A 8 b-wide single-ended simultaneous bidirectional transceiver test chip in a 0.4 /spl mu/m CMOS process allows study of the major challenges in high-performance, low-cost parallel-link design. This paper shows the I/O cell and placement of I/O pads in each chip. Data pins are laid out with different signal return configurations to study cross-talk in parallel links. In the I/O cell, the open-drain output driver is broken down into 4 legs ratioed 1:2:4:4 for swing control. The line is terminated on each side with a pMOS resistor, whose gate voltage is adjusted externally for impedance control. Two externally-adjustable reference voltages (VrefH and VrefL) are multiplexed to generate the local reference voltage (Vref) to decode incoming data. The I/O design operates at a bit time equal to 4 fanout-of-4 delays (FO4=delay of an inverter driving a load of 4 identical inverters and is 193 ps at a 3.3 V supply in this run). The bidirectional links operate at 2.4 Gb/s/pin (1.2 Gb/s in each direction), with 200 mV minimum signal swing on each side for the pins with worst-ease cross-talk.
You are a physician working at a regional trauma center. Your unit's committee, which is responsible for standardization of care, is considering using tranexamic acid to treat trauma patients arriving 3 hours after injury. Almost all the information on this topic is derived from a single, blinded trial that randomized trauma patients to tranexamic acid or placebo. The original publication reported that 99% of the enrolled patients were followed up and there was a reduction in all-cause mortality (relative risk [RR], 0.91; 95% CI. 0.85-0.97) with no apparent subgroup effect. A subsequent publication focused on an additional analysis addressing death from bleeding and reported a powerful subgroup effect with a large benefit for patients treated within 3 hours of injury and possible harm if treated 3 or more hours after injury. The committee's mandate is to decide whether tranexamic acid should not be given to patients 3 hours or more after injury. The credibility you place on the subgroup analysis will determine your decision.
Abstract Introduction Infectious diseases carry a large global burden and have implications for society at large. Therefore, reproducible, transparent research is extremely important. To assess the current state of transparency in this field, we investigated code sharing, data sharing, protocol registration, conflict of interest and funding disclosures in articles published in the most influential infectious disease journals. Methods We evaluated transparency indicators in the 5340 PubMed Central Open Access (PMC OA) articles published in 2019 or 2021 in the 9 most-cited specialty journals in infectious disease. We used a previously validated text-mining R package, rtransparent . The approach was manually validated for a random sample of 200 articles for which study characteristics were also extracted in detail. Main comparisons assessed 2019 versus 2021 articles, 2019 versus 2021 non-COVID-19 articles, and 2021 non-COVID-19 articles versus 2021 COVID-19 articles. Results A total of 5340 articles were evaluated (1860 published in 2019 and 3480 in 2021 (of which 1828 on COVID-19)). Text-mining identified code sharing in 98 (2%) articles, data sharing in 498 (9%), registration in 446 (8%), conflict of interest disclosures in 4209 (79%) and funding disclosures in 4866 (91%). There were substantial differences across the 9 journals in the proportion of articles fulfilling each transparency indicator: 1-9% for code sharing, 5-25% for data sharing, 1-31% for registration, 7-100% for conflicts of interest, and 65-100% for funding disclosures. There were no major differences between articles published in 2019 and non-COVID-19 articles in 2021. In 2021, non-COVID-19 articles had more data sharing (12%) than COVID-19 articles (4%). Validation-corrected imputed estimates were 3% for code sharing, 11% for data sharing, 8% for registrations, 79% for conflict of interest disclosures and 92% for funding disclosures. Conclusion Data sharing, code sharing, and registration are very uncommon in infectious disease specialty journals. Increased transparency is required.
<p>PDF - 418KB, Global distribution of EGRP-supported cancer epidemiology consortia (CEC)-affiliated teams.</p>
SRT dividers are common in modern floating point units. Higher division performance is achieved by retiring more quotient bits in each cycle. Previous research has shown that realistic stages are limited to radix-2 and radix-4. Higher radix dividers are therefore formed by a combination of low-radix stages. In this paper, we present an analysis of the effects of radix-2 and radix-4 SRT divider architectures and circuit families on divider area and performance. Using analytical modeling and simulation, we evaluate the performance and area of a wide variety of divider architectures and implementations. We conclude that divider performance is only weakly sensitive to reasonable choices of architecture but is significantly improved by aggressive circuit techniques.
There is considerable variation in the amount of evidence from randomised controlled trials for each of the 16 major neglected tropical diseases. Even in diseases with substantial evidence, such as leishmaniasis and geohelminth infections, some recommended treatments have limited supporting data and lack head to head comparisons.
