dataesr/scientific-paragraphs-categorization huggingface dataset:
Double deep Q-network-energy management system with the memory counter capability We propose to equip DDQN with a new memory called MemC of capacity G and denoted by C (MemC). The purpose of C is to keep track of the maximum step reached during an episode before restarting at the beginning. The capacity of C is equal to G, meaning that C consists of the last step numbers of F I G U R E 3 Rare events are highlighted by red dots in the dataset. (Blue dots are non rare events. ).
ACKNOWLEDGEMENTS This work was supported by the  CSE & AI R&D of Total S. A  and the  TREND-X research program  of École polytechnique. The measurements from the SIRTA's (http:// sirta. ipsl. fr)outdoors photovoltaic test bench are done in a joint collaboration of four laboratories (GeePs, LMD, Limsi and LPICM). Philippe Preux acknowledges the support of  Inria  and the members of SequeL for the very friendly, fruitful, stimulating, and exciting atmosphere of the group. Tanguy Levent  thanks the  LPICM laboratory of École Polytechnique  and the  CSE & AI R&D of Total S. A team  for the equipment, the fascinating discussions and their delightful supports.
5. 2. 1 | 21 Double deep Q-network-energy management system with the memory counter capability We propose to equip DDQN with a new memory called MemC of capacity G and denoted by C (MemC). The purpose of C is to keep track of the maximum step reached during an episode before restarting at the beginning. The capacity of C is equal to G, meaning that C consists of the last step numbers of F I G U R E 3 Rare events are highlighted by red dots in the dataset. (Blue dots are non rare events. ).
Glutamate concentration was reported to sample protein concentration. Results were expressed as percent of medium glutamate concentration used for the stimulation. In addition, Aera Under the Curve (AUC) over the 48 h of test were calculated (Arbitrary Unit-AU) using MicroCal software. For intracellular glutamate and glutamine concentrations, cell suspensions were centrifuged for 30 min, 13,000× g, at 4 • C, and cellular pellets were homogenized in sodium acetate buffer (0. 05 M, pH 5. 0) with sonification. Supernatants were collected and underwent the standard dosage as described previously.
Quantitative RT-PCR Quantitative RT-PCR was performed using Taqman technology (Applied technologies, Cleveland, OH, USA) as described previously  [23]. Briefly, total RNA was isolated from Müller cells culture using TRIzol reagent (Ambion, Life Technologies, Carlsbad, CA, USA), quantified and reverse transcripted (Superscript iii reverse transcriptase, Invitrogen, Life Technologies). Real-time PCR reactions were performed in the Mx3005P Agilent (Applied, Life Technologies) with fivefold dilution of cDNA, 200 nM of each Taqman primer using the Expression Master Mix (Applied, Life Technologies). Data were normalized to the reference standard RNA 18S, analyzed by ∆∆Ct method and expressed as LogFC (Fold Change) compared to basal condition (-SC). Each measurement was performed three times.
Expression Arrays Total RNA was extracted using TRIzol reagent (Invitrogen, Waltham, MA, USA) according to the manufacturer's instruction. Genome-wide transcriptional profiling was performed by Mice Exon 1. 0 ST arrays (Affymetrix, Santa Clara, CA, USA) following manufacturer's instructions and as described previously  [24, 25]. Each condition was performed in duplicate. GeneChip CEL files were analyzed using GeneSpring (Agilent Technologies, Santa Clara, CA, USA). The log2 values were obtained and the microarray data were normalized using RMA16 algorithm. Statistical data analyses were done on all samples for each group. Similarly, enrichment rankings were based on all samples per group. Data were filtered with GeneSpring analysis using a cutoff of at least 1. 5 times up or down (Fold Change 1.
5, FC1. 5). Subsequently, one-way ANOVA was performed on a filtered list. The Benjamini-Hochberg false discovery rate (FDR) was set to 5%, and the P value was set to <0. 05 (ANOVA test). The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to find gene ontology-enriched terms. Data were confirmed by quantitative RT-PCR. Quantitative RT-PCR were performed using Taqman technology (Applied Technologies) as described previously  [25]. Data were analyzed by ∆∆Ct method and normalized to the reference standard RNA 18S. Each measurement was performed three times.
