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Domestication has led to substantial phenotypic and genetic variation in domestic animals. In pigs, the size of so called minipigs differs by one order of magnitude compared to breeds of large body size. We used biallelic SNPs identified from re-sequencing data to compare various publicly available wild and domestic populations against two minipig breeds to gain better understanding of the genetic background of the extensive body size variation. We combined two complementary measures, expected heterozygosity and the composite likelihood ratio test implemented in "SweepFinder", to identify signatures of selection in Minipigs. We intersected these sweep regions with a measure of differentiation, namely FSelective sweep analysis identified putative sweep regions for growth and subsequent gene annotation provided a comprehensive set of putative candidate genes. A long swept haplotype on chromosome X, descending from the Goettingen Minipig founders was associated with a reduction of adult body length by 3% in FThe resulting set of genes in putative sweep regions implies that the genetic background of body size variation in pigs is polygenic rather than mono- or oligogenic. Identified genes suggest alterations in metabolic functions and a possible insulin resistance to contribute to miniaturization. A size QTL located within the sweep on chromosome X, with an estimated effect of 3% on body length, is comparable to the largest known in pigs or other species. The androgen receptor AR, previously known to influence pig performance and carcass traits, is the most obvious potential candidate gene within this region.

Figure S1. Multi-Dimensional-Scaling of the distance matrix underlying the phylogenetic tree, based on chromosomes 1, 8 and 13. (DOCX 95 kb)

In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form ofswitching between regimes) and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e., on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) computer-generated series, obtained by randomly switching between two nonlinear processes, (2) a time series from the Santa Fe Time Series Competition (the light intensity of laser in chaotic state), and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several timescales. The main results are (1) the gating network correctly discovers the different regimes of the process, (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the subprocesses), and (3) there is less overfitting compared to single networks (homogeneous multilayer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.

Three genetically different clones of Toxoplasma gondii, also different in mouse virulence, were studied by experimental infection in chickens. For the experiments, four chicken lines were used, which differed in phylogenetic origin and performance level: two white egg layer lines, one with high laying performance (WLA), one with low (R11) and two brown layer lines, also displaying high (BLA) and low (L68) egg number. Chickens were intraperitoneally infected with three different T. gondii isolates representing type IIxIII recombinant clones, i.e. showing both, type II- and type III-specific alleles. These clones (K119/2 2C10, B136/1 B6H6, K119/2 A7) had exhibited virulence differences in a mouse model. In chickens, a significantly higher mortality was observed in white layer lines, but not in brown layer lines, suggesting that differences in the phylogenetic background may influence the susceptibility of chickens for toxoplasmosis. In addition, antibody (IgY) levels varied in surviving chickens at 31 days post infection. While low to intermediate antibody levels were observed in white layers, intermediate to high levels were measured in brown layers. Infection with a T. gondii clone showing low chicken virulence resulted in higher antibody levels in all chicken lines compared to infection with T. gondii clones of intermediate or high chicken virulence. This was in agreement with the parasite load as determined by real-time PCR. Overall, results show that progeny resulting from natural sexual recombination of T. gondii clonal lineages, may differ in their virulence for mice and chickens., Facultad de Ciencias Veterinarias

