Matrix-free multigrid block-preconditioners for higher order Discontinuous Galerkin discretisations

Efficient and suitably preconditioned iterative solvers for elliptic partial differential equations (PDEs) of the convection-diffusion type are used in all fields of science and engineering. To achieve optimal performance, solvers have to exhibit high arithmetic intensity and need to exploit every form of parallelism available in modern manycore CPUs. The computationally most expensive components of the solver are the repeated applications of the linear operator and the preconditioner. For discretisations based on higher-order Discontinuous Galerkin methods, sum-factorisation results in a dramatic reduction of the computational complexity of the operator application while, at the same time, the matrix-free implementation can run at a significant fraction of the theoretical peak floating point performance. Multigrid methods for high order methods often rely on block-smoothers to reduce high-frequency error components within one grid cell. Traditionally, this requires the assembly and expensive dense matrix solve in each grid cell, which counteracts any improvements achieved in the fast matrix-free operator application. To overcome this issue, we present a new matrix-free implementation of block-smoothers. Inverting the block matrices iteratively avoids storage and factorisation of the matrix and makes it is possible to harness the full power of the CPU. We implemented a hybrid multigrid algorithm with matrix-free block-smoothers in the high order DG space combined with a low order coarse grid correction using algebraic multigrid where only low order components are explicitly assembled. The effectiveness of this approach is demonstrated by solving a set of representative elliptic PDEs of increasing complexity, including a convection dominated problem and the stationary SPE10 benchmark.Comment: 28 pages, 10 figures, 10 tables; accepted for publication in Journal of Computational Physic

Effect of iodine on early stage thyroid autonomy

AbstractThyroid autonomy is a frequent cause of thyrotoxicosis in regions with iodine deficiency. Epidemiological data suggest that iodide may influence the course of pre-existing thyroid autonomy.Making use of FRTL-5 cells stably expressing a constitutively activating TSH receptor mutation as an in vitro model of thyroid autonomy, we investigated the impact of iodide on proliferation, function and changes in global gene expression.We demonstrate that iodine inhibits growth in TSHR WT and L629F mutant FRTL-5 cells and downregulates e.g. protocadherin cluster (Pcdha1–13) and thyroid responsive element (Thrsp). In addition functional genes e.g. iodotyrosine deiodinase (iyd) and oncogen junB are upregulated, while sodium-iodide-symporter (Nis) and thyroid peroxidase (Tpo) are downregulated by iodide.Iodide tunes down the biological activity of autonomous thyrocytes and may thus be of therapeutic benefit not only to prevent the occurrence of somatic TSHR mutations, causing thyroid autonomy, but also to slow down the development of clinically relevant disease

The ABCG2 efflux transporter from rabbit placenta: Cloning and functional characterization

AbstractIn human placenta, the ATP-binding cassette efflux transporter ABCG2 is highly expressed in syncytiotrophoblast cells and mediates cellular excretion of various drugs and toxins. Hence, physiological ABCG2 activity substantially contributes to the fetoprotective placenta barrier function during gestation. Developmental toxicity studies are often performed in rabbit. However, despite its toxicological relevance, there is no data so far on functional ABCG2 expression in this species. Therefore, we cloned ABCG2 from placenta tissues of chinchilla rabbit. Sequencing showed 84–86% amino acid sequence identity to the orthologues from man, rat and mouse. We transduced the rabbit ABCG2 clone (rbABCG2) in MDCKII cells and stable rbABCG2 gene and protein expression was shown by RT-PCR and Western blot analysis. The rbABCG2 efflux activity was demonstrated with the Hoechst H33342 assay using the specific ABCG2 inhibitor Ko143. We further tested the effect of established human ABCG2 (hABCG2) drug substrates including the antibiotic danofloxacin or the histamine H2-receptor antagonist cimetidine on H33342 accumulation in MDCKII-rbABCG2 or -hABCG2 cells. Human therapeutic plasma concentrations of all tested drugs caused a comparable competitive inhibition of H33342 excretion in both ABCG2 clones. Altogether, we first showed functional expression of the ABCG2 efflux transporter in rabbit placenta. Moreover, our data suggest a similar drug substrate spectrum of the rabbit and the human ABCG2 efflux transporter

Review of automated time series forecasting pipelines

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting

Review of automated time series forecasting pipelines

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting

Chiral dynamics and the growth of the nucleon's gluonic transverse size at small x

We study the distribution of gluons in transverse space in the nucleon at moderately small x (~10^{-2}). At large transverse distances (impact parameters) the gluon density is generated by the 'pion cloud' of the nucleon, and can be calculated in terms of the gluon density in the pion. We investigate the large-distance behavior in two different approaches to chiral dynamics: i) phenomenological soft-pion exchange, ii) the large-N_c picture of the nucleon as a classical soliton of the pion field, which corresponds to degenerate N and Delta states. The large-distance contributions from the 'pion cloud' cause a \~20% increase in the overall transverse size of the nucleon if x drops significantly below M_pi/M_N. This is in qualitative agreement with the observed increase of the slope of the t-dependence of the J/psi photoproduction cross section at HERA compared to fixed-target energies. We argue that the glue in the pion cloud could be probed directly in hard electroproduction processes accompanied by 'pion knockout', gamma^* + N -> gamma (or rho, J/psi) + pi + N', where the transverse momentum of the emitted pion is large while that of the outgoing nucleon is restricted to values of order M_pi.Comment: 20 pages, revtex4, 10 eps figure

