dataesr/scientific-paragraphs-categorization huggingface dataset:
Example data As input genomic regions, we used NRF1 ChIP-seq peaks in mouse embryonic stem cells from our previous study  (20)  available on GEO (ID GSM1891641). The data was aligned to the mouse reference genome assembly mm10 using bowtie2  (21)  and peaks were called using peakzilla  (22). Peaks are centered on their summits by peakzilla by default. Selected motifs from the JASPAR database are NRF1 (MA0506. 1), the TF of interest, as well as other TFs expressed in mouse embryonic stem cells: MYCN (MA0104. 4), GABPA (MA0062. 3), CTCF (MA0139. 1), REST (MA0138. 2) and SP1 (MA0079. 3). Control regions are generated randomly (see below).
Data analyses The overlap between input genomic and control regions and TF motif occurrences is performed using intersectBed from BEDTools  (24). The position of gene transcription start sites was derived for all coding gene transcripts from the ENSEMBL genome annotation (Homo sapiens. GRCh38. 87 for hg38, Homo sapiens. GRCh37. 71 for hg19, Mus musculus. GRCm38. 87 for mm10 and Mus musculus. NCBIM37. 67 for mm9). Genomic tracks for the UCSC genome browser (25) are generated using bedToBigBed  (26). Further data processing, statistics and plots are generated using R (https://www. R-project. org/). Interactive plots are generated using plot ly in R (https://plot. ly/).
Web server implementation TFmotifView was implemented in R using the shiny package (https://shiny. rstudio. com/) and the following libraries: shinydashboard, RColorBrewer, stringr, shinyjs, colourpicker, png, DT, data. table, foreach, doMC, shinyBS, htmltools and plotly. TFmotifView was deployed using the open-source Shiny Server and runs on a dedicated server using an Intel Xeon Silver CPU (10 cores, 20 threads, 2. 2 GHz and 96GB RAM). In order to enable simultaneous connections, TFmotifView was containerized using Docker (https: //www. docker. com/; image deposited on DockerHub) and using Traefik as load-balancer (https://docs. traefik. io/).
Information about individual motifs such as motif name, strand, exact sequence and P-value score, can be accessed by clicking on the motif. Gene transcription start sites of protein coding transcripts are indicated as directional arrows. The legend indicates the motif sequence logos and the colors used for display. Results can be downloaded in a PDF format (limited to maximum 10 pages). Motif genomic track. In order to integrate the motif occurrences with other genomic datasets, a motif track, representing the position of the selected motifs with the selected P-value threshold along the whole genome, can be generated and directly uploaded onto the UCSC genome browser or downloaded as a BED file. Results interpretation. The NRF1 ChIP-seq peak regions contain many occurrences of the NRF1 and SP1 motifs (70% and 51% respectively) and fewer GABPA and MYCN motifs (30% and 15% respectively) (Figure  3 ).
DISCUSSION Understanding the regulation of gene expression by TFs is an active field of research. TF motifs are a great resource to better comprehend the binding specificities of TFs. However, they should not only be exploited in a static genome but rather integrated to a wide range of condition-specific genomic datasets. Toward this aim, we developed TFmo-tifView, a web server that allows to study the distribution of known TF motifs in genomic regions. As a webserver, the analyses from TFmotifView are accessible to researchers with no coding experience or compute resources. It directly takes genomic coordinates as input, which are generally provided by common genomic data processing pipelines. It uses an original approach to set the default P-value threshold dynamically based on each motif information content or allows the user to set the threshold of their choice.
It allows to integrate the corresponding motif track (with the selected P-value threshold) into the UCSC genome browser for integration with other genomic datasets. Finally, it generates graphical representations of the motif enrichments, which allows for a better interpretation of the results. These different features represent an added value compared to other existing methods available to study TF motif distributions in genomic regions (Table  1 ). We believe that TF-motifView will contribute to integrate TF motif information with a wide range of genomic datasets and is relevant for a broad range of questions toward the goal to better understand the regulation of gene expression by transcription factors.
