dataesr/scientific-paragraphs-categorization huggingface dataset:
Twenty 1mg fractions were subsequently analyzed by Q-Exactive HF LC-MS/MS system (Thermo Scientific) for each replicate sample. Raw data was analysed using SEQUEST in Proteome Discoverer 2. 1 (Thermo Scientific) against combined human (Uniprot) and P. falciparum (PlasmoDB) reference proteome databases with 25 ppm precurson mass tolerance, 0. 05 Da fragment mass tolerance with Decoy database search using 0. 01 and 0. 05 strict and relaxed target FDR cutoffs respectively. The searches included static modifications: TMT6plex on N-terminus and K, Carbamidomethylation on C, and dynamic modifications Acetylation on N-terminus, Oxidation on M, and Deamination on N and Q.
MS-IDTR-CETSA and analysis The assay was carried out in two biological replicates, as described with minor modifications  (Dziekan et al. , 2020). In brief, lysate from saponin-liberated mature (32 ± 6 hpi) P. falciparum parasites was incubated for 3min with different concentrations (1. 5nM-100mM) of Darapladib (MedChemExpress) or the DMSO control and subsequently heated for 3min to 50 C, 55 C or 60 C to denature unstable protein subsets, or incubated at 37 C. Soluble protein fractions were isolated by centrifugation and analysed by quantitative mass spectrometry as described before  (Dziekan et al. , 2020). Data analysis was carried out in R environment using mineCETSA package. Resulting protein stability profiles under thermal challenge conditions (i. e. > 37 C) were evaluated based on observed change in protein stability [reading delta Area Under the Curve (DAUC) relative to the non-denaturing (37 C) condition, the Fold Change (FC) in protein abundance relative to vehicle control treated sample], as well as protein stability profile adherence to expected sigmoidal shape of dose response (R 2 ).
Differential scanning fluorimetry of PRDX6 Melt curves of recombinant PRDX6 with and without inhibitors were determined using differential scanning fluorimetry. The assay was conducted 384-well plates with a final volume of 10mL. A PBS-based solution of PRDX6 (2mM) and SYPRO Orange (5x) was incubated for 60min at RT with 25mM Darapladib, 25mM Varespladib or DMSO. Melt curves were determined on a BioRad CFX384 qPCR thermocycler (40-70 C at 1. 2 C/min, FRET Channel). Data was analysed using the CFX manager software (BioRad). Recombinant expression of P. falciparum GluPho Recombinant N-terminal His 6 -tagged P. falciparum glucose-6-phosphate dehydrogenase-6-phosphogluconolactonase (PfGluPho) was produced according to Jortzik et al. (Jortzik et al. , 2011)  in the vector pQE30 in E. coli M15 cells (Qiagen) with pRAREII (Novagen).
Parasites were treated with 0. 5 mM or 2 mM Darapladib, 10 mM Varespladib or DMSO for 2h at late trophozoite/early schizont stage (38-42 hpi). After treatment for 2 h, parasites were pelleted by centrifugation and resuspended in labelling solution (5 mM BODIPY 581/591 C11, 10 mM Hoechst 33342 in cRPMI) and incubated for 1 h at 37 C, 5% O 2 , 5% CO 2. Parasites were measured on a MACSquant flow cytometer: BODIPY 581/591 C11 reduced, l Ex 488nm, l Em 614/50nm, ''Red''; BODIPY 581/591 C11 oxidised l Ex 488nm, l Em 525/50 nm, ''Green''; Hoechst 33342, l Ex 405nm, l Em 450/50 nm. Data was analyzed using FlowJo 10 (FlowJo, LLC). After exclusion of doublets, debris and merozoites by size, infected RBCs were gated for by selecting Hoechst 33342 positive cells. The MFI of ''Red'' and ''Green'' channels of infected RBCs was calculated.
Relative successful transport of cytosolic LysoSensor to the FV was expressed as the ratio of integrated densities of ''Yellow'' and ''Blue'' channels. For flow cytometric measurement, the cells were stained with 0. 5 mM SYTO 61 for 30 min at 37 C, washed once in cRPMI afterwards and measured on a MACSquant flow cytometer: LysoSensor pH 7, l Ex 405 nm, l Em 450/50 nm, ''Blue''; LysoSensor pH 5, l Ex 405 nm, l Em 525/50 nm, ''Yellow'' and SYTO 61, l Ex 561 nm, l Em 695/50 nm. Data was analyzed using FlowJo 10 (FlowJo, LLC). Briefly, after exclusion of doublets, debris and merozoites by size, infected RBCs were gated for by selecting SYTO 61 positive cells. The MFI of ''Yellow'' and ''Blue'' channels of SYTO 61 + infected RBCs was calculated. Relative successful transport of cytosolic LysoSensor to the FV was expressed as the ratio of the MFI of ''Yellow'' and ''Blue'' channels.
