dataesr/scientific-paragraphs-categorization huggingface dataset:
To overcome this problem, Bayesian models may be filtered using external database information on miRNA binding sites  [21]. However, filtering does not address the problem of miRNA effect sizes nor takes into account the possibility that miRNA-target interactions could be indirect eventhough there is evidence for a binding site in external databases. Expression-based approaches also involve support vector machine analysis  [22] , Gaussian process regression model  [23]  and network inference such as weighted gene correlation network analysis (WGCNA), the latter approach which has been used, for example, for modelling miRNA regulation in hepatitis C  [24]  and in HD knock-in mice (Hdh mice)  [3]. Although network inference methods such as Bayesian analysis and WGCNA may provide insights into the features of miRNA regulation, they may be prone to aggregation of a large number of hypotheses around strongly deregulated entities  [3, 20] , lacking discriminative power and biological precision, and impairing data prioritization.
Here, we addressed this problem by developing an approach in which network-based analysis for reducing data complexity is followed by robust random-forest (RF) analysis for selecting explanatory variables (i. e. miRNAs best explaining targets, with a P-value computed for each predictor variable and each predictor variable stable across RF iterations involving different seeds) and shape analysis (surface matching) for building discriminative and accurate ensembles of negatively correlated miRNA-mRNA pairs. We used RF analysis for feature selection as this method does not make any prior hypothesis on the existence of a relationship, whether direct or indirect, between a miRNA and a target. To select the most interesting miRNAs, this analysis was supplemented with evidence for binding sites as instructed from multiple databases and followed by data prioritization using criteria such as CAGrepeat-length dependence and the fold change of target expression.
We applied this approach to the analysis of multidimensional data in the allelic series HD knock-in mice (Hdh mice), currently the largest and more comprehensive datasets (6 CAG-repeat lengths, three age points, several brain areas: miRNA, mRNA and proteomic data) to understand how miRNA regulation may work on a system level in neurodegenerative diseases  [2]. We focused on the study of miRNA regulation mediated by mRNA degradation as the coverage and dynamics of proteomic data in the allelic series of Hdh mice is limited compared to miRNA and mRNA data. As developed below, we found that, on a global level, miRNA data explains a very small proportion of the CAG-repeat-and age-dependent dynamics of gene deregulation in the striatum (and none in cortex) of Hdh mice, retaining 31 miRNA-mRNA pairs implicated in neuronal activity and cellular homeostasis, among which only five pairs are of high interest.
Multimodal selection of miRNA targets To understand how the dynamics of miRNA regulation may work on a system level in the brain of Hdh mice, we applied miRNA regulation analysis via multimodal integration (miRAMINT), a pipeline in which novelty is to combine shape analysis with random forest analysis (Fig. 1 ). As a first step, we performed a signed WGCNA analysis  [26]  of mRNA and miRNA expression profiles to reduce data complexity through building co-expression modules. The expression profiles of genes (respectively miRNA) in each cluster were summarized using the eigen-gene (respectively eigen-miRNA)  [27]. We then selected the miRNA module(s) where the eigen-miRNAs are negatively correlated with the eigen-genes. This analysis retained 8 miRNA co-expression modules and 18 target co-expression modules in the striatum and 4 miRNA co-expression modules and 14 gene coexpression modules in the cortex (Table  S1 , see http:// www.
broca. inserm. fr/MiRAMINT/index. php for edge lists). Amongst all possible associations (144) between miRNA modules and target modules, 12 negative correlations between eigen-vectors (false discovery rate lower than 1%) were retained in the striatum and in the cortex (Table  1 ). We then tested whether the log fold change (LFC) for miRNA expression across the 15 CAG-repeat and agedependent conditions tested in Hdh mice might explain target expression levels across these conditions. To this end, we applied RF analysis, which allows this question to be addressed in an unbiased manner (i. e. with no a Fig. 1  MiRAMINT analysis of miRNA regulation. This protocol integrates network-based analysis for reduction of data complexity followed by (i) random forest (RF) analysis for selecting explanatory variables, with a p-value computed for each predictor variable using the Altmann's approach  [25]  and this RF analysis iterated (which involves different seeds in each iteration) until the number of hypotheses is stable across consecutive iterations (see also Materials and Methods), (ii) surface-matching analysis for high precision in matching the miRNA and mRNA expression profiles across conditions (herein as defined by 6 CAG repeat alleles and 3 age points), (iii) evidence for binding sites and (iv) data prioritization for selecting predicted miRNA-mRNA pairs of high interest.
