dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion: Similarities and differences relatively to VectorPU Let us compare the formal definition of the coherence protocol, Figure  5  (valid for simple memory locations or arrays), with the VectorPU implementation of the protocol for simple arrays, Figure  3. Except the order of operations and minor changes, the code is similar. The main difference is that there is no view of abstract vs. concrete variables, however, if we consider that transfer operations on abstract variables have no effect, and that validity status of concrete variables can be abstracted away, the code is the same as the formalisation. Taking a more global point of view, no verification is performed by the Vec-torPU framework and thus, the property of "well-declared programs" is not checked currently by the framework.
In the current state of the library, the property "well-declared programs" must be ensured by the programmer. The implementation of VectorPU relies on the hypothesis that the function declarations are correct, because of this the current formalisation is a significant step forward as it allows us to express precisely what assumption is made by the library on the programmer's code. Such a check could either be done by runtime verification checking that each function performs exactly the required access, or statically by constraints on the program and a static analysis (involving some approximations, meaning some correct programs could be rejected). The first solution is not acceptable considering the target application domain because of the overhead involved by the dynamic checks. Let us now try to figure out how difficult it would be to ensure that a given program verifies the "well-declared" property statically.
Conclusion and future works In this article we provided a formal approach to verify the consistency of the memory accesses in heterogeneous computer systems made of two memory spaces. We formalise the operations of memory accesses and memory synchronisation between the two memory spaces and prove that a program adequately annotated with informations on the memory accesses always access valid memory spaces and tracks correctly which of the memory space contains the up-to-date data. The practical result is that we can verify the coherency mechanism used by the VectorPU library and ensure that, additionally to the significant performance benefits of the approach, the VectorPU mechanisms is correct and ensures the consistency of the memory accesses. We also extended our model for studying the effect of operations made on overlapping arrays.
The reverse transcription (RT) was performed, using ProtoScript II (in general) or SuperScript IV (for RT test--Supplement Figure  S11 ), during 1 h at 50 DNA library quantification and quality control. DNA libraries were quantified using the Qubit dsDNA HS (High Sensitivity) Assay Kit with the Invitrogen benchtop fluorometer Qubit 2. 0. Each library (amplicon and synthetic oligonucleotide) was analysed, and quality evaluation by automated electrophoresis on the Agilent 2100 Bioanalyzer system was performed, following the Bioanalyzer High Sensitivity DNA Kit instructions. Sequencing method. Libraries were subjected to highthroughput sequencing using an Illumina MiSeq instrument with single-read (SR50) or paired-end (PE75) mode. Prior to loading on the sequencing chip, the multiplexed libraries were diluted to 6-8 pM final concentration.
Bioinformatic analysis NOseq alignment. NOseq Illumina output demultiplexed FastQ files were unzipped. The nucleotide sequences (FASTA format), read information (identity number) and their corresponding quality scores (encoded in ASCII) were extracted, compiled and analysed using Python. The bioinformatic pipeline is single file dependent and each file is associated to a reference sequence. Prior to the alignment of the reads, the whole reference sequence was divided into smaller overlapping segments with identical size (k-mer of size k = 11 per default) and indexed by starting position. In order to increase the algorithm speed, k-mers were alphabetically ordered. The alignment process is relying on seed-and-extend strategy. Sequenced reads were individually segmented into k-mers of size k, scanned against the reference sequence k-mers and scored according to the asymmetric substitution matrix (Supplement Figure  S5 ).
In case of matching seeds, the alignment was then extended as well as the score associated. Regarding the +1/-1 reward/penalty system and by allowing mismatches according to partial deamination (Figure  1 ), only the reads with a score equal to their length were kept. Furthermore, in order to avoid shifting of the reading frame, sequencing errors and to have a homogeneous coverage, only reads starting at the first nucleotide of the reference sequence were selected for further evaluation. In addition, this strict selection process is applied in order to limit the occurrence of NGS errors within the short sequencing reads  (54). Finally, the totality of perfectly aligned reads was used for alignment analysis. NOseq data analysis. From the aligned reads, the number of adenosine (A), cytidine (C), guanosine (G) and thymidine (T) was summed for each position and corresponding frequencies were plotted using matplotlib python library.
