Towards a Unified Theory of Timed Automata

Timed automata are finite-state machines augmented with special clock variables that reflect the advancement of time. Able to both capture real-time behavior and be verified algorithmically (model-checked), timed automata are used to model real-time systems. These observations have led to the development of several timed-automata verification tools that have been successfully applied to the analysis of a number of different systems; however, the practical utility of timed automata is undermined by the theories underlying different tools differing in subtle but important ways. Since algorithmic results that hold for the variant used by one tool may not apply to another variant, this complicates the application of different tools to different models. The thesis of this dissertation is this: the theory of timed automata can be unified, and a practical unified approach to timed-automata model checking can be built around the paradigm of proof search. First, this dissertation establishes the mutual expressivity of timed automata variants, thereby providing precise characterizations of when theoretical results of one variant apply to other variants. Second, it proves powerful expressive properties about different logics for timed behavior, and as a result, enlarges the set of verifiable properties. Third, it discusses an implementation of a verification tool for an expressive fixpoint-based logic, demonstrating an application of this newly developed theory. The tool is based on a proof-search paradigm; verifying timed automata involves constructing proofs using proof rules that enable verification problems to be translated into subproblems that must be solved. The tool's performance is optimized by using derived proof rules, thereby providing a theoretically sound basis for faster model checking. Last, this dissertation utilizes the proofs generated during verification to gain additional information about the vacuous satisfaction of certain formulae: whether the automaton satisfied a formula by never satisfying certain premises of that specification. This extra information is often obtained without significantly decreasing the verifier's performance

GraPE: fast and scalable Graph Processing and Embedding

Graph Representation Learning methods have enabled a wide range of learning problems to be addressed for data that can be represented in graph form. Nevertheless, several real world problems in economy, biology, medicine and other fields raised relevant scaling problems with existing methods and their software implementation, due to the size of real world graphs characterized by millions of nodes and billions of edges. We present GraPE, a software resource for graph processing and random walk based embedding, that can scale with large and high-degree graphs and significantly speed up-computation. GraPE comprises specialized data structures, algorithms, and a fast parallel implementation that displays everal orders of magnitude improvement in empirical space and time complexity compared to state of the art software resources, with a corresponding boost in the performance of machine learning methods for edge and node label prediction and for the unsupervised analysis of graphs.GraPE is designed to run on laptop and desktop computers, as well as on high performance computing cluster

GRAPE for fast and scalable graph processing and random-walk-based embedding

Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as competitive edge- and node-label prediction performance. GRAPE comprises approximately 1.7 million well-documented lines of Python and Rust code and provides 69 node-embedding methods, 25 inference models, a collection of efficient graph-processing utilities, and over 80,000 graphs from the literature and other sources. Standardized interfaces allow a seamless integration of third- party libraries, while ready-to-use and modular pipelines permit an easy-to- use evaluation of graph-representation-learning methods, therefore also positioning GRAPE as a software resource that performs a fair comparison between methods and libraries for graph processing and embedding

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of \u3e530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy

Demographic, multi-morbidity and genetic impact on myocardial involvement and its recovery from COVID-19 : protocol design of COVID-HEART-a UK, multicentre, observational study

Acknowledgements CB acknowledges British Heart Foundation support (RE/18/6134217). GPM is funded by a NIHR Research Professorship (RP‐2017‐08‐ST2‐007). CM is funded by a NIHR Clinician Scientist Award (CS‐2015‐15‐003). VMF and SN acknowledge the NIHR Oxford BRC for support of this study. CBD is in part supported by the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. Additional support was provided by the NIHR Leicester Biomedical Research Centre and the NIHR Leeds Clinical Research Facility. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. We thank the patients and staff who have supported this project. Dr. Warren J. Manning served as a Guest Editor for this manuscript. Study Management and Recruitment centres: Grant applicants: JP Greenwood (chief investiga‐ tor), GP McCann, C Berry, M Dweck, J Moon, CM Miller, A Chiribiri, S Prasad, VM Ferreira, C Bucciarelli‐Ducci, D Dawson. Data repository and statistical analysis: Glasgow Clinical Trials Unit. Senior study statistician: Prof A McConnachie, GCTU. Local Principle Investigators and Recruitment Centres: Prof John Green‐ wood, Leeds Teaching Hospitals NHS Trust, UK; Prof Gerry McCann, Glenfield Hospital, Leicester, UK; Prof Dana Dawson, Aberdeen Royal Infirmary, UK; Prof Marc Dweck, Royal Infirmary of Edinburgh, UK; Prof Vanessa Ferreira, JohnRadcliffe Hospital, Oxford, UK; Prof Colin Berry, Queen Elizabeth University Hospital, Glasgow, UK; Dr Peter Swoboda, Pinderfields Hospital, Wakefield, UK; Dr Richard Steeds, Queen Elizabeth Hospital, Birmingham, UK; Prof James Moon, UCL Hospital London, UK; Dr Christopher Miller, Wythenshawe Hospital, Manchester, UK; Dr Timothy Fairbairn, Liverpool Heart and Chest Hospital, UK; Dr Andrew Flett, Southampton General Hospital, UK; Prof Marianna Fontana, Royal Free Hospital, London, UK; Dr Thomas Green, Northumbria NHS Trust, UK; Prof Amedeo Chiribiri, St Thomas’ Hospital, London, UK; Dr Chiara Bucciarelli‐Ducci, University Hospitals Bristol and Weston NHS Trust, UK; Dr Graham Cole, Hammersmith Hospital, London, UK; Prof Sanjay Prasad, Royal Brompton Hospital, London, UK; Dr Adam McDiarmid, Freeman Hospital, New‐ castle Upon Tyne, UK; Dr Nicholas Bunce, St Georges Hospital, London, UK; Dr Prathap Kanagala, Aintree University Hospital, Liverpool, UK; Prof Nicholas Bellenger, The Royal Devon and Exeter Hospital, UK; Dr Tishi Ninan, Swansea Bay University Hospital, UK; Dr Khaled Alfakih, Lewisham University Hospital, London, UK; Prof James Moon, St Bartholomew’s Hospital, London, UK. Funding COVID‐HEART is funded by the National Institute for Health Research (NIHR) and UK Research and Innovation (UKRI) COVID‐19 Rapid Response Rolling Call (Grant Number COV0254), and sponsored by the University of Leeds, UK. The study has been endorsed by the British Society of Cardiovascular Magnetic Resonance (BSCMR) Research Group, and nationally prioritised, and received both BHF‐NIHR Cardiovascular Partnership Flagship Status, and the NIHR Urgent Public Health Group identified it as an Urgent Public Health (UPH) study. Funding for the translation of the patient information leaflets into non‐ English languages was provided by the West Yorkshire and Humber Clinical Research Network (CV070).Peer reviewedPublisher PD