This approach retained 3983 pairs (involving 141 explanatory miRNA variables and 350 dependent genes variables) in the striatum and 49 pairs (involving 16 explanatory miRNA variables and 3 dependent genes variables) in the cortex (Table  S2 ). Next, we tested whether the shape of the surface defined by the LFC values for explanatory miRNAs is negatively correlated with that defined by the LFC values for the corresponding targets (see Methods). Surface-matching retained 219/3983 relationships in the striatum, and 23/49 relationships in the cortex (Table  S2 ). Finally, in these latter groups of miRNA-target relationships, we retained those showing evidence for binding sites as indicated in the TargetScan  [11] , MicroCosm  [30]  and miRDB  [31]  databases, which generated a final number of 31 predictions (14 miRNAs explaining 20 targets) in the striatum and 9 predictions (6 miRNAs explaining 3 targets) in the cortex (Table  S2 ).
Data prioritization upon miRAMINT analysis Although selective, data analysis in miRAMINT enables a diversity of profiles in terms of CAG-repeat dependence, age dependence and magnitude of effects across conditions to be retained. Several criteria may then be used for prioritizing the most interesting pairs, including (i) the overall shape of the gene deregulation plane (e. g. linear effects, biphasic effects, local effects) and the maximal amplitude of gene deregulation at any point in the CAG repeat-and age-dependent plane, (ii) the strength of plane matching (i. e. the Spearman's score for surface-matching), (iii) the number of databases concluding to a binding site between miRNA(s) and predicted target(s) and (iv), if available, positive correlations between changes in the expression of proteins and of genes encoding these proteins.
The analysis retained 31 miRNA-mRNA pairs in the striatum, among which 17 top pairs corresponding to either binding sites found in more than one miRNA target database or highest Spearman's score for surface matching, or both (Fig. 2a ), including 5 pairs for which from the maximally-achieved log fold change of target is greater than or equal to 0. 5 (Fig. 2b ). Biological annotations suggested this group of miRNA-target pairs may be notably implicated in Jak-STAT signaling, Th1 and Th2 cell differentiation, ether lipid metabolism and Nglycan biosynthesis signaling pathway (Fig. 2a ). In the cortex, miRAMINT retained 9 miRNA-target pairs that tend to show a biphasic (deregulation at 6 months, then return to initial level) age-dependent profile, including 6 miRNAs and 3 targets annotated for inflammatory pathways (Tnfrs11a) such as NF-kappa B signaling, a pathway involved in neuronal apoptosis  [36] , and for cell genesis and death (protogenin, cadherin 9) (Fig.
This conclusion is reminiscent of a similar trend detected in the brain of wild-type mice, where miRNA regulation may be poorly correlated to gene expression signatures across cell types  [37]. This conclusion is even more stringent for the cortex of Hdh mice, suggesting that miRNA regulation do not play a critical role in truly responding to HD in this brain area. In so far, our model significantly differs from a previous analysis  [3]  of the RNA-seq time series data in the allelic series of Hdh mice  [2]  in which global (eigenvalue-based) negative correlation between miRNAs and target modules (using WGCNA) was used to build a model of miRNA regulation. Although some of the   S3  for the full list of miRAMINT miRNA-target pairs in striatum). The targets are contained in rectangles and the miRNAs in ellipses.
A thick edge means that evidence for binding sites is available from at least two miRNA databases. A thin edge means that evidence for binding sites is available from only one miRNA database. A thick rectangle means that the maximal LFC of the target is greater than 0. 5. A cross arrow indicates the miRNA that is best paired with a target when this target has several possible miRNA regulators. Biological annotations of miRNA targets correspond to GO Biological processes or KEGG pathways at the result of STRING analyzes using stringent criteria (i. e. STRING score > 0. 7, Databases and Experiments only, 20 neighbors added on the first shell) the KEGG pathways are those with, at least, 3 genes implied, the GO Biological processes are those with, at least, 5 genes implied. b Examples of 3D-graphs for top miRNA-target pairs (LFC amplitude of the target above 0.