Ruskeissa kananmunissa esiintyvä kalanhaju on aiheuttanut mittavia ongelmia niin kananjalostukselle, tuottajille kuin kaupallekin. Ongelma koskettaa erityisesti eurooppalaisia kuluttajia, sillä EU-alueella yli 90% kulutetuista munista on ruskeita. Suomessa ongelma on toistaiseksi ollut marginaalinen, sillä kulutustottumukset ovat suosineet valkoisia munia, ruskeiden myyntiosuuden jäädessä alle kymmeneen prosenttiin. Kuluttajan näkökulmasta ongelma kärjistyy ruoanlaitossa: hajun on toisinaan havaittu voimistuvan käsiteltäessä munia kuten esimerkiksi keitettäessä tai paistettaessa. Kaupan laadunvalvonnan satunnaistesteistä löydetyt kalanhajuiset munat voivat johtaa koko sisäänostoerän hylkäämiseen. Pahimmassa tapauksessa tuottaja joutuu vaihtamaan koko kanalansa tuotantoeläimet muutaman haisevan munan takia. Hajun aiheuttava geeni tunnistettiin geenikartoituksen avulla. Taustalla on geenivirhe, joka johtaa hajuhaittaan, kuitenkin vain yhdessä tietynlaisen, yleensä rypsi- tai rapsipohjaisen ruokinnan kanssa. Valkoisilta kanoilta tämä geenivirhe puuttuu. Ruskeidenkaan kanojen munat eivät haise, elleivät geenivirheen perineet kanat ole saaneet rehua, joka sisältää hajun aiheuttajaa. Yleensä noin noin 5-10 prosenttia ruskeista kanoista voi tuottaa haisevia munia. Perinteisesti DNA-tutkimuksiin on vaadittu verinäytettä, jonka ottaminen voi pahimmillaan olla kanalle stressaava ja kivulias kokemus. Sen sijaan on mahdollista käyttää testauksessa höyheniä: jo yhdessä höyhentupessa on riittävä määrä DNA:ta, josta testi voidaan tehdä. Geenivirheen testaamiseksi onkin kehitetty höyhenestä tehtävä nopea ja yksinkertainen laboratoriomenetelmä. Menetelmän ansiosta voidaan ruskeita kanalinjoja valita risteytettäviksi niin, ettei jälkeläisissä ilmene hajuhaittaa aiheuttavaa geenivirhettä. Testausmenetelmälle on haettu kansainvälistä patenttia. Tällä menetelmällä jalostettujen kanalinjojen markkinointi aloitetaan vuoden lopussa ja ensimmäiset hajuttomat untuvikot ovat myynnissä elokuussa 2006. Nämä kanat aloittavat munintansa vuoden 2007 alussa. Testin ansiosta luomutuotannossa on mahdollista käyttää rehun valkuaislähteinä kotimaista rypsiä ja rapsia, mahdollisesti siirryttäessä 100 %:n luomuruokintaan.

The goal of the workshop on Implicit Measures was to explore how various implicit measures of user interest can be used in information retrieval and filtering applications. In most information retrieval or filtering applications, it is difficult to get explicit feedback from users about the relevance of the results, the appropriateness of the presentation, and more generally about the quality of their experience. Yet explicit judgments are assumed by researchers for many activities like the tuning and selection of ranking algorithms, information combination, user modeling, information presentation, etc. This workshop explored how implicit measures of user interest (such as dwell time, click through, and user activities like annotation, printing, and purchasing) can be used to develop predictive models for a variety of purposes. In the context of information retrieval such models could be used to: improve ranking and relevance assessment (e.g., the extent to which implicit measures can be used to evaluate the quality of systems, ranking algorithms and recommendations, or as input to relevance feedback algorithms); personalize search, filtering or presentation; personalize considering both individual and aggregate data; adaptive link generation or web site design; etc. An examination of theoretical issues such as modeling approaches (Bayesian techniques and other predictive models), gold standards for user behavior (e.g., relevance judgments, purchases), combining implicit and explicit preferences, and biases introduced by reliance on implicit measures were also encouraged. Since this was the first workshop on the topic at SIGIR, we encouraged participation from people with different backgrounds and perspectives including theoretical modeling, experimental analysis, and applications development. Applications from information retrieval, collaborative filtering, e-commerce, user modeling, and human-computer interaction were encouraged. Participation from both academia and industry was encouraged. As an outcome of the workshop we hoped to identify key theoretical modeling issues, systematize engineering principles and best practices, and spark new research directions. The workshop was held on August 1, 2003 in Toronto, Canada, immediately following the SIGIR 2003 conference. The workshop drew about 35 participants, representing a nice mix of academia and industry, and information retrieval and learning. To encourage interaction among participants, we limited the number and length of the talks to 15 minutes. This was followed by a 5 minute discussion by one of the organizers and additional open discussion among participants. This seemed to work well as a way to encourage interaction. We also had poster presentations available during the lunch break, which worked well, although only two posters were presented.

