dataesr/scientific-paragraphs-categorization huggingface dataset:
The plausibility of calcium was confirmed by the web server CheckMyMetal  (27). Without the cofactor, the coordination sphere is uncompleted but is predicted to be octahedral. The root mean square deviation (RMSD) observed in the angle geometry (gRMSD) was 13. 9°, which is only 0. 4°higher than the acceptable value for this parameter. The coordination distances measured and compared to the distances of the structures in the Cambridge Structural Database (CSD) are in agreement with those for a calcium ion (Fig. 3B ). Finally, after refinement, the calcium presents a B-factor of 33. 9 Å 2 , which is coherent with the average of 38. 4 Å 2 for its environment. The calcium is directly coordinated by the side chain of Asp242 and the carbonyl of the Ser240 and is completed by two water molecules.
A third water molecule completes this wellorganized network (Fig. 3B ). This organization is conserved in the native crystal, where the calcium ion is replaced by a water molecule. The addition of the catalytic calcium and its coordinating water molecules was not sufficient to fully fit the additional density. The remainder of the density was too large and too dense to be filled only by water molecules (Fig. 4 ) but too small to correspond to the entire PQQ molecule, presumably indicating a weak and partial occupancy of the site (Fig. 4 ). In addition to PQQ, several molecules were tested to occupy the extra electron density, but no satisfactory result was obtained. As the occupancy was too weak to place the PQQ molecule without ambiguity, the PDB file was deposited only with the catalytic calcium and without PQQ (PDB accession number 6I1T).
Structural homologs. Structural homologs of TrAA12 were searched with the Dali server  (28). The closest structures (with Z-scores of about 30) were from the bacterial soluble glucose dehydrogenase (sGDH) family. sGDHs use PQQ as their cofactor and utilize a calcium-assisted mechanism to oxidize mono-or disaccharides to the corresponding lactones  (29). Several structures of sGDHs are available in PDB, but only the structure of the Acinetobacter calcoaceticus enzyme (PDB accession number 1CQ1) with the catalytic calcium, the PQQ, and the substrate was solved  (12). The superimposition of TrAA12 with A. calcoaceticus sGDH shows that the global fold is well conserved (RMSD, 2. 4 Å with an alignment done on 284 amino acids with 17% sequence identity). Three calcium sites were identified in the A.
This reorganization results in a narrower substrate binding site (Fig. 4 ). In the sGDH family, the oxidation mechanism involves a direct hydride transfer between substrate C-1 and the C-5 of the PQQ, followed by a general basecatalyzed proton abstraction  (12). Based on the quaternary structure of A. calcoaceticus sGDH, His144 is the best candidate catalytic base, functionally assisted by calcium and Arg228 to enhance the reactivity of the C-5-O-5 bond of PQQ  (12, 32). The orientation of the L-fucose, found by docking simulation on TrAA12, is the same as that observed for the D-glucose in the structure of A. calcoaceticus sGDH (PDB accession number 1CQ1)  (12). The proton at the C-1 position of the L-fucose is well oriented and points down toward C-5 of PQQ, which would be coherent with a catalytic mechanism similar to that of sGDH.
Expression vector construction and fungal transformation. The sequence encoding amino acids 24 to 435 of the T. reesei family AA12 protein (TrAA12; GenBank accession number EGR46829. 1) was codon optimized for A. niger, synthesized by GeneArt Gene Synthesis (Life Technologies, Germany), and cloned into pAN52. 3 (GenBank accession number Z32689) by using restriction enzymes NcoI and HindIII. In the expression vector, the A. nidulans glyceraldehyde-3-phosphate dehydrogenase gene (gpdA) promoter, the 5= untranslated region of gpdA mRNA, and the A. nidulans trpC terminator were used to drive the expression of the gene coding for TrAA12. Fungal cotransformations were carried out as described by van den Hondel and colleagues using the expression vectors and pAB4-1 containing the pyrG selection marker in a 5:1 ratio (40, 41).
