dataesr/scientific-paragraphs-categorization huggingface dataset:
The KPiX user module consists of one Producer connecting KPiX via shared KPiX DAQ libraries, one Data Collector linked with KPiX binary data format libraries, two DataConverter units to interpret a RawEvent to the StdEvent and the LCIO format, and one Run Control to ensure Stop and Reset functioning for all run modes with KPiX. Moreover, the KPiX user module contains a customized GUI inherited from the core GUI for printing more detailed information like the Run Rate, and one analysis executable called lycorisCliConverter for producing a set of straightforward plots like the ADC distribution of each channel to a ROOT file from an EUDAQ2 RawEvent. This user module has been used for data taking in multiple test beam campaigns. Figure  5  shows the good spatial correlation between two L strip sensor modules taken with this EUDAQ2 module.
Conclusion EUDAQ2 is a modular, modern and versatile data acquisition framework that has been developed as a successor of EUDAQ. A core library is utilized to manage the readout, data collection and steering. The core only depends on standard C++ functionalities, allowing for platform independent developments. Hardware specific code, a Producer, is linked to the core and can utilize more specific external libraries. EUDAQ2 is shipped together with Producers for a trigger logic unit and the EUDET-type telescopes, which are provided as common hardware on test beam facilities at DESY, ELSA and CERN. The EUDAQ2 software, together with a detailed operation manual is available online under the LGPLv3 open-source licence. Already in the development phase, EUDAQ2 has been used successfully by several test beam groups.
The Allergy Diary The mobile technology of MASK is the Allergy Diary, an app (Android and iOS) freely available for patients with AR and asthma in 23 countries (16 European Union countries, Argentina, Australia, Brazil, Canada, Mexico, Switzerland, and Turkey) and 16 languages (translated and back-translated, culturally adapted, and legally compliant; Fig  3 ). 6 Anonymized users fill in a simple questionnaire on asthma and rhinitis on registration and daily assess the effect of the disease by using a visual analog scale  62  for global allergy symptoms, rhinitis, conjunctivitis, asthma, and work. Moreover, a questionnaire is applied every week to assess disease effect on patients' quality of life (EuroQol). 21 power others to act on the vision. Organizational processes and structures are in place and are aligned with the overall organizational vision.
Clinical decision support system Clinical decision support systems are software algorithms that advise health care providers on diagnosis and management based on the interaction of patient data and medical information. They should be based on the best evidence to aid patients and health care professionals to jointly determine treatment (SDM). In patients with AR, the MASK clinical decision support system is incorporated into a tablet interoperable with the Allergy Diary  65  for health care professionals (ARIA Allergy Diary Companion). 6, 50 This is based on an algorithm to aid clinicians to select pharmacotherapy for patients with AR and to stratify their disease severity. 66 This approach will be adapted for the patient's guided self-care in the context of SDM.
g. Western blot). In iTRAQ, a correct peptide and protein quantitation needs a correct interpretation of MS/MS spectra, which depends on a trustworthy peptide and protein identification. The identification and quantitation can invariably be performed by commercial built-in algorithms integrated to companion software packages; they fully retreat data generated by the manufacturer's mass spectrometers (e. g. Paragon -ProteinPilot for ABSciex mass spectrometers). Alternatively, analyses may be split into separate stages through a composite suite of commercial or free algorithms integrated to manufacturer-independent software (e. g. Mascot in Mascot Server, jTRAQx). It is noteworthy that, although tools to compute RPA are widely available, the computation of the ratios of RSPI is not supported by any available companion software, and so, it requires an alternative suite.
The first aim of our work was to compare these two strategies of quantitation (RPA and RSPI) to the classical Western blot technique, commonly used as a non-MS validation technique for iTRAQ-based quantitation. The second aim was to further develop an all-in-one protocol, from sample preparation to result reporting, based on the best strategy of quantitation followed by in-house data processing. This study was carried out on the respective effects of the CalciNeurin inhibitors (CNI) Cyclosporine A (CsA) and Tacrolimus (Tac, a. k. a. FK506) on renal proximal tubular cells, used as a study model. iTRAQ was combined to nano-scale liquid chromatography online with Q-Q-TOF tandem mass spectrometry on proximal tubular cells to investigate whether CsA and Tac nephrotoxicity results from the inhibition of calcineurin or from the modulation of other intracellular pathways targeted by immunophilins.
