dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank  Dr. Maria Moriel-Carretero  for her very insightful comments about the manuscript. Dr. Laguette  ( IGH, Montpellier ) and  Dr. Seitz  ( IGH, Montpellier ) for HEK293T and HCTT116 cell lines, respectively. Dr. Patrick Hsu  ( Salk, US ) for providing us the plasmids of CasRx, dCasRx and processed gRNA through Addgene. We thank  Dr. Ling-Ling Chen Lab  ( State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, China. ) for providing us the plasmids for PspCas13b, dPspCas13b, dPguCas13b and crRNA backbone.
(Figure 5. 5 Figure 5. dCasRx can be used to identify regulatory mechanisms of splicing. (A) RNA binding motifs along CTNND1 exon 2 pre-mRNA predicted by at least two of the four publicly available RNA motif databases used (RBPDB, RBPMAP, SFMAP and Spliceaid). The genomic position of the gRNAs most impacting CTNND1 splicing are also shown in a dotted square. (B) CTNND1 exon 2 relative inclusion levels in HEK293T cells upon shRNA-mediated knock-down of the splicing factors which motifs are found in (A). RT-qPCR levels are normalized by TBP and represented as mean +/-SD relative to non-targeting control shRNA in n=4 biological replicates. (C) Knock-down efficiencies of the shRNA tested in (B). RT-qPCR levels are normalized by TBP and represented as the mean +/-SD relative to NT shRNA in at least 3 biological replicates.
Environmental objectives In order to compute the three environmental objectives (i. e. human health (in years), on ecosystem quality (in species x year) and on resource availability), the LCA model by  (Quéheille et al. , 2022)  is used. It is a parametric and holistic LCA model to assess the end-of-life environmental impacts of any building. Following the standard EN 15804 (CEN, 2016), the LCA system (Fig. 2 ) includes stages which connect to the multi-objective optimization model. After deconstruction (C1), waste is transported (C2) to treatment plants where waste is recovered (C3) or land lled (C4). The potential environmental bene ts through waste recovery are estimated at stage D. The model is developed with OpenLCA software (GreenDelta, 2019a) and Ecoinvent 3. 3 cut-off database  (Ecoinvent, 2016).
Implementation The optimization model has been implemented in Java with the Eclipse IDE1. The many-objective optimization engine uses the Java open-source framework MOEA Framework2. The Improved Decomposition-Based Evolutionary Algorithm (I-DBEA) algorithm is used to perform the optimization. DBEA demonstrated its interest for many-objective problems  (Asafuddoula et al. , 2015)  and was already used for demolition optimization  (Quéheille et al. , 2019). One issue to address was the inclusion of LCA software's results. OpenLCA is kept to correctly manage the signi cant database and computations for LCA. The Jython language3 (GreenDelta, 2019b) is used to connect OpenLCA in python with the optimization model in Java. Thus, for each solution, when it comes to study the environmental objectives, the optimization model transfers the input data to OpenLCA, which estimates the endpoint results and provides them to the optimization model.
Brightway is an opensource framework which enables high-speed computations and most probably an easier connection to the optimization model, which would reduce the computation time of the whole model. Computational features could also be considered, such as a more powerful computer or parallel processing of computations. Another limitation of the tool is that the uncertainties of the objectives results are not estimated. Indeed, there are various uncertainties in these calculations: LCA uncertainties (which can be provided by the Ecoinvent database), energy consumption of machines, driving speed of lorries which transport waste to the treatment plants… Additional work is required for the tool to consider the totality of uncertainties involved in the objectives calculations.
For lipids with an exact corresponding internal standard, concentrations were calculated directly based on the lipid to internal standard ratio and internal standard concentration, while ensuring that all peaks were within the linear dynamic range of the instrument. For other lipids, concentrations were derived from comparisons with an external calibration curve of the ratio of the specific lipid standard to a deuterated internal standard. Limit of quantitation used signal:noise of >5:1 and at least 5 data points per peak. All further analysis was conducted using R and GraphPad Prism version 8. 4. 3, and MetaboAnalyst version 4. 0 and higher (metaboanalyst. ca). Data processing. A MongoDB database was populated with clinical parameters, soluble biomarker measurements and summary statistics of flow cytometry data.
After stopping the reactions by placing the samples on ice, the samples were surface stained with primary mouse anti-bovine CD11c (clone BAQ153A) and CD14 (clone CAM36A) to identify macrophages, and mouse anti-bovine granulocytes (clone CH138A). Cells were then fixed with BD FACS lysis buffer, washed, and resuspended in FACS buffer for flow cytometry analysis as previously described  (Mahmoud et al. , 2020). Samples were analyzed using a BD FACS Canto flow cytometer and analyzed using Flowjo software  (version 10. 8. 1; Becton, Dickinson and Company).
