dataesr/scientific-paragraphs-categorization huggingface dataset:
) or those who are self-employed  [24]. Patients were extracted from the database with the ICD10 diagnosis codes H401 (normal tension glaucoma, primary open-angle glaucoma, and open-angle glaucoma) and H409 (unspecified glaucoma) and with prescription claims for the eye drops to treat glaucoma between April 1, 2011 and March 31, 2016. In addition, we extracted the clinical laboratory data, such as anthropometric measurements, blood pressure, blood glucose levels, blood lipid levels, liver function test values, hematologic values, and urine test values, from the health checkup database. We included those who had received monotherapy for 1 year or longer without surgery including laser treatment and then switched to a fixed-combination therapy as second-line treatment (the fixed-combination cohort) or those who received an additional eye drop, such as timolol as an addition to latanoprost or vice versa, as second-line treatment (the unfixed-combination cohort).
1, followed by a stepwise (forward and backward) logistic regression model. The odds ratios (OR) and Wald 95% confidence intervals (CIs) were presented. The subject persistence rates at 6 and 12 months with a pointwise 95% CI were estimated using the Kaplan-Meier method, and the differences between the two treatment cohorts were tested by log-rank test. All the statistical analyses were performed using the SAS version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
Results In total, 57,899 patients were diagnosed with glaucoma and prescribed with any IOP-lowering drug in the database, of whom 309 patients were in the fixedcombination cohort and 329 patients were in the unfixed-combination cohort (Fig. 1 ). The two cohorts had similar baseline demographic and clinical characteristics (Table  1 ). The mean age was 56-57 years and clinical measurement values were within the normal ranges. Comorbidities reported in > 20% of patients were hypertension, hyperlipidemia, diabetes mellitus, and cancer. In both cohorts, over 80% of patients lived in East Japan (especially residents of Tokyo or large cities). The eye drops claimed at the index date are summarized in Fig. 2. In the fixed-combination cohort, PG/BB (78. 0%) and CAI/BB (22. 0%) were used by drug class, and the most commonly used eye drop was latanoprost/ timolol (39.
Moreover, other common risk factors were previously identified  [12] [13] [14]. All of these risk factors need to be solved by education, communication with patients, and patient training in improving proficiency of selfadministration of eye drops  [42]. Specific reasons on why physicians prescribed unfixed combinations for certain patients would be interesting to identify. Such reasons may include a risk of adverse events caused by the concentration of the fixedcombination, preservative formulations, and dosage adjustment of eye drops. If so, developing a new fixedcombination therapy would be worthwhile. This study, however, has limitations. First, this study assumes that patients took eye drops for the number of days that they were prescribed, because the claims database does not track whether the patient actually administrated the eye drops as prescribed.
However, we set a 30-day grace period so that the PDC did not underestimate adherence. Second, the study did not consider baseline PDC in patients that had being continuing monotherapy for 12 months prior to the index date, although baseline may or may not differ between the groups. Third, we used the MinaCare database to gather healthcare information covering nearly 2 million individuals, of which 90% were aged 20-59 years who worked for a nationwide large company, of which, most individuals lived in Tokyo or large cities in East Japan, and their dependents in private corporate health insurance societies  [24, 25]. Therefore, regional and income biases might have affected the results. Moreover, only elderly patients were included in this study. However, this study is worth considering for treating glaucoma because young patients are more likely to have glaucoma progression during their long life-spans.
Fourth, the patients between the two cohorts possessed an uncontrolled nature, which includes the numerous drug types used as monotherapy during the pre-index period and the severity of glaucoma. Although background information and baseline characteristics were limited, they were similar between the two cohorts. Finally, the present analyses were built according to the assumption that all of the claimed drugs were used by the patients. To address these limitations, we need to conduct further studies using the real-world data combined with clinical data.
Data and Methods The evolution through time of wet and dry extremes has primarily been studied through indices derived directly from precipitation () time series, and through relationships between , 20 evapotranspiration (), potential evapotranspiration (  ), soil moisture (), runoff () and drought indices such as the Palmer Drought Severity Index (PDSI) and the SPEI. The SPEI  (Beguería, Vicente-Serrano, & Angulo-Martínez, 2010; Beguería, Vicente-Serrano, Reig & Latorre, 2014 ) is a scalar index reported monthly. It is built after fitting a distribution on the cumulative  -  over the window of interest (e. g. , 12 months). The dataset used is based on CRU  (Harris, Jones, Osborn & Lister, 2014)  data for both precipitation and potential evapotranspiration and is accessible at http://spei.
