First application of Markov chain Monte Carlo-based Bayesian data analysis to the Doppler-shift attenuation method

Motivated primarily by the large uncertainties in the thermonuclear rate of the 30P(p,γ)31S reaction that limit our understanding of classical novae, we carried out lifetime measurements of 31S excited states using the Doppler Shift Lifetimes (DSL2) facility at the TRIUMF Isotope Separator and Accelerator (ISAC-II) facility. The 31S excited states were populated by the 3He(32S,α)31S reaction. The deexcitation γ rays were detected by a clover-type high-purity germanium detector in coincidence with the α particles detected by a silicon detector telescope. We have applied modern Markov chain Monte Carlo-based Bayesian statistical techniques to perform lineshape analyses of Doppler-shift attenuation method γ-ray data for the first time. We have determined the lifetimes of the two lowest-lying 31S excited states. First experimental upper limits on the lifetimes of four higher-lying states have been obtained. The experimental results were compared to shell-model calculations using five universal sd-shell Hamiltonians. Evidence for γ rays originating from the astrophysically important Jπ=3/2+, 260-keV 30P(p,γ)31S resonance with an excitation energy of Ex=6390.2(7) keV in 31S has also been observed, although strong constraints on the lifetime will require better statistics

Differentiation-inducing and anti-proliferative activities of isoliquiritigenin and all-trans-retinoic acid on B16F0 melanoma cells: Mechanisms profiling by RNA-seq

Melanoma is a cancer that arises from melanocytes, specialized pigmented cells that are found predominantly in the skin. The incidence of malignant melanoma has significantly increased over the last decade. With the development of therapy, the survival rate of some kind of cancer has been improved greatly. But the treatment of melanoma remains unsatisfactory. Much of melanoma's resistance to traditional chemotherapy is believed to arise intrinsically, by virtue of potent growth and cell survival-promoting genetic alteration. Therefore, significant attention has recently been focused on differentiation, therapy, as well as differentiation inducer compounds. In previous study, we found isoliquiritigenin (ISL), a natural product extracted from licorice, could induce B16F0 melanoma cell differentiation. Here we investigated the transcriptional response of melanoma differentiation process induced by ISL and all-trans-retinoic acid (RA). Results showed that 390 genes involves in 201 biochemical pathways were differentially expressed in ISL treatment and 304 genes in 193 pathways in RA treatment. Differential expressed genes (DGEs, fold-change (FC) >= 10) with the function of anti-proliferative and differentiation inducing indicated a loss of grade malignancy characteristic. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated glutathione metabolism, glycolysis/gluconeogenesis and pentose phosphate pathway were the top three relative pathway perturbed by ISL, and mitogen-activated protein kinase (MAPK) signaling pathway was the most important pathway in RA treatment. In the analysis of hierarchical clustering of DEGs, we discovered 72 DEGs involved in the process of drug action. We thought Cited1, Tgm2, Xaf1, Cd59a, Fbxo2, Adh7 may have critical role in the differentiation of melanoma. The evidence displayed herein confirms the critical role of reactive oxygen species (ROS) in melanoma pathobiology and provides evidence for future targets in the development of next-generation biomarkers and therapeutics. (C) 2016 Elsevier B.V. All rights reserved

Effect of strain rate on yielding strength of a Zr-based bulk metallic glass

Uniaxial tension and compression experiments were performed on a typical Zr52.5Cu17.9N44.6Al10Ti5 (Vit 105) bulk metallic glass over a wide range of strain rates at room temperature. It is found that the strain rate effect of the yielding strength will change from insensitive to negative with increasing strain rate above a critical value. This phenomenon can be quantitatively described by a modified cooperative-shear model of shear transformation zones that takes the adiabatic temperature rise into account. The model predicts well the present and other experimental data

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

Asymmetric Migration of Human Keratinocytes under Mechanical Stretch and Cocultured Fibroblasts in a Wound Repair Model

Keratinocyte migration during re-epithelization is crucial in wound healing under biochemical and biomechanical microenvironment. However, little is known about the underlying mechanisms whereby mechanical tension and cocultured fibroblasts or keratinocytes modulate the migration of keratinocytes or fibroblasts. Here we applied a tensile device together with a modified transwell assay to determine the lateral and transmembrane migration dynamics of human HaCaT keratinocytes or HF fibroblasts. A novel pattern of asymmetric migration was observed for keratinocytes when they were cocultured with non-contact fibroblasts, i.e., the accumulative distance of HaCaT cells was significantly higher when moving away from HF cells or migrating from down to up cross the membrane than that when moving close to HF cells or when migrating from up to down, whereas HF migration was symmetric. This asymmetric migration was mainly regulated by EGF derived from fibroblasts, but not transforming growth factor alpha or beta_1 production. Mechanical stretch subjected to fibroblasts fostered keratinocyte asymmetric migration by increasing EGF secretion, while no role of mechanical stretch was found for EGF secretion by keratinocytes. These results provided a new insight into understanding the regulating mechanisms of two or three-dimensional migration of keratinocytes or fibroblasts along or across dermis and epidermis under biomechanical microenvironment

