dataesr/scientific-paragraphs-categorization huggingface dataset:
The exact search strategy is provided in Additional file 1. A Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram depicting the selection process and a PRISMA Checklist are provided in Fig. different terms covering barriers, evidence-based medicine, and nutrition. 12, 2016) (including the Cochrane Data- base of Systematic Reviews (CDSR), CENTRAL, National Health Service Economic Evaluation Database (NHSEED), and Database of Abstracts of Reviews of Effects (DARE, U. S. Library of Medicine)); MEDLINE (Ovid SP) (1946 to May 2016); EMBASE (Ovid SP) (1974 to May 2016); and Science Citation Index Expanded (1900 to May 2016) using.
For the pertinent choice of biologically relevant targets, computerguided analysis of structural databases is needed. From another perspective, crystallization is a self-assembly process that can occur in the bulk of crowded fluids, with crystals being supramolecular assemblies. Life also uses selfassembly and supramolecular processes leading to transient, or less often stable, complexes. An integrated view of supramolecularity implies that proteins crystallizing either in vitro or in vivo or participating in cellular processes share common attributes, notably determinants and antideterminants that favour or disfavour their correct or incorrect associations. As a result, under in vivo conditions proteins show a balance between features that favour or disfavour association. If this balance is broken, disorders/diseases occur.
Adapted from Tables 4, 6 and 7 in  Giege(2013). Newer references are given here for co-crystallization with macrocyclic peptides  (Hipolito et al. , 2014) , co-crystallization with nanobodies  (Pardon et al. , 2014)  and the 'adsorption and desorption' method  (Guo et al. , 2014)  In the 1990s, when the demand for macromolecular crystals became crucial, pioneers tried to understand the factors that make many proteins resistant to crystallization. A few explored the inter-protein contacts in crystal lattices and compared them with the contacts occurring in oligomer interfaces. The examination of rather small sets of structures ($200 PDB entries) showed subtle differences in size and amino-acid composition within contact patches, with a tendency for smaller patches rich in polar residues and looser interactions in packing contacts  (Janin & Rodier, 1995).
, 2009). In the same way, basic knowledge on crystal architectures was exploited to engineer proteins for enhanced crystallizability. Thus, knowledge of the packing contacts in ferritins guided the mutagenesis of human ferritin to enhance crystallization  (Lawson et al. , 1991). Likewise, surface-entropy reduction strategies, in which surface lysine, glutamine or glutamate residues with mobile side chains are replaced by smaller alanine residues, produced crystallizable mutants of proteins  (Goldschmidt et al. , 2014). In parallel, analyses of crystallization databases helped to optimize crystallization screens  (Fazio et al. , 2014) , for example using PEG-based cocktails  (Chaikuad et al. , 2015). Another promising approach came from the improved success rate of liganded protein or nucleic acid crystallizations guided by biophysical diagnostics  (Chung, 2007; Da Veiga et al.
, 2014). Other tools are aimed towards a microscopic picture of the dynamics of protein crystals and a realistic modelling of crystallization conditions  (Kuzmanic & Zagrovic, 2014)  and, more radically, towards computational tools to guide crystallization  (Altan et al. , 2016). Furthermore, new methods will help in the preparation of nanocrystals suitable for data collection on SFX and XFEL instruments  (Boudes et al. , 2016). Also, the toolbox for mining structure and sequence databases is being enlarged, for example for automated evolution-based assessment of protein-protein interfaces  (Baskaran et al. , 2014). Finally, the tools developed in softmatter physics for studying colloidal assemblies will offer new insights into problems of protein crystallization  (Fusco & Charbonneau, 2016).
Analysis of genomic databases, which are much larger than the PDB, should provide guidelines for pertinent choices of the proteins to be crystallized (for example from the proteomes of eukaryotic human pathogens). Also, the crystallization of prokaryal-like mitochondrial proteins, which are likely to be different from their human cytosolic homologues, is of interest for rational drug design and applications in medicine. On the other hand, since many protein structures are partly disordered, stable domains should be dissected from genes and overexpressed for crystallization. Flexible or unstructured proteins could also be stabilized in natural or artificial complexes that are more prone to crystallization. Using macrocyclic peptides  (Hipolito et al. , 2014)  or camelid antibody fragments (i.
