dataesr/scientific-paragraphs-categorization huggingface dataset:
The discovery of natural bioactive oligopeptides has been widely studied in recent years. To detect new and unexpected biomarkers, an untargeted peptidomics approach is often applied to characterize the entire oligopeptidic profile. Such studies usually start with the purification and the extraction of the peptidome (peptides~0. 4-12 kDa). The peptide-enriched samples are then analyzed by tandem mass spectrometry (MS/ MS)  8, 9  so that the most abundant peptides are identified by matching fragment masses with predicted MS/MS spectra of "digested" protein sequences in the database (Mascot 10 , MaxQuant  11  and others). In addition to untargeted peptidomics, oligopeptidic biomarkers have been reported from MS-based untargeted metabolomic studies  12, 13. By investigating the complete set of low-molecular-weight metabolites (majorly <1 500 Da) in a biological system  14  , untargeted metabolomics, especially high resolution MS-based, can cover a part of the peptidome that consists of shorter oligopeptides  15.
In other words, suppose an unknown mass signal (e. g. 780. 475 Da) is detected, how do we decide whether the signal could be an oligopeptide without knowing its fragments? Searching the m/z value in a metabolite library seems to be a straightforward solution. However, such databases contain a limited number of oligopeptides since the number of possible amino acid combinations could easily reach millions with increased peptide length  19. For instance, the METLIN database  20  currently contains di-, tri-and some of tetra-and penta-peptides, whereas the 100-1 500 Da mass range could cover longer peptides (e. g. the MW of the heptapeptide β-casein f169-175 is 780. 475 Da  21  ). In addition, these databases are usually limited to peptides that consist of proteinogenic amino acids. A database-independent solution to annotate any potential oligopeptides is to decompose the m/z value into a combination(s) of amino acids.
Böcker et al. 22  has related this task to the well-known Money Changing Problem (MCP) and developed an efficient algorithm called DECOMP. Their web server (http://bibiserv2. cebitec. uni-bielefeld. de/decomp) can decompose thousands of masses in a short time. We have tested several metabolomics datasets on their server. Based on the results, each mass signal from an untargeted MS screening can be annotated to: i) unique amino acid combination (UAAC) ii) multiple amino acid combinations (MAAP) or iii) no possible combination. UAAC mass signals are interesting targets for LMW peptidome studies. Firstly, their sequences could be easily validated by fragmentation and de novo sequencing  8. Furthermore, by studying the amino acid difference between two UAAC signals (sequenced or not), we could generate interesting biochemical hypotheses.
In the fMDN, two nodes (two mass signals) are connected if their mass difference (edge between nodes) agrees with one of user-defined repetitive functional units. In the PDN, to mimic the peptide degradation, we restrict the functional units to the loss of one or a combination of amino acids. Therefore, each node represents a UAAC mass signal, and a higher MW signal (e. g. A2P2) is connected to a lower one (e. g. P2) only if it contains all its amino acids. The OligoNet web server that we developed allows rapid annotation of UAAC and MAAP signals from untargeted metabolomics data, followed by the PDN construction, visualization and network structure analysis. As a complementary study, UAAC signals are annotated in the KEGG database  28  and visualized in metabolic pathways along with other putative metabolites in the dataset.
Program description Implementation details. OligoNet is a web server developed in R. On the server-side, m/z values from MS screening experiments are first decomposed into a combination(s) of user-defined amino acid. The closest related MCP can be resolved in two ways on OligoNet: i) using a recursive function written entirely in R, ii) using the third-party web server DECOMP  22. For the second solution, metabolomic data are first converted to the JSON format and requests to DECOMP are performed using the Rcurl library  29. The directed PDN is built as an igraph object  30  based on all identified UAAC signals. OligoNet uses the visNetwork package for the network visualization. The KEGGREST library is used for the mass annotation in the KEGG database and for the visualization of metabolic pathways.
