dataesr/scientific-paragraphs-categorization huggingface dataset:
High Energy Detectors The requirements of both small pixel size (100µm) and large arrays (22cm 2 × 22cm 2 ) are very challenging and require developments in rupture. Hard X-ray imaging detectors with 50µm pitch are already available  (Ruat & Ponchut 2012; Poikela et al. 2014 ) but with poor spectral resolution not suitable for space astronomy. The promising developments of fine-pitch hard X-ray spectroscopic imagers imply CdTe double-sided strip detectors (150µm pitch,  Furukawa et al. (2019) ) and CdTe pixel detectors (250µm,  Wilson et al. (2015) ). A dedicated R&D program will be of high importance to reach the science goals of the PHEMTO mission and includes several development axis that could be shared between European research labs and industrial in 2020-2040. These developments are in synergy with industrial motivations for medical applications (cancer tomography.
They combine memory work and skills involving visual-spatial ability  [31]. Considering the high cognitive load involved by high-fidelity simulators, which may negatively impact the ability to learn medical gestures  [32] , we followed the recommendations of Mayer and Moreno  [33]  and kept the exercises and the virtual environment simple, focusing on the gestures (trajectories and tool synchronization). To avoid high computing costs, we used open-source software and affordable material  [34]. This low-fidelity simulator is a computer-based training system. The hardware includes a computer (Microsoft Windows 7 ® Intel ® Core ™ i7-6500 CPU clocked at 2. 50GHz, Nvidia ® Geforce GTX940M, 8Go RAM), and a standard monitor displaying virtual 3D environment, associated with two real standard laparoscopic surgical instruments, paired each one with a force-feedback device thus simulating tactile and kinesthetic feedback (Geomagic Touch ® haptic device, 3D System Inc.
). The latter is a 6 degreesof-freedom device using three electrical motors as actuators, often used in medical simulators  [35]. We developed the software with Microsoft Visual Studio ® 2015 1 and we used the open-source Haptic Framework CHAI3D 3. 1. 1 2 to make the haptic devices, the 3D virtual world, and the Open-GL 4. 4 3 renderer communicating with each other. Fig. 1  Overview of the simulator. The user manipulates real MIS tools connected to the haptic interface hidden under the cover which mimics the patient's skin. The trainee monitors his gestures in the virtual environment through the monitor We designed all virtual objects with the 3DS Max software 2016. 4 We finally collected and processed performance data (affine velocity, motion duration, number of collisions) through Matlab ® R2015a (Mathworks).
As a result, the parameters used in these previous works could not be used for MIS. Therefore, we had to determine those for MIS, which should fit every motion involved in this hands-on training. This is explained in the next subsection. Affine velocity was utilized to provide a quantitative measure reflecting the quality of the kinematics of users' trajectories. Affine velocity was used instead of the velocity itself because it provides valuable information about the quality and smoothness of the trajectory. Data from (1) v = v a • κ • α • |τ | • β each training session (trajectories, moments, and numbers of collisions, right/left-hand distributions) were collected through CHAI3D into a file and post-processed using Matlab to evaluate the trainees' performance. In future versions, these assessment algorithms will be integrated into the simulator software.
The first step to tune the affine velocity computation was to get enough data to determine the best α and β val- ues for this simulator. We collected an adequate sample of 46 trajectories ranging from novices to experts (see the next section). We independently verified their skill level so that they could serve as references. After the process proposed in  [42] , we first interpolated the trajectories of both tooltips into cubic splines and computed the values of the Euclidean velocity v, the curvature κ , and the tor- sion τ (measuring how much the trajectory twists out of the plane of curvature). Then, we performed a logarithmic linearization of (1): We performed multivariate linear regression using a gradient descent algorithm to determine these parameters. The average values of α and β should fit the best lapa- roscopic medical skills to allow a realistic computation of affine velocity.
