dataesr/scientific-paragraphs-categorization huggingface dataset:
This provides data profiling with a different, more user-friendly, perspective of the result set. Another application is business rule acquisition and data cleaning. The aim of business rule acquisition is to identify those constraints which are meaningful in a given application domain. This is usually done with the help of domain experts, who have commonly no knowledge of database constraints but can readily assess samples and provide feedback. Given the sample above, for instance, a domain expert may spot that different books with the same author and title occur in the list. If the original intention of the integration process was to not list multiple books with the same author and title, then the sample would immediately alert domain experts to a violation of this constraint. The feedback to the business analysts would then result in the specification of the p-key ({author, title}, β 1 ).
Query processing We demonstrate the benefit of p-keys on query processing. Therefore, we add the attribute p-degree to the relation schema TRACKING with attributes zone, time, rfid, object. Suppose we are interested in finding out which grizzly bears have been tracked in which zone, but we are only interested in answers that come from 'certain' or 'quite possible' tuples in the database. A user might enter the following SQL query: SELECT DISTINCT zone, rfid, p-degree FROM TRACKING WHERE p-degree = α 1 OR p-degree = α 2 ORDER BY p-degree ASC zone rfid p-degree Z0 H0 α 1 Z1 H1 α 1 Z1 H2 α 1 Z3 H2 α 1 Z3 H3 α 2 which removes duplicate answers, and orders them with decreasing p-degree. When applied to the p-relation from Table  1 , the query returns the answers on the right. Firstly, our framework allows users to ask such queries, since the p-degrees of tuples is available.
Secondly, answers can be ordered according to the p-degree, which is a huge benefit for users in terms of ranking outputs. Thirdly, the example shows how our framework can be embedded with standard technology, here SQL. Finally, recall our p-key ({zone, rfid}, β 3 ) which holds on the set of tuples that have p-degree α 1 or α 2. Consequently, the query answers satisfy the key {zone, rfid} and the DISTINCT clause becomes superfluous. A query optimizer, capable of reasoning about p-keys, can remove the DISTINCT clause from the input query without affecting its output. This optimization saves response time when answering queries, as duplicate elimination is an expensive operation and therefore not executed by default in SQL databases. P-keys, and the ability to reason about them, have therefore direct applications to query processing.
Acquisition tools New applications benefit from the ability of data engineers to acquire the p-keys that are semantically meaningful in the domain of the application. For that purpose, data engineers communicate with domain experts. Unfortunately, they have to overcome a communication problem which is caused by a mismatch in expertise. Indeed, data engineers know database concepts but not the domain, while domain experts know the domain but not database concepts. It is therefore helpful to translate the knowledge of the data engineers into a format that can be understood by the domain experts. As humans learn a lot from good examples, the goal is to visualize the abstract set of p-keys that data engineers currently perceive as meaningful in the form of a data sample. Here, the data sample should be representative of the abstract p-key set.
This means the data sample satisfies all the elements of the set, but violates all the p-keys not implied by the abstract set. The intuitive idea is that domain experts will easily spot cases in which actually meaningful p-keys are violated by the data sample, because they are incorrectly perceived as meaningless by the data engineers. The notion of a representative data sample is also known as an Armstrong database in the research literature. In this section we will establish two major tools that help data engineers to effectively communicate with domain experts. We follow the framework in Fig. 3. We will first establish an algorithm that data engineers can use to visualize abstract p-key sets in the form of some Armstrong p-relation r. This p-relation is then inspected jointly with the domain experts.
P-keys and p-relations Our goal is now to extend these results to p-relations and p-keys Our first aim is to characterize the structure of Armstrong p-relations. We recall two notions from relational databases. The agree set of two tuples t, t ′ over R is the set ag(t, t ′ ) = {a ∈ R | t (a) = t ′ (a)} of attributes on which t and t ′ have matching values. The agree set of a relation is the set ag(r) = {ag(t, t ′ ) | t, t ′ ∈ r ∧ t = t ′ }. Let denote a set of keys over relation schema R. An anti-key of R with respect to is a subset A ⊆ R such that does not imply the key A over R and for all a ∈ R -A, implies the key A ∪ {a} over R. We denote by -1 the set of all anti-keys of R with respect to. Example 12. Consider the possible world r 4 of the p-relation r in Table  1. Then the set of agree sets of r 4 consists of {time, object}, {object}, {zone, object}, {rfid, object}, {zone, time, object}, {zone, rfid, object}, and {time, rfid, object}.
