dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, an additional iteration on the tiers is performed based on upstream or downstream consistency with neighbouring paleoH sites to identify potential outliers. The source literature of Antarctic exposure ages does not always report the sample position relative to the mean flow of the surrounding ice. This proves to be an issue when comparing ice-thinning histories reconstructed by continental-scale ice sheet models with histories based on paleoH data because the exposure ages can be heavily biased depending on the nunatak flank, where the samples were collected  (Mas E Braga et al. , 2021). This information is not broadly accessible in the source literature, causing a limitation which propagates into the AntICE2 database, and this must be considered within the error model when scoring a reconstruction against paleoH data.
PaleoExt data curation The past-ice-extent dataset is also divided into tiers based on specific data type criteria. Firstly, the interpretation of the facies is extracted from the source literature and assigned one of the following classes (Figs. 4 and S103-S180 in the Supplement): proximal to the GL (PGL), sub-ice-shelf (SIS), or open-marine conditions (OMC). Each core is then sorted according to the 14 C dating method, i. e. whether the 14 C age is obtained from biogenic carbonate or organic matter, with the latter dates typically considered to be less reliable (see Sect. 2. 2). Down-core 14 C ages obtained from organic matter in sediment cores from the Antarctic continental shelf are often corrected by subtracting the core top age rather than the marine reservoir effect only (e. g. Domack et al.
Data standards and expert assessment Fundamental and recurring issues, which exacerbate the challenges of evaluating data quality, remain across many studies and data types. They relate to the data availability. For example, some studies make the entirety of uncorrected, corrected, and calibrated 14 C ages available  (Heroy and Anderson, 2007; Bentley et al. , 2014b) , while others provide only those with robust interpretations. This makes it challenging to assess the entirety of a broad dataset by the same standards since some data rely on the implicit assessments made by their respective study. Ideally, all associated data should be made available, and the data should be categorized into quality tiers based on explicitly specified criteria. The expert quality control by the principal investigators who collected the samples and analysed and evaluated the data is exceedingly valuable and should be included with the data.
This enables a broader consensus on quality control as various experts converge towards specific quality criteria. Moreover, when new proxy data of various qualities are introduced in the future, including potential novel constraints, it will be possible to re-assess the categorical quality tiers if their criteria are clearly specified. The data made available should contain all the necessary information to recalibrate the data with clearly specified uncertainties. This will facilitate future data calibration, standardization, and quality assessment once integrated properly within an online repository, such as the ICE-D repository  (Balco, 2020). 3. 1. 8 Data-system and system-model uncertainties The data presented in the AntICE2 database include datasystem uncertainty, typically referred to as observational uncertainty, which consists of measurement uncertainty and indicative meaning uncertainty.
The former represents uncertainties affiliated with inherent instrumental uncertainties when taking measurements, such as the elevation at which a sample for exposure dating is collected. The latter represent uncertainties relating to the interpretation of data and how these data represent a proxy observation for physical characteristics of the system, for instance a fossil mollusc fragment and how it relates to past sea level. In the AntICE2 database, we include the indicative meaning uncertainty from the source literature when available and otherwise do not attempt to specify it. On the other hand, we do specify a baseline measurement uncertainty when this is absent in the source publication or when it is clearly understated. As the observational uncertainty specifies the data-system relationship, meaningful data-model comparison also requires specification of the relationship between the model and the physical system.
Potential future and rejected data types Radiostratigraphy of the Greenland Ice Sheet has been used to infer the age structure of the ice sheet  (MacGregor et al. , 2015). Proof-of-concept age-tracking simulations of a 3D slice through the Greenland summit have demonstrated the potential constraint value of such data  (Born, 2017). The age structure of the ice is inferred from internal radar reflectors (reflective isochrones) visible in the radiostratigraphic profiles, which are dated at major ice core sites  (Cavitte et al. , 2021). The AntICE2 database does not include any direct age structure constraints for the Antarctic Ice Sheet. It would be extremely valuable to have an age structure for the entire AIS because this would provide constraints for many regions that are lacking any paleo-constraints.
