Phylogenetic position, biodiversity, phylogeography and biology of the Placozoa

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The Role of Homology and Orthology in the Phylogenomic Analysis of Metazoan Gene Content

Resolving the relationships of animals (Metazoa) is crucial to our understanding of the origin of key traits such as muscles, guts, and nerves. However, a broadly accepted metazoan consensus phylogeny has yet to emerge. In part, this is because the genomes of deeply diverging and fast-evolving lineages may undergo significant gene turnover, reducing the number of orthologs shared with related phyla. This can limit the usefulness of traditional phylogenetic methods that rely on alignments of orthologous sequences. Phylogenetic analysis of gene content has the potential to circumvent this orthology requirement, with binary presence/absence of homologous gene families representing a source of phylogenetically informative characters. Applying binary substitution models to the gene content of 26 complete animal genomes, we demonstrate that patterns of gene conservation differ markedly depending on whether gene families are defined by orthology or homology, that is, whether paralogs are excluded or included. We conclude that the placement of some deeply diverging lineages may exceed the limit of resolution afforded by the current methods based on comparisons of orthologous protein sequences, and novel approaches are required to fully capture the evolutionary signal from genes within genomes.W.P. was supported by the French National Research Agency (ANR) grant Ancestrome (ANR-10-BINF-01-01), and by grants from the National Science Foundation (DEB1556615, DEB-1256993). D.P. was supported by a NERC BETR (NE/P013643/1) grant. G.W. was supported by LMU Munich’s Institutional Strategy LMUexcellent within the framework of the German Excellence Initiative, and the German Research Foundation (DFG) grant Wo896/19-1, and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 764840 (ITN IGNITE

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Graph-based prediction of missing KPIs through optimization and random forests for KPI systems

Key performance indicators (KPIs) are widely used to monitor and control the production in industry. On an aggregated level, often represented as graphs or interrelated KPI systems, a comprehensive overview is given. However, missing or inaccurate sensor data and KPIs, as well inconsistencies in KPI based management are a major hurdle disturbing operations. To counter the impact of such missing KPIs, we propose a value optimization based approach to reconstruct the values of missing KPIs within a KPI system. While the approach shows successful reconstruction in the case study, the value optimization can be sped up through a random forest prediction of the initial optimization set. Thus, the inclusion of previous knowledge about the system behavior proves beneficial and superior to the pure optimization based approach, as validated by both randomized and simulation-based measurement data

Anaerobic metabolism of Foraminifera thriving below the seafloor

Foraminifera are single-celled eukaryotes (protists) of large ecological importance, as well as environmental and paleoenvironmental indicators and biostratigraphic tools. In addition, they are capable of surviving in anoxic marine environments where they represent a major component of the benthic community. However, the cellular adaptations of Foraminifera to the anoxic environment remain poorly constrained. We sampled an oxic-anoxic transition zone in marine sediments from the Namibian shelf, where the genera Bolivina and Stainforthia dominated the Foraminifera community, and use metatranscriptomics to characterize Foraminifera metabolism across the different geochemical conditions. Relative Foraminifera gene expression in anoxic sediment increased an order of magnitude, which was confirmed in a 10-day incubation experiment where the development of anoxia coincided with a 20-40-fold increase in the relative abundance of Foraminifera protein encoding transcripts, attributed primarily to those involved in protein synthesis, intracellular protein trafficking, and modification of the cytoskeleton. This indicated that many Foraminifera were not only surviving but thriving, under the anoxic conditions. The anaerobic energy metabolism of these active Foraminifera was characterized by fermentation of sugars and amino acids, fumarate reduction, and potentially dissimilatory nitrate reduction. Moreover, the gene expression data indicate that under anoxia Foraminifera use the phosphogen creatine phosphate as an ATP store, allowing reserves of high-energy phosphate pool to be maintained for sudden demands of increased energy during anaerobic metabolism. This was co-expressed alongside genes involved in phagocytosis and clathrin-mediated endocytosis (CME). Foraminifera may use CME to utilize dissolved organic matter as a carbon and energy source, in addition to ingestion of prey cells via phagocytosis. These anaerobic metabolic mechanisms help to explain the ecological success of Foraminifera documented in the fossil record since the Cambrian period more than 500 million years ago