dataesr/scientific-paragraphs-categorization huggingface dataset:
Catchments and hydrological model In all, 16 catchments spread over France were selected from the database used by  Nicolle et al. (2014). Using a set of catchments helps getting more general conclusions (see e. g. Andréassian et al. , 2009; Gupta et al. , 2014). However, it should be noted that identifying relations between performances and catchment characteristics is outside the scope of this study. These catchments are dominated by a pluvial regime and the quality of their streamflow data during low flows is good. Additionally, the selected catchments have an average solid fraction of precipitation below 10 %. Their location, hydrological regimes and main characteristics are presented in Fig. 1  and Table  1 , respectively. In these catchments, low flows are observed between May and October, i.
Here, sharpness was computed as the average difference between the 95th and the 5th percentiles of the forecast distribution over the evaluation period  (Gneiting et al. , 2007). It thus corresponds to the average 90 % interquantile range (IQR). Reliability refers to the statistical consistency between observed frequencies and forecast probabilities. Reliability was evaluated with the probability integral transform (PIT;  Gneiting et al. , 2007; Laio and Tamea, 2007)  diagram. The PIT diagram represents the cumulative distribution of the positions of the observation within the distribution of forecast values. The PIT diagram of a perfectly reliable forecast is superposed with the 1 : 1 diagonal, meaning that the observation uniformly falls within the forecast distribution. To numerically compare results for large datasets,  Renard et al.
Acknowledgements. The first author was partly funded by the  DROP  project (benefit of governance in DROught adaptation) of the  Interreg IVB NWE programme of the European Union. The second and the third authors were partly funded by the  IMPREX  project supported by the  European Commission  under the  Horizon 2020 Framework programme , with grant no. 641811. The authors thank  Météo-France  and  SCHAPI  for providing climate and hydrological data, respectively, and  ECMWF  for providing seasonal forecast data and for hosting the first author for 2 weeks. Edited by: A. Wood Reviewed by: two anonymous referees.
Antisense Transcripts Produced by Transcriptional Read-Through Occur during Oncogene-Induced Senescence at Convergent Gene Loci In order to identify functions and regulatory mechanisms of antisense RNAs in humans, we asked whether senescence induced by oncogene activation in WI38 fibroblasts could be associated with the appearance of antisense transcripts produced by loss of accurate transcription termination at convergent gene loci. For clarity, in the convergent gene pair, we called the gene from which the putative read-through transcript originates the ''forward gene'' and the gene to which the putative read-through is antisense the ''reverse gene'' (Figure  1A ). On two independent replicates of strand-specific RNAsequencing (RNA-seq) datasets (proliferation and senescence), we applied a multisteps pipeline (see Supplemental Experimental Procedures for a detailed description) to identify the genomic boundaries of read-through antisense transcripts at convergent gene loci, whose expression changes in senescence.
Metadata analyses of the 2 RNA-seq datasets in proliferation (PROLIF, green) or in senescence (SEN, red) at the 91 START loci are shown. Each gene and intergenic regions were divided into 20 equal regions. For each of these regions, the mean of the normalized number of aligned reads per base was computed for each gene, and the median within the gene population is plotted. The forward strand and the reverse strand of RNA-seq datasets are shown with a full lane and a dashed lane, respectively. (C) Boxplots showing the log 2 of the expression change in senescence, log 2 (sen/prolif), of the forward genes and of different analyzed regions in the 91 START RNAs: the antisense (AS) parts, the entire read-through (RT) transcript domains, corresponding to the entire START RNAs, as well as their first (read-through, part 1) and second (read-through, part 2) halves.
Two independent datasets are shown. The read-through transcript domains and the AS parts of the 91 START RNAs are significantly more increased in senescence than their forward genes (paired Wilcoxon test). Note that the second halves of START RNAs are significantly more increased in senescence than the first halves (paired Wilcoxon test). See also Figures  S1  and S2  and Tables S1  and S2. REV3L were confirmed by reverse transcription followed by qPCR (qRT-PCR) (Figures  2C  and 2D ). We confirmed that the increase in antisense expression was not due to an increase in the expression from the upstream genes, since ARHGAP18 and KIAA1919 gene expression did not vary much upon senescence induction (Figure  2C ). ARHGAP18 and KIAA1919 START RNAs Are Produced by a Defect in Transcription Termination of Their Forward Gene ARHGAP18 and KIAA1919 START RNAs could be produced through an accumulation of transcriptional read-through in senescence or through transcription initiation at a promoter located close to the 3 0 end of the ARHGAP18 or KIAA1919 gene.
