dataesr/scientific-paragraphs-categorization huggingface dataset:
Name OA  fitting model was found to be appropriate to describe these systems. The existence of a layer contaminated by electrolyte anions (here oxalates) has been confirmed and quantified. We also found that the length in the fitting model is a critical parameter and corresponds to the longitudinal correlation length. Its determination is thus required to fit SANS data in absolute units. Finally, this original approach, based on a detailed and complete description of the SANS data, can be applied to AAOs prepared in different electrolytes (sulfuric or phosphoric acids) and extended to a variety of nanoporous media and will allow the further investigation of condensed matter under confinement.
Data Analysis We began by investigating handwriting performances with an analysis of letter legibility. Two raters determined whether each letter that was produced was legible or not, using the Evaluation Tool of Children's Handwriting (ETCH;  Amundson, 1995;  see also  Alamargot et al. , 2014). According to Evaluation Tool criteria  (Amundson, 1995) , a letter is non-legible if it is not quickly recognizable out of context and at first glance, is poorly formed, distorted, reversed or greatly rotated, is confused with another letter or numeral, has additional or missing parts, is sloppy or intentionally hatched, overlaps with another letter, or is not proportional. Based on these criteria, each correctly formed letter was scored one point in both the letter-handwriting task (total of 20 letters produced in isolation) and the pseudowordhandwriting task (6 letters per item).
Statistical analysis The results are reported as mean ±standard deviation or median (range) for continuous variable according to the distribution of the variable. Continuous variables are compared between groups using a paired Student t-test or a Mann-Whitney U-test as appropriate. Categorical variables are compared using a Chi-square test. ANOVA analysis was used when more than 2 groups had to be compared. Significant difference between groups was considered when p-value< 0. 05. Statistical analysis were performed with Statview software (version 5. 0 for Windows; SAS Institute, Cary, NC).
Echo 550 Reverse Transfection The Echo 550 device was set to 384LDV_AQ_B2 mode. Three picklists were generated from a Microsoft Excel macro file to design the position of each source well, the expected transferred volume, and the destination wells (step 1: diluent dispensing; step 2: DNA transfer; step 3: transfection reagent mix dispensing). Generated picklists were imported in the Echo Cherry Pick 1. 5. 3 software for transfers. A 384LDV Microplate (Labcyte) and a 384-well Microplate µClear Black (Greiner Bio-one) were used as the source plate and destination plate, respectively. In the optimized protocol, diluent was dispensed using the MicroFlo dispenser (BioTek, 1 µL cassette).
YAP co-activates migration and invasion Page 10 of 53 Shah, SR et al Cancer Genome Atlas (TCGA). Specifically, we explored whether there was a correlation between YAP and either TRIO or TIAM1 expression in these two GBM datasets. To enable this analysis, we first derived a list of genes significantly down-regulated in shYAP compared to shCtrl mRNA samples from JHGBM651 and JHGBM612 cells (See Experimental Procedures) (Table  S2 ). We refer to these YAP-regulated genes as the GB-YAP signature. Gene signatures allow more robust estimation of YAP activity from gene expression datasets than mRNA alone, since many transcriptional regulators, including YAP, undergo significant post-transcriptional regulation. We found that the GB-YAP signature of GBM cells correlated with YAP signatures derived from other cell types  (Cordenonsi et al.
, 2011)  (Fig. S5l ). Furthermore, the REMBRANT data suggested that the patients with elevated GB-YAP signature genes also have significantly higher expression of TRIO, but not TIAM1 (Fig. 3k ). Indeed, patients with TRIO overexpression were significantly over-represented among patients with high GB-YAP signature expression in this dataset (comparing all observed patient group sizes to random expectation using Fisher's exact test; Fig. 3l ). Taken together, these results derived from patient-derived tumor tissues support our in vitro observations that YAP modulates TRIO expression.
YAP co-activates migration and invasion Page 13 of 53 Shah, SR et al environments. Interestingly, in both the TCGA and REMBRANDT tumor gene expression datasets, the GB-YAP signature is associated with higher expression of an invasive gene signature (IGS)  (Liu et al. , 2007)  (Fig. 5a , Fig. S9a ). In a reciprocal fashion, patients with elevated expression of the IGS were significantly over-represented among patients with high GB-YAP signature expression (Fig. S9b ). Consistent with these observations, we observed that fewer shYAP and more YAP OE cells invaded through Matrigel-coated Boyden chambers than their respective control cells in vitro (Fig. 5b, c , Fig. S9c ). This led us to explore the mechanisms of YAP-mediated regulation of invasion by surveying genes implicated in ECM remodeling, degradation, and proteolysis  (Jun and Lau, 2011; Overall and Lopez-Otin, 2002).
