dataesr/scientific-paragraphs-categorization huggingface dataset:
For survival analysis, patients with a follow-up data >1 month were included, in order to reduce the bias caused by noncancer death. 10 In addition, nontumor brain tissues were obtained from apparently healthy individuals or chronic epilepsy patients with pathological evidence of other neurological or psychiatric diseases in each dataset. The G-CIMP status was determined by K-means (k = 3) clustering on the 1503 probes reported by Noushmehr et al  14.
MGMT (O-6-methylguanine-DNA methyltransferase) promoter methylation status was determined by a logistic regression model using two CpGs, that is, cg12434587 and cg12981137. 15 Batch effects from different datasets and platforms were adjusted by a nonparametric empirical Bayes approach (ber package). 16 Patients were then classified into high-risk or low-risk groups with the cutoff point as the median risk score from the combined training set. The validation phase was performed in GSE60274 and datasets of LGGs and in particular those with wide-type IDH.
| MiRNA target gene prediction and pathway analysis The online databases TargetScan (http://www. targetscan. org/ vert_72/. Accessed November 11, 2018), miRanda (http://www. microrna. org/microrna/home. do. Last update: 2010-11-01), and miRDB (http://mirdb. org/. Accessed November 11, 2018) were used to identify the target genes of the relative miRNAs. Genes appeared in all three databases were included for the following analysis. 21  were considered to be significant.
| Cell cycle and apoptosis analysis For cell cycle analysis, cells were harvested, fixed in 70% ethanol on ice, and stained with propidium iodide in phosphate-buffered saline containing RNase. The DNA contents were analyzed by flow cytometry. For cell apoptosis analysis, Annexin V-fluorescein isothiocyanate and propidium iodide double staining (Roche Diagnostics, Germany) was used to sort cells in early or late apoptotic phase. a Included all high-risk samples of three datasets.
| Validation of the five-GpG miRNA methylation signature for prognostication To validate the prognostic performance of the 5-CpG miRNA methylation signature, we applied it to the independent validation set of GSE60274. With the prespecified cutoff, patients were classified into a low-risk group (n = 28) and a high-risk group (n = 31). Consistent with the training sets, low-risk patients were associated with longer OS than high-risk ones (log-rank P = 0. 013; Figure  2B ). We also observed a significant correlation between progressionfree survival (PFS) and the miRNA methylation-based risk groups in Rennes cohort (Figure  2C ). In addition, we applied the GBM-derived signature to independent validation cohorts of IDH wild-type LGGs. The miRNA methylation signature failed to yield significant OS differences between the risk subgroups within both grade III and II gliomas using their median risk scores as cutoffs, respectively, which supported the signature as a GBM-specific prognostic model (Figure  2D ).
| Target gene prediction of the 5-CpG signaturerelated miRNAs Targetscan, miRanda, and miRDB databases were used to predict the target genes of miR-132, miR-127, miR-433, miR-1284, miR-1248, and miR-759. To ensure the specificity and sensitivity of our prediction, we kept the identical targets predicted in all three databases without setting additional criterion. Totally 1578 target genes left for further functional analysis (Figure  5 ). Among them, 139 genes were candidate targets for two miRNAs, seven genes were common targets of three miRNAs, and one was target of four miRNAs.
28 wever, in newly diagnosed GBM patients, recent randomized trials failed to yield clear survival benefits when applied bevacizumab plus Stupp regimen,  29   subgroups. Accordingly, distinct survival benefits were observed in Rennes cohort with the use of bevacizumab at progression: High-risk patients seemed to benefit more from the bevacizumab-contained therapy. These results suggest that the 5-CpG signature is of potential use to optimize bevacizumab therapy by identifying appropriate patient candidates. For biological features of this 5-CpG signature, target genes of relative miRNAs were analyzed with bioinformatic methods. Results showed these miRNAs regulated a great many genes and might cooperate with each other to regulate specific genes. The profound characteristics of the target genes were analyzed based on GO database and KEGG database.
The renal cell carcinoma pathway includes HIF-α pathway and strongly correlates with VEGF and PDGF production. This is consistent with the above GSEA results on gene expression data of TCGA and reminds the effects of these signature relative miRNAs. To further explore the biological relevance of the 5-CpG signature, we selected one miRNA (miR-1284) for functional experiments. MiR-1284 has been reported to have tumor-inhibiting roles in lung, gastric and ovarian cancers,  [30] [31] [32]  but its roles in GBMs were still unknown. The in vitro experiments confirmed the anti-tumor role of miR-1284 as inhibiting glioma cell proliferation and migration and inducing glioma cell apoptosis. Interestingly, when treated with demethylation agent, the expression of miR-1284 was significantly decreased, which indicated a positive correlation between its DNA methylation and expression.
