A method for multi-objective topology optimization of acoustic and fluid flow properties

A framework for multi-objective topology optimization is presented with the purpose to simultaneously optimize both fluid flow and acoustic quantities. The proposed method uses a coupled approach on fixed grids with immersed solid boundaries. For the fluid flow part the incompressible Navier-Stokes equations are solved and the immersed boundaries are modeled with a Brinkman penalization method. The acoustic field is computed by an acoustic/viscous splitting technique and the solution of the resulting linearized Euler equations. The reflecting boundaries are modeled by a mismatch in the acoustic impedance between solid and fluid. To describe the geometry of the boundaries a NURBS-based approach is introduced. Two test cases are investigated to validate the immersed boundary method for the fluid flow problem and the acoustics, respectively. Finally, the capability for topological changes of the proposed method is shown with a multi-objective optimization test case, which is solved with the gradient-free evolutionary algorithm NSGA-II

Induction and function of virus-specific CD4+ T cell responses

CD4+ T cells -- often referred to as T-helper cells -- play a central role in immune defense and pathogenesis. Virus infections and vaccines stimulate and expand populations of antigen-specific CD4+ T cells in mice and in man. These virus-specific CD4+ T cells are extremely important in antiviral protection: deficiencies in CD4+ T cells are associated with virus reactivation, generalized susceptibility to opportunistic infections, and poor vaccine efficacy. As described below, CD4+ T cells influence effector and memory CD8+ T cell responses, humoral immunity, and the antimicrobial activity of macrophages and are involved in recruiting cells to sites of infection. This review summarizes a few key points about the dynamics of the CD4+ T cell response to virus infection, the positive role of pro-inflammatory cytokines in the differentiation of virus-specific CD4+ T cells, and new areas of investigation to improve vaccines against virus infection

Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants

The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD. 2022, The Author(s).T. Kessler is supported by the Corona-Foundation (Junior Research Group Translational Cardiovascular Genomics) and the German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02). T.J. was supported by a Medical Research Council DTP studentship (MR/S502443/1). J.D. is a British Heart Foundation Professor, European Research Council Senior Investigator, and National Institute for Health and Care Research (NIHR) Senior Investigator. J.C.H. acknowledges personal funding from the British Heart Foundation (FS/14/55/30806) and is a member of the Oxford BHF Centre of Research Excellence (RE/13/1/30181). R.C. has received funding from the British Heart Foundation and British Heart Foundation Centre of Research Excellence. O.G. has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). P.S.d.V. was supported by American Heart Association grant number 18CDA34110116 and National Heart, Lung, and Blood Institute grant R01HL146860. The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I), R01HL087641, R01HL059367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. We thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by grant UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. The Trøndelag Health Study (The HUNT Study) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology), Trøndelag County Council, Central Norway Regional Health Authority and the Norwegian Institute of Public Health. The K.G. Jebsen Center for Genetic Epidemiology is financed by Stiftelsen Kristian Gerhard Jebsen; Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology; and Central Norway Regional Health Authority. Whole genome sequencing for the HUNT study was funded by HL109946. The GerMIFs gratefully acknowledge the support of the Bavarian State Ministry of Health and Care, furthermore founded this work within its framework of DigiMed Bayern (grant DMB-1805-0001), the German Federal Ministry of Education and Research (BMBF) within the framework of ERA-NET on Cardiovascular Disease (Druggable-MI-genes, 01KL1802), within the scheme of target validation (BlockCAD, 16GW0198K), within the framework of the e:Med research and funding concept (AbCD-Net, 01ZX1706C), the British Heart Foundation (BHF)/German Centre of Cardiovascular Research (DZHK)-collaboration (VIAgenomics) and the German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02), the Sonderforschungsbereich SFB TRR 267 (B05), and EXC2167 (PMI). This work was supported by the British Heart Foundation (BHF) under grant RG/14/5/30893 (P.D.) and forms part of the research themes contributing to the translational research portfolios of the Barts Biomedical Research Centre funded by the UK National Institute for Health Research (NIHR). I.S. is supported by a Precision Health Scholars Award from the University of Michigan Medical School. This work was supported by the European Commission (HEALTH-F2–2013-601456) and the TriPartite Immunometabolism Consortium (TrIC)-NovoNordisk Foundation (NNF15CC0018486), VIAgenomics (SP/19/2/344612), the British Heart Foundation, a Wellcome Trust core award (203141/Z/16/Z to M.F. and H.W.) and the NIHR Oxford Biomedical Research Centre. M.F. and H.W. are members of the Oxford BHF Centre of Research Excellence (RE/13/1/30181). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. C.P.N. and T.R.W. received funding from the British Heart Foundation (SP/16/4/32697). C.J.W. is funded by NIH grant R35-HL135824. B.N.W. is supported by the National Science Foundation Graduate Research Program (DGE, 1256260). This research was supported by BHF (SP/13/2/30111) and conducted using the UK Biobank Resource (application 9922). O.M. was funded by the Swedish Heart and Lung Foundation, the Swedish Research Council, the European Research Council ERC-AdG-2019-885003 and Lund University Infrastructure grant ‘Malmö population-based cohorts’ (STYR 2019/2046). T.R.W. is funded by the British Heart Foundation. I.K., S. Koyama, and K. Ito are funded by the Japan Agency for Medical Research and Development, AMED, under grants JP16ek0109070h0003, JP18kk0205008h0003, JP18kk0205001s0703, JP20km0405209 and JP20ek0109487. The Biobank Japan is supported by AMED under grant JP20km0605001. J.L.M.B. acknowledges research support from NIH R01HL125863, American Heart Association (A14SFRN20840000), the Swedish Research Council (2018-02529) and Heart Lung Foundation (20170265) and the Foundation Leducq (PlaqueOmics: New Roles of Smooth Muscle and Other Matrix Producing Cells in Atherosclerotic Plaque Stability and Rupture, 18CVD02. A.V.K. has been funded by grant 1K08HG010155 from the National Human Genome Research Institute. K.G.A. has received support from the American Heart Association Institute for Precision Cardiovascular Medicine (17IFUNP3384001), a KL2/Catalyst Medical Research Investigator Training (CMeRIT) award from the Harvard Catalyst (KL2 TR002542) and the NIH (1K08HL153937). A.S.B. has been supported by funding from the National Health and Medical Research Council (NHMRC) of Australia (APP2002375). D.S.A. has received support from a training grant from the NIH (T32HL007604). N.P.B., M.C.C., J.F. and D.-K.J. have been funded by the National Institute of Diabetes and Digestive and Kidney Diseases (2UM1DK105554). EPIC-CVD was funded by the European Research Council (268834) and the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). The coordinating center was supported by core funding from the UK Medical Research Council (G0800270; MR/L003120/1), British Heart Foundation (SP/09/002, RG/13/13/30194, RG/18/13/33946) and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute.Scopu