ACKNOWLEDGMENTS We thank  Gordon Langsley  for fruitful discussions and critical inputs. We also thank the electron microscopy  Ultrastructural BioImaging (UBI) platform, Institut Pasteur, Paris , for providing ready access to the electron microscopy facility. The authors are grateful to the  Central Animal Facility of Institut Pasteur  for providing support with animal importation, care, housing, and breeding. We thank the  Image Analysis Hub (IAH), Institut Pasteur, Paris , for their help with image analysis. M. P. W. was part of the  Pasteur -Paris University (PPU)   International PhD Program  and  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no. 665807. This work was funded by the following grants:  Fondation pour la Recherche Medicale grant  ( FDT202001010791 ) to M.
P. W. ;  Agence Nationale de la Recherche  grant ( ANR-17-CE15-0010 ) to C. E. C. ;  Pasteur Innov grant  from  Institut Pasteur to C. E. C. ; Laboratoire d'Excellence (LabEx) ''French Parasitology Alliance For Health Care''  ( ANR-11-LABX-0024-PARAFRAP ) to C. E. C. and D. M. ;  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases' ' (grant no. ANR-10-LABX-62-IBEID ) to P. F. ;  NTU Presidential Postdoctoral Fellowship grant  ( NTU/PPF/2019 ) to J. M. D. ;  Singaporean Ministry of Education  grant ( MOE-T2EP30120-0015 ) to J. M. D. and Z. B. ; and LOEWE  Center DRUID  (Projects  B3 ) within the  Hessian Excellence Program  to I. B.
Acknowledgments: We thank  Rup Lal  for the kind gift of the plasmid pRL60 and  Marick Esberard  and  Philippe Bouloc  for the kind gift of S. aureus strains. We thank  Jorddy Benoist ,  Anaïs da Costa  and  Gaëtan Pavard  for their technical help and motivation during their internships. We thank  Stéphanie Bury-Moné  for critical reading of the manuscript and helpful discussions.
Data Availability Statement All data used in this work are publicly available: GHCN-D v2 is available at https://climexp. knmi. nl/selectdailyseries. cgi. De Bilt TXx was constructed from the daily time series of The Bilt at https://climexp. knmi. nl/getdutchstations. cgi?TYPE=tx. The CMIP5 TXx values can be found at https://climexp. knmi. nl/selectfield_ cmip5_annual. cgi. The 55 RCM/GCM combinations at 11 km resolution of the CORDEX ensemble are available via the ESGF as linked from https://cordex. org. The Community Earth System Model Large Ensemble (CESM1 LENS) is available at https://www. cesm. ucar. edu/projects/community-projects/LENS/data-sets. html.
Objective 4: Enabling Technologies and the Sharing of Resources The completion of a high-quality wheat genome sequence was expected to be a "game changer", and this has proved to be the case. Since the completion of the first reference quality sequence led by the International Wheat Genome Sequencing Consortium (IWGSC), a wheat pan-genome has been completed, and sequences of durum wheat and several wild tetraploid and diploid wheat progenitors have become available (for example,  [20] ). Access to wheat data and information is now provided through the Wheat Information System (WheatIS) and WheatVIVO. These databases and data access tools are being continually updated to provide information on diverse genomics resources, wheat researchers, research organisations, projects and publications. The rapid expansion of information makes these systems essential for the research community.
Data Access and Standards As with many areas of modern biological research, problems around the utilisation of large and complex datasets abound. This encompasses concerns about data standards, access and reliability. Consistency in data collection and processes for ensuring reliability are fundamental to support the exchange of information and allowing researchers to collaborate effectively. The complexity of many datasets and issues around the management and utilisation of "big data" permeate many research areas. The scale and complexity are likely to increase as new tools for data generation and analysis become available. The pool of data and interest in mining these datasets are expected to extend from research and breeding operations through to farmers and processors.
Wheat Initiative role: The broad expertise base available through the EWGs provides an ideal opportunity to support the development and application of new technologies and capabilities to small wheat improvement programs. Through the identification of the capabilities and needs of wheat research and delivery programs, options to expand capabilities by linking groups, providing support for staff exchanges and targeted training activities will help build the necessary capacity. In some cases, the support can be provided remotely, for example with crop and climate modellers, but in other cases, staff will need to move between groups. In addition to identifying the needs of individual programs, the Wheat Initiative can develop an expertise database that can be called on to provide specific support.
