dataesr/scientific-paragraphs-categorization huggingface dataset:
To better pinpoint when startups are independent and active, we supplement VentureOne data with information from Sand Hill Econometrics. As  Hall and Woodward (2010)  report, Sand Hill provides more accurate estimates on the timing of unsuccessful entrepreneurial outcomes, thus enabling us to better capture when startups disband and leave the risk pool for lending. Each company is tracked through 2008, our last year of reliable financing data, or until exit. The initial sample comprises 3,414 companies. To identify startups with patents, we search the Delphion database for U. S. patents assigned to all current and former names listed for each startup as reported in VentureOne. Of the 3,414 startups, 1,519 receive at least one U. S. patent by 2008 or exit, averaging 9. 5 patents per company.
In the combined set of 14,514 patents, 51 percent are issued to 483 medical devices companies, 23 percent are issued to 197 semiconductor devices companies, and the remaining 26 percent are awarded to 839 software startups. The maximum portfolio size is 199 patents. The summary statistics and analyses below are based on this patenting-startup sample. The main dataset is an unbalanced panel with 1,519 startups and 11,298 startup-calendar year observations, a subset of which is used in our difference-in-differences (DD) analysis. Startups are retained in the sample through 2008 or the year in which they went public, were acquired, or were disbanded.
Startup Receipt of Debt Financing Our outcome variable, !"#$ !" , indicates if one or more patents owned by a startup is used to secure a loan in a given year. To obtain information on patent security assignments, we extend the method introduced by Serrano (2010) and extract assignment records for each of the 14,514 patents from the USPTO Patent Assignment Database. 9  We then identify, on a patent-by-patent basis, all instances where a patent "security interest" is assigned to a third party, i. e. , a patent has been pledged as collateral 10. For each record, we track the date of the transaction (execute date), the date the transaction was recorded (recorded date), the entity that assigned the security interest (assignor), the entity that received it (assignee), and the patent numbers involved in the transaction.
As expected, Silicon Valley Bank (SVB), an entity that specializes in banking services for startups, is the most common lien holder. SVB supplies loans to 35. 2 percent of the 547 sample startups and an even larger share (42 percent) in the two IT-related sectors. In total, we identify 239 annual debt deals between Silicon Valley Bank and patenting startups. Of those, only eight (3 percent) are listed in the VentureOne database.
Supplemental Fund-Vintage Data, Estimation Sample, and Descriptive Evidence Implementing this methodology requires information about the vintage of funds managed by each startup's syndicate of VC investors as of early 2000. We therefore match investors in sample startups reported in VentureOne to supplemental data on VC funds reported in PREQIN. 17 To start, we identify investors in a startup's most recent syndicate prior to the bubble's collapse, and compute the age of those investors' most recent VC funds as of the year 2000. Since the typical investment period in a VC fund is five years, we set RecentFund to one when the average age of the most recent funds within the syndicate is less than five years in early 2000. This approach allows the capital-constraints introduced by the market's collapse to be less (more) binding when a startup's investors have relatively recent (older) funds at the time of the crash.
Procedure and materials Participants in the two immersive conditions came to the laboratory three times. During sessions one and three, participants filled in the same online questionnaire designed to collect data on their energy saving perceived norms, attitudes and strategies. At the end of session one, we took photos of the participants and recorded their voice by asking them to read a script (the text that the avatar would say). During session two, they received energy saving instructions in IVR for about 3 min and afterwards, filled in a short questionnaire. The instructions were delivered by participant's doppelganger speaking with their own voice (DG condition, N = 55) or by an unknown avatar of the same gender (UA condition, N = 56). For each instruction, the avatar showed appropriate gestures, such as pointing at the fridge when explaining how to save energy related to fridge management.
Participants were immersed in virtual reality by wearing a headmounted display headset and were tracked in the 3D environment using the HTC Vive Pro system as the hardware and Vizard from WorldViz as the VR software. Avatars were created using 3ds Max toolkit. The doppelgangers were developed using participants' photos. The unknown avatars (i. e. , one male and one female) were developed using photos of lab members unknown to the participants. Specifically, we developed one male and one female unknown avatars such that participants from the UA condition saw an avatar of the same gender as their own. This approach ensured that the quality of the avatars was comparable across conditions. Participants in the two traditional conditions came to the laboratory twice. During both sessions, they filled in the same questionnaire used during sessions one and three of the immersive conditions.
