dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection and preparation All predictive models were trained from the MIMIC-III database (version: January 2020). This data warehouse is an open-access database that collected anonymized care data in 46,520 patients from 19 critical care units of the Beth Israel Deaconess Medical Center in Boston, USA, between 2001 and 2012. Only the first ICU stay of each patient in the MIMIC-III database was used. Patients aged over 100 years were excluded, as well as patients under 15 in order not to mix pediatric patients with adult patients and to keep a homogeneous population, as their conditions, risk factors and vital prognosis highly differ. Patients with missing information on vital status at hospital discharge and those with an ICU length of stay lower than 48 h were also excluded, as the method requires the collection of data over 36 h to predict mortality after a 12-hour gap following the end of this observation period.
Nineteen predictors used in these scores were selected (Table  1 ), including medical history, vital signs, and blood tests, as well as administrative features such as previous hospitalization wards, which can provide information on the most common complications. To assess the relevance of using longitudinal data, predictive models derived solely from admission data were built from the same dataset (thus only addressing patients alive and still in the ICU 48 h after admission to predict death anytime during their stays).
This third approach considered a large extent of available predictors without limiting the sample size, as would be required by the complete-case analysis. A new set of 70 predictors was defined according to their availability among patients, which allowed us to keep patients for whom data were available for at least one of these 70 predictors. These predictors were selected solely based on their availability, regardless of their expected clinical relevance or collinearity (e. g. , several predictors could describe the same measure performed by different devices, Appendix Table  A. 1). Continuous predictors were log-transformed when required to improve normality. Ten imputed datasets were generated using randomeffect linear and logistic regressions for quantitative and binary variables, respectively, and polytomous regression for other categorical variables.
Neural network architectures and statistical analyses Four neural network architectures were set up to predict mortality in ICU inpatients: a fully connected neural network (FCN), a convolutional neural network (CNN)  [23, 24] , a bidirectional long short-term memory (LSTM) recurrent neural network  [20]  and a CNN-LSTM network  [17] , which concatenated the information from the two previous networks. A fine-tuning of the hyperparameters was performed for each of the neural networks. Trained on two imputed datasets from the Imputed-19 and the Imputed-70 cohorts, a grid search was performed based on the AUC scores averaged over 5-fold cross-validation to determine the optimal model architecture for both data formats. Different hyperparameter combinations were iteratively tested by varying the number of layers and neurons per layer for the FCN, the number of neurons for the LSTM, and the number of layers and filters, the kernel size and the stride length for the CNN.
The alpha and lambda parameters (relative weight of ridge and lasso penalties and shrinkage parameter, respectively) were optimized using cross-validation over a grid search. The best performances were obtained for an alpha value of 0. 001 (i. e. , almost considering coefficients from only ridge regression) and a lambda value of 0. 5 for all cohorts. Performances of these predictive models were assessed by evaluating their discrimination and calibration abilities, with the use of multiple 5-fold cross-validation: patients were split into 5 subsets, 4 of which were used for model training and the 5th for performance evaluation. This procedure was repeated so that all 5 subsets were used for evaluation. This 5-fold cross-validation was carried out 10 times with different partitions of the dataset.
The models' ability to discriminate patients at higher risk of death was evaluated using the average area under the ROC curve (AUC). Pairwise AUC comparisons were performed between models using linear mixed models with a random intercept for the cross-validation dataset partition, a method derived from the Hanley and McNeil test to account for the correlation between cross-validation datasets  [27]. Fixed effects were tested directly for the complete-case analyses and after pooling with Rubin's rule for imputed datasets. Calibration was graphically assessed with calibration plots comparing observed and predicted probabilities after rescaling predictions according to the imbalance weights used for model training. Neural networks were built using Keras version 2. 3. 1 and the application programming interface of Tensor-Flow version 2.
