dataesr/scientific-paragraphs-categorization huggingface dataset:
For each gene, raw read counts and TPM (Transcripts Per Million) numbers were calculated  [98]. We performed a differential expression analysis on data obtained from the 'Garnet' samples. First, data were filtered by removing lowly expressed genes (average read count <3), genes not expressed in most samples (read counts = 0 in more than 75% of the samples); and genes presenting little change in expression between samples (coefficient of variation <0. 3). Then, differentially expressed genes (DEGs) between non dormant and dormant stages were assessed using DEseq2 R Bioconductor package  [99] , in the statistical software R (R Core Team 2018), on filtered data. Genes with an adjusted p-value (padj) < 0. 05, using the Benjamini-Hochberg multiple testing correction method, were assigned as DEGs (Additional file 2: Table  S2 ).
Principal component analyses and hierarchical clustering Distances between the DEGs expression patterns over the time course were calculated based on Pearson's correlation on 'Garnet' TPM values. We applied a hierarchical clustering analysis on the distance matrix to define ten clusters (Additional file 2: Table  S2 ). For expression patterns representation, we normalized the data using zscore for each gene: z score ¼ TPM ij -mean i À Á Standard Deviation where TPM ij is the TPM value of the gene i in the sample j, mean i and standard deviation i are the mean and standard deviation of the TPM values for the gene i over all samples. Principal component analyses (PCA) were performed on TPM values from different datasets using the prcomp function from R. For each cluster, using data for 'Garnet', 'Regina' and 'Cristobalina', mean expression pattern was calculated as the mean z-score value for all genes belonging to the cluster.
adjust {stats} function available in R. A list of predicted regulation between transcription factors and target genes is available for peach in PlantTFDB  [37]. We collected the list and used it to analyse the overrepresentation of genes targeted by TF, using Hypergeometric {stats} available in R, comparing the number of appearances of a gene controlled by one TF in one cluster against the number of appearances on the overall set of DEG. p-values obtained were corrected using a false discovery rate as described above. We only present results obtained for TFs that are themselves DEGs. Predicted gene homology to Arabidopsis thaliana and functions were retrieved from the data files available for Prunus persica (GDR, https://www. rosaceae. org/species/prunus_persica/genome_v2. 0. a1).
Primers used in this study for qPCR are available in (Additional file 2: Table  S7  ). Primers were tested for non-specific products by separation on 1. 5% agarose gel electrophoresis and by sequencing each amplicon. Realtime data were analyzed using custom R scripts. Expression was estimated for each gene in each sample using the relative standard curve method based on cDNA diluted standards. For the visualization of the marker genes' relative expression, we normalized the RT-qPCR results for each marker gene by the average RT-qPCR data for the reference genes PavRPII and PavEF1.
Bud stage predictive modelling In order to predict the bud stage based on the marker genes transcriptomic data, we used TPM values for the marker genes to train and test several models. First, all samples were projected into a 2-dimensional space using PCA, to transform potentially correlated data to an orthogonal space. The new coordinates were used to train and test the models to predict the five bud stage categories. In addition, we tested the model on RT-qPCR data for samples harvested from the 'Fertard' cultivar. For the modelling purposes, expression data for the seven marker genes were normalized by the expression corresponding to the October sample. We chose the date of October as the reference because it corresponds to the beginning of dormancy and it was available for all cultivars.
For each date, the October-normalized expression values of the seven marker genes were projected in the PCA 2-dimension plan calculated for the RNA-seq data and they were tested against the model trained on 'Cristobalina', 'Garnet' and 'Regina' RNA-seq data. We tested five different models (Multinomial logistic regression -LR, Random forest classifier -RF, k-nearest neighbour classifier -KNN, multi-layer perceptron -MLP, and support vector machine classifier -SVM) for 500 different combination of training/testing RNA-seq datasets, all implemented using the scikit-learn Python package  [101]  (See Additional file 3 for details on the used parameters). The models were 5-fold cross-validated to ensure the robustness of the coefficients and to reduce overfitting. The models F1-scores, which are used in multi-class cases and are calculated as the weighted average of the precision and recall of each class, were calculated for the RNA-seq testing sets and the RT-qPCR datasets.