Summary Two Chinese domestic yak populations representing the Plateau type and the Huanhu Alpine type were analysed with 12 bovine microsatellite primers. All primer pairs functioned in the yak genome and polymorphism was found at all loci. The allele size ranges and frequencies of the two yak populations were similar and there was considerable overlap with the allele size ranges observed in cattle. Data for European cattle breeds was obtained from the Cattle Diversity Database (CaDBase) to interpret the heterozygosity and genetic distance estimates in yak populations. Heterozygosity estimated for the two yak populations was comparable with that of European cattle while Nei's Genetic Distance DA between the two yak populations was less than distances between the most closely related German cattle breeds. Bovine microsatellite primers proved to be a valuable tool for characterization of yak populations. Zusammenfassung Untersuchungen von zwei chinesischen Yak (Bos grunniens) Herden mittels Primersequenzen von Rindermikrosatelliten Primersequenzen von Rindermikrosatelliten wurden verwendet, um zwei chinesische Yak-Herden zu analysieren, eine vom Plateau-Typ und eine andere vom Huanhu-Bergtyp. Alle 12 Primerpaare fuhrten zu polymorphen Amplifikationsprodukten mit den DNA-Proben der Yaks. Der Allelgrosenbereich und die Allelfrequenzen aller 12 Marker waren in den beiden Yak-Herden sehr ahnlich. Zum Vergleich wurden Typisierungsergebnisse der 12 Mikrosatellitengenorte von 15 europaischen Rinderassen aus der Rinderdiversitatsdatenbank CaDBase entnommen. Ein groser Teil der Yak-Allele hatte die gleiche Grose wie beim Rind und auch der geschatzte Heterozygotiegrad der beiden Yak-Populationen war vergleichbar mit dem durchschnittlichen Heterozygotiegrad der 15 Rinderrassen. Die genetische Distanz (Nei's DA) zwischen beiden Yak-Herden wurde jedoch geringer geschatzt als zwischen zwei Paaren eng verwandter Rinderrassen. Die Ergebnisse zeigen, dass Sequenzinformationen von Rindermikrosatellitenprimern fur die Charakterisierung von Yak-Populationen auf der molekularen Ebene nutzlich sein konnen.

Contents: Part I:Neuroscience. T. Rebotier, J. Droulez, Sigma-Pi Properties of Spiking Neurons. H.S. Wan, D.S. Touretzky, A.D. Redish, Towards a Computational Theory of Rat Navigation. H.T. Blair, Evaluating Connectionist Models in Psychology and Neuroscience. Part II:Vision. J. Sirosh, R. Miikkulainen, Self-Organizing Feature Maps with Lateral Connections: Modeling Ocular Dominance. A.K. Bhattacharjya, B. Roysam, Joint Solution of Low, Intermediate, and High Level Vision Tasks by Global Optimization: Application to Computer Vision at Low SNR. T.B. Ghiselli-Crippa, P.W. Munro, Learning Global Spatial Structures from Local Associations. Part III:Cognitive Modeling. D. Ascher, A Connectionist Model of Auditory Morse Code Perception. V. Dragoi, J.E.R. Staddon, A Competitive Neural Network Model for the Process of Recurrent Choice. A.M. Lindemann, A Neural Network Simulation of Numerical Verbal-to-Arabic Transcoding. T. Lund, Combining Models of Single-Digit Arithmetic and Magnitude Comparison. I.E. Dror, Neural Network Models as Tools for Understanding High-Level Cognition: Developing Paradigms for Cognitive Interpretation of Neural Network Models. Part IV:Language. F.J. Eisenhart, Modeling Language as Sensorimotor Coordination. A. Govindjee, G. Dell, Structure and Content in Word Production: Why It's Hard to Say Dlorm. P. Gupta, Investigating Phonological Representations: A Modeling Agenda. H. Schutze, Y. Singer, Part-of-Speech Tagging Using a Variable Context Markov Model. M. Spivey-Knowlton, Quantitative Predictions from a Constraint-Based Theory of Syntactic Ambiguity Resolution. B.B. Tesar, Optimality Semantics. Part V:Symbolic Computation and Rules. K.G. Daugherty, M. Hare, What's in a Rule? The Past Tense by Some Other Name Might Be Called a Connectionist Net. A. Almor, M. Rindner, On the Proper Treatment of Symbolism -- A Lesson from Linguistics. L.F. Niklasson, Structure Sensitivity in Connectionist Models. M. Crucianu, Looking for Structured Representations in Recurrent Networks. I. Tchoumatchenko, Back Propagation with Understandable Results. M.W. Craven, J.W. Shavlik, Understanding Neural Networks via Rule Extraction and Pruning. A-H. Tan, Rule Learning and Extraction with Self-Organizing Neural Networks. Part VI:Recurrent Networks and Temporal Pattern Processing. J.F. Kolen, Recurrent Networks: State Machines or Iterated Function Systems? F. Cummins, R.F. Port, On the Treatment of Time in Recurrent Neural Networks. J.D. McAuley, Finding Metrical Structure in Time. C. Stevens, J. Wiles, Representations of Tonal Music: A Case Study in the Development of Temporal Relationships. M.A.S. Potts, D.S. Broomhead, J.P. Huke, Applications of Radial Basis Function Fitting to the Analysis of Dynamical Systems. M.E. Young, T.M. Bailey, Event Prediction: Faster Learning in a Layered Hebbian Network with Memory. Part VII:Control. S. Thrun, A. Schwartz, Issues in Using Function Approximation for Reinforcement Learning. P. Sabes, Approximating Q-Values with Basis Function Representations. K.L. Markey, Efficient Learning of Multiple Degree-of-Freedom Control Problems with Quasi-Independent Q-Agents. A.L. Tascillo, V.A. Skormin, Neural Adaptive Control of Systems with Drifting Parameters. Part VIII:Learning Algorithms and Architectures. R.C. O'Reilly, Temporally Local Unsupervised Learning: The MaxIn Algorithm for Maximizing Input Information. V.R. de Sa, Minimizing Disagreement for Self-Supervised Classification. S.N. Lindstaedt, Comparison of Two Unsupervised Neural Network Models for Redundancy Reduction. Z. Ghahramani, Solving Inverse Problems Using an EM Approach to Density Estimation. M. Finke, K-R. Muller, Estimating A-Posteriori Probabilities Using Stochastic Network Models. Part IX:Learning Theory. A.S. Weigend, On Overfitting and the Effective Number of Hidden Units. R. Dodier, Increase of Apparent Complexity Is Due to Decrease of Training Set Error. G.B. Orr, T.K. Leen, Momentum and Optimal Stochastic Search. R. Garces, Scheme to Improve the Generalization Error. M.P. Perrone, General Averaging Results for Convex Optimization. R.A. Caruana, Multitask Connectionist Learning. Z. Cataltepe, Y.S. Abu-Mostafa, Estimating Learning Performance Using Hints. Part X:Simulation Tools. A. Jagota, A Simulator for Asynchronous Hopfield Models. A. Linden, An Object-Oriented Dataflow Approach for Better Designs of Neural Net Architectures.

