dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description In the frame of a partnership between academia and industry, a collaboration was established in 2017 between the Laboratory of Phytochemistry and Bioactive Natural Products at the University of Geneva (LPBNP), Switzerland, and the Pierre Fabre Laboratories (PFL) in Toulouse, France. This collaboration had the objective to explore and exploit the chemodiversity of a large collection of plant extracts (furnished by the industrial partner) using stateof-the-art mass spectrometric profiling methods and associated data-mining solutions (performed and developed by the academic partner). The main goal of this research project is to establish a precise and exploitable description of the wide chemical diversity displayed in the plant extracts collection in order to orient further isolation efforts aiming to discover, in a first move, novel antitrypanosomatid scaffolds and deepen chemotaxonomic knowledge of such a large set.
The first samples of plant parts were collected in 1998, and when the HTS program ended in December 2015, the collection contained just       ). On the left, the barplot represents the overall coverage at main taxa level and up to the phylum Streptophyta. On the right, the taxonomical coverage is represented using a taxonomical tree of all families within the Streptophyta phylum. The families present within the current collection are highlighted in red. The Open Tree of Life (ott3. 3) was used for taxonomy resolving. Download pdf version here. over 17,000 unique samples. Multiple scientific articles originating from the exploration of this unique collection have been published over those years  [4] [5] [6] [7]. Hereafter is a selection of natural products-based scaffolds that entered antitumor medicinal chemistry programs: dimeric derivatives of artemisinin (Q426921)  [8] , flavagline derivatives (Q3073444)  [9] , griseofulvin (Q416096)  [10] , narciclasine (Q18379239) and pancratistatin (Q7130395)  [11] , neoboutomellerone  [12] , and triptolide (Q906351)  [13].
The PFL collection, which is among the largest collection of plant samples worldwide with over 17,000 unique samples, was registered on 1 April 2020 at the European Commission under the accession number 03-FR-2020. This official registration recognizes the legality of the access and management process. It means that the collection meets the criteria set out in the European Access and Benefit Sharing Regulation, which implements at the European level the requirements of the Nagoya Protocol regarding access to genetic resources and the fair and equitable sharing of benefits arising from their utilization [14]. To date, 3 European collections are recognized  [15]. In 2015, the Nature Open Library program was launched in order to share this unique PFL ressource with industrial or academic partners and to foster the research, development, and industrialization of plant assets.
In this context, PFL provided access to their private plants collection, including some rare species  [16]. Upon restructuration, this program was discontinued as of 2018. Some academic collaborations established at this time were kept active. The partnership established between LPBNP and PFL in 2017 had as principal objective the chemical characterization of the full PFL plant extracts collection. In order to evaluate the feasibility of such an ambitious program, a pilot project was defined. It focused on a selection of 1,600 samples (corresponding to approximately 10% of the extracts prepared in the full PFL collection). The results of this pilot study-namely, the generated data and outcomes-are shared in this Datanote. Note: from now on, in this article, the full collection (17,000 extracts) is referred to as the "PFL collection".
Sample collection Aa previously detailed, all samples from the PFL collection were collected with respect to the different regulations around genetic resources at the time of collection. After drying and grounding, they were stored at room temperature, in the dark, in high-density polyethylene barcoded 0. 5-L or 2L-pots. The storage room is access secured and protected by an automatic fire protection system with inert gas (50% nitrogen, 50% argon). Precise localization of the geographical sampling sites, unique IDs, barcodes, and quantities are stored in the PFL internal data management system. Furthermore, to perform later sample inspections (e. g. , in case of dubious botanical identifications), all dry plant parts are also sampled and stored in their intact form. Several constraints were followed while building this collection.
