dataesr/scientific-paragraphs-categorization huggingface dataset:
The clustering or grouping suggests that sequons with the same amino acid at the -1 position exhibited similar conformations or low-energy states. Similar grouping was not observed for sequons with the same residue at the +1 position (Figure  3B ). This high-level analysis of low-energy conformations for the entire dataset suggested that the -1 position plays a dominant role in determining the low-energy conformation(s) exhibited by a sequon and that the +1 position contributed in a secondary capacity. In the following sections, we present hypotheses to explain how each position (-1, +1) contributed in a characteristic manner to determine the low-energy conformations exhibited by a sequon and how these conformations, in turn, related to experimentally determined specificities. We characterized the low-energy conformations by a selected set of relevant features.
To compare our predictions with experiments, we employed logistic regression and, unless otherwise indicated, we labeled all sequons with experimental glycosylation efficiencies greater than 10% (efficiency threshold) as glycosylatable and those with efficiencies less than 10% as un-glycosylatable, in line with previous work  34. In Table  1  (and SI Table  1 ), for a chosen value of efficiency threshold, we have tabulated the area-under the curve (AUC) of the receiver operating curve (ROC). Since the dataset of glycosylation efficiency measurements is highly imbalanced with only 46/361 (12. 7%) glycosylatable sequons (positive samples) and 315/361 ( 87. 3%) unglycosylatable sequons (negative samples), in Table  1  (and SI Table  2 ), for each metric, we also report the true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) and the balanced accuracy ( BA = ( TPR + TNR ) / 2, where the true positive rate TPR = TP / ( TP + FN) and the true negative rate TNR = TN / (TN + FP) and false positive rate ( FPR = FP / ( FP + TN ) ).
Note that a naïve classifier that classifies all 361 sequons as unglycosylatable has an accuracy (ACC = ( TP + TN ) / (TP + TN + FP + FN) ) of 87. 3% (= 315/361), a BA of 50% (= (0/46 + 315/315)/2) and an FPR of 0%. Since accuracy (as opposed to balanced accuracy) higher than such a naïve classifier may come at the expense of FPs, we report balanced accuracy instead of accuracy for our dataset.
Methods Starting structure for enzyme-peptide complex The primary starting structure of the enzyme-peptide complex was obtained from the crystal structure of the active conformation of GalNAcT2 from the crystal structure of the complex (pdb id: 2ffu). Since the sugar is absent from that structure, we used a second GalNAcT2 structure (pdb id: 4d0z) with bound peptide (mEA2), manganese and UDP-GalNAc-5S. While the sugar bound to UDP in 4d0z has a modification (sulfur instead of oxygen in the ring), it aligns exactly with 2ffu with the additional sugar (SI 12). To generate the starting structure for each sequon, we used the crystal structure of the complex replacing the mEA2 peptide peptide with A-2X-1T0X+1A+2P+3R+4C+5, where X is any amino acid residue except cysteine. Residues at positions -1 and +1 (denoted by Xs) were mutated to the target sequon for all 361 sequons studied in the work by Kightlinger et al.
We generated 2000 decoys per sequon. Larger number of decoys (8000) were not found to alter the results. This protocol is available in the Rosetta suite. See Supplementary Information for the complete list of steps to run the protocol. The protocol is run from command-line as follows: >mucintypeglycosylation. <system><compiler><mode> @flags System=linux; compiler=gcc; mode=release Where "flags" is a plain text file and contains the following options: -in:file:s <input pdb file>.
Specificity prediction with MFPred We used MFPred as described:  34  1) The starting structure was relaxed. 2) The lowest energy decoy from relax step was used as the starting structure for the FastRelax protocol for each sequon. 3) The lowest energy decoy for each sequon from the FastRelax protocol was processed by the GenMeanFieldMover. All calculations were performed with Rosetta version [details in Supplementary Information; www. rosettacommons. org/software]. 42.