Data were reported as a percentage of the total lipids in the sample. Determination of fatty acid composition: Lipids were transmethylated using boron trifluoride in methanol according to  Morrison and Smith (1964)   [29]. The fatty acid methyl esters formed by transmethylation were analyzed on a Trace 1310 gas chromatograph (Thermofisher, Waltham, MA, USA) fitted with a CPSIL-88 column (100 m × 0. 25 mm i. d. , film thickness 0. 20 µm; Varian, Les Ulis, France). Hydrogen was used as a carrier gas (inlet pressure 210 kPa). The oven temperature was held at 60 • C for 5 min, increased to 165 • C at 15 • C/min and held for 1min, then increased to 225 • C at 2 • C/min, and finally held at 225 • C for 7 min. The injector and the flame ionization detector were maintained at 250 • C. Fatty acid methyl esters were identified by comparison with commercial and synthetic standards.
Results and Discussion The TGA and DSC analysis are presented in Figure  1. It is essential to determine the thermal properties to provide the suitability of PLA and PCL for various applications that require integrity at high temperatures, such as particleboard production. Thanks to TGA analysis, the temperature limits (temperature of biopolymers degradation) were recorded, which should not be exceeded during further research with these materials. The data presented in Table  1  displays the thermal stability established for 50% and 80% weight loss of the samples. The higher temperature obtained for PCL is related to higher thermal resistance. According to the literature data on UF resin, the thermal resistance drops significantly in the temperature range of 200-250  [40]. They confirmed that exceeding the temperature of 200 causes the formaldehyde relived from the dimethylene ether groups, which breaks the polymer chain.
ACKNOWLEDGMENTS The authors thank  S. Johanson  for constructive discussions, and the volunteers at the Walter and  Eliza Hall Volunteer Blood Donor Registry. This work was supported by grants and fellowships from the  National Health and Medical Research Council of Australia  (T. M. J. # 1124081 , R. S. A. and T. M. J. # 1049307 , # 1100451 , G. K. S. # 1058892 , R. S. A. and G. K. S. # 1158531 , C. R. K. # 1125436 , D. J. H # 1113577 , L. C. H. # 1080887  and # 1037321 ). H. D. C. was supported by an  H. C. Marian  and  E. H. Flack Fellowship. This study was made possible through  Victorian State Government Operational Infrastructure Support and Australian Government NHMRC Independent Research Institute Infrastructure Support scheme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Chromosome shuffling While considering the chromosomes three-dimensional character, the centre positions of all chromosomes are randomly distributed within the nuclear space until all chromosomes fit and no overlapping is observed between chromosomes. Due to space constraints shuffling was not possible for every nucleus attempted. All images were stored within an OMERO Database  (Allan et al. , 2012) , processing and analysis were then automated using the TAPAS home system  (Whitehead, 2018).
The resultant Hi-C library was amplified with Phusion Polymerase (Thermo), size-selected and purified with AMPure XP magnetic beads (Beckman) and sequenced on an Illumina NextSeq 500 to produce 81-bp paired-end reads. Approximately 200 million read pairs were generated for one biological replicate. HiC libraries for CD4 + T cells, CD8 + T cells and B cells are from GSE105776. The data pre-processing and analysis was performed with the diffHic pipeline  (Lun and Smyth, 2015)  in R with changes in parameters  (Johanson et al. , 2018a). Where biological replicates were available, the libraries were summed after pre-processing. Chromosomal looping interactions were detected using a method described by Rao et al. (Rao et al. , 2014)  and in  (Johanson et al. , 2018b). In brief, read pairs were counted in bin pairs of 50 kbp anchors for all libraries.
X-ray data collection, phasing and structure refinement X-ray diffraction data from a single crystal of each of G4C2 DNA, G4C2 RNA-DNA hybrid and the Ni II (Chro) 2 -DNA complex were collected at the synchrotron radiation facility of the National Synchrotron Radiation Research Center, Taiwan. Diffraction data integration and reduction were performed using the HKL-2000 package  (24). Phases for the G4C2 DNA structure were determined in the P3 1 21 space group by molecular replacement using Phaser MR in a python-based hierarchical environment for integrated xtallography (PHENIX) (version 1. 10. 1) and using a modelled A-DNA structure with WinCoot (version 0. 8. 9. 1) as a template  (25). The G4C2 DNA structure was then used to determine phases for the G4C2 RNA-DNA hybrid in the P2 1 2 1 2 1 space group, using a molecular replacement protocol.
The phases for the Ni II (Chro) 2 -DNA complex structure were determined by molecular replacement in the P6 5 22 space group with Phaser MR in the PHENIX suite using the partial structure of the [Mg 2+ -(Chro) 2 ]d(TTGGCCAA) 2 complex (PDB ID: 1VAQ) as a starting template. The resulting well-defined electron density maps were used to build initial models using the molecular graphics programs MIFit and crystallographic object-oriented toolkit (COOT). Initial models of the G4C2 DNA, G4C2 RNA-DNA hybrid duplexes and the Ni II (Chro) 2 -DNA complex were rebuilt and refined with COOT and PHENIX. Structure refinements were performed using phenix. refine in PHENIX (version 1. 10. 1) and Refmac5 in CCP4i (Oxon, UK); the crystallographic and refinement statistics of the structures are listed in Supplementary Table S1  (25, 26).