5) miRNAs retained by miRAMINT analysis were also retained in this former study  [3]  (see Table  S3 : 12/14 miRNAs common to the two studies), miRAMINT miRNA-target pairs are in smaller numbers (before data prioritization: 31 miRAMINT predictions in striatum, instead of 7514 WGCNA predictions contained in 55 negative correlations between miRNA and target modules in striatum; 9 miRAMINT predictions in cortex, instead of 186 WGCNA-based predictions contained in 9 negative correlations between miRNA and target modules) and, importantly, except to one case (Mir132-Pafah1b1), they are associated with different targets. These differences are likely due to the higher accuracy associated with tree-based analysis combined with surface matching in miRAMINT compared to using a global (eigenvalue-based) negative correlation scheme between target modules and miRNAs  [3].
2 , Table  S3 ) that are known to play a role in neuronal activity and homeostasis. This feature applies to miRNAs that are upregulated in the striatum of Hdh mice. Mir132 (upregulated and paired with 2310030G06Rik, the Guanine Monophosphate Synthase Gmps, Interferon Lambda Receptor Ifnlr1, Ribonucleoprotein Domain Family Member   S3  for the full list of miRAMINT miRNA-target pairs in cortex). The targets are contained in rectangles and the miRNAs in ellipses. A thick edge means that evidence for binding sites is available from at least two miRNA databases. A thin edge means that evidence for binding sites is available from only one miRNA database. All LFC are below 0. 5. A cross arrow indicates the miRNA that is best paired with a target when this target has several possible miRNA regulators.
Biological annotations of miRNA targets correspond to GO Biological processes or KEGG pathways at the result of STRING analyzes using stringent criteria (i. e. STRING score > 0. 7, Databases and Experiments only, 5 neighbors added on the first shell) the KEGG pathways are those with, at Larp1b, Platelet Activating Factor Acetylhydrolase 1b Regulatory Subunit Pafah1b1 and Tripartite Motif-Containing ProteinTrim26) is associated to brain vascular integrity in zebrafish  [37] , spine density  [39]  and synaptogenesis  [40]. Knocking down Mir1b (upregulated and paired with Ventral Anterior Homeobox 2, Vax2) significantly alleviated neuronal death induced by hypoxia  [41]. miR139 (paired with the zinc finger protein 189 Zfp189) modulates cortical neuronal migration by targeting Lis1 in a rat model of focal cortical dysplasia  [42].
Conclusions In summary, we addressed the problem of accurately modeling the dynamics of miRNA regulation from the analysis of multidimensional data. Our study puts forth the added value of combining shape analysis with feature selection for predictive accuracy and biological precision in modeling miRNA regulation from complex datasets, as illustrated by precise self-organised learning from multidimensional data obtained in the striatum and cortex of HD knock-in mice. MiRAMINT provides a convenient framework for researchers to explore how combining shape analysis with feature selection can enhance the analysis of multidimensional data in precisely modeling the interplay between layers of molecular regulation in biology and disease.
Source data RNA-seq (mRNA and miRNA) data were obtained from the striatum and cortex of Hdh knock-in mice (allelic series Q20, Q80, Q92, Q111, Q140 and Q175 at 2 months, 6 months and 10 months of age) as previously reported  [2]. The GEO IDs for transcriptome profiling data in Hdh mice are GSE65769 (Cortex, miRNAs), GSE65773 (Striatum, miRNAs), GSE65770 (Cortex, mRNAs) and GSE65774  (Striatum, mRNAs).
Differential expression analysis mRNA and miRNA significant read-count data for eight individuals (four males and four females) as available in the RNA-seq data in the allelic series of Hdh mice was fed into Deseq2 implemented in the R package DESeq2  [24]  in order to obtain a log-fold-change (LFC) vector for each condition (CAG-repeat length, age) and a vector indicating if the genes are up-regulated (LFC > 0 and pvalue < 0. 05), down-regulated (LFC < 0 and p-value < 0. 05) or unchanged (p-value ≥0) for each condition. The set age is k, and Q20 was used as reference for each condition at age k and Q > 20.