Upload metabolomics data. The required metabolomics data (File 1) for OligoNet is a mass peak list from high-field MS experiments (neutral masses or positive/negative m/z). The first line of the file must contain a header with desired column names. The dataset has to be organized as follows: i. First column: unique IDs assigned for each mass signal. ii. Second column: a list of experimental or theoretical masses without any adducts, such as Na, K, Br and Cl. It is not recommended to include masses higher than 1 600 Da. Masses should be rounded to the same decimal places. iii. Third column: the intensity of each mass signal (in case of direct-infusion MS) or the area of their corresponding chromatograph peaks (in case of LC/GC-MS). iv. More columns (optional): users could add more intensities or areas from the 4th column if multiple samples are available.
It is recommended to provide at least 10 samples for a better evaluation of correlations between network nodes. User could add all additional information about mass signals in File 2 (optional), such as the retention times, statistical scores and p-values. This information will be displayed when visualizing the PDN. File 2 must have the same number of mass signals arranged in the same order as File 1 and its first column must be IDs. File 1 and File 2 must have identical IDs. All datasets uploaded must be tab-separated. txt or. dat files. Perform mass decomposition. By default, OligoNet uses 19 proteinogenic amino acids as subunits of mass decomposition (instead of 20) because of the two ambiguous by mass (Leucine/Isoleucine). Users could customize the subunits in File 3 if users do not want to use the default 19 common amino acids or wish to include non-standard amino acid/post-translational modifications.
We note that the first line of the file must be the molecular weight loss of the condensation reaction (by default the loss of a H 2 O molecule). The two columns of the file should be the names or symbols of the subunits and their corresponding neutral exact mass. It is compulsory to specify the tolerance window (in Dalton) of annotation, with 0 if you have theoretical exact masses. The annotated UAAC and MAAP signals, along with absolute annotation errors (in ppm), are displayed in the tab-panels "Mass decomposition-UAAC-Annotation" and "MAAP-Annotation". They can be also downloaded as a data matrix in a ". txt" file, where each row corresponds to the decomposition results of each mass feature. Compared to the third-party server DECOMP, output of OligoNet is more easily combined with other information about the mass features (statistical analysis, retention time, etc), and is more adapted to metabolomics studies.
The default implementation for mass decomposition (the R recursive function) is recommended for datasets that contain fewer than 500 mass signals. Otherwise, if users prefer to request the third-party server DECOMP, the checkbox "Use DECOMP server" needs to be ticked. Users could also perform the mass decomposition directly on their website (https://bibiserv2. cebitec. uni-bielefeld. de/decomp). During the data submission, the checkboxes "chemically plausible decompositions" and "actual mass for each decomposition" must be disabled and "deviation from query mass for each decomposition" must be enabled. OligoNet could then display UAAC and MAAP annotations and perform further analysis based on the DECOMP job id. Network construction and visualization. The PDN is a directed acyclic graph (DAG) G = (V, E) in which the set of vertices, V, represents annotated UAAC signals, and the set of edges, E, represents "degraded amino acids", or the amino acid differences (ΔAA) between UAAC annotations.
In fact, the high correlations indicate a strong biochemical dependence between two network nodes  31. An interactive network visualization is provided once the network is built. Users could export a Cytoscape-compatible  32  network file for a visualization outside OligoNet. Network visualization and topological analyses are available in the tab-panel "Network results" (details in the Results session). Oligopeptides in the metabolic network. All mass signals in File 1 are also annotated in the KEGG compound database (http://www. genome. jp/kegg/compound/). The compound IDs of annotated peptides are available in the ". txt" file along with peptide annotations. Furthermore, OligoNet displays in the tab-panel "Peptides in KEGG" metabolic pathways in which these peptides are involved for an organism chosen.
Annotation of yeast LMW peptidome. To illustrate the results of a typical OligoNet job, we use a previously published dataset derived from the extracellular metabolic profiles (exo-metabolome) of 15 yeast Saccharomyces cereviesae strains  12. Mass spectra of 45 grape wines fermented by yeast (biological triplicates for each strain) were acquired in positive full scan mode on a Bruker solariX 12 Tesla FT-ICR-MS (Bruker Daltonics, Bremen, Germany). With a time domain of 4 megawords in the 100-1 000 m/z range, the resolving power of the mass spectra reached 400 000 at m/z = 400 and the internal calibration error was under 0. 1 ppm. After spectra alignment and elemental formula annotation using the mass difference network (MDiN) approach  27, 33  , the dataset uploaded to OligoNet contains 18253 neutral theoretical masses detected in 45 samples (m/z values and intensities).
