dataesr/scientific-paragraphs-categorization huggingface dataset:
Correlative measurement selection Ground-based data records obtained by ozonesondes and stratospheric ozone lidars are used as a transfer standard against which the OPERA and RAL GOME-2 retrievals are compared. As described in Sect. 3, the selection and postprocessing of reference data is included in the first segment of the evaluation scheme (Appendix A). Like for the satellite data, prior to searching for co-locations, data screening has been applied to the ground-based correlative measurements, both on the entire profiles and on individual levels. The recommendations of the ground data providers to discard unreliable measurements are followed. Next to that, measurements with unphysical (e. g. negative or NAN) pressure, temperature, or ozone readings are rejected automatically. Ozonesonde measurements at pressures below 5 hPa (beyond 30-33 km) and lidar measurements outside of the 15-47 km vertical range are rejected as well.
Ozonesondes In situ measurements of ozone are carried out regularly by ozonesondes on-board small meteorological balloons launched at numerous sites around the world. They measure the vertical profile of ozone partial pressure with order of 10 m vertical sampling (100-150 m actual vertical resolution) from the ground up to the burst point of the balloon, usually between 30 and 33 km. An interfaced radiosonde provides the pressure, temperature and GPS data necessary to geolocate each measurement and to convert ozone partial pressure into other units. Normalization factors are not applied. Different types of ozonesondes were developed over the years. Those still in use today are based on the electrochemical reaction of ozone with a potassium-iodide sensing solution. Laboratory tests and field campaigns indicate that between the tropopause and about 28 km altitude all sonde types produce consistent results when the standard operating procedures are followed  (Smit and ASOPOS panel, 2011).
However, interference by aerosols, signal induced noise in photomultipliers and saturation of the data acquisition system can degrade the quality of the measurements. Unreliable mea-surements can be discarded based on the reported precision, which decreases with altitude. The estimated bias and precision are about ±2 % between 20 and 35 km and increase to ±10 % outside this altitude range where the signal-to-noise ratio is smaller  (Keckhut et al. , 2004). The consistency between six lidars in the NDACC network was recently studied by  (Nair et al. , 2012)  using various satellite data sets. They concluded that the different lidar records agree within ±5 % of the space-based observations over the range of 20-40 km. This work uses data from all stratospheric ozone lidars that have been operational in the NDACC network during the year 2008.
Co-location settings The setting of coincidence criteria and the subsequent extraction of co-located ozone profiles from both the satellite and reference data sets is included in the fourth part of the validation scheme (see Appendix A). From the screened GOME-2 station overpass data sets and the approved NDACC (ozonesonde and lidar) and WOUDC (ozonesonde only) ground-based measurement data, several co-located subsets have been extracted. Only co-locations with a maximal spatial distance r of 200 km and a maximal time difference t of 2 h for ozonesondes and 12 h for lidars were allowed. The spatial co-location window is thus chosen to be of the order of the horizontal resolution of the co-added satellite pixels, while the limit temporal differences are a compromise between a sufficient overlap of the sampled air masses and a sufficiently large sample for statistical analysis (see next section).
Lidar data on the other hand are mostly given in number density and in general do not contain temperature profiles for a beforehand ND to VMR conversion by use of Eq. (B1). The latter has therefore been accomplished by consistently applying pressure and temperature fields that were extracted from the latest ERA-Interim reanalysis  (Dee et al. , 2011). Diverse unit conversion and kernel smoothing approaches can (and sometimes must) be applied (see 12 boxes in section six of the flowchart in Appendix A): ground-based ozone profiles in VMR or number density units require conversion to sub-columns for comparison with OPERA, while for RAL a more direct comparison in VMR or ND is possible as well. Unit conversion of ground-based reference data anyhow optimally (in order to minimize data manipulation) takes place before kernel smoothing for OPERA (pre-conversion) and after kernel smoothing for RAL (post-conversion) because of the partial column and number density units of the initial OPERA and RAL averaging kernel matrices, respectively.
