dataesr/scientific-paragraphs-categorization huggingface dataset:
We examined some parameters that could influence repeatability. The stage speed did not significantly affect the values (data not shown, varied rates from 1 -22 mm/s). We examined the reproducibility of the measurements by performing acquisitions for five consecutive times. We observed that there was almost no variability in some cases, and in other cases, it varied a factor of two. Figure  7b  shows the results for four different stages. This is a strong indicator that the repositioning measurements do not depend exclusively on the stage characteristics but the entire system stability. We examined the influence of the stage displacement distance using three different values (0. 3 mm, 3 mm and 30 mm) for both axes for five different stages from the same constructor (Märzhäuser) and with similar datasheets.
Camera noise characterization The read noise was calculated from 3 CCD, 4 EMCCD, and 22 sCMOS cameras from various constructors and most often for both standard and fast camera readout modes (30, 100, 200 or 500MHz for sCMOS, 10 and 17 or 30MHz for EMCCD, 10 and 20MHz for CCD). Read noise maps and read noise distributions are indicated in Figure  8a , b. The difference in the measured read noise and DSNU value is shown in Figure  8b  when the blemish correction (warm pixels or defects) is ON or OFF. Extreme pixel values are smoothed with the nearest neighbors, and the calculated noise is closer to the one in the datasheet. For each sCMOS camera, the dark offset and the DSNU distribution map were also calculated (Figure  8c ). Data for each pixel was averaged for 100 frames, and a narrow look-up-table was used for visualization to render small differences in the columns visible.
The calculated DSNU is never higher than the read noise, as expected from theory for this kind of cameras. For precise DSNU evaluation, one should in principle acquire thousands of frames to eliminate the read noise. To define the optimal dataset size, we introduced a quality criterion that compares the DSNU obtained with datasets of different size. A threshold of 10% yielded an optimal dataset size of 100 frames (i. e. , the DSNU value difference obtained with larger datasets is less than 10% of the DSNU value calculated with 100 frames, Figure  S10 ). Hence, we recommend using a 100 dark frames dataset for DSNU evaluation. The evolution over a six months of read noise values of different cameras, as measured with the STABnoise metrics, is shown in Figure  8f. STABnoise was always higher than 97%, meaning that camera read noise is relatively stable, and any fluctuation indicates serious detector issues.
Microscope Lateral and Axial Resolution (PSF) The analyzed dataset was collected across ten microscopy facilities, 91 objective lenses of various magnifications and corrections and a wide range of instruments. The PSF measurements were at an average of 1. 54x, 1. 52x and 1. 31x larger than the lateral, theoretical resolution for wide-field, single point scanning and spinning-disk confocal microscopes, respectively. At the same time, axial performances were 1. 16x, 1. 15x and 1. 41x larger than the respective theoretical values (Figure  2b ). The observed wide-field higher dispersion of the measured/theoretical axial resolution ratio, as compared to both confocal types, could be associated with some spherical aberration issues. Hence, the confocal effect at 1 A. U. crops the elongated PSF of a spherical aberration-affected objective, reducing the ratio distribution dispersion that such PSF induces in the wide-field mode.
In the future, the possibility of such protocols being performed with a single sample slide by software in an all automatized acquisition process would be an advantage, since the human factor can be skipped. Every acquired data needs to be analyzed in a reproducible manner as well. To allow this, we developed and proposed the usage of the ImageJ/Fiji plugin MetroloJ_QC. For quality monitoring, we performed many acquisitions over time and proposed several metrics and limit values. When possible, we tried to compare the measured values with the values given by the theory, such as the resolution for PSF, the co-registration or even the mechanical drift. For some other cases we referred to mechanical specifications given by constructors as for the stage repositioning or the read noise for detectors.
We also tried as much as we could, to choose the limit value relevant for the experiments in biology, what we call "Lab practical" in Figure  9. Figure  9  shows in a heuristic way an idea of what Quality Control can be for fluorescence microscopy. We describe the essential aspects of Acquisition and Analysis Tools, Acquisition protocols and find some experimental tolerance values (Table  2 ). We consider that these metrics complement the ISO norm  13  and hopefully will be further enriched by the QUAREP-LiMi, an international initiative started in April 2020 and whose role is to establish guidelines for quality assessment in light microscopy  51. These metrics have to be monitored over time, first in a simple table. However, ideally, an image data management database would be the best solution to process the analysis and obtain the metrics as quality control images are imported  [52] [53] [54].
