dataesr/scientific-paragraphs-categorization huggingface dataset:
The FANTOM databases The FANTOM Consortium aims at producing a functional annotation of the mammalian genome that extends ENCODE's own endeavour  [18, 25]. The research and the data produced since 2000 by the FANTOM Consortium have been packaged as iterations, from FANTOM1, a functional annotation of a mouse cDNA collection, to FANTOM6, a functional analysis of non-coding RNAs. For this study, we relied on the output of FANTOM5, which compiles an atlas of mammalian promoters, enhancers, lncRNAs and miRNAs using a cap analysis gene expression (CAGE) approach  [23, 24]. More specifically, FANTOM5 provided us with a list of genes, categorised as coding or non coding in order to evaluate the benefit of using one category or the other in terms of tissue specificity. Additionally, FANTOM5's CAGE data allowed us to get an idea of the location of each gene's transcription start site (TSS), and we needed this because the features used for the tissue identification are fragmentomic features extracted around the TSSs.
Technically, TSSs were identified from clusters of decomposition-based peaks (DPI for decomposition-based peak identification). Finally, the CAGE expression profiles were available for a number of cell lines and tissues, known as "ontologies", which allowed us to derive lists of active or inactive genes (referred to as "gene classes") for each tentative tissue. TSS positions and gene categories were obtained from processing three data files originating from the FANTOM project  [23] : "FANTOM DB1" a database providing for each gene ID the DPI cluster corresponding to the most robust TSS, "FANTOM DB2" a database providing for each gene ID the gene category (coding genes, pseudogenes, etc. ) and "FANTOM DB3" a database providing from CAGE alignment the genomic locations of the DPI clusters. All three databases were combined for the purpose of associating each of the 59,110 genes to a gene class (see "Gene classes based on transcription and tissue specificity") and its TSS's genomic location.
In this study, we relied on "FANTOM DB4", a set of 347 data files recording for each ontology the specificity of each one of the 59,110 genes in terms of fold-change and p-value. We also analysed fragmentation patterns around the TSSs of housekeeping genes. We built a gene class of "ubiquitous genes" using the database of housekeeping promoters "FANTOM DB5" (supplementary table 4 in  [24] ). This table of 10,787 genes was filtered to keep only the strongest DPI cluster of the robust FANTOM set (our FANTOM DB1) leaving us with 6524 genes, including 5884 coding genes and 53 lincRNAs. The final table contained 5711 coding genes and 48 lincRNAs (when intersected with our using our FANTOM DB2, which only considers the genes located on the autosomes). Finally, we used expression data to provide a convenient 2D representation of the ontologies, onto which we mapped some of our key results.
More precisely, given a window size w at each genomic position, the WPS was calculated as S = O -F , where O denotes the number of inserts that fully overlapped the window and F the number of inserts that only partially overlapped with the region (only one end fell within the region). A width w = 101 was chosen, in accordance with Snyder et al. [15]. Fragmentomic profiles were then restricted to regions centred on the TSS of each coding gene or intergenic lncRNA. This was done using the FANTOM databases DB1 and DB3, assuming the TSS corresponded to the position of the strongest DPI (decomposition based peak identification) cluster identified in Ref. [23]. Besides, for merely technical reasons, the analysis pipeline at this stage operated separately on the 21,069 coding genes and on the 13,105 intergenic lncRNAs using the gene category provided by FANTOM DB2.
TSS-centred regions were taken to be 10,001 nt wide: 5000 nt upstream and 5000 nt downstream of the TSS, in addition to the TSS itself. The protection score was calculated over these regions, in accordance to what had been described in a previous study  [15]. (Nucleotides were counted relative to the TSS with position zero corresponding to the first transcribed nucleotide. ) Genes located on the reverse strand of each chromosome were turned around before proceeding with data processing.
For each single FANTOM ontology (347) used in this study  [23] , FANTOM DB4 provides a dataset informing us on each gene specificity in terms of fold-change and p-value. These datasets were used to define a number of "gene classes", which depend on what ontology selected (see Fig. 3 ): • Select a gene category: either coding genes  (21, 069)  or intergenic lncRNAs  (13, 105 ). • Sort by decreasing fold-change "FC sorting" (high fold-changes appear at the top) or increasing p-value "P sorting" (significant p-values appear at the top); this allowed us to define three possible classes: (1) top, (2) bottom or (3) middle of the list. • Additionally, focus on positive fold changes (genes that are specifically over-expressed in an ontology) or negative fold changes (genes that are specifically under-expressed in an ontology).
