dataesr/scientific-paragraphs-categorization huggingface dataset:
e. AOT and PM2. 5 day and night for the two pollution episodes sampled by lidar, shows no correlation with a Pearson correlation coefficient R² ≈ 0. 16. However, a group of points stands out from this dataset and is associated with a PBL top below 600 m AMSL. It appears that most of these points are also associated with PM2. 5 values above the information threshold (50 µg m -3 ). The linear regression conducted without this set of points (black solid line of Figure  11 ) shows a better correlation (R² ≈ 0. 66). It suggests that the direct correlation between AOT and PM2. 5 to assess air quality, as proposed by  Wang and Christopher (2003) ,  Gupta et al. (2006)  and  Kacenelenbogen (2006) , cannot be used under low PBL height conditions. Scatterplots (not shown) made with PM10 instead of PM2.
Conclusion In this paper we investigate the lidar-derived optical properties of two major APEs of the winter of 2016/2017, found to be part of the most severe pollution events of the 2007-2017 decade. The data collected for this study highlights the maximum AEC in the PBL as an optical parameter that offers the possibility to assess the surface concentration of PM2. 5. This work is achieved through a synergy between: i) ground-based active and passive remote sensing devices, ii) spaceborne instruments, iii) air quality network measurements and iv) meteorological reanalyses. Although limited in time, this lidar dataset comes to enrich the scientific literature, which was lacking severe winter pollution data. These episodes are rare (eight days in one decade, split between four separate events), but harmful for the citizens' health and still difficult to forecast.
Acknowledgements. This work was supported by the  Commissariat à l'Energie Atomique  et aux énergies alternatives (CEA). The  Centre National d'Etude Spatial (CNES)  helped maintain the Raman-lidar instrument. The authors would like to thank  Pascal Genau  ( CNRS/LATMOS ) and  Cristelle Cailteau-Fischbach  ( UPMC/LATMOS ) for their support in operating the lidar data and the welcoming on the site of  University Pierre et Marie Curie (UPMC). The authors would like to thank the  Airparif network  for collecting data. The authors would like to thank the  AERONET network  for sunphotometer products (at https://aeronet. gsfc. nasa. gov/). The authors acknowledge the MODIS Science, Processing and  Data Support Teams  for producing and providing MODIS data (at https://modis. gsfc. nasa. gov/data/dataprod/), the  Atmospheric Science Data Center (ASDC)  at  NASA Langley Research Center (LaRC)  for the data processing and distribution of CALIPSO products (level 4.
Materials and methods Informed consent was obtained from all patients involved in the study, and each patient had the opportunity to opt out of the study at all time points. We performed a retrospective review of multicenter, prospectively collected data from all patients undergoing primary TSA or RTSA by 16 different surgeons. All surgeons used the same implantdthe Equinoxe Primary Shoulder System (Exactech, Gainesville, FL, USA)dand the same surgical technique. 2 The inclusion criteria included patients undergoing primary TSA or RTSA with a minimum of 2 years of follow-up and recorded preoperative and postoperative functional scores. Patients were selected to undergo surgery based on the surgeon's discretion, and the indications for surgery were recorded in an encrypted database. Postoperative complicationsdwhich consisted of loosening, dislocation, infection, neuropathy, pulmonary embolism, stroke, and periprosthetic fracturedwere also recorded.
Patient scores were assessed at the last preoperative visit before surgery and at the most recent clinic follow-up date. The minimal clinically important difference in outcome scores was defined according to previous literature as follows  21, 22  : ASES score, 21 points; SST score, 2. 4 points; and Constant score, 8 points. Postoperative physical therapy regimens were not standardized across the different study sites. All continuous data are described using means and standard deviations, whereas categorical data are described as counts. Comparison of the 2 groups defined by sex was performed using Student t tests. A paired t test was used initially to compare preoperative values, postoperative values, and preoperative-topostoperative improvements in outcome scores and range of motion. Next, a multivariable linear regression analysis was performed to adjust P values for differences in outcomes while controlling for covariates including type of surgery, age, body mass index, history of surgery, and preoperative diagnosis.
