Advances in proteomics and sequencing have highlighted many non-annotated open reading frames (ORFs) in eukaryotic genomes. Genome annotations, cornerstones of today's research, mostly rely on protein prior knowledge and on ab initio prediction algorithms. Such algorithms notably enforce an arbitrary criterion of one coding sequence (CDS) per transcript, leading to a substantial underestimation of the coding potential of eukaryotes. Here, we present OpenProt, the first database fully endorsing a polycistronic model of eukaryotic genomes to date. OpenProt contains all possible ORFs longer than 30 codons across 10 species, and cumulates supporting evidence such as protein conservation, translation and expression. OpenProt annotates all known proteins (RefProts), novel predicted isoforms (Isoforms) and novel predicted proteins from alternative ORFs (AltProts). It incorporates cutting-edge algorithms to evaluate protein orthology and re-interrogate publicly available ribosome profiling and mass spectrometry datasets, supporting the annotation of thousands of predicted ORFs. The constantly growing database currently cumulates evidence from 87 ribosome profiling and 114 mass spectrometry studies from several species, tissues and cell lines. All data is freely available and downloadable from a web platform (www.openprot.org) supporting a genome browser and advanced queries for each species. Thus, OpenProt enables a more comprehensive landscape of eukaryotic genomes’ coding potential.
Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.
Luminescent colloidal nanosystems based on europium-doped biomimetic apatite were prepared and investigated. The colloids were synthesized by soft chemistry in the presence of a phospholipid moiety, 2-aminoethylphosphoric acid (AEP), with varying europium doping rates. Physicochemical features, including compositional, structural, morphological, and luminescence properties, were examined. Experimental evidence showed that suspensions prepared from an initial Eu/(Eu + Ca) molar ratio up to 2% consisted of singlephased biomimetic apatite nanocrystals covered with AEP molecules. The mean particle size was found to depend closely on the AEP content, enabling the production of apatite colloids with a controlled size down to ca. 30 nm. The colloids showed luminescence properties typical of europium-doped systems with narrow emission bands and long luminescence lifetimes of the order to the millisecond, and the data suggested the location of Eu 3+ ions in a common crystallographic environment for all the colloids. These systems, stable over time and capable of being excited in close-to-visible or visible light domains, may raise interest in the future in the field of medical imaging.
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