dataesr/scientific-paragraphs-categorization huggingface dataset:
File name: Supplementary_File_11_Stage_29 Title of data: Late swimming larva (Stage 29) Description of data: A. Larva, left view. CLSM. ATEN, DCEN, RTEN, VCEN: cilium of an anterior trunk, dorsal caudal, rostral trunk, and ventral caudal epidermal neuron, respectively. B. Medial sagittal optic section of the larval trunk. CLSM. C-C I. Two frontal sections of the same larva, at the level of atrial siphon primordia (C) and endodermal strand (esr) (C I ). Light microscopy, Toluidine blue. Enlargement is the same in C-C I. D-D VII. Eight selected transverse sections from a complete dataset of a serially sectioned larva from anterior (D) to posterior (D VII ). Light microscopy, Toluidine blue.
Omics Analysis One-hundred ng of total RNA from each fraction was labeled and hybridized onto Agilent mouse RNA and miRNA microarrays, Release 18. 0 (Agilent Technologies, Santa Clara, CA, USA) following the standard Agilent Protocol with RNA and miRNA Complete Labelling and Hybridization Kits, including Agilent RNA Spike-ins. Results were scanned using an Agilent Microarray Scanner G2565CA. Scanned TIFF image files were processed using Agilent Feature Extraction Software (v10. 7. 3. 1) to extract the raw data. Proteomics analysis was performed via mass spectrometry as described previously  [8]. Briefly, samples were shortly separated in a polyacrylamide gel, and proteins were reduced with dithiothreitol, alkylated with iodoacetamide, and digested with trypsin. After extraction, the peptides were finally resuspended in 0.
1% trifluoroacetic acid. Approximately 500 ng of peptides were subsequently separated on an Ultimate 3000 rapid separation liquid chromatography system (Thermo Fisher Scientific, Dreieich, Germany) and analyzed on an Orbitrap Elite (Thermo Fisher Scientific, Dreieich, Germany) hybrid mass spectrometer. All omics data were normalized with the quantile method. PCA and the hierarchical clustering of genes and samples were performed with one minus correlation metric and the unweighted average distance linkage method. To find the statistically significant molecules and their GO enrichment, we used the method described previously  [9]  with the selection threshold θ DEG = θ DEM = 4, θ DEP = 2, and significance threshold α DEG = α DEM = α DEP = 0. 001. For each mRNA sample, we used 4 biological replicates; for each miRNA sample 3 biological replicates, except 2 for ECs and KTs; for the proteomics data 9 biological replicates for ECs, 7 for ETs, 6 for ICs and ITs, and 5 for KCs and KTs.
miRNA Validation with miRNA RT-qPCR The microarray data of three of the most relevant miRNAs were validated with real-time PCR (RT-qPCR) using a miRCURY LNATM Universal RT miRNA PCR system (Exiqon, Denmark), following the manufacturer's instructions. Also, 50 ng of total RNA was used for cDNA synthesis using a Universal cDNA synthesis kit II (Exiqon, Denmark). cDNA was amplified in triplicate in a 7900 HT Fast Real Time System (Life Technologies, Carlsbad, CA, USA) using miRNA LNA primer sets for the specified miRNAs (Exiqon, Denmark) and ExiLENT SYBR ® Green master mix (Exiqon, Denmark). The expression of miRNAs was normalized to the SNORD68 control gene, and the relative expression was calculated with the 2 -∆∆Ct method.
Algorithm to Search for miRNAs Targeting Genes To search for miRNA target genes, we developed software in MATLAB ® (MathWorks ® ), miRDiana that collects the union of mouse validated targets from the TargetScan  [11] , MicroCosm  [12] , mirTarBase  [13]  and miRWalk 2. 0  [14]  databases. Firstly, the software downloads each database and preprocesses by standardizing the miRNA and gene names. It strips the miRNA names from the species ids and converts the gene names to the official symbols of the National Center for Biotechnology Information (NCBI) database. Next, for each potential gene target, it calculates an incidence matrix with all the miRNAs of each database targeting such genes. Finally, it builds a consensus matrix with the cases of the appearance of each miRNA in the four analyzed miRNA databases.
