dataesr/scientific-paragraphs-categorization huggingface dataset:
PPIMem and other datasets The PPI template-based prediction algorithm and server PRISM2. 0  [85]  also uses the 3D structural similarity of interfaces with respect to templates to propose other protein complexes. PRISM is not specific for MPs and requires the 3D structure of the targets to propose an interaction, whereas PPIMem is specific for TMs and does not require the 3D structure of the subunits composing the putative complex. Thus, when having an interface template corresponding to an MP, PRISM may propose not only MP protein complexes but also globular protein complexes. Therefore, many of our TM template interfaces are absent in the PRISM dataset. Although most datasets based on experimental approaches cover the entire human proteome, again, MPs are underrepresented (for an overview of the major high-throughput experimental methods used to detect PPIs, see  [86, 87] ).
Thus, the experimental work of Rolland and colleagues on the human interactome network  [25]  found only 41 interactions between MPs. Twentyeight of these proteins were found to interact in the IntAct DB. Nevertheless, none of the interactions we extracted from the structural PDBsum DB were found among the 41 interactions above. It seems that some of these interactions bear between the juxtamembrane regions of the MPs reported by Rolland et al. for MPs. We did not find either any of our predictions in their results. In the HI-III-20/HuRI updated dataset  [12] , none of the high-quality binary PPIs are MPs (log 2 odds ratio < 0, Extended Data Figure  7a.
of Luck et al. )! To verify our predictions of TM-TM complexes, we confronted our PPIMem predictions at 0-20% mutation rate for human proteins to several datasets like FpClass of predicted human PPIs, Bio-Plex 3. 0 of experimentally determined PPIs (uses the TAP-MS technology), MENTHA, IID, HMRI, and BIPS. In Table  S9  we compare our PPIMem predictions to FpClass and to BioPlex with the aim of representing an independent qualitative validation of our method. For example, PPIMem predicts the interaction between isoform 2 of the human c-aminobutyric acid receptor subunit b-3 (GABRB3, P28472-2) and subunit a-6 (GABRA6, Q16445). In the BioPlex 3. 0 dataset, the interaction is detected with a probability !0. 99. The ComplexPortal DB (https://www. ebi. ac. uk/complexportal/home) reports the same interaction as Complex AC: CPX-2164 and CPX-2951.
Given that BioPlex reports $2000 MP interactions (all membranes included) with score !0. 78, the present non-specific correspondence between the two datasets is of $2%, considering the 35 interactions described in Table  S9. Searching the PPIMem human proteins in FpClass results in 8857 predicted PPIs with a score !0. 5, out of 26,456 interactions for 278 PPIMem proteins. In the end, we obtained 74 interactions in common between the two datasets, representing a correspondence of 0. 8%. As a reminder, the BioPlex and PPIMem sets are independent, i. e. , there is no intended intersection between them, and the BioPlex dataset is for the HEK293T cell, whereas our dataset is for no specific cell at all. In addition, the FpClass and PPIMem sets are orthogonal and 139 PPIMem human proteins were not found in FpClass.
Despite the difficulties of comparing different datasets, we show how some of our predicted data for H. sapiens is found in other datasets: -The MENTHA experimentally determined direct protein interactions DB presents also the P28472-Q16445, as well as the P28472-P47870 (GABRB2) interaction. -The IID DB validates experimentally the Vascular endothelial growth factor receptor 2 (P35968) -receptor 3 (P35916) interaction. -The HMRI DB, which seems to list only heteromers and for which not all the interactions are between MPs, shows a correspondence for the heterotypic pair TYROBP-KLRC2 (p value = 0,034341). -BIPS  [30]  predicts putative interactions and is based on sequence homology between proteins found in PPI DBs and templates. We find several correlations between BIPS and PPI-Mem. For instance, we propose an interaction between T-cell surface glycoprotein CD3 f chain (P20963) and high immunity immunoglobulin e receptor subunit c (P30273).
