dataesr/scientific-paragraphs-categorization huggingface dataset:
Bias in the Prognostic Value when Using the Uncurated Cohort We assessed whether the clinical or biological data from the non-PDAC patients impacted survival analyses performed on the TCGA_PAAD cohort. While the median PFS and OS of the uncurated cohort was marginally longer than that of the pure PDAC cohort (median PFS: 17. 0 versus. 15. 9 months and median OS 20. 1 vs. 20. 2 months respectively), the non-PDAC patients had a much longer PFS and OS compared to the pure PDAC cohort (n = 150) (median PFS: 27. 3 vs. 15. 9 months p = 0. 07; median OS: unattained vs. 19. 6 months p = 0. 03; five year overall survival: 54. 6% vs. 19. 7%) (Figure  3a. ). When the gene expression data were considered as continuous values; 5312 and 4221 genes were significantly associated (FDR 5%) with the PFS and OS respectively in the uncurated cohort (where a total of 17,302 genes were tested, uncorrected log-rank test: 6618 and 7260 genes at alpha = 5%).
The progression-free and overall survival of cases with the top and bottom 25% expression were then compared in the uncurated and the curated cohort. While some genes such as ERBB2, HK2, SLC2A1 were significantly or nearly significantly associated with the prognosis in both cohorts, others such as MUC1/LOX/TWIST1/PI3K lost their prognostic significance in the pure PDAC cohort (TWIST1 as an example in Figure  4a ). Similar findings were observed when key mutations in PDAC were assessed. Here we included the seven PDAC cases with the DNA data available (but no RNAseq). The results were similar when using the minimal 150 PDAC cohort. While the mutational status of KRAS and TP53 were strongly associated with the overall survival in the uncurated cohort, these mutations lost their prognostic significance in the pure PDAC cohort (Figure  3b, c ).
In a recent study, Shi et al. searched in silico miRNAs associated with the prognostic significance in the TCGA_PAAD cohort and reported that a five miRNA signature had a strong prognostic value  [7]. Unfortunately, they used the uncurated cohort. As a consequence, when these miRNAs were assessed in the pure PDAC cohort, they lost their prognostic significance (mir-203 as an example, Figure  4d ). Similar findings were observed when key mutations in PDAC were assessed. Here we included the seven PDAC cases with the DNA data available (but no RNAseq). The results were similar when using the minimal 150 PDAC cohort. While the mutational status of KRAS and TP53 were strongly associated with the overall survival in the uncurated cohort, these mutations lost their prognostic significance in the pure PDAC cohort (Figure  3b, c ).
Web-Based Application to Query the Curated TCGA-PAAD Dataset In order to quickly assess the prognostic significance of a gene in the curated TCGA_PPAD, we developed a web-based application  [8]. The application requires a gene symbol and displays survival curves by splitting patients into groups depending on their level of expression of the selected gene, if it is available in the TCGA_PAAD RNAseq data. The patients are either separated in two groups, low versus high, or separated by a given percentile cut, 50% by default. In this splitting case, all 144 patients were used for the survival analysis. The patients could have also been divided by interval in which only the patients with the highest expression levels would be shown against the patients with the lowest levels of expression of the analyzed gene.
Discussion Clinical validation of their findings is often a bottleneck step for basic scientific laboratories. Consortium efforts such as those of the TCGA and the ICGC have been a tremendous help for this purpose. In addition, it facilitated alternative approaches based on in silico discovery completed by clinical validation. Here, we presented a thorough analysis of the TCGA dataset, which was dedicated to pancreatic neoplasms and highlighted the heterogeneity of the data sources and samples. In addition, we demonstrated that the curation of this dataset led to the exclusion of almost 20% of the cases but was a mandatory step, as it prevented false results on prognostic analyses. TCGA data may be retrieved through numerous platforms. Confusion for researchers may arise from the fact that while data for a particular sample are homogeneous across all platforms, the list of available samples is heterogeneous with little to no information on the sample of most platforms.