CRC Patients and Samples All experiments in this study comply with the current Spanish and European Union legal regulations. Samples and data from patients were provided by the Basque Biobank for Research-OEHUN. All patients were informed and gave written consent for the use of their tissue in this project by signing a document approved by the Ethical and Scientific Committees of the Basque Country Public Health System (CEIC 11/51 and CEIC 18/37). To build TMAs, paraffin-embedded liver metastases from 34 CRC patients were identified and collected from 28 males (mean age: 65. 1 years) and 6 females (mean age: 64. 7 years). Eleven of these samples presented with synchronous metastasis (i. e. , they were detected in the moment of the first diagnosis (Stage IV)) and the remaining 23 cases had metachronous metastases with four patients diagnosed with Stage I, ten with Stage II, and nine with Stage III at the time of clinical presentation.
Interestingly, Brca1 itself displayed very low expression in all control samples but was highly expressed in all TME samples in Figure  6A. In the tumor samples (TM and TP), all of the BASC genes displayed increased expression, suggesting that Brca1 is the last BASC gene to become activated in the TME samples. To further investigate the behavior of BASC genes in CRC, we downloaded two independent datasets with transcriptomics data of liver metastasis from the GEO database. In the dataset with non-tumor colorectal tissue, primary colorectal tumor, and colorectal liver metastasis from 51 patients  [39]  (GSE81558), we found that ATM is middle-expressed in the three types of samples, around a third of the BASC genes (MLH1, MSH2, MSH6, RAD50) are highly expressed in the three types of samples, and more than the half (BLM, BRAT1, BRCA1, MRE11A, NBN, RCF1, RFC2, RCF3) are up-regulated in cancer samples (primary colorectal tumor and colorectal liver metastasis) in relation to the non-tumor colorectal tissue in Figure  6B.
Thus, generally, the BASC gene expression is higher in cancer than in non-cancer samples in Figure  6C. In the dataset with primary colorectal tumor and colorectal liver metastasis from 133 patients  [40]  (GSE41568), MRE11A and NBN are low-expressed in primary colorectal tumor and colorectal liver metastasis, MLH1, ATM, and RFC1 and homogeneously high-expressed in all the samples, and the rest of the BASC genes, including BRCA1, are high-expressed, with some variation between patients, MSH6 and MHSH2 being the highest and lowest variable ones, and BRCA1 being high-expressed with a middle variability among patients in Figure  6D. These results support the hypothesis that BRCA1 is the last BASC gene activated in colorectal liver metastasis.
Funding: D. G. and M. J. A. -B. have been supported by Grants  DFG113/18  from  Diputación Foral de Gipuzkoa (DFG), Spain, Ministry of Economy and Competitiveness, Spain ,  MINECO  Grant  BFU2016-77987-P  and  Instituto de Salud Carlos III  ( AC17/00012 ) Grant co-funded by the  European Union (Eracosysmed/H2020  Grant Agreement No. 643271 ) and  European Union  ( H2020-FETOPEN, Project 899417 ). D. G. , M. J. A. -B. and I. B. have been supported by  Grants Health Department of the Basque Government (Spain) , RIS3 call, Exp. No. 2020333039  and  2020333001.
Data Availability: The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO)  [61]  and are accessible through GEO Series accession number GSE156431 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE156431). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [62]  partner repository with the dataset identifier PXD020913. Computer Code and Software: Our newly developed software package miRDiana to search for miRNA target genes is freely available at https://sourceforge. net/projects/mirdiana/.
, 2009]. Generally, the purpose of web crawlers is to collect, maintain, manage the index of web pages, and keep the database in the repositories up-to-date  [Jain and Subodh, 2018]. With a swiftly growing Internet, web crawling hence is an essential technique to collect data and keep up with web data for search engine systems. Web crawlers can also be applied for automatic maintenance for websites, including validating links or verifying HTML code. [Komal and Ashutosh, 2016]  Moreover, some may use them to collect particular kinds of information from webpages, like gathering e-mail addresses, typically for spam  [Desai, 2017]. 2 Literature Review "WebCrawler" was first launched in 1994, with the world's first full-text crawler and search engine  [Pinkerton, 1994]. The "WebCrawler" allowed users to search web document contents rather than keywords and descriptors, lessening confusing results, and providing better search capacities.
