dataesr/scientific-paragraphs-categorization huggingface dataset:
The minimum is, however, not well defined for m ≫ n, which is often the case in the cancer survival analysis setting. Tumor databases typically include several hundreds of patients characterized for over 20,000 gene expression values. A remedy is provided by adding a regularization term, the most popular being ridge and lasso, or their combination into an elastic net  (Zou and Hastie (2005) ). In this work, we use the standard lasso term penalty P λ (β) λ β 1 , ( 2 ) where λ is a non-negative constant corresponding to the strength of the regularization. Finding β that minimizes ℓ(β) + P λ (β) - i: Ci 1 x i • β -log j: Yj ≥ Yi e xj•β ⎛ ⎝ ⎞ ⎠ + λ β 1 (3) produces a sparse solution where some of the coefficients are reduced to zero. However, while such regularization usually improves survival predictions, one of the important limitations remains excessive variation in selected genes across models trained on even slightly varying data (e.
Model Optimization To find β minimizing the loss functions of lasso, group lasso and multi-task group lasso models, we used the Adam optimizer implemented in the PyTorch package  (Kingma and Ba (2014) ). Moreover, in case of group lasso or multi-task group lasso, we truncated β g to zero when all elements from a group g were below a threshold of 0. 001 in absolute value. FIGURE 1 | Typical solutions minimizing the Cox loss function with either the lasso (left) or the group lasso regression term (right), here computed for the UCEC gene expression data: these two models predict survival of cancer patients based on expression values for genes from downstream targets of 69 signaling pathways. The number of inputs varies between the two examples since the implementation of latent group lasso duplicates genes that appear in more than one group (i.
Training on Synthetic Data To check the validity of our multi-task learning approach and corresponding code, we simulated the following synthetic data set: Two "toy" cancer gene expression and survival data sets T 1 and T 2 drawn from a normal sampling distribution generated from two TCGA cancers COADREAD and STAD. Both T 1 and T 2 comprised nearly 10,000 genes, and 300 and 200 patients respectively. Moreover, we assumed that the patients' survival is fully determined by two pathways each where one is being shared among the two toy cancer types. The corresponding "true" β coefficients were obtained as the first principal component coefficients of the genes included in the pathway over the combined COADREAD and STAD data sets. To each patient i, we randomly assigned either event C i 1 (with probability 70%) or censorship C i 0 (30%).
Training on TCGA Data We examined all possible pairs between 30 cancer types in the TCGA data set. For each pair, we selected hyperparameters using a 10-fold cross-validated random search. We then performed 30 80-20 training-test splits, computed β on the training sets and evaluated the c-value on the test sets for both cancers. We computed the paired t-test statistics value and its associated pvalue for each pair with respect to the latent group lasso without multi-tasking. Finally, the false discovery rate (FDR) correction for the number of pairs tested per cancer was applied.
Latent Group Lasso As despite the popularity of group lasso, we could not find a comparison between standard lasso and group lasso model for the cancer survival prediction on gene expression data, we first evaluated and compared accuracies of these two models on 16 cancer types from the TCGA database with at least 300 patients per set (see Section 2. 1 for data set description). Out of the 16 cancers tested, five had a significantly higher prediction accuracy (c-value) for simple lasso, seven were significantly higher for latent group lasso and there was no significant difference for the remaining four cancers (Figure  2A ). Also, model reproducibility measured through the averaged congruence coefficients (see Section 2. 4) was better for the group lasso model for 12 out of the 16 cancers tested (Figure  2B ).
Validating the Multi-Task Penalty on Synthetic Data To explore the efficacy of the multi-task penalty (7) we designed, we first applied our approach to synthetic data sets T 1 and T 2 comprising 300 and 200 samples respectively (see Section 2. 6 for the detailed data set description). Our simulation results showed that while the latent group lasso without multi-tasking generally   (A-B ) and multi-task group lasso (C-D) on T 1 and T 2 data sets. Out of the 69 total pathways, only pathways with at least one non-zero coefficient are shown. By design, activity of pathways 1 and 2, and one and three were predictive for patient survival for T 1 and T 2 , respectively. The average congruence coefficient of β is 0. 81 (T 1 ) and 0. 96 (T 2 ) for single tasking, and 0. 80 (T 1 ) and 0. 91 (T 2 ) for multitasking (e-f) c-values and box plots of the paired difference for 100 random seeds for single and multitasking for T 1 and T 2 synthetic data.
