dataesr/scientific-paragraphs-categorization huggingface dataset:
A manual verification was then made. In order to associate peaks with pigments, all peaks from all spectra were extracted with the stat_peaks function from the ggspectra  [46]  R package (V. 0. 3. 5) and compared with a homemade bibliographic database (897 peaks obtained from 36 marine molluscan pigments extracted from 65 species in 32 studies, see Supplementary Materials Table  S3 ). Since biological materials are known to produce noisy spectra, three different methods were used to compare peaks between the different shells analyzed  [19, 44, 47]. First, a visual screening was operated to identify peaks matching or not expected signals. They were classified as: (i) clear peak, (ii) putative peak, and (iii) no peak. Second, the intensity of each selected peak was measured (the value of the lowest point of the peak minus the value of the peak).
88 for red vs. green and Log2FC: 3. 006 for red vs. yellow). The "terpenoid backbone biosynthesis pathway" (ec00900) was found to be associated with the red phenotype. Finally, although we cannot exclude their involvement in color expression, none of the yellow-and greenassociated DEGs had sufficient background literature or sequence similarity to a gene of known function for us to draw a direct link to pigmentation. Among the DEGs present in only one pairwise comparison, we identified four interesting candidate genes linked to the green phenotype. The first two of these candidates, which were found under-represented in the green vs. yellow comparison and encode two glutathione-S-transferase (GST) enzymes (Log2FC: -1. 02 and -1. 27), were also found in the "glutathione transferase activity" (GO:0004364) molecular function in RBGOA and are involved in "glutathione metabolism" (ec00480), "metabolism of xenobiotics by cytochrome P450" (ec00980), and in "drug metabolism-cytochrome P450" (ec00982).
Individual spectra display the characteristic peaks of high intensity corresponding to the CaCO 3 biomineral in its aragonitic and calcitic forms. This high intensity reflects that the shell is essentially composed of these two polymorphs  [61]. At a lower intensity, dozens of peaks are present and illustrate the high chemical complexity of this biomaterial, with more than 78 different Shell Matrix Proteins (90% of the organic matrix  [61, 62] ), at least 4 different lipid's family (fatty acids, cholesterol, phytadienes, and ketones; 0. 15 to 0. 29% of the organic matrix  [61] ); 10 different traces of heavy metals  [63] , polysaccharides, and secondary metabolites  [61]. Among the 174 peaks included in our homemade database (see Supplementary Materials Table  S3 ), 40 peaks were found in the spectra obtained from our red, yellow, and green individuals (Table  2  and Supplementary Materials Figure  S1 ).
Supplementary Materials: The following are available online at https://www. mdpi. com/2073-442 5/12/3/421/s1, Figure  S1 : Raman spectra and enlargement corresponding to Raman signatures of interest, Table  S1 : Detailed parameters of the bioinformatic pipelines, Table  S2 : Detailed protocol of the homology modeling, Table  S3 : Homemade bibliographic database referencing Raman peaks specific to pigments, Table  S4:   S5 : Transcriptome annotation, Table  S6 : DEGs and corresponding BlastX results for the comparison "Red versus Yellow". The colored cells correspond to the color-specific DEGs that can be found in two comparisons, Table  S7 : DEGs and corresponding BlastX results for the comparison "Red versus Green". The colored cells correspond to the color-specific DEGs that can be found in two comparisons, Table  S8 : DEGs and corresponding BlastX results for the comparison "Yellow versus Green".
Contributions: J. V. -D. , C. -L. K. and S. P. designed and supervised the study. J. D. and H. D. performed the Raman. P. -L. S. analyzed the data. P. -L. S. and J. V. -D. draft the manuscript. P. D. and L. Q. annotated the transcriptome. Funding was obtained by C. -L. K. , S. P. and J. V. -D. , J. V. -D. , J. D. , C. -L. K. , S. P. , C. R. corrected the main manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by grants from the Direction des Ressources Marines, through the AmeliGEN project (# 10065/MEI/DRMM). Pierre-Louis Stenger's PhD was funded by AmeliGEN and the doctoral school of the Pacific (ED 469). This study is set within the framework of the Laboratoire d'Excellence (LABEX) TULIP (ANR-10-LABX-41) and CORAIL (ANR-10-LABEX).
