Prenatal development of skull and brain in a mouse model of growth restriction

Patterns of covariation result from the over-lapping effect of several developmental processes. By perturbing certain specific developmental processes, ex-perimental studies contribute to a better understanding of their particular effects on the generation of phenotype. The aim of this work was to analyze the interactions among morphological traits of the skull and the brain during late prenatal life (18.5 days postconception) in mice exposed to maternal protein undernutrition. Images from the skull and brain were obtained through micro-computed tomography and 3D landmark coordinates were digitized in order to quantify shape and size of both structures with geometric morphometric techniques. The results highlight a systemic effect of protein restriction on the size of the skull and the brain, which were both significantly reduced in the under-nourished group compared to control group. Skull shape is partially explained by brain size, and patterns of shape variation were only partially coincident with previous re-ports for other ontogenetic stages, suggesting that allomet-ric trajectories across pre- and postnatal ages change their directions. Within the skull, neurocranial and facial shape traits covaried strongly, while subtle covariation was found between the shape of the skull and the brain. These find-ings are in line with former studies in mutant mice and reveal the importance of carrying out analyses of pheno-typic variation in a broad range of developmental stages. The present study contributes to the basic understanding of epigenetic relations among growing tissues and has di-rect implications for the field of paleoanthropology, where inferences about brain morphology are usually derived from skull remains.Los patrones de covariación entre rasgos fenotí-picos resultan de la acción de diversos procesos que se sola-pan durante el desarrollo. Los estudios experimentales cons-tituyen la aproximación más adecuada para evaluar el efecto de procesos específicos en la generación de tales patrones. El objetivo de este trabajo es analizar las interacciones entre rasgos morfológicos craneofaciales y cerebrales durante la vida prenatal tardía (18,5 días posconcepción) en ratones ex-puestos a desnutrición proteica materna. Se obtuvieron imá-genes del cráneo y cerebro a partir de microtomografía com-putada y se digitalizaron landmarks en 3D para cuantificar la forma y tamaño con técnicas de morfometría geométrica. Los resultados subrayan un efecto sistémico de la restricción proteica en el tamaño del cráneo y el cerebro. La forma del cráneo es parcialmente explicable por el tamaño cerebral y los patrones de variación en forma fueron sólo en parte coin-cidentes con los reportados antes para otras edades, lo cual sugiere que las trayectorias alométricas a lo largo de la vida pre- y posnatal cambian su dirección. Los rasgos de forma del neurocráneo y el esqueleto facial covariaron fuertemen-te, aunque se encontró una asociación débil entre la forma del cráneo y del cerebro. Estos resultados concuerdan con estudios previos en ratones mutantes y revelan la relevancia de analizar la variación fenotípica en distintas etapas. El pre-sente estudio contribuye al conocimiento básico de las inte-racciones epigenéticas entre tejidos en crecimiento y tiene implicancias en el campo paleoantropológico en el que las inferencias acerca de la morfología cerebral son usualmen-te derivadas del análisis del cráneo.Fil: Barbeito Andrés, Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; ArgentinaFil: Gonzalez, Paula Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria "Ing. Fernando Noel Dulout". Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Genética Veterinaria; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; ArgentinaFil: Hallgrimsson, Benedikt. University of Calgary; Canad

ASAP: An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner out- performs all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners’ performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a plan- ner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings–planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans

Ordered Landmarks in Planning

Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and to use them for guiding search, in the hope of speeding up the planning process. We go beyond the previous approaches by considering ordering constraints not only over the (top-level) goals, but also over the sub-goals that will necessarily arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We extend Koehler and Hoffmann's definition of reasonable orders between top level goals to the more general case of landmarks. We show how landmarks can be found, how their reasonable orders can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant runtime performance improvements when used as a control loop around state-of-the-art sub-optimal planning systems, as exemplified by FF and LPG

Planification Evolutionnaire par Décomposition

Ce rapport présente l'approche Divide-and-Evolve pour la résolution générique des problèmes de planification temporelle par décomposition. L'idée principale de l'approche est la recherche des solutions dans l'espace des décompositions en états intermédiaires à l'aide d'un algorithme évolutionnaire: les solutions candidates sont des séquences d'états intermédiaires qui définissent successivement les plans partiels du problème initial. Nous nous sommes intéressés à la résolution des problèmes de type "simple temporal planning problems". La résolution des séquences d'états intermédiaires et la détermination d'une solution globale se font à l'aide du planificateur CPT. Ce rapport formalise l'approche, définit l'algorithme Divide-and-Evolve et compare les résultats obtenus à ceux trouvés par les meilleurs planificateurs existants à notre connaissance

On the Online Generation of Effective Macro-operators

Macro-operator (“macro”, for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training