LC-MS/MS analyses Chromatographic separation was performed on a Waters Acquity UPLC I-Class system interfaced to a Q-Exactive Focus mass spectrometer (Thermo Scientific, Bremen, Germany), using a heated electrospray ionization (HESI-II) source. Thermo Scientific Xcalibur 3. 1 software was used for instrument control. The LC conditions were as follows: column, Waters BEH C18 50 × 2. 1 mm, 1. 7 μm; mobile phase, (A) water with 0. 1% formic acid; (B) acetonitrile with 0. 1% formic acid; flow rate, 600 μL•min -1 ; injection volume, 2 μL; and gradient, linear gradient of 5% to 100% B over 7 minutes and isocratic at 100% B for 1 minute. The optimized HESI-II parameters were as follows: source voltage, 3. 5 kV (pos); sheath gas flow rate (N2), 55 units; auxiliary gas flow rate, 15 units; spare gas flow rate, 3.
Data processing and molecular networking The MS data were converted from. RAW (Thermo) standard data format to. mzXML format using the msconvert module, part of ProteoWizard (RRID:SCR_012056)  [19]. The converted files were treated using the MZmine (RRID:SCR_012040) software suite v. 2. 53  [20]. The parameters were adjusted as follows: the centroid mass detector was used for mass detection with the noise level set to 1. 0E4 for MS level set to 1 and to 0 for MS level set to 2. The ADAP chromatogram builder was used and set to a minimum group size of scans of 5, minimum group intensity threshold of 1. 0E4, minimum highest intensity of 5. 0E5, and m/z tolerance of 12 ppm  [21]. For chromatogram deconvolution, the algorithm used was the wavelets (ADAP). The intensity window S/N was used as the S/N estimator, with a signal to noise ratio set at 10, a minimum feature height at 5.
0E5, a coefficient area threshold at 130, a peak duration ranges from 0. 0 to 0. 5 minutes, and the retention time (RT) wavelet range from 0. 01 to 0. 03 minutes. Isotopes were detected using the isotopes peaks grouper with an m/z tolerance of 12 ppm, a RT tolerance of 0. 01 minutes (absolute), and the maximum charge set at 2, and the representative isotope used was the most intense. Each feature list was filtered before alignment to keep only features with an associated MS2 scan and an RT between 0. 5 and 8. 0 minutes using the feature filtering. Peak alignment was performed using the join aligner method (m/z tolerance at 40 ppm), absolute RT tolerance at 0. 2 minutes, weight for m/z at 2, and weight for RT at 1 and a weighted dot-product cosine similarity of 0. 3. The aligned feature list (119,182 features) was exported using the export to GNPS module.
Features occurring in QC samples only or in blanks were removed before molecular networking, resulting in a final feature list of 117,005 features. The MZmine parameters used for the data treatment are available at the referenced link  [22]. The TMAP approach is employed to display the >100,000 spectra resulting from the alignment of the 1,600 samples untargeted MS/MS profiles. In this TMAP, each dot represents a feature's spectrum, and they are linked together according to their similarity. In A, blue dots (36% of the total amount of spectra) correspond to annotated spectra while gray dots (64%) correspond to unannotated spectra. In B, dots are colored according to the botanical family of the sample where the highest MS1 peak area for the corresponding feature was recorded. In C and D, dots are colored according to the NPClassifier superclass and class, respectively, of their annotation (for annotated dots).
g. , principal component analysis, principal coordinates analysis) indicated a strong batch effect that could be tracked down to a specific date and attributed to a change of column during the course of the mass spectrometry analysis of the full collection. Such batch effects are almost inevitable when samples are profiled on long periods (here spanning over several months) and are particularly complicated to mitigate especially when dealing with chemodiverse datasets where poor overlap among the samples is expected. This incentivized us to develop a novel computational mass spectrometry solution for MS2 BasEd SaMple VectOrization (MEMO -[biotools:memo-ms]). MEMO allows organizing samples in large and chemodiverse collections in a retention-time agonist fashion, thus strongly mitigating the batch effect and allowing the comparison of samples acquired over heterogeneous chromatographic conditions.