(C) WI38 hTERT RAF1-ER cells were induced (+ 4-HT) or not induced (-4-HT) to enter senescence for 3 days. Total RNA was extracted and subjected to random qRT-PCR using the indicated primers: e, exon; e-e, exon-exon junction; and down, downstream of the TTS of the gene. Data were normalized to GAPDH mRNA expression and calculated relative to 1 in proliferative cells for each experiment. Data are indicated as means ± SD from 3 independent experiments. (D) Same as in (C), except that total RNA was subjected to strand-specific qRT-PCR and analyzed using the indicated primers (i, intron). See also Figure  S3. tended to test whether these transcripts antisense to LAMA2 or REV3L were produced from a long RNA originating from the promoter of the forward gene (ARHGAP18 and KIAA1919, respectively). We used an siRNA (small interfering RNA)-based approach because, in a previous study, we successfully used this strategy to deplete nuclear ncRNAs in these cells  (Lazorthes et al.
72 hr after transfection, total RNA was extracted and subjected to random qRT-PCR using the indicated primers (left) or analyzed by strand-specific qRT-PCR to monitor the expression of the region antisense to LAMA2 (right). Data were normalized to GAPDH mRNA expression and calculated relative to 1 in siRNA Ctrl-treated cells for each experiment. Data are indicated as means ± SD from 3 independent experiments. (C) Same as in (B), except that cells were transfected using an siRNA targeting the read-through region (ARHG R-th) in the intergenic region between ARHGAP18 and LAMA2 (38. 7 kb downstream of the TTS of ARHGAP18). (D) Same as in (B), except that cells were transfected using an siRNA targeting KIAA1919 read-through region (KIAA R-th, 2. 5 kb downstream of the TTS of KIAA1919), and strand-specific qRT-PCR was performed to monitor the expression of the region antisense to REV3L (right panel).
LAMA2 mRNA expression (e64-65) was analyzed by random qRT-PCR using primers in an exon-exon junction. Data were normalized to GAPDH mRNA expression and calculated relative to 1 in siRNA Ctrl-treated cells for each experiment. Data are indicated as means ± SD from 3 independent experiments. (B) Same as in (A), except that cells were transfected using an siRNA targeting KIAA1919 read-through region (KIAA R-th). REV3L (pre-mRNA) sense expression was monitored by strand-specific qRT-PCR in an intron (left panel, i32) or by random qRT-PCR using primers in an intron located far away from the read-through antisense transcript (right panel, i2). The spliced REV3L mRNA was assessed by random qRT-PCR using primers in an exon-exon junction (right panel, e32-e33). (C) Total RNAs from proliferative or senescent cells were extracted and subjected to strand-specific RNA-seq experiments.
For the 91 loci harboring START RNAs, the log 2 of the variation in senescence (log 2 (sen/prolif)) of the expression of the reverse genes was computed and represented as a boxplot for both RNA-seq experiments (#1 and #2). Note the significant decrease (Wilcoxon test) in the expression of the reverse gene population compared to all expressed genes to which an antisense transcript is increased in senescence (2,135 and 1,349 genes in the first and second RNA-seq replicates, respectively). (D) Senescent cells were transfected using the indicated siRNAs. Transfected cells were then subjected to a colony formation assay in duplicate. Colony number was counted twice in each plate and calculated relative to 1 for the control siRNA. Data are indicated as means ± SD from four entirely independent experiments.
Thus, some siRNAs targeting START RNAs can allow a significant fraction of senescent cells to enter S phase. We also performed a clonogenic assay, which is different from measuring EdU incorporation as it allows measuring sustained cell proliferation, even in a small fraction of senescent cells. We found that depletion of LINC01433 or ARHGAP18 START RNAs in senescent cells consistently led to an increase in the number of clones (Figures  4D  and S4C ). Altogether, the data in Figures  4  and S4  indicate that some siRNAs targeting START RNAs can reverse the permanent cell proliferation arrest associated with senescence. Importantly, in the case of the siRNAs targeting ARHGAP18 and SERBP1 START RNAs at least, this reversal was not due to forward gene depletion. Indeed, using siRNAs targeting SERBP1 or ARHGAP18 mRNAs, which decrease their respective mRNA at least as efficiently as the siRNA targeting the START RNA (compare the effects of siRNAs targeting the START RNA or the mRNA on ARGHAP18 mRNA expression in Figures  3B  and 3C  or on SERBP1 mRNA expression in Figure  S4F ), no effect on cell proliferation could be observed (Figures  S4D  and S4E ).