DNA parameter analysis A standard A-DNA structure with the same sequence as G4C2 DNA and G4C2 RNA-DNA hybrid DNA duplexes was generated using Discovery Studio Client software (version-19. 1. 0. 18287) (Dassault Systems Biovia Corp, Vélizy-Villacoublay, France) for comparison. DNA structural parameters, including helix, base and base-pair parameters were analysed with the Web 3DNA 2. 0 server and the CURVES+ program  (27, 28). RMSDs were calculated in PyMOL and van der Waals interactions were monitored using LigPlot+  (29). DNA parameter values for typical A-and B-DNA were used for comparison  (30).
MD simulation protocol Structures of metal II (Chro) 2 -DNA complexes obtained from the crystal structures were used for MD simulations. These were performed using the Discovery Studio software v19. 1. 0. 18287 and the CHARMm force field  (31). A 1000 step smart-minimizer algorithm was used, and the models were immersed in an orthorhombic water box built based on a modified TIP3P system  (32). The dimensions of the water box were selected such that the distances of non-hydrogen atoms in a nucleic acid duplex were at least 7 Å from the edge of the box. The total charge on each system under study was neutralized by randomly adding sodium ions. The system was then subjected to 5000-step Steepest Descent (SD) and 10 000-step Conjugate Gradient (CG) minimizations followed by 20 ps heating from 50 to 300 K with position constraints imposed on the nucleic acid duplexes.
The following 20 ps equilibration run was performed with position constraints on nucleic acid duplexes. The production run was carried out with an NPT ensemble with periodic boundary conditions. During the entire simulation, all covalent bonds involving hydrogen were constrained using the SHAKE algorithm, where the time step was set to 2 fs. The particle mesh Ewald method was used for treating longrange electrostatic interactions  (33). The production simulations was extended up to 500 ps for the Ni II (Chro) 2 -DNA and Mg II (Chro) 2 -DNA complex. RMSF values were analysed using the 'Analyse Trajectory' function in Discovery Studio software v19. 1. 0. 18287. Final graphs were plotted using Origin Pro8 v8. 0721 (OriginLab Corp. , Northampton, MA, USA)  (34).
For the Drosophila activity assay, equal amounts of males and females were individually placed in Drosophila Activity Monitoring System (TriKinetics Inc, Waltham, MA, USA) testing chambers that had been capped with regular food at one end. The flies were grown on a 12-h:12-h light: dark cycle at 25 • C for 4 weeks. Locomotor data were collected in the light cycle at 25 • C. Locomotor activity averages of each day during the fourth week were calculated. Data were assayed in eight flies from each group at a time.
This average twist value corresponds to unwinding angles from B-DNA of 3 • and 5 • , respectively  (36). To compare the crystal structures, a uniform model of the ideal A-form was built using the Discovery studio client package v19. 1 with the same sequence. The all-atom root-mean-square deviation (RMSD) of ideal A-form DNA to the G4C2 DNA and G4C2 RNA-DNA hybrid duplexes was 1. 3 and 1. 6 Å, respectively. Superimposing the G4C2 DNA duplex with that of the hybrid duplex resulted in an all-atom RMSD of 1. 4 Å. These results suggested that the three structures were globally identical, albeit with significant local differences (Figure  2C ). The crystal structure of the G4C2 DNA duplex is remarkable in that the structure exists as a hybrid A-form with some B-form character, having average twist angles and rise steps of 35 • and 3.
Although the two complexes in this structure are significantly different, the central GGGCCG tetranucleotide core, upon Ni II (Chro) 2 binding, adopts A-DNA-like characteristics, as reflected by the glycosyl torsion angle values that are in the range -156 • to -179 • with C3 -endo sugar pucker conformations for most of the residues and show a high degree of similarity, with an RMSD of 0. 5 Å (Supplementary Figure  S7 , Supplementary Table  S4 ). This is very distinct from the previously-reported Mg II (Chro) 2 -d(TTGGCCAA) 2 complex structure [Protein Data Bank (PDB): 1VAQ], which adopts a B-like character with a pronounced kink at the central 4-bp segment  (41). The overall DNA structure of the complexes preserves right-handed helical features with significant unwinding of base pairs at the GpG and CpC steps of the GGCC central core.