Acknowledgements The authors would like to thank  Sebastian Beer  and  Bruno Emica  from  Hamamatsu Photonics  and  Gerhard Holst  from PCO for their valuable support and discussion on the metrics of the QC of detectors. We would like to thank  Kees van der Oord (Nikon) ,  Christian Schulz  ( Leica Microsystems ) and  Ralf Wolleschensky (Zeiss)  for their helpful input on PSF issues. We also thank  Arnd Rühl  (Märzhäuser) for his invaluable advice on the stages part of this manuscript. We are deeply indepted to  Ulrike Boehm  for her critical reading of the manuscript. We finally thank the french  RTmfm community  for the fruitful exchanges throughout the past years. The  Montpellier Ressources Imagerie (BCM) ,  PICT (Institut Curie), Bordeaux Imaging Center and IMAG'IC (Institut Cochin ) are part of the  National Infrastructure France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
Using suitable inducers, some fungal strains can secrete high amounts of oxidoreductases  (11)  including copper-containing enzymes. For instance, hypersecretory fungal strains from the Pycnoporus genus have been selected to produce laccases and use them in applied settings  (12, 13). Most of the copper-dependent enzymes are classified within the Auxiliary Activities (AA) class  (14)  in the CAZy database (www. cazy. org) (15). The founding member of copper radical oxidases (CROs), a galactose oxidase (FgrGalOx -EC 1. 1. 3. 9), was discovered in 1959 from the secretome of Fusarium graminearum  (16, 17). In 2013, fungal CROs have been classified within the AA5 family and further classified into subfamilies AA5_1 and AA5_2. AA5_1 contains glyoxal oxidases (GLOX -EC 1. 2. 3. 15) that catalyze the oxidation of aldehydes to carboxylic acids, and AA5_2 gathers enzymes that oxidize the alcohol function of diverse substrates (e.
We therefore elaborated two liquid growth media: (i) "YG" (for "Yeast-extract, Glucose"), and (ii) 'YG-CME" (for "YG-Copper, Ethanol, Manganese"), with the same composition as YG but supplemented with CuSO 4 , manganese and ethanol. A time-course analysis was performed on each strain in both media at three time points (day 3, day 7 and day 14) and proteomic analyses were performed on the 16 secretomes generated (note that no samples were harvested at day 3 for C. graminicola in both YG and YG-CME due to insufficient growth). The quantification of total proteins revealed an increasing amount of proteins over time (except for C. destructivum) (Figure  S2 ) while capillary electrophoresis analysis revealed distinct profiles over time and between fungal strains (Figure  1 ). By performing proteomic analyses of the secretomes using LC-MS/MS and mass matching, we detected a high number of secreted proteins (between 150 and over 400 different proteins, depending on the culture conditions; Supplementary Data Set; Figure  2A ) encompassing a large diversity of enzymes.
Natural and recombinant strains The following fungal strains were used: C. graminicola (M1. 001, CBS 130836, (  35 )), C. destructivum (LARS 709, CBS 520. 97, (70)) and C. tabacum (N150, CPC 18945, (  70 )). The DNA sequences of CdeAlcOx and CtaAlcOx were obtained by blasting against the "raw" unannotated genomes of C. destructivum and C. tabacum and further intron prediction was done using Augustus web tool (http://bioinf. uni-greifswald. de/augustus/). DNA cloning and transformation of P. pastoris X33 with codon-optimized genes (for expression in P. pastoris) encoding CdeAlcOx and CtaAlcOx was carried out as described previously for other constructions  (71). The recombinant Pichia pastoris X33 strains containing CgrAlcOx gene (GenBank EFQ30446. 1) was recovered from previous work  (18, 21).
Microfluidic capillary gel electrophoresis The concentrated secretomes samples were diluted so that 87. 5 µL of the initially harvested secretomes (before concentration) were submitted to electrophoresis, except for C. tabacum YG-CME day 14 which was further diluted by 1/3 to avoid too high protein concentration. Samples were prepared in a 96-well plate using the manufacturer "HT Protein Express Assay Sample Buffer" (PerkinElmer, USA) supplemented with 36. 4 mM (final concentration) of dithiothreitol. The samples were heated at 95°C for 10 min and subjected directly to LabChip GX II capillary gel electrophoresis (PerkinElmer) according to the manufacturer's instructions. The electropherograms were analyzed using the LabChip GX II software (PerkinElmer).