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database

3D printing metals like thermoplastics: Fused filament fabrication of metallic glasses

Whereas 3D printing of thermoplastics is highly advanced and can readily create complex geometries, 3D printing of metals is still challenging and limited. The origin of this asymmetry in technological maturity is the continuous softening of thermoplastics with temperature into a readily formable state, which is absent in conventional metals. Unlike conventional metals, bulk metallic glasses (BMGs) demonstrate a supercooled liquid region and continuous softening upon heating, analogous to thermoplastics. Here we demonstrate that, in extension of this analogy, BMGs are also amenable to extrusion-based 3D printing through fused filament fabrication (FFF). When utilizing the BMGs’ supercooled liquid behavior, 3D printing can be realized under similar conditions to those in thermoplastics. Fully dense and amorphous BMG parts are 3D printed in ambient environmental conditions resulting in high-strength metal parts. Due to the similarity between FFF of thermoplastics and BMGs, this method may leverage the technology infrastructure built by the thermoplastic FFF community to rapidly realize and proliferate accessible and practical printing of metals

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics

Dynamics of central and peripheral immunomodulation in a murine glioma model

Immunosuppression by gliomas contributes to tumor progression and treatment resistance. It is not known when immunosuppression occurs during tumor development but it likely involves cross-talk among tumor cells, tumor-associated macrophages and microglia (TAMs), and peripheral as well as tumor-infiltrating lymphocytes (TILs). We have performed a kinetic study of this immunomodulation, assessing the dynamics of immune infiltration and function, within the central nervous system (CNS) and peripherally. PDGF-driven murine glioma cells were injected into the white matter of 13 mice. Four mice were sacrificed 13 days post-injection (dpi), four mice at 26 dpi, and five mice at 40 dpi. Using multiparameter flow cytometry, splenic T cells were assessed for FoxP3 expression to identify regulatory T cells (Tregs) and production of IFN-γ and IL-10 after stimulation with PMA/ionomycin; within the CNS, CD4+ TILs were quantified, and TAMs were quantified and assessed for TNF-α and IL-10 production after stimulation with LPS. Peripheral changes associated with tumor development were noted prior to effects within the CNS. The percentage of FoxP3+ regulatory T cells (Tregs) increased by day 26, with elevated frequencies throughout the duration of the study. This early increase in Tregs was paralleled by an increase in IL-10 production from Tregs. At the final time points examined (tumor morbidity or 40 dpi), there was an increase in the frequency of TAMs with decreased capacity to secrete TNF-α. An increase in TIL frequency was also observed at these final time points. These data provide insight into the kinetics of the immunosuppressive state associated with tumor growth in a murine model of human gliomas. Functional impairment of TAMs occurs relatively late in the course of GBM tumor growth, potentially providing a window of opportunity for therapeutic strategies directed towards preventing their functional impairment

Distinct cardiovascular phenotypes are associated with prognosis in systemic sclerosis: a cardiovascular magnetic resonance study

AIMS: Cardiovascular involvement in systemic sclerosis (SSc) is heterogeneous and ill-defined. This study aimed to: (i) discover cardiac phenotypes in SSc by cardiovascular magnetic resonance (CMR); (ii) provide a CMR-based algorithm for phenotypic classification; and (iii) examine for associations between phenotypes and mortality. METHODS AND RESULTS: A retrospective, single-centre, observational study of 260 SSc patients who underwent clinically indicated CMR including native myocardial T1 and T2 mapping from 2016 to 2019 was performed. Agglomerative hierarchical clustering using only CMR variables revealed five clusters of SSc patients with shared CMR characteristics: dilated right hearts with right ventricular failure (RVF); biventricular failure dilatation and dysfunction (BVF); and normal function with average cavity (NF-AC), normal function with small cavity (NF-SC), and normal function with large cavity (NF-LC) sizes. Phenotypes did not co-segregate with clinical or antibody classifications. A CMR-based decision tree for phenotype classification was created. Sixty-three (24%) patients died during a median follow-up period of 3.4 years. After adjustment for age and presence of pulmonary hypertension (PH), independent CMR predictors of all-cause mortality were native T1 (P  0.14). Hazard ratios (HR) were statistically significant for RVF (HR = 8.9, P < 0.001), BVF (HR = 5.2, P = 0.006), and NF-LC (HR = 4.9, P = 0.002) groups. The NF-LC group remained significantly predictive of mortality after adjusting for RVEF, native T1, and PH diagnosis (P = 0.0046). CONCLUSION: We identified five CMR-defined cardiac SSc phenotypes that did not co-segregate with clinical data and had distinct outcomes, offering opportunities for a more precision-medicine based management approach