PROPHECY—a database for high-resolution phenomics

The rapid recent evolution of the field phenomics—the genome-wide study of gene dispensability by quantitative analysis of phenotypes—has resulted in an increasing demand for new data analysis and visualization tools. Following the introduction of a novel approach for precise, genome-wide quantification of gene dispensability in Saccharomyces cerevisiae we here announce a public resource for mining, filtering and visualizing phenotypic data—the PROPHECY database. PROPHECY is designed to allow easy and flexible access to physiologically relevant quantitative data for the growth behaviour of mutant strains in the yeast deletion collection during conditions of environmental challenges. PROPHECY is publicly accessible at http://prophecy.lundberg.gu.se

Ladarixin, a dual CXCR1/2 inhibitor, attenuates experimental melanomas harboring different molecular defects by affecting malignant cells and tumor microenvironment.

CXCR1 and CXCR2 chemokine receptors and their ligands (CXCL1/2/3/7/8) play an important role in tumor progression. Tested to date CXCR1/2 antagonists and chemokine-targeted antibodies were reported to affect malignant cells in vitro and in animal models. Yet, redundancy of chemotactic signals and toxicity hinder further clinical development of these approaches. In this pre-clinical study we investigated the capacity of a novel small molecule dual CXCR1/2 inhibitor, Ladarixin (LDX), to attenuate progression of experimental human melanomas. Our data showed that LDX-mediated inhibition of CXCR1/2 abrogated motility and induced apoptosis in cultured cutaneous and uveal melanoma cells and xenografts independently of the molecular defects associated with the malignant phenotype. These effects were mediated by the inhibition of AKT and NF-kB signaling pathways. Moreover, systemic treatment of melanoma-bearing mice with LDX also polarized intratumoral macrophages to M1 phenotype, abrogated intratumoral de novo angiogenesis and inhibited melanoma self-renewal. Collectively, these studies outlined the pre-requisites of the successful CXCR1/2 inhibition on malignant cells and demonstrated multifactorial effects of Ladarixin on cutaneous and uveal melanomas, suggesting therapeutic utility of LDX in treatment of various melanoma types

Analyzing the Role of Model Uncertainty for Electronic Health Records

In medicine, both ethical and monetary costs of incorrect predictions can be significant, and the complexity of the problems often necessitates increasingly complex models. Recent work has shown that changing just the random seed is enough for otherwise well-tuned deep neural networks to vary in their individual predicted probabilities. In light of this, we investigate the role of model uncertainty methods in the medical domain. Using RNN ensembles and various Bayesian RNNs, we show that population-level metrics, such as AUC-PR, AUC-ROC, log-likelihood, and calibration error, do not capture model uncertainty. Meanwhile, the presence of significant variability in patient-specific predictions and optimal decisions motivates the need for capturing model uncertainty. Understanding the uncertainty for individual patients is an area with clear clinical impact, such as determining when a model decision is likely to be brittle. We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.Comment: Published in the ACM Conference on Health, Inference, and Learning (CHIL) 2020. Code available at https://github.com/Google-Health/records-researc

Performance Targets in Production Processes (PT-PRO)

One of the main actions of the European Environmental Technologies Action Plan (ETAP) is: ¿Setting ambitious targets to improve the environmental performance of technologies within a given timeframe. This should encourage technological development while preparing the markets to accept and prepare for these high standard environmental technologies¿. On request of DG Environment, the JRC-IPTS launched a project on Performance Targets for Industry Processes (PT-PRO project), with the objective to further define concepts and to identify the conditions necessary to implement the concept of Performance Targets. The project was carried out with the help of ITA, ÖAW, GMV/IVL, FEA and TNO. This report provides a definition of the main elements of Performance Targets and an overview of the general principles for their implementation. Based on four case study sectors (the iron & steel, cement, pulp & paper and the textile industries), an illustration of these different elements is provided in relation with a range of industry sectors and their environmental challenges, existing regulations, technical potentials, market situations and organisational structure. The report also discusses the best conditions and limitations of setting Performance Targets for the industry and the possible value-added.JRC.J.2-Competitiveness and Sustainabilit