, 2007)  and climate  (Eicher et al. , 2016). We therefore do not include air content observations in the AntICE2 database. An alternative method to determine past elevation changes at ice core sites is through model inferences, where model simulations are locally constrained by ice core data  (Barbante et al. , 2006; Neumann et al. , 2008; Steig et al. , 2001; Waddington et al. , 2005; Parrenin et al. , 2007). The issue with using model inferences to constrain a model is that they integrate all the assumptions involved in making those inferences, and these are often not explicitly specified. Moreover, the uncertainties in the ice core site elevation model inferences are often inadequately explored and hence underestimated and would benefit from a greater exploration of the range of uncertainties  (Steig et al.
, 2001). If included in a calibration, this would propagate ill-defined uncertainties and could invalidate the calibration. Therefore, we opt to exclude this dataset since it is too far removed from direct field observations and comes associated with significant and ill-defined model uncertainties. As previously mentioned when discussing the GPS data, geodetic observations have been used to constrain the GIA response signal associated with past ice sheet changes  (Martín-Español et al. , 2016; Sasgen et al. , 2017). The justification for excluding the inversion-based Antarctic GIA reconstructions as a constraint lies in the assumptions behind the elastic corrections associated with contemporary mass loss. Like the ice-core-inferred elevation changes, the elastic corrections come associated with ill-defined model uncertainties which could invalidate the calibration if implemented without expanded uncertainties.
Subglacial sediments contain mixtures of eroded and reworked detritus initially deposited at different times. Therefore, a 14 C date obtained from the organic matter of such subglacial sediments typically provides an integrated age, derived from the mixing of relatively young with old and even 14 C-dead material, which increases the uncertainty of how to interpret such a date. Kingslake et al. (2018)  opted to use the 14 C dates as evidence of an early Holocene GL retreat upstream of its PD position, thereby arguing that the 14 C dates do not represent true ages for sediment deposition ages. Given these uncertainties and notwithstanding further studies  (Neuhaus et al. , 2021)  on the 14 C dates from the till samples along the Siple Coast, these observations have not been included in the An-tICE2 database as paleoExt GL retreat constraints because they do not yet represent a firm and reliable age constraint on GL position.
The main outstanding issues with the AntICE2 database are the temporal and spatial data gaps. As shown in Figs. 1, 2, and 8, only a small number of dates extend beyond 20 ka, and the spatial distribution of the data leaves many regions, particularly in East Antarctica, completely devoid of observational constraints. The ramification of the data deserts is that calibrated models will likely have large uncertainties in regions with limited observational constraints. A few new data types, discussed above, could ameliorate the situation, with the most promising being the wide-scale age structure of the AIS, inferred from airborne and on-ice radar mapping of internal layering connected to sites of dated ice cores. This data type could constrain changes in the AIS far beyond 30 ka and even cover regions with little or no data due to a lack of rock outcrops and boreholes.
Summary In this study, we provide the second major iteration of the Antarctic Ice Sheet Evolution observational constraint (An-tICE2) database. The AntICE2 database is a curated observational constraint database intended for the calibration of models of the Antarctic Ice Sheet and Antarctic glacial isostatic adjustment over the last glacial cycle. It can also be used to constrain a paleo-model spin up of the AIS to initialize PD simulations. This will lead to a more accurate understanding of contemporary and future changes of the AIS. The AntICE2 database includes a large variety of observational constraints necessary for model calibration. The data types included are as follows: PD geometry and surface velocity, PD uplift rates, borehole temperature profiles, past ice extent, past ice surface elevation, and past relative sea level.
All the 14 C ages in the database are recalibrated and share a consistent reservoir age correction wherever appropriate. The AntICE2 database represents a curated dataset with specified quality tiers. This was achieved by establishing and applying criteria for the different data types. Future efforts should be geared toward refining the criteria for the quality tier assignment since a community consensus would benefit datamodel integration. An ongoing effort involves automating the selection and curation process from a raw database (ICE-D) to a recalibrated curated subset (i. e. AntICE2). This would render the AntICE2 database more manageable and updatable when more data are being collected in the future. To contribute to the AntICE2 database, one can contact the corresponding author with data or publications, contribute data to the ICE-D databases, or offer data type criteria modifications to help revise the data curation process.