The following options were applied: white lig correction, fluorescence correction, cosmic ray rejection, and a smart background optio The camera temperature was -51 °C. Omnic v. 9. 2 software from Thermo Fisher Scienti was used for data collection and analysis. Spectra were compared using the common sca mode. Scanning electron microscopy images of the silver substrate were obtained with Carl Zeiss (Jena, Germany) MERLIN™ field emission scanning electron microscope (F SEM) with an accelerating voltage of 20. 0 kV. Absorbance of TBARS samples was measured using a Shimadzu UV1700 Pha maspec spectrophotometer (Kyoto, Japan).
The following options were applied: white light correction, fluorescence correction, cosmic ray rejection, and a smart background option. The camera temperature was -51 • C. Omnic v. 9. 2 software from Thermo Fisher Scientific was used for data collection and analysis. Spectra were compared using the common scale mode. Scanning electron microscopy images of the silver substrate were obtained with a Carl Zeiss (Jena, Germany) MERLIN™ field emission scanning electron microscope (FE-SEM) with an accelerating voltage of 20. 0 kV. Absorbance of TBARS samples was measured using a Shimadzu UV1700 Pharmaspec spectrophotometer (Kyoto, Japan).
Acknowledgments: Warm and sincere thanks are given to  Thermo Fisher Scientific  for the fruitful collaboration and effective exchange of experience and knowledge. M. Wrona  acknowledges the  FPU grant  (reference number  AP2012-2716 ) received from the  MEC, Ministerio de Educación, Cultura y Deporte, Spain. J. Lours  acknowledges the  IUT Lyon  1 through the scholarship EXPLO'RA sup of the  Region Rhône-Alpes. We also thank  Gobierno de Aragón and Fondo Social Europeo  for the financial help to GUIA T53_20R. The authors would like to acknowledge the use of  Servicio General de Apoyo a la Investigación-SAI, Universidad de Zaragoza.
Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy associated with rapid progression and an abysmal prognosis. Previous research has shown that chronic pancreatitis can significantly increase the risk of developing PDAC. The overarching hypothesis is that some of the biological processes disrupted during the inflammatory stage tend to show significant dysregulation, even in cancer. This might explain why chronic inflammation increases the risk of carcinogenesis and uncontrolled proliferation. Here, we try to pinpoint such complex processes by comparing the expression profiles of pancreatitis and PDAC tissues. Methods: We analyzed a total of six gene expression datasets retrieved from the EMBL-EBI ArrayExpress and NCBI GEO databases, which included 306 PDAC, 68 pancreatitis and 172 normal pancreatic samples.
This is especially true in cases of chronic pancreatitis (an inflammatory pathophysiological disease of the pancreas), with the predisposing genes associated with a higher risk of developing pancreatic cancer. Hence, it is crucial to identify the underlying mechanisms, cellular processes, and inflammatory pathways, which can further help us design drugs targeting these biomarkers  (Khalafalla and Khan, 2017; Zheng et al. , 2013). The present study analyzes the microarray data of PDAC and pancreatitis tissues from publicly available datasets to derive the biological meaning of differentially expressed genes (DEGs) using bioinformatics methods. The results of this study provide valuable biological insights which could be further explored to identify novel therapeutic targets in PDAC.
Retrieval of gene expression datasets The microarray data for normal, PDAC, and pancreatitis tissues were obtained from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI ArrayExpress. A total of 6 datasets (GSE15471, GSE32676, GSE46234, E-MTAB-1791, E-GEOD-71989, and E-MEXP-2780) were analyzed in this study  (Athar et al. , 2019; Barrett et al. , 2013). Since data was generated using different platforms, all the datasets belonging to the respective platforms (Affymetrix GPL570 [HG-U133_Plus_2] and Illumina human WG6 BeadChip v3) were processed and analyzed independently. The results obtained were later pooled for a more comprehensive analysis. The detailed description of the methodology followed in the study is represented in Figure  1.