FUNDING Systems Biology Cancer Plan grant from the  ITMO Cancer AVIESAN (French National Alliance for Life Sciences & Health)  [ 18CB008-00  to A. F. B. ]; initiative of excellence IDEX-Unistra from the  French national programme 'Investment  for the future' [part of the  ANR-10-IDEX-0002-02  to A. F. B. ]. Funding for open access charge:  Systems Biology Cancer Plan grant  from the  ITMO Cancer AVIESAN  managed by  CNRS UMR7242.
SNP Panels We genotyped four wild populations of C. gigas and C. angulata from both their native Asian and introduced Atlantic Ocean areas (see Biological Material section below) using two panels of SNPs. The first panel contained 384 SNPs developed in  [49]  and the second panel contained 10,144 Restriction-site Associate DNA sequencing (RAD-seq) derived SNPs from  [42]. From each of the two published genotype datasets, we sorted SNPs according to their between-species F ST value. We chose unlinked SNPs with the highest F ST values to develop a panel of ancestry informative markers differentiating C. gigas and C. angulata. The characteristics of the 15 selected markers are given in Table  S1. A second panel was developed in order to further characterize within-species genetic structure among the cupped oyster populations.
Biological Material Used to Develop the Ancestry Informative Panel (Dataset 1) We sampled four wild populations of C. gigas and C. angulata from both their native Asian and introduced Atlantic Ocean areas. They are presented in Table  1  and Figure  1  and are the same populations as the ones used in  [42] , except that more individuals have been genotyped in the present dataset, and that some population name abbreviations have been modified here because of the sampling of the same populations at different times. Hence, the introduced French Marennes-Oléron Bay population (LAF) and Portuguese population (SAD) from  [42]  have been, respectively, renamed LAF1 and SAD1 in the present dataset. These two populations together with the native Japanese population (JAP) and the Taiwanese population (KEE) were used in the development of the 15 SNPs panel (Table  1 ).
Briefly, data generated from the BeadXpress ® TM reader were analyzed with GenomeStudio for automated genotype clustering and calling. Clusters were then visually inspected to ensure high data quality. The RAD-derived SNP genotyping data were taken from  [42]. A Sanger-based amplicon sequencing method was used for LAFG0, SADG0, HYB, and TAV1 samples. PCR primers were designed using the online Primer3 software package  [51]  based on the sequences available in  [49]  and  [42]. Polymerase Chain Reaction (PCR) products were purified by alcohol precipitation using ammonium acetate. They were then sequenced in both directions using the ABI prism BigDye v3 Terminator Cycle sequencing Kit (Applied Biosystems, Foster City, USA) and the sequences were analyzed on an ABI 3130 XL genetic analyzer (Applied Biosystems) via Sequencing Analysis 5.
2 to detect single-nucleotide polymorphisms. SNP genotyping was performed for the 27 populations included in Dataset 3 using the iPLEX Gold chemistry following  [52]  on a MassARRAY System (Agena Bioscience). Data analysis was done using MassARRAY Typer Analyzer 4. 0. 26. 75 (Agena Biosciences). We excluded monomorphic SNPs, loci with weak or ambiguous signal and loci high missing data. As described above, the samples were genotyped by several methods, with often some common samples between the methods in order to confirm the identity of the genotypes. In those cases, no   1 ) used to characterize the cupped oyster diversity.
Biological Material Used to Confirm the Power of the Ancestry Informative Panel (Dataset 2) In order to confirm the power of the 15 SNPs panel to differentiate C. gigas and C. angulata populations, we added four samples to the four previous populations (Table  1  and Figure  1 ). We obtained samples from another population from Portugal in the Algarve region (TAV1) as well as new alive animals from Marennes-Oléron Bay (LAFG0) and Sado River (SADG0). These were also used to experimentally produce a reference sample of F1 hybrids (HYB). Briefly, nine F1 biparental families were produced June 9, 2012, involving, respectively, five C. gigas females, five C. angulata males, four C. angulata females, and four C. gigas males (9 LFG0 and 9 SADG0). Between 11 and 12 animals of each family were sampled for this study for a total number of 104 HYB animals.
