dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSIONS AND FUTURE DIRECTIONS With this new release of OrthoInspector, we provide improvement in two main areas: proteome coverage and information design. The new databases boast a massive increase in the number of species across the three domains of life and provide the most comprehensive ortholog relations resource in terms of species coverage. Nevertheless, this increase did not involve simply adding a substantial number of species. Special attention was paid to both quality of proteomes and taxonomic coverage. With the increasing rate of genome sequencing, our scheduled strategy to ensure scalability will include regular updates of the current proteome content and the addition of new species while maintaining our standard of proteome quality. This will come with an updating procedure directly added to the software suite to allow any user to easily update their local databases with the latest data.
In terms of accessibility, the installation process of local databases using the software suite has been simplified and more importantly, the web interface of the OrthoInspector precomputed databases has been significantly reorganized. The new design offers improved access to orthologous data in the three domains of life. In addition, we believe that the implementation of original and user-friendly comparative genomics tools will be useful for anyone interested in comparative genomics and evolutionary studies of protein families. The next step for OrthoInspector will be the automated definition and analysis of orthologous families among 'model species' by exploiting our experience in multiple sequence alignment construction  (25, 26)   (Kress, in prep). This will allow the exploration of protein evolution through the three life domains at different levels of resolution, from presence/absence of orthologs to subtler patterns of differential conservation at domain or block levels.
FUNDING Agence Nationale de la Recherche  [ BIPBIP :  ANR-10-BINF-03-02 ,  ReNaBi-IFB :  ANR-11-INBS-0013 ,  Labex Agro :  ANR-10-LABX-0001-01  to B. L. ,  Labex CeMEB :  ANR-10-LABX-0004  to B. L. , Labex NUMEV:  ANR-10-LABX-20  to B. L. ]; Institute funds from the  Centre National de la Recherche Scientifique  and the  Université de Strasbourg. Funding for open access charge:  Centre National de la Recherche Scientifique.
D414 Nucleic AcidsResearch, 2019, Vol. 47, Database issue to orthology relations, taxonomic distribution and proteins with similar distribution (detailed below). data. Orthologous relationships are presented in the 'Orthologs and taxonomic distribution' section of the protein page. A menu allows users to choose display options, depending on their needs: Orthology • Domain's model organisms: only orthologs found in the 'model organisms' of Eukaryotes, Bacteria or Archaea are shown in this tab. This view is used to find orthologs in popular species and avoids overwhelming the user with superfluous information. The page shown by de- fault should meet the requirements of most users and thus serves as a suitable entry point. • Whole domain: orthologs in all species of the in-domain databases are shown in this tab.
Apart from supporting many genomes and predictive scoring models, another distinguishing feature of CRISPOR is that it tries to help with cloning, expressing and validating of guide sequences. Overlapping oligonucleotides, primers for target validation and relevant restriction enzymes are displayed, can be downloaded as tables and in a few cases we link to the respective wet-lab protocols. In the following, we give a quick overview of the software and then describe changes made last year in more detail.
UCSC Genome Browser tracks The UCSC Genome Browser provides interactive visualization of genome sequences and extensive annotations including gene exon-intron structures that is essential to design of genome editing experiments. As described in this year's NAR database issue (5), the UCSC Genome Browser now contains a track 'CRISPR' that shows pre-computed CRISPOR results for various model organisms and the sp-Cas9 nuclease. The guides are coloured by efficiency score by default, details pages show the predicted off-targets and other scores. A guide can be sent directly to CRISPOR to show more annotations and primers. For computational reasons, the CRISPR guides are only available for exons and ±200 bp around them. Mouse is currently the only exception, the guides cover ±10 kb around exons, so most of the genome.
Additional genomes or longer regions can be added by requesting them from the UCSC Genome W244 Nucleic Acids Research, 2018, Vol. 46, Web Server issue Browser group. The data files can be downloaded from the UCSC Table Browser or the Genome Browser's download server for processing, e. g. for customized library design. For genomes that do not have a CRISPR track already, or regions that are not yet available on the CRISPR track, any genome sequence can be submitted directly to CRISPOR from the UCSC Genome Browser via the menu 'View--in external tools--CRISPOR'.