Discovery We will now discuss our experiments with the possibilistic key finder tool that implemented Algorithm 4. The algorithm was applied to possibilistic variants of seven real-world data sets plus one synthetic data set, called fdreduced-30. The synthetic data set was generated with the dbtesma data generator  [51, 52]. All the other data sets have been obtained from the UCI Machine Learning Repository  [42]. Table  4  shows the basic characteristics of the various data sets. They comprise a reasonable range of column and row numbers. It should be pointed out here that big data sets, with say 50 columns and 1 million rows, resist state-of-the-art data dependency mining algorithms  [51, 52]. As indicated previously, the data sets are not possibilistic, so they were made possibilistic by randomly assigning values between 1 and k for each tuple in the data set.
Results of experiments with given data sets We will now present our findings of applying Algorithm 4 to the possibilistic variants of the given data sets. Several of the data sets contained duplicate tuples, i. e. tuples which had matching values on all the underlying attributes. In such cases, no key can exist, and we therefore only kept one of the tuples for which duplicate tuples exist. The efficiency of the key finder tool is measured in terms of the computation time in seconds, the number of p-keys found, and the total number of attributes that occur in the output. We provide these measures for each data set, and each value of k = 1, 5, 10, and 15, and distinguish between the output (RC) derived directly from merging the p-keys found in each possible world, and the non-redundant cover (NRC) derived from the previous output by removing any redundant p-keys.
For each data set, and for each value of k, the experiments were repeated 5 times and the  averages were computed for time taken, the number of p-keys discovered, and the size of the output, for RC and NRC, respectively. Table  5  shows all these measures for the k-values 1 and 5, and Table  6  shows the measures for the k-values of 10 and 15. The value ML refers to the event that the experiment exceeded the memory limit of 4 GB. For the non-redundant cover (RC), computation time refers to the time taken to compute the non-redundant cover (NRC) from the redundant cover (RC). The mining work the fastest on the smallest data set, Iris, which also has the least number of p-keys found. The Abalone data set has significantly more rows than iris and breast-cancer. The values of k have limited impact on the computation times for this data set.
For the breast-cancer data set the computation time is faster than for abalone. For this data set, the value of k does again not affect the computation time much. The adult data set more rows and columns than the previous data sets. In this case, the computation time is also much higher than that on the previous data sets. The value of k is seen to have a considerable effect on the efficiency. The letter data set has more columns than the above mentioned data sets, but has only 20,000 rows while adult has 48,842 rows. For this data set, p-key mining takes less time than that of the adult data set, even though there are more columns. The hepatitis data set has considerably more columns  (20)  and few rows. In this case, the computation time increases proportionally with the increases of k. The horse data set has a high number of columns  (27)  but few rows.
In this case the computation time increases substantially with increases in k. The synthetic data set fd-reduced-30 has a high number of rows and columns. In this scenario, the algorithm does not return an output as the data set does not fit into memory. Regarding our experiments we make the following observations. The algorithm performs well on data sets which have a modest number of rows or columns. Data sets that have a larger number of columns (e. g. 30) and a larger number of rows (e. g. 250,000) are difficult to handle. The savings achieved by removing redundant p-keys can be significant (and the larger data sets are the more savings will be made in terms of the time required to validate the p-keys). In addition, the removal of redundant p-keys takes little time in comparison to the computation of the p-keys.
For data sets with more columns and few rows, such as horse and hepatitis, the size of the cover is larger than that for data sets with few columns and more rows, such as adult and letter. This may be attributed to the fact that with fewer tuples it takes more columns to generate non-empty intersections for the participating larger disagree sets. For larger numbers of rows, for example in the letter and adult data sets, it can be seen from Fig. 10c  and Fig. 10b  that the main contribution to computation time comes from tuple comparisons during the computation of the disagree sets. As the value of k increases, it is expected that the number of keys increase. This behavior is attributed to the increase in possible worlds when k grows. The algorithm conforms to this behavior.
