dataesr/scientific-paragraphs-categorization huggingface dataset:
12 This was comparable to the 13. 2 months in the CAPRISTANA observational study, where 85% of patients were in second line. 13 eyond survival, QoL and pain are major issues in these often elderly patients with advanced metastatic disease. In CAPRIS-TANA, QoL was maintained or improved in 72. 5% of patients, and 53. 6% of patients reported pain improvement. 13 t the time of market approval of cabazitaxel in France in October 2011, the French Health Authorities requested a postauthorization study in real life to evaluate the impact of cabazitaxel on survival, QoL, pain, and analgesic use in patients with mCRPC. A first study confirmed survival characteristics of patients with mCRPC treated with cabazitaxel. 12 The present prospective study was set up to study QoL and pain, using real-world patientreported data over 6 months.
Study Design FUJI (Follow-Up of Jevtana in real life) is a French multicenter observational cohort study of patients with mCRPC starting treatment with cabazitaxel. A first historical study of 18-month survival in patients starting cabazitaxel in 2013 to 2015 has been reported elsewhere. 12 In the present study, FUJI-QoL, data on QoL and pain were collected prospectively over a 6-month treatment period in patients starting cabazitaxel between March 1, 2016 and February 28, 2017.
Data Collection Baseline data were collected from patient files into an electronic case report form by a dedicated clinical research assistant and validated by the participating physician. These included the date of first prescription of cabazitaxel, patient demographics, medical and treatment history, cabazitaxel treatment modalities, clinical, biological (prostate-specific antigen [PSA]) and radiologic outcome (response, progression or death), analgesic consumption, and adverse events (AEs) reported during cabazitaxel treatment. AEs were coded using the current MedDRA classification, and their severity was coded according to the grading system of the National Cancer Institute's Common Terminology for the Classification of Adverse Events (NCI-CTCAE v4. 0). AEs requiring hospitalization were identified.
Statistical Analysis The target sample size in the QoL cohort (60 subjects) was determined to estimate the FACT-P score with a precision of 3. 3 Clinical Genitourinary Cancer October 2020 -e511 points and the BPI-SF with a precision of 10. 5%. QoL and pain were analyzed from raw data. Sensitivity analyses tested multiple imputation of missing values using the Monte Carlo Markov Chain method. OS and PFS were estimated using Kaplan-Meier survival analysis. All statistical analyses were performed with SAS (SAS Institute, version 9. 4, Cary, NC).
Ethics The study was done in accordance with all relevant national legislation and guidelines for observational studies, with approval from the French national data protection agency (CNIL). The study protocol was submitted and approved by the French health authorities as part of the post-marketing commitments of the manufacturer of cabazitaxel (Sanofi-Aventis). All patient data in the study database were rendered anonymous. Written informed consent was obtained from all patients prior to inclusion. The study was registered with the European Union PAS registry (ENCEPP/SDPP/10391).
Study Population A total of 56 physicians agreed to participate in this prospective QoL and pain study. Of these, 22 invited 63 patients who were starting cabazitaxel treatment to participate in the study. Two patients refused. The remaining 61 patients were included and provided with the questionnaires. Complete evaluable FACT-P and BPI-SF data (ie, questionnaire at inclusion and at least 1 during follow-up) were available for 49 (80. 3%) and 44 (72. 1%) patients, respectively. The patient recruitment process is illustrated in Figure  1 , and baseline characteristics are in Table  1. There was no difference in patient characteristics between patients who were analyzable and those who were not because of missing data. The median time from prostate cancer diagnosis to cabazitaxel initiation was 6.
Weaknesses and Strengths This is a prospective study using primary patient data and specific questionnaires for mCRPC. Data for death and progression were abstracted from the clinical files. Patients came from various cancer treatment centers in France, and appear to be typical of the population of patients with prostate cancer in terms of age, previous cancer therapy lines, or concomitant illnesses. In a previous historical cohort study of cabazitaxel users in 401 patients with mCRPC in France, the mean age was 70, and clinical characteristics were similar. The median PFS was 3. 9 months in that population, compared with 3. 7 months here. 12 The median OS was 11. 9 months, whereas it was not measurable here owing to the shorter follow-up. In PROSELICA, 10 comparing 20 mg/m 2 with 25 mg/m 2 as initial dose, the median time to progression (PFS) was 2.