The dynamic behaviour of these individual base pairs in both complexes was determined in terms of RMSF values. The RMSF plot indicates a similar residue fluctuation profile for the central GGGCCG motif with an average RMSF of 1. 1 Å, indicating higher stability due to (Chro) 2 binding (Supplementary Figure  S12A ). The terminal residues T1:A20 and T2:A19 show extreme fluctuations, suggesting that Chro binding did not affect the terminal residues. Interestingly, this specificity was not observed in the previously reported structure of Mg II (Chro) 2d(TTGGCCAA) (PDB ID-1VAQ), where the flanking base pair is A:T (Supplementary Figure  S12B ). This suggests that the Ni II (Chro) 2 complex has extended specificity due to the indirect binding effects of the flanking G3:C18 and G8:C13 base pairs in the G4C2 sequence.
DATA AVAILABILITY The atomic coordinates and structure factors for the reported crystal structures have been deposited in the PDB under the accession numbers 6L75 for d(GTGGGCCGAC/GTCGGCCCAC) (G4C2 DNA duplex), 7BPV for r(GUGGGCCGAC)/d(GTCGGCCCAC) (G4C2 RNA-DNA hybrid duplex) and 6L76 for the Ni II (Chro) 2 -d(TTGGGCCGAA/TTCGGCCCAA) complex.
β-CD@Cur 4 and (β-CD) 2 @Cur 5 resulting from single and double "click" conjugations between alkyne-modified Cur (1 or 2) and azide-functionalized β-CD 3 were obtained as an orange powder and as a yellowish powder with 40% and 55% yields, respectively (Scheme 1C). The purity of the obtained nanoconjugates was determined by analytical HPLC and NMR analysis. The HPLC purity of both nanoconjugates was determined using reverse phase and based on the UV-visible detection (254 and 420 nm). As shown in Figure  2 , both β-CD@Cur 4 and (β-CD) 2 @Cur 5 nanoconjugates showed an excellent purity (94. 2% and 95. 3% for 4 and 5, respectively). The purity and the successful synthesis of both nanoconjugates were characterized with 1D and 2D NMR ( 1 H, 13 C, HSQC and HMBC) and HRMS analyses. All 1D and 2D NMR spectra of nanoconjugates 4 and 5 are included in supplemental data (Figures  S1-S8 ).
MTT Test The neuronal viability of the different molecules was assessed using the 3-(4,5 dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT, Sigma-Aldrich (Saint-Quentin Fallavier, France)) reduction activity assay. MTT solution (4 mg/mL, 37 • C) was added to each well and the plates were then incubated for 1 h at 35 • C in 6% CO 2. Media was removed and replaced with DMSO followed by incubation while shaking at room temperature for 10 min. The absorbance of formazan was measured at wavelength of 570 nm [(Fluostar Galaxy, Biolise software) (BMG Lab technologies, Offenberg, Germany)]. Cell viability was expressed as percentage of living cells compared to values obtained from control untreated cells.
Data processing The parameters of the models and their standard errors were adjusted with Origin® (OriginLab, Northampton, MA, United States) software using linear or non-linear curve fitting with the Levenberg and Marquardt iteration algorithm. Simulink® (MATLAB and Statistics Toolbox Release 2012b, The MathWorks, Inc. , Natick, Massachusetts, United States. ) software was used to build the global model of yeast growth. As shown in Figure  2 , the CTPM secondary model fit the experimental data well (RMSE 0. 04).
Acknowledgments: This work was supported by the  Frenchoc Premium  project (20 th  Fond Unique Interministériel)  and its three actors: the  French Center for Agricultural Research for International Development (CIRAD) , the companies  CEMOI and Lallemand S. A. S (Lallemand S. A. S -B. P. 59 -31702  Blagnac Cedex -France ). We thank  Lallemand  for providing the Saccharomyces cerevisiae LM strain.
Quality control was assessed using a D1000 ScreenTape on a TapeStation (Agilent, Santa Clara, California, USA), and quantity was determined via the Quant-it dsDNA Assay kit (ThermoFischer, Waltham, MA, USA) on a Mithras LB 940 (Berthold Technologies, Bad Wildbad, Germany). All libraries were normalized to nM, pooled, and denatured before end-of-pair sequencing (v3-v4, 2 x 300 bp) on a MiSeq device (Illumina, San Diego, California, USA). The targeted bacterial regions were sequenced according to the manufacturer's instructions  [25]. The 16S rRNA amplicon sequencing data are available from the European Nucleotide Archive (ENA) database (http://www. ebi. ac. uk/ena) under the accession number PRJEB43183. Overall, 10,483,756 paired-reads were sequenced (137,944. 16 +/-41,883. 18 reads/sample, length: 25-251bp).