In addition, while the genomic data for a sample may be exploitable, the RNAseq data may not. The TCGA data portal provided the most comprehensive information on the nature of the samples, but it required a deeper exploration within the platform to find it. Due to most platforms providing the official clinical data, but not a detailed manifest of the samples analyzed, it is important to retrieve the final list of "good samples" and to only assess these, a function available on some platforms but not all. In the TCGA_PAAD, there were three main reasons for sample exclusion. The first reason, valid across all the data (DNA, RNA, etc. ) was the histology of the tumors. PDAC have a well-described biology (mutational and transcriptional patterns) and a clinical behavior, which is very distinct from other pancreatic neoplasm such as neuroendocrine neoplasms (NEN), acinar cell carcinomas or intraductal papillary neoplasm (IPMN).
Well-differentiated NEN for instance, have a completely different mutational and transcriptional pattern from PDAC and a much longer survival rate. Therefore, any alteration specific to PDAC will artificially see its prognostic value increased. The second reason was that the sample analyzed was not a tumor. It was either a normal pancreas or from a PDAC-look alike histological lesion, often atrophic fibrosis with a stroma-like appearance. These patients had a prolonged survival compared to PDAC patients. As a result, the prognostic value was strongly biased in any alteration present in PDAC. Finally, for one sample, the single nucleotide variation data were not available (TCGA-L1-A7W4-01). Peran et al. compared the clinical characteristics of the TCGA_PAAD cohort with that of the SEER and the national cancer database and reported that the TCGA_PAAD cohort had less locally advanced and metastatic tumors and therefore a much better chance of survival  [5].
We observed that many molecular alterations (gene mutation, mRNA or miRNA aberrant expression level) had a prognostic value only in the uncurated cohort. This is not surprising as most of these were absent from the good prognostic non-PDAC subgroup. Improper data curation led to the description of many PDAC prognostic factors that lost their value in the curated dataset  [9, 10]. This is in line with the study of Peran et al. that described on a limited number of genes how the use of a curated cohort led to the loss of their prognostic value. We therefore developed a free web-based app to quickly assess the prognostic value (progression-free and overall survival) of the expression of any gene on the curated dataset. This is to our knowledge the only "click and play" tool to reliably assess the prognostic value of any gene using either a cut off based on the median expression value or any interval, and retrieve the survival and expression data.
Data Query from the Main Data Repository The following data repository were queried to retrieve a sample list and biological annotations, whole normalized level 3 RNAseq and miRNA data, specific DNA mutation data (KRAS, TP53, SMAD4) and clinical data: TCGA (https://gdc. cancer. gov), the Broad Institute (https://gdac. broadinstitute. org), the University of California Santa Cruz (http://xena. ucsc. edu), cBioportal (http://www. cbioportal. org) and the Human Protein Atlas (https://www. proteinatlas. org). The sample list and the clinical data used in the TCGA group publication were also retrieved from the supplemental data  [5].
Data from the Multicenter Ductal Adenocarcinoma Cohort For comparison with the TCGA_PAAD cohort, we used a previously published cohort of 471 consecutive patients who underwent curative intent surgery for PDAC at 5 university centers between September 1996 and August 2009. Subjects were excluded if they had received preoperative treatment and macroscopically incomplete resection (R2) or if their tumor histology was not a ductal adenocarcinoma. Patients who died of postoperative complications within 30 days following surgery were also excluded.
Data Analysis Survival analyses with gene expression were performed in R using the survival package. The gene expression or miRNA expression association to survival was evaluated by fitting a Cox proportional hazards regression model. The explanatory variable of the survival model fitted for each gene was either the continuous expression values or the discretized expression value (e. g. , below versus over the median expression). The association to survival in the curated or uncurated cohorts was assessed for 17,302 genes. For each of them, the p-value of the log-rank test was retrieved and corrected using the Benjamini-Hochberg multiple comparison approach for controlling the false discovery rate. When defining the number of prognostic genes, the log-rank test was used and adjusted to obtain a False Discovery Rate of 5%.
Web Based App A web app to associate gene expression to overall and progression-free survival in the 144 TCGA patients (6 of the 150 curated sample/patient pair had missing clinical data for effective PFS analysis) is available at  [8]  (executable code available:  [11] ). The TCGA data used for the application were obtained from the Broad's Institute firehose portal (https://gdac. broadinstitute. org, 20,160,128 release). The PDAC survival web application can be used to associate gene expression to overall and progression-free survival in three different ways: by splitting the series of patients into two subseries around a gene expression threshold (e. g. , median), by separating the series of patients into two extremes intervals (e. g. , upper quartile vs. lower quartile) or by directly associating the continuous gene expression values to the survival.