In addition, only A positions in the reference sequence were selected and associated A and/or G rates were plotted. From this analysis, the cumulative density function of the normal distribution was calculated for each A position using the python package scipy. stats. norm and plotted together with the detection threshold at 0. 95 (red line) (see Figure  2  as example). Above this limit, 5% of the most distant A rates to the overall distribution was observed, highlighting A sites with a high probability to behave differently to the other positions, i. e. presenting m 6 A candidate sites under deamination conditions. NOseq UMI assessment. NOseq data were analysed in order to determine the read redundancy by assessment of the unique molecular identifiers (UMI). The UMI sequence (7 nt) of every aligned read was determined and the dataset was checked for duplicates (reads with same UMI sequences).
A range of conditions leading to partial deamination of RNA could be determined (with Ato-I conversion between 10% and 50%), within which the sequencing output was optimized in terms of aligned reads and m 6 A signal strength, i. e. the data were evaluated for the fraction of aligned reads and maximum m 6 A signal intensity. Importantly, partial deamination prevented the use of conventional alignment algorithms. As shown in Figure  1C  (and Supplement Figure  S3 ), native adenosine residues gave rise to either adenosine or guanosine signals, the latter deriving from the fraction having undergone deamination to inosine. Similarly, cytidines yielded cytidine or thymidine signals, respectively. At guanosine positions in the reference sequence, the analysis yielded a vast majority of guanosine signals, with a small remainder of adenosine.
ACKNOWLEDGEMENTS We are grateful to  Dr. Lilia Ayadi  and  Valérie Igel-Bourguignon  ( EpiRNA-Seq Core Facility ,  UMS2008   IB-SLor ,  CNRS-Université de Lorraine-INSERM, Nancy, France)  for excellent technical assistance in Illumina MiSeq sequencing, the  IMB Core Facility Genomics  for their helpful support and use of its  Illumina NextSeq machine  ( INST 247/870-1 FUGG ), and  Anke Busch  from the  IMB Core Facility Bioinformatics  for analyses of miCLIP data. We want to further thank  Dr. Lyudmil Tserovski  for his contributions during early stages of this project.
We demonstrate the high competitiveness of the proposed algorithm compared to four state-of-the-art algorithms. As an additional assessment, we show an application of using the algorithm to detect community structures in complex networks. • Third, this work advances the state-of-the-art in terms of effective solving of the general minimum conductance graph partitioning problem. Given that the studied problem has a number of applications, the proposed algorithm can be usefully applied to these practical problems (we will make the code of our algorithm publicly available). Finally, the ideas of some underlying algorithmic components are of general interest and could be advantageously adapted to design effective algorithms for other large graph optimization problems. The reminder of the paper is organized as follows.
Benchmark instances The two sets of benchmarks include 60 large and massive graphs which are from two sources and have 60,005 to 23,947,347 vertices. The 10th DIMACS Implementation Challenge Benchmark. This dataset contains 50 graphs which are dedicated to two related problems of graph partitioning and graph clustering. These graphs belong to 6 families: Clustering instances, Delaunay graphs, Redistricting, Walshaw's graph partitioning archive, Co-author and citation networks, and Sparse matrices. They are available at: https://www. cc. gatech. edu/dimacs10/downloads. shtml. The Network Data Repository online. This dataset contains 10 massive real-world network graphs  [35]. This dataset can be found at: http: //networkrepository. com/index. php. Additionally, we also test our approach for community detection on 3 popular real-world complex networks (Zachary's Karate Club, College Football Network and Bottlenose Dolphin Social Network) available at: http://www-personal.
Experimental setting The proposed MAMC algorithm was coded in C++ 1  and compiled using GNU g++ 6. 3. 0 compiler with the "-O3" flag. The experiments were conducted on a computer running Ubuntu Linux 16. 04, using 6 cores of AMD Opteron 4184 CPU @ 2. 80GHz and 32GByte RAM. To evaluate our results, we adopt as our references the following four state-ofthe-art algorithms in the literature. • Metis  [21] : This is a general and popular graph partitioning package which has been used to generate partitions in several studies on MC-GPP  [1, 22, 25]. As shown in  [29] , even if Metis does not directly optimize the conductance criterion (it minimizes the number of cut edges), it can produce partitions of relatively low conductance within a very short computation time. In our study, Metis is used to serves as a baseline reference, as well as to create initial partitions of the MQI and SaBTS methods (see below).