Chicken genetic resources comprise a wide range of breeds and populations including red jungle fowl (the assumed progenitor of all domestic breeds), native and fancy breeds, middle level food producers, industrial stocks and specialised lines. Based on the suggestion that the more distant a breed or population is the more likely it is to carry unique genetic features, the assessment of genetic distances by means of molecular marker information may provide useful information for initial evaluation of chicken genetic resources. During the last two decades several molecular marker classes have become available. Variable numbers of tandem repeat loci, in particular microsatellites, have been successfully used in chicken diversity studies. Genetic diversity measures using the highly polymorphic variable number of tandem repeat lociyield reliable and accurate information for the study of genetic relationships between chicken populations. First results of the European project on chicken biodiversity (AVIANDIV) obtained from microsatellite typing in DNA pools of 51 diverse chicken breeds showed that jungle fowl populations, traditional unselected breeds and broiler lines appear to be widely heterogeneous populations that may include a large portion of the genetic diversity of the tested breeds. In contrast, highly selected strains (layers and experimental lines) are characterised by a lower polymorphism. They behave as outliers from the set of breeds sampled. Single nucleotide polymorphism is a new and very promising molecular marker system which offers opportunities to assess the genetic diversity in farm animal species differently by investigating the mode and extent of changes in certain positions in the genome.

This paper presents ‘hidden Markov experts’, a framework for predicting conditional probability distributions of future values of a time series. On daily S&P500 data, the out-of- sample performance is compared to several baselines including GARCH and ‘gated experts’. The evaluation of the full density shows improvement over all competitors. Since the performance for point-predictions is comparable to the other methods, the main advantage of hidden Markov experts is their use for conditional density forecasting. Copyright © 2000 John Wiley & Sons, Ltd.