1), while on the wet side it will be (   , 0. 9). Months for which the SPEI is below (above) these thresholds are 15 marked as belonging to a dry (wet) event. It should be noted that CRU may not provide adequate spatial coverage far back in time, especially in the Southern Hemisphere. This may affect the SPEI. For our first analysis, we consider the global land area exposed. Each event is then weighted by the area of the grid-block it corresponds to divided by the total land area. Further, using CRU data, we consider extremes in  and   : for both of these variables, we aggregate 20 monthly values over 12-month windows and define wet and dry events at a given location as above (inversing thresholds for   ). In the supplementary material (Fig. S18 ), we also compare the results with those based on data from the 20CR reanalysis project  (Compo et al.
, 2011) , accessible here: https://www. esrl. noaa. gov/psd/data/gridded/data. 20thC_ReanV2c. html (the variable names are "prate" for precipitation and "pevpr" for potential evapotranspiration). We use the  and   data of 20CR to compute a version of the SPEI. In each case, we study the proportion of the area of the world affected by dry or wet, dry, wet and dry and wet events in a given year. 5 To apply our method to economic portfolios, data is collected from (SNL, 2016) to find the location of producing bauxite, copper, gold and iron ore copper mines in 2014. Each event is weighted with the 2014 share of production of each mine. Further, we perform wavelet and multitaper analysis to test important cycles in the data and 10 covariation patterns with climate indices. Wavelet analysis uses base functions differing in time and frequency resolutions from specific families of oscillatory functions that attenuate to zero.
Multitaper analysis uses different orthogonal data tapers separately to obtain different realizations of the power spectrum, and average over them, thus reducing bias in spectral estimation. Coherence between spectra enables one to identify common oscillatory behavior 15 between time series. Using package biwavelet  (Gouhier, Grinsted, & Simko, 2016) , coherence is characterized by warmer colors, with significant regions circled in black. Using  (Rahim, Burr & Rahim, 2017)  common oscillatory behaviors from multitaper analysis are marked by spikes in.
Results The key findings from our analyses are illustrated in Fig. 1  and 2. We use the 1901-2014 data of the SPEI12 index, thus a measure of net water availability based on the cumulative difference between precipitation and potential evapotranspiration for a 12 month duration at each location, which is computed for each month in the record, and then mapped to a probability distribution, 5 yielding monthly time series. We consider the 90 th (10 th ) percentile of yearly maximum (minimum) of the SPEI time series at each location as a "dry" ("wet") threshold, corresponding to a 10 year return period event. The occurrence of exceedance of this threshold at a given location in each year of the climate record is then weighted by the production (assumed here to be constant) at that location and spatially aggregated to provide an estimate of annual exposure.
3. To evaluate intraobserver and interobserver variability for 3DPD analysis (random sample of 60 placenta). 4. To assess the predictive value of the markers for the occurrence of other pregnancy complications (neonatal mortality and morbidity, placental abruption, preterm delivery). 5. If we confirm the main hypothesis, we will develop an algorithm for the prediction of PE and/or IUGR presence, including 3DPD, UAD, biomarkers and maternal characteristics, using the prospective Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) cohort data using a 2/3 random sample (logistic regression model with stepwise variable selection). The model validation will be done in the 1/3 random sample. 6. To constitute a biological collection of blood samples in order to determine diagnostic markers or severity predictive markers for PE and/or IUGR.
Doppler acquisitions and blood sampling The 3DPD and UAD acquisitions will be performed immediately after the first trimester ultrasound screening and followed by blood sampling (20 mL in total) for biomarker analysis (PAPP-A and P1GF) and biological collection. Investigators and midwives of each centre will be trained to use the ultrasound system (Voluson S8) in a standardised fashion, with standardised acquisition protocol and machine settings, to secure optimal reproducibility (table  2 ). To determine an optimal 3DPD machine setting in a general population (secondary objective 2), several predetermined settings will be used for 3DPD acquisition. Image analysis VOI will be drawn using VOCAL software and three 3DPD indices will be automatically calculated: VI, FI, VFI (figure  1 ).