Peptide attribution and protein annotation were performed by comparing mass spectrometry data to predicted proteins in the genomes of tabacum strains. The internal contaminant database X!TandemPipeline software (X!TandemPipeline version 0. 2. 30, France) was also used. The protein annotation was completed manually using the CAZy database for the CAZymes annotation and using the BlastP tool from the NCBI database (https://blast. ncbi. nlm. nih. gov) for other proteins.
A total of 15,792 individuals participated in the baseline examination in 1987-1989, but only baseline fasting blood from the Minnesota field center were analyzed for participants with all data plus plasma fatty acids (n=3494). Fatty acids were measured in EDTA plasma that had been frozen at -70˚C. Fatty acid assays were performed at the Collaborative Studies Clinical Laboratory at Fairview University Medical Center (Minneapolis, MN) as previously described 8. Lipids were extracted with chloroform/methanol and separated by thin layer chromatography. Fatty acid methyl esters were prepared from the phospholipid fraction and separated by gas chromatography using an HP-5890 gas chromatograph (Hewlett-Packard, Palo Alto, CA) with a 100-m capillary Varian CP7420 column. We identified 29 fatty acids.
Thirty seven different fatty acids were identified with their retention times compared with those of commercial standards and expressed each level as percentage of total phospholipid fatty acids (mol%). Pentadecanoic acid (15:0) showed coefficient of variation 11. 9%; palmitic acid (16:0), 1. 6%; heptadecanoic acid (17:0), 4. 2%; stearic acid (18:0), 2. 0%; arachidic acid (20:0), 15. 3%; behenic acid (22:0), 10. 3%; tricosanoic acid (23:0), 18. 9%; and lignoceric acid (24:0), 14. 7%. We used human and equine plasma (Sera Laboratories International, West Sussex, UK) for quality control. Ascertainment of incident T2D involved a review of the existing EPIC datasets at each centre using multiple sources of evidence including self-report, linkage to primary-care registers, secondary-care registers, medication use (drug registers), hospital admissions and mortality data.
Finnish Diabetes Prevention Study (FDPS), Finland 14-16 The Finnish DPS is a was a randomised study aimed at preventing type 2 diabetes through intensive lifestyle intervention, carried out at five study clinics in Finland. The randomised trial started in November 1993, the recruitment period lasted until June 1998 and the intervention period lasted until the end of 2001. The last follow-up data has been collected in 2009 with a follow-up over 13 years from baseline. Participants were recruited at five field centers (Kuopio, Helsinki, Tampere, Turku and Oulu). The cohort consists of 522 non-institutionalized men and women. Originally, subjects at baseline were all with impaired glucose tolerance defined by repeated oral glucose tolerance tests (OGTT) (mean value of 2-hour plasma glucose concentrations 7.
8-11. 0 mmol/l in two OGTTs). For the fatty acid measurements, only subjects who were non-diabetic by the revised WHO 1999 criteria at baseline and had stored serum samples available from the active study period (n=407). From these, three subjects have lost follow-up and do not have data on diabetes incidence after baseline. Therefore, a total of 396 adults free of type 2 diabetes at baseline with available data on circulating fatty acids and diabetes incidence were eligible for the current analysis. The total serum fatty acid composition was measured by TETHYS Bioscience Inc. (Emeryville, CA) in 2010 as in [Takkunen et al. Longitudinal associations of serum fatty acid composition with type 2 diabetes risk and markers of insulin secretion and sensitivity in the Finnish Diabetes Prevention Study.
The fatty acid composition of erythrocyte samples were analyzed by gas chromatography equipped with a SP 2560 capillary column after direct transesterification for 10 minutes in boron trifluoride/ methanol and hexane at 100 C as previously described. 18 This technique generates fatty acids primarily from erythrocyte glycerophospholipids. Erythrocytes were isolated from blood drawn after a 10-12 h fast and frozen at -80 °C immediately after collection. All fatty acids present at >1% abundance had CVs of ≤7%. Genotyping was conducted using the Affymetrix 500K SNP chip, with imputation for markers with genotype call rates below 97% or small (<1x10 -6 ) HWE p-values using Mach and the CEU HapMap dataset for reference. Incident diabetes was defined as fasting glucose concentration ≥ 7. 0mmol/L, HBA1C ≥ 6.
