• 2015-2024close
• Open Access close
• Embargo close
• Publications close
• Research software close
• Other research products close
• Natural Sciences and Engineering Re... close
• US close
• English close

In this thesis, we present a new procedure for mesh adaptation for wakes. The approach starts by tracking the wake centerline with an initial isotropic unstructured mesh. A vertex-centered finite volume method is used, and the velocity field is obtained from solution reconstruction. The velocity data is integrated numerically using an adaptive fourth-order Runge-Kutta method. We insert the wake centerline into the existing unstructured mesh as an internal boundary and use a metric-based anisotropic mesh adaptation to generate anisotropic cells in regions with large second derivatives of flow variables. In the second step, the problem is solved on adapted mesh and a new wake centerline is tracked. We then move the previous wake centerline (which is now a part of adapted mesh) to match the centerline obtained from the adapted mesh. To move the wake centerline, a solid mechanics analogy is used and the linear elasticity equation is solved on the adapted mesh. As a result, the displacement is propagated throughout the mesh and the already adapted regions along the wake centerline are preserved. The process is then followed for subsequent cycles of anisotropic mesh adaptation to obtain a more accurate approximation of the wake centerline. As an alternate strategy for obtaining an anisotropic mesh in the wake, we take the first geometry, together with the captured wake centerline from an unstructured triangular mesh, as an initial geometry to produce a quad dominant mesh, using an advancing layer method. The correctness of the streamline tracking algorithm is verified using an analytical velocity field. The mesh morphing approach is tested using the method of manufactured solutions, demonstrating that the linear finite element solution is second-order accurate. The results of laminar flow test cases for the attached and separated flow are presented and compared with some well-established numerical results in the literature. Our results show that the advancing layer mesh is more efficient in resolving the wake. In the end, one case for turbulent subsonic flow is considered. For turbulent flow, a cell-centered finite volume method is used and we only track the wake centerline at different angles of attack.

The beam-helicity asymmetry was measured, for the first time, in photoproduction of $\pi^{0}\eta$ pairs on carbon, aluminum, and lead, with the A2 experimental setup at MAMI. The results are compared to an earlier measurement on a free proton and to the corresponding theoretical calculations. The Mainz model is used to predict the beam-helicity asymmetry for the nuclear targets. The present results indicate that the photoproduction mechanism for $\pi^{0}\eta$ pairs on nuclei is similar to photoproduction on a free nucleon. This process is dominated by the $D_{33}$ partial wave with the $\eta\Delta(1232)$ intermediate state.

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (DDB2, PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% (DDB2, CD8A, TALDO1, PCNA, EIF4G2, LCN2, CDKN1A, PRKCH, ENO1, and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

We report a measurement of cross section σðνμ þ nucleus → μ− þ XÞ and the first measurements of the cross section σðν¯μ þ nucleus → μþ þ XÞ and their ratio Rð σðν¯Þ σðνÞ Þ at (anti) neutrino energies below 1.5 GeV. We determine the single momentum bin cross section measurements, averaged over the T2K ν¯=ν-flux, for the detector target material (mainly carbon, oxygen, hydrogen and copper) with phase space restricted laboratory frame kinematics of θμ 500 MeV=c. The results are σðν¯Þ ¼ ð0.900 0.029ðstatÞ 0.088ðsystÞÞ × 10−39 and σðνÞ¼ð2.41 0.022ðstatÞ 0.231ðsystÞÞ × 10−39 in units of cm2=nucleon and Rð σðν¯Þ σðνÞ Þ ¼ 0.373 0.012ðstatÞ 0.015ðsystÞ.

