'Good-genes' and 'compatible-genes' effects in an Alpine whitefish and the information content of breeding tubercles over the course of the spawning season.

Some models of sexual selection predict that individuals vary in their genetic quality and reveal some of this variation in their secondary sexual characteristics. Alpine whitefish (Coregonus sp.) develop breeding tubercles shortly before their spawning season. These tubercles are epidermal structures that are distributed regularly along the body sides of both males and females. There is still much unexplained variation in the size of breeding tubercles within both sexes and with much overlap between the sexes. It has been suggested that breeding tubercles function to maintain body contact between the mating partners during spawning, act as weapons for defence of spawning territories, or are sexual signals that reveal aspects of genetic quality. We took two samples of whitefish from their spawning place, one at the beginning and one around the peak of spawning season. We found that females have on average smaller breeding tubercles than males, and that tubercle size partly reveals the stage of gonad maturation. Two independent full-factorial breeding experiments revealed that embryo mortality was significantly influenced by male and female effects. This finding demonstrates that the males differed in their genetic quality (because offspring get nothing but genes from their fathers). Tubercle size was negatively linked to some aspects of embryo mortality in the first breeding experiment but not significantly so in the second. This lack of consistency adds to inconsistent results that were reported before and suggests that (i) some aspects of genetic quality are not revealed in breeding tubercles while others are, or (ii) individuals vary in their signaling strategies and the information content of breeding tubercles is not always reliable. Moreover, the fact that female whitefish have breeding tubercles of significant size while males seem to have few reasons to be choosy suggests that the tubercles might also serve some functions that are not linked to sexual signaling.

Discovery of the faithfulness gene: a model of transmission and transformation of scientific information.

The purpose of this paper is to study the diffusion and transformation of scientific information in everyday discussions. Based on rumour models and social representations theory, the impact of interpersonal communication and pre-existing beliefs on transmission of the content of a scientific discovery was analysed. In three experiments, a communication chain was simulated to investigate how laypeople make sense of a genetic discovery first published in a scientific outlet, then reported in a mainstream newspaper and finally discussed in groups. Study 1 (N=40) demonstrated a transformation of information when the scientific discovery moved along the communication chain. During successive narratives, scientific expert terminology disappeared while scientific information associated with lay terminology persisted. Moreover, the idea of a discovery of a faithfulness gene emerged. Study 2 (N=70) revealed that transmission of the scientific message varied as a function of attitudes towards genetic explanations of behaviour (pro-genetics vs. anti-genetics). Pro-genetics employed more scientific terminology than anti-genetics. Study 3 (N=75) showed that endorsement of genetic explanations was related to descriptive accounts of the scientific information, whereas rejection of genetic explanations was related to evaluative accounts of the information.

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelationbetween variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics

Decoding stimulus-related information from single-trial EEG responses based on voltage topographies

Neuroimaging studies typically compare experimental conditions using average brain responses, thereby overlooking the stimulus-related information conveyed by distributed spatio-temporal patterns of single-trial responses. Here, we take advantage of this rich information at a single-trial level to decode stimulus-related signals in two event-related potential (ERP) studies. Our method models the statistical distribution of the voltage topographies with a Gaussian Mixture Model (GMM), which reduces the dataset to a number of representative voltage topographies. The degree of presence of these topographies across trials at specific latencies is then used to classify experimental conditions. We tested the algorithm using a cross-validation procedure in two independent EEG datasets. In the first ERP study, we classified left- versus right-hemifield checkerboard stimuli for upper and lower visual hemifields. In a second ERP study, when functional differences cannot be assumed, we classified initial versus repeated presentations of visual objects. With minimal a priori information, the GMM model provides neurophysiologically interpretable features - vis à vis voltage topographies - as well as dynamic information about brain function. This method can in principle be applied to any ERP dataset testing the functional relevance of specific time periods for stimulus processing, the predictability of subject's behavior and cognitive states, and the discrimination between healthy and clinical populations.

Native homing endonucleases can target conserved genes in humans and in animal models.

In recent years, both homing endonucleases (HEases) and zinc-finger nucleases (ZFNs) have been engineered and selected for the targeting of desired human loci for gene therapy. However, enzyme engineering is lengthy and expensive and the off-target effect of the manufactured endonucleases is difficult to predict. Moreover, enzymes selected to cleave a human DNA locus may not cleave the homologous locus in the genome of animal models because of sequence divergence, thus hampering attempts to assess the in vivo efficacy and safety of any engineered enzyme prior to its application in human trials. Here, we show that naturally occurring HEases can be found, that cleave desirable human targets. Some of these enzymes are also shown to cleave the homologous sequence in the genome of animal models. In addition, the distribution of off-target effects may be more predictable for native HEases. Based on our experimental observations, we present the HomeBase algorithm, database and web server that allow a high-throughput computational search and assignment of HEases for the targeting of specific loci in the human and other genomes. We validate experimentally the predicted target specificity of candidate fungal, bacterial and archaeal HEases using cell free, yeast and archaeal assays.

A qualitative study of the impact of electronic journals on scholarly information behavior

A qualitative study of the impact of electronic journals on the information behavior of academics at Catalan universities shows that academics now read more, and more widely. However, their reading is becoming more superficial; they are compelled to improve their discrimination skills in order to decide what to read in more depth. The electronic accessibility of journals means that academics now make fewer library visits. Web browsing and TOC e-mail alerts are replacing physical browsing, and searching is a very popular option for keeping up to date with developments. Internet search engines, especially Google and Google Scholar, are becoming important sources of information for academics. However, they face problems in managing their personal scientific information.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Resting-brain functional connectivity predicted by analytic measures of network communication.