Biological Material Used with the 80 SNPs Panel to Characterize the European Cupped Oyster Diversity (Dataset 3) A large spatial scale sampling was performed in 2017 along the French Atlantic coast from Normandy to Arcachon Bay and along the Mediterranean Sea coast. It was completed by samples collected earlier (2010) in Brittany, Marennes-Oléron, and Arcachon Bay. The objective here was to characterize the genetic diversity of French cupped oyster populations across space and time, a decade after the beginning of important mortalities on spat, and more recently, adult oysters. Reference populations of C. gigas and C. angulata were added in this third study in order to characterize each species genetic background (C. gigas and C. angulata), as well as reference the North European population of C.
2 to detect single-nucleotide polymorphisms. SNP genotyping was performed for the 27 populations included in Dataset 3 using the iPLEX Gold chemistry following  [52]  on a MassARRAY System (Agena Bioscience). Data analysis was done using MassARRAY Typer Analyzer 4. 0. 26. 75 (Agena Biosciences). We excluded monomorphic SNPs, loci with weak or ambiguous signal and loci high missing data. As described above, the samples were genotyped by several methods, with often some common samples between the methods in order to confirm the identity of the genotypes. In those cases, no discrepancies were observed between the results obtained by the different methods. The genotypes of the three datasets are available at https://doi. org/10. 17882/71545 [53].
Population Genetics Analyses Genetic differentiation between pairs of populations was estimated SNP by SNP using F ST  [54]  in Genetix 4. 1  [55]. Within-and between-population components of genetic diversity were decomposed using a discriminant analysis of principal components (dAPC), in order to maximize genetic variation between populations while minimizing within-population variation. The dAPC analysis was performed with the R package Adegenet  [56]. Bayesian clustering of the genetic data was performed using Structure version 2. 1  [57, 58]. We ran Structure with K varying from 1 to 10, with 10 runs for each K value, to find the K value with the highest posterior probabilities, and used the ∆K statistics to evaluate the change in likelihood  [59]. Our parameters were 10,000 burn-in periods and 50,000 Markov chain Monte Carlo repetitions after burn-in.
This reflected a stronger effort to detect the most strongly differentiated markers in the RAD dataset compared to the BeadXpress SNP dataset. frequencies within each population and estimated FST between pairs of populations at each SNP. Although we did not find any fixed difference in allele frequency between the two species using the four populations sampled, we sorted all the SNPs according to the FST between pairs of interspecific populations and chose the seven and eight SNPs with the higher values respectively in the BeadXpress (384) and RAD sequencing  (10, 144)  panels. The frequency of the most frequent allele in C. angulata (named the "angulata" allele) at each SNPs in each of the four populations is presented in Figure  2. SNPs named ANGI1 to ANGI7 come from the BeadXpress panel  [49]  and SNPs named ANGI8 to ANGI15 were developed from the RAD sequencing panel  [42].
angulata. On the basis of two previously published sets of SNP markers  [42, 49] , we selected SNPs showing the highest F ST values between pairs of interspecific populations. Although our final panel consisted of 15 SNPs that were evenly chosen from the two initial datasets, the most informative markers were mainly obtained from the RADseq SNPs panel (e. g. , ANGI9, 10, 14, and 15). This simply reflects the fact that this high-dimensional SNP dataset offered a far higher number of SNPs, among which the most significant outlier loci could be searched. This was also illustrated in Figure  2 , in which the SNPs from the RADseq SNPs panel (e. g. , ANGI8 to 15) showed more extreme allele frequency differences between C. gigas and C. angulata populations. The overall same amount of variance was explained by axis 1 (19.
52%) on the discriminant analysis of Dataset 2 than in  [42]. Since we used an extended sampling to evaluate the discrimination power of these SNPs, this result confirms that the 15 "outlier" SNPs used in this panel are sufficient to evaluate individual ancestry with a reasonable precision. This new panel of 15 SNPs is clearly more powerful than the few markers already available  [31, 32]  even though none of these SNPs individually appeared to be fully diagnostic. This reflects the fact that the genomic landscape of divergence between C. gigas and C. angulata does not contain any region with fixed differences, as previously observed using more than 10 thousand SNPs  [42]. In this context, the interest of the tool developed here lies in its capacity to quantify mixed ancestries between the two species with a reasonable degree of precision and a very low cost.