In this study, we are not interested in the possible sequences of each peptide, but rather in how each mass can be decomposed into "monomers". Therefore, to simplify the DECOMP algorithm, glutamine (Q) and asparagine (N) were removed from the 19 default subunits for not being monomers: the glutamine is a dimer consisting of one glycine (G) and one alanine (A), while the asparagine (N) a dimer of glycines (G). With the new 17 amino acid settings, the DECOMP algorithm has annotated in total 602 UAAC and 1 004 MAAP signals in the dataset. Such a high amount of putative annotations unraveled an unprecedented diversity of oligopeptides in the yeast exo-metabolome. Among all 1 606 annotated mass signals, only 98 (free amino acids included) were also annotated in the KEGG database. In this study, the mass decomposition had a clear advantage in revealing the LMW peptidome richness over database annotation: putative annotations obtained by mass decomposition are extremely diverse in terms of peptide length and amino acid composition.
Characteristics of the peptide degradation network. The PDN is a hypothetical metabolic network that collects all potential hydrolysis reactions between annotated peptides. The PDN building is merely based on the metabolic profile, a static snapshot of physiology of the cell. As for other metabolomic data-driven networks, we need to evaluate the potential of the PDN to allow unbiased and comprehensive studies of peptide degradation systems. Indeed, the behavior of well-known complex biological systems usually emerges from the pairwise dependence between its components  34. In the PDN, such dependence can be proved through statistical correlations between its nodes. The PDN that we evaluated was built from UAAC signals of the yeast metabolomics dataset. Free amino acids were removed since they have a strong influence on the network topology by connecting to most UAAC signals, and might lead to biased interpretations.
Spearman correlation coefficient ρ was calculated for each connection of the PDN, and the density plot of 2 134 correlation coefficients was compared to random mass pairs selected from the entire FT-ICR-MS dataset and from all UAAC annotations (Fig. 1 ). We observed that the correlations in the MS dataset was clearly a zero-centered Gaussian (Fig. 1A ). When we sampled only from UAAC signals, a second Gaussian distribution appeared at ρ = 0. 8 (Fig. 1B ). This distribution was more pronounced for the correlations of PDN edges (Fig. 1C ). These comparisons proved the biochemical dependence between annotated peptides, and even stronger dependence between PDN connected components. To further explore the biological details of the PDN and origins that contribute to its formation, we performed a series of topological analysis on the yeast PDN.
Results of OligoNet on a lower resolution metabolomics dataset. OligoNet was tested on a lower resolution metabolomics dataset. Same 45 wine samples were studied on a TOF-MS system (Synapt HDMS aoQTOF, Waters, Milford, MA) coupled to the ACQUITIY UPLC (Waters, Milford, MA). The reversed-phase (RP) gradient and positive ionization mode MS settings for LC-MS metabolic profiling was described in Liu et al. 12. The resolving power and mass accuracy of the TOF-MS was 10 000-50 000 and 1 ppm to 5 ppm, respectively. Metabolic features were extracted based on their mass and retention time (RT) pairs after spectra alignment. Isomers with the same mass and different RTs were considered as separate features. We used experimental neutral masses for mass decomposition with default subunit settings. With a tolerance window of 0.
Some connected vertices in PDN clearly reflected these influencing factors. For instance, the node "A1P1" has considerably lower retention time than 6 out of 8 surrounding longer peptides that contain "P1L1" and other residues (Fig. 3A ). In summary, OligoNet reflects the chemical diversity of LMW peptidome from yeast metabolomics data with lower mass resolution. Despite much lower mass accuracy (because of the TOF-MS and the fact that the masses were not annotated), hypotheses generated by PDN seem to be comparable and consistent between FT-ICR-MS and LC-MS data for the same set of samples.
