dataesr/scientific-paragraphs-categorization huggingface dataset:
Challenge metrics • Mean absolute error (MAE). Methods were required to provide an accurate prediction for the anomalous diffusion exponent α for a single trajectory (T1) or for a part of a trajectory after segmentation (T3). Method performance was thus quantified by the MAE between the predicted value and the ground truth: MAE = 1 N N i=1 |α i,p -α i,GT |, ( 9 ) where N is the number of trajectories in the dataset, and α i,p and α i,GT represent the predicted and ground truth values of the anomalous exponent of the i-th trajectory, respectively. • F 1 -score. For T2 and T3, the methods have to provide the probability for a trajectory (or a segment) to be assigned to one of the five diffusion models. Predictions for which the highest probability value corresponded to the the ground truth model were identified as true positives (TP).
As a summary statistics for model classification, we used the F 1 -score. In the case of a balanced dataset (as the one considered in the challenge, for which each class is equally represented), the F 1 -score with micro-average can be calculated as F 1 = TP N , ( 10 ) where N is the total number of trajectories in the dataset. • Root mean square error (RMSE). The trajectory segmentation problem in T3 requires the location of the point where a trajectory undergoes a change in anomalous diffusion. The most important consideration for a changepoint method is how accurately it localizes the changepoint itself. The quantification of this accuracy was performed through the RMSE between the predicted and ground truth position: RMSE = 1 N N i=1 t i,p -t i,GT 2 , ( 11 ) where t i,p and t i,GT represent the predicted and ground truth values of the changepoint position, respectively.
Although the choice of a different timescale or the use of an independently calculated localization precision can produce better results  [16] , we intentionally limited the code to a simple fitting algorithm with a straightforward criterion for the choice of the number of data points to fit. As shown in Fig. 2 , such a simple fit produces results comparable with the best methods for ergodic models with high SNR and long trajectories. The code is available at https://github. com/AnDiChallenge/ANDI_datasets. Besides the MSD, another popular methodology for the quantification of the anomalous diffusion exponent is the moment scaling spectrum MSS  [73, 74]. MSS considers several high-order moments of the displacement distribution to obtain their scaling exponents and use them to calculate the slope of the exponent curve versus the moment order, which is found proportional to α.
CODE AND DATA AVAILABILITY All software used for the Challenge is available at https://github. com/AnDiChallenge. The code of the andi-datasets package used to generate the competition datasets is available at https://github. com/ AnDiChallenge/ANDI_datasets. Simulated datasets used for the competition are available for download at http://andi-challenge. org. Groundtruth for datasets used in the first phase of the competition for training are also available. Empirical probability distributions of the difference between the predicted (αp) and the ground-truth exponent (αGT ) for every method participating in T1. 3D. The expectation value of the bias θ is reported in the plot. A dashed line representing the zero value is included as a guide-to-the-eye. Teams are ordered according to to their ranking in the leaderboard.
Team K: TSA Contact: Erez Aghion Max-Planck institute for the physics of complex systems Dresden, Gemany Reference: F 1 -score 1 -2 1 2 1 -4 1 4 1 -6 1 6 1 -8 1 8 1 -1 0 1 1 0 1 -1 2 1 1 2 1 -1 4 1 1 4 1 -1 6 1 1 6 1 -1 8 1 1 8 G M L E F J C O I D A B K CTRW G M E L J C F I O D B K A FBM G M L E F I J O C B D A K LW M E L J G C F B D A K O I SBM E G F M J L O C B K 0. 1 0. 2 0. 3 0. 4 0. 5 MAE 2D L E G F M J O C B K G L E J B F O M K C L E F J O G B K C M L J E G B F K M O C L F E G C M B K Team 0. 1 0. 2 0. 3 0. 4 0. 5 MAE 3D L G F E M C B K Team L G E F B C K M Team L F E G B C K M Team L E G F B K C M Team [15] Method: Scaling analysis, and feature engineering (for T2) Platform: Python Open-access: https://github. com/ErezAgh/ANDI-challange-codeshttps://github. com/AnDiChallenge/AnDi2020_TeamK_TSA Description: This approach is based on theory, as opposed to pure data-driven methods.
The answers of each question can be "yes" (= 1) or "no" (= 0) (or "maybe" (= 2)). An example for a question about the exponents: Is (J -0. 5) > (M -0. 5) + (L -0. 5)? Namely, is the effect of autocorrelations on the Hurst exponent stronger than the combined effect of the increment distribution? This question separates between FBM and LW on the one side, and ATTM and SBM on the other. An example for a question beyond the exponents, is given by the comparison of the autocorrelations of the increments of the process, versus that of their absolute value. This question is highly selective for distinguishing Lévy walk from all the others. For each trajectory in the competition data set: we generate a set of answers using the same algorithm, and then generate an array of probabilities for this set to be either ATTM, CTRW, FBM, LW, or SBM.
