dataesr/scientific-paragraphs-categorization huggingface dataset:
These features make of 13 C NMR a useful tool for NP dereplication. A general review article about NP mixture analysis has been recently published  [21] ; it includes references to the methods that benefit from taxonomy focused NMR databases such as CARAMEL  [10] , DerepCrude  [12] , or MixONat  [13] , but in which the step of 13 C NMR chemical shift prediction still constitutes a bottleneck.
Materials and Methods The LOTUS DB was searched through its web interface  [6]. Calculated NMR chemical shift values were obtained from Advanced Chemistry Development, Inc. (ACD/Labs, Toronto, Canada) CNMR Predictor and DB software, version 2020. 1. 0 [22], as produced for chemical shift checking. The Python computer language interpreter, version 3. 7. 1, was supplied by Anaconda  [23]. Structure file transformations were carried out using the RDKit cheminformatics library  [24]  and locally developed Python scripts publicly available from the CNMR_Predict directory of the KnapsackSearch Github project  [25].
Results and Discussion The creation of a taxonomy focused compound library with predicted 13 C NMR chemical shift values included is illustrated here for turnip, or Brassica rapa subsp. rapa L. Scheme 1 helps to follow the successive steps of the creation process. The related files are available from the Turnip subdirectory of directory CNMR_Predict  [25]. Submitting species name Brassica rapa as keyword (thus including subspecies other than turnip) for a simple search in the LOTUS DB resulted in 121 hits. The search result was downloaded as file lotus_simple_search_result. sdf in V3000 SDF format  [26]  and stored in a local computer directory, in which all files related to the turnip project were stored. A new ACD/Labs DB file, turnip. NMRUDB, was created and filled with data from lotus_simple_search_result.
sdf. Exporting this database in SDF format as file turnip. sdf converted it to the V2000 SDF format. This conversion (Scheme 1, step a) is useful if an SDF file reader that does not decode properly the V3000 format is used for structure viewing. The turnip. sdf file then underwent three preparatory operations (Scheme 1, step b) before it could be supplemented with chemical shift values. The turnip. sdf file may have contained identical structures, apparently because different InChI  [27]  character strings in LOTUS may have resulted in the production of structures in lotus_simple_search_result. sdf that were in turn recoded as identical InChI strings by the RDKit library. A python script, uniqInChI. py, retains only a single occurrence of duplicated structures according to InChI equality and was applied to file turnip.
sdf, in which only one compound out of 121 was removed. Many structures downloaded from LOTUS were produced by the decoding of InChI strings. This process has the very visible side effect of replacing secondary and primary amide functions by their tautomeric iminol forms. As the central carbon atom in enamine and iminol functional groups have their 13 C NMR chemical shift values not identically predicted, it appeared to be necessary to transform aliphatic iminol substructures into their amide tautomer, as achieved by the tautomer. py script applied to file turnip. sdf. It should be noticed that the systematic nomenclature  [28]  of iminol-containing compounds in LOTUS is determined as if they were really iminols and not amides, resulting, for example, in the difficult identification of peptidic bonds in peptides.
Script tautomer. py relies on RDKit to write SDF files and makes use of reaction SMARTS  [29]. Electrically charged atoms in structures written by RDKit include a nondefault specification for the nonstandard valence of such atoms (such as four for the nitrogen atom in an ammonium group), in accordance with SDF specification. Such a structure description is not properly interpreted by the ACD/Labs software, thus precluding the prediction of chemical shifts. The rdcharge. py script resets the valence data piece to the default, nonblocking value and was applied to turnip. sdf. The first step toward the automatic calculation of 13 C NMR chemical shifts (Scheme 1, step c) was to let the ACD/Labs software consider that experimental values were stored in an SDF file it produced, something feasible by supplementing the SDF file with data lines under the purposely created CNMR_SHIFTS SDF tag.
These fake data lines include the fake chemical shift value 99. 99, one per carbon atom in each molecule. The fakefakeACD. py script applied to turnip. sdf transforms it into file fake_acd_turnip. sdf. For chemical shift prediction (Scheme 1, step d), a new ACD/Labs DB file, fake_acd_ turnip. NMRUDB, was created and filled by importation from the file fake_acd_turnip. sdf. All carbon atoms appeared with their arbitrarily given 99. 99 chemical shift value. The presence of these values allows ACD/Labs DB to check all chemical shift values of all molecules from a single mouse click. Checking the chemical shifts of a DB that does not contain chemical values fails to give a meaningful result, thus justifying the resorting to the fakefakeACD. py script. Exporting the current DB as file fake_acd_turnip_exported.