Genomic variants For organisms with well-known variant databases, like 1000 Genomes in humans or the Sanger Mouse Strain variants, genome nucleotide variants are shown above the sequence. Variants below a given frequency can be removed with an input box. If a genome does not have a variant database, users can still mark SNPs with N characters in the input sequence which will remove guides that overlap Ns from the table. This information helps to exclude guide RNAs that may be inactive, when the variant affects the PAM, or less efficient, when variants lead to mismatches with the target sequence  (9).
The Doench 2016 score is recommended when guides are expressed inside cells from exogenous promoters such as U6 whereas the Moreno-Mateos score should be taken into account when using guides transcribed in vitro from the T7 RNA polymerase promoter. CRISPOR results can be dowloaded in different spreadsheet file formats (. tsv or. xls). To facilitate further analysis, results can now also be downloaded in eight different Genbank-like formats for various third party sequence editors. The respective files are adapted to the sequence editor, e. g. by adding colors or special feature types, in order to make the display in the third party tool as convenient as possible. Finally, the PCR primer designer allows users to choose the T m and amplicon length optimal for their sequencing method and assay.
LC-ESI-QTOF MS/MS analysis A Waters Q-TOF Xevo G2S mass spectrometer equipped with an Acquity UHPLC system and Lockspray source was used for the acquisition of the LC-ESI-QTOF MS and MS/MS data. The digested peptide sample was injected into an Acquity UPLC BEH300 C18 column (1. 7 μm, 2. 1 mm ID × 150 mm L, Waters). The elution of the peptides was performed with a 10-70% gradient of buffers B/ A over 10 min [solvent A composition: dH 2 O/formic acid, 99. 9/0. 1 (v/v) and solvent B composition: ACN/formic acid, 99. 9/0. 1 (v/v)]. The eluted solution was directly injected into the coupled MS system. Acquisition and analysis of the peptide samples were carried out in the positive mode, within a mass range of m/z 100-2000 using the Agilent MassLynx software version 4. 1 (Waters). The mass spectrometer settings for the MS analyses were: capillary voltage, 0.
5 kV; cone voltage, 40 V; source temperature, 150 °C; desolvation temperature, 600 °C; gas flow, 80 L/h; and gas desolvation, 1000 L/ h. MS data were acquired using a data-dependent acquisition method (DDA) for which MS/MS data were acquired using CID activation mode based on mass and charge state of the candidate ions. For calibration, an external lock mass was used with a separate reference spray (LockSpray) using a solution of leucine enkephalin eluted at a flow rate of 5 μL/min. The calibration was based on the MS detection of m/ z 278. 1141 and 556. 2771 ions at collision energy of 23 eV. The resulting MS/MS spectra data were analyzed by de novo sequencing using PEAKS® studio version 5. 2 software (Bioinformatics Solutions Inc. ) with the following settings: trypsin, or V8 protease; carbamidomethyl (C) as a fixed modification; mass accuracy for MS/MS data at 0.
N-terminal sequence determination Amino acid sequence determination based on Edman degradation was performed using an Applied Biosystems gas-phase sequencer model 492 (s/n: 9510287J). Phenylthiohydantoin amino acid derivatives generated at each sequence cycle were identified and quantitated online with an Applied Biosystems Model 140C HPLC system using the data analysis system for protein sequencing from Applied Biosystems (software Procise PC v2. 1). The PTH-amino acid standard kit (Perkin-Elmer P/N 4340968) was used and reconstituted according to the manufacturer's instructions. The procedures and reagents used were as recommended by the manufacturer. Chromatography was used to identify and quantify the derivatized amino acid removed at each sequence cycle. Retention times and integration values of peaks were compared to the chromatographic profile obtained for a standard mixture of derivatized amino acids.
Search of peptides analogues The highest scoring sequences of the identified peptides were compared to the protein databases (SwissProt, BLASTP 2. 6. 1+) through the BLAST website (http://blast. ncbi. nlm. nih. gov) for protein similarities. Only proteins from the database with high identity percentages (at least 60%) were selected. The theoretical molecular masses of the identified peptides were calculated online using ExPASy ProtParam website (https://web. expasy. org/protparam).