Negative interactions The importance of recording negative results of PPI assays in interatomic DBs, i. e. , those indicating the tested proteins do not interact, has been raised  [89]. However, their identification is less straightforward. This feature should eventually lead us to define a set of true negative interactions with the goal of training a predictor of TMPPIs as the sampling of negatives is crucial for optimal performance  [90, 91]. Looking for negative interactions in the IntAct DB for our PPIMem proteins, we find two negative interactions of two PPI-Mem proteins, but with two non-MPs. Analogously, the Negatome datasets  [92, 93]  and Stelzl  [94]  compile sets of protein pairs that are unlikely to engage in direct physical interactions. We observed that spanning the Negatome dataset with our predicted positive interactions for H.
In general, if complex formation is feasible as the interacting surfaces of the individual proteins manage to face each other in the docked complex provides a partial validation of the method. There are many caveats of any analysis comparing PPIs from multiple sources  [95] , as there are large discrepancies and dramatic differences in the content between experimental PPI data collected by the same or different techniques, reflecting inherent limitations to each detection method, such as errors and ambiguities leading to false positives (FP). Consequently, our attempt to compare our predictions to experimental data is more haphazard. Indeed, the intersections between various interaction maps that employ altogether diverse approaches are very small  [96, 97]. For example, though interactions in our template set exist as PDB structures, surprisingly most of them do not show in PPI databases.
Moreover, the presence of orthologs and splice isoforms makes the research more cumbersome, not to mention that reported interactions are often functional associations and not necessarily direct, physical interactions between proteins leading to a complex  [49]. To this, we have to add differences in curation policies  [98]. Thus, the comparison between our predicted set of TM complexes and other datasets is necessarily qualitative. Disagreements occur also because TMs are not well probed in experiments, so that the screening of the huge potential interaction space is not complete. Because of all these factors, we do not have a large, validated dataset to compare to and assess the FPs. Nevertheless, to reduce the FPs we considered only motifs with six or more contact residues. Searching for a given protein in the database may result in too many entries in the PPIMem webpage.
Complementary to the sequence-based co-evolution PPI prediction methods  [8, [99] [100] [101] , our approach encodes 3D into 1D and thus adds the spatial dimension to a given TM interactome. This may lead to pioneering biological hypotheses concerning PPIs at the membrane level, to genotype-phenotype relationships, to investigation of the effect of pathological mutations on the interaction between TMs, and to propose molecular mechanisms of action. Recovering PPIMem predictions for human TM complexes in several experimental PPI datasets obtained by different methods (BioPlex 3. 0, MENTHA, IID, HMRI) highlights the pertinence of our approach. The predicted TM-TM interactions could be verified experimentally with specific techniques like video microscopy, FRET  [102] , or by our exclusive Microtubule Bench approach  [103].
Data availability Fig. S4  shows a screen capture of PPIMem's first page. The corresponding opensource code and instructions for running the pre-diction algorithm have been deposited at https://github. com/ PPIMem. The database with the annotated predicted interactions is implemented as a web application that supports sorting and filtering. The output data can be downloaded as a csv file and the predictions can be accessed at https://transint. univ-evry. fr. Several pertinent notes are to be found in the Supplemental Material.
Ice cover on lakes is subject to atmospheric forcing from above and the influence of water dynamics and heat flux from below. One characteristic example of these influences in some large lakes, such as Lake Baikal in Russia, are the giant ice rings and the associated eddies under the ice cover. In April 2020 a giant ice ring appeared in southern Baikal, and a lens-like eddy was detected below the ice. We analysed the temporal changes of ice cover using satellite images from multiple satellite missions -MODIS on Terra and Aqua, Sentinel-1 SAR, Sentinel 2 MSI, Landsat 8, PlanetScope, satellite photography from the International Space Station, and radar altimetry data from Jason-3. Satellite imagery and meteorological data show unusual temporal changes of ice colour in April 2020, which were explained by water infiltration into the ice followed by the competing influences of cold air from above and the warm eddy below the ice.
Multitemporal satellite data in various parts of the electromagnetic spectra together with meteorological data may reveal some of the physical processes behind these changes. Various available high-resolution satellite imagery  (Landsat, Sentinel, etc. )  for the period of observation of this ice ring showed the various stages of ice ring development, but the time between available images made understanding difficult. Daily MODIS imagery is potentially useful but has low spatial resolution. Adding daily high-resolution PlanetScope satellite images to the analysis helps to complete the picture. In this paper we first identify the types of satellite data analysed and describe the geographical location of the study. Then we analyse ice cover changes and metamorphism in the context of meteorological data and of what we currently know about ice rings and eddies.
