dataesr/scientific-paragraphs-categorization huggingface dataset:
However, they still do not integrate any learning techniques. Moreover, in the market area, many start-up companies with several tools have entered the market recently, all with the promise of solving the coding skill conundrum. While no-code or low-code concepts become a trend (following the recent Google patent  (Arkadyev, 2017) ), companies such as Salesforce promote plug-and-play offers. Some testing tools such as Testcraft 1  , Ranorex 2  , Tricentis 3  , Leapwork 4  , etc. advertise that they can provide this functionality by building a user-friendly UI on top of the code layer and also enabling switching between two modes. The companies promote that testers can still write scripts and receive detailed feedback but skip the coding part. However, when it comes to test automation, an issue is often raised on how codeless is being interpreted.
Selenium Selenium  (Selenium, 2020)  is a suite of automation testing techniques and tools which allows the testers to run the tests directly on the target browser, drive the interactions on the required web pages and rerun them without any manual inputs. Selenium has become very popular among testers because of the various advantages it offers. With its advent in 2004, Selenium made the life of automation testers easier, highly improved the testing time, costs, detected failures and is now a favorite tool for many automation testers. Selenium was invented with the introduction of a basic tool named as JavaScriptTestRunner, by Jason Huggins at ThoughtWorks to test their internal Time and Expenses application. Now it has gained popularity among software testers and developers as an open-source portable automation testing framework.
Nowadays, Selenium is currently used in production in many large companies as Netflix, Google, HubSpot, Fitbit, and more. According to  (Enlyft, 2020) , there are 42,159 companies that use Selenium, taking 26. 83% market share in software testing tool (see Figure  1 ). The primary reason behind such overwhelming popularity of Selenium is that it is open source. This not only helps keep the costs in check but also ensures that companies are using a tool that will get continually updated. Other reasons include the multidimensional flexibility that it offers in terms of scripting languages, operating systems, browsers, and integration with other tools. This widens the scale of reach and test coverage, enabling enterprises to deliver a web application that is highly reliable and functional. Selenium test scripts can be written in Java, Python, C#, PHP, Ruby, Perl and.
Net. This allows a large number of testers to easily use it without any language barriers. It can be carried out on Windows, MacOS, and Linux, using any browsers out of Mozilla Firefox, Internet Explorer, Chrome, Safari, and Opera. This enables a thorough cross browser compatibility testing with strong environment support. Selenium suite includes three major components (Figure  2 ), each tool has its own approach for automation testing. The testers or developers can choose tools out of it depending upon the testing requirements. • Selenium IDE. Selenium IDE, earlier known as Selenium recorder, is a tool used to record, edit, debug and replay functional tests. Selenium IDE is implemented as an extension to the Chrome browser and add-on in Firefox browser. With Selenium IDE plugin, the testers can do simple record-andplayback of interactions with the browser, they can also export tests in any of the supported programming languages like Ruby, Java, PHP, Javascript, etc.
• Selenium Grid. Selenium Grid allows the testers to run parallel automated tests on multiple machines and browsers at the same time. The main function of this tool is to save time. If the test suite is large, the testers can use Selenium Grid to reduce the time running. Considering how scripts normally run slow on a browser, using performance-improving techniques such as parallel testing can help with the problem. Testers can also use it to test one application in different browsers in parallel, when one machine is running Firefox, the other Chrome, and so on. Testers can also create different configurations with Grid, combining different versions of browsers and operating systems. Needless to say that when used in large production environments, Grid is a huge time-saver.
Codeless Testing Despite the advantages above listed, Selenium still has limitations. The most challenge of using Selenium in automation testing is steep learning curve. Testers require high technical skills to accurately design and maintain test automation. Maintenance including modifications and updating Selenium test code in an efficient way is a very common problem in automated test. The impact of the changes occur in the web page or application under test could suffer during its development. Changes or modifications from web User Interface (UI) or from its structure to its elements or its attributes could make the whole test suites collapse. For that purpose, one of automated testing trends, namely Codeless Testing, was introduced to try resolving those issues. Codeless Testing for web services refers to the methodology which utilizes a generic test case to test multiple websites.
