dataesr/scientific-paragraphs-categorization huggingface dataset:
We queried 8,820 sequence changes in 1,990 human proteins, corresponding to 16,765 IMEx mutation annotations (see table III and figure  3 ). 29% (4,804) of the mutation annotations (figure  3a ) and 12% (1,073) of the sequence changes (figure  3b ) were fully mapped to natural variants. We also checked cases in which there is a variant described in the same position as a mutation reported in the IMEx data set, but the amino acid change is different in the two datasets (positional matches), and also those where mutations span more than one residue and only some of the residue changes or positions are matched in UniProtKB (partial matches). 16% (2,671) of the mutation annotations (figure  3a ) and 16% (1,415) of the sequence changes (figure  3b ) are positional or partial matches. The biological significance of positional and partial mappings does not go beyond stating that the region or position in question is important for interaction and is variable.
However, we believe this information might be useful for researchers interested in exploring specific regions in more detail. We also checked how many of the mapped variant annotations have been linked to disease according to UniProtKB. Disease associations were complemented with data from the DisGeNET database  29. There were disease-associated variants for 42% (840) of the proteins queried, with a median value of 4 disease variants mapped per protein. As seen in Table  III , 20% (3,432) of IMEx mutation annotations have been tagged as related to disease, with over 900 known disease variants represented in the data set. UniProtKB derives disease annotations for variants from both manual curation  30  and imports of cross-referenced data from ClinVar 31 via Ensembl 32 , while DisGeNET also includes variants from the GWAS Catalog  33  , and from text-mining the scientific literature.
Variant effect annotation: computational predictions and literature curation There is currently a variety of computational tools used to annotate variation data sets  35. These tools can report the effect of variation on protein function, folding or binding, usually based almost exclusively on sequence or structural data, or can also report genome-derived parameters such as allele frequencies or conservation scores. We wanted to study how variation annotations provided by these tools align with experimental effect over interaction as reported in the literature. For this purpose, we used mutfunc (www. mutfunc. com)  36 , a database reporting the effect of almost any possible mutation on protein stability, interaction interfaces, posttranslational modifications, protein translation, conserved regions, and regulatory regions.
The experimentally-observed impact on binding stability that we report in our data set may also be reflected on these parameters. This assumption was partially confirmed using two independent measurements. First, we used the 'sorting intolerant from tolerant' (SIFT) method  37  , observing that the proportion of variants with low tolerance scores was significantly higher in all groups where an effect was reported vs the 'no effect' reference (figure  4c ). We also checked allele frequencies as derived from ExAC data. Again, mutations with a reported effect seemed to have significantly lower allele frequencies (figure  4d ) and a higher proportion of alleles with extremely low frequencies (figure  5e ) than those reported to have no effect over interaction. The interaction-perturbing effects reported in the IMEx data set can be caused by modifying overall protein structure or by alteration of binding interfaces.
Literature bias in the IMEx mutations data set IMEx databases have a wide scope when selecting publications for curation and it is reasonable to assume that the proteins in this data set are representative of the interaction data that has been explored in the literature. Socially-driven, literature bias is a well-known phenomenon previously reported for literature-curated data sets  24, 41  so we decided to explore to what extent it affects the data set. First, we checked whether the number of annotations and variants found in the dataset and the number of publications in which the affected protein is reported are correlated. As seen in figures 5a and 5b, the data set contains examples of both heavilyresearched proteins with a low number of annotations and variants and vice versa. If we fit linear models between the number of annotations / variants and number of publications in which a protein is reported we find a slight positive correlation, especially in the case of disease-related variants.
This observation is compatible with socially-induced bias, with known disease-related proteins and variants being more often reported in the literature. Then we set out to find if the proteins represented in the mutations data set are involved in distinctive pathways versus all proteins for which IMEx has interaction information. To avoid database-specific biases we performed annotation enrichment analysis using PathDIP (http://ophid. utoronto. ca/pathdip), an analysis tool that integrates information from 20 source databases  42. Human proteins were divided in different sets depending on the effect reported for their mutations and their pathway annotation enrichment was calculated using all the human proteins in IMEx as background. Pathways obtained from these sets have substantial overlap (figure  5c , 885 pathways).
These results suggest that the proteins whose mutation effect on interactions have been collected in this dataset may be biased, possibly due to specific interest of the researchers exploring variation influence on molecular interactions. Specifically, in the group of mutations that show an effect on interactions, pathways related to the immune system, signalling, disease and cell cycle control ranked on the top (suppl. figure  5 , see suppl. table  2  for full details), with little difference between effect categories. There seems to be a predominance of cancer-related pathways, with representatives in both the 'disease' and the 'signaling' categories, which agrees with the observation reported in figure  5b  that the literature is biased towards diseaserelated variants.
Discussion Here we present a unique resource containing experimental, publicly available information about the impact of sequence changes on specific protein-protein interaction outcomes. This is a direct result of the IMEx Consortium full-detail curation policies and represents an example of how expert curation, resulting in structured and standardized representations, is required in order to make the most of published experimental results. In comparison to similar, pre-existing data sets recording variation influence over interactions, this resource represents a leap forward in depth, size and scope (table II). A previous, relatively small study  43  reported a curated list of about 100 mutations influencing interactions. This was used as benchmark in a study investigating the link between disease-related variation and interaction interfaces  1  , showing an application of this type of data, despite obvious limitations due to its size.
Analysis of variation is a fundamental tool in basic and clinical research, with direct application in the clinic through translational genomics. Variation effects are explored mainly through statistical analysis of large population datasets, GWAS studies, or by quantitative analysis of its influence on expression via identification of eQTLs. However, in order to unravel the mechanisms behind detected effects, it is key to explore how molecular interactions are affected  46. Currently, most of the mechanistic insight into variation effects is generated by computational annotation and predictions, using tools that are based on relatively small reference sets, generally based on structural data. As an example, the widely used FoldX algorithm is generated from protein complex structures and has been tested against a library of 1,008 mutants  39.
Our current data set already provides interaction effects for over 10 times more individual variants and is not limited to structural data. The wide scope of experimental setups represented (figure  1c ) allows the capture of effects on proteins and protein regions that might be intrinsically non-structured  47. We show that the data set gives a currently unparalleled and representative overview about which residues are key for protein interactions, with the results being in good accordance with commonly used variant annotators (figure  4 ). IMEx curation practices originally did not enforce capturing sequence changes that had no effect over interaction outcome, but as a result of consultations with tool developers and data users this policy has been amended and the data set now features a growing number of mutations with no effect that can be used as a training negative set for the development of computational annotation tools.
Software and packages used The quality control pipeline for mutation annotations was developed and integrated within the production code used in the IntAct database. The code is written in Java and makes use of the Hibernate and Spring frameworks for interaction with the core SQL database and application implementation. Specific implementation details are available upon request. Statistical analysis, plots, mutation re-annotation checks and mappings were performed using the R programming language  48  through the RStudio programming suite  49. The following R packages were used in the study: data. table, dplyr, ggplot2, ggpubr, gridExtra, gsubfn, httr, jsonlite, plyr, RCurl, reshape2, scales, seqinr, splitstackshape, XML, Biostrings, biomaRt.
Mutations re-annotation effort After the development of PSI-XML3. 0 and the 'resulting sequence' field in the IMEx schemas to capture amino acid change in participant features of the type 'mutation (MI:0118)' and children, it was necessary to populate the field with legacy data from the participant feature short label. This free-text, manually-entered field was prone to contain typographical errors and was difficult to keep updated. Curators used a set of simple rules to depict amino acid substitution, deletions and insertions. As a first step towards populating the 'resulting-sequence' field, we wrote ad hoc parsing scripts to evaluate and extract the information stored in the short labels. Several rounds of corrections took place until the data set got to its current state. Of the 27,868 records of the data set, 20,161 had to be corrected, with around 2,000 of them manually corrected.