Applied to the current plant extract collection, MEMO allows efficient reduction of the observable batch effect and clustering samples according to their content  [23]. See the referenced link for a view of the batch effect on the classically aligned dataset and its mitigation using MEMO fingerprints  [24]. To analyze the spectral diversity of the profile collection, a molecular network (MN) was created on the GNPS (RRID:SCR_0 19012) website  [25]  using the. mgf spectra file generated at the previous step  [26]. The precursor ion mass tolerance was set to 0. 02 Da and an MS/MS fragment ion tolerance of 0. 02 Da. A network was then created where edges were filtered to have a cosine score above 0. 7 and more than 6 matched peaks. Further, edges between 2 nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes.
Via the GNPS Explorer interface, it is possible to efficiently navigate through the uploaded spectral file and its associated metadata, see referenced link  [29]. For example, the name of a plant of interest can be typed under the NCBI Taxonomy header, resulting in the direct filtering of the dataset for this specific plant. Individual spectral files can then be selected (and eventually compared if multiple are selected) and viewed using the GNPS dashboard. See, for example, this interactive view of the total ion chromatogram of Desmodium heterophyllum Hook. & Arn (Q10770714) aerial parts. In addition to the MN, spectra (from the same. mgf file used for MN) were organized using TMAP visualization  [30]. In this case, the TMAP corresponds to a minimum spanning tree built from a dense network of spectral similarity.
For the establishment of this visualization, spectra were first translated to documents (2 decimals were used, i. e. , a peak at 100. 3897 would be translated as "peak@100. 39") using matchms (biotools:matchms) and spec2vec (biotools:spec2vec) packages  [31, 32] , with calculation of neutral losses (up to 400 m/z) to the precursor. The spectral doc-uments (i. e. , a list of peaks and losses without intensity information) were then hashed using the MinHash scheme and indexed in an locality-sensitive hashing (LSH) forest that was used to generate the TMAP visualization based on the presence/absence of peaks and losses in spectra  [30, 33]. The generated TMAP is presented in Fig. 2 , an interactive visualization is available  [34] , and the code used for the TMAP generation is available online  [17].
Experimental spectral libraries search The full spectral dataset corresponding to the 1,600 plant extract collection has been uploaded on the MassIVE repository  [36] , and a continuous identification workflow is automatically carried against GNPS experimental spectral libraries. The latest results of this continuous identification workflow can be observed online  [37]. The latest iteration at the time of writing  (18 April 2022)  indicated that a total of 2,665 unique compounds were spectrally matched  [38].
Theoretical spectral libraries search In addition to an experimental spectral library search, we have shown that spectral matching against theoretical spectral libraries of natural products was an efficient way to cover a much wider, yet relevant, spectral space  [39]. Furthermore, we showed that taking into account the taxonomical distance between the biological source of candidate structure and the biological source of the annotated extracts greatly improved the overall quality of the annotation results  [40]. Thus, in addition to the spectral search performed at the molecular networking step against publicly available spectral libraries (see previous section), a taxonomically informed metabolite annotation was performed. For this, we first established a large theoretical spectral database of natural products following a previously established methodology  [39].
This spectral database and associated biological sources metadata were constructed using chemical structure and information compiled during the LO-TUS Initiative's first project (biotools:lotus-db) aiming to establish an open and evolutive resource compiling natural product biological occurrences  [41]. The theoretical spectral database is publicly available  [42]. The biological sources metadata are available online  [43]. The taxonomically informed metabolite annotation was performed using the met_annot_enhancer scripts  [44]. The parameters used for the taxonomically informed metabolite Downloaded from https://academic. oup. com/gigascience/article/doi/10. 1093/gigascience/giac124/6980761 by Université de Genève user on 18 January 2023 annotation process and the resulting tables can be found online  [45].