We found that MMP2 and MMP9 (as well as the YAP-target control CTGF) mRNA expression was decreased in shYAP GBM cells (Fig. S9d ). Given that these genes are transactivated by STAT3  (Dechow et al. , 2004; Hilfiker-Kleiner et al. , 2004; Xie et al. , 2004) , a transcription factor known to promote invasion and metastasis  (Yu et al. , 2009) , we hypothesized that YAP may modulate STAT3 activity. Indeed, we found evidence of association between elevated expression of the GB-YAP signature and a published GB-STAT3 signature  (Carro et al. , 2010) , with significant overrepresentation of patients with elevated expression of both signatures in GBM samples from both clinical datasets (Fig. 5d , e, Fig. S9e ). This prompted us to further investigate this correlation between YAP and STAT3 activity.
YAP co-activates migration and invasion Page 16 of 53 Shah, SR et al GB-YAP signature expression increased with glioma grade in the REMBRANDT dataset (Fig. S11a ); notably, STAT3 and TRIO have also been reported to increase with glioma grade  (Salhia et al. , 2008). Additionally, the percentage of glioma patients overexpressing the GB-YAP signature compared to non-cancer cortex increased with glioma grade (Fig. 7a ). Having found that YAP is hyperactive in GBMs, we focused on its prognostic value for these patients as determined by Kaplan-Meier analyses. Strikingly, higher expression of the GB-YAP signature predicted poor prognosis of GBM patients from both TCGA (Fig. 7b ) and REMBRANDT cohorts (Fig. S11b ). In addition, the YAP gene signature was more predictive than 91. 5% of 1 million simulated, size-matched gene sets (empirical p = 0.
, 2011). Consistently, Bhat et al  (Bhat et al. , 2011)  observed no modulation of STAT3 upon TAZ knockdown in GBM cells. These results raised the possibility that YAP might modulate the Mesenchymal phenotype associated with the eponymous sub-class in a way distinct from TAZ, through activation of STAT3. Indeed, there was a significant over-representation of GB-YAPhigh patients among the Mesenchymal GBMs, but not among other subclasses (Fig. 7c ). Furthermore, we found that YAP-TRIO signaling regulated the activity of STAT3 by promoting   , 10 ), suggesting that YAP may be a driver of mesenchymal reprogramming in GBMs. Consistent with this hypothesis, higher expression of the GB-YAP signature corresponded to higher expression of Mesenchymal subclass genes in both GBM datasets (Fig. 7d ). In vitro, we observed decreased expression levels of several Mesenchymal subclass genes after YAP knockdown in all three GBM cell lines (Fig.
Strikingly, higher expression of the GB-YAP signature predicted rapid recurrence in all the GBM subclasses except the G-CIMP subtype (Fig. S11c-f ). This suggests that YAPtargeted therapies may be effective for patients with non-G-CIMP tumors, and especially the Mesenchymal subclass. We then investigated whether YAP-TRIO-STAT3 signaling axis is clinically relevant in GBMs. First, using the TCGA dataset, we determined the distribution of patient groups with hyperactivation of either one, two, or all three of the network components (i. e. YAP, TRIO, and STAT3). Hyperactivation was determined using the GB-YAP signature, TRIO transcript, and GB-STAT3 signature (Fig. 7f ). Indeed, we found a significant over-representation of GBM. CC-BY 4. 0 International license a.
e. YAP+TRIO) without high YAP activity did not appear to be associated with poor patient prognosis in this dataset (Fig. S12 ), emphasizing the prognostic power of YAP-dependent signaling. Overall, these analyses emphasize the co-dependence of TRIO and STAT3 on YAP to confer poor patient prognosis. These results should be viewed as preliminary, as the tumor purity, mRNA degradation, and cohort size might be confounding factors in this analysis. Larger patient cohorts with matched expression data, which are currently unavailable, would help to validate these results. Nevertheless, collectively, our study provides important initial evidence of the clinical value of the YAP-driven pro-migratory and invasive genetic circuit in predicting patient outcomes (Fig. 7j ).
