dataesr/scientific-paragraphs-categorization huggingface dataset:
We acknowledge that the reliability of the configured lake type depends on the accuracy and completeness of the global river networks, and some of the "isolated" lakes may actually be connected through non-channelized surface runoff and/or subsurface flow (associated with surficial terrain). In addition, since lake-river connectivity can be intermittent, an "isolated" lake may also be connected back to the network during high-flow conditions. For these reasons, we offer two a priori lake type attributes to allow for more flexibility (Table  1 ). The first is "Lake_type," where lake drainage types were configured based on the drainage paths connecting the entirety of global lakes (hereafter "inter-lake connecting reaches"), which we delineated from the 3-arc-second MERIT Hydro hydrography dataset (Section 2.
4). In other words, the inter-lake connecting reaches are composed of a set of global drainage networks, where drainage density is determined by the observed spatial variability of lake density. "Lake_type" ensures that all global lakes are topologically connected and eliminates any "isolated" and "headwater inflow" lakes from lake drainage configuration. The number of identified headwater, flow-through, endorheic, and coastal lakes as "Lake_type" are 57. 3%, 42. 5%, 0. 1%, and 0. 1% of the total lakes, respectively. The other attribute is "Laktyp_mhv". Lake types in "Laktyp_mhv" attribute was configured based on MERIT Hydro-Vector  (Lin et al. , 2021) , a high-resolution global river network dataset which accounts for variable drainage density across the land surface (see Section 3. 1 for more details).
Considering that isolated lakes have the potential to be connected to the other lakes, 15. 5% of the isolated, 16. 3% of the headwater, and 12. 7% of the inflow-headwater lakes from "Laktyp_mhv" become flowthrough lakes in "Lake_type", leading to a 30. 7% increase in the flow-through lake frequency. The dominant proportion for flow-through lakes further accentuates the intrinsic roles of lakes in the global drainage system and the necessity of building this global-scale lake topology dataset. Endorheic or terminal lakes also cover a significant proportion of the global lake area (~21%), mainly due to large terminal saline lakes such as the Caspian Sea, the Great Salt Lake, and Lake Balkhash, as well as the cluster of terminal lakes across the endorheic Tibetan Plateau (Fig. 2a-b ).
Inter-lake reaches The inter-lake reach features contain a set of detailed reach networks that tie any lake in the world to its upstream and downstream lakes (if any) through the outlets. Together with the topology attributes (Table  1 ), each inter-lake reach network depicts how the lakes in the headwater drain to other lakes downstream in a cascade pattern, which eventually reaches a drainage terminus, i. e. , an inland sink or the ocean. Because these networks are determined by lake distribution, their drainage density varies with lake density across the continental surface. Such inter-lake networks can be different from other river datasets such as MERIT Hydro-Vector, which were not tailored for lake-determined drainage density. More specifically, each reach in an inter-lake network starts from the outlet of one lake and ends with the outlet of the next downstream lake, but along the way, the reach is often segmented further by the confluences with tributary reaches from other lakes.
Input and validation data Lake-TopoCat v1. 0 was developed based on HydroLAKES v1. 0  (Messager et al. , 2016)  primarily using the 3-arc-second MERIT Hydro (v1. 0. 1) hydrography dataset  (Yamazaki et al. , 2019) , together with the river flowlines in MERIT Hydro-Vector  (Lin et al. , 2021)  to assist in lake and reach type configurations. The developed lake topology and catchments were validated against: (1) the high-quality National Hydrography Dataset (NHD)  (McKay et al. , 2012)  in part of the US; and (2) the qualitycontrolled lake basin boundaries on the endorheic Tibetan Plateau  (Liu et al. , 2020; Liu et al. , 2021). More details of each of the input and validation datasets are given below.
Lake mask Constructing global lake topology requires two essential types of data: a global lake inventory with detailed lake boundaries; and a DEM or hydrography dataset depicting accurate drainage directions. For the former, we selected the widely used HydroLAKES, which contains about 1. 4 million lake polygons larger than 10 ha (0. 1 km 2 ) compiled from various digital and remote sensing products  (Messager et al. , 2016). HydroLAKES is, to our knowledge, one of the most comprehensive globalscale lake and reservoir inventories publicly available. Associated with each lake boundary, HydroLAKES also provides the outlet point and a number of attributes such as lake mean depth, lake volume, and catchment area. The lake outlet points and catchment areas in HydroLAKES were derived from the 15 arc-second (~500 m at the equator) HydroSHEDS database  (Lehner et al.