The AntICE2 database represents the most comprehensive observational constraint dataset of high-quality data relating to the past evolution of the Antarctic Ice Sheet. The dataset facilitates data integration with AIS and GIA simulations. This dataset compilation also facilitates data-model scoring by processing and curating large raw and disparate datasets from online repositories (e. g. ICE-D) and source publications. Finally, a call to the community is made to make raw data with complete and clearly specified uncertainties publicly available and to make efforts towards establishing data quality criteria in order to facilitate data curation and hence produce meaningful data-model comparisons. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-3573-2023-supplement.
Author contributions. BSL and LT led and designed the study. BSL compiled and recalibrated the datasets. BSL wrote the paper and generated all the figures. GB and PS contributed pastice-thickness and past-ice-extent data via ICE-D. CDH contributed some past-ice-extent datasets and assisted in its interpretation. CB, CR, MLL, and RM contributed some ice core borehole datasets. PLW and MJB contributed some relative-sea-level datasets. JB provided the GPS datasets. All authors participated in establishing the data quality criteria to curate the database and provided feedback on the paper.
The highest number of DEGS was found  (5, 701)  during the first phase switch (L1 to D1), 55% of these genes were upregulated and 45% were downregulated. The four next switches, i. e. D3 to C1, C1 to C2, C2 to C3 and C3 to R1, had 1,894; 3,326; 1,074 and 323 DEGs respectively, and at least 75% of the genes were downregulated in each of these switches. The last developmental phase switch (R4 to E1) had 2,098 DEGs of which 80% were upregulated. It is interesting to note that the passages from D1 to D3 that characterize early stages of dedifferentiation do not appear to be remarkable switches. This is also the case for R1 to R4 stages that characterize redifferentiation and where no remarkable switches occurred either. DEGs for the six key developmental phase switches were compared against The Arabidopsis Information Resource database (TAIR, www.
SERK2 had a high expression level while CLV3 had a low expression profile. Both genes were switched off during the primary callus-to-embryogenic callus switch, and hence were not expressed in embryogenic cells, embryogenic cell clusters or embryos; (iii) BBM, ABI3, LEC1, AGL15, WOX2 and WUS expression was induced during the primary callus-to-embryogenic callus switch and remained expressed in embryogenic Fig. 3  Functional categorization of the Arabica differentially expressed genes (DEGs) during the six key developmental switches. DEGs were first compared against The Arabidopsis Information Resource database (TAIR, www. arabi dopsis. org) using BLASTP with an e-value cut-off of 1 × 10 -4. Functional categorization of Arabidopsis orthologs in all 6 developmental phase switches was performed using the Parametric Analysis of Gene set Enrichment (PAGE) tool in agriGO v2.
Illumina sequencing RNA sequencing was carried out by Nestlé Research (Lausanne, Switzerland). The cDNA libraries were generated using the TruSeq Stranded mRNA Kit (Illumina), followed by PCR amplification for sequencing on Illumina HiSeq 2500. Paired-end cDNA libraries were generated from all samples, and sequencing was performed to generate the ~ 125 bp paired-end reads. FastQC software (v0. 11. 5) was used for quality control, and assessment of raw Illumina reads in FASTQ format to obtain per base quality, guanine-cytosine (GC) content, and sequence length distribution. Low-quality reads, adapters, and poly-N-containing reads were removed from the raw data. Approximately 95% of high quality reads were obtained in each library from generated data. An average of 90 million paired-end reads was obtained for each library.
