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

Precise point positioning on the reliable detection of tropospheric model errors

Precise point positioning (PPP) is one of the well-known applications of Global Navigation Satellite System (GNSS) and provides precise positioning solutions using accurate satellite orbit and clock products. The tropospheric delay due to the neutral atmosphere for microwave signals is one of the main sources of measurement error in PPP. As one component of this delay, the hydrostatic delay is usually compensated by using an empirical correction model. However, how to eliminate the effects of the wet delay during a weather event is a challenge because current troposphere models are not capable of considering the complex atmosphere around the receiver during situations such as typhoons, storms, heavy rainfall, et cetera. Thus, how positioning results can be improved if the residual wet delays are taken into account needs to be investigated . In this contribution, a real-time procedure of recursive detection, identification and adaptation (DIA) is applied to detect the model errors which have the same effects on both phase and code observables; e.g., the model error caused by the tropospheric delay. Once the model errors are identified, additional parameters are added to the functional model to account for the measurement residuals. This approach is evaluated with Global Positioning System (GPS) data during two rainfall events in Darwin, Australia, proving the usefulness of compensated residual slant wet delay for positioning results. Comparisons with the standard approach show that the precision of the up component is improved significantly during the periods of the weather events; for the two case studies, 72.46% and 64.41% improvements of root mean squared error (RMS) resulted, and the precision of the horizontal component obtained by the proposed approach is also improved more than 30% compared to the standard approach. The results also show that the identified model errors are concentrated at the beginning of both heavy rainfall processes when the front causes significant spatial and temporal gradients of the integrated water vapor above the receiver.Mathematical Geodesy and Positionin

An Optimal Troposphere Tomography Technique Using the WRF Model Outputs and Topography of the Area

The water vapor content in the atmosphere can be reconstructed using the all-weather condition troposphere tomography technique. In common troposphere tomography, the water vapor of each voxel is represented by an unknown parameter. This means that when the desired spatial resolution is high or study area is large, there will be a huge number of unknown parameters in the problem that need to be solved. This defect can reduce the accuracy of troposphere tomography results. In order to overcome this problem, an optimal voxel-based troposphere tomography using the Weather Research and Forecasting (WRF) model is proposed. The new approach reduces the number of unknown parameters, the number of empty voxels and the role of constraints required to enhance the spatial resolution of tomography results in required areas. Furthermore, the effect of considering the topography of the study area in the tomography model is examined. The obtained water vapor is validated by radiosonde observations and Global Positioning System (GPS) positioning results. Comparison of the results with the radiosonde observations shows that using the WRF model outputs and topography of the area can reduce the Root Mean Square Error (RMSE) by 0.803 gr/m3. Validation using positioning shows that in wet weather conditions, the WRF model outputs and topography reduce the RMSE of the east, north and up components by about 17.42, 10.46 and 20.03 mm, which are equivalent to 46.01%, 35.78% and 53.93%, respectively.Mathematical Geodesy and Positionin

Comprehensive review on the transport and reaction of oxygen and moisture towards coupled oxidative ageing and moisture damage of bitumen

Oxidative ageing and moisture damage are key factors in bitumen degradation and asphalt pavement deterioration. The effects of oxygen and moisture on bitumen are governed by their transport and reaction processes. This paper provides an overview of theories and concepts developed to describe the kinetics, thermodynamics and mechanisms of transport and reaction of moisture and oxygen within bitumen. The moisture- and oxygen-induced changes of the physicochemical and mechanical properties of bitumen are also discussed. The aim is to summarize literature findings and conclusions and discuss the possibilities of establishing coupled moisture-oxygen models to be used for long-term pavement performance predictions.Pavement Engineerin

ANANKE: a Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows: Technical Report DS-2017-001

Complex workflows that process sensor data are useful for industrial infrastructure management and diagnosis. Although running such workflows in clouds promises reduces operational costs, there are still numerous scheduling challenges to overcome. Such complex workflows are dynamic, exhibit periodic patterns, and combine diverse task groupings and requirements. In this work, we propose ANANKE, a scheduling system addressing these challenges. Our approach extends the state-of-the-art in portfolio scheduling for datacenters with a reinforcement-learning technique, and proposes various scheduling policies for managing complex workflows. Portfolio scheduling addresses the dynamic aspect of the workload. Reinforcement learning, based in this work on Q-learning, allows our approach to adapt to the periodic patterns of the workload, and to tune the other configuration parameters. The proposed policies are heuristics that guide the provisioning process, and map workflow tasks to the provisioned cloud resources. Through real-world experiments based on real and synthetic industrial workloads, we analyze and compare our prototype implementation of ANANKE with a system without portfolio scheduling (baseline) and with a system equipped with a standard portfolio scheduler. Overall, our experimental results give evidence that a learningbased portfolio scheduler can perform better (5–20%) and cost less (20–35%) than the considered alternatives.Distributed System