The NA62 experiment reports an investigation of the K+→π+νν¯ mode from a sample of K+ decays collected in 2017 at the CERN SPS. The experiment has achieved a single event sensitivity of (0.389 ± 0.024) × 10−10, corresponding to 2.2 events assuming the Standard Model branching ratio of (8.4 ± 1.0) × 10−11. Two signal candidates are observed with an expected background of 1.5 events. Combined with the result of a similar analysis conducted by NA62 on a smaller data set recorded in 2016, the collaboration now reports an upper limit of 1.78 × 10−10 for the K+→π+νν¯ branching ratio at 90% CL. This, together with the corresponding 68% CL measurement of (0.48−0.48+0.72) × 10−10, are currently the most precise results worldwide, and are able to constrain some New Physics models that predict large enhancements still allowed by previous measurements.

We express our gratitude to members of the Plateforme d’analyses génomiques and bioinformatics platform at IBIS. We also thank Tariq Elsayegh (Royal College of Surgeons in Ireland) for his assistance with annotation of polymyxin resistance genes. JJ is supported by a Cystic Fibrosis Canada postdoctoral fellowship. RL is funded by Cystic Fibrosis Canada and by a CIHR-UK team grant. CW, JF, and MM are supported by the UK Cystic Fibrosis Trust. CW and NL would like to acknowledge funding from Fight for Sight. JB was supported by NIH grant P30 DK089507. SB is supported by a Queensland Health Fellowship. SB, TK, PR, and KG were supported by grants by NHMRC (#455919) and the TPCH Foundation. TK is the recipient of an ERS-EU RESPIRE2 Marie Skłodowska-Curie Postdoctoral Research Fellowship— MC RESPIRE2 1st round 4571-2013. IL is supported by grants from CureKids, Cystic Fibrosis New Zealand, and the New Zealand Lotteries Board (Health). AM holds a Cisco Research Chair in Bioinformatics, supported by Cisco Systems Canada, Inc. GD Wright’s laboratory is funded by the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council (NSERC), and a Canada Research Chair. NW is supported by a CIHR Doctoral research award. GW and FB acknowledge Cystic Fibrosis Foundation Therapeutics and Genome Canada for support. ED’s laboratory is supported by Canadian Institutes of Health Research (CIHR) operating grant MOP-142466 and a Canada Research Chair. The International Pseudomonas aeruginosa Consortium is sequencing over 1000 genomes and building an analysis pipeline for the study of Pseudomonas genome evolution, antibiotic resistance and virulence genes. Metadata, including genomic and phenotypic data for each isolate of the collection, are available through the International Pseudomonas Consortium Database (http://ipcd.ibis.ulaval.ca/). Here, we present our strategy and the results that emerged from the analysis of the first 389 genomes. With as yet unmatched resolution, our results confirm that P. aerugihosa strains can be divided into three major groups that are further divided into subgroups, some not previously reported in the literature. We also provide the first snapshot of P aeruginosa strain diversity with respect to antibiotic resistance. Our approach will allow us to draw potential links between environmental strains and those implicated in human and animal infections, understand how patients become infected and how the infection evolves over time as well as identify prognostic markers for better evidence-based decisions on patient care. The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2015.01036 info:eu-repo/semantics/publishedVersion

We examined biases in the global GEOS-Chem chemical transport model for the period of February–May 2010 using weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation and dry-air mole fractions of CH₄ (XCH₄) from the Greenhouse gases Observing SATellite (GOSAT). The ability of the observations and the WC 4D-Var method to mitigate model errors in CH₄ concentrations was first investigated in a set of observing system simulation experiments (OSSEs). We then assimilated the GOSAT XCH₄ retrievals and found that they were capable of providing information on the vertical structure of model errors and of removing a significant portion of biases in the modeled CH₄ state. In the WC 4D-Var assimilation, corrections were added to the modeled CH₄ state at each model time step to account for model errors and improve the model fit to the assimilated observations. Compared to the conventional strong-constraint (SC) 4D-Var assimilation, the WC method was able to significantly improve the model fit to independent observations. Examination of the WC state corrections suggested that a significant source of model errors was associated with discrepancies in the model CH₄ in the stratosphere. The WC state corrections also suggested that the model vertical transport in the troposphere at middle and high latitudes is too weak. The problem was traced back to biases in the uplift of CH₄ over the source regions in eastern China and North America. In the tropics, the WC assimilation pointed to the possibility of biased CH₄ outflow from the African continent to the Atlantic in the mid-troposphere. The WC assimilation in this region would greatly benefit from glint observations over the ocean to provide additional constraints on the vertical structure of the model errors in the tropics. We also compared the WC assimilation at 4∘ × 5∘ and 2∘ × 2.5∘ horizontal resolutions and found that the WC corrections to mitigate the model errors were significantly larger at 4∘ × 5∘ than at 2∘ × 2.5∘ resolution, indicating the presence of resolution-dependent model errors. Our results illustrate the potential utility of the WC 4D-Var approach for characterizing model errors. However, a major limitation of this approach is the need to better characterize the specified model error covariance in the assimilation scheme.