Phylogenetic analysis. Phylogenetic analysis was performed on 59 fungal sequences downloaded from GenBank and from the fungal genome portal of the JGI (https://mycocosm. jgi. doe. gov/mycocosm/ home). All selected sequences were from published genomes  (20, 21). The amino acid sequences were aligned by use of the MAFFT program  (42). We used the option -maxiterate 1000 and -global pair to obtain high accuracy, where -maxiterate  [number]  indicates the maximum number of iterative refinement and -global pair forces global pairwise alignment. To generate the tree, we built a matrix of maximum likelihood distances based on model-of-substitution LG  (43). Finally, the phylogeny reconstruction tree was created using the BIONJ program  (44). Purification of recombinant protein. The best isolate corresponding to the transformant that produced an intense protein band was inoculated under the same conditions described above for the screening procedure.
The kinetic parameters of recombinant TrAA12 were determined from a nonlinear regression model using the Michaelis-Menten equation. Three replicates were taken for each point. Crystallization and data collection. The first crystals of TrAA12 were obtained by the sitting drop method in a commercial screen (Stura; Molecular Limited Dimension, Suffolk, UK) containing 60% polyethylene glycol (PEG) 550 monomethyl ether (MME), 0. 1 M HEPES, pH 8. 2. This commercial condition was optimized, and the best crystallization conditions were 55 to 60% PEG 550 MME, 0. 1 M HEPES, pH 8. 0 to 8. 5. The protein concentration for crystallization was 24 mg ml Ϫ1 in the purification buffer. The PEG 550 MME concentration was sufficient to protect the crystals during nitrogen flash freezing. Iodide crystals were obtained by use of a 1-to 5-min soak in reservoir solution to which 1 M potassium iodide was added, followed by a back soak and then freezing.
Several soaks were done using calcium, PQQ, and L-fucose to obtain a holoenzyme structure. The final concentrations in the drop during the soaking were approximately 1 mM CaCl 2 , 5 M PQQ, and 500 mM L-fucose. Crystals were collected at the French national synchrotron facility, Soleil, on the Proxima 2 beamline for the native data set and on the Proxima 1 beamline for the iodide crystal. The crystals containing calcium were collected at ESRF on the ID30B beamline. Structure determination and refinement. The crystals belong to the P4 3 2 1 2 space group with cell parameters with unit cell dimensions of 83 Å for a and b and 143 Å for c. One protein is present in the asymmetric unit. Single-wavelength anomalous dispersion was used to obtain the phase. Data were collected at a wavelength of 1.
653 Å. The initial phases were calculated with the Phaser program suite  (46, 47). Phase improvement was done by use of the Parrot tool in the CCP4 program suite  (47). Only one iodide ion was found in the structure. The first model was built by use of the buccaneer tool and refined with the Refmac5 tool in the CCP4 program suite  (47) (48) (49). The Coot program was used to finalize the model building  (50). The other structures were solved by molecular replacement using the Refmac5 and Coot programs  (47, 49, 50). The assignment of the two calcium ions was verified by using the web server CheckMyMetal  (27). The crystallographic data collection and refinement statistics are presented in Table  1. Molecular docking studies. To estimate the binding mode of the cofactor with the substrate, molecular docking studies were performed using the Autodock Vina program  (51).
The cofactor (PQQ) and substrate (L-fucose:␣-L-fucopyranose) were built and minimized with Gasteiger charges in the UCSF Chimera system  (52). Further, minimized cofactor and substrate structures were prepared for docking studies by applying the standard ligand docking protocol. The structure of TrAA12 solved with the active calcium was chosen as the protein model. For protein preparation, all hydrogen atoms added and nonpolar hydrogens were also merged. The Kollman united atom charge and atom type parameters were added. The calcium ion present in the active site was assigned a ϩ2 charge. For the cofactor and substrate, two separate docking sites were set in such way that included the cofactor binding site and the active site. Thirty dock poses were generated for each ligand. The dock conformation with the lowest docking energy, the lowest metal ion distance, and the best superimposition on the reported homologue complex structure (PDB accession number 1CQ1) from the bacterium Acinetobacter calcoaceticus was chosen  (12).
Methods We used each hospital's individual FINESS code to compare the number of pulmonary resections and deaths recorded in Epithor to the figures found in the PMSI. Centers were classified into either the good-quality data (GQD) group or the low-quality data (LQD) group. To demonstrate the influence of case-mix quality on the ranking of centers with low-quality data, we used 2 methods to estimate the standardized mortality rate (SMR). For the first (SMR1), the expected number of deaths per hospital was estimated with risk-adjustment models fitted with low-quality data. For the second (SMR2), the expected number of deaths per hospital was estimated with a linear predictor for the LQD group using the coefficients of a logistic regression model developed from the GQD group.