Data analysis Data were analyzed using SPSS 21. 0 (SPSS Inc. )  6. To assess content evidence, we applied a one-factor ANOVA to compare groups with post hoc Tukey correction tests. A level of p ≤ 0. 05 was considered statistically different. Cronbach alpha test measured the internal consistency of the questionnaire with the α-value set at 0. 7). We used non-parametric tests to compare groups regarding time, number of collisions, and affine velocity (discriminative protocol), as the number of samples was too low for parametric tests. The Kruskal-Wallis test compared the three groups, associated with the Kolmogorov-Smirnov for two-by-two comparisons. The statistical threshold was set at p ≤ 0. 05. Finally, correlation coefficients evalu- ated the temporal stability of the simulator's measure.
This confirms the existing literature, stating that time and collision are indirect indicators of laparoscopic proficiency, needed to be associated with motion characteristics, provided by the affine velocity  [15, 36]. The evolution of the simulator could incorporate new skills such as suturing and knot tying with the aim to test more advanced techniques. As previously proposed, introducing specific surgical techniques remains an open option. This would require additional software development, however without hardware changes. This would also require coding new exercises in C++, based on the Chai3D library. From our own experience, we estimate that the conception of a single new exercise testing.
Funding Funding has been provided by the  ANR (French National Agency for Research)  in the context of the " Learning Strategies for Healthcare Professions in Immersive Environment" (SAMSEI)  project ( ANR-11-IDFI-0034 ) under the supervision of Pr. X. Martin , in support and conjunction with  INSA Lyon and Lyon 1 University. This funding covered the prototyping costs, a Ph. D. salary, and the current scientific publication. This study report has been provided to SAMSEI which let the authors freely build this experimental protocol and has accepted that experimental data be published.
The number of submissions to diagnostic databases of pathogenic variants, such as ClinVar  16  , has become a good surrogate for the prevalence of rare disorders. When submissions to ClinVar of disease genes with pathogenic mutations are plotted in decreasing order, most of the supported syndromes are on the left, indicating relatively high prevalence (Figure  1 ). For instance, Cornelia de Lange syndrome (CdLS), which has been modeled by multiple tools  4, 11  , is caused by mutations in NIPBL, SMC1A, and HDAC8, as well as other genes, and has been linked to hundreds of reported mutations. However, more than half of the genes in ClinVar have fewer than ten submissions each (Figure  1 ). As a result, most phenotypes have not been modeled because su cient data are lacking. Thus, the need to train on large numbers of photographs is a major limitation for the identi cation of ultra-rare syndromes.
Results The feature encoder of GestaltMatcher computes a Facial Phenotypic Descriptor (FPD) for each portrait image (Figure  2a ). Each FPD can be thought of as one coordinate in the CFPS (Figure  2b ). The distances between the FPDs in the CFPS form the basis for syndrome classi cation and patient clustering. The complete dataset used to construct the CFPS consisted of 33,350 images from 21,836 subjects who had been diagnosed with a total of 1,362 syndromes, each supported by at least two cases. We divided the dataset into categories of distinct (rare syndromes with facial dysmorphism recognized by DeepGestalt), non-distinct (rare syndromes without described facial dysmorphism, not recognized by DeepGestalt), and target (ultra-rare syndromes with facial dysmorphism that we hope to be able to identify, not evaluable by DeepGestalt).
Top-10 accuracy plateaus when GestaltMatcher is trained on more than 100 syndromes Earlier de nitions of the FPD were mainly based on training a network with a small selection of common and highly characteristic syndromes  4, 8. In principle, we could train GestaltMatcher's encoder on all 1,362 different syndromes in our dataset. However, most of the phenotypes that have recently been linked to a gene are either ultra-rare or less distinctive, and using a very unbalanced training set with many ultra-rare disorders linked to only few cases may add noise without substantial additional bene t. We therefore analyzed the in uence of the number of syndromes on model training by incrementally increasing their number starting with the most frequent ones (Figure  3 ). The top-10 accuracy improved with an increase in the number of syndromes until 110 syndromes was reached, uctuated as the number of syndromes further increased to 190, and became saturated after 190 syndromes.