Read mapping and differential gene expression analysis The pre-processed reads were aligned to the C. arabica genome sequence  [86]  using the STAR (v2. 5. 3a) software. The uniquely mapped reads to each gene locus were quantified with a maximum of 10 mismatches per pairedend alignment using the Partek E/M algorithm originally described by Xing et al. [87]  and principal component analysis (PCA) was performed to check the homogeneity of the replicates. On average, 74% of sequenced reads per sample were uniquely mapped to the reference genome  [86]. DESeq2  [88]  was used to standardize reads across libraries and for differential expression analysis. Differential expression was considered at a threshold value of FDR ≤ 0. 001 and the absolute value of log2Ratio ≥ 3. A heatmap of all differentially expressed genes was generated in R (https:// www.
Functional gene expression analysis All differentially expressed genes (DEGs) were compared against The Arabidopsis Information Resource database (TAIR, www. arabi dopsis. org) using BLASTP with an e-value cut-off of 1 × 10 -4. The resulting annotation was used to analyze gene ontology (GO) using the Parametric Analysis of Gene set Enrichment (PAGE) tool in agriGO v2. 0 with default functions  [91]. Significant GO terms were found using the default FDR < 0. 05 cutoff value. Obtained Z-scores were plotted on a heatmap generated in R (https:// www. R-proje ct. org) using the Com-plexHeatmap package  [89]  or in a bar diagram in Excel.
Validation of RNA-seq by RT-qPCR analysis To validate the RNA-seq study, RT-qPCR experiments were carried out on five SE developmental stages L1, C1, C2, C3, and E1 as previously described by Marraccini et al. [94]. Based on published data, we targeted three of the circadian clock key genes in C. arabica: GI (Cara019g022520), LHY (Cara00s376g005010) and ELF4 (Cara003g011430), two genes involved in photosynthesis PORA (Cara009g016160) and CAB1 (Cara011g016820), and two genes involved in starch degradation ISA3 (Cara016g026000) and GWD1 (Cara021g020800). Primers were designed using Primer3Plus online software (http:// www. bioin forma tics. nl/ cgi-bin/ prime r3plus/ prime r3plus. cgi). All reactions were performed in triplicate. The specificity of the PCR products generated for each set of primers was confirmed by analyzing the Tm (dissociation) of amplified products.
PCR efficiency (E) was estimated using absolute fluorescence data captured during the exponential phase of amplification of each reaction with the Eq. (1 + E) = 10 (-1/slope)  [95]. Expression levels were calculated by applying the formula (1 + E) -ΔΔCt where ΔCt target = Ct target gene -Ct ref- erence gene and ΔΔCt = ΔCt target -ΔCt reference sample , with the L1 samples being used as references for each construction. Expression levels were normalized by taking the geometric mean of two internal control genes, 24S (Cara005g012900) and PP2A (Cara00s700g005000)  [48, 96]. The statistical differences were analyzed by ANOVA based on Fisher's LSD (P < 0. 05). No statistical differences were noted (Fig. S4 ).
Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. All raw transcriptomic sequence data generated and used to carry out the present study on Coffea arabica GPFA116 intraspecific hybrid variety are available at the National Center for Biotechnology Information Sequence Read Archive under accession number PRJNA744419. Coffea arabica reference genome (Et39 cultivar) sequencing, assembly and annotation files are available at the National Center for Biotechnology Information Sequence Read Archive under accession number PRJNA698600 (manuscript under publication).
h at ADC(3) and all descriptors at DFT/ MRCI and MRCI levels were obtained externally with Theo-DORE  73  analysis package using a pre-release of version 3. 0. To investigate the cycloaddition of ethylene, we performed a relaxed scan keeping the linear combination of C1-C3 and C2-C4 (Fig. 1 ) at xed distances. Ground state geometry optimizations were done at DFT level using B3LYP/cc-pVTZ. Excitedstate energies and wave functions were obtained at MRCI+P (8,8)/cc-pVDZ level. All calculations considered D 2h symmetry. CAS (8,8) wave functions with 6 states in the average (four A g and two B u ) were used as references. MRCI calculations considered 4 frozen core orbitals (belonging to a g , b 3u , b 2u , and b 1g representations), and 1 orbital in the active space for each of the 8 irreducible representations of the D 2h point group.