HDACi Activates Different Transcriptional Cascades in Response to CFC in the Hippocampus and Striatum. To further understand the molecular mechanisms by which cognitive epigenetic priming leads to improved memory performance, we next used bulk RNAseq in the hippocampus and striatum to determine which genes are activated when CFC is combined with HDACi treatment. For this, we extracted and sequenced total mRNA from whole-tissue homogenates 1 h after behavior, using the same experimental setup as for the electrophysiological recordings. In the hippocampus, consistent with previous data (29), we found no differentially expressed genes (DEGs) (P ≤ 0. 05; log 2 fold-change [FC] ≥ 0. 4) between CFC and context-only exposure in VEH-treated animals (Fig. 2  A, Far Left). Likewise, when comparing CFC with the context-only group in HDACitreated animals, no DEGs were detected, indicating that CFC alone is not sufficient to induce detectable transcriptional changes (Fig.
2  B, Center Right). In contrast to the hippocampus, however, no further increase in the number of DEGs was detected when HDACi was paired with CFC (Fig. 2 B , Right, and SI Appendix, Fig. S5 ). Lists of pairwise differential expression for both brain regions can be found in Dataset S1. When comparing strongly up-regulated genes (with a stringent log 2 FC ≥ 3 cutoff) after the combined HDACi and CFC treatment in both the hippocampus and the striatum, (Fig. 2C ), we detected 4. 5× more hippocampal DEGs, which also had a higher magnitude of activation than striatal DEGs (Student's t test of slope values, P = 2. 304e-05) (Fig. 2C ). These hippocampal DEGs (Dataset S2) were enriched in iontransport ontologies and included transthyretin, Ttr, which provides neuroprotection in aged mice and is associated with enhanced memory  (30, 31).
In contrast, highly expressed striatal genes were primarily predicted genes (Dataset S2). This indicates that, in the hippocampus, the expression of these DEGs is further enhanced when the HDACi is paired with CFC, while pairing HDACi with CFC had no such effect in the striatum. Next, we set out to identify transcriptional patterns of CFC and HDACi treatment. To this end, all DEGs (P ≤ 0. 05, log 2 FC ≥ 0. 4) underwent decision tree clustering, in which they were grouped based on their FC expression pattern from the VEH-Context group (Fig. 2 D-F ; see also details in Materials and Methods). Four major up-regulated clusters were identified as trajectories of interest (Fig. 2D ). In the hippocampus, this analysis yielded: 1) genes that were up-regulated by the HDACi treatment alone (HDACi-VEH) and further increased when HDACi was paired with CFC (HDACi-CFC), which we termed "primed active" (n = 62); 2) genes that were increased by HDACi treatment but showed no further CFC-driven increase, which we termed "primed stable" (n = 937); 3) genes that were enriched by HDACi treatment, but were reduced when the HDACi was paired with CFC, which we termed "primed silenced" (n = 579); and 4) genes that were only activated when combining HDACi with CFC, but not by either condition alone, which we termed "non-primed active" (n = 726).
In the striatum, the order of magnitude of DEGs was similar (Fig. 2F  and Dataset S3). qPCR analysis confirmed the expression changes of these genes (SI Appendix, Fig. S6 ). Gene ontology (GO) analysis revealed brain and trajectoryspecific gene expression differences that are relevant for synaptic plasticity. We found that in the hippocampus, the primed active cluster was enriched for the ERK cascade, which has been implicated in synaptic plasticity as well as learning and memory  (32, 33). Conversely, in the striatum, the primed active cluster was not enriched for any ontologies involved in MAPK/ERK signaling or learning and memory (Fig. 2E  and Dataset S4). Furthermore, the primed stable cluster was characterized by learning and memory-related pathways, such as cognition and regulation of calcium ion transport in the hippocampus, but not the striatum.
S7C  and Dataset S4). Taken together, these results illustrate that HDACi treatment is a generic driver of differential transcription between the hippocampus and the striatum. It enhances MAPK signaling in both the hippocampus and the striatum, as seen in the comparisons of the primed stable groups. However, when paired with CFC, HDACi treatment results in a specific increase of learning-related genes in the primed active state selectively in the hippocampus.