Results presented in (Additional file 1: Figure  S10 ) show that, although the highest model F1-scores were obtained for the RF and MLP when considering only the RNA-seq training dataset, the best results based on the RT-qPCR dataset were obtained for the SVM and the LR models. We selected the LR model for this study because the coefficients are more easily described, with two coefficients for each dormancy stage (Additional file 1: Figure  S9b ). The LR model used in this study was optimised using the LogisticRegressionCV function with default parameters, multi_class: 'multinomial', max_iter: 1000 and the 'lbfgs' solver for the optimization.
Acknowledgments We thank the  Fruit Experimental Unit of INRA (Bordeaux-France)  for growing and managing the trees, and  Teresa Barreneche ,  Lydie Fouilhaux ,  Jacques Joly ,  Hélène Christman  and  Rémi Beauvieux  for the help during the harvest and for the pictures. Many thanks to  Dr. Varodom Charoensawan  ( Mahidol University, Thailand ) for providing scripts for mapping and gene expression count extraction.
N. Hiranuma et al. :  A comprehensive characterization of ice nucleation Abstract. We present the laboratory results of immersion freezing efficiencies of cellulose particles at supercooled temperature (T ) conditions. Three types of chemically homogeneous cellulose samples are used as surrogates that represent supermicron and submicron ice-nucleating plant structural polymers. These samples include microcrystalline cellulose (MCC), fibrous cellulose (FC) and nanocrystalline cellulose (NCC). Our immersion freezing dataset includes data from various ice nucleation measurement techniques available at 17 different institutions, including nine dry dispersion and 11 aqueous suspension techniques. With a total of 20 methods, we performed systematic accuracy and precision analysis of measurements from all 20 measurement techniques by evaluating T -binned (1 • C) data over a wide T range (-36 • C < T < -4 • C).
These samples were shared with all collaborators, and immersion freezing experiments were conducted individually at each institution to obtain immersion freezing data as a function of multiexperimental parameters (see  Sect. 3. 1). The motivation of using multiple types of cellulose was to (1) examine the immersion freezing abilities of both predominantly supermicron (MCC and FC) and submicron (NCC) cellulose particles to assess a wide size range of chemically uniform biological particles and (2) look into diverse surface structure (Table  1 ) A total of 20 measurement techniques are used in this study to compile a comprehensive dataset for evaluating immersion freezing properties of cellulose samples. The dataset is analyzed to understand functional dependence of various experimental parameters and of cellulose particle characteristics.
35, n W15 s,geo =    (1. 40 × 10 12 ) × 1 -(exp((-2. 00 × 10 -10 exp(-2. 34 × T ))))    geometric SSA Snomax , ( 10 ) T ∈ [-38, -2]; log(n s,geo )/ T = 0. 88(-2 • C < T < -10. 7 • C), n U17 s,geo = exp(150. 577 -(0. 517 × (T + 273. 150))), (11) T ∈  [-30, -14] ; log(n s,geo )/ T = 0. 22, n A13 s,geo = 10 4 × exp(-1. 038 (T + 273. 150) + 275. 260) (12) × BET -SSA K-feldspar geo -SSA K-feldspar , T ∈ [-25, -5]; log n s,geo )/ T = 0. 45. For H15MCC (wet), the n m (T )-to-n s,geo (T ) conversion was performed using SEM-based SSA constants of 0. 068 m 2 g -1. The geometric SSA value of 7. 99 m 2 g -1 was used for W15. This SSA value was derived from the polydisperse particle size distribution measurements of Snomax obtained during AIDA studies, whose IN data are included to compute immersion freezing results reported in  Wex et al.
1  are used. A total of five panels are presented. First, a summary comparison of two method categories (dry dispersion and aqueous suspension) in a temperature range )/log(n s,avg ) values are presented for (a. i-c. i) ratios of the log average to dry dispersion measurements (DD) or aqueous suspension measurements (AS) to the log average to all the data (All), (a. ii-c. ii) ratios of the individual DD measurements to all, (a. iii-c. iii) ratios of the individual AS measurements to all, (a. iv-c. iv) ratios of the individual particle dispersion measurements to DD and (a. v-c. v) ratios of the individual aqueous suspension measurements to AS. The black dotted line represents log(n s,ind. )/log(n s,avg ) = 1. (c. iv) is left blank since only one dataset is available at each temperature; thereby, no differences can arise.