After quality checking with the FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/, v0. 11. 9) software, reads were filtered for quality that was less than Q20 (using a sliding window of 5pb) and minimal length of 100bp (using trimmomatic, v0. 39)  [26]. Remaining quality paired-end reads were merged using the FLASH2 software (v2. 2. 0, https://github. com/dstreett/FLASH2/blob/master/README) [27] , yielding 120,716. 70 +/-38,924. 65 (87. 4 +/-5. 6%) combined paired per sample. We then employed MALT (v0. 4. 1, https://software-ab. informatik. uni-tuebingen. de/download/malt/welcome. html) and Megan6 (v6. 17. 0)  [28]  software with the rRNA SILVA database (v132)  [29]  for taxonomical assignation, using parameters for 16S ("SemiGlobal" / "mif" / "LCA"). Abundances were exported as a Biological Observation Matrix (BIOM) for further analyses (S1 File).
Data analysis. Gender, associated diseases, and diabetes differences were assessed using Chi-squared testing (GraphPrism 8), while age and body mass index (BMI) differences were assessed via t-testing (normal distribution was assessed using the D'Agostino-Pearson test). Analyses were performed including age and BMI of patients as co variable. Values were considered statistically significant when p <0. 05. PCoA, figures, and differential microbiota analysis were performed using the Shiny Application for Metagenomic Analysis (Shaman), from Institut Pasteur de Paris (http://shaman. pasteur. fr/) [30]. For the differential abundance analysis, we used the BIOM Table and the "target" File that associates each sample with its explanatory variables (see suppl data: S1 File and S3 Table ). Abundances of bacteria were summarized at the Phylum and Genus levels.
The statistical analysis in SHAMAN is based on the DESeq2/R package which model abundance counts with a negative binomial distribution. For the experimental design, we selected the variables of gender, BMI, age, diagnosis (Control/CCA), and associated diseases. We also included the interaction between diagnosis and associated diseases in the model. We used defaults parameters, such as the "weighted non-null normalization," which was introduced by Volant et al  [30]  and accounts more accurately for matrix sparsity. SHAMAN/DESeq2 yields baseMean and FoldChange (and log2FoldChange) and an adjusted p-value. Outputs were analyzed using Benjamini-Hochberg adjustment method. To be retained as differentially abundant, a taxon (phylum or genus) had to fulfill the following criteria: p value adjusted <0.
Acknowledgments The authors would like to thank all the patients for accepting to contribute to the current research, in addition the employees of the Henri Mondor (Creteil, France) and  Firouzgar (Tehran, Iran ) hospitals, who were involved in the study. They also wish to thank the department  Dean ,  Prof. Farhad Zamani , for supporting the study without any financial support from  Iran  to the  French lab EC2M3. Finally, many thanks go to  Vanessa Demontant  for her technical help.
Data are presented as treatment mean ± SE (n = 4). Data followed by the same letter are not significantly different by LSD test at p < 0. 05. lowed by washings with double distilled water. The sterilized seeds were sown in 23-cm diameter earthen pots filled with acid-washed sand, as described earlier  41. Plants in each pot were supplemented with 300 mL Hoagland nutrient solution on alternate days, and 100 mM NaCl was added at ten days after sowing (DAS) and then again at 20 DAS (at an interval of 10 days). Plants at the age of 20 DAS were treated with 200 µL L -1 ethephon (2-chloroethyl phosphonic acid; an ethylene releasing compound) on foliage individually or in combination with 6% Glu in the presence or absence of NaCl. The effect of ethephon in reducing Glu sensitivity in the presence of salt was assessed by recording observations on growth and photosynthetic parameters, oxidative stress as the content of H 2 O 2 and lipid peroxidation.
Raw reads were trimmed for low quality (quality score, 0. 05), Illumina adapters and sequences shorter than 15 nucleotides and aligned to the L. bicolor v2 reference transcripts available in the JGI database https://mycocosm. jgi. doe. gov/Lacbi2/Lacbi2. home. html (last accessed on 6 December 2021) using CLC Genomics Workbench v8. For read mapping, the CLC genomic workbench parameters were the following: minimum length fraction, 0. 9; minimum similarity fraction, 0. 8; mismatch cost = 2; insertion cost = 3; deletion cost = 3; and the maximum number of hits for a read was set to 10. The unique and total mapped read numbers for each transcript were determined and then normalized to RPKM (reads per kilobase of exon model per million mapped reads). We calculated the differential expression of genes with DESeq2  [26].