dataesr/scientific-paragraphs-categorization huggingface dataset:
Position-Specific Amino Acid Residue Frequency Calculation Complete protein sequences were retrieved from NIH source data openly available (NCBI SARS-CoV-2 variant data packages; SARS-CoV-2 Resources-NCBI (https://www. ncbi. nlm. nih. gov/, accessed on 3 May 2022) and NCBI Virus; NCBI Virus (https://www. ncbi. nlm. nih. gov/, accessed on 3 May 2022) in the fasta format for different SARS-CoV-2 variants. Sequences for the whole proteome from each of the variants were extracted, followed by separating the reading frames into Orf3a and E protein. Once the respective protein sequences for each variant were identified, we used the median length of the sequences as a selection filter. Any reported sequence lesser and greater than the median of the length of all proteins were discarded (incomplete or truncated sequences were hence excluded from the analysis).
Homology Modelling of the Orf3a Protein and E Protein The structure of the Orf3a protein of SARS-CoV-2 has been elucidated using cryo-EM microscopy and determined as a dimeric protein (PDB id: 7KJR)  [29]. This structure has been used as a template for structure-based homology modelling of variant-specific Orf3a structures. Pentameric E protein structure has been elucidated using solid-state NMR from SARS-CoV-2 (PDB-id: 7K3G)  [26]  which has been templated to generate refined structural models for further analysis. All the homology modelling procedures have been performed using MODELLER  [46]  and SWISS-MODEL  [47]. Model refinement and further structural fine-tuning of unreliable structural regions were performed using the GalaxyWeB server  [48]. The structures obtained were validated by scores obtained from the MolProbity  [49, 50].
148), and hence could not be assigned as a stable mutation (Figure  2D ). In order to ascertain the stability of mutations observed, we calculated the consensus sequences for Orf3a and E protein for each variant. Additionally, a similar count matrix generated for E protein intra-variant position-specific frequency also showed lower frequencies in certain positions compared to a stable score of 1 (Figure  2E ). The fluctuation in position-specific frequencies was quite low (range: 0-0. 064), much lower than its Orf3a counterpart (Figure  2F ). The relative probability of observing a mutational event in the specific positions is highlighted by their frequencies. This estimation is performed on a large number of sequence datasets and provides a framework of statistical estimates of the mutations observed in a position-specific manner.
Structural Modelling and Analysis of Representative Mutations in Orf3a across SARS-CoV-2 Variants In correlation to previous results, we have identified representative mutations across the Orf3a sequence specific for different SARS-CoV-2 variants (Figure  3A ). All the variants except the Alpha have one or more random mutations. The structure of Orf3a dimer has been elucidated using Cryo-EM at 2. 1 angstrom, which spans from 40th to 236th amino acid, in complex with human apolipoprotein A (PDB id: 7KJR). The dimeric structure of Orf3a was extracted from the pdb file which was used as the reference (Figure  3B ) for homology modelling of the representative Orf3a structures across SARS-CoV-2 variants (Supplementary Figure  S2 ). The refined Orf3a structures of the variants with the best parameters, i.
Molecular Dynamics Simulation of the TM Region of Orf3a (40-125) from SARS-CoV-2 in Endosomal Membrane Mimicking System The SARS-CoV-2 Orf3a dimeric protein structure has been determined using cryo-EM at 2. 0 angstroms resolution (PDB id: 7KJR)  [29]. The transmembrane region of the protein ranges from 40 to 125th residue of the protein. We performed membrane insertion of the truncated structure of Orf3a (40-125) using CHARMM-GUI. It comprises lipid components mimicking the human endosomal membrane (Table  1 ). Then a molecular dynamics simulation of 5 ns was performed using NAMD to understand the channelling activity of the upper and lower channels of the protein (Supplementary Movie S1). We observed the presence of the upper channel in the protein-membrane system (Figure  4A  black dotted circle).
Molecular Dynamics of the TM Region of SARS-CoV-2 E Protein in an ERGIC Membrane Mimic Shows Water Movement The pentameric E protein from the SARS-CoV-2 has been modelled taking its solidstate NMR structure as the template (PDB id: 7K3G)  [26] , whose viroporin activity through the single channel formed by its TM region  has been analysed in a previous study  [13]  (Figure  5A ). The protein-membrane complex was equilibrated energetically followed by 5 ns of MD simulation to analyse the continuous water channel formation through the proposed pore (Supplementary Movie S2). The water dynamics was observed at several intervals for 2 ns from the initial timestep starting at 0 ns (Figure  5B. We observed that from 1 ns to 1. 5 ns, the water molecules reach the proposed F26 bottleneck region  [13]  inside the pore of the E protein (Figure  5B (iii-viii)).