Hydrography We considered two global hydrography datasets for building lake drainage topology: HydroSHEDS  (Lehner et al. , 2008)  and MERIT Hydro  (Yamazaki et al. , 2019). The HydroSHEDS hydrography database is available at three different resolutions. Limited by the spatial coverage of SRTM DEM  (Farr et al. , 2007)  technique on MERIT DEM. This technique applied multi-source waterbody datasets to derive flow directions that were more realistically aligned with the observed drainage networks. Small dummy depressions were then removed by an inland basin detection algorithm to further smooth the drainage continuity. For these reasons, we selected MERIT Hydro to configure lake outlets, catchments, and interconnecting drainage paths. As one of the supplementary layers, MERIT Hydro also produced a hydrologically adjusted DEM, where elevations were ensured to comply with the condition that downstream should not be higher than upstream.
This hydrologically adjusted DEM was used to calculate the drainage gradient (slope) of each inter-lake reach. Furthermore, we used the vector-based global river network dataset, MERIT Hydro-Vector  (Lin et al. , 2021) , to assign the type of each lake based on its drainage position in the network (attribute "Laktyp_mhv"). In this dataset, the spatial variability of drainage density was considered when the networks were delineated from the original MERIT Hydro. A machine learning technique was employed to optimize the drainage density per watershed (with a flexible channelization threshold down to 1 km 2 ) in relationship with various hydroclimate factors. About 58 million river reaches were delineated, with a total length of ~75 million km. It is important to note that the point of MERIT Hydro-Vector was not to simply vectorize MERIT Hydro to the finest spatial detail (as something done by Hydrography90m  (Amatulli et al.
Validation Detailed and accurate information of lake topology and basins is lacking on a global scale, so we benchmarked TopoCat against two high-quality regional hydrographical geofabrics. NHDPlusV2. 1  (McKay et al. , 2012) , a value-added version of the 1:100,000 NHD dataset (USGS 2001), was used to validate our derived lake catchment areas in the continental US, and lake topology in part of the Prairie Pothole Region in the US, one of the world's most lake-dense areas. NHDPlusV2. 1 contains catchment boundaries for NHD on-network waterbodies and their drainage topology based on flow directions. Here, onnetwork waterbodies are those which intersect the NHD flowline network. For the waterbodies off the NHD network, we obtained catchments and drainage topology from the LakeCat dataset  (Hill et al.
, 2018). Together, these datasets represent the most up-to-date and comprehensive lake catchment and drainage topology information for the US. In addition, we used the watershed boundaries on the Inner Tibetan Plateau produced by  Liu et al. (2021)  to validate the endorheic lake catchments for the same region in TopoCat. The Inner Tibetan Plateau hosts the world's largest cluster of terminal lakes. Delineating lake catchments in arid and endorheic basins can be challenging for at least two reasons. First, small pseudo depressions due to errors or noise in the elevation data often coexist with actual inland basins, and eliminating them from the hydrography data is difficult  (Yamazaki et al. , 2019). Second, many terminal lakes on the Tibetan Plateau are rapidly expanding due to a wetting and warming climate  (Chen et al.
, 2022b; Wang et al. , 2018; Yao et al. , 2018). Such lake dynamics have led to ongoing drainage re-organizations such as basin merging and annexation, meaning that the catchments derived from a static lake mask, (such as HydroLAKES) are prone to boundary migration through time. Despite the availability of other inland lake catchment data for this region (e. g. ,  Yan et al. , 2019) , the dataset from Liu et al. (  2021 ) is, to our knowledge, the only one that considered the ongoing drainage changes on the Tibetan Plateau due to lake inundation dynamics. They used a lake-oriented approach  (Liu et al. , 2020) , together with the calibration of multi-temporal lake mappings, to refine the basin boundaries initially derived from MERIT DEM. The dataset offers the endorheic lake catchments for the years 2000 and 2018 (hereafter "Liu21 endorheic basins 2000 and 2018"), with a total catchment number of 434 and 421, respectively.