Reference to the dataset the samples where used in: 1 = development of the ancestry informative panel; 2 = confirmation of the power of the ancestry informative panel; 3 = characterization of the European cupped oyster diversity. Genes 2020, 11, 451 5 of 17 Genes 2020, 11, x FOR PEER REVIEW 5 of 17 characterize each species genetic background (C. gigas and C. angulata), as well as reference the North European population of C. gigas from Denmark. The 80 SNPs panel was used to genotype these 985 individuals.
Estimated and observed heterozygosity in the large-scale population Dataset 3. TableS1: List of the ancestry informative SNP panel. TableS2: List of the 80 SNPs used to characterize the cupped oyster genetic diversity. Author Contributions: S. L. and P. -A. G. wrote the manuscript with input from S. H. , F. C. , and E. G. ; experimental crosses as well as data curation were managed by S. H. and F. C. ; E. G. and S. H. managed the different genotyping strategies. S. L. , S. H. , and P. -A. G. performed population genomic analyses; S. L. conceived the project and managed financial support. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by Ifremer, the ANR project HYSEA (no. ANR-12-BSV7-0011) and DPMA (Direction des Pêches Maritimes et de l'Aquaculture) project ECOSCOPA.
Acknowledgments: We are very grateful to all our colleagues who contributed to the collection of samples:  F. Batista ,  S. Pouvreau ,  H. Palvadeau ,  J. F. Bouget ,  P. Soletchnik ,  J. -L. Seugnet ,  P. Geairon ,  F. D'Amico ,  F. Lagarde. We thank  J. Haure ,  M. Chapat , and the hatchery team of  Ifremer La Tremblade  for their help in the production of some biological material. MassARRAY genotyping was performed at the  Genome Transcriptome Facility of Bordeaux  with the help of  Adline Delcamp  and  Marie Massot.
38 3 * 3 Reference to the dataset the samples where used in: 1 = development of the ancestry informative panel; 2 = confirmation of the power of the ancestry informative panel; 3 = characterization of the European cupped oyster diversity. Genes 2020, 11, 451 5 of 17 Genes 2020, 11, x FOR PEER REVIEW 5 of 17 characterize each species genetic background (C. gigas and C. angulata), as well as reference the North European population of C. gigas from Denmark. The 80 SNPs panel was used to genotype these 985 individuals.
These defects can be minimized by using commercial software such as AdditiveLab, Simufact, Virfac, etc. , which helps to find the best support structure placement based on the thermomechanical simulations. A lot of time and money is consumed to find best-fit process parameters such as laser power, scan speed, hatch distance, layer thickness, etc. , to reduce the third type of defects called "process defects". All the parameters mentioned above can be combined in the empirical formula of 'Energy density (E)' given by Equation  (1) , where P, v, h, and t represent laser power, scan speed, hatch distance, and layer thickness, respectively. E = P v × h × t (1) Each paramete in Equation (  1 ) has its effect on the final quality of the part. For example, at lower scan speed and high laser power, the energy density is higher, and its attributes to porosity defects are due to entrapment of gas.
e. , one data point for every 10 µm to achieve a spatial resolution of 10 µm. Most of the researchers have reported a sampling rate of 50 kHz and used more than two sensing devices, such as a digital camera, photodiode, or combination of both. Therefore, if a single data value is stored in binary fashion, it occupies 32 bits. With a 50 kHz data sampling rate and with five sensing devices, the data will be stored at 1 MB/s. Therefore, if the machine runs for three days, it will generate at least 233 GB of data. The post-processability of this dataset and storage is difficult  [78]. Secondly, linking the obtained in situ sensing data to the quality of the product is a significant challenge. In recent years, the research community is working on integrating ML methods into AM. Machine learning (ML) is a subset of Artificial Intelligence (AI) that can learn and improve in an automated fashion from experience without being explicitly programmed.
