A database of naturally occurring human urinary peptides and proteins for use in clinical applications

Owing to its availability, ease of collection and correlation with (patho-) physiology, urine is an attractive source for clinical proteomics. However, the lack of comparable datasets from large cohorts has greatly hindered development in this field. Here we report the establishment of a high resolution proteome database of naturally occurring human urinary peptides and proteins - ranging from 800-17,000 Da - from over 3,600 individual samples using capillary electrophoresis coupled to mass spectrometry, yielding an average of 1,500 peptides per sample. All processed data were deposited in an SQL database, currently containing 5,010 relevant unique urinary peptides that serve as classifiers for diagnosis and monitoring of diseases, including kidney and vascular diseases. Of these, 352 have been sequenced to date. To demonstrate the applicability of this database, two examples of disease diagnosis were provided: For renal damage diagnosis, patients with a specific renal disease were identified with high specificity and sensitivity in a blinded cohort of 131 individuals. We further show definition of biomarkers specific for immunosuppression and complications after transplantation (Kaposi's sarcoma). Due to its high information content, this database will be a powerful tool for the validation of biomarkers for both renal and non-renal diseases

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Cloud condensation nuclei spectra derived from size distributions and hygroscopic properties of the aerosol in coastal south-west Portugal during ACE-2

In this work we propose and test a method to calculate cloud condensation nuclei (CCN) spectra basedon aerosol number size distributions and hygroscopic growth factors. Sensitivity studies show thatthis method can be used in a wide variety of conditions except when the aerosol consist mainly oforganic compounds. One crucial step in the calculations, estimating soluble ions in an aerosol particlebased on hygroscopic growth factors, is tested in an internal hygroscopic consistency study. The resultsshow that during the second Aerosol Characterization Experiment (ACE-2) the number concentrationof inorganic ions analyzed in impactor samples could be reproduced from measured growth factorswithin the measurement uncertainties at the measurement site in Sagres, Portugal. CCN spectra were calculated based on data from the ACE-2 field experiment at the Sagres site.The calculations overestimate measured CCN spectra on average by approximately 30%, which iscomparable to the uncertainties in measurements and calculations at supersaturations below 0.5%. Thecalculated CCN spectra were averaged over time periods when Sagres received clean air masses and airmasses influenced by aged and recent pollution. Pollution outbreaks enhance the CCN concentrationsat supersaturations near 0.2% by a factor of 3 (aged pollution) to 5 (recent pollution) compared to theclean marine background concentrations. In polluted air masses, the shape of the CCN spectra changes.The clean spectra can be approximated by a power function, whereas the polluted spectra are betterapproximated by an error function

Cloud condensation nuclei spectra derived from size distributions and hygroscopic properties of the aerosol in coastal south-west Portugal during ACE-2

In this work we propose and test a method to calculate cloud condensation nuclei (CCN) spectra based on aerosol number size distributions and hygroscopic growth factors. Sensitivity studies show that this method can be used in a wide variety of conditions except when the aerosol consist mainly of organic compounds. One crucial step in the calculations, estimating soluble ions in an aerosol particle based on hygroscopic growth factors, is tested in an internal hygroscopic consistency study. The results show that during the second Aerosol Characterization Experiment (ACE-2) the number concentration of inorganic ions analyzed in impactor samples could be reproduced from measured growth factors within the measurement uncertainties at the measurement site in Sagres, Portugal. CCN spectra were calculated based on data from the ACE-2 field experiment at the Sagres site. The calculations overestimate measured CCN spectra on average by approximately 30%, which is comparable to the uncertainties in measurements and calculations at supersaturations below 0.5%. The calculated CCN spectra were averaged over time periods when Sagres received clean air masses and air masses influenced by aged and recent pollution. Pollution outbreaks enhance the CCN concentrations at supersaturations near 0.2% by a factor of 3 (aged pollution) to 5 (recent pollution) compared to the clean marine background concentrations. In polluted air masses, the shape of the CCN spectra changes. The clean spectra can be approximated by a power function, whereas the polluted spectra are better approximated by an error function

Non-Standard Errors

URL des documents de travail : https://centredeconomiesorbonne.cnrs.fr/publications/Documents de travail du Centre d'Economie de la Sorbonne 2021.33 - ISSN : 1955-611XVoir aussi ce document de travail sur SSRN: https://ssrn.com/abstract=3981597In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants