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Offspring size is a fundamental trait in disparate biological fields of study. This trait can be measured as the size of plant seeds, animal eggs, or live young, and it influences ecological interactions, organism fitness, maternal investment, and embryonic development. Although multiple evolutionary processes have been predicted to drive the evolution of offspring size, the phylogenetic distribution of this trait remains poorly understood, due to the difficulty of reliably collecting and comparing offspring size data from many species. Here we present a dataset of 10,449 morphological descriptions of insect eggs, with records for 6,706 unique insect species and representatives from every extant hexapod order. The dataset includes eggs whose volumes span more than eight orders of magnitude. We created this dataset by partially automating the extraction of egg traits from the primary literature. In the process, we overcame challenges associated with large-scale phenotyping by designing and employing custom bioinformatic solutions to common problems. We matched the taxa in this dataset to the currently accepted scientific names in taxonomic and genetic databases, which will facilitate the use of these data for testing pressing evolutionary hypotheses in offspring size evolution. Machine-accessible metadata file describing the reported data (ISA-Tab format)

This paper presents a multiscale corner detection method in planar shapes, which applies an undecimated Mexican hat wavelet decomposition of the angulation signal to identify significant points on a shape contour. The advantage of using this wavelet is that it is well suited for detecting singularities as corners and contours due to its excellent selectivity in position. Thus, this wavelet plays an important role in our approach because it identifies changes in non-stationary angulation signals, and it can be extended to multidimensional approaches in an efficient way when approximating this wavelet by difference of Gaussians. The proposed algorithm detects peaks on a correlation signal which is generated from different wavelet scales and retains relevant points on the decomposed angulation signal while discards poor information. Our approach assumes that only peaks which persist through several scales correspond to corners. Furthermore, we introduce a novel procedure to tune parameters for the corner detection algorithms that corresponds to the best relation between Precision and Recall measures. This technique guides the parameter adjustment of the algorithms according to the image database and it improves their performance with regard to true corner detection. Concerning the performance assessment of the algorithms, we compare the proposed one to other corner detectors by using Precision and Recall measures which are based on ground-truth information. Tests were carried out using more than a hundred images from a non-homogenous database that contains noisy and non-noisy binary shapes.

The addition of nucleating agents to semicrystalline polymersis largely used in the processing industry of plastic materials to improve some properties of polymers as well as for economical and technological reasons. In this work, the influence of talc concentration on the nucleation efficiency of poly(propylene) (PP), as well as on the non-isothermal kinetics of the crystallization of that system were determined by differential scanning calorimetry (DSC). The nucleating efficiency was determined by Fillon's method, and the dynamic nucleation by Ozawa's method at cooing rates of 2, 5 and 10°C min–1. The results show that both the degree of crystallinity and the crystallization temperature increase with the filler content and decrease at higher cooling rates and that Ozawa's (n,) exponent and the nucleation efficiency increase with temperature and filler content. It was also shown that the nucleating efficiency of talc in poly(propylene) is comparable to the best heterogeneous nucleating agents available.