Editorials20 January 2009Personalized Genetic Prediction: Too Limited, Too Expensive, or Too Soon?John P.A. Ioannidis, MDJohn P.A. Ioannidis, MDFrom the University of Ioannina, Iaonnina 45110, Greece.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-150-2-200901200-00012 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Genetic epidemiology has identified many common genetic variants that are associated with common diseases, and the list is growing monthly (1, 2). This success has boosted expectations for personalized genetic prediction. According to these expectations, genetic information can tell people about their risk for various diseases and which medications they should use or avoid. However, 2 articles in this issue (3, 4) suggest that this promise may be exaggerated and premature.Paynter and colleagues (3) evaluated the predictive performance of rs10757274 for cardiovascular disease (CVD) in 22 129 white women. This gene variant has emerged from genome-wide association studies, and its ...References1. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356-69. [PMID: 18398418] CrossrefMedlineGoogle Scholar2. Manolio TA, Brooks LD, Collins FS. A HapMap harvest of insights into the genetics of common disease. J Clin Invest. 2008;118:1590-605. [PMID: 18451988] CrossrefMedlineGoogle Scholar3. Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3. Ann Intern Med. 2009;150:65-72. LinkGoogle Scholar4. Eckman MH, Rosand J, Greenberg SM, Gage BF. Cost-effectiveness of using pharmacogenetic information in warfarin dosing for patients with nonvalvular atrial fibrillation. Ann Intern Med. 2009;150:73-83. LinkGoogle Scholar5. Schunkert H, Götz A, Braund P, McGinnis R, Tregouet DA, Mangino M, et al; Cardiogenics Consortium. Repeated replication and a prospective meta-analysis of the association between chromosome 9p21.3 and coronary artery disease. Circulation. 2008;117:1675-84. [PMID: 18362232] CrossrefMedlineGoogle Scholar6. Kathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med. 2008;358:1240-9. [PMID: 18354102] CrossrefMedlineGoogle Scholar7. Zheng SL, Sun J, Wiklund F, Smith S, Stattin P, Li G, et al. Cumulative association of five genetic variants with prostate cancer. N Engl J Med. 2008;358:910-9. [PMID: 18199855] CrossrefMedlineGoogle Scholar8. Lango H, Palmer CN, Morris AD, Zeggini E, Hattersley AT, McCarthy MI, et al; UK Type 2 Diabetes Genetics Consortium. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes. 2008;57:3129-35. [PMID: 18591388] CrossrefMedlineGoogle Scholar9. Seddon JM, Francis PJ, George S, Schultz DW, Rosner B, Klein ML. Association of CFH Y402H and LOC387715 A69S with progression of age-related macular degeneration. JAMA. 2007;297:1793-800. [PMID: 17456821] CrossrefMedlineGoogle Scholar10. Pharoah PD, Antoniou AC, Easton DF, Ponder BA. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008;358:2796-803. [PMID: 18579814] CrossrefMedlineGoogle Scholar11. Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH, Rioux JD, et al; NIDDK IBD Genetics Consortium. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat Genet. 2008;40:955-62. [PMID: 18587394] CrossrefMedlineGoogle Scholar12. Hunter DJ, Khoury MJ, Drazen JM. Letting the genome out of the bottle—will we get our wish? N Engl J Med. 2008;358:105-7. [PMID: 18184955] CrossrefMedlineGoogle Scholar13. Little J, Higgins JPT, Ioannidis JPA, Moher D, Gagnon F, von Elm E, et al. Strengthening the Reporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. Ann Intern Med. 2009. [Forthcoming]. Google Scholar14. Ioannidis JP. Molecular evidence-based medicine: evolution and integration of information in the genomic era. Eur J Clin Invest. 2007;37:340-9. [PMID: 17461979] CrossrefMedlineGoogle Scholar15. Ioannidis JP, Boffetta P, Little J, O'Brien TR, Uitterlinden AG, Vineis P, et al. Assessment of cumulative evidence on genetic associations: interim guidelines. Int J Epidemiol. 2008;37:120-32. [PMID: 17898028] CrossrefMedlineGoogle Scholar16. Janssens AC, Gwinn M, Bradley LA, Oostra BA, van Duijn CM, Khoury MJ. A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions. Am J Hum Genet. 2008;82:593-9. [PMID: 18319070] CrossrefMedlineGoogle Scholar17. Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for common chronic adult diseases: a systematic review. JAMA. 2008;299:1320-34. [PMID: 18349093] CrossrefMedlineGoogle Scholar18. Ioannidis JPA. Limits to forecasting in personalized medicine: an overview. Int J Forecast. [Forthcoming]. Google Scholar Author, Article, and Disclosure InformationAffiliations: From the University of Ioannina, Iaonnina 45110, Greece.Disclosures: None disclosed.Corresponding Author: John P.A. Ioannidis, MD, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, University Campus, Ioannina, 45110, Greece; e-mail, [email protected]uoi.gr. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoCardiovascular Disease Risk Prediction With and Without Knowledge of Genetic Variation at Chromosome 9p21.3 Nina P. Paynter , Daniel I. Chasman , Julie E. Buring , Dov Shiffman , Nancy R. Cook , and Paul M. Ridker Cost-Effectiveness of Using Pharmacogenetic Information in Warfarin Dosing for Patients With Nonvalvular Atrial Fibrillation Mark H. Eckman , Jonathan Rosand , Steven M. Greenberg , and Brian F. Gage Metrics Cited byA new non-invasive diagnostic tool in coronary artery disease: artificial intelligence as an essential element of predictive, preventive, and personalized medicinePersonalized Respiratory Medicine: Exploring the Horizon, Addressing the Issues. Summary of a BRN-AJRCCM Workshop Held in Barcelona on June 12, 2014Genetic Prediction in the Genetic Analysis Workshop 18 Sequencing DataPrevalence of genetic polymorphisms of CYP2C9 and VKORC1 — Implications for warfarin management and outcome in Croatian patients with acute strokeGenetics and Genomics for the Prevention and Treatment of Cardiovascular Disease: UpdateRe-examining the Gene in Personalized GenomicsUtility of Genome-Wide Association Study findings: prostate cancer as a translational research paradigmGenotype-based personalised nutrition for obesity prevention and treatment: are we there yet?Direct-to-Consumer Genetic TestingReconsidering the Criteria for Evaluating Proposed Screening Programs: Reflections From 4 Current and Former Members of the U.S. Preventive Services Task ForceThe Death of the Cancer CellInherited genetic markers discovered to date are able to identify a significant number of men at considerably elevated risk for prostate cancerDirect-to-Consumer Genetic TestingUse of genomic profiling to assess risk for cardiovascular disease and identify individualized prevention strategies—A targeted evidence-based reviewA Survey of UK Public Interest in Internet-Based Personal Genome TestingExpectations, validity, and reality in omicsConsiderations for the Impact of Personal Genome Information: A Study of Genomic Profiling among Genetics and Genomics ProfessionalsThe Prospect of Genome-guided Preventive Medicine: A Need and Opportunity for Genetic CounselorsNew Approaches to Stroke Prevention in Atrial FibrillationEthical implications of the use of whole genome methods in medical researchPredictive and prognostic molecular markers for cancer medicineEvaluation of genetic tests for susceptibility to common complex diseases: why, when and how?Die Verflüssigung der Norm: Selbstregierung und personalisierte GesundheitPublic Health GenomicsDo DNA Microarrays Tell the Story of Gene Expression?Clinical pharmacology, biomarkers and personalized medicine: education pleaseCollaborative Meta-analysis: Associations of 150 Candidate Genes With Osteoporosis and Osteoporotic FractureJ. Brent Richards, MD, MSc, Fotini K. Kavvoura, MD, PhD, Fernando Rivadeneira, MD, PhD, Unnur Styrkársdóttir, PhD, Karol Estrada, MSc, Bjarni V. Halldórsson, PhD, Yi-Hsiang Hsu, MD, ScD, M. Carola Zillikens, MD, Scott G. Wilson, PhD, Benjamin H. Mullin, BSc, Najaf Amin, MSc, Yurii S. Aulchenko, PhD, L. Adrienne Cupples, PhD, Panagiotis Deloukas, PhD, Serkalem Demissie, PhD, Albert Hofman, MD, PhD, Augustine Kong, PhD, David Karasik, PhD, Joyce B. van Meurs, PhD, Ben A. Oostra, PhD, Huibert A.P. Pols, MD, PhD, Gunnar Sigurdsson, MD, PhD, Unnur Thorsteinsdottir, PhD, Nicole Soranzo, PhD, Frances M.K. Williams, MD, PhD, Yanhua Zhou, MSc, Stuart H. Ralston, MD, Gudmar Thorleifsson, PhD, Cornelia M. van Duijn, PhD, Douglas P. Kiel, MD, MPH, Kari Stefansson, MD, PhD, André G. Uitterlinden, PhD, John P.A. Ioannidis, MD, PhD, and Tim D. Spector, MD, MSc, for the GEFOS (Genetic Factors for Osteoporosis) ConsortiumPopulation-Wide Generalizability of Genome-Wide Discovered AssociationsComparative effectiveness research and genomic medicine: An evolving partnership for 21st century medicineExpanding the Public Health Research Agenda for Ovarian CancerEnabling personalized medicine through the use of healthcare information technologyThe Scientific Foundation for Personal Genomics: Recommendations from a National Institutes of Health–Centers for Disease Control and Prevention Multidisciplinary WorkshopReporting genetic association studies: the STREGA statementPersonalized Medicine: Reality and Reality Checks 20 January 2009Volume 150, Issue 2Page: 139-141KeywordsBreast cancerCardiovascular diseasesClinical geneticsGenetic diseasesGeneticsHemorrhageMedical risk factorsPharmacogeneticsPopulation statistics ePublished: 20 January 2009 Issue Published: 20 January 2009 Copyright & PermissionsCopyright © 2009 by American College of Physicians. 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Abstract Despite over 30 000 publications on proteomics in the last decade, and the accumulation of extensive interesting information on the human proteome in diverse observations, the clinical translation of proteomics to‐date has had major setbacks. I review here a roadmap for improving the success rate of clinical proteomics. The roadmap includes steps for improvements that need to be made in analytical tools, discovery, validation, clinical application, and post‐clinical application appraisal. It is likely that most if not all of the components that are necessary for clinical success are either readily available, or should be possible to put in place with more rigorous research standards and concerted efforts of the research community, clinicians, and health agencies. Enthusiasm for the clinical impact of proteomics may need to be tempered currently until robust evidence can be obtained, but some clinical successes should eventually be feasible.
