DNA barcodes for soil animal taxonomy

The biodiversity of soil communities remains very poorly known and understood. Soil biological sciences are strongly affected by the taxonomic crisis, and most groups of animals in that biota suffer from a strong taxonomic impediment. The objective of this work was to investigate how DNA barcoding - a novel method using a microgenomic tag for species identification and discrimination - permits better evaluation of the taxonomy of soil biota. A total of 1,152 barcode sequences were analyzed for two major groups of animals, collembolans and earthworms, which presented broad taxonomic and geographic sampling. Besides strongly reflecting the taxonomic impediment for both groups, with a large number of species-level divergent lineages remaining unnamed so far, the results also highlight a high level (15%) of cryptic diversity within known species of both earthworms and collembolans. These results are supportive of recent local studies using a similar approach. Within an impeded taxonomic system for soil animals, DNA-assisted identification tools can facilitate and improve biodiversity exploration and description. DNA-barcoding campaigns are rapidly developing in soil animals and the community of soil biologists is urged to embrace these methods.

Diffusion dynamics on multiplex networks

We study the time scales associated with diffusion processes that take place on multiplex networks, i.e., on a set of networks linked through interconnected layers. To this end, we propose the construction of a supra-Laplacian matrix, which consists of a dimensional lifting of the Laplacian matrix of each layer of the multiplex network. We use perturbative analysis to reveal analytically the structure of eigenvectors and eigenvalues of the complete network in terms of the spectral properties of the individual layers. The spectrum of the supra-Laplacian allows us to understand the physics of diffusionlike processes on top of multiplex networks.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

Bayesian networks and the evaluation of scientific evidence in forensic science

Résumé Ce travail de thèse étudie des moyens de formalisation permettant d'assister l'expert forensique dans la gestion des facteurs influençant l'évaluation des indices scientifiques, tout en respectant des procédures d'inférence établies et acceptables. Selon une vue préconisée par une partie majoritaire de la littérature forensique et juridique - adoptée ici sans réserve comme point de départ - la conceptualisation d'une procédure évaluative est dite 'cohérente' lors qu'elle repose sur une implémentation systématique de la théorie des probabilités. Souvent, par contre, la mise en oeuvre du raisonnement probabiliste ne découle pas de manière automatique et peut se heurter à des problèmes de complexité, dus, par exemple, à des connaissances limitées du domaine en question ou encore au nombre important de facteurs pouvant entrer en ligne de compte. En vue de gérer ce genre de complications, le présent travail propose d'investiguer une formalisation de la théorie des probabilités au moyen d'un environment graphique, connu sous le nom de Réseaux bayesiens (Bayesian networks). L'hypothèse principale que cette recherche envisage d'examiner considère que les Réseaux bayesiens, en concert avec certains concepts accessoires (tels que des analyses qualitatives et de sensitivité), constituent une ressource clé dont dispose l'expert forensique pour approcher des problèmes d'inférence de manière cohérente, tant sur un plan conceptuel que pratique. De cette hypothèse de travail, des problèmes individuels ont été extraits, articulés et abordés dans une série de recherches distinctes, mais interconnectées, et dont les résultats - publiés dans des revues à comité de lecture - sont présentés sous forme d'annexes. D'un point de vue général, ce travail apporte trois catégories de résultats. Un premier groupe de résultats met en évidence, sur la base de nombreux exemples touchant à des domaines forensiques divers, l'adéquation en termes de compatibilité et complémentarité entre des modèles de Réseaux bayesiens et des procédures d'évaluation probabilistes existantes. Sur la base de ces indications, les deux autres catégories de résultats montrent, respectivement, que les Réseaux bayesiens permettent également d'aborder des domaines auparavant largement inexplorés d'un point de vue probabiliste et que la disponibilité de données numériques dites 'dures' n'est pas une condition indispensable pour permettre l'implémentation des approches proposées dans ce travail. Le présent ouvrage discute ces résultats par rapport à la littérature actuelle et conclut en proposant les Réseaux bayesiens comme moyen d'explorer des nouvelles voies de recherche, telles que l'étude de diverses formes de combinaison d'indices ainsi que l'analyse de la prise de décision. Pour ce dernier aspect, l'évaluation des probabilités constitue, dans la façon dont elle est préconisée dans ce travail, une étape préliminaire fondamentale de même qu'un moyen opérationnel.