Introduction Epithor, a French national database for thoracic surgery, has been in operation since 2003. Participation is on a voluntary basis. It has led to the publication of several research articles  [1] [2] [3] [4] [5] [6] ) and, most notably, Falcoz et al. [7]  used it to develop the Thoracoscore, a predictive score that is widely used by European surgeons according to the latest European recommendations  [8]. A number of existing publications have highlighted the importance of data quality in medical databases  [9, 10] , particularly since missing or biased data can lead to erroneous conclusions regarding hospital quality  [11, 12]. At this stage of development, it seems to us that the quality of the data within the Epithor database needs to be assessed. We therefore used the "Programme de Medicalisation des Systèmes d'information" (PMSI), a French national medicoadministrative database that collects exhaustive data regarding pulmonary resection for lung cancer, as a point of reference  [13].
National medico-administrative database All the data for patients who underwent pulmonary resection for lung cancer (LC) from January 2016 to December 2017 were collected from the French national medico-administrative database. The completeness and validity of PMSI data have already been assessed  [13]. Routinely collected medical information includes the principal diagnosis, secondary diagnoses and the procedure performed on the patient. Diagnoses identified during the hospital stay are coded according to the International Classification of Diseases, tenth revision (ICD-10)  [14]. We selected patients with a principal discharge diagnosis of primary lung cancer (codes C34, C34. 0, C34. 1, C34. 2, C34. 3, and C34. 9). Procedures were coded according to the CCAM (Classification Commune des Actes Medicaux).
For all patients, LC was confirmed by pathology analyses according to the 2004 World Health Organization classification of LC  [15]. Surgery-related variables included the surgical approach (thoracotomy or video assisted thoracic surgery), and the type of resection (limited resection, lobectomy, bi-lobectomy or pneumonectomy). In this study, data access was based on special permissions given by the National Health Insurance. All data were completely anonymous. Patients consent is not required. Ethics approval, use of this database was approved by the National Commission for Data protection (CNIL No 1576793) and this study adhered to the tenets of the declaration of Helsinsksi.
The Epithor national clinical database Epithor was modified in 2016 so that each surgeon could upload their patient data directly to a website called "web Epithor". Between 1 st January 2016 and 31 st December 2017, 100 centers that operated patients for lung cancer provided data to Epithor. Baseline demographic data included sex, age, body mass index (BMI), performance status, American Society of Anesthesiologists (ASA) score, forced expiratory volume (FEV), dyspnea score, and smoking status. The comorbidities selected for our analysis were smoking status, chronic bronchitis, arrhythmia, chronic heart failure, peripheral artery disease, alcoholism, cirrhosis, stroke, diabetes, coagulopathy, hematologic disease, history of neoplasm, surgical history, severe malnutrition, pulmonary embolism, valvulopathy, neurological history, psychiatric history, asthma, respiratory failure, infectious disease, cardiac malformation, endocrine disorder, anemia, immunosuppression, and steroid treatment.
The details of the surgery included surgical approach (open thoracotomy or video-assisted thoracoscopy) and type of surgery (segmentectomy, lobectomy, bilobectomy, or pneumonectomy). For clinical database, all data were completely anonymous. Patients consent is not required. Ethics approval, use of this database was approved by the National Commission for Data protection (CNIL No 809833) and this study adhered to the tenets of the declaration of Helsinki.
Comparison of databases The PMSI database was used to classify the hospitals that participated in data collection for the Epithor database. We used hospital FINESS codes to compare the number of pulmonary resections and related deaths recorded in Epithor to the figures recorded in the PMSI during the same period. We calculated the ratio of the number of lung resections in the PMSI to the number of lung resections in Epithor. For deaths, the calculation of the ratio was the same as for the number of pulmonary resections. The hospitals in the Epithor database were then divided into two groups according to the ratios obtained for pulmonary resections and deaths. Group 1was considered good-quality data (GQD) and included hospitals with ratios between 1 and 0. 7 for both measures (75th percentile).