With the recent dramatic increase in electronic access to documents, text categorization—the task of assigning topics to a given document—has moved to the center of the information sciences and knowledge management. This article uses the structure that is present in the semantic space of topics in order to improve performance in text categorization: according to their meaning, topics can be grouped together into “meta-topics”, e.g., gold, silver, and copper are all metals. The proposed architecture matches the hierarchical structure of the topic space, as opposed to a flat model that ignores the structure. It accommodates both single and multiple topic assignments for each document. Its probabilistic interpretation allows its predictions to be combined in a principled way with information from other sources. The first level of the architecture predicts the probabilities of the meta-topic groups. This allows the individual models for each topic on the second level to focus on finer discriminations within the group. Evaluating the performance of a two-level implementation on the Reuters-22173 testbed of newswire articles shows the most significant improvement for rare classes.

This article exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits, and shows potential pitfalls of ignoring variability across splits. Using a bootstrap or resampling method, we compare the uncertainty in the solution stemming from the data splitting with neural network specific uncertainties (parameter initialization, choice of number of hidden units, etc.). We present two results on data from the New York Stock Exchange. First, the variation due to different resamplings is significantly larger than the variation due to different network conditions. This result implies that it is important to not over-interpret a model (or an ensemble of models) estimated on one specific split of the data. Second, on each split, the neural network solution with early stopping is very close to a linear model; no significant nonlinearities are extracted.

In the analysis and prediction of real world systems two of the key problems are nonstation arity (often in the form of switching between regimes) and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts consisting of a nonlinear gating network and several also nonlinear competing experts. Each expert learns to predict the conditional mean and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert given the input. This article focuses on the case where the gating network bases its decision on infor mation from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state s i e on the output of the gating network at the previous time step as well as to averaging over several predictors. In contrast, gated experts soft partition the input space. This article discusses the underlying statistical assumptions, derives the w...

This study aimed to analyse the genetic diversity and population structure of five Ethiopian chicken ecotypes (N = 155), which were compared with six commercial purebreds (N = 180). For the analysis of genetic diversity, 26 AVIANDIV microsatellite markers were used. The number of alleles in Ethiopian ecotypes ranged from 2 to 19 per locus, with a mean of 6.1. The average observed heterozygosity within ecotype varied between 0.53 and 0.57. The overall heterozygote deficiency (F(IT)) in Ethiopian ecotypes was 0.124 ± 0.037. Over 68% of F(IT) was because of within-ecotype deficiency (F(IS)). In the phylogenetic tree, Ethiopian ecotypes clustered into two groups. The analysis of the relationship between populations using the structure program provided further evidence for the occurrence of at least two subgroups in the Ethiopian ecotypes. Findings of this study may provide the background for future studies to identify the origin of the two gene pools representing the Ethiopian chicken ecotypes and to characterize the gene variants influencing economically important traits.

Many studies in human genetics compare informativeness of single-nucleotide polymorphisms (SNPs) and microsatellites (single sequence repeats; SSR) in genome scans, but it is difficult to transfer the results directly to livestock because of different population structures. The aim of this study was to determine the number of SNPs needed to obtain the same differentiation power as with a given standard set of microsatellites. Eight chicken breeds were genotyped for 29 SSRs and 9216 SNPs. After filtering, only 2931 SNPs remained. The differentiation power was evaluated using two methods: partitioning of the Euclidean distance matrix based on a principal component analysis (PCA) and a Bayesian model-based clustering approach. Generally, with PCA-based partitioning, 70 SNPs provide a comparable resolution to 29 SSRs. In model-based clustering, the similarity coefficient showed significantly higher values between repeated runs for SNPs compared to SSRs. For the membership coefficients, reflecting the proportion to which a fraction segment of the genome belongs to the ith cluster, the highest values were obtained for 29 SSRs and 100 SNPs respectively. With a low number of loci (29 SSRs or ≤100 SNPs), neither marker types could detect the admixture in the Gödöllö Nhx population. Using more than 250 SNPs allowed a more detailed insight into the genetic architecture. Thus, the admixed population could be detected. It is concluded that breed differentiation studies will substantially gain power even with moderate numbers of SNPs.