Note that T 1 and T 2 are sorted independently, so their random seed numberings do not correspond. The t-test p-values are 1. 35•10 -7 (T 1 ) and 0. 49 (T 2 ). selected the correct pathways for T 1 (pathways 1, 2) and T 2 (pathways 1, 3) the model also assigned non-zero coefficients to a number of the irrelevant pathways (Figures  4A, B ). However, when the multi-task penalty was added, the number of irrelevant pathways included in the model usually reduced for both data sets (Figures  4C, D ), and no correctly included pathways were lost. Furthermore, the average c-value increased significantly for T 1 when the multi-task penalty was included, and did not change significantly for T 2 (Figures  4E, F ). From these results, we concluded that the multi-task penalty we designed was acting as intended.
Multi-Task Group Lasso Model on the TCGA Data To check the efficacy of the multi-task group lasso model for the survival prediction, we applied it to 30 TCGA cancer data sets (see Section 2. 1 for more details). For each pair of cancer types, we compared the resulting model accuracies (c-values) calculated for 100 random splits for the individual group lasso and multi-task group lasso models. Although based on the results of the model validation on synthetic data, we expected the multi-task setting to improve predictions, little significant difference was observed after multiple testing correction (Figure  5 ). For one cancer type, LUSC, significant improvements were observed when the cancer was paired with a number of other cancer types. Further, while not significant after multiple testing correction, significant uncorrected differences were observed for PRAD.
In particular, combining PRAD with CHOL, COADREAD and GBM each led to an improvement of c-value over 0. 08. Finally, several other combinations showed a marginal significant improvement, e. g. , BLCA with STAD, KIRC wth KIRP, or UCEC with COADREAD. No significant improvements in the model stability, measured by congruence between model coefficients β, were observed with the addition of multi-tasking (Figure  6 ). The mean congruence decreased for almost every cancer pair tested. Of note, other multi-tasking approaches using the same data and similar validation strategies, such as VAECox  (Kim et al. (2020) ), have reported similar results, with only limited improvements over standard lasso. VAECox observed a microaverage concordance across 10 cancers from TCGA of 0. 649; using the same microaverage method for those 10 cancers, our multi-task approach gave results in the range 0.
e. , congruence between model coefficients when training using different random seeds, appeared to be higher for latent group lasso regularization, suggesting potential improvements in biological interpretability. Next, we tested our multi-tasking model a on syntetic data set designed so that it closely mimicked real cancer data (including strong gene collinearity). We used two toy sets drawn from a sample distribution associated with COADREAD and STAD, and then determined the patients' hazard scores from two overlapping gene groups each. We randomly censored 30% of the patients and adjusted for their survival time uncertainty. In order to leverage similarities between cancers, we introduced a rather low number of patients-300 and 200 respectively. Our model showed a comparably high c-value for both toy cancers separately, and a significant improvement in the accuracy of the first set after multi-tasking.
A two step-methodology combining quantitative and qualitative approaches We focus on the first three years of the Mayotte seismic-volcanic crisis, more precisely from 10 May 2018 to 1 April 2021. We build our analysis on the following methodology and datasets. We searched the archives and in particular the web archives of the scientific and state institutions involved in the monitoring and management of the crisis. We collected and analyzed all the documents made public by the authorities and scientists during these first three years such as press releases, scientific bulletins, news on websites and public notes (table  1 ). Hereafter, we are citing scientific bulletins and websites as references (including their URL when existing) while authorities' press releases are given in the supplementary dataset (ministerial press releases as well as those from the Préfecture of Mayotte).
