dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the recent approval of bedaquiline and delamanid, patients with gatifloxacin/moxifloxacin MICs. 2 mg/L might be more reliably cured by substituting one or the other of these drugs. Identifying low-level FQ resistance, on the other hand, may become important in the presence of resistance to injectable drugs; this could not be determined from this dataset. The strongest risk factor for a bacteriologically unfavourable outcome with the Bangladesh regimen was high-level FQ resistance, particularly when compounded by initial pyrazinamide resistance. 14 In our study population comprising only patients without baseline resistance to kanamycin, treatment outcome was not associated with resistance to companion drugs for the QRDR mutants, corroborating the impact of high-level gatifloxacin resistance.
Our sample size was too small to identify treatment outcome differences between heterogeneous and homogeneous populations, but their MIC levels were in the same range. Detection limits of different phenotypic and molecular assays and platforms to find these heteroresistant populations remain to be further investigated. The lack of data on levofloxacin is a limitation of this study, as this remains the FQ of choice in many recommended MDR-TB treatment regimens. This study, based on a large dataset, provides an analysis of the relationship between phenotypic and genotypic characteristics of pretreatment isolates and the treatment outcome with a standardized MDR-TB treatment regimen. It suggests using 0. 5 and  gyrA predictors of TB treatment outcome.
Material A Dell computer equipped with E-prime2 software (Psychology Software Tools, Inc. , USA) was used to run the experiment and record initiation times. Liquid-crystal goggles (Plato Translucent Technologies, Toronto, ON, USA) were used to control the subjects vision and a home-made spherical trigger button of 4 cm diameter was connected to the computer and used to collect gesture initiation times. The tools were placed on a board measuring 40 cm by 50 cm.
Materials All compounds were from the Sigma-Aldrich. Mouse anti-IRS-1, rabbit anti-phospho-IRS1 (Ser 307 ), rabbit anti-Akt, rabbit anti-phospho-Akt (Ser 473 ), rabbit anti-glycogen synthase kinase (GSK)-3β, rabbit anti-phospho-GSK-3β (Ser 9 ), rabbit anti-AS160, rabbit anti-phospho-AS160 (Thr 642 ), rabbit anti-NF-κB p65, rabbit and IL-18 (Mm_Il18_1_SG, cat. no. QT00171129). The PCR protocol conditions were as follows: HotStarTaq DNA polymerase activation step at 95°C for 30 s, followed by 40 cycles at 95°C for 5 s and 55°C for 10 s. All samples were run in duplicate. At least two nontemplate controls were included in all PCR runs. The transcript of the reference gene ribosomal RNA 18S (Mm_Rn18s_3_SG, cat. no. QT02448075) was used to normalize mRNA data, and the quan ti fication data analyses were performed by using the Bio-Rad CFX Manager Software, version 1.
( 31 ) With this spectral transformation, it is easier to get the least damped quasi-geostrophic modes, imposing σ = iω with ω 1. Only eigenvalues with absolute residuals |(A -σB) -1 BX -µX| < 10 -12 are accepted as good eigenvalues. We wrote a Python code, nicknamed Singe (Spherical INertia-Gravity Eigenmodes), that relies on the SLEPc library  [Hernandez et al. , 2005]  to solve the eigenvalue problem using the restarted Krylov-Schur method  [Hernandez et al. , 2009]  together with the shift-and-invert strategy. SLEPc is an open-source scientific library developed to efficiently solve large and sparse eigenvalue problems on parallel computers. It is itself based on PETSc  [Balay et al. , 1997 [Balay et al. , , 2014]] , another open-source library intended to solve scientific problems modelled by partial differential equations.