02 upon alkylation, illustrates that actiflagelin should contain ten cysteine residues. In addition, the differences in molecular masses would also suggest that all these cysteine residues engage in five disulfide bridges (Fig. 3a ). Samples of the reduced/alkylated actiflagelin were then digested overnight at 37 °C with either trypsin (w/w trypsin/actiflagelin with a ratio of 1/20) or V8 protease (w/w V8 protease/actiflagelin ratio of 1/20). All digested fragments were purified by All ions were analyzed by LC/ESI QTOF MS/MS and de novo sequencing software (PEAKS® studio version 5. 2 software) used to define the amino acid sequence of each actiflagelin fragment. An example of de novo sequencing by LC-ESI-QTOF MS/MS is provided in Fig. 3c  for the m/z 755. 8411 [+2] ion. The resulting sequence of 12 amino acids was RW(L/I)GCAKTCTEE.
Thus, there is still a missing molecular mass gap of 96. 48 Da that is compatible with a C-terminal Pro residue at the end of actiflagelin (115. 13 Da -18 Da of H 2 O when engaged in peptide bond = 97. 13 Da). If, in addition, the peptide is amidated (-1 Da), then Pro would perfectly match the missing residue in the sequence to provide the correct missing mass. This, in turn, would reach a theoretical mass of actiflagelin that matches the observed experimental mass of our sperm motility activator. Finally, we performed a Blast search using the almost fully reconstructed sequence of actiflagelin, which yielded a match with several animal toxins (Fig. 4b ). Sequence identity with these matches ranged between 65% for toxin S6C6 and 86% for bucandin. The sequence of actiflagelin was deposited in the UniProt database (http://www.
Funding We acknowledge financial support from the  Egyptian government  for a joint supervision scholarship to  Tarek Abd El Aziz. We thank  Smartox Biotechnology  for financial support and its staff for providing technical assistance to venom fractionation and LC-ESI-QTOF MS analyses. We thank  INSERM  for financial support to  Michel De Waard. This program is supported by a grant from the  Agence Nationale de la Recherche  to the  LabEx Ion Channels, Science and Therapeutics  under the code number  ANR-11-LABX-0015.
The purpose of this chapter is to: • illustrate the implementation of a decision aiding process from start to finish in the context of integrating managerial preferences into the qualitative evaluation of team members, • promote the use of the MCDA package, which aims at providing a repository of MCDA methods that can easily be linked together within the statistical programming language R, • serve as a blueprint for implementing similar decision aiding processes within other application domains. The readers will be able to familiarize themselves, in Section 2, with the teaming problem within the free/libre/open source software (FLOSS) domain and the methodology of extracting and then selecting the criteria used to evaluate team members. In Section 3 they will gain a general understanding of the field of MCDA, the preference models and methods that will be used in the illustrative examples as well as how to set up the MCDA package for R.
As our claim is that our technique helps detecting those differences, it was necessary to choose a domain where it has been clearly established that there is something to detect, and FLOSS is a place where such differences have been documented. Taking the example of communication skills, certain behaviors can be deemed 'toxic' in some projects, but completely acceptable in others  [12]. The difference in perception can be seen in public statements by FLOSS team leaders. For example, the Dreamwidth community prides itself on its inclusivity1, while Linus Torvalds, the founder of Linux, has famously said, "Talk is cheap. Show me the code,"2 suggesting that technical capability/proposals are highly valued in his community over other attributes. Finally, existing processes for evaluating team members in FLOSS groups have clear limitations.
However, these are 1 Denise Paolucci and Mark Smith, Web 2. 0 Expo SF, 2010, https://www. slideshare. net/dreamwidth/build-your-own-contributors-one-part-at-a-time. 2 Linux kernel mailing list. Google Groups, https://groups. google. com/d/msg/fa. linux. kernel/iQtWFALi4JA/eSzv64_tOvoJ. Other quotations from L. Torvalds may be found in Wikiquote: https://en. wikiquote. org/wiki/Linus_Torvalds. coarse-grained, and incorporate some measure of technical skills with descriptors such as "functional expertise. " Consequently, we relied on the personality indicator method described by Driskell et al. [20]. This work examined the relationship between team member personality traits and team effectiveness, basing the traits on the ten personality indicators (BFI-10)  [40]. The five indicators which describe the relationship between team members and team effectiveness are: communication, commitment, collaboration, handling of pressure, and creativity  [20].
