Inferring the mode of origin of polyploid species from next-generation sequence data.

Many eukaryote organisms are polyploid. However, despite their importance, evolutionary inference of polyploid origins and modes of inheritance has been limited by a need for analyses of allele segregation at multiple loci using crosses. The increasing availability of sequence data for nonmodel species now allows the application of established approaches for the analysis of genomic data in polyploids. Here, we ask whether approximate Bayesian computation (ABC), applied to realistic traditional and next-generation sequence data, allows correct inference of the evolutionary and demographic history of polyploids. Using simulations, we evaluate the robustness of evolutionary inference by ABC for tetraploid species as a function of the number of individuals and loci sampled, and the presence or absence of an outgroup. We find that ABC adequately retrieves the recent evolutionary history of polyploid species on the basis of both old and new sequencing technologies. The application of ABC to sequence data from diploid and polyploid species of the plant genus Capsella confirms its utility. Our analysis strongly supports an allopolyploid origin of C. bursa-pastoris about 80 000 years ago. This conclusion runs contrary to previous findings based on the same data set but using an alternative approach and is in agreement with recent findings based on whole-genome sequencing. Our results indicate that ABC is a promising and powerful method for revealing the evolution of polyploid species, without the need to attribute alleles to a homeologous chromosome pair. The approach can readily be extended to more complex scenarios involving higher ploidy levels.

Towards Machine Consciousness: Grounding Abstract Models as pi-Processes

We present a two-level model of concurrent communicating systems (CCS) to serve as a basis formachine consciousness. A language implementing threads within logic programming is ¯rstintroduced. This high-level framework allows for the de¯nition of abstract processes that can beexecuted on a virtual machine. We then look for a possible grounding of these processes into thebrain. Towards this end, we map abstract de¯nitions (including logical expressions representingcompiled knowledge) into a variant of the pi-calculus. We illustrate this approach through aseries of examples extending from a purely reactive behavior to patterns of consciousness.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

Size and fat content of gynes in relation to the mode of colony founding in ants (Hymenoptera; Formicidae)

In ants, there are two main processes of colony founding, the independent and the dependent modes. In the first case young queens start colony founding without the help of workers, whereas in the second case they are accompanied by workers. To determine the relation between the mode of colony founding and the physiology of queens, we collected mature gynes of 24 ant species. Mature gynes of species utilizing independent colony founding had a far higher relative fat content than gynes of species employing dependent colony founding. These fat reserves are stored during the period of maturation, i.e. between the time of emergence and mating, and serve as fuel during the time of colony founding to nurture the queen and the brood. Gynes of species founding independently but non claustrally were found to have a relative fat content intermediate between the values found for gynes founding independently and those founding dependently. This suggests that such gynes rely partially on their fat reserves and partially on the energy provided by prey they collect to nurture themselves and the first brood during the time of colony founding. Study of the fat content of mature gynes of all species has shown that it gives a good indication of the mode of colony founding.

Differentiation of blue ballpoint pen inks by positive and negative mode LDI-MS

Usually, the differentiation of inks on questioned documents is carried out by optical methods and thin layer chromatography (TLC). Therefore, spectrometric methods were also proposed in forensic literature for the analysis of dyes. Between these techniques, laser desorption/ionization mass spectrometry (LDI-MS) has demonstrated a great versatility thanks to its sensitivity to blue ballpoint ink dyes and minimal sample destruction. Previous researches concentrated mostly on the LDI-MS positive mode and have shown that this analytical tool offers higher discrimination power than high performance TLC (HPTLC) for the differentiation of blue ballpoint inks. Although LDI-MS negative mode has already been applied in numerous forensic domains like the studies of works of art, automotive paints or rollerball pens, its potential for the discrimination of ballpoint pens was never studied before. The aim of the present paper is therefore to evaluate its potential for the discrimination of blue ballpoint inks. After optimization of the method, ink entries from 33 blue ballpoint pens were analyzed directly on paper in both positive and negative modes by LDI-MS. Several cationic and anionic ink components were identified in inks; therefore, pens were classified and compared according to their formulations. Results show that additional information provided by anionic dyes and pigments significantly increases the discrimination power of positive mode. In fact, it was demonstrated that classifications obtained by the two modes were, to some extent, complementary (i.e., inks with specific cationic dyes not necessarily contained the same anionic components).