Gluon Radiation and Coherent States in Ultrarelativistic Nuclear Collisions

We explore the correspondence between classical gluon radiation and quantum radiation in a coherent state for gluons produced in ultrarelativistic nuclear collisions. The expectation value of the invariant momentum distribution of gluons in the coherent state is found to agree with the gluon number distribution obtained classically from the solution of the Yang-Mills equations. A criterion for the applicability of the coherent state formalism to the problem of radiation in ultrarelativistic nucleus-nucleus collisions is discussed. This criterion is found to be fulfilled for midrapidity gluons with perturbative transverse momenta larger than about 1-2 GeV and produced in collisions between valence partons.Comment: 15 pages, 6 figures, RevTeX (with epsf, psfig style files

Anatomical phenotyping and staging of brain tumours

Unlike other tumors, the anatomical extent of brain tumors is not objectified and quantified through staging. Staging systems are based on understanding the anatomical sequence of tumor progression and its relationship to histopathological dedifferentiation and survival. The aim of this study was to describe the spatiotemporal phenotype of the most frequent brain tumor entities, to assess the association of anatomical tumor features with survival probability and to develop a staging system for WHO grade 2 and 3 gliomas and glioblastoma. Anatomical phenotyping was performed on a consecutive cohort of 1000 patients with first diagnosis of a primary or secondary brain tumor. Tumor probability in different topographic, phylogenetic and ontogenetic parcellation units was assessed on preoperative MRI through normalization of the relative tumor prevalence to the relative volume of the respective structure. We analyzed the spatiotemporal tumor dynamics by cross-referencing preoperative against preceding and subsequent MRIs of the respective patient. The association between anatomical phenotype and outcome defined prognostically critical anatomical tumor features at diagnosis. Based on a hypothesized sequence of anatomical tumor progression, we developed a three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma. This staging system was validated internally in the original cohort and externally in an independent cohort of 300 consecutive patients. While primary central nervous system lymphoma showed highest probability along white matter tracts, metastases enriched along terminal arterial flow areas. Neuroepithelial tumors mapped along all sectors of the ventriculocortical axis, while adjacent units were spared, consistent with a transpallial behavior within phylo-ontogenetic radial units. Their topographic pattern correlated with morphogenetic processes of convergence and divergence of radial units during phylo- and ontogenesis. While a ventriculofugal growth dominated in neuroepithelial tumors, a gradual deviation from this neuroepithelial spatiotemporal behavior was found with progressive histopathological dedifferentiation. The proposed three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma correlated with the degree of histological dedifferentiation and proved accurate in terms of survival upon both internal and external validation. In conclusion, this study identified specific spatiotemporal phenotypes in brain tumors through topographic probability and growth pattern assessment. The association of anatomical tumor features with survival defined critical steps in the anatomical sequence of neuroepithelial tumor progression, based on which a staging system for WHO grade 2 and 3 gliomas and glioblastoma was developed and validated

Association of perioperative adverse events with subsequent therapy and overall survival in patients with WHO grade III and IV gliomas

Background Maximum safe resection followed by chemoradiotherapy as current standard of care for WHO grade III and IV gliomas can be influenced by the occurrence of perioperative adverse events (AE). The aim of this study was to determine the association of AE with the timing and choice of subsequent treatments as well as with overall survival (OS). Methods Prospectively collected data of 283 adult patients undergoing surgery for WHO grade III and IV gliomas at the University Hospital Zurich between January 2013 and June 2017 were analyzed. We assessed basic patient characteristics, KPS, extent of resection, and WHO grade, and we classified AE as well as modality, timing of subsequent treatment (delay, interruption, or non-initiation), and OS. Results In 117 patients (41%), an AE was documented between surgery and the 3-month follow-up. There was a significant association of AE with an increased time to initiation of subsequent therapy (p = 0.005) and a higher rate of interruption (p < 0.001) or non-initiation (p < 0.001). AE grades correlated with time to initiation of subsequent therapy (p = 0.038). AEs were associated with shorter OS in univariate analysis (p < 0.001). Conclusion AEs are associated with delayed and/or altered subsequent therapy and can therefore limit OS. These data emphasize the importance of safety within the maximum-safe-resection concept

Classical Gluon Radiation in Ultrarelativistic Nuclear Collisions: Space-Time Structure, Instabilities, and Thermalization

We investigate the space-time structure of the classical gluon field produced in an ultrarelativistic collision between color charges. The classical solution which was computed previously in a perturbative approach is shown to become unstable on account of the non-Abelian self-interaction neglected in the perturbative solution scheme. The time scale for growth of the instabilities is found to be of the order of the distance between the colliding color charges. We argue that these instabilities will eventually lead to thermalization of gluons produced in an ultrarelativistic collision between heavy nuclei. The rate of thermalization is estimated to be of order $g^2 \mu$, where $g$ is the strong coupling constant and $\mu^2$ the transverse color charge density of an ultrarelativistic nucleus.Comment: 11 pages, REVTeX, eps-, aps-, and psfig-style files, 7 figs., figs. 2-5 in gif-format, a uucompressed version of this paper including all figures (ca. 2.2 Mb) is available at ftp://nt1.phys.columbia.edu/pub/stabil/stab.u