For this work, we used the latest release Metis 5. 1. 0 available at: http://glaros. dtc. umn. edu/gkhome/metis/metis/overview. • MQI  [22] : This is a max-flow quotient-cut improvement algorithm which refines a given initial partition. In previous studies and this work, MQI starts with a partition provided by the fast Metis tool. To our knowledge, MQI is one of the best algorithms for MC-GPP. We used the latest implementation of MQI available at: https://github. com/kfoynt/ LocalGraphClustering. • SaBTS  [29] : This is the most recent metaheuristic algorithm for MC-GPP, which combines a dedicated tabu search procedure and a self-adaptive perturbation procedure. The intensive experiments reported in  [29]  show that SaBTS is able to consistently improve a partition provided by Metis and can also reduce the conductance of partitions given by MQI (see below).
The code of SaBTS is available at: http://www. info. univ-angers. fr/ ~hao/ mcgpp. html. • MQI+SaBTS  [29] : This hybrid approach uses SaBTS to refine a partition produced by MQI. Thanks to the combination of these two powerful approaches (MQI and SaBTS), MQI+SaBTS performs the best compared to other existing approaches. The proposed MAMC algorithm requires 3 parameters: population size p, tabu tenure management factor α and depth of tabu search d. Following previous studies  [14, 44] , we adopted a small population size and set p = 20. For α and d, they were fixed according to the analysis reported in Section 5. 1. For our experiments, we consistently used the parameter setting shown in Table  2  to run our MAMC algorithm to solve all instances. This parameter setting can also be considered to be the default setting of MAMC.
The quantities P s (1) and P s (r f ) are called efficiency and robustness of s respectively. Fig. 3  shows the performance profiles of our MAMC algorithm as well as the reference algorithms which have been drawn with the software perprof-py  [39]. From the figure, we observe that MAMC has a very good performance, surpassing the four reference algorithms in terms of conductance value. Indeed, MAMC has the highest value of P s (1) among the compared algorithms, meaning that MAMC can quickly find the lowest conductance for the tested instances. Besides, MAMC also attains a good robustness by quickly solving all the instances (corresponding to the fact that MAMC arrives at P s (r f ) first). This experiment thus demonstrates the competitiveness of the proposed MAMC algorithm for solving MC-GPP compared to the 4 state-of-the-art reference methods.
The chemical diversity of biologically active fungal strains from 42 Colletotrichum, isolated from leaves of the tropical palm species Astrocaryum sciophilum collected in pristine forests of French Guiana, was investigated. The collection was first classified based on protein fingerprints acquired by matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) correlated with cytotoxicity. Liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-HRMS/MS) data from ethyl acetate extracts were acquired and processed to generate a massive molecular network (MN) using the MetGem software. From five Colletotrichum strains producing cytotoxic specialized metabolites, we predicted the occurrence of peptide and cytochalasin analogues in four of them by MN, including a similar ion clusters in the MN algorithm provided by MetGem software.
This method compares microbial peptide/protein fingerprint against reference databases  16. To improve classification when using large microorganism collections, machine learning technique, such as the Aristotle Classifier, has been introduced in the literature  17. Moreover, the identification accuracy is increased by combining the data from MALDI-TOF MS and FTIR spectroscopy and hierarchical clustering analysis  18. Alternatives to MALDI profiling has been recently introduced by the group of Takats demonstrating that automated laser-assisted rapid evaporative ionisation mass spectrometry (LAREIMS) platform can chemically screen over 450 yeast colonies in under 4 h, while simultaneously generating recoverable glycerol stocks of each colony in real-time  19. First dedicated to the identification of human pathogenic or opportunistic yeasts and bacteria, some studies recently developed spectral databases for a more systematic identification of filamentous fungi  20, 21.