This operation was carried out by the script CNMR_predict. py acting on file fake_acd_turnip_exported. sdf to produce file true_acd_turnip. sdf. A new ACD/Labs DB file, lotus_turnip. NMRUDB was finally created and filled with compounds from file true_acd_turnip. sdf. The DB lotus_turnip. NMRUDB was then ready for compound identification according to 13 C NMR chemical shift values using the compound search tool included in the ACD/Labs software. The file true_acd_turnip. sdf also contains SDF tags that make dereplication possible by the MixONat software. The script ACD_to_DerepCrude. py formatted the predicted chemical shifts for its use with the DerepCrude software. Both DerepCrude  [12]  and MixONat  [13]  are dedicated to the dereplication by 13 C NMR either on crude NP extracts or on extract fractions, as alternatives to the now well established CARAMEL dereplication procedure  [10, 30].
The values that were calculated for the chemical shift checking of an entire DB file, written under the CNMR_CALC_SHIFTS SDF tag in file fake_acd_turnip_exported. sdf, were not exactly those produced by the ACD/Labs CNMR Predictor when run in a compound by compound procedure, but the origin of the difference is difficult to track as no information is available on the details of the underlying algorithms. The creation of DB lotus_turnip. NMRUDB is a process that alternates the execution of python scripts from a terminal window and the handling (create/import/predict/export/ close) of ACD/Labs DB files. A template text file is proposed with the CNMR_Predict project files so that the actions to perform sequentially can be easily accomplished. Figure  1  illustrates the content of this template file.
CNMR_Predict is a follow up of the Knapsack-Search project that made use of nmrshiftdb2 for the prediction of 13 C NMR chemical shift values  [31]. These predicted values may be formatted as experimental values under the CNMR_SHIFTS SDF tag and a template file is also available for this option. Creating a file such as lotus_turnip. NMRUDB with the ACD/Labs software from initial file lotus_simple_search_result. sdf without CNMR_Predict would require a tedious compound by compound operation, lasting about one minute per structure unless some presently undisclosed script is available for calculation process automation  [32]. The prediction involving CNMR_Predict lasts less than one second per structure, making it easy to use for the creation of taxonomically focused collections of natural products.
For example, species Brassica rapa is related to family Brassicaceae and searching for this taxon in LOTUS results in 2271 hits. A ready-to-search database of compounds from Brassicaceae can be thus produced in less than one hour on a standard laptop computer, an hour during which the computer is the only one that performs the repetitive work. The choice of the appropriate taxon type (order, family, genus, species, etc. ) is left to the user and can be adapted according to the size of the chemical space to investigate. This taxonomy focused approach is more flexible than the one consisting in precalculating the chemical shifts for all the compounds present in a snapshot of a database such as LOTUS since its content may be steadily updated. It should be noticed that DBs refer to published data and can propagate errors.
For example, glucosinolates constitute an emblematic group of compounds related to the family of Brassicaceae (and more generally to the order of Capparales) that contain an O-sulfated anomeric (Z)-thiohydroximate function in which the double bond configuration may appear in DBs with the double bond in the (E) configuration or left undefined  [33]. The library of the compounds from Brassica rapa, a Brassicaceae species, reported by LOTUS contain such erroneous or incompletely defined structures that would be also found in general purpose databases such as that of the Chemical Abstract Service  [34].
Conclusions The CNMR_Predict project presented in this article allows one to easily and quickly combine structural and taxonomic data from the LOTUS NP database with 13 C NMR data predicted by the ACD/Labs CNMR Predictor in order to facilitate the 13 C NMR based dereplication of natural products. Future works will include the prediction of 1 H NMR chemical shifts and, possibly, of 2D NMR spectra.