38 MiR-433 was reported to be commonly dysregulated in GBMs and suppressed glioma cell proliferation, migration, invasion, and enhanced sensitivity to TMZ therapy. 39, 40 Regarding miR-759 and miR-1248, no biological or clinical evidences have been reported in cancers so far. Our study has several limitations. First, as a retrospective study, the identification and validation of the signature were based on open source databases which had already been uploaded before. The follow-up information of these researches could not be considered in our study. Also, clinical information such as drug data and recurrent therapy of some cases was not detailed enough, which made it hard to make more subtle analysis. Second, bias caused by the differences among these selected trial platforms should be considered even with compensatory statistical measure.
The survival correlation of the five-CpG signature in each dataset. A, The five-CpG signature predicted overall survival (OS) in training sets. B, The signature was validated by yielding apparent OS difference in GSE60274. C, The five-CpG signature was also able to predict PFS in Rennes cohort. D, The signature could not identify patients with different prognoses in IDH wide-type LGG (grade III or II) F I G U R E 3 Molecular and clinical characteristics of the 5-CpGs miRNA methylation signature. A, the heat maps of K-means (k = 2) clustering on the 5-CpGs methylation signature according to the M-value from all GBM groups; each column represented a sample; for each sample (n = 238), subgroup correlation was indicated; P values for Fisher' exact test and chi-square test were accordingly shown; B, GSEA enrichment plots for representative functional gene sets enriched in high-risk tumors from TCGA.
Variables Training set Validation set Rennes cohort TCGA GSE60274 be significant. Enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed and visualized using ClueGO (Version 2. 5. 3), 23,24 a Cytoscape (version 3. 7. 1, http://cy- toscape. org/) plug-in. The main parameters for constructing the network were as follows: ontologies/pathways: KEGG (321 terms/ pathways with 7454 available unique genes, December 7, 2018), GO term/pathway selection: Min Gene = 3 & Min Percentage = 3. 0%, Kappa Score Threshold = 0. 5, Statistical Test Used = Enrichment/ Depletion (Two-sided hypergeometric test), Correction Method Used = Bonferroni step down. Only pathways with P value < 0. 05 GO analysis was then performed with PANTHER (ver- sion 14. 0 Released 2018-12-03) based on the GO database (http:// www.
Glioblastoma (GBM) is the most lethal primary brain tumor in adults and is known to be particularly aggressive and resistant to anti-cancer therapies, mainly due to the presence of GBM stem cells (GBMSC). By in vitro approaches supported by analysis from patients' databases, we determined how α6-integrin and Fibroblast Growth Factor Receptor 1 (FGFR1) work in concert to regulate proliferation and stemness of GBMSC. We showed that α6-integrin regulates the expression of FGFR1 and its target gene Fokhead Box M1 (FOXM1) via the ZEB1/YAP1 transcription complex. These results were in accordance with the positive correlation observed in GBM between α6-integrin expression and its target genes ZEB1/YAP1, FGFR1, and FOXM1 in the databases, TCGA and Rembrandt. In addition, the clinical data demonstrate that GBM patients with high levels of the five genes signature, including α6-integrin, ZEB1/YAP1, FGFR1 and FOXM1, have a significantly shorter overall survival.
A-E ) GBMSC derived from GBM biopsy specimens (GC1, GC2) were transfected with an α6-integrin siRNA (si-α6 (1)), a FGFR1 siRNA (si-FGFR1), a combination of both siRNA (si-α6 (1) + si-FGFR1), or a scramble control (si-Scr). Propidium iodide staining was performed as described in section "Materials and Methods", and the DNA content was analyzed using flow cytometry. The % of cells in sub-G1, G1, S and G2/M phases were quantified by the BD Accuri C6 software. (E) Cells number was measured by using the cell counter Countess II FL. Quantifications of 3 independent experiments are presented as means ± SD. *** p < 0. 001; ** 0. 001 < p < 0. 01.
Recently, it has been reported that ZEB1 directly binds to the Hippo pathway effector YAP1 and turns into a transcriptional co-activator of common ZEB1/YAP1 target genes involved in   A-E ) GBMSC derived from GBM biopsy specimens (GC1, GC2) were transfected with an α6-integrin siRNA (si-α6 (1)), a FGFR1 siRNA (si-FGFR1), a combination of both siRNA (si-α6 (1) + si-FGFR1), or a scramble control (si-Scr). Propidium iodide staining was performed as described in section "Materials and Methods", and the DNA content was analyzed using flow cytometry. The % of cells in sub-G1, G1, S and G2/M phases were quantified by the BD Accuri C6 software. (E) Cells number was measured by using the cell counter Countess II FL. Quantifications of 3 independent experiments are presented as means ± SD. *** p < 0.
