dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and data evaluation The primary endpoint of this phase II study was local response after concurrent RT and paclitaxel regimen in patients with unresectable LRR of breast cancer. Secondary endpoints included acute toxicity and follow-up. The patients were routinely followed with clinical examinations, CT or PET/CT every 3 months for the first 2 years and every 6 months thereafter. Tumor response was evaluated using clinical examinations and CT according to the RECIST criteria at 3 months after concurrent CRT. Adverse events were assessed at least weekly during RT by the National Cancer Institute (NCI) common toxicity criteria (version 3. 0). Sample size estimation was performed based on the Simon's optimal two-stage design  [14, 15]. We hypothesized that the local response rate could be increased from 84%  [16]  to 95%.
With a unilateral alpha error of 5% and a statistical power of 80%, a planned of 46 subjects were needed. With an anticipated drop-out rate of 10%, a total of 51 subjects were finally necessary. The different parameters of each individual patient were entered into a database and analyzed using SPSS 17. 0 statistical software. The local progression-free survival rate, distant progression-free survival rate and overall survival rate were summarized using Kaplan-Meier methods. The local progression-free survival was defined as the time interval between the end of CRT and the first observation of local disease progression. Similarly, distant progression-free survival was defined as the time interval between the end of CRT and the first observation of distance metastasis.
QUANTIFICATION AND STATISTICAL ANALYSIS Confocal image analysis-Images were analyzed using ImageJ software for the parameter outlined for each experiment. At least 3 sections per lesion were imaged per animal, at evenly spaced intervals (e. g. one in every 4 sections). The lesion area was imaged using tiling. For cell density calculations, the number of cells per parameter was divided by the total volume of the z-stacked image (length × width × stacked depth) to give cells/um3. Measurements were then multiplied by 10 6 to give cells/10 6 um 3. Images of cingulate white matter and corpus callosum white matter up to the dorsal edge of, but not including, the subventricular zone, were taken at the same laser intensity and microscope acquisition settings for 3 sections per animal. ImageJ was used to image the WM area, form z-stacks and adjust the threshold.
Images were minimally adjusted for clarity and size using Adobe Photoshop software. Electron microscope image analysis-Low magnification images were used to qualitatively assess the overall extent of myelination; high magnification images were used for quantitative analyses. The percent myelinated and unmyelinated axons was determined and G ratio analysis was conducted. Any axon with a single wrap of an oligodendrocyte process was classified as "myelinated"; G ratio analyses were limited to only myelinated axons and were calculated by dividing the average axon diameter determined by measuring the longest and shortest diameter by the average axon diameter plus the width of the thickest and narrowest span of the myelin sheath in cross section. For myelin thickness measurements, regions with expanded myelin due to structural artifact were not used.
Statistical analysis-All statistical analyses were performed with Sigmaplot 13, and all values are expressed as mean ± SEM, and illustrations were created using SigmaPlot 13. Neonatal mice sometimes died from early tamoxifen injections, so these samples could not be analyzed. The specific numbers of animals or cultures are denoted in each figure legend. Significance was determined using SigmaPlot13 software, with Student's unpaired, twotailed T-tests for comparisons between two groups and one-way ANOVA with Holm-Sidak post hoc tests for multiple comparisons. N values denote independent experiments. The degree of statistical significance was indicated with asterisks as defined in figure legends.
ACKNOWLEDGEMENTS This work was supported by the  National Multiple Sclerosis Society   RG4706A4/2  and  RR-1512-07066  (V. G and BNO), and partially supported by  FISM   2015/R/13  (V. G) and  VA I01BX002565-05  (J. D). Confocal and fluorescence microscopy, and neurobehavioral analysis portions of this project were performed in the Neuroimaging and Neurobehavior Cores, supported by Award number  1U54HD090257  from the  NIH , District of  Columbia Intellectual and Developmental Disabilities Research Center Award (DC-IDDRDC, VG). Its contents are solely the responsibility of the authors and do not represent the official views of the DC-IDDRC or  NIH. Further support was provided through the  Fondation pour l'Aide à la Recherche sur la Sclérose en Plaques (ARSEP, BNO)  and the " Investissements d'avenir "  ANR-10-IAIHU-06  (BNO).