, 2015). Another aspect concerns the optimization of crystal perfection. For this purpose, several approaches are possible, most based on crystal growth under diffusive regimes (for example counter-diffusion and gelled media). To guarantee growth under a uniform regime, control of the thermodynamics and kinetics at all stages of crystallization is needed. Diagnostic tools [for example dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), calorimetry, interferometries and microscopies] and computational database mining are essential to uncover the relative importance of the crystallization parameters. Finally, efficient methods to generate tiny crystals for SFX and XFEL crystallography and large crystals for neutron crystallography are needed. Protein nanocrystals can be found and characterized in reversible precipitates or produced on purpose, for example in special batch systems controlled by DLS  (Schubert et al.
Acknowledgements This essay is based on my keynote lecture at the 16th International Conference on the  Crystallization of Biological Macromolecules  ( ICCBM16 ) in Prague ( 2-7 July 2016 ). I thank the support from  Universitede Strasbourg, CNRS  and  UPR9002  (Architecture et Reactivitede l'ARN), as well as my colleagues from  Strasbourg  and worldwide from the ICCBM communitee who continually stimulated my interest in biological crystallogenesis. Special thanks to the scientific editor who helped to optimize the manuscript, and to  Pascale Romby  for support and help.
Filgotinib treatment of healthy volunteers for up to 10 days with doses up to 450 mg once daily was well tolerated and safe  (13). Significant inhibition of JAK-1 pathways but not JAK-2 was found in pharmacodynamic (PD) assays at daily doses of $100 mg. In healthy volunteers, the exposure of filgotinib was well in excess of that showing efficacy in animal models of RA. We present data obtained in two 4-week trials with filgotinib, one proof-of-concept study and one dose-ranging study. The observed safety and efficacy in RA patients provide initial evidence that selective inhibition of JAK-1 may represent a future way to treat RA.
PATIENTS AND METHODS Study design and treatments. Two 4-week exploratory, randomized, double-blind, placebo-controlled studies were performed in RA patients who had an inadequate response to MTX. Both studies aimed to evaluate the preliminary safety, efficacy, pharmacokinetics (PK), and PD of filgotinib added onto a stable regimen of MTX. Study 1 (GLPG0634-CL-201; NCT01384422) was a phase IIa proofof-concept study enrolling 36 patients and evaluating daily doses of 200 mg of filgotinib, given at 200 mg once daily or 100 mg twice daily, versus placebo. The study was conducted at a single site in the Republic of Moldova. Study 2 (GLPG0634-CL-202; NCT01668641) was a phase IIa doseranging study in which 91 patients were enrolled to receive filgotinib once daily at 30 mg, 75 mg, 150 mg, or 300 mg versus placebo.
Safety assessments. AEs, vital signs, concomitant medications, routine hematology, serum biochemistry, coagulation, urinalysis, and other clinical laboratory test results were collected at each visit. A 12-lead electrocardiogram (EKG) was carried out at baseline and follow-up, and also at week 4 for study 1. A physical examination was performed at screening and follow-up. Data were summarized in a descriptive manner. No formal statistical comparisons of safety data were performed. PK. Plasma concentrations of filgotinib and its major metabolite were measured in samples collected from a subset of patients in both studies (12 patients in study 1 and 15 patients in study 2) to assess individual steady-state PK of filgotinib and its major metabolite. Blood samples were collected before the morning dose and 1, 2, 3, 5, and 8 hours after the morning dose at either the week 2 or week 4 visit.
PD. In plasma samples collected at the baseline and week 4 visits from all participants in study 2, levels of various marker proteins were assessed using 2 different technologies. The measurement of the concentration of the YKL-40 factor (chitinase 3-like protein 1) was performed using an enzymelinked immunosorbent assay from R&D Systems. The other markers (vascular cell adhesion molecule 1 [VCAM-1], intercellular adhesion molecule 1 [ICAM-1], matrix metalloproteinase 3 [MMP-3], haptoglobin, IL-18) were quantified at Myriad RBM. Data were normalized to the baseline value for each patient and plotted as the percentage of change from baseline. Statistical analysis. No formal statistical power calculation was used to determine sample sizes, as the studies were purely exploratory. All randomized patients who received at least one dose of study drug and had at least one postbaseline efficacy assessment (intent-to-treat population) were included in the efficacy analyses.