Methodology and algorithm The TopoCat constructing algorithm is illustrated by the schematic diagram in Fig. 8. In brief, the algorithm started with an iterative process that used MERIT Hydro's flow accumulation layer to identify all possible outlets per lake (i. e. , pour points to all possible outbound directions from this lake) and each of their associated unit catchments. The connecting reaches among the lake outlets were then delineated using the flow direction layer. The majority of attributes for lake outlets, unit catchments, and inter-lake reaches (Table  1 ) were then generated using the hydrography, hydrologically adjusted DEM, and the spatial relationships among the delineated features. This resulted in the "preliminary TopoCat" database, which consists of the feature geometries and topological attributes in the finest spatial detail possible, based on all identified lake outlets.
The existing attributes were then re-calculated according to the updated network. The lake-network basins were delineated using the terminal points of all the available inter-lake reach networks. In the last step, a few additional lake outlet attributes were populated and assigned to the associated lake polygons, and MERIT Hydro-Vector was used to determine lake types ("Laktyp_mhv") according to their hydrological position in the river networks. To enable parallel processing, we partitioned the MERIT Hydro dataset into multiple Pfafstetter level-2 basins. A total number of 61 Pfafstetter level-2 basins were obtained from HydroBASINS  (Lehner and Grill, 2013) , which were originally generated from the HydroSHEDS hydrography data  (Lehner et al. , 2008). Lin et al. (  2021 ) redefined these level-2 basin boundaries to make them compatible with MERIT Hydro.
Delineating preliminary lake outlets and unit catchments Although HydroLAKES provides lake outlet points derived from the 15 arc-second HydroSHEDS data, we regenerated the lake outlets using the higher-resolution (5 arc-second) MERIT Hydro dataset. Different from the outlets available in HydroLAKES, we were not constrained by the "one-outlet for one-lake" assumption in order to capture possible lake multifurcation. However, we had to assume that multifurcation can only occur at the lake level, not the outlet level, because pixel-based bifurcation was not assumed in MERIT Hydro. Typically, the outlet location of a lake can be identified by intersecting the lake shoreline with the flow direction layer and then locating the intersecting cell(s) flowing outward from the lake  (Tokuda et al. , 2021).
However, there is no guarantee that the lake outlets always fall exactly at the shoreline cells. An example is an endorheic lake, where the drainage outlet (the sink in this case) is located inside the lake boundary. Other examples are some of the lagoon polygons in HydroLAKES, which can overshoot the terrestrial boundary of MERIT Hydro due to resolution or precision inconsistency between the lake and hydrography datasets. To address these issues, we adopted a flow-accumulation-based approach, which identified the lake outlet as the location of maximal flow accumulation within the lake boundary. Lakes are shown as striped polygons, lake unit catchments as color-filled polygons, lake outlets as red dots, and inter-lake reaches as blue lines. (a) Delineated primary lake outlets (i. e. , no iteration) with their associated unit catchments and connecting reaches.
Here, lake boundaries are the same as HydroLAKES  (Messager et al. , 2016) , and map background from Esri, Maxar, Earthstar Geographics, and the GIS User Community in both figures. A total of 1,261 outlets were identified as "endorheic-check". Among them, 291 were confirmed to be pour points of incomplete endorheic lake polygons, and 14 of these 291 outlets shared drainage sinks with other outlets. The locations of the other 277 outlets were snapped to their connected drainage sinks, and the drainage catchments were re-delineated. As exemplified in Fig. 11 , the result of this QA/QC was a complete endorheic catchment and corrected topological attributes despite an incomplete lake extent in the original HydroLAKES dataset.
Quality assessment and validation The quality of TopoCat was first assessed by comparing its lake catchment areas and lake outlet locations with those in HydroLAKES or LakeATLAS with reference to high-accuracy regional hydrography datasets: LakeCat and NHDPlusV2 for the continental US (CONUS). Since the lake polygons, lake outlets, and catchment or watershed area in LakeATLAS are the same as in HydroLAKES, we only referred to LakeATLAS hereafter, but results are also true for the HydroLAKES. This comparison is followed by a more detailed validation against the lake topology acquired from NHDPlusV2 and LakeCat in a lake-dense area of Minnesota. Finally, the performance of our delineated endorheic basins in TopoCat were assessed against Liu21 endorheic basins 2000 and 2018 for the endorheic Tibetan Plateau (refer to Section 3.