Reads Alignment and Differential Expression Analysis of RNA Seq Two sequence reads (FASTQ format) were provided by the Iowa State University Sequencing Core Facil-ity, one for each sequencing lane. Counts were merged between lanes by averaging counts between the 2 files. Sequence reads were analyzed for quality metrics using FastQC (version 0. 11. 7; Babraham Bioinformatics) and MultiQC (version 1. 5; Seqera), and all reads passed the basic quality statistics (i. e. , read length, sequence length distribution, per base sequence quality, overrepresented sequences). The sequence reads were aligned to the bovine reference genome (UMD3. 1) using the HISAT2 program (version 2. 2. 1, release 7/24/2020; http: / / daehwankimlab. github. io/ hisat2/ ). Differential gene expression was performed with the program edgeR version 3.
32. 1  (Dai et al. , 2014)  using a general linearized model with control and treatment as factors. The Benjamini and Hochberg correction was used to estimate the false discovery rate (FDR). Sequences were normalized using the reads per kilobase per million mapped read method. Hierarchical clustering analysis was performed using the program MClust version 5. 4. 9  (Scrucca et al. , 2016)  for genes that were found to be significantly different (FDR <0. 05) using the average of normalized counts across samples with the same treatment. Functional enrichment analysis was performed with the PANTHER Database version 11. 1 (released on October 24, 2016; http: / / pantherdb. org/ ) classification system utilizing Bos taurus as the reference organism using the Benjamini-Hochberg FDR procedure for multiple test correction  (Mi et al.
Validation of Transcriptome Sequencing Using Real-Time Quantitative PCR Eleven differentially expressed genes (DEG) were selected from the sequencing data and verified using real-time quantitative (qRT) PCR. The genes are AIRE, CCL1, CCR8, CD8A, CD79B, FCRLA, ICOS, IFNG, MZB1, TIGIT, and XCR1. Primers used for these genes can be found at (https: / / doi. org/ 10. 6084/ m9. figshare. 24036855. v1). Primers were designed using the National Center for Biotechnology Information PrimerBlast software. The original ante-and postmortem BAL RNA preparations were used for cDNA synthesis as previously described  (McGill et al. , 2018; McDonald et al. , 2021). Reactions were conducted in duplicate and performed using a QuantStudio 5 qPCR machine (Thermo Fisher Scientific) with the following cycling conditions: 2 min at 50°C, 10 min at 95°C, 40 cycles of 15 s at 95°C, and 1 min at 60°C, and a dissociation step (15 s at 95°C, 1 min at 60°C, 15 s at 95°C, 15 s at 60°C).
Statistics The larger experiment was analyzed as a randomized complete block design. Individual calves were considered the experimental unit. For data in which assessments were performed on multiple days, results were analyzed by a linear mixed-effects model, fit using REML with the Geisser-Greenhouse correction, followed by Sidak's test for multiple comparisons. The model used the fixed effects of time, treatment (CON vs. SCFP), and treatment × time interaction. Calf was considered a random effect. Data are reported as least squares means ± standard error of the mean. Differences of P ≤ 0. 05 were considered significant. Graphs were constructed and statistical analyses were performed using GraphPad Prism v9. 0. 2 (GraphPad Software Inc. ).
As seen in the Venn diagram in Figure  1B , 73 of these genes were shared between the 2 tissue types (Figure  1B , https: / / figshare. com/ articles/ dataset/ _/ 24036852). A heat map was generated for 537 DEG identified in both NLL and LL samples and organized by supervised hierarchical clustering using the log 2-fold change between SCFP and CON calves, which was calculated from the average of normalized counts across samples with the same treatment (Figure  1C ). Four clusters were identified and labeled as 1, 2, 3, and 4 (Figure  1C ), and functional enrichments for Gene Ontology (GO) terms for biological, cellular, and molecular processes were assessed for each cluster (Table  2 ). In each cluster, a selection of the top enriched pathways among the predicted target genes are shown. In cluster 1, LL samples from SCFP had distinctly higher expression of genes enriched in plasminogen activating cascade (FDR 8.