SARS-CoV-2 has acquired several mutation hotspots in the spike (S) protein across all its variants  [78, 79]  which contributes to the increased pathogenicity of the variants  [17]. However, there are several other important viral proteins that play decisive roles in the viral life cycle and have remained conserved across the variants. We performed large-scale sequence data analysis for the ion channelling viral proteins of SARS-CoV-2: Orf3a and envelope protein, where we calculated the position-specific frequency of each amino acid from sequences reported in the NCBI datasets for these two proteins. The positions harbouring the mutations reported from the analysis were either identical to the residue in the corresponding position of the reference sequence with lower frequency, or different residue with higher frequency.
These changes are random mutations and cannot be concluded as significant, as the sample space of the number of sequences was quite low in some of the variants. The variants with the highest number of sequences (Alpha and BA lineage of Omicron-Figure  1B ) did not show any major mutations in the proteins. Nevertheless, we aligned the sequences of the variants and specified the random mutations in the ion channel forming region of the proteins. All the mutant structures were structurally aligned with the reference structure (PDB id: 7KJR) which showed no significant structural variability in the channel-forming region. Afterwards, we looked into the membrane permeation and channel-forming mechanism of the Orf3a and E protein from SARS-CoV-2. One of the most studied viroporins is the M2 channel of the influenza virus, which is essential for viral replication and homeostasis.
Time-Lapse Microscopy and Single-Cell Image Analysis Agarose pads  (Young et al. , 2011)  spotted with 5 µl of a 1:10 dilution of GBS cell cultures collected at mid-log phase of growth (OD600 0. 5 in THY medium) were flipped and transferred to an imaging dish sealed with parafilm. Timelapse imaging was performed using a Leica DMi8 widefield microscope, equipped with a 100× oil immersion objective (Leica HC PL Fluotar 100×/1. 32 OIL PH3), a Leica DFC9000 sCMOS camera and driven by Leica LASX software. Experiments were performed using an environmental microscope incubator set at 37 • C and bacteria were imaged in phase contrast, every 5-min and up to 6 h. Manual segmentation of individual cells and analysis of image stacks were performed using the ImageJ 1. 52a software, as previously described  (Manina et al.
RNA-Sequencing and Analysis RNA-Seq was performed on four independent biological replicates for each strain. rRNA was depleted using the QIAseq FastSelect -5S/16S/23S Kit (QIAGEN). RNA was sequenced using Illumina sequencing technology (BMR-Genomics, Padua). For RNA-Seq data analysis, raw reads were quality checked using FASTQC 1  and processed by Trimmomatic  (Bolger et al. , 2014)  to trim the adaptor sequences and remove low-quality reads. Clean reads were mapped onto the reference genome of Streptococcus agalactiae BM110 (Accession: NZ_LT714196. 1) using Bowtie2  (Langmead, 2010). To quantify the known transcripts, the alignment results were input into featureCounts  (Liao et al. , 2014). Lastly, the R package DESeq2  (Love et al. , 2014)  was used to test for differential expression. We defined genes as differentially expressed using the following criteria: | Log2 Fold Change| ≥ 1 and adjusted p-value FDR < 0.
Cell adhesion was quantified using HeLa and A549 immortalized cell lines. The WT and the codY mutant contain the empty vector pTCV P tet or the complementing vector pTCV P tet _codY (+ codY ). The percent of adhesion of each strain was calculated relative to the initial inoculum and was normalized to the adhesion of the WT strain, set as 100%. Represented data are the average ± SD of six independent experiments, each performed in duplicate. Statistically significant differences are indicated (One-way ANOVA). * * p < 0. 01, * * * * p < 0. 0001. model in which bacteria are inoculated intravenously. Under these conditions, the codY mutant displayed a considerably decreased ability to persist in the blood and to cause lethal encephalitis compared to WT bacteria (Figures  2D, F, H ).
