dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation on SBMnet dataset The SBMnet dataset [41] (http://scenebackgroundmodeling. net/, accessed on 20 November 2021) is composed of 79 sequences, which have been selected to cover a wide range of challenges and are representative of typical indoor and outdoor visual data captured today in surveillance, smart environment, and video database scenarios. The dataset includes the following eight categories with associated challenges: basic, intermittent motion, clutter, jitter, illumination changes, background motion, very long and very short. Although this dataset is freely available on the SBMnet website, ground truth images are publicly available for only 18 frame sequences, either on the SBMnet website or on the SBI dataset website. In order to benchmark a new algorithm, one has to submit the predicted fixed background images associated with each frame sequence to the website, which performs the evaluation of the submitted results.
Evaluation on SBI Dataset The SBI dataset  [42]  (https://sbmi2015. na. icar. cnr. it/SBIdataset. html, accessed on 20 November 2021) is composed of 14 image sequences. Ground truth backgrounds are available for all sequences. We use the Matlab tool available on the SBI website for fair comparison with other models, but do not report the CQM results considering that other sequences were evaluated with a Matlab tool which included a bug for the CQM computation, as indicated in the SBI website. We run the proposed model on the SBI dataset using the same hyperparameters as those used for the SBMnet dataset. The results of this evaluation are listed in Table  3  and show that the proposed model obtains better results than all other compared unsupervised models for the evaluation criteria AGE, MS-SSIM and PSNR, and is ranked second for the criteria pEPs and pCEPs.
Ablation Study In order to check the contribution of the various weights described in this paper, we provide results obtained using truncated versions of the proposed model while keeping the hyperparameters fixed: Version 0 does not use any weight and does not remove motionless frames, and is then equivalent to temporal median filtering. Version 1 uses only the optical flow weights and does not remove motionless frames. Version 2 uses both optical flow weights and global weights and does not remove motionless frames. Version 3 uses bootsrap weights, global weights and optical flow weights, but does not remove motionless frames. The AGE scores obtained by these truncated models on the 18 videos of the SBMnet dataset for which a ground truth is available and using the evaluation tool available on the SBMnet website are provided in Table  4.
Computation Time We have performed computation times measurements and tested the impact of reducing the number of optimization iterations, while keeping all other parameters frozen, excluding the learning rate. The results of these experiments are provided in Table  5. The total computation times necessary to reconstruct the 79 backgrounds from the associated video sequences of the SBMnet dataset are estimated by performing a sequential computation for all the videos, so that the computation times indicated in this table are the sum of the computation times of each of the 79 videos. If we divide the number of frames of the full dataset (73,355) with the total computation time of the proposed model, which is 1409 s, we obtain an average of 52 frames per second (fps). Table  5  shows, however, that the number of optimization iterations can be reduced from 3000 to 250, increasing the average speed to 187 fps, without major impact on the overall accuracy of the algorithm.
Because of this analogy to solid-on-solid heteroepitaxy, this highly textured nucleation and growth, which in the literature is often referred to as fiber texture  46  , was termed quasi-epitaxial growth. It was not clear, however, whether this phenomenon is specific to PbFBr or represents a more general growth mode at liquid metal interfaces. Especially, the specific influence of the two different halide anion species on this process was not clear. Here we present in situ X-ray scattering results for a series of deposition systems, in which only one halide anion is present at a time. The influence of the halide species is studied by deposition from aqueous electrolytes of composition NaX + PbX 2 with X = Br, Cl, F. We investigate by XRR the presence and structure of precursor adlayers and correlate this with GID data on the formed bulk deposit as well as complementary electrochemical data and optical microscopy observations.
Acknowledgements The authors acknowledge financial support by the  DFG  through project  MA 1618/18  (O. M. M. ) and funding for LISA by the  BMBF  through project  05K16FK1/05K19FK2  (O. M. M. , B. M. M. ). We acknowledge  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF , for the provision of experimental facilities. Parts of this research were carried out at PETRA III and we would like to thank  Florian Bertram  and  Rene Kirchhof  for assistance in using P08 and  Milena Lippmann  for her assistance in using the  PETRA III chemistry laboratory. Beamtime was allocated for proposals I-20200512, I-20180496, and II-20170014. Furthermore, we would like to thank  Wulf Depmeier  for his feedback on the laurionite crystal structure.