Finally, Singe uses the SHTns library  [Schaeffer, 2013]  for the spherical harmonic transforms. The Singe code was benchmarked with the theoretical solutions of  Zhang et al. [2001] ,  Liao et al. [2001]  and  Zhang et al. [2003]  in the full sphere. As benchmarks for the spherical shell geometry, we used the numerical results of  Rieutord et al. [1997]  without stratification and of  Dintrans et al. [1999]  with a constant stratification in the shell. Singe is available at https://bitbucket. org/nschaeff/singe as free software.
, 2011]. We expect that the fast Rossby modes remain insensitive to more complex magnetic fields. Unfortunately, our numerical code Singe is not well suited to take into account complex imposed fields, because they lead to an increasing number of coupled harmonic degrees, which are cumbersome to express and translate into matrix coefficients. A possible solution would be to use computer algebra systems (CAS) to write down the coupling operators between spherical harmonics in a symbolic way [as in  Schmitt, 2010] , and then to export them into a language suitable for numerical computations. Another more flexible approach is to use so-called matrix-free methods, which avoid to explicitly form the matrices, but require to provide appropriate preconditioners. These methods may come forward in the forthcoming years.
Acknowledgments The authors would like to thank the whole geodynamo team for fruitful discussions. The most demanding computations were performed using the Froggy platform of the CIMENT infrastructure (https://ciment. ujf-grenoble. fr),supported by the  Rhône-Alpes region  ( GRANT CPER07 13 CIRA ), the OSUG@2020 labex (reference  ANR10 LABX56 ) and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ). Plots were produced using matplotlib [Droettboom et al. , 2014, http://matplotlib. org]. ISTerre is part of  Labex OSUG@2020  ( ANR10 LABX56 ).
All 1 H-NMR spectra were obtained on a Bruker DRX-600-Avance NMR spectrometer operating at 600. 13 MHz for 1 H resonance frequency using an inverse detection 5mm 1 H-13 C- 15 N cryoprobe attached to a CryoPlatform (the preamplifier cooling unit). The 1 H-NMR spectra were acquired at 300K using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse sequence with pre-saturation, as described previously  [13] , with a total spin echo delay (2nτ) of 240ms to attenuate broad signals from proteins and lipoproteins. A total of 128 transients were collected in 32,000 data points using a spectral width of 12 ppm, a relaxation delay of 2. 5 sec, and an acquisition time of 2. 28 sec. The spectra were Fourier transformed by multiplication of the FIDs by an exponential weighting function corresponding to a line-broadening of 0.
3Hz. All spectra were manually phased and baseline corrected and referenced to TMSP using Bruker TopSpin 2. 1 software (Bruker, GMBH, Karlsruhe, Germany). To confirm the chemical structure of metabolites of interest, 2D 1 H-1 H COSY (Correlation Spectroscopy) and 2D 1 H- 13 C-HSQC (Heteronuclear Single Quantum Coherence Spectroscopy) NMR experiments were performed on selected samples. Spectral assignment was based on matching 1D and 2D data to reference spectra in a home-made reference database, as well as with other databases (http://www. brmb. wisc. edu and http://www. hmdb. ca), and reports in the literature.
Data reduction and multivariate statistical analyses Data were reduced using the AMIX software (version 3. 9, Bruker, Rheinstetten, Germany) to integrate 0. 01 ppm wide regions corresponding to the δ 9. 0-0. 70 ppm region for serum samples and to the 10. 0-0. 80 ppm region for aqueous liver extracts, respectively. The 5. 10-4. 40 and 5. 0-4. 50 ppm regions, comprising the water resonances for serum samples and aqueous liver extracts, respectively, were excluded. The 3. 7-3. 6 ppm and 1. 25-1. 10 ppm regions, corresponding to ethanol resonances, were also excluded for serum samples. A total of 738 (serum) and 871 (liver) NMR buckets were included in the data matrices. To account for differences in sample amount, each integrated region was normalized to the total spectral area. Measurements were performed on 6 to 10 rats per treatment group and per measurement time-point.