Selection of patients After exclusion of patients aged < 15 or > 100 years, those with missing data on vital status and those staying < 48 h in the ICU, 17,373 patients with unique admission remained in the dataset. According to missing data management, three cohorts were defined. Patients with no missing data in the 19 initially selected predictors, at least for the first time slot, defined the "Complete-case" cohort (n = 4276 patients, 787 deaths). The "Imputed-19" cohort included patients with data available for at least one of the 19 main predictors (n = 10,089 patients, 1499 deaths), whereas the "Imputed-70" cohort did the same with the extended selection of 70 predictors (n = 16,532 patients, 2395 deaths). Models were derived from these two latter cohorts after multiple imputation. Figure  2  summarizes this selection process.
The CNN showed the best performance (AUC = 0. 783 ± 0. 003), although, as for the Imputed-19 cohort, no significant difference was found among the four best methods. Figure  3  summarizes the discrimination and calibration performances of the compared modeling methods for all cohorts, except for the FCN, which demonstrated poor performance in all analyses. ROC curves and calibration plots represent the average estimates over the 10 repeated 5-fold cross-validation and over the imputed datasets for the Imputed-19 and Imputed-70 cohorts. All models globally present a fairly satisfactory calibration.
The thick gray line shows values expected for a perfect calibration, with observed probabilities equal to predicted probabilities. All estimates are averaged over the 10 repeated 5-fold cross-validation datasets and over the imputed datasets for the Imputed-19 and Imputed-70 cohorts outperform more conventional penalized regressions in our study. An explanation might be that available data did not take full advantage of the time slot format  [37] : although some predictors, such as vital signs or blood tests, were frequently updated, medical conditions likely to dramatically impact prognosis, such as the occurrence of shock or organ failure, were collected retrospectively but not on time to be used as a predictor. This limitation is due to the nature of the MIMIC-III database and may be present in other health care data warehouses, yet we assume that a timely collection of medical diagnoses and relevant symptoms may be insightful to enhance predictive performances.
16S rDNA metabarcoding analyses of microbial communities Bacterial and archaeal 16S rRNA gene V4 hypervariable regions were amplified by PCR using the 515F/806R universal primer set  (Caporaso et al. , 2011) , with a barcode on the forward primer, as previously described by  Dowd et al. (2008) , and were sequenced by the MiSeq Illumina platform of the Molecular Research Laboratory (TX, USA). Sequence data were processed using the MR DNA analysis pipeline (MR DNA, Shallowater, TX, USA). In summary, sequences were joined (overlapping pairs) and grouped by samples following the barcodes before removing them. Short sequences (<150 bp) and sequences with ambiguous base calls were removed. Remaining sequences were denoised, operational taxonomic units (OTUs) were generated, and chimeras were checked using UCHIME and removed  (Edgar et al.
, 2011). OTUs were clustered at 97% of similarity with USEARCH  (Edgar, 2010)  followed by removal of singleton sequences. Finally, OTUs were taxonomically classified using BLASTn against NCBI non-redundant (NR) reference database, and the top hit was taken as a taxonomic classification. The 16S rRNA gene sequences of the dominant OTUs have been deposited in the Genbank database under the accession numbers OQ551354-OQ551410. Raw sequence data were submitted to the NCBI SRA under BioProject PRJNA974011, BioSamples SAMN35158175-SAMN35158185.
Aspartate aminotransferase (AST) (p = 0. 016084) and total cholesterol (TC) (p = 0. 014987) levels were higher among individuals with liver cirrhosis versus the no fibrosis group. As well, the prevalence of portal hypertension (HT) and thrombocytopenia (platelet count below 150,000) were much higher in the cirrhotic group. DNA samples from all subjects included in the study were successfully analyzed, and high-quality genotyping data was generated for all eleven SNPs. The distribution of genotypes did not follow the Hardy-Weinberg equilibrium (HWE) for the liver fibrosis, cirrhosis, and no fibrosis group except for rs225014, rs2016520, rs4794067 in cirrhotic patients, and rs2016520 in no fibrosis group that was consistent with HWE (p > 0. 5). Surprisingly in the HCC group, only rs12031994 (AKT3) displayed deviation from HWE.