However, it is difficult to determine the actual causes of these differences as they are ultimately multifactorial in nature. Various studies have suggested that female sex is a risk factor for complications postoperatively. 4, 8, 13, 17, 18, 27 Menendez et al  13  and Dunn et al  4  used large, national databases to perform retrospective analyses of patients undergoing TSA. Both studies showed that women are at an increased risk of extended hospital stays, and they postulated that longer hospital stays may be associated with a greater risk of complications and higher likelihood of a less-thandesirable outcome. 4, 13 Matsen et al  12  analyzed 17,311 patients from the New York Statewide Planning and Research database and found female sex to be associated with a longer length of hospital stay after SA.
Limitations This study has several potential limitations, which are due to the multicenter design and retrospective nature. The multicenter retrospective design introduces the possibility of operator bias, patient selection bias, and performance bias based on nonstandardized rehabilitation protocols. This could not be controlled for given the retrospective nature of the study. However, all surgeons performed RTSA based on the implant surgical technique without major variations. Only 1 arthroplasty system was used for all patients in this study; thus, these results may not be transferable to other patient populations in which other implant systems are used. In a large database, failure of consistent and standardized coding can lead to biases in reported results. However, our study was well coordinated, and each facility was briefed in detail on coding specifics of the database.
Replacement therapy in severe hemophilia A leads to factor VIII (FVIII) inhibitors in 30% of patients. Factor VIII gene (F8) mutation type, a family history of inhibitors, ethnicity and intensity of treatment are established risk factors, and were included in two published prediction tools based on regression models. Recently investigated immune regulatory genes could also play a part in immunogenicity. Our objective is to identify bio-clinical and genetic markers for FVIII inhibitor development, taking into account potential genetic high order interactions. The study population consisted of 593 and 79 patients with hemophilia A from centers in Bonn and Frankfurt respectively. Data was collected in the European ABIRISK tranSMART database. A subset of 125 severely affected patients from Bonn with reliable information on first treatment was selected as eligible for risk stratification using a hybrid tree-based regression model (GPLTR).
The analysis plan should explain the purpose of the use of the data and confirm the intention to use the data only for replication studies concerning anti-drug inhibitors, since this is the limitation of the ethical permission on how this data can be used. The contact persons of the ABIRISK steering committee to whom the requests should be sent are Pierre Dnnes (pierre@scicross. com) and Marc Pallardy (marc. pallardy@inserm. fr). Association ) member companies in the IMI JU and costs related to their part in the research were carried by the respective companies as in kind contributions under the IMI JU scheme. Sanofi provided support in the form of salaries for author AHM, SciCross AB provided support in the form of salaries for author PD, GlaxoSmithKline provided support in the form of salaries for author JD who also held held stocks/shares in GlaxoSmithKline, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The linear part is used to model the effect of the known classic risk factors and the nonparametric tree part is used to capture the distributional shape of the data using the complex joint effects of multiple genetic explanatory variables. The analyses were performed on cohorts of hemophilia A patients with a long follow-up from two German centers that are reference laboratories for anti-FVIII antibody testing. From this dataset, we evaluated the combined role of clinical and genetic factors in the immunogenicity of FVIII products on a selected population of severe HA PUPs in order to reduce the magnitude of any potential confounding variables associated with therapeutic changes over time.
Design and study population The population eligible for inclusion in this historical cohort analysis was selected from two German sites (Bonn and Frankfurt) under the leadership of the ABIRISK EU-consortium. For Bonn, follow-up data was collected prospectively for HA patients (children and adults) treated with FVIII products since 1967. The Bonn database was created in 1978 and became an electronic system in 1990. For Frankfurt, data for HA patients having entered services since 1979 was collected in an electronic database created in 2005. For both databases, patients could have been treated previously elsewhere before registering at the current sites. In all, 593 patients with severe HA from the Bonn database and 79 from the Frankfurt database were included in the present study. Data were fully anonymised prior to access for the analysis.