H. ; team H: N. F. , J. A. C. , O. G. O. ; team I: C. M. ; team J: T. B. ; team K: E. A. , P. G. M. ; team L: Gior. V. , A. G. ; team M: O. G. O. , J. A. C. ; and teams N,O: H. L. -O. , P. K. , J. S. D. K. , C. M. , and A. W. provided experimental datasets. The article was written by C. M. , G. M. -G. , Giov. V. , and M. A. G. -M. with input from all authors.
ACKNOWLEDGMENTS The authors would like to thank:  Paula Kowalek  for the graphical illustrations;  Matthias Weiss  and  Maria Garcia-Parajo  for sharing experimental data;  Daniel Adam  for help with compiling the data of singleatom trajectories. G. M. -G. , B. R. , and M. L. acknowledge support from  ERC AdG NOQIA ,  Agencia Estatal de Investigación "Severo Ochoa" Center of Excellence   CEX2019-000910-S, Plan National FIDEUA PID2019-106901GB-I00/10. 13039 / 501100011033 , FPI),  Fundació Privada Cellex ,  Fundació Mir-Puig , and from  Generalitat de Catalunya (AGAUR  Grant No. 2017 SGR 1341 ,  CERCA program , QuantumCAT  U16-011424  , co-funded by  ERDF Operational Program  of Catalonia 2014-2020),  MINECO-EU QUANTERA MAQS  (funded by  State Research Agency (AEI)   PCI2019-111828-2 / 10. 13039/501100011033 ),  EU Horizon 2020 FET-OPEN OPTOLogic  (Grant No  899794 ), and the  National Science Centre, Poland-Symfonia  Grant No.
Sampling We aimed to sample at least one representative for all genera of Sapindaceae and sought to include collections belonging to type species, although this was not always possible, in which case, we used collections from the DNA and Tissue Collection at Royal Botanic Gardens, Kew (http://dnaba nk. scien ce. kew. org/homep age. html) or from our own field-collected samples. Species representing the type genus of each tribe were also included to support the development of a tribal classification. The resulting sample set included representatives of 123 of the 144 currently recognized genera (86%), 31 of which had never been sequenced before or were not included in any previous family-wide phylogenetic analyses. All samples are vouchered by collections deposited in one or more of the following herbaria: BM (The Natural History Museum, London, UK), G (Conservatoire et Jardin botaniques de Genève, Geneva, Switzerland), K (Royal Botanic Gardens, Kew, UK), L (Naturalis, Leiden, Netherlands), MO (Missouri Botanical Garden, St, Louis, MO, USA), and P (Muséum national d'Histoire naturelle, Paris, France) (see Appendix S1 with Supplementary Data and Data Availability section for more details on data repository).
Gene recovery and phylogenetic tree reconstructions Gene-coding sequences were recovered from each specimen using HybPiper version 1. 2  (Johnson et al. , 2016)  to find matching orthologous sequences from the Angiosperms353 target gene set  (Johnson et al. , 2019 ; https://github. com/mossm atter s/Angio sperm s353). First, specimen read sequences were trimmed using Trimmomatic  (Bolger et al. , 2019)  with the following parameters: ILLUMINACLIP: <AdapterFastaFile>: 2:30:10:2:true; LEADING: 10; TRAILING: 10; SLIDINGWINDOW: 4:20; and MINLEN: 40. Successfully trimmed read pairs and singletons were then assembled in HybPiper default mode (read mapping to amino acid target   (Radlkofer, 1931 -1934 and Buerki et al. , 2009)     sequences with BLASTX) except the --cov_cutoff parameter was set to 4.
Sequence recovery and phylogenetic tree reconstructions For the species sequenced specifically for this study (i. e. , excluding Aesculus pavia, which was obtained from OneKP; www. onekp. com), we recovered on average 2,634,488 reads per accession (range: 58,883-11,035,138) of which 451,877 (range: 7853-2,302,320) were on target (16. 9%; range 1. 14-31. 95%). Of the 353 genes targeted by the Angiosperms353 probe set, we retrieved on average 335 (range: 45-349). Both ASTRAL and concatenated analyses were performed on 343 gene alignments. All statistics concerning sequence recovery are presented in Appendix S1 (with data on vouchers and DNA accession numbers). Angiosperms353 target gene sequences were generated for 123 genera (86%) represented by 135 samples (see Appendix S1 and Data Availability section for details on data repository).