Reduction of data complexity via network analysis To reduce data complexity, we used WGCNA analysis. To this end, we used the R package WGCNA (https:// horvath. genetics. ucla. edu/html/CoexpressionNetwork/ Rpackages/WGCNA/). We applied standart settings as previously described  [26]  to generate signed WGCNA modules from RNA-seq (miRNA and mRNA separately) data in the allelic series of Hdh mice at 2 months, 6 months and 10 months of age, for striatum and cortex, by computing the correlation coefficient across the various CAG repeat lengths. Briefly, we constructed a matrix of pairwise correlations between all gene pairs across condidtions and samples. We removed all genes having less than two counts in all samples. We then constructed a "signed" pairwise gene co-expression similarity matrix and we raised the co-expression similarities to the power β = 6 to generate the network adjacency matrix.
Feature selection To select the miRNAs that best explain the expression of target genes in the miRNA and mRNA space defined by the paired miRNA:mRNA WGCNA modules, we used RF analysis. Random forests are collections of decision trees that are grown from a subset of the original data. This non parametric method has the advantage of dealing with non-linear effects and of being well-suited to the analysis of data in which the number of variable p is higher than the number of observation. Firstly, we removed the mRNA WGCNA nodes that show no significant deregulation across CAG-repeat lengths and age points. For each target, we then considered all miRNAs in the paired module(s) as possible explanatory variables of the target expression profile across conditions. Then, RF analysis implemented in the R package Ranger was performed by using the Altmann's approach  [27].
Comparison to proteomic data Previous studies have shown that RNA-seq may validate proteomic data whereas few proteomic data may validate gene deregulation  [2]. Nonetheless, we tested whether the deregulation of gene targets retained by MiRAMINT might be also observed at the protein level. To this end, we used the protein data as processed in the HdinHD database (https://www. hdinhd. org/). These data cover 6 CAG-repeat lengths across 3 age points, similarly to RNA-seq data. Briefly, the label-free quantification (LFQ) of the proteins was obtained as previously described  [2]. We used the log10 ratio provided in the HDinHD database. This ratio compares the LFQ of the protein for a given CAG repeat length versus the LFQ at Q20 for each age. To test for correlation between the deregulation of the mRNA and the deregulation of the protein product, we computed the Spearman's score between the log-fold-change of the gene and the log10 ratio of the protein.
Availability of data and materials The full list of WGCNA edges that define miRNA and mRNA expression either in the cortex or striatum and a 3D-visualization database of all miRNA-target pairs retained by miRAMINT analysis are available at http://www. broca. inserm. fr/MiRAMINT/index. php. The source code developed for running miR-AMINT, written using R, is available http://www. broca. inserm. fr/MiRAMINT/ index. php.
In the framework of a collaborative project between three African countries (Ivory Coast, Senegal and Uganda) and France (WaSAf project, https://humbert19. wixsite. com/wasaf), we performed a first pilot study on the feasibility of setting-up a citizen monitoring of cyanobacteria in the African context. The three main goals of our study were to evaluate (i) the potentials and constraints for the mobilization of the local populations for the visual survey of cyanobacteria in aquatic ecosystems, (ii) the consistency between the data provided by the citizen monitoring of cyanobacteria and those obtained using a classical approach based on water sampling and cell counting and (iii) the contribution of citizen monitoring to awareness in the neighboring populations regarding the cause and health consequences of cyanobacterial blooms.
For each entry in the WaSAf project (S1 Fig), users completed a short questionnaire asking for the presence of (i) a green water color and a visible mass of algae suspended in the water, (ii) traces of fuel contamination, (iii) dead fish, (iv) garbage accumulation and/or if there was nothing to report or if they wanted to add other observations. For each questionnaire, users were asked to update their geo-location and to add a picture if they wished. Users can choose to respond anonymously but they could also provide their phone number. This optional information could be used by the project team to provide feedback and to confirm the importance of the collected data (two SMS campaigns were sent). At the end of each questionnaire, users recorded their data and sent them to the website if a data connection was available.