Nevertheless, these databases still focus on clinical isolates. Few studies reported MALDI-TOF MS as a tool for strain identification for environmental microorganisms  22  and demonstrated the correlation of MALDI classification of microorganism together with production of specialized metabolites  23. Forty-two endophytic and cultivable Colletotrichum strains from healthy leaves of a tropical palm species, Astrocaryum sciophilum Miq. (Pulle), Arecaceae were studied. A. sciophilum is a long-lived palm that grows in the understory of tropical forests of the Guiana Shield of Amazonia, and can be locally abundant  24. A methodology was developed to acquire protein MS profile of Colletotrichum strains by MALDI-TOF MS to decipher the variability of our collection. Colletotrichum strains with similar phenotypes were recognized thereby reducing the number of strains of interest.
For each isolate, a protein extract was analyzed by MALDI-TOF MS. Fingerprints showed that some of our Colletotrichum isolates share the same protein profiles whereas fifteen strains are characterized by a unique one (Figure  S1  and Figure  S3-S22 ). Considering previous reports on strain identification by MALDI-TOF MS and the specificity of protein fingerprints among species  21, 22  , we assumed that each protein fingerprint refers to a Operational Chemically Unit (in reference and comparison with Operational Taxonomic Unit in genetic or OTU) within the genus. This expectation has been confirmed through the classification by MALDI-TOF protein profiling and hierarchical clustering data analyses of the complete collection of environmental microorganisms at CNRS-ICSN, containing more than 1000 unique strains.
Thus, from the 20 different protein fingerprints drawn from the 42 Colletotrichum isolates (Figure  S1 ), our collection can be realigned into 18 different chemotypes OCU. These OCUs (noted from Colletotrichum sp. 1 to Colletotrichum sp. 18) are classified following a hierarchical clustering based on their protein fingerprint (Fig. 1 ). Inactive BSNB-0703, BSNB-0290 and BSNB-0646 isolates do not appear in this hierarchical clustering because of the low quality of the recorded spectra. The chemical diversity of extracts was analyzed to prioritize the isolation of active metabolites. To this end, a massive molecular network (MN) was generated from the liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-HRMS/MS) data acquired from the EtOAc extracts of our 42 Colletotrichum isolates (Fig.
2D ). The MN did not explain the different biological activities among the OCU Colletotrichum sp. 6 strains. BSNB-0627 was then excluded from further chemical investigation. As OCU Colletotrichum sp. 1 and sp. 12 had the same metabolite composition, we finally focused on four different metabolite profiles coming from the five cytotoxic taxa. The study of OCU Colletotrichum sp. 14 and sp. 11 extracts was undertaken by searching known compounds and analogues in the MS/MS databases available from the MetGem software  26, 29. Thanks to this methodology, cyclopeptide analogues could be annotated (Fig. 2C  and 3 ). After a scaled-up culture of BSNB-0580, three cyclopeptides were isolated from the EtOAc extract and identified by NMR, MS/MS analysis and manual fragmentation assignments. The amino acid composition was deduced from the immonium ions in the low mass range of the MS/MS spectra 30 : ions at m/z 86.
12 and sp. 1 (Fig. 2B ). Indeed, the MN approach ranks the clusters by size, from the largest to the smallest (selfloop), but does not give access to the inter-cluster distance. To overcome this limitation, data analysis using the  t-SNE algorithm of the MetGem software (Fig. 5 ) regroups ions with related scaffolds that had been separated into different clusters by the MN  28. The t-SNE method shows that ions from a MN cluster of Colletotrichum sp. 12 and sp. 1 are spatially close to ions annotated as analogues of peptides and cytochalasin E (Fig. 5 ). After searching standards in the MS/MS databases, a node at m/z 496. 2326 was putatively assigned to protonated cytochalasin E ([M + H] + ). Assignment of the two related clusters was performed after cultivation of the strain BSNB-0536 (OCU Colletotrichum sp.