Results of OligoNet compared to publicly-available databases. To validate the potential biological meaning of OligoNet annotations, metabolomics datasets were additionally annotated by the bioactive peptide database PeptideDB  46. We present here the results of two additional example datasets "Positive_Doing. txt" and "Wine_data. txt" available on the OligoNet server. 18 and 12 masses were assigned by PeptideDB from Doping and Wine, respectively. All 30 masse were annotated by OligoNet, and the amino acid compositions suggested by OligoNet were in concordance with the database results (Table  S2 ). This validation confirms the potential of OligoNet to discover bioactive peptides. To give a more rigorous validation of how precisely OligoNet could annotate real peptides, we extracted randomly 467 masses from the publicly available HeLa label-free proteomics dataset  47  (identifier: PXD000612 on proteomexchange).
All masses in this dataset were annotated by the human Uniprot FASTA database along with their sequences. Before importing this mass list to OligoNet, a random error of an amplitude of 0. 01 Da was added to each exact mass to mimic the instrumental mass deviations. Despite the added error, all 467 masses were annotated by OligoNet, and the amino acid compositions of all 467 masses suggested by OligoNet were in agreement with the peptide sequence deduced by the standard proteomics approach (Table  S3 ). These findings indicate that OligoNet is sensitive to detect peptide features and could provide reliable annotations for experimental mass signals.
Discussion To illustrate how the web server could add values into an untargeted metabolomics study, a workflow for biomarker discovery using the above-mentioned FT-ICR-MS-based yeast metabolic profiling data is provided (Fig. 4 ). Aiming at discovering new yeast-derived peptides involved in the stimulation of bacteria-driven malolactic fermentation (MLF), this study is a continuation and extension of the work described in Liu et al. 12. In this earlier work, metabolic profiling was performed for 15 yeast strains (S1-S15) in triplicates. These strains held reproducible MLF-compatibility scores (from 5 to 1, 5 being the most compatible, 1 being the least compatible). The FT-ICR-MS dataset was statistically evaluated to extract mass features whose level was positively correlated with MLF-compatibility scores.
Liu et al. has suggested oligopeptides as an important family of MLF-stimulatory compounds via elemental formula and database annotation of these discriminant features. To further characterize the biochemical nature of potential LMW peptides, the web server was used in the current workflow to allow: i) a fast identification of potential peptides among discriminant features (Fig. 4C ); ii) the association of specific subnetwork of PDN with MLF stimulation (Fig. 4D ). Based on i), we performed LC-MS 2 experiments on discriminant masses that could be decomposed into combination(s) of amino acids, and putative structures of some MLF-stimulatory peptides were determined via de novo sequencing (Figure  S5  and Table  S1 ). We confirmed that the results of de novo sequencing agreed with their annotated amino acid combinations (Table  S1 ).
Conclusion OligoNet is a web-service that facilitates the extraction of potential oligopeptides from untargeted metabolomics datasets. After a MS full scan experiment, OligoNet provides a rapid, database-free solution to find the amino acid composition of all potential peptides under 1 500 Da. However, the main purpose of the software is the biological analysis of oligopeptides with 3-8 residues (300-900 Da) for which little information is available in metabolomics or proteomics databases. Our software was designed to provide more information about them and to fill in this blank space of databases. The software is adapted for both ultrahigh resolution and high resolution direct infusion or LC/GC-MS, and for both annotated and original MS data. Considering that no specific peptide enrichment or separation techniques were introduced, the outputs of OligoNet on two yeast metabolic profiling datasets unraveled an unprecedented diversity of oligopeptidic features including both known and unknown peptides.
That's why multiple biochemical hypotheses can be generated from specific sub-networks such as the high-degree vertices and long network paths. From these detected structures, or "patterns", some potential functions of the LMW peptidome can be inferred based on statistical analysis (e. g. p-values of vertices in the pattern) and literatures (such as the antihypertensive "A1P1"  40  ). Also, we have shown in the manuscript the possibility and a workflow to validate biologically the observed patterns. Characterization of yeast-derived peptides involved in MLF-stimulation using the web server OligoNet (A) Mass features that could be decomposed into a combination(s) of amino acids were considered as potential peptides. (B) Each potential peptide was evaluated statistically for biomarker discovery.