Data analysis Data were exported from VITIMES to Microsoft Excel (Excel) and analysed using both Excel 2010 and Stata version 14 (Stata Corp LLC, 4905 Lakeway college station, Texas, USA). The proportion of TB patients with a known HIV status was calculated by taking the number of TB patients with an HIV test result recorded in the TB register and dividing by the total TB cases registered during the same period. The proportion of TB patients with a positive HIV test was calculated by taking the number of TB patients with a positive HIV test result and dividing by the number of all TB patients with an HIV test result. We used nptrend test (Cuzick test) for assessment the trend of non-parametric data. Point estimates are shown with 95% confidence intervals for proportions. A P-value < 0. 05 was regarded as statistically significant.
3D ). To compare the overall loss of antibodies, the change in antibody levels was calculated at two time points following clinical (A) Malaria-naïve healthy individuals underwent inoculation with 1800 or 2800 viable P. falciparum 3D7-parasitized RBCs, and peripheral parasitemia was measured by qPCR. Participants were treated with anti-malarial drugs at day 7 or 8 of infection. Blood samples from 60 volunteers (from 10 independent cohorts) were collected before infection (day 0), at peak infection (day 7 or 8), and 14 or 15 and 28 to 36 days after inoculation; in analysis, these time points are grouped as days 0, 7, 14, and 30. Data are representative of 10 individuals from four cohorts. (B) IgM and IgG levels were measured against MSP2, AMA1, and EBA175 (RII) at days 0, 7, 14, and 30 following controlled human P.
falciparum infection in malaria-naïve adults (n = 40). Antibody responses are expressed as optical density (OD) after background subtraction of no-serum wells. Positivity threshold was calculated as means + 3SDs of responses at day 0 (dotted line). P values are Wilcoxon matched-pairs signed-rank test. (C) IgM and IgG responses to several merozoite antigens were measured at day 30 following infection in CHMI participants (n = 20). Prevalence of antibody responses was considered as a positive response if antibody levels were greater than means + 3SDs of responses at day 0. Data are proportion positive, and error bars are upper 95% confidence interval calculated by the Wald method. P value is calculated by Fisher's exact test. (D) Magnitude of responses relative to day 0 was assessed as arbitrary units (AU = OD day 30/means + 3SDs of responses at day 0).
P values are Wilcoxon matched-pairs signed-rank test. (E) Affinity of IgM and IgG to MSP2 was assessed by the dissociation of antibodies using increasing concentrations of ammonium thiocynate. Data are means ± SEM of eight individuals tested in two assays in duplicate, expressed as the percentage of OD in PBS. P values indicate Sidak's multiple comparison test of IgM verses IgG from two-way analysis of variance (ANOVA), taking into account repeat measures for each individual (*P < 0. 05 and **P < 0. 01). malaria, between 30 and 60 days or between 180 and 300 days. There was no difference in the change in IgM compared to IgG at either time points, indicating that IgM was not more rapidly lost than IgG following malaria (Fig. 3E ).
Data are means ± SD of two assays performed in duplicate. Significant differences are indicated as P < 0. 05 by Mann-Whitney U test. *P < 0. 05. (B) Isolated IgM and IgG fractions were tested for deposition of complement components (C1q, C3b, and C5b-C9) on intact merozoites or recombinant MSP2 by ELISA with 10% normal sera from malaria-naïve donors as a source of complement. Data represent the level of complement binding measured in OD values minus deposition observed using naïve IgM and IgG. (C) IgM and IgG fractions were tested for deposition of complement components on recombinant MSP2 by ELISA with 10% C1q-depleted sera and C1q-depleted sera reconstituted with purified C1q (10 g/ml). Data are means ± SEM of two independent assays performed in duplicate. (D) The level of C1q-fixing antibodies and IgM and IgG1/IgG3 isotypes to merozoites was quantified in 50 individuals with a clinical malaria episode from Sabah.
Plasma was collected from lithium heparin whole-blood samples according to standard procedures, snap frozen in dry ice, and stored at -70°C. All studies were registered with the Australian and New Zealand Clinical Trial Registration scheme. Participants were healthy males and females between 18 and 55 years with no prior exposures to malaria or residence in malaria-endemic regions. Two groups of participants were tested. The first group (inoculated with 2800 viable P. falciparum 3D7-parasitized RBCs; n = 40) were drawn from clinical trials nos. NCT02867059, NCT02783833, NCT02431637, and NCT02431650. The second group (inoculated with 1800 viable P. falciparum 3D7-parasitized RBCs; n = 20), tested only on days 0 and 30, was drawn from clinical trial nos. NCT02453581, ACTRN12613001040752, and ACTRN12613000565741.