In this work, we validated the RSPI methodology and we built an automated data processing algorithm called Customizable iTRAQ Ratio Calculator (CiR-C) to refine critical parameters related to peptide confidence and selection. The composite suite made up of the Mascot algorithm, for peptide and protein identification, end-to-end with the jTRAQx software for computation of RSPI at the peptide level and the CiR-C algorithm for data integration and definitive protein quantitation turned out to be a successful combination. It has provided a great improvement compared to already available solutions for iTRAQ-based high-throughput quantitative proteomics and multiplexed analysis of biological systems.
1 % FA) over 120 min at a flow rate of 200 nL/min. Data acquisition was carried out by IDA (Information-Dependent Acquisition) mode of Analyst 1. 7 TF software (ABSciex). The data from MS and MS/MS were continuously recorded with a cyclic duration of 3 s. For each MS scan, up to 50 precursors were selected for fragmentation based on their intensity (greater than 20,000 cps), their charge state (2+, 3+) and if the precursor had already been selected for fragmentation (dynamic exclusion). The collision energies were automatically adjusted according to charge state, ionic mass of selected precursors and iTRAQ labelling. The *. group results files (1 per experiment) were exported as Peptide Summaries.
After three 5-min washes in TBS-T, secondary antibody incubation was performed in BLOTTO for 1 h at room temperature then washed again. Membranes were incubated in a 1: 1 mix of SuperSignal™ West Pico PLUS Chemiluminescent Substrate kit (#34577, ThermoFisher) and analyzed by the ChemiDoc Imaging system (Bio-Rad) for chemiluminescent signal detection and acquisition. Quantitation was computed via the ImageLab software (Bio-Rad).
COMPARISON BETWEEN THE DATA PROCESSING OUTPUTS: MASCOT -jTRAQx VERSUS PARAGON -PROTEINPILOT Multiplex analysis of CNI-exposed partial proteomes was conducted and optimized in vitro using the epithelial tubular proximal cell line LLC PK-1 serving as a model. Five series of samples were prepared and processed according to the optimized custom-made iTRAQ protocols (Scheme 1A). The samples labeling strategy is summarized in Table  1. The CiR-C shell script excluded irrelevant data according to criteria summed up in Table  2 : i) identification confidence: peptides are retained if the probability that the observed positive match is a random match is below 5 % (p < 0. 05, Mascot score > 30; Paragon confidence score > 95); ii) quantification confidence: peptides are retained if all iTRAQ ratios have been successfully calculated i.
e. , peptides with 0. 0 ratios or uncalculated ratios (null ratios) are discarded; iii) peptides related to 'Fragment'-and 'REVERSED'-annotated proteins are discarded. After irrelevant data removal, CiR-C drew up an exhaustive catalogue of identified peptide sequences with their associated Swiss-Prot or TrEMBL accession IDs. Peptides were assigned to a frequency index of positive matches (identification in {1;2;3;4;5} out of 5 independent experiments (biological replicates)) and CiR-C drew a second catalogue of peptides with the highest frequency index (n=5). Protein ratios were calculated as both overall and seriesspecific median values of peptide ratios associated with a given accession ID and frequency index. CiR-C discarded 7597 ± 3291 (75. 4 % ± 9. 0) peptides quantified by RPA and 4377 ± 2367 (29.
4 % ± 5. 5) peptides quantified by the RSPI (Table  2 ). Table  1. Summary of experimental design. Proteins from five independent replicates of four distinct experimental conditions were extracted from LLC PK-1 cells. Peptides were labeled with iTRAQ 4-plex reagents and analyzed by nano-HPLC MS/MS. 4-plex distribution rotated between independent experiments (biological replicates) to circumvent tag-related bias. Table  2. Data processing summary. The MS/MS data from the five replicate experiments were submitted to identification (Mascot versus Paragon), primary quantification (jTRAQx versus ProteinPilot) and data refining, statistical analysis (CiR-C). This table sums up data inclusion criteria, identification output and data refining yield, as numbers of identified, discarded of retained peptides.