System Architecture of Web Crawler Template-based web crawlers are normally used to extract data from web-pages, however, specific template from each website is vary and challenging for this technique. Therefore, in this paper, a new web crawler is proposed to extract web-pages from blocks by blocks, attaching with each title and content, (as shown in Figure  1 ) making the crawling data produce contents similar to an article format. Its operation process is indicated in Figure  2  and described as follows. 1. Using URLs, websites were crawled in forms of HTML files, which includes different web pages; 2. Those pages were then extracted into a number of blocks, including pictures and texts; filter out and select only text blocks for the next experiment; 3. The text blocks were manually verified and labeled by operators, through a UI (user interface); 4.
Crawling Sub-module The crawling sub-module is designed to be implemented on the crawling server. By using URL, for example, a website sample is crawled as an HTML file, as shown in Figure. 4. The crawling sub-module runs as a batch in the background, taking input from CSV/JSON URL files or the database. The output of this module is a JSON string that contains a web page's text blocks and other attributes, as indicated in Figure  5. Then, the string is inserted into a local database to verify it in the next step.
Tuning Method: Cross Validation During a machine learning process, the dataset is normally split into training and test sets. [Goras and Fira, 2009]  Afterward, the training set is used to train the model and the test set is handled to evaluate the model performance. [Ge et al. , 2017]  However, this method may lead to variance problems, in which the accuracy obtained on one test is somewhat different from the accuracy obtained on another test set using the same algorithm. [Sebastian, 2018]  In order to solve this issue, K-Fold Cross-Validation is used in this research to evaluate the performance. From there, the data is divided into K folds, and K-1 sets are used for training whereas the remaining set is used for testing. The algorithm is consequently trained and tested K times, each time using a new set as the test set, and then using the remaining sets for training.
Dataset Collection The website dataset was collected from 495 URLs, which is corresponding to 495 web pages. The final blocks extracted from the Crawling Sub-module are 2082 text blocks, with 777 blocks are classified as "good" (accounting for 37,32%, containing content information of the website) and 1305 blocks are classified as "bad" (accounting for 62. 68%, containing navigations, contact boxes, Ads, etc. ). There are also 15 numeric features and 1 compound score. The dataset feature details are shown in Figure  12.
Experiments To standardize values, the dataset which contains raw HTML files was preprocessed by using Selenium  [Shaumik and Pradeep, 2019]. The next step was to extract the files into length and width values, before clustering by Density-based spatial clustering of applications with noise (DBSCAN) algorithm  [Ester, 1996]. Then, the blocks were labeled and extracted their features. Finally, the dataset was split into an unstratified training test with 20% samples and was delivered to the training process. Therefore, there is a total of 1665 training text blocks and 417 testing text blocks in the end. First, the model suggested by GridSearchCV was used to train and test the dataset. The features used include Kernel SVM, gamma = 'scale', class weight= {0: 1. 9}, and penalty = 1000. Using this model, the training set obtained remarkably high accuracy and precision scores.
Conclusions In this paper, a web crawler model was designed and developed, which extracted websites into article-like contents. Firstly, the web crawler module was designed into 4 different sub-modules, crawling, verifying, training, and classifying. The website dataset was collected from 495 URLs, then extracted into 2082 text blocks, and split its 20 percent for the training set. The model used the Support Vector Machine model to train, Cross-Validation for tuning, and Grid Search for parameter selection. After experience on different models, the optimal suggested is the GridSearchCV, where it may deliver 92. 01% for accuracy and 96. 43% for precision on the training set, and 81. 06%, 84. 62% respectively on the testing set. It is also noticeable that the efficiency, regarding the accuracy, precision, and recall values are higher when the training size increases.
ure 3. Each sub-module implements specific tasks as detailed below. 1. Crawling: getting data from web pages, extracting text blocks, and saving them into a local database; 2. Verifying: verifying each text block by operators and submitting results into the database via an API; 3. Training: training Machine Learning models using data from database and export reports; 4. Classifying: classifying text blocks in the database using the trained Machine Learning model, exporting as files or database.