In a second step, and in order to further take into account the variation over time and the relationship between variables (multivariate data), the A-SCA method was applied to study the effect of Time and Dose on the metabolome  [14] [15] [16]. A-SCA was developed to analyze metabolomic datasets taking into consideration the underlying experimental design. A-SCA combines ANOVA and PCA and works in two steps. ANOVA was first applied to split data according to the experimental factors, namely Time, Dose, and the Time Ã Dose interaction, as follows (Eq 1): X ¼ X Time þ X Dose þ X TimeÃDose þ X Residual : Eq 1 A permutation test  [20]  was used to check the significance of the experimental factors of Eq 1(10,000 iterations). Next, each data block (i. e. each sub-model) was individually analyzed using PCA.
Discriminant variables were determined using loadings or VIP (Variable Importance in the Projection), for A-SCA and PLS-DA, respectively. Then, the Kruskal-Wallis test (significance threshold = 0. 05) was applied to assess the significance of the difference. The Kruskal-Wallis test is a non-parametric method for comparing two or more groups  [21]. When rejecting the null hypothesis of the test, pairwise comparisons were performed to identify which pairs were significantly different. Multiple testing corrections were used to avoid false positives. SIMCA-P + software (V13, Umetrics AB, Umea, Sweden) and MATLAB (Mathworks, Inc. , Natick, MA) were used to perform the multivariate analyses. A-SCA and permutation tests were performed according to the MATLAB functions from Jansen et al. (2005)  and  Vis et al.
(2007)   [14, 20]. Using this software available at http://www. bdagroup. nl. , the original function estimates were obtained for the Time, the Dose and the Time Ã Dose interaction, respectively, as detailed in Eq 1. Since the focus of the study was on BPA effects (e. g. , to separate the Dose factor), we modified the original MATLAB function to enable a distinction between the Dose factor and the interaction effect. In addition, an in-house MATLAB function was written to search sets of time points and BPA doses for which the Time Ã Dose interaction was significant. At least 3 time points were included in this set. A-SCA and permutation tests were applied for all combinations of k time points (k = 3 to 5) and h doses (h = 2 to 5). Metabolomics datasets generated from either biofluids or tissues include hundreds of endogenous metabolites.
A-SCA analyses, serum samples We further explored the means to overcome the limits of PLS-DA for single time point / multiple dose comparisons by using A-SCA. The A-SCA method, which works in two stages (ANOVA and PCA), was applied to the entire dataset (5 time points and 5 doses). The first step of A-SCA allowed us to determine that the overall variability in serum samples was split among the experimental factors, with contributions of 23. 6%, 0. 9% and 4. 6%, for the Time factor, the Dose factor, and the Time Ã Dose interaction, respectively. Time was the only significantly altered factor (p-value = 0. 0001). Residuals contributed to a major part of the variability (69. 9%). In the second step of A-SCA, we focused on the Time factor. In this context, the first principal component (PC) of PCA explained 61.
4% of the mean dynamic variation. Twenty-six metabolites were identified as significantly different between these five time-points (S2 Table ). We further took advantage of the A-SCA method to identify Time Ã Dose significant interactions. To this end, we examined all combinations of k time points and h treatment groups. Among these combinations, A-SCA enabled us to identify a significant Time Ã Dose interaction for the PND21, PND90 and PND140 groups, when exposed perinatally to either 0. 25μg or 25μg BPA/kg BW/day. For this "reduced" dataset, the Time factor represented 20. 0% of the total variation (p-value<0. 0001), the Dose factor 0. 9% and the Time Ã Dose interaction 7. 3% (pvalue = 0. 0385). For the Time sub-model, the first PC explained 86. 6% of the dynamic mean variation. The corresponding list of discriminant metabolites (provided in S3 Table ) shows that the metabolism of amino-acids, glucose and fatty acids was modified along the time course.