Probabilistic evidential assessment of gunshot residue particle evidence (Part I): likelihood ratio calculation and case pre-assessment using Bayesian networks

Well developed experimental procedures currently exist for retrieving and analyzing particle evidence from hands of individuals suspected of being associated with the discharge of a firearm. Although analytical approaches (e.g. automated Scanning Electron Microscopy with Energy Dispersive X-ray (SEM-EDS) microanalysis) allow the determination of the presence of elements typically found in gunshot residue (GSR) particles, such analyses provide no information about a given particle's actual source. Possible origins for which scientists may need to account for are a primary exposure to the discharge of a firearm or a secondary transfer due to a contaminated environment. In order to approach such sources of uncertainty in the context of evidential assessment, this paper studies the construction and practical implementation of graphical probability models (i.e. Bayesian networks). These can assist forensic scientists in making the issue tractable within a probabilistic perspective. The proposed models focus on likelihood ratio calculations at various levels of detail as well as case pre-assessment.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Speed-cohesion trade-offs in collective decision making in ants and the concept of precision in animal behaviour

In decision making, speed-accuracy trade-offs are well known and often inevitable because accuracy depends on being well informed and gathering information takes time. However, trade-offs between speed and cohesion, that is the degree to which a group remains together as a single entity, as a result of their decision making, have been comparatively neglected. We combine theory and experimentation to show that in decision-making systems, speed-cohesion trade-offs are a natural complement to speed-accuracy trade-offs and are therefore of general importance. We then analyse the decision performance of 32 rock ant, Temnothorax albipennis, colonies in experiments in which accuracy of collective decision making was held constant, but time urgency varied. These experiments reveal for the first time an adaptive speed-cohesion trade-off in collective decision making and how this is achieved. In accord with different time constraints, colonies can decide quickly, at the cost of social unity, or they can decide slowly with much greater cohesion. We discuss the similarity between cohesion and the term precision as used in statistics and engineering. This emphasizes the generality of speed versus cohesion/precision trade-offs in decision making and decision implementation in other fields within animal behaviour such as sexually selected motor displays and even certain aspects of birdsong. We also suggest that speed versus precision trade-offs may occur when individuals within a group need to synchronize their activity, and in collective navigation, cooperative hunting and in certain escape behaviours.

Iowa Animal Industry News, 2016, Vol. 17, no. 1

Newsletter produced by Department of Agriculture and Land Stewardship about the animal industry in Iowa. Previously titled Animal Industry News.

Bayesian Networks and the Value of the Evidence for the Forensic Two-Trace Transfer Problem

Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions. Up to now, scientists and statisticians have derived LR formulae using an algebraic approach. However, this approach reaches its limits when addressing cases with an increasing number of variables and dependence relationships between these variables. In this study, we suggest using a graphical approach, based on the construction of Bayesian networks (BNs). We first construct a BN that captures the problem, and then deduce the expression for calculating the LR from this model to compare it with existing LR formulae. We illustrate this idea by applying it to the evaluation of an activity level LR in the context of the two-trace transfer problem. Our approach allows us to relax assumptions made in previous LR developments, produce a new LR formula for the two-trace transfer problem and generalize this scenario to n traces.

Desempenho animal e características de pasto de capim-xaraés sob diferentes intensidades de pastejo, durante o período das águas

O objetivo deste trabalho foi avaliar o valor nutritivo, a estrutura do dossel, a ingestão de forragem e a produção animal em pastos de capim-xaraés (Urochloa brizantha cv. Xaraés), manejados a 15, 30 e 45 cm de altura, sob lotação contínua, durante o período das águas. Mensalmente, foram realizadas amostragens para estimar as características dos pastos e determinar o peso dos animais. A produção de forragem decresceu com o aumento da intensidade de pastejo; no entanto, o pasto com 15 cm de altura apresentou o maior valor nutritivo. O consumo de matéria seca foi menor no pasto com 15 cm (1,89% peso vivo - PV) do que nos com 30 cm (2,26% PV) e 45 cm (2,34% PV). O ganho médio diário foi semelhante entre as diferentes alturas de manejo, em média, 730 g por novilho. As taxas de lotação foram menores para os pastos com 45 cm (2,0 UA ha-1) e 30 cm (2,5 UA ha-1) do que para o com 15 cm (3,5 UA ha-1). Houve maior ganho por área no pasto com 15 cm (678 kg ha-1) do que no com 45 cm (324 kg ha-1).

Efficiency of informational transfer in regular and complex networks

We analyze the process of informational exchange through complex networks by measuring network efficiencies. Aiming to study nonclustered systems, we propose a modification of this measure on the local level. We apply this method to an extension of the class of small worlds that includes declustered networks and show that they are locally quite efficient, although their clustering coefficient is practically zero. Unweighted systems with small-world and scale-free topologies are shown to be both globally and locally efficient. Our method is also applied to characterize weighted networks. In particular we examine the properties of underground transportation systems of Madrid and Barcelona and reinterpret the results obtained for the Boston subway network.

Models of social networks based on social distance attachment

We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by extensive numerical simulations), showing that the model reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities. The model is confronted with the social network formed by people that shares confidential information using the Pretty Good Privacy (PGP) encryption algorithm, the so-called web of trust of PGP.