The highest performance estimates, in the best configuration (cf. Fig. 7A ), were observed for the hematopoietic system (UBERON 0002390), the hematopoietic cells (CL 0000988), the myeloid cells (CL 0000763) and the eosinophils (CL 0000771) with an accuracy of 93%, 92%, 91% and 91% respectively (see Fig. 7B ). This is in line with the results obtained by  [15]  (on the very same dataset BH01) and by  [17]. Among the lymphomyeloid cluster, the lowest performance estimates, in the best model configuration, were obtained for the following ontologies: CD8 positive alpha beta T cell (ontology CL 0000625), whole blood (UBERON 0000178), mast cell (CL 0000097) and Langerhans cells (CL 0000453) with an accuracy less than 60%. It must be noted that mast cells and Langerhans cells are immune cells not located in the bloodstream.
Finally, we looked at various combinations of fragmentomic features around TSSs. In the optimal model configuration, good performance was achieved by most of the ontologies of the lymphomyeloid cluster (median accuracy greater than 75%). It can be reasonably assumed the low performance ontologies contribute no or little cfDNA to the bloodstream (because of physical barriers, low cell counts, low turn-over or a combination thereof). But high accuracy for a given ontology can either be indicative that the corresponding tissue or cell type may contribute a significant fraction of cfDNA or, another explanation, the result of functionally-similar yet different tissue or cell type contributing a significant amount of cfDNA. It appears that lincRNAs using the tissue-specific genes (genes with a positive fold-change with most significant p-values in the FANTOM database) vs non-specific genes (as evidenced by high p-values) using all the features worked best (Fig.
Data and code availability Pre-processing and analysis were carried out by scripts written in Python (version 3. 6. 9) using NumPy (1. 19. 5), Pandas (1. 1. 5), Scikit-Learn (0. 24. 2) and Pysam (0. 19. 1). t-SNE was performed using R ( 4. 2. 2), Rtsne (0. 15), data. table (1. 14. 0). The GRCh37 reference was used throughout this study, since that was the reference genome used for mapping the reads in the original study  [15]. Scripts used to perform these steps are available. BAM.
files are available from http://shendurelab. github. io/cfDNA/. The FANTOM databases are freely available from the FANTOM website: FANTOM DB1 from https://fantom. gsc. riken. jp/5/suppl/Hon_et_al_2016/data/assembly/ lv3_robust/FANTOM_CAT. lv3_robust. info_table. gene. tsv. gz, FANTOM DB2 from https://fantom. gsc. riken. jp/5/suppl/Hon_et_al_2016/data/supp_table/ supp_table_11. cell_type_gene_association. tsv, FANTOM DB3 from https://fantom. gsc. riken. jp/5/suppl/Hon_et_al_2016/data/assembly/lv3_ robust/FANTOM_CAT. lv3_robust. CAGE_cluster. bed. gz, FANTOM DB4 from https://fantom. gsc. riken. jp/5/suppl/Hon_et_al_2016/data/expression/ sample_ontology_association/ FANTOM DB5 is available as the supplementary table 4 in [24]. FANTOM DB6a from https: //fantom. gsc. riken. jp/5/suppl/Hon_et_al_2016/data/expression/expression_ atlas/FANTOM_CAT.
expression_atlas. gene. lv3_robust. count. tsv. gz FANTOM DB6b from https://fantom. gsc. riken. jp/5/suppl/Hon_et_al_2016/ data/supp_table/supp_table_10. sample_ontology_information. tsv, which were published alongside [23]. Scripts used to perform these steps are available from our GitHub repository https://github. com/MuShuw/cnam_cfdna. January 16, 2023 21/25.
Dissolved inorganic carbon (DIC), pCO 2, and saturation states of aragonite (Ω arag ) were calculated with the software CO2SYS, using pH (NBS scale), A T , temperature, values for the K1 and K2 carbonic acid constants from  Lueker et al. (2000) , for KHSO 4 from  Dickson (1990) , for KHF from  (Perez & Fraga, 1987) , for [B] I from  (Lee et al. , 2010) , and an averaged atmospheric pressure of 1. 015 atm (source: Meteo France).