Scheme 1. Database creation using CNMR_Predict: (a) initial creation of a multistructure SDF file in V2000 format from a downloaded LOTUS search result; (b) SDF file normalization: removal of duplicated structures, tautomer correction, and valence data adjustment for electrically charged atoms; (c) creation of a DB with fake chemical shift data; (d) incorporation in the DB of the predicted chemical shift values; (e) transfer of the predicted chemical shift values for future dereplication studies.
Scheme 1. 1 Scheme 1. Database creation using CNMR_Predict: (a) initial creation of a multistructure SDF file in V2000 format from a downloaded LOTUS search result; (b) SDF file normalization: removal of duplicated structures, tautomer correction, and valence data adjustment for electrically charged atoms; (c) creation of a DB with fake chemical shift data; (d) incorporation in the DB of the predicted chemical shift values; (e) transfer of the predicted chemical shift values for future dereplication studies.
Material and methods This retrospective observational single-center study about proper FEP use was performed in a 1495-bed French teaching hospital with medical, surgical and obstetrical wards. All patients hospitalized in the University Hospital, whatever their age, and whose treatment with FEP was started between March 1st, 2018 and February 28th, 2019 were included. We excluded the medical and surgical ICUs because the absence of computerized prescriptions did not allow us to surveil FEP prescriptions as soon as necessary. The FEP dispensations were recorded using Pharma ® (Computer engineering, Paris) inventory management software. Crossway R ® (Maincare solution, Cestas) made it possible to consult the patient files and laboratory results. We collected information on: • clinical and biological data: demographic characteristics, hospitalization ward, site of infection, glomerular filtration rate (GFR) calculated according to the Chronic Kidney Disease EPIdemiology equation (CKD-EPI); • microbiological data: bacteria species identification by the hospital microbiology laboratory, presence of an ESBL ECC, presence of an ECC 3GC-R, performance of an antibiogram and the FEP minimum inhibitory concentration (MIC); • therapeutic data: FEP indication, antimicrobial stewardship, dosage match with the indication, kidney function, reassessment between 48 and 72 h and between the 7th and the 10th day, adverse effects attributed to FEP, treatment duration.
These data were analyzed by comparison with the proper FEP use guidelines we had established using the French and the American Summary of Product Characteristics, the guidelines of the European Committee on Antimicrobial Susceptibility Testing (EUCAST, 2019) and the "French Health Authority" (HAS) on the management of enterobacteria treatments  [3, 12, 21, 22]. These criteria were validated with an infectious disease (ID) specialist a microbiology specialist from the institution (Table  1 ). The proper use of FEP was evaluated according to 9 criteria: • an indication was acceptable if it matched with the guidelines or if the antimicrobial stewardship team advised treatment with FEP; • antimicrobial stewardship was acceptable if there was a note in the patient's file from the antimicrobial stewardship team or if the prescriber of the treatment was an ID specialist; • dosage was acceptable for the indication if it matched with the guidelines; • dosage was adapted to kidney function (CKD-EPI) as proposed by the specialized online GPR website [21,23]; • bacteriological documentation was acceptable if a bacteriological sample had been sent to the laboratory; • if an antibiogram was created, it was considered acceptable; if the sample was sterile or contaminated, the creation of an antibiogram was not evaluated; • the reassessment between 48 and 72 h was acceptable if it was available in the computer file or if the treatment had been modified between 48 and 72 h; • the reassessment between the 7th day (D7) and the 10th day (D10) was acceptable if it was available in the computer file or if the treatment had been modified between D7 and D10; b Care-associated infection with a history of infection with cefepimesusceptible 3GC-R group 3 enterobacteria or P.
Nucleotide Clustering Nucleotides were clustered by the KMeans algorithm as vectors of reactivities obtained at 37, 53, 65, 74 and 85 °C. Clustering was done using scikit-learn and a custom python script available at https://github. com/gdebissc/DiLCrz, accessed on day month year. Data were log2-transformed and standardized (nucleotide-wise then temperaturewise). Undetermined reactivities were removed prior to clustering. Dimensionality reduction by principal component analysis reveals that nucleotides can be grouped in at least three or four clusters. The optimal number of clusters was further determined using the elbow method. Briefly, the within-cluster sum of squared errors, or distortion, was calculated for each cluster number between 1 and 10 (Figure  S5B ). The result graphically indicates that increasing the cluster number above four does not significantly reduce the distortion.