There are still about 2,500 records for which no fix was possible without fully amending the original entry. These have been left out of the dataset until being revisited by an IMEx curator in due course. An automated quality control pipeline has been put in place to handle newly-created entries and future changes in UniProtKB (details in Supplementary materials). Finally, we have also adapted the participant feature short labels to the Human Genome Variation Society (HGVS) recommendations for variant annotation  50  , which can be accessed at http://varnomen. hgvs. org/recommendations/protein/. Mapping IMEx mutations to UniProtKB and the genome UniProtKB accessions for human proteins were extracted from the IMEx mutations data set, retaining isoform identifiers, and used to query the EMBL-EBI Protein API  25.
The API's 'variation' method was used to extract large-scale variation annotation from UniProtKB, regardless of its origin. Annotations extracted through this method were then mapped to the IMEx mutations data set using UniProtKB accession, sequence position and resulting amino acid for 'full' mappings and only UniProtKB accession and position for 'positional' mappings. Cases where the IMEx-reported mutation spans more than one amino acid position were split into individual substitutions and only labelled as 'full' matches if every individual position matches an annotation in UniProtKB. Otherwise, they were considered 'partial' mappings. Disease annotations were extracted from the API's output, along with rsIDs. These rsIDs were then used in DisGeNET to search for additional disease annotations that were brought in as well.
Predicting impact on protein interaction interfaces Experimental and homology modelled structures for protein interactions were obtained from the Interactome3D database  38. Relative solvent accessibility (RSA) for all residue atoms was computed using NACCESS  51  for proteins individually and in the interaction complex. Interface residues were defined as those with any change in RSA. The impact of variant on interface stability was computed using FoldX v. 4. 0. All binary interface structures were repaired using the RepairPDB command, with default parameters. The Pssm command is then used to predict ∆G with numberOfRuns=5. This performs the mutation multiple times with variable rotamer configurations, to ensure the algorithm has achieved convergence. The average ∆G of all runs is computed and the ∆∆G is computed as the difference between the wildtype and mutant and provides a predictive estimate of how destabilising the mutant is to the interaction interface.
https://github. com/omarwagih/siftr) was used to generate SIFT scores with parameters ic_thresh=3. 25 and residue_thresh=2. Allele frequencies A total of 3,198,692 coding variants in H. sapiens for over 65,000 individuals was collected from the ExAC Consortium 27 in the ANNOVAR 53 output format along with corresponding adjusted allele frequencies. Ensembl transcript positions were mapped to UniProt by performing Needleman-Wunsch global alignment of translated Ensembl transcript sequences against the UniProt sequence using the pairwiseAlignment function in the Biostrings R package. The mapping between Ensembl transcript IDs (v81) and UniProt accessions was obtained from the biomaRt R package. In the case that multiple alleles mapped to the sample single amino acid substitution, the one with the highest adjusted allele frequency was retained.
mutations data set are listed here, along with their IMEx database affiliation:  Sara Abbani  (DIP),  Mais G Ammari  (HPIDB),  Alan Bridge (UniProt) ,  Fiona Broackes-Carter  ( I2D ),  Nancy H Campbell  ( BHF-UCL ),  Carol Chen (InnateDB) ,  Marta Iannuccelli  (MINT),  Sruthi Jagannathan  (MBInfo),  Jyoti Khadake  (IntAct),  Luana Licata  (MINT),  Ruth C Lovering  ( BHF-UCL ),  Usha Mahadevan  ( Molecular Connections ),  Anna N Melidoni  ( BHF-UCL, IntAct ),  Simona Panni  (IntAct),  Arathi Raghunath  ( Molecular Connections ),  Sylvie Ricard-Blun  ( MatrixDB ),  Milagros Rodriguez-Lopez  ( BHF-UCL, IntAct ),  Bernd Roechert  (UniProt),  Lukasz Salwinski  (DIP),  David Thorneycroft  (IntAct) and  Kim van Roey (IntAct). Principal Investigators that supported their work are  Gianni Cesareni  (MINT),  David Lynn  (InnateDB) and  Fiona M McCarthy  (HPIDB).