Rates of gross photosynthesis (P g ) were calculated as: Data were normalized per surface area of the fragment as described below. P net , P g, and R dark are expressed in mg O 2 cm -2 h -1 and then converted in μmol O 2 cm -2 h -1. Photosynthesis to respiration ratio (P g :R) was calculated using the value of daylight hours equal to 12 to calculate an integrated P g :R ratio for a 24 h day as follows:.
| Growth rate Growth rates of the three coral species (Figure  2 ) were significantly different according to corals' origin (p ≤. 003) and pH conditions (p ≤. 044), but not their interactions, with the exception of M. digitata showing a p =. 092 for pH (two-way ANOVA, Table  2  and Table  S7  for all data analyses). The mean growth rates of the three corals originating from the reference and maintained at control pH, generally decreased when maintained at future, extreme, and also variable pH. This decrease was more evident in the two branching corals than in Porites sp. For instance, the calcification rates of A. tenuis, M. digitata, and Porites sp. originating from the reference site decreased by 9. 3%, 19. 0%, and 17. 6%, respectively, in the future pH (pH NBS 7. 76) F I G U R E 1 Seawater pH T variations recorded with a SeaFet during a 48-h cycle in the four monitored experimental pH conditions (Control; Future; Extreme; and Variable).
Funding information  ARC Discovery Early Career Research Award, Grant/Award Number :  DE190100142 ;  Brittany region (France) ARED ;  Centre National de la Recherche Scientifique -MITI  (Appel à projets Adaptation du vivant à son environnement);  ISblue, Grant/ Award Number :  ANR-17-EURE-0015 ;  Ministère des Affaires Etrangères et du Développement International (MAEDI) Fonds Pacifique , Grant/Award Number:  Project SuperCoraux  no  1976 ;  University of Western Brittany (UBO)  PhD grant.
Thus, the true utilization of hydrogenation methodology relies on finding an optimum catalyst that can hydrogenate an ester with high activity and selectivity and under mild conditions (e. g. , room temperature, and ambient pressure). Our current conventional approach of catalysis development partly fails to achieve this due to its multiple limitations such as (a) empirical screening of several parameters such as solvent, temperature, pressure, time, additive, etc. can only be limited to certain extent; (b) syntheses of well-defined ruthenium catalysts often involve complex multi-step processes limiting the scope of complexes that can be studied, (c) lack of mechanistic understanding of new complexes limits its application in catalysis, and (d) intrinsic limitation of the human brain to find a pattern in large data collection restricts us to a smaller dataset.
Chemoinformatics provides an attractive alternative to the conventional empirical approach. Although a mechanistic understanding of the underlying class of reactions can be highly beneficial in such a venture, one can often find patterns in large datasets of chemical reactions even without this. This can be accomplished by deploying novel machine-learning methodologies and architectures tailored to such applications. 4 This can facilitate the discovery of desirable catalyst designs and catalytic conditions, bypassing the complexity of empirical synthesis and screening. Although powerful, the application of the tool of machine learning (ML) for the discovery of molecular catalysts is in its nascent phase and growing. A few reports have been published on the development of predictive models for catalytic reactions using various machine learning architectures dictated by the nature of the reaction, molecular catalyst, and available dataset.
18 onsidering the contemporary interests in developing sustainable catalysts for the hydrogenation of esters, we report here the application of ML to predict the outcome of ester hydrogenation using well-defined ruthenium catalysts. Our approach involves the following three steps: (1) dataset construction, and exploratory data analysis, (2) creation of chemical descriptors for catalysts, and esters, and (3) development of predictive models using ML frameworks such as neural networks (NNs), and Gaussian processes (GPs). 1. Dataset construction: We created a dataset of reactions involving hydrogenation of esters by well-defined ruthenium complexes from existing (peer-reviewed) literature. This choice is guided by two factors. Firstly, homogeneous ruthenium catalysts are known for their high activity towards hydrogenation of esters making this family a suitable choice for potential superior catalysts.
Secondly, substantial research outputs in the past on several types of ruthenium catalysts present systematic data needed for ML studies. Only those examples have been included in the dataset where the structure of ruthenium catalysts are well-defined, and they operate under neutral or basic conditions. Mechanistically, all the catalysts (with a few exceptions) have been proposed to operate via non-redox metal-ligand cooperation. Thus, our dataset consists of 460 hydrogenation reactions involving 85 ruthenium catalysts and 114 esters (Figure  1 ). Each reaction or datapoint is characterized by 12 parameters -catalyst structure, ester structure, amount of ester (mmol), catalyst loading (mol%), base structure, base loading (mol%), temperature ( o C), pressure of H2 (bar), reaction time (h), solvent structure, solvent amount (mL), and yield (%).