Candidate variables Data on patient and disease characteristics, such as the mutation type of the F8 gene classified as "high risk" (large deletions, nonsense mutation, intron 22 or intron 1 inversions) versus "low risk" (small deletions/insertions of <200 base pairs, missense mutations, and other Pfizer for research support and speakers bureau, grants and personal fees from Shire for research support and speakers bureau, grants and personal fees from Sobi for research support and speakers bureau, personal fees from Roche for expert testimony, outside the submitted work. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no  [115303] , resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.
J. E. Davidson, A. Hincelin-Mery: belong to EFPIA (European Federation of Pharmaceutical Industries and Association) member companies in the IMI JU and costs related to their part in the research were carried by the respective companies as in kind contributions under the IMI JU scheme. This does not alter his adherence to PLOS ONE policies on sharing data and materials. All the company-employed authors declare that this does not alter their adherence to PLOS ONE policies on sharing data and materials. mutations including splice site defects), family history of factor VIII inhibitors, and blood group, were considered in the present study. The type of the first FVIII product (plasma-derived versus recombinant product) was available for a restricted sub-cohort of patients who were first treated in the center hosting the database after its creation, or who had a reliable documentation of their first treatment if they were treated elsewhere.
For readability, this sub-cohort is referred to by the term "well-documented" population. The genetic factors mentioned in the previous section were also considered as potential markers. Data was standardized into a common format and entered into the ABIRISK tranSMART database, which integrates immunogenicity data from a number of European countries in a well-structured format. The data is harmonized across different cohorts and the format is based on the CDISC standards (http://www. cdisc. org/). Where no previous variable description can be found in CDISC, a local variable description is used. Data was prepared by data custodians using a data load plan describing the variable semantics and format. Anonymized data was uploaded into the ABIRISK database, which is based on the tranSMART platform  [22].
Statistical analyses The selection of patients for the different analyses is described in a flowchart in Fig 1. In the Bonn entire cohort, people were born from 1920 to 2012. This interval was cut in three smaller intervals: before 1978, 1978-1995 and after 1995. Inhibitor positivity rates were different in these three categories. A descriptive analysis of patient, disease and treatment characteristics for the entire dataset was performed with univariate logistic regressions taking into account birth cohort effect and "well-documented" status. To avoid blurring any association between a variable and the outcome, these analyses were run separately on Bonn and Frankfurt datasets: a first exploration was carried out on Bonn, then a confirmation on Frankfurt's dataset. If results were consistent, analyses were performed on the pooled cohort.
This procedure is not intended to give prediction since our dataset is underpowered for reaching such objective. However, it is a way to evaluate the stability of the association results. More precisely, the bagging procedure enables computation of variable importance measures that assess the relevance of each variable across the set of trees. It provides a way to rank the variables according to their discriminative power. Variables that are associated with the outcome have large values as compared to those who are not associated with. These latter results provide us some arguments regarding the reliability of the selected optimal GPLTR tree. Statistical analyses were performed using R software (version 3. 3. 1. software). Haplotype analyses were performed using the 'HaploStats' R package.
Results The dataset comprised 593 and 79 patients with severe HA from the Bonn and the Frankfurt databases respectively. Univariate analyses for well-established risk factors were performed separately. A pooled analysis was performed when results exhibited the same trend. As shown in Fig  1 , the analyses excluded 442 patients for whom we had no reliable information on the first exposure to FVIII products. Of the 586 patients from Bonn and the 79 patients from Frankfurt, respectively 113 (19%) and 32 (41%) developed inhibitors. The analysis of birth cohort effect showed that, in Bonn, the incidence of inhibitors increased over time: 12% of the patients born before 1978 developed inhibitors, 23% between 1978 and 1995 and 44% after 1995. There was no significant difference between birth cohorts in Frankfurt (22% before 1995 and 19% after 1995).