Author Contributions: J. V. -D. , C. -L. K. and S. P. designed and supervised the study. J. D. and H. D. performed the Raman. P. -L. S. analyzed the data. P. -L. S. and J. V. -D. draft the manuscript. P. D. and L. Q. annotated the transcriptome. Funding was obtained by C. -L. K. , S. P. and J. V. -D. , J. V. -D. , J. D. , C. -L. K. , S. P. , C. R. corrected the main manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by grants from the Direction des Ressources Marines, through the AmeliGEN project (# 10065/MEI/DRMM). Pierre-Louis Stenger's PhD was funded by AmeliGEN and the doctoral school of the Pacific (ED 469). This study is set within the framework of the Laboratoire d'Excellence (LABEX) TULIP (ANR-10-LABX-41) and CORAIL (ANR-10-LABEX).
We refer to these ecosystems as urban greenspaces hereafter  (1). Natural areas near these cities were unmanaged or seminatural forests, shrublands, and grasslands, many of them relict ecosystems. Urban greenspaces and natural ecosystems strongly differ in vegetation structure and supported lower levels of plant species richness (fig. S2  and table  S2 ). Agricultural lands were not included in this study. Our database comprises a wide range of urban areas including relatively small (population < 50,000; e. g. , Alice Springs, Australia), medium (population, 50,000 to 1 million; e. g. , Ljubljana, Slovenia), and large cities (population > 5 million; e. g. , Beijing, China; table  S1 ), and spanned a large range in vegetation types (forests, grasslands, and shrublands) and climatic regions (arid, temperate, tropical, and cold ecosystems; table  S1 ).
(C) The relationship between soil pH and within-group (urban or natural) community similarity for archaea, bacteria, fungi, and protists. **P < 0. 01; *P < 0. 05. S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 5 of 14 Identifying the members of the soil microbiome that commonly reside in urban greenspaces is fundamental for the appropriate management of these habitats, as revealed by studies on macro-organisms  (19). Here, we identified the indicative members of the soil microbiome of urban greenspaces across the globe (urban greenspaces taxa hereafter). We focused on those microbial phylotypes (ASVs) that (i) were relatively ubiquitous (>25% of all cities), (ii) were classified as "species indicators" of greenspaces using the algorithm of the "indicspecies" R package (see Materials and Methods), and (iii) showed statistically significantly higher proportions in urban rather than natural environments (see Materials and Methods for a more detailed description of these analyses).
5  and figs. S12 and S13). These patterns are likely the result of fertilizer applications to greenspace soils, given that similar taxonomic shifts have been observed in grasslands worldwide receiving elevated nutrient inputs  (25). Thus, our findings suggest that changes in temperature, as well as differences in important socioeconomic and management factors, are largely associated with the soil microbiome of our greenspaces. To gain a deeper understanding of the functional attributes of the urban greenspace soil microbiome, we obtained shotgun metagenomic data from a subset of 54 soil samples (one of the soil composite samples from a selection of 27 cities and their corresponding natural ecosystems). This selection covered a wide range of cities from contrasting climates and populations and 17 countries from both hemispheres (table  S1 ).
Microbial functional diversity A composite sample per plot was sequenced for the entire metagenome in 27 paired urban/natural ecosystems (54 samples). More than 500 ng of DNA per soil sample was isolated for shotgun metagenomic  (34, 35)  sequencing using the DNeasy PowerSoil DNA Isolation Kit (Qiagen Inc. , USA) according to the manufacturer's protocol. Sequencing was performed using an Illumina HiSeq (Illumina Inc. , USA) at Majorbio in Shanghai, China. Raw reads {PE150 [150-base pair (bp) paired-end reads]} were trimmed to remove low-quality reads as follows. First, the SeqPrep software (https:// github. com/jstjohn/SeqPrep) was used to remove the adapter sequences. Second, the library sickle (https://github. com/najoshi/sickle) was used to trim the reads from the 5′ end to the 3′ end using a sliding window (size, 50 bp; 1-bp step).