Z in Proliferative Cells (A) WI38 hTERT RAF1-ER cells were induced, or not induced, to enter senescence by the addition of 4-HT for 3 days. Total cell extracts were analyzed by western blot using the indicated antibodies. One representative experiment out of 7 is shown. (B) Proliferative cells were transfected using the indicated siRNAs. 72 hr following transfection, total RNA was prepared and subjected to random qRT-PCR using the indicated primers. Data were normalized to GAPDH mRNA expression and calculated relative to 1 in siRNA control (Ctrl)-treated cells for each experiment. Data are indicated as means ± SD from 3 independent experiments. (C) Same as in (B), except that the H2A. Z#2 siRNA was used, and one representative experiment out of two is shown (means ± SD from the qPCR sample triplicates).
The ratio between the indicated sequence and a control sequence (GAPDH e1) was then performed and calculated relative to 1 in the H2A. Z ChIP in proliferative cells for each experiment. Data are indicated as means ± SD from 4 or 5 independent experiments. (F) Proliferative and senescent cells were subjected to an H2A. Z ChIP-seq analysis (ChIP-seq replicate #2). Boxplots show the log 2 of the variation in senescence (log 2 (sen/prolif)) of H2A. Z occupancy at the forward gene TSS and the intergenic regions for the loci harboring START RNAs with an intergenic region >1 kb (to limit the analysis to intergenic regions because of the ChIP resolution of around 500 bp [69/91]). Note that H2A. Z occupancy significantly decreased in senescence among this population on these two regions. The p values of the difference to 0 are 5.
Bioinformatic Analysis Boxplots, metadata, and analysis of RNA-seq datasets in senescence and proliferation or after siRNA treatments as well as ChIP-seq datasets are fully described in the Supplemental Experimental Procedures. siRNA and Primers siRNAs and primers were purchased from Eurogentec or Dharmacon (p21 siRNA). The sequences are described in Table  S3. If several transcript variants exist, transcript variant #1 was used as a reference for numbering the exons or the introns. Clonogenic Assays WI38 hTERT RAF1-ER cells were seeded at a density of 250,000 cells per dish into 35-mm dishes, induced to senescence for 3 days, and then transfected with siRNAs. 48 hr after transfection, 1/15 of cells was seeded into 60-mm dishes and cultured for 14 days before being stained with a solution of 0.
EdU and Hoechst Staining Detailed procedures for EdU and Hoechst staining are fully described in the Supplemental Experimental Procedures. EdU and Hoechst staining were analyzed using the Operetta High-Content Imaging System (PerkinElmer, Harmony Imaging Software 4. 1). After data acquisition, subsequent analyses were performed with the Columbus 2. 5. 0 software (PerkinElmer).
The forward strand and the reverse strand of RNA-seq datasets are shown with a full lane and a dashed lane, respectively. (C) Boxplots showing the log 2 of the expression change in senescence, log 2 (sen/prolif), of the forward genes and of different analyzed regions in the 91 START RNAs: the antisense (AS) parts, the entire read-through (RT) transcript domains, corresponding to the entire START RNAs, as well as their first (read-through, part 1) and second (read-through, part 2) halves. Two independent datasets are shown. The read-through transcript domains and the AS parts of the 91 START RNAs are significantly more increased in senescence than their forward genes (paired Wilcoxon test). Note that the second halves of START RNAs are significantly more increased in senescence than the first halves (paired Wilcoxon test).
The chromosome strand of the analyzed region is annotated. The values for the forward gene (ARHGAP18 or KIAA1919), the intergenic region of the readthrough (RT) transcript, the read-through entire domain, the antisense (AS) part of the readthrough (AS part to LAMA2 or REV3L), and the reverse gene (LAMA2 or REV3L) are shown. (C) WI38 hTERT RAF1-ER cells were induced (+ 4-HT) or not induced (-4-HT) to enter senescence for 3 days. Total RNA was extracted and subjected to random qRT-PCR using the indicated primers: e, exon; e-e, exon-exon junction; and down, downstream of the TTS of the gene. Data were normalized to GAPDH mRNA expression and calculated relative to 1 in proliferative cells for each experiment. Data are indicated as means ± SD from 3 independent experiments. (D) Same as in (C), except that total RNA was subjected to strand-specific qRT-PCR and analyzed using the indicated primers (i, intron).
