Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Sediment dynamics on intertidal mudflats: A study based on in situ measurements and numerical modelling

Tidal flats provide essential ecosystem services (e.g. coastal protection and function as habitats sustaining coastal food webs). They are also under pressure due to climate change and human interventions. To investigate the sediment dynamic processes on tidal flats for a better understanding of the morphological development, in situ measurements were carried out on three tidal flats in the Yangtze Estuary (China) and in the Westerschelde Estuary (the Netherlands). The results show that erosion and deposition stages alternate due to the competition between hydrodynamic forces and bed strength, and due to variability of suspended sediment concentration. A bed-level change (BLC) model has been developed based on this understanding. Sediment dynamic processes on tidal flats are governed by tidal forcing exhibiting neap-spring variation, as well as by random wind events. The impacts from wind events were investigated by an integrated approach based on in situ measurements and the BLC model. This integrated approach also helped to reveal the spatial variability of bed erosional characteristics of tidal flats under influence of diatoms. This work not only improves the prediction of morphological changes of tidal flats, but also has implications for coastal engineering and for studies of coastal ecology and the coastal environment.Coastal Engineerin

The application perspective of mutatoin testing

The main goal of this thesis is to investigate, improve and extend the applicability of mutation testing. To seek the potential directions of how to improve and extend the applicability of mutation testing, we have started with a systematic literature review on the current state of how mutation testing is applied. The results from the systematic literature review have further guided us towards three directions of research: (1) speeding up mutation testing; (2) deepening our understanding ofmutation testing; (3) exploring new application domains ofmutation testing. For the first direction, we have leveraged compression techniques and weak mutation information to speed up mutation testing. The results have shown our proposed mutant compression techniques can effectively speed up strong mutation testing up to 94.3 times with an accuracy > 90%. Given the second direction, we are interested in gaining a better understanding of mutation testing especially in the situation where engineers cannot kill all the mutants by just adding test cases. We have investigated the relationships between code quality regarding the testability and observability, and the mutation score. We have observed a correlation between observability metrics and the mutation score. Furthermore, relatively simple refactoring operations/adding tests enable an increase in the mutation score. As for the third direction, we have explored two new application domains: one is physical computing, and the other is GPU programming. In both application domains, we have designed new mutation operators based on our observations of the common mistakes that could happen during the implementation of the software. We have found promising results in that mutation testing can help in revealing weaknesses of the test suite for both application domains. In summary, we have improved the applicability of mutation by proposing a new speed-up approach and investigating the relationship between testability/observability and mutation testing. Also, we have extended the applicability of mutation testing in physical computing and GPU programming domains.Software Engineerin

Mutation Testing for Physical Computing

Physical computing, which builds interactive systems between the physical world and computers, has been widely used in a wide variety of domains and applications, e.g., the Internet of Things (IoT). Although physical computing has witnessed enormous realisations, testing these physical computing systems still face many challenges, such as potential circuit related bugs which are not part of the software problems, the timing issue which decreasing the testability, etc.; therefore, we proposed a mutation testing approach for physical computing systems to enable engineers to judge the quality of their tests in a more accurate way. The main focus is the communication between the software and peripherals. More particular, we first defined a set of mutation operators based on the common communication errors between the software and peripherals that could happen in the software. We conducted a preliminary experiment on nine physical computing projects based on the Raspberry Pi and Arduino platforms. The results show that our mutation testing method can assess the test suite quality effectively in terms of weakness and inadequacy.Accepted author manuscriptSoftware Engineerin

Massively Parallel, Highly Efficient, but What about the Test Suite Quality? Applying Mutation Testing to GPU Programs

Thanks to rapid advances in programmability and performance, GPUs have been widely applied in High Performance Computing (HPC) and safety-critical domains. As such, quality assurance of GPU applications has gained increasing attention. This brings us to mutation testing, a fault-based testing technique that assesses the test suite quality by systematically introducing small artificial faults. It has been shown to perform well in exposing faults. In this paper, we investigate whether GPU programming can benefit from mutation testing. In addition to conventional mutation operators, we propose nine GPU-specific mutation operators based on the core syntax differences between CPU and GPU programming. We conduct a preliminary study on six CUDA systems. The results show that mutation testing can effectively evaluate the test quality of GPU programs: conventional mutation operators can guide the engineers to write simple direct tests, while GPU-specific mutation operators can lead to more intricate test cases which are better at revealing GPU-specific weaknesses. Software Engineerin

A Systematic Literature Review of How Mutation Testing Supports Quality Assurance Processes

Mutation testing has been very actively investigated by researchers since the 1970s, and remarkable advances have been achieved in its concepts, theory, technology, and empirical evidence. While the most influential realisations have been summarised by existing literature reviews, we lack insight into how mutation testing is actually applied. Our goal is to identify and classify the main applications of mutation testing and analyse the level of replicability of empirical studies related to mutation testing. To this aim, this paper provides a systematic literature review on the application perspective of mutation testing based on a collection of 191 papers published between 1981 and 2015. In particular, we analysed in which quality assurance processes mutation testing is used, which mutation tools and which mutation operators are employed. Additionally, we also investigated how the inherent core problems of mutation testing, ie, the equivalent mutant problem and the high computational cost, are addressed during the actual usage. The results show that most studies use mutation testing as an assessment tool targeting unit tests, and many of the supporting techniques for making mutation testing applicable in practice are still underdeveloped. Based on our observations, we made 9 recommendations for future work, including an important suggestion on how to report mutation testing in testing experiments in an appropriate manner.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin

Speeding-Up Mutation Testing via Data Compression and State Infection

Mutation testing is widely considered as a high-end test criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, its scalability issue remains in practice. In this paper, we introduce a novel method to speed up mutation testing based on state infection information. In addition to filtering out uninfected test executions, we further select a subset of mutants and a subset of test cases to run leveraging data-compression techniques. In particular, we adopt Formal Concept Analysis (FCA) to group similar mutants together and then select test cases to cover these mutants. To evaluate our method, we conducted an experimental study on six open source Java projects. We used EvoSuite to automatically generate test cases and to collect mutation data. The initial results show that our method can reduce the execution time by 83.93% with only 0.257% loss in precision.Software Engineerin

ShuffleFL: Addressing Heterogeneity in Multi-Device Federated Learning

Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative deep learning model training across distributed data silos. Despite its importance, FL faces challenges such as high latency and less effective global models. In this paper, we propose ShuffleFL, an innovative framework stemming from the hierarchical FL, which introduces a user layer between the FL devices and the FL server. ShuffleFL naturally groups devices based on their affiliations, e.g., belonging to the same user, to ease the strict privacy restriction-"data at the FL devices cannot be shared with others", thereby enabling the exchange of local samples among them. The user layer assumes a multi-faceted role, not just aggregating local updates but also coordinating data shuffling within affiliated devices. We formulate this data shuffling as an optimization problem, detailing our objectives to align local data closely with device computing capabilities and to ensure a more balanced data distribution at the intra-user devices. Through extensive experiments using realistic device profiles and five non-IID datasets, we demonstrate that ShuffleFL can improve inference accuracy by 2.81% to 7.85% and speed up the convergence by 4.11x to 36.56x when reaching the target accuracy.Embedded System