The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures-search information and path transitivity-which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.

Models that include supercoiling of topological domains reproduce several known features of interphase chromosomes.

Understanding the structure of interphase chromosomes is essential to elucidate regulatory mechanisms of gene expression. During recent years, high-throughput DNA sequencing expanded the power of chromosome conformation capture (3C) methods that provide information about reciprocal spatial proximity of chromosomal loci. Since 2012, it is known that entire chromatin in interphase chromosomes is organized into regions with strongly increased frequency of internal contacts. These regions, with the average size of ∼1 Mb, were named topological domains. More recent studies demonstrated presence of unconstrained supercoiling in interphase chromosomes. Using Brownian dynamics simulations, we show here that by including supercoiling into models of topological domains one can reproduce and thus provide possible explanations of several experimentally observed characteristics of interphase chromosomes, such as their complex contact maps.

Employment risk, unemployment insurance and search strategies: a disaggregated equilibrium approach with application to the Swiss labour market in the 1990-ies

The Organization of the Thesis The remainder of the thesis comprises five chapters and a conclusion. The next chapter formalizes the envisioned theory into a tractable model. Section 2.2 presents a formal description of the model economy: the individual heterogeneity, the individual objective, the UI setting, the population dynamics and the equilibrium. The welfare and efficiency criteria for qualifying various equilibrium outcomes are proposed in section 2.3. The fourth section shows how the model-generated information can be computed. Chapter 3 transposes the model from chapter 2 in conditions that enable its use in the analysis of individual labor market strategies and their implications for the labor market equilibrium. In section 3.2 the Swiss labor market data sets, stylized facts, and the UI system are presented. The third section outlines and motivates the parameterization method. In section 3.4 the model's replication ability is evaluated and some aspects of the parameter choice are discussed. Numerical solution issues can be found in the appendix. Chapter 4 examines the determinants of search-strategic behavior in the model economy and its implications for the labor market aggregates. In section 4.2, the unemployment duration distribution is examined and related to search strategies. Section 4.3 shows how the search- strategic behavior is influenced by the UI eligibility and section 4.4 how it is determined by individual heterogeneity. The composition effects generated by search strategies in labor market aggregates are examined in section 4.5. The last section evaluates the model's replication of empirical unemployment escape frequencies reported in Sheldon [67]. Chapter 5 applies the model economy to examine the effects on the labor market equilibrium of shocks to the labor market risk structure, to the deep underlying labor market structure and to the UI setting. Section 5.2 examines the effects of the labor market risk structure on the labor market equilibrium and the labor market strategic behavior. The effects of alterations in the labor market deep economic structural parameters, i.e. individual preferences and production technology, are shown in Section 5.3. Finally, the UI setting impacts on the labor market are studied in Section 5.4. This section also evaluates the role of the UI authority monitoring and the differences in the Way changes in the replacement rate and the UI benefit duration affect the labor market. In chapter 6 the model economy is applied in counterfactual experiments to assess several aspects of the Swiss labor market movements in the nineties. Section 6.2 examines the two equilibria characterizing the Swiss labor market in the nineties, the " growth" equilibrium with a "moderate" UI regime and the "recession" equilibrium with a more "generous" UI. Section 6.3 evaluates the isolated effects of the structural shocks, while the isolated effects of the UI reforms are analyzed in section 6.4. Particular dimensions of the UI reforms, the duration, replacement rate and the tax rate effects, are studied in section 6.5, while labor market equilibria without benefits are evaluated in section 6.6. In section 6.7 the structural and institutional interactions that may act as unemployment amplifiers are discussed in view of the obtained results. A welfare analysis based on individual welfare in different structural and UI settings is presented in the eighth section. Finally, the results are related to more favorable unemployment trends after 1997. The conclusion evaluates the features embodied in the model economy with respect to the resulting model dynamics to derive lessons from the model design." The thesis ends by proposing guidelines for future improvements of the model and directions for further research.

Impact of community structure on information transfer

The observation that real complex networks have internal structure has important implication for dynamic processes occurring on such topologies. Here we investigate the impact of community structure on a model of information transfer able to deal with both search and congestion simultaneously. We show that networks with fuzzy community structure are more efficient in terms of packet delivery than those with pronounced community structure. We also propose an alternative packet routing algorithm which takes advantage of the knowledge of communities to improve information transfer and show that in the context of the model an intermediate level of community structure is optimal. Finally, we show that in a hierarchical network setting, providing knowledge of communities at the level of highest modularity will improve network capacity by the largest amount.

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Case studies and mathematical models of ecological speciation. 1. Cichlids in a crater lake.

A recent study of a pair of sympatric species of cichlids in Lake Apoyo in Nicaragua is viewed as providing probably one of the most convincing examples of sympatric speciation to date. Here, we describe and study a stochastic, individual-based, explicit genetic model tailored for this cichlid system. Our results show that relatively rapid (<20,000 generations) colonization of a new ecological niche and (sympatric or parapatric) speciation via local adaptation and divergence in habitat and mating preferences are theoretically plausible if: (i) the number of loci underlying the traits controlling local adaptation, and habitat and mating preferences is small; (ii) the strength of selection for local adaptation is intermediate; (iii) the carrying capacity of the population is intermediate; and (iv) the effects of the loci influencing nonrandom mating are strong. We discuss patterns and timescales of ecological speciation identified by our model, and we highlight important parameters and features that need to be studied empirically to provide information that can be used to improve the biological realism and power of mathematical models of ecological speciation.