dataesr/scientific-paragraphs-categorization huggingface dataset:
Primate polymorphism data We aggregated high-coverage whole genomes of 809 primate individuals across 233 primate species, including 703 newly sequenced samples and 106 previously sequenced samples from the Great Ape Genome project  (19). Samples that passed quality evaluation were then aligned to 32 high-quality primate reference genomes (110) and mapped to the GRCh38 human genome build. We developed a random forest (RF) classifier to identify false positive variant calls and errors resulting from ambiguity in the species mapping. In addition, we removed variants that fell in primate codons that did not match the human codon at that position, as well as those residing in primate transcripts with likely annotation errors. We also devised quality metrics based on the distribution of RF scores and Hardy-Weinberg equilibrium, and developed a unique mapping filter to exclude variants in regions of non-unique mapping between primate species.
PrimateAI-3D Model PrimateAI-3D is a 3D convolutional neural network that uses protein structures and multiple sequence alignments (MSA) to predict the pathogenicity of human missense variants. To generate the input for a 3D convolutional neural network, we voxelized the protein structure and evolutionary conservation in the region surrounding the missense variant. The network was trained to optimize three objectives: distinction between benign and unknown human variants; prediction of a masked amino acid at the variant site; per-gene variant ranks based on protein language models.
Protein structures and multiple sequence alignments For 341 species, we used vertebrate and mammal MSAs from UCSC Multiz100 (112, 113) and Zoonomia (114). Another 251 species appeared in Uniprot for least 75% of all human proteins (115). For each protein, alignments from all 341+251=592 species were merged. Human protein structures were taken from AlphaFold DB (June 2021)  (73). Proteins that did not sequencematch exactly to our hg38 proteins (2590; 13. 5%) were homology modeled using HHpred  (74)  and Modeller (116).
3D fill-in-the-blank We removed all atoms of a target residue before voxelization, discarding any information about the residue from the input tensor to the network. The network was then trained to predict a 20-length vector, labeled 0 (benign) for amino acids that occur at the target site in any of the 592 species and 1 (pathogenic) otherwise. All human protein positions with at least one possible missense variant were included in this dataset. Variant ranks from language models For each gene, we took the average pathogenicity ranking from two protein language models, PrimateAI language model (PrimateAI LM, described below) and our reimplementation of the EVE variational autoencoder algorithm which we extended to all human proteins (EVE*)  (67). We calculated the pairwise logistic rank loss as described in  Pasumarthi et al.
PrimateAI Language Model The PrimateAI language model (PrimateAI LM) is a MSA transformer  (83)  for fill-in-the-blank residue classification, which was trained end-to-end on MSAs of UniRef-50 proteins (118, 119) to minimize an unsupervised masked language modelling (MLM) objective  (81). Our model requires ~50x less computation for training than previous MSA transformers due to several improvements in architecture and training (Fig. S9 ).
Model training procedure Each batch had the same number of samples from each of the three variant datasets (~33 with a batch size of 100). For the language model ranks dataset, all 33 samples had to come from the same protein. The number of times a protein was chosen for a batch was proportional to the length of the protein. In order to make our model robust against protein orientations, we randomly rotated the protein atomic coordinates in 3D before voxelizing a variant.
Model Evaluation We compared performance of our model and other models  (84)  on variants for which all models had scores. Deep mutational scanning assays were available for 9 human genes: Amyloid-beta (102), YAP1  (96) , MSH2 (120), SYUA (101), VKOR1 (121), PTEN (99, 100), BRCA1 (122), TP53  (123) , and ADRB2 (124). For each assay and prediction model, we calculated the absolute Spearman rank correlation between prediction and assay scores. The UK Biobank dataset  (79, 80)  contains 42 gene-phenotype pairs which were significantly associated by rare variant burden testing using all rare missense variants, without applying missense pathogenicity prioritization. The evaluation was the same as with DMS assays, except that correlations were calculated from the quantitative phenotypes of individuals carrying the variant, instead of the assay score for the variant.
