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

Model order reduction and substructuring methods for nonlinear structural dynamics

Dynamic analysis of large-size finite element models has been commonly applied by mechanical engineers to simulate the dynamic behavior of complex structures. The ever-increasing demand for both detailed and accurate simulation of complex structures forces mechanical engineers to pursue a balance between two conflicting goals during the simulations: low computational cost and high accuracy. These goals become extremely difficult for geometric nonlinear structural dynamical problems. When geometrical nonlinearities are introduced, the internal force vector and Jacobians are configuration dependent, and the corresponding updates are computationally expensive. This thesis presents nonlinear model order reduction techniques that aim to perform detailed dynamic analysis of multi-component structures with reduced computational cost, without degrading the accuracy too much. Special attention is given to flexible multibody system dynamics.For multi-component structures featuring many interface degrees of freedom, standard substructuring dynamics can be combined with interface reduction techniques to obtain compact reduced order models. Chapter~2 summarized a variety of interface reduction techniques for the well-known Craig-Bampton substructuring method. These approaches are reviewed and compared in terms of both computational cost and accuracy. A multilevel interface reduction method is presented as a more generalized approach, where a secondary Craig-Bampton reduction is performed when the subsystems are assembled within localized subsets. The multilevel interface reduction method provides an accurate representation of the full linear model with significantly lower computational cost. In Chapter~3, we extend the Craig-Bampton method to geometric nonlinear problems by augmenting the system-level interface modes and internal vibration modes of each substructure with their corresponding modal derivatives. The modal derivatives are capable of describing the bending-stretching coupling effects exhibited by geometric nonlinear structures. Once the reduced order model is constructed by Galerkin projection, the upcoming challenge is the computation of the reduced nonlinear internal force vectors and tangent matrices during the time integration. The evaluation of these objects scales with the size of the full order model, and it is therefore expensive, as it needs to be repeated multiple time within every time step of the time integration. To address this problem, we directly express the reduced nonlinear vectors and matrices as a polynomial function of the modal coordinates, using substructure-level higher-order tensors with much smaller size. This enhanced Craig-Bampton method offers flexibility for reduced modal basis construction, as modal derivatives need to be computed only for substructures actually featuring geometrical nonlinearities, and do not need the prior knowledge of the nonlinear response of the full system with training load cases.For flexible multibody systems, each body undergoes both overall rigid body motion and flexible behavior. To describe the dynamic behavior of each body accurately, the floating frame of reference is commonly applied. In Chapter~4, the enhanced Craig-Bampton method, as proposed in Chapter~3, is embedded in the floating frame of reference. We consider here structures modeled with von-Karman beam elements. Interface reduction methods are in this context unnecessary since the adjacent bodies are connected through a single node. The proposed reduction method constitutes a natural and effective extension of the classical linear modal reduction in the floating frame.For more complex geometries, like wind turbine blades, extremely simplified beam models can not capture the complexity of the real three-dimensional structure, and therefore the dynamic behavior might not be accurately modeled. In Chapter~5, we present an enhanced Rubin substructuring method for three-dimensional nonlinear multibody systems. The standard Rubin reduction basis is augmented with the modal derivatives of both the free-interface vibration modes and the attachment modes to include bending-stretching coupling effects triggered by the nonlinear vibrations. When compared to the enhanced Craig-Bampton method proposed in Chapter~4, the enhanced Rubin method better reproduces the geometrical nonlinearities occurring at the interface, and, as a consequence, higher accuracy can be achieved.In Chapter~6, the overall conclusions are drawn and recommendations for further study are provided.Dynamics of Micro and Nano SystemsStructural Optimization and Mechanic

Testing STT-MRAM: Manufacturing Defects, Fault Models, and Test Solutions

As STT-MRAM mass production and deployment in industry is around the corner, high-quality yet cost-efficient manufacturing test solutions are crucial to ensure the required quality of products being shipped to end customers. This dissertation focuses on STT-MRAM testing, covering three abstraction levels: manufacturing defects, fault models, and test solutions. We apply the advanced device-aware test (DAT) approach to STT-MRAM defects, including resistive defects on interconnects and STT-MRAM device-internal defects such as pinhole defects, synthetic anti-ferromagnet flip defects, intermediate state defects. With the derived accurate defect models calibrated by silicon data, a comprehensive fault analysis based on SPICE circuit simulations is performed. STT-MRAM unique faults are identified, including both permanent faults and intermittent faults. Based on the obtain fault models, high-quality test solutions are proposed. Additionally, this dissertation also explores the impact of magnetic coupling and density on STT-MRAM performance for robust designs.Computer Engineerin