If the mean quality of bases inside a window dropped below 20, then the remainder of the read below the quality threshold was trimmed. Quality-trimmed reads that were shorter than 50 bp or containing N (ambiguous bases) were discarded. The original sequences of the 54 samples were annotated using Subsystem Technology [MG-RAST (metagenomics rapid annotation using subsystem technology); www. mg-rast. org)] (36) to perform quality control and automated annotation as well as produce taxonomic and functional assignments. MG-RAST generates taxonomic assignments based on the SEED subsystem database by DIAMOND software (version 0. 9. 32) by best-hit classification with a maximum E value of 1 × 10 -5 , a minimum identity of 60%, and a minimum alignment length of 25 amino acids for proteins and functional categories.
The resulting table was parsed at SEED subsystem level 3 by software SUPER-FOCUS. The proportion of 1276 groups of functional genes was determined from these analyses. This information was used to analyze patterns in the community composition and similarity of functional genes. Moreover, we investigated particular groups of genes that are known to be associated with pathogenesis, greenhouse gas emissions, nutrient cycling, and abiotic stress (Fig. 7 ). The richness of functional genes was determined from a rarefied gene table including 5. 8 million annotated reads per sample.
Identifying the soil microbiome of urban greenspaces To assess the soil microbiome associated with urban greenspaces (urban greenspaces taxa), we followed the three steps. First, using the information from the plot-level ASV abundance table, we kept those microbial phylotypes (bacterial, fungal, and protist ASVs) that were present in at least 25% of the studied cities  (40). Second, we then used the function "multipatt" in the R package indicspecies  (41)  to identify those microbial phylotypes that were significant indicators of city greenspaces compared with natural ecosystems. In particular, this function studies the association between species patterns and combinations of groups of sites and identifies what species are most likely to be indicators of a given group of sites (here, urban greenspaces).
Spearman rank correlations are a nonparametric approach that does not require normality of data or homogeneity of variances and measures the strength and direction of the association between two ranked variables. In addition, unlike Pearson correlations, Spearman rank correlations can be used to associate two variables regardless of whether they are ordinal, interval, or ratio. Soil radiation and climatic information were extracted from the WorldClim database (www. worldclim. org/data/index. html). Soil pH was measured with a pH meter, in a 1:2. 5 mass:volume soil and water suspension. Total nitrogen (N) in the soil was analyzed using an elemental analyzer (C/N Flash EA 112 Series-Leco Truspec). Total soil P was determined, after nitric-perchloric acid digestion, using an inductively coupled plasma optical emission spectrometer (ICAP 6500 DUO; Thermo-Scientific, Waltham, MA, USA).
Population density was determined using the latest available city censuses using official national statistical sources. We used population density rather than the total population because it is a more complete index of the human pressure on urban environments. Population density was positively correlated to total population (Spearman  = 0. 33; P = 0. 010). Moreover, we collected information about mowing, irrigation, and fertilization treatments in the urban greenspaces. GDP per capita, which provides information on the monetary value of final goods and services in the regions for the cities surveyed, was extracted from that dataset in  (43) , to provide information on the economic activity for each location. Plant structure data (plant diversity and cover) were determined in the three 30-m transects within each plot (fig.
Acknowledgments: We would like to thank  C. Walsh  and  R. Ochoa-Hueso  for advice on bioinformatics and statistical analyses. We also thank  M. Martin  for revising the English of the manuscript. In addition, we thank  J. Owojori  for connecting us with our sampling collaborator in Nigeria, A. R. Bamigboye. Funding: M. D. -B. and this project were supported by a 2019 Leonardo Grant for Researchers and Cultural Creators,  BBVA Foundation  (URBANFUN) and by the  BES  grant agreement no. LRB17\1019  (MUSGONET). M. D. -B. is also supported by a  Ramón y Cajal grant  from the  Spanish Ministry of Science and Innovation  ( RYC2018-025483-I ). N. F. was supported by grants from the  U. S. National Science Foundation  ( DEB1556090  and  DEB1542653 ). L. T. acknowledges support from  Norway-Baltic  collaboration grant  EMP442  and  Estonian Science Foundation  grant  PRG632.