The lists showing the genes expressed are provided at https: / / figshare. com/ articles/ dataset/ _/ 24036852. Clustering analysis of clusters 1 and 2 showed that after infection SCFP-treated calves had lower transcriptional signatures of processes involved in glutathione catabolic process (FDR 2. 95e-02), leukocyte chemotaxis involved in inflammatory response, (FDR 1. 39e-03), interleukin-2-mediated signaling pathway (FDR 3. 83e-02), B cell receptor signaling pathway (FDR 1. 23e-10), and phagocytosis, recognition (FDR 5. 31e-06) compared with CON calves. These pathways involved genes such as IL2RB, IL21R, CD4, IL1R2, CD2, ICOS, CD8A, IFNG, LAMP3, ITK, CCL20, and CTLA4. Similarly, we observed high expression of genes involved in leukocyte chemotaxis and inflammatory response (FDR 3. 96e-02) and cytokine receptor binding (FDR 4.
Differential Gene Expression and Functional Annotation in BAL Samples from SCFP and CON Calves Before Experimental Coinfection Both lung tissues (Figure  1 ) and BAL samples (Figure  2 ) from calves supplemented with SCFP have a transcriptional response that indicates that animals experienced a less severe bacterial-viral challenge. To provide additional insights on the SCFP mode of action, we explored transcriptional changes of BAL samples before infection, and a volcano plot of the identified 678 DEG was generated (Figure  3B ) and the target genes annotated (https: / / figshare. com/ articles/ dataset/ _/ 24036852). Most of these changes involved an increase in transcription (positive log 2-fold change) when calves were supplemented with SCFP (Figure  3B ). Functional enrichment of the upregulated DEG such as CD19, CD79, FCRLA, MZB1, ICOS, CD2, CD8A, CTL4, CCL20, MHC II, IL12RB1, IRF4, IL17A, CCDC13, TNF superfamily member, and C-X-C motif chemokine receptor (https: / / figshare.
com/ articles/ dataset/ _/ 24036852) were associated with 8 biological pathways corresponding to processes related to immune processes that included regulation of T cell differentiation, and immune system development, among others (Figure  3C ). To validate these results, 11 DEG (AIRE, CCL1, CCR8, CD8A, CD79B, FCRLA, ICOS, IFNG, MZB1, TIGIT, and XCR1) from preinfection and necropsy BAL samples were selected and tested by qRT-PCR to test reproducibility and repeatability of the DEG in RNA seq data. The RPS9 gene was used as the housekeeping gene, and all primers used in this study are listed at https: / / figshare. com/ articles/ dataset/ _/ 24036855. All 11 DEG identified by RNA seq followed the same expression pattern when confirmed by qRT-PCR method.
ACKNOWLEDGMENTS This project was funded by  Diamond V Mills Inc. (Cedar Rapids, IA). Authors T. W. Maina ,  M. I. Sardi ,  A. Rogers , and  I. Yoon  are employees of  Diamond V Mills or Cargill Technologies R&D (Elk River, MN ). T. W. Maina  performed this work while employed by  Iowa State University (Ames, IA ). The authors have not stated any other conflicts of interest.
Network analysis reveals regulators of signaling, membrane trafficking and thioreductase redox state involved in BoNT/A intoxication Among the positive regulators of the screen, 135 hits did not influence significantly surface SV2 levels and are thus likely to function in post-endocytic processes (Supplementary Tables 2). 92 positive regulators (required for intoxication, in red) and 43 negative regulators (reducing intoxication, blue) were mapped to their intracellular localities (Figure  5A ). Several gene products were localized to endosomes and 16 were associated with the Golgi and ER. At the Golgi, one hit was the glycosylation enzyme B4GALT4, known to be involved in the biosynthesis of the ganglioside co-receptor of BoNT/A, G T1b. Using the STRING database, a protein-protein interaction network of hits was generated (Figure  5B )  (Szklarczyk et al.
Acknowledgements and conflict of interest: This study was funded by a grant from  IPSEN  and by an  Industry Alignment Fund Grant  from  A*STAR. Jacquie Maignel ,  Laurent Pons , and  Matthew Beard  are employees of  Ipsen. Keith Foster  and  Omar Loss  are former employees of  Ipsen. F. Bard is funded by a grant " Leader in Oncology " from the  Fondation " ARC pour la recherche sur le cancer " and by a  Chaire d'Excellence  from  AMIDEX :  AMX-20-CE-03.
Analysis of the probes Two sets of probes were used in the SHAMAN benchmark described in the previous section. The first set of 8 probes (Tab. S7) was previously used in the development of SILCS-RNA  31  and was mostly composed of aliphatic compounds selected to represent specific types of interaction with the RNA target. This set includes: acetate (ACEY), benzene (BENX), dimethyl-ether (DMEE), formamide (FORM), imidazole (IMIA), methyl-ammonium (MAMY), methanol (MEOH), and propane (PRPX). A second set of 5 probes (Tab. S8) was generated in this work using a fragmentation protocol (Materials and Methods) applied to ligands present in i) the HARIBOSS  22  database of RNA-ligand resolved structures (https://hariboss. pasteur. cloud); and ii) the R-BIND  26  database of bioactive small molecules targeting RNA (https://rbind.