In Fig. 3B , each of the annotated structures within the 1,600 plant extract collection is displayed (strong blue) within the rest of the reported structures in LOTUS (light gray) indicating a relatively heterogeneous structural coverage of the annotations with some denser areas. Finally, the color coding in Fig. 3C  allows one to distinguish chemical classes in the overall TMAP. The NPClassifier classification is employed  [49]. The framed barplot displays the most frequently annotated chemical classes in the present dataset (strong color) versus the total count for each class in LOTUS (light color). Multiple factors can explain discrepancies between the repartition of chemical classes annotated in the 1,600 extracts collection versus the overall repetition observed in LOTUS. For example, cyclic peptides (green bar in the framed barplot) are poorly covered.
Reusability Regarding data reuse, multiple options exist for such a dataset. Hereafter, we describe some selected use cases. For example, GNPS continuous identification can be explored and retrieved online  [52]  and can be used to compare or combine the results of experimental library matches to theoretical spectral library matches. Since the dataset is also available through the ReDu interface, multiple reanalysis possibilities can be explored. For example, through the https://redu. ucsd. edu/metadataselection  [53]  interface and after selection of the PF dataset by its DatasetAccession value (MSV000087728), a filter for all files of the dataset can be established. Filters of individual species can be obtained by selecting their corresponding NCBITaxonomy terms. Later on, the full set or subset thereof can be selected for Molecular Networking or Library Search at GNPS, for example.
See the complete ReDu documentation for details  [54]. The recently developed Mass Spec Query Language (MassQL) allows efficient search for specific patterns within large spectral datasets  [55]. For example, researchers can look for ionization patterns characteristic of dimeric monoterpene indole alkaloids (MIAs) using the following MassQL query. The results of the query on the 1,600 extracts collection is available here. As an example, this query returns that a feature with a mass of 687. 3570 corresponds to the searched criteria. This page displays the overlap of the MassQL query criteria and the corresponding MS1 and MS2 spectra, and it also indicates that these spectra were found in sample VGF152_B02_pos. mzXML. We can then turn to the GNPS Explorer interface for the 1,600 plants dataset, filter for the sample ID, and see that this sample is an extract of Tabernaemontana coffeoides (Q15376858), which is known to contain dimeric MIA.
Availability of Source Code and Requirements The scripts used to generates the figures of this Data Note are available as follows: Project name: Scripts for the PF1600 project Project homepage: https://github. com/mandelbrot-project/pf_1 600_datanote Operating system: Platform independent Programming language: R (RRID:SCR_001905) and Python (RRID: SCR_008394) License: GNU Affero General Public License v3. 0.
Data Availability All. RAW (Thermo),. mzXML, and. mzML datafiles in positive and negative ionization modes along with metadata and metabolite annotation tables are available on the MassIVE repository under accession number MSV000087728  [36]. Molecular networking job results are publicly available  [56]. Furthermore, all supporting data and materials are also available in the GigaScience GigaDB database  [57].
We tested the model using two independent cross-sectional data sets collected from Instagram and Snapchat users. Our findings, which align with TCIU, suggest that FoMO is positively associated with information overload and compulsive use for both Instagram and Snapchat users. For Snapchat users, FoMO is also positively associated with communication overload and OSWB. The overuse aspect associated with TCIU is explained in the strong positive associations between FoMO and compulsive use among both Instagram and Snapchat users. In addition, OSWB, information overload, and compulsive use are positively associated with social media fatigue for users of both platforms. In contrast, communication overload significantly predicts social media fatigue for Snapchat users only.
Method The data analysis involved two steps. First, we examined reliability and validity using confirmatory factor analysis. The second step involved testing the applicability of the proposed hypotheses using structural equation modeling. We utilized IBM AMOS 25 for the structural model testing and executed descriptive and correlational analyses, common method bias analyses, and mediation analyses using IBM SPSS 25. The mediation effects in the theoretical model were examined by testing a multi-mediation model using bootstrapping  (MacKinnon et al. , 2004).
