dataesr/scientific-paragraphs-categorization huggingface dataset:
Re-description of H. uarnak. A species in the genus Himantura, as re-defined by  Last et al. (2016a). CO1 gene sequences cluster into a distinct lineage in the phylogenetic tree of the genus Himantura (Fig. 3 ). Nucleotide sequence of partial CO1 gene of neotype, comprised between homologous nucleotide sites no. 69 and no. 699 of the CO1 gene in H. leoparda (GenBank accession no. NC_028325;  Shen et al. 2016) is 5'-C G G T G C G T G A G C A C G G A T A G T G G G T A C T G G C C T T A G C C T G C T T A T T C G G A C A G A G C T A A G C C A A C C A G G C G C A T T A C T G.
List of nucleotide sequences at the CO1 locus used for the present work, with GenBank and BOLD identification nos. and other details including sampling locations and references. Sequences were retrieved on 23 March 2020 from the GenBank and BOLD public databases (N = 201) or donated by X. Chen (Wenzhou Medical College, China) (N = 2) or produced through the present survey (N = 13). Sequences are arranged by species and presented in alphabetical order Supplementary Table  S3  Main morphological characters used in the diagnoses of the four Himantura spp. species considered valid by  Last et al. (2016b Last et al. ( , 2016c) )  and  Weigmann (2016). Diagnosticity: utility of character to diagnose at least one (One sp. ) or all four species (All spp. ); N, sample size; NA not specified.
Acknowledgements Foremost, thanks are due to  M. Tietbohl  for donating tissue samples, assisting in field collections, and providing laboratory assistance. Additionally, we are grateful to  J. Spaet  and members of  KAUST's Reef Ecology Lab  for their support at various stages of this project; to  D. Catania  and  L. A. Rocha  for allocating a CAS collection number to the neotype of H. uarnak; to  X. Chen  for sharing reticulate whipray sequences; to  M. Bennett-Smith  for sharing excellent photographs; to  S. Bogorodsky ,  H. Debelius  and  R. Fricke  for insightful discussions about the taxonomy and nomenclature of reticulate whiprays; to  G. Lim  ( ICZN ) for confirming the validity of the present neotype designation. Useful taxonomic literature was communicated to us by  C. -W. Chang ,  K.
The construction of terraces is a key soil conservation practice on agricultural land in China providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and non-terraced types through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10 875 test samples and achieved values of 94 % and 0.
Note that mediumresolution images have been used in many previous largescale agricultural land cover classification studies  (Massey et al. , 2018; Phalke and Özdogan, 2018). Although the amount of data decreases when conducting classification at medium resolution compared with highresolution images, remote-sensing-based land cover mapping at the scale of a large country like China still requires considerable data storage and processing capabilities, which is a major challenge for software packages. The Google Earth Engine (GEE) platform provides a multi-petabyte analysisready data catalog and high-performance, intrinsically parallel computational service  (Gorelick et al. , 2017). Thus, the handling of large geospatial datasets and access to substantial computational resources can be easily met by GEE, improving the efficiency of geospatial analysis.
Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data Topographic features being the most distinctive features of terraces, it is crucial to consider them for terrace identification. We selected the 30 m SRTM DEM data for characterizing topographic features. SRTM is an international research effort that obtained digital elevation models on a near-global scale  (Farr et al. , 2007). The product at 1 arcsec (30 m) resolution is available on GEE, which has undergone a voidfilling process using open-source data (ASTER GDEM2, GMTED2010, and NED)  (USGS, 2015). Note that the acquisition time of the SRTM DEM data is 2000, and we suppose the terrain has changed little in decades.
GlobeLand30 To improve terrace mapping efficiency, we limited the classification to cropland area of GlobeLand30, a well-established and widely used source of land cover information, generated by the integration of pixel-based and objected-based methods with knowledge (POK) using multisource data  (Chen et al. , 2015). Its accuracy for cultivated land type is above 80 % (J. Chen et al. , 2017). The latest version of this land cover dataset is for 2010, which does not correspond to our classification year (2018). However, according to national survey statistics, the cropland area in China changed little in recent years. We thus accepted the use of 2010 data as a mask for our 2018 classification. GlobeLand30 was first downloaded from the website (http://www. globeland30. org, last access: 15 March 2021) and then uploaded into GEE.
