Metabolic Sensor Governing Bacterial Virulence in Staphylococcus Aureus
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Reproducibility, Replicability, and Generalization in the Social, Behavioral, and Economic Sciences
REPRODUCIBILITY, REPLICABILITY, AND GENERALIZATION IN THE SOCIAL, BEHAVIORAL, AND ECONOMIC SCIENCES Report of the Subcommittee on Replicability in Science of the SBE Advisory Committee to the National Science Foundation 13 May 2015 Presentation at SBE AC Spring Meeting by K. Bollen. Report of the Subcommittee on Replicability in Science Advisory Committee to the NSF SBE Directorate Subcommittee Members: Kenneth Bollen (University of North Carolina at Chapel Hill, Cochair) John T. Cacioppo (University of Chicago, Cochair) Robert M. Kaplan (Agency for Healthcare Research and Quality) Jon A. Krosnick (Stanford University) James L. Olds (George Mason University) Staff Assistance Heather Dean (National Science Foundation) TOPICS I. Background II. Subcommittee Report III. Definitions IV. Recommendations V. Final Comments Background • Spring, 2013 o NSF SBE Advisory Committee establishes subcommittee on how SBE can promote robust research practices • Summer & Fall 2013 o Subcommittee proposal for workshop on “Robust Research in the Social, Behavioral, and Economic Sciences.” • February 20-21, 2014 o Workshop convened o Participants drawn from variety of universities, funding agencies, scientific associations, and journals o Cover broad range of issues from extent and cause of problems to possible solutions o Details are in the appendix of the circulated report • Post-workshop period o Document & digest workshop content o Discuss and propose recommendations o Complete report Subcommittee Report • DEFINITIONS o No consensus in science on the meanings -
Evaluating Alternative Hypotheses to Explain the Downward Trend in the Indices of the COVID-19 Pandemic Death Rate
Evaluating alternative hypotheses to explain the downward trend in the indices of the COVID-19 pandemic death rate Sonali Shinde1, Pratima Ranade1 and Milind Watve2 1 Department of Biodiversity, Abasaheb Garware College, Pune, Pune, Maharashtra, India 2 Independent Researcher, Pune, Maharashtra, India ABSTRACT Background: In the ongoing Covid-19 pandemic, in the global data on the case fatality ratio (CFR) and other indices reflecting death rate, there is a consistent downward trend from mid-April to mid-November. The downward trend can be an illusion caused by biases and limitations of data or it could faithfully reflect a declining death rate. A variety of explanations for this trend are possible, but a systematic analysis of the testable predictions of the alternative hypotheses has not yet been attempted. Methodology: We state six testable alternative hypotheses, analyze their testable predictions using public domain data and evaluate their relative contributions to the downward trend. Results: We show that a decline in the death rate is real; changing age structure of the infected population and evolution of the virus towards reduced virulence are the most supported hypotheses and together contribute to major part of the trend. The testable predictions from other explanations including altered testing efficiency, time lag, improved treatment protocols and herd immunity are not consistently supported, or do not appear to make a major contribution to this trend although they may influence some other patterns of the epidemic. Conclusion: The fatality of the infection showed a robust declining time trend between mid April to mid November. Changing age class of the infected and Submitted 7 December 2020 Accepted 3 March 2021 decreasing virulence of the pathogen were found to be the strongest contributors to Published 20 April 2021 the trend. -
Principles of Validation of Diagnostic Assays for Infectious Diseases1