For ClinVar (4), we filtered to high-quality 2-star variants and evaluated model performance by calculating per-gene area under the receiver operating characteristic curve (AUC). For the rare disease cohorts, we collected de novo missense mutations from patients with developmental disorders  (85) (86) (87) , autism spectrum disorders  (88) (89) (90) (91) (92) (93) (94)  or congenital heart disorders  (95). For all three datasets, we compared against DNMs from healthy controls  (88) (89) (90) (91) (92) (93). We applied the Mann-Whitney U test to measure how well each model's prediction scores could distinguish patient variants from control variants.
agreement No. 864203  to TMB),  BFU2017-86471-P  ( MINECO/FEDER ,  UE  to TMB), " Unidad de Excelencia María de Maeztu" , funded by the  AEI  ( CEX2018-000792-M  to TMB),  NIH   1R01HG010898-01A1  (to TMB) and  Secretaria d'Universitats i Recerca and CERCA Programme del Departament d'Economia i Coneixement de la Generalitat de Catalunya  ( GRC 2021 SGR 00177  to TMB),  Howard Hughes International Early Career  (to TMB),  Obra Social "La Caixa " and internal funds from  Baylor College of Medicine. HLR receives funding from  Illumina, Inc  to support rare disease gene discovery and diagnosis. JPB, RMDB, IG and DV were supported by a  UKRI Grant NERC  ( NE/T000341/1 ). We thank  Dr. Praveen Karanth  ( IISc ),  Dr. H. N. Kumara  ( SACON ) for collecting and providing us with some of the samples from India.
Common primate variants are largely benign in human. (A) Counts of missense (solid green) and synonymous (shaded grey) variants from primates compared to the gnomAD database. Missense : synonymous counts and ratios are displayed above each bar. (B) Fractions of all possible human synonymous (grey) and missense variants (green) observed in primates. (C) Counts of benign (grey) and pathogenic (red) missense variants with two-star review status or above in the overall ClinVar database (left pie chart), compared to ClinVar variants observed in gnomAD (middle), and compared to ClinVar variants observed in primates (right). Conflicting benign and pathogenic annotations and variants interpreted only with uncertain significance were excluded. (D) Observed gnomAD (green) or primate (blue) missense variants in each amino acid position in the CACNA1A gene.
IV. RESULTS AND DISCUSSIONS In order to prove the effectiveness of SMA we present the simulation results of applying the proposed algorithm on the IEEE 9-bus and 30-bus systems, under different scenarios. The cost and NO x coefficients for the IEEE 9-bus and 30-bus systems are taken from  [14, 15]. Simulations were programmed using MATLAB and were carried out for the three following case studies:  Case study 1: EED problem without VPE consideration.  Case study 2: EED problem with VPE consideration.  Case study 3: EED with VPE consideration in the presence of renewable energy. We note that in all the studied cases, the transmissions lines power losses are taken into consideration and calculated with the use of (  13 ) where the loss coefficients matrix of the IEEE 9-bus and 30-bus systems are given in (20) and (21).
As a result, there are two negative images of the star on the side of the central coronagraphic image. The detector integration time was set to 6 ms to avoid saturation in the background. Ten frames were acquired and stacked in each DSM chopper position, out of which the first two were discarded to suppress any point spread function (PSF) smearing due to the DSM settling time. The recorded data have thus a time frame of 160 ms. Simple chopping leaves some low spatial frequency residuals due to inhomogeneities of the thermal footprint. Nodding was thus also applied to calibrate these residuals. The nodding offset was parallel and of the same amplitude as the chopping so that the target was always kept within the detector's field of view. A temporal binning of chopped-nodded frames was performed to produce a data cube in which each frame corresponds to an integration time of about 5 s.
VISIR archival data Non-coronagraphic observations of β Pictoris were obtained with VISIR on 2004 -program ID 60. A-9234(A) -and 2015program ID 95. C-0425(A) -with the VLT. The observations were performed in a standard chopping-nodding mode. The data were reduced by combining all the chop-nodded frames into a single one. Each observation was typically 1 h long. The data were consequently flux calibrated by observing with the same settings an infrared standard (HD 42540, spectral type K2/3III, J = 2. 90, H = 2. 24, K = 2. 09) just before the β Pictoris observations. The reduced data are further examined in Sect. 5 to perform a comparison with the NEAR observations. A35, page 3 of 15 A&A 675, A35 (2023) Fig. 2. Contrast curve at 5σ as measured along the disk spine across a width of 1 ′′ , as measured on the PCA image.