Finally, two different AK smoothing methods are common practice but yield somewhat dissimilar results (see Sect. 7). The first contains a direct multiplication of the AKM with a regridded ground-based ozone profile (coarse method), while the second is based on a multiplication of a row-interpolated AKM with the full ground profile (fine method), which maximally exploits the high-resolution reference measurement without adding information to the retrieval data  (Ridolfi et al. , 2006). Both procedures will be applied and compared in this work, resulting in nine evaluation approaches to be studied in total (corresponding to the nine boxes with black text in the sixth part of Fig. A1 ), as denoted by Roman numerals. -Comparison of OPERA (I) and RAL (II) nadir ozone sub-column profiles with regridded and unit converted ground-based data.
(13) -Comparison of RAL partial column profiles with kernel smoothed and post-converted ground-based data. The kernel smoothing steps then are identical to those in the previous case, but both the coarse (VIII) and fine (IX) methods are now followed by a post-conversion of the form: x PC g = M c x ND g. (14) The sub-column nadir ozone profiles that are provided in the RAL data sets are however calculated from their ND retrievals quite differently (see Sect. 3. 3). RAL's VMR integration over pressure differences on a 41 level grid after conversion and interpolation of the kernel smoothed number density profile yields a slightly different result from Eq. (  14 ) and is therefore also considered in the result discussion. It should be noted that the kernel smoothing outlined in the above equations can also be expressed differently.
For the fine-gridded kernel smoothing approaches for example, one can write (ignoring unit conversions): x g = x p + A * x g -x * p. (15) Despite the mathematical identity of both forms, it should be clear that for the latter an additional interpolation of the prior satellite profile from the coarse to the fine grid is required, denoted as x * p. As it is however preferential to minimize data manipulation, the outlined Eqs. (  9 ) to (  14 ) contain the privileged fine-gridded approach. Analogous to the OPERA and RAL GOME-2 data, the bottom ozonesonde and lidar partial ozone columns in the comparison data have always been attributed to the full fixed lowest retrieval layer, i. e. roughly from 0-6 km in altitude, although they represent the integrated amount of ozone from the surface upwards.
Full comparison results Absolute and relative difference statistics for all GOME-2 2008 closest ozonesonde and lidar co-locations and all outlined evaluation approaches (as discussed in Sect. 6) are collected in Fig. 8. Results are provided for (from top to bottom) OPERA v1. 26 in partial columns, RAL v2. 1 in partial columns, RAL v2. 1 in partial columns determined using RAL's high-resolution VMR integration method, and RAL v2. 1 number densities. Within each subplot, comparisons with regridded, coarse resolution AK smoothed, and fine resolution AK smoothed ground-based data are shown (in green, blue, and red, respectively). Satellite random uncertainties and satellite-ground combined uncertainties have been included for direct bias and spread appraisal, respectively. Median vertical sensitivities are also added to each plot.
Below about 20 hPa clear differences between ground-based profile regridding on the one hand and its kernel smoothing on the other hand begin to appear and increase towards the ground surface. In the troposphere and UT/LS, median differences get opposite signs for both the OPERA and RAL retrieval results. Here, comparison spreads are up to the order of 10 % higher for the regridded groundbased data in the same vertical region. A forced reduction of the vertical smoothing difference between ground-based and satellite measurements, by application of the averaging kernels to the former, thus has a substantial impact on their resultant difference figures. Small differences can nevertheless be observed be-tween coarse and fine resolution smoothing approaches as well. Again in the troposphere and around the UT/LS, the partial column bias of the fine resolution smoothing often seems to be slightly shifted (∼ 3 %) towards more positive values with respect to the coarse smoothing.
Acknowledgements. The reported work was funded by  ESA  via the  CCI -ECV   Ozone  project, with support from the  Belgian Federal Science Policy Office (BELSPO)  and  ProDEx  via project  A3C. The ground-based ozonesonde and lidar data used in this publication were obtained as part of  WMO's Global Atmospheric Watch (GAW)  programme, including the  Network for the Detection of Atmospheric Composition Change (NDACC)  and  NASA's Southern Hemisphere Additional Ozonesonde programme (SHADOZ) , and are publicly available via the NDACC Data Host Facility, the SHADOZ archive, and  World Ozone  and  Ultraviolet Data Center (WOUDC)  (see http://www. ndacc. org/,http://croc. gsfc. nasa. gov/shadoz/,and http://www. woudc. org/,respectively). We warmly thank several members of the NDACC ozonesonde and lidar working groups for fruitful discussions.