Conclusions In conclusion, we highlighted in this study the heterogeneity of the data available through the main repositories and the lack of a proper sample description leading to the inclusion in many studies dedicated to PDAC of non-PDAC tumor samples or even non-tumor samples. We confirmed that it introduced a major bias in biomarker prognostic value analysis and we provided a comprehensive list of the curated dataset together with a free web-based app to assess on the curated dataset the prognostic value of gene expression levels.
Material and Methods Plasmid construction. cDNAs encoding full-length human SEPT2 (GenBank NM_004404), SEPT5 (GenBank NM_002688), SEPT6 (GenBank NM_145799) and SEPT7 (GenBank NM_001788), were amplified from a fetal human brain cDNA library (Clontech) by PCR and checked by automated sequencing. Complexes (5-6-7 or 2-6-7) were co-expressed using two bi-cistronic vectors. For the first expression vector, the cDNA encoding SEPT7 (residues 29-437) was subcloned into pETDuet TM -1 (NOVAGEN), using BamHI and PstI restriction sites. SEPT7 was therefore produced in fusion with an N-terminal His-tag allowing for its purification by metal affinity chromatography, together with its interaction partners. For the second expression vector construct, the coding sequence for SEPT5 (or SEPT2) was inserted into either pRSFDuet TM -1 (NOVAGEN) or pRSFMBP (details below), using EcoRI e SalI restriction sites in both cases.
After the affinity chromatography, the purified complexes were concentrated and loaded onto a Superdex 200 10/300 GL column (GE Healthcare) pre-equilibrated with buffer B. The purity and integrity of the septin complexes were analyzed on 12% SDS-PAGE. Fractions containing the complex were concentrated to 1mg / mL and aliquots of 20µL were frozen in liquid N 2 and stored at -80 °C. Mass spectrometry. The individual components of the complex were identified by LC-MS/MS analysis on an ESI-micrOTOF-Q II 10234 mass spectrometer (Bruker Daltonics) and the data analyzed with MASCOT, available from http://www. matrix science. com/search_form_select. html. Analysis of nucleotide content. Nucleotides were extracted from the purified protein complex samples according to the method described by Seckler et al, with some modifications  [25].
Micrographs of MBP-SEPT2-SEPT6-SEPT7 were taken on a Talos Arctica (Thermo Fisher Scientific, USA) and were recorded using a Ceta camera with a pixel size of 2. 51 Å at a defocus range of 1-3 mm underfocus. Images of high-salt (500mM) samples incubated with mouse monoclonal anti-SEPT5 were taken on a Talos F200C (Thermo Fisher Scientific, USA) microscope at 200 kV and were also recorded using a Ceta camera with a pixel size of 2. 6 Å. Imagic4D software (Image Science, Germany) was used for image processing  [27]. Class averages of selected particles were computed after alignment by classification and subsequent rounds of multivariate statistical analysis (MSA) using selected masks  [28, 29]. For better visualization of the MBP location, due to the flexible linker, one of the masks used was constructed as a rectangle on the hexameric complex, so that only information from the MBP was used for classification.
Characterization of the SEPT5-SEPT6-SEPT7 heterocomplex In order to investigate the prediction of  Kinoshita (2003)  that SEPT5 should be able to replace SEPT2 as a component of a viable heterocomplex, we co-expressed SEPT5, SEPT6 and SEPT7 in E. coli Rosetta (DE3) cells. SEPT5 was produced with or without an MBP-tag and an N-terminal His-tag was added to SEPT7 to facilitate purification by metal affinity chromatography. Size exclusion chromatography proved essential for eliminating aggregates and contaminants, yielding a symmetrical peak corresponding to the stoichiometric SEPT5-SEPT6-SEPT7 complex (Fig. S1 ). The identity of all components was confirmed by mass spectrometry (data not shown).