Resulting cracks form a complex spatial pattern and may extend for several hundreds of metres and sometimes for several tens of kilometres. Snow cover on the ice of Lake Baikal is very thin due to low precipitation and the influence of the wind that blows snow away to the coast. In some regions such as middle Baikal the wind can completely clear large extents of ice, polishing it to perfection with drifting snowflakes. People sometimes refer to Lake Baikal as a giant skating rink, and the ice is one of the main tourist attractions in winter. Ice cover on Lake Baikal thus presents a heterogeneous pattern changing in space and in time. To analyse ice cover changes, we have used various sources of remote sensing data in different parts of the electromagnetic spectrum. Before presenting them in detail, we would like to broadly describe the main groups of data and what kind of signal satellites receive from different types of ice and snow and from open water (Table  1 ).
Satellite imagery To analyse day-to-day changes in ice cover on large spatial scales, we have used MODIS imagery (Moderate Resolution Imaging Spectroradiometer, on board Terra and Aqua satellites) that has 250 m spatial resolution in the visible range. Both Terra and Aqua provide daily images covering the whole of Lake Baikal. More detailed analysis was done using high-resolution data from the Landsat 8 and Sentinel-2 satellites. Landsat 8 OLI (Operational Land Imager) has 15 m spatial resolution in panchromatic and 30 m in the visible, near (NIR) and short-wave (SWIR) infrared ranges; the satellite has a 16 d repeat cycle. We have also used images from Landsat 8 TIRS (Thermal Infrared Sensor) instrument that has 100 m spatial resolution. These images in thermal infrared (TIR) show not reflectance but emission and are closely linked to the temperature of a surface.
Radar altimetry Additional information was obtained by satellite radar altimetry. While the main mission of radar altimeters is the monitoring of water level over the ocean or large water bodies, the return signal also provides valuable information on the state of ice-covered or open water surfaces. We have used data from the Jason-3 satellite, track 79 passing across Kultuk Bay (see Fig. 1 ), cycles 141-157 (9 December 2019-16 May 2020) for the analysis. We used data from the nadirlooking radar altimeter operating in the Ku band (13. 6 GHz), with the backscatter parameter processed with the ice retracker. The backscatter coefficient is the ratio of the power reflected from the surface to the incident power emitted by the onboard radar altimeter, expressed in decibels (dB). A rough water surface typically has a low backscatter coefficient, while over ice cover it is high.
11 ). Actually the backscatter may have been even higher but was cut out by the ice retracker algorithm at 58 dB. The detached ice floe presented in the northern part of the track in Fig. 11  apparently drifted away between 11:57 (time of Landsat image) and 20 h (time of Jason-3 observation). The consolidated ice edge remained the same and is resolved by Jason-3 data with high spatial accuracy (290 m). This ability of radar altimetry missions for robust discrimination between open water and ice was noted in our work for T/P-Jason, GFO and ENVISAT-AltiKa series over various Eurasian lakes and rivers  (Kouraev et al. , 2007 (Kouraev et al. , , 2008 (Kouraev et al. , , 2015;; Zakharova et al. , 2021). However one may also note the spatial variability of backscatter over consolidated ice, such as the decrease in signal over a white (potentially rough and hummocked) ice field (Fig.
This helps to focus field research, as well as to find new eddies. Satellite monitoring provides statistics on the locations, lifetime and behaviour of ice rings and lens-like eddies. Further research with the use of multi-satellite imagery, in situ measurements, and numerical and laboratory modelling will bring further information on eddies under ice, their influence on ice cover development, their role in horizontal and vertical heat and mass exchange, and their impact on chemistry and biology of the lakes and on human activity. Financial support. This research has been supported by the CNES TOSCA LakeIce, LAKEDDIES and TRISHNA, ESA CCI+ Lakes, and CNRS-Russia IRN TTS projects. Andrey G. Kostianoy was partially supported in the framework of the P. P. Shirshov Institute of Oceanology RAS (Russian Academy of Sciences) budgetary financing (project N 0128-2021-0002).