Acknowledgements F. P. thanks  Ignacio Fernández Galván  and  Anna I. Krylov  for discussions about wave function analysis methods. F. P. has received funding from the  UK EPSRC  (reference  EP/V048686/1 ) and acknowledges access to Loughborough's high-performance computing system lovelace. M. T. do C. , M. B. , and J. M. T. have received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No  828753  (BoostCrop). M. B. and J. M. T. thank the  European Research Council (ERC)  advanced grant  SubNano (Grant agreement   832237 ). M. T. do C. is thankful for the support of the PhD mobility program funded by  AMUtech. Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high performance computing resources.
4) using the green (488 nm), the blue (405 nm) and the red (561 nm) channels for the detection of PVs, nuclei and amastigotes, respectively  (Lamotte et al. , 2019). Images were transferred to the Columbus Conductor™ Database (Perkin Elmer Technologies) and analysed using the integrated Image analysis building blocks  (Lamotte et al. , 2019). Quantitative values obtained for host cell and parasite numbers were exported to Excel and SigmaPlot for further analysis and graphical representations.
Relative protein expression was calculated by densitometric analysis using the Amersham ImageQuant analysis software. For every band, the ratio between the values obtained for the target protein and the YY1 (nuclear extracts) and the GAPDH (cytoplasmic extracts) normalization controls were calculated. Fold changes were calculated using the control sample as calibrator, control values of uninfected and unstimulated samples being set to 1. Modulations of the LSD1 protein content were statistically determined by the nonparametric Wilcoxon rank-sum test using the GraphPad Prism 7. 03 software.
Determination of parasite load in live macrophages by epifluorescence imaging Parasite load was estimated by the level of the mCherry fluorescence of live hosting macrophages. Nuclei from macrophages were stained by 5 M Hoechst 33342 (ThermoFisher Scientific) for 15 min at room temperature. Image acquisition was done with the EVOS M5000 Imaging System on live cells after 4 days of infection and 3 days of treatment, using the DAPI light cube filter for detection of Hoechst-stained nuclei (Excitation: 357/44 nm; Emission: 447/60 nm) and the Texas red light cube for parasite mCherry detection (Excitation: 585/29 nm; Emission: 624/40 nm). Five fields were acquired with the 20x objective. Image analysis was performed using the ImageJ software package on at least 100 cells. Parasite loads were determined based on the Raw Integrated Density of the mCherry fluorescence of macrophages in the absence or presence of LSD1i.
Viability assay for Leishmania promastigotes and amastigotes Anti-leishmanial activity of compounds on free parasites was evaluated using an adapted resazurin-based dye reduction (DRA) assay with cell-cycling promastigotes from logarithmic growth phase maintained in M199 supplemented medium. Fixed concentrations or serial dilutions of the inhibitors were done in triplicate or quadruplicate, at 27°C for promastigotes and 34°C for amastigotes in 96-well plates or 384-well plates. DMSO vehicle and AmB were used as controls. Two days later, resazurin was added (10 μL per well for a final concentration of 5 µg/ml) and resorufin-derived fluorescence intensity was measured 24 h later using a Tecan Safire 2 reader (excitation 558 ± 4. 5 nm; emission 585 ± 10 nm). Following background subtraction (complete parasite culture medium with resazurin in absence of parasites), data were expressed as percentages of growth compared to DMSO-treated controls (POC).
Ligation products were submitted to PCR amplification. The quality of the obtained libraries was controlled using a Bioanalyzer DNA1000 Chips (Agilent, # 5067-1504) and quantified by spectrofluorimetric analysis (Quant-iT™ High-Sensitivity DNA Assay Kit, #Q33120, Invitrogen). Sequencing was performed on the Illumina Hiseq2500 platform to generate single-end 65 bp reads bearing strand specificity. Reads were cleaned using cutadapt version 1. 11 and only sequences at least 25 nt in length were considered for further analysis. STAR version 2. 5. 0a  (Dobin et al. , 2013) , with default parameters, was used for alignment on the reference genome (GRCm38 from Ensembl database 94). Genes were counted using featureCounts version 1. 4. 6-p3  (Liao et al. , 2014)  from the Subreads package (parameters: -t gene -s 0).