This is the interface which is susceptible to high salt concentrations leading to depolymerization under these conditions. This interface is therefore fundamental to the polymerization process and it has been shown in yeast that it is the formation of end-toend contacts between core complexes via lateral diffusion within the membrane which leads to polymerization  [40]. The question then arises as to which of the two interfaces would be expected to more labile -the SEPT7-SEPT7 G interface or the SEPT2-SEPT2 NC interface? PISA was used to analyse both. The G interface was taken directly from the high-resolution crystal structure of the SEPT7 G-domain (pdb code 6N0B) and a model for the NC interface was generated from two incomplete crystal structures (pdb codes 2QNR and 2QA5). The two interfaces have approximately equal buried surface areas (1677 and 1651 Å 2 per subunit respectively for NC and G) but the predicted binding energy of interaction is slightly more favourable for the G interface (-21.
Author Contributions DCM, JNM, SLG planed and performed the expression and complex purifications. DCM, AC and RP performed TEM measurements and single particle analysis. JNM and RI were responsible of GUVs experiments. FLB performed molecular dynamic simulations and analyzed the data. RCG and APUA conceived and supervised the research study and secured the funding. DCM, RCG, RP and APUA wrote the manuscript, with input from the other authors. receive fellowships from CNPq. FAPESP is thanked for financial support via thematic project 2014/15546-1 to RCG and APUA and regular project 2015/16116-3 to FLBDS.
Acknowledgements We thank  LNNano/CNPEM  for access to the EM facility via projects  TEM-16717 ,  TEM-20359  and  TEM-24386. Andressa P. A. Pinto ,  Isabel D. Moraes ,  Edson Crusca ,  Andreza Barbosa Gomide ,  Humberto M. Pereira  and  Diego Leonardo  are thanked for excellent technical assistance and  Prof. William Trimble  for fruitful discussions. DCM, JNM and SLG were recipients of  FAPESP  scholarships and APUA, RCG and RI.
Balsari et al. [7]  used, for example, a laser diffraction system (Malvern SprayTec instrument) coupled with a SprayTec software to obtain droplet size data from different sprayers. We can also cite the PDPA (Phase Doppler Particle Analyze) method for example  [8]. Other optical methods include the use of a non-contact optical sensor to detect and measure droplets in flight  [9]  or interferometric techniques (IPI: Interferometric Particle Imaging)  [10]. The characterization of leaf coverage surface has also interested researchers. The techniques used were mostly based on water-sensitive papers (WSP)  [11] , more recently coupled with imaging  [12] [13] [14]. In Reference  [14] , the authors propose a comparative study of three methods for using data collected on sensitive paper: visual assessment of coverage rate, visual droplet counting and measurement by an imaging system.
One of the objectives of the PCA will first be to identify the outliers, corresponding to aberrant or atypical spectra to clean the dataset. Another objective will consist in highlighting the differences or similarities between the spectra. PCA consists in trying to define new variables, which will cause as little information loss as possible. Several formalisms can be used, such as matrix writing  [26]. Thus, we can write the matrix X of the initial data (a matrix of dimensions N × P, N corresponding to observations-light transmission and P to variables-wavelengths), as the sum of A terms (A being the dimensionality of the new basis, lower than N and P), which explains most of the data and a residual matrix E corresponding to "noise" (part of the data that are not explained by the analysis).
Each of the A terms is itself the product of two vectors t i and p i ' , so that the matrix X of the initial data can be written as in Figure  2 : Sensors 2019, 19, x FOR PEER REVIEW 5 of 12 volumes. But several parameters can interfere with the measurement, such as the position of the droplet on the waveguide, the temperature or the light injection efficiency; indeed, this latter can vary due to misalignment between series of similar experiments or to laser sources fluctuations between reference (I0) and measured (I) spectra; this makes it difficult to exploit the raw spectra. One of the objectives of the PCA will first be to identify the outliers, corresponding to aberrant or atypical spectra to clean the dataset. Another objective will consist in highlighting the differences or similarities between the spectra.
The performances of the cross-validations (using S1 and S2 sets), and prediction (using the independent set S3) were assessed through the number of latent variables used in the models, the coefficient of determination R 2 and the standard error of cross-validation (SECV) and standard error of prediction (SEP) corrected from the bias. All the computations were performed with Matlab software (Matlab R2015b, Mathworks).
