dataesr/scientific-paragraphs-categorization huggingface dataset:
HCSC: Spanish Ministry of Health   PI15/00059 ,  PI16/01292 , and CB-161200301  CIBERONC  from  ISCIII (Spain) , partially supported by  European Regional Development FEDER funds. HEBCS: Helsinki University Hospital Research Fund ,  Academy of Finland  ( 266528 ), the  Finnish Cancer Society  and the  Sigrid Juselius Foundation. HEBON: the  Dutch Cancer Society  grants  NKI1998-1854 ,  NKI2004-3088 ,  NKI2007-3756 , the  Netherlands Organization of Scientific Research  grant  NWO 91109024 , the  Pink Ribbon  grants  110005  and  2014-187. WO76 , the  BBMRI  grant  NWO 184. 021. 007/CP46  and the Transcan grant  JTC 2012 Cancer 12-054. HUNBOCS: Hungarian Research Grants  KTIA-OTKA CK-80745  and  NKFI_OTKA K-112228. HVH ( University Hospital Vall d'Hebron ) This work was supported by  Spanish Instituto de Salud Carlos III (ISCIII)  funding, an initiative of the  Spanish Ministry of Economy  and Innovation partially supported by  European Regional Development FEDER Funds :  FIS PI12/02585  and  PI15/00355.
IOVHBOCS:  Ministero della Salute  and " 5 × 1000" Istituto Oncologico Veneto  grant. IPOBCS:  Liga Portuguesa Contra o Cancro. kConFab: The  National Breast Cancer Foundation , and previously by the  National Health and Medical Research Council (NHMRC) , the  Queensland Cancer Fund , the  Cancer Councils of New South Wales, Victoria , Tasmania and  South Australia , and the  Cancer Foundation of Western Australia. MAYO:  NIH  grants  CA116167 ,  CA192393  and  CA176785 , an  NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 ), and a grant from the  Breast Cancer Research Foundation. MCGILL:  Jewish General Hospital Weekend to End Breast Cancer, Quebec Ministry of Economic Development, Innovation and Export Trade. Marc Tischkowitz  is supported by the funded by the  European Union   Seventh Framework Program  ( 2007Y2013 )/ European Research Council  (Grant No.
The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors.
nlmixr is a freely available, open-source package for R  1  that implements a number of parameter estimation algorithms in the field of nonlinear mixed effect modeling; a stable version is freely available on the Comprehensive R Archive Network (CRAN),  2  and the development version is available from GitHub. 3  The package is primarily intended for the pharmacometric community and allows pharmacometric models to be implemented through the application of ordinary differential equations (ODEs). The pharmacometric accent manifests itself through the availability of fully flexible dosing definitions in terms of the type (e. g. , bolus doses or infusions), timing, and number of doses and their amounts, which can vary between and within individuals. nlmixr builds on RxODE,  4, 5  a fast and efficient R package for simulating nonlinear mixed effect models using ODEs with rapid execution as a result of compilation in C.
Comprehensive online documentation  6  and an nlmixr tutorial  7  are available. nlmixr implements a number of parameter estimation algorithms that can be accessed through a common model definition language. These algorithms currently comprise nlme,  8  implemented as a well-established package in R; stochastic approximation expectation-maximization (SAEM);  9  and first-order conditional estimation with interaction (FOCEI). 10 urther advances such as implementation of the Gaussian Quadrature algorithm in the common model definition language are in active development. Additional R packages have been developed as add-ons to nlmixr to assess general goodness of fit (xpose. nlmixr  11 ) and to provide a project management interface (shinyMixR  12  ), with other packages actively developing support for nlmixr (e.
Single-dose data for 10,000 subjects were simulated. The population was split into four equal-sized groups that received doses of 10, 30, 60, or 120 mg, and four time points were randomly sampled within the 24 hours after the dose. A total of 500 data sets containing 120 subjects each were resampled from these 10,000 subjects and stratified by dose so that 30 subjects in each resampled data set received one of the four doses using the bootstrap tool of Perl-speaks-NONMEM (PsN). 16 Each resampled data set was then analyzed using the same structural model that was used for simulation using Monolix's SAEM algorithm, NONMEM's FOCEI algorithm, and nlmixr's SAEM and FOCEI algorithms to allow a paired comparison for each simulated data set of the analysis outcomes. Richly sampled data sets fit using different models Richly sampled profiles were simulated for four different dose levels of 30 subjects each, for a range of test models with the following: • One-compartmental or two-compartmental disposition • Oral (first-order absorption), intravenous bolus, or intravenous infusion administration • Linear or Michaelis-Menten clearance.