Analysis of the most significantly increased metabolite peaks in this set revealed one at m/z 465. 3339 Da (Fig. 1c , Supplementary Table  1 ). This corresponds to the mass of the known substrate of NSDHL, 4-b-methylzymosterol-4-a-carboxylate  11, 20. The dosedependent increase in intensities of this peak is consistent with inhibition of the NSDHL enzyme. A second peak at 467. 3496 could represent 3-b-hydroxy 4b-methyl 5a-cholest-7-ene 4-acarboxylate, that differs from 4-b-methylzymosterol-4-acarboxylate by two hydrogens (Supplementary Table  1 ). This may represent an alternative reduced form of the substrate for NSDHL that would not normally be produced by these cells, but accumulates on blockage of the biosynthetic flux by FR171456 treatment. Reviewing this dataset for other known sterols revealed that calcidiol (m/z 401.
cerevisiae Erg26p. In the absence of a crystal structure for S. cerevisiae Erg26p, or its human and plant homologues, a homology model of the enzyme was built based on the crystal structure of Pseudomonas aeruginosa UDP-N-acetylglucosamine 4-epimerase complexed with UDP-Nacetylgalactosamine (PDB ID: 1SB8) (Supplementary Fig. 8a ). The homology model also shows conserved structural domains with three other templates in Protein Data Bank (Supplementary Fig. 8b-d ), but the 1SB8-derived model scored the best overall using multiple criteria (see Methods). To improve the accuracy of the model we determined the nuclear magnetic resonance (NMR) structure of the natural substrate following its purification from yeast inhibited with FR171456 (Methods, Fig. 3d , Supplementary Fig. 7 ). The docked substrate and NAD þ cofactor interactions with the S.
The elution gradient was from 30% acetonitrile in water rising to 100% within 60 min, the mobile phase contained 0. 01% trifluoroacetic acid (TFA). Combined fractions resulted in 9. 9 mg of purified FR171476. The structure of FR171476 was determined by infrared spectroscopy, mass spectroscopy (MS) and NMR spectroscopy. The structure has been previously reported  14. FR171456 can be described chemically as (1R,2aR,3R,5aR,5bS,7aR,8S, 9S,11aS,12aR)-1,5b,9-trihydroxy-2a,8-dimethyl-3-((R)-6-methyl-5-methyleneheptan-2-yl)- The absolute configuration of FR171456 has been identified by high resolution X-ray structure determination. The structure of the compound is shown in Fig. 3d  and Supplementary Fig. 9. X-ray data for FR171456 is available in Table  1. This is the first report of the absolute 3D structure of this compound.
The column was developed using the following solvents (A: 0. 03% formic acid, B: acetonitrile containing 0. 03% formic acid). The column was first washed with a solution of 70% B for 5 min followed by a gradient from 70% B to 100% B in 25 min yielding in 1. 1 mg desired substrate. 1H-NMR was used to determine the structure of the substrate. The parameters for the instrument were: 660. 13 MHz frequency, PROBHD: 1. 7 MM CPTCI1, DMSO solvent, PULPPROG ZG30, TD 65536. The NMR data for the compound:  1   HCV replicon inhibitor screening. The inhibitor screening was performed in 1,536-well plate with 3,000 cells per well, using Huh-7 cells containing the HCV replicon subgenomic genotype 1b, maintained in DMEM, supplemented with (1 Â ) L-glutamine (L-Glue), 1 Â non-essential amino acids, 10% heat-inactivated fetal bovine serum (FBS), and 1 mg ml -1 G418, and incubated with compounds for 48 h at 37 °C, 5% CO 2.
The total mass range m/z 100-2,000 was segmented into 48 scan events with a width of mass window m/z 50 each. Mass resolution at m/z 400 was 100,000. Each sample was measured in triplicate in positive ionization mode in random order. For quality and instrument stability control, the samples were interspersed with test mix infusion and pooled samples. Blank spectra were also recorded and the observed mass peaks of solvent and impurities were then suppressed in subsequent analysis. The measured raw spectra were imported with in-house software written in the Igor package (Wavemetrics Inc. ) for alignment, peak detection, and integral normalization. The table of exact peak masses and intensities (Supplementary Data, file Supplementary Dataset 1) were exported into the statistical software R, version 2.