The manager's perspective A total of six DMs participated in the first part of our experiment. Table  2  illustrates the criteria each of them selected as 'important' or 'very important' according to the second step of the proposed methodology. Each of the dimensions were considered useful by at least one DM, while no DM felt that all of the criteria were relevant. We observe that the dimensions related to the commitment of contributors, their capacity to work together, their perspective of the project as a whole and their ability to document their work were unanimously considered important factors by all DMs. Furthermore, most were also concerned with the quality of code produced. This result supports the premise of the article: if a set of variables can describe what is observable and important for evaluating a team member, managers will only use a subset of these variables.
The MCDA R package as a support tool In this section we present the software which can be used to support the previously presented method, namely the MCDA package for R. R is an open-source functional programming language and environment mainly centered around data analysis  [29, 70]. In recent years it has grown in popularity with the IEEE identifying it as the 9th most popular programming language in 2014, the 6th most popular in 2015 and the 5th most popular in 2016  [28]. Due to the large community of R users, many tools in the form of functions within packages have been proposed, many dealing with handling different data formats, data pre-processing, data filtering and interactive visualizations. An interactive tutorial for learning R may be found at  [1]. The MCDA package  [7]  that we propose to use follows the philosophy of R, by encompassing a growing array of MCDA algorithms that may be used to decompose a decision aiding process into sub-steps.
The package mainly targets MCDA practitioners that are familiar with the decision aiding process, giving them the possibility to construct this process as they see fit. As very often during a decision aiding process the DM does not have a clear picture of his/her problem  [61] , being able to quickly adapt the process as new information is made available is of great importance. Finally, the MCDA package may benefit both MCDA practitioners and data analysts, as MCDA practitioners could further apply methods linked to data analysis throughout the decision aiding process, while data analysts could use their data for reaching an objective in addition to analyzing it. At the time of writing, the package is still young and consequently is far from covering all of the algorithms from the classical MCDA literature.
However, functions supporting various steps of the MCDA process have been implemented in the MCDA R package. They can be categorized as follows: • state of the art aggregation algorithms; • state of the art preference elicitation algorithms; • data manipulation functions; • plot functions. In order to be able to use this package, the user should first install either GLPK, CPLEX or both on their machine. GLPK is a free and open-source, while a free version of CPLEX can be easily acquired through the Academic Initiative of IBM. We recommend using CPLEX for testing the case studies that we present in Section 5, as it is much faster than GLPK. On a Debian-based Linux machine, installing GLPK is as simple as: sudo apt-get install libglpk-dev. Within R, the MCDA package and its dependencies can be installed using the following command: # install the MCDA package.
Experimental case: FLOSS Although six DMs agreed to participate in our research, three of them only participated in the criteria selection step, whereas one of the remaining three stopped mid-way through the presented protocol. We therefore illustrate below the proposed methodology for the two DMs who reached the end of our proposed methodology. Both of these DMs selected the same five criteria in order to evaluate their contributors, therefore we present them below: • Commitment to the project (g 1 ), psycho-sociological; • Working with others (g 2 ), psycho-sociological; • Quality of code produced (g 3 ), technical; • Global picture: understands the tools/technology/domain, & processes behind the project (g 4 ), technical; • Documentation & testing (g 5 ), technical. For each of these criteria, a five-level ordinal scale ranging from very bad to very good ({vb, b, n, g, vg}) was selected, as well as a final three-level ordinal evaluation scale (K = {Bad, N eutral, Good}).