DECLARATION OF INTERESTS: Microarray data of shCtrl and shYAP JHGBM651 and JHGBM612 cells have been deposited with NCBI Gene Expression Omnibus under accession code (upon acceptance of manuscript). The YAP constitutively-active overexpression lentiviral vector was generated using pCMV-Flag YAP127/128/131/381A plasmid (Addgene) and Duet011 lentiviral plasmid. Primary GB, MCF10A, and NIH-3T3 cells were transduced with equal titers of concentrated virus in complete growth media supplemented with 1 μg/ml polybrene (Sigma) for 24 hours. Following transduction, cells were given 24 hours to recover before selection in 0. 25 μg/ml (JHGBM651, JHGBM640, JHGBM1A) or 0. 5 μg/ml (JHGBM612, MCF10A, and NIH-3T3) puromycin (Sigma) for a minimum of 6 days. Minimum effective puromycin or hygromycin concentrations were determined using kill curves of non-transduced cells (data not shown).
A single observer used a custom MATLAB script to view time-lapse images and manually mark Intracranial xenograft model and immunohistochemistry. All animal protocols were approved by the Johns Hopkins Animal Care and Use Committee. Briefly, JHGBM612 cells were resuspended at 75,000 cells/μl in complete growth media and placed on ice. 1-month old male NOD-SCID gamma-null (NSG) mice were anesthetized prior to intracranial injections following an established protocol  (Gonzalez-Perez et al. , 2010). 2 μl of shCtrl or shYAP cells were injected into the striatum; specifically (X: 1. 5, Y: 1. 34, Z: -3. 5) mms from the bregma (n = 3 per group). 5 weeks post-injection, mice were anesthetized prior to trans-cardiac perfusion with saline followed by 10% formalin. Brains were then harvested and fixed overnight at 4 °C in 10% formalin.
YAP co-activates migration and invasion Page 40 of 53 Shah, SR et al cDNA Synthesis Kit (Invitrogen). Quantitative real-time PCR using SYBR Green (Applied Biosystems) was performed on a 7300 Cycler (Applied Biosystems) and the included data analysis software. GAPDH primers were used as a loading control. See the table below for all primer sequences used.
YAP co-activates migration and invasion Page 42 of 53 Shah, SR et al datasets  (Carro et al. , 2010) ) were required to map to a single Entrez Gene ID. Redundant probesets mapping to the same gene were eliminated after differential expression analysis. To derive gene signatures, each cell line was analyzed for differential expression independently (GB-YAP, GB-STAT3, and GB-C/EBPβ datasets). Genes with q <. 05 and absolute fold-change > 1. 5 were considered as differentially expressed genes, although q < 0. 1 was used when no genes passed q < 0. 05. This relaxed FDR was excused because the final signatures were taken as the genes significantly differentially expressed in both cell lines, at least one of which required q <. 05. To stratify patients into groups of low, intermediate, and high expression of a given gene signature, we used the gene set z-score  (Irizarry, 2009).
g, h, i, G-LISA analysis of Rac1-GTP levels in CONT or YAP OE JHGBM612 cells, CONT or TRIO OE JHGBM612 cells, and shCtrl or shTRIO JHGBM651 cells, respectively. j, Correlation plot of relative YAP and TRIO protein expression (normalized to β-actin) in patient derived primary GBM tissues based on densitometric quantification of immunoblots in Fig. 4i and Supplemental Fig. 5k. k, TRIO and TIAM1 expression in GB-YAP high, intermediate, or low patient groups in the REMBRANDT GBM dataset. i, Percentage of.
g-i, Kaplan-Meier graphs of cumulative progression-free survival in patient groups defined by the GB-YAP signature, TRIO transcript, and STAT3-conserved signature expression in TCGA dataset. P=Log-Rank test. j, Schematic of the YAP-driven pro-migratory and invasive signaling pathway in a single cell on a layer of extracellular matrix. Dashed lines demonstrate movement of cell body during migration and invasion. Darker shading of extracellular matrix around the invading edge of the cell represents degradation of the surrounding tissue.
For Affymetrix data (GB-YAP, TCGA, and Rembrandt datasets)//statistics. berkeley. edu/sites/default/files/tech-reports/745. pdf) using a custom CDF with updated probeset mappings (Dai et al. , 2005 ) (BrainArray v15. 1. 0). Differential expression analysis was performed using the sam function of the siggenes package (version 1. 28. 0) in R 2. 15. 2. For Illumina beadchip data, signature probesets (GB-STAT3 and GB-C/EBPβ.
