dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank the six water agencies and private labs for participating in the collection of biotic data. We are also grateful to the lake team of  Irstea Aix-en-Provence  for the lake database management. Many thanks are going to one of our team engineer,  S. Boutry , for the map in Fig. 1. We kindly thank the anonymous referees for their useful comments on the previous version of this manuscript. We are also thankful to  D. M. John  for the English edits. This work was supported by the  French Ministry  of the ecological and solidarity transition (MTES) and funded by the  "Office Français de la Biodiversité" (OFB)  through the convention between  INRAE  and OFB between 2016 and 2018. It was also funded by the  Labex  COTE "Continental To coastal Ecosystems: evolution, adaptability and governance" within the Cluster of  Excellence COTE  ( ANR-10-LABX-45 ) and the  Adour-Garonne water agency  for some personnel costs.
Code availability The software Phytobs, used for some trait compilation or calculation (biovolumes), was created by our team in a first version in 2009 by Hadoux and Laplace-Treyture  51  for phytoplankton counting purposes. It was upgraded over the years to allow biovolume calculations and to integrate taxonomy and synonymy  31. This free software is now in version 3. 2 (French, English and Spanish languages) and publicly available on https://hydrobio-dce. inrae. fr/phytobs-software/.
Proportion of the dataset taxa in the different phytoplankton groups. 36. 5% 11. 8% 2. 2% Chlorophytes 3% Chrysophytes 2. 4% Cryptophytes 2. 5% Cyanobacteria 16. 4% Desmids Diatoms 6. 4% 18. 9% Dinophytes Euglenophytes Xanthophytes Fig. 3 56. 8% 10. 4% Cell 32. 9% Colony Filament.
Taken as a whole, these data showed that the majority of FCER2 + cells belong to a small number of activated, nonproliferating LZ B GC -cells that might have been diverted from a PC fate. In contrast, FCER2-negative LZ B GC -cells were more heterogeneous in their distribution and presented a relative prevalence in the PB subset by taking into account the significant low number of FCER2-positive cells in this compartment. In mice, it has been suggested that most pre-plasmablasts are derived from positively selected cells i. e. , expressing MYC gene  (13, 20) , we found among the 1,518 human MYC-positive LZ B GC -cells from Holmes et al. (26)  dataset that 316 (21%) and 1,202 (79%) were, respectively, positive and negative for FCER2 expression. This result was consistent with data obtained recently in mice  (24).
Histo-Immunofluorescence Staining Human tonsils and reactive lymph nodes were embedded in Optimal Cutting Temperature Compound (OCT, Sakura) and conserved at -80°C. Cryostat sections (18 µm thick) were fixed with 4% paraformaldehyde for 15 min at room temperature (RT). Sections were then incubated during 1 h with a blocking solution (PBS, 2% Bovine Serum Albumin, 4% donkey serum and 0. 1% saponin) at RT and incubated in a humidified chamber overnight at 4°C with primary antibodies. Sections were washed with PBS 0. 1% saponin and incubated with secondary antibodies for 1 h at RT. Finally, tissue sections were mounted with Mowiol (Merck) antifade reagent containing 20µM of SytoxBlue nucleic acid stain (Thermo Fischer) and analyzed by confocal microscopy on a SP8 (Leica Microsystems). ImageJ software (National Institutes of Health) was used for image analysis.
After cell segmentation and nuclear detection pSTAT6, CD20, PAX5 and PD1 threshold intensities were set up. Six GCs in three different tonsils were analyzed. Double CD20 or PAX5 and pSTAT6-positive cells and total CD20-or PAX5-positive cells were quantified and a percentage of positive pSTAT6 B cells was assessed. Information About RNA-Seq Datasets Used Throughout the Paper Bulk RNAseq datasets used to select genes analyzed by sc-qPCR in this study are available in the Gene Expression Omnibus database under accession no. GSE136990. Single-cell-RNAseq datasets from Holmes et al. 's paper  (26)  are available in the Gene Expression Omnibus database under accession no. GSE139833. sc-gene expression data are available under accession no. GSE139891.
