86 research outputs found
Investigating potential future changes in surface water flooding hazard and impact
Surface water flooding (SWF) is a recurrent hazard that affects lives and livelihoods. Climate change is projected to change the frequency of extreme rainfall events that can lead to SWF. Increasingly, data from Regional Climate Models (RCMs) are being used to investigate the potential waterârelated impacts of climate change; such assessments often focus on broadâscale fluvial flooding and the use of coarse resolution (>12 km) RCMs. However, highâresolution (<4 km) convectionâpermitting RCMs are now becoming available that allow impact assessments of more localised SWF to be made.
At the same time, there has been an increasing demand for more robust and timely realâtime forecast and alert information on SWF. In the UK, a realâtime SWF Hazard Impact Model framework has been developed. The system uses 1âkm gridded surface runoff estimates from a hydrological model to simulate the SWF hazard. These are linked to detailed inundation model outputs through an Impact Library to assess impacts on property, people, transport, and infrastructure for four severity levels.
Here, a set of highâresolution (1.5 km and 12 km) RCM data has been used as input to a gridâbased hydrological model over southern Britain to simulate Current (1996â2009) and Future (~2100s; RCP8.5) surface runoff. Counts of thresholdâexceedance for surface runoff and precipitation (at 1â, 3â and 6âhr durations) are analysed. Results show that the percentage increases in surface runoff extremes, are less than those of precipitation extremes. The higherâresolution RCM simulates the largest percentage increases, which occur in winter, and the winter exceedance counts are greater than summer exceedance counts. For property impacts, the largest percentage increases are also in winter; however, it is the 12âkm RCM output that leads to the largest percentage increase in impacts. The addedâvalue of highâresolution climate model data for hydrological modelling is from capturing the more intense convective storms in surface runoff estimates
Ultrabithorax confers spatial identity in a context-specific manner in the Drosophila postembryonic ventral nervous system.
BACKGROUND: In holometabolous insects such as Drosophila melanogaster, neuroblasts produce an initial population of diverse neurons during embryogenesis and a much larger set of adult-specific neurons during larval life. In the ventral CNS, many of these secondary neuronal lineages differ significantly from one body segment to another, suggesting a role for anteroposterior patterning genes. RESULTS: Here we systematically characterize the expression pattern and function of the Hox gene Ultrabithorax (Ubx) in all 25 postembryonic lineages. We find that Ubx is expressed in a segment-, lineage-, and hemilineage-specific manner in the thoracic and anterior abdominal segments. When Ubx is removed from neuroblasts via mitotic recombination, neurons in these segments exhibit the morphologies and survival patterns of their anterior thoracic counterparts. Conversely, when Ubx is ectopically expressed in anterior thoracic segments, neurons exhibit complementary posterior transformation phenotypes. CONCLUSION: Our findings demonstrate that Ubx plays a critical role in conferring segment-appropriate morphology and survival on individual neurons in the adult-specific ventral CNS. Moreover, while always conferring spatial identity in some sense, Ubx has been co-opted during evolution for distinct and even opposite functions in different neuronal hemilineages
Pericoronary and periaortic adipose tissue density are associated with inflammatory disease activity in Takayasu arteritis and atherosclerosis.