CodY Is a Global Regulator of Gene Expression in Group B Streptococcus To determine the transcriptional changes associated with codY deletion, an RNA-Seq experiment was performed on WT and codY bacteria during exponential growth in rich THY medium, i. e. , under conditions of maximal CodY activity. A total of 277 genes (out of 2,128 analyzed genes) were differentially expressed at least twofold (adjusted p-value < 0. 05) in the codY strain, demonstrating a global regulatory role for CodY (Supplementary Datasets 1A-C). Among these, 256 genes were up-regulated (Supplementary Dataset 1B) and 21 genes were down-regulated (Supplementary Dataset 1C) in the mutant, supporting a role for CodY mainly as a repressor of gene expression (Figure  5A ). Overall, fold changes associated with negative regulation were higher than those associated with positive regulation.
Notably, 55% (140/256) of the overexpressed genes were located in four prophages. The 98 genes whose expression was affected by codY deletion at least fourfold (94 up-regulated and 4 down-regulated genes) (Supplementary Datasets 1D,E) could be classified into seventeen categories by the Cluster of Orthologous Genes (COGs) analysis (Supplementary Dataset 2 and Figure  5B ). Among these, the most represented groups included genes involved in "amino acid transport and metabolism, " "cell wall/membrane/envelope biogenesis, " and "mobilome: prophages, transposons. " Specifically, CodY-repressed genes (Supplementary Dataset 1D and Figure  5A ) included those encoding BCAAs transporters (braB, brnQ, all the genes belonging to the livK-G operon), the (oligo)peptide permease OppA1-F, adhesins, and serine peptidases anchored to the cell wall through the LPxTG motif, as well as proteins involved in DNA replication, recombination, and repair.
Green dots correspond to non-significant (adjusted p-value > 0. 05) differentially transcribed genes. (B) COGs associated to downregulated and upregulated genes. over-expressed in the absence of CodY. On the contrary, the gene encoding the CAMP factor pore-forming toxin Cfb was under positive regulation by CodY (Supplementary Dataset 1E and Figure  5A ). Using the FIMO Motif Search Tool  (Grant et al. , 2011) , the genome of BM110 was scanned to search for sequences matching the conserved AATTTTCWGAAATT CodY binding motif. One hundred and one matches were retrieved from the genomic regions located upstream of the coding sequences of the genes, using a p-value lower than 0. 0001. At least one of these sites was located in the proximity of the coding sequence of eighteen genes differentially expressed in the codY strain (Supplementary Datasets 1A-D and Supplementary Table  4 ), predicting that these genes might be targets of direct CodY-mediated regulation.
Three genes encoding BCAAs transporters and identified by RNA-Seq analysis (Supplementary Dataset 1D and Figure  5A ) and qRT-PCR (Supplementary Figure  3 ) as subjected to different levels of CodY-mediated repression during growth in rich THY medium were included in the analysis. In the WT strain, transcription of all three genes increased when BCAA were less abundant in the defined medium, in accord with expected decrease in CodY activity. All three target genes were further and significantly over-expressed in the codY mutant compared to the WT strain under both conditions tested, however, the extent of this overexpression was higher in livK (82-fold and 11-fold increase in CDM + 1500 µM and in CDM + 50 µM ILV, respectively) and lower in braB (3. 4-fold and 2. 3-fold increase in CDM + 1500 µM and in CDM + 50 µM ILV, respectively) and brnQ (4.
g. from the Gravity Recovery and Climate Experiment, GRACE;  Tapley et al. , 2019)  is equal to assessed changes of water mass (in solid, liquid, or gaseous state) outside the ocean, which are dominated by mass changes of land ice (glaciers and ice sheets) and water stored on land as liquid water or snow. Misclosure of these budgets indicates errors in the assessment of some of the components (including effects of undersampling) or contributions from unassessed elements in the budget. Clearly, as a prerequisite of progress in SLB assessments, datasets on the mentioned budget elements must be accessible. Over the course of its six assessment reports and its recent Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019), the Intergovernmental Panel on Climate Change (IPCC) has documented a significant improvement in our understanding of the sources and impacts of global sea-level rise.
Today, the SLB for the period since 1993 is often considered closed within uncertainties  (Church et al. , 2013; Oppenheimer et al. , 2019). Recent studies that reassessed the SLB over different time spans and using different datasets include the studies by  Rietbroek et al. (2016) ,  Chambers et al. (2017) ,  Dieng et al. (2017) ,  Chen et al. (2017 Chen et al. ( , 2020)) ,  Nerem et al. (2018) ,  Royston et al. (2020) ,  Vishwakarma et al. (2020) , and  Frederikse et al. (2020). In the context of the Grand Challenge of the World Climate Research Programme (WCRP) entitled "Regional Sea Level and Coastal Impacts", an effort involving the sealevel community worldwide  (WCRP Global Sea Level Budget Group, 2018)  assessed the various datasets used to estimate components of the SLB during the altimetry era  (1993 to present).