ACKNOWLEDGMENTS L. M. was supported by fellowships from  La Ligue Nationale contre le Cancer  and from the  Fondation ARC (Association pour la Recherche sur le Cancer ). M. K. D. and S. L. were recipients of studentships from the  French Ministry of Science , the  Ligue Nationale Contre le Cancer , and from the  Fondation ARC. M. A. was supported by the  FRM (Fondation pour la Recherche Medicale). E. N. is a researcher from the  INSERM. This work was also supported by grants from the  ANR (Agence Nationale de la Recherche ,  Pings program ), the  INCa (Institut National du Cancer , PLBIO 2012 -DA No. 2012-123 ), and the  Fondation ARC (Progamme ARC ,  PGA120150202320 ) to D. T. We would like to thank  Dr. K. Belguise  for her help. Operetta high-throughput analysis was performed at the  Toulouse Reseau Imagerie (TRI) facility  located at the  Centre de Biologie Integrative.
For each of these regions, the mean of the normalized number of aligned reads per base was computed for each gene, and the median within the gene population is plotted. The forward strand and the reverse strand of RNA-seq datasets are shown with a full lane and a dashed lane, respectively. (C) Boxplots showing the log 2 of the expression change in senescence, log 2 (sen/prolif), of the forward genes and of different analyzed regions in the 91 START RNAs: the antisense (AS) parts, the entire read-through (RT) transcript domains, corresponding to the entire START RNAs, as well as their first (read-through, part 1) and second (read-through, part 2) halves. Two independent datasets are shown. The read-through transcript domains and the AS parts of the 91 START RNAs are significantly more increased in senescence than their forward genes (paired Wilcoxon test).
Flow cytometry To fulfill the criteria of the International Society for Cellular Therapy, MSCs were analyzed for the expression of CD34, CD45, CD73, CD90, CD105, and CD44 (fluorochrome-conjugated monoclonal antibodies from BD Bioscience) by flow cytometry (FACSCanto II Becton Dickinson, BD Bioscience, USA). Cellular apoptosis was evaluated by Annexin V and 7AAD staining, detected by FACSCanto II Becton Dickinson (BD Bioscience, USA). Unstained MSCs were used as negative controls to assess background fluorescence. Cell analysis was performed using FlowJo software (TreeStar Inc. , USA).
MGMT methylation analysis For 73/104 (70%) tumor DNA samples, MGMT methylation was analyzed using the PyroMark MGMT kit (Qiagen). Chemically methylated and unmethylated human genomic DNA controls (EpiTect PCR Control DNA Set, Qiagen) were included in each batch. In brief, 40 ng of tumor DNAs were extracted from paraffin-embedded tissue blocks using the FFPE DNA Extraction Kit (Zymo Research, Orange, CA). DNAs were then bisulfitemodified using the EZ DNA methylation kit (Zymo Research, Orange, CA) according to the manufacturer's recommendations. The CpG pyrosequencing methylation assay using the Qiagen kit was performed on a PSQ 96 MA system (Qiagen) according to the manufacturer's protocol. The PyroMark MGMT kit quantifies the level of methylation at five individual CpG sites within exon 1 of MGMT using the Pyromark CpG software (Qiagen).
Furthermore, average age in this study was 71 years while our study only included patients 70 years or more, with a median age of 75 years. However, this study brings interesting data concerning "moderate" elderly patients, in excellent general status, who had extensive surgery and for which a Stupp regimen might considered over HFRT + TMZ. Minniti et al. [31]  also compared the outcomes of elderly (≥ 65 years) GBM patients receiving either short (HFRT; 40 Gy, 15 sessions) or standard-course (60 Gy, 30 sessions) irradiation plus concomitant TMZ (Table  3 ). To limit the potential bias of such a retrospective study, they designed a propensity-matched analysis. Patients given standardcourse irradiation + TMZ were more likely to be younger (median age of 68 years vs 71) and to undergo total/ subtotal resection.
Those eyes requiring additional treatment due to post-operative pain or inflammation were excluded from the study. Efficacy was defined as increase in CMT of <10% from pre-operative value. The pre devised proforma was completed endorsing subject's demography, ocular examination and investigations findings. Statistical Analysis: Data was evaluated and analyzed using Statistical Program for Social Sciences (SPSS) version 17. Mean and standard deviation was reported for continuous variables (Age, CMT) while frequency and percentage for nominal data (gender, laterality of eyes, efficacy). Shapiro Wilk's test was used to check normality of data. Post normality testing, independent 't' test was used to compare quantitative variables, and Chi Square test was used to compare qualitative variables between two groups.