REFINMENT OF THE CiR-C ALGORITHM The CiR-C script uses two thresholds for peptide selection: a peptide significance threshold (given by the Mascot score) and a peptide frequency threshold (defined as the minimum number of experiments in which a given peptide must be identified and quantified). To establish the best compromise between reliability of iTRAQ quantitation and time/cost efficiency, we explored how threshold tuning may affect (strengthen or weaken) CiR-C results. The RSPI calculated after tuning the first threshold to either a permissive (Mascot score > 20) or a stringent (Mascot score > 40) cut-off value were compared to Western blot standards (Figure  4 ). When opening to more peptides (lower Mascot score) the linear correlation was lost (zero slope; Pearson correlation coefficient test, r = 0.
iTRAQ results in rich and complex MS/MS datasets which require thorough processing and solid statistics to reach relevant conclusions. In this respect, the choice of a tag ratio calculation method is crucial. Two potential strategies have been proposed: either RPA or RSPI. The two methods are not equivalent since peak area measurement suffers from a major bias originated from the way reporter ion signals are processed, as described by Boehm et al. (13). It is worth noting that peak intensities are proportional to reporter mass ion counts, whereas peak areas are not (Scheme 2). Therefore, the RSPI is more likely to provide a reliable rendering of the actual ion count detected by mass spectrometer. Numerous studies reported the necessity of RPSI-based quantitation workflows to obtain robust, precise, accurate and sensitive when using high resolution MS platforms (Orbitraps and TripleTOF 5600)  (25) (26) (27)  We confirmed the superiority of RSPI since the commercially available RPA strategy failed to report significant biological variations assessed by Western Blot analysis of LLC-PK-1 protein extracts and iTRAQ-nanoLC-MS/MS benchmark analysis of UPS1 spiked-in LLC-PK-1 protein extracts.
001) and the test of scores 10-fold apart from our initial choice (s = 20, p = 0. 01) confirmed how close it is from a potential optimum for the size of our datasets. Indeed, this threshold highly depends on the number of peptides characterized in MS/MS. The occurrence threshold was set to the largest possible values -the more peptides, the better -hence the restriction to ubiquitous peptides, i. e. , peptides retrieved from all five biological replicates. The main source of variations when using iTRAQ is of biological origin (± 25 %)  (43). The number of biological replicates was set to five independent experiments (biological replicates), corresponding to the use of a complete given set of iTRAQ tags. However, as a matter of time and cost efficiency, the possibility to reduce the number of independent experiments (biological replicates) had to be addressed.
As expected, considering biological variations are the most impactful source of variability of iTRAQ, we showed that using four independent replicates is the lowest limit to obtain reliable results, five replicates appearing optimal under our experimental conditions. In summary, this work demonstrated that RSPI outperform the commercially available RPA in quantifying biological modifications using iTRAQ. Furthermore, we propose a Mascot -jTRAQx -CiR-C strategy as a simple yet powerful answer to the need for an all-inclusive suite for iTRAQ data processing.
MASS SPECTROMETRY DATA PROCESSING AND RELATIVE PROTEIN IDENTIFICATION / QUANTIFICATION QUANTITATION METHOD BASED ON RSPI MS and MS/MS data for five independent experiments (biological replicates) (*. wiff, 1 per fraction, 12 files per experiment) were submitted to Mascot Server 2. 2. 03 via ProteinPilot (version 5. 0, ABSciex) for protein identification, and searched against two complementary Sus scrofa databases: a Swiss-Prot database (2015_10 release) and a TrEMBL database (2015_10 release). Carbamidomethyl (C) was defined as a fixed modification. Oxidation (O), iTRAQ4plex (K), iTRAQ4plex (Y), iTRAQ4plex (N-term) were defined as variable modifications. MS/MS fragment mass tolerance was set at 0. 3 Da. Precursor mass tolerance was set at 0. 2 Da. Mascot raw data files (*. dat, 1 per experiment) were saved for further isobaric tags-based peptide and protein quantitation with the Java implementation of the Quant algorithm, jTRAQx (version 1.
