dataesr/scientific-paragraphs-categorization huggingface dataset:
13  New standardized and scalable tools allowing the integration of different datasets can potentially improve the efficiency of conventional breeding. 5  Linking food science measurements such as instrumental textural profile analysis (ITPA), color analysis and functional properties of the food product to the overall liking of food samples from different varieties is needed to establish which parameters represent what consumers prefer. This is crucial to facilitate the selection of genotypes for specific, dual or multipurpose food uses. 9  It is also essential to overcome the paucity of information regarding the stability of acceptability traits of cassava-eba food across cultural and environmental settings among its diverse consumers. Earlier research  4, 6, 15, 16, 18  has established that processor and consumer preferences are mainly related to the texture and color of the food products, which informed our focus on laboratory parameters related to these.
To achieve this, tricot combines an incomplete block design and statistical models that work with ranking data (Plackett-Luce model). [18] [19] [20]  Tricot mobilizes consumers' cognitive mechanisms of perception and judgement to avoid overburdening them by limiting the evaluation to only three samples per consumer. It aims to reflect how consumers evaluate samples in their use contexts to ensure the external validity of the test results. [18] [19] [20]  According to Awoyale et al. ,  7  the information on the suitability of varieties for cassava foods contributes to improving breeding programs. Tricot offers an analytical method related to other conventional breeding and hybrid food science approaches and datasets. The digital platform ClimMob (https://climmob. net/ blog) was custom-made to manage tricot projects, from design to analysis and reporting.
This decreases data management and cleaning work and enables quick feedback to citizen science partners. The platform and associate R packages have the functionality to systematically store, analyze and combine data on a year-to-year basis in line with breeders' common practice. 21  Tricot also easily allows the integration of traditional food science tools such as just-about-right and a check-all-that-apply test. 22  Soon, ClimMob will be able to export these data to breeders' databases, such as BreedBase and CassavaBase,  5  to integrate these data seamlessly into decisionmaking by RTB breeding programs. In the present study, tricot was used to identify consumer acceptability of eba made from improved and commonly used commercial checks. Understanding the relationship between varieties and food products' overall acceptability is critical in meeting the food preference of gendered and socially diverse consumers.
4, 20  The novel approach was used to triangulate consumer-testing datasets with food science laboratory results to validate the inclusion and prioritization of preferred laboratory-measurable quality traits to inform breeding product profiles and allow for selection towards such product profiles. Specific objectives included: (i) examining the influence of environment, cultural settings and social dimensions of the consumers on the acceptability of cassava-eba; (ii) investigating the relation between laboratory measurements physical and functional properties and ITPA and consumer acceptability (what are they, and how do they differ concerning the environment cultural settings and the social dimensions of the consumers?); and (iii) evaluating consumer acceptability of new cassava genotypes compared to the typical farmer and processor-preferred clones.
ClimMob was used to design the project and generate the sequentially balanced incomplete block design, where each consumer is offered an incomplete block of three eba samples to test (https://climmob. net/blog). Open Data Kit (ODK) was configured and installed on tablets or Android phones for trained enumerators to collect information, which is automatically stored and uploaded in ClimMob. Consumers were asked qualitative openended questions. 12, 16  Laboratory assessment of roots and gari/eba food products The roots were processed in the field into gari by the champion processors following the traditional methods. Gari samples were taken to the laboratory and processed into eba using the standard operating procedures (SOP) for eba preparation in the laboratory. 25  Per locations, around 2 kg of gari, per variety from each of the three champion processors (a total of 6 kg per variety), was taken to the laboratory for the preparation of eba and analyzed for textural properties using SOP developed in the RTB Foods project.
Statistical analysis Consumer testing and laboratory results were analyzed in ClimMob (https://climmob. net/blog). 21  Data were analyzed using the Plackett-Luce model. 26, 27  The model applies Luce's axiom that estimates the probability that an item (or a given variety) has of outperforming all the other items/varieties in a set. The Plackett-Luce is implemented in R by the package Plackett-Luce. To account for the effect of covariates, the model-based recursive partitioning  28  on variety preference was applied, comprising a decision-tree approach. To account for the effect of biophysical features of genotypes (texture profile and color spectrum) on eba preference, the alternating directions method of multipliers (ADMM) algorithm  29  was applied, which estimates the linear predictor for log-worth by genotype features.