This trend persists regardless of sample type. Further discussion on the observed deviations and diversity between dry and aqueous suspension measurement techniques is beyond the scope of this study. Some discussions regarding potential sources and explanations of deviations, which warrant future studies, are given in Sects. S4, S5, S9 and S10. Table  3. List of the Gumbel cumulative distribution fit parameters to the n s,geo (T ) for T -binned ensemble datasets of MCC, FC and NCC (All). The datasets are fitted in the log space. In addition to all, fit parameters for ensemble maximum values (All max ), ensemble minimum values (All min ), suspension subset (AS) and the dry-dispersed particle subset (DD) are also included in this table. The correlation coefficient, r, for each fit is also shown.
WISDOM Over the investigated temperature range given in Table  2 , WISDOM (WeIzmann Supercooled Droplets Observation on Microarray) shows n s,geo,MCC > n s,geo,NCC (Figs. 3r  and 5j ). The MCC result exhibits a broader spectrum with log(n s,geo )/ T of 0. 26 than NCC ( log(n s,geo )/ T = 0. 31). The observed relation between widening of spectra and increased n s,geo (T ) suggests that supermicron-sized particles have increased diversity in immersion freezing compared to submicron particles. Looking at the overall NCC data (Fig. 1c. iii), nearly all aqueous suspension techniques, independently of the drop volume, agree with the WISDOM data and all point towards the AIDA data. We remark that the WISDOM team followed the suggested sample handling details described in Sect. 3. 1.
Conclusion and future outlook This paper presents the immersion freezing efficiencies of cellulose particles of three different types evaluated by a total of 20 IN instruments at supercooled temperature condi-tions. Three cellulose samples examined in this study showed a propensity to nucleate ice, and their ice nucleation activity is comparable to that of another test system (i. e. , illite NX) that we have previously evaluated. On average, supermicronsized cellulose samples are more ice active than the nanocellulose samples at a T lower than -20 • C, although the difference is not apparent for all temperatures when considering experimental uncertainty. Our work also provides a comprehensive dataset of experimental variables in INP measurement techniques to complement our insufficient knowledge regarding inter-method diversity that, when filled, will enhance the credibility of our experiments to evaluate INP abundance in the atmosphere.
Data availability. Within the framework of INUIT, we established a new community database including the laboratory results on ice nucleation with access for registered users. The tabulated data are available in a publically accessible MySQL portal at https://imk-aaf-s1. imk-aaf. kit. edu:8081/inuit/ (KIT-INK-AAF, 2019). This database helps the users to evaluate and interpret the comprehensive laboratory ice nucleation results measured over the past years. It also provides a good basis for a database with a wider public access. All data associated with this study will be made available without any barriers to the user. But, please first contact Ottmar  Möhler (ottmar. moehler@kit. edu). Any disputes about the use of other groups' data, particularly with respect to publications, will be resolved by the INUIT coordinators.
Figure2. T -binned ratios of the interpolated individual measurements to the average of the data, log(n s,ind. )/log(n s,avg ), based on the geometric surface area (n s,geo ) for MCC (a), FC (b) and NCC (c). T -binned log(n s,ind. )/log(n s,avg ) values are presented for (a. i-c. i) ratios of the log average to dry dispersion measurements (DD) or aqueous suspension measurements (AS) to the log average to all the data (All), (a. ii-c. ii) ratios of the individual DD measurements to all, (a. iii-c. iii) ratios of the individual AS measurements to all, (a. iv-c. iv) ratios of the individual particle dispersion measurements to DD and (a. v-c. v) ratios of the individual aqueous suspension measurements to AS. The black dotted line represents log(n s,ind. )/log(n s,avg ) = 1. (c. iv) is left blank since only one dataset is available at each temperature; thereby, no differences can arise.
For offline microscopy, KA per-formed electron microscopy and image analyses. RU contributed to the database work. All authors discussed the results and contributed to the final version of the paper. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Results from the ice nucleation research unit (INUIT) (ACP/AMT inter-journal SI)". It is not associated with a conference. Acknowledgements. INUIT (IceNuclei research UnIT) is a multiinstitutional project (FOR1525, https://www. ice-nuclei. de/, last access: 20 March 2019) funded by the German Research Foundation (DFG). The objective of INUIT is to comprehensively study the atmospheric heterogeneous ice formation with collaboration among various research institutions.