ACKNOWLEDGMENTS Cell sorting was performed at the  Biosit Flow Cytometry and Cell Sorting Facility (CytomeTRI, University of Rennes 1, Rennes, France ). Histologic immunofluorescence analyses were performed at the Biosit H2P2 and PRISM facilities ( University of Rennes 1, Rennes, France ). We thank  L. Deleurme  and A. Saintamand for technical assistance, and  I. Papa  for discussions. KS received a doctoral fellowship from the  University of Rennes 1. SL is funded by the chair " Cancer & Innovation " of the  Rennes 1 Foundation  and the  LabEx IGO  project (reference:  ANR-11-LABX-0016-01 ). MH received a doctoral fellowship from the  FHU CAMIn, university hospital of Rennes, Fance. We thank  David Fraser  at  Biotech Communication  for editing services.
DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. These data can be found here: 1) The dark zone and light zone bulk singlecell RNAseq gene expression data are available in the Gene Expression Omnibus database under accession no. GSE139833. sc-gene expression data are available under accession no. GSE139891; 2) CD23-positive and CD23-negative RNA-seq data have been deposited in the Gene Expression Omnibus: GSE136990.
We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. The semantic distance is derived from BERT, and the lexical quality is based on GPT2 perplexity. To solve this multi-objective search problem, we propose two algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS). We provide an extensive set of experiments on 5 datasets with a rigorous reproduction and validation for several state-of-the-art paraphrase generation algorithms. These experiments show that, although being non explicitly supervised, our algorithms perform well against these baselines.
The available aligned paraphrase corpora are often biased toward specific problems like question answering or image captioning. For instance, a transformer or a seq2seq model trained on a question answering corpus will typically turn any input sentence into a question. With the lack of generic aligned datasets, it remains challenging to train generic paraphrase models in a supervised manner. On the other hand, with the availability of largescale non-supervised language models like BERT and GPT2, the assessment of a given candidate paraphrase in terms of semantic distance from its source and lexical quality has become much more tractable. Leveraging from these metrics, we propose to cast the paraphrase generation task as a multicriteria search problem. We use PPDB 2. 0  (Pavlick et al. , 2015) , a large-scale database of rewriting rules derived from bilingual corpora, to potentially generate billions of 'naive' candidate paraphrases by edition from the source sentence.
PPDB rule Edited sentence is → is found he is found speaking on june 14. is speaking → 's talking he 's talking on june 14. speaking → speak now he is speak now on june 14. 14 → 14th he is speaking on june 14th. Table  1 : PPDB rules applied to a source sentence sieve of three criteria: semantic similarity, syntax quality, and lexical distance. We propose and evaluate two search algorithms: MCPG and PTS. We also provide an extensive set of experiments on English datasets with a rigorous reproduction and validation methodology for several state-of-the-art paraphrase generation algorithms. This article is organized as follows: The candidate paraphrase generation scheme is presented in Section 2. In Section 3, we develop the different criteria we use to qualify correct paraphrases. The two search algorithms (MCPG and PTS) are described in Section 4.
Paraphrase generation scheme We model paraphrase generation as a sequence of editions and transformations from a source sentence into its paraphrase. In this work, we only consider local transformations, i. e. replacement of certain words or group of words by others that have the same or similar meanings, but the method we propose should work with more sophisticated transformations as well. The Paraphrase Database (PPDB)  (Ganitkevitch et al. , 2013; Pavlick et al. , 2015)  is a large collection of scored paraphrase rules that was automatically constructed from various bilingual corpora using a pivot alignment method  (Callison-Burch, 2008). The database is divided into increasingly large and decreasingly accurate subsets 1. We used the XL subset, and we removed the rules labeled as "Independent".
This left us with a set of 5. 5 million rewriting rules. We give some examples of these rules in Table  1. By iteratively applying the rules from a source sentence like the one in Table  1 , we obtain a vast lattice of candidate paraphrases. Some of these candidates like "he's talking on june 14" are wellformed, but many are syntactically broken, like "he is speak now on june 14". The number of rules that apply depends on the source sentence's size and the words it contains. For instance, on the MSRPARAPHRASE dataset (see section 6. 2), sentences are quite long and the median number of PPDB-XL rules that apply is around 450. After two rewriting steps, the median number of candidates is around 10 5 , and by iterative rewriting, we quickly reach a number of paraphrase candidates that is greater than 10 8.
