Turing degrees of limit sets of cellular automata

Cellular automata are discrete dynamical systems and a model of computation. The limit set of a cellular automaton consists of the configurations having an infinite sequence of preimages. It is well known that these always contain a computable point and that any non-trivial property on them is undecidable. We go one step further in this article by giving a full characterization of the sets of Turing degrees of cellular automata: they are the same as the sets of Turing degrees of effectively closed sets containing a computable point

Pattern Recognition in Bioinformatics - 8th IAPR International Conference, PRIB 2013

International audienceIn the post-genomic era, a holistic understanding of biological systems and pro- cesses, in all their complexity, is critical in comprehending nature’s choreogra- phy of life. As a result, bioinformatics involving its two main disciplines, namely, the life sciences and the computational sciences, is fast becoming a very promis- ing multidisciplinary research field. With the ever-increasing application of large- scale high-throughput technologies, such as gene or protein microarrays and mass spectrometry methods, the enormous body of information is growing rapidly. Bioinformaticians are posed with a large number of difficult problems to solve, arising not only due to the complexities in acquiring the molecular information but also due to the size and nature of the generated data sets and/or the limi- tations of the algorithms required for analyzing these data. The recent advance- ments in computational and information-theoretic techniques are enabling us to conduct various in silico testing and screening of many lab-based experiments be- fore these are actually performed in vitro or in vivo. These in silico investigations are providing new insights for interpreting and establishing new direction for a deeper understanding. Among the various advanced computational methods cur- rently being applied to such studies, the pattern recognition techniques are mostly found to be at the core of the whole discovery process for apprehending the under- lying biological knowledge. Thus, we can safely surmise that the ongoing bioin- formatics revolution may, in future, inevitably play a major role in many aspects of medical practice and/or the discipline of life sciences.The aim of this conference on Pattern Recognition in Bioinformatics (PRIB) is to provide an opportunity to academics, researchers, scientists, and industry professionals to present their latest research in pattern recognition and compu- tational intelligence-based techniques applied to problems in bioinformatics and computational biology. It also provides them with an excellent forum to interact with each other and share experiences. The conference is organized jointly by the Nice Sophia Antipolis University, France, and IAPR (International Association for Pattern Recognition) Bioinformatics Technical Committee (TC-20).This volume presents the proceedings of the 8th IAPR International Confer- ence on Pattern Recognition in Bioinformatics (PRIB 2013), held in Nice, June 17–19, 2013. It includes 25 technical contributions that were selected by the In- ternational Program Committee from 43 submissions. Each of these rigorously reviewed papers was presented orally at PRIB 2013. The proceedings consists of five parts:Part I Bio-Molecular Networks and Pathway Analysis Part II Learning, Classification, and ClusteringPart III Data Mining and Knowledge DiscoveryPart IV Protein: Structure, Function, and Interaction Part V Motifs, Sites, and Sequences AnalysisPart I of the proceedings contains six chapters on “Bio-Molecular Networks and Pathway Analysis.” Rahman et al. propose a fast agglomerative cluster- ing method for protein complex discovery. A new criterion is introduced that combines an edge clustering coefficient and an edge clustering value, allowing us to decide when a node can be added to the current cluster. Maduranga et al. use the well-known random forest method to predict GRNs. The problem of in- ferring GRNs from (limited) time-series data is recast as a number of regression problems, and the random forest approach is used here to fit a model to this. Winterbach et al. evaluate how well topological signatures in protein interaction networks predict protein function. They compare several complex signatures and their own simple signature. They find that network topology is only a weak predictor of function and the simple signature performs on par with the more sophisticated ones. De Ridder et al. propose an approach for identifying putative cancer pathways. This approach relies on expression profiling tumors that are induced by retroviral insertional mutagenesis. This provides the opportunity to search for associations between tumor-initiating events (the viral insertion sites) and the consequent transcription changes, thus revealing putative regulatory in- teractions. An important advantage is that the selective pressure exerted by the tumor growth is exploited to yield a relatively small number of loci that are likely to be causal for tumor formation. Ochs et al. apply outlier statistics, gene set analysis, and top scoring pair methods to identify deregulated pathways in can- cer. Analysis of the results on pediatric acute myeloid leukemia data indicate the effectiveness of the proposed methodology. Pizzuti et al. present some variants of RNSC (restricted neighborhood search clustering) for prediction of protein com- plexes that are based on new score functions and evolutionary computation. It is shown via computational experiments that the proposed methods have better prediction accuracies (in F-measure) than the basic RNSC algorithm.Part II of the proceedings contains three chapters on “Learning, Classifica- tion, and Clustering.” Marchiori addresses a limitation of the RELIEF feature weighting algorithm that maximizes the sample margin over the entire training set, or the sum of the possibly competing feature weights. Her work proposes, instead, a conditional weighting algorithm (CCFW) and classifier (CCWNN) to improve feature weighting and classification. Mundra et al. propose a sample se- lection criterion using a modified logistic regression loss function and a backward elimination based gene ranking algorithm. On the basis of the classifier margin for sample points, points on or within the margin are more important than those outside, the sample selection criterion based on T-score is proposed. Li et al. describe a generalization of sparse matrix factorization (SMF) algorithms and showcase a few very concisely described applications in bioinformatics. The main merit of the work is the fact that a unified representation for SMF algorithms is proposed, as well as an optimization algorithm to solve this problem.Part III of the proceedings contains six chapters on “Data Mining and Knowl- edge Discovery.” Hsu et al. consider prediction of RNA secondary structure in the “triple helix” setting for which they argue existing methods are inade- quate. Their approach uses a Simple Tree Adjoining Grammar (STAG) coupledwith maximum likelihood estimation (MLE), implemented via an efficient dy- namic programming formulation. Higgs et al. present an algorithm for generating near-native protein models. It combines a fragment feature-based resampling algorithm with a local optimization method that performed best, for protein structure prediction (PSP), among a set of five optimization techniques. Com- putational experiments show that the use of local optimization is beneficial in terms of both RMSD and TM score. Spirov et al. discuss a method for trans- formation of variables, in order to normalize Drosophila oocyte images acquired via confocal microscopy. The paper describes an interesting problem, namely, the experimental determination of intrinsic Drosophila embryo coordinates, and proposes an approach using evolutionary computation by genetic algorithms. Rezaeian et al. propose a novel and flexible hierarchical framework to select dis- criminative genes and predict breast tumor subtypes simultaneously. Dai et al. tackle an important problem in drug-target interaction research and present an interesting application of machine learning methods to the analysis of drugs. Gritsenko et al. make an adaptation of their previously developed protocol for building and evaluating predictors, in order to introduce a framework that en- ables forward engineering in biology. An experimental test is performed in the biological field of codon optimization and the results obtained are comparable with those produced by the reference tool JCat.Part IV of the proceedings contains six chapters on “Protein: Structure, Func- tion, and Interaction.” Xiong et al. propose an active learning-based approach for protein function prediction. The novelty of the proposal is the use of a pre- processing phase that uses spectral clustering before selecting candidates for labeling with graph centrality metrics. Experimental results show that cluster- ing reveals a valid pre-processing step for the active learning method. Gehrmann et al. address the problem of integrating multiple sources of evidence to predict protein functions. The paper proposes to use a conditional random field (CRF) to represent protein functions as random variables to be predicted and different sources of evidence as conditioning variables. Inference and learning algorithms based on MCMC are described and the proposed method is applied to a yeast dataset. Dehzangi et al. describe a new approach to protein fold recognition, a problem that has been widely studied over the past decade. The main contribu- tion is the proposal of a new set of global protein features based on evolutionary consensus sequences and predicted secondary structure, and local features based on distributions and auto covariances of these features over segments. An RBF SVM using these features is applied to two benchmark datasets in an extensive comparison with a number of existing methods and is demonstrated to work well. Dehzangi et al. present a novel approach to using features extracted from the position specific scoring matrix (PSSM) to predict the structural class of a protein. The authors propose two new sets of features: a global one based on the consensus sequence of a PSSM and a local one that takes the auto-covariance in sequence segments into account. The features extracted are used to train an RBF SVM and are shown to lead to good results (better than other state-of-the-art algorithms) on two benchmarks. Chiu et al. discuss a new method for detecting associated sites in aligned sequence ensembles. The main idea is derived from the concept of granular computing, where information is extracted at different levels of granularity or resolution. The experimentation was focused on p53 and it has been demonstrated that the extracted association patterns are useful in discov- ering sites with some structural and functional properties of a protein molecule. Tung presents a new method for predicting the potential hepatocarcinogenicity of non-genotoxic chemicals. The proposed method based on chemical–protein interactions and interpretable decision tree is compared with other data-mining approaches and shows very good performances in both accuracy and simplicity of the found model.Part V of the proceedings contains four chapters on “Motifs, Sites, and Se- quences Analysis.” Pathak et al. present an algorithm that exploits structural information for reducing false positives in motifs prediction. They tested the validity of the algorithm using the minimotifs stored in the MnM database. Lacroix et al. present a workflow for the prediction of the effects of residue sub- stitution on protein stability. The workflow integrates eight algorithms that use delta-delta-G as a measure of stability. The workflow is designed to populate the online resource SPROUTS. A use case of the workflow is presented using the PDB entry 1enh. Malhotra et al. present an algorithm for inferring haplotypes of virus populations from k-mer counts obtained from next-generation sequencing (NGS) data. The algorithm takes as input read counts for a set of k-mers and produces as output a predicted number of haplotypes, their relative frequen- cies and, for reads covering SNPs, can assign reads to a haplotype. The novel feature of the algortihm is that it does not rely on having a reference genome. The authors report that it performs well on synthetic data compared with the existing algorithm ShoRAH, which relies on a reference genome. Comin et al. discuss and improve the Entropic Profile method introduced in the literature for detecting conservation in genome sequences. The authors propose a linear-time linear-space algorithm that captures the importance of given regions with re- spect to the whole genome, suitable for large genomes and for the discovery of motifs with unbounded length.Many have contributed directly or indirectly toward the organization and success of the PRIB 2013 conference. We would like to thank all the individ- uals and institutions, especially the authors for submitting the papers and the sponsors for generously providing financial support for the conference. We are very grateful to IAPR for the sponsorship. Our gratitude goes to the Nice Sophia Antipolis University, Nice, France, and IAPR (International Association for Pat- tern Recognition) Bioinformatics Technical Committee (TC-20) for supporting the conference in many ways.We would like to express our gratitude to all PRIB 2013 International Pro- gram Committee members for their objective and thorough reviews of the sub- mitted papers. We fully appreciate the PRIB 2013 Organizing Committee for their time, efforts, and excellent work. We would also like to thank the Nice Sophia Antipolis University for hosting the symposium and providing technical support. We sincerely thank the EDSTIC doctoral school for providing grants toa number of students attending the conference. We also thank “Region PACA” and the University of Salerno (Italy) for partially funding the invited speakers. Last, but not least, we wish to convey our sincere thanks to Springer forproviding excellent professional support in preparing this volume