Validation of lake topology Validating lake topology is challenging because there are only a few regional hydrography datasets that consider lake components, and additionally, lake topology is not readily available in these datasets and retrieving it requires a substantial amount of pre-processing. In this study, we benchmarked our TopoCat lake topology against the reference data in a lake-dense area of Minnesota (Fig. 13 ), located at the intersection of the Canadian Shield (the Laurentian Plateau) and the Prairie Pothole Region. We selected this validation site for two reasons. The first reason was the availability of high-quality hydrography data (in this case, NHDPlusV2 and LakeCat). The second reason is that the accuracy of TopoCat is expected to be relatively low in this region given the difficulty of using gridded hydrography to portray the geological and hydrological complexity in such lake-rich areas.
Thus, our rationale is that if the performance of TopoCat is acceptable in this lake-rich area, it may work equally well or better for the rest of the world. The TopoCat and benchmark datasets were pre-processed to enable a comparison at equivalent levels. First, the waterbodies in NHDPlusV2 were intersected with the lake polygons in TopoCat within the validation site to retrieve the common lakes in both datasets. A linking ID between the common lakes was established. In occasional cases where one TopoCat lake covers multiple NHDPlusV2 lakes, the link was established for the most downstream NHDPlusV2 lake among the ones intersecting TopoCat. This way, the topological orders among the linked subset of NHDPlusV2 lakes were still maintained. A total of 427 linked lake pairs were established between NHDPlusV2 and TopoCat, where 112 intersect the NHDPlusV2 flowline network (hereafter referred to as "on-network lakes").
Topological orders of the on-network lakes were obtained from the NHDPlusV2 dataset using the topological orders of their associated catchments. Several on-network NHDPlusV2 lakes intersect no lake in TopoCat. In such cases, we assumed that the lake is absent in TopoCat (i. e. , the HydroLAKES polygons), so such lakes were removed from the assessment, and the topological orders of the remaining on-network lakes were updated accordingly. The topological orders of the off-network lakes in NHDPlusV2 (315 in total) were obtained from the LakeCat database (i. e. , from associated lake catchment orders). Similar to the pre-processing for on-network lakes, any off-network lake intersecting no lake in TopoCat was removed from the assessment, followed by an update of the topological order. The updated off-network lake topology was then appended to the updated on-network ones to form a completely pre-processed validation or benchmark lake topology (hereafter "NHDPlusV2 topology").
It is noteworthy to mention that, only on-network waterbodies were burnt into the DEM in development of the NHDPlusV2 hydrography dataset (i. e. , flow direction, flow accumulation). On the other hand, the LakeCat directly used the flow direction of the NHDPlusV2 to derive the topology of off-network lakes, instead of developing a hydrologically adjusted flow direction through burning off-network lakes in DEM. Therefore, the reference topology of the on-network lakes is more reliable than the off-network lakes. Akin to the benchmark data processing, TopoCat lakes intersecting no lakes in NHDPlusV2 were excluded from the assessment, and all attributes in TopoCat were updated to accommodate these changes. The updated TopoCat topology was compared with NHDPlusV2 to assess the skill in identifying the downstream lake, as well as the skill in identifying the upstream lake abundance (i.
Although the input hydrography and lake mask data are different in the two datasets, their basin boundaries show a fairly good agreement. The overall CSI value of all the 245 endorheic basins is ~0. 85, with the individual basin CSI values ranging from 0. 03 to 0. 99. The largest difference occurs in the endorheic basin of the Pangong lake, which was misidentified as part of the exorheic Indus Basin in TopoCat. Our visual examination attributed this omission error to the automated process of dummy depression removal in MERIT Hydro, where the adopted threshold modification volume was too large for this basin.
g. ,  Lehner et al. , 2022; Meyer et al. , 2020). However, lake catchment perimeters are not directly available from these studies and often do not accurately represent the actual drainage areas of the lakes  (Meyer et al. , 2020). Together with other thematic datasets, the high-resolution (i. e. , 3-arc second) lake catchment boundaries in TopoCat provide users with more accuracy and flexibility to customize the calculations of basin-level limnological and socioeconomic attributes. Furthermore, the lake drainage topology along with lake basin boundaries will help users better understand lake water quality issues, such as tracking temperature and nutrient flows from the upstream lakes and catchments that affect downstream lake algae contributions and water color (e. g. ,  Hou et al. , 2022; Yang et al.