Univariate analysis Patients with an F8 mutation risk considered as "high risk" had a 3. 61 times greater risk (95% CI 2. 13-6. 40) of developing inhibitors compared to patients with a "low risk" according to the pooled analysis of the Bonn and Frankfurt data (Table  2 ). Among patients with a history of hemophilia A, the presence of a family history of inhibitors was associated with a 5. 94 times greater risk (95% CI 2. 73-13. 29) of developing inhibitors in the Bonn dataset. This information was not available in the Frankfurt dataset. Patients with a blood group other than O were more likely to develop inhibitors than those with group O. This association did not reach statistical significance in the pooled dataset, with patients with blood group other than O having a 1. 46 times greater risk (95% CI 0.
94-2. 31). As shown in the flow-chart (Fig  1 ), the type of FVIII at first exposure could only be analyzed in the "well-documented" subgroup of the patients. In the Bonn subgroup, patients first exposed to recombinant FVIII developed more inhibitors than those exposed to plasma-derived products (OR = 2. 18, 95% CI 0. 97-4. 99). This association was not observed in the Frankfurt database. HLA class II markers and immune response genes IL-10, TNF-a, CTLA-4, CD32, MAPK9 and CD86 were analyzed for their association with inhibitor development in 142 patients from the Bonn database. (Fig  1 ). We found no statistically significant deviation from the Hardy Weinberg equilibrium for any of the candidate SNPs. Table  3  gives the univariate odds ratios for each genetic factor. For HLA markers, HLA-DRB1 � 01 was associated with a lower risk (OR = 0.
Discussion In this study, we investigated the association of bio-clinical and genetic markers with the development of FVIII inhibitor taking into account potential genetic high order interactions. The classic bio-clinical factors selected by our analyses were in line with those reported in previous studies. The F8 mutation type and a family history of FVIII inhibitors were confirmed as being associated with inhibitor development in severe hemophilia A patients. The type of FVIII product (recombinant versus plasma-derived) was also selected in our analyses, this result being consistent with those of SIPPET. These bio-clinical variables were considered in the linear part of the model for both the logistic and the tree-based model. Intensity of the first treatments was not available in the dataset and could not be assessed.
It is also worth noting that the GPLTR model provided a better fit for the data according to the Akaike information criterion compared to a classic multivariate logistic regression approach. Concerning blood group, patients in blood group O were less likely to develop inhibitors but the association was not statistically significant in the univariate analysis combining the Bonn and Frankfurt cohorts (p = 0. 10). In the eligible subset of the Bonn cohort, blood group was also not significant in the multivariate analysis. This could result from a lack of power. It is worth noting that this variable is classified as the nineth in terms of fit scores by the bagged GPLTR procedure. An association between blood group and inhibitors has previously been reported  [27]. Given the data on the potential role of von Willebrand factor (VWF) in immune recognition of FVIII  [28]  and inhibitor development  [2] , it is of note that levels of VWF differ in individuals with different blood groups.
This association warrants further investigation. The ABIRISK project, as a collaborative initiative, enables data from different sites to be pooled. Investigation of the main clinical factors (patient-, disease-and treatment-related) was based on the historical cohorts of Bonn and Frankfurt. Datasets were however rather different in terms of data availability, especially concerning genetic information. In addition, a selection of population in terms of homogeneity in birth cohort and follow-up was necessary. While the descriptive analyses were carried out on the whole population, a small subpopulation from the Bonn cohort was used for the multivariate analyses. Mostly patients treated previously elsewhere were excluded as some were patients treated a long time ago and as there were no reliable information on first treatment for these patients.
With this unbiased but reduced population, however, comes a decrease in power for the statistical analyses. Our study has some limitations that should be mentioned. Since it is an observational study, we could not exclude some bias between negative and positive inhibitor patients, these latter been more well documented. The majority of the population are Caucasian but we could not exclude some heterogeneity in the ancestry background of our population. Moreover, our dataset has a reduced sample size and additional studies with larger sample size are required to strengthen our findings.