Acknowledgements. We would like to thank  Alexander I. Beketov  ( Ust-Barguzin, Russia ) for helpful discussions on ice metamorphism and  Igor G. Polikarpov  ( KISR, Kuwait ) for remarks on the manuscript. We acknowledge the use of imagery from the  NASA  Worldview application (https://worldview. earthdata. nasa. gov,last access: 20 September 2021), part of the NASA Earth Observing System Data and Information System (EOSDIS). Sentinel-1 and Sentinel-2 images are from Modified Copernicus Sentinel data [2019-2020]/Sentinel Hub. The Landsat 8 image is courtesy of the  U. S. Geological Survey. Jason-3 data were extracted from the geophysical research data records (GDR) distributed by AVISO+ data portal (https://www. aviso. altimetry. fr/es/data/data-access. html, last access: 20 September 2021).
Code availability. Satellite imagery has been processed by the ESA SNAP software, which is publicly available (https://step. esa. int/ main/download/snap-download/, ESA, 2021). Data availability. All satellite data used in this study are publicly available, except PlanetScope. However, scientific access schemes to the PlanetScope data exist (https://www. planet. com/markets/ education-and-research/, Planet Team, 2021).
Fungal lectins are a large family of carbohydrate-binding proteins with no enzymatic activity. They play fundamental biological roles in the interactions of fungi with their environment and are found in many different species across the fungal kingdom. In particular, their contribution to defense against feeders has been emphasized, and when secreted, lectins may be involved in the recognition of bacteria, fungal competitors and specific host plants. Carbohydrate specificities and quaternary structures vary widely, but evidence for an evolutionary relationship within the different classes of fungal lectins is supported by a high degree of amino acid sequence identity. The UniLectin3D database contains 194 fungal lectin 3D structures, of which 129 are characterized with a carbohydrate ligand.
In the genomic era, a very common discovery strategy entails in silico sequence analysis. However, information extraction from protein and genome sequence databases is significantly hampered by the poor biochemical and functional knowledge of fungal lectins, resulting in low-quality annotation. Furthermore, 3D structural data are still scarce and do not reflect the variety described, for example in a recent review reporting 26 fungal lectins displaying 10 different folds in 100 crystal structures  [23]. Using the hierarchical 3D-fold based classification of the curated UniLectin3D database (www. unilectin. eu/ unilectin3D)  [24] , conserved sequence motifs were identified in each of the 109 lectin classes. The classification defined in UniLectin3D applies to collected lectins across all living kingdoms.
The conserved motifs are characteristic profiles that can be used to map the lectin repertoire, called lectome, of a given species. With this approach, the lectin repertoire of thousands of eukaryotic, bacterial and archaebacterial species was generated and stored in LectomeXplore, another module of the UniLectin platform  [25]. In the present study, the objective was to adopt this strategy with a special focus on Fungi and to accurately predict and classify lectins. To that end, we used the UniLectin3D-based motifs to screen fungal genomes available at the Joint Genome Institute (JGI) MycoCosm genomics portal (http://jgi. doe. gov/fungi) [26]. This database collects genomes representative of most fungal lifestyles and supports the integration, analysis and dissemination of fungal genome sequences as well as other "omics" data through interactive web-based tools  [26].
In the 2020 release of this database used in the present study, approximately 1419 genomes of 1223 species were stored. Sequence processing resulted in the identification of 35,460 lectin domains in 33,485 putative lectins. These predictions can be searched and analyzed in the MycoLec module of UniLectin (https://www. unilectin. eu/mycolec/). The potential impact of the mode of nutrition and lifestyles on the lectin gene repertoire was investigated in the fungal species inhabiting forest ecosystems. A large part of the fungal species whose genome is available at the JGI MycoCosm and NCBI databases populates these terrestrial ecosystems. They include specialized saprotrophs (litter/soil decayer, wood decayer, etc. ) and symbiotrophs (e. g. , ectomycorrhizal fungi), with most species belonging to the Agaricomycetes class.