A-SCA analysis, liver samples When applied to the entire dataset, A-SCA first led us to determine that the variability in liver extract samples was associated with the Time factor (46. 2% of the variability), the Dose factor (2. 5%) and the Time Ã Dose interaction. Both the Time and Dose factors were significant (p = 0. 001), and the residual variability (48. 5%) was lower than that for serum. For the Time sub-model, 89. 2% of the average dynamic variation was explained by a single PC. Concentrations of alanine, glucose, glycine, lactate and lysine was higher in PND21 extracts, compared to PND50, PND90, PND140 and PND200, respectively. For betaine, creatine, glutamate, glutathione, oxidized glutathione, glycogen, inosine and valine, it was the opposite (S2 Table ). For the Dose sub-model, the first PC explained 88.
7 0. 950 0. 815 -0. 200 17 2 79. 5 0. 548 0. 422 -0. 238 0. 25 Control / 2. 5 20 3 98. 8 0. 938 0. 755 -0. 264 17 3 99. 4 0. 961 0. 578 -0. 444 Control / 25 19 2 99. 8 0. 997 0. 344 -0. 296 18 2 99. 0 0. 919 0. 600 -0. 367 Control / 20 2 94. 9 0. 763 0. 603 -0. 246 19 3 99. 6 0. 968 0. 493 -0. 504 250 PND 5 treatment 50 2 42. 5 0. 378 0. 022 -0. 197 49 2 43. 4 0. 396 0. 051 -0. 209 200 groups Control / 20 4 98. 8 0. 896 0. 750 -0. 392 20 3 97. 5 0. 807 0. 514 -0. 397 0. 25 Control / 2. 5 20 2 93. 7 0. 670 0. 646 -0. 178 19 2 97. 7 0. 888 0. 372 -0. 309 Control / 25 20 3 99. 8 0. 989 0. 454 -0. 467 19 3 98. 9 0. 913 0. 487 -0. 480 Control / 21 2 98. 8 0. 817 0. 633 -0. 322 19 2 92. 2 0. 633 0. 415 -0. 281 250 a number of observations in the dataset b number of latent variables included in the PLS-DA model c % of variance explained by the PLS-DA model d predictive ability of the PLS-DA model calculated by 7-fold cross-validation e Intercept values (R2/Q2) of permutation test.
Comparisons were performed with the Chi-square and the Mann-Whitney test, or the Mood's median test when appropriate, for categorical and continuous variables, respectively. Probabilities of overall survival (OS) were estimated by the Kaplan-Meier method, using the log-rank test for univariate comparisons; the cumulative incidence of IFI and NRM was calculated by a competing risk analysis, with the Gray test for univariate comparisons. Death was the competing event when calculating IFI, relapse or progression was competing event when calculating NRM. A p-value <0. 05 was considered significant. Statistical analysis was performed with SPSS software (version 15. 0) and R (version 2. 12. 2), package "cmprsk". Results. From January 2009 to May 2013, a total of 99 patients were identified; 30 patients were treated with micafungin, and 69 patients were treated with itraconazole (n=50) or fluconazole (n=19).
In order to validate this finding, we analyzed 99 LMS cases collected at different institutions from the years 1991 to 2012 as an independent cohort, and performed expression profiling with 3′end RNA-sequencing (3SEQ)  (14) (15) (16) (17) (18) (19) (20). In addition, we analyzed the publically available expression profiling dataset from TCGA on 82 cases. Our analysis confirms the existence of three molecular subtypes in LMS. Using two novel markers we could distinguish the three subtypes by immunohistochemistry and correlate the subtypes with clinical outcome. The recognition of these molecular subtypes in LMS may have clinical significance as the subtypes vary in their expression of a number of genes for which novel targeted therapies either already exist or are being developed.