Results Geochemical characterization of Southern Africa potential source areas. The complete REE concentrations and Sr-Nd-Pb isotopic composition database of the samples presented in this study are presented in Supplementary Data 1. The Southern Africa PSAs signature of the windblown samples presented in this study reflect the compositional variability of the locally outcropping rocks and sediments: a chemical and isotopic fingerprint that is the result of the mixing between the Damara Orogenic system including 2. 0-1. 2 Ga gneissic basement overlaid by Neoproterozoic to Paleozoic metasediments intruded by 840-460 Ma granitoid plutons, and the Orange River sediments  42, 43  (see more details in Supplementary Note 1 and Supplementary Fig. 1 ). In Fig. 2 , REE patterns for the Southern Africa PSAs normalized to the upper crust (UCC  44  ) are plotted.
This figure indicates that no significant difference in the REE composition prevails among all four areas characterized in this study and a regional differentiation is difficult. In general, samples show flat REE patterns with a minor decrease in the heavy REE abundances (Fig. 2a ). Note that for the Omaruru catchment only the REE data for three out seven samples are available. Both the bulk and the fine fractions from Huab and Kuiseb exhibit Eu/Eu* < 1, with Omaruru displaying slightly higher values (Fig. 2b ) for this ratio. For the Namib Sand Sea region, only one data point for a bulk sample is available in terms of REE and its composition is similar to the Huab area. The Eu anomaly (Eu/Eu*) was calculated with the equation Eu/Eu*  2b  also shows that the REE composition of the bulk and the fine fractions from the Huab area are similar, suggesting bulk samples are representative of the fine particles and that Southern Africa REE signature is independent of the grain-size fraction of sediments.
The nebkhas from the Huab catchment, which drains the Paleoproterozoic basement of the Kaoko belt  43  , display a narrower range of isotopic values for both the bulk and fine fractions: 87 Sr/ 86 Sr = 0. 725-0. 728 and εNd(0) = -18 to -15. We also observe a Nd isotopic trend with values becoming progressively less radiogenic from north to south. This is, the Huab region (in the north of the Namib coastline), exhibits the less radiogenic Nd composition with εNd(0) between -18 and -15, continuing towards the Omaruru and Kuiseb valleys with intermediate Nd compositions before reaching the southernmost location of the Namib Sand Sea region which exhibits εNd values between -10. 5 and -8. We also observe a Sr isotopic offset between our fine fraction samples from the Namib Sand Sea dunes and the previously published values on the same samples 3 with our dataset showing higher 87 Sr/ 86 Sr values (>0.
However, due to the limited number of sediment samples and their distinct geochemical signature with respect to EA, they concluded isotopic data was not conclusive enough to support a dust contribution from SAF, excluding this region from the provenance puzzle. Contrarily, the new and expanded dataset of Southern Africa Sr-Nd-Pb isotopic ratios and REE concentrations presented in this study indicate that, SAF emerges as PSA that together with high-latitudes sources in Southern South America (i. e, Patagonia, Southern Central Western Argentina, and the Puna-Altiplano Plateau) can explain the glacial/interglacial dust signature observed in East Antarctica ice cores, as well as the signature of marine sediments from the South Atlantic sector of the Southern Ocean (Fig. 6 ). In general, the 87 Sr/ 86 Sr and εNd(0) values are more diagnostic than the  206, 208  Pb/ 207 Pb for identifying the glacial/interglacial dust provenance in paleo-archives.
This means that even though the Pb isotopic data is usually less indicative because of the Pb input related to local (Antarctic) volcanoes in comparison to the dust related Pb 8 , the Pb data in the present study add critical support to our interpretations. This is, the new Pb isotope data of SAF samples define a field that, together with Southern South America PSAs can better explain the entire range of Pb isotopic compositions observed in the East Antarctic dust from Vostok and EDC ( 208 Pb/ 207 Pb values from 2. 44-2. 50) (Fig. 6a ). It should also be noticed that the very radiogenic Pb data similar to Kuiseb envelope are also those of the Antarctic local volcanoes (Mt. Erebus); contribution that has been recognized in previous studies  8, 9, 67  and cannot be denied. Thus, our results show that EA dust samples show two main compositional trends that comprise different end-members (orange dashed lines in Fig.