A-G ) GBMSC derived from GBM biopsy specimens (GC1, GC2) were transfected with an α6-integrin siRNA (si-α6 (1)), a FGFR1 siRNA (si-FGFR1), a combination of both siRNA (si-α6 (1) + si-FGFR1), a scramble control (si-Scr) or a ZEB1 siRNA (si-ZEB1). (A-C) Olig2, Tuj1, FGFR1, and ZEB1 proteins expression was analyzed using Western blot. Images are representative of 3 independent experiments. Actin was used as a loading control. (D-F) Spheres formation was analyzed as described in section "Materials and Methods". Micrographs from representative fields were taken ×20 (E), ×40 (G). (C) neurospheres number was counted under the microscope. (F) The spheres size was measured using the Image J software. Quantifications of 3 independent experiments are presented as means ± SD. *** p < 0. 001; ** 0. 001 < p <0.
α6-Integrin Expression Correlates with the Target Genes ZEB1, YAP1, FGFR1, and FOXM1 The glioblastoma database of the Cancer Genome Atlas, (TCGA, n = 539) was used to analyze the correlations between α6-integrin expression and several potential target genes including ZEB1, YAP1, FGFR1, and FOXM1. As shown in Table  1 , a significant positive correlation was observed between α6-integrin expression and each of the target genes tested. Except for FOXM1, these results were confirmed in the Rembrandt database (n = 184) (Table  2 ). Finally, we queried if the five genes signature, α6-integrin/ZEB1/YAP1/FGFR1/FOXM1, correlates with the survival of GBM patients. For this purpose, we analyzed two different databases, TCGA and Rembrandt. In the TGCA database, analyses were performed on 184 patients treated with standard radio-chemotherapy for primary GBM without prior glioma history (n = 184).
Although the risk score was not found to be significantly associated with overall survival as a continuous variable (Hazard Ratio (HR) = 2. 72 (0. 97; 7. 61), p = 0. 057), the five genes signature was significantly associated with overall survival when patients samples were stratified by the highest and lowest quartile (high versus low risk: HR = 1. 99  (1. 16; 3. 44) , p = 0. 013, Figure  7A ). In the Rembrandt database, the risk score and risk groups based on the same five genes were found to be significantly associated with the overall survival survival (HR = 2. 49 (1. 05; 5. 89), p = 0. 038; 4th quartile vs. 1st quartile: HR = 1. 86 (1. 16; 2. 95), p = 0. 009, Figure  7B ), indicating that higher levels of this five-genes signature predicted significantly shorter overall survival as compared to patients with lower expression.
An intracellular cross-talk has also been reported in endothelial cells between integrins and FGFR1. Fibronectin, through the activation of β1 or β3, has been shown to trans-activate FGFR1 by phosphorylation via the non-receptor tyrosine kinase Src  [31]. Finally, in breast cancer cells, α3-integrin has been reported. High expression of the five genes signature: α6-integrin/ZEB1/YAP1/FGFR1/FOXM1 is prognostic of survival of GBM patients. Kaplan-Meier curves depict the probability of overall survival based on the expression of a five genes signature: α6-integrin/ZEB1/YAP1/FGFR1/FOXM1. Analysis was performed from TGCA (A) or Rembrandt (B) databases. Patients were stratified by the upper or lower quartiles and the statistical analysis performed as described in section "Materials and Methods".
In particular, α3-integrin has been shown to regulate YAP1 expression in GBM  [35] , whereas α1-integrin has been involved in YAP1 nuclear relocation in hepatocarcinoma  [36]. Very few studies have reported the regulation of FGFR expression by YAP1. In lung cancer, YAP1 increases FGFR1 expression, and in cholangiocarcinoma, cross-talk between YAP1 and FGFR1, 2 and 4 has been shown  [37, 38]. Our results were in accordance with the positive correlation observed between α6-integrin expression and its target genes ZEB1, YAP1, FGFR1, and FOXM1 in GBM from two different databases, including respectively 539 and 184 patients. In addition, consistent with our pre-clinical data in vitro, the analyses from two distinct clinical datasets, TGCA and Rembrandt databases, demonstrate that GBM patients with high levels of the five genes signature, including α6-integrin and its targets, ZEB1, YAP1, FGFR1, and FOXM1, have a significantly shorter overall survival as compared to patients with lower expression.