Open Aircraft Performance Modeling: Based on an Analysis of Aircraft Surveillance Data

A large number of stakeholders exist in the modern air traffic management ecosystem. Air transportation studies benefit from collaboration and the sharing of knowledge and findings between these different players. However, not all parties have equal access to information. Due to the lack of open-source tools and models, it is not always possible to undertake comparative studies and to repeat experiments. The barriers to accessing proprietary tools and models create major limitations in the field of air traffic management research. This dissertation investigates the methods necessary to construct an aircraft performance model based on open data, which can be used freely and redistributed without restrictions. The primary data source presented in this dissertation is aircraft surveillance data that can be intercepted openly with little to no restriction in most regions of the world. The eleven chapters in this dissertation follow the sequence of open data, open models, and performance estimations. This order corresponds to the three main parts of the dissertation. In the first part of the dissertation, open surveillance data is explored. Methods are developed to decode and process this data. Extraction of information is also made possible thanks to machine learning algorithms. The second part of the dissertation examines the main components of the open aircraft performance model. Models related to kinematics, thrust, drag polar, fuel flow, and weather are investigated. The third part of the dissertation looks into the possibility of using surveillance data to estimate aircraft performance parameters, for example, aircraft turn performance, aircraft mass, and thrust settings, for individual flights. With the goal of making future air traffic management studies more transparent, comparable, and reproducible, the models and tools proposed in this dissertation are fully open. The final aircraft performance model, OpenAP, proposed in this dissertation has proven to be an efficient open alternative to current closed-source models.Control & Simulatio

Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets

AUTOMATIC dependent surveillance–broadcast (ADS-B) [1,2] is widely implemented in modern commercial aircraft and will become mandatory equipment in 2020. Flight state information such as position, velocity, and vertical rate are broadcast by tens of thousands of aircraft around the world constantly using onboard ADS-B transponders. These data are identified by a 24-bit International Civil Aviation Organization (ICAO) address, are unencrypted, and can be received and decoded with simple ground station set-ups. This large amount of open data brings a huge potential for ATM research. Most studies that rely on aircraft flight data (historical or real-time) require knowledge on the flight phase of each aircraft at a given time [3–7]. However, when dealing with large datasets such as from ADS-B, which can contain many tens of thousands of flights, exceptions to deterministic definitions of flight phases are inevitable, due to large variances in climb rate, altitude, velocity, or a combination of these. In this case, instead of using deterministic logic to process and extract flight data based on flight conventions, robust and versatile identification algorithms are required. In this paper, a twofold method is proposed and tested: 1) a machine learning clustering step that can handle large amounts of scattered ADS-B data to extract continuous flights, and 2) a flight phase identification step that can segment flight data of any type of aircraft and trajectory by different flight phases.Control & Simulatio

WRAP: An open-source kinematic aircraft performance model

Open access to flight data from Automatic Dependent Surveillance-Broadcast (ADS-B) has provided researchers with more insights for air traffic management than aircraft tracking alone. With large quantities of trajectory data collected from a wide range of different aircraft types, it is possible to extract accurate aircraft performance parameters. In this paper, a set of more than thirty parameters from seven distinct flight phases are extracted for common commercial aircraft types. It uses various data mining methods, as well as a maximum likelihood estimation approach to generate parametric models for these performance parameters. All parametric models combined can be used to describe a complete flight that includes takeoff, initial climb, climb, cruise, descent, final approach, and landing. Both analytical results and summaries are shown. When available, optimal parameters from these models are also compared with the Base of Aircraft Data and the Eurocontrol aircraft performance database. This research presents a comprehensive set of methods for extracting different aircraft performance parameters. It also provides the first set of open parametric performance data for common aircraft types. All model data are published as open data under a flexible open-source license.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.Control & Simulatio

Aerosol Absorption from Global Satellite Measurements in the Ultra-Violet: From Qualitative Aerosol Index to Quantitative Aerosol Absorptive Properties

