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Poly (ADP-ribose) polymerase inhibitors (PARPis) have demonstrated clinical activity in patients with BRCA1 and/or BRCA2 mutated breast, ovarian, prostate, and pancreatic cancers. Notably, BRCA mutations are associated with defects in the homologous recombination repair (HRR) pathway. This homologous recombination deficiency (HRD) phenotype can also be observed as genomic instability in tumour cells. Accordingly, PARPi sensitivity has been observed in various tumours with HRD, independent of BRCA mutations. Currently, four PARPis are approved by regulatory agencies for the treatment of cancer across multiple tumour types. Most indications are specific to tumours with a confirmed BRCA mutation, mutations in other HRR-related genes, HRD evidenced by genomic instability, or evidence of platinum sensitivity. Regulatory agencies have also approved companion and complementary diagnostics to facilitate patient selection for each PARPi indication. This review aims to summarise the biological basis, clinical validation, and clinical relevance of the available diagnostic methods and assays to assess HRD. • Several biomarker tests are available to determine patient selection for PARPis. • These tests identify tumours with HRD and help determine sensitivity to PARPis. • We reviewed biomarker tests to assess HRD and their relevance for PARPi treatment. [ABSTRACT FROM AUTHOR]

The paper presents a new efficient algorithm for computing worst case execution time (WCET) of systems modelled as timed automata (TA). The algorithm uses a set of abstraction techniques that improve significantly the efficiency of WCET analysis of TA models with cyclic behaviour. We show that the proposed abstractions are exact with respect to the WCET problem in the sense that the WCET computed in the abstract model is equal to the one computed in the concrete model. We also compare our algorithm with the one implemented in the model checker UPPAAL which shows that when infinite cycles exist (i.e. cycles that can be run infinitely often), UPPAAL's algorithm may not terminate, and when largely repetitive finite cycles exist (i.e. cycles that can be run a large number of times but finite), UPPAAL's algorithm suffers from the state space explosion, thus leading to a low efficiency or resource exhaustion. [ABSTRACT FROM AUTHOR]

In this paper we present refinement modal logic . A refinement is like a bisimulation, except that from the three relational requirements only ‘atoms’ and ‘back’ need to be satisfied. Our logic contains a new operator ∀ in addition to the standard box modalities for each agent. The operator ∀ acts as a quantifier over the set of all refinements of a given model. As a variation on a bisimulation quantifier, this refinement operator or refinement quantifier ∀ can be seen as quantifying over a variable not occurring in the formula bound by it. The logic combines the simplicity of multi-agent modal logic with some powers of monadic second-order quantification. We present a sound and complete axiomatization of multi-agent refinement modal logic. We also present an extension of the logic to the modal μ -calculus, and an axiomatization for the single-agent version of this logic. Examples and applications are also discussed: to software verification and design (the set of agents can also be seen as a set of actions), and to dynamic epistemic logic. We further give detailed results on the complexity of satisfiability, and on succinctness. [ABSTRACT FROM AUTHOR]

Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems. [Display omitted] • Process focused mechanistic models are widely used but are application specific. • Deep learning (DL) has applications in prediction, control, sensing, and data analytics. • DL is data-hungry and the lack of high-quality data sets is a key issue for training. • Improve training by transfer learning, and unfitted mechanistic models. [ABSTRACT FROM AUTHOR]

Potential biomarkers were identified for in vitro sensitivity to the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib in head and neck cancer. Gefitinib sensitivity was determined in cell lines, followed by transcript profiling coupled with a novel pathway analysis approach. Eleven cell lines were highly sensitive to gefitinib (inhibitor concentration required to give 50% growth inhibition [GI(50)] < 1 microM), three had intermediate sensitivity (GI(50) 1-7 microM), and six were resistant (GI(50) > 7 microM); an exploratory principal component analysis revealed a separation between the genomic profiles of sensitive and resistant cell lines. Subsequently, a hypothesis-driven analysis of Affymetrix data (Affymetrix, Inc., Santa Clara, CA, USA) revealed higher mRNA levels for E-cadherin (CDH1); transforming growth factor, alpha (TGF-alpha); amphiregulin (AREG); FLJ22662; EGFR; p21-activated kinase 6 (PAK6); glutathione S-transferase Pi (GSTP1); and ATP-binding cassette, subfamily C, member 5 (ABCC5) in sensitive versus resistant cell lines. A hypothesis-free analysis identified 46 gene transcripts that were strongly differentiated, seven of which had a known association with EGFR and head and neck cancer (human EGF receptor 3 [HER3], TGF-alpha, CDH1, EGFR, keratin 16 [KRT16], fibroblast growth factor 2 [FGF2], and cortactin [CTTN]). Polymerase chain reaction (PCR) and enzyme-linked immunoabsorbant assay analysis confirmed Affymetrix data, and EGFR gene mutation, amplification, and genomic gain correlated strongly with gefitinib sensitivity. We identified biomarkers that predict for in vitro responsiveness to gefitinib, seven of which have known association with EGFR and head and neck cancer. These in vitro predictive biomarkers may have potential utility in the clinic and warrant further investigation.