Google Earth images Google Earth images on GEE were used as auxiliary data for sample collection. This dataset is a composited product combining multiple sets of satellite imagery which are provided by different commercial image providers or government agencies at different zoom level  (Potere, 2008). Its highest resolution can reach less than 1 m. With more detailed information (e. g. , texture) provided by the highresolution Google Earth images, we can visually distinguish the samples more accurately.
Feature calculation In total 39 features were calculated from the Landsat 8 SR images and the SRTM DEM data, including the 25th, 50th, and 75th percentiles of seven bands and four indexes, as well as six topographic factors (Table  2 ). These features will be used to distinguish the terraces and non-terraces. Time-series data play an important role in land cover classification, especially for cropland classification, since they can describe phenological traits  (Jia et al. , 2014; Knight et al. , 2006; Matton et al. , 2015). However, taking numerous time-series Landsat SR imagery as input for classifiers can lead to performance deterioration due to the Hughes phenomenon (i. e. , the "curve of dimensionality", accuracy decreases as dimensions increase)  (Hughes, 1968). To reduce the dimension of temporal-spectral features, we extracted the percentiles of six optical bands (blue, green, red, NIR, SWIR1, and SWIR2) and one thermal infrared band (TIRS2) from the Landsat 8 time-series images during the whole year.
e. , eastern hilly plains region, southeastern low-middle mountain region, north China and Inner Mongolia eastern-central mountain and plateau region, and southwestern middle and low mountain, plateau, and basin region). These samples were used for training general classification rules and identifying terraces with typical features. To classify terraces with local or confusing features, a total of 3989 local samples were added to each province to the areas with a poor classification visual effect according to the initial classification results. Cropland in some provinces has similar features, such as in Heilongjiang, Inner Mongolia, Liaoning, Jilin, Tibet, and Xinjiang. Since these provinces have a large area, setting regional samples can reduce the workload of sample collection greatly. Therefore, 54 samples in Heilongjiang were taken as All test samples were randomly generated in a hexagonal grid (icosahedral Snyder equal area discrete global grid, ISEA DGG) created using the DGGRID software  (Sahr, 2019).
Uncertainty analysis In addition to the terrace map itself, the associated map quality analysis is also important, which can provide valuable information for users to select data and indicate the limitations and future improvements. In the following subsections, the mapping uncertainty was assessed using the traditional accuracy evaluation method at the sampling unit level, terrain level, and province level, i. e. , calculating the accuracy of different sampling units, terrain, and provinces. Then, the probability of terrace versus non-terrace was calculated to evaluate the uncertainty at the pixel level.
In recent years, some classification studies have produced continuous fractions of land cover at the pixel scale through unmixing to address subpixel heterogeneity  (Atzberger and Rembold, 2013; Deng and Wu, 2013; Xie et al. , 2016). Therefore, to tackle the mixed pixel issue, future work should focus on improving the image resolution or the estimation of subpixel terrace distribution. In addition to the above difficulties in terrace identification, both the terrain data (SRTM data) and the cropland extent data extracted from GlobeLand30 caused some limitations in the mapping results. On the one hand, the inconsistent year of terrain data and classification led to the uncertainties. Terrain may change due to land use and/or cover transformations during 2010-2018. However, relative to the vertical accuracy of terrain data, most transformations had little impact on terrain.
Furthermore, the non-correspondence of cropland extent data year and terrace/non-terrace classification year also had an impact on the results. Compared to using a cropland map from 2018, the major limitation of using a cropland map from 2010 is that some terraces located in the newly increased cropland area will be omitted. We quantified the omission caused by the cropland mask using the test samples. Only 119 of the total 1092 terrace test samples were located outside the cropland extent of GlobeLand30 2010, indicating that the maximum possible omission errors caused by the non-corresponding year was 10. 90 %. These uncertainties can be avoided by using accurate cropland extent data. However, we had tried several other sources of the best cropland-extent data at present to limit the classification area, such as cropland of National Land Cover Database for China (NLCD-C)  (Zhang et al.