Common method bias Because the study employed a self-reported cross-sectional design, we needed to confirm the absence of common method bias. We followed a multi-pronged approach for this purpose. First, we adopted a rigorous questionnaire development procedure by conducting a pilot survey and adopting survey measures with sufficient content validity and reliability. We also carefully selected respondents. Second, we conducted Harman's single-factor test using SPSS  (Podsakoff et al. , 2003). The variance explained was 23. 86 % for Instagram users and 37. 29 % for Snapchat users. The variance explained for both data sets was <50 %, suggesting the absence of bias. Third, we applied a marker-variable technique (Zeugner-Roth and Žabkar, 2015), confirming that bias was not an issue.
Results of indirect effects We performed a mediation analysis to investigate the possible mediating influence of information overload, communication overload, OSWB, and compulsive social media usage on the association between FoMO and SMF (Tables  5  and 6 ). We employed Model 4 PROCESS macro in SPSS to examine the direct and indirect effects  (Hayes, 2012). As prior scholars have suggested  (Efron and Tibshirani, 1993) , the absence of zero in the confidence intervals proves the presence of indirect or direct effects. As mentioned above, FoMO had an insignificant direct effect on SMF among both Snapchat and Instagram users. Even when the model included information overload, communication overload, OWSB, and compulsive social media usage, the direct relationship between FoMO and fatigue continued to be insignificant for both Snapchat and Instagram users (see Table  5 ).
Limitations and future work The present study has two main limitations. The first relates to the context in which the data were collected, i. e. , young adult Instagram and Snapchat users from India. Due to this limited context, the transferability or generalizability of the findings to other cultural or demographic settings (i. e. , other age groups) may likewise be limited. The second limitation relates to the study design. Our research utilized a cross-sectional design, and it is, therefore, unable to indicate causal effects between the study measures and provide evidence for associations at a singular point in time. Such studies cannot reveal changes in these relationships over time. To address these limitations, we recommend possibilities for future work. For example, scholars should evaluate our research model with participants recruited from other cultural and demographic settings, i.
To deal with this issue, we propose forming mutants that are in some sense natural; meaning that the mutated code/statement follows the implicit rules, coding conventions and generally representativeness of the code produced by competent programmers. We define/capture this naturalness of mutants using language models trained on big code that learn (quantify) the occurrence of code tokens given their surrounding code. We introduce µBert, a mutation testing tool that uses a pre-trained language model (CodeBERT) to generate mutants. This is done by masking a token from the expression given as input and using CodeBERT to predict it. For example, given the masked sequence int a = <mask>;, CodeBERT predicts that 0, 1, b, 2, and 10 are the (five) most likely tokens/mutants to replace the masked one (ordered in descending order according to their score -likelihood).
Theorem 4. 4 (Ganian et al. [24]) There exists an algorithm working in time O(|V (G)| 3 ) which transforms any rooted treecut decomposition (T , X ) of G into a nice treecut decomposition of the same graph, without increasing its width or number of nodes. The following property of nice decompositions is extremely useful in designing the dynamic programming algorithms. Auxiliary problems and algorithms. To show that ComPath is fixed-parameter tractable with respect to the treecut-width we provide a dynamic-programming algorithm on the treecut decomposition. In the individual steps of the dynamic program we will need to solve the more general problem of finding compatible vertex-disjoint paths between given pairs of vertices. We now introduce this problem and a restricted variant that occurs in a special case of the dynamic program, and we provide a fixed-parameter algorithm for the more restricted variant.
Let G be a graph. We say that W is a set of terminal pairs of G, if it consists of pairwise-disjoint two-element subsets of V (G). If W is clear from the context, we also simply call the elements of W terminal pairs and each of them consists of terminals. Recall that by T G we denote the transition system of G (again, we omit the subscript if it is clear from the context). Below we will also say that a set of pairwise disjoint paths as above is a solution to the instance (G, T G , W ). Clearly, an instance (G, T G , s, t) of ComPath is equivalent to an instance (G, T G , {{s, t}}) of ComVDP. Our dynamic-programming approach is inspired by the XP-algorithm for finding edge-disjoint paths between given pairs of terminals, described by Ganian and Ordyniak  [25]. However, we need modified dynamic-programming records and a more careful analysis to be able to ensure that subsolutions respect the transitions.