9 research outputs found
More on the Ethics of E-Discovery: Predictive Coding and Other Forms of Computer-Assisted Review
No full textThis paper was circulated at a TAR conference hosted by the Bolch Judicial Institute (then the Center for Judicial Studies) in 2015. With the author\u27s permission, the paper has been archived in the scholarship repository.
This document does not represent the views of Duke Law School, Duke University, their faculties, or any other organization
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More on the Ethics of E-Discovery: Predictive Coding and Other Forms of Computer-Assisted Review
No full textThis paper was circulated at a TAR conference hosted by the Bolch Judicial Institute (then the Center for Judicial Studies) in 2015. With the author\u27s permission, the paper has been archived in the scholarship repository.
This document does not represent the views of Duke Law School, Duke University, their faculties, or any other organization
Importance and vulnerability of the world’s water towers
No full text\ua9 2019, The Author(s), under exclusive licence to Springer Nature Limited.Mountains are the water towers of the world, supplying a substantial part of both natural and anthropogenic water demands1,2. They are highly sensitive and prone to climate change3,4, yet their importance and vulnerability have not been quantified at the global scale. Here we present a global water tower index (WTI), which ranks all water towers in terms of their water-supplying role and the downstream dependence of ecosystems and society. For each water tower, we assess its vulnerability related to water stress, governance, hydropolitical tension and future climatic and socio-economic changes. We conclude that the most important (highest WTI) water towers are also among the most vulnerable, and that climatic and socio-economic changes will affect them profoundly. This could negatively impact 1.9 billion people living in (0.3 billion) or directly downstream of (1.6 billion) mountainous areas. Immediate action is required to safeguard the future of the world’s most important and vulnerable water towers
Community estimate of global glacier mass changes from 2000 to 2023
No full text\ua9 The Author(s) 2025. Glaciers are indicators of ongoing anthropogenic climate change1. Their melting leads to increased local geohazards2, and impacts marine3 and terrestrial4,5 ecosystems, regional freshwater resources6, and both global water and energy cycles7,8. Together with the Greenland and Antarctic ice sheets, glaciers are essential drivers of present9,10 and future11, 12–13 sea-level rise. Previous assessments of global glacier mass changes have been hampered by spatial and temporal limitations and the heterogeneity of existing data series14, 15–16. Here we show in an intercomparison exercise that glaciers worldwide lost 273 \ub1 16 gigatonnes in mass annually from 2000 to 2023, with an increase of 36 \ub1 10% from the first (2000–2011) to the second (2012–2023) half of the period. Since 2000, glaciers have lost between 2% and 39% of their ice regionally and about 5% globally. Glacier mass loss is about 18% larger than the loss from the Greenland Ice Sheet and more than twice that from the Antarctic Ice Sheet17. Our results arise from a scientific community effort to collect, homogenize, combine and analyse glacier mass changes from in situ and remote-sensing observations. Although our estimates are in agreement with findings from previous assessments14, 15–16 at a global scale, we found some large regional deviations owing to systematic differences among observation methods. Our results provide a refined baseline for better understanding observational differences and for calibrating model ensembles12,16,18, which will help to narrow projection uncertainty for the twenty-first century11,12,18
Glacier Inventory of the South Patagonian Icefield
No full text<p>###############################################################################################<br>REFERENCE</p>
<p>This set of ShapeFile vector files contains the glacier inventory used and published in the paper:</p>
<p>De Angelis. H. 2014. Hypsometry and sensitivity of the mass balance to changes in equilibrium line altitude: the case of the South Patagonian Icefield. Journal of Glaciology 219(60), 14-28.</p>
<p>The inventory was compiled by visual interpretation of cloud-free Landsat 5 TM imagery acquired on 2001-03-12. The interpretation was made with the aid of a shaded relief model (16x vertical exaggeration) and artificially computed ''water drainage flow lines'', both derived from Shuttle Radar Topography Mission (SRTM) data. See the paper for more details.</p>
