Mutation identification by direct comparison of whole-genome sequencing data from mutant and wild-type individuals using k-mers.

Genes underlying mutant phenotypes can be isolated by combining marker discovery, genetic mapping and resequencing, but a more straightforward strategy for mapping mutations would be the direct comparison of mutant and wild-type genomes. Applying such an approach, however, is hampered by the need for reference sequences and by mutational loads that confound the unambiguous identification of causal mutations. Here we introduce NIKS (needle in the k-stack), a reference-free algorithm based on comparing k-mers in whole-genome sequencing data for precise discovery of homozygous mutations. We applied NIKS to eight mutants induced in nonreference rice cultivars and to two mutants of the nonmodel species Arabis alpina. In both species, comparing pooled F2 individuals selected for mutant phenotypes revealed small sets of mutations including the causal changes. Moreover, comparing M3 seedlings of two allelic mutants unambiguously identified the causal gene. Thus, for any species amenable to mutagenesis, NIKS enables forward genetics without requiring segregating populations, genetic maps and reference sequences.

Ecological assembly rules in plant communities--approaches, patterns and prospects.

Understanding how communities of living organisms assemble has been a central question in ecology since the early days of the discipline. Disentangling the different processes involved in community assembly is not only interesting in itself but also crucial for an understanding of how communities will behave under future environmental scenarios. The traditional concept of assembly rules reflects the notion that species do not co-occur randomly but are restricted in their co-occurrence by interspecific competition. This concept can be redefined in a more general framework where the co-occurrence of species is a product of chance, historical patterns of speciation and migration, dispersal, abiotic environmental factors, and biotic interactions, with none of these processes being mutually exclusive. Here we present a survey and meta-analyses of 59 papers that compare observed patterns in plant communities with null models simulating random patterns of species assembly. According to the type of data under study and the different methods that are applied to detect community assembly, we distinguish four main types of approach in the published literature: species co-occurrence, niche limitation, guild proportionality and limiting similarity. Results from our meta-analyses suggest that non-random co-occurrence of plant species is not a widespread phenomenon. However, whether this finding reflects the individualistic nature of plant communities or is caused by methodological shortcomings associated with the studies considered cannot be discerned from the available metadata. We advocate that more thorough surveys be conducted using a set of standardized methods to test for the existence of assembly rules in data sets spanning larger biological and geographical scales than have been considered until now. We underpin this general advice with guidelines that should be considered in future assembly rules research. This will enable us to draw more accurate and general conclusions about the non-random aspect of assembly in plant communities.

High-performance payload data handling system for Gaia

Gaia is the most ambitious space astrometry mission currently envisaged and is a technological challenge in all its aspects. We describe a proposal for the payload data handling system of Gaia, as an example of a high-performance, real-time, concurrent, and pipelined data system. This proposal includes the front-end systems for the instrumentation, the data acquisition and management modules, the star data processing modules, and the payload data handling unit. We also review other payload and service module elements and we illustrate a data flux proposal.

Faktorenanalytische Untersuchung und Rehabilitätsbestimmung der statischen und dynamischen Pupillometrie bei Normalpersonen und psychopathologischen Gruppen [Factor analytic study and determination of rehabilitation of static and dynamic pupillometry in normal persons and psychopathologic groups]

Variables measured during static and dynamic pupillometry were factor-analyzed. Following factors were obtained regardless whether investigations were carried out in normals or in psychiatric patients: A static factor, a dynamic factor, a stimulus-specific factor and a restitution-dependent factor. Evaluation of reliability in normals demonstrated a high reliability for the static variables of pupillometry.

On the Schoenberg transformations in data analysis: theory and illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.

The study club program : suggestions relating to courses of study and aids furnished by the Traveling Library, 1909

This leaflet includes information on programs for Women's Clubs. Some possible subjects for the club's discussions are literature, history and fine arts. Several methods of study are covered. Groups are encouraged to use public libraries and the Iowa Traveling Library to obtain reference materials for their topics.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Rethinking study and management of agricultural systems for policy design

There is a concern that agriculture will no longer be able to meet, on a global scale, the growing demand for food. Facing such a challenge requires new patterns of thinking in the context of complexity and sustainability sciences. This paper, focused on the social dimension of the study and management of agricultural systems, suggests that rethinking the study of agricultural systems entails analyzing them as complex socio-ecological systems, as well as considering the differing thinking patterns of diverse stakeholders. The intersubjective nature of knowledge, as studied by different philosophical schools, needs to be better integrated into the study and management of agricultural systems than it is done so far, forcing us to accept that there are no simplistic solutions, and to seek a better understanding of the social dimension of agriculture. Different agriculture related problems require different policy and institutional approaches. Finally, the intersubjective nature of knowledge asks for the visualization of different framings and the power relations taking place in the decision-making process. Rethinking management of agricultural systems implies that policy making should be shaped by different principles: learning, flexibility, adaptation, scale-matching, participation, diversity enhancement and precaution hold the promise to significantly improve current standard management procedures.

Four decades of the independent study programmes

Sobre l'Independent Studies Programme (ISP) i el Programa d’Estudis Independents (PEI) del MACBA

Study and modeling of the desintegration kinetics of coated paper

The disintegration of recovered paper is the first operation in the preparation of recycled pulp. It is known that the defibering process follows a first order kinetics from which it is possible to obtain the disintegration kinetic constant (KD) by means of different ways. The disintegration constant can be obtained from the Somerville index results (%lsv and from the dissipated energy per volume unit (Ss). The %slv is related to the quantity of non-defibrated paper, as a measure of the non-disintegrated fiber residual (percentage of flakes), which is expressed in disintegration time units. In this work, disintegration kinetics from recycled coated paper has been evaluated, working at 20 revise rotor speed and for different fiber consistency (6, 8, 10, 12 and 14%). The results showed that the values of experimental disintegration kinetic constant, Ko, through the analysis of Somerville index, as function of time. Increased, the disintegration time was drastically reduced. The calculation of the disintegration kinetic constant (modelled Ko), extracted from the Rayleigh’s dissipation function, showed a good correlation with the experimental values using the evolution of the Somerville index or with the dissipated energy