AimsTo examine pericoronary adipose tissue (PCAT) and periaortic adipose tissue (PAAT) density on coronary computed tomography angiography for assessing arterial inflammation in Takayasu arteritis (TAK) and atherosclerosis.Methods and resultsPCAT and PAAT density was measured in coronary (n = 1016) and aortic (n = 108) segments from 108 subjects [TAK + coronary artery disease (CAD), n = 36; TAK, n = 18; atherosclerotic CAD, n = 32; matched controls, n = 22]. Median PCAT and PAAT densities varied between groups (mPCAT: P P = 0.0002). PCAT density was 7.01 ± standard error of the mean (SEM) 1.78 Hounsfield Unit (HU) higher in coronary segments from TAK + CAD patients than stable CAD patients (P = 0.0002), and 8.20 ± SEM 2.04 HU higher in TAK patients without CAD than controls (P = 0.0001). mPCAT density was correlated with Indian Takayasu Clinical Activity Score (r = 0.43, P = 0.001) and C-reactive protein (r = 0.41, P P = 0.002). mPCAT density above -74 HU had 100% sensitivity and 95% specificity for differentiating active TAK from controls [area under the curve = 0.99 (95% confidence interval 0.97-1)]. The association of PCAT density and coronary arterial inflammation measured by 68Ga-DOTATATE positron emission tomography (PET) equated to an increase of 2.44 ± SEM 0.77 HU in PCAT density for each unit increase in 68Ga-DOTATATE maximum tissue-to-blood ratio (P = 0.002). These findings remained in multivariable sensitivity analyses adjusted for potential confounders.ConclusionsPCAT and PAAT density are higher in TAK than atherosclerotic CAD or controls and are associated with clinical, biochemical, and PET markers of inflammation. Owing to excellent diagnostic accuracy, PCAT density could be useful as a clinical adjunct for assessing disease activity in TAK
Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial
Background
Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy
Pharmaceuticals and personal care products in the environment: What are the big questions?
Background: Over the past 10-15 years, a substantial amount of work has been done by the scientific, regulatory, and business communities to elucidate the effects and risks of pharmaceuticals and personal care products (PPCPs) in the environment. Objective: This review was undertaken to identify key outstanding issues regarding the effects of PPCPs on human and ecological health in order to ensure that future resources will be focused on the most important areas. Data sources: To better understand and manage the risks of PPCPs in the environment, we used the "key question" approach to identify the principle issues that need to be addressed. Initially, questions were solicited from academic, government, and business communities around the world. A list of 101 questions was then discussed at an international expert workshop, and a top-20 list was developed. Following the workshop, workshop attendees ranked the 20 questions by importance. Data synthesis: The top 20 priority questions fell into seven categories: a) prioritization of substances for assessment, b) pathways of exposure, c) bioavailability and uptake, d) effects characterization, e) risk and relative risk, f) antibiotic resistance, and g) risk management. Conclusions: A large body of information is now available on PPCPs in the environment. This exercise prioritized the most critical questions to aid in development of future research programs on the topic.Centro de Investigaciones del Medioambient
Career guidance and the changing world of work: Contesting responsibilising notions of the future.
Career guidance is an educational activity which helps individuals to manage their participation in learning and work and plan for their futures. Unsurprisingly career guidance practitioners are interested in how the world of work is changing and concerned about threats of technological unemployment. This chapter argues that the career guidance field is strongly influenced by a âchanging world of workâ narrative which is drawn from a wide body of grey literature produced by think tanks, supra-national bodies and other policy influencers. This body of literature is political in nature and describes the future of work narrowly and within the frame of neoliberalism. The âchanging world of workâ narrative is explored through a thematic analysis of grey literature and promotional materials for career guidance conferences. The chapter concludes by arguing that career guidance needs to adopt a more critical stance on the âchanging world of workâ and to offer more emancipatory alternatives.N/
NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods
Glycosylation is a topic of intense current interest in the
development of biopharmaceuticals because it is related
to drug safety and efficacy. This work describes results of
an interlaboratory study on the glycosylation of the Primary
Sample (PS) of NISTmAb, a monoclonal antibody
reference material. Seventy-six laboratories from industry,
university, research, government, and hospital sectors
in Europe, North America, Asia, and Australia submit-
Avenue, Silver Spring, Maryland 20993; 22Glycoscience Research Laboratory, Genos, Borongajska cesta 83h, 10 000 Zagreb, Croatia;
23Faculty of Pharmacy and Biochemistry, University of Zagreb, A. KovacË icÂŽ a 1, 10 000 Zagreb, Croatia; 24Department of Chemistry, Georgia
State University, 100 Piedmont Avenue, Atlanta, Georgia 30303; 25glyXera GmbH, Brenneckestrasse 20 * ZENIT / 39120 Magdeburg, Germany;
26Health Products and Foods Branch, Health Canada, AL 2201E, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9 Canada;
27Graduate School of Advanced Sciences of Matter, Hiroshima University, 1-3-1 Kagamiyama Higashi-Hiroshima 739â8530 Japan; 28ImmunoGen,
830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College,
ul. Michalowskiego 12, 31â126 Krakow, Poland; 30Department of Pathology, Johns Hopkins University, 400 N. Broadway Street Baltimore,
Maryland 21287; 31Mass Spec Core Facility, KBI Biopharma, 1101 Hamlin Road Durham, North Carolina 27704; 32Division of Mass
Spectrometry, Korea Basic Science Institute, 162 YeonGuDanji-Ro, Ochang-eup, Cheongwon-gu, Cheongju Chungbuk, 363â883 Korea
(South); 33Advanced Therapy Products Research Division, Korea National Institute of Food and Drug Safety, 187 Osongsaengmyeong 2-ro
Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, 363â700, Korea (South); 34Center for Proteomics and Metabolomics, Leiden
University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; 35Ludger Limited, Culham Science Centre, Abingdon,
Oxfordshire, OX14 3EB, United Kingdom; 36Biomolecular Discovery and Design Research Centre and ARC Centre of Excellence for Nanoscale
BioPhotonics (CNBP), Macquarie University, North Ryde, Australia; 37Proteomics, Central European Institute for Technology, Masaryk
University, Kamenice 5, A26, 625 00 BRNO, Czech Republic; 38Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse
1, 39106 Magdeburg, Germany; 39Department of Biomolecular Sciences, Max Planck Institute of Colloids and Interfaces, 14424
Potsdam, Germany; 40AstraZeneca, Granta Park, Cambridgeshire, CB21 6GH United Kingdom; 41Merck, 2015 Galloping Hill Rd, Kenilworth,
New Jersey 07033; 42Analytical R&D, MilliporeSigma, 2909 Laclede Ave. St. Louis, Missouri 63103; 43MS Bioworks, LLC, 3950 Varsity Drive
Ann Arbor, Michigan 48108; 44MSD, Molenstraat 110, 5342 CC Oss, The Netherlands; 45Exploratory Research Center on Life and Living
Systems (ExCELLS), National Institutes of Natural Sciences, 5â1 Higashiyama, Myodaiji, Okazaki 444â8787 Japan; 46Graduate School of
Pharmaceutical Sciences, Nagoya City University, 3â1 Tanabe-dori, Mizuhoku, Nagoya 467â8603 Japan; 47Medical & Biological Laboratories
Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464â0858 Japan; 48National Institute for Biological Standards and Control, Blanche Lane, South
Mimms, Potters Bar, Hertfordshire EN6 3QG United Kingdom; 49Division of Biological Chemistry & Biologicals, National Institute of Health
Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158â8501 Japan; 50New England Biolabs, Inc., 240 County Road, Ipswich, Massachusetts
01938; 51New York University, 100 Washington Square East New York City, New York 10003; 52Target Discovery Institute, Nuffield Department
of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom; 53GlycoScience Group, The National Institute for
Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland; 54Department of Chemistry, North
Carolina State University, 2620 Yarborough Drive Raleigh, North Carolina 27695; 55Pantheon, 201 College Road East Princeton, New Jersey
08540; 56Pfizer Inc., 1 Burtt Road Andover, Massachusetts 01810; 57Proteodynamics, ZI La Varenne 20â22 rue Henri et Gilberte Goudier 63200
RIOM, France; 58ProZyme, Inc., 3832 Bay Center Place Hayward, California 94545; 59Koichi Tanaka Mass Spectrometry Research Laboratory,
Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho Nakagyo-ku, Kyoto, 604 8511 Japan; 60Childrenâs GMP LLC, St. Jude Childrenâs
Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1â5 Muromati 1-Chome, Nishiku,
Kobe, 651â2241 Japan; 62Synthon Biopharmaceuticals, Microweg 22 P.O. Box 7071, 6503 GN Nijmegen, The Netherlands; 63Takeda