Data pre-processing and differentially expressed genes (DEGs) screening The datasets were pre-processed, normalized, and analyzed for differential expression using BRB-Array tool 4. 6. 1 (Stable Version)  (Simon et al. , 2008). The pre-processing and normalization criteria included -(i) If the spot intensity is below the minimum value i. e. , 10, then threshold the intensity at the minimum value (ii) Average the replicate spots within an array (iii) Exclude a gene if the 50th percentile of intensities < 500 or the percentage of data filtered/missing > 50% (iv) Each array was normalized using quantile normalization. The DEGs screened conformed to the following cutoff criteria: |logFC| > 2 and a high significance threshold of 0. 001 of univariate tests. Only DEGs with a false discovery rate (FDR) < 0.
Ontology and pathway enrichment analysis Gene ontology (GO) terms describe non-overlapping information on biological process (BP), cellular component (CC), and molecular function (MF) of individual gene products  (Hill et al. , 2008). In contrast, the ontologies and comprehensive information on human diseases are described in the Disease Ontology (DO)  (Schriml et al. , 2019). KEGG is a database resource encompassing the functional meaning of a biological system derived mainly from high-throughput experiments  (Kanehisa and Goto, 2000). We used the R Bioconductor package, clusterProfiler, which integrates the data from the above resources to perform ontology and enrichment analysis  (Yu et al. , 2012).
Protein-Protein Interaction (PPI) and module analysis PPIs are crucial for several biological functions in the body, and any dysregulation can often indicate diseases  (Gonzalez and Kann, 2012). In this study, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database and the Cytoscape software (version 3. 8. 2) for the construction and visualization of interaction networks  (Shannon et al. , 2003; Szklarczyk et al. , 2015). MCODE (Molecular Complex Detection) was used to identify the densely interconnected regions in the network with the following analysis parameters: node score cutoff = 0. 2, k-score = 2, degree cutoff = 2, node density cutoff = 0. 1, and max depth = 100  (Bader and Hogue, 2003).
Pathway reanalysis, potential druggability, and gene expression analysis The hub genes obtained were then reanalyzed to identify core genes. The potential druggability was determined using the Drug-Gene Interaction Database (DGIdb). DGIdb is a web-based resource providing information about druggable candidate genes and potential drug-gene interactions  (Freshour et al. , 2021). The expression of hub genes was evaluated using the GPEIA2 tool  (Tang et al. , 2019). GEPIA2 performs gene expression analysis using the data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTex), which helps us to validate and correlate the expression profiles between normal and PDAC tissues. The results of the KEGG pathway reanalysis were visualized using the R package, circlize  (Gu et al. , 2014).
Identification of differentially expressed genes A total of 6 datasets were retrieved that included 172 samples of normal pancreatic tissue, 68 samples of pancreatitis, and 306 samples of PDAC. The microarray analysis was performed to identify the differentially expressed genes in PDAC and pancreatitis. The results Figure  1. Flowchart Describing the Overview of the Methodology Followed in the Present Study. This involved collection of raw data & preprocessing, screening and identification of overlapping DEGs, ontology and pathway enrichment analysis, protein-protein interaction, and analysis of hub genes. conferring tensile strength, and glycosaminoglycan binding (Figure  2b ). (iii) In the cellular component group, the DEGs were significantly connected with the collagen-containing ECM, collagen trimer, and its complex and endoplasmic reticulum lumen (Figure  2c ).
PPI network and identification of hub genes The Protein-protein interaction network (PPI) constructed for the overlapping DEGs using the STRING database with a combined score > 0. 4 (default threshold) showed 45 nodes with a total of 152 edges, representing a densely interconnected network (Figure  4a ). Module analysis using MCODE revealed 15 hub genes (with 92 edges), including -COL6A3, COL1A1, FBLN1, COL8A1, THBS2, CDH11, COL5A2, SPARC, COL3A1, THBS1, COL6A1, LUM, COL1A2, COL6A2, and COL5A1 (Figure  4b ).