Principal Component Analysis A principal component analysis (PCA) was performed on the whole dataset. It allowed identifying two principal components PC1 and PC2, explaining 95. 81% of the variance between the different recorded spectra (85. 72% for PC1 and 10. 09% for PC2). We can therefore consider that the main information is contained in these two principal components, the rest can be considered as noise. To go further in the analysis, we plotted the scores on each principal component for each sample, corresponding to the different volumes and measurement days (3 times 25 different volumes). Note that scores represent new coordinates of the different samples on the generated components (Figure  5a  for PC1 and Figure  6a  for PC2). We also plotted the loadings versus wavelength for each principal component (Figure  5b  for PC1 and Figure  6b  for PC2).
To end, we realized a score plot on PC1 and PC2 generated from the whole dataset (Figure  7 ). To go further in the analysis, we plotted the scores on each principal component for each sample, corresponding to the different volumes and measurement days (3 times 25 different volumes). Note that scores represent new coordinates of the different samples on the generated components (Figure  5a  for PC1 and Figure  6a  for PC2). We also plotted the loadings versus wavelength for each principal component (Figure  5b  for PC1 and Figure  6b  for PC2). To end, we realized a score plot on PC1 and PC2 generated from the whole dataset (Figure  7 ). In Figure  5a  we can observe that, except for four points, scores on PC1 are homogeneously distributed around the axis corresponding to a score of 0. It signifies that there is no correlation between PC1 and the droplet volume (color coded).
Prediction Model A PLS model was built with a calibration set containing spectral dataset from S1 + S2, after having removed the outliers. The results of the calibration model are presented in Figure  8a  where the predicted volumes obtained are plotted versus the real values. The metrological performance of the system (laser + probe + detector + model) can then be extracted. Repeatability is given by the standard error of cross-validation (SECV) or the variance, accuracy by the bias, and precision by the coefficient of correlation R 2. This latter is equal to 0. 803, the bias is 0. 00508, and the variance is of 0. 299 µL. So, it tends to show that the precision of the model for the calibration set is mainly limited by the variance, since most of it is due to physical perturbation (PC1) and limits the learning process.
Prediction Model A PLS model was built with a calibration set containing spectral dataset from S1 + S2, after having removed the outliers. The results of the calibration model are presented in Figure  8a  where the predicted volumes obtained are plotted versus the real values. The metrological performance of the system (laser + probe + detector + model) can then be extracted. Repeatability is given by the standard error of cross-validation (SECV) or the variance, accuracy by the bias, and precision by the coefficient of correlation R². This latter is equal to 0. 803, the bias is 0. 00508, and the variance is of 0. 299 µl. So, it tends to show that the precision of the model for the calibration set is mainly limited by the variance, since most of it is due to physical perturbation (PC1) and limits the learning process.
The low value of the bias may be interpreted as the fact that variance of S1 + S2 is symmetric and has little impact on mean error. The result is quite encouraging. In order to validate the predictive model in real conditions, it was then tested on the independent spectral dataset from the series S3. The predicted volumes obtained are plotted versus the actual volumes in Figure  8b. The coefficient of correlation is now of 0. 665 and the standard error of prediction or the variance in the predicted volume increased from 0. 299 to 0. 391 µL. The bias is now 50 times much stronger (at a value of -0. 105), which negatively impacts the R². The failure of the coefficient of determination (66. 5%) isn't due to the volume but to the test conditions: Light injection quality, droplet position on the waveguide, spectrometer setting, etc.
A first physical improvement is related to light injection, this can be optimized and made robust versus wavelength changes and mechanical vibrations by studying injection alignment impact, pigtailing the fibre from the light source with the waveguide and monitoring directly the output signal of the light source. Also, the temperature may slightly modify the evanescent field extent and thermal monitoring can quantify the temperature explained variance. Finally, droplet lateral position modifies the surface of interaction between water and light and thus varies the output signal; this In order to validate the predictive model in real conditions, it was then tested on the independent spectral dataset from the series S3. The predicted volumes obtained are plotted versus the actual volumes in Figure  8b.