Overall presentation of the evolution of the number and locations of collected reports during the study period During the first period of this pilot study (from October 2017 to August 2018), 443 reports have been recorded in the WaSAf project hosted by the Epicollect5 platform (see https://five. epicollect. net/project/wasaf). Among these 443 reports, phone numbers were only provided in 37 of them. During the second period of the study (from September 2018 to August 2019), 171 questionnaires were collected and phone numbers were provided in only 7 of them. During the whole study period, it appears that 92. 8% of the collected data with the smartphone application were anonymous, which makes it impossible to estimate the number of participants who have contributed to the monitoring. During the first period of the study, an average of 44 reports was collected each month in the web site of our application, while during the second period, an average of 13 reports were collected monthly (Fig  3 ).
This difference in the number of collected reports during the two periods was expected since we changed the recommendation for the sentinels from sending a picture at least once a week (regardless of whether cyanobacteria were observed) to sending pictures only if cyanobacteria or other degradation signs were observed. As shown in Fig  4A , the geographical origins of the data collected during this study were distributed all around the lagoon and not only near the three villages. Many data points were collected in the western part of the lagoon by the sentinel from the Debarcadère village (Fig  4B ). This sentinel performed visual monitoring at four points in this part of the lagoon. Finally, it also appears on the map that some of the data were not located on the shoreline of the lagoon.
Comparison of the data provided by the participatory monitoring with those from the Pasteur Institute It was difficult to compare the data collected by the Pasteur Institute of Abidjan with that collected by the citizens. Indeed, the citizen monitoring was performed on the shores of the lagoon while the monitoring of cyanobacteria by the Pasteur Institute of Abidjan was performed in six sampling points located more than 150 m away from the shores (except for S3). Moreover, water samples were collected by Pasteur Institute once a month while the smartphone reports were sent by citizens as the days went by. For these reasons, we have only compared the monthly number of reports mentioning the presence of a green water color/visible algae with the monthly variations in the total cyanobacterial biovolumes estimated at each sampling stations.
Acknowledgments We are extremely grateful to the three sentinels ( Mr. Blaise Lattho in Aghien-Te  ´legraphe,  Mr. Kouandjo Guy Rolland Adon  in Akandje, and  Mr. Kameleman Siaka Issoufou  in Debarcadère) and the inhabitants and chiefdoms of the three villages ( Aghien-Telegraphe, Akandjeand Debarcadère ) for their decisive contribution to this work. Finally, we would like also to thank the three reviewers of this paper for their constructive comments.
gambiae and An. coluzzii in the An. gambiae complex  [40]. Tag SNPs were computationally ascertained in the genomic database of natural variation for these species (Ag1000G;  [41] ), and high throughput assays to genotype candidate tags in individual mosquitoes were developed based on probe hybridization and amplicon sequencing  [42]. We found that the two molecular methods performed comparably when applied to DNA from the same set of previously cytogenetically karyotyped mosquitoes, and they rivaled traditional cytogenetic karyotyping not only in genotyping speed but also in accuracy. Here, we implement a strategy similar to that used in An. gambiae s. l. to achieve high throughput genotyping of three of the most highly polymorphic and geographically widespread inversions in An. funestus, notably those used to discriminate the Folonzo and Kiribina ecotypes (3Ra, 3Rb, 2Ra).
In the absence of a database comparable to Ag1000G for An. funestus, we performed deep Illumina sequencing of 186 previously cytogenetically karyotyped An. funestus from Burkina Faso, where Folonzo and Kiribina co-occur, and 15 unkaryotyped An. funestus from six other countries. After calling variants in the rearranged regions with respect to the recent chromosome-based An. funestus assembly  [43] , we computationally ascertained candidate tag SNPs and developed high throughput hybridization and amplicon sequencing assays to genotype the tags. We validated the candidate tags using DNA extracted from a second collection of cytogenetically karyotyped An. funestus from Burkina Faso. We achieved relatively high concordances between molecularly and cytogenetically assigned karyotypes for each of the targeted inversions in An.