Descriptive statistics were calculated by dose for each of the PK parameters for filgotinib and its major metabolite. Exploratory between-group comparisons were also performed. Safety data were summarized for all randomized patients who received at least one dose of study drug (safety population). Descriptive statistics were calculated for each parameter at every time point and in each treatment group. A treatment-emergent AE analysis was performed. PD data are expressed as the mean 6 SEM. Comparisons of PD marker levels among the groups were made using a nonparametric Kruskal-Wallis test followed by Dunn's post hoc test.
At steady state, attained within 2 days and 4 days of dosing for filgotinib and its metabolite, respectively, exposure to the major metabolite was 13-fold higher than exposure to the parent filgotinib within the 75-300 mg dose range. PD findings. The concentration of several known markers of RA and of inflammation was measured in plasma from participants in study 2 treated with filgotinib at 75, 150, or 300 mg or treated with placebo. Data presented in Figure  2  demonstrate a dosedependent effect of filgotinib on levels of these circulating markers after 4 weeks of treatment. * Adverse events (AEs) were classified as treatment-emergent if they started on or after the date of the first dose of study treatment. AEs with partial or missing start dates were classified as treatment-emergent unless the nonmissing components of the start date confirmed otherwise.
The Maïdo high-altitude observatory located in Reunion Island (21°S, 55. 5°E) is equipped with Lidar1200, an innovative Raman lidar designed to measure the water vapor mixing ratio in the troposphere and the lower stratosphere. The calibration methodology is based on a GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor) dataset and 20 lamp measurements. The mean relative standard error on the calibration coefficient is around 2. 7%. Two years of lidar water vapor measurements from November 2013 to October 2015 are now processed. By comparing CFH (Cryogenic Frost point Hygrometer) radiosonde profiles with the Raman lidar profiles, the ability of the lidar to provide accurate measurements is possible up to 22 km. The ability of measuring water vapor mixing ratios of a few ppmv in the lower stratosphere is demonstrated with a 48-hours integration time period, an absolute error lower than 0.
1 Introduction To monitor potential climate changes, observations of essential climate variables (ECV) such as atmospheric water vapor  (Bojinski et al. , 2014)  are necessary. Long-term series allow the international community to progress on important climatological issues, such as the contributions of stratospheric water vapor to decadal changes in the rate of global warming 35  (Solomon et al. , 2010). Selection criteria are important for the certification of networks. These criteria include: long-term stability and regularity. When supplying the databases, very precise and detailed metadata files of the physical quantities have to be provided. And most importantly, an uncertainty has to be associated to the data. This last exercise is realized through the examination of the data processing algorithms and calibration methodologies.
al. , 1999a). All the Raman water vapor lidars of the NDACC used Vaisala sondes to calibrate their database. Different measurements have been tested and intercompared to calibrate the data (GNSS, radiometer and optimized matching methods when using radiosoundings) during different campaigns: DéméVap (Développements Méthodologiques pour le sondage de la Vapeur d'eau dans l'atmosphère;  Bock et al. , 2013) , MOHAVE (Measurements Of Humidity in the Atmosphere and Validation Experiments;  Leblanc et al. , 2012) , and HOPE (HD(CP)² Observational Prototype Experiment;  Foth et al. , 2015). The 5 uncertainty on the calibration factor calculated by the different instruments was between 5 to 10%. One of the conclusions of these studies is that the GNSS IWV (Integrated Water Vapor) is appropriate to calibrate the Raman lidar, the resulting uncertainty being around 7% during DéMéVap  (Bock et al.