The microarray dataset of JHGBM651 and JHGBM612 cells treated with shCtrl or shYAP in triplicate will be made available on the Gene Expression Omnibus prior to publication. The file named "Suppl Figs. 1-11" contains the following 11 supplemental figures with their associated legends listed below:SupplementalFigure 1. YAP regulates migration. a, Densitometric analysis of YAP and YAP/β-actin expression in Fig. 1a. b, mRNA expression of YAP target genes, Mcl-1 and CTGF, in glioblastoma tissues compared to non-cancer cortex. c, Top: Representative immunoblot of YAP and p-YAP expression in primary GBM cell cultures. Bottom: Representative immunoblot of YAP expression in nuclear and cytosolic fractions of primary GBM cells. * = cells used in functional assays. Numbers correspond to Patient Sample ID.
ACKNOWLEDGEMENTS: We thank the  Department of Neurological Surgery and Oncology at Johns Hopkins Hospital  for access to intraoperatively-obtained glioblastoma tissues;  Lakesha Johnson  and  Liron Noiman  for establishing primary cell cultures of these primary glioblastoma specimens;  Colette Ap Rhys  for designing and preparing lentiviruses;  Conover Talbot Jr. for technical support and helpful discussions of microarray analysis;  Tom Schaffer  for technical support with the RhoA-GTP pulldown assay; and  Kaisorn L. Chaichana  for glioblastoma patient MRI images. This research was funded by  NIH   R01 NS070024  and  NIH   5K08 NS055851  (A. Q. -H), and  U54 CA209992  and  U01 CA155758  (A. L). S. R. S is a recipient of the  National Science Foundation Graduate Research Fellowship. A. M. is a recipient of the  Maryland Stem Cell Postdoctoral Research Fellowship.
f, Distribution of patient groups expressing either one, two, or all three of GB-YAP signature, TRIO transcript, and STAT3-conserved signature expression patterns. g-i, Kaplan-Meier graphs of cumulative progression-free survival in patient groups defined by the GB-YAP signature, TRIO transcript, and STAT3-conserved signature expression in TCGA dataset. P=Log-Rank test. j, Schematic of the YAP-driven pro-migratory and invasive signaling pathway in a single cell on a layer of extracellular matrix. Dashed lines demonstrate movement of cell body during migration and invasion. Darker shading of extracellular matrix around the invading edge of the cell represents degradation of the surrounding tissue.
Design and setting This retrospective cohort study was based on data routinely collected by the GCR, which has recorded all incident cancers occurring in Geneva, Switzerland (approximately 480,000 inhabitants in 2014) since 1970. The data recorded include sociodemographic variables, tumour characteristics coded according to the International Classification of Diseases for Oncology (ICD-O)  [31] , stage at diagnosis (coded according to the Tumor, Node, Metastasis Classification of Malignant Tumors [32]), and treatment received within 6 months of diagnosis, including the identity of the physician in charge of the first treatment for the patient, for the private sector.
Patient characteristics The patient characteristics extracted from the GCR database included age (<50, 50-69, 70-79, ≥80 years), period of diagnosis  (2000-2002, 2003-2005, 2006-2009) , socioeconomic status coded according to the last occupation (high, medium, low, unknown), country of birth (Switzerland, Southern Europe, other), method of breast cancer detection (mammography screening, clinical screening, breast self-examination, other [including symptoms or incidental finding], unknown), and familial risk of breast cancer (high, medium, none, unknown).
Indicators of quality of care State-of-the-art breast cancer management was defined according to the quality indicators described by the European Society of Breast Cancer Specialists (EUSOMA)  [33]. We selected nine indicators for which information was available from the GCR database: (1) reported hormone receptor immune-activity, tumour size, and grading; (2) histological assessment before surgery; (3) a single operation for the primary tumour (excluding reconstruction); (4) sentinel lymph node excision for clinically negative axillae; (5) ≥10 lymph nodes removed when axillary dissection performed; (6) breast-conserving surgery for tumours ≤3 cm; (7) radiotherapy if indicated (after breast-conserving surgery if no metastasis or after mastectomy for pT3 or pT4 or positive margin or ≥pN2a); (8) endocrine therapy for oestrogen-receptor positive tumours; and (9) chemotherapy for oestrogen-receptor negative tumours >1 cm or with a positive lymph node (we also considered an age of ≤35 years as an indication for chemotherapy, according to the 2003 Saint Gallen Consensus  [34] ).