This paper describes the fundamental change in paradigm in time series prediction that has occurred in the last decade. The examples are drawn from the Santa Fe Time Series Prediction and Analysis Competition . In 1979, the results of an earlier competition were published by the Royal Society. In that competition, and also in the follow up five years later, relatively large numbers of time series were provided (111 and 1001, respectively), taken from business (forecasting sales), economics (predicting recovery from the recession), finance, and the social sciences. However, all of the series used were very short, generally less than 100 values long. Most of the algorithms entered were fully automated, and most of the discussion centred around linear models. In contrast, the Santa Fe Competition focused on only six data-sets, ranging from 1000 to 100 000 points. All of the successful entries were fundamentally nonlinear and, even though significantly more computer power was used to analyse the larger data-sets with more complex models, the application of the techniques required more careful manual control than in the past. There was a general failure of simplistic ‘black-box’ approaches. In all successful entries, exploratory data analysis preceded the application of the algorithm. The Santa Fe Competition showed examples of nonlinear results going far beyond what is possible within the canon of linear systems analysis, but also showed that there are unprecedented opportunities for the analysis to go astray.

Inclusive production of ϕ,K*0, and\(\overline {K*^0 } \) mesons has been measured in γp, π±p andK± p collisions at beam energies of 65 GeV

In this study, we assessed the genetic diversity of three Egyptian local chicken strains (Fayoumi, Dandarawi and Sinai) and six synthetic breeds derived from Fayoumi and Sinai by intercrossing with Barren Plymouth Rock, Rhode Island Red or White Cornish. Diversity measures were based on interrogation of 29 microsatellites. We identified three main clusters of chicken populations encompassing selected Fayoumi lines and Doki-4 (cluster-1), native Dandarawi (cluster-2) and Sinai, and all six synthetic breeds (cluster-3). Dandarawi and Fayoumi lines exhibited lower intra-population genetic diversity and allelic privacy than Sinai and synthetic breeds. The global inbreeding (F(IT) ) was 0.11, among-population differentiation (F(ST) ) was 0.07, and within-population differentiation (F(IS) ) was 0.04. The between-population marker-estimated kinship was lower than within-population estimates. The cluster analysis classified the Fayoumi lines, Dandarawi and Gimmizah as clearly separated populations. The other strains were configured in mosaic admixed groups.

The objectives of this study were to estimate conservation potential of Vietnamese local breeds and to investigate optimal allocation of conservation funds to maximize genetic diversity conserved between these breeds. Twenty-nine microsatellites were genotyped in 353 individuals from nine Vietnamese local chicken breeds and two chicken breeds of Chinese origin. The Vietnamese chicken breeds were sampled from the northern and southern parts of Vietnam while the two Chinese breeds have been kept as conservation flocks at the National Institute of Animal Sciences, Hanoi. The Weitzman approach was used to assess alternative strategies for conserving genetic diversity between breeds. Three different models, which reflect the range of possible functions in typical conservation situations, were applied. An average extinction probability of 48.5% was estimated for all Vietnamese chicken breeds. The highest conservation potential was found in the Te, Dong Tao and Ac chicken breeds, whereas the lowest corresponding values were observed in the Ri and Mia chicken breeds. The conservation funds were mainly allocated to the same three breeds (Te, Dong Tao and Ac) under all three models. This study suggests that conservation potential of the Vietnamese chicken breeds varies considerably. Population priorities for allocation of conservation funds in this study do not depend on the cost model used. The three breeds (Te, Dong Tao and Ac) with the highest conservation potential should be the prime candidates to be allocated conservation funds if the conservation budgets are limited.

This study aimed to assess genetic diversity within and between nine Vietnamese local chicken breeds and two Chinese breeds included for comparison. Genotyping 29 microsatellites revealed high diversity of both Vietnamese and Chinese breeds. Cluster analysis using the STRUCTURE software suggested six clusters as the most likely grouping of the 11 breeds studied. These groups encompassed four homogeneous clusters, one formed by the two Chinese breeds and the other three representing a single breed each: the Mekong Delta breed Ac, the South Central Coast breed Choi, and the Red River Delta breed Dong Tao. The six remaining breeds formed two additional admixed clusters.