Data availability The China terrace proportion map at 1 km resolution (calculated from 30 m resolution map) is available to the public at https://doi. org/10. 5281/zenodo. 3895585 (Cao et al. , 2020). The map values indicate the proportion of terraces within 1 km×1 km grid cell. The China terrace map at 30 m resolution will be available after publication. The Landsat 8 SR imagery and SRTM DEM data used in this study were available at the GEE platform. The GlobeLand30 data can be downloaded from http://www. globeland30. org (Chen et al. , 2014).
Detecting direct interactions supported by high-resolution structural data To further evaluate the extent to which different binary PPI assays can detect directly interacting proteins, we compared the recovery of the 31 hsPRS-v2 PPIs for which highresolution structures are available in the RCSB protein data bank (PDB)  23  (hsPRS-PDB), to that of the 29 remaining interactions (hsPRS-non-PDB) (Figure  2A ). Our analysis revealed that structurally supported PPIs are detected with higher success rates in comparison to interactions for which structural information is not currently available (Figure  2B ). This was the case for: 1) our previous benchmarking of five binary assays combined (Five assays union, 80. 6% hsPRS-PDB vs. 48. 3% hsPRS-non-PDB); 2) the union of 12 N2H assay versions (N2H union, 71.
To map interactions between subunits of the LAMTOR, MIS12 and BRISC complexes, we systematically tested all possible pairwise combinations with the LuTHy and mN2H assays (Source Data Figures  3 4 5 ). Except for LAMTOR5, which was not available in the human ORFeome 8. 1 collection, all 16 ORFs encoding the selected target proteins were sequence verified and cloned into both LuTHy and N2H expression plasmids. A resulting search space of 256 pairwise combinations, corresponding to a total of 16 subunits for the three complexes (16x16 matrix; Figure  3B ), was thus systematically explored with the LuTHy and mN2H assays. To score a tested interaction as positive, we rationalized that true binary PPIs should only be found between the respective subunits of a given complex (i. e. intra-complex pairs), but not between subunits belonging to different complexes (i.
e. inter-complex pairs). Therefore, we treated all inter-complex pairs as negative controls, similar to protein pairs from a random reference set. We observed that LuTHy and mN2H fusion constructs showed a broad distribution of interaction scores among those inter-complex pairs (Supplementary Figure  3A-F ). Therefore, we defined construct-specific cutoffs under conditions of maximal specificity, where none of the inter-complex protein pairs are scored positive with any of the assay versions. Using this strategy, we found that LuTHy and mN2H recovered 35 (68. 6%) and 23 (45. 1%) of the 51 intra-complex interactions, respectively, while 42 of those protein pairs (82. 4%) were detected when combining both assays (Figure  3C ; Supplementary Figure  4 ). We then compared those results to the ones from the BioPlex dataset  7, 11, 14.
Differentiating direct interactions from indirect associations Differentiating direct interactions from indirect associations within each multiprotein complex identified by AP-MS-based methods is an important, unresolved challenge. In fact, it was estimated that only ~32% of the detected protein associations in the BioPlex dataset are direct interactions  11. Therefore, we evaluated if the binary PPI detection assays, LuTHy and mN2H, could identify directly interacting subunits within multiprotein complexes. To define direct interactions and indirect associations between the subunits of each complex, we used 3D structural information available in three public databases: PDB 23 , Interactome3D 25 and PDBePISA  26. While directly interacting proteins share a physical interface, we defined indirect associations as pairs of subunits that do not share an interaction interface (Figure  4A ).
6% of the structure-based direct interactions, and 85. 7% of the indirect associations (Supplementary Figure  5A, B ). In comparison, according to the BioPlex dataset, 87. 1% of the direct interactions along with 100% of the indirect associations were recovered by AP-MS (Supplementary Figure  5A, B ). To further evaluate if we could confidently differentiate between direct interactions and indirect associations, we analyzed the quantitative scores obtained with LuTHy and mN2H. We observed that direct interactions generate significantly higher scores compared to indirect associations in the LuTHy-BRET (Figure  4B ), LuTHy-LuC (Figure  4C ) and mN2H (Figure  4D ) assays. Since LuTHy recovered both direct interactions and indirect associations, we applied a machine learning-based clustering algorithm on the LuTHy-BRET and LuTHy-LuC scores to classify LuTHy-positive protein pairs as direct or not-direct interactions.