, 2016). The third approach groups other methods, which can be based on hints such as 2D sections of the part for example  (Muraleedharan et al. , 2018). Other methods have been proposed to recognize patterns, based on neural networks  (Jun et al. , 2001)  or genetic algorithms  (Pal et al. , 2005). However, these approaches require a large and annotated database, which is out of our scope. Finally, we highlight methods which use systems of rules based on three previously described approaches  (Wang and Yu, 2014; Vilmart et al. , 2018). These rule systems can be used to infer new information or construct a hierarchical system of features, for example. However, existing methods seem to have difficulties correctly separating intersecting features. Moreover, some information used to recognize features are specific to a feature type, which can be a problem to extend the method to other features.
Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0. 0001 km 2 (100 m 2 ) to 1 km 2. We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions.
, 2015). The new Permafrost Pond and Lake (PeRL) database provides high-resolution (5 m or better) information on current and historical waterbody surface areas -which we will refer to as "size" throughout this paper  (Muster et al. , 2017). With the availability of the PeRL database we are now in a position to characterize and analyze waterbody size distributions in the Arctic, including for the first time waterbodies as small as 0. 0001 km 2. Here, we used statistical moments to investigate (1) current waterbody size distributions in 30 Arctic permafrost lowland regions, (2) spatial factors affecting these distributions, and (3) the change in distributions over time in two regions on the Northern Seward Peninsula in Alaska.
Preprocessing of Satellite Data and Aerial Photographs We used waterbody maps from the PeRL database by  Muster et al. (2017)  for the assessment of waterbody size distributions. PeRL waterbody maps provide areas of open water. Many shallow ponds and pond or lake margins are characterized by vegetation growing or floating in the water. This type of water with vegetation could not be classified from the singleband imagery, which was the main source for PeRL classifications  (Muster et al. , 2017). Open water, on the other hand, can be classified confidently with high accuracy from most imagery. Accuracy of the waterbody maps ranged from 89% to more than 95%  (Muster et al. , 2017). Accuracy was assessed using ground surveys of lakes and ponds or aerial imagery with resolutions of less than 1 m.
Areas of open water were extracted using a density slice or an unsupervised k-means classification in ENVI software (v4. 8) on the panchromatic or near-infrared band for optical data and the X-Band (HH-polarization) for the TerraSAR-X data. Full descriptions of the image properties and the classification procedures used can be found in  Muster et al. (2017). Classifications were converted from raster representations to vector representations (ESRI shape-files). The shape-files were then manually edited to remove rivers, streams, ocean in coastal areas, waterbody fragments at the image boundaries, and cloud or topographic shadows. Waterbodies smaller than 0. 0001 km 2 -corresponding to 4 pixels at the lowest resolution of 5 m -were removed from all datasets.
To cope with the different spatial resolutions and coverages, as well as with the wide range of waterbody sizes, we (i) band-limited the data to between 0. 0001 and 1 km 2 , and (ii) broke down study regions into boxes measuring 10 × 10 km, which were analyzed individually before averaging the statistics over the entire region. We selected waterbodies with sizes between 0. 0001 and 1 km 2 for our analyses. Waterbodies larger than 1 km 2 were excluded from the analysis because of the small sample size (10 of the 30 regions included fewer than 5 waterbodies larger than 1 km 2 and some of the regions had no such waterbodies). On average, waterbodies larger than 1 km 2 contributed less than 1% (+/-0. 8 % standard deviation) to the total number of waterbodies (Figure  3 ). Subgrid analyses were conducted by breaking study regions into 10 × 10 km boxes.
Environmental Analysis Each study region was described in terms of a set of climate, permafrost, and terrain variables (Supplementary Tables  2, 3 ), which were extracted from global or regional databases to ensure similar spatial scales for all regions (Supplementary Table  4 ). The climate variables included air temperature, thawing degree days (TDD), precipitation (P), evapotranspiration (ET), and precipitation minus evapotranspiration (P-ET) (Supplementary Table  2 ). We chose these climatic parameters because we were interested in characterizing the net water inputs across the wide range of conditions in the study regions, and the potential for long-term temperature conditions to affect waterbody size distributions. ERA-INTERIM data was used to calculate long-term averages (1979 to year of image acquisition) for the mean annual air temperature (T) as well as ET, P, and P-ET during the snow-free period.