Fatty acid concentrations were measured in stored total plasma and erythrocyte samples using gasliquid chromatography. Concentrations of individual circulating fatty acids were expressed as a percentage of total fatty acids either in plasma or erythrocyte membranes Plasma intra-assay CVs are as follows: EPA: 7%, DPA: 13%, DHA: 10%, ALA: 4%. The corresponding erythrocyte CVs are as follows: EPA: 12%, DPA: 13%, DHA: 14%, ALA: 12%. Incident cases of diabetes are identified by self-reports on mail questionnaires and confirmed by supplementary information collected about the diagnosis using the following criteria from the National Diabetes Data Group (NDDG) up until 1998: (1) manifestation of classic symptoms such as excessive thirst, polyuria, weight loss and hunger, in conjunction with elevated fasting glucose ≥7.
4%, 2. 3%, and 3. 8%, respectively. Genomic DNA was extracted from whole blood using a QIAmp DNA blood kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Extracted DNA was dissolved in 20 μL Tris-HCl buffer (10 mmol/L, pH 8. 0) containing 1 mmol/L EDTA and was stored at -80°C until use. Genotyping was performed by using of either the multiplex polymerase chain reaction-based invader assay (Third Wave Technologies) 17 or TaqMan assay (Applied Biosystems) in a blinded manner to the clinical information of study subjects. The databases from the cohort of the Hisayama study has been scrutinized in order to identify subjects with information of genotype and phenotype regarding diabetic status. A total of 3,230 subjects who participated in the health examination in 2002 consented to a genomic research and were genotyped for tagging single nucleotide peptides (SNPs).
Three City Study (3C), France  37, 38  3C study is an ongoing multicenter prospective cohort study of vascular risk factors for dementia which started in 1999-2000 and included 9,294 community dwellers in three French cities: Bordeaux (n=2,104), Dijon (n=4,931) and Montpellier (n=2,259). Individuals living in one of these cities, aged 65 years and over and not institutionalized were eligible for recruitment into the 3C study. The protocol of the 3C study has been approved by the Consultative Committee for the Protection of Persons participating in Biomedical Research of the Kremlin-Bicêtre University Hospital (Paris). All participants gave their written informed consent. The baseline data collection included socio-demographic and lifestyle characteristics, symptoms and complaints, main chronic conditions, medication use, neuropsychological testing, clinical examination including blood pressure measurement, electrocardiogram (ECG) and blood sampling.
Individual fatty acid methyl esters were separated using a gas chromatograph (Focus GC, Thermo Scientific, France) equipped with a flam ionization detector and a split injector. A fused silica capillary column (BPX 70, 60m x 0. 25mm internal diameter, 0. 25mm film; Phenomenex, Germany) was used with H2 as the carrier gas (inlet pressure: 1 bar). The column temperature was programmed to increase from 150 to 200°C at 1. 5°C/min for 25 min, and then from 200 to 225°C at 20°C/min and was held at 225°C until the completion of the analysis (20 min). The injection port and detector were maintained at 250 and 280°C, respectively. Data were integrated using the ChromQuest Software (Thermo Scientific). Individual fatty acid methyl esters were identified by comparing their retention times with those of authentic standards eluted in the same conditions (Sigma Chemical Co.
Women's Health Initiatives Memory Study (WHI), USA The fatty acid composition of RBC samples were analyzed by gas chromatography equipped with a SP 2560 capillary column after direct transesterification for 10 minutes in boron trifluoride/ methanol and hexane at 100 C as previously described. This technique generates fatty acids primarily from RBC glycerophospholipids. During the aliquoting phase, the RBC samples were stored improperly at -20°C for a period of approximately 2 weeks, causing oxidative degeneration of the PUFAs before measurement. The original FA levels were estimated with multiple imputations using independent data on fatty acid degradation and length of time the samples were exposed to -20°C. 41 All fatty acids present at >1% abundance had CVs of ≤6. 5%. Genotyping was conducted using the Human Omni Express Exome-8v1_B, with imputation using the 1000 genomes reference panel.
Verification of diabetes cases in EPIC-Netherlands was additionally funded by Netherlands Agency grantIGE05012 and an incentive grant from the board of the University Medical Centre Utrecht. NJW and NGF acknowledge support from the National Institute for Health Research Cambridge Biomedical Research Centre [IS-BRC-1215-20014] and NJW is an NIHR Senior Investigator. Finnish Foundation for Cardiovascular Research, Unilever, and Competitive Research Funding from Tampere, Kuopio and Oulu University Hospitals. The study sponsors had no role in the design and conduct of the study; the collection, analysis and interpretation of the data; or the preparation, review or approval of the manuscript. Framingham Heart Study The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No.