PRINCIPLES OF VALIDATION OF DIAGNOSTIC ASSAYS FOR INFECTIOUS DISEASES1 R. H. JACOBSON, New York State College of Veterinary Medicine, Cornell University, Ithaca, New York, USA Abstract PRINCIPLES OF VALIDATION OF DIAGNOSTIC ASSAYS FOR INFECTIOUS DISEASES. Assay validation requires a series of inter-related processes. Assay validation is an experimental process: reagents and protocols are optimized by experimentation to detect the analyte with accuracy and precision. Assay validation is a relative process: its diagnostic sensitivity and diagnostic specificity are calculated relative to test results obtained from reference animal populations of known infection/exposure status. Assay validation is a conditional process: classification of animals in the target population as infected or uninfected is conditional upon how well the reference animal population used to validate the assay represents the target population; accurate predictions of the infection status of animals from test results (PV+ and PV−) are conditional upon the estimated prevalence of disease/infection in the target population. Assay validation is an incremental process: confidence in the validity of an assay increases over time when use confirms that it is robust as demonstrated by accurate and precise results; the assay may also achieve increasing levels of validity as it is upgraded and extended by adding reference populations of known infection status. Assay validation is a continuous process: the assay remains valid only insofar as it continues to provide accurate and precise results as proven through statistical verification. Therefore, the work required for validation of diagnostic assays for infectious diseases does not end with a time-limited series of experiments based on a few reference samples rather, to assure valid test results from an assay requires constant vigilance and maintenance of the assay, along with reassessment of its performance characteristics for each unique population of animals to which it is applied. -
New NIH Guidelines to Improve Experimental Rigor and Reproducibility
New NIH guidelines to improve Experimental Rigor and Reproducibility Oswald Steward Professor, Department of Anatomy & Neurobiology Senior Associate Dean for Research University of California School of Medicine What I’ll tell you 1) Background: Increasing reports of failures to replicate 2) Previous actions by NIH to enhance rigor 3) New requirements for grants submitted after January 2016 4) What NIH will be looking for in new required sections 5) Some suggested best practices to meet new standards for scientific rigor • The Problem: Numerous recent reports of findings that can’t be replicated. Failure to replicate key results from preclinical cancer studies: 1) Prinz et al. (2011) Believe it or not: How much can we rely on published data on potential drug targets? Nature Reviews Drug Discovery. Inconsistencies were found in 2/3 studies 2) Begley and Ellis (2012) Raise standards for preclinical cancer research. Nature. Only 6/53 “landmark” papers replicated 3) Begley (2013) Six red flags for suspect work. Nature. -Were experiments performed blinded? -Were basic experiments repeated? -Were all the results presented? -Were there positive and negative controls? -Were reagents validated? -Were statistical tests appropriate? • In ALS field, Scott et al., (2008) re-assessed 70 drugs that had been reported to increase lifespan in an animal model of ALS (SOD1-G93A knockout mice). • 221 separate studies were carried out over a five year period that involved over 18,000 mice. • Some drugs were already in clinical trials. • Outcome: There was no statistically significant positive effect for any of the 70 compounds tested. • Conclusion: Previously published effects were measurements of noise in the distribution of survival means as opposed to an actual drug effect. -
Studies of Staphylococcal Infections. I. Virulence of Staphy- Lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R
Journal of Clinical Investigation Vol. 43, No. 11, 1964 Studies of Staphylococcal Infections. I. Virulence of Staphy- lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R. MCCABE t (From the Research Laboratory, West Side Veterans Administration Hospital, and the Department of Medicine, Research and Educational Hospitals, University of Illinois College of Medicine, Chicago, Ill.) Many of the determinants of the pathogenesis niques still require relatively large numbers of and course of staphylococcal infections remain staphylococci to produce infection (19). Fatal imprecisely defined (1, 2) despite their increas- systemic infections have been equally difficult to ing importance (3-10). Experimental infections produce in animals and have necessitated the in- in suitable laboratory animals have been of con- jection of 107 to 109 bacteria (20-23). A few siderable assistance in clarifying the role of host strains of staphylococci have been found that are defense mechanisms and specific bacterial virulence capable of producing lethal systemic infections factors with a variety of other infectious agents. with inocula of from 102 to 103 bacteria (24) and A sensitive experimental model would be of value have excited considerable interest (25-27). The in defining the importance of these factors in virulence of these strains apparently results from staphylococcal infections, but both humans and an unusual antigenic variation (27, 28) which, the usual laboratory animals are relatively re- despite its interest, is of doubtful significance in sistant. Extremely large numbers of staphylo- human staphylococcal infection, since such strains cocci are required to produce either local or sys- have been isolated only rarely from clinical in- temic infections experimentally. -
Virulence During Newcastle Disease Viruses Cross Species Adaptation
viruses Review Virulence during Newcastle Disease Viruses Cross Species Adaptation Claudio L. Afonso Base2bio, LLC, Oshkosh, WI 54905, USA; [email protected]; Tel.: +1-800-817-7160 Abstract: The hypothesis that host adaptation in virulent Newcastle disease viruses (NDV) has been accompanied by virulence modulation is reviewed here. Historical records, experimental data, and phylogenetic analyses from available GenBank sequences suggest that currently circulating NDVs emerged in the 1920–19400s from low virulence viruses by mutation at the fusion protein cleavage site. These viruses later gave rise to multiple virulent genotypes by modulating virulence in opposite directions. Phylogenetic and pathotyping studies demonstrate that older virulent NDVs further evolved into chicken-adapted genotypes by increasing virulence (velogenic-viscerotropic pathotypes with intracerebral pathogenicity indexes [ICPIs] of 1.6 to 2), or into cormorant-adapted NDVs by moderating virulence (velogenic–neurotropic pathotypes with ICPIs of 1.4 to 1.6), or into pigeon-adapted viruses by further attenuating virulence (mesogenic pathotypes with ICPIs of 0.9 to 1.4). Pathogenesis and transmission experiments on adult chickens demonstrate that chicken-adapted velogenic-viscerotropic viruses are more capable of causing disease than older velogenic-neurotropic viruses. Currently circulating velogenic–viscerotropic viruses are also more capable of replicating and of being transmitted in naïve chickens than viruses from cormorants and pigeons. These evolutionary virulence changes are consistent with theories that predict that virulence may evolve in many directions in order to achieve maximum fitness, as determined by genetic and ecologic constraints. Keywords: NDV; evolution; virulence; host adaptation Citation: Afonso, C.L. Virulence during Newcastle Disease Viruses Cross Species Adaptation. -
On the Reproducibility of Meta-Analyses.Docx
On the Reproducibility of Meta-Analyses 1 1 RUNNING HEAD: ON THE REPRODUCIBILITY OF META-ANALYSES 2 3 4 On the Reproducibility of Meta-Analyses: Six Practical Recommendations 5 6 Daniël Lakens 7 Eindhoven University of Technology 8 Joe Hilgard 9 University of Missouri - Columbia 10 Janneke Staaks 11 University of Amsterdam 12 13 In Press, BMC Psychology 14 15 Word Count: 6371 16 17 Keywords: Meta-Analysis; Reproducibility; Open Science; Reporting Guidelines 18 19 Author Note: Correspondence can be addressed to Daniël Lakens, Human Technology 20 Interaction Group, IPO 1.33, PO Box 513, 5600MB Eindhoven, The Netherlands. E-mail: 21 [email protected]. We would like to thank Bianca Kramer for feedback on a previous draft 22 of this manuscript. 23 24 Competing Interests: The authors declared that they had no competing interest with respect 25 to their authorship or the publication of this article. 26 27 Author Contributions: All authors contributed writing this manuscript. DL drafted the 28 manuscript, DL, JH, and JS revised the manuscript. All authors read and approved the final 29 manuscript. 30 31 Email Addresses: D. Lakens: [email protected] 32 Joe Hilgard: [email protected] 33 Janneke Staaks: [email protected] On the Reproducibility of Meta-Analyses 2 34 Abstract 35 Background 36 Meta-analyses play an important role in cumulative science by combining information 37 across multiple studies and attempting to provide effect size estimates corrected for 38 publication bias. Research on the reproducibility of meta-analyses reveals that errors are 39 common, and the percentage of effect size calculations that cannot be reproduced is much 40 higher than is desirable. -