Conclusion and future work Possibilistic keys have been introduced to efficiently identify tuples in uncertain data. Uncertainty is modeled qualitatively by applying the AI framework of possibilistic logic to the fundamental database concept of keys. Tools were established to efficiently reason about possibilistic keys, and applications of p-keys in constraint maintenance, data cleaning, and query processing were outlined in detail. It was characterized which non-redundant families of p-keys attain maximum cardinality, a result that enables data engineers to simulate data processing under heavily.
Acknowledgement The authors would like to thank a reviewer whose comments led to a clearer positioning of the contributions of the research. In particular, the model of uncertainty is primarily intended for the application areas that have been identified, including database design. We remark that the model of uncertainty is only of limited use in the area of query processing, due to its limited expressiveness. This research is partially supported by the  Marsden Fund Council  (grant no  3701176 ) from  New Zealand Government  funding.
Mass spectrometry The nanoUPLC was coupled online through a nanoESI emitter (10 μm tip; New Objective; Woburn, MA) to a quadrupole orbitrap mass spectrometer (Q Exactive HF, Thermo Scientific) using a FlexIon nanospray apparatus (Proxeon). Data were acquired in DDA mode, using a Top20 method. MS1 resolution was set to 120,000 (at 400 m/z), and maximum injection time was set to 20 ms. MS2 resolution was set to 60,000 and maximum injection time of 60 ms.
Data processing and bioinformatic analysis Raw data were processed using MaxQuant v1. 6. 0. 16. MS/MS spectra were searched using MaxQuant's builtin search engine, Andromeda. Data were searched against the Pseudobacteroides cellulosolvens ATCC 35603 DSM 2993 sequences in UniprotKB (Additional file 1: Table  S1 ). Fixed modification was set to carbamidomethylation of cysteines, and variable modifications were set to oxidation of methionines and deamidation of glutamine and asparagine. Protein identifications were filtered, such that the global false discovery rate was maximum of 1%. Comparative analysis of LFQ intensities was done in Perseus (v1. 6. 0. 7) to determine fold changes and p-values, adjusted with multiple-comparison correction. Proteins resulting in the MaxQuant file of tryptic digestion were filtered to remove reverse sequences and known mass spectrometry contaminants.
Protein annotation was based on the CAZy database (http:// www. cazy. org/) and a previous publication of the P. cellulosolvens genome  [26, 33]. Unsupervised hierarchical clustering was done using the Euclidian method with average linkage. The resulting heatmaps and PCA projection  [34]  were generated using the Partek Genomics Suite software, version 7. 0. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (http://www. ebi. ac. uk/pride ) partner repository with the dataset identifier PXD012663.
c Venn diagram depicting the overlap in the number of proteins, which were detected in replicates of the samples and/or between the different carbon sources. d Number of proteins containing CAZy-and cellulosome-related modules, which were detected among the 1510 proteins identified in this study (detectable in the secretome, not necessarily differentially expressed above a certain threshold). Magenta-proteins detected in this study, blue-proteins coded in the genome. Most of the cellulosomal modules are expressed. Full list of protein names and intensities is given in Additional file 1: Table  S1  Table  S2A ), and 245 proteins showed significant difference between CB and MCC in peak II (Fig. 3b , Additional file 3: Table  S2B ).
The second largest primary scaffoldin, ScaA2, showed relatively high intensity for MCC I (3. 03 times lower than ScaA1) and MCC II (6. 25 times lower than ScaA1) but appeared in much lower amounts than ScaA1 in all other fractions. This fact is surprising since ScaA2 is a large   S3A ); b 30 (of 166) selected most abundant dockerin-containing proteins (Additional file 4: Table  S3B ). Names of genes (locus tags) and their CAZy modules are noted. Heatmaps were generated using LFQ intensities (log2), where zero intensity values were imputed to 10. Rows were standardized and clustered by partitional clustering using the Euclidian method. Full list of gene names and intensities is detailed in Additional file 1: Table  S1. Numbers from 1 to 3 at top represent different triplicates from the two growth substrates.