We used the detected hsPRS-PDB PPIs as a positive training set for direct interactions, and the hsRRS-v2 pairs as a negative training set for not-direct interactions (Supplementary Table  4 ). Next, we applied the trained cluster algorithm to the 23 direct interactions and six indirect associations detected by LuTHy (Supplementary Figure  5A, B ). Using this unbiased approach, we were able to classify 14 of the LuTHy-positive direct interactions (45. 2%) as true direct PPIs, without wrongly classifying any of the indirect associations (Figure  4E ,F, Supplementary Figure  5C ). After combining these results with the ones from the mN2H assay, we were able to confidently recover and classify 67. 7% of the direct interactions among the LAMTOR, BRISC and MIS12 complexes. In comparison, protein pair associations reported in the BioPlex database are currently neither classified as direct PPIs or indirect associations (Figure  4F , Supplementary Figure  5C )  7, 11, 14.
e. hsPRS-PDB) as references (Supplementary Figure  8A , Source Data Figures 1-2 and 3-5). However, many of these protein pairs are, on average, structurally covered by less than 75% of their respective full-length sequences (Supplementary Figures  8B  and 9 ; Supplementary Tables  5  and 6 , Source Data Figures  3 4 5 ). Therefore, for the calibration, we selected a subset of 44 protein pairs (i. e. eight different interactions in multiple taggingconfigurations) where the tagged protein termini are fully resolved in the associated structures. For this subset of structurally resolved protein pairs, we generated a standard curve by plotting the cBRET ratios against the measured distances. We observed a strong negative correlation between the cBRET ratios and the reported distances (R = -0. 58), which is best described by a sigmoidal fit (Supplementary Figure  8C ).
LuTHy data analysis Data analysis was performed as previously described  19. In brief, the LuTHy-BRET and LuTHy-LuC ratios from BRET and co-precipitation measurements are calculated as follows: (1) !"#$ &'()* = !"! #"! --. with -. = !"! !"#$% #"! !"#$% with LWL and SWL being the detected luminescences at long (520-570 nm) and short (370-480 nm) wavelengths, respectively. The correction factor (Cf) represents the donor bleedthrough value from the PA-NL only construct. The corrected BRET (cBRET) ratio is calculated by subtracting the maximum BRET ratios of control 1 (NL/PA-mCit-Y), or of control 2 (NL-X/PA-mCit) from the BRET ratio of the studied interaction (NL-X/PA-mCit-Y). For the LuC readout, the obtained luminescence precipitation ratio (PIR) of the control protein PA-NL (PIRPA-NL) is used for data normalization, and is calculated as follows: (2) 23" $%&'! = '! &'( ( * '! )$ With NLOUT being the total luminescence measured after co-IP and NLIN the luminescence measured in the cell extracts, directly after lysis.
Processing publicly available interaction data Publicly available binary protein interaction datasets used in this study came from the original Choi et al publication  18. The hsPRS-v2 PPI recovery rates were extracted as published, in conditions where none of the hsRRS-v2 pairs are scored positive. To generate hsPRS-PDB, structurally supported interactions from hsPRS-v2 were identified using interactome insider (http://interactomeinsider. yulab. org)  52 , and using both, co-crystal structures and homology modeling (Source Data Figures  1 2 ). The BioPlex dataset  7, 11, 14  was downloaded from https://bioplex. hms. harvard. edu/interactions. php on May 10, 2021.
Scoring hsPRS-v2 PPIs under conditions where no hsRRS-v2 pair is scored positive for LuTHy Source data for each assay version performed in this study are presented in the Source Data Figures  1 2. Each binary PPI experiment with LuTHy-BRET and LuTHy-LuC was performed six times, with two biological and three technical replicates. For each protein pair X-Y, we calculated the corrected BRET and LuC ratios as described above. To identify protein pairs that scored positive at a threshold of no hsRRS-v2 detection for a given tagging configuration, only cBRET or cLuC ratios higher than the highest hsRRS-v2 score for that tagging configuration were considered. All hsPRS-v2 pairs that did not meet this criterion were not scored positive and therefore defined as not detected in the corresponding assay version.
