dataesr/scientific-paragraphs-categorization huggingface dataset:
Naive Bayes Classification Model One of the first approaches of machine learning classifiers on nucleotide signatures was the application of naive Bayes (NB) models on 28 genomic data present in the genomic databases in 2001  (Sandberg et al. , 2001). This work was the foundation for the development of the application of NB classification on shotgun metagenomic data by  Rosen et al. (2008) , who trained their classifier using 635 microbial genomes to construct k-mer frequency profiles of the genomes, then tested the classification of simulated fragments and metagenomic reads into different classes (strain, species, and genus). The term "naive" in NB refers to the fact that the Bayes theorem assumes that the values of a particular feature are independent of the value of any other feature, which simplifies the problem and gives a starting point to estimate the degree of complexity of the problem, here, the metagenomic classification.
However, in a 2017 benchmark study of 11 classifiers, the NB classifier was evaluated using simulated metagenomic data and experimental metagenomic mock communities, obtaining one of the lowest precision and recall in the benchmark (using three precision levels: strain, species, and genus)  (McIntyre et al. , 2017) , hence showing the limitations of NB models. The low accuracy can be explained by the simplicity of the model itself or by the fact that the model did not integrate new genomes present in the tested datasets.
Support Vector Machine Models Support Vector Machine (SVM) models are another supervised learning methodology applied to metagenomic read classification. SVMs compute the distance between the points of the datasets and try to find the hyperplane that represents the largest separation between two classes, generally using maximum margin as loss function  (Han et al. , 2017). Such hyperplane is determined by a kernel function (e. g. , linear and Gaussian)  (Steinwart and Christmann, 2008). In comparison to NB models, SVM models can handle the non-linearities of the data and take into account the interactions between data inputs. To our knowledge, the first use in metagenomics was in 2007, when  McHardy et al. (2007)  developed a multiclass SVM model to analyze the sequence composition of assembled metagenomic contigs to classify them into taxonomic ranges.
e. , performing each pair of comparison one vs. all), and the contigs are assigned to a class using a voting mechanism. To train and test the model, the genome sequences were divided into training and test data of a defined length (1, 5, 10, and 15 kb, according to the mean length of contigs retrieved in metagenomic assembling). Training data and test data came from the same genomes, but sequences sampled in training datasets were excluded from the testing datasets. Using these data, they defined the capacity of prediction using the class outputs from genus to domain taxonomic ranges, according to the length of the contig. In terms of gene level, the sensitivity of the classifier was close to 90% for the long fragments, whereas the 1-kb fragments had a very low sensitivity percentage, close to 0.
The authors tested their tools on real assembled data from different metagenomes and used as ground truth the taxonomic annotation made by state-of-the-art alignment-based tools from 2007 in the corresponding studies, making it difficult to consider as exact reality. The model was then implemented into a dedicated web server to annotate metagenomes  (Patil et al. , 2012). Another SVM-based approach was recently developed in 2016 by  Vervier et al. (2016) , where an SVM model supports the expansion of genome sequences data availability. The authors highlighted the limit of compositional vectors approach (k-mer profile of 4, 5, and 6) for SVM model training, because the size of genome sequences is in millions of bases and the genomes available in databases increase exponentially. To overcome the problem, they optimized their model using a stochastic gradient descent, which allowed the optimization of the gradient using only one term at each step.
To construct the training and test datasets, they selected three different quantities of genomes to evaluate the impact of genome numbers on the model prediction accuracy. Considering that certain alignment classifiers develop a lower common ancestor approach that allows classification of reads at different taxonomic levels, the authors built a rankflexible approach that chooses the most adapted level to classify the reads based on the maximum score obtained with each of different rank-specific models. If a read is rejected at a specific taxonomic level, then it can be classified in upper levels if the score achieved the required threshold for the upper level. These thresholds are tunable parameters that can be optimized by taxon or set globally. The models were then tested on the remaining genomes available affiliated to the same species as the genome sequences in the training set.