2 ± 0. 2 •  (Lagrange et al. 2012). The most notable difference of several degrees with the NEAR 2019 data could be related to the lack of an astrometric calibration procedure related to these data, especially since VISIR was moved to the VLT UT4 for the NEAR experiment. Still, this mismatch has no impact on the following analysis. We note that in scattered light images, the midplane and the warp are observed as two distinct components owing to the edge-on orientation combined with the radiation pressure effect  (Golimowski et al. 2006; Lagrange et al. 2012). Milli et al. (2014)  have also referred to a warp in the L band, while, in fact, the disk image shows a single component that reveals a very similar trend to the one observed in the mid-IR: a single disk component with a misalignment on the two sides.
Temporal evolution of the southwest clump C1 The β Pictoris system has been observed in the mid-IR on several occasions with 8-m class telescopes since December 2003, when C1 was first identified by  Telesco et al. (2005). Given the 16-yr baseline with respect to our NEAR observations, the study of its evolution becomes relevant and can potentially allow us to address questions around its origin. Table  1  presents the data available in the mid-IR that we used for comparison with our data set. Both  Telesco et al. (2005)  and  Li et al. (2012)  located the position of C1 by performing a centro-symmetrical subtraction of the disk image, subtracting the emission in the fainter NE wing from the SW wing, and vice-versa. This yielded a residual emission, which was then assumed to be the main contribution of the clump.
Temporal evolution of C1's projected distance We note that the overall profile of C1 has a two-mode shape with a nearly Gaussian part inward and a plateau outward, indicative that the actual three-dimensional shape of the clump is more complex. In particular, the clump can be azimuthally and/or radially extended. In the latter, the differential Keplerian rotation will modify the projected intensity profile with time, an effect which could already be suspected in Fig. 11. Furthermore, we considered studying the evolution of the size of the clump, however, the current data do not allow us to accurately quantify such evolution. Follow-up observations with similar or better angular resolution than NEAR will be key to future investigations of the actual morphology of the clump. Carrying out a quantitative comparison of the disk intensity profiles is impractical, given the absolute flux is not known accurately enough in each dataset.
Conclusion This paper presents the first high contrast imaging data in the mid-IR of the β Pictoris system, observed with NEAR. Here, we A35, page 12 of 15  Skaf, N. , et al. : A&A proofs,  summarize the main results of our analysis of these data, along with a comparison with previous observations. -The planet β Pictoris b was not detected with NEAR. However, by taking into account the transmission of the coronagraph, we derived the planetary flux upper limit from the contrast curve at 5σ. We collected spectro-photometry from different instruments and presented a combined spectrum the first spectra of β Pictoris b acquired with SPHERE during several epochs. The upper limit of the NEAR data does not allow us to put meaningful constraints on atmospheric scenarios. -Given the upper limit on the planetary flux, we investigated which corresponding amount of dust, present around the planet, could reproduce such a limit.
Although this quantity scales with the distance to the planet, we concluded that the presence of a dust cloud around the planet was unlikely. If dust particles were located at the Roche radius, it would correspond to the collisional debris of a 5 km size asteroid. -The disk is uniquely resolved in these mid-IR data, allowing us to identify structures that were never observed prior to those observations. The southwesterern clump, previously reported in the literature, is distinctively detected in the NEAR data set, at 56. 1 +0. 3 -0. 3 au. On the northeastern side of the disk, there is a clear detection of a new clump at -35. 2 +0. 3 -0. 3 au. We note the possible presence of two other clumps at 29. 3 +0. 3 -0. 3 au at -15. 2 +0. 3 -0. 3 au. Further observations will be required to confirm their existence.