Creation of chemical descriptors: In order to successfully use the dataset to develop a generalizable ML model, it is important to transform the structures and properties of catalysts, esters, bases, and solvents to informative numbers called chemical descriptors as also recently highlighted by Grzybowski and co-workers. 19 1] [22] [23]  Aspuru-Guzik and Balcells have used graph or connectivity-based chemical descriptors computed using autocorrelation function from DFT optimized structures for a transition-metal complex. 24 Choice of descriptors and their calculations using DFT, experiments, or ML can also depend on the size of the dataset. In general, there is however a tradeoff between descriptor accuracy and computational cost. In the case of large datasets, graph-or connectivity-based descriptors can be used successfully in ML approaches with minimum computational cost.
24 0] [31]  We used a combination of DFT-based as well as experimentally estimated descriptors in addition to graph-based descriptors (Figure  2 ). Catalysts have been represented by three types of chemical descriptors. The first type is the graph-based descriptor calculated using an autocorrelation function of depth 4. 24  The second type is sterics-based descriptors calculated using topographic steric maps (%Vfree, %Vfree quadr. ) through DFToptimized structures and the Morfeus software (buried volumes and solvent accessible surface area and volumes). The third one is electronics-based descriptors calculated using DFT (HOMO-LUMO-gaps, dipole moment, as well as the NBO charge on the central Ru-atom). Similarly, esters have also been represented by three types of chemical descriptors: (a) graph-based descriptors using an autocorrelation function of depth 4,  24.
Prediction of yield: We started our investigations by the input of all the data points and the corresponding descriptors calculated using DFT, experiments (spectral data), and ML (one-hot-encoding) to the ML architectures. The dataset was divided into a training set and test set containing randomly selected 80%, and 20% of the data respectively. The initial investigations showed that the mean accuracy for the prediction of yields was 78% (training set), and 74% (test set) using GP, whereas NN resulted in the mean accuracy of 79% (training set), and 75% (test set). Upon analyzing the plots of predicted yields vs true yields (see SI, Figure  S2 ), we observed that the deviation of the low yielding data (<50%) was significantly higher than those of the high yielding data (>50%). We speculate this is due to a relatively low number of available data points for the reactions giving yields less than 50% (see Figure  S1 , SI).
30 datasets were created using various combinations of these five types of chemical descriptors. Our experiments showed that the highest accuracy (e. g. 94% test set for the dataset of yield>60%) is obtained by just using electronics of catalysts and esters. 4] However, the accuracy is not significantly different from the case when some other selected descriptors are used. Interestingly, just using one-hot encoding without using a chemical descriptor, also resulted in the mean accuracy of 86% only slightly lower than our best result of 94% using selected descriptors. The results obtained using one-hot encoding were found to be very similar to those when all the descriptors were used. The studies were conducted using the dataset of different sizes containing data points of yields higher than a certain percentage (e.
Prediction of catalyst: Having developed an ML model for the prediction of yields, we diverted our attention to develop a model for the inverse problem -i. e. to predict a catalyst structure for the desired yield. To simplify the problem and demonstrate a proof of concept of using the ML approach for catalyst prediction, we turned this to a multi-channel classification problem asking our model to predict a particular catalyst given the reaction conditions and yields from the dataset. Gratifyingly, our model using the NN architecture predicted the corresponding catalysts (one-hot-encoding) with a mean accuracy of 85% (Figure  5 ). We employed a simple MLP (multilayer perceptron) architecture and linear regression to predict the various catalyst descriptors. In total we have 8 steric descriptors, and three electronic descriptors.
Since neural networks are good at making end-to-end predictions, we attempted to predict all these features simultaneously. We conducted two different sets of experiments. In the first set of experiments [Expt. A], we divided our dataset into a train-test split of 80-20 and built an MLP model that aims to predict all such catalyst descriptors simultaneously. We compared our outcomes against linear models (realized through linear regression) for each of these features. In the second set of experiments [Expt. B], we divided the dataset into two disjoint parts such that each catalyst features in exactly one of the sets. This is in line with our goal of predicting new catalysts or their properties. We state at the outset that our initial efforts in this direction have not yielded in predictions of high accuracy.