Funding This research was supported by Grant No. 1349  from the  Israel Science Foundation  (ISF) and by the  United States-Israel Binational Science Foundation (BSF)  (Grant no. 2013284 ), Jerusalem, Israel. In addition, a  joint Research Grant  (No. 2566/16 ) from the  Israel Science Foundation (ISF)-National Natural Science Foundation of China (NSFC)  is gratefully acknowledged. The authors appreciate the support of the  European Union ,  Area NMP. 2013. 1. 1-2:Self-assembly of naturally occurring nanosystems: CellulosomePlus Project number:  604530  and  European Union Horizon 2020  contract: Sustainable production of next generation biofuels from waste streams: Waste2Fuels. EAB is the incumbent of The Maynard I. and  Elaine Wishner Chair of Bio-organic Chemistry.
IG/ MED/MICR/00/01), the French Ministry of Foreign Affairs (French Embassy in Oman) (grants nos. 402419B, 402415K and 339660F), the CNRS-Sciences de la Vie (grants no. 01N92/0745/1 and 02N60/1340), the CNRS-Direction des Relations internationales (grants no. 01N92/0745 and 02N60/1340, and the PICS-CNRS no. 06249: FRANC-INCENSE), the University of Perpignan and the National Institute of Health (NIH) (grant no. R01-AI133749 T. J. C. A. ; Subaward no. 53409 H. M. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. arise de novo or are segregating as standing variation, is a central question in evolutionary biology and public health, and key to predicting the effective "shelf life" of new drug treatments  [3, 5]. There is growing evidence for resistance to anti-helminthics in several helminths infecting humans, including Onchocerca volvulus  [6] , the filarial nematode causing river blindness, and in soil transmitted helminths (hookworm, whipworm and Ascaris roundworms)  [7]  which cumulatively infect over one billion people worldwide  [8].
5 kb flanking haplotype, is sampled from both West African and Caribbean schistosomes. This result strongly suggests that p. E142del alleles were present in West Africa prior to the transatlantic slave trade (1501-1867)  [19] , and were transferred to In silico evaluation of mutations on protein stability. We computed the difference in free enthalpy (ΔΔG) between the mutated and the wild type proteins. The higher the ΔΔG, the more unstable is the mutated protein. Only single amino acid changes within the resolved crystal structure were examined. For completeness, we included mutations from our current dataset and from previous studies  [10, 13]. Grey bars correspond to known sensitive alleles. Red bars correspond to validated resistance alleles. Grey labels correspond to mutations identified previously from South America  [13].
We assessed genome copies by quantifyng the S. mansoni α-tubulin single copy gene  [64]. We prepared exome capture libraries on the selected African samples and on all the successfully amplified Omani samples using the pre-capture pooling method of the SureSelect XT 2 Target Enrichment System (Agilent)  [64]. Finally, we sequenced libraries on a HiSeq 2500 and data were demultiplexed using the Casava pipeline. Raw sequencing data are accessible from the NCBI Sequence Read Archive under BioProject accession numbers PRJNA439266, PRJNA560069, PRJNA560070.
Alignment, variant calling and phasing We aligned sequences as described in Le Clec'h et al. [64]. Briefly, we aligned data against the v5 S. mansoni genome using BWA and SAMtools, realigned around indels using GATK, PCR duplicates were marked using picard and Q-score were recalibrated using GATK. To identify variants in the SmSULT-OR (Smp_089320) gene, we performed a variant calling using FreeBayes (v1. 1. 0-46-g8d2b3a0) [65]  in the first 3 Mb of the chromosome 6. We used a minimum base quality (-q) of 20 and a minimum mapping quality (-m) of 30. We included mutation sites identified previously  [10, 13]  in order to genotype these sites specifically for further comparison. We defined 4 populations: Caribbean (HR), West Africa (SN and NE), East Africa (TZ) and Middle-East (OM). We excluded variants supported by less than 4 reads using VCFtools (v0.