GestaltMatcher performs similar to DeepGestalt with better scalability To validate the GestaltMatcher approach, we rst worked with the 323 images of patients with 90 syndromes from the London Medical Database (LMD)  19  that were already used for benchmarking the performance of DeepGestalt 11. When using the distinct gallery, which contains syndromes that DeepGestalt currently supports, GestaltMatcher achieved 74. 30% and 89. 78% accuracy within the top-10 and top-30 ranks, respectively, which was lower than the 84. 52% top-10 accuracy and 91. 64% top-30 accuracy achieved with DeepGestalt (Table  2  and Supplementary Table  1 ). However, when we used the gallery of all 1,362 syndromes for GestaltMatcher (distinct, non-distinct, and target), the top-10 and top-30 dropped by only 3. 78% and 4.
For our cluster analysis, we sampled subjects in our database with subtypes of four large phenotypic series and found high inter-syndrome separability in addition to considerable intra-syndrome substructure in e. g. Noonan syndrome, CdLS, or mucopolysaccharidosis. A t-SNE  25  projection of the FPDs into two dimensions yielded the best visualization results (Supplementary Figure  4 ). Although any projection into a smaller dimensionality might cause a loss of information, the clusters are still clearly visible for the 743 subjects sampled from these four phenotypic series. This observation provides further evidence that characteristic phenotypic features are encoded in the FPDs. To demonstrate the separability of syndromes with facial dysmorphism, we also used t-SNE to project 4,353 images of the ten distinct syndromes with the largest number of subjects and 872 images of ten non-distinct syndromes into 2D space.
In addition, we calculated the Silhouette index 26 for both of these datasets. The FPDs of the distinct syndromes showed ten clear clusters of subjects (Supplementary Figure  5 ), but the t-SNE projection of subjects with non-distinct syndromes created no clear clusters. Moreover, the Silhouette index of the distinct syndromes (0. 11) was higher than that of the non-distinct syndromes (-0. 005); the negative Silhouette index indicates poor separation of the non-distinct syndromes.
Thus, GestaltMatcher can match a syndrome with a distinguishable facial gestalt even if it is of extremely low prevalence. This enables us to avoid the long development ow currently required to support and discover novel syndromes (Supplementary Figure  1 ). Instead, matching can be offered instantly for all undiagnosed cases with available frontal images for which consent has been provided for inclusion in the tool. GestaltMatcher's framework also allows us to abstract the encoding of a dataset away from the classi cation task. For example, one can evaluate both phenotypic series and pleiotropic genes within a single CFPS, or obtain the most-similar patients for each of the matched syndromes, with minor computational cost (i. e. , in real time). Furthermore, the GestaltMatcher framework computes the similarity between each of the test set images across the entire dataset of images.
This similarity can be computed using different metrics, e. g. , cosine or Euclidean distance. The results are then aggregated according to the chosen con guration. For example, image similarity can be aggregated at the patient level or the syndrome level. Furthermore, the dataset can be ltered according to different parameters (such as ethnicity, number of affected genes, or age) to further customize the evaluation. One of the most important features of GestaltMatcher is the ability to match patients with highly similar facial features. Clinicians are often faced with the challenge of nding enough patients with a similar phenotype to statistically link the phenotype to a gene. This is especially true when dealing with presumed novel or extremely rare Mendelian disorders. Several online platforms, such as GeneMatcher, MyGene2 (https://mygene2.
Datasets We collected images of subjects with clinically or molecularly con rmed diagnoses from the Face2Gene database (https://www. face2gene. com). Extracted, deidenti ed data were used to remove poor-quality or duplicated images from the dataset without viewing the photos. After removing images of insu cient quality, the dataset consisted of 33,350 images from 21,836 subjects with a total of 1,362 syndromes (Supplementary Table  4 ). GestaltMatcher was designed to distinguish syndromes with different properties. We separated syndromes by the number of affected subjects and whether they had already been learned by the DeepGestalt model. Supplementary Figure  7  provides an overview of how the dataset was divided. The current DeepGestalt approach requires at least seven subjects to learn a novel syndrome.