3SEQ library construction and bioinformatics analysis Paraffin blocks of 99 Leiomyosarcoma (LMS), 4 Myometrium, 3 Leiomyoma and 6 Undifferentiated Pleomorphic Sarcomas (UPS) from 1991 to 2012 from 9 hospitals were obtained with IRB approval and a waiver of consent due to the archival nature of the specimens. Multiple 2mm-diameter cores were re-embedded into paraffin blocks longitudinally and sectioned again to ensure the purity of tumor cells. After RNA isolation, 3SEQ libraries for next generation sequencing-based expression profiling were sequenced and analysis was performed as described previously  (14) (15) (16) (17) (18) (19) (20). All gene expression profiling data used for this study have been deposited in the Gene Expression Omnibus (GEO) and are publicly accessible through GSE45510, GSE53844 and GSE54511.
After filtering data to genes with a standard deviation greater than 100, transforming the data by log2 and gene-based centering, the Consensus Clustering (R package ConsensusClusteringPlus)  (21)  was used to determine the number of subtypes and to assign the subtype for each LMS case. This was ran over 1000 iterations with setting "Distance -(1-Pearson correlation), a 80% sample resampling, 80% gene resampling, a maximum evaluated k of 12, and agglomerative hierarchical clustering algorithm". Silhouette width (R package cluster)  (22)  was then calculated based on the assignment from ConsensusClusteringPlus to measure the accuracy of assignments from ConsensusClusteringPlus. Separately, RNASeq data of 82 LMS cases were downloaded from the TCGA database. To compare with 3SEQ data, the TCGA data were normalized into TPM and analyzed with ConsensusClusteringPlus and Silhouette analysis as performed for 3SEQ data.
Subclass mapping  (23)  was performed to determine the common LMS subtypes identified in 3SEQ and TCGA RNASeq datasets. In order to get subtype specific genes, SAMSeq  (24)  was performed on both datasets between each subtype and all other subtypes with a FDR of 0. 05. Kaplan-Meier plots were used to compute the survival curves, and Log-rank (Mantel-Cox) test was used to determine the statistical significance of survival between groups by using GraphPad Prism5 software. Significance of contingency analysis between one subtype and the other subtypes was measured by two tail Fisher's exact test and Chi-square test with GraphPad Prism5 software. Univariate and multivariate analysis by the Cox proportional hazard method was performed using the survival package in R. Analysis of gene ontology (GO) was done using DAVID Bioinformatics Resources version 6.
7  (25, 26). CSF1-signature  (27)  positive, and CINSARC-signature (28) positive cases were identified as those cases that coordinately highly expressed these signature genes with >0. 3 correlation with the centroid as performed previously  (19, 27, 29, 30). Principal component analysis (PCA) was performed on square root transformed TPM with R package pcaMethods, ellipse contour of principal components (PC) was computed and drawn with R package car.
TMA construction, Immunohistochemistry staining and scoring The tissue microarrays used in this study (TA-201 and TA-381) were constructed with Tissue Arrayer (Beecher Instruments, Sun Prairie, WI). TA-201 included the LMS cases with clinic outcome data, while TA-381 was comprised of LMS cases analyzed by 3SEQ. For Immunohistochemistry staining, sections underwent antigen retrieval and were stained with anti-ARL4C antibody (1:120, Sigma, CAT#HPA028927) and anti-LMOD1 antibody (1:20, Sigma, CAT#HPA028325) with the EnVision+system (Dako). The staining results were scored as follows: 3 (+++), strong staining (>30% positivity); 2 (++), weak staining (10-30%); 1 (+), equivocal or uninterpretable (<10%); 0 (-), absence of any staining. Hierarchical clustering of IHC results was performed with software "Cluster3" with "uncentered Correlation" and "Centroid Linkage" and a clustered heatmap was visualized with Java TreeView  (31, 32).