The case of the FMN riboswitch The Flavin MonoNucleotide (FMN) riboswitch is an RNA molecule found in bacteria that regulates FMN gene expression via binding the FMN metabolite  51. Being the target of ribocil  18  , one of the few FDA-approved compounds targeting RNA, the FMN riboswitch constitutes a natural test case for our drug-design purposes. As of today, 19 X-ray structures of the FMN riboswitch are deposited in the PDB database, 3 in apo and 16 in holo conformations. The 9 unique small molecules resolved in the holo structures fall into three main families: the cognate FMN family, the synthetic ribocil family, and the tetracyclic DKM binder (Fig. S3 ). The ligands belonging to the FMN and ribocil families share a Ushaped conformation and occupy the same binding site, buried into the RNA structure within the junctional region of the six stems between the A-48 and A-85 bases (Fig.
I. Input stage The initial input of SHAMAN consists of the 3D structures of the target RNA and of a set of N probes. Starting from this information, we generate a reference mother system, including the RNA and possibly structural ions, and N replicas, each one with the addition of a different probe. Setup of the mother simulation. The 3D structures of all the systems (Tab. S1) were obtained from the PDB database  64. In the case of RNA structures determined by NMR, the first model was selected. In case of holo structures, the ligand was removed. Furthermore, to correctly model the RNA with our forcefield, the following elements were also eliminated, if present: crystal waters, PO3 group in the 3' terminal, modified residues at both terminals, and ions not modeled by our forcefield (SO4 in PDB 3tzr, 3ski and 7kd1).
The resulting model was then prepared by adding hydrogen atoms using UCSF Chimera 65 at pH=7. 4 and processed by the OpenMM library 66 v. 7. 7. 0 to generate an initial configuration and topology files. The forcefield used for RNA was AMBER99SB-ILDN* 67 with the BSC0 correction on torsional angles  68  and the 𝜒 "#$ correction on anti-g shifts  69. Ions were modeled using the Joung and Cheatam parameters  70  with the Villa et al. correction for magnesium  71. Water molecules were modelled with the OPC force field  72. Forcefield parameters were obtained from https://github. com/srnas/ff. Setup of the replica simulations. The 3D structures of the probe were generated as described in the section Details of the probes. One replica of the mother system was generated for each probe. A single probe conformer was generated using the RDkit python library v.
Details of the SHAMAN benchmark Details of the target RNAs. For our SHAMAN simulations, we selected 7 RNA systems, whose structures in complex with at least one ligand were deposited in the PDB databank  64  (Tab. S1). To initiate the simulations, we selected 1 holo structure per system and, when available, an apo structure of the same RNA molecule. In total we performed 12 SHAMAN simulations. A summary of all simulations performed along with details about the systems are reported in Tab. S2.
Software and data availability SHAMAN simulations can be run with the development version (GitHub master branch) of PLUMED (https://github. com/plumed/plumed. github. io). The GROMACS topology files and PLUMED input files used in our benchmark are available on PLUMED-NEST (www. plumed-nest. org), the public repository of the PLUMED consortium, as plumID:23. 031. Scripts to facilitate the preparation of the input files and the analysis of the results as well as a complete tutorial will be released soon under a license "free for academics, not for commercial use".
0 Figure 2. Figure 3. Figure 4. 0234 Figure 2. Assessment of the SHAMAN accuracy. A)A cartoon-surface representation of the four riboswitches in our benchmark set (Tab. S1), with the corresponding name in the upper left of each panel. In the lower part, the PDB id of the starting structure used in our SHAMAN simulations is reported in a brown and cyan box for the holo-like and apo case (when available), respectively. The cartoon representations correspond to the holo-like structures. B) As in panel A), for the three viral RNAs of our benchmark set (Tab. S1). C) Definition of the validation distance (Eq. 10) as the distance between the free-energy weighted center of an interacting site and the center of mass of the experimental ligand. D) 𝛥𝛥𝐺 distribution of the probes that correctly identified known experimental pockets for holo-like (brown) and apo simulations (cyan).