Wilcoxon matched-pairs signed-rank test was used for comparisons of responses between acute (day 0) and convalescent time points (days 7 and 28) in matched samples. Mann-Whitney U test was used to compare responses between different populations (acute versus control) and for invasion inhibition assays. Spearman's rank correlation coefficient was used to assess correlations between antibody responses and C1q fixation. To assess the relationship between C1q and IgM and IgG subclasses in Sabah individuals, ELISA OD data were transformed into arbitrary units, relative to the highest responder in the cohort.
Acknowledgments: RBC and human serum used for parasite culture and naïve controls were provided by the  Australian Red Cross blood bank (Melbourne and Darwin). We thank  J. Richards  for assistance with collating returned traveler samples, all participants and parents or guardians involved in the clinical studies, and the staff of the  Papua New Guinea Institute for Medical Research , along with Malaysian Ministry of Health hospital directors and clinical staff at Kudat,  Kota Marudu , and Pitas district hospitals and at  Queen Elizabeth Hospital  ( Kota Kinabalu ) and with  Kenya Medical Research Institute (Kilifi, Kenya). We would like to thank the Director General of  Health Malaysia  for permission to publish this work. This work is published with permission from the director of KEMRI.
Taking into account this fluctuating parameter makes it possible to improve the power curves and therefore the operating point of the turbines  [8]. C p (λ,β) is aerodynamic efficiency and is defined by a nonlinear function of the tip-speed ratio (TSR) λ and blade pitch angle β. The TSR is defined as λ = ωR v ( 2 ) where ω is rotor speed. For each wind value, there is a rotor speed for which the C p is maximum and this value corresponds to the optimal tip-speed-ratio λ *. This is illustrated in Figure  2 , which represents a typical λ-β-C p curve for AWT-27 wind turbine model. The calculation was performed using FAST Software (NREL, Golden, CO, USA). Data post-processing was done using MATLAB (The Mathworks, Natick, MA, USA). Table  1  presents a summary of AWT-27 configuration.
The transition zone between Region II and Region III, named transition region, is important in the control strategy in order to reach rated power. This is illustrated in Figure  3 , which represents the power operating region for AWT-27. Calculations was performed using FAST Software (NREL, USA). Data post-processing was done using MATLAB (The Mathworks, Natick, MA, USA). Region I corresponds to the start of the wind turbine. In Region I, the turbine is stopped due to the low level of wind velocities, which do not allow extracting significant power. Generally, the cut-in wind speed is 5 m/s. In Region II, the power content of the wind varies with the cube of the average wind speed (Equation (  1 )). The output power requires an appropriate control of torque and speed turbine to maximize energy production.
Statistical analysis Statistical analyses were performed using R statistical software (v3. 3. 1) at α = 0. 05. For organosomatic index and vitellogenin, two-way ANOVA test was performed after that normality and homogeneity were tested, using respectively Shapiro and Levene tests. When normality or homoscedasticity were not met, data were transformed using log, or square root method. We first analysed the effect of sex on the different parameters at each time-point independently, using two-way ANOVA. Sex and exposure conditions were considered as factors. Since there was a significant sex-effect, data were then analysed on males and females taken separately. For that, two-way ANOVA test was performed, using exposure and time conditions as factors. Tukey's HSD post-hoc test was performed to compare conditions at each time-point.
S6G ). Of importance, depletion of SMC1 or MPP8 also triggered decreased levels of H3K9me3 at rDNA (Fig. 6I, J ), further strengthening the role of cohesin and the HUSH complex in mediating H3K9me3 deposition. Collectively, these data suggest that following DSB production in rDNA, the cohesin complex mediates transcriptional shutdown of ribosomal genes in a manner that depends on the HUSH complex and SUV39H1/2-mediated H3K9me3. The LINC complex subunit SUN1, the actin nucleator ARP3, and the myosin regulator UNC-45A promote DSB mobilization downstream from transcriptional repression We next aimed at characterizing the mechanisms that ensure rDNA DSB mobilization at the periphery. Previous studies in yeast and mammals showed a role for the microtubule network in the movement of DSBs  (Lottersberger et al.