Scoring interactions within multiprotein complexes Data for LuTHy and mN2H mapping of multiprotein complexes can be found in Source Data Figures  3 4 5. Recovery of interactions by LuTHy-BRET, LuTHy-LuC and mN2H was calculated based on construct-specific cutoffs. Therefore, the quantitative scores were determined as described in the LuTHy and mN2H methods sections, respectively. For each construct, X or Y, involved in the X-Y interaction, a cutoff was defined as the highest intercomplex pair score for each of the two respective constructs (X and Y). The final cutoff for a tested protein pair between constructs X and Y was then determined as the maximum of the two cutoff values.
Cluster analysis of LuTHy data For supervised classification of direct and not-direct interaction clusters, a Gaussian finite mixture model using the R package mclust  53  was used. First, the LuTHy-BRET and LuTHy-LuC quantitative scores (i. e. cBRET and cLuC ratios) from 63 LuTHy-positive interactions among hsPRS-PDB and from 585 protein pairs in hsRRS-v2 (Supplementary Table  3 ) were used as positive and negative training sets, respectively. The MclustDA discriminant analysis function was applied to those training sets using the eigenvalue decomposition discriminant analysis (EDDA) method. The training set performed at a 2. 36% classification error rate. Next, the trained algorithm was used to cluster 117 intra-complex protein pairs (Supplementary Table  3 ) that scored above the construct-specific cutoffs in LuTHy assays.
Determining interaction interface areas, complex surface areas and molecular distances between protein termini Interaction interface and total complex surface areas were determined according to PDBePISA 26 (https://www. ebi. ac. uk/msd-srv/prot_int/pistart. html) on April 16, 2021. Protean 3DTM (DNASTAR ® ) was used to measure molecular distances between protein termini of interactions in the hsPRS-PDB as well as between direct interactions and indirect associations within the multiprotein complexes: LAMTOR (RCSB PDB: 6EHR), MIS12 (RCSB PDB: 5LSJ) and BRISC (RCSB PDB: 6H3C). The determined molecular distances can be found in Source Data Figures  3 4 5. Nonlinear and linear regressions between cBRET ratios and molecular distances between protein termini were calculated using GraphPad Prism 9 and the fits were compared using the extra-sum-of-squares F test with a bottom constraint to 0.
GraphPad Prism 9 was also used to interpolate molecular distances and their 90% confidence intervals using the measured cBRET ratios. (A) Structures of the protein complexes analyzed in this study: LAMTOR (PDB: 6EHR), MIS12 (PDB: 5LSK), and BRISC (PDB: 6H3C). (B) Binary interaction approach to systematically map PPIs within distinct complexes. Every protein subunit from each complex was screened against every other one (all-by-all, 16x16 matrix). (C) Results of the all-by-all interaction screen for the selected multiprotein complexes. Each protein pair was systematically tested in every possible configuration (i. e. C-C, C-N, N-C, N-N), and as donor and acceptor in LuTHy, or as F1 and F2 NanoLuc fusions in mN2H. LuTHy union corresponds to the combination of both LuTHy-BRET and LuTHy-LuC results.
(C) Schematic representations of the HTTQ23 and HTTQ145 intramolecular LuTHy-BRET sensors. (D,E) Cryo-EM structures of the apo-HTT protein (D, PBD: 6RMH) and the HTT-HAP40 complex (E, PDB: 6EZ8) where the distances between residues T91 and L2680 are indicated (black) either in the absence or presence of HAP40. (F) BRET ratios measured for the intramolecular LuTHy-BRET sensors in the absence or presence of HAP40, for HTTQ23 and HTTQ145 (the bar plots show means ± sd, statistical significance was calculated by two-way ANOVA with Tukey's multiple comparisons test, n=3). The BRET ratios were used to interpolate distances between the Nterminus of HTT and residue L2680 for HTTQ23 and HTTQ145, which are indicated in D and E. 51 terminus of LAMTOR3, the current study uses the full-length sequences for subunits of the LAMTOR complex.