6b ). This is, a common end-member with low values in both  208  Pb/ 207 Pb, and 206 Pb/ 207 Pb elucidated by either the Namib Sand Sea (fine fraction) or the Patagonia/Southern Central Western Argentina sources in combination with a more radiogenic end-member, that is, either Kuiseb PSA or the local Antarctic volcanism with a contribution from the Puna Altiplano Plateau (PAP) and the Huab region, especially during interglacial times (Fig. 6b ). This is also in part supported by Sr and Nd isotopic data (Fig. 6c, d ), where the new SAF dataset defines a compositional field that turns out to be a key end-member for describing the composition of dust deposited in the South Atlantic and East Antarctica. In general, during the last glacial climatic periods of the late Quaternary, EA data is very uniform converging to an unequivocal signature from Patagonia with additional inputs from other lower latitude areas in SSA, such as, the Southern Central Western Argentina (SCWA) and Southern Puna 10 (Fig.
This has important implications especially at ice core sites located on the high-elevated plateau areas (~3000-4000 m) that can be particularly isolated from extra-Antarctic dust sources, and where AGCM experiments struggle to reproduce dust concentrations in ice core samples  18, 20, 72. While recognizing the limitations of modeling in representing the global dust cycle, a more recent study introduced a novel tool known as DustCOMM (Dust Constraints from joint Observational-Modelling-experimental analysis)  73  ; a continuously expanding dataset of constraints on the global dust cycle that combines observational and experimental analyses on dust properties and dust abundance with an ensemble of global aerosol model simulations. DustCOMM showed that while SSA accounts for the large majority (70%) of the dust deposited in almost the entire Antarctic continent, smaller contributions from Australia (20%) and southern Africa (10%) are also observed  73.
Sr, Nd and Pb isotope ratios of Antarctic modern dust recovered from snow samples collected along coastal sites of the Dronning-Maud Land area (DML-East Antarctica), have shown similar composition to the Southern Africa sediments  77. This data is consistent with the results observed using back trajectory FLEXPART particle dispersion transport model where a mix of modern Southern Africa and Southern South America dust sources was clearly spotted in the coastal area of East Antarctica 78. Likewise, using a statistical analysis a study revisited the only EDML ice core rare earth elements dataset available from  17  , and observed that the Southern Africa fingerprint appears at ~15 ky as an important mixing endmember that together with SSA, can explain the East Antarctica interglacial dust signature  79.
Acknowledgements This work was supported through  Belgian Federal Science Policy Office (BELSPO)  fellowship (Convention  BR/175/A2/CHASE 1&2 ). Co-authors would like to warmly thank  Jeroen De Jong  ( ULB ) for his assistance during isotopic analyses and the  F. R. S-FNRS  for the financial support of the analytical platform (e. g. , Large Equipments (GEQ) No. 2. 5016. 12& 40007934 ). CNRS-INSU  is gratefully acknowledged for supporting CESAM as an open facility through the  National Instrument  and AERIS datacenter (aeris-data. fr)for hosting simulation chamber data. The CESAM chamber has received funding from the  European Union 's  Horizon 2020 research and innovation program  through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement No. 730997. Sample collection was supported by the  Natural Science and Engineering Research Council of Canada  ( Discovery Grant   RGPIN-2016-05417 ).
Statistical analysis Statistical analyses were performed with SPSS (Statistical Package of Social Science) version 25 (IBM, Armonk, NY, USA). Five-year recurrence-free survival (RFS) was estimated using the Kaplan-Meier's methodology and compared between groups using the log-rank test. The diagnostic performance of p53 IHC was determined by calculating the accuracy, sensitivity and specificity of p53 IHC compared to TP53 NGS analysis. A two-sided p-value < 0. 05 was considered statistically significant.
3C ). In the fourth case the p53 IHC slide was overstained with nuclear positivity of the stroma and was therefore not reliably interpretable (Fig. 3D ). Restaining p53 IHC in the latter case confirmed unequivocal wildtype p53 expression. The p53 IHC assignment of these 4 misinterpreted cases was corrected in the database for further analyses in this study (including the molecular landscape and survival analyses). The remaining 6 POLEwt and MMRp EC showed true discrepancies between IHC and NGS results: one case assigned as NSMP EC, based on wildtype p53 IHC, had a missense TP53 mutation. Re-examination of the p53 IHC revealed unequivocal wildtype p53 staining. Four cases had the mutant overexpression p53 IHC pattern without a detected TP53 mutation (Supplementary Fig. S2 ). All of these cases showed unambiguous mutant-type overexpression with optimal staining.