Our work benefitted from the availability of crystal structures for the peptide-enzyme complex but may be less accurate in the absence of crystal structures. Our approach also suffered from inaccuracies in the Rosetta energy function, the limitations of MCM sampling, and the use of implicit solvation models to name a few. We further note that an MD-based simulation, though computationally prohibitive for a large dataset, may be better suited for generating thermodynamically accurate ensembles and for characterizing the density of multiple stable states. We investigated a range of features to predict the glycosylation efficiency of GalNAcT-2 (Table  1 ) and found that the features dHB and RMSDpeptide are able to recapitulate binarized glycosylation specificity with a balanced accuracy of 88.
Methods for biomonitoring of freshwater phytoplankton are evolving rapidly with eDNA-based methods, offering great complementarity with microscopy. Metabarcoding approaches have been more commonly used over the last years, with a continuous increase in the amount of data generated. Depending on the researchers and the way they assigned barcodes to species (bioinformatic pipelines and molecular reference databases), the taxonomic assignment obtained for HTS DNA reads might vary. This is also true for traditional taxonomic studies by microscopy with regular adjustments of the classification and taxonomy. For those reasons (leading to non-homogeneous taxonomies), gap-analyses and comparisons between studies become even more challenging and the curation processes to find potential consensus names are time-consuming.
Here, we present a web-based application (Phytool), developed with ShinyApp (Rstudio), that aims to make the harmonisation of taxonomy easier and in a more efficient way, using a complete and up-to-date taxonomy reference database for freshwater microalgae. Phytool allows users to homogenise and update freshwater phytoplankton taxonomical names from sequence files and data tables directly uploaded in the application. It also gathers barcodes from curated references in a user-friendly way in which it is possible to search for specific organisms. All the data provided are downloadable with the possibility to apply filters in order to select only the required taxa and fields (e. g. specific taxonomic ranks). The main goal is to make accessible to a broad range of users the connection between microscopy and molecular biology and taxonomy through different ready-to-use functions.
To assess and identify microalgae, both techniques have their advantages and pitfalls and this point will not be discussed in this paper. However, the paper will describe the current Shin-yApp, "Phytool", which has been created with the goal to connect both approaches by making easier comparisons of data resulting from those two methods. The rapid increase in the use of molecular techniques comes along with an amount of new DNA sequences (i. e. DNA barcodes) that are generally made available online (e. g. National Center for Biotechnology Information, NCBI https://www. ncbi. nlm. nih. gov/) in libraries (e. g. GenBank,  Sayers et al. 2019). Only some of these DNA sequences benefit from expert taxonomical curation and can be found in curated reference libraries of DNA barcodes. In the case of phytoplankton species, some curated reference libraries are available as for example PhytoRef (del  Campo et al.
That is why taxonomic homogenisation is required to perform comparisons between data coming from molecular techniques and microscopy, but also to be able to compare the occurrences of taxa through the different existing DNA barcode libraries. The proposed application, Phytool, is an innovative tool that enables users to homogenise taxonomic names collected from different types of files: DNA sequences in FASTA format (fulfilling some conditions, see §2. 2. 1 in Results section) for molecular biologists and simple dataframes for microscopists or taxonomists. Phytool uses the up-to-date taxonomy of freshwater microalgae as proposed in Phytobs  (Laplace-Treyture et al. 2017) , software designed to help people who make microscopic counts of phytoplankton for freshwater in the framework of lake or rivers monitoring.
The Phytobs taxonomy is based on AlgaeBase and the most recent publications, with last update being made in May 2021. AlgaeBase is considered to provide the most complete and up-to-date taxonomy available for microalgae and was, thus, chosen as reference for the taxonomical homogenisation process. Phytool gathers different DNA barcodes, namely for the first release: rRNA16S; rRNA18S; rRNA23S. These barcodes, available for freshwater microalgae, were gathered from the curated databases cited above. The selection of these genetic markers has been established, based on investigations (e. g. literature review, reference libraries completion) and in silico tests made in the framework of a project founded by the OFB (Office Français de la Biodiversité). An in-depth investigation was done on different genetic markers to test their ability to target easily (i.