We also included the academic papers published during our 3 years period of study  (Cesca et al. , 2020; Famin et al. , 2020; Feuillet et al. , 2021; Lemoine et al. , 2020; Tzevahirtzian et al. , 2021). We coded this dataset by date of publication and by publishing institution/author, and quantitatively analysed it to show the time evolution of the publication rate by the different actors of the risk management (see Figure  4 ). Using the catalog of the felt seismicity provided by EMSC for the period from May 2018 to April 2021 (EMSC-CSEM, 2021), we compare this publication rate to the number of earthquakes felt by Mayotte citizens and its evolution in time  (Figure 3, 4). This analysis was made using the R software package. This allows us to quantitatively put the scientist's and authorities' communication effort in perspective with the evolution of the geophysical signal that directly affected the population.
However, to understand people's feeling of a lack of communication and how the communication has been managed by the scientists and authorities as the crisis developed, we also needed more qualitative approaches: i. e. qualitative analysis of the content of the different documents. We thus studied the content of all the documents in the above dataset and the way it has evolved in time. We also aimed to identify the main stages of scientific monitoring and to understand how the circulation and transfer of knowledge has been managed by the different actors. With this objective, we conducted semi-structured interviews with scientists from the main institutions in charge of the geophysical monitoring of the crisis (7 interviews lasting from 1 to 3 hours within BRGM, IPGP, CNRS and REVOSIMA) and with local and national risk managers (6 interviews lasting from 40 minutes to 2 hours within the Préfecture of Mayotte, the DIRMOM, the Ministries of Research, Environment and Interior).
In particular, we use selected citations extracted from STTM facebook posts to illustrate citizens' feelings about the management of the crisis by the scientists and authorities. Hereafter, we anonymised these facebook excerpts, and provide our own english translation (anonymised French original versions of the facebook posts are given in the supplementary dataset). If this Facebook group gathers more than 10 000 members and has been identified as a key interlocutor by local authorities, it is not representative of the sociology of the whole population of Mayotte. Nevertheless, these excerpts allow us to identify misunderstandings and grasp ways in which communication could be improved.
We include in our database the five academic papers (one is a preprint version of a submitted paper) dedicated to the crisis that were published during our period of study  (Cesca et al. , 2020; Famin et al. , 2020; Feuillet et al. , 2021; Lemoine et al. , 2020; Tzevahirtzian et al. , 2021)  and commented by the press and/or the members of STTM facebook group.
It should however be noted that, for BRGM, as for the other scientific organizations involved in monitoring before the creation of REVOSIMA, real-time data processing is organized thanks to scientists' voluntary commitment. But the situation is difficult as crucial data are missing. Only the largest magnitude earthquakes (M>5) are reported by global seismic networks while the existing local network -the few accelerometric stations in Mayotte completed by few regional stations in Comoros and in Madagascar -does not allow a good record of the surge of moderate magnitude earthquakes felt by the population. Because of this inadequate network the BRGM operators initially encountered difficulties in accurately locating the earthquakes and assessing their epicentral depths. In June 2018, the persistence of the seismic crisis leads other actors to become involved.
It is important to outline again that, before the creation of REVOSIMA, realtime data processing is organized through the voluntary commitment of scientists. During the first weeks of the crisis, the scientific reports and official press releases follow one another within a few hours. BRGM publishes its bulletins on the BRGM website 2 , while the Préfecture sends press releases to the press and publishes them on Facebook. These official press releases generally reproduce the elements communicated by the expert body. They remain often very technical, recalling the number of earthquakes recorded per day, their magnitude, the time at which they were detected and their distance from the island (the reports mention uncertainties of the order of 10-15 km). The Préfecture's press releases can contain additional elements about impacts (injuries, building damage) and often recall safety instructions.
public catalog of the seismic data collected since the beginning of the crisis  (Bertil et al. , 2018; Lemoine et al. , 2019). BRGM continues to publish a monthly bulletin dedicated to the monitoring of the seismicity but communication from the Préfecture of Mayotte becomes more episodic. It focuses on relaying BRGM's situation points (with the list of events -among which the felt ones -in the past months) and on announcing the arrival of Tellus Mayotte scientific campaigns. It is worth pointing out that the press release of 3 April 2019 mentions a "scientific volcanological mission" which aims at "consolidating knowledge of the tectonic and volcanic history of Mayotte and at highlighting the tectonic structures of the island by means of dating of magmatic rocks, or analyses of the composition of soil gases".