Software and hardware Monolix (version 2019R1  14  ) using the SAEM algorithm was used as a comparator for the SAEM estimation algorithm implemented in nlmixr. The SAEM algorithm in Monolix was applied using the default settings. NONMEM (version 7. 4. 3  9  ) using the FOCEI algorithm was used as a comparator for the FOCEI estimation algorithm implemented in nlmixr. The ADVAN13 module was used for estimation with settings of TOL = 6, NSIG = 2, and SIGL = 6 and using the NOABORT and NOOBT options to prevent premature termination of the estimation procedure and to prevent NONMEM's default boundary test for between-subject random effects, respectively. ADVAN13 was used because it implements the Livermore solver for ODEs  18  that is also used in nlmixr. If convergence was not obtained, TOL was increased to 7 or 8.
NONMEM was supplemented with PsN, version 4. 6. 0  15  to sample and analyze the 500 sparse-sample data sets with the bootstrap tool. R software (64 bit, version 3. 6. 1  1  ) and the RxODE package (version 0. 9. 1-3 of August 6, 2019) and nlmixr package (version 1. 1. 1-1 of August 23, 2019 2 ) obtained from CRAN were used for parameter estimation. Both the FOCEI algorithm and the SAEM algorithm in nlmixr were applied using the default settings. The 500 sparse sample analyses were run side by side using the doParallel R package; the required code is available in the Supplemental Materials. All models were implemented using differential equations, and the parameters were estimated on the logarithmic scale. In all cases, IIV was implemented using so-called mu-referencing, in which IIV is expressed linearly with the population typical parameters, and residual error was implemented using the constant coefficient of variation (proportional) residual error model.
nlmixr/FOCEI vs. NONMEM/FOCEI The correspondence between estimates obtained using nlmixr/FOCEI and NONMEM/FOCEI is provided in Figure  2. The results indicate that the estimates of population-typical parameters, IIVs, and SEs for the one-compartment sparse-data model are very highly correlated, with the notable exception of IIV for ka (Figure  2 , middle row, right panel). The data were simulated using an IIV of 30%, and NONMEM/FOCEI resulted in an IIV estimate for ka near zero in 7. 6% of the runs, whereas for nlmixr/FOCEI none of the runs estimated an IIV near zero. In practice, near-zero estimates for IIVs are often an indication of insufficient capacity of the software to estimate this parameter. The slightly lower correlation coefficient for SE of ka is attributable to a number of outlying SE estimates for NONMEM/FOCEI.
Historically, the radial alignment between reflectometer and radiometer has been a challenge due to the requirement that alignment is achieved within a radial correlation length (< 5 -10 mm). This challenge is significantly alleviated by using the CECE channel comb arrangement and the maximal coherence between reflectometer and radiometer can be unambiguously captured. Measurements of these quantities have been made in an AUG L-mode plasma, at the same radial location and have provided simultaneous quantitative constraints on realistic gyrokinetic simulations [Physics of Plasmas 25, 055903 (2018)] using the gyrokinetic code GENE. Here we present diagnostic detail for this study.
Acknowledgements This work is supported by the  US DOE  under grants  DE-SC0006419  and  DE-SC0017381 , and was performed in the framework of the  Helmholtz Virtual Institute on Plasma Dynamical Processes and Turbulence Studies using Advanced Microwave Diagnostics. It has also been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. The gyrokinetic simulations have been performed at the MARCONI supercomputer at CINECA and the MPCDF supercomputing facilities.