II. 2. Taxonomic homogenisation process Within Phytool application, the tab "Homogenise taxonomy" allows users to upload files from the computer to homogenise and update the taxonomical names included in them. The input files can be FASTA files with DNA sequences (. fasta only) or data tables (. txt;. csv); more details about the specificities for each file types are provided in the corresponding section dealing with Phytool functionalities (see Results section). The reference used for the taxonomic homogenisation process is the data table displayed at the main page of the application ("Taxonomic browser" tab). Briefly, the process works as follows: in the uploaded files, the R algorithm looks for the pattern corresponding to both genus and species names in each row of the file. If the pattern is present in the reference database, then the ascendant taxonomy is changed (if the taxonomic rank is different from the one in input file) or added (if absent in the input file) with the one matching in the reference list.
II. 3. Phytool barcode library: data origin and curation process To date, the molecular data added in Phytool v. 1. 0 come from curated reference barcoding libraries only. These are represented in Table  1. After being downloaded from the web, the collected sequences (FASTA format) were re-arranged (on Linux terminal) in order to be comparable (identical FASTA format with same taxonomical ranks). A curation process (sche-  matic shown in Figure  2 ) has been applied for each database to avoid conflicts (i. e. different sequences associated to one species) and redundant taxa (i. e. a taxon with several identical sequences). For the same reason, if the barcode was covered by several different databases, the curation process was then applied to the full database (merging of the different libraries used for the target barcode).
III. 1. General overview Phytool application is available online at the following address https://caninuzzo. shinyapps. io/phytool_v1/. It allows free access with a user-friendly interface (the functioning is explained more in details ahead). The number of taxa per phyla and per barcode (16S, 18S, 23S) gathered in Phytool are summed up in Figure  3 ; the number of taxa per phyla available in Phytobs are also given. As a reminder, the number of sequences available in Phytool barcode libraries results from the curation process (detailed previously in "Methodology" section). Table  2  gives a summary of the number of sequences kept after this curation process. All the numbers supplied here (Table 2; Figure  3 ; etc. ) are specific to the first version of Phytool and, thus, likely to change through next updates of the application.
As shown by the pie chart (Figure  3 ), many taxa present in Phytobs suffer from a lack of barcode representation. This is particularly true, for instance, for the Bacillariophyta phylum in which more than 2000 taxa are registered in Phytobs and only 302 taxa have a 16S barcode (1137 and 36 for 18S and 23S, respectively). Another thing to notice is the huge proportion of barcodes that cannot be assigned at species level (i. e. unidentified species "sp. ") as shown in the stacked barplot (Figure  3 ). This explains that only about 25% of the species contained in Phytobs have an associated barcode.
III. 2. 1. Taxonomic browser The "Taxonomy browser" tab enables the display and download of the different species registered in Phytool and to check if DNA barcodes are available in reference  barcode libraries. An interactive table enables users to choose amongst different fields: the taxonomic ranks of the species, their potential synonym (i. e. potential other species name that is no longer accepted and refer to the current accepted 'Genus_species') and the different barcodes implemented in Phytool (SSU16S; SSU18S and LSU23S). Ticking a checkbox on the left panel will display the associated column on the table; it is also possible to select rows by clicking directly on them within the table (click again to remove selection). The different fields are searchable in order to target species or lineages easily; finding a pattern within the complete table is also possible through search input at the top-right of the table.
III. 2. 3. Barcode reference libraries The "Barcode libraries" tab displays the three different barcode reference libraries with the barcodes gathered in Phytool, which are (as a reminder) prokaryotic and eukaryotic small subunit ribosomal (16S and 18S, respectively) and prokaryotic large subunit ribosomal (23S). After selecting one of the three barcode reference libraries, the functioning of the interactive table is similar to the "Taxonomy browser". Amongst the different selectable (and searchable) fields provided, the original barcode reference library, in which the sequence was found, is available, as well as its original id number. The different taxonomic ranks, homogenised with Phytool, the potential synonyms and the size of the sequences (in base pairs) are provided. Users can choose to download the complete database or just a selection.