A large number of available quality datasets were used for each component, from which ensemble means for each component were derived for the budget assessment. Significant challenges remain. The IPCC SROCC reported the sum of assessed sea-level contributions for the  1993-2015 period (2006-2015 period)  to be 2. 76 mm yr -1 (3. 00 mm yr -1 , respectively), and this was 0. 40 mm yr -1 smaller (0. 58 mm yr -1 smaller) than the observed GMSL rise at 3. 16 mm yr -1 (3. 58 mm yr -1 )  (Oppenheimer et al. , 2019, Table 4. 1). While the misclosure was within the combined uncertainties of the sum of contributions and the observed GMSL, these uncertainties were large, with a 90 % confidence interval width of 0. 74 to 1. 1 mm yr -1. Determining the LWS contribution to sea level is a particular challenge (WCRP Global Sea Level Budget Group, 2018): hydrological models generally suggest LWS losses and therefore a positive contribution from LWS to GMSL rise  (Dieng et al.
, 2017; Scanlon et al. , 2018; Cáceres et al. , 2020). Initial GRACE-based estimates indicated a gain of LWS  (Reager et al. , 2016; Rietbroek et al. , 2016) , while newer GRACEbased estimates  (Kim et al. , 2019; Frederikse et al. , 2020)  agree with global hydrological modelling results on the sign of change (loss of LWS). Moreover, in view of the high interannual variability of LWS, the determined trend strongly depends on the selected time period and method of trend determination. Challenges of making SLB assessments include the question of consistency among the various involved datasets and their uncertainty characterisations. For example, the study by  WCRP Global Sea Level Budget Group (2018)  assessed each budget element from a large number of available datasets generated in different frameworks and used ensemble means of these datasets in the budget assessment.
It is specific to SLBC_cci, as well as complementary to the WCRP initiative, that SLBC_cci concentrated on datasets generated within CCI or by project members. The thorough insights into the genesis and uncertainty characteristics of the datasets facilitated progress towards working in a consistent framework of product specification, uncertainty characterisation, and SLB analysis. In this paper we present the methodological framework of the SLBC_cci budget assessments (Sect. 2). We describe the datasets used, including summaries of the methods of their generation and details on their uncertainty characterisation (Sect. 3). We report and discuss results of our OMB and SLB assessments (Sects. 4 to 7), address the data availability in Sect. 8, and conclude in Sect. 9 with an outlook on suggested work in the sequence of this initial CCI cross-ECV study.
The requirement to refer z(t) consistently to the mean over the 2006-2015 reference period entailed adaptations of the uncertainty characterisation for some of the elements. For each budget element, uncertainties of the linear trends were assessed by the project partners who contribute the datasets on the budget element. By accounting for temporal error correlations, the trend uncertainties are typically larger than the formal uncertainty that would arise from the leastsquares regression (Eq. 10). Our concept of treating the trend purely as a mathematical functional of the full time series through which uncertainties can be propagated implies that our evaluated uncertainties in trends arise only from uncertainties in z(t) and not from statistical fitting effects, such as any true nonlinear evolution of z(t) or sampling any assumed underlying trend from a short series of data.
Methods and product We use time series of GMSL anomalies derived from satellite altimetry observations. For the period January 1993-December 2015, the GMSL record is version 2. 0 of the ESA (European Space Agency) Sea Level CCI project (https: //climate. esa. int/en/projects/sea-level/, last access: 13 January 2022). The CCI sea-level record combines data from the TOPEX/Poseidon, Jason-1 and Jason-2, GFO (Geosat Follow-On), ERS-1 and ERS-2 (European Remote Sensing), Envisat, CryoSat-2, and SARAL (Satellite with ARgos and ALtiKa) ALtiKa (Ka-band altimeter) missions and is based on a new processing system  (Ablain et al. , 2015 (Ablain et al. , , 2017a;; Quartly et al. , 2017; Legeais et al. , 2018). It is available as a global gridded 0. 25 • × 0. 25 • dataset over the 82 • N-82 • S latitude range.