Inferring the preferences of DM1 We begin with illustrating the inference of the first decision maker's (DM1) preferences. We will additionally show how the proposed protocol was implemented in R. The entire R script for this case study is available in the MCDA package using: # path to the R script of the example system. file("examples","exampleFLOSS. R",package="MCDA") We started by defining the criteria and the categories: # load the MCDA library library(MCDA) # define the criteria and their preference directions criteria <-c("c1","c2","c3","c4","c5") criteriaMinMax <-c("max","max","max","max","max") # define the categories and their ranks categories <-c("Good","Neutral","Bad") categoriesRanks <-c(1,2,3).
colnames(pT) <-criteria In order for the reader to be able to reproduce our experiment, the provided R script does not include this code, but simply loads step by step the profiles that were generated using this method. Given the 25 generated team member profiles, we asked DM1 to assign them to one of the three categories, while explaining his reasoning to one of the chapter's authors. DM1's assignments are illustrated in Table  3. This corresponds to step 2 in the proposed protocol. Using this information, we then tried to construct a MR-Sort model with a simple majority rule (step 3 of the protocol). # define the category assignments of the generated alternatives assignments <-c("Bad", "Neutral", "Bad", "Bad", "Neutral", "Good", "Neutral", "Good", "Bad", "Bad", "Good", "Neutral", "Bad", "Bad", "Bad", "Bad", "Bad", "Neutral", "Bad", "Neutral", "Bad", "Bad", "Neutral", "Bad", "Bad").
names(assignments) <-rownames(pT) m1 <-MRSortInferenceExact(pT, assignments, categoriesRanks, criteriaMinMax, veto = FALSE, readableWeights = TRUE, readableProfiles = TRUE) Running the R code up to this point gives us the following result contained in m1: > m1 $solverStatus [1] "Failed ( invalid bounds )" This means that no MR-Sort model with only the majority rule is able to capture the assignments given by DM1 on the 25 profiles (step 4). In order to determine whether we need to use a more expressive model, we first generate the minimal sets of incompatible assignments (step 5). The result, containing of sets of indexes corresponding to the profiles that would need to be assigned to different categories in order for us to use an MR-Sort model, is given by: > incomp $incompatibleSets $incompatibleSets[[1]] [1] "2" "12".
The function also allows to plot several alternatives along with their assignment, however, at this point we are only interested in seeing the model. The output of this code is given in Figure  4. The first diagram shows the limit between the Bad and Neutral categories, while the second shows the boundary between Neutral and Good. The lines correspond to the category limits, while the dark regions represent the range of values which would trigger a veto. At this point, the model was not yet presented to DM1, as at least a couple of iterations of the protocol need to be followed. An additional set of 10 profiles were generated and presented to DM1 (step 9). The code below illustrates the generation procedure, however, the script that can be loaded from the MCDA package uses the same evaluations that were used in this experiment, instead of regenerating them, in order for us to be able to reproduce the original results.
names(assignments) <-rownames(pT) Using these new alternatives and their assignments, we again tested whether an MR-Sort model with vetoes was able to capture them (step 3): > m2 <-MRSortInferenceExact(pT, assignments, categoriesRanks, criteriaMinMax, veto = TRUE, readableWeights = TRUE, readableProfiles = TRUE) > m2 $solverStatus [1] "Failed ( invalid bounds )" As the code above illustrates, this model did not completely fit the assignments. The MRSortInferenceExact function reported that the mixed-integer linear solver did not find a feasible solution. We therefore proceeded to check if DM1 had any hesitations in his assignments which would allow this model to be used. We again identified the sets of alternatives with incompatible assignments (step 5): incomp <-MRSortIdentifyIncompatibleAssignments(pT, assignments, categoriesRanks, criteriaMinMax, veto = TRUE) > incomp $incompatibleSets $incompatibleSets[[1]] [1] "2" "16" $incompatibleSets[[2]] [1] "2" $incompatibleSets[[3]] [1] "2" "35".
Using the MCDA R package as a support tool In this chapter, we proposed to support the multi-criteria evaluation process throughout all of its steps by use of a single environment, the R statistical software. This prevents analysts and DMs from having to resort to using multiple tools at different stages of the decision aiding process, adding an additional level of difficulty. The choice of using R throughout the process was motivated by its focus towards data analysis, its open source and package-based philosophy, as well as its large community of users and contributors. While the MCDA package currently contains only a few MCDA algorithms, its authors wish to continue developing it in the future so that as many of the MCDA algorithms can be found within it. Furthermore, functions linked to the presentation of the results, for instance graphically, will also be added to complement the existing ones.