Acknowledgment The authors wish to thank ADERA for legal, human resource and management support that made this study possible. The authors would like to thank the investigators for their participation in the study (supplementary list of participating centers and investigators follows). This study was funded by  Sanofi-Aventis , the company that markets cabazitaxel. The funder had no impact on the final protocol, which was approved by the French regulatory authorities, on the study conduct, on data analysis, or the content of the present paper, though it was given the opportunity to comment, according to the ENCEPP Code of conduct.
We use a generalized additive model to ensemble the random forest and extreme gradient boosting predictions with weights that vary spatially and by the magnitude of the predicted residual. The 200 m models also perform well, with ten-fold cross-validated R 2 of 0. 79, 0. 79, and 0. 85, MAE of 0. 4, 0. 3, and 0. 3, and RMSE of 0. 6, 0. 4, and 0. 5 (Rmin, Rmean, and Rmax, respectively). Our model will reduce bias in epidemiological studies in France by improving Ta exposure assessment in both urban and rural areas, and our methodology demonstrates that MODIS and Landsat thermal data can be used to generate gap-free timeseries of daily minimum, maximum, and mean Ta at a 200 x 200 m 2 spatial resolution.
, 2016; Molina and Saldarriaga, 2017; Shi et al. , 2015). We also add a local stage that uses an ensemble of machine learning algorithms to predict the residuals of the 1 km model in urban areas based on higher spatial resolution predictors including thermal data from the Landsat 5, 7, and 8 satellites. This allows us to predict daily Ta over 17 years at a 200 m spatial resolution which better captures intra-urban Ta variation across 103 urban areas.
7), 7% are in peri-urban areas (within 5 km of an urban area), and the remaining 90% are rural. The stations calculate daily Tmin as the lowest Ta observed from 18 UTC the previous day until 18 UTC on the day; daily Tmax is the highest Ta observed from 6:00 UTC on the day until 6:00 UTC the following day. Most stations calculate Tmean as the mean of all (at least 24) Ta observations from 0 UTC on the day until 0 UTC the following day. However, about 40% of the Tmean observations were calculated as the average of Tmin and Tmax. We exclude these observations, meaning our final dataset has fewer observations for Tmean than for Tmin or Tmax. Daily Ta at the included stations during the study period ranged from Tmin of -31. 2 °C to Tmax of 44. 1 °C; mean Tmean was 11. 3 °C with a standard deviation of 7.
The 16-day revisit time of the Landsat satellites means that Tb is unavailable for many locations on many days. Cloud cover and sensor malfunctions also contribute to these data gaps and can increase error in Tb retrieval. To reduce error, we discard all scenes with cloud cover > 75%. We also discard all scenes captured during periods of instrument malfunction, which we identified by checking summary statistics of each scene for unrealistic values (e. g. mean Tb > 100 °C). We then trim the edges of Landsat 5 scenes by 2. 5 km to remove abnormalities  (Robinson et al. , 2017)  and mask pixels identified as high-or mediumconfidence cloud in the pixel quality assessment band. We mask any remaining pixels where Tb ≤ -25 °C or Tb ≥ 50 °C. Finally, for each calendar month we composite all Tb retrievals during the entire study period (e.
Stage 3: increasing spatial resolution to 200 m across large urban areas In stage 3 we increase the spatial resolution of our predictions over large urban areas. We Populationi is the population of cell i; xi and yi are the geographical coordinates of cell i; j is the Julian day; and εij is the error for cell i on day j. We use the R packages ranger  (Wright and Ziegler, 2017) , XGBoost  (Chen and  Guestrin, 2016), and mlr  (Bischl et al. , 2016)  to train the random forest and XGBoost models. We tune the models using the sequential model-based optimization of package mlrMBO  (Bischl et al. , 2017). Briefly, mlrMBO estimates optimal hyperparameter values by iteratively training and evaluating a model using hyperparameter values that are chosen based on the performance of previous iterations.