At the end of 2019, EOST also announces the arrival of the second mission of the BCSF-RéNaSS macro-seismic intervention group in Mayotte. The continuation of REVOSIMA decided at the December 2019 interministerial meeting is not really announced, at least publicly. In January 2020, a team of French and German researchers, not members of REVOSIMA, publishes in Nature Geoscience the first academic paper analysing the time evolution and the dynamics of the ongoing volcanic activity  (Cesca et al. , 2020). This paper, mostly based on seismic data acquired by worldwide seismic networks, mentions the discovery of the new volcano before its publication by the scientists directly involved in the survey campaigns and the close monitoring of the crisis. The CNRS and the University of Toulouse, which hosts the second author of this paper, publishes a press release in French (CNRS & Université de Toulouse III, 2020) bearing a sketch section of the proposed magmatic plumbing system, which is largely commented by the STTM group: "So much questions !!! In particular on the position of the magma chamber  […]  One or Two? 1 or 2 chambers? The island is moving east, towards the supposed chamber near the volcano??? And there's another one just below under the doormat on our front door", "Silly question, but does that portend a big disaster for us?" (excerpts from STTM Facebook group, 8 Jan 2020) In January, EOST also announces the results of the GIM mission and of a pickathon organized by the REVOSIMA to get help in relocating earthquakes.
Two information leaflets are also issued that describe the release and recovery of OBS (MAYOBS 13-1) and the acquisition of underwater acoustic data (MAYOBS 13-2). While surprisingly, no press release followed the MayOBS 5 to 12 missions, REVOSIMA issues in May 2020 a detailed report about MayOBS13 results (REVOSIMA, 2020), which is relayed on the websites of partner institutions (IPGP, BRGM, IFREMER) on 4 June 2020. The same day, the government publishes a press release that summarizes the main scientific results and thanks all the staff for their commitment in these missions. Two more scientific papers are published in June 2020, one on the volcanological and seismotectonic context of the seismo-volcanic crisis  (Famin et al. , 2020) , the other one, led by BRGM scientists, analyses the seismic and GNSS data from the first year (2018-2019) of the seismo-volcanic episode  (Lemoine et al.
But the situation is difficult as crucial data are missing. Only the largest magnitude earthquakes (M>5) are reported by global seismic networks while the existing local network -the few accelerometric stations in Mayotte completed by few regional stations in Comoros and in Madagascar -does not allow a good record of the surge of moderate magnitude earthquakes felt by the population. Because of this inadequate network the BRGM operators initially encountered difficulties in accurately locating the earthquakes and assessing their epicentral depths. In June 2018, the persistence of the seismic crisis leads other actors to become involved. Ministries in charge of civil protection and disaster risk prevention send an interministerial mission. Mid-June 2018, a team of seismologists from BCSF-RéNass is sent to "estimate the levels of damage induced by this seismic swarm according to the vulnerability of the buildings at the date of the field analysis" (Sira et al.
agu. confex. com/agu/fm19/meetingapp. cgi/Paper/560238 Devès et al. (2021). Media coverage of the sismo-volcanic crisis of Mayotte: from science to risk. In Preparation. Donovan, A. , Oppenheimer, C. , & Bravo, M. (2012). Social studies of volcanology: knowledge generation and expert advice on active volcanoes. Bulletin of Volcanology, 74(3), 677-689. Donovan, A. (2019). Critical volcanology? Thinking holistically about risk and uncertainty. Bulletin of Volcanology, 81(4), 1-20. Edwards, R. , & Holland, J. (2013). What is Qualitative Interviewing? A&C Black. EMSC-CSEM. (2021). EMSC-CSEM/Earthquake_Sequence_Mayotte_2018_2021: Second release. Zenodo. https://doi. org/10. 5281/zenodo. 4734032 EOST. (2018a, June 11). Mission du Groupe d'Intervention Macrosismique à Mayotte. Retrieved April 26, 2021, from https://eost.