Discussion Cancer is one of the complex diseases resulting from various phenomena, including significant gene-environment interactions that result in disordered cell proliferation. Although there has been a massive improvement in the treatment for PDAC, mortality and incidence rates are still increasing at an unprecedented rate. Several studies have been carried out to uncover the molecular mechanisms involved in the onset, growth, and progression of PDAC. It has also been reported that chronic cases of pancreatitis can increase the risk of developing PDAC by 16-fold  (Carrière et al. , 2009; Kirkegård et al. , 2017). The present study aims to identify dysregulated genes, pathways, and biochemical processes shared between pancreatitis and PDAC. Analysis of six different datasets obtained from publicly available databases revealed a total of 45 overlapping DEGs between pancreatitis and PDAC.
Proteoglycans are another important biomolecule of interest, having multiple functions in angiogenesis and cancer. They often influence cell growth through their interaction with growth factors and can sometimes cause deregulation of cell proliferation  (Knelson et al. , 2014; Wang et al. , 2011). Thus, their integration in tumor cell diagnostics can facilitate early diagnosis, as demonstrated in a few studies  (Effenberger et al. , 2018; Kurihara et al. , 2008). We further constructed a protein-protein interaction network and performed a module analysis. The module consisted of 15 nodes with 92 edges. Expression analysis using the GEPIA2 tool revealed all 15 hub genes to be significantly expressed in PDAC. Interestingly, the expression of most of the hub genes was independent of factors such as gender, drinking habits, race, and pancreatitis status, which suggests that these genes can be used as biomarkers on a global scale for advancing PDAC treatment.
Potential druggability determined using the DGIdb database showed that 14 out of 15 genes were in the druggable genome category and thus have a potential value for developing targeted drugs. Next, to understand which genes were significantly involved in the pathways analyzed before, we performed KEGG pathway reanalysis for the 15 hub genes. Based on this, we identified five core genes -COL1A1, COL1A2, THBS1, THBS2, and COL3A1. Among these, three were protein-coding collagen genes. Recently, a few studies have demonstrated how various collagen genes can play a role in tumorigenesis leading to poor clinical outcomes   (Kita et al. , 2009; Wu et al. , 2013). Further, differential expression of genes COL1A1, COL1A2, and THBS1 has been reported in several cancers, including colorectal cancer, hepatocellular carcinoma, and melanoma  (Bonazzi et al.
, 2011; Hayashi et al. , 2014; Zhang et al. , 2018). Researchers have also demonstrated the utilization of Thrombospondin-2 (THBS2) as a biomarker for risk prediction and early detection of PDAC and as a robust prognostic indicator in colorectal cancer  (Kim et al. , 2017; Tian et al. , 2018). Taken together, this study analyzed a total of six different datasets by comprehensive bioinformatics analysis to identify critical genes and various biochemical processes thought to play crucial roles in certain events leading to carcinogenesis and its progression in PDAC. There were a few limitations to this study -Firstly, this study compared pancreatitis and PDAC samples but did not consider the stage of individual samples. Secondly, the clinical data of samples was not analyzed due to inaccessibility.
Despite these drawbacks, the integrative approach used in the present study offers more precise findings compared to other studies that analyzed only a single dataset. Majorly, we identified 45 DEGs shared between PDAC and Pancreatitis, including 15 hub genes, namely, COL6A3, COL1A1, FBLN1, COL8A1, THBS2, CDH11, COL5A2, SPARC, COL3A1, THBS1, COL6A1, LUM, COL1A2, COL6A2, and COL5A1. In-depth experimental research is needed to elucidate the role and exact molecular mechanisms of these genes. This can further help identify novel therapeutic targets to improve PDAC treatment. Such personalized therapies can significantly reduce the sequelae of cancer treatment while improving patients' quality of life and overall survival rate.
METHODS Patients and blood samples Sixty patients (≥18 years of age) with histologically confirmed metastatic NSCLC (stage III and IV) were included in the prospective ALCINA 1 clinical trial (NCT02866149) from June 2016 to August 2018 to assess circulating biomarkers in different cancer types. The protocol was approved by the Montpellier University Hospital Centre ethics committee. This sample size of patients was employed to evaluate a technique's viability and offer proof of concept, serving as the basis for more detailed investigations on a particular technique or indication. Blood sampling was performed at diagnosis, before the first treatment (n = 9), or later, at progression, before the next therapeutic line (n = 45). All patients gave their written informed consent. CTCs were analysed prospectively  [8] , whereas ctDNA and sEVs were evaluated retrospectively after all patients were included.