The IntAct database and EMBL-EBI-based authors received funding from  EMBL core funding and Open Targets  (grant agreement  OTAR-044 ). The DIP database is funded by  NIH  grant  R01GM123126. MINT received support from  ERC  grant " DEPTH  project of the  European Research Council  (grant agreement  322749 )". UniProt curation activities at  EMBL-EBI  and the  Swiss Institute of Bioinformatics  are funded by  NIH  grants  U41HG007822  and  U24HG007822. The  British Heart Foundation-University College of London (BHF-UCL ) curation team is funded with the  British Heart Foundation  grant  RG/13/5/30112. DisGeNET is supported with  EU - FP7  funds from  ISCIII-FEDER  ( CP10/00524 ,  CPII16/00026 ),  IMI-JU  (grant agreement no. 116030 , TransQST) and EFPIA companies in kind contribution, and the  EU   H2020 Programme 2014-2020  (grant agreements no.
634143 ,  MedBioinformatics  and no. 676559 ,  Elixir-Excelerate ). The  Research Programme on Biomedical Informatics (GRIB)  is a member of the  Spanish National Bioinformatics Institute (INB) ,  PRB2-ISCIII  and is supported by grant  PT13/0001/0023 , of the PE I+D+i 2013-2016, funded by  ISCIII  and  FEDER. The DCEXS is a " Unidad de Excelencia María de Maeztu ", funded by the  MINECO  (ref:  MDM-2014-0370 ). I. J. and group supported in part by  Krembil Foundation ,  Ontario Research Fund  (# 34876 ), and  Canada Foundation for Innovation  (CFI # 225404 , # 30865 , # 33536 ).
( https://github. com/omarwagih/siftr) was used to generate SIFT scores with parameters ic_thresh=3. 25 and residue_thresh=2. Allele frequencies A total of 3,198,692 coding variants in H. sapiens for over 65,000 individuals was collected from the ExAC Consortium 27 in the ANNOVAR 53 output format along with corresponding adjusted allele frequencies. Ensembl transcript positions were mapped to UniProt by performing Needleman-Wunsch global alignment of translated Ensembl transcript sequences against the UniProt sequence using the pairwiseAlignment function in the Biostrings R package. The mapping between Ensembl transcript IDs (v81) and UniProt accessions was obtained from the biomaRt R package. In the case that multiple alleles mapped to the sample single amino acid substitution, the one with the highest adjusted allele frequency was retained.
We show that, from this point of view, differences in the stability across models have little influence on circulation, and hence precipitation, differences. Though differences in the heating appear to explain differences in circulation across the models, these differences are not associated with processes that usually come to mind, such as radiation or surface fluxes. Rather it appears necessary to account for differences in the structure of the circulation itself, rather than just its magnitude, to explain differences in precipitation across the models. The outline of the remainder of the manuscript is as follows. After describing the COOKIE simulations in more detail (section 2), we compare the intermodel spread in different precipitation and circulation characteristics of the tropics between simulations with active and inactive cloud-radiation interaction (section 3).
The paper presents an evaluation of the combined use of the HYDRUS and SWI2 packages for MODFLOW as a potential tool for modeling recharge in coastal aquifers subject to saltwater intrusion. The HYDRUS package for MODFLOW solves numerically the one-dimensional form of the Richards equation describing water flow in variablysaturated media. The code computes groundwater recharge to or capillary rise from the groundwater table while considering weather, vegetation, and soil hydraulic property data. The SWI2 package represents in a simplified way variable-density flow associated with saltwater intrusion in coastal aquifers. Combining these two packages within the MODFLOW framework provides a more accurate description of vadose zone processes in subsurface systems with shallow aquifers, which strongly depend upon infiltration.
Alternatively, less complex models can be used also to describe flow in the vadose zone, especially close to the land surface. These models are often based on relatively simple water balance principles (e. g. , SWAT  (Neitsch et al. , 2011)  or HELP  (Schroeder et al. , 1994) ) or kinematic wave routing (e. g. , the UZF1 package for MODFLOW  (Niswonger et al. , 2006) ). The usefulness of these type of models has been confirmed in a number of studies (e. g. ,  Chang et al. , 2016; Holding and Allen, 2015; Luoma and Okkonen, 2014). They are typically very efficient computationally. However, due to their simplified nature, they may not be able to represent properly some important features of unsaturated flow. For example, neither UZF1 nor HELP allows for upward flow due to capillary action, while SWAT greatly simplifies water flow below the root zone.
