We demonstrate the effect of parameter updating applied to the internal dielectric constant parameter on the out-of-sample prediction accuracy as measured by cross-validation.     

Four-state models and regulation of contraction of smooth muscle. I. Physical considerations, stability, and solutions.

Cyclic four-state models are frequently used in biology to represent a variety of molecular behaviors. A common experimental strategy to test such models is to follow the behavior of the real system after some of the rate constants are changed in a stepwise manner. We analyze the mathematical behavior of a simple example of such a model applicable to the regulation of contraction of smooth muscle, but our results apply in general to any linear, cyclic four-state model. We discuss detailed balance and requirements for linearity. We find that the only way to have sustained oscillations is for the rate constants of the model to be themselves oscillatory. We state conditions for decaying oscillations and find that in models that do not follow strictly first-order kinetics and do not satisfy detailed balance, these conditions can hold. We show analytically that the response of any state to step changes in the rate constants is the sum of three weighted exponentials plus a constant term, the steady-state value. We provide explicit expressions for the time dependence of all state variables. We discuss a simple way to use these results to obtain numerical solutions in cases where the rate constants change in an arbitrary way.     

On the design of closed recapture experiments

We propose a method to plan the number of occasions of recapture experiments for population size estimation. We do so by fixing the smallest number of capture occasions so that the expected length of the profile confidence interval is less than or equal to a fixed threshold. In some cases, we solve the optimization problem in closed form. For more complex models we use numerical optimization. We detail models assuming homogeneous, time‐varying, subject‐specific capture probabilities, behavioral response to capture, and combining behavioral response with subject‐specific effects. The principle we propose can be extended to plan any other model specification. We formally show the validity of the approach by proving distributional convergence. We illustrate with simulations and challenging examples in epidemiology and ecology. We report that in many cases adding as few as two sampling occasions may substantially reduce the length of confidence intervals.     

Online clustering algorithms

We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.     

The influence of sexually active non-reproductive groups on persistent sexually transmitted diseases

We describe several population models exposed to a mild life-long sexually transmitted disease, i.e. without significant increased mortality among infected individuals and providing no immunity/recovery. We then modify these models to include non-reproductive groups consisting of those isolated from sexual contact and those who are sexually active but infertile due to choice, medical or other reasons. We analyse the potential effect on the dynamics of the population. We are interested in how the isolated class may curb the growth of the infected group while keeping the healthy population at acceptable levels. We also analyse the difference between being sexually active and abstained within the non-reproductive class and its impact on the epidemic reproductive number and the nature of the bifurcation around the disease-free equilibrium. We provide a comparison with our models introduced in a previous article, which include only the isolated from sexual contact class.     

Partial‐Likelihood Analysis of Spatio‐Temporal Point‐Process Data

Summary We investigate the use of a partial likelihood for estimation of the parameters of interest in spatio‐temporal point‐process models. We identify an important distinction between spatially discrete and spatially continuous models. We focus our attention on the spatially continuous case, which has not previously been considered. We use an inhomogeneous Poisson process and an infectious disease process, for which maximum‐likelihood estimation is tractable, to assess the relative efficiency of partial versus full likelihood, and to illustrate the relative ease of implementation of the former. We apply the partial‐likelihood method to a study of the nesting pattern of common terns in the Ebro Delta Natural Park, Spain.     

A Hierarchical Bayesian Design for Phase I Trials of Novel Combinations of Cancer Therapeutic Agents

Summary We propose a hierarchical model for the probability of dose‐limiting toxicity (DLT) for combinations of doses of two therapeutic agents. We apply this model to an adaptive Bayesian trial algorithm whose goal is to identify combinations with DLT rates close to a prespecified target rate. We describe methods for generating prior distributions for the parameters in our model from a basic set of information elicited from clinical investigators. We survey the performance of our algorithm in a series of simulations of a hypothetical trial that examines combinations of four doses of two agents. We also compare the performance of our approach to two existing methods and assess the sensitivity of our approach to the chosen prior distribution.     