The next release will mainly focus on enriching the barcode reference libraries by manual curation of the sequences rejected in this first release of Phytool from reference libraries. New barcodes will be implemented on Phytool in the future and these will also be deposited to the NCBI library. The project in which the current application was developed, focuses on the development of eDNA tools applicable for phytoplankton biomonitoring. We, therefore, selected specific barcodes within the two marker genes (rRNA16S and rRNA23S) allowing us to target the entire freshwater phytoplankton community. These barcodes will thus be enriched in the next releases of the application. Users who want to contribute in the enrichment process (for the same barcodes or other ones) are welcome to participate.
Monitoring sea-level changes The monitoring of sea-level changes in Venice relies on both in situ data acquired by tide gauges (Sect. 2. 1) and remote sensing observations provided by satellite radar altimetry (Sect. 2. 2). Tide gauges record sea-level heights with reference to a permanent benchmark on land. Therefore, they provide measurements of RSL embedding the effects of vertical land motion (Sect. 3). Tide-gauge datasets consist of local, long-term measurements acquired at high frequency and accuracy  (Zerbini et al. , 2017). Satellite radar altimetry, in contrast, measures GSL changes  (Fu and Cazenave, 2001; Stammer and Cazenave, 2017). These are independent of variations in the local land level changes  (Gregory et al. ,   2019) and hence unaffected by the potentially key component of RSL  (Wöppelmann and Marcos, 2016; Gregory et al.
it/it/content/centro-previsioni-e-segnalazioni-maree, last access: 5 August 2021). Further details on the tide gauges installed in the Venice Lagoon up to the early 20th century are reported by  Magrini and Gnesotto (1908). Dorigo (1961)  reviews the sea-level observations in Venice and summarizes the main development stages of the observational network in the Venice Lagoon, including lists of active and decommissioned tide-gauge stations. Battistin and Canestrelli (2006)  provide the most recent review of tide-gauge data for Venice and collect qualitychecked published and unpublished records of high and low waters since 1872. Linking the data from the various tide gauges to provide one continuous dataset of long-term sea-level change requires an accurate knowledge of the corresponding reference levels (or datums) on land.
For example, the Jason-3 mission that continues the climatic sea-level record started in 1993 with TOPEX/Poseidon has only four tracks crossing the Adriatic Sea and does not cover the Venice area. To improve the monitoring, it is possible to take advantage of the data collected by various radar altimeters flying at the same time. However, this requires a characterization of the inter-mission biases and the development of suitable interpolation schemes of the independent ground tracks. Multimission datasets are typically distributed over regular grids. An additional potential limiting factor for the Venice area is the degradation of the technique performance towards the   Battistin and Canestrelli, 2006). coast resulting from the contaminating presence of land in the satellite footprint and from the enhanced inhomogeneity of the local ocean surface.
Limitations and possible perspectives of coastal altimetry in the Adriatic Sea have been discussed in several studies since the late 1990s  (Cipollini et al. , 2008; Fenoglio-Marc et al. , 2012; Vignudelli, 1997; Vignudelli et al. , 2011 Vignudelli et al. , , 2019a)). This has motivated further investigations  (Cipollini et al. , 2013; Passaro et al. , 2014)  based on the latest coastal altimetry datasets (e. g. , CTOH; see  Birol et al. , 2017)  and/or reprocessing initiatives (e. g. , ALES; see  Passaro et al. , 2014). In the northern Adriatic, these studies analyzed data around Venice and Trieste, including their validation against tide-gauge measurements. The results show that a reasonable increase in quantity and quality of data can be achieved compared to standard products up to a few kilometers from the coastline.
, 2018). The SLCCI product, distributed over a homogenous grid of 0. 25 • , contains data close to the coast, e. g. , 10 km near Trieste, and was used for the assessment of coastal sea-level trends  (Rocco, 2015; Vignudelli et al. , 2019b). The sufficient maturity of the algorithms and processing in coastal altimetry offered the opportunity to extend the SLCCI product to the coastal zone. During the bridging phase in 2018 a new product with an along-track spacing of about 350 m for estimating GSL trends was developed in selected regions, including the Adriatic Sea. The experimental dataset only covered the period from July 2002 to June 2016 and the Jason-1 and Jason-2 missions. It combines the postprocessing strategy of X-TRACK  (Birol et al. , 2017)  and the advantage of the ALES re-tracker  (Passaro et al.