The CNR-ISAC team acknowledges funding for their research from the European Union's Seventh Framework Programme FP7-ENV-2013 project BACCHUS (grant no. 603445) and Franco Corticelli (Institute for Microelectronics and Microsystems IMM-CNR) for SEM observations. The FRIDGE group acknowledges funding by DFG in the Research Unit FOR 1525 (INUIT) under BI 462/3-2. For the LACIS work, Heike Wex was funded within the INUIT subproject WE 4722/1-2. The MRI cloud simulation chamber experiments were partly supported by JSPS KAKENHI grant numbers 23244095 and 17H00787. Support of the PNNL-CIC work for Gourihar Kulkarni was provided by the U. S. Department of Energy (DOE) Office of Biological and Environmental Research (OBER) Atmospheric Research Systems Program (ASR). The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RLO 1830.
By contrast, Wnt/BMP LOF produced defects such as non-fusing neural folds (arrowheads in Figure  2B ), structures that were either absent in Wnt LOF embryos or normal in BMP LOF embryos. Changes to ZGA caused by the single or combined LOF of Wnt, Nodal, and/or BMP were then determined at the late blastula stage on a transcriptome-wide scale using deep RNA sequencing (RNA-seq). Analysis was limited to the 3,315 zygotic genes for which spatiotemporal expression data are available  (Blitz et al. , 2017; Owens et al. , 2016)  and where reduced expression (R50% loss of exonic and/or intronic transcript counts, false discovery rate [FDR] %10%) could be detected in a-amanitin-injected embryos (Figure  2C  and Table  S3 )  (Gentsch et al. , 2018b). a-Amanitin-mediated inhibition of RNAPII elongation impedes the morphogenetic tissue movements of gastrulation and ultimately leads to early embryonic death  (Gentsch et al.
ACKNOWLEDGMENTS We thank  Abdul Sesay ,  Leena Bhaw ,  Harsha Jani ,  Deborah Jackson , and  Meena Anissi  for deep sequencing;  Mareike Thompson  for critical reading of the manuscript; and the  Smith laboratory  for discussions and advice. G. E. G and J. C. S. were supported by the  Medical Research Council  (program number  U117597140 ) and are now supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001-157 ), the  UK Medical Research Council  ( FC001-157 ), and the  Wellcome Trust  ( FC001-157 ).
DATA AND CODE AVAILABILITY The accession numbers for the sequencing reads (FASTQ files) and raw RNA-Seq read counts reported in this paper are GEO: GSE113186 and GEO: GSE122551. All analyses were performed in R v3. 5. 1 (Bioconductor v3. 8), Perl v5. 18. 2 (https://www. perl. org) and Python v2. 7. 12 (http://www. python. org) as detailed above. The R code, genome annotation, intermediate datasets and graphs are available on GitHub at https://github. com/gegentsch/SpatioTemporalControlZGA. Original datasets are also available on Mendeley Data at http://dx. doi. org/10. 17632/jn466b4n8v. 1.
None of the four variant were detected. However, an atypical profile of the melting curve relative to rs3918290 genotyping was observed (Figure  1 ). As the presence of an uncommon SNP was suspected, sequencing of DPYD exon 14 was performed which revealed that the patient harbored a SNP in a heterozygous state: c. 1903A>G (p. Asn635Asp) (chromosome position 1:97915617, A/G). To the best of our knowledge, this is a novel variant and is not identified in any of the specific Ensembl, dbSNP, ExAC, Alfred, HGMD or LOVD databases. In order to evaluate the impact of the proximity of this variant to the intron 14 mRNA splice donor site, we used the most popular in silico tools to predict potential functional alterations involving splicing sites. SIFT (Scale-Invariant Feature Transform, http://sift.
" Furthermore, the SwissModel web tools (http://swissmodel. expasy. org/, accessed 28 September 2018) indicated that the missense variant did not affect the final structure of the protein. Therefore, in order to characterize the potential pathogenic features of the identified variant, a peripheral blood sample stored in PAXgene Blood RNA Tube (Qiagen, Hilden, Germany) was sent to the Oncogenomic Research Center (University of Bari, Italy) for further molecular analysis. Total RNA was extracted from whole blood using the PAXgene Blood RNA Kit (PreAnalytiX GmbH, Hombrechtikon, Switzerland) and reverse transcribed into cDNA with an iScript cDNA Synthesis Kit (BioRad, Hercules, CA, USA). Using a pair of primers spanning exons 13 and 15 of DPYD, the cDNA was amplified and sequenced using a 3500 Genetic Analyzer (Applied Biosystems, 3500 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA).