The β Pictoris system has been extensively studied at different wavelengths and it serves an archetypal system for our understanding of planet-disk interactions and planetary formation. This study shows that the use of adaptive optics, along with a coronagraph in the N band, does bring a considerable improvement with regard to the data quality. The NEAR data have allowed us to put constraints, for the first time, on the presence of circumplanetary material around a directly imaged planet. We could indeed expect giant planets to have circumplanetary disks, as all the giant planets in our solar system do indeed have some. Similarly, we detected disk structures that had never been observed before. Further observations are needed to confirm some of those structures and understand their origin.
Acknowledgements. We want to thank the  ESO , the  Breakthrough Foundation , and everyone involved in the NEAR project. The observations were carried out under the  ESO program  id: 60. A -9107 (K). We want to thank the referee for the constructive feedback that contributed to improving the quality of this paper. N. S. acknowledges support from the  PSL IRIS-OCAV  project. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( COBREX ; grant agreement # 885593 ). French co-authors also acknowledge financial support from the  Programme National de Planétologie (PNP). We thank  J. Chilcote  for sharing the GPI spectrum of β Pic b. C. D. acknowledges financial support from the  State Agency for Research of the Spanish MCIU  through the " Center of Excellence Severo Ochoa " award to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ) and the Group project Ref.
Collection The corpus was collected from the online databases Pub-Med, Web of Science (WoS), and Scopus. Since we aimed to analyze titles, abstracts, and keywords, we included article that had an abstract in English, regardless of the article language. Following a keywords frequency analysis, three groups of Medical Subject Headings (MeSH)-terms were selected, as shown in Appendix B. Group A included ART terms and Group B included terms indicating a relation to ELSI across disciplines within humanities and social sciences. Group C was formed to exclude irrelevant articles. We aimed to find balance between false-positive (inclusion of articles with medical-clinical nature) and false-negative (exclusion of articles concerning social sciences and humanities). We used the PubMed API  [18]  to query for articles with "One MeSH-term from group A" AND "One MeSH-term from group B" AND "Humans (MeSH)" AND "1999-2019" NOT "Any MeSH-term from group C.
" We extracted 14,394 and 12,588 articles from WoS and Scopus, respectively. In addition to the 6333 extracted from PubMed, 33,315 articles were merged from all three databases. Following the removal of duplicates of titles, abstract, and DOI, 17,247 articles remained, of which 14,283 had available title and abstract in English. We repeated the cleaning methods previously applied on the PubMed query (explained above), and removed 154 articles due to short abstract (less than 50 words), and 99 from journals of biodiversity. 14,030 articles remained.
Classification The abstracts were processed by merging the title and abstract into one string ("code"), harmonized, tokenized 2  , and lemmatized. We formed a list of terms to be replaced with acronyms or abbreviations in order to unify the text and allowing us to identify repetitions. Next, we extracted a list of terms by the frequency of "codes" in which they appear and divided the most frequent technical-medical terms into  10   (10)  Assisted Insemination. An article was assigned into an ART field (or several) if one term, associated to that field, was found in its title, abstract, or keywords. The articles which included none of these terms were assigned by two researchers reading their titles and abstracts. The remaining articles (2267/7714) were appointed as "General. " Subsequently, for each publication, all obtainable metadata was extracted from WoS, Scopus, and PubMed, merged and unified under one template.
Finally, we cleaned the abstracts from all those technical-medical terms that were used to define the 10 ART fields and then: (1) Uploaded the database to the VOSviewer software tool for constructing and visualizing bibliometric networks. (2) Applied Topic Modeling (TM) via Latent Dirichlet Allocation (LDA) (Blei, Ng, and Jordan 2003; Kapadia, 2019) to all "codes. " To do so, we have built a corpus dictionary using gensim 3  opensource library. 4  The LDA method assumes that the observed distribution of words in a textual corpus is determined by a statistical model that fixes both a word-topic and an articletopic association  [7]. We defined the number of topics by computing the coherence score as a function of the number of topics  5  and by assessing the results in relation to the VOSviewer analysis.