Follow-up No specific follow-up is organised for patients ending, prematurely or not, their participation in the study. After childbirth, the pregnancy monitoring and outcome data (medical routine follow-up) will be collected for further analysis. Only routine data will be collected, routine pregnancy monitoring will not be changed. The data will be collected from the patient's medical file. Pregnancy course and outcome of all participants will be recorded on electronic case report forms (eCRFs) and added to the database.
Sample size consideration The sample size is based on mean comparison for IUGR and PE compared with uncomplicated pregnancy and has been estimated using MedCalc Software. In Hafner et al study, the mean 3DPD index measured was about 35. 6±12. 9 in the uncomplicated group, 31. 4±13. 9 in the group with IUGR and 19. 4±11. 35 for PE group. So, to show a significant difference between uncomplicated pregnancy and IUGR in 3DPD measurements, with a 100/10 ratio a specified power of 90% and a type I error of 1% (to control multiple comparisons); we need to include in statistical analysis 1961 women. Considering that in the obstetrical field the expected lost to follow-up women is relatively low, around 10%, we should include 2200 women in the SPIRIT cohort. As expected differences between uncomplicated pregnancy and PE are bigger than those for IUGR, the sample size will also allow to show a significant difference between uncomplicated pregnancy and PE.
Data collection and management The collection of personal history, biomarkers, 3DPD and UAD acquisitions will be performed at the time of inclusion. 3DPD and UAD acquisitions will be carried out by specially trained midwives, including the collection and handling of blood samples for biomarkers assays and biological collection. The quantitative results of the various tests will be collected and will remain blinded to the investigators and pregnant women. Data for this study will be recorded on an eCRF and are presented in table  3. The data from all women will be centrally stored in Nancy. Data management will be carried out by Nancy CIC-IT (INSERM CIC 1433). The conditions for data Open access transfer of all or part of the study database are decided by the study sponsor and are the subject of a written contract.
The data images will be transferred to Nancy CIC-IT and stored after verification, in the ArchiMed database declared to the French authority (CNIL declaration number: 1410005). This study and the data collected fall within the scope of Reference Methodology MR001. After data collection, women will be allocated to a group including: ► Uncomplicated pregnancy. ► Complicated pregnancy. -By PE -By IUGR -By PE and IUGR.
These values will be compared with each other. The interobserver and intraobserver reproducibility for 3DPD analysis will be evaluated by calculating an intraclass correlation coefficient (and its 95% CI), complemented by a Bland-Altman graph. For ancillary study, the interobserver and intermachine reproducibility will be evaluated by calculating, on a sample of 60 women, intraclass correlation coefficients (and its 95% CI), complemented by Bland-Altman graphs. After quantification of uteroplacental vascularisation, an algorithm for the early prediction of PE and/or IUGR based on 3DPD will be developed using the cohort of 2200 participants. The data will randomly split into two parts: 2/3 to develop the model to predict PE and/or IUGR occurrence (train data) and the remaining 1/3 to measure its performance (test data).
1121-3 and R. 5121-13 of the French Public Health Code) the persons having direct access to source data will take every precaution required to ensure the confidentiality of information relating to investigational medicinal products, studies, participants, notably concerning the identity of these, as well as the results obtained. These persons, as the investigators themselves, are subject to professional confidentiality. During the study, or at its conclusion, data collected regarding participants that is sent to the sponsor by the investigators (or all other specialists involved) will be coded by the inclusion number of the patient in the study. At no point should the names of participants or their addresses appear unencrypted. The presentation of the data processing results can not in any case allow the direct or indirect identification of persons lending themselves to research.
This point will also be addressed in our study and that is one of its strengths. The strategy used for the study design was not just to test one new biomarker vs another. In order to achieve a good prediction tool, we think it is necessary to consider the respective potential advantages of the different approaches. Therefore, all markers already tested in other studies will be collected. First, these data will be compared with 3DPD, then, it will be associated with 3DPD markers to propose a decision algorithm. The evaluation of placental development by exploiting imaging technologies, such as 2D colour Doppler and 3DPD represents the state-of-the-art in the obstetrics research field, and such an approach has not yet reached the clinic. Inclusion of these non-invasive imaging techniques in a PE/IUGR risk assessment procedure will give a considerable benefit in terms of regulatory approval of patient stratification methods and clinical implementation of these techniques.