The process of learning starts with data observation and data pattern recognition to allow algorithms to learn automatically and adjust actions accordingly. The main aim of the ML is to learn and improve from data without human intervention. The ML algorithms are used in AM for design and process optimization, in situ monitoring, inspection and validation, and cost estimation. The data used for training the ML algorithms decide the effectiveness of the ML approach. In other words, the ML algorithms are as good as the training dataset  [79]. ML algorithm can be broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning. In supervised machine learning, the algorithm is trained with a labeled dataset. The labeled dataset is the dataset in which the output is known for the input variables.
Therefore, the model is trained based on inferring the functional relationship between input and output variable. Supervised learning can be used for regression and classification problems. For instance, classifying the part quality as "good" and "bad" is a classification problem, whereas predicting the porosity level or mechanical properties of the part is a regression problem. In unsupervised learning, the output labels are not known for the input training dataset. The algorithm separates the training dataset into different clusters based on the relationship among input data. Unsupervised learning is useful in clustering problems such as detecting anomalous conditions. The third ML category, called reinforcement learning (RL), is learning by interacting with an environment. The RL algorithm learns from the consequences of its actions, and it selects its actions based on its experience and new choices, which is trial and error learning.
The RL algorithm receives a numerical reward which encodes the success of an action's outcome. The RL algorithm seeks to learn to select actions that maximize the accumulated reward over time. Applications of the RL algorithm are game theory and self-driving cars. The list of various ML algorithms is shown in Figure  13   [80]. There is another type ML algorithm called "semi-supervised learning", which combines unsupervised and supervised learning. Semi-supervised learning combines the small or limited amount of the labeled dataset with a large amount of the unlabeled dataset during training the algorithm  [81]. This type of learning is beneficial in AM applications, where obtaining the labeled dataset is a very challenging task. The decision for using a type of ML approach is based on the available dataset, and specific output preferred.
The use of the abovementioned ML approaches for monitoring the L-PBF process is discussed in the next sections. unsupervised and supervised learning. Semi-supervised learning combines the small or limited amount of the labeled dataset with a large amount of the unlabeled dataset during training the algorithm  [81]. This type of learning is beneficial in AM applications, where obtaining the labeled dataset is a very challenging task. The decision for using a type of ML approach is based on the available dataset, and specific output preferred. The use of the abovementioned ML approaches for monitoring the L-PBF process is discussed in the next sections.
3. Lack of labeled data: As discussed earlier, the supervised learning ML approach requires a labeled dataset for training. Therefore, a better understanding of the acquired data is needed to label it. However, sometimes it is challenging to label the data accurately and effectively. For example, in the case of melt pool in situ monitoring data, it is very tricky to identify the "acceptable" melt pool profile from the "unacceptable" melt pool profile. There are high chances of human error and misclassification. It can affect the accuracy of the whole model.
4. Machine learning training problems: Overfitting and underfitting are the most common problems that can occur while training machine learning models. Overfitting occurs when the model tries to fit every data point precisely in the training dataset. It makes the model vulnerable to noise and outliers. Usually, overfitting is most likely with non-parametric and non-linear models that are more flexible while learning a target function. For example, the Decision tree (DT) is a nonparametric algorithm that is very flexible and subjected to overfitting training data. Overfitting problem can be eliminated by running a tree after it has learned, to remove some of the detail it has picked up. Underfitting occurs when the model is unable to extract a reasonable relationship among training data points.
Supervised Learning Approach Supervised learning is one of the most commonly used ML approaches for detecting the defect in the L-PBF process. The first step in supervised learning is to prepare a labeled training dataset. Labeling the in situ data is the most challenging and error-prone task, which can affect the overall accuracy of the supervised ML model. Therefore, labeling the in situ data, such as IR images, photodiode signals, pyrometer signals, can be done via making a statistical comparison between in situ data and CT images, which captures the ground truth labels. The same can be used for labeling the different clusters obtained by unsupervised learning. Gobert et al. and Petrich et al. [84, 85]  used a 36. 3-megapixel digital single-lens reflex (DSLR) CCD camera (Nikon D800E) mounted inside the build chamber of EOS M280 (L-PBF system) to detect discontinuity defects such as overheating, pores and unmolten powder.