3SEQ yielded an average of 18 million total reads after quality filtering and an average of 2 million uniquely mapping reads against human transcriptome (refMrna) per sample. All reads that uniquely mapped to an individual gene (UCSC genome browser, hg19) were combined to obtain a quantification of the expression level for that gene. The genes with the highest degree of variation in expression levels across all samples were identified by filtering the dataset for genes with a standard deviation ≥100, yielding 1,300 genes. These were used to perform Consensus Clustering  (21) , a method that uses data-resampling followed by iterative re-clustering to estimate cluster stability and to help determine the optimal number of clusters in a dataset. As shown in Figure  1A  and 1B, the greatest increase in the area under the CDF curve (empirical cumulative distribution function) is seen when 3 molecular subtypes are assumed.
Molecular subtypes of LMS can be reproduced in an independent dataset To further explore the reproducibility of the three LMS subtypes, we downloaded gene expression data for 82 LMS cases from the TCGA database. Of these samples, 80 cases were primary LMS while 2 cases were recurrences. The origin of the tumors differed from the 99 cases analysed by 3SEQ with 19 LMS originating from the uterus, 13 cases from the extremities and 32 cases from other extrauterine sites. The remaining 18 cases had an unknown primary site. In addition to the differences in the site of origin for the tumors examined there was a significant difference in the percentage of samples derived from primary lesions versus recurrences between the two cohorts. In the 3SEQ cohort 37% of the cases were recurrences or metastases while only 2% of TCGA LMS cases were recurrent and none were metastatic (Supplementary Table  S2 ).
The 2 datasets differed in two additional aspects. First, the TCGA dataset used fresh frozen material whereas our 3SEQ analysis was performed on FFPE material. Second, whole transcriptome RNASeq was used for the TCGA dataset while 3SEQ determines the gene expression level based on quantification of 3′end reads. Despite these differences, analysis of the TCGA dataset produced results very similar to those found in the 3SEQ cohort. The TCGA dataset yielded 1,174 genes when filtered for a standard deviation ≥100 (1,300 genes in the 3SEQ set). By Consensus Clustering and Silhouette analysis, the TCGA dataset also showed the greatest increase in area under the CDF curve when three subtypes were used (Figure  2A ). Compared to the cases studied by 3SEQ in our cohort the homogeneity of the subtypes in the TCGA cohort was greater as determined by consensus clustering (Figure  2B ), possibly due to the fact that frozen material and not FFPE material was used for the cases in the TCGA cohort.
To measure the reproducibility between the 3SEQ and TCGA cohorts the cases with positive Silhouette values from both sets were analysed by Subclass mapping  (23). The 3SEQ-subtype I and -subtype II types were significantly reproduced as subtype-C3 and -C1 in the TCGA cohort with FDR corrected p-values of 0. 0090 for both. The 3SEQsubtype III did not reach statistical significance when it was compared with the remaining TCGA-subtype C2 (p = 0. 0959) (Figure  2C ). A substantial number of genes identified by SAMSeq in the 3SEQ and TCGA datasets showed overlap between 3SEQ subtypes I and -II and the corresponding TCGA subtypes (Supplementary Table  S3 ). Importantly, five genes for which we previously used immunohistochemical markers  (13, 33)  to identify subtype I LMS (CASQ2, ACTG2, MYLK, SLMAP, and CFL2) were shown to be highly expressed on the mRNA level in TCGA-subtype C3, as was the new subtype I marker LMOD1.
Clinical features of LMS molecular subtypes in three datasets In the 3SEQ dataset, approximately 26%, 59% and 92% of subtype I, subtype II and subtype III LMS were derived from the uterus with uterine LMS being significantly associated with subtype III (p = 0. 0005, Chi-square test). However, when evaluating only those 34 cases arising in the uterus, we found that uterine LMS had a roughly equal chance of belonging to each of the molecular subtypes with 9, 13, and 12 cases belonging to subtype I, II, and III, respectively. In contrast, the extrauterine LMS were overrepresented in subtype I and II, with 25, 9 and 1 cases belonging to subtype I, II and III, respectively. These results indicate that a subset of uterine LMSs behave as independent molecular subtype (subtype III) while another subset of uterine LMS together with extrauterine LMS belong to subtype I and subtype II LMS.