Data were analysed with the Typer software version 4. 0. 26. 74 (Agena Bioscience, San Diego, CA, USA). The signal intensity for the mutant allele was normalized to the capture control peaks. An intensity value = 1 indicates that the peak intensity in the mutant allele is equal to the peak intensity of the average of the five capture control peaks. The capture control peaks are biotin-labelled, non-reactive oligonucleotides that are added to the extension reaction and used as an internal control for the streptavidin-bead capture and elution of the mutant extension product steps. Mutant allele calls were returned by an automated software report specific for the UltraSEEK™ Lung Panel. A signal-to-noise ratio ≥6 and a z-score ≥7 were considered significant. For allele calling, the reporter algorithm considered the instrument-specific baseline for each mutation assay.
4a, b ). Higher PD-L1 + sEV concentration was associated with higher risk of death, and the survival probability decreased with higher PD-L1 + sEV concentrations. Conversely, total sEV concentration was not associated with OS (HR = 1. 00, 95% CI = 0. 93-1. 08, P = 0. 953). PD-L1 + sEV and total sEV concentrations were not correlated with PFS (HR = 1. 08, 95% CI = 0. 99-1. 18, P = 0. 095; HR = 0. 99, 95% CI = 0. 93-1. 06, P = 0. 813, respectively). Interaction between PD-L1 + sEV concentration and CTC detection. 51 patients were included in the model (n = 2 patients with missing values for sEVs or for CTC status). Comparison of the patients' characteristics before and after the exclusion of these two patients did not highlight any significant difference (data was not shown). The interaction analysis did not find any significant interaction of PD-L1 + sEV and total sEV concentration with OS in function of CTC presence/absence (P interaction = 0.
We previously showed that PD-L1 + CTCs were significantly correlated with OS and PFS and that prognosis is worse in patients with PD-L1 + CTCs than with PD-L1 -CTCs  [8]. However, here, the PD-L1 + CTC subgroup was not included as a risk factor in the combinatory analysis, due to the small number of patients (N = 5). As previous and current results confirmed the importance of PD-L1 expression in CTCs and sEVs, we think that assessing PD-L1 expression in CTCs and sEVs can provide reliable information specifically in patients receiving immunotherapy. An ongoing clinical trial (NCT04025541) in patients with NSCLC receiving immunotherapy evaluates the importance of CTCs, sEVs, ctDNA, and immune cell changes and their combined analysis for predicting the response to tumour-related events (e. g.
ACKNOWLEDGEMENTS We thank  Dr. Elisabetta Andermarcher  for assistance with her comments and proofreading which greatly improved the manuscript. The authors thank  Professors Francois-Clément Bidard  and  Jean-Yves Pierga ,  Mrs. Souhir Neffati , and  Marie-Emmanuelle Legrier  at the  Institut Curie Paris , for the design of the ALCINA study. The figures were Created with BioRender. com.
METHODS Given the nature and the complexity of the empirical research carried out, it is based on four main types of studies (Figure  1 ): i) exploratory study, ii) performance study, iii) attitudinal study and iv) teacher training study. The selection of the four types of studies was conditioned by the limited bibliographical background and resources, and by the benefits of data triangulation in order to improve the quality of the results obtained. The research entails a mixed methodology that combines qualitative and quantitative studies  (León & Montero, 2015). The data were analysed with IBM ® SPSS ® Statistics v. 22 software.
Exploratory Study In order to establish the reliability of the instrument administered in the pilot study, Cronbach's Alfa procedure was used to determine the internal consistency index. Alfa values of 0. 685 and 0. 681 were obtained for the Technical Aspects and Facility of Use aspects, respectively (total value for the questionnaire: 0. 682). In the two dimensions analyzed, the descriptive analysis of the students' answers to the questionnaire reflects scores that are much higher than 3. 5, the scale's mean. The data reveal medium-high homogeneity of the answers, indicating evident uniformity of the students' opinions regarding the AR applications. Given the small number of students that participated in the study, it is assumed that the sample does not present normality in respect to the sample populations, which is why the non-parametric Kruskal-Wallis test was applied (values shown in Table  3 ).