, 2022). Despite the improvements over existing datasets identified above and a variety of potential applications of TopoCat, we would like to acknowledge a few notable limitations. First, TopoCat aims to construct a priori drainage topology and connectivity among global lakes. Thus, the "headwater" extent in our delineated inter-lake reach network was determined by the presence of the most upstream lakes rather than river channels. This is partially due to the ambiguity of what and where should be considered a headwater river and the variability of drainage density across the continents  (Lin et al. , 2021). However, our lakebased drainage network can be potentially nested into a broader and more detailed drainage network to enable a more complete global lake-river integration. Second, although our algorithm includes a mechanism to consider possible lake multifurcation, and this mechanism design did successfully capture some of the multifurcation cases (see the example in Fig.
These errors have not been fully considered in the current TopoCat version and may require future manual QA/QC and validation. A higher-resolution, remote-sensing-based, and quality-controlled lake mask, such as the SWOT a priori lake database  (Sheng et al. , 2016) , may also help improve the representation of lake multifurcation. Third, the accuracy of the delineated basin boundaries in Lake-TopoCat v1. 0 depends on the quality of MERIT-Hydro, which applied an automatic approach to remove dummy depressions from the DEM  (Yamazaki et al. , 2019). This automated DEM processing occasionally lumped actual small endorheic basins to their adjacent exorheic or endorheic basins, which could lead to incomplete lake boundaries and misclassification of the lake drainage types. For instance, the Pangong Lake (33.
, 2012; Sakura et al. , 2016; Sangaréet al. , 2016; Halleé et al. , 2018a; Halleé et al. , 2018b). However, the percentage of identity between T. gondii sortilin and its human counterparts is only about 27%. Interestingly, four peptide insertions are present in the b-propeller domains B, H, 10CCb and the C-terminus tail in all apicomplexan parasite's sortilins identified up-to-date  (Sloves et al. , 2012). The presence of conserved specific peptide insertions exclusively in sortilins of apicomplexan parasites suggests that they may have peculiar 3-D structures compared to the classical VPS10 backbone that can be exploited for future therapeutic interventions. In the present study, we established optimal conditions for expression of a secreted soluble N-terminus luminal end of TgSORT in the methylotrophic yeast Pichia pastoris for structural analysis including a powerful computational predictive program AlphaFold to propose a 3D-model of TgSORT.
Pichia pastoris expression vector The N-terminus luminal coding region of TgSORT (named here Tg-NSORT) from amino acid 37 to 789 (TgSORTLR ToxoDB accession Number TGME49_290160) was designed with Flag and 6xHistidine epitopes added to the C-terminus end. The synthesis and cloning of this Flag and 6xHis tagged TgSORT coding nucleotide sequence in pPINKaHC were performed by Genscript based on P. pastoris codon usage. This vector contained the Saccharomyces cerevisiae a-factor signal peptide added upstream to Tg-NSORT (N-terminus) to allow the traffic of expressed protein to the secretory pathway. The expression was under the control of the methanol-inducible promoter AOX1.
All these systems were fully controlled by Thermo Xcalibur 3. 0 (Thermo Fisher Scientific). All data files (*. raw) collected were processed with a specific workflow designed in Proteome Discoverer 1. 4 (Thermo Fisher Scientific). MS/MS data was interpreted using two search engine Mascot (version 2. 4. 1, Matrix Science, London, UK) and Sequest HT (Thermo Fisher Scientific). Searches were performed against T. gondii (TGVEG, TGME49 and TGGT1 strains) protein sequences downloaded from www. toxodb. org. The Mascot ion score were >20 and Sequest HT XCorr >1. 5. The decoy database search allowed us to control and to estimate the false positive identification rate.
Negative staining of TgN-SORT Samples were analyzed by conventional electron microscopy using the negative staining method. Three mL suspension (0. 05 mg mL -1 ) were deposited on an airglow-discharged carbon-coated grid. Excess liquid was blotted, and the grid rinsed with 2% w/v aqueous uranyl acetate. The grids were visualized at 100 kV with a TECNAI Spirit (FEI) transmission electron microscope (ThermoFisher, New York NY, USA) equipped with a K2 Base 4k × 4k camera (Gatan, Pleasanton CA, USA). Final magnification was at 34. 500 x, corresponding to a pixel size at the level of the specimen of 0. 14 nm. Data were recorded under low-dose conditions (dose rate 20 e A -2 ).