This approach is further justified given that we have attempted to include a representative of the type genus for each previously recognized tribe (as defined by  Radlkofer [1931 Radlkofer [ -1934] ]  and  Acevedo-Rodríguez et al. [2011 , 2017] ). These genera and their suggested group placements, as indicated in previous phylogenetic studies, are as follows: Averrhoidium Baill. (Dodonaea group;  Buerki et al. , 2009) , Cardiospermum L. (Paullinia group;  Buerki et al. , 2009; Chery et al. , 2019)   Buerki et al. , 2011b). We were unable to include DNA sequences of these latter genera in the analyses presented here since they were represented only by plastid or nuclear ribosomal DNA sequences (rather than nuclear genes, targeted by the Angiosperms353 baiting kit; see above). RAxML and ASTRAL phylogenetic trees are available in Appendices S2 and S3 (newick trees are available here: https://github.
Porocystis (2 species), distributed in South America, was placed in Sapindeae by  Radlkofer (1931 Radlkofer ( -1934)). A matK sequence of P. toulicioides Radlk. was produced by Clark, Pennington and Dexter (GenBank accession MH024824) but has to be taken with caution since it is not associated with a publication or a cited voucher (at least not as of 1 June 2021). A BLAST analysis revealed that this species is genetically closely related to species of Alectryon Gaertn. and Mischarytera (Radlk. ) H. Turner, which would suggest that Porocystis may belong to clade 21. Finally, we are unable to comment on the affinities of the monotypic African Chonopetalum, since it is only known from the type specimen and is in critical need of further study.
Janssens et al. (2020)ra he assigned to this tribe were recovered in clade 21, but apart from Lecaniodiscus, none occur in mainland Africa. A matK sequence (GenBank accession MN370324) of L. dananensis Aubrév. &Pellegr. wasproduced byJanssens et al. (2020)for DNA barcoding analyses. A BLAST analysis on this sequence suggested a close relationship with species of the African genus Chytranthus, which would instead place Lychnodiscus in clade 9. Namataea (1 species in Cameroon) was regarded as a member of Lepisantheae by.
ACKNOWLEDGMENTS This work was funded by grants from the  Calleva Foundation  and the  Sackler Trust  to the Plant and Fungal Tree of Life Project (PAFTOL) at the  Royal Botanic Gardens ,  Kew. Fieldwork  by S. B. , M. W. C. , J. M. , and P. P. L. was supported by the  Idaho Botanical Research Foundation. We thank the curators at the following herbaria for making their collections available for our research: BM,  BRI, CNS, G, K, KLU, L, MO, MPU, NOU, P, SAR, SAN, SUVA, SING. We are grateful to  Michail Belov  (http://www. chileflora. com),  Philippe Chassot  (https://philou. i234. me), Christopher Davidson  (https://flora ofthe world. org), Claire De Schrevel ,  G. Curt Fiedler  (http://www. umijin. com), Olga Martha Montiel ,  Diosdado Nguema ,  Pete Phillipson ,  Charles Rakotovao , and  Germaine A.
DATA AVAILABILITY The data (a spreadsheet summarizing sampling as well as ASTRAL and RAxML phylogenetic trees in newick format) and R code (scripts to produce Table  1 , Fig. 1 , and Appendices S2 and S3) underpinning this study are available on GitHub: https://github. com/ svenb uerki/ Angio 353_Sapin daceae ENA (European Nucleotide Archive) accession numbers for all samples included in this study are available in Appendix S1.
Subsequently, we identified 50 independent tests using parallel analysis and permutation test (Methods)  1, 20, 21. Thus, besides conventional genome-wide threshold (P = 5×10 -8 ), we set the stricter study-wide threshold to P = 1×10 -9 (P = 5×10 - 8 /50) after Bonferroni correction. In the discovery datasets, we identified 156 genomewide significant variants (P < 5×10  -8  ) after condition analysis and peak selection (Methods). In which, 81 variants are study-wide significant (P < 1×10  -9  ). Majority of variants (120 out of 156 genome-wide variants, 76 out of 81 study-wide variants) are replicated at nominal threshold (P < 0. 05). To increase statistical power, we performed a meta-analysis using Stouffer's method to combine the P-values obtained from the discovery and replication cohort  1, 22.