Analysis In addition to the VOSViewer tool, we conducted an analysis based on the year of publication, countries of corresponding  6  authors, and countries mentioned in codes as an indicator for the focus of the article. For the last, we searched the codes (abstracts, titles, and keywords) using a list of countries, cities (with no duplications), and nationalities. 7 Furthermore, we analyzed the database according to the predefined ART fields, the topics identified by the TM, and regions  8. We tested for Spearman's correlations between the ART fields and the topics (Appendix 4). Those correlations were used for two purposes: first, to group ART fields in order to simplify the presentation of results, i. e. , [Egg (C1) and Sperm donation (C2)], and [Stem-cell Research (C6) PGT (C7) and Genome Editing (C9)]; second, to verify a relationship between ART fields and topics.
These trends were evident for the entire corpus, but were particularly noticeable in international research. As seen in Fig. 4 , the topic "Bioethics" had decreased prominence under case studies and under international research compared with the general literature. In contrast, "Law and Policy," which was the growing and most central topic at the end of the period for the entire literature, was more intensely engaged by international research. Hence, a comparison of laws and regulations between countries has become one focus of the literature over time. International research of the other topics was more limited, mainly concerning "Psychology. " Nevertheless, due to the inclusion criteria for the database selection, this topic includes only.
This could lead to some mistakes. Third, a country was defined as the research subject based on its being mentioned in the abstract (including names of cities). We made a great effort to manually identify and correct mistakes resulting from this methodology. However, we assume that few errors could have remained unnoticed. Finally, topic modeling by LDA has some limitations  [37]. The algorithm assumes a certain probabilistic distribution behind the word/article association, which may not hold in reality. Moreover, there is no "standard" or objective way of fixing the number of topics, which remains a free parameter, adjusted in accordance with different metrics. Consequently, due to the number of topics one eventually selects, some smaller topics may remain hidden. Despite these limitations, our findings are based on a comprehensive corpus collected from three major databases, a relatively strong LDA, and an additional method of categorization.
In terms of future research, we may further exploit the collected database by dividing it into ART fields and topics which enable various systematic literature reviews and meta-analyses by extracting a single ART field (or topic) and presenting its distributions of study designs, key issues, research questions, and outcomes, according to geographic location and timeline. Furthermore, building upon our findings, future research could investigate the factors contributing to the shifts in research focus across various fields and topics that we have demonstrated, as well as explore some of the hypotheses we have raised. By examining the underlying reasons for these shifts, their relationships with ELSI research trends, and potential policy implications, scholars can deepen the understanding of the dynamics within the broader context of assisted reproduction.
Phantom acquisitions and reconstructions All acquisitions were performed on a Discovery NM/CT 670 camera (D670, GE Healthcare, MA, USA). The main characteristics of this camera and the acquisition protocols used are described in Table  1. All SPECT images were reconstructed using the 3D ordered subset expectation maximisation (OSEM) algorithm available on the XELERIS workstation (software version 4. 1, GE Healthcare, MA, USA). Images were corrected for attenuation using CT-based attenuation maps, scatter using a triple energy window (TEW) method, and resolution recovery.
Comparison of different dosimetric methods The Torso phantom was used to study the impact of different dosimetric methods. Assuming that the 99m Tc activity distribution reflects the 90 Y-ms distribution in the compartments of interest  [14] , the 99m Tc-SPECT images were converted from counts to 90 Y-ms cumulative activity. Absorbed dose maps were then generated using the PM, DVK, and LDM methods with an in-house Python tool. These methods were compared with the reference fMCD obtained using the Gate MC simulation (Section 2. 3).
Convex hull computation. The most common algorithm for computing the convex hull of a given set of points is the classical quickhull algorithm  [BDH96]. The most famous dD implementation is Qhull. 1 However this program represents point coordinates with floatingpoint numbers and may return erroneous and approximate convex hulls when points are not in general position. This is a considerable issue when dealing with lattice points, where co-sphericities are extremely common. Another common dD implementation is CGAL, 2  which has the advantage of offering kernels with guaranteed results, at the price of slower computations. Another drawback is that it always outputs a triangulation, so cosphericities are arbitrarily triangulated. Note also that there exists GPU implementations of quickhull  [MZXZ18].