Simulation and Validation To validate the two proposed models, an industrial standard vehicle dynamics simulation software, CARSIM ® was used to carry out simulations  [16, 19, 20]. A standard SUV model was chosen from CARSIM ® vehicle models as shown in Figure  8. Simulation and validation results of vehicle roll behavior were carried out under steering wheel (SW) input 10 • condition as an example case to facilitate better analysis of vehicle roll behavior. Based on the above analysis, half-car and full-car models proposed in Section 4 were simulated in MATLAB to study performance of vehicle roll behavior with or without road excitation (two representatives). It is assumed that the tire does not loose ground contact. Here, the J-turn maneuver  [15]  was utilized to illustrate roll behavior for steering wheel at 10 • at a velocity of 80 km/h with presence of road excitation.
Simulation and Validation To validate the two proposed models, an industrial standard vehicle dynamics simulation software, CARSIM ® was used to carry out simulations  [16, 19, 20]. A standard SUV model was chosen from CARSIM ® vehicle models as shown in Figure  8. Simulation and validation results of vehicle roll behavior were carried out under steering wheel (SW) input 10° condition as an example case to facilitate better analysis of vehicle roll behavior. Based on the above analysis, half-car and full-car models proposed in Section 4 were simulated in MATLAB to study performance of vehicle roll behavior with or without road excitation (two representatives). It is assumed that the tire does not loose ground contact. Here, the J-turn maneuver  [15]  was utilized to illustrate roll behavior for steering wheel at 10° at a velocity of 80 km/h with presence of road excitation.
Roll Behavior without Road Excitation Here, using vehicle dynamics model from Section 4, performance of vehicle roll behavior is simulated without presence of road input. Initial conditions used in the simulation are listed in Tables  1  and 2. Corresponding performance of roll behavior result parameters are shown in Figure  9. It can be seen from Figure  9  that without road excitation, half-car and full-car model are approximately equivalent. The E-Class (SUV) level car model in CARSIM ® was used as benchmark results. Calculations carried out in MATLAB were found to be consistent with the CARSIM data. This suggests that vehicle dynamics model presented in Section 4 can be considered accurate to depict system dynamics under this condition.
Based on the above analysis, simulation results show that accuracy of statistical features calculation for 6-DOF model reached a maximum of 65% for all situations. Lower accuracy of statistical features follows increase in the angle of steering wheel input; accuracy of statistical features calculated for 9-DOF model was higher than 85%. Comparison of simulation results from MATLAB and CARSIM ® allows verification of the proposed 9-DOF full-car model for roll behavior with or without road excitation condition. A 6-DOF half-car model was found to be suitable in conditions without road excitation. Influence of vehicle model on roll behavior should be adopted for the full-car model while analyzing vehicle roll behaviors with large steering wheel input and considering road excitation.
Evaluation of Time Lag Between the Peaks of Chl a and OM Sedimentation In order to quantify TL we identified the temporal difference of the peaks in water column Chl a (P Chl ) and peaks in sedimented total carbon (P Sed ) and extrapolated to 100 m as described in the next section. Chl a concentrations were used, as PP data were only available for two out of the four experiments and the low temporal resolution of these data did not allow for a precise determination of TL. C/Chl ratios did vary at the different locations, but Chl a concentrations were still the most reliable bloom indicator available. Phytoplankton blooms were identified using the threshold method  [Siegel et al. , 2002; Brody et al. , 2013]. Briefly, median Chl a concentrations were calculated for each experiment and the first value equal to or above the median prior to the peak marks the bloom start date (BSD).
The same method was applied to the sedimented total carbon concentrations to estimate mass flux initiation. The time from BSD to the peak in Chl a was evaluated for each mesocosm of the respective experiments and is hereafter referred to as duration of Chl a buildup. Note that we focused on inorganic nutrient-fueled phytoplankton blooms in this analysis of mesocosm data as we could only detect Chl a and subsequent sinking flux peaks in such settings. In later stages of the experiments OM production was often fueled by organic nutrients and resulted in smaller and more irregular pulses in OM sedimentation, thus making a precise assignment of peaks impossible.