2014; Ryu et al. 2015) , via nuclear actin polymerization in Drosophila  (Caridi et al. 2018). We hence further asked whether rDNA DSB mobilization also necessitates the actin network. Indeed, depletion of UNC-45A (also known as general cell UNC-45 [GC-UNC-45], a myosin regulator)  (Lehtimäki et al. 2017 ) and ARP3 (an actin nucleator-mediating branched actin network), both previously involved in heterochromatic DSB mobilization in flies  (Caridi et al. 2018) , also decreased DSB-induced cap formation (Fig. 7C, D ) without major changes to cell cycle distribution (Supplemental Fig. S7H ). Strikingly, depletion of all three factors (SUN1, UNC-45A, and ARP3) did not affect transcriptional repression following DSB induction (Fig. 7E, F ; Supplemental Fig. S7I ). Collectively, these data suggest that the NE/LINC complex and actin network (ARP3 and UNC-45A) ensure rDNA DSB mobilization outside of the nucleolus downstream from transcriptional inhibition.
Moreover, depletion of the LINC/actin pathway impairs mobilization without affecting the upstream transcriptional inhibition. Of note, this rDNA mobilization pathway is specific to DSB formation, since depletion of SMC1, Suv39H1/2, Sun1, UNC45, and Arp3 did not affect rDNA segregation observed after actinomycin D (data not shown). Importantly, so far, we could not identify any conditions under which transcriptional arrest is impaired and DSBs are still mobilized, which might suggest that transcriptional repression is a prerequisite for DSB movement.
Alternatively, or in addition, the LINC complex and NE invaginations could also directly regulate resection, which could further foster DSB mobilization to the periphery of the nucleolus. Our data also indicate that rDNA movement relies on the actin network, as reported for Drosophila heterochromatic breaks  (Caridi et al. 2018)  and in line with the contribution of actin regulation in DSB repair  (Belin et al. 2015; Spichal et al. 2016; Aymard et al. 2017; Schrank et al. 2018 ). One possibility is that rDNA DSB mobilization involves the formation of nuclear/nucleolar actin filaments as found in other contexts  (Belin et al. 2015; Caridi et al. 2018; Schrank et al. 2018 ). Alternatively, since cytoplasmic filamentous actin can be found in NE invaginations  (Jorgens et al. 2017) , one could envisage that cytoplasmic actin cables/filaments regulate NE deformation and the dynamics of invaginations, which could further contribute to the relocalization of the rDNA DSBs outside of the nucleolus thanks to a physical link between the DSBs and NE-embedded LINC complex.
BLESS analysis on rDNA BLESS sequences from asynchronized cells or G1 and G2 cell populations were retrieved from our previous work  (Aymard et al. 2017; Clouaire et al. 2018; Mourad et al. 2018). Barcodes were removed using Cutadapt, and BLESS data were aligned on a custombuilt genome composed of hg19 plus rRNA unit U13369. 1 from GenBank using bwa in paired-end mode (bwa aln and bwa sampe). Fragments were reconstituted from paired reads using Rsamtools and GenomicAlignments (R packages). Fragments with lengths >500 bp were dropped, and the remaining fragments were deduplicated (fragments with the exact same start and end positions were considered as duplicates). For the G1 and G2 BLESS data analysis, proximal positions were extracted from fragments based on their relative positions to the DSB.
The appropriate Alexa-conjugated secondary antibodies diluted in 3% BSA/PBS (mouse or rabbit Alexa 594 or Alexa 488 used at a 1:1000 dilution [Invitrogen]) were then added to cells for 1 h at room temperature followed by DAPI or Hoechst 33342 staining. Coverslips were mounted in Citifluor (Citifluor, AF-1), and images were acquired using a wide-field microscope (Leica, DM6000) equipped with a cooled charge-coupled device camera (Cool-SNAP HQ2) using 40× or 100× objectives and MetaMorph. Images of total RPA and SUN1 immunostainings were processed by nonlinear deconvolution with Huygens software (Scientific Volume Imaging). Classical maximum likelihood estimation was used to estimate positive fluorescent signal after deconvolution. An adaptive estimation of background was done for each image, the signal to noise ratio was set to 40 (RPA) or 20 (SUN1), and 50 iterations were used during the minimization.