Natural land movements The Venice area is naturally subsiding. This process is characterized by a long-term component controlled by tectonics and geodynamics and sedimentation, active on time spans of about 10 6 -10 4 years, and a short-term component controlled by glaciation cycles and due to GIA processes acting on periods of 10 3 -10 4 years  (Antonioli et al. , 2017; Cuffaro et al. , 2010; Stocchi et al. , 2005). Depending on the time interval considered, different datasets are available for investigating the rate of vertical land movement. Subsidence rates up to 2 Myr ago can be inferred from the thickness of the different layers of Quaternary sediments. Over this time frame, sedimentation rates are equivalent to subsidence rates since the entire sedimentary sequence was deposited in shallow marine to continental environments  (Massari et al.
Late Pleistocene and Holocene After the Last Glacial Maximum several lagoons developed along the Adriatic Sea, formed by the rivers flowing into the sea. Only two of them, the Grado and Venice lagoons, still exist today, while the rest have been infilled by sedimentation  (Tambroni and Seminara, 2006). Recent stratigraphic information about Holocene sea levels (2-6 cal kyr BP) was obtained from lagoonal deposits found in boreholes between the Tagliamento River and the city of Venice. Other data were derived from archeological markers reported in the abundant literature available for the Venice Lagoon and its mainland  (Antonioli et al. , 2009; Fontana et al. , 2017; Lambeck et al. , 2011). The shell base of the lagoon indicates a subsidence rate over the last 7. 3 kyr of 1. 6 ± 0. 3 mm/year (average of ratios of values in columns H and G for sites 26 and 28 in Table  1  of  Antonioli et al.
, 2004) , the GIA predictions for the northern Adriatic had a larger uncertainty, with a range of RSL rise between ∼5 and ∼2 m in the last 5000 years. This shows that GIA models are constantly being updated due to improvements in the constraining RSL datasets and in modeling techniques. Based on the work quoted above, the rate of long-term RSL change in Venice due to the melting of the Late Pleistocene ice sheets does not appear to be tightly constrained  (Tosi et al. , 2013). Further uncertainties arise from the effects of the melting of the Würm Alpine ice sheet, whose chronology remains uncertain regarding several aspects  (Spada et al. , 2009). Nonetheless, the long-term rate of Venetian RSL change due to GIA can be assessed in the range between -0. 2 and +0. 5 mm/year based on the published works cited above.
Several altimetric datasets have been used to estimate sea-level trends in the Venice area. Fenoglio-Marc et al. (  2012 ) estimated a trend of 5. 9 ± 1. 4 mm/year over the period 1993-2008 for an along-track point about 80 km away from Venice (see their Table  2 ). Rocco (2015)  obtained trends of 4. 18 ± 0. 92 mm/year (period 1993-2014) and 3. 40 ± 0. 99 mm/year (period 1993-2013) for the closest grid point to the Venice tide gauge in the AVISO and SLCCI V1 products, respectively, with both estimates consistent with each other within errors. A reprocessing of the SLCCI V2 dataset over the period 1993-2015 yielded a trend of 4. 25 ± 1. 25 mm/year  (Vignudelli et al. , 2019b) , further reduced to 4. 03 ± 1. 27 mm/year after removing the seasonal signal  (ESA CCI, 2019). Explanations for the differences between trend estimates in these studies include the different time spans, especially for Fenoglio-Marc et al.
8a, b ). The analysis is performed on data from version 3 of the NOAA-CIRES-DO Twentieth Century Reanalysis, which contains objectively analyzed fourdimensional weather maps and their uncertainty from the early 19th century to the 21st century  (Slivinski et al. , 2019). For each considered season, the years were identified using the 10th and 90th percentiles of the seasonal sea-level time series as thresholds. Results indicate that in both autumn and winter relatively high sea level in Venice is associated with anomalously low sea-level pressure over the Mediterranean region, which intensifies over the northern portion of the basin, and with extensively significant northeastward wind anomalies over the Ionian Sea -corresponding to blowing meridional sirocco wind -as well as with an eastward wind anomaly over the Strait of Gibraltar.