15. 1, for subsequent univariate statistics and pathway analysis. For peak identification, the KEGG database was searched for matches of the observed peaks with endogenous metabolite entries. Metabolite masses with adducts of H þ , Na þ , or K þ were considered a hit and possible identity of a peak if the difference between its exact monoisotopic mass and the observed mass plus adducts was below 3 p. p. m. Focused metabolomics in C. albicans. Approximately 1. 25 Â 10 8 C. albicans cells from a saturated YPD culture were inoculated into 100 ml Synthetic Medium (SM) and grown at 30 °C to an OD of 1. 0. Cells were washed once in modified SM (25 mM KPO 4 pH 7. 0, with glucose omitted) and suspended to 5 OD 600 units per ml in modified SM. Labelling reactions were performed in 12-ml glass tubes with PTFE-lined caps (Corning) and containing 1 ml cell suspension (B6 Â 10 7 cells), 10 ml DMSO or 10 ml compound dissolved in DMSO, 12 ml 1M D-Glucose (U -13 C6 glucose), and 10 ml 1 M (1,2 -13 C2) sodium acetate (Cambridge Isotope Laboratories, Inc).
To do this we consider the adjusted MADL scores (a 1 ,. , a n ) of a strain over n experiments. Usually, the z-score transformation of score a i is defined as z i ¼ ða i À aÞ=s where a is the mean and s the s. d. of the values a i. However, due to the special nature of the HIP-HOP data it is advantageous to introduce a number of changes to the computation of the adjusted MADL z-score transformation. Since an (adjusted) MADL score of zero indicates a relative growth rate of a strain in the compound treated sample that is equal to the relative growth rate of the strain in the untreated control, we set a ¼ 0, for all strains. What remains is to estimate s as a measure of the variability of the strain which is described in the following. In order to avoid biasing the computation of the s. d. of a strain profile we allow at most five entries of the same compound/concentration combination in the data set.
The objective was to standardize the assessment of existing assays and to validate novel fluid biomarkers for AD and PD. To support the validation of novel biomarkers and assays, a central and a virtual biobank for body fluids and associated data from subjects with neurodegenerative diseases have been established. In the central biobank, cerebrospinal fluid (CSF) and blood samples were collected according to the BIOMARKAPD standardized pre-analytical procedures and stored at Integrated BioBank of Luxembourg. The virtual biobank provides an overview of available CSF, plasma, serum, and DNA samples at each site. Currently, at the central biobank of BIOMARKAPD samples are available from over 400 subjects with normal cognition, mild cognitive impairment (MCI), AD, frontotemporal dementia (FTD), vascular dementia, multiple system atrophy, progressive supranuclear palsy, PD, PD with dementia, and dementia with Lewy bodies.
clinical Data The central biobank collected information on age, gender, education, clinical history [e. g. , diagnosis, medication use, a selection of co-morbid disorders (cardiovascular, cerebrovascular, neurological, endocrine, somatic, and psychiatric disorders)], smoking habits and alcohol intake, physical examination [i. e. , blood pressure, height, weight, and body mass index (BMI)], general cognition (CDR and MMSE), neuropsychological test performance for the domains of memory, fluency, visuospatial construction, attention, and executive functioning (expressed as raw scores and as z-scores according to local norms corrected for age, gender, and education), procedures for sample collection and processing, and the availability of imaging data (e. g. , MRI, PET). Clinical data were collected within a timeframe of 6 months around blood/CSF collection.
All other samples were centrifuged at room temperature at 2,000 × g (min 1,800 × g, max 2,200 × g) and stored at -80°C. A maximum of 2 h was allowed between collection and freezing. A more detailed description of the SOP used for the collection of samples for the central biobank can be found elsewhere (13). For every subject 2 mL CSF, 2 mL serum, and 2 mL plasma were stored in 0. 5 aliquots (in 0. 5 mL Matrix 2D Thermo tubes) and 4 mL blood was stored for DNA isolation. Primary specimens and samples derivatives were coded with a three-letter center code and a subject number. Samples were at first stored locally, and then shipped on dry ice to IBBL for long-term storage. DNA extraction was performed at the IBBL. Samples and associated data were processed and stored at IBBL in compliance with ISO 9001:2008, NF S96-900: 2011, and ISO 17025:2005 standards and the ISBER Best Practices.