ACKNOWLEDGEMENTS We thank  Tessa Rutten ,  Natalja ter Haar  and  Enno Dreef  ( Department of Pathology, Leiden University Medical Center, Leiden, Netherlands ) for their technical support. We thank  Maurice de Haan  for his assistance in the NGS analysis. We thank  Tom van Wezel  ( Department of Pathology, Leiden University Medical Center, Leiden, Netherlands ) for his help with the TP53 mutational analysis.
Acknowledgments We want to thank all our field consultants, in particular  Constantin Bartning  and  Aymann Azzawi  from  REFUEAT , and  Marieke Schöning ,  Rabea Haß ,  Jule Schröder ,  Victoria Hugelshofer ,  Sara Naffati ,  Clara Speer  and  Hameed Khasawnih  from  Über den Tellerand/Kitchen  on the Run. This work has also been supported by the team of the  FOOD2GATHER  project ( HERA JRP -H2020 ). Our thanks go also to  Regina Bendix ,  Rick Dolphijn ,  Jenny Herman , and the anonymous reviewers for their invaluable feedback on this and previous versions of this paper.
On the other hand, underdiagnosis and undernotification of these conditions are recognized as key factors to the potential increased number of avoidable reactions, treatment/hospitalization, and risk of death. Misclassification based on personal history of FHs alone impact on inaccurate epidemiological data, may limit nutrition options and can lead to the use of more expensive alternatives, such as formulas and eventually nutritional deficiencies. Trends in international FHs morbidity and mortality rates would be able to provide a useful parameter of the burden of these conditions and the impact of changes in management and prevention. Administrative databases, electronic health records, and disease registries contain a plethora of health data that can be used to ascertain these health outcomes in clinical practice.
3 trillions in 2013 and USD 2. 6 trillions in 2014 according to the US National Center for Health Statistics. 16 All European Member States use the ICD, which has been translated in 43 languages. The ICD-10 was endorsed by the forty-third World Health Assembly in May 1990 and came into use in WHO Member States since 1994. 15, 16 stitutional databases worldwide increasingly use the ICD system to classify diagnoses, health services utilization and death data. 15 e misclassification of disorders in the ICD system contributes to a lack of ascertainment and recognition of their importance for healthcare planning and resource allocation. It also hampers clinical practice and prevention actions. This results in poor understanding of their natural history and lack of knowledge of their epidemiology.
In other words, aligning the clinical diagnosis to ICD classification and codes provides a realistic recognition of allergic and hypersensitivity conditions and allows a better management of allergic patients. Standardized criteria are critical for facilitating adequate and accurate patient recruitment into clinical trials and translational research. The built "Food hypersensitivity" subsection also intends to support personalized translational medicine through providing tools of reaching standard food allergy phenotypes recognized by health professionals worldwide by supporting the stratification of subjects for multicentric clinical and/or research studies. As the ICD-11 allows descriptions regarding the diagnosis (Table  1 ), the new framework will support the correct patients' FHs/ food allergy labeling in administrative databases.
Results Fusexin genes in Archaea. To search for fusexins we used the crystallographic structures of C. reinhardtii, A. thaliana, and T. cruzi HAP2 (Cr/At/TcHAP2)  23, 25, 26  to build dedicated Hidden Markov Models (HMMs) for scanning the Uniclust30 database. We detected 24 high-confidence candidates in prokaryotes: 8 belong to isolated and cultivated archaea, and the remaining 16 to metagenome-assembled genomes (MAGs, Supplementary Table  1 ). We then built HMMs of the candidate ectodomains and compared them to HMMs of sexual, somatic, and viral fusexins. Figure  1a  shows that the prokaryotic candidates have detectable sequence similarities with HAP2, with E-values below 0. 001 and HHblits-derived probabilities higher than 0. 95 (Supplementary Fig. 1a ). We named these proteins Fusexin1 (Fsx1).