B. K. S. acknowledges a research award by the  Humboldt Foundation  and funding from the  Australian Research Council  ( DP190103714 ). F. A. is supported by  ANID   FONDECYT 11180538  and  1170995. S. A. is funded by  ANID   FONDECYT 1170995  and  ANID ANILLO ACT192027. F. B. and J. L. M. acknowledge support from the  Spanish Ministry and FEDER  funds for the project  AGL2017-85755-R , the i-LINK+ 2018 (LINKA20069) from  CSIC , as well as funds from " Fundación Séneca " from  Murcia Province  ( 19896/GERM/15 ). C. P. acknowledges support from the  Spanish State Plan for Scientific and Technical Research and Innovation  ( 2013-2016 ), award reference  AGL201675762-R (AEI/FEDER , UE). M. B. acknowledges support from a  Juan de la Cierva Formación grant  from the  Spanish Ministry of Economy and Competitiveness  ( FJCI-2018-036520-I ).
T. P. M. would like to acknowledge contributions from the  National Research Foundation of South Africa  and cities involved in the South African survey. Slovenian coauthors were supported by the research project  J4-1766  " Methodology approaches in genome-based diversity and ecological plasticity study of truffles from their natural distribution areas "  and the Research Program in Forest Biology, Ecology, and Technology  ( P4-0107 ) of the  Slovenian Research Agency. J. D. and  A. Rey  acknowledge support from the  FCT  ( IF/00950/2014  and  SFRH/BDP/108913/2015 , respectively), as well as from the  MCTES, FSE, UE , and the  CFE  ( UIDB/04004/2020 ) research unit financed by  FCT/MCTES  through national funds (PIDDAC). J. P. V. acknowledges financial support from  SERB (Science and Engineering Research Board)  ( EEQ/2017/000775 ) India.
As long as we have a fitting embedding model, this approach could be further extended to analogies between domains that are usually considered too complex, such as analogies between functions or other complex models. This paper is organized as follows. We recall in Section II a simple formalization of analogy and present related works, in particular, the approach of Lim et al. [11]  by which our framework is inspired. In Section III we explain how we adapt this approach to morphological analogy. An empirical study carried out on the Sigmorphon2016  [16]  and Japanese Bigger Analogy  [17]  datasets is presented in Section IV, in which we compare our results to other state of the art approaches to morphological analogy, that show the competitiveness of our approach. We also explore how the models trained on each language perform when transferred to the other languages in Section V, that reveal interesting similarities between them.
Good results can be obtained with 1000 receptions  [22]. A more empirical approach was proposed by Murena et al. (2020)  [8] , which relaxes the formal definition of analogical proportion. Following preliminary evidences that humans may follow a simplicity principle when solving analogies  [24, 25, 26] , the authors propose to solve analogical equations A : B :: C : x by finding the x that minimizes the total description length (or Kolmogorov complexity) of A : B :: C : x. The total description length is evaluated using a simple description language for character strings and an associated binary code. Both Alea and Fam and Lepage's approaches were shown to be outperformed by Murena et al. 's Kolmogorov complexity approach on analogies, on the Sigmorphon2016 dataset.
However, as mentioned in  [14] , those simplistic rules for analogy do not match human performance in all cases. Additionally, some strict properties of formal analogy can contradict human intuition, like uniqueness which is problematic when dealing with (quasi-)synonyms, which should be interchangeable solutions of analogical equations on word semantics. Hence, it becomes necessary to relax the model of analogy to match human performance on analogies. While Murena et al. [8]  follow a simplicity principle on the transformation between words, Lim et al. [11]  propose to learn an analogy operator directly from analogies as an elegant way to get out of the simplistic models of parallelogram and cosine analogy. They use simple neural networks to learn analogies from a dataset of semantic analogies, and propose different models for classification and regression purposes.
For example for German, one of the most readily available languages of the Sigmorphon2016 dataset, we initially experimented with a pretrained German GloVe model 1. However, this rather large GloVe model only has a coverage of 51. 26% (8872 of 17308 words) of the words present in the training set and 49. 10% (9798 of 19955 words) for the test set. This means a large portion of the analogies have at least one word unknown by the model, which is thus replaced by an embedding full of 0. Similar issues happened with other pretrained embeddings we tried. Our other preliminary experiments using GloVe and Bert Multilingual  [29]  confirmed the poor performance of such embedding methods for extensive morphological analogy even if some marginal results can be observed. Due to these issues we will not compare to them further in this article.