Alzheimer's disease: Old problem,new views from transgenic and viral models

Alzheimer's dementia is developing ever more as a complex syndrome with various unknown genetic and epigenetic contributions. These are compounded on and exacerbating the underlying amyloid and tau pathology that remain the basis of the pathological definition of Alzheimer's disease. Here, we present a selection of aspects of recent bigenic and virus-based mouse strains, developed as pre-clinical models for Alzheimer's disease. We discuss newer features in the context of the characteristics defined in previously validated transgenic models. We focus on specific aspects of single and multiple transgenic mouse models for Alzheimer's disease and for tauopathies, rather than providing an exhaustive list of all available models. We concentrate on the content of information related to neurodegeneration and disease mechanisms. We pay attention to aspects and defects that are predicted by the models and can be tested in humans. We discuss implications that help translate the fundamental knowledge into clinical, diagnostic and therapeutic applications. We elaborate on the increasing knowledge extracted from transgenic models and from newer adeno-associated viral models. We advocate this combination as a valuable strategy to study molecular, cellular and system-related pathogenic mechanisms in AD and tauopathies. We believe that innovative animal models remain needed to critically test current views, to identify and validate therapeutic targets, to allow testing of compounds, to help understand and eventually treat tauopathies, including Alzheimer's disease.     

Gene mapping via the ancestral recombination graph

We present a multilocus gene mapping method based on linkage disequilibrium, which uses the ancestral recombination graph to model the history of sequences that may harbor an influential variant. We describe the construction of a recurrence equation used to make inferences about the location of a trait-influencing mutation. We demonstrate how a Monte Carlo algorithm combined with a local importance sampling scheme can be used for mapping. We explain how to simulate the timing of events in the coalescent in the presence of recombination and mutation, which accomodates variable population size. We provide an example to illustrate the use of the method, which can be easily extended to more general situations. Although the method is computationally intensive and variation in the likelihood profiles can occur, the method offers a great deal of promise.     

The community context of species' borders: ecological and evolutionary perspectives

Species distributional limits may coincide with hard dispersal barriers or physiological thresholds along environmental gradients, but they may also be influenced by species interactions. We explore a number of models of interspecific interactions that lead to (sometimes abrupt) distribution limits in the presence and absence of environmental gradients. We find that gradients in competitive ability can lead to spatial segregation of competitors into distinct ranges, but that spatial movement tends to broaden the region of sympatry between the two species, and that Allee effects tend to sharpen these boundaries. We generalize these simple models to include metapopulation dynamics and other types of interactions including predator–prey and host–parasite interactions. We derive conditions for range limits in each case. We also consider models that include coevolution and gene flow and find that character displacement along environmental gradients can lead to stable parapatric distributions. We conclude that it is essential to consider coevolved species interactions as a potential mechanism limiting species distributions, particularly when barriers to dispersal are weak and environmental gradients are gradual.     

The influence of sexually active non-reproductive groups on persistent sexually transmitted diseases

We describe several population models exposed to a mild life-long sexually transmitted disease, i.e. without significant increased mortality among infected individuals and providing no immunity/recovery. We then modify these models to include non-reproductive groups consisting of those isolated from sexual contact and those who are sexually active but infertile due to choice, medical or other reasons. We analyse the potential effect on the dynamics of the population. We are interested in how the isolated class may curb the growth of the infected group while keeping the healthy population at acceptable levels. We also analyse the difference between being sexually active and abstained within the non-reproductive class and its impact on the epidemic reproductive number and the nature of the bifurcation around the disease-free equilibrium. We provide a comparison with our models introduced in a previous article, which include only the isolated from sexual contact class.     