Definition of the Parameters To get insights into the relationship between the structure and the reactivity, we computed several quantities along the MD simulation. First, we computed the hydrogen bonds and the stacking for each frame using the software MC-Annotate  (69) (70) (71). The hydrogen bonds were classified using the Leontis/Westhof classification (24, 72) and we detailed if the HBs are formed between the O2′ and an atom in the Watson-Crick (W), Hoogsten (H) or the B side (the position is between the W and H side) or between OP and an atom in these sides  (70). Second, we computed the multiplets (triplets, quadruplet, etc. ) and the secondary structure for each frame using the software DSSR (dissecting the spatial structure of RNA) v. 2  (73). For each hydrogen bond and multiplet, we computed the percentage of the occurrence.
Using Probing Data for Secondary Structure Modeling The probing results performed with the different SHAPE probes at 37 °C in the presence, or absence of Mg 2+ ions were used as constraints for secondary structure prediction software. Most of the secondary structure was correctly predicted by RNAstructure and RNAfold, which both do not predict pseudoknots. However, in most of the proposed models the P7 part of the pseudoknot nucleotides are incorrectly paired (see Figure  S2 ). In many of the models, A112-G115 are paired to C70-U73 compromising not only P7 but also the prediction of P4 and P6. Such misprediction can be expected as the P7 pseudoknot nucleotides are unreactive to the SHAPE reagents in native conditions, and only the 3′ part of the pseudoknot becomes reactive in absence of Mg 2+.
Interestingly, when informed with the reactivity data obtained using NMIA in the presence or absence of Mg 2+ IPANEMAP yields one model which captures most of the secondary structure, but the two base-pair P6 (Figure  S7B ). In another approach, we sought to predict the P7 pseudoknot pairings from the experimental results. To this end, we generated artificial constraints files aiming at indicating nucleotides potentially involved in pseudoknots to the prediction software. Reactivity of positions for which the SHAPE values significantly vary between two conditions was set to -0. 2 while the value for other nucleotides was set to 5. When using such artificial constraints obtained from the reactivity with or without magnesium no base pair was predicted independently of the prediction software utilized.
Informing Prediction Software to Integrate Pseudoknots and Other Tertiary Motifs Although pseudoknots are present in many RNA, predicting them is still a challenge in terms of modeling and computation. Thus, as exemplified in this study, failing to take them into account may lead to incorrect secondary structure models. While more general and exact methods seem precluded, predicting pseudoknots in two rounds has been applied successfully; most prominently in ShapeKnots (43): a first round predicts several models for secondary structure, and a second round searches for complementarity between the remaining single stranded nucleotides; identified pseudoknots are rewarded with a thermodynamic bonus. Using the 1M7 reactivity pattern obtained in this study, ShapeKnots delivers ten possible models among which the second most stable is a model including the P7 pseudoknot and otherwise correct but for the absence of P6 and the kissing-loop between P5 and P2.
In the near future we will extend the IPANEMAP workflow to infer tertiary interactions based on multi-condition probing data. The system will be informed of nucleotides with a high probability to form tertiary structure. The software will at first exclude them from secondary structure modeling, which allows it to identify pseudoknot helices and even recurrent tertiary structure motifs in a second modeling phase. For instance, comparing the profiles of the three probes on DiLCrz highlights A161, a nucleotide part of a GAAA tetraloop that interacts with a receptor. Such motifs are frequent within RNA structures, the combination of multiple SHAPE probing and the signature sequence (GNRA) could help to identify such structures ab initio. One of the challenges in modeling DiLcRz structure is the prediction of the L2.
1/L5 kissing loop. Both temperature-and Mg 2+ -differential probing strategies detect most of the nucleotides involved in the kissing loop (4 and 5 out of 6 nt, respectively). However the informed second round of modeling does not predict 2 bp long interactions to avoid the prediction of spurious pairings. In contrast, predicting three base pairs long range or cross interactions should be possible-it would require detection of all the nucleotides involved. Comparing SHAPE data performed in different conditions or with different probes reveals not only the secondary but also some elements of the tertiary contacts. In this context, it is crucial to further understand the mechanisms behind SHAPE reactivities and the interplay between SHAPE probes, their reactivities, and RNA structures using methods at different length scales and by comparing diverse experimental data on a RNA benchmark dataset.