Materials and methods Patients. This study included 284 patients with confirmed CHB infection (HBsAg positive for more than 6 months) from the ANRS CO22 HEPATHER cohort (ClinicalTrials. gov registry number: NCT01953458). All the subjects were of Caucasian ethnicity and had no other concomitant liver etiologies (viral coinfection, autoimmune or metabolic). Patients were excluded if they were currently treated or had undergone antiviral treatment within 6 months before the initiation of the study. Serum samples were collected before liver fibrosis assessment, and underwent the standard procedure in the local clinical center laboratory, including hepatitis serologic variables (HBsAg, HBsAb, HBeAg, HBeAb, HBcAb, HBV DNA levels). Fibrosis scores were assessed by non-invasive transient elastography by using FibroScan (Echosens, Paris, France), and the METAVIR scoring system  62  was used for patient classification.
Acknowledgements We thank participants and participating clinicians of the ANRS CO22 HEPATHER cohort at each study site. We thank  F. Carrat ,  S. Pol , and  H. Fontaine , the coordinators of the cohort;  C. Dorival , the project manager; and the  ANRS   CO22  HEPATHER scientific committee. The  ANRS   CO22  HEPATHER cohort is sponsored and funded by  Inserm-ANRS  ( France REcherche Nord&sud Sida-vih Hepatites );  ANR  ( Agence Nationale de la Recherche) ;  DGS  ( Direction Générale de la Santé ); and  MSD ,  Janssen ,  Gilead ,  AbbVie ,  BMS  and  Roche.
Collection and Optimization of Bioactive Compounds For the current study, a total of 1000 biologically important active phytochemicals were collected from the literature and different databases. The IMPPAT (Indian medicinal plants, phytochemistry and therapeutics) database [29] (https://cb. imsc. res. in/imppat/; accessed on 2 December 2022), and TCMSP (the traditional Chinese medicine systems pharmacology) database  [30]  (https://tcmsp-e. com/tcmsp. php; accessed on 2 December 2022) were used to search the active phytochemicals with reported anticancer activity. PubChem database  [31]  (https://pubchem. ncbi. nlm. nih. gov/; accessed on 4 December 2022) was used to retrieve the chemical structures of these plant phytochemicals in. sdf format. The phytochemicals' energy was minimized before the molecular docking study.
Retrieval and Preparation of Receptor Proteins The epidermal growth factor receptor (EGFR) and caspase-9 were selected as receptor proteins and used for molecular docking studies. The three-dimensional (3D) structures of receptor proteins (i. e. , EGFR with PDB ID: 4LQM and caspase-9 with PDB ID: 2AR9) were downloaded from Protein Data Bank in. pdb format (https://www. rcsb. org/; accessed on 8 December 2022). The binding pockets of the receptor proteins were predicted using Molecular Operating Environment software  [32]. To further prepare the receptor proteins for molecular docking, already bound ligand(s) were removed (if any), the water molecules were deleted, hydrogen atoms were added, and 3D protonation and energy minimization was performed to further prepare the receptor proteins for molecular docking.
Molecular Docking The molecular docking study was performed to obtain compatible leading drug candidates for HCC treatment. PyRx software  [33]  was used for molecular docking of ligands to the active amino acids of the binding pocket of the receptor proteins. The Discovery Studio  [34]  software was used for the visualization of interactions between the receptor proteins and key active compounds. After docking, the conformations with best docking scores and RMSD (root-mean-square deviation) values were selected for further studies.
Introduction: In the phase 3 KEYNOTE-604 study (NCT03066778), pembrolizumab plus etoposide and platinum chemotherapy (EP) significantly (p ¼ 0. 0023) improved progression-free survival versus placebo plus EP in previously untreated extensive-stage SCLC (ES-SCLC). We present health-related quality of life (HRQoL) results from KEYNOTE-604.
3, 4  The phase 3, randomized, double-blind KEYNOTE-604 study (NCT03066778) evaluated pembrolizumab plus etoposide and platinum chemotherapy (EP) versus placebo plus EP in patients with previously untreated ES-SCLC. 5  In KEYNOTE-604, progression-free survival (PFS) was significantly improved with pembrolizumab plus EP (median [95% confidence interval (CI)]: 4. 5 [4. 3-5. 4] mo) versus placebo plus EP (4. 3 [4. 2-4. 4] mo; hazard ratio [HR] ¼ 0. 75 [95% CI: 0. 61-0. 91], p ¼ 0. 0023). 5  Estimated 12-month PFS rates were 13. 6% and 3. 1%, respectively. The median (95% CI) overall survival (OS) with pembrolizumab plus EP was 10. 8 (9. 2-12. 9) months compared with 9. 7 (8. 6-10. 7) months with placebo plus EP (HR [95% CI]: 0. 80 [0. 64-0. 98], p ¼ 0. 0164), which did not meet the threshold for statistical significance for OS.