Development and application of experimental and modeling tools for In vivo kinetic analysis in S. Cerevisiae

Abstract not availableApplied Science

An Ultra-Low-Power ADPLL for BLE Applications

In recent years, wireless personal area network (WPAN) applications have triggered the needs for low-cost and low-power PLLs which also provide good performance. All-digital phased-locked loops (ADPLLs) are preferred over their analog counterparts in nanoscale CMOS technology due to their flexibility, configurability, small area and easy portability. However, fractional spurs and insufficiently low power dissipation are main problems related to conventional TDC-based structures. In this work, a sub-half mW 2.2 GHz - 3 GHz fractional-N ADPLL is presented for Bluetooth Low Energy (BLE) applications. Coarse-fine DTC based phase predictor with dynamic element matching (DEM) ability and clock gated phase error freezer are proposed to reduce the power while maintaining good phase noise and fractional spur performance. This prototype ADPLL was taped out on Sep. 11th 2014 in GlobalFoundries 40 nm Low Power (40 nm-LP) technology. Based on post-layout simulations and modelling, it is expected to consume less than 450 ?W with integrated rms jitter of 1.5 ps for the close integer channel and 800 fs for the rest of channels, leading to a potential state-of-art FoM below -240 dB. Design of the full ADPLL in terms of system level analysis, digital logic, mixed-signal and RF design is presented in the thesis.ELCAMicroelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc

On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural networks contain at least three types of layers: convolutional, pooling, and dense layers. Although the definition of pooling layers causes a large impact on neural network performance, a study on pooling hyperparameters effect on side-channel analysis is still not provided in the academic community. This paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets.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.Cyber Securit

Modal Derivatives based Reduction Method for Finite Deflections in Floating Frame

Model order reduction techniques are widely applied in the floating frame of reference. The use of linear vibration modes, however, is not applicable when the elastic deformations become finite. In this paper, the non-linear elastic formulation, where the higher-order terms will be included in the strain energy expression to consider the bending-stretching coupling effect, is applied in the floating frame of reference. In this case, the complexity of the formulation diminishes the advantages of the floating frame of reference formulation because of the relatively high computational cost. Therefore, the linear reduction basis of vibration modes is augmented with the relevant modal derivatives to accurately reproduce the nonlinear elastic deformation on the reduced basis. The numerical results presented in this paper demonstrate that the proposed approach can be applied to accurately investigate problems featuring arbitrary large rigid body rotations and finite elastic displacements.Precision and Microsystems EngineeringMechanical, Maritime and Materials Engineerin

Nonlinear model order reduction for flexible multibody dynamics: A modal derivatives approach

An effective reduction technique is presented for flexible multibody systems, for which the elastic deflection could not be considered small. We consider here the planar beam systems undergoing large elastic rotations, in the floating frame description. The proposed method enriches the classical linear reduction basis with modal derivatives stemming from the derivative of the eigenvalue problem. Furthermore, the Craig–Bampton method is applied to couple the different reduced components. Based on the linear projection, the configuration-dependent internal force can be expressed as cubic polynomials in the reduced coordinates. Coefficients of these polynomials can be precomputed for efficient runtime evaluation. The numerical results show that the modal derivatives are essential for the correct approximation of the nonlinear elastic deflection with respect to the body reference. The proposed reduction method constitutes a natural and effective extension of the classical linear modal reduction in the floating frame.Precision and Microsystems EngineeringMechanical, Maritime and Materials Engineerin

Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders

In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that deep learning attacks will always succeed. Various countermeasures make attacks significantly more complex, and such countermeasures can be further combined to make the attacks even more challenging. An intuitive solution to improve the performance of attacks would be to reduce the effect of countermeasures.This paper investigates whether we can consider certain types of hiding countermeasures as noise and then use a deep learning technique called the denoising autoencoder to remove that noise. We conduct a detailed analysis of six different types of noise and countermeasures separately or combined and show that denoising autoencoder improves the attack performance significantly.Cyber Securit