/hariboss. pasteur. cloud) and RBind 26 (https://rbind. chem. duke. edu) databases, respectively. The molecules were then prepared using Epik 85 at pH 7. 4. All molecules were converted into canonical SMILES using RDkit v. 2022. 3 and then processed using the RDkit Murcko Scaffolds KNIME node. probes was generated using the following KNIME 4. 6 protocol. We first extracted 265 experimentally validated RNA binders and 159 bioactive binders from the HARIBOSS 22 (https:/The two sets were compared using the 'Compare Ligand Sets' node provided by Schrodinger v. 2022. 3 and 6 Murcko scaffolds were identified as common. Finally, the RECAP fragmentation method 86 and for the most part originally developed for proteins. It is composed of: acetate (ACEY), benzene (BENX), dimethyl-ether (DMEE), formamide (FORM), imidazole (IMIA), methyl-ammonium (MAMY), methanol (MEOH), and propane (PRPX).
ACKNOWLEDGMENTS This work was supported by the  Swiss National Science Foundation  ( Sinergia  Grant  CRSII3_141814  to C. R, Y. A. and D. S), by the  Foundation Provisu  (to YA, to CK and VH), by the  Fondation "En souvenir du   Dr Georges Borel " (CK), by the  Fondation Asiles des Aveugles (YA) , by the  Novartis foundation for biomedical research  (to YA).
Subcloning in pGEMT allowed to fuse IRBP (240 bp, position 1175 to 1415 Genbank X53044. 1) to GRK1 (298 bp, 1760 to 2088 from AY32780. 1), FCBR2 (456 bp, position 10338 to 10794 Genbank NG_028125. 2) to F0. 4 (459 bp, position 4554 to 5013 Genbank NG_028125. 2) and to subclone FCBR1-F0. 4 (1098 bp, position 3949 to 5013 of Genbank NG_028125. 2). Human FAM161A short (HS, 1969 bp, position 20 to 1989 of Genbank sequence NM_032180. 3) and long isoform (HL, 2150 bp, position 20 to 2170 of Genbank sequence NM_001201543. 2) cDNAs were obtained after amplification from human retinal cDNA and cloning of the PCR product. A mutated Woodchuck hepatitis virus Posttranscriptional regulatory Element (WPRE4) devoid of promoter activities or open-reading frames.
Data and Method AKR emission is selected from amongst a complex superposition of phenomena in Wind/WAVES/RAD1  (Bougeret et al. , 1995)  data using the W21 technique (described in detail by  Waters et al. (2021b) ); a subset of these data are available online  (Waters et al. , 2021a). Using this W21-selected data, the AKR intensity is integrated over 100-650 kHz as in  Waters et al. (2021b); Fogg et al. (2022)  (using the same technique as  Lamy et al. (2010 Lamy et al. ( , 2008)) ). Using the same approach as  Waters et al. (2021b) , AKR intensity is calculated over a full frequency sweep in Wind/WAVES data, roughly 183 seconds (with some fraction of seconds that varies from integration to integration). Since the geomagnetic indices which AKR will be compared with are provided at integer minute resolution, one or other of the datasets must be interpolated to allow point to point comparison.
Acknowledgements. A. R. F. 's work was supported by  Irish Research Council Government of Ireland Postdoctoral  Fellowship  GOIPD/2022/782  and  Science Foundation Ireland  Grant  18/FRL/6199. C. M. J. 's work was supported by the  Science Foundation Ireland  Grant  18/FRL/6199. S. C. C. acknowledges  AFOSR  grant  FA8655-22-7056  and an  ISSI Johannes Geiss Fellowship. J. E. W. 's work was supported by the  EPSRC   Centre for Doctoral Training in Next Generation Computational Modelling Grant  No. EP/L015382/1. The authors acknowledge  CNES  ( Centre National d-Etudes Spatiales) ,  CNRS (Centre National de la Recherche Scientique) / INSU (Institut national des sciences de l'Univers).
density and speed. No clear trend in MI as a function of lag was found, and indeed sometimes the MI was below the random phase surrogate threshold. Therefore only results for parameters with clear MI vs lag trends (i. e. AE, AU, AL, PC(N), SYM-H and B Z ) are presented for brevity. Additionally, any time intervals with missing data (which are only found in PC(N) and B Z ) were removed from both the parameter data set and the AKR dataset before running the MI-lagging analysis. https://doi. org/10. 5194/egusphere-2023-2055 Preprint. Discussion started: 13 September 2023 c Author(s) 2023. CC BY 4. 0 License.
Statistical analyses Differences in continuous variables were evaluated by the nonparametric Mann Whitney U test (two groups) or the parametric Student's t test (two groups) or Kruskal Wallis with Dunn's post-test (multiple groups). Chi-square/Fisher's exact test was used to evaluate associations of categorical variables. Correlations were evaluated by Spearman's bivariate analysis. Statistical analyses were performed using GraphPad Prism-v9 software (Graphpad, San Diego, CA, USA). Data are shown as median and interquartile range and p-values < 0. 05 were considered significant.