The former consists in revising only the concept solution whereas the latter revises the solution at the Inventive Principle level by choosing a different IP and interpreting the concept solution accordingly. • Generative adaptation: the user does not find any Inventive Principle useful to solve his problem so he reformulates his problem at a more detailed level using Su-Field model 2  and retrieve the solution in the Effects database  3. In order to organize the transformational adaptation in a systematic way, we propose a three-level adaptation scheme to store the adapted solutions: the IP groups level, the IP level and the concept solution level as it is illustrated in the rectangle at the right of figure 2 (based on  18  ). We apply the LSA method  5  to group the 40 IPs into three groups (Geometrical, Physical and Chemical) and in each IP group, we store the IPs based on their common semantic meanings.
Case study In order to illustrate the proposed approach, we have developed a software prototype, with Java 1. 8 as programming language, with a MySQL 5. 7. 14 database on a Windows environment. We used also myCBR  4  to build our CBR system. The initial case base is composed of 47 old cases that has been solved by engineering students and experts in different domains. These domains include human necessities; performing operations, transporting; chemistry, metallurgy; textiles, paper; fixed constructions; mechanical engineering, lighting, heating, weapons, blasting; physics and electricity. To evaluate the feasibility of our proposal, we will show step by step how to solve a new specific problem: Recycling bins are commonly seen in public spaces with the function of sorting glass waste and normal waste in order to better recycle them.
The scalable extension (SHVC) of the High Efficiency Video Coding (HEVC) allows encoding in layers a video with multiple quality level such as resolution, bit-depth or Signal to Noise Ratio (SNR). Compared to the equivalent HEVC simulcast, the SHVC extension provides inter-layer prediction mechanisms enabling significant bit-rate savings. Moreover these inter-layer prediction mechanisms are less complex than those from former standards. Therefore, SHVC seems a promising solution for both broadcast and storage applications and is considered in the ATSC 3. 0 as video coding solution. Indeed the spatial scalability is an application use-case considered in the ATSC 3. 0. This paper proposes a scalable multi-layer architecture combining pipelined software HEVC encoders. The proposed architecture provides a good trade-off between parallelism over layer and latency.
This scalable extension is called the Scalable High efficiency Video Coding (SHVC)  [7]  and enables scalable video coding including spatial and bit-depth and Signal to Noise Ratio (SNR) and color gamut and codec scalability features. The HEVC reference software (HM)  [8]  and the Scalable HEVC reference software (SHM)  [9]  provides both an encoding and decoding solution for HEVC and SHVC, respectively. The different video quality levels enable the scalable video coding to adapt the encoded bitstream according to decoder capabilities and network conditions. For example, it enables preventing errors resilience from traffic congestions. These requirements can be found in applications such as video streaming and video conferencing applications for instance  [10]. Alternatives to scalable video coding in such applications includes simulcast approach.
Moreover the Advanced Television Systems Committee (ATSC) 3. 0  [13]  considers the SHVC as a video coding solution candidate. The ATSC defines a broadcast solution mainly employed in the US. This broadcast solution enables applications such as video conferencing that consider a realtime constraint on the employed video coding solution. In previous works  [14] ,  [15]  authors presented the employed architecture for scalable video coding with a spatial scalable application. In this paper, authors propose an enhancement of this spatially-scalable software SHVC encoder based on a professional real-time HEVC encoder. With an ATSC broadcast context, the proposed SHVC encoder performs real-time encodings. It integrates enhancements in the coding rate thanks to various optimisations on filtering operations that enable interlayer prediction on I and P slices in real-time.
To the best of our knowledge, there are real-time and parallel software SHVC decoders  [16]  but there is no real-time SHVC encoder. The proposed encoder is the first real-time and parallel spatiallyscalable SHVC encoder addressing broadcast target. The rest of this paper is organised as follows. The SHVC extension is presented in Section II. Section III details the proposed real-time SHVC encoder architecture and its optimisations. Performances in terms of coding rate and bit-rate savings of the solution are presented in Section IV. Finally, Section V concludes this paper.