For SMR2, the expected number (E2) of deaths per hospital was estimated with the linear predictor for the low-quality data using the coefficients of a logistic regression model developed from the GQD group, as described by EW Steyerberg  [16]  (S1 Appendix). The effect of the different models on the overall between-hospital variation was quantified by calculating the interquartile ranges of SMR1 and SMR2. We used the kappa (k) statistic to assess the level of agreement between methods for outliers. The statistic measures the proportion of observed-to-expected agreement, and we adopted the principle that k>0. 8 indicates excellent agreement, k = 0. 6-0. 8 indicates satisfactory agreement, k = 0. 41-0. 6 indicates moderate agreement, and k = 0. 21-0. 4 indicates low agreement  [19]. Calculations were done with STATA 14 statistical software (StataCorp, College Station, Tex) and R statistical software for which we used Harrell's Design library.
Discussion This study demonstrates the limitations of a clinical database with voluntary participation. We found that comorbidities were generally under-coded in the low-quality data group of hospitals and that the underestimation of observed mortality influenced the construction of the risk-adjustment model. These elements can lead to hospital misclassification  [20] [21] [22] [23]. However, the number of patients, specifically the number of patients who have a given event per hospital, have a much greater impact on accuracy  [22]. The between-hospital difference in SMR was substantial (interquartile range) for both estimates. Our work shows the ability of the risk-adjustment model for identifying high-mortality outliers and low-mortality outliers with marginal agreement between the two methods.
Some work on the comparison of medico-administrative databases and clinical databases can be found in the literature; these studies emphasize that both types of database have their limits  [21] [22] [23] [24]. The observed mortality rates vary between databases for the same types of surgery  (25). Co-morbidities can also vary significantly in two databases that include the same type of surgery  [25]. These differences, which can be the result of non-standardized end-points or the misclassification of cases  [25] , have a direct influence on the construction of risk-adjustment models for estimating the standardized mortality ratio. One published study compared the European and the North American general thoracic surgery databases  [26]. The authors revealed considerable disparities in the rates of certain comorbidities, including coronary artery disease.
This poses a problem in the interpretation of results seeing as this discrepancy can hardly be explained by patient characteristics. Two potential reasons could be put forward: firstly, that the European database under-codes certain comorbidities, or, secondly, that the databases do not use the same definitions. Further in the article, missing data for the TNM stage are reported. Missing data were more numerous in the European database, corroborating the theory that certain variables were under-reported  [26]. The definition of an outcome such as mortality, which is the most commonly used indicator in surgery, can vary across databases. For some, mortality means hospital mortality, defined as any death occurring during the same hospital stay as the surgery, but mortality can also be 30-day mortality.
The reported rate depends on the definition used, leading to discrepancies in the logistic model  [20, 22, 27, 28]. Our work is different from other publications comparing databases. Contrary to one study that used the clinical database as a reference to validate the medico-administrative database, we used the medico-administrative database to validate the completeness of the data from the centers participating in Epithor  [25]. We used the PMSI, the exhaustive medico-administrative database, as the gold standard against which we compared the number of pulmonary resections and deaths recorded in Epithor. All private and public centers in France are required to use the PMSI database to finance their activity. Death is systematically reported as one of reasons for patient discharge from the hospital.
We showed that this changes the estimate of the standard mortality rate (SMR) (appendix) and consequently the determination of the outliers. This difference in case-mix can influence the comparison of standardized mortality ratios as demonstrated Manktelow et al. [11]. In addition, it has been shown that the death rate and the volume of activity have a significant influence on whether the hospital is considered a high mortality or low mortality outlier  [11, 20, 30]. The observed mortality rate in the LQD group was significantly lower than that of the GQD group; this is explained by an under-reporting of events (as shown in Fig  2  where these hospitals had a ratio significantly lower than 0. 7). The main limit of our study was the use of the FINESS code of each hospital to link the two databases  [31].
This code was used to identify the hospital in the PMSI data and then manually match it with the Epithor database. This method was a reliable means to compare the number of lung resections and the number of deaths. In France it is very difficult to link two databases using patient identifiers because personal data is strictly protected by the CNIL  [32]. Our assessment of the Epithor database is limited, but it remains enlightening regarding the need to apply enhanced measures for quality control. Recently, dashboards have been put into place so that each center can see its fill rate for different items compared to a national reference. Future onsite audits are planned, similar to what is currently in place for the STS database  [33]. This study was essential for improving the quality of the Epithor database and it has been used to select the hospitals and surgeons providing the most complete data.