Conclusion The newly generated collection of 42 endophytic Colletotrichum strains has been chemically investigated by upto-date analytical tools, i. e. MALDI-TOF profiling and molecular networking dereplication, to identify secondary cytotoxic metabolites. We found that Colletotrichum strains produce specific specialized metabolites depending on the Operational Chemically Unit OCU highlighted by the MALDI-TOF MS clustering. This has finally led to the isolation and complete de novo characterisation of 10 Colletotrichum specialised metabolites. Cytochalasins and peptides have been isolated in three cytotoxic extracts (BSNB-0615, -0652 and -0580), but chemical diversity remains unknown for the 8 remaining cytotoxic extracts. At this point, this lack of information can be due to incomplete MS/MS databases or to the fact that extracts are actually composed of currently unknown specialized metabolites.
Acquisitions were performed in linear positive ion mode. The laser intensity was set just above the ion generation threshold to obtain peaks with the highest possible signal-to-noise (S/N) ratio without significant peak broadening. The mass spectrometer was externally calibrated using a mixture of proteins (Insulin, Cytochrome C, Myoglobin and Ubiquitin I). All data were processed using the program FlexAnalysis (Bruker Daltonics, Bremen).
The leaf fragments were cultured for a maximum of one month. All isolated endophytic strains have been deposited at the ICSN/CNRS Strain library France. Strains are maintained in triplicate in 2 ml Eppendorf tubes containing 1 ml of a solution of glycerol and water (1:1) at -80 °C. Phylogenetic analyses. Fungal strains were identified using nucleotide sequencing of the rDNA ITS region (ITS1-5,8S-ITS2). ITS sequences were blasted on NCBI GenBank (accessed 2018-03-12) to search for closest strains (See supplementary Table  S2 ). 16    57. The resulting alignment was saved into Phylip4 format. A phylogenetic tree was constructed by maximum likelihood (ML) inference on the CIP-RES server (https ://www. phylo. org)  58. The ML analysis was performed using RaxML-HPC2 on XSEDE (8. 2. 10). Branch support was evaluated by a bootstrapping method with 1000 replicates.
Hierarchical cluster tree. A hierarchical cluster tree was constructed using the Mass-up software  59. Each MALDI TOF MS spectra was preprocessed using the following parameters: intensity was square-root transformed, smoothing was done using moving average and baseline correction using TopHat. Peak detection was set to Mass SpecWavelet with a minimum peak intensity chosen in order to detect only the major peak. Peak matching on peak lists was done with a tolerance of 700 ppm for intra-sample matching. Clustering analysis was generated using Jaccard distance function and conversion values of presence. Cultures and extraction of secondary metabolites. Each strain was cultivated at 28 °C in 10 Petri dishes (10 cm diameter) of PDA (Potato Dextrose Agar) media. Then, culture media was extracted with ethyl acetate (EtOAc) at room temperature during 24 h.
UPLC-MS/MS and data analysis. Each EtOAc extract of Colletotrichum strain was profile on a UPLC-MS/MS to acquire mass data for the implementation of molecular network  55. Samples were pre-treated by Solid phase extraction using Discovery® SPE 96 well plate (bed wt 100 mg/well) and plate prep vacuum Manifold. Each extract was then dissolved in 500 µL of 5% milliQ water in HPLC methanol and loaded on the cartridge bed. Elutes were injected at a concentration of 4 mg/mL for Orbitrap analysis. For UHPLC-HRMS analysis, chromatographic separation was performed on an Acquity UHPLC system interfaced to a Q-Exactive Plus mass spectrometer (Thermo Scientific, Bremen, Germany), using a heated electrospray ionization (HESI-II) source. Thermo Scientific Xcalibur 2. 1 software was used for instrument control and data analysis.
MZmine 2 data-preprocessing parameters. The complete procedure has been Raw files were converted into mzXML files using MSConvert software. Then mzXML files were processed using MZmine 2. 37  60, 61. Mass detection was realized with centroid mass detector with the noise level set to 1. 0E5 for MS level set to all. The ADAP chromatogram builder  62  was achieved using a minimum group size of scans of 5, minimum group intensity threshold of 1. 0E5, minimum highest intensity of 1. 0E5 and m/z tolerance of 0. 002 or 5 ppm. Wavelets (ADAP) algorithm was used for chromatogram deconvolution with the following settings: S/N threshold of 10, intensity window SN, minimum feature height of 1000, coefficient area threshold of 100, peak duration range between 0. 01 and 0. 5 and the RT wavelet range between 0.