C. Training Process The model was trained for binary classification using binary cross entropy, where the positive class is analogies and the negative is non-analogies. Since the Sigmorphon2016 dataset we use in our experiments contains only positive examples (i. e. correct analogical quadruples), we use the same data augmentation process as in  [11]  to generate negative examples (i. e. invalid analogical quadruples). This process was designed based on the properties of formal analogies, resulting in 8 equivalent forms for every analogy A : B :: C : D:.
IV. EXPERIMENTS In this section we detail our experimental setup on the Japanese Bigger Analogy Test Set  [17]  and the Sigmor-phon2016 dataset  [16]  (both detailed in Section IV-A). We also present how we used the approaches from  [8, 22]  as baselines (Section IV-B). The code used for our experiments is written in Python 3. 9 and PyTorch and is available in the repository https://github. com/AmandineDecker/nn-morpho-analogy. Experiments presented in this paper were carried out using computational clusters equipped with GPU from the Grid'5000 testbed (see https://www. grid5000. fr). Namely, the grele and grue clusters of were used.
A. Dataset For our experiments, we use two datasets: the Sigmor-phon2016  [16]  dataset and the Japanese Bigger Analogy Test Set  [17]   3. 1) Sigmorphon2016: The Sigmorphon2016 dataset  [16]  is a large inflexion dataset over 10 languages: Arabic (Romanized), Finnish, Georgian, German, Hungarian, Maltese, Navajo, Russian, Spanish, and Turkish. Most of those languages are considered to have rich inflexions. It is separated in 3 subtasks: inflexion, reinflexion and unlabled reinflexion. In our experiments, we focus on the data from the inflexion task, which is composed of triples of a source lemma A (ex: "do"), a set of features F (ex: present participle) and the corresponding inflected form B (ex: "doing"). For example from the Finish data we have the triple: A ="lenkkitossut", F ="pos=N,case=ON+ESS,num=PL" (the transformation corresponds to the nominative to essive cases of a noun, for the plural) and B ="lenkkitossuilla".
To obtain morphological analogies from this dataset, for any pair of triples A, F, B , A , F , B that have the same set of features (F = F ), we consider A : B :: A : B an analogical proportion. Note that for each pair of triple, we only generate one analogy, i. e. , if we generate A : B :: A : B we do not generate A : B :: A : B as it will be generated by the data augmentation process. Also, analogies of the form A : B :: A : B will be generated as the set of features is the same (F = F ). The number of analogies obtained by this process is detailed in Table  I. During training and evaluation we generate for each analogy all the permutations and negative examples as mentioned in Section III-C, resulting in a total of 8 valid analogies and repressively 3 and 24 invalid analogies for training and evaluation.
Several approaches were proposed to solve these subtasks, they can be grouped into three trends as mentioned in  [16] : NLP pipelines using edit operations, neural approaches, and approaches based on linguistic heuristics. None of the systems originally submitted relied on analogy solving, which is the approach we explore in this paper, in a similar manner as the approach to the task developed in  [8]. 2) Japanese Bigger Analogy Test Set: The Japanese Bigger Analogy Test Set contains pairs of words related by a particular morphological relation, each relation is separated in a particular file. From the available relations we focus on inflectional and derivational morphology, and process the data in a similar manner as Sigmorphon2016: for every two pairs of words A, B , A , B related by the same relation we generated an analogical proportion A : B :: A : B , and we only generate one analogy per pair of word pairs.
The selected relations correspond to 1017 pairs of words and 26410 analogies before augmentation. 3) Data for Training and Testing: The model trained on Japanese was trained on 18487 randomly selected analogies (70%) and tested on the remaining 7923. On Sigmorphon2016, as the amount of available data is very large, the models of each language were trained on 50000 and tested on 50000 analogies randomly selected from the train and test data respectively. After data augmentation, this correspond to 400000 positive and 150000 negative examples for training, To maintain a reasonable training time, we trained the models for 20 epochs which correspond to around 6h with our computational resources. 4) Lepage's analogies from Sigmorphon2016: For some of our comparability experiments we use the analogies extracted from Sigmorphon2016  [16]  by Lepage  [30] , as it is the dataset used to compare the baselines in  [8].