, 2012; Sherlock et al. , 1999b). In October 2012, this new lidar system has been set up at a higher altitude: the Maïdo facility located at 2160 m asl  (Baray et al. , 2013). At the same time, most of the instruments of the OPAR (Observatoire de Physique de l'Atmosphère de la Réunion) moved to the observatory. In order to optimize the instrumental configuration and to evaluate a first set of data, two measurement campaigns were organized: MALICCA-1 (MAïdo LIdar Calibration CAmpaign) in April 2013  (Keckhut et al. , 2015)  and MALICCA-2 in November 2013. The 20 MALICCA-1 dataset shows a good agreement between the water vapor data measured by the lidar and those measured by the other instruments (Vaisala RS92, MLS,  Dionisi et al. , 2015). The relative difference between the lidar profiles and 15 simultaneous radiosoundings (Vaisala RS92) was lower than 10% for the lower and middle troposphere and between 10 and 20% for the upper troposphere.
It is noteworthy that this Maïdo Raman water vapor lidar (called hereafter "Lidar1200") was recently provisionally affiliated within the NDACC. The conclusive affiliation occurs when absence of fluorescence and a stable calibration method are both demonstrated using validation campaigns involving frost-point hygrometer measurements. The main objectives of this paper are to assess the ability of this lidar to monitor the water vapor in the lower tropical 35 stratosphere on a long-term basis and to introduce its 2-years dataset of water vapor profiles. We will describe the instrument, the data processing and the calibration methodology. A routine calibration methodology, based on GNSS observations, has been developed in order to improve the reliability of the calibration coefficient and the robustness of the measurements.
This methodology is detailed in Sect. 2. The validation of the profiles by comparing the lidar data with CFH radiosoundings will be presented in the Sect. 3. The evaluation of the performances of the instrument is detailed in Sect. 4 from fine scale structures 40 detection to the potential establishment of climatologies and then trends in the UT/LS. Finally, an overview of the 2-years dataset is given (Sect. 5). Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2017 -32, 2017  Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 4 April 2017 c Author(s) 2017. CC-BY 3. 0 License. 2. Description of the system, the data processing and the calibration methodology 2. 1 Routine Raman system measurements Before being transferred to the Maïdo Observatory in 2012, the Raman water vapor lidar operated in the north of Reunion Island, at Saint-Denis, at sea level  (Hoareau et al.
5 kg. m -2 have been observed regarding the localization of the measurements but few sources of uncertainties still need to be investigated  (Bock et al. , 2013). A TRIMBLE-NETR9 receiver (MAIG) has been installed at the Maïdo Observatory in 2013 in order to receive the 5 GNSS data (see http://rgp. ign. fr/STATIONS/#MAIG). The GNSS network used to calculate IWV above the station includes 20 other local stations disseminated over the island, and in order to ensure efficiently high numbers of baselines for the differential data processing through GAMIT software v10. 32  (King and Bock, 2007) , 15 overseas stations in the Indian Ocean are included. The typical cutoff elevation angle is fixed to 10° to ensure the most local sounded area of water vapor as possible. Currently, the database produces hourly IWV, 24 hours at the Observatory elevation.
Description of the methodology Usually a calibration coefficient is the ratio between a reference instrument, here it is the GNSS IWV, and the uncalibrated lidar IWV data. In this section, the hourly calibration coefficient, named "hourly coefficient" represents the ratio between the coefficients of one night that will be named "nightly coefficients". In the next subsections, the "calibration coefficient" will correspond to the coefficient calculated with the methodology described below, it represents the coefficient used to calibrate the Lidar1200 water vapor dataset for supplying the national and international networks. If no instrumental change occurs, the calibration coefficient is supposed to be almost constant. To determine the calibration coefficient of the Lidar1200's water vapor profiles, the ratios between the GNSS and 5 lidar IWV data are compared.
The temporal coincidence between the datasets consists of taking the hourly GNSS IWV and integrating the lidar data on a 1-hour window around the hourly mean GNSS. The principle can be illustrated as followed: if the GNSS measurement corresponds to 18:00 LT, the lidar data are integrated between 17:30 and 18:25 LT (with a minimum of 45 minutes). The time series of the nightly coefficients (Fig. 2 ) has been used in order to identify the periods with a quasiconstant ratio. The nightly coefficient varies in time, probably due to the fact that the integration methods are different. Even 10 if the GNSS and the lidar are collocated, they do not measure exactly the same volume of water vapor (in space and in time). Nevertheless, periods of quasi-stationarity of the nightly coefficient can be defined inside periods in which the lidar was considered as instrumentally stable.