PROPHECY—a yeast phenome database, update 2006

Connecting genotype to phenotype is fundamental in biomedical research and in our understanding of disease. Phenomics—the large-scale quantitative phenotypic analysis of genotypes on a genome-wide scale—connects automated data generation with the development of novel tools for phenotype data integration, mining and visualization. Our yeast phenomics database PROPHECY is available at . Via phenotyping of 984 heterozygous diploids for all essential genes the genotypes analysed and presented in PROPHECY have been extended and now include all genes in the yeast genome. Further, phenotypic data from gene overexpression of 574 membrane spanning proteins has recently been included. To facilitate the interpretation of quantitative phenotypic data we have developed a new phenotype display option, the Comparative Growth Curve Display, where growth curve differences for a large number of mutants compared with the wild type are easily revealed. In addition, PROPHECY now offers a more informative and intuitive first-sight display of its phenotypic data via its new summary page. We have also extended the arsenal of data analysis tools to include dynamic visualization of phenotypes along individual chromosomes. PROPHECY is an initiative to enhance the growing field of phenome bioinformatics

Instability in clinical risk stratification models using deep learning

While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised. We study the stability of different model architectures trained on electronic health records, using a set of outpatient prediction tasks as a case study. We show that repeated training runs of the same deep learning model on the same training data can result in significantly different outcomes at a patient level even though global performance metrics remain stable. We propose two stability metrics for measuring the effect of randomness of model training, as well as mitigation strategies for improving model stability.Comment: Accepted for publication in Machine Learning for Health (ML4H) 202

Mechanism of Direct Molecular Oxygen Insertion in a Palladium(II)−Hydride Bond

The mechanism of the direct insertion of molecular oxygen into a palladium hydride bond has been elucidated using quantum mechanics (B3LYP/LACVP^(**) with the PBF continuum solvent model). The key step is found to be the abstraction of the hydrogen atom resulting in the formation of a Pd^I/HO_2 (triplet) radical pair, which then proceeds to form a singlet palladium hydroperoxo species. Potential palladium(0) pathways were explored and were found to be inaccessible. The results are in agreement with recent experimental results and are consistent with our previously predicted mechanism for an analogue system

Analysis of complete mitochondrial genomes from extinct and extant rhinoceroses reveals lack of phylogenetic resolution

<p>Abstract</p> <p>Background</p> <p>The scientific literature contains many examples where DNA sequence analyses have been used to provide definitive answers to phylogenetic problems that traditional (non-DNA based) approaches alone have failed to resolve. One notable example concerns the rhinoceroses, a group for which several contradictory phylogenies were proposed on the basis of morphology, then apparently resolved using mitochondrial DNA fragments.</p> <p>Results</p> <p>In this study we report the first complete mitochondrial genome sequences of the extinct ice-age woolly rhinoceros (<it>Coelodonta antiquitatis</it>), and the threatened Javan (<it>Rhinoceros sondaicus</it>), Sumatran (<it>Dicerorhinus sumatrensis</it>), and black (<it>Diceros bicornis</it>) rhinoceroses. In combination with the previously published mitochondrial genomes of the white (<it>Ceratotherium simum</it>) and Indian (<it>Rhinoceros unicornis</it>) rhinoceroses, this data set putatively enables reconstruction of the rhinoceros phylogeny. While the six species cluster into three strongly supported sister-pairings: (i) The black/white, (ii) the woolly/Sumatran, and (iii) the Javan/Indian, resolution of the higher-level relationships has no statistical support. The phylogenetic signal from individual genes is highly diffuse, with mixed topological support from different genes. Furthermore, the choice of outgroup (horse <it>vs </it>tapir) has considerable effect on reconstruction of the phylogeny. The lack of resolution is suggestive of a hard polytomy at the base of crown-group Rhinocerotidae, and this is supported by an investigation of the relative branch lengths.</p> <p>Conclusion</p> <p>Satisfactory resolution of the rhinoceros phylogeny may not be achievable without additional analyses of substantial amounts of nuclear DNA. This study provides a compelling demonstration that, in spite of substantial sequence length, there are significant limitations with single-locus phylogenetics. We expect further examples of this to appear as next-generation, large-scale sequencing of complete mitochondrial genomes becomes commonplace in evolutionary studies.</p> <p><it>"The human factor in classification is nowhere more evident than in dealing with this superfamily (Rhinocerotoidea)." G. G. Simpson (1945)</it></p