The Social Burden of Resilience: A Historical Perspective

We examine the social burden associated with resilience to environmental shocks in pre-modern societies. We argue that analyses of state-level interventions to mitigate the consequences of catastrophic events tend to isolate these measures from their larger social contexts and thereby overlook the uneven distribution of their burden across different groups. We use three cases of pre-modern societies in the northeastern Mediterranean - the sixth century Roman Empire, the tenth century Byzantine Empire, and the sixteenth century Ottoman Empire. We demonstrate how the adaptive processes that reinforced resilience at the state level incurred different burdens for those at lower levels of the social hierarchy. We found that some groups sustained losses while others gained unexpected benefits in the context of temporary systemic instability. We also found that although elites enjoyed enhanced buffers against the adverse effects in comparison with non-elites, this did not consistently guarantee them a better outcome. We conclude that the differentiated burden of resilience could in some cases entrench existing political or economic configurations, and in other cases, overturn them. Our case studies indirectly address the pressing issue of environmental justice.     

Functional and structural analyses of novel Smith-Kingsmore Syndrome-Associated MTOR variants reveal potential new mechanisms and predictors of pathogenicity

Smith-Kingsmore syndrome (SKS) is a rare neurodevelopmental disorder characterized by macrocephaly/megalencephaly, developmental delay, intellectual disability, hypotonia, and seizures. It is caused by dominant missense mutations in MTOR. The pathogenicity of novel variants in MTOR in patients with neurodevelopmental disorders can be difficult to determine and the mechanism by which variants cause disease remains poorly understood. We report 7 patients with SKS with 4 novel MTOR variants and describe their phenotypes. We perform in vitro functional analyses to confirm MTOR activation and interrogate disease mechanisms. We complete structural analyses to understand the 3D properties of pathogenic variants. We examine the accuracy of relative accessible surface area, a quantitative measure of amino acid side-chain accessibility, as a predictor of MTOR variant pathogenicity.We describe novel clinical features of patients with SKS. We confirm MTOR Complex 1 activation and identify MTOR Complex 2 activation as a new potential mechanism of disease in SKS. We find that pathogenic MTOR variants disproportionately cluster in hotspots in the core of the protein, where they disrupt alpha helix packing due to the insertion of bulky amino acid side chains. We find that relative accessible surface area is significantly lower for SKS-associated variants compared to benign variants. We expand the phenotype of SKS and demonstrate that additional pathways of activation may contribute to disease. Incorporating 3D properties of MTOR variants may help in pathogenicity classification. We hope these findings may contribute to improving the precision of care and therapeutic development for individuals with SKS.     

Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

We study the orientation and speed tuning properties of spatiotemporal three-dimensional (3D) Gabor and motion energy filters as models of time-dependent receptive fields of simple and complex cells in the primary visual cortex (V1). We augment the motion energy operator with surround suppression to model the inhibitory effect of stimuli outside the classical receptive field. We show that spatiotemporal integration and surround suppression lead to substantial noise reduction. We propose an effective and straightforward motion detection computation that uses the population code of a set of motion energy filters tuned to different velocities. We also show that surround inhibition leads to suppression of texture and thus improves the visibility of object contours and facilitates figure/ground segregation and the detection and recognition of objects.     

Description of base motion and role of excitations in the melting of poly(dG).poly(dC).

We study the contribution of various vibrational modes to the melting of poly(dG).poly(dC). We find that the principal contribution comes from the H-bond breathing modes that have been observed in Raman scattering and that we have associated with helix melting. We show the softening of these modes on approach to melting in agreement with the observed behavior. We also describe the contribution to melting from base rotation modes that others have suggested are important in melting.     

Mathematical models of bacterial growth,inhibition and death under combined stress conditions

Summary In this report we review the history of growth theories. We show how classical growth models may be derived as special cases of a generic growth rate equation. We show how growth models may be modified to represent survival data. We use linear combinations of growth and survival models to represent complex growth/survival curves and give practical examples utilizing nonlinear regression analysis. We show that traditional methods of estimating D values are inappropriate for complex, multiphasic growth/survival data. We show how such data may be modeled mathematically and illustrate methods for estimating true D values from such data.