RIC belongs to the Astex Diverse Data Set,  75  recently used to validate the AutoDockFR docking software, which models receptor flexibility by explicitly specifying a set of flexible side chains. 76 n cross-docking experiments using the apo conformations of the receptors, AutoDockFR outperformed AutoDock Vina 77 in terms of both number of correct poses and their ranking. However, none of the aforementioned software was able to find any solution within a ligand root-mean-square deviation (RMSD) of 2. 5 Å from the experimental structure of the complex. The third target is the allose binding protein (ABP) (Figure  1c ,f), which is also representative of targets undergoing hingebending motions upon binding of their ligands, in this case D- allose (ALL). 78 The conformational changes for ABP are larger than those occurring in BGT.
It is therefore important to validate our method on proteins that undergo only limited conformational changes upon binding. Furthermore, previous work has already pointed out how the performance of enhanced-sampling versus standard MD simulations in reproducing a relevant fraction of holo-like conformations is target-dependent. 63 he subtle conformational changes of RIC upon binding of NEO (Figure  1b ,e) were hardly handled by algorithms exploiting the flexibility of the binding site through sidechain torsional angles. 76 RIC has proven to be a very difficult target for both rigid and flexible docking calculations starting from the apo X-ray structures using both AutoDock Vina  77  and the recently introduced AutoDockFR 76 software (see Table  1  in ref 76). In contrast, both metadynamics and standard MD simulations were able to reproduce the geometry assumed by the binding site in the holo structure (although EDES did find conformations closer to the holo structure than those obtained from MD apo/holo ; see Table  1  and Figure  4 ).
9 ± 0. 1 Å). It should be noted that for BGT and RIC the results were virtually independent of the number of EDES windows used. Clearly, further studies will be needed to optimize the number and the width of the windows used, possibly exploiting a set of intrinsic properties of each protein so as to set up targetdependent rules. Druggability Assessment. We used the software fpocket  84  to assess the druggability of the binding site within the ensembles of conformations generated by EDES (see Materials and Methods). For each conformation, we evaluated the druggability score D,  85  which ranges between 0 and 1 with higher values identifying more druggable geometries. It is customary to associate scores >0. 5 to putative binding sites. 85 able  4  shows that EDES generated a much larger set of druggable structures than MD apo for BGT and ABP (actually, no druggable conformation was generated from this trajectory for the latter system), while the performance of the two sets was similar for RIC, as expected.
41, 63 inally, ABP was one of the two targets used in ref 63 to investigate the performance of accelerated  54  versus conventional MD simulations in reproducing near-native conformations of the ABP-ALL complex. Accelerated MD simulations (∼2. 5 μs in cumulative simulation time) were performed with both the single-and double-boost approaches. As expected, only the biased simulations were able to sample closed (hololike) conformations of the binding site (defined by any residue with at least one atom within 5 Å from the ligand), reaching values of the RMSD BS as low as 1. 42 Å. Using the same definition of the binding site, we obtained RMSD BS values as low as 1. 20 Å for EDES 4w and EDES 3w , with 8. 5% and 9. 3% of structures, respectively, having RMSD BS < 2 Å (see Table  1 ). In ref 63 the docking was performed with the package Glide  79, 80  on representatives of the 10 (out of the 50 extracted) most populated clusters obtained with a volumetric approach developed by the authors in order to collect structures with maximal differences in the accessible space within the binding site.
Johnson and Karanicolas  41, 89  implemented within the Rosetta software suite 90 an approach to enhance the sampling of protein conformations suitable for small-molecule binding by biasing the fluctuations of the volume of surface pockets. The conformations generated by their approach resemble known inhibitor-bound structures more closely than equivalent ensembles of unbiased conformations. Oleinikovas et al. 64  developed a method based on Hamiltonian replica-exchange cosolvent MD simulations that effectively enhance the discovery of cryptic pockets for different protein targets. By scaling of the nonbonded interactions between apolar atoms of the protein and water molecules, the properties of the latter shift toward ligand-like behavior, increasing cryptic site opening. These methodologies are similar to EDES in scope, although their application was focused rather on enhancing the discovery (opening) of druggable (including cryptic) sites.