Quantifications of immunofluorescence For DSB mobilization, the number of cells displaying at least one nucleolar cap was counted manually using the 40× objective in >150 cells per experiments and for a minimum of three independent experiments using either RNA pol I or UBF as markers. The number of caps (RNA Pol I staining) per nucleus was counted manually in >80 cells. Resection was quantified using RPAS33P antibody in >100 cells acquired with a 40× objective and analyzed using Columbus software (Perkin Elmer). Nucleoli were detected thanks to the RNA Pol I (using method A), and the sum of RPA2S33 spot (method D) intensity inside the nucleolar region was determined. At least three independent experiments were performed; only one representative experiment is shown as a box plot. In Supplemental Figure  S3F , the presence of repair protein in either unsegregated or segregated nucleoli was quantified in cells with at least one nucleolar cap.
Secondary antimouse Alexa 647 (1:500 in 1% BSA/PBS; Molecular Probes, A21235) was incubated for 30 min at 37°C in the presence of 80 U/m RNasinL followed by DAPI staining. Image acquisition with a 20× objective lens was automated to obtain at least 36 fields per well, allowing the visualization of a total of >1000 cells (three wells were acquired for each condition). Each picture was analyzed with the integrated Columbus software. Briefly, the DAPI-stained nuclei were selected (method B), and the size and roundness of nuclei were used as parameters to eliminate false positive compounds. The 5-FUrd staining attributed to the nucleoli was delineated using the find spot method D, and the sum of the 5-FUrd intensity was measured and is represented as box plots. For cell cycle analysis, the sum of the DAPI intensity and the mean of the EdU intensity were plotted in order to separate G1, S, and G2 cells.
DNA FISH combing and analysis The combing and FISH probing of the rDNA fibers were carried out before or after 24 h of OHT treatment (Genomic Vision). The detection and rDNA fibers and the attribution of rDNA-specific probes  (Caburet et al. 2005 ) was carried out using Genomic Vision's provided Web interface. Analysis of rearrangements was performed using a script that was designed to count the total number of units (as defined when comprised between two "IGS" regions) and assess the occurrence of changes when comparing one unit with the next. The source code was deposited on GitHub (https://github. com/LegubeDNAREPAIR/rDNA/blob/master/ get_break_event. py).
Acknowledgments We thank  T. Clouaire  and  N. Puget  for their advice throughout the project and comments on the manuscript. We thank  E. Watrin  ( Rennes ) for the Flag-GFP-SMC1 construct. We thank the high-throughput microscopy platform of the  Centre de Biologie Intégrative (Toulouse) , and the mass spectrometry platform from the  Institut de Pharmacologie et de Biologie Structurale (Toulouse). Funding in E. P. R. 's laboratory was provided by  Labex Ecofect  ( ANR-11-LABX-0048 ),  Fondation Innovations en Infectiologie (Finovi) , and the  European Research Council  ( ERC-StG-LS6-805500 ) under the European Union's  Horizon 2020 research  and innovation programs. Funding in G. L. 's laboratory was provided by grants from the  European Research Council  ( ERC-2014-CoG 647344 ), the  Agence Nationale pour la Recherche  ( ANR-14-CE10-0002-01  and  ANR-13-BSV8-0013 ), the  Institut National Contre le Cancer (INCA) , and the  Ligue Nationale Contre le Cancer (LNCC).
Diagnosis was confirmed by two trained psychiatrists of the Schizophrenia Expert Centers Network. After initial examination, patients were seen every year at the Expert Center during a 3 years follow-up, using a standardized and systematic assessment. Only one-year follow-up data are available to date. The study was carried out in accordance with ethical principles for medical research involving humans (WMA, Declaration of Helsinki). The assessment protocol was approved by the relevant ethical review board (CPP-Ile de France IX,  January 18, 2010). All data were collected anonymously. A non-opposition form was signed by all participants as this study including data coming from regular healthcare assessments.
Statistical Analysis Sociodemographics, clinical data, and treatments are presented as the mean +/-the standard deviation (SD) for continuous variables and frequency distribution for categorical variables. We performed unsupervised machine learning algorithm using clustering analysis based on kmeans estimation. In order to check the stability of our cluster, we generated a simple random sample without replacement of 500 patients from the original dataset and rerun the cluster analysis (not shown). Analyses of (co)variance were then performed to compare each group in regard to socio-demographics factors, clinical characteristics, co-morbidities, therapeutics and cognitive parameters. Continuous variables were analyzed with the Student t-test for normally distributed data and the Mann-Whitney test in case of non-normal distribution.