fsx1 genes found in cultivated and isolated prokaryotes are restricted to the Haloarchaea class (also called Halobacteria, Euryarchaeota superphylum) whereas MAGs containing Fsx1s include all major Archaea superphyla (Supplementary Table  1 ). Next, we used this Fsx1 sequence set to search the Metaclust database, which comprises 1. 59 billion clustered proteins from over 2200 metagenomic/metatranscriptomic datasets. Performing a scan pipeline using PHMMER, PSI-BLAST, HMM-HMM comparisons and topology filtering we found 96 high-confidence fsx1 genes. The identified fsx1s come from different environments (with preeminence of saline samples) and a wide temperature range (-35 to 80 °C, see Supplementary Data 1). Fsx1 is a structural homolog of HAP2/GCS1. To experimentally investigate the presence of fusexin-like proteins in Archaea, a selection of the candidate genes was expressed in mammalian cells (Supplementary Fig.
The presence of fsx1 genes in Haloarchaea IMEs is consistent with gene transfer in the opposite direction. Eukaryogenesis, and by extension the emergence of sex, is marked by massive horizontal gene transfer events to the archaeal ancestor of eukaryotes  52  , disregarding if it belonged to the Asgard superphylum or to a sister group of Archaea  53. During the First Comparisons by one-way ANOVA followed by Bonferroni's test against the vector (black) and against Fsx1 (red). ns = non-significant, ***p < 0. 001, ****p < 0. 0001. Source data are provided as a Source Data file. c Representative merged images from the experiments in (b): magenta (RFP); green (GFP) and blue (DAPI). Fused cells with RFP and GFP (arrows). Scale bars, 20 µm. See also Supplementary Fig. 7f. d Immunoblot of EFF-1-V5, control (untransfected cells) and Fsx1-V5 expressing cells.
Methods Initial fusexin search using structurally guided MSAs. HMMs were prepared using structurally guided multiple sequence alignments (MSAs) of known eukaryotic HAP2 sequences (ectodomains only). Structural MSAs were derived using I-TASSER  68  -generated models of HAP2 homologs for Erythranthe guttata (A0A022QRC8), Phytomonas sp. isolate EM1 (W6KUI1), Plasmodium falciparum (A0A1C3KGX6), Chlorella variabilis (E1Z455) and the HAP2 crystal structures for Chlamydomonas reinhardtii (PDB 6E18 [https://www. rcsb. org/structure/6E18]  65 , 6DBS [https://www. rcsb. org/structure/6DBS]  26 ) and Arabidopsis thaliana (PDB 5OW3 [https://www. rcsb. org/structure/5OW3]  25  ). Searches for fusexin homologs using structurally guided MSAs were performed for 3 iterations on the Uniclust database  69  using default HHblits 70 parameters.
HMM-based distance matrices. A taxonomically representative list of known viral and eukaryotic fusexin homologs, covering major lineages, was manually curated. A MSA was built for each homolog by using the sequence as a query on the Uniclust database with HHblits for three iterations. This set of MSAs was compiled into an HH-suite database and each MSA was used as a query against this database to establish a profile-based distance matrix using the probability of homology (Fig. 1a  and Supplementary Fig. 1 ). Metaclust database search pipeline. We searched the Metaclust 71 dataset (nr50) using an HMM made of Fsx1 sequences found in PCGs and MAGs (Supplementary Data 1; see also codes, notebooks and datasets available at Zenodo 63 ). Fsx1 sequences were aligned using ClustalO  72  with default settings for 3 iterations and the resulting MSA was used as a query with HMMER hmmsearch  73  against the Metaclust50 dataset 71.
The 11 segments analyzed correspond to the cluster marked in Supplementary Fig. 9. pHLsec3  77. The protein construct that yielded the final high-resolution dataset included residues D25-S535 and contained a T369C substitution, introduced by PCR mutagenesis with the aim of facilitating heavy atom derivatization for experimental phasing. Oligonucleotides were from Sigma-Aldrich or IDT and all constructs were verified by DNA sequencing (Eurofins Genomics or Macrogen). To generate pCI::GFPnes plasmid (see list of plasmids in Supplementary Table  6 ), an oligo DNA encoding for the nuclear export signal (LQKKLEELELD) was cloned downstream the region encoding EGFP of the pCAGIG plasmid using the enzyme BsrGI. Then, the GFPnes coding sequence was amplified, cut with BmgBI and BglII and used to replace the H2B-GFP coding sequence of the pCI::H2B-GFP plasmid (see list of primers in Supplementary Table  7 ).