Random Fruits on the Zielonka Tree

Stochastic games are a natural model for the synthesis of controllers confronted to adversarial and/or random actions. In particular, $\omega$-regular games of infinite length can represent reactive systems which are not expected to reach a correct state, but rather to handle a continuous stream of events. One critical resource in such applications is the memory used by the controller. In this paper, we study the amount of memory that can be saved through the use of randomisation in strategies, and present matching upper and lower bounds for stochastic Muller games

The rostro-caudal gradient in the prefrontal cortex and its modulation by subthalamic deep brain stimulation in Parkinson’s disease

Acknowledgements The authors thank Benjamin Rahm (University of Freiburg) and Michael Fox (Harvard Medical School) for valuable comments on a previous version of this manuscript. This work was supported by a grant of the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, grant number EXC 1086) to C.P.K., F.A., T.P., B.O.S., C.W, and V.A.C.; A.H. was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, Emmy Noether Stipend 410169619 and 424778381 – TRR 295) as well as Deutsches Zentrum für Luft- und Raumfahrt (DynaSti grant within the EU Joint Programme Neurodegenerative Disease Research, JPND). Funding Open Access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

D1.4 Critical evaluation of material criticality and product-related circularity approaches

The deliverable presents a critical evaluation of existing approaches addressing material criticality and circularity

Posterior Association Networks and Functional Modules Inferred from Rich Phenotypes of Gene Perturbations

Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the ‘search space’ for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html

Acute effects of intracranial hypertension and ARDS on pulmonary and neuronal damage: a randomized experimental study in pigs

Abstract PURPOSE: To determine reciprocal and synergistic effects of acute intracranial hypertension and ARDS on neuronal and pulmonary damage and to define possible mechanisms. METHODS: Twenty-eight mechanically ventilated pigs were randomized to four groups of seven each: control; acute intracranial hypertension (AICH); acute respiratory distress syndrome (ARDS); acute respiratory distress syndrome in combination with acute intracranial hypertension (ARDS + AICH). AICH was induced with an intracranial balloon catheter and the inflation volume was adjusted to keep intracranial pressure (ICP) at 30-40 cmH2O. ARDS was induced by oleic acid infusion. Respiratory function, hemodynamics, extravascular lung water index (ELWI), lung and brain computed tomography (CT) scans, as well as inflammatory mediators, S100B, and neuronal serum enolase (NSE) were measured over a 4-h period. Lung and brain tissue were collected and examined at the end of the experiment. RESULTS: In both healthy and injured lungs, AICH caused increases in NSE and TNF-alpha plasma concentrations, extravascular lung water, and lung density in CT, the extent of poorly aerated (dystelectatic) and atelectatic lung regions, and an increase in the brain tissue water content. ARDS and AICH in combination induced damage in the hippocampus and decreased density in brain CT. CONCLUSIONS: AICH induces lung injury and also exacerbates pre-existing damage. Increased extravascular lung water is an early marker. ARDS has a detrimental effect on the brain and acts synergistically with intracranial hypertension to cause histological hippocampal damage

HETEAC: The Aerosol Classification Model for EarthCARE

We introduce the Hybrid End-To-End Aerosol Classification (HETEAC) model for the upcoming EarthCARE mission. The model serves as the common baseline for development, evaluation, and implementation of EarthCARE algorithms. It shall ensure the consistency of different aerosol products from the multi-instrument platform as well as facilitate the conform specification of broad-band optical properties necessary for the EarthCARE radiative closure efforts. The hybrid approach ensures the theoretical description of aerosol microphysics consistent with the optical properties of various aerosol types known from observations. The end-to-end model permits the uniform representation of aerosol types in terms of microphysical, optical and radiative properties

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

“Whoa! we’re going deep in the trees!”: patterns of collaboration around an interactive information visualization exhibit

In this paper we present a qualitative analysis of natural history museum visitor interaction around a multi-touch tabletop exhibit called DeepTree that we designed around concepts of evolution and common descent. DeepTree combines several large scientific datasets and an innovative visualization technique to display a phylogenetic tree of life consisting of over 70,000 species. After describing our design, we present a study involving pairs of children interacting with DeepTree in two natural history museums. Our analysis focuses on two questions. First, how do dyads negotiate their moment-to-moment exploration of the exhibit? Second, how do dyads develop and negotiate their understanding of evolutionary concepts? In order to address these questions we present an analytical framework that describes dyads’ exploration along two dimensions: coordination and target of action. This framework reveals four distinct patterns of interaction, which, we argue, are relevant for similar interactive designs. We conclude with a discussion of the role of design in helping visitors make sense of interactive experiences involving the visualization of large scientific datasets

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria

Abstract: Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Variant annotation was supported by software resources provided via the Caché Campus program of the InterSystems GmbH to Alexander Teumer