Toxoplasma gondii infection in alpine red deer (Cervus elaphus): Its spread and effects on fertility

In contrast to the depth of knowledge on the pathological effects of parasitism in domestic animals, the impact of the vast majority of parasites on wildlife hosts is poorly understood and, besides, information from domestics is rarely usable to disclose the parasites' impact on free-ranging populations' dynamics. Here we use Toxoplasmosis as a study model since, until now, the infection process and the protozoan's effects in natural conditions has received little attention. We analysed 81 sera from red deer (Cervus elaphus) sampled in Italian Alps and through generalized linear models we evaluated (1) the epidemiological factors influencing T. gondii infection dynamics; (2) its impact on female fertility [corrected]. High seroprevalence of T. gondii infection was recorded in yearling (1 year-old; prevalence = 52.4%) and adult (>2 year-old; prevalence = 51.3%) red deer, while calves (<1 year-old) did not contract the infection suggesting horizontal transmission as the main route of infection. The stable prevalence between yearlings and adults and the higher serological titres of younger individuals lead to two alternative infection processes suggesting a difference between age classes or in acquiring the infection or in responding to the pathogen. No associations between T. gondii serological titres and pregnancy status was observed indicating no direct effect on the probability of being pregnant; nevertheless a relation between females' higher serological titres and lower foetal development emerged, suggesting potential effects of the parasite infection on deer reproduction. The results demonstrate high seroprevalence of T. gondii infection in free-ranging red deer and, furthermore, the effect on foetal development suggests the potential impact of the parasite on red deer fertility and thus on its population dynamics

Conservation Laws in Cellular Automata

If X is a discrete abelian group and B a finite set, then a cellular automaton (CA) is a continuous map F:B^X-->B^X that commutes with all X-shifts. If g is a real-valued function on B, then, for any b in B^X, we define G(b) to be the sum over all x in X of g(b_x) (if finite). We say g is `conserved' by F if G is constant under the action of F. We characterize such `conservation laws' in several ways, deriving both theoretical consequences and practical tests, and provide a method for constructing all one-dimensional CA exhibiting a given conservation law.Comment: 19 pages, LaTeX 2E with one (1) Encapsulated PostScript figure. To appear in Nonlinearity. (v2) minor changes/corrections; new references added to bibliograph

