Una planta del Pirineu amaga el secret de la llarga vida

Els científics estudien una espècie vegetal que arriba a viure fins a 350 anys i que no mor a causa de l'envelliment, sinó per infeccions o condicions climàtiques adverses

GPS to LRM: Integration of Spatial Point Features with Linear Referencing Methods, 2001

Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.

Sharing data between LSDBs and central repositories.

Several Locus-Specific DataBases (LSDBs) have recently been approached by larger, more general data repositories (including NCBI and UCSC) with the request to share the DNA variant data they have collected. Within the Human Genome Variation Society (HGVS) a document was generated summarizing the issues related to these requests. The document has been circulated in the HGVS/LSDB community and was discussed extensively. Here we summarize these discussions and present the concluded recommendations for LSDB data sharing with central repositories.

Use of Remote Sensing for Collection of Data Elements for Linear Referencing Systems, 2001

This report evaluates the use of remotely sensed images in implementing the Iowa DOT LRS that is currently in the stages of system architecture. The Iowa Department of Transportation is investing a significant amount of time and resources into creation of a linear referencing system (LRS). A significant portion of the effort in implementing the system will be creation of a datum, which includes geographically locating anchor points and then measuring anchor section distances between those anchor points. Currently, system architecture and evaluation of different data collection methods to establish the LRS datum is being performed for the DOT by an outside consulting team.

Aggregation invariance in general clustering approaches

General clustering deals with weighted objects and fuzzy memberships. We investigate the group- or object-aggregation-invariance properties possessed by the relevant functionals (effective number of groups or objects, centroids, dispersion, mutual object-group information, etc.). The classical squared Euclidean case can be generalized to non-Euclidean distances, as well as to non-linear transformations of the memberships, yielding the c-means clustering algorithm as well as two presumably new procedures, the convex and pairwise convex clustering. Cluster stability and aggregation-invariance of the optimal memberships associated to the various clustering schemes are examined as well.

Bayes linear spaces

Linear spaces consisting of σ-finite probability measures and infinite measures (improper priors and likelihood functions) are defined. The commutative group operation, called perturbation, is the updating given by Bayes theorem; the inverse operation is the Radon-Nikodym derivative. Bayes spaces of measures are sets of classes of proportional measures. In this framework, basic notions of mathematical statistics get a simple algebraic interpretation. For example, exponential families appear as affine subspaces with their sufficient statistics as a basis. Bayesian statistics, in particular some well-known properties of conjugated priors and likelihood functions, are revisited and slightly extended

Tamanho de amostra para estimação do coeficiente de correlação linear de Pearson entre caracteres de milho

O objetivo deste trabalho foi determinar o tamanho de amostra para a estimação do coeficiente de correlação linear de Pearson entre caracteres de três híbridos de milho. Para as análises, foram tomadas aleatoriamente 361, 373 e 416 plantas, respectivamente, de híbridos simples, triplo e duplo. Para cada planta, os seguintes caracteres foram mensurados: diâmetro maior e menor do colmo, altura da planta e altura, peso, comprimento e diâmetro da espiga, número de fileiras por espiga, peso e diâmetro de sabugo, massa de cem grãos, número de grãos por espiga, comprimento e produtividade de grãos. Para cada um dos 91 pares de caracteres e híbridos, foi determinado o tamanho de amostra a partir de "bootstrap", com reposição de 1.000 amostras, de cada tamanho de amostra simulado. Na estimação do coeficiente de correlação linear de Pearson com a mesma precisão, o tamanho de amostra (número de plantas) aumenta na direção de pares de caracteres com menor intensidade de relação linear, independentemente do tipo de híbrido. Para os 91 pares de caracteres estudados, 252 plantas são suficientes para a estimação do coeficiente de correlação linear de Pearson, no intervalo de confiança de "bootstrap" de 95%, igual a 0,30

Smooth height/age curves from stem analysis with linear programming

[Abstract]

Secret de dones o com les dames "s'alcen i reguen el seu jardí" : L'irreverent "Col·loqui de dames", un divertimento de l'eròtica renaixentista

Es proposa fer un estudi d'un col·loqui valencià de l'època baix medieval, molt poc conegut i emmarcat en la literatura satírica valenciana. Es contextualitza l'obra en el corrent satíric valencià de l'època i s'analitza el text i els personatges en relació als tòpics del gènere (anticlericalisme, misogínia, erotisme i incapacitat sexual). L'anàlisi dels personatges descriu tots els tipus establerts (la vídua, el vell, l'alcavota, el clergue, el burgès, etc.). L'estudi fa també un acostament comparatiu a d'altres obres satíriques de l'època en d'altres literatures, com el Decameró i Els contes de Canterbury.

A connectome-based comparison of diffusion MRI schemes.

Diffusion MRI has evolved towards an important clinical diagnostic and research tool. Though clinical routine is using mainly diffusion weighted and tensor imaging approaches, Q-ball imaging and diffusion spectrum imaging techniques have become more widely available. They are frequently used in research-oriented investigations in particular those aiming at measuring brain network connectivity. In this work, we aim at assessing the dependency of connectivity measurements on various diffusion encoding schemes in combination with appropriate data modeling. We process and compare the structural connection matrices computed from several diffusion encoding schemes, including diffusion tensor imaging, q-ball imaging and high angular resolution schemes, such as diffusion spectrum imaging with a publically available processing pipeline for data reconstruction, tracking and visualization of diffusion MR imaging. The results indicate that the high angular resolution schemes maximize the number of obtained connections when applying identical processing strategies to the different diffusion schemes. Compared to the conventional diffusion tensor imaging, the added connectivity is mainly found for pathways in the 50-100mm range, corresponding to neighboring association fibers and long-range associative, striatal and commissural fiber pathways. The analysis of the major associative fiber tracts of the brain reveals striking differences between the applied diffusion schemes. More complex data modeling techniques (beyond tensor model) are recommended 1) if the tracts of interest run through large fiber crossings such as the centrum semi-ovale, or 2) if non-dominant fiber populations, e.g. the neighboring association fibers are the subject of investigation. An important finding of the study is that since the ground truth sensitivity and specificity is not known, the comparability between results arising from different strategies in data reconstruction and/or tracking becomes implausible to understand.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

A random linear functional approach to efficiency bounds

This paper suggests a method for obtaining efficiency bounds in models containing either only infinite-dimensional parameters or both finite- and infinite-dimensional parameters (semiparametric models). The method is based on a theory of random linear functionals applied to the gradient of the log-likelihood functional and is illustrated by computing the lower bound for Cox's regression model