S9B ). These clusters were then assigned to known cell types by comparing expression of cell type-specific genes taken from previously published datasets (38-42) (SI Appendix, Fig. S9C ). This identified 10 distinct cell types: 4 clusters of excitatory neurons that split based on hippocampal location (5,175 DG nuclei, 2,871 CA1 nuclei, 1,657 CA3 nuclei, and 507 nuclei with no location marker); 794 inhibitory neurons; 4 glial clusters (1,960 oligodendrocytes, 254 oligo-precursors, 763 astrocytes, and 880 microglia); and a cluster of 478 nuclei (NA), which could not be assigned to a single cell-type based on its expression profile (Fig. 3A  and SI Appendix, Fig. S9D ). In line with previous work  (43) , neuronal clusters had more expressed genes than glial clusters (SI Appendix, Fig. S9E ) and the proportions of cell types were similar to those reported for the hippocampal region in the Blue Brain Atlas (44) (SI Appendix, Fig.
S9F ). We then explored whether pairing CFC with HDACi induces distinct responses across cell types. Augur, a tool prioritizing a population's responsiveness to experimental perturbations  (45) , reported a similar global responsiveness for all clusters (SI Appendix, Fig. S10A ), and with the exception of oligoprecursors, HDACi treatment did not change cluster cell-type composition (SI Appendix, Fig. S10B ). However, HDACi treatment differentially regulated a distinct set of genes in each cell type (Fig. 3B ), which were highly cluster-specific (SI Appendix, Fig. S10  C-F and Dataset S5), with excitatory neurons having the largest HDACi response for up-regulated genes (SI Appendix, Fig. S10C ). Interestingly, we found an HDACi-induced separation for excitatory neurons in the DG and for glia, but not for any other cluster (Fig.
3C ). This drug-induced split was mainly mediated by the up-regulated genes within the DG, as removing those genes and rerunning the dimension reduction in silico remerged the split DG cluster (Fig. 3D ). Conversely, there was no cluster remerging when removing up-regulated DEGs from CA1, glia, or from any other cell type (SI Appendix, Fig. S11A ). Furthermore, DG cluster remerging was specific to the up-regulated genes, as removing only down-regulated genes had no effect either (SI Appendix, Fig. S11B ). Among the HDACi-induced up-regulated genes within each cluster, we found that excitatory neurons were predominatly enriched for genes involved in neuronal functioning and synaptic signaling (Fig. 3E  and Dataset S6). Among the HDACi-induced down-regulated genes within each cluster, which were also cluster-specific (SI Appendix, Fig.
S10F ), there were only a few GOs significantly represented, which included regulation of metabolic and cellular processes in the excitatory neurons cluster (Dataset S6). Importantly, the differential expression patterns in the snRNA-seq dataset were in line with the bulk RNA-seq results (SI Appendix, Fig. S12 ), since most DEGs from both analyses were differentially expressed in the same direction (Dataset S7), with genes up-regulated in both experiments associated with cell signaling and communication, and genes down-regulated in both experiments mostly associated with RNA metabolism, DNA repair, and other nuclear processes important for cell maintenance (Dataset S8). Together, these results provide evidence that pairing CFC with HDACi treatment transcriptionally activates different gene sets across cell types, with a particularly strong response among up-regulated genes in the DG that is relevant for synaptic signaling.
HDACi Combined with CFC Enriches H3K27ac at Genes Involved in Synaptic Communication. To better understand the HDACiinduced epigenetic regulation of the up-regulated genes in DG excitatory neurons, we next characterized histone acetylation in these cells using ChIP-seq. We focused on H3K27ac, a known marker of active enhancers that is enriched at activity-dependent regulatory elements after neuronal activation  (29, (46) (47) (48) (49) , and correlates with gene transcription  (29, 50). Furthermore, H3K27ac, alongside H3K9ac and H4K12ac, is directly influenced by CI-994, as revealed by Western blotting (SI Appendix, pooled DG from five mice, following fluorescence activated nuclear sorting of NeuN + nuclei (Fig. 4A ). Differential enrichment analysis (Diffbind, DEseq2: data in Dataset S9) revealed that CFC in VEH-treated animals led to only marginal changes in H3K27ac enrichment (Fig.
Next, we performed a decision tree analysis for H3K27ac peaks at active enhancers, the chromatin state with the largest number of peaks (Fig. 4C  and SI Appendix, Fig. S14 ; other chromatin states are included in SI Appendix, Fig. S15 ), focusing on the same four trajectories as in the RNA-seq (Fig. 2  D-F ). The primed active cluster for active enhancers was the smallest, containing 179 peaks (Fig. 4D  and Dataset S10). This cluster represented ontologies associated with dendritic locations (Fig. 4E  and Dataset S11) and included peaks associated  with NMDA receptor 2A (Grin2a) and the calcium voltagegated channel subunit (Cacna1e). In the primed stable cluster, there were 378 active enhancers for which H3K27ac was increased after HDACi treatment but not further enriched after CFC. This cluster included ontologies specific to synaptic locations, and a previously described enhancer of cFos, whose regulation by histone acetylation was recently validated by targeted dCas9-p300 manipulations  (47).