Statistical Analyses Health-related quality-of-life analyses included all randomized patients who received at least one dose of the study treatment and completed at least one PRO assessment. The completion rate for the PRO instruments was defined as the percentage of patients who completed at least one questionnaire item, divided by the number of randomized patients at each time point. Because the completion rate was anticipated to decrease as patients discontinued treatment, the study also assessed PRO compliance rates, defined as the percentage of patients who completed at least one item, divided by the number of patients who were expected to complete the PRO assessment. For analysis purposes, relative visit days were mapped onto the analysis visits. The mean change in scores for each continuous end point defined was analyzed using a constrained longitudinal data analysis method, as described by Liang and Zeger.
Patients Between May 15, 2017, and July 30, 2018, 453 patients were randomly assigned to pembrolizumab plus EP (n ¼ 228) or placebo plus EP (n ¼ 225). The median time from randomization to data cutoff (December 2, 2019) was 21. 6 (range: 16. 1-30. 6) months. As reported previously, baseline characteristics were similar between the groups. 5  With the exception of four patients in the pembrolizumab plus EP group and three in the placebo plus EP group, all enrolled patients received at least one dose of the assigned study treatment. Of the patients who received at least one dose of the study treatment, the PRO analysis population comprised 439 patients (pembrolizumab þ EP, n ¼ 221; placebo þ EP, n ¼ 218) who completed at least one QLQ-C30 and at least one QLQ-LC13 assessment. Compliance and completion rates with the QLQ-C30 were each !94% in both treatment groups at baseline (Supplementary Table  1 ).
There are, however, some limitations associated with the current PRO analysis. First, in this study, PRO data were collected up through the 30-day safety followup period; therefore, it is not possible to ascertain longer-term changes in HRQoL effects from this analysis. Second, as was expected for a patient population with an aggressive disease such as ES-SCLC, the PRO completion rates decreased over time, limiting data toward the later time points; however, compliance rates remained high throughout the study duration, suggesting that questionnaires were completed by most of the patients at the given time. Finally, all p values noted here are nominal and there were no adjustments for multiplicity. In summary, the addition of pembrolizumab to EP as first-line therapy for patients with previously untreated ES-SCLC was not associated with reductions in GHS/QoL and functioning or disease symptoms.
Data-Sharing Statement Merck Sharp & Dohme LLC, a subsidiary of Merck & Co. , Inc. , Rahway, NJ, USA (Merck Sharp & Dohme), is committed to providing qualified scientific researchers access to anonymized data and clinical study reports from the company's clinical trials for the purpose of conducting legitimate scientific research. Merck Sharp & Dohme is also obligated to protect the rights and privacy of trial participants and, as such, has a procedure in place for evaluating and fulfilling requests for sharing company clinical trial data with qualified external scientific researchers. The Merck Sharp & Dohme data-sharing website (available at: http://engagezone. msd. com/ds_ documentation. php) outlines the process and requirements for submitting a data request. Applications will be promptly assessed for completeness and policy compliance.
Feasible requests will be reviewed by a committee of Merck Sharp & Dohme subject matter experts to assess the scientific validity of the request and the qualifications of the requestors. In line with data privacy legislation, submitters of approved requests must enter into a standard data-sharing agreement with Merck Sharp & Dohme before data access is granted. Data will be made available for request after product approval in the United States and European Union or after product development is discontinued. There are circumstances that may prevent Merck Sharp & Dohme from sharing requested data, including country-or region-specific regulations. If the request is declined, it will be communicated to the investigator. Access to genetic or exploratory biomarker data requires a detailed, hypothesisdriven statistical analysis plan that is collaboratively developed by the requestor and Merck Sharp & Dohme subject matter experts; after approval of the statistical analysis plan and execution of a data-sharing agreement, Merck Sharp & Dohme will either perform the proposed analyses and share the results with the requestor or will construct biomarker covariates and add them to a file with clinical data that is uploaded to an analysis portal so that the requestor can perform the proposed analyses.