The average nightly coefficient of each quasi-stationary period is considered as the calibration coefficient of the associated profiles. Nevertheless, for the near-real time treatment, the profile is calibrated with the former validated coefficient. It remains necessary to check if there are instrumental changes in order to detect possible 15 alteration of the coefficient, and to recalculate it if required to compile the final dataset. At the beginning of each night of measurements, a systematic lamp measurement is made in order to detect potential sudden instrumental changes due to nonidentified causes. The lamp measurement allows to look at the changes that occur only on the reception part of the system. The lamp measurement consists in the emission of a white light on the telescope which lightens the sensors.
20 Finally, the lamp measurement value corresponds to the ratio of the signal of the Raman H2O channel and the Raman N2 channel. This value is independent from the altitude and is impacted by the laser power, the losses due to the reception optics and the efficiency of the sensors. Some of the instrumental changes affecting the calibration coefficient are known. The logbook can be checked to reduce non-automatic detections of instrumental changes. With hindsight on the dataset, the identification of the periods with a stable nightly/hourly coefficient allows to overcome the problem of automatic detection of 25 instrumental change. Once the periods are identified, the calibration coefficients are validated and, thus, the data can be calibrated and used for geophysical purposes. evaluate the robustness of the GNSS calibration, the method described above needed to be further investigated based on more comparisons.
6 and 3. 6% between 2013 and 2015 (Table  1 ). The mean standard error on the calibration for the whole dataset is 2. 7%. GRUAN exercise with multiple sondes flights (Vaisala, Modem and CFH) was performed during that campaign. After that major campaign, the Lidar1200 was part of LIDEOLE-2. An 8-hours measurement session for the water vapor lidar was 20 performed to test the optimal performances of the instrument on a long duration session. Within the scope of the evaluation of the performances of the lidar water vapor data, the dataset offered by those campaigns is exceptional, particularly the CFH dataset that will be part of the comparison.
It just highlights the difficulty of optimizing the instrumental comparison. To conclude, the results for the comparison analysis in the troposphere (2. 2-14 km) are good. In the upper troposphere (Fig. 6B  and 6b ), the lidar data used for the comparison represent the average of the 5 night-sessions coincident with the CFH launches during the campaign. In the lower stratosphere (Fig. 6A  30 and 6a), the lidar data used for the comparison represent the integration of the whole 47-hours dataset of the campaign. Comparisons between lidar and CFH data are as good or better than expected in the 14-17 km layer, and they are especially good in the 17-22 km layer. The clearest evidence of this is to be found in the fact that the mean CFH profile is nearly always inside the error bar of the Lidar1200 profile (Fig.
The Lidar1200 -and the Maïdo Observatory -being designed for long term operations, the goal here is to build up over decades a database containing monthly UT/LS water vapor profiles. Such a database can be useful to make out climatology and long term trends of UT/LS hydration 35 on this unique subtropical site. Hoareau et al. (2012)  estimated the performances of the Lidar1200 for the water vapor measurements based on numerical simulations. The results were that with 30 minutes of integration, the statistical error would be of 15% at 14. 6 km and 30% at 16. 3 km and that, with a sufficient number of data, the profiles could reach the stratosphere. The study of the 40- measurements would reach 9. 7 km asl and 11. 1 km asl for 30% (Fig. 7 ). When looking at the total error (after filtering), it would be 15% at 13.
1 km asl and of 30% at 14. 2 km asl (Fig. 7 ). The simulation of performances of  Hoareau et al. (2012)  has been realized for a clear sky, without the moon and using two wavelengths (355 and 532 nm). In practice, the Lidar1200 is operating with only one wavelength and for different moon phases. The mean profile here has been calculated on the whole dataset without taking into account the moon or the cloud fraction effects. The measurement of water vapor of 9 hours of 5 September 2015 showed that the moon phase has a non-negligible influence on the quality of the data (see Sect. 4. 2). Based upon a situation corresponding to any night of measurements in routine (i. e. potentially submitted to the influence of the moon and variable meteorological conditions), the Lidar1200 shows, for a 240-minutes integration time, a total error of 15% at 16.