This work was funded by a FRM grant (DBI20141231337) and ANR Grants (ANR-19-CE45-0023-02 and ANR-19-CE30-0021-03) to BS and YP. Work in BS lab is funded by CNRS and Université de Paris. MD simulations were carried out thanks to a generous allocation of computer time on the CINES supercomputer OCCIGEN to EF (Grant DARI A0080711431). GDB was the recipient of a PhD fellowship from the French Ministry for Research and Education. BM is supported by the Interdisciplinary Thematic Institute IMCBio, as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm, by IdEx Unistra (ANR-10-IDEX-0002), and by SFRI-STRAT'US project (ANR 20-SFRI-0012) and EUR IMCBio (ANR17-EURE-0023) under the framework of the French Investments for the Future Program. Institutional Review Board Statement: Informed Consent Statement:.
0 EU. One additional long-term vegetation change site completes the reference site list for the PARTY SOC v2. 0 global model. This site is a 56-year chronosequence of oil palm plantations (with C 3 plants) established on former pastures (with C 4 plants) located in South America (La Cabaña in Colombia) and sampled as a space-for-time substitution  (Quezada et al. , 2019). For each reference site, data on total SOC content in topsoil (0-10 to 0-30 cm depending on the site; Supplement Table  S2 ) were obtained from previously published studies  (Barré et al. , 2010; Cécillon et al. , 2018; Franko and Merbach, 2017; Körschens et al. , 1998; Quezada et al. , 2019). Total SOC content was measured by dry combustion with an elemental analyser (SOC EA , g C kg -1 ) according to ISO 10694 (1995) after the removal of soil carbonates using an HCl treatment for the topsoils of Grignon.
200 • C) of Rock-Eval ® (PseudoS1, g C kg -1 ; after  Khedim et al. , 2021) ; the ratio of PseudoS1 to PC (PseudoS1 / PC, unitless); the ratio of Pseu-doS1 to TOC RE6 (PseudoS1 / TOC RE6 , unitless); the ratio of S2 to PC (S2 / PC, unitless; after  Poeplau et al. , 2019) ; the ratio of PC to TOC RE6 (PC / TOC RE6 , unitless); and the ratio of HI to OI RE6 (HI / OI RE6 , mg HC mg -1 O 2 ). TOC RE6 , MinC, PC, HI, and OI RE6 were obtained as default parameters from the software of the Rock-Eval ® apparatus (Vinci Technologies, France). All other Rock-Eval ® parameters were calculated from the integration of the five thermograms using R version 4. 0. 0 (R Core Team, 2020; RStudio Team, 2020) and functions from the R packages hyperSpec  (Beleites and Sergo, 2020) , pracma  (Borchers, 2019) , and stringr (Wickham, 2019).
The relative importance (i. e. ranking) of each selected Rock-Eval ® parameter in the regression models was computed as the unscaled permutation accuracy  (Strobl et al. , 2009). The performance of PARTY SOC v2. 0 and PARTY SOC v2. 0 EU was assessed by statistical metrics comparing the predicted vs. the estimated values of their reference topsoil sample set using three complementary validation procedures. First, the predictive ability of both models was assessed by an "internal" procedure that used their respective whole reference topsoil sample sets (n = 105 samples for PARTY SOC v2. 0, n = 90 samples for PARTY SOC v2. 0 EU ). For this procedure, performance statistics were calculated only for the "out-of-bag" topsoil samples of the whole reference sets using a random seed of 1 to initialize the pseudorandom number generator of the R software.
Out-of-bag samples are observations from the training set not used for a specific regression tree that can be used as a "built-in" test set for calculating its prediction accuracy  (Strobl et al. , 2009). Second, the predictive ability of the models was assessed by a "random splitting" procedure that randomly split their respective reference topsoil sample sets into a test set (made of n = 30 samples) and a training set (n = 75 samples for PARTY SOC v2. 0, n = 60 samples for PARTY SOC v2. 0 EU ). This procedure was repeated 15 times using random seeds from 1 to 15 in the R software. Third, a fully independent "leave-one-site-out" procedure was used to assess the predictive ability of the models. This procedure successively excludes topsoil samples of one reference site from the training set and uses them as a test set (n = 15) for the models.