, 2016; Vaswani et al. , 2017). Several papers like  (Prakash et al. , 2016a; Cao et al. , 2017)  set the paraphrase generation task as a supervised sequence-to-sequence problem. As confirmed by our experiments in Section 6. 4, this approach is efficient for specific types of paraphrases. It is also able to produce relatively long-range transformations, but it requires huge and high-quality sentence-level aligned datasets for training. The paraphrase generation literature mostly reports results on MSCOCO  (Chen et al. , 2015)  and QQP  (Kornél Csernai, 2017)  datasets which are built respectively from image captions and semiduplicate questions. These datasets are very specific: MSCOCO is strongly biased toward image description sentences, and QQP is dedicated to questions. The Transformer model we trained on QQP typically transforms any input into a question.
Experiments The evaluation metrics and datasets are described respectively in Section 6. 1 and 6. 2. We paid attention to set up a rigorous validation protocol for our experiments. The reproducibility and methodology issues that we faced are discussed in Section 6. 3. We compare our model with state-of-theart supervised methods and with CGMH, another search-based algorithm. The technical details on these algorithms are developed in Section 6. 4. The results are detailed in Section 6. 5.
Evaluation datasets Table  4  gives a summary of the datasets we used. On one hand, we have large datasets like MSCOCO or OPUSPARCUS (OPUSPAR. ) that are noisy and very specific. On the other hand, we have MSR-PARAPHRASE (MSRPARA. ), a high-quality but small human-labeled dataset. We also report a rough distribution of the BERT score (B) and the Levenshtein distance (L) computed on the corpora paraphrases pairs. The 'PPDB@x' columns give the median number of candidates that PPDB-XL generates from one sentence respectively at one and three rewriting steps. The size of the PPDB rewriting lattice is correlated to the length of sentences. The MSCOCO-2017 set  (Chen et al. , 2015)  contains image captions, assuming that captions associated with the same picture are paraphrases. The strengths of MSCOCO are its size and the fact that each source sentence is associated with four reference paraphrases.
However, the sentences are biased towards a descriptive style, and the quality of the paraphrases is often questionable. The OPUSPARCUS dataset  (Creutz, 2018)  has been extracted from movies and TV shows subtitles. It contains mostly informal dialogues. Quora Question Pairs dataset (QQP) (Kornél Csernai, 2017) is a paraphrase identification corpus dedicated to question-answering systems. The MSRPARAPHRASE dataset  (Dolan and Brockett, 2005)  is mostly build with pieces of news. The sentences of this corpus are quite long. It is a small but high-quality paraphrase identification corpus that was labeled by humans. PAWS wiki (PAWS)  (Zhang et al. , 2019b; Yang et al. , 2019b ) is a paraphrase identification corpus that contains several lexicallysimilar but hard-to-classify pairs like "Flights to Florida from New York" and "Flights from Florida to New York".
Methodology and reproducibility issues In the paraphrase generation literature, most of the papers report results on MSCOCO and QQP corpora. In table 5, we provide the BLEU scores as reported in  (Prakash et al. , 2016b; Gupta et al. , 2017; Fu et al. , 2019; Miao et al. , 2018b)  and (Egonmwan and Chali, 2019a). However, even if the dataset names coincide, and even if each evaluation methodology is correct on its own, the discrepancies between methodologies render these values impossible to compare with each other. The strange gap between the residual LSTM performance of  (Prakash et al. , 2016b)  and the one Model applied on MSCOCO BLEU ↑ RESIDUAL LSTM  (Fu et al. , 2020)  23. 7 LBOW-TOPK  (Fu et al. , 2020)  25. 3 RESIDUAL LSTM  (Prakash et al. , 2016a)  37. 0 VAE-SVG-EQ  (Gupta et al. , 2017)  39.
Regarding the sentence lengths, Prakash et al. (2016b) and  Gupta et al. (2017)  shrunk all sentences to 15 words. Fu et al. (2019)  set the maximum length to 16 while  Egonmwan and Chali (2019a)  set it to 15 and 10 respectively for the input and target sentences. Knowing that roughly 56% (resp. 5%) of MSCOCO target sentences are strictly longer than 10 (resp. 15) words, these small changes can have a great impact on the results. The vocabulary considered also differs. Fu et al. (2019)  used a vocabulary of 8k and 11k tokens from the train sets of QQP and MSCOCO respectively, whereas Egonmwan and Chali (2019a) had a vocabulary of approximately 15k words that was constructed on both the train and test sets. The scripts used to compute metrics did also differ from one paper to another, and it is known that BLEU scores can vary wildly with different parameterizations  (Post, 2018).