10 Things to Know About Reproducibility and Replicability
10 Things to Know About Reproducibility and Replicability One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some argue that such an observed inconsistency can be an important precursor to new discovery while others fear it may be a symptom of a lack of rigor in science. When a newly reported scientific study has far-reaching implications for science or a major, potential impact on the public, the question of its reliability takes on heightened importance. 1 The terms reproducibility and replicability take on 6 Not all studies can be replicated. While scientists are a range of meanings in contemporary usage. The report able to test for replicability of most studies, it is impossible to do distinguishes and defines the terms as follows: Reproducibility so for studies of ephemeral phenomena. means obtaining consistent results using the same input data, computational steps, methods, and conditions of analysis; it is synonymous with computational reproducibility. Replicability 7 One type of scientific research tool, statistical means obtaining consistent results across studies aimed at inference, has an outsized role in replicability discussions answering the same scientific question, each of which has due to the frequent misuse of statistics and the use of a obtained its own data. p-value threshold for determining “statistical significance.” Biases in published research can occur due to the excess reliance on and misunderstanding of statistical significance. 2 Reproducibility and replicability matter. -
Virulence of Japanese Encephalitis Virus Genotypes I and III, Taiwan
Virulence of Japanese Encephalitis Virus Genotypes I and III, Taiwan Yi-Chin Fan,1 Jen-Wei Lin,1 Shu-Ying Liao, within a year (8,9), which provided an excellent opportu- Jo-Mei Chen, Yi-Ying Chen, Hsien-Chung Chiu, nity to study the transmission dynamics and pathogenicity Chen-Chang Shih, Chi-Ming Chen, of these 2 JEV genotypes. Ruey-Yi Chang, Chwan-Chuen King, A mouse model showed that the pathogenic potential Wei-June Chen, Yi-Ting Ko, Chao-Chin Chang, is similar among different JEV genotypes (10). However, Shyan-Song Chiou the pathogenic difference between GI and GIII virus infec- tions among humans remains unclear. Endy et al. report- The virulence of genotype I (GI) Japanese encephalitis vi- ed that the proportion of asymptomatic infected persons rus (JEV) is under debate. We investigated differences in among total infected persons (asymptomatic ratio) is an the virulence of GI and GIII JEV by calculating asymptomat- excellent indicator for estimating virulence or pathogenic- ic ratios based on serologic studies during GI- and GIII-JEV endemic periods. The results suggested equal virulence of ity of dengue virus infections among humans (11). We used GI and GIII JEV among humans. the asymptomatic ratio method for a study to determine if GI JEV is associated with lower virulence than GIII JEV among humans in Taiwan. apanese encephalitis virus (JEV), a mosquitoborne Jflavivirus, causes Japanese encephalitis (JE). This virus The Study has been reported in Southeast Asia and Western Pacific re- JEVs were identified in 6 locations in Taiwan during 1994– gions since it emerged during the 1870s in Japan (1). -
Lab Services Accuracy and Reproducibility
Accuracy and Reproducibility ABOUT The Laboratory Services Committee of the American Thyroid Association® (ATA) conducted a survey of ATA® members to identify areas of member interest for education in pathology and laboratory medicine. In response to the results of the survey, the Lab Service Committee developed a series of educational materials to share with the ATA® membership. The topics below were ranked as high educational priorities amongst the membership. FIGURE 1A: FIGURE 1B: Single perfectly accurate result. Three inaccurate results that ACCURACY AND PRECISION are accurate on average WHAT ARE THE INDICATORS OF TEST RELIABILITY? The most commonly used descriptors of analytical performance for laboratories are