Climate Driven Life Histories: The Case of the Mediterranean Storm Petrel

Seabirds are affected by changes in the marine ecosystem. The influence of climatic factors on marine food webs can be reflected in long-term seabird population changes. We modelled the survival and recruitment of the Mediterranean storm petrel (Hydrobates pelagicus melitensis) using a 21-year mark-recapture dataset involving almost 5000 birds. We demonstrated a strong influence of prebreeding climatic conditions on recruitment age and of rainfall and breeding period conditions on juvenile survival. The results suggest that the juvenile survival rate of the Mediterranean subspecies may not be negatively affected by the predicted features of climate change, i.e., warmer summers and lower rainfall. Based on considerations of winter conditions in different parts of the Mediterranean, we were able to draw inferences about the wintering areas of the species for the first time.     

Evolution of green lacewings (Neuroptera: Chrysopidae): a molecular supermatrix approach

We present a time‐calibrated phylogeny of the charismatic green lacewings (Neuroptera: Chrysopidae). Previous phylogenetic studies on the family using DNA sequences have suffered from sparse taxon sampling and/or limited amounts of data. Here we combine all available previously published DNA sequence data and add to it new DNA sequences generated for this study. We analysed these data in a supermatrix using Bayesian and maximum likelihood methods and provide a phylogenetic hypothesis for the family that recovers strong support for the monophyly of all subfamilies and resolves relationships among a large proportion of chrysopine genera. Chrysopinae tribes Leucochrysini and Belonopterygini were recovered as monophyletic sister clades, while the species‐rich tribe Chrysopini was rendered paraphyletic by Ankylopterygini. Relationships among the subfamilies were resolved, although with relatively low statistical support, and the topology varied based on the method of analysis. Greatest support was found for Apochrysinae as sister to Nothochrysinae and Chrysopinae, which is in contrast to traditional concepts that place Nothochrysinae as sister to the rest of the family. Divergence estimates suggest that the stem groups to the various subfamilies diverged during the Triassic‐Jurassic, and that stem groups of the chrysopine tribes diverged during the Cretaceous.     

Colorectal Cancers Mimic Structural Organization of Normal Colonic Crypts

Colonic crypts are stereotypical structures with distinct stem cell, proliferating, and differentiating compartments. Colorectal cancers derive from colonic crypt epithelia but, in contrast, form morphologically disarrayed glands. In this study, we investigated to which extent colorectal cancers phenocopy colonic crypt architecture and thus preserve structural organization of the normal intestinal epithelium. A subset of colon cancers showed crypt-like compartments with high WNT activity and nuclear β-Catenin at the leading tumor edge, adjacent proliferation, and enhanced Cytokeratin 20 expression in most differentiated tumor epithelia of the tumor center. This architecture strongly depended on growth conditions, and was fully reproducible in mouse xenografts of cultured and primary colon cancer cells. Full crypt-like organization was associated with low tumor grade and was an independent prognostic marker of better survival in a collection of 221 colorectal cancers. Our findings suggest that full activation of preserved intestinal morphogenetic programs in colon cancer requires in vivo growth environments. Furthermore, crypt-like architecture was linked with less aggressive tumor biology, and may be useful to improve current colon cancer grading schemes.     

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.