The maximum sentence length is set to 1024 tokens which the default setting in fairseq. For decoding, we did a beam search with a beam of size 5. Weakly-supervised paraphrase generator For the weakly-supervised strategy CGMH introduced by  Miao et al. (2018b)  we used the official code. We managed to reproduce their results on QQP. On our test set we achieve a BLEU score of 22. 5 while they reported 18. 8. We then extended the experiment to other datasets and metrics.
Results Table  6  summarizes the results of the comparison of our models, supervised encoder-decoder neural networks and the weakly-supervised method CGMH. Overall, these results are mixed: it is however important to keep in mind that contrary to the supervised baselines which are retrained for each dataset, the parameters of the CGMH, MCPG and PTS models are left unchanged. On the MSCOCO and QQP datasets, the supervised baselines achieve clearly better results, but MCPG and PTS achieve better results on OPUSPAR-CUS and PAWS except with the BERT score for which the TRANSFORMER model achieves similar results. On MSRPARAPHRASE, the encoderdecoder neural networks models perform poorly. This result can be explained by the small number of training examples available on this corpus (See Table  4 ).
On the weakly-supervised side, MCPG and PTS models outperform the CGMH baseline on all corpora except on the MSCOCO dataset where the results are similar. These results prove that even without a specialized training sets, generic search-based methods are competitive for paraphrase generation. However, it is a fact that encoder-decoder networks have excellent performances for text generation and have the potential to generate more complex paraphrases than those obtained by simple local transformations as in our models. Training a general -all-purpose -paraphrase generation network would require a huge volume of data. And there is yet much less aligned corpora available for paraphrase than for translation.
Data-augmentation experiment In order to get the best of both worlds, one option is to enrich the training set of a TRANSFORMER with the results of a search-based method. To test this idea, we used our models to augment the MSRPARAPHRASE training set. For that purpose we created new pairs of paraphrases from unused sentences of MSRPARAPHRASE (the pairs labeled as "not paraphrases") using MCPG and PTS. We then trained a new supervised TRANSFORMER models on the augmented training sets. Having no guarantee that our models generate syntactically perfect sentences, we inverted the pairs, thus taking the generated paraphrases as input and the dataset's sentences as output. This trick, called back-translation, forces the target model to generate correct sentences  (Sennrich et al. , 2016a; Edunov et al.
Conclusion We experimented with two search-based approaches for paraphrase generation. These approaches are pragmatic and flexible. Being generic, our approaches did not overfit on small datasets. We performed extensive experiments with a rigorous evaluation methodology that we applied both on our algorithms and on the other related methods that we tried to reproduce faithfully. These experiments confirm that our two methods, namely MCPG and PTS, are comparable to supervised stateof-the-art baselines despite being less tightly supervised. When compared with CGMH, another search-based and weakly-supervised method, our algorithms proved to be faster and more efficient. We plan to refine the scoring with deep reinforcement learning techniques and enrich the edition rules with more sophisticated patterns like phrase permutations.
The fourth candidate emphasizes the utility of the spell-checker. The last candidate, on the fifth row, achieves our equilibrium goal. We show on a second part, the paraphrases generated by our models MCPG an PTS, and on a third part, we give the reference paraphrase from the dataset. 1. 00 Score isolines 0. 98 BERT Score 0. 90 0. 92 0. 94 0. 96 Pareto-Front 0 1 -GPT2 160 140 120 100 80 0. 88 0. 86 0. 0 0. 1 0. 2 Levhenstein Distance 0. 3 0. 4 0. 5 Figure 1: The cloud of candidates generated from the sample of Table 2. The optima of any positive combi- nation of BERT score, normalized Levenshtein distance, and GPT2 perplexity belong to the Pareto front (orange dots). We plotted the projections of MCPG combined score (1) with dashed isolines. The BERT score and Levenshtein distance being clearly anti-correlated, the balance between these two criteria is difficult to tune.