Discussion Spatiotemporally-resolved Ta at high resolutions is essential to understanding, monitoring, and managing the health effects of Ta, a pressing issue in a warming, urbanizing world. We have developed the longest  (2000 -2016) , highest spatial resolution (1 km) model of daily Ta available for continental France aimed at public health research. Furthermore, our model provides an unprecedented spatial resolution of 200 m over large urban areas. A key feature of our model is its ability to capture spatial variation in Ta. Previous epidemiological research in France linked geographical variation in mortality risk to both typical  (Laaidi et al. , 2006)  and extreme Ta  (Le Tertre et al. , 2006 ) using weather stations. Recent studies in the USA showed that a daily 1 km Ta dataset similar to ours was needed to detect associations with low birth weight  (Kloog et al.
97 with RMSE of 1. 29 whereas an initial version achieved R 2 of 0. 96 with RMSE of 1. 52  (Kloog et al. , 2017). We also demonstrate that a GAM ensemble of machine learning models can use higher spatial resolution predictors including Landsat thermal data to account for some of the residual error in our daily 1 km Ta predictions. Adding this local stage both increases the spatial resolution of our model and improves performance. One limitation of our method is its reliance on historical satellite thermal data. Our model is restricted to the MODIS period of record, which starts in 2000. Older thermal data is available from other satellites (e. g. Landsat), but not with a twice-daily revisit time. In the USA,  Oyler et al. (2015)  showed that an anomaly-climatology approach could model daily Tmin and Tmax since 1948 from 8-day composite MODIS LST, although their approach may have smoothed spatiotemporal Ta trends.
Our model's low MAE (typically less than 1. 5 °C) suggests that it produces good Ta estimates despite this; incorporating high temporal-resolution (e. g. hourly) LST from geostationary satellites might improve performance. A fourth limitation of our model is the need to fill gaps in satellite thermal data. This can introduce error and may make modelling impossible in some areas or time periods. Landsat data is particularly challenging due to the satellites' 16-day revisit time; parts of France have no usable Landsat observations during some winters. The few previous studies that used Landsat thermal data to model Ta limited their analysis to days and locations where Landsat data was available  (Pelta and Chudnovsky, 2017)  or used a few scenes that were deemed typical of hot summer days  (Ho et al.
, 2016 (Ho et al. , , 2014;; Wicki et al. , 2018). We fill gaps in Landsat Tb by compositing all scenes for each calendar month across 17 years. This smooths spatial patterns and means we rely entirely on MODIS to capture short-term temporal variation in LST. Combining data from Landsat 5, 7, and 8 may also introduce error as the sensors operate at different wavelengths and spatial resolutions (Table  1 ). Future studies may benefit from the forthcoming Landsat Surface Temperature product  (Malakar et al. , 2018)  which might be more consistent, and would allow using LST as a predictor rather than brightness temperature. Future studies could also make use of high spatial-resolution LST from forthcoming satellites. Landsat 9 will have a spatial resolution and revisit time similar to the previous Landsat satellites, but should offer better LST retrieval thanks to the correction of the stray light contamination that affects Landsat 8  (Hair et al.
Acknowledgements This study was funded by the  Climate Health Effects In Pregnant Women And Children-A Multi-Cohort Study In France And Israel grant  ( CNRS PRC 2018-2020 ), the  Fondation de France  (n°  00081169 ), and the  Effects of Urban Microclimate Variability and Global Climate Change on Heat-Related Cardiovascular Outcomes in the Semi-Arid Environment of Southern.
Data were acquired on a CytoFLEX flow cytometer (Beckman Coulter) and analyzed using Kaluza Analysis v2. 1 software (Beckman Coulter). To calculate absolute values of cells per microliter of blood, the numbers of lymphocytes on routine clinical blood counts from the same day were collected from the patient charts. Based on these parameters, we estimated what proportion of cells in the blood of the patients was represented by each cell type analyzed by flow cytometry. Gating strategies F I G U R E 2 Gating strategy for analysis of lymphocyte subpopulations. Gating of lymphocytes was sequentially done on CD45 + and low SSC-A (A). CD3 + were selected (B), and CD4+ (D) and CD8 + (C) cells were discriminated according to the expression of CD4 and CD8 in CD3 + cells. Tregs cells were identified in CD4 + cells as CD127 -CD25 + FOXP3 + (E).