, 2008ab, 2016). HYDRUS-1D simulates the transient movement of water, solute, and heat in variably-saturated soil profiles using finite element discretization in space and fully implicit discretization in time. The model has been validated in a large number of applications  (Šimůnek et al. , 2008b, 2016) , including several related to recharge (e. g. ,  Scanlon et al. , 2002; Šimůnek et al. , 2016)  and saltwater intrusion  (Werner and Lockington, 2004). The HYDRUS package for MODFLOW was originally written for MODFLOW-2000  (Seo et al. , 2007). Its evaluation and comparison with other MODFLOW-related packages for vadose zone flow was presented by  Twarakavi et al. (2008)  and  Bailey et al. (2013). Applications to regional scale groundwater flow, as well as comparisons with the UZF1 package, are described in  Leterme et al.
(2013 Leterme et al. ( , 2015)). Currently, the package is being developed further in cooperation between research groups from the University of California Riverside, the Belgian Nuclear Research Centre (SCK•CEN), and the Gdańsk Univer-sity of Technology  (Beegum et al. , 2018). The most recent version of the HYDRUS package is compatible with MOD-FLOW-2005  (Harbaugh, 2005). So far, only a fraction of the processes implemented in the original standalone HYDRUS-1D program has been ported to HYDRUS for MODFLOW. Much potential exists for further enhancements, including features such as contaminant transport, macropore flow, or improved models of the hydraulic functions. The SWI2 package  (Bakker et al. , 2013)  is the latest release of the Seawater Intrusion (SWI) Package for MODFLOW. This package allows simulations of variable-density groundwater flow and seawater intrusion in multi-aquifer coastal systems.
Our research reported in this contribution has two main goals. The first objective is to carry out an evaluation of the HYDRUS package for MODFLOW as a tool for simulating recharge in conjunction with the SWI2 package  (Bakker et al. , 2013)  for saltwater intrusion modeling. While HYDRUS-1D has been used previously in studies related to saltwater intrusion, the HYDRUS package for MODFLOW has to the best of our knowledge not yet been applied in this particular context. The second objective is to investigate the influence of selected factors on the amount of recharge as well as evolutions of the groundwater table and the saltwater-freshwater interface during the simulations. These factors include uncertainty in soil hydraulic parameters (describing the retention and hydraulic conductivity functions of unsaturated soils), the presence of vegetation, and the temporal resolution of weather data (precipitation and reference evapotranspiration).
MATERIALS AND METHODS HYDRUS code Numerical simulations were performed using a modified version of MODFLOW-OWHM  (Hanson et al. , 2014) , which includes both the SWI2  (Bakker et al. , 2013)  and HYDRUS packages for MODFLOW  (Twarakavi et al. , 2008). We used the updated version of HYDRUS for MODFLOW  (Beegum et al. , 2018) , which represents an improvement over the original version as described by  Seo et al. (2007)  and  Twarakavi et al. (2008) , and which is compatible with MODFLOW-2005 and MODFLOW-OWHM. Here, we provide a more detailed description of the current version of the HYDRUS package, while details about MODFLOW-OWHM and SWI2 can be found in  Hanson et al. (2014)  and  Bakker et al. (2013) , respectively. The HYDRUS package performs simulations of vertical flow in the vadose zone overlying the MODFLOW model domain.
When the pressure head boundary condition is used, the potential flux is reduced to the actual flux (e. g. , actual infiltration and/or evaporation). Details about the numerical implementation of the atmospheric boundary conditions can be found in  Šimůnek et al. (2008a). Another important feature of the HYDRUS package is an explicit representation of water uptake by plant roots. The root zone depth and root density distribution is specified for each soil profile, according to available data on vegetation. The maximum potential transpiration rates are specified by the user at a temporal resolution matching the precipitation and evaporation data. At each HYDRUS time step, the current maximum transpiration rate is distributed among the nodes in the root zone, proportionally to the defined root density distribution, to calculate the nodal potential root water uptake.