Model parameters were held constant among case studies (flow accumulation: 2000 [except for in Cabo Delgado, where we used 500 due to larger spatial resolution of analysis]; Borselli's kb: 1. 8; IC0: 0. 5, SDR max: 0. 8). We quantified impacts of regulated development and Averted Loss approaches as the difference between current and future landscapes within the compensation sites that impacted biodiversity. For Improvement, impacts equalled half the sum of differences between preclearing and current landscapes within compensation sites that impacted biodiversity. Finally, we quantified case-study-wide biodiversity outcomes, as counterfactual biodiversity losses and gains, minus impacts from regulated development plus impacts of compensation activities. Case studies and data sources. We applied our modelling approach to four case studies: the Brigalow Belt in Queensland, Australia; the Iron Quadrangle in Minas Gerais, Brazil; East Kalimantan in Indonesia; and Cabo Delgado in Mozambique (Fig.
1 ; Supplementary Figs. 5 6 7 8 ). Case studies differed in ways that allowed us to assess the influence of local context on compensation performance. For example, they varied in their extent of native vegetation remaining; land use and land use transitions; and compensation requirements (e. g. policy scope and extent of future Step 4. Quantify impacts of compensation on biodiversity (shown as green polygons in box on right) and two ecosystem services (using spatial models), and aggregate their conservation outcomes (i. e. combined impacts of regulated development and compensation, and any counterfactual losses and gains). regulated development) (Table  1 ). Supplementary Methods 1-4 and Supplementary Tables 1-25 contain details of local context for each case study, along with specific data sources used in our analysis.
Not only would this information permit more certainty in policy evaluations, but also to inform the multipliers required to achieve NNL, which rapidly decline as success improves. Given the large extent of our study regions and the assumptions of our modelling methodology (Fig. 6 ), our results are likely influenced by several sources of uncertainty. While parameter values used by our compensation policies (i. e. multipliers and compensation success) are based on current practice, these likely vary across and among sites. Further, errors are likely evident in the many datasets used to calibrate and validate our land use change models (which were then used to simulate counterfactual biodiversity losses and gains) and ecosystem services models (used to examine trade-offs from biodiversity compensation).
Acknowledgements This research was supported in part by the  Science for Nature and People Partnership (SNAPP)  Compensatory Conservation Working Group, a partnership of  The Nature Conservancy , the  Wildlife Conservation Society  and the  National Center for Ecological Analysis and Synthesis (NCEAS) at University of California, Santa Barbara. L. J. S. acknowledges  Australian Research Council   Discovery Early Career Research Award  ( DE170100684 ); M. M. acknowledges  Australian Research Council   Future Fellowship  ( FT140100516 ). M. M. and J. S. S. were supported by The  Australian Government's National Environmental Science Program  through the  Threatened Species Recovery Hub ; and multiple co-authors received support from  COMBO (COnservation, impact Mitigation and Biodiversity Offsets in Africa ), which is funded by the  Agence Française de Développement , the  Fonds Français pour l'Environnement Mondial  and the Mava foundation, among others, and implemented by the  Wildlife Conservation Society ,  Forest Trends  and Biotope.
4 Limited data exist on the long-term follow-up after a DBPCFC, including the frequency and treatment of accidental allergic reactions, diet, and HRQL. We sought to evaluate 1) allergic reactions and HRQL in children with peanut allergy and 2) peanut introduction and HRQL in peanut-tolerant children. The current study is a 3-year follow-up of 83 children who participated in a prospective study in The Netherlands and all underwent a DBPCFC for peanut. 5 hree years after the DBPCFC, all parents and children aged 8 years or older were contacted by mail and asked to fill out a questionnaire on accidental allergic reactions and current diet and the Food Allergy Quality of Life Questionnaire (FAQLQ). Details on data collection and statistical methods are presented in the Methods section in this article's Online Repository at www.