Change of direction asymmetry across different age categories in youth soccer

Background In youth, the development of change of direction (COD) and sprint performance is a key component for successfully competing in soccer across age. During a COD, the presence of directional asymmetries may be detrimental due to the unpredictable nature of the sport. Therefore, the aims of the study were to investigate asymmetries in COD ability and to examine the differences in COD and sprint performance across age in young soccer players. Methods Sixty-eight sub-elite soccer players of different age categories (U18, U17, U16, U15) were tested on a 10-m linear sprint test and 90°COD (5-m entry and exit) test in both directions. Asymmetric index (AI) of COD deficit was obtained for dominant (fastest) and non-dominant directions (slowest). Results The results showed that U16 were more asymmetrical than U18, U17, and U15 from large to moderate effects. The sprint time improved linearly across age with U18 and U15 displaying the fastest and slowest 10-m sprint performance (p 0.05). Conclusion Given the results of this study, practitioners are encouraged to assess asymmetries between dominant and non-dominant directions rather than solely players’ COD ability in young soccer players

Atmospheric aerosols at the Pierre Auger Observatory and environmental implications

The Pierre Auger Observatory detects the highest energy cosmic rays. Calorimetric measurements of extensive air showers induced by cosmic rays are performed with a fluorescence detector. Thus, one of the main challenges is the atmospheric monitoring, especially for aerosols in suspension in the atmosphere. Several methods are described which have been developed to measure the aerosol optical depth profile and aerosol phase function, using lasers and other light sources as recorded by the fluorescence detector. The origin of atmospheric aerosols traveling through the Auger site is also presented, highlighting the effect of surrounding areas to atmospheric properties. In the aim to extend the Pierre Auger Observatory to an atmospheric research platform, a discussion about a collaborative project is presented.Comment: Regular Article, 16 pages, 12 figure

Increased hormonal stress response of Apennine chamois induced by interspecific interactions and anthropogenic disturbance

Responses of animals to environmental changes and their interactions with other species play an important role in conservation. Sharing a common habitat may lead to interspecific competition for resources, but field assessment of these biological events is not always easily accomplished. By using a non-invasive method, we evaluated the physiological stress responses of Apennine chamois (Rupicapra pyrenaica ornata) to the presence of cattle, sheep and goat, red deer (Cervus elaphus), people (hikers), and predators to identify which factors may affect this endangered species. During September 2012, November 2012, and July 2013, a total of 318 faecal samples were collected in representative sites and analysed for faecal cortisol metabolites (FCM). FCM concentration was analysed through linear mixed-effect models. A significant increase in FCM values in Apennine chamois sharing their habitat with domestic animals was recorded during all study periods. On the contrary, stress responses to red deer and people were limited in time and emerged only during summer months, when hikers are more frequent and red deer extend their altitudinal range reaching chamois\u2019 habitat. The observed effects of domestic animals, red deer, and hikers should be considered in future Apennine chamois management plans, which should include the regulation of pastured domestic livestock, anthropogenic disturbances, and possible interferences with other wild species within parks

Study of a New Trigger on Multiplicity and Primary Interaction Vertex using the ALICE Silicon Pixel Detector

New trigger inputs for the ALICE Central Trigger Processor (CTP) are proposed. They are based on the use of Fast Multiplicity (FM) output signals generated by the ALICE Silicon Pixel Detector (SPD). These can be used for a multiplicity based centrality trigger and for a fast on-line computation of the primary vertex. A simple algorithm for primary vertex location at the trigger level is proposed. The precision that can be achieved with this method on centrality selection and primary vertex location, is discussed for interactions with different pseudo-rapidity density level. The feasibility of background rejection is also considered

Molecular identification of cryptic cysticercosis : Taenia ovis krabbei in wild intermediate and domestic definitive hosts

The complex life cycle of taeniids represents an ideal model of a multi-host system. The complexity of these parasites can therefore cover the epidemiological issues of the interface between wild and domestic animals, especially once spatial overlap between wild and domestic definitive and intermediate hosts occurs. Here we use the occurrence of Taenia ovis krabbei in two model areas as an example of this epidemiological complexity. In two contiguous areas in the Italian northern Apennines, two hunted roe deer (Capreolus capreolus) showed numerous cysticerci in the muscles of their whole body and an adult tapeworm was recorded in a semi-stray dog (Canis lupus familiaris). Through molecular typing of the mitochondrial cytochrome c oxidase I (cox1) gene, cysticerci and the adult tapeworm of T. krabbei were identified. Taenia krabbei cysticercosis was recorded for the first time in Italy. Although the role of dogs in the parasite's life cycle emerges, the overlap between wild and domestic definitive hosts and the increase of wild population densities raise concerns about the temporal (old or new) introduction and the spread of this parasite by one of these canid species (wolf (Canis lupus) or dog). Although T. krabbei is not a public health issue, economic concerns emerged for hunters and meat producers, related to the damage of carcasses by cysticerci. Therefore, there is a need to evaluate the spread of T. krabbei in the intermediate and definitive host populations, and to ensure the relevant sanitary education for hunters in order to avoid practices that could favour the spread and maintenance of its life cycle