The two types of networks were compared to the 16S ribosome database project (RDP) classifier, a NB classifier for 16S data  (Wang et al. , 2007) , which demonstrated that the CNN model had the best accuracy. The benchmark has its limitations because it has not been compared to alignment classifiers specialized in shotgun metagenomic data. In 2020,  Liang et al. (2020)  analyzed different deep learning architectures for metagenomic taxonomic classification and developed a model to classify metagenomic reads based on bidirectional long short-term memory (LSTM) plus a selfattention mechanism, called DeepMicrobes. The input data used for training were taxonomically characterized MAGs from human gut metagenomes that were transformed into simulated metagenomic reads with the HiSeq 2500 Illumina sequencing error model.
It allows the model to focus on specific regions of the DNA sequence and the comparison of sequences with different read lengths. In fine, it increased the precision/recall score of the tested input. The best model was then compared to 2020 state-of-the-art classifiers: kraken2  (Wood et al. , 2019) , Centrifuge  (Kim et al. , 2016) , Kaiju  (Menzel et al. , 2016) , and CLARK-S  (Ounit and Lonardi, 2016). As they mentioned, because there is no real metagenomic dataset that can serve as ground truth, one possibility is to simulate metagenomic samples by taking isolated sequencing reads. They thus created mock community and compared their results at the genus level because some classifiers did not contain related genomes in their native database. Globally, the DeepMicrobes model performed better than the different tools in terms of precision, recall, and estimation of abundance of the genus level.
One limitation of this article is the lack of comparison on the species level, as this information provides key insight into biological interactions. However, with the two best competitors, kraken2 and Kaiju, they obtained good results for abundance estimation at the species level even if less reads were classified. Kraken2 accuracy might have been improved by the fact that it can support larger databases than the native small database, allowing the detection of more species. To justify this key point of database dependency, they analyzed the detection of 121 genomes where species are absent from all databases of all tools and demonstrated that their model proposed less false positive results. Going through the literature highlighted that k-mer embedding encoding was already proposed for metagenomic classification  (Menegaux and Vert, 2019) , in a study that compared their model to the already described SVM model  (Vervier et al.
APPLICATION OF MACHINE LEARNING TO FUNCTIONAL ANNOTATION AND OTHER SPECIFIC CASES Machine learning models have not been yet fully applied on metagenomic functions. The only article that mentioned the utilization of machine learning models was the WOODS program, which developed a two-step pipeline, a first step of machine learning classification, and a second step of alignment annotation  (Sharma et al. , 2015). The machine learning step acts as pre-filter to align the reads against a specific functional category of genes. The alignment tool selected was RAPsearch2  (Zhao et al. , 2012) , and the functional database was eggNOG3  (Huerta-Cepas et al. , 2019). The genes were regrouped into 22 functional categories, and different machine learning models were evaluated. Random forest was the best model to classify the test data, and the global pipeline achieved good results compared to the BLAST reference.
However, the model was developed on complete ORF or amino acid sequences with a length larger than 500, and this makes the software useful only for assembled metagenomic data. For non-assembled metagenomic reads, this leads to the direct use of RAPsearch2. Machine learning has also been applied to a specific functional case, the detection of antibiotic resistance gene (ARG). The screening of antibiotic resistance (AR) determinants in microbiome is a hot topic of research, as the increase of microbial resistance is a worldwide concern  (Zaman et al. , 2017; Nathan, 2020). To retrieve ARGs in microbiome, the analysis of shotgun metagenomic data is one of the most exhaustive ways, bypassing the culturing step. However, to retrieve these genes researchers are dependent on alignment tools and related databases.
As alignment-based methods are not perfect and can produce false positive results (AR can be derived from non-ARG such as 1 https://fasttext. cc/ efflux pump) and false negative results (no detection of genes variants from databases), applying learning models can be an efficient way to detect these genes. This was tested by Arango-Argoty et al. (  2018 ) they proposed a new tool named DeepARG that contains two deep learning models to retrieve 30 classes of antibiotic determinants in metagenomic reads or full gene sequences, respectively. The model took as input a dissimilarity matrix based on the alignment bitscore of reads/genes mapped to an ARG database. Although the tool was also based on alignment score, the accuracy of ARG classes prediction compared to the alignment-based method was improved.