N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or conclusions of the Framingham Heart Study or the NHLBI. The Kuopio Ischaemic Heart Disease Risk Factor Study was supported mainly by grants 41471 and 1041086 from the Academy of Finland, Helsinki, Finland to Jukka T. Salonen. The Melbourne Collaborative Cohort Study recruitment was funded by VicHealth and Cancer Council Victoria and was further supported by grants 209057, 251553, 126403, and 504711 from Australia's National Health and Medical Research Council and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.
N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or conclusions of the Framingham Heart Study or the NHLBI. The Kuopio Ischaemic Heart Disease Risk Factor Study was supported mainly by grants 41471 and 1041086 from the Academy of Finland, Helsinki, Finland to Jukka T. Salonen. The Melbourne Collaborative Cohort Study recruitment was funded by VicHealth and Cancer Council Victoria and was further supported by grants 209057, 251553, 126403, and 504711 from Australia's National Health and Medical Research Council and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database Allele Alpha Omega Cohort Study rs340874_T rs7578597_C rs2943641_T rs780094_T rs7593730_T rs4607103_T rs1801282_G rs11708067_G rs10010131_A rs864745_C rs972283_A rs2191349_G rs13266634_T rs10811661_C rs13292136_T rs1111875_T rs231362_A rs1552224_C rs7957197_A rs11634397_A rs10923931_T rs243021_A rs4402960_T rs4457053_G rs10946398_C rs4607517_A rs896854_T rs12779790_G rs7901695_C rs5215_C Study Age, Gene/Environment Susceptibility Study Atherosclerosis Risk in Communities Study Chin-Shan Community Cardiovascular Study Cardiovascular Health Study Blood Institute (European Prospective Investigation Gene PROX1 THADA IRS1 GCKR RBMS1 ADAMTS9 PPARG ADCY5 WFS1 JAZF1 KLF14 DGKB SLC30A8 CDKN2AB CHCHD9 HHEX KCNQ1 CENTD2 HNF1A ZFAND6 NOTCH2 BCL11A IGF2BP2 ZBED3 CDKAL1 GCK TP53INP1 CDC123 TCF7L2 KCNJ11 Office of Dietary Supplements, NIH contract N01-AG012100, the NIA Intramural Research Program, Hjartavernd (the Weight wt1=0.
(B) (JP16H05850, JP16H05557, and JP17H04126) and (C) (JP15K09267, JP15K08738, JP15K09835, JP16K09244, InterAct investigators acknowledge funding from the following sources: Medical Research Council Epidemiology Unit JP17K09114, JP17K09113, and JP17K01853) from the Ministry of Education, Culture, Sports, Science and (grants MC_UU_12015/1 and MC_UU_12015/5), Medical Research Council Human Nutrition Research (grant MC_UD99999906), and Cambridge Lipidomics Biomarker Research Initiative (grant G0800783); Cancer Research UK; Finnish Diabetes Prevention Study The DPS study has been financially supported by the Academy of Finland (128315, 129330), Ministry of Education, Novo Nordisk Foundation, Yrjö Jahnsson Foundation, Juho Vainio Foundation, Finnish Diabetes Research Technology of Japan; by Health and Labour Sciences Research Grants of the Ministry of Health, Labour and Welfare of Japan (H25-Junkankitou [Seishuu]-Sitei-022, H29-Junkankitou-Ippan-003, and H27-Shokuhin-[Sitei]-017); and by the Japan Agency for Medical Research and Development (JP17dk0207025, JP17ek0210082, JP17gm0610007, JP17ek0210083, JP17km0405202, JP17ek0210080).
Health Professionals Follow-up Study The Health Professionals Follow-up Study was supported by research grants U01 CA167552, R01 HL35464, AA11181, CA55075, HL60712, and P30 DK46200 from the National Institutes of Health. Kuopio Ischaemic Heart Disease Risk Factor Study Melbourne Collaborative Cohort Foundation, Hisayama Study Study rs10830963_G MTNR1B wt31=0. 0953 rs7961581_C TSPAN8 wt32=0. 0677 rs1531343_C HMGA2 wt33=0. 0583 rs8042680_A PRC1 wt34=0. 0677 rs4430796_A HNF1B wt35=0. 0953 The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human.