D Spheroid Formation Cells derived from GBM biopsy specimens, transfected with a specific α6-integrin siRNA, a specific FGFR1 siRNA or both were seeded in 96 wells plates (100 cells/well). After 8-10 days, the number of neurospheres/wells was counted under the microscope. The spheres area was measured using the Image J software (version 1. 49V, free download at www. 01net. com) by using the scale bar obtained with the Nikon software NIS Elements (version 4. 0, Nikon, Champigny sur Marne, France). Experiments were performed independently 3 times with 12 wells/conditions on GBMSC neurospheres at 3 different passages between passages 4 and 12.
Cell Cycle Analysis 24 h post-transfection, cells were fixed in 70% ice-cold ethanol for 1H at 4 • C. After washing, the cell pellet was resuspended in propidium iodide (PI)-staining buffer (50 µg/mL PI, 10 µg/mL RNAse A) and incubated for 15 min at 37 • C. The DNA content was analyzed by flow cytometry (BD AccuriTM C6 cytometer). The % of cells in sub-G1, G1, S and G2/M phases were quantified with the BD Accuri C6 software (Le Pont de Claix, France). Experiments were performed independently 3 times in duplicates on GBMSC neurospheres at 3 different passages between passages 4 and 12.
Genes Correlations in TGCA and Rembrandt Databases The glioblastoma database of the Cancer Genome Atlas, (TCGA, n = 539) (https://genome-cancer. ucsc. edu/) and Rembrandt database (n = 184) (http://www. betastasis. com/glioma/rembrandt/) were used to analyze the correlations between α6-integrin expression and several potential target genes including ZEB1, YAP1, FGFR1, and FOXM1. Links between biomarkers were assessed using Spearman's rank correlation coefficient and tests were considered significant at alpha 5% level. Statistical analyses were performed using R. 3. 5. 1 software (free download at www. 01net. com).
Association Between Five Genes: Integrin α6/ZEB1/YAP1/FGFR1/FOXM1 and Overall Survival in GBM Patients Survival analyses were performed on the glioblastoma database of The Cancer Genome Atlas (https://genome-cancer. ucsc. edu/), on patients treated with standard radio-chemotherapy for primary GBM, excluding patients with prior glioma history (n = 184). Overall survival was estimated using the Kaplan-Meier method. A risk score was created based on the linear predictor given by the multivariable Cox proportional hazard model on overall survival using a set of 5 genes (α6-integrin/ZEB1/YAP1/FGFR1/FOXM1). This score was then divided into four groups by taking the quartiles. To confirm the prognostic ability of our risk score, coefficients found on the TCGA database were applied on REMBRANDT patients (http://www.
Consequently, we chose a cheap car toy (Figure  2 ). We only kept the chassis frame, which provides a cargo lift of around 15kg, the wheel motors, the steering system, as well as some very well known Raspberry Pi 5 outfitted with movie camera, WiFi connection and display screen. An SMT32F103 micro-controller was used for the low level control and a CAN bus was used for the communication between STM32 and Raspberry. The students added a lot of electric devices : ultrasonic sensors, GPS, additional movie camera, lidar, other micro-controllers. The Raspberry system is based on a free Linux distribution, ensuring full open access.
Progeny were assessed for all phenotypic markers. To calculate intersister recombination, R(1)2, y 1 w hd80k17 f 1 / y 1 females with desired genotype were crossed to y 1 w 1118 and progeny was scored for phenotypic markers. Exceptional progeny (progeny resulting from meiotic nondisjunction) were subtracted from total progeny and rates were adjusted to reflect only normal (non-exceptional) progeny, as in  [21]. For complete dataset and calculation details, refer to S1 Table.
Slides were incubated swith 400 μL DAPI (1 ug/ml) in 1x PBS for 10 minutes in dark and washed in 1x PBS. Coverslips were mounted with ProLong Gold. Antibodies for C(3)G  [49] , Corolla  [14] , SMC1  [10] , and CID (Active Motif) were used. Fig 5A : Images were taken on Zeiss LSM880 confocal laser scanning microscope using 63x/0. 65 NA oil immersion objective with a 2x zoom using ZEN software. Images were saved as. czi files and processed using FIJI  [50]. Fig 2D : Images were taken on Nikon N-SIM using Elements software. Images were saved as. nd2 files and processed using FIJI  [50]. For one BAC clone DNA template, the following was added into a 0. 5 mL tube: 5 μL 10X DNA Pol I buffer, 2. 5 μL dNTP mix (1 mM each of dCTP, dATP, dGTP), 2. 5 μL biotin-11-dUTP (1 mM), 5. 0 μL 100 mM BME, 10 μL of freshly diluted dDNase I, 1 μL DNA Pol I, 1 ug of template DNA, water up to 50 μL.