We excluded autism from the non-distinct group of syndromes in this study because it had many more subjects than other non-distinct syndromes, leading to an imbalanced dataset. For target syndromes, we sought to demonstrate that GestaltMatcher could predict a syndrome even if facial images were publicly available for only a few subjects. It is noteworthy that, for more than half of all known disease-causing genes, fewer than ten cases with pathogenic variants have been submitted to ClinVar (Figure  1 ). Of the 1,362 syndromes in the entire dataset, 296 were distinct, 242 non-distinct, and 824 target. DeepGestalt cannot yet be applied to non-distinct and target syndromes. We further divided each of these three datasets into a gallery and test set. The gallery is the set of subjects that we intend to match, given a subject from the test set.
First, 90% of subjects with each distinct syndrome were used for training models, and the remaining 10% of subjects were used to validate DeepGestalt training; the 90% then became the distinct gallery and the 10% were assigned to the distinct test set. For the target and non-distinct datasets, we performed 10-fold cross-validation. In each syndrome, 90% and 10% of subjects were assigned to the gallery and test set, respectively. Matching only within a dataset would not represent a real-world scenario. Therefore, the galleries of the three datasets were later combined into a uni ed gallery that was used to search for matched patients.
London Medical Dataset validation analysis We compiled 323 images of patients diagnosed with 90 distinct syndromes from the LMD  19  and used this as the validation set for distinct syndromes. We rst evaluated the validation set using softmax, which is a DeepGestalt method. To compare the performance with that of GestaltMatcher, we evaluated the performance of GestaltMatcher on two different galleries: a gallery of distinct syndromes consisting of 20,091 images of patients with 296 syndromes, and a uni ed gallery consisting of 27,826 images of patients with 1,362 syndromes. We then reported the top-k accuracy and compared the results of these three conditions (DeepGestalt with softmax, GestaltMatcher with distinct gallery, and GestaltMatcher with uni ed gallery).
GeneMatcher validation analysis We selected 14 publications in which GeneMatcher was used to match patients with facial dysmorphism from unrelated families. In total, these studies contained 104 photos of 89 subjects from 77 families. The details are shown in Table  3. We performed leave-one-out cross-validation on this dataset, i. e. , we kept one photo as the test set, and we assigned the rest of the photos to a gallery of 3,636 photos with 824 target syndromes to simulate the distribution of patients with unknown diagnosis. We then evaluated the performance by top-1 to top-30 rank. If a photo of another subject with the same disease-causing gene from an unrelated family was among the top-k rank, we called it a match. Moreover, we used top-k rank to measure how many unrelated families were connected.
The pipeline consists of our own scripts as well as a number of previously published software, which were carefully selected based on their performance, popularity and own experience. For example, reference-guided assembly of transcriptomes (also referred to as ab initio assembly), which takes advantage of a genome sequence to which RNA-Seq reads are aligned using splice-aware software, is done with StringTie  [10] ; the tool outperforms other software for transcriptome assembly, such as Cufflinks  [11]  and Bayesembler  [12]. The identification of lncRNAs, on the other hand, benefits from a number of lncRNA features that are employed to differentiate mRNAs from noncoding RNAs  [13]. For instance, Coding Potential Calculator  [14] , a part of our lncRNA search algorithm, is focused on the coding capability of transcripts and much relies on sequences of already known protein-coding genes from public databases.
Docker container The Nextflow script is transparently executed in a Docker container. There are two options:  (1)  to build the image from scratch using Dockerfile and the docker build command; (2) to use the spirit678/lncrna_conservation:latest repository from Docker Hub [https ://hub. docke r. com] (default option; may be changed in docker. config). As a base, continuumio/miniconda3:4. 7. 12 is used with procps installed, while the list of software that is used is declared within the Conda environment files environment. yml and cpc2. yml. The applied Conda channels are conda-forge, bioconda, khourhin, cbp44 and defaults.