CONCLUSION Machine learning has been applied because the beginning of metagenomic annotation, but the increase of available microbial genomic data in databases leads to the obsolescence of the first models, too simple to accommodate the size and complexity of the data. Their accuracy was reduced in comparison to k-merbased tools in the reviewed benchmark. Because the integration of genomic data is feasible in deep learning models, two models have been published for taxonomic annotation. The first one was not compared with enough benchmarks to conclude on their progress, and the second named DeepMicrobes demonstrated good performance, even compared to state-of-the-art alignmentbased classifiers. The tool highlighted the benefit of k-mer embedding for the input treatment and the use of networks such as LSTM that learns important long-range interactions and "forgets" information not discriminant to build the model.
The comparisons to the other tools were mostly achieved at the genus level, but a benchmark to the species level would have been of interest in terms of interpretation. In functional annotation, deep learning technologies have been applied to specific questions, or to build a model for pre-classification, but remain to be studied for a full functional annotation. Because no real microbiomes are known without the prism of metagenomic tools, the benchmarks in metagenomic annotation are based on simulated data or mock communities that present a reduced diversity. This leads the benchmarks to be case specific, and the tools developed to be overfitted to the generated data. Moreover, the data available in databases represent a low percentage of the overall microbial diversity, leading to the construction of models specific to what is known in databases.
Specific machine learning algorithms have been proposed to answer these specific cases, as active learning that allows the selection of relevant data from the training set to improve the models and not overfit to the data. Active learning may be a framework that facilitates the building of models with high accuracy, by selecting certain data of the input to train the models  (Settles, 2009). Despite the possibility of biases due to the targeted sampling, it may overcome the pitfalls of metagenomics (i. e. , database orientation to certain bacterial species, the use of reconstructed genomes with no taxonomic annotation, and the deletion of non-informative sequences). Finally, as bacterial genome sequences in databases are still in expansion, current developed models have to be regularly tested/updated to remain up to date.
This bactericidal activity was confirmed by plating of the remaining ss18b-lux bacteria following 7 days of antibiotic exposure on streptomycin containing plates and cfu counting. Data clearly show that exposure to high concentrations of isoniazid had no significant bactericidal activity against nonreplicating ss18b-lux, while rifampicin and bedaquiline were bactericidal, as previously reported  25, 26. [f] Compound R Z E. coli [b] mc 2 155 [c] 1 -H CH 3. In this assay, both 11 and 12 showed a concentration-dependent killing of ss18b-lux, with concentrations of 4. 8 µM leading to a 1-2 log drop in bacterial counts, similar to that seen for 1 µM bedaquiline and 0. 6 µM rifampicin (Figure  1d ). While the potency of the 11 and 12 are lower than that observed on replicating bacteria.
Target identification of TriSLa To gain insight into the mechanism of action of the TriSLa series, resistant Mtb mutants were isolated and characterised. After exposure to compound 1 at 14 and 28 µM, resistant H37Rv isolates were selected at a frequency of around 1 x 10 -8 (6 and 4 colonies respectively, following the plating of 50 µL of OD600 = 50), with confirmation of resistance to 1 by REMA (MIC>100 µM). Whole genome sequencing and variant analysis of two resistant isolates found one (RC14. 2) to carry a 97 bp base-pair deletion in the ndhA (Rv0392c) promoter (including a 17 bp truncation of the 5' end of neighbouring, non-essential Rv0393 (encoding a 13E12 repeat family protein)) (Figure  S5 ), and the other (RC28. 2) to carry a non-synonymous single nucleotide polymorphism (SNP) in ndh (Rv1854c) leading to Tyr403Cys (Table  S2 ) (sequences available at NCBI project number, PRJNA808942).
Next, efficacy studies were performed as previously  34  , where zebrafish larvae infected with M. marinum (expressing mWasabi) were exposed to different doses of TriSLa (refreshed daily) for a 4-days period (from 1 to 5 days post-infection (dpi)), followed by a 5-day period in the absence of antibiotics where survival was monitored (Figure  3a ). Data confirmed that 4-day exposure compounds 11, 12 and 13 significantly prolonged the survival of the infected zebrafish in a dosedependent manner, starting from doses of 0. 3, 0. 3 and 1. 2 µM (respectively), with higher doses of 4. 8, 4. 8 and 9. 6 µM (respectively) prolonging survival by 3 days (Figure  3b-d ). In complement, the bacterial load of the infected zebrafish was determined by fluorescence microscopy during the treatment phase of the study (at 2 and 4 days of exposure).