The inclusive production of ρ0 mesons was measured in γp andh ± p collisions at beam energies of 65 GeV≦E γ≦175 GeV andE h =80, 140 GeV, respectively, whereh is π orK. Cross sections were determined for all beams and energies as functions ofx F (−0.1≦x F≦1.0),p T (0≦p T≦3.5 GeV/c) and the polar decay angle of the ρ0 by fitting the ρ0 signal in π+π- mass distributions. The ρ0 line shape is found to be distorted from a pure Breit-Wigner distribution throughout most of thex F−p T plane for both photon and hadron beams and a simple explanation is suggested. Throughout the paper emphasis is put on the comparison of photon and hadron beam data. The comparison of cross sections of γp andhp data provides a measure of the Vector Meson Dominance factor throughout thex F−p T range of the ρ0. The ρ0 production at lowp T can be described for both photon and hadron beams by a triple regge model at largex F. Similarly central production is well described by the quark-antiquark fusion model. At largep T there is an excess of ρ0 photoproduction which is consistent with the expected onset of pointlike photon interactions.

Measurements are reported of inclusive production of π0-mesons in the beam fragmentation region in γp, πp andKp collisions. Results include the ratio of π0 production inKp and πp collisions, showing reduced production from fragmentation of theK-meson, and the ratio of π0 production in photon and hadron collisions which shows agreement with modified Vector Meson Dominance at lowPT, and departures at higherPT signalling the onset of direct photon reactions. The pattern of departure from Feynman scaling at highPT points to a contribution of hard parton-parton collisions in both γp and πp collisions.

Photo- and hadroproduction data in the beam energy range 65–175 GeV have been studied with a view to isolating higher-twist processes in photoproduction from other point-like and hadron-like contributions. With selection of charged tracks havingp T >2 GeV/c and 0.28

The charged particle asymmetry, (n+−n−)/(n++n−), in inclusive photoproduction of charged particles on protons has been studied as a function ofxF andpT in the rangesxF>0 and 0

Energy-flow distributions for charged hadrons from interactions of photons, pions and kaons on hydrogen are presented as functions of ΣpT2 in the event plane. Data cover the range 0.0

We investigate the effectiveness of connectionist architectures for predicting the future behavior of nonlinear dynamical systems. We focus on real-world time series of limited record length. Two examples are analyzed: the benchmark sunspot series and chaotic data from a computational ecosystem. The problem of overfitting, particularly serious for short records of noisy data, is addressed both by using the statistical method of validation and by adding a complexity term to the cost function ("back-propagation with weight-elimination"). The dimension of the dynamics underlying the time series, its Liapunov coefficient, and its nonlinearity can be determined via the network. We also show why sigmoid units are superior in performance to radial basis functions for high-dimensional input spaces. Furthermore, since the ultimate goal is accuracy in the prediction, we find that sigmoid networks trained with the weight-elimination algorithm outperform traditional nonlinear statistical approaches.

Copyright holders have been investigating technological solutions to prevent distribution of copyrighted materials in peer-to-peer file sharing networks. A particularly popular technique consists in "poisoning" a specific item (movie, song, or software title) by injecting a massive number of decoys into the peer-to-peer network, to reduce the availability of the targeted item. In addition to poisoning, pollution, that is, the accidental injection of unusable copies of files in the network, also decreases content availability. In this paper, we attempt to provide a first step toward understanding the differences between pollution and poisoning, and their respective impact on content availability in peer-to-peer file sharing networks. To that effect, we conduct a measurement study of content availability in the four most popular peer-to-peer file sharing networks, in the absence of poisoning, and then simulate different poisoning strategies on the measured data to evaluate their potential impact. We exhibit a strong correlation between content availability and topological properties of the underlying peer-to-peer network, and show that the injection of a small number of decoys can seriously impact the users' perception of content availability.

Fishy odor of urine and other secretions is a characteristic of trimethylaminuria in humans, resulting from loss-of-function mutations in the flavin-containing mono-oxygenase isoform FMO3. A similar phenotype exists in cattle, in which a nonsense mutation in the bovine orthologue causes fishy off-flavor in cow's milk. The fishy odor is caused by an elevated level of excreted odorous trimethylamine (TMA), due to deficient oxidation of TMA. We report the mapping of a similar disorder (fishy taint of eggs) and the chicken FMO3 gene to chicken chromosome 8. The only nonsynonymous mutation identified in the chicken FMO3 gene (T329S) changes an evolutionarily highly conserved amino acid and is associated with elevated levels of TMA and fishy taint in the egg yolk in several chicken lines. No differences in the expression of FMO3 were found among individuals with different associated genotypes, indicating that the trait is not caused by a linked polymorphism causing altered expression of the gene. The results support the importance and function of the evolutionarily conserved motif FATGY, which has been speculated to be a substrate recognition pocket of N-hydroxylating siderophore enzymes and flavin-containing mono-oxygenases.