ANANKE: a Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows

Complex workflows that process sensor data are useful for industrial infrastructure management and diagnosis. Although running such workflows in clouds promises reduced operational costs, there are still numerous scheduling challenges to overcome. Such complex workflows are dynamic, exhibit periodic patterns, and combine diverse task groupings and requirements. In this work, we propose ANANKE, a scheduling system addressing these challenges. Our approach extends the state-of-the-art in portfolio scheduling for data centers with a reinforcement-learning technique, and proposes various scheduling policies for managing complex workflows. Portfolio scheduling addresses the dynamic aspect of the workload. Q-learning, allows our approach to adapt to the periodic patterns of the workload, and to tune the other configuration parameters. The proposed policies are heuristics that guide the provisioning process, and map workflow tasks to the provisioned cloud resources. Through real-world experiments based on real and synthetic industrial workloads, we analyze and compare our prototype implementation of ANANKE with a system without portfolio scheduling (baseline) and with a system equipped with a standard portfolio scheduler. Overall, our experimental results give evidence that a learning-based portfolio scheduler can perform better and consume fewer resources than state-of-the-art alternatives, in particular for workloads with uniform arrival patterns.Distributed System

Assessing the performance of multi-GNSS PPP-RTK in the local area

The benefits of an increased number of global navigation satellite systems (GNSS) in space have been confirmed for the robustness and convergence time of standard precise point positioning (PPP) solutions, as well as improved accuracy when (most of) the ambiguities are fixed. Yet, it is still worthwhile to investigate fast and high-precision GNSS parameter estimation to meet user needs. This contribution focuses on integer ambiguity resolution-enabled Precise Point Positioning (PPP-RTK) in the use of the observations from four global navigation systems, i.e., GPS (Global Positioning System), Galileo (European Global Navigation Satellite System), BDS (Chinese BeiDou Navigation Satellite System), and GLONASS (Global’naya Navigatsionnaya Sputnikova Sistema). An undifferenced and uncombined PPP-RTK model is implemented for which the satellite clock and phase bias corrections are computed from the data processing of a group of stations in a network and then provided to users to help them achieve integer ambiguity resolution on a single receiver by calibrating the satellite phase biases. The dataset is recorded in a local area of the GNSS network of the Netherlands, in which 12 stations are regarded as the reference to generate the corresponding corrections and 21 as the users to assess the performance of the multi-GNSS PPP-RTK in both kinematic and static positioning mode. The results show that the root-mean-square (RMS) errors of the ambiguity float solutions can achieve the same accuracy level of the ambiguity fixed solutions after convergence. The combined GNSS cases, on the contrary, reduce the horizontal RMS of GPS alone with 2 cm level to GPS + Galileo/GPS + Galileo + BDS/GPS + Galileo + BDS + GLONASS with 1 cm level. The convergence time benefits from both multi-GNSS and fixing ambiguities, and the performances of the ambiguity fixed solution are comparable to those of the multi-GNSS ambiguity float solutions. For instance, the convergence time of GPS alone ambiguity fixed solutions to achieve 10 cm three-dimensional (3D) positioning accuracy is 39.5 min, while it is 37 min for GPS + Galileo ambiguity float solutions; moreover, with the same criterion, the convergence time of GE ambiguity fixed solutions is 19 min, which is better than GPS + Galileo + BDS + GLONASS ambiguity float solutions with 28.5 min. The experiments indicate that GPS alone occasionally suffers from a wrong fixing problem; however, this problem does not exist in the combined systems. Finally, integer ambiguity resolution is still necessary for multi-GNSS in the case of fast achieving very-high-accuracy positioning, e.g., sub-centimeter level.Mathematical Geodesy and Positionin

Sustainable designed pavement materials

This Special Issue "Sustainable Designed Pavement Materials" has been proposed and organized as a means to present recent developments in the field of environmentally-friendly designed pavement materials. For this reason, articles included in this special issue relate to different aspects of pavement materials, from industry solid waste recycling to pavement materials recycling, from pavement materials modification to asphalt performance characterization, from pavement defect detection to pavement maintenance, and from asphalt pavement to cement concrete pavement, as highlighted in this editorial.Pavement Engineerin

Thermodynamics and kinetics of moisture transport in bitumen

Moisture in bitumen and at the bitumen-aggregate interface affects the cohesive and adhesive properties of asphalt mixtures, which are critical for the service performance and durability of pavements. This paper aims to investigate the kinetics and thermodynamics of moisture transport in bitumen at various temperatures and relative humidity for different bitumen types. Transport models are introduced to study the moisture transport mechanisms. A parameter optimization approach combined with the finite element method is applied to simulate moisture transport behavior. Results show salient sorption increase at higher relative humidity levels (more than 70%), indicating the occurrence of clustering of water molecules in bitumen, which can lead to a significant decrease of the diffusion coefficient. Transport models show great quality in simulating experimental results, in which the S-Cluster model provides a detailed explanation of the moisture transport mechanisms and describes better the performance at high sorption levels. The diffusion coefficient, cluster size and activation energy were determined and were found to be linked to the bitumen chemical and structural properties. The transport kinetics and thermodynamics are expected to contribute to a comprehensive understanding of moisture transport behavior in bitumen and further of pavement moisture damage at complex and interacting environmental conditions.Pavement Engineerin

Basis of Design for a process to refine green plants

DelftChemTechApplied Science