Acknowledgments This study was supported by  Institut National de la Santeet de la Recherche Medicale (INSERM)  and  Centre National de la Recherche Scientifique (CNRS). The research leading to these results was supported by the  Innovative Medicines Initiative Joint Undertaking ABIRISK (Anti-Biopharmaceutical Immunization Risk)  project under grant agreement # 115303 , the resources of which comprise financial contribution from the  European Union 's  Seventh Framework Program  (FP7/2007-2013) and in-kind contributions from EFPIA companies. For further information, please refer to www. abirisk. eu. All the authors of this article are members of the  ABIRISK consortium. Philippe Broe  ¨t (philippe. broet@inserm. fr) is a leader in this consortium. We thank the eTRIKS consortium (https://www.
etriks. org)for their support in the construction of the tranSMART database. List of ABIRISK partners, leaders and task responsible persons:  Medizinische Universita ¨t Innsbruck (Florian Deisenhammer) ,  UCB Pharma SA (Louis Christodoulou) ,  GlaxoSmithKline  ( Dan Sikkema ,  Amy Loercher ,  Julie Davidson ,  Andy Lawton ,  Steve Etheridge ,  Sally Miles ),  INSERM  ( Marc Pallardy ,  Sophie Tourdot ,  Xavier Mariette ,  Sebastien Lacroix-Desmazes ,  Philippe Broet ,  Delphine Bachelet ,  Nadia El-Hamdi ),  Academisch Medisch Centrul bij de Universiteit van Amsterdam  ( Niek De Vries, Anne Musters ),  Assistance Publique Hopitaux de Paris (Aline Doublet ), Groupe d'etudes therapeutiques des affections inflammatoires du tube digestif ( Matthieu Allez ,  Sabrina Williams ),  Universitaetsklinikum Bonn  ( Johannes Oldenburg ,  Thilo Albert ),  Karolinska Institutet  ( Anna Fogdell Hahn ,  Malin Ryner ,  Ryan Ramanujam ),  Pfizer  ( Tim Hickling ),  Merck Serono (Elisa Bertotti) ,  Ipsen  ( Julie Le  Grand),  University College London  ( Claudia Mauri ,  Liz Jury ),  Sanofi-Aventis Research and Development (Vincent Mikol ,  Agnès Hincelin-Mery ,  Catherine Prades ,  Pauline Loas ),  Università di Firenze (Enrico Maggi) ,  Novartis Pharma AG  ( Annette Karle ,  Sebastian Spindeldreher ,  Verena Romach-Riegraf ),  Fondazione per l'Istituto di Ricerca in Biomedicina  ( Antonio Lanzavecchia ),  Klinikum rechts der Isar der Technischen Universitaet Muenchen (Bernhard Hemmer ),  Commissariat à l'Energie Atomique (Bernard Maillere) ,  Novo Nordisk  ( Christian Ross Pedersen ),  Scicross AB  ( Pierre Do  ¨nnes),  Bayer Schering Pharma AG  ( Jeannette Lo ,  Pascale Buchmann ), eTRIKS ( Fabien Richard ),  Paul-Ehrlich-Institut (Christine Keipert) ,  ALTA Ricerca e Sviluppo in Biotecnologie S.
ABIRISK does not own the data analyzed in this study, which were collected in the context of other past and current projects by both ABIRISK partners and non-ABIRISK research groups in the different centers and provided to ABIRISK exclusively for the purpose of the performed analyses upon official request and ethical approval. For this reason access to the minimal dataset underlying the findings cannot be made publicly available, but can be obtained by interested researchers upon request to the ABIRISK steering committee by submission.
This study was supported by Institut National de la Santeet de la Recherche Medicale (INSERM) and Centre National de la Recherche Scientifique (CNRS). The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no [115303], resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013)and EFPIA companies. J. E. Davidson, A. Hincelin-Mry: belong to EFPIA (European Federation of Pharmaceutical Industries and.