Discovery of nitrogen starvation detecting and signaling mutants in Chlamydomonas reinhardtii

Nitrogen (N) is a fundamental macronutrient that constitutes nucleic/amino acids. Therefore, organisms require signaling mechanisms to sense external and internal N and regulate essential processes like cell growth, development, and energy metabolism. The responses to changes in cellular N status in bacteria/fungi are mediated by monitoring metabolites of the N assimilation pathway ending with glutamine synthetase (GS) and glutamate synthase (GOGAT) enzymes, forming the GS/GOGAT cycle. In photosynthetic eukaryotes, N assimilation relies on the GS/GOGAT cycle, however, whether similar signaling mechanisms are operant remains unknown. Breakthroughs in the N status signaling of bacteria/fungi were made by utilizing N sources that activated N starvation responses during growth, defined as 'derepressive'. While it is known that NH₄⁺ and NO₃- repress N starvation responses in photosynthetic eukaryotes, no derepressive N sources have been established. The unicellular green alga, Chlamydomonas reinhardtii, can survive on a wider array of N sources than land plants, of which arginine-feeding was known to derepress gametogenesis, a N starvation-specific response. This work investigated the use of arginine as a derepressive N source in photosynthetic eukaryotes and documented that N starvation responses were activated (Chapter 3 and 4). Using this derepressive condition, screens for defective N starvation responses collected mutants displaying nitrogen insensitivity (nsi) or constitutive nitrogen starvation responses (cns). Five nsi mutants disrupted the same region, NSI1, encoding a divergent glutamine synthetase (GLN4) (Chapter 5). NSI1 homologs were found in chlorophytes, and sequence features common to this family suggested it is unlikely to catalyze the reduction of NH₄⁺ but may detect N status via its potential binding to glutamate (Chapter 5). The cns mutants suggested a negative regulation of N starvation responses, whose further study may elucidate how NH₄⁺ rapidly represses N starvation responses (Chapter 6). Collectively, the work done in this thesis has led to the discovery of a hypothetical signaling network that responds to cellular N status in photosynthetic eukaryotes involving a detection mechanism of N flux through the GS/GOGAT cycle, comparable to the N status signaling counterparts in bacteria/fungi. Whether an analogous system for N status signaling exists in land plants remains to be investigated.Science, Faculty ofGraduat

A method for multi-objective topology optimization of acoustic and fluid flow properties

A framework for multi-objective topology optimization is presented with the purpose to simultaneously optimize both fluid flow and acoustic quantities. The proposed method uses a coupled approach on fixed grids with immersed solid boundaries. For the fluid flow part the incompressible Navier-Stokes equations are solved and the immersed boundaries are modeled with a Brinkman penalization method. The acoustic field is computed by an acoustic/viscous splitting technique and the solution of the resulting linearized Euler equations. The reflecting boundaries are modeled by a mismatch in the acoustic impedance between solid and fluid. To describe the geometry of the boundaries a NURBS-based approach is introduced. Two test cases are investigated to validate the immersed boundary method for the fluid flow problem and the acoustics, respectively. Finally, the capability for topological changes of the proposed method is shown with a multi-objective optimization test case, which is solved with the gradient-free evolutionary algorithm NSGA-II