The tomato (Solanum lycopersicum) fruit has a thick, astomatous cuticle that has become a model for the study of cuticle formation, structure, and properties in plants. Tomato is also a major horticultural crop and a long-standing model for research in genetics, fruit development, and disease resistance. As a result, a wealth of genetic resources and genomic tools have been established, including collections of natural and artificially induced genetic diversity, introgression lines of genome fragments from wild relatives, high-quality genome sequences, phenotype and gene expression databases, and efficient methods for genetic transformation and editing of target genes. This mini-review reports the considerable progresses made in recent years in our understanding of cuticle by using and generating genetic diversity for cuticle-associated traits in tomato.
INTRODUCTION Cultivated tomato (Solanum lycopersicum L. ) is a major horticultural crop that has long been a model for the Solanaceae crop species (tomato, potato, eggplant, pepper …) and for fleshy fruit development and disease resistance  (Rothan et al. , 2019). Tomato is very suitable for laboratory studies (grown in greenhouse, miniature cultivars, short life cycle, autogamy, and easy genetic transformation) has extensive genetic resources, high-quality reference genome  (Tomato Genome Consortium, 2012) , sequences of hundreds of accessions  (Zhu et al. , 2018; Gao et al. , 2019; Alonge et al. , 2020) , and available phenotype and gene expression databases. 1 hanks to these resources and tools, genes underlying trait variations can be identified and their relationships with phenotypic variations can be established through in planta functional analysis.
, 2018). The resulting Tomato Expression Database (TEA) available at SGN 2 can be mined for clusters of genes co-expressed in specific developmental stages and cell types including inner and outer epidermis, which gives precious information on genes functionally linked to cuticle formation. Gene expression profiling can be further combined with various multi-omics technologies, typically metabolome  (Fernandez-Moreno et al. , 2016)  and proteome  (Szymanski et al. , 2017) , and with large scale sequencing of genetic diversity  (Szymański et al. , 2020)  to assign putative functions to the genes and explore their role in cuticle formation. Novel functions revealed by biochemical approaches, e. g. , the cutin:cutin-acid endo-transacylase (CCT) enzyme activity  (Xin et al. , 2021) , may also ultimately lead to the isolation of the encoding gene.
DISCOVERING CUTICLE-ASSOCIATED GENES THROUGH FORWARD GENETICS APPROACHES IN TOMATO Though the list of cuticle-associated genes can be considerably shortened by mining existing databases and literature, their systematic analysis in planta may remain problematic because of gene redundancy, pleiotropic effect, etc. This may restrict the focus to known gene families, thus impeding the discovery of original gene functions. An alternative approach is to screen natural or artificially induced genetic diversity for phenotypesof-interest, e. g. , fruit surface defects induced by cutin deficiency  (Isaacson et al. , 2009; Petit et al. , 2014) , and then identify causal genetic variation.
, 2014). Many more mutants can be found by screening tomato mutant collections for obvious cuticle defects  (Petit et al. , 2014)  and by browsing associated phenotypic databases (reviewed in  Rothan et al. , 2016) , such as TOMATOMA 4   (Saito et al. , 2011). EMS mutant collections can also be screened by TILLING  (Targeting Induced Local Lesions IN Genomes; Okabe et al. , 2011)  though mutated alleles of target genes are now efficiently generated by gene editing  (Rothan et al. , 2019). As detailed in Table  1 , mutant collections have been instrumental for: (i) discovering novel gene functions (SlCUS1;  Yeats et al. , 2012b) ; (ii) isolating allelic variants of genes implicated in wax biosynthesis (SlCER6;  Vogg et al. , 2004; Smirnova et al. , 2013) , cutin biosynthesis (SlCYP86A69;  Shi et al.
0. 27. 10 software package. Peptides were separated on a C18 PepMap100 precolumn, and a C18 2 µ, Acclaim PepMap RSLC column (Thermo Scientific) using a 15 min gradient of 5 to 36% acetonitrile with 0. 1% formic acid at a flow rate of 300 nL/ min. Peptides were analyzed using Data Dependent Acquisition (DDA) mode. Full scan MS mode (400-1500 m/z) was operated at a resolution of 120,000 with automatic gain control (AGC) target of 4 × 10 5 ions, and a maximum ion transfer of 35 ms. Selected ions for MS/MS were analyzed using the following parameters: resolution 120,000; AGC target of 4 × 10 5 ; maximum ion transfer of 35 ms; 1. 6 m/z isolation window; for CID normalized collision energy of 35% was used; and dynamic exclusion of 60. 0 s. The Easy-IC was used for internal calibration. Protein identification and data analysis.