<p>The inventory is also part of the Randolph glacier inventory:</p>
<p>Arendt, A., T. Bolch, J.G. Cogley, A. Gardner, J.-O. Hagen, R. Hock, G. Kaser, W.T. Pfeffer, G. Moholdt, F. Paul, V. Radić, L. Andreassen, S. Bajracharya, M. Beedle, E. Berthier, R. Bhambri, A. Bliss, I. Brown, E. Burgess, D. Burgess, F. Cawkwell, T. Chinn, L. Copland, B. Davies, \textbf{H. De Angelis}, E. Dolgova, K. Filbert, R. Forester, A. Fountain, H. Frey, B. Giffen, N. Glasser, S. Gurney, W. Hagg, D. Hall, U.K. Haritashya, G. Hartmann, C. Helm, S. Herreid, I. Howat, G. Kapustin, T. Khromova, C. Kienholz, M. Koenig, J. Kohler, D. Kriegel, S. Kutuzov, I. Lavrentiev, R. LeBris, J. Lund, W. Manley, C. Mayer, E. Miles, X. Li, B. Menounos, A. Mercer, N. Moelg, P. Mool, G. Nosenko, A. Negrete, C. Nuth, R. Pettersson, A. Racoviteanu, R. Ranzi, P. Rastner, F. Rau, J. Rich, H. Rott, C. Schneider, Y. Seliverstov, M. Sharp, O. Sigurðsson, C. Stokes, R. Wheate, S. Winsvold, G. Wolken, F. Wyatt, N. Zheltyhina. 2012, Randolph Glacier Inventory [v2.0]: A Dataset of Global Glacier Outlines. Global Land Ice Measurements from Space, Boulder Colorado, USA. Digital Media.</p>
<p>###############################################################################################<br>LICENSE</p>
<p>This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/deed.en_US.</p>
<p>You are free:<br>to Share — to copy, distribute and transmit the work<br>to Remix — to adapt the work<br>to make commercial use of the work</p>
<p>Under the following conditions:<br>Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).<br>Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one.</p>
<p>###############################################################################################<br>GEOGRAPHIC PROJECTION</p>
<p>Universal Transverse Mercator zone 18F South referred to the WGS84 ellipsoid.</p>
<p>PROJCS["WGS 84 / UTM zone 18S",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],UNIT["metre",1,AUTHORITY["EPSG","9001"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-75],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",10000000],AUTHORITY["EPSG","32718"],AXIS["Easting",EAST],AXIS["Northing",NORTH]]</p>
<p>###############################################################################################<br>DESCRIPTION OF DATA COLUMNS</p>
<p>The columns in the database should be self-explanatory, but are given here for clarity:</p>
<p>NAME glacier name, unnamed glaciers referred to as "UN"<br>CENTER_LON geographical longitude of centroid<br>CENTER_LAT geographical latitude of centroid<br>CENTER_X x coordinate in meters (UTM18S,WGS84)<br>CENTER_Y y coordinate in meters (UTM18S,WGS84)<br>AREA_KM2 glacier area (km2)<br>AREA_ERROR error in glacier area (km2)<br>HMAX_M maximum elevation (m), as derived from SRTM<br>HMIN_M minimum elevation (m), as derived from SRTM<br>HAVG_M average elevation (m), as derived from SRTM<br>HMEDIAN_M median elevation (m), as derived from SRTM<br>HMODE_M mode of elevation (m), as derived from SRTM<br>SNLE_M elevation (m) of average snowline (closely corresponding to equilibrium line) [see paper for derivation]<br>SNLE_ERROR error in elevation (m) of average snowline (closely corresponding to equilibrium line) [see paper for derivation]<br>AAR accumulation area ratio<br>AAR_ERROR error in AAR<br>SLOPE_ELA glacier slope (degrees) at ELA<br>SLOPE_AVG average glacier slope (degrees)</p>
<p> </p
Long-term medical imaging use in children with central nervous system tumors
No full textBackgroundChildren with central nervous system (CNS) tumors undergo frequent imaging for diagnosis and follow-up, but few studies have characterized longitudinal imaging patterns. We described medical imaging in children before and after malignant CNS tumor diagnosis.ProcedureWe conducted a retrospective cohort study of children aged 0-20 years diagnosed with CNS tumors between 1996-2016 at six U.S. integrated healthcare systems and Ontario, Canada. We collected computed topography (CT), magnetic resonance imaging (MRI), radiography, ultrasound, nuclear medicine examinations from 12 months before through 10 years after CNS diagnosis censoring six months before death or a subsequent cancer diagnosis, disenrollment from the health system, age 21 years, or December 31, 2016. We calculated imaging rates per child per month stratified by modality, country, diagnosis age, calendar year, time since diagnosis, and tumor grade.ResultsWe observed 1,879 children with median four years follow-up post-diagnosis in the U.S. and seven years in Ontario, Canada. During the diagnosis period (±15 days of diagnosis), children averaged 1.10 CTs (95% confidence interval [CI] 1.09-1.13) and 2.14 MRIs (95%CI 2.12-2.16) in the U.S., and 1.67 CTs (95%CI 1.65-1.68) and 1.86 MRIs (95%CI 1.85-1.88) in Ontario. Within one year after diagnosis, 19% of children had ≥5 CTs and 45% had ≥5 MRIs. By nine years after diagnosis, children averaged one MRI and one radiograph per year with little use of other imaging modalities.ConclusionsMRI and CT are commonly used for CNS tumor diagnosis, whereas MRI is the primary modality used during surveillance of children with CNS tumors
A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity
No full textATENCIÓ: la correcció està també al DDD, cal relacionar??? https://ddd.uab.cat/record/226203Altres ajuts: The following studies and consortia have contributed to this manuscript. Amsterdam dementia Cohort (ADC): Research of the Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. The clinical database structure was developed with funding from Stichting Dioraphte. Genotyping of the Dutch case-control samples was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW projectnumber 733051061). 100-Plus study: We are grateful for the collaborative efforts of all participating centenarians and their family members and/or relations. This work was supported by Stichting Alzheimer Nederland (WE09.2014-03), Stichting Diorapthe, horstingstuit foundation, Memorabel (ZonMW projectnumber 733050814) and Stichting VUmc Fonds. Genotyping of the 100-Plus Study was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW projectnumber 733051061). German Study on Ageing, Cognition and Dementia in Primary Care Patients (AgeCoDe): This study/publication is part of the German Research Network on Dementia (KND), the German Research Network on Degenerative Dementia (KNDD; German Study on Ageing, Cognition and Dementia in Primary Care Patients; AgeCoDe), and the Health Service Research Initiative (Study on Needs, health service use, costs and health-related quality of life in a large sample of oldest-old primary care patients (85+; AgeQualiDe)) and was funded by the German Federal Ministry of Education and Research (grants KND: 01GI0102, 01GI0420, 01GI0422, 01GI0423, 01GI0429, 01GI0431, 01GI0433, 01GI0434; grants KNDD: 01GI0710, 01GI0711, 01GI0712, 01GI0713, 01GI0714, 01GI0715, 01GI0716; grants Health Service Research Initiative: 01GY1322A, 01GY1322B, 01GY1322C, 01GY1322D, 01GY1322E, 01GY1322F, 01GY1322G). Alfredo Ramirez was partly supported by the ADAPTED consortium: Alzheimer's disease Apolipoprotein Pathology for Treatment Elucidation and Development, which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115975. Brain compendium: This work was funded by the UK Medical Research Council (13044). P.F.C. is a Wellcome Trust principal Fellow (212219/Z/18/Z) and a UK NIHR Senior Investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.Clinical AD, Sweden: We would like to thank UCL Genomics for performing the genotyping analyses. Danish data: The studies behind the Danish long-lived cases received funding from The National Program for Research Infrastructure 2007 (grant no. 09-063256), the Danish Agency for Science Technology and Innovation, the Velux Foundation, the US National Institute of Health (P01 AG08761), the Danish Agency for Science, Technology and Innovation/The Danish Council for Independent Research (grant no. 11-107308), the European Union's Seventh Framework Programme (FP7/2007-2011) under grant agreement no. 259679, the INTERREG 4 A programme Syddanmark-Schleswig-K.E.R.N. (by EU funds from the European Regional Development Fund), the CERA Foundation (Lyon), the AXA Research Fund, Paris, and The Health Foundation (Helsefonden), Copenhagen, Denmark. The GOYA study was conducted as part of the activities of the Danish Obesity Research Centre (DanORC, www.danorc.dk) and The MRC centre for Causal Analyses in Translational Epidemiology (MRC CAiTE). The genotyping for GOYA was funded by the Wellcome Trust (WT 084762). GOYA is a nested study within The Danish National Birth Cohort which was established with major funding from the Danish National Research Foundation. Additional support for this cohort has been obtained from the Pharmacy Foundation, the Egmont Foundation, The March of Dimes Birth Defects Foundation, the Augustinus Foundation, and the Health Foundation. Fundació ACE (FACE): We would like to thank patients and controls who participated in this project. We are indebted to Trinitat Port-Carbó and her family for their support of Fundació ACE research programs. Fundació ACE collaborates with the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED, Spain) and is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). Agustín Ruiz has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking ADAPTED Grant No. 115975 and by grants PI13/02434 and PI16/01861. Acción Estratégica en