Input data As an input, the pipeline only requires paired-end FASTQ files and genome assembly IDs, such as GRCh38, Pan_tro_3. 0, or the species' names (here, human and chimpanzee, respectively) (Fig. 1 ). Currently, we support only species that are available in the ENSEMBL repository. A Python script is used to find a proper assembly version with ENSEMBL mart using a pybiomart library [https ://jrder uiter. githu b. io/pybio mart/] to create the download queues and prepare additional datasets, if they are available (representing a set of ribosomal RNAs (rRNAs) and a set of known lncRNAs). The requests, ftplib and urllib Python libraries are used for downloading the sequences of rRNAs and Fig. 1  A schematic representation of the lncEvo workflow. There are two sequential parts: (1) ab initio transcriptome assembly from RNA-Seq data followed by identification of lncRNAs, and (2) the search for conserved counterparts in the two species of interest toplevel FASTA files for a given genome assembly as well as the reference gene annotations in GTF format.
Then, mapping against the corresponding genome is performed with STAR  [19]  using the recommended settings [https :// githu b. com/alexd obin/STAR/blob/maste r/doc/STARm anual. pdf ]. For this, the genome index is built from a FASTA file and additional ENSEMBL annotations in GTF format to improve the accuracy of the splice mapping. The obtained BAM files (one per sample) represent the input for ab initio transcriptome assembly with StringTie [10] using annotations from ENSEMBL as a reference. The resulting GTF files with custom transcriptomes (one per sample) are merged with StringTie into a single transcriptome. The transcriptome is then compared against reference annotations with Cuffcompare  [20] , and transcripts belonging to the class codes c, e, p or s, which represent potential errors in transcriptome assembly, are removed.
The sequences of transcripts are extracted from the corresponding genome with gffread  [21]. The estimation of expression levels is Fig. 2  A schematic representation of the computational steps for ab initio transcriptome assembly and identification of lncRNAs. Data flow is represented with green arrows, whereas the metadata relationships are marked with red dotted lines. A more detailed representation of the workflow is available on the tool webpage performed using Salmon  [22]  and summarized with a custom Python script utilizing the pandas library. The identification of lncRNAs consists of several filtering steps that are applied to the assembled transcriptome. In the first step, the transcriptome is compared against the reference annotations using Cuffcompare with the -R and -C options.
Then, lncRNA identification is performed using a set of Python scripts according to the following criteria: Transcripts with Cuffcompare class codes = j or o are discarded if the reference gene is not classified as a lncRNA in ENSEMBL; Transcripts shorter than 200 bases are removed; Transcripts containing open reading frames (ORFs) identified by TransDecoder  [23]  with the -m 100 (minimum protein length; default value) and -S (strand-specific) options are discarded; Transcripts classified as encoding proteins by the Coding Potential Calculator (CPC)  [14]  with the default settings are eliminated as well; Each transcript is required to meet the expression threshold of 1 TPM in at least one sample. Regardless of the TransDecoder and CPC results, retained are all expressed RNAs classified as lncRNAs in ENSEMBL.
Conservation study Conservation analysis is performed in a pairwise manner between the two species of interest. The set of predicted lncRNAs in both species and the species names represent the input. One of the species is the target, providing the reference set of lncRNAs, while the query species is the source of potentially conserved counterparts (Fig. 3 ). A set of Python scripts is used to preprocess the data and build slncky-compatible annotations for the target and query transcriptomes. Based on our previously described methodology  [4] , we redesigned the process of the generation of cross-species genome alignments, which allowed us to significantly reduce the required computational time while maintaining comparable sensitivity. The alignment itself is performed with lastal from the LAST package  [25].
A reference set of lncRNAs is divided into batches of 2500 transcripts for efficient computation, and each batch is processed independently. The results are merged into a single file with all orthologs and separately into a file with only the topranked orthologs to collect only the best query-target associations as determined by the calculated exonic identity of the two given lncRNAs. Exploratory data analysis (EDA) is used to analyze the obtained data and provide the main characteristics of the datasets with the use of visual methods. To automate the EDA in the version of the pipeline designed for the local executor, summary reports are generated with a patched version of SweetViz [https ://githu b. com/fbdes ignpr o/sweet viz]. The resulting HTML files provide visualizations as well as summary statistics for the predicted set of lncRNAs and the results of the conservation analysis.