13, (16)). Reporter mass tolerance was set at 0. 05 Da while iTRAQ correction factors were implemented as provided by ABSciex. This tool generated one. jpf file (tab-delimited text file) for each series. QUANTITATION METHOD BASED ON RPA MS and MS/MS data for five independent experiments (biological replicates) (*. wiff, 1 per fraction, 12 files per experiment) were submitted to protein identification using the Paragon algorithm as implemented in the ProteinPilot software (version 5. 0, AB SCIEX) and searched against two complementary Sus scrofa databases: a Swiss-Prot database (2015_10 release, 1422 entries) and a TrEMBL database (2015_10 release, 47465 entries). Quantitation was conducted with or without auto bias correction, an available option implemented in the ProteinPilot software to normalize uneven protein across the multiplex samples, improving further quantitation.
2003) , DSSAT  (Jones et al. 2003) , SAFYE  (Duchemin et al. 2008) , AquaCrop  (Steduto et al. 2007 ) etc. For this study, the FAO-AquaCrop model is considered since it focuses on water and water productivity normalized for atmospheric evaporative demand and CO 2 concentration that confer the model an extended extrapolation capacity, to diverse locations, seasons, and climate, including future climate scenarios. Besides, the green canopy cover used in AquaCrop can be related directly to easily accessible data from visual field observations and remote sensing such as vegetation indices which make the calibration and validation task over large areas easier  (Foster et al. 2017). Several authors reported on the accuracy of this model to estimate yield and biomass, e. g. ,  Garcia-Vila et al. (2009) for cotton in Spain,  Salemi et al.
Dataset and methodology In order to assess the effect of topography on AquaCrop performance in simulating water balance and biomass production, three experimental fields with different slopes and aspects are considered. The experiment was conducted on wheat during the growing season of 2013. Measurements of climatic factors, needed as input data for AquaCrop, and other variables, required for the model evaluation, i. e. , crop evapotranspiration, soil moisture and biomass production, were carried out from January to May. Climatic parameters and actual evapotranspiration were monitored in three wheat fields (A, B, and C) by three flux stations measuring energy fluxes and basic meteorological variables. Field (A) covers 1. 2 ha area; it has a fairly homogeneous terrain slope (6. 0%) south-east oriented, versus dominant wind.
Instrumental equipment and determination of latent heat flux Flux stations collected measurements of the land surface energy balance components (net radiation, soil heat flux, sensible and latent heat fluxes). Each station was equipped with a CR3000 data logger (Campbell Scientific). Sensible (H) and latent (LE) heat fluxes were determined using 20 Hz data of wind vertical velocity, temperature, and air humidity generated by sonic anemometers (CSAT3, Campbell Scientific) and Krypton hygrometers (KH20, Campbell Scientific). Net radiation (Rn) and soil heat flux (G) were measured by a differential radiometer (NR01, Hukesflux) and three soil heat flux sensors (HPF01, Hukesflux). For each flux station, the three soil heat flux sensors were distributed few meters around the station and were buried at 5 cm below the soil surface; soil heat flux (G) was estimated by averaging the measurements collected by the three soil heat flux sensors.
3 ). Comparison between measured and estimated values shows that AquaCrop simulates water balance relatively well, particularly for flat field C (Fig. 4 ). Coefficient of determination R 2 is 0. 73 for field C and around 0. 2 for sloping fields A and B (Table  2 ). RMSE varies between 41 and 67 mm/m, SWC is under-estimated by the model for sloping fields. The deviation could be a result of the overestimation of ET and/or runoff module.
Conclusion Actual evapotranspiration is the main driver of biomass and water contents state variables in crop yield simulation models. However, ET is characterized by high spatial heterogeneity especially in hilly watersheds where topography may affects radiation and aerodynamic processes. In this work, we evaluated the performance of FAO-AquaCrop model for simulating wheat yield in a hilly topography. Data measured in three wheat fields with different configurations slope/aspect were used to evaluate the impact of relief on evapotranspiration, soil water content, and biomass and compare measured values to the model'sou tput. Th etopography effects were detected by different values of the ratio ET/ETo representing the FAO-crop coefficient Kc. Results show coherence between simulated and observed values and differences between sloping and flat fields.