A P-gp classification model was also developed in the authors' previous study  [11]. On the other hand, structure-based methods (e. g. , molecular docking) allow the investigation of ligand-receptor interactions at atomistic level when high-resolution structures of the receptors are available. Until 2019, when the cryoEM structure of human ABCB1 was resolved (PDB ID: 6QEX)  [12] , the docking studies on P-gp for understanding the binding site interaction profiles had been limited due to the availability of the experimentally solved structure of human P-gp (hP-gp)  [13] [14] [15] , thus the use of homology models became necessary for studying the ligand-hP-gp interactions. The first homology models developed and utilized in molecular docking studies relied on bacterial homologues used as templates, such us the bacterial transporters Sav1866 and MsbA structures  [16] [17] [18] , representing different catalytic states of the transport cycle.
In 2009, the crystal structure of the mouse P-gp (mP-gp) complex with a cyclic tetrapeptide (PDB ID: 3G5U) [19] was resolved, hence representing a ligand binding competent conformation of the protein. The mP-gp, with 87% sequence identity is a well-suited template for homology modeling of the hP-gp and provides a better model for structure-based approaches. In the present study, we developed a hP-gp homology model based on mP-gp multiple templates that can be used in further docking simulations. We used the available knowledge on the interaction of substrates and inhibitors with P-gp to apply a molecular docking approach in order to first elucidate if molecular docking is able to differentiate between active and non-active P-gp compounds; and second, for determining to what extent the amino acids predicted by molecular docking are consistent with experimental data available.
Homology Modeling of hP-gp The crystal structure of mP-gp (PDB ID: 4M1M), which has an 87% sequence identity to the hP-gp, was selected as the most suitable template for developing the homology models of the 3D structure of hP-gp. However, the models built with Discovery Studio Client/Modeler 9. 12  [20, 21]  and I-TASSER  [22] [23] [24]  tools were based on the alignment utilizing more than one template, among these the crystal structure of Caenorhabditis elegans P-gp (PDB ID: 4F4C), included in the model generated with the Discovery Studio 4. 1 Client/Modeler tool 9. 12  [20, 21]. The alignments between the hP-gp sequence and the templates used are provided in the Supplementary materials, Figure  S1. Several models were built with each of the tools and one model per tool was selected for further evaluation and validation.
SWISS-MODEL A total of five initial hP-gp models were created with the SWISS-MODEL tool  [25]  using as template the crystal structure of the mP-gp (PDB ID: 4M1M). The obtained models were scored using the GMQE (global model quality estimation) and QMEAN  [26]  scoring functions. The Model 1 (Figure  1a ) was selected for further evaluation and validation as it shows the best quality factors in the set of models developed (Table  1 ). Even though, in general terms, the selected 3D structure was assessed as a good quality model, some regions are yet poorly modeled and the residues involved can be easily identified looking at the Local Quality Plot in Figure  2. According to this plot, a majority of the residues in the structure are in good agreement with the estimated native structure, apart from the residues belonging to the linker region (a disorganized coil region of approximately 75 residues long).
Homology Modeling of hP-gp The crystal structure of mP-gp (PDB ID: 4M1M), which has an 87% sequence identity to the hPgp, was selected as the most suitable template for developing the homology models of the 3D structure of hP-gp. However, the models built with Discovery Studio Client/Modeler 9. 12  [20, 21]  and I-TASSER  [22] [23] [24]  tools were based on the alignment utilizing more than one template, among these the crystal structure of Caenorhabditis elegans P-gp (PDB ID: 4F4C), included in the model generated with the Discovery Studio 4. 1 Client/Modeler tool 9. 12  [20, 21]. The alignments between the hP-gp sequence and the templates used are provided in the Supplementary materials, Figure  S1. Several models were built with each of the tools and one model per tool was selected for further evaluation and validation.
I-TASSER Five models were generated by the I-TASSER tool using as template the crystal structures of mPgp (PDB IDs: 4M1M, 5KO2, 5KOY, 3G61, 3G5U). The resulting models were scored using C-score, TM-score  [27] , and root mean square deviation (RMSD) scoring functions. The C-scores for the five models generated are reported in Table  2 , while the TM-score and RMSD are reported just for the Model 1. The correlation between C-score and TM-score is weak for lower rank models; thus, they are not calculated. However, the C-score, Number of decoys and Cluster density for all models are reported for a reference. The Model 1, shown in Figure  1b , was selected for further evaluation and validation as it presents the best quality factors in the set of models generated; a positive C-Score value of 0.