It has been validated using different approaches including a comparison with tide gauge records as well as ocean reanalyses and climate model outputs. While our study focusses on utilising CCI products, the CCI sea-level product did not cover the year 2016. We therefore extended the GMSL record with the Copernicus Marine Environment Monitoring Service dataset (CMEMS, https://marine. copernicus. eu/, last access: 13 January 2022) from January 2016 to December 2016. The TOPEX-A instrumental drift due to ageing of the TOPEX-A altimeter placed in the TOPEX/Poseidon mission from January 1993 to early 1999 was corrected for in the GMSL time series following the approach of  Ablain et al. (2017b). It was derived by comparing TOPEX-A sea-level data with tide gauge data. The TOPEX-A drift value based on this approach amounts to -1.
Ensemble mean steric product for 1993-2016 Since the Argo-based steric product developed within SLBC_cci (see Sect. 3. 2. 2 below) does not cover the full P1 period, for P1 we resort to the ensemble mean steric product by  Dieng et al. (2017) , updated to include the year 2016. It comprises the following three datasets for the period 1993-2004: the updated versions of  Ishii and Kimoto (2009) , the NOAA dataset  (Levitus et al. , 2012) , and the EN4 (version 4 of the Met Office Hadley Centre "EN" series) dataset  (Good et al. , 2013). Over recent years, these datasets have integrated Argo data from IPRC (International Pacific Research Center, http://apdrc. soest. hawaii. edu/projects/ Argo/data/gridded/On_standard_levels/, last access: 13 January 2022), JAMSTEC (Japan Agency for Marine-Earth Science and Technology, http://www.
The GIA uncertainty assessment used the ensemble of the three GIA correction options mentioned above and in Table  1. For the degree-one uncertainty assessment we made a choice of 10 different series in the attempt to represent a balanced sample of different methods and input datasets. This choice includes three To estimate the uncertainty that arises from leakage, in conjunction with buffering and rescaling, we performed a simulation study based on synthetic mass change data from the ESA Earth System Model (ESM;  Dobslaw et al. , 2015). The ESM data were processed according to the settings of the SLBC_cci OMC analysis, and the results (simulated observations) were compared with the OMC that arises from the full-resolution ESM data (simulated truth). In order to derive statistics for multi-year trends, we calculated linear trends of the simulated observations and of the simulated truth and of their misfit for every interval of a length between 9 and 12 years contained in the ESA ESM period.
A detailed description of the model is provided by  Marzeion et al. (2012). There are four global model parameters that need to be optimised: (i) the air temperature above which melt of the ice surface is assumed to occur; (ii) the temperature threshold below which precipitation is assumed to be solid; (iii) a vertical precipitation gradient used to capture local precipitation patterns not resolved in the forcing datasets; and (iv) a precipitation multiplication factor to account for effects from (among other processes) wind-blown snow and avalanching, which are not resolved in the forcing dataset. For each of the eight forcing datasets cited above, we performed a multi-objective optimisation for these four parameters, using a leave-one-glacier-out cross validation to measure the model's performance on glaciers for which no mass balance observations exist.
We used annual in situ observations from about 300 glaciers, covering a total of almost 6000 mass balance years (WGMS, 2018). In the optimisation, the temporal correlation of observed and modelled mass balances is maximised; the temporal variance of modelled mass balances is brought close to that of observed mass balances (aiming for a realistic sensitivity of the model to climate variability and change); and the model bias is minimised (to avoid an artificial trend in modelled glacier mass). Using the mean of the seven atmospheric datasets described above results in the overall best model performance. Compared to the results in  Marzeion et al. (2012) , the correlation of annual glacier mass change was increased from 0. 60 to 0. 64; the bias was changed from 5 to -4 kg m -2 (both statistically indistinguishable from zero); and the ratio of the temporal variance of modelled and observed mass balances was improved from 0.
Methods and product The surface elevation changes estimates are based on satellite radar altimeter observations for the period 1992-2017 and include data from the missions ERS-1, ERS-2, Envisat, and CryoSat-2. The temporal evolution of surface elevation is estimated by a combination of crossover, repeat-track, and least-squares methods covering the entire GrIS and for the entire time span covered by the different missions, on a 5 km common uniform grid  (Sørensen et al. , 2018). The different characteristics of the missions (the conventional radar altimetry of the ERS-1, ERS-2, and Envisat missions and the novel interferometric SAR -synthetic-aperture radar -altimetry of CryoSat-2) and the different orbital characteristics call for special care in the combinations of the different datasets and in the determination of the uncertainties.