The products 4a and 4b were obtained from (2S)-2-aminopropan-1-ol using general protocol 1. The crude product was purified by preparative HPLC using MeCN +0. 1%HCOOH and H2O + 0. 1%HCOOH gradient (9/1 to 0/10). A basification and an extraction at 10 pH with a saturated solution of Na2CO3 were carried out on the recovered fraction and the combined organic layers were dried over MgSO4. The solvent was removed under reduced pressure the desired to afford products 4a and 4b (Yield = 46%, yellow oil), as a mixture of two diastereoisomers (d. r. (4a/4b) = 7/3). Data for the major diastereoisomer 4a:.
05, ** P < 0. 01, **** P < 0. 0001). (e) Representative sequential fluorescence images of M. marinum (mWasabi) infected zebrafish embryos following 2 or 4 days of treatment (3 and 5 dpi) with compound 12 at 0. 25, 0. 5 and 2 µg/mL, relative to untreated zebrafish (UNT. ) Scale bar, 210 µm. (f) M. marinum infection burden in zebrafish embryo as quantified following 2 (3 dpi) and 4 (5 dpi) days of compound 12 exposure (0. 25, 0. 5 and 2 µg/ml) by pixel count (fluorescent events) using the ImageJ software. The data presented are the cumulative data of three independent experiment (each containing 20 to 25 embryos per group), with each data point representing one infected zebrafish larva. The error bar represents the mean and standard deviations of the cumulative dataset. Statistical comparison of the different groups was performed using a Mann-Whitney's t test, (*** P < 0.
Acknowledgements: We thank  F. Capet  and  P. Roussel  from the  X-ray platform of Chevreul Institute  ( FR CNRS 2638 ), for supporting data collection and solving the x-ray crystal structure. We thank  V. Landry  ( U1177-Drugs and Molecules for Living Systems ) for cytotoxicity experiments. We thank  BLS-3 Facility  staff (N. Vandenabeele,  R. Prath  and  S. Marin ) at the  Institut Pasteur de Lille  for technical support. We thank  E. Anoz Carbonell  for unbiased MIC analysis. We are grateful to  P. Richard  and  M. Plays  for zebrafish husbandry. This research was financially co-funded by  NL4Tb ,  Atip-Avenir ,  SMARt-Lab  and CPER grants. The NL4Tb grant was funded by the  French National Research Agency  ( ANR-19-CE18-0034-01 ). The SMARt-Lab grant was funded by  European Union  under the  European Regional Development Fund (ERDF) , by the  Hauts De France Regional Council  (Contract n°NP0020070) and by  I-Site ULNE  ( ANR-16-IDEX-0004 ULNE ).
The CPER grants were funded by  European Union  under the  European Regional Development Fund (ERDF)  and by the  Hauts de France regional Council  (contract n°20002842 and contract n° 18006176), the MEL (contract  n°2017_ESR_14  and  contract_2020_ESR_06 ), and the  French State  (contract n°  2018-R3-CTRL-Phase2  and contract  n°2020-R3-CTRL_IPL_Phase4 ). The compound management and physicochemical/ADME properties measurement were supported by  ChemBioFrance  through the  ARIADNE platform (Lille, France ). ABBREVIATIONS BDQ, bedaquiline; cfu, colony forming unit; dpi, days post-infection; FPC, fluorescent pixel count; hpf, hours post-fertilization; INH, isoniazid; MBP, maltose-binding protein; MDR, multi-drug resistant; Mtb, Mycobacterium tuberculosis; REMA, resazurin microtiter assay; RIF, rifampicin; XDR, extensively drug resistant.
Data availability Atomic coordinates and structure factors reported in this paper (crystal structure of BDM44410, 1) have been deposited in the Cambridge Structural Database under accession number CCDC 2143694. Whole genome sequencing data (fastq files) for parental and BDM44410 (1) resistant H37Rv isolates (RC14. 2, RC28. 1) have been deposited at NCBI (BioProject ID: PRJNA808942).