This volume selects the best contributions from the Fourth International Conference on Neural Networks in the Capital Markets (NNCM). The conference brought together academics from several disciplines with strategists and decision makers from the financial industries.The various chapters present and compare new techniques from many areas including data mining, information systems, machine learning, and statistical artificial intelligence. The volume focuses on evaluating their usefulness for problems in computational finance and financial engineering.Applications — risk management; asset allocation; dynamic trading and hedging; forecasting; trading cost control. Markets — equity; foreign exchange; bond; commodity; derivatives; Approaches — data mining; statistical AI; machine learning; Monte Carlo simulation; bootstrapping; genetic algorithms; nonparametric methods; fuzzy logic.The chapters emphasizes in-depth and comparative evaluation with established approaches.

Independent component analysis is a new signal processing technique. In this paper we apply it to a portfolio of Japanese stock price returns over three years of daily data and compare the results obtained using principal component analysis. The results indicate that the independent components fall into two categories, (i) infrequent but large shocks (responsible for the major changes in the stock prices), and (ii) frequent but rather small fluctuations (contributing little to the overall level of the stocks). The small number of major shocks indicate turning points in the time series and when used to reconstruct the stock prices, give good results in terms of morphology. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price does not show the same results at all. Independent component analysis is shown to be a potentially powerful method of analysing and understanding driving mechanisms in financial time series.

This note summarizes the remarks as discussant of Blake LeBaron’s paper An Evolutionary Bootstrap Strategy for Selecting Dynamic Trading Strategies at NNCM/CF97 in London. First, the main contributions of the paper are placed in a general modeling framework. Second, some ideas for further analysis are given. Third, possible relationships between minimal architectures and poor local minima are discussed.

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system.In this framework, empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility. We compare these results with ordinary autoregres-sive models and find that autoregressive models underestimate the relaxation times by about two orders of magnitude due to their ignoring the distinction between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities.

This paper explores the appliation of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensinal hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used. Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).

In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes), and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes; (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state); and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are: (1) the gating network correctly discovers the different regimes of the process; (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes); and (3) there is less overfitting compared to single networks (homogeneous multilayer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.

Most traditional prediction techniques deliver a single point, usually the mean of a probability distribution. For multimodal processes, instead of predicting the mean, it is important to predict the full distribution. This article presents a new connectionist method to predict the conditional probability distribution in response to an input. The main idea is to transform the problem from a regression problem to a classification problem. The conditional probability distribution network can perform both direct predictions and iterated predictions, the latter task being specific for time series problems. We compare this new method to fuzzy logic and discuss important differences, and also demonstrate the architecture on two time series. The first is the benchmark laser series used in the Santa Fe competition, a deterministic chaotic system. The second is a time series from a Markov process which exhibits structure on two time scales. The network produces multimodal predictions for this series. We compare the predictions of the network with a nearest-neighbor predictor and find that the conditional probability network is more than twice as likely a model.

We present a new method, inspired by the bootstrap, whose goal it is to determine the quality and reliability of a neural network predictor. Our method leads to more robust forecasting along with a large amount of statistical information on forecast performance that we exploit. We exhibit the method in the context of multi-variate time series prediction on financial data from the New York Stock Exchange. It turns out that the variation due to different resamplings (i.e., splits between training, cross-validation, and test sets) is significantly larger than the variation due to different network conditions (such as architecture and initial weights). Furthermore, this method allows us to forecast a probability distribution, as opposed to the traditional case of just a single value at each time step. We demonstrate this on a strictly held-out test set that includes the 1987 stock market crash. We also compare the performance of the class of neural networks to identically bootstrapped linear models.

We present a new method for obtaining local error bars, i.e., estimates of the confidence in the predicted value that depend on the input. We approach problem of nonlinear regression in a maximum likelihood framework. We demonstrate our technique first on computer generated data with locally varying, normally distributed target noise. We then apply it to the laser data from the Santa Fe Time Series Competition. Finally, we extend the technique to estimate error bars for iterate predictions, and apply it to the exact competition task where it gives the best performance to date.