Support Vector Machines A support vector machine (SVM)  (Vapnik and Vapnik, 1998 ) is a linear classifier defined by a separating hyperplane that determines the decision surface for the classification. Given a training set (supervised learning), the SVM algorithm finds a hyperplane to classify new data. Consider a binary classification problem, with a training dataset composed of pairs (x 1 , y 1 ),. , (x l , y l ), where each vector x i ∈ R n and y i ∈ {-1, +1}. The SVM classifier model is a hyperplane that separates the training data in two sets corresponding to the desired classes. Equation (1) defines a separating hyperplane f (x) = w T x + b = 0 (1) where w ∈ R n and b ∈ R are parameters that control the function. Function f gives the signed distance between a point x and the separating hyperplane.
A ML-BASED WEB TESTING FRAMEWORK The architecture overview of our codeless testing framework is illustrated in Figure  3. We implemented our scraping tool in Python. Besides, we utilize BeautifulSoup  (Richardson, 2020)  as a web scraping library to pull data out of DOM in HTML or XML format. • Processing Data Component. This component has a role to process the data after pulling from HTML DOM file, its main goal is to clean up and extract the useful data, then transform the cleaned data to the next stage for training. • SVM Model. A machine learning model to recognise and/or predict the search element pattern which appears in HTML data of testing website. The general architecture of our scraping tool is showed in Figure  4. Its main target is exactly as its name -to collect HTML data and their position in the DOM tree from the website which is driven by Selenium Webdriver.
We use both Selenium Webdriver and Request library to send a HTTP request to the URL of the webpage we want to access. We discover that some websites with heavy render JavaScript will not response to the request module. Also, some websites can detect the request is performed in an automated way (not by human browsing but expected to be a bot) by their tool, it will deny to establish the connection. Selenium Webdriver works fine with almost all major websites, however the speed is recorded as very slow since Selenium needs to open the real browsers to perform tasks. Moreover, using different browsers (Chrome, Firefox, Opera, etc. ) may lead to obtain different behaviours of navigation websites. For example, some websites are optimized for Chrome but not Firefox or vice versa, or some website may crash down on Microsoft Edge browser.
Therefore, we use both modules Selenium Webdriver and Request library (Pypi, 2020) to ensure that our scraping tool can adapt with most of the websites to collect as most as data it can. First, using request library for speeding up the test, if a request fails, then Selenium Webdriver will easily detect it. Moreover, each website has its different DOM structure, thousands of websites will have thousands of DOM structures, therefore it is a challenge process to retrieve in an automated way the data from multiple of websites con-currently. Nevertheless, our scraping tool enables to scrape and collect variety of websites. Our scraping tool follows two tasks: 1. Query the website using requests or Selenium WebDriver and return its HTML content. 2. Using BeautifulSoup  (Richardson, 2020)  and LXML (LXML, 2020) libraries to go through the HTML structure of each website, parse HTML content and extract the expected data useful to tested the targeted functionality embedded in the website.
Our SVM Model In considering that web HTML elements are keep changing dynamically in both structure and attribute values, it is a very challenging task to adapt test suites with the websites changes. In our proposed framework, the objective of SVM model is to recognize the pattern of web elements corresponding to the search box in each website as illustrated in Figure  5. Specifically, the SVM model will learn the HTML structure of each website, in case of any modification in terms of web elements, the SVM model will find the similar pattern of HTML web and adjust its model according to the changes. For the next testing step, if the website has changed, the SVM model detects its changes and guides the Selenium code to adapt automatically with it. In this case, the test code will not be broken and can be reused.
Data Collection and Analysis In order to have the dataset for our training SVM model, we need to gather the large amount of data. By using the scraping tool described above, we are successful to retrieve the data from the list of 5000 websites provided by the Alexa database. This database contains more than one million URLs on top ranking of Alexa  (Helme, 2020). Our tool can scrape any website that has the search functionality, it pulls down all the web elements corresponding to the search box. However, these elements are collected as raw data in an undesired format, unorganized, and extremely large. Furthermore, these elements may be different from one page to another. We need, for further steps, to use the processing tool to enhance the data quality and, related to our experiments, in particular to extract the elements linked to the search box (our testing purpose).