Patients with premature MI We reviewed in our database all patients referred to Ambroise Paré University Hospital from 01 January 2014 to 31 December 2018 for type I premature MI (aged ≥ years). Type I MI was defined as follows  [18] : detection of a rise and/or fall in cardiac troponin concentration, with at least one value above the 99th percentile upper reference limit, and at least one of the following: (1) symptoms of acute MI; (2) new ischaemic electrocardiogram changes; (3) development of pathological Q waves; (4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality in a pattern consistent with an ischaemic aetiology; and (5) identification of a coronary thrombus by coronary angiography. Data regarding cardiovascular risk factors (definitions are given in the Online material), personal and family history of CVD, clinical presentation and lipid profile at admission, medication at admission and discharge and angiographic and revascularization data were assessed systematically.
Study limitations Our study has several limitations. First, although the CARVAR 92 cohort is a prospective cohort, patients with premature myocardial infarction were included retrospectively from our database. Thus, not all data concerning family history of hypercholesterolaemia, xanthomata or arcus cornealis were available for the calculation of the complete DLCN score However, previous FH studies  [15, 28]  have assessed FH using a simplified DLCN score, which we used. Furthermore, the prevalence of these criteria is low, and has little effect on the interpretation of FH, as demonstrated by these previous studies  [17, 21]. Second, we did not systematically perform genetic molecular analysis to identify mutations associated with FH. However, in most studies, the frequency of detectable mutations in patients with clinically ''definite'' or ''probable'' FH is between 60% and 80%  [39, 40].
Acknowledgements We kindly thank the municipalities and medical teams from our partner cities Antony, Asnières-sur -Seine, Bagneux, Châtenay-Malabry, Colombes, Fontenay-aux-Roses, Gennevilliers, Issy-les-Moulineaux, Le Plessis-Robinson, Montrouge, Nanterre, Foch Hospital in Suresnes, Neuilly-Courbevoie Hospital and Marie-Thérèse Health Centre in Malakoff , and all physicians involved in the screening campaign. We thank the  Caisse Primaire d'Assurance Maladie of the Hauts de Seine ,  Christian Collard  its general director, his predecessor  Alain Bourez , and the board members President Patric Mourgère and  President Aurélie Le Galoudec.
Introduction In newly diagnosed acute myeloid leukemia (AML), high remission rates are achieved with intensive chemotherapy (IC), but remissions are often transient. 1 A variety of therapies, including hypomethylating agents, have been investigated in clinical trials for use as maintenance therapy in patients with AML in remission. ] [4] [5] [6] [7] [8] [9] [10] [11]  Oral azacitidine (oral-AZA; formerly CC-486) is a hypomethylating agent that allows for extended dosing schedules to prolong drug exposure over each treatment cycle. 12, 13 Oral-AZA has a distinct pharmacokinetic and pharmacodynamic profile from that of injectable azacitidine, and the 2 formulations are not bioequivalent nor interchangeable. 12, 14 In the randomized, phase 3 QUAZAR AML-001 trial (clinicaltrials. gov #NCT01757535), treatment with oral-AZA significantly prolonged median OS by 9.
Within 4 months (plus or minus 7 days) of achieving CR/CRi, patients were randomized 1:1 to receive oral-AZA 300 mg or placebo once daily on days 1 to 14 of repeated 28-day treatment cycles. Randomization was stratified by age, prior history of myelodysplastic syndromes or chronic myelomonocytic leukemia, cytogenetic risk at diagnosis, and receipt of consolidation chemotherapy. Baseline MRD status was established using the initial bone marrow aspirate taken at the time of screening (after completion of induction plus or minus consolidation and #28 days before randomization). The data cutoff date was 15 July 2019.
22, 25, 36 erall, the longitudinal, MFC-based MRD analyses in QUAZAR AML-001 showed that, compared with placebo, older patients with AML in first remission who received maintenance treatment with oral-AZA had improved OS and RFS independent of MRD status at study entry were more likely to achieve MRD negativity and had a longer overall duration of MRD negativity. Persistence of MRD after IC was significantly and independently predictive of inferior outcomes, even after controlling for randomized treatment. Additional MRD assessments of QUAZAR AML-001 data are ongoing and may offer further insights into the impact of oral-AZA on suppression of residual leukemic clones in specific patient populations.