Pharmaceuticals International Co., 40 Landsdowne Street Cambridge, Massachusetts 02139; 64Department of Chemistry and Biochemistry,
Texas Tech University, 2500 Broadway, Lubbock, Texas 79409; 65Thermo Fisher Scientific, 1214 Oakmead Parkway Sunnyvale, California
94085; 66United States Pharmacopeia India Pvt. Ltd. IKP Knowledge Park, Genome Valley, Shamirpet, Turkapally Village, Medchal District,
Hyderabad 500 101 Telangana, India; 67Alberta Glycomics Centre, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 68Department
of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 69Department of Chemistry, University of California, One Shields Ave,
Davis, California 95616; 70HorvaÂŽ th Csaba Memorial Laboratory for Bioseparation Sciences, Research Center for Molecular Medicine, Doctoral
School of Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Egyetem ter 1, Hungary; 71Translational Glycomics
Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprem, Egyetem ut 10, Hungary;
72Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; 73Proteomics Core Facility, University
of Gothenburg, Medicinaregatan 1G SE 41390 Gothenburg, Sweden; 74Department of Medical Biochemistry and Cell Biology, University of
Gothenburg, Institute of Biomedicine, Sahlgrenska Academy, Medicinaregatan 9A, Box 440, 405 30, Gothenburg, Sweden; 75Department of
Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy at the University of Gothenburg, Bruna Straket 16, 41345 Gothenburg,
Sweden; 76Department of Chemistry, University of Hamburg, Martin Luther King Pl. 6 20146 Hamburg, Germany; 77Department of Chemistry,
University of Manitoba, 144 Dysart Road, Winnipeg, Manitoba, Canada R3T 2N2; 78Laboratory of Mass Spectrometry of Interactions and
Systems, University of Strasbourg, UMR Unistra-CNRS 7140, France; 79Natural and Medical Sciences Institute, University of Tuš bingen,
Markwiesenstrae 55, 72770 Reutlingen, Germany; 80Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical
Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; 81Division of Bioanalytical Chemistry, Amsterdam Institute for
Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; 82Department
of Chemistry, Waters Corporation, 34 Maple Street Milford, Massachusetts 01757; 83Zoetis, 333 Portage St. Kalamazoo, Michigan 49007
Authorâs ChoiceâFinal version open access under the terms of the Creative Commons CC-BY license.
Received July 24, 2019, and in revised form, August 26, 2019
Published, MCP Papers in Press, October 7, 2019, DOI 10.1074/mcp.RA119.001677
ER: NISTmAb Glycosylation Interlaboratory Study
12 Molecular & Cellular Proteomics 19.1
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ted a total of 103 reports on glycan distributions. The
principal objective of this study was to report and compare
results for the full range of analytical methods presently
used in the glycosylation analysis of mAbs. Therefore,
participation was unrestricted, with laboratories
choosing their own measurement techniques. Protein glycosylation
was determined in various ways, including at
the level of intact mAb, protein fragments, glycopeptides,
or released glycans, using a wide variety of methods for
derivatization, separation, identification, and quantification.
Consequently, the diversity of results was enormous,
with the number of glycan compositions identified by
each laboratory ranging from 4 to 48. In total, one hundred
sixteen glycan compositions were reported, of which 57
compositions could be assigned consensus abundance
values. These consensus medians provide communityderived
values for NISTmAb PS. Agreement with the consensus
medians did not depend on the specific method or
laboratory type. The study provides a view of the current
state-of-the-art for biologic glycosylation measurement
and suggests a clear need for harmonization of glycosylation
analysis methods. Molecular & Cellular Proteomics
19: 11â30, 2020. DOI: 10.1074/mcp.RA119.001677.L
Stroke genetics informs drug discovery and risk prediction across ancestries
Previous genome-wide association studies (GWASs) of stroke â the second leading cause of death worldwide â were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (Pâ<â0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries
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Culture Clash: Laborâs Economic Agenda and Taft-Hartley Trusteesâ Interpretation of ERISA
In this paper we analyze the investment practices of two Taft-Hartley pension funds in order to discover the extent, impacts, and limits of investments targeted to produce labor-friendly collateral benefits
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