The main steps for preprocessing data are formatting and cleaning. Data cleaning is applied to remove messy data, duplicate data and manage missing values. Formatting is required to ensure that all variables within the same attribute are consistently written. Once structured data is extracted for each HTML element, they have to be turned into proper feature vectors. After this preprocessing phase, vectors with relevant features is obtained, our training dataset is built. For that purpose, an important task of labelling is then performed to classify our vectors depending on the structure of the "search box". We labeled 1000 webpages to provide three classes according to the three cases detailed in Section 5. 1. From this training dataset, an SVM model has been obtained as above explained.
By analyzing the multiple search activities on various websites, we group the search scenario in three use cases: • Case 1 (Traditional Search): we call it traditional because the search box is appeared directly in the website interface, the user can easily locate it and enter the search query. We studied that 80-90% search websites are designed in this case 1 (Fig. 6 ). For example: google, youtube, yahoo, amazon, etc. • Case 3 (Pattern/Condition Search): the websites have many filters/criteria for search, the users must select the right criteria to proceed. For instance, some hotel or housing websites only allow users to select the special search key in their pattern database (region, type of house, size of house, price, number of rooms, etc. ). Furthermore, particular websites only allow the users to perform the search on specify content provided in their database.
Experiments Setup In the experiment phase, we test our framework on the dataset as described in Section 5. 3. 1. Testbed. • OS: x86-64 Microsoft Windows 10 Pro, version 10. 0. 18363, Build 18363 • Processor: Intel Core i7-7500U CPU @ 2. 70GHzx2 • Memory: 16GB • SSD: 512GB • Dataset: -Metadata: 1. 3 Mb in csv format. 1000 rows x 7 columns -Training set: 70% metadata in npy format (numpy array) -Test set: 20% metadata in npy format (numpy array). From this training dataset, our tool processed the SVM model in average 7 minutes using the dependencies below mentioned. • Browsers are detailed in the Table 1 In order to run the experiments, we setup our system environment as in the following: • Python 3. 6. 8, • Dependencies: Keras/Tensorflow, scikit-learn: core platform to analyse/process data and training SVM model, • Libraries: Selenium, Request HTTP, Beautiful Soup, LXML, • Jupyter Notebook App: online web-based platform to visualize the data.
Automation Testing against Multiple Websites To evaluate the efficiency of our framework, we conducted the tests using a generic test case through 1000 websites chosen randomly on the list of Alexa described above. First, the learning approach has been processed. A sample of raw data with 10 features has been scraped (Fig. 9 ). The preprocessing phase and specifically the cleaning step allowed to obtain the seven features and one thousand lines of our training dataset. An illustration of these lines are presented in the Fig. 10. Our testing search functionality has been implemented in Selenium, the testing results are showed in the table below (Table  2 ). First, we may be surprised by the number of fail and error verdicts. Nevertheless, the high percentage of results getting error does not mean that our framework is defective.
The reason is that we modified our Selenium scripts in order to detect such pop-up windows and then to add such an extension when it occurs. Therefore, the webdriver had to trigger automatically the extension in the targeted browser, reducing at the same time the performance of our tests (in terms of response timeobtained verdicts). For the experiments, we chose two extensions below due to their efficiency in blocking pop-up windows, there are more than 10 millions users using those extensions: • Ublock: (ubl, 2020) is a free and open-source, cross-platform browser extension for contentfiltering, including ad-blocking. • I dont care about cookie:  (Kladnik, 2020)  This browser extension removes cookie warnings from almost all websites and saves thousands of unnecessary clicks, very useful for our codeless testing approach.