I-TASSER Five models were generated by the I-TASSER tool using as template the crystal structures of mP-gp (PDB IDs: 4M1M, 5KO2, 5KOY, 3G61, 3G5U). The resulting models were scored using C-score, TM-score  [27] , and root mean square deviation (RMSD) scoring functions. The C-scores for the five models generated are reported in Table  2 , while the TM-score and RMSD are reported just for the Model 1. The correlation between C-score and TM-score is weak for lower rank models; thus, they are not calculated. However, the C-score, Number of decoys and Cluster density for all models are reported for a reference. The Model 1, shown in Figure  1b , was selected for further evaluation and validation as it presents the best quality factors in the set of models generated; a positive C-Score value of 0.
Discovery Studio/Modeler A total of 20 initial models were generated by Discovery Studio 4. 1 Client/Modeler 9. 12 using as template the crystal structures of mP-gp (PDB IDs: 6FN4, 4M1M, 5KPI, 4M2S, 5KO2, 3G60) and the crystal structure of C. elegans P-gp (PDB ID: 4F4C). The resulting models were scored using the the Discrete Optimized Protein Energy (DOPE) score and PDF Total Energy scoring functions (Table  3 ). As all the models have similar Probability Density Function (PDF) Total Energy, the DOPE score was then used for selecting the best-ranked model. The Model 16 (Figure  1c ) was selected for further evaluation via the Verify Protein protocol in Discovery Studio 4. 1 Client. According to the scores presented in Table  4 , the model 16 is of good quality, as the verify score obtained is higher than the Verify Expected Low Score value and very close to the Verify Expected High Score value.
Models Validation The quality of the models was assessed in order to verify that they are reliable and suitable for carrying out further molecular docking simulations. The validation of the selected models from each modeling tool was done utilizing available online quality structure assessment tools, such as PROCHECK  [28] , Verify 3D  [29] , ERRAT  [30] , and PROVE  [31]. The stereochemical properties of the hP-gp models were evaluated using the PROCHECK software via the Ramachandran Plot and the results were compared with those obtained from the crystal structure of mP-gp (PDB ID: 4M1M). The resulting Ramachandran Plots of the predicted hPgp models are shown in Figure  5  and the related statistics are reported in Table  5. The plots revealed that the phi (φ) and psi (ψ) backbone dihedral angles in the hP-gp models are reasonably accurate as a majority of the residues are inside the allowed regions; less than 1.
0% of the residues are in the disallowed regions in all the models evaluated. The residues in the disallowed region are located mainly in the NBDs of the protein, apart from one residue (Y710) in the I-TASSER model, which is located in the TM domain helix; however, none of the residues of the binding pocket are in the disallowed regions. Considering the φ/ψ distribution of the amino acids in the modeled hPgp structures, the results are consistent with those obtained from the experimentally available mP-gp structure (PDB ID: 4M1M) reported in Table  5. In summary, the stereochemical quality of the models is satisfactory, with similarities to that of the template used.
Models Validation The quality of the models was assessed in order to verify that they are reliable and suitable for carrying out further molecular docking simulations. The validation of the selected models from each modeling tool was done utilizing available online quality structure assessment tools, such as PROCHECK  [28] , Verify 3D  [29] , ERRAT  [30] , and PROVE  [31]. The stereochemical properties of the hP-gp models were evaluated using the PROCHECK software via the Ramachandran Plot and the results were compared with those obtained from the crystal structure of mP-gp (PDB ID: 4M1M). The resulting Ramachandran Plots of the predicted hP-gp models are shown in Figure  5  and the related statistics are reported in Table  5. The plots revealed that the phi (ϕ) and psi (ψ) backbone dihedral angles in the hP-gp models are reasonably accurate as a majority of the residues are inside the allowed regions; less than 1.
0% of the residues are in the disallowed regions in all the models evaluated. The residues in the disallowed region are located mainly in the NBDs of the protein, apart from one residue (Y710) in the I-TASSER model, which is located in the TM domain helix; however, none of the residues of the binding pocket are in the disallowed regions. Considering the ϕ/ψ distribution of the amino acids in the modeled hP-gp structures, the results are consistent with those obtained from the experimentally available mP-gp structure (PDB ID: 4M1M) reported in Table  5. In summary, the stereochemical quality of the models is satisfactory, with similarities to that of the template used. In order to assess the overall folding of the models, a structural comparison of the hP-gp models developed was done in Discovery Studio 4.