Apparatus and stimuli The experiment was executed in an instrumented fixed-base simulator with sensors on the pedals and the gearbox. The driving scenario was designed with OpenDS Pro Complete software. This includes tools for creating various experimentation components (databases and files) needed to describe the environment and progress of experiments. The experiment room was temperature-controlled (M = 24 °C, SD = 1. 23 °C). Participants were seated in a car seat 3. 5 m in front of a screen (dimensions: 2. 0 m wide × 1. 4 m high; field-of-view: 31° horizontally, 24° vertically). A high-speed digital camera (S-MOTION) was used to record head movements, with the noise tip being the region of interest (ROI). The camera was placed just above the screen in the middle. The head movement analysis was performed using Imaging Studio software where the movements of the noise tips were measured in pixels along the X and Y axes.
In any case, it is fundamental to consider that the Mixed GCS-AS condition could have generated some interference with driving due to the multiple sources of stimulation, and this was not the case when GCS and AS were administrated together, yielding a significant improvement in average speed. Finally, regarding the correlation between motions sickness and average speed has rarely been analyzed given the aforementioned described null effect of MS in driving variables. However, previous research found a similar data pattern in the current research. For example,  Helland et al. , (2016)  showed that simulator sickness is associated with a reduction in average speed. Future research must be performed to replicate and generalize our results. First, head sway during the simulator experience was used as a measure of body balance.
One way to relax the assumptions of Nash is to forego belief modeling (which are typically unobservable anyway), and concentrate on immediate impulses from experienced utilities (payoffs). This approach of reinforcement learning had emerged in natural sciences, and has been introduced in economics by  Roth and Erev (1995; 1999) , and further developed in the experience weighted attraction (EWA) model  (Camerer and Ho, 2000). It is worth notice, however, that this approach is by no means exclusive nor universal to all contexts. In fact, most models fall short of explaining the underlying generating process by which human players learn in economic decision tasks  (Salmon, 2001). Selten, Chmura and Goerg (2011)  compare descriptive performance of several models of learning against an empirical dataset based on more than 800 subjects playing for 200 rounds.
As in the previous case (model-based RL with counterfactual updating), this leaves unspecified how much to decrease or increase the PB. DL has been found to describe accurately a large body of experimental data, but it does not furnish testable quantitative predictions, since it does not prescribe how to adjust actions upon miss other than the direction in which the actions should be adjusted. In this study, we have created and fitted several adaptive learning models based on DL. The numerous schemes we tried on the dataset are succinctly described next: Naive DL. This is the basic model, which we did not implemented in practice, but it is the basis of all other DL models used in the current study. It consists in simply 'nudging' the bid up and down by a fixed amount n, contingent on the outcome of the transaction: The naive learning model stipulates adjustment from the previous trial is a function of success or failure only, and is being made at a constant rate: If the current bid is rejected: A t +1 (m)= A t (m) -n (8) If the current bid is accepted: A t +1 (m)= A t (m)+n, (9) where n is a constant to be estimated.
DL algorithms exhibited generative sufficiency in the sense that simulated artificial bidders pit against the same pre-recorded dataset as humans generated behavior resembling that of human subjects: the simulated learning curves of agents enacted by DL algorithms playing against the prerecorded dataset were qualitatively in close agreement to human behavioral learning curves (Figure  11 ). By contrast, learning curves of agents enacted by RL algorithms hardly exhibited any learning (not shown). Finally, our empirical results convincingly show that individual buyers reveal risk aversion when facing potentially scrambled signals. Figure  12  shows the distributions of buyers' bids in the three information conditions. Intuitively, it seems obvious that partial (T1) and full (T3) information conditions result in quite similar bidding patterns, which are not significantly different (Wilcoxon-Mann-Whitney p-value <0.