We measured pH in the cerebellum to assess the integrity of the tissue RNA as described previously  23. At least one informant per case agreed to an interview for the purpose of a psychological autopsy, which was performed according to our previously reported method  24  ; based on the SCID a DSM-IV consensus diagnostic determination was made in suicide decedents by an experienced psychiatrist (J. J. M. ). Psychological autopsy results for Axis I and Axis II disorder diagnoses have been validated  24. We used the Suicide History Form (Columbia University, New York, NY) to record past suicidal acts and a checklist for demographic and developmental data and other clinical details related to medical and psychiatric past treatment. A review of hospital records was done by an experienced psychiatrist (J.
Statistical analysis and design of the analysis Statistical analyses on coded samples and figures were generated using the "R/Bioconductor" statistical open source software (version 3. 4. 3)  28. Differences between groups were analyzed using the non-parametric Wilcoxon rank-sum test. p-values < 0. 05 were considered as statistically significant. The calculation of the relative PDE8A isoform proportion takes into account all possible deviations between two sets of experimental conditions and is defined through the following formula: ΔDeviation PDE8A ¼ %Editing value condition2 À%Editing value condition1 The following two criteria were considered for further detailed analysis: variations of the median value of relative proportion of RNA editing above 20% and p-values < 0. 05 using the one-sample Wilcoxon rank-sum test, where null hypothesis H0: median variation of % editing value = 0.
Subjects A paired case-control design was used to control demographic and assay variance. Potential confounding factors age, race of subjects, post-mortem interval, extracted tissue weight or tissue pH were not statistically different between control and MDD-suicide groups (Table  1 ). In order to avoid potential sex differences, only male subjects were studied. RIN values (a metric commonly used to assess RNA quality) were all >7, suggesting very limited RNA degradation, if any, during the brain collection, storage and tissue isolation procedure. Raw data of the complete PDE8A RNA-editing profiles are shown in Supplementary Tables  S1  and S2. Characterization of editing site combinations in intron 9 of the PDE8A pre-mRNA in human brain Prediction of the secondary structure of the 175 bp sequence used in this study was performe using the Vienna RNA websuite  30  (Fig.
5% (w/v) agarose gels. Triplicate PCR products were combined, and each of these was purified using SPRI beads (Agencourt R AMPure R XP, Beckman Coulter, CA, United States). The DNA concentration in the purified samples was determined as described above. Samples were diluted to identical concentrations of 2 ng/µl prior to pooling the diluted PCR products together in equal volumes (10 µl) in one composite sample (including positive and negative controls). The composite samples were paired-end sequenced at the Vrije Universiteit Amsterdam Medical Center (Amsterdam, Netherlands) on a MiSeq Desktop Sequencer with a 600-cycle MiSeq Reagent Kit v3 (Illumina) according to manufacturer's instructions. High-throughput sequencing raw data were demultiplexed using bcl2fastq software version 1. 8. 4 (Illumina) and primers were trimmed using Cutadapt  (Martin, 2011).
pl (Pylro et al. , 2014). UPARSE was used to dereplicate, filter chimeras, discard OTUs (operational taxonomic units) detected less than two times and OTU clustering at 97% similarity  (Edgar, 2010 (Edgar, , 2013)). The OTU taxonomy was assigned using the UCLUST algorithm  (Edgar, 2010)  on QIIME  (Caporaso et al. , 2010)  using SILVA compatible taxonomy mapping files (Silva database release 128)  (Quast et al. , 2013; Yilmaz et al. , 2014)  and aligned using align_seqs. py in QIIME  (Caporaso et al. , 2009). Taxonomy was manually curated and refined up to genus level based on 97% similarity of reference sequences. The reference tree was calculated using the make_phylogeny script in QIIME  (Price et al. , 2009). We generated a BIOM file using make_otu_table. py on QIIME  (Caporaso et al. , 2010).
Prior to further analysis we produced an OTU table and a taxonomy table using BIOM scripts  (McDonald et al. , 2012). The OTUs detected in negative controls and procedural blanks were manually removed from the dataset. After quality filtering, taxonomy assignment (97% identity), subtraction of contaminating sequences and removal of singletons, the dataset comprised 1,817,287 reads which were attributed to a total of 4,270 OTUs. Next, a filtering step was performed as described by  Bokulich et al. (2013) , where OTUs representing fewer than 0. 005% of the total reads were removed from the dataset. This trimmed dataset resulted in 1,748,123 reads and a total of 1,315 OTUs (Supplementary Table  S1 ).
