Extensive telomere repeat arrays in mouse are hypervariable

In this study we have analysed mouse telomeres by Pulsed Field Gel Electrophoresis (PFGE). A number of specific restriction fragments hybridising to a (TTA-GGG)4 probe in the size range 50-150kb can be detected. These fragments are devoid of sites for most restriction enzymes suggesting that they comprise simple repeats; we argue that most of these are likely to be (TTAGGG)n. Each discrete fragment corresponds to the telomere of an individual chromosome and segregates as a Mendelian character. However, new size variants are being generated in the germ line at very high rates such that inbred mice are heterozygous at all telomeres analysable. In addition we show that specific small (approximately 4-12kb) fragments can be cleaved within some terminal arrays by the restriction enzyme MnII which recognises 5'(N7)GAGG3'. Like the complete telomere-repeat arrays (TRA's) these fragments form new variants at high rates and possibly by the same process. We speculate on the mechanisms that may be involved

Complex genetic diseases:controversy over the Croesus code

The polarization of views on how best to exploit new information from the Human Genome Project for medicine reflects our ignorance of the genetic architecture underlying common diseases: are susceptibility alleles common or rare, neutral or deleterious, few or many? Single-nucleotide polymorphism (SNP) technology is almost in place to dissect such diseases and to create a personalized medicine, but success is critically dependent on the biology and "Nature to be commanded must be obeyed" (Francis Bacon, 1620, Novum Organum)

Delayed Decision-making in Real-time Beatbox Percussion Classification

This is an electronic version of an article published in Journal of New Music Research, 39(3), 203-213, 2010. doi:10.1080/09298215.2010.512979. Journal of New Music Research is available online at: www.tandfonline.com/openurl?genre=article&issn=1744-5027&volume=39&issue=3&spage=20

Geo-additive models of Childhood Undernutrition in three Sub-Saharan African Countries

We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighboring countries in Southern Africa using the 1992 Demographic and Health Surveys. We estimate models of undernutrition jointly for the three countries to explore regional patterns of undernutrition that transcend boundaries, while allowing for country-specific interactions. We use semiparametric models to flexibly model the effects of selected so-cioeconomic covariates and spatial effects. Our spatial analysis is based on a flexible geo-additive model using the district as the geographic unit of anal-ysis, which allows to separate smooth structured spatial effects from random effect. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques. While the socioeconomic determinants generally confirm what is known in the literature, we find distinct residual spatial patterns that are not explained by the socioeconomic determinants. In particular, there appears to be a belt run-ning from Southern Tanzania to Northeastern Zambia which exhibits much worse undernutrition, even after controlling for socioeconomic effects. These effects do transcend borders between the countries, but to a varying degree. These findings have important implications for targeting policy as well as the search for left-out variables that might account for these residual spatial patterns

Additive Nonparametric Reconstruction of Dynamical Systems from Time Series

We present a nonparametric way to retrieve a system of differential equations in embedding space from a single time series. These equations can be treated with dynamical systems theory and allow for long term predictions. We demonstrate the potential of our approach for a modified chaotic Chua oscillator.Comment: accepted for Phys. Rev. E, Rapid Com

Fast stable direct fitting and smoothness selection for Generalized Additive Models

Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing `whole model' criteria these problems disappear, but until now attempts to do this have employed finite difference based optimization schemes which are computationally inefficient, and can suffer from false convergence. This paper develops the first computationally efficient method for direct GAM smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes based on working-model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models

Euclidean Gibbs states of interacting quantum anharmonic oscillators

A rigorous description of the equilibrium thermodynamic properties of an infinite system of interacting $\nu$-dimensional quantum anharmonic oscillators is given. The oscillators are indexed by the elements of a countable set $\mathbb{L}\subset \mathbb{R}^d$, possibly irregular; the anharmonic potentials vary from site to site. The description is based on the representation of the Gibbs states in terms of path measures -- the so called Euclidean Gibbs measures. It is proven that: (a) the set of such measures $\mathcal{G}^{\rm t}$ is non-void and compact; (b) every $\mu \in \mathcal{G}^{\rm t}$ obeys an exponential integrability estimate, the same for the whole set $\mathcal{G}^{\rm t}$; (c) every $\mu \in \mathcal{G}^{\rm t}$ has a Lebowitz-Presutti type support; (d) $\mathcal{G}^{\rm t}$ is a singleton at high temperatures. In the case of attractive interaction and $\nu=1$ we prove that $|\mathcal{G}^{\rm t}|>1$ at low temperatures. The uniqueness of Gibbs measures due to quantum effects and at a nonzero external field are also proven in this case. Thereby, a qualitative theory of phase transitions and quantum effects, which interprets most important experimental data known for the corresponding physical objects, is developed. The mathematical result of the paper is a complete description of the set $\mathcal{G}^{\rm t}$, which refines and extends the results known for models of this type.Comment: 60 page

Improved functional prediction of proteins by learning kernel combinations in multilabel settings

Background We develop a probabilistic model for combining kernel matrices to predict the function of proteins. It extends previous approaches in that it can handle multiple labels which naturally appear in the context of protein function. Results Explicit modeling of multilabels significantly improves the capability of learning protein function from multiple kernels. The performance and the interpretability of the inference model are further improved by simultaneously predicting the subcellular localization of proteins and by combining pairwise classifiers to consistent class membership estimates. Conclusion For the purpose of functional prediction of proteins, multilabels provide valuable information that should be included adequately in the training process of classifiers. Learning of functional categories gains from co-prediction of subcellular localization. Pairwise separation rules allow very detailed insights into the relevance of different measurements like sequence, structure, interaction data, or expression data. A preliminary version of the software can be downloaded from http://www.inf.ethz.ch/personal/vroth/KernelHMM/.ISSN:1471-210

Chemical laboratories 4.0: A two-stage machine learning system for predicting the arrival of samples

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Automated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interesting predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.This work has been supported by FCT – Funda ̧c ̃ao para a Ciˆencia e Tecnologiawithin the R&D Units Project Scope: UIDB/00319/2020. The authors also wishto thank the chemical company staff involved with this project for providing thedata and also the valuable domain feedback