Acknowledgments We are grateful to all the French thoracic surgeons who participated in this study for their essential collaboration:  Dr Michel ALAUZEN  (Montpellier),  Dr Jean-Franc  ¸ois ANDRO (Quimper),  Dr Maxime AUBERT  (Grenoble),  Dr Jean Philippe AVARO  ( Marseille) ,  Pr Jacques AZORIN (Bobigny) ,  Dr Patrick BAGAN  (Argenteuil),  Dr Francois BELLENOT  ( Cergy Pontoise ),  Dr Vincent BLIN  (Vannes),  Dr Philippe BOITET (Harfleur ),  Dr Laurent BORDIGONI (Toulon) ,  Pr Pierre-Yves BRICHON (Grenoble) ,  Dr Gilles CARDOT  ( Boulogne-sur-Mer ),  Dr Jean Michel CARRIE  ( Saint Jean ),  Pr Pierre CORBI (Poitiers) ,  Dr Michel DEBAERT  (Lille),  Dr Bertrand DEBRUERES  (Ploemeur),  Dr Jean DUBREZ  (Bayonne),  Dr Xavier DUCROCQ  (Strasbourg),  Dr Antoine DUJON  ( Bois Guillaume ),  Pr Pascal DUMONT (Tours) ,  Dr Philippe FERNOUX  ( Chalon sur Saone ),  Pr Marc FILAIRE  ( Clermont-Ferrand ),  Dr Eric FRASSINETTI  ( Chambery ),  Dr Gil FREY  ( Saint Etienne ),  Dr Dominique GOSSOT  (Paris),  Pr Gilles GROSDI-DIER (Nancy) ,  Dr Benoit GUIBERT  (Lyon),  Dr Olivier HAGRY  ( Chalon sur Saone ),  Dr Sophie JAILLARD  (Lille),  Dr Jean-Marc JARRY  ( Aix en Provence ),  Dr David KACZMAREK  ( Saint Etienne ),  Dr Yves LABORDE  (Pau),  Dr Bernard LENOT  ( Saint Brieuc ),  Dr Francis LEVY (Bordeaux ),  Dr Laurent LOMBART  (Beziers),  Dr Eric MARCADE  ( Saint Gregoire ),  Dr Jean Paul MARCADE  ( La Rochelle ),  Dr Jean MARZELLE  ( Creteil ),  Pr Gilbert MASSARD  (Strasbourg),  Dr Florence MAZERES  (Bayonne),  Dr Eric MENSIER  (Lille),  Dr David METOIS  (Orleans),  Dr JL MICHAUD/Edouard PARIS  (Nantes),  Dr Philippe MONDINE  (Brest),  Dr Michel MON-TEAU  (Reims),  Dr Jean-Michel MOREAU  (Nantes),  Pr Jerome MOUROUX (Nice) ,  Dr Antoine MUGNIOT  (Nantes),  Dr Pierre MULSANT  (Lyon),  Dr Nidal NAFFAA (Avignon) ,  Dr Pierre NEVEU  (Talant),  Dr Ge  ´rard  PAVY (Arras) ,  Pr Christophe PEILLON (Rouen) ,  Pr Francois PONS (Percy) ,  Pr Henri PORTE (Lille) ,  Pr Jean-Francois REGNARD (Paris) ,  Pr Marc RIQUET  (Paris),  Dr Babak SADEGHI LOOYEH  (Morlaix),  Pr Pascal THOMAS (Marseille) ,  Pr Olivier TIFFET  ( Saint Etienne ),  Dr Bruno TREMBLAY  (Meaux),  Dr Jean VALLA  ( Charenton le Pont ),  Pr J JOUGON  (Bordeaux),  Dr Bernard WACK  ( Metz) ,  Dr Jean-Didier WAGNER  (Colmar),  Dr Didier WOELFFE  (Valenciennes).
Correct answers were determined according to the latest French recommendations (According the Haute Autorité de Santé [HAS]) and were validated by specialist physicians  [5] [6] [7]. The primary endpoint was the number of GPRs who correctly answered the 5 questions dealing with management for each clinical case. Statistical analyses were performed using SAS software, Version 9. 4 (SAS Institute Inc. , Cary, NC, USA). Categorical variables were expressed in numbers of GPRs and percentages. Comparisons across the three clinical cases were performed using logistic regression models for repeat measures followed by Tukey multiple comparison tests. A p value <0. 05 was considered as statistically significant. Statistical analyses were performed by the CIC 1414 team.