As such, 130 of the 244 leading variants below the genome-wide threshold are novel, while 65 of the 151 leading variants below the study-wide threshold are novel. (Supplementary Table  2 ). We used FUMA and GREAT to annotate the leading variants  24, 25. As a result, we identified 204 potential genes associated with facial variation. In which, 100 genes are not reported in previous facial GWAS studies (Supplementary Table  2 ). Besides, we found that the genes associated with the leading variants are highly enriched for the processes of skeletal system development and morphogenesis related biological processes (Extended Data Fig. 2a ). Moreover, from the epigenome and transcriptome datasets from variable tissue or cell types, the leading variants are mainly enriched for enhancers in craniofacial tissues, and candidate genes are significantly highly expressed in mesenchyme compared to in ectoderm at late fetal development (Extended Data Fig.
Genotyping and imputation The DNA samples of the participants in the discovery dataset were extracted from their blood samples using the MagPure Blood DNA KF Kit. These DNA samples were genotyped on the Illumina Infinium Global Screening Array that investigates 707,180 variants which is a fully custom array designed by WeGene Since we used two different genotyping platforms in the discovery and replication datasets, we chose to impute the two data sets separately. Prior to imputation, samples with a genotyping missing rate > 0. 05 were excluded. Haplotypes were estimated from the genotypes using SHAPEIT2 2. Then, the samples were imputed to the 1000 Genomes Project Phase 3 reference panel using IMPUTE2  3. After imputation, variants with an INFO score < 0. 8 or certainty score < 0. 9 were excluded.
3D image registration and quality control After image acquisition, the 3D images were imported into MeshMonk, a 3D registration software, from wavefront. obj format files to perform a spatially dense surface registration process  4. The nose tip landmark was manually identified as the point of origin in each 3D image to trigger an initial but crude pose alignment as input to MeshMonk. Afterwards, a symmetric anthropometric mask of 7,906 landmarks was non-rigidly mapped onto all 3D images. In each dataset (discovery and replication), generalized Procrustes analysis (GPA) was performed on a stack of the mapped 3D images and their reflection so that any difference in position, orientation, and size of both original and reflected shapes was eliminated. The average of an original and its reflected facial shape resulted in symmetrized facial shape.
Facial phenotyping Similar to the approach of Claes et al, we performed a semi-supervised facial segmentation based on the phenotypic correlation between facial landmarks using the discovery dataset  5, 6. To calculate the phenotypic correlations, we first corrected the symmetrized facial shapes for the covariates of age, age squared, sex, BMI, and four SUGIBS components using a partial least-squares regression (PLSR, function plsregress from MATLAB TM 2018a) to obtain the residual facial shapes  7. We then calculated the pairwise RV coefficients as the phenotypic 3D correlations between the landmarks of the residual facial shapes. In addition to the RV similarity matrix, a scaled Euclidean distances matrix (the closest points scaled to 1; the most far points scaled to 0) between landmarks on the anthropometric mask was calculated.
Besides, the correlation can be tested for significance based on Rao's exact F-test (one-sided, right tail)  11. For each variant, we obtained a direction  i in the shape PCA space most correlated with the genotype of that variant and a P-value representing the strength of correlation in the discovery phase. In the replication phase, we calculated the Pearson's correlation between the phenotypic scores and the genotypes of a given variant in the replication cohort. To test the correlation's significance, we used the Student's t-test where the t-statistics is defined as. We performed a one-sided right tail test for each variant to ensure that the effective direction of the variant within the two datasets is the same. Next, the P-values obtained in the discovery and replication phase were combined in a meta-analysis using Stouffer's method weighted by the sample sizes  12.
Gene annotation We used three gene-mapping criteria implemented in Functional Mapping and Annotation (FUMA) to identify the candidate gene for each lead variant  15. First, we tried to map each variant to genes based on physical distance (within a 10,000 base pair window) from the known protein-coding genes in the human reference assembly. Second, we also included the genes which have a significant eQTL association with the leading variants. We extracted the cis-eQTLs analysis, which uses a linear regression evaluated association between leading variants and expression levels of nearby genes (1 Mb distance to the leading variant), using 10 tissue types from the GTEx v8 database  [16] [17] [18]. We used a false discovery rate (FDR) of 0. 05 to define significant eQTL associations. Finally, we also identified candidate genes for each leading variant if there is a 3D DNA-DNA interaction between the leading variant region and the gene region, or in other words, chromatin interaction mapping.
https://www. wegene. com/). In the replication dataset, participants' DNA samples were extracted from blood samples using the GENEray TM DNA extraction kit. These DNA samples were genotyped on the illumine HumanOmniZhongHua-8 array which captures 776,213 variants. Relatedness among the individuals was inferred using KING-robust separately in the discovery and replication datasets1. Any relative up to the second-degree was excluded. As a result, 6968 (n = 4,089 in NHC cohort, n = 2,879 in NSPT cohort) and 2706 unrelated individuals in the discovery and replication dataset are used for further analysis.