Mineralogical Analysis Semi-quantitative X-ray diffraction (XRD) analyses were conducted to determine the composition and relative abundance of the crystalline phases in the bulk sediments. The samples were dried at 70 • C for 48 h, ground in a bead-mill to a particle size <100 µm and measured using a Bruker D8 Advance diffractometer equipped with an X-ray Cu source at the Centre de Diffractométrie of the University Lyon, France. Disoriented measurements were made over a 2Theta range of 3 • -70 •. Determining the presence and identification of clays could not be done accurately with the used approach, so these will be largely left out of the analysis. XRD patterns were analyzed using the Bruker DIFFRAC. SUITE EVA software. Mineralogical fits were performed by comparing D-spacing values to those of minerals listed in the International Center for Diffraction Data database and the Crystallography Open Database  (Kabekkodu et al.
Carbon and Nitrogen Measurements Samples were powdered using heat sterilized mortar and pestle and subsequently dried at 60 • C for 24 h. Analysis was carried out using a FlashEA 1112 (Thermo Scientific), with EAGER300 operating software and a thermo conductive detector. For carbon and nitrogen measurements, approximately 30 mg of each sample was weighed in silver holder cups. An empty holder cup was used as a blank. Two to three milligram of EDTA was used as standard (7. 522% N, 32. 237% C, 4. 836% H, 34. 386% O). For the removal of inorganic carbon, cups containing the samples were placed upright in a desiccator containing 12M HCl. Samples were exposed overnight to the vapors. Complete removal of inorganic carbon was tested by adding 10 µl of 10% HCl to a cup in order to determine if vapors were produced (which would indicate presence of more inorganic carbon).
Alpha diversity measurements were performed using the plot_ richness function from the Phyloseq package in R (Figure  4A ). In order to equalize library sizes for downstream analysis, a rarefaction step with a sampling depth of 2350 was performed (see Figure  5  for the rarefaction curves). One subsurface sample from Lake Lefroy (L2), which had only 820 sequence reads, was excluded and hence 22 of the 23 investigated samples are included in the rarefied dataset. Alpha diversity of the samples decreased after rarefaction (Figure  4B ), while only a very small fraction of total OTU's (operational taxonomic units) was removed from the dataset (Supplementary Table  S1 ). This is explained by the observed loss of table density after rarefaction, indicating that a larger fraction of the OTUs is represented in lower abundances and, as a result, is represented in fewer samples (but still included within the dataset).
The bulk of the sequence reads (76%) originates from four dominant phyla, namely: Euryarchaeota (23%), Proteobacteria (22%), Bacteroidetes (22%), and Gemmatimonadetes (9%). Another 11% of the sequences are not related to known phyla while the remainder of reads (13%) is shared between 27 phyla (Figure  6A ). The extremely halophilic Archaeal family Halobacteriaceae (phylum Euryarchaeota) dominates the dataset at family level comprising approximately 23% of the reads and representing over 30 known genera. The archaeal community profile in each sediment sample is specified in Figure  6B. Most of the samples contain similar (in terms of species represented) profiles for the family of Halobacteriaceae. However, the relative contribution of Halobacteriaceae differs significantly between samples from different lake environments.
Samples from Lake Dundas and Lake Lefroy form a more separated and somewhat denser cluster in the unweighted PCoA plot (Figure  7A ). This indicates that samples from these two environments have lower intra-lake variation and are also more similar to each other in terms of species that are represented when compared to samples from Lake Cowan and Lake Yellowdine. The spread observed in samples from the other two lake environments is much higher and reveals strong intra-and interlake variation. However, using unweighted metrics only explains a small percentage of the variation that is present within the dataset, as is shown by the low variation percentages at the primary and secondary axis. The weighted PCoA plot (Figure  7B ) shows a different distribution in which much more overlap is found between samples from the different lake environments.