1. 14) [66]. We then used vcf-subset from VCFtools to remove sites with reference allele only. We adjusted variant positions using vt normalize (v0. 5772-60f436c3)  [67]. We finally used Beagle (v4. 1)  [68]  to first estimate genotypes and to then phase data. This approach resolves haplotypes using statistical models rather than by direct linkage of SNPs on short reads. Unphased variant calling data and scripts used to analyze the data are available on Zenodo (DOI: 10. 5281/zenodo. 2850876 and 10. 5281/zenodo. 3370039, respectively).
Genetics analysis We performed all the genetics analysis using phased exome data. We handled the VCF data in R (R v3. 5. 0  [69] ) using the vcfR package (v1. 8. 0. 9)  [70]. We determined the longest haplotype block from the samples carrying the p. E142del using a custom R code after filtering invariable sites in these samples. We built the haplotype network using the R package poppr (v2. 8. 1)  [71, 72]  after selecting variants within the longest haplotype coordinates showing no more than 20% of missing data. We computed the frequency of the OXA-R allele using a custom R script and functional annotation obtained through a custom bash script. We generated haplotype sequences of the coding sequence only for the Old World samples using the updated phased VCF data exported from R and the BCFtools (v1.
2)  [73]. We generated haplotypes of the South American samples from previous genotyping using a custom bash script. We included the SmSULT-OR homologue sequence from S. rodhaini previously identified  [13]. We aligned the haplotypes using Clustal Omega (v1. 2. 2)  [74]. We determined nucleotide diversity, number of synonymous and non-synonymous sites using DnaSP software (v6. 12. 01)  [75]. Populations were defined as described in the previous section. Scripts used to analyze the data are available on Zenodo (DOI: 10. 5281/zenodo. 3370039).
Recombinant SmSULT-OR protein production and OXA activation assay We produced recombinant SmSULT-OR proteins following Chevalier et al. [13]. Briefly, mutations were introduced in the cloned SmSULT-OR gene sequence (Smp_089320; GenBank accession no. HE601629. 1) and introduced into Escherichia coli for protein production. Proteins were extracted from bacterial culture and purified on an affinity chromatography column. Histag was removed using Tobacco etch virus (TEV) protease, the solution was dialyzed overnight and passed through affinity chromatography again to remove His-tag and TEV protease. The sample was loaded onto a GE-pre-packed Q anion exchange column and eluted, and the pooled fractions were dialyzed overnight. The protein was finally concentrated at 10 mg. mL -1. We tested the impact of a subset of the mutations observed on OXA activation using an in vitro assay  [13].
Evaluation of impact of mutations on protein stability We first visually assessed the scored mutations on the SmSULT-OR protein structure (PDB code 4MUB  [10] ) using the mutagenesis function of PyMol software (v2. 3. 0; Schro ¨dinger, LLC). We then used the Rosetta package (v3. 9) to test the impact of scored mutations on protein stability. We used the ddg_monomer application to compute the difference in free enthalpy (ΔΔG) between the mutated (amino acid substitutions only) and the wild-type protein. The higher the ΔΔG, the more unstable is the mutated protein. For this, we prepared ligand files (OAQ and A3P) by downloading SDF files containing all ligand structures from SmSULT-OR crystals available from the Protein Data Bank website. We modified SDF files to add hydrogens using Openbabel (v2.
4. 0)  [76] , and generated params files using molfile_to_params. py from Rosetta. We used the structure of SmSULT-OR to generate a constraint file using the minimi-ze_with_cst application and the convert_to_cst_file. sh scripts. We removed ligand information from the constraint file. For each mutation, we finally generated a resfile and ran the ddg_monomer application with 50 iterations and options adapted from the protocol 16 of Kellogg et al. [77]. Scripts used to run this analysis are available on Zenodo (DOI: 10. 5281/ zenodo. 3370039).
W. L was supported by a Cowles fellowship from Texas Biomed. CONTRAST was funded by the European Commission (FP6 STREP contract no. 032203). SCORE (https://score. uga. edu/) activities were funded by the University of Georgia Research Foundation Inc. (prime award no. 50816, sub awards RR374-053/4785416 and RR374-053/4785426), which is funded by the Bill & Melinda Gates Foundation for SCORE projects.