Acknowledgements We would like to thank Ellina Nurullina, Anna Drozdova, and Yulia Biryulina for assistance in recruiting subjects and collecting data, and Maria Yurevich and Karina Akopyan for providing instructions text translations. the randomization due to random-opponent matching). The same learning algorithms were fitted to the same dataset modified to comply with a coarser bid space of only 11 bins for model comparison purposes, but no substantial differences were found. Following the usual approach in this kind of estimation problems  (Daw et al. , 2006) , first parameters were yoked (fixed) across all market types and subjects and optimized in a global objective likelihood function encompassing all subjects (Table  4 , FFX column): l μ (θ yoked |D )=∑ s ∈ S ∑ n ∈ N ∑ m∈ M log ( P μ ( b sn |θ yoked , f sn , m) ) , (B.
Nevertheless, in order to confirm that the fitted parameters were reasonable, an additional fitting routine was accomplished individually for each subject, and between-subject standard errors of the mean parameter values and BIC scores were calculated too for each subject s (Table  4 , RFX column): l μ , s (θ s |D s )= ∑ n ∈ N ∑ m ∈ M log ( P μ (b sn |θ s , f sn ,m) ) (B. 2) Code implementing artificial bidders, model fits, and simulation results is available under the MIT license on the hosting service GitHub (https://github. com/mmartinezsaito/adaptive-learning-inauctions).
Market situations: Market situations will vary depending on the quantity of sellers and buyers on the market. First of all, there may be either one or two sellers on the market. Secondly, you may be the only buyer on the market or there may be another one. During each trial (one financial operation) you will be informed about the quantity of buyers and sellers. The sellers and buyers have participated in previous sessions of the experiment (see section "players"). Each trial players are selected randomly from the database consisting of 16 sellers and 16 buyers (participants in previous experimental sessions). You will not recognize the participants you will be playing with. At the end of the game the participants who played with you will get extra cash bonus which depends on the results of the financial operations during the game (see more in section "payment").
Numerical Manifold Method (NMM) The NMM consists two of important components: the cover system and the block dynamics. The cover system is a dual cover system, which includes a mathematical cover (MC) of three-node triangle elements and a physical cover (PC) that includes the boundary, the material interface, and the crack. The block dynamics is used to solve the mechanical behaviour of block systems under loading and block contact. The NMM program runs on the MATLAB platform. Figure  3  shows the dual cover system containing MC and PC. We can clearly see in Figure  3 (a) that there are three MCs that are coloured yellow, blue and red, that every MC is a hexagon consisting of 6 triangles, and that the node of every triangle is the centre of the MC. If there a crack that cuts through the triangle ABC (such as D1-D5 in Figure  3   Figure  5  NMM model of size and multi-crack distributions When the SIFs at the crack tips are lower than the fracture toughness Kc, both the real and virtual cracks will propagate according to the SCG theory.
Conclusions In this paper, the time-dependent deformation of granite is modelled by combining SCG and NMM on the MATLAB platform. This combined method replaces the traditional creep component model method. In the model, cracks propagate according to the SCG theory. When K is smaller than the fracture toughness Kc, the crack growth length is linked to the local damage (using an exponential material softening law) to reduce the granite strength. When K is larger than Kc, the cracks propagate according to the MCSSC. Parameter sensitivity analysis for the improved NMM program was performed and validated against experimental results. Crack length, confining pressure and differential stress were then investigated. The results show that the microstructure of granite has a great influence on time-dependent deformation.
The main infrastructure providers, who depend on the DREQ, are the developers of the Climate Model Output Rewriter (CMOR) package, 4  those developing quality control software and those doing planning for the Earth System Grid Federation (ESGF) data delivery services. 5 The relationship with the CMOR team is especially important as the DREQ and CMOR intersect in supporting the metadata specifications for CMIP6 output. Users are mainly expected to use portal search interfaces (e. g. the ESGF search interface) to locate existing CMIP6 data, but, especially in early stages, may also rely on the DREQ to determine what data may eventually be found there.
9 he framework element defines how the configuration will be specified and provides some basic tools. It is designed to be flexible and provide some basic software functionality to support the development and use of the DREQ. It specifies that the content will consist of a collection of sections, each of which contains some header information and a list of data records. Each data record is a list of key-value pairs, with a specific set of keys defined for each section. Each key is, in turn, defined by a record, as explained further in Sect. 4. 2 below.