Salud, integrated in the Spanish National R + D + I Plan and financed by ISCIII (Instituto de Salud Carlos III)-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER- "Una manera de Hacer Europa"), by Fundación bancaria "La Caixa" and Grifols SA (GR@ACE project). Genetics of Healthy Ageing Study (GEHA - NL): The work described in this paper was funded mainly by the EU GEHA Project contract no. LSHM-CT-2004-503-270. Gothenburg Birth Cohort (GBC) Studies: We would like to thank UCL Genomics for performing the genotyping analyses. The studies were supported by The Stena Foundation, The Swedish Research Council (2015-02830, 2013-8717), The Swedish Research Council for Health, Working Life and Wellfare (2013-1202, 2005-0762, 2008-1210, 2013-2300, 2013-2496, 2013-0475), The Brain Foundation, Sahlgrenska University Hospital (ALF), The Alzheimer's Association (IIRG-03-6168), The Alzheimer's Association Zenith Award (ZEN-01-3151), Eivind och Elsa K:son Sylvans Stiftelse, The Swedish Alzheimer Foundation. International FTD-Genomics Consortium (IFGC): International FTD-Genomics Consortium (IFGC): The authors thank the IFGC for providing relevant data to support the analyses presented in this manuscript. Further acknowledgments for IFGC (https://ifgcsite.wordpress.com/), e.g. full members list and affiliations, are found in the online supplementary files. IPDGC (The International Parkinson Disease Genomics Consortium): We also would like to thank all members of the International Parkinson Disease Genomics Consortium (IPDGC). See for a complete overview of members, acknowledgements and funding http://pdgenetics.org/partners. Kompetenznetz Multiple Sklerose (KKNMS): This work was supported by the German Ministry for Education and Research (BMBF) as part of the "German Competence Network Multiple Sclerosis" (KKNMS) (grant nos. 01GI0916 and 01GI0917) and the Munich Cluster for Systems Neurology (SyNergy). TA was supported by the BMBF through the Integrated Network IntegraMent, under the auspices of the e:Med Programme (01ZX1614J). BH was supported by the EU Horizon 2020 project MultipleMS.Longitudinal Aging Study Amsterdam (LASA) is largely supported by a grant from the Netherlands Ministry of Health, Welfare and Sports, Directorate of Long-Term Care. The authors are grateful to all LASA participants, the fieldwork team and all researchers for their ongoing commitment to the study. Leiden Longevity Study: This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology, and the Netherlands Consortium for Healthy Ageing (Grant 050-060-810), all in the framework of the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research (NWO) and by Unilever Colworth.Maria Carolina Dalmasso: Georg Forster Research Award (Alexander von Humboldt Foundation). Mayo Clinic AD, DLB, PD, PSP: We thank the patients and their families for their participation, without whom these studies would not have been possible. Funding for this work was supported by National Institute on Aging [RF AG051504 to NET.; U01 AG046139 to NET]; and National Institute of Neurological Disorders and Stroke [R01 NS080820 to NET; P50 NS072187]. The Mayo Clinic is a Lewy Body Dementia Association (LBDA) Research Center of Excellence, American Parkinson Disease Association (APDA) Information and Referral Center and Center for Advanced Research, NINDS Tau Center without Walls (U54-NS100693) and is supported by Mayo Clinic AD and related dementias genetics program, The Little Family Foundation, the Mangurian Foundation for Lewy body research and NINDS R01 NS078086 (to OAR). The PD program at the Mayo Clinic Florida is also supported by the Mayo Clinic Center for Regenerative Medicine, Mayo Clinic Center for Individualized Medicine, Mayo Clinic Neuroscience Focused Research Team (Cecilia and Dan Carmichael Family Foundation, and the James C. and Sarah K. Kennedy Fund for Neurodegenerative Disease Research at Mayo Clinic in Florida), the gift from Carl Edward Bolch, Jr., and Susan Bass Bolch, and The Sol Goldman Charitable Trust. Samples included in this study are from the brain bank at Mayo Clinic in Jacksonville which is supported by CurePSPThe online version of this article (10.1007/s00401-019-02026-8) contains supplementary material, which is available to authorized users
Climate and Bioinvasives drivers of change on South African Rocky shores?
No full textIncludes abstract.Includes bibliographical references.The overall aims of the thesis were to assess spatio-temporal change in macro species assemblages at sites located around the South African coast. Detected changes were considered in parallel with regional patterns of bioinvasion and climate change driven shifts in temperature trends over comparable time scales