Our models can predict the reaction yields, and catalyst properties with high accuracies of up to 91%, and 85%, respectively. Our studies also indicate that the use of certain selected chemical descriptors (e. g. electronics parameters) in the model can outperform the prediction accuracies obtained using several other descriptors or just one-hot-encoding although these differences are in the range of 3-6%. Moreover, we can also predict certain chemical descriptors with mean accuracies >90%. We note that the small size of the dataset with skewed population density (e. g. low datapoints for low yields, see Figure  S1 ) is a limitation to our study which is primarily due to (a) difficulty in generating a large dataset of high quality by conducting high throughput/automation experiments that would need sophisticated equipment for a high pressure/temperature conditions, and (b) the limited availability of data (in particular of low yields) in the literature.
ACKNOWLEDGMENT We sincerely thank  Prof. Neil Lawrence  ( Department of Computer Science and Technology, University of Cambridge ) for his valuable advice and feedback on this project. AK thanks the  Leverhulme Trust  for an early career fellowship ( ECF-2019-161 ). NVW and EB thank the  IdEx Université de Paris  ( ANR-18-IDEX-0001 ) for funding. CM is supported by a Fellowship by the  Accelerate Program for Scientific Discovery at the Computer Laboratory, University of Cambridge. The authors acknowledge the  GENCI-CINES  center for HPC resources (Projects  A0080810359  and  A0100810359 ).
12 Version of Record yellow, and mature fruits red) were scored as polymorphic. Presence of stone cells in fruits was coded from taxonomic monographs, field observations, or herbarium specimens, as they can be easily seen (and felt) in both fresh and dried specimens if they are present. Trait evolution. -Stochastic character mapping (SIMMAP;  Huelsenbeck & al. , 2003; Bollback, 2006)  was performed using the phytools v. 0. 6-99 package  (Revell, 2012)  in R (R Core Team, 2021). All traits were treated as unordered and equally weighted. Polymorphic clades were coded with equal probability of all states found to be present. Three different transition rate models were used: (1) Equal Rates (ER), i. e. , a single rate for all possible transitions between states; (2) Symmetrical Rates (SYM); and (3) All Rates Different (ARD).
■ ACKNOWLEDGEMENTS We thank  National Science Foundation (NSF)  grant (to LB & SK) " PBI Solanuma world  treatment"  DEB-0316614 ,  Sibbald Trust Fellowship  (to RH), the  Fundación Ceiba  and the  GEME lab  (Agreement 566 from 2014 between the  Universidad Nacional de Colombia  [https://unal. edu. co/] and Colciencias [now called Minciencias https://minciencias. gov. co/]) (to AO), and  CNPq  ( 427198/2016-0  and  422191/2021-3 ) and  FAPESPA/CAPES  (Proc. AUXPE 88881. 159124/2017-01  to LG) for funding and supported the research;  Rocío Deanna  for discussions about character states; and  João Renato Stehmann ,  Richard Olmstead , and  Stacey Smith  for critical yet insightful comments that helped to enhance the manuscript. We thank the following people for images:  Michael Benedito ,  Rocío Deanna ,  Martin Gardner ,  Paulina Hechenleitner ,  David Rae ,  Pedro Acevedo Rodríguez ,  Mario Vallejo-Marín , and  Maria S.
Discussion Because of the excellent reproducibility of 1 H-NMR spectroscopy, its quantitative accuracy and its ability to identify structures  17  , this instrumental approach was of great interest to characterize the metabolome of different egg compartments such as yolk, amniotic fluid 8 and allantoic fluid. In birds, the composition of allantoic fluid and its evolution during embryonic development are still poorly described. The OPLS-DA performed on the spectral dataset clearly identified three clusters highlighting differences in the metabolic profiles obtained at E10, E14 and E17, regardless of the line. It also revealed an increase in inter-individual variability during embryonic development. With the exception of formate, nicotinate and phenylalanine, all metabolites accumulated in the allantoic fluid, especially amino acids, peptides and analogues, the most represented class among those identified with 12 distinct metabolites (alanine, betaine, creatine, glutamate, glycine, histidine, isoleucine, leucine, lysine, phenylalanine, tyrosine and valine).