(If using IDT-generated probes, these steps were not performed. ) Ovarioles were mounted on a slide in 35 μL of DAPI + fluoromount. Antibody for C(3)G  [49]  was used. Figs 1B and 1C and 4D: Images of whole mount germaria were taken on Zeiss LSM880 confocal laser scanning microscope using 63x/0. 65 NA oil immersion objective with a 2x zoom using ZEN software. Fig 2B : Images were obtained using AIRY-Scan on Zeiss LSM880 confocal laser scanning microscope using 63x/0. 65 NA oil immersion objective. Images were saved as. czi files and processed using FIJI  [50].
To account for potential staining heterogeneity, fluorescence intensities were normalized to total nuclear CID fluorescence intensity, which was assumed to be unperturbed. Fluorescence intensities represent raw integrated densities of maximum intensity projected z-stacks. Nucleus selection, background subtraction and fluorescence intensity measurements were performed semi-automatically using custom FIJI-based plugins  [50]. The custom FIJI-based plug-ins are publicly available at GitHub (https://github. com/viboud12/Germarium_Meiosis).
Live cell imaging tracking The use of PAR1::GFP on live germaria allowed the identification of the different cyst stages. For live germaria, images shown are the projection of all Z-series of a single (t) projection. Three-dimensional tracking of spinning-disc data was performed using Imaris software (Bitplane). The CID::RFP signal was tracked using the 'spots' function with an expected diameter of 0. 3 μm. Automatically generated tracks were then edited manually to eliminate inappropriate connections, including connections between foci in different nuclei or between foci of different sizes or intensity when more likely assignments were apparent or multiple spots assigned to the same focus. To remove global movements of the germarium, each nucleus containing a CID::RFP focus was assigned to the nearest fusome foci.
Then, the position of the reference fusome was subtracted from each CID::RFP focus for each time point of the tracking to get the relative tracks. These relative tracks were then compiled using a custom MATLAB (MathWorks) routine that computes the minimum volume of the ellipsoid that encloses all of the three-dimensional points of the trajectory. To analyze centromere trajectories: Positions of individual centromeres were tracked every 10 seconds for 8 minutes to quantify the volume covered by each centromere. This raw volume was then corrected both for overall movements of the tissue and for variations in total nuclear volume. First, we subtracted the motion of the germarium using the position of the fusome as a reference within each cyst. Second, to take into account the nuclear volume at 8cc, we computed the relative volume, which is the raw volume divided by the mean value of the nuclear volume at 8cc stage.
WT and Mcm5 A7 data are repeated from Fig 4 and are represented as X-probe plus 3R-probe early pachytene data. Significance comparing Mcm5 A7 and nos>Smc1, Mcm5 A7 : �� p = 0. 0002, chi-square d. Crossover levels on chromosome 2L as shown in cM in WT (n = 4222) [54], Mcm5 A7 (n = 2070), and nos>Smc1; Mcm5 A7 (n = 933). ��� p < 0. 001, chi-square. Data are represented as mean ± 95% CI. Refer to S2Table for full 2L crossover dataset. e.
Acknowledgments We thank the  Sekelsky Lab, Scott Hawley , the  Hawley Lab , and  Abby Dernburg  for critical review of this manuscript. We thank  Sharon Bickel  (SMC1 antibody and spreading protocol),  Cathy Lake  and  Scott Hawley  (C(3)G and Corolla antibodies),  Kim McKim  (UAS::Smc1-HA transgenic fly), and  Bloomington Stock Center  for generously providing reagents and protocols. Thanks to  Tony Perdue  and  UNC Biology Microscopy Core  for microscopy assistance.
MRI scans Perfusion images measuring cerebral blood flow (CBF) at rest were acquired for all participants using a 3D pulsed continuous arterial spin labelling (ASL) sequence with spiral filling of the K space (TR/TE: 4428/10. 5 ms, Post Labeling Delay: 1025 ms, flip angle: 155°, matrix size: 128 x 128 , field of view: 24 x 24 cm, with 34 axial slices at a thickness of 4 mm). CBF quantification from ASL tagged and control images was performed and warrant by General Electric 3D ASL software  [26]. In addition, all participants underwent a 3D T1-weighted FSPGR sequence.