From the experiments, the results show that our framework can be efficient to perform the automation testing with most of standard websites by using the generic test case without rewriting the test code into Selenium. From this work, we intend several perspectives. First, we have to tackle all the use cases mentioned in our paper and in particular the third case in which constraints are given on the search functionality. Furthermore, we aim at testing several behaviors at the same time. This aspect will need another testing architecture to consider the huge amount of data needed to be trained and analysed through our framework. Besides, we expect to propose APIs and generic test scripts to the community in order to assess our technique on diverse functional tests. This is will lead to interesting and relevant comparisons with other approaches.
, 2012). Validation of EVs isolation is often based on fluorescent microscopy, scanning electron microscopy, and nanoparticle tracking analysis (NanoSight)  (Murgoci et al. , 2018). Under an electron microscope, exosomes show characteristic cup-shaped morphology, appearing as flattened spheres with diameters ranging from 30 to 150 nm  (Murgoci et al. , 2018). To identify the proteins, present at the surface of EVs and the ones within EVs, we performed a proteomic approach after Trypsin/Lys C treatment and Amicon filtration. Using this procedure, we were able to identify plasmatic membranes as well as luminal proteins. At the same time, we were interested in looking for ghost proteins present in and on the surface of vesicles. Ghost proteins or alternative proteins (AltProts) represent the invisible part of the proteome, because they are not currently identified, annotated or added to the current database using bottom-up proteomics  (Delcourt et al.
, 2018). In fact, mass spectrometric (MS) proteomic strategies are based on protein identification against databases of annotated proteins. However, it has been shown that a large number of proteins have not yet been referenced to these databases, in particular because of the rules used to predict the proteins. Thus, AltProts represent proteins of lower molecular weight than reference proteins (RefProts) and are derived from regions of the RNA described as non-coding. AltProts can be translated from 5 0 UTR or 3 0 UTR or even by shifting on the open reading frame by +2 or +3. Proteins from these regions do not share common sequences with the RefProts produced from the same mRNA and are therefore not isoforms. Some of these AltProts are also described as encoded by non-coding (ncRNA) and long non-coding RNA.
LIMITATIONS OF THE STUDY In agreement with the fact that the AltProts are not referenced, it is complicated at present to monitor and quantify the AltProts by classical approach such as the western blot based on the use of antibodies. The MS strategy remains the best method for identifying this type of protein. The expression of RNA transcripts can also provide information on the expression capacity of these proteins, although the presence of an RNA does not guarantee the production of the protein. The MS strategy uses AltProt prediction; if the databases are getting richer and safer, certain models like the RAT still present a not well-annotated genome. Thus the AltProt database based on Rnor_6. 0 genomic data needs to evolve. Finally, if we demonstrate the presence of AltProt in variable amounts under different conditions, their present function remains.
Studies of the AltProt interactome have previously been undertaken  (Cardon et al. , 2019a (Cardon et al. , , 2019b) )  and have given some clues and showed that they may be involved in phenotypic changes, and in the signaling pathways of gene expression  (Chen et al. , 2020). Table  2. Continued AltProts identification: accession number according to OpenProt annotation correlated to the gene produced the AltProt; column ''type'' lists the kind of AltProt based on the location in the RNA: AltProts come from the 5 0 and 3 0 UTRs, by a shift in the CDS, or from ncRNA. The transcript accession is also mentioned permitting the identification of the RNA and gene; in the AltProt sequence in amino acid and nucleic acid, the sequence of the unique peptide identified has been underlined.
ACKNOWLEDGMENTS This research was supported by a collaboration between the PRISM and grants from  Ministere de L'Education Nationale, L'Enseignement Superieur et de la Recherche, Inserm, France  (M. S. , I. F. ), and  University de Lille 1, France  (A. -N. M. ),  APVV 15-0613 , Slovakia (D. C. ), STEFANIK, France and Slovakia (M. S. , D. C. ),  VEGA 1/0376/20 , Slovakia (D. C. ),  ERA-NET AxonRepair ,  Netherlands  (D. C. ).
