Markov Random Fields in Cancer Mutation Dependencies

Even though a large amount of evidence would suggest that PP2A serine/threonine protein phosphatase acts as a tumour suppressor the genomics data to support this claim is limited. We fit a sparse binary Markov random field with individual sample's total mutational frequency as an additional covariate to model the dependencies between the mutations occurring in the PP2A encoding genes. We utilize the data from recent large scale cancer genomics studies, where the whole genome from a human tumour biopsy has been analysed. Our results show a complex network of interactions between the occurrence of mutations in our twenty examined genes. According to our analysis the mutations occurring in the genes PPP2R1A, PPP2R3A, and PPP2R2B are identified as the key mutations. These genes form the core of the network of conditional dependency between the mutations in the investigated twenty genes. Additionally, we note that the mutations occurring in PPP2R4 seem to be more influential in samples with higher number of total mutations. The mutations occurring in the set of genes suggested by our results has been shown to contribute to the transformation of human cells. We conclude that our evidence further supports the claim that PP2A acts as a tumour suppressor and restoring PP2A activity is an appealing therapeutic strategy.

Random path sampling applied to vector models of magnetism

The recently reported Monte Carlo Random Path Sampling method (RPS) is here improved and its application is expanded to the study of the 2D and 3D Ising and discrete Heisenberg models. The methodology was implemented to allow use in both CPU-based high-performance computing infrastructures (C/MPI) and GPU-based (CUDA) parallel computation, with significant computational performance gains. Convergence is discussed, both in terms of free energy and magnetization dependence on field/temperature. From the calculated magnetization-energy joint density of states, fast calculations of field and temperature dependent thermodynamic properties are performed, including the effects of anisotropy on coercivity, and the magnetocaloric effect. The emergence of first-order magneto-volume transitions in the compressible Ising model is interpreted using the Landau theory of phase transitions. Using metallic Gadolinium as a real-world example, the possibility of using RPS as a tool for computational magnetic materials design is discussed. Experimental magnetic and structural properties of a Gadolinium single crystal are compared to RPS-based calculations using microscopic parameters obtained from Density Functional Theory.

Random Mating Between Two Widely Divergent Mitochondrial Lineages of Cryptolestes ferrugineus (Coleoptera: Laemophloeidae): A Test of Species Limits in a Phosphine-Resistant Stored Product Pest

Effective pest management relies on accurate delimitation of species and, beyond this, on accurate species identification. Mitochondrial COI sequences are useful for providing initial indications in delimiting species but, despite acknowledged limitations in the method, many studies involving COI sequences and species problems remain unresolved. Here we illustrate how such impasses can be resolved with microsatellite and nuclear sequence data, to assess more directly the amount of gene flow between divergent lineages. We use a population genetics approach to test for random mating between two 8 ± 2% divergent COI lineages of the rusty grain beetle, Cryptolestes ferrugineus (Stephens). This species has become strongly resistant to phosphine, a fumigant used worldwide for disinfesting grain. The possibility of cryptic species would have significant consequences for resistance management, especially if resistance was confined to one mitochondrial lineage. We find no evidence of restricted gene flow or nonrandom mating across the two COI lineages of these beetles, rather we hypothesize that historic population structure associated with early Pleistocene climate changes likely contributed to divergent lineages within this species.

A Random Walk down Main Street

US suburbs have often been characterized by their relatively low walk accessibility compared to more urban environments, and US urban environments have been characterized by low walk accessibility compared to cities in other countries. Lower overall density in the suburbs implies that activities, if spread out, would have a greater distance between them. But why should activities be spread out instead of developed contiguously? This brief research note builds a positive model for the emergence of contiguous development along “Main Street” to illustrate the trade-offs that result in the built environment we observe. It then suggests some policy interventions to place a “thumb on the scale” to choose which parcels will develop in which sequence to achieve socially preferred outcomes.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Being more certain about random assignment in social policy evaluations

Social experiments have been widely utilised in evaluations of social programmes in the US to identify ‘what works’, whilst in the UK their use is more controversial. This paper explores the paradigmatic, technical and practical issues evaluators confront in using randomised experiments to evaluate social policies. Possible remedies to some of these problems are outlined. It is argued that although no evaluation methodology is problem-free, policy makers and researchers should be more confident about the merits of using random assignment, provided it is used in conjunction with other methodologies more suited to understanding why and how interventions work.

Bayesian alignment of continuous molecular shapes using random fields

Statistical methodology is proposed for comparing molecular shapes. In order to account for the continuous nature of molecules, classical shape analysis methods are combined with techniques used for predicting random fields in spatial statistics. Applying a modification of Procrustes analysis, Bayesian inference is carried out using Markov chain Monte Carlo methods for the pairwise alignment of the resulting molecular fields. Superimposing entire fields rather than the configuration matrices of nuclear positions thereby solves the problem that there is usually no clear one--to--one correspondence between the atoms of the two molecules under consideration. Using a similar concept, we also propose an adaptation of the generalised Procrustes analysis algorithm for the simultaneous alignment of multiple molecular fields. The methodology is applied to a dataset of 31 steroid molecules.

Random matrix theory eta aplikazioa kaos kuantikoan

[eus] Gradu amaierako lan honetan ausazko matrizeen teoriari, RMT-ri, buruzko sarrera orokor bat egiten da ondoren aplikazio fisiko bat emateko. Teoriaren aplikazioa egiteko Kaos kuantikoa deritzon fisikaren arloa erabiliko da. Lehenik eta behin, RMT-ren kontzeptu batzuk azalduko dira helburutzat lehen auzokideen distantziaren distribuzioaren espresio lortzea izanik. Izan ere, distribuzio honek erakutsiko baititu Kaosak kuantikoki uzten dituen aztarnak. Bigarren kapituluan, aplikazio fisikoa azalduko da. Lehenengo Kaosean RMT nola aplikatzen den ikusiko da, ondoren adibide batzuen bidez argituz, eremu magnetiko batean dagoen hidrogeno atomoa eta billar kuantikoak izenarekin ezagutzen diren sistemak, batik bat.

Shifting Processes with Cyclically Exchangeable Increments at Random

International audience

Analysis of a stochastic SIR epidemic on a random network incorporating household structure

This paper is concerned with a stochastic SIR (susceptible-infective-removed) model for the spread of an epidemic amongst a population of individuals, with a random network of social contacts, that is also partitioned into households. The behaviour of the model as the population size tends to infinity in an appropriate fashion is investigated. A threshold parameter which determines whether or not an epidemic with few initial infectives can become established and lead to a major outbreak is obtained, as are the probability that a major outbreak occurs and the expected proportion of the population that are ultimately infected by such an outbreak, together with methods for calculating these quantities. Monte Carlo simulations demonstrate that these asymptotic quantities accurately reflect the behaviour of finite populations, even for only moderately sized finite populations. The model is compared and contrasted with related models previously studied in the literature. The effects of the amount of clustering present in the overall population structure and the infectious period distribution on the outcomes of the model are also explored.

Darboux Transformations and Random Point Processes

International audience