Acknowledgements This work benefited from the data of felt seismicity collected by the  EMSC  (https://doi. org/10. 5281/zenodo. 4734032)whose activities is supported by the  Fondation SCOR pour la Science. The authors would also like to thank  Rémy Bossu ,  Laure Fallou  and  Matthieu Landès  from  EMSC  who provided very useful comments at different stages of the work. People interviewed during these three years are also to be thanked for their invaluable contribution to our thinking process. The authors also want to thank scientists and officials from the  DIRMOM , the  Préfecture of Mayotte  and the REVOSIMA for their help in setting up exhaustive records on the communication elements put in place by the scientists and the authorities during the course of the crisis. Many thanks also to  Aline Peltier , scientific director of the REVOSIMA, who kindly agreed to read and comment on the paper before its submission.
Data availability EMSC data on the felt seismicity are available from https://doi. org/10. 5281/zenodo. 4734032. Instrumental seismicity plotted on Figure  1  is from  Lemoine et al. (2020)  dataset, and from REVOSIMA catalog (not yet available for distribution, these data will be included in  Saurel et al. , 2021). Press releases from the Préfecture de Mayotte and French ministries are given in supplementary dataset. French version of STTM post excerpts are also given in supplementary dataset. Full verbatim of interviews from which we extracted cited excerpts are not public for confidentiality. All other data used in this paper are available from cited references.
RNA quality and quantity were determined on a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, United States). One µg of RNA from each pool was used to produce RNA libraries for sequencing, with a TruSeq Stranded mRNA kit (Illumina, San Diego, CA, United States). Sequencing was performed in a paired-end 100 bp mode using Illumina Hiseq2500 platform. The cDNA ensemble corresponding to 18 libraries was deposited at the NCBI GEO database with accession ID GSE180716.
Bioinformatic Analyses Eighteen paired-end libraries were obtained from Illumina HiSeq 2500 sequencing. The raw sequences were analyzed under Galaxy Platform ABIMS from Biologist Station of Roscoff 1  , until finding the differentially expressed transcripts between control and treatment samples (Figure  1 ). Firstly, the raw sequences were checked for quality, passing all of them the filter criterion with all nucleotides with > 30 phred score, excepting 9 first and last one nucleotides. For these reasons, the first 9 nucleotides and the last one of all raw reads were trimmed with Trim sequences program. Then, a reference transcriptome was assembled using all libraries with Trinity  (Grabherr et al. , 2011)  and annotated with Trinotate. Alignment analyses were performed for every sample with Bowtie2  (Langmead et al.
, 2009)  to assess Transcriptome quality showing aligned percentage between 81. 68 and 84. 78% with an average of 83. 64% evidencing a high accurate assembly. This reference transcriptome assembly was used for aligning and counting reads for each sample with the normalization method "Transcripts Per Millions" (TPM) using Salmon program  (Patro et al. , 2017). The TPM normalized counts were used to compare generate expressions matrixes of transcript expression among experimental conditions. Then, these matrixes were used to obtain the differentially expressed transcripts (DET) using EdgeR software  (Robinson et al. , 2009) , considering differentially expressed with a fold-chage > 1. 5 and False discovery rate < 0. 05. The files with differentially expressed transcripts were exported from Galaxy and their list of transcripts with Suisseprot abbreviation were used to obtain interaction networks and enriched GOterms with STRING online program  (Szklarczyk et al.
Transcriptome Sequencing Stats After exposure to experimental condition, RNA isolation, library preparation and sequencing 674,021,026 paired-end reads were yielded, with 37,445,612. 56 reads per sample average. Using the total 18 paired-end libraries, we assembled a high-quality reference transcriptome containing 175,629 transcripts. This transcriptome has a N50 of 1,093 nucleotides and an average contig 1,071. 13 nucleotides based on all transcript contigs. Regarding completeness of contigs, comparing with Arthropoda database, the BUSCO software revealed a 97% of contigs as complete (40. 3% of contigs as complete and single copy and 56. 7% complete and duplicated), 2% as fragmented and 1% as missing (Table  1 ).