accuracy and precision. Accuracy is the degree to which the test results approximate the value that would be obtained by a reference method, and precision is the reproducibility of test results among measurements, days, and operating conditions. The metrics of analytical performance are distinct from the metrics of clinical performance, which are focused on sensitivity, specificity, and predictive values. FIGURE 1C: Three perfectly precise but ACCURACY (TRUENESS) inaccurate results. A test method is said to be accurate when the values it produces are close to the values that are obtained from a reference method. Thus, PRECISION (REPEATABILITY) the concept of accuracy is highly dependent on the correct choice of The description of accuracy above demonstrates why this metric alone a reference method as all judgments of accuracy treat the reference is insufficient to characterize how a test performs. Three arrows shown method as truth; deviations from the reference method are therefore in (Figure 1B) are all very inaccurate, but they average to be accurate. -
A Novel High Throughput, Parallel Infection Assay for Determining The
viruses Article A Novel High Throughput, Parallel Infection Assay for Determining the Replication Capacities of 346 Primary HIV-1 Isolates of the Zurich Primary HIV-1 Infection Study in Primary Cells Audrey E. Rindler 1,2,3 , Herbert Kuster 1,2 , Kathrin Neumann 1,2 , Christine Leemann 1,2, Dominique L. Braun 1,2 , Karin J. Metzner 1,2,* and Huldrych F. Günthard 1,2,* 1 Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, 8091 Zürich, Switzerland; [email protected] (A.E.R.); [email protected] (H.K.); [email protected] (K.N.); [email protected] (C.L.); [email protected] (D.L.B.) 2 Institute of Medical Virology, University of Zürich, 8057 Zürich, Switzerland 3 Life Sciences Graduate School, University of Zürich, 8057 Zürich, Switzerland * Correspondence: [email protected] (K.J.M.); [email protected] (H.F.G.) Abstract: HIV-1 replication capacity is an important characteristic to understand the replication competence of single variants or virus populations. It can further aid in the understanding of HIV-1 pathogenicity, disease progression, and drug resistance mutations. To effectively study RC, many assays have been established. However, there is still demand for a high throughput replication capacity assay using primary cells which is robust and reproducible. In this study, we established Citation: Rindler, A.E.; Kuster, H.; such an assay and validated it using 346 primary HIV-1 isolates from patients enrolled in the Neumann, K.; Leemann, C.; Braun, Zurich Primary HIV Infection study (ZPHI) and two control viruses, HIV-1 JR-CSF and HIV-1 D.L.; Metzner, K.J.; Günthard, H.F. -
1 Maximizing the Reproducibility of Your Research Open
1 Maximizing the Reproducibility of Your Research Open Science Collaboration1 Open Science Collaboration (in press). Maximizing the reproducibility of your research. In S. O. Lilienfeld & I. D. Waldman (Eds.), Psychological Science Under Scrutiny: Recent Challenges and Proposed Solutions. New York, NY: Wiley. Authors’ Note: Preparation of this chapter was supported by the Center for Open Science and by a Veni Grant (016.145.049) awarded to Hans IJzerman. Correspondence can be addressed to Brian Nosek, [email protected]. 1 Alexander A. Aarts, Nuenen, The Netherlands; Frank A. Bosco, Virginia Commonwealth University; Katherine S. Button, University of Bristol; Joshua Carp, Center for Open Science; Susann Fiedler, Max Planck Institut for Research on Collective Goods; James G. Field, Virginia Commonwealth University; Roger Giner-Sorolla, University of Kent; Hans IJzerman, Tilburg University; Melissa Lewis, Center for Open Science; Marcus Munafò, University of Bristol; Brian A. Nosek, University of Virginia; Jason M. Prenoveau, Loyola University Maryland; Jeffrey R. Spies, Center for Open Science 2 Commentators in this book and elsewhere describe evidence that modal scientific practices in design, analysis, and reporting are interfering with the credibility and veracity of the published literature (Begley & Ellis, 2012; Ioannidis, 2005; Miguel et al., 2014; Simmons, Nelson, & Simonsohn, 2011). The reproducibility of published findings is unknown (Open Science Collaboration, 2012a), but concern that is lower than desirable is widespread -