Fungal Genomes The 1223 fungal proteomes of the 2020 release of the MycoCosm platform (mycocosm. jgi. doe. gov) [26, 27]  were downloaded. The full taxonomy of each corresponding fungal strain was retrieved using the ncbitax2lin software tool (github. com/zyxue/ncbitax2 lin). When ncbitax2lin did not identify the full taxonomy of the fungal strains, taxonomy was assigned manually with the support of JGI (taxonomy. jgi-psf. org) and UniProt (www. uniprot. org/taxonomy). The ecological guild of all fungal strains was automatically annotated using FUNGuild  [28] , and expert ecological annotation was performed for the 125 Agaricomycetes strains available at the time of this study.
Identification and Scoring of Candidate Fungal Lectins For each class, the lectin 3D domain sequences from which histidine tags were removed were aligned with MUSCLE  [29]. The obtained alignments were used to generate a Hidden Markov Model (HMM) for each domain. HMMSEARCH  [30]  was then run to identify lectins in the dataset, with a e-value threshold at 10 -2. Results were uploaded in the MycoLec database to provide direct web access to the identified lectin candidates. Lectins were further filtered by a score, reflecting sequence similarity between the reference lectin domain consensus sequence and the predicted lectin domain sequence  [25]. This score was computed using the amino acid sequence alignment generated by HMMER during the search. At each position of the alignment, a cumulative counter was incremented by 1 if amino acids were identical, else by a normalized BLOSUM62 substitution score.
Transcriptomics Differentially expressed genes of 14 fungal-plant interactions leading to mycorrhizae formation were collected from the literature (detailed in Table  S1 ). In this published work, the authors compared the expression of fungal genes between established mycorrhizae stage with a compatible plant and free-living mycelia (control condition) in vitro  [28, [31] [32] [33]. In all studies, genes were considered as differentially expressed during mycorrhization compared with control when a log2 fold change above 2 or below -2 and an adjusted p-value below 0. 05 were observed. Among the fungal strains, three are ericoid mycorrhizae (ERM), two are orchids mycorrhizae (ORM) and the remaining are ectomycorrhizae (ECM) (Table  S1 ). All 14 fungal strains pertained to our genomic dataset.
Structural Classification of Fungal Lectins in UniLectin3D The 2021 version of the Unilectin3D database contains 549 distinct lectin sequences and 2278 lectin 3D structures, including 1456 with their interacting ligands  [24]. Among these lectins, 194 structures belong to fungal species. The known 3D structures of fungal lectins are partitioned in 12 different folds (for a total of 35 in the database), expanding into 20 classes (for a total of 109 in the database) (Figure  1  and Figure  S1 ). Except for the ß-prism III fold, identified fungal lectin folds are shared with other organisms. The most abundant lectin 3D structures in the surveyed fungi are the ß-sandwich/Con-A-like fold (24%), the ß-propeller fold (18%) and the ß-trefoil fold (17%). When looking at classes, some are very specific of fungi, for example fungal fruit body lectins.
The most represented classes include galectin-like (18%), PA14 yeast adhesin (15%), AAL-like (12%), fungal fruit body lectin (11%) and Boletus and Laetiporus ß-trefoil lectin (7%) (Figure  1 ). While lectins are often forming multivalent protein complexes through monomer oligomerization, several fungal lectins generate multivalent binding sites through internal repeats, as in the case of ß-trefoils and ß-propellers  [34]. J. Fungi 2021, 7, x FOR PEER REVIEW 4 of 13 mycorrhizae (ERM), two are orchids mycorrhizae (ORM) and the remaining are ectomycorrhizae (ECM) (Table  S1 ). All 14 fungal strains pertained to our genomic dataset.
Structural Classification of Fungal Lectins in UniLectin3D The 2021 version of the Unilectin3D database contains 549 distinct lectin sequences and 2278 lectin 3D structures, including 1456 with their interacting ligands  [24]. Among these lectins, 194 structures belong to fungal species. The known 3D structures of fungal lectins are partitioned in 12 different folds (for a total of 35 in the database), expanding into 20 classes (for a total of 109 in the database) (Figures 1 and Figure  S1 ). Except for the ß-prism III fold, identified fungal lectin folds are shared with other organisms. The most abundant lectin 3D structures in the surveyed fungi are the ß-sandwich/Con-A-like fold (24%), the ß-propeller fold (18%) and the ß-trefoil fold (17%). When looking at classes, some are very specific of fungi, for example fungal fruit body lectins.