Acknowledgments Funding for this research was provided by  Merck Sharp & Dohme LLC , a subsidiary of  Merck & Co. , Inc. , Rahway, NJ, USA. We thank the patients and their families and caregivers for participating in this study, along with all investigators and site personnel. Medical writing assistance was provided by  Christabel Wilson, MS , and  Autumn Kelly, MA , of  ICON plc (Blue Bell, Pennsylvania ).
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Eddy covariance and environmental variable measurements Each eddy covariance system consisted of an enclosed-path CO 2 /H 2 O analyser (LI-7200, LI-COR) to measure CO 2 and H 2 O concentrations, an open-path CH 4 analyser (LI-7700, LI-COR) to measure CH 4 concentrations and a three-dimensional sonic anemometer (WindMaster Pro 3-Axis Anemometer, Gill Instruments) to measure the orthogonal components of wind-speed fluctuations. Eddy covariance sensors were Article mounted at the top of each tower to ensure complete exposure in all directions (Fig. 1 ). The filters of the CO 2 analyser were manually cleaned, either biweekly or if the flow drive increased above 80% (indicating filter clogging). The mirrors of the CH 4 analyser were cleaned automatically either at 05:00 local time every day or if the received-signal-strength indicator dropped below 20%, because CH 4 data become noisy below this threshold.
Furthermore, the upper and lower mirrors of the CH 4 analyser were manually cleaned on a biweekly basis. The raw eddy covariance turbulence data were recorded at 10 Hz using an analyser interface unit (LI-7550, LI-COR) and were stored on a removable flash disk (Industrial Grade USB Flash Disk, APRO). A quantum sensor (LI-190SL-50, LI-COR) was mounted at the top of each tower to measure incoming photosynthetic photon flux density (PPFD). A radiometer (CNR4, Kipp & Zonen) was also mounted at the top of each tower to measure global and net radiation. Vertical profiles of relative humidity and air temperature were measured using air-temperature and humidity probes (HMP155, Vaisala), which were installed inside ventilated radiation shields at five heights from the ground surface, 3, 7, 13, 23 and 40 m for the plantation site, 3, 7, 14, 21 and 40 m for the degraded site and 4, 11, 20, 29 and 48 m for the intact site.
Vertical profiles of the CO 2 concentrations were measured by air sampling at four heights, 3, 12, 22 and 40 m for the plantation site, 3, 14, 21 and 40 m for the degraded site and, 4, 11, 29 and 48 m for the intact site, to calculate the flux-storage  56  term below the measurement height using a closed-path CO 2 analyser (LI-8100, LI-COR). The air-sampling intakes were automatically changed every 90 s and the CO 2 concentration was measured for the last 10 s of every 90-s sampling time at each sampling height and recorded using a data logger (LI-8100, LI-COR); therefore, one rotation of measurements took 6 min in every 30 min. Both the enclosed-path and closed-path CO 2 analysers were calibrated every three months using reference gases with concentrations of 396 and 444 ppm CO 2 in air (certified grade ±1 ppm) and ultrahigh-purity nitrogen as the zero-point gas.
The soil temperature was measured at 0. 15 m below the hollow peat surface using a temperature probe (HydraProbe II, Stevens Water Monitoring Systems) from September 2017 until November 2018 and from October 2016 until June 2020 with three replicates at the intact and plantation sites, respectively. From November 2019 until May 2022, the soil temperatures were measured at the intact site and from November 2019 until May 2021 at the plantation site with two replicates using a temperature probe (AquaCheck, South Africa). The soil temperatures were not measured at the degraded site owing to site logistic issues. All meteorological sensors took measurements every second and were recorded as 1-min averages using a data logger (Model 9210 XLite, Sutron). Each measuring system was powered using five solar panels (65-W solar panel, SunWize), along with eight rechargeable batteries (6 V and 305 Ah, Sun Xtender).