Nanoparticle tracking analysis (NTA) Isolated vesicles were analyzed using the NanoSight LM 10 instrument (Merkel technologies LTD. , UK) to characterize size and concentration. After the last ultracentrifugation step followed by Tryspin/Lys digestion, the pellet obtained was diluted in particle-free PBS (1:100). To analyze the particles, 5 videos of 60 s for each sample were recorded. A monochromatic laser beam at 488nm was used for analyses. Particle movement was investigated with NTA software (version 3. 2, NanoSight). NTA post-acquisition settings were kept constant between samples.
Data analyses For the Alternative Proteins (AltProt), Proteome Discoverer V2. 3 (Thermo Scientific) was used, the protein database was downloaded from Openprot (https://openprot. org/) and includes RefProts, novel isoforms and AltProts predicted from both Ensembl and RefSeq annotations (Rnor_6. 0. 84, Rnor_6. 0) for a total of 293303 entries. The following processing and consensus parameters are used with: Trypsin as enzyme, 2 missed cleavages, methionine oxidation as variable modification and carbamidomethylation of cysteines as static modification, Precursor Mass Tolerance: 10 ppm and Fragment mass tolerance: 0. 1 Da. The validation was performed using a Percolator with a protein strict FDR set to 0. 001%, according to Brunet et al. , for AltProt database, the FDR needs to be more restrictive to remove maximum of false positive  (Brunet et al.
, 2019). A consensus workflow was then applied for the statistical arrangement, using the high confidence protein identification. Results are filtered to keep master protein and high confidence protein FDR. For the Reference Proteins (RefProts) the MS data was processed with MaxQuant (version 1. 5. 8. 3)  (Cox and Mann, 2008)  using the Andromeda search engine. Proteins were identified by searching MS and MS/MS data against the Decoy version of the complete proteome for Rattus norvegicus of the UniProt database(UniProt Consortium, 2012) (Release June 2017, 8022 entries reviewed). Trypsin specificity was used for the digestion mode with N-terminal acetylation and methionine oxidation selected as the variable. Carbamydomethylation of cysteines was set as a fixed modification and we allowed up to two missed cleavages.
For MS spectra, an initial mass accuracy of 6 ppm was selected, and the MS/MS tolerance was set to 20 ppm for HCD data. For identification, the FDR at the peptide spectrum matches (PSMs) and protein level was set to 0. 01. Relative, label-free quantification of proteins was performed using the MaxLFQ algorithm  (Cox et al. , 2014 ) integrated into MaxQuant with the default parameters. Analysis of the proteins identified was performed using Perseus software (http://www. perseus-framework. org/) (version 1. 6. 0. 7). The file containing this information from identification was used with hits to the reverse database, and proteins only identified with modified peptides and potential contaminants removed. Then, the LFQ intensity was transformed by the log2[x] function. Categorical annotation of rows was used to define different groups depending on the following criteria: 1) localization on the exosome and 2) time of enzyme treatment.
Datasets including MaxQuant files and annotated MS/MS datasets, were uploaded to ProteomeXchange Consortium via the PRIDE database, and was assigned the dataset identifier PXD016944 (Username: reviewer95445@ebi. ac. uk, Password: wHS6rIOg). Functional annotation and characterization of identified proteins were obtained using PANTHER software (version 9. 0, http://www. pantherdb. org) and STRING (version 9. 1, http://string-db. org). The Elsevier's Pathway Studio version 9. 0 (Ariadne Genomics/Elsevier) was used to deduce relationships among differentially expressed protein candidates using the Ariadne ResNet database  (Bonnet et al. , 2009; Yuryev, Kotelnikova and Daraselia, 2009). "Subnetwork Enrichment Analysis" (SNEA) algorithm was selected to extract statistically significant altered biological and functional pathways pertaining to each identified set of protein hits.
SNEA utilizes Fisher's statistical test used to determine if there are non-randomized associations between two categorical variables organized by specific relationships. SNEA starts by creating a central "seed" from all relevant entities in the database, and retrieving associated entities based on their relationship with the "seed" (i. e. binding partners, expression targets, protein modification targets, regulation). The algorithm compares the sub-network distribution to the background distribution using one-sided Mann-Whitney U-Test and calculates a p-value indicating the statistical significance of the difference between two distributions.