Atmospheric aerosols are solid or liquid particles suspended in the air. The majority of them are produced by natural processes, including sea salt from oceans, mineral dust from (semi-)arid regions, carbon containing particles from wildfires, and sulfates and ash from volcanic activities. Anthropogenic aerosols are produced by industrial activities, power generation, transportation, agriculture, and human-induced biomass burning events. Depending on the meteorological conditions, aerosol particles can stay in the atmosphere for several hours to several months and can be transported over long distances, causing adverse effects on human health, visibility and climate.This thesis focuses on the aerosol optical properties, particularly the light absorption of the aerosol particles that has significant effects on the Earth’s climate system. This thesis starts with a general introduction of atmospheric aerosols, including its sources, categories, physical properties and measurement techniques (Chapter 1). Next, the Ultra-Violet Aerosol Index (UVAI) is introduced, which is calculated from satellite measurements of the radiance at two wavelengths in the UV. UVAI contains information of aerosol absorption, and it has a very long andalmost continuous data record starting in 1978. Direct use of UVAI is challenging because it is not a geophysical quantity, but a numerical index. The objective of this thesis is to derive quantitative properties on aerosol absorption from the UVAI (e.g. single scattering albedo, absorption aerosol optical depth) that can be directly used in aerosol radiative transfer assessments. Two types of methods have been developed, i.e. physically-based methods and statistically-based methods. The first compares the observed UVAI to the one simulated by radiative transfer models. The second uses Machine Learning algorithms trained by existing data sets.The physically-based methods have been applied to quantify aerosol absorption of several large scale wildfires (Chapter 2 and 3). An important challenge of these method is that assumptions have to be made on the aerosol micro-physical properties, leading to significant uncertainties in the results, whereas theMachine Learning-based methods can avoid this kind of assumptions. Chapter 3 investigates the feasibility to quantify aerosol absorption from UVAI using a Machine Learning algorithm. Despite the higher computational efficiency and better results, the application of such data-driven methods is still restricted by the limited data on the aerosol vertical distribution. Therefore, in Chapter4, a database of aerosol height is created from a chemistry transport model. This database is applied in Chapter 5, where a Deep Neural Network method is used to derive the quantitative aerosol absorptive properties from the OMI/Aura UVAI for the period from 2006 to 2019. In comparison to ground-based observations, the results of the Deep Neural Network agree better than satellite retrievals and also better than chemistry transport model simulations.This thesis demonstrates the feasibility of deriving quantitative aerosol absorptive properties from the satellite retrieved UVAI.We use traditional radiative transfer simulations meanwhile investigating the new possibilities of data-driven methods in aerosol remote sensing. Although the retrieval results are encouraging, there remain limitations and challenges which need to be addressed. These are discussed in Chapter 6 with corresponding suggestions and prospects. Despite the challenges, it is expected that a synthetic database of global aerosol absorption can be derived fromUVAI observations provided by multiple satellite products. Such a data set will make great contributions to quantify the effect of absorbing aerosols on the climate system.Atmospheric Remote Sensin

Develop a LES-based air quality model by nesting DALES in LOTOS-EUROS

Nitrogen dioxide (NO2) is one of the nitrogen oxides (NOx) pollutants. Not only the NO2 itself is toxic to human health, but also the precursors of a number of hazardous secondary air pollutants, such as nitric acid, tropospheric ozone and nitrate component of particulate matters. Besides, NO2 is also an essential substances involving in ozone destruction in the stratosphere. The main source of NO2 over urban is combustion processes from traffic. Jeopardized by the severe situation, the monitoring and observation to this harmful trace gas is important. For urban regions, the in-situ and remote sensing techniques are combined. However, these measurements can be problematic due to the meteorological conditions or atmospheric processes, such as clouds. Besides, the retrieval of the measurements provides limited information on concentration fields under various a-priori assumptions. Alternatively, the atmospheric dispersion modeling is in use to study the air quality, which provides a more complete deterministic description of pollutants dispersion problem. Currently, the dominating atmospheric dispersion models are based on the parameterization. These models are efficient to simulate meso-scale or macro-scale atmospheric dispersion, with spatial resolution of magnitude of kilometers. Considering on urban scale, however, this resolution is too coarse to resolve the air pollutants, where the emission sources are close to receptors. Instead, a more effective technique is large eddy simulation (LES). It applies a low-pass filter that effective removes small-scale turbulences from numerical calculation. By nesting DALES (Dutch Atmospheric Large Eddy Simulation) into LOTOS-EUROS (LOng Term Ozone Simulation-EURopean Operational Smog model), an air quality module is developed to evaluate the LES-based air quality model by comparing with LOTOS-EUROS. The conclusion of this thesis consists of two parts. The first one is the sensitivity study, where the properties of DALES original chemical module are explored. These properties includes the sensitivity of NO2 concentration to background ozone level, reaction rate coefficient, clouds perturbation and turbulent control. In the second part, simulation over Rotterdam is operated on a relative coarse resolution, as the consequence of the limitation of restore space and process capability. The slab averaged profiles are not significantly different because of the strongly constrained concentration boundary condition by LOTOS-EUROS. Although attempt to study the difference of concentration field due to the dynamics scheme is not achieved, DALES still has much higher resolution compared with LOTOS-EUROS. Hence, the spatial variability in DALES is more detailed. Conclusively, the DALES air quality module performs consistently with LOTOS-EUROS. The improvement in terms of chemical mechanism, the emission inventory, the capability of processing. etc. will complete this module in future.Civil Engineering and GeosciencesGeoscience and Remote Sensin