Likely, certain conditions present specifically in Lake Cowan allow for carbonate precipitation while the other lakes lack these conditions. The high values for calcium for the samples D4 and D5 from Lake Dundas correlate with the high values of gypsum (CaSO 4 ) determined for these samples by XRD. Adonis tests showed significant p-values (<0. 05) for aluminum, iron, sodium, potassium, and titanium when using the unweighted unifrac analysis, suggesting that these elements may affect the species variation observed within the microbial dataset. Using weighted unifrac analysis, aluminum, iron and potassium gave significant p-values, showing that these elements also contribute to the relative species abundances observed within the sediments. However, the effect sizes of the Adonis tests were small (<0.
Prominent detection of halite in the sediments by our XRD analysis indicates salt-rich environments, which is further confirmed by the high abundances of extremely halophilic phylotypes within the sequence dataset. Since the focus of this study was on the characteristics of the sediments, no measurements were done on the lake waters. However, previous studies have reported salinity values for the waters of several of the environments investigated here. It should be noted though that due to strong fluctuations in the volumes of the water bodies these values are not per se representative for the specific sample locations and time points in our study. Bowen and Benison (2009)  report salinity values (total dissolved solids) for Lake Lefroy (5 g/L), Lake Dundas (100 g/L), and Lake Cowan (26-100 g/L).
The most dominant OTUs in the dataset are related to (extremely) halophilic and halotolerant microorganisms, of which members of the Halobacteriaceae family are the most prominent. This family comprises 47 known genera with a total of 165 species of which many have a surprising metabolic versatility and unusual characteristics that may assist in their survival or even growth on Mars  (Oren, 2014). Halobacteriaceae also developed a specialized strategy to cope with high salt levels, known as the 'salt-in' strategy  (Lanyi, 1974). To counteract the osmotic pressure, cells accumulate high concentrations of K + and Cl -in their interior. The evolutionary development of this strategy aligns with considerable adaptations in the protein and enzyme machinery of these species. Besides Halobacteriaceae, only few other known species are known to use this strategy, which include the extremely halophilic bacteria Salinibacter and members of the order Halanaerobiales  (Oren et al.
, 2006). Although OTUs related to such microorganisms are not clearly identified in the dataset, some 16S sequences are novel to these lake environments and thus perhaps from species with specialized metabolic properties. Gypsum was mainly present in the sediments of Lake Dundas and Lake Lefroy, although the distribution was very patchy. Its contributions were generally low but were very high in two samples from Lake Dundas (D4 and D5). Notably, these two samples had a higher pH and element analysis revealed high abundances of calcium. Both elevated pH and elevated concentration of calcium would facilitate precipitation of gypsum from the lake waters. The variation in evaporite mineralogy occurs owing to the solubility of minerals in the fluids. The solubility increases in the order from carbonate to gypsum to halite.
Furthermore, comparing the major element analysis to the XRD results shows that the two datasets are generally in agreement. Silicates, iron and aluminum are the dominant elements reflecting the dominant mineral phases. Magnesium, calcium, and sodium also contribute significantly to the major elements as would be expected based on the mineralogy of the samples. Trace element analysis showed the presence of a large variety of elements with some in very high concentrations  (i. e. , strontium, chromium, barium, nickel, zirconium). Although we saw some variations between the different lakes in the concentrations of certain trace elements, these are relatively minor. This is likely because the regional ground-waters dissolve and transport such elements throughout the region where they are locally deposited in the sediments  (Mann, 1983).
Patients We conducted a retrospective analysis of our prospective database using information from women with DE without bowel involvement who underwent surgery from January 2006 to December 2014 at Tenon University Hospital in Paris, France. For each woman, the following parameters were recorded: age at surgery, body mass index (BMI), smoking status, presence of endometrioma and adenomyosis on magnetic resonance imaging (MRI), fertility before surgery, symptoms, previous surgery for endometriosis, type of surgery, surgical route (laparoscopy or laparotomy), Enzian and ASRM scores calculated during surgery. All patients gave their consent to participate in the study. The study was approved by the Ethics Committee (CEROG) of the College National des Gynécologues et Obstétriciens Français (CNGOF) (reference number: CEROG 2012-GYN-10-03).