Virtual Biobank The virtual biobank provides an estimation of the number of samples, and clinical (i. e. , age, gender, education, CDR scores, MMSE scores, Parkinson scales, neuropsychological test results, information on medication use, and co-morbid disorders) and other biomarker data (i. e. , MRI data, amyloid PET, dopamine SPECT) available at each center of subjects with normal cognition, MCI, AD, PD, PD with dementia, DLB, FTD, VaD, PSP, MSA, and other types of dementia. Retrospectively collected samples had been collected according to the center's own SOPs. Centers that changed to the standardized BIOMARKAPD SOP during the project reported the transition date. All samples remained stored on site. this needle was unavailable (n = 239), it was impossible to collect CSF with this needle (n = 19) or the neurologist preferred a traumatic needle (n = 79).
Virtual Biobank Currently, 21 centers have contributed data to the virtual biobank of BIOMARKAPD. The virtual biobank contains information on CSF samples from 7,550 subjects, EDTA plasma samples from 8,676 subjects, and serum samples from 8,141 subjects. So far, 11 centers have reported that they followed, or changed to, the BIOMARKAPD SOP for sample collection and processing. Table  3  lists the number of subjects per diagnostic group with CSF, EDTA plasma, and serum samples available.
results central BiobankSample collection for the central biobank was performed in the period October 2013-December 2015. A total of 14 European centers have contributed samples and data to the central biobank. Currently, the central biobank database contains clinical information on 419 subjects, of which 49 had normal cognition, 117 MCI, 164 AD, 24 FTD, 3 VaD, 11 DLB, 25 PD, 5 PD with dementia, 3 PSP, 1 MSA, and 18 other types of dementia (i. e. , either unknown or mixed pathology). From almost all subjects CSF samples (n = 410), plasma samples (n = 413 subjects), serum samples (n = 414), and DNA samples (n = 414) are available at the central biobank. At the local sites, MRI imaging data are available from 299 subjects, SPECT from 6 subjects, amyloid PET from 14 subjects, and FDG-PET from 28 subjects.
Isolates were stored at Ϫ80°C in a preservative medium (Bacterial Preservers; Technical Service Consultant, Ltd. ). Gene amplification and sequencing. Genomic DNA was obtained using the MasterPure DNA purification kit (Epicentre, Madison, WI). Multilocus sequence typing (MLST) (acsA, aroE, guaA, mutL, nuoD, ppsA, and trpE for P. aeruginosa and lepA, nrdA, nusA, nuoL, gltB, rpoB, and eno for Achromobacter) was performed as previously described  (27, 28). Amplification products were sequenced on an ABI 3730xl automatic sequencer (Cogenics, France). Sequence comparison and allele affiliation were performed using http://www. pubmlst. org. The combination of allele numbers for each isolate defined the sequence type (ST). The sequences were deposited into the GenBank database, and the undescribed ST was deposited in the pubMLST (P.
aeruginosa) database as ST1753. PFGE and genome comparison. For P. aeruginosa, restriction fragment length polymorphism (RFLP) in PFGE after digestion by SpeI was performed according to Corne et al. (29). The PFGE patterns were photographed under UV light and visually compared. The Lambda concatemer ladder (Bio-Rad) was used as a molecular size marker. The PFGE patterns of P. aeruginosa ST309 DCC isolates were compared in silico with the complete genome sequence of P. aeruginosa strains BL09 and BL20 (accession no. GCA_000480965. 1 and GCF_000480745. 1, respectively). Multiplex rep-PCR. DNAs were extracted using the MasterPure DNA purification kit (Epicentre, Madison, WI) as recommended by the supplier. The amplification of repetitive extragenic palindromic (REP) sequences, the enterobacterial repetitive intergenic consensus (ERIC), and BOX was done with the primers previously described  (30, 31).
The biofilm assay was performed three times. The averages and standard deviations were calculated for all repetitions of the experiment. The cutoff OD (OD C ) for the microtiter plate test corresponded to 3 standard deviations above the mean OD of the negative control. In all experiments, the ratio of OD to ODc was calculated. Strains were categorized as nonadherent or weakly, moderately, or strongly adherent according Stepanovic et al. (34). For the biofilm formation with cocultured Achromobacter sp. and P. aeruginosa, we mixed diluted cultures equally, and we transferred this culture into the microplates for 48 h. The number of adhering bacteria within the biofilm was determined according to Lopez et al. (35). Nucleotide sequence accession numbers. The sequences of the isolates determined in this study have been deposited into the GenBank database under accession no.