The 1,480 enhancers of the primed silenced cluster were also associated with genes that are involved in synaptic assembly and signaling (Datasets S10 and S11). Finally, the non-primed active cluster was the largest and contained 6,493 active enhancer peaks, the ontologies for which were also associated with regulation of synaptic signaling. This cluster included enhancers for multiple plasticity and memoryrelated genes: for example, Fosb, Jun, Junb, and JunD, which are members of the AP-1 complex, known to be involved in neuronal plasticity processes during CFC  (36, 49) ; calcium-dependent protein kinases, which are crucial for signaling at glutamatergic synapses  (51) ; and genes in the MAPK/ERK signaling cascade, which regulates H3 acetylation during CFC and helps to establish the stabilization of long-term memory  (5, 33, 37).
Transcriptional Activation and H3K27ac Accumulation Occur at Genes Involved in Synaptic Communication. When we related H3K27ac accumulation at active enhancers to the expression changes of their associated genes, we found that multiple genes underwent trajectory changes (Fig. 5 A  and B ). The most pronounced trajectory change occurred for genes associated with active enhancers that were in the non-primed active cluster in the ChIP dataset, of which 58% changed to being transcriptionally activated by HDACi regardless of whether CFC had occurred (primed stable). This indicates that, while CFC was needed to drive their acetylation changes, it was no longer required for transcription. These genes were enriched for ontologies, including "positive regulation of signal transduction" and "nervous system development" (Fig.
5E ), all of which contribute to glutamatergic synapse communication. Finally, when comparing all three datasets together-namely enhancer acetylation, bulk and snRNA-seq transcriptional changes-we found the MAPK pathway to be predominantly activated (Fig. 5E ). The MAPK/ERK pathway is necessary for memory consolidation  (53, 54)  and, once activated, ERK phosphorylates protein targets that are implicated in histone acetylation, gene transcription, protein synthesis, and synaptic plasticity  (5, 33). Importantly, previous studies using different HDACis (i. e. , sodium butyrate) have already demonstrated that HDACiinduced learning requires the activation of the MAPK pathway: knockout mice for mitogen-and stress-activated protein kinase 1 (Msk1), a member of the MAPK pathway, showed no HDACiinduced memory enhancement compared to wild-type mice  (55, 56).
In addition to the MAPK pathway, 18 more genes with H3K27ac enhancer enrichment were transcriptionally activated in all experiments (Dataset S12), of which two genes-autism susceptibility candidate 2 (Auts2) and cortactin binding protein 2 (Cttnbp2)-protect against autism-like behavior and regulate object recognition memory  (57, 58). Taken together, these data suggest that genes involved in synaptic communication and MAPK signaling are the primary epigenetic and transcriptional targets of HDACi treatment, which in turn indicates these pathways to underlie HDACi-mediated memory enhancement.
Signals were amplified with an Axopatch 200B (Molecular Devices) digitized by a Digidata 1322A interface (Molecular Devices) and sampled at 10 kHz. Recordings were acquired using Clampex (Molecular Devices) and analyzed with Clampfit (Molecular Devices). LTP was measured as percent of baseline fEPSP slope recorded over a 10-min period before HFS delivery. This value was taken as 100% of the excitatory postsynaptic potential and all recorded values were normalized to this. HDAC Activity Assay. HDAC activity of hippocampal and striatal hemispheres was assessed using the Fluor de Lys HDAC fluorometric activity kit (Enzo Life Science, BML-AK500) (for details see SI Appendix).
RNA-Seq. RNA extraction and library preparation. Total RNA was isolated from hippocampal and striatal hemispheres using TRIzol Reagent (Life Technologies), and purified using the RNase-Free DNase Set (Qiagen, Cat# 79254) and the RNAeasy Mini Kit (Qiagen). Libraries were prepared using the TruSeq Stranded mRNA Preparation Kit (Illumina) then multiplexed and sequenced on the Illumina HiSEq. 4000, yielding 100-bp, paired-end reads. RNA-seq analysis. Truseq adapter sequences were trimmed from FASTQ files using bcl2fastq (v2. 20. 0, Illumina). STAR (v2. 6) (80) aligned FASTQ reads to the mouse mm10 reference genome with annotations from Ensembl release 93  (81). Custom R scripts were used to count reads mapping to exonic regions.