The Optical Spectrograph and Infra-Red Imager System (OSIRIS) and the Atmospheric Chemistry Experiment (ACE) have been taking measurements from space since 2001 and 2003, respectively. This paper presents intercomparisons between ozone and NO2 measured by the ACE and OSIRIS satellite instruments and by ground-based instruments at the Polar Environment Atmospheric Research Laboratory (PEARL), which is located at Eureka, Canada (80° N, 86° W) and is operated by the Canadian Network for the Detection of Atmospheric Change (CANDAC). The ground-based instruments included in this study are four zenith-sky differential optical absorption spectroscopy (DOAS) instruments, one Bruker Fourier transform infrared spectrometer (FTIR) and four Brewer spectrophotometers. Ozone total columns measured by the DOAS instruments were retrieved using new Network for the Detection of Atmospheric Composition Change (NDACC) guidelines and agree to within 3.2%. The DOAS ozone columns agree with the Brewer spectrophotometers with mean relative differences that are smaller than 1.5%. This suggests that for these instruments the new NDACC data guidelines were successful in producing a homogenous and accurate ozone dataset at 80° N. Satellite 14–52 km ozone and 17–40 km NO2 partial columns within 500 km of PEARL were calculated for ACE-FTS Version 2.2 (v2.2) plus updates, ACE-FTS v3.0, ACE-MAESTRO (Measurements of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation) v1.2 and OSIRIS SaskMART v5.0x ozone and Optimal Estimation v3.0 NO2 data products. The new ACE-FTS v3.0 and the validated ACE-FTS v2.2 partial columns are nearly identical, with mean relative differences of 0.0 ± 0.2% and −0.2 ± 0.1% for v2.2 minus v3.0 ozone and NO2, respectively. Ozone columns were constructed from 14–52 km satellite and 0–14 km ozonesonde partial columns and compared with the ground-based total column measurements. The satellite-plus-sonde measurements agree with the ground-based ozone total columns with mean relative differences of 0.1–7.3%. For NO2, partial columns from 17 km upward were scaled to noon using a photochemical model. Mean relative differences between OSIRIS, ACE-FTS and ground-based NO2 measurements do not exceed 20%. ACE-MAESTRO measures more NO2 than the other instruments, with mean relative differences of 25–52%. Seasonal variation in the differences between NO2 partial columns is observed, suggesting that there are systematic errors in the measurements and/or the photochemical model corrections. For ozone spring-time measurements, additional coincidence criteria based on stratospheric temperature and the location of the polar vortex were found to improve agreement between some of the instruments. For ACE-FTS v2.2 minus Bruker FTIR, the 2007–2009 spring-time mean relative difference improved from −5.0 ± 0.4% to −3.1 ± 0.8% with the dynamical selection criteria. This was the largest improvement, likely because both instruments measure direct sunlight and therefore have well-characterized lines-of-sight compared with scattered sunlight measurements. For NO2, the addition of a ±1° latitude coincidence criterion improved spring-time intercomparison results, likely due to the sharp latitudinal gradient of NO2 during polar sunrise. The differences between satellite and ground-based measurements do not show any obvious trends over the missions, indicating that both the ACE and OSIRIS instruments continue to perform well.