Study participants This study was approved by the Bloomsbury Ethics Committee (no. 08H071382), the Oxford GI biobank (no. 09/H1204/30), and the Comit e de Protection des Personnes (ID-RCB/EUDRACT 2014-A01017-40) in France. We obtained written informed consent from all family members and adolescent healthy control subjects with local ethics approval (REC 11/LO/0330).
Genetic mapping and sequencing All 5 family members were genotyped with the Illumina cytoSNP12 chip (Illumina, San Diego, Calif). Two hundred nanograms of DNA was isothermally amplified overnight and then enzymatically fragmented. The fragmented DNA was incubated on a BeadChip overnight. These were imaged with the Illumina iScan System. Regions of homozygosity were identified by using the Illumina BeadStudio with the loss-of-heterozygosity detector plugin (version 1. 0. 3). The minimum number of contiguous homozygous single nucleotide polymorphisms was set to 100. Whole-exome sequencing was completed by using the Illumina platform and sequenced on the Illumina HiSeq2000. Raw sequence data were aligned to the human reference genome by using the Burrows-Wheeler Aligner algorithm. Variant calling was performed with the Genome Analysis Toolkit, and variants were annotated with wANNOVAR.
Structural modeling Structural models and figures were prepared by using the ICM software package (MolSoft, San Diego, Calif). A homology model for human DNase II was prepared by using the existing structure from B thailandensis (PDB 5UNB) as the model template. Double-stranded DNA (PDB 2BNA) was positioned manually at the dimer interface to indicate the potential DNA-binding surface, as previously highlighted. E2  All coordinate files were obtained from the Protein Data Bank (http://www. rcsb. org).
In all conditions, 2 hours before the end of incubation, macrophages were stained with LysoTracker Deep Red (Thermo Fisher, Waltham, Mass) 1:2000 solution. Cells were fixed with PBS-paraformaldehyde 4% solution for 10 minutes at 378C and then washed. Slides were mounted with a glass coverslip by using VECTASHIELD solution (Vector Laboratories, Burlingame, Calif). Images were captured with a Nikon eclipse ME600 fluorescence microscope (Nikon, Tokyo, Japan) and analyzed with ImageJ software.
RNA expression analysis in total blood, fibroblasts, and macrophages Whole blood was collected into PAXgene tubes (BD Biosciences). We used a PreAnalytix RNA isolation kit for whole blood and the RNeasy Micro Kit (Qiagen) for RNA isolation from macrophages and fibroblasts. The RNA concentration was assessed with a Nanodrop (FLUOstar Omega; Labtech, Orternberg, Germany). Total RNA was retrotranscribed to cDNA by using the Reverse Transcription kit (Applied Biosystems). We performed quantitative PCR analysis using the iTaq Universal SYBR Green Supermix (172-5121; Bio-Rad Laboratories, Hercules, Calif) and the TaqMan Gene Expression Assay (FAM; Applied Biosystems). The relative abundance of each of 6 ''interferon score'' targets (IFL27, IFIT1, IFI44L, ISG15, and RSAD2) and other proinflammatory transcripts was normalized to the expression level of HPRT1 or RPLPO assessed by using Bio-Rad CFX Maestro software.
Data obtained from the sequence alignment are based on the following National Center for Biotechnology Information accession numbers: Homo sapiens, AAH10419. Rattus norvegicus, NP_612548. 1; Pan troglodytes, XP_001170388. Canis lupus, XP_533902. 3; Mus musculus, NP_034192. Drosophila melanogaster, NP_650672. 1; Caenorhabditis elegans, NP_491414; Burkholderia thailandensis, WP_009895252. 1; and Burkholderia pseudomallei, YP_111989. 1.
3 ; 1 ; 1 ; 311 Data obtained from the sequence alignment are based on the following National Center for Biotechnology Information accession numbers: Homo sapiens, AAH10419. Rattus norvegicus, NP_612548. 1; Pan troglodytes, XP_001170388. Canis lupus, XP_533902. 3; Mus musculus, NP_034192. Drosophila melanogaster, NP_650672. 1; Caenorhabditis elegans, NP_491414; Burkholderia thailandensis, WP_009895252. 1; and Burkholderia pseudomallei, YP_111989. 1.