The development of the Silurian trilobite Aulacopleura koninckii reconstructed by applying inferred growth and segmentation dynamics: A case study in paleo-evo-devo

Fossilized growth series provide rare glimpses into the development of ancient organisms, illustrating descriptively how size and shape changed through ontogeny. Occasionally fossil preservation is such that it is feasible to test alternative possibilities about how ancient development was regulated. Here we apply inferred developmental parameters pertaining to size, shape, and segmentation in the abundant and well-preserved 429 Myr old trilobite Aulacopleura koninckii that we have investigated previously to reconstruct the post-embryonic ontogeny of this ancient arthropod. Our published morphometric analyses associated with model testing have shown that: specification of the adult number of trunk segments (polymorphic in this species) was determined precociously in ontogeny; that growth regulation was targeted (i.e., compensatory), such that each developmental stage exhibited comparable variance in size and shape; and that growth gradients operating along the main body axis, both during juvenile and adult ontogeny, resulted from a form of growth control based on positional specification. While such developmental features are common among extant organisms, our results represent the oldest evidence for them within Metazoa. Herein, the novel reconstruction of the development of Aulacopleura koninckii permits visualization of patterns of relative and absolute growth and segmentation as never before possible for a fossilized arthropod ontogeny. By conducting morphometric analysis of appropriate data sets it is thus possible to move beyond descriptive ontogenetic studies and to address questions of high interest for evolutionary developmental biology using data from fossils, which can help elucidate both how developmental processes themselves evolve and how they affect the evolution of organismal body patterning. By extending similar analyses to other cases of exceptional preservation of fossilized ontogeny, we can anticipate beginning to realize the research program of “paleo-evo-devo.

Financial time series representation using multiresolution important point retrieval method

Financial time series analysis usually conducts by determining the series important points. These important points which are the peaks and the dips indicate the affecting of some important factors or events which are available both internal factors and external factors. The peak and the dip points of the series may appear frequently in multiresolution over time. However, to manipulate financial time series, researchers usually decrease this complexity of time series in their techniques. Consequently, transfonning the time series into another easily understanding representation is usually considered as an appropriate approach. In this paper, we propose a multiresolution important point retrieval method for financial time series representation. The idea of the method is based on finding the most important points in multiresolution. These retrieved important points are recorded in each resolution. The collected important points are used to construct the TS-binary search tree. From the TS-binary search tree, the application of time series segmentation is conducted. The experimental results show that the TS-binary search tree representation for financial time series exhibits different performance in different number of cutting points, however, in the empirical results, the number of cutting points which are larger than 12 points show the better results

A niching memetic algorithm for simultaneous clustering and feature selection

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data

Modeling of evolving textures using granulometries

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