The values of the experimental crop coefficient Kc during the well-watered mid-season stage (March 1-April 15) were respectively 0. 81, 0. 74, and 1. 03 for A, B, and C fields, lower than the 1. 15-1. 20 range reported in the FAO paper 56. The lower rates of evapotranspiration observed in the sloping fields A and B affected water balance and biomass production modules and resulted in higher levels of simulated soil water content and contrasting biomass production. The difference between fields A and B could be attributed to the orientation vs dominant wind and/or to the sensitivity of AquaCrop to soil texture since field A is fine textured compared to B and C. Errors in estimating ET seem to have a large impact on the results of the models because of the non-linear and dynamic nature of the interaction between evapotranspiration, soil water status, and biomass production variables.
We present a measurement of the baryon acoustic oscillation (BAO) scale at redshift z = 2. 35 from the three-dimensional correlation of Lyman-α (Lyα) forest absorption and quasars. The study uses 266,590 quasars in the redshift range 1. 77 < z < 3. 5 from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14). The sample includes the first two years of observations by the SDSS-IV extended Baryon Oscillation Spectroscopic Survey (eBOSS), providing new quasars and re-observations of BOSS quasars for improved statistical precision. Statistics are further improved by including Lyα absorption occurring in the Lyβ wavelength band of the spectra. From the measured BAO peak position along and across the line of sight, we determined the Hubble distance D H and the comoving angular diameter distance D M relative to the sound horizon at the drag epoch r d : D H (z = 2.
2018; Pâris et al. 2018)  obtained in the extended Baryon Oscillation Spectroscopy Survey (eBOSS) program  (Dawson et al. 2016 ) of SDSS-IV  (Blanton et al. 2017). This data set has been previously used to measure the cross-correlation between quasars and the flux in the "CIV forest" due to absorption by triply-ionized carbon  (Blomqvist et al. 2018). Besides the addition of new quasars and forests, our analysis differs in a few ways with that of dMdB17. Most importantly, we expand the wavelength range of the forest from the nominal Lyα forest, 104. 0 < λ rf < 120. 0 nm, to include Lyα absorption (λ α = 121. 567 nm) in the Lyβ region of the spectra, 97. 4 < λ rf < 102. 0 nm, thus increasing the statistical power of the sample. The procedure for fitting the correlation function is also slightly modified by including relativistic corrections  (Bonvin et al.
2014; Iršič et al. 2016). Furthermore, we divide the data to report BAO measurements for two redshift bins. We have not developed new sets of mock spectra beyond those used in dMdB17. We refer to Section 6 of dMdB17 for the analysis of those mocks and the tests used to justify the analysis procedure. The organization of this paper follows closely that of dMdB17. Section 2 describes the DR14 data set used in this study. Section 3 summarizes the measurement of the fluxtransmission field. Section 4 describes the measurement of the cross-correlation of the transmission field with quasars and the associated covariance matrix. We also derive the "distortion matrix" that describes how the measured cross-correlation is related to the underlying physical cross-correlation. Section 5 describes our theoretical model of the cross-correlation.
Section 6 presents the fits to the observed correlation function and section 7 combines these results with those from the Lyα autocorrelation function presented in a companion paper (de Sainte  Agathe et al. 2019). Section 8 summarizes the constraints on cosmological parameters derived from these results and those from de Sainte  Agathe et al. (2019). Our conclusions are presented in Section 9. The measurements presented in this paper were made using the publicly available Python package picca 1 developed by our team.
Data sample and reduction The quasars and forests used in this study are drawn from SDSS DR14. This release includes data from DR12 taken in the first two generations SDSS-I/II, in the BOSS program of SDSS-III and in the eBOSS pilot program SEQUELS  (Myers et al. 2015). These data were used in the measurement of the quasar-forest cross-correlation of dMdB17. Here, we use in addition data from the first two years of the eBOSS program and the completed SE-QUELS. The quasar target selection for BOSS, summarized in  Ross et al. (2012) , combines different targeting methods described in  Yèche et al. (2010) ,  Kirkpatrick et al. (2011), and Bovy et al. (2011). The methods employed for eBOSS quasar target selection are described in  Myers et al. (2015)  and  Palanque-Delabrouille et al. (2016). The catalog of identified quasars, DR14Q  (Pâris et al.