Acknowledgements This project was funded by the following grants and contracts:  Shanghai Municipal Science and Technology Major Project  ( 2017SHZDZX01 ,  2018SHZDZX01 );  National Key Research and Development  Project  2018YFC0910403 ;  CAS Interdisciplinary Innovation Team Project ; " Strategic Priority Research Program " of the  Chinese Academy of Sciences  (Grant No. XDB38020400 );  Max Planck-CAS   Paul Gerson Unna Independent Research Group Leadership Award ;  National Natural Science Foundation of China  (Grant No. 31521003 , Grant No. 31900408 ); National Science & Technology Basic Research Project  2015FY111700 ;  CAMS Innovation Fund for Medical Sciences   2019-I2M-5-066 ; The  111  Project  B13016 ;  China Postdoctoral Science Foundation  (No. 2019M651352 , No. 2020M670984 ). We thank for all suggestions collected during the poster exhibition of ASHG 2019 Annual Meeting.
Data availability The participants making up the NSPT, NHC and TZL datasets were not collected with broad data sharing consent. Given the highly identifiable nature of both facial and genomic information and unresolved issues regarding risk to participants, we opted for a more conservative approach to participant recruitment. Broad data sharing of the raw data from these collections would thus be in legal and ethical violation of the informed consent obtained from the participants. This restriction is not because of any personal or commercial interests. Additional details can be requested from Li Jin and Sijia Wang. Publicly available data used were: The 1000GP Phase 3 data: (ftp://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502/), The Roadmap Epigenomics Project: (https://egg2. wustl.
edu/roadmap/web_portal/imputed. html#chr_imp) The transcriptome resource from separated ectoderm and mesenchyme of the developing mouse face (GSE62214). GWAS summary statistics are available on the National Omics Data Encyclopedia (NODE) (https://www. biosino. org/node/project/detail/OEP002283). The project ID of our study is OEP002283. Data usage shall be in full compliance with the Regulations on Management of Human Genetic Resources in China.
Code availability KU Leuven provides the MeshMonk spatially dense facial-mapping software, free to use for academic purposes (https://github. com/TheWebMonks/meshmonk). The statistical analyses in this work were based on MATLAB TM 2018a, SHAPEIT2 (v2. 17), IMPUTE2 (v2. 3. 2), SNPLIB (https://github. com/jiarui-li/SNPLIB), MeshMonk, FUMA (v1. 3. 6), GREAT, Metascape, REHH2 (v3. 2. 0), plink 1. 9, R (> v. 3. 6), ggplot2 (v3. 1. 0), and Python (v3. 5. 0) as mentioned throughout the Methods. Extended Data Fig. 6: EAS-FS of the 244 leading variants on the EUR-EAS difference. The distributions (blue) of EAS-FS derived from 244 leading variants associated with a) whole face and b) -k) 10 anatomical segments. The black dotted line is the EAS-FS threshold of each region (mean + 3×SD). The red arrow is the variant over threshold.
( https://www. wegene. com/). In the replication dataset, participants' DNA samples were extracted from blood samples using the GENEray TM DNA extraction kit. These DNA samples were genotyped on the illumine HumanOmniZhongHua-8 array which captures 776,213 variants. Relatedness among the individuals was inferred using KING-robust separately in the discovery and replication datasets1. Any relative up to the second-degree was excluded. As a result, 6968 (n = 4,089 in NHC cohort, n = 2,879 in NSPT cohort) and 2706 unrelated individuals in the discovery and replication dataset are used for further analysis.
Flow cytometry. The surface expression of NTCP, ERp46, ERp57 and ERp72 was quantified by FACS analysis from 10 6 live cells using antibodies added to cells for 1h at 4°C. After washing, the binding of antibody to the cell surface was detected using PE (Phycoerythrin)-conjugated anti-mouse antibodies. Fusion peptide prediction. The HBV surface sequence used was taken from the UniProt database, with accession number P03138. Hydropathy plots were obtained with Membrane Protein eXplorer software  (Snider et al. , 2009)  using as input the reference sequence. Hydropathy plots were also used to evaluate the effect of residue mutations. Sequences with a propensity to partition into the lipid bilayer were identified using interfacial settings and pH=5. 0.