This can also explain the preference for low values on adhesiveness in Nigeria (Table  4 ) because a less integrated and merged eba (with eba particles still visible) will tend to be less cohesive and thus stick to the fingers more. Stickiness to the fingers was determined as a crucial trait dislike by consumers. 6, 15  At the same time, a preference exists for lower gumminess, maybe because people still prefer a relatively soft product that is easy to swallow at the same time as maintaining shape (cohesiveness). However, further research is recommended to establish the exact underlying causes of the results observed. Alamu et al. 33  analyzed the consumer testing data for Nigeria separately and found a similar divide in how laboratory parameters were related to the overall liking by consumers equally, pointing mainly to differences in the degree of fermentation.
Furthermore, solubility, as well as redness and brightness of the color, are positively related to overall liking, whereas yellowness is negatively related for ethnic groups practicing a higher degree of fermentation. For the category of ethnic groups, who practice relatively less fermentation, only the redness was positively correlated to overall liking. Merging the datasets of Osun and Benue with the Littoral zone in Cameroon has provided more data points and more contrasting varieties (Fig. 3 ), especially with regards to texture. This results in texture parameters being more related to the overall liking among those who practice a higher degree of fermentation. The Cameroon case provided data points related to an even smaller degree of fermentation than practiced in Benue and Osun. In Cameroon, representing the least fermentation, it is clear that consumers do not like eba, which is gummy or has a high springiness value (Table  4 ).
ACKNOWLEDGMENTS We acknowledge all of the support, suggestions and comments from contributors who could not be listed as co-authors of this paper, especially Esther Biaton Njeufa, Ngaoundere, Isabelle Linda Nguiadem Chomdom, Noel Tchuente Takam, Germaine Alice Wakem, Durodola Owoade, Adedayo Ogunade, Gospel Edughaen, Peter Iluebbey, Ismail Rabbi, Peter Kulakow and Elizabeth Parkes. We thank Hernán Ceballos for his editorial comments and Clair Hershey for the final proofreading of the submitted manuscripts. Their contributions largely improved the manuscript. We greatly thank all the study participants who participated as consumers in this study. Funding statement: This work was funded by the RTBfoods project https://rtbfoods. cirad. fr through a grant INV-008567: Breeding RTB Products for End User Preferences (RTBfoods), to the French Agricultural Research.
Code and data availability. The combined time series used in this study -including daily SAOZ means and noon measurements of Brewer DS, Brewer GI, and GUV -are provided at https://doi. org/10. 5281/zenodo. 6760259 (Bernet et  al. , 2022). The full Brewer GI dataset can be found at https://doi. org/10. 5281/zenodo. 6760244  (Svendby et al. , 2022). The full time series of Brewer DS daily means are available at the World Ozone and Ultraviolet Radiation Data Centre (https://doi. org/10. 14287/10000001,  NILU et al. , 2021). SAOZ data are available at http://www. ndacc. org/ (last access: 4 July 2022; Network for the Detection of Atmospheric Composition Change, 2022). GUV data (v2. 0) are available at https://doi. org/10. 5281/zenodo. 4773478  (Svendby, 2021)  and are used here with some updates (extended to 2020, and calibrated in 2019 and 2020).
The NRAS mutated subtype, which accounts for a quarter of all melanoma cases, lacks such a targeted approach but ongoing clinical trials are assessing the effects of combined MEK and CDK4/ 6 inhibitors (NCT01781572; NCT02065063). However, tolerance or resistance to such targeted treatments inevitably develops. MicroRNAs have been shown to be involved in resistance to treatment. miRNAs are 20-22 nucleotides short RNAs, which in complex with the Argonaute (AGO) protein, act as posttranscriptional regulators of gene expression  [12]. Canonically, miRNAs destabilise mRNAs by binding to a complementary sequence within the 3' UTR, this resulting in downregulation of the mRNA and the encoded protein  [13]. Experimental methods based on immune-precipitation and sequencing have been developed to profile miRNA-mRNA interactions.
This resulted in the creation of nine conditionspecific iCell networks, by multiplying the G1 matrix resulting from the data fusion and its transpose (which is equivalent to computing the dot product between gene pairs) and considering as interactions the 1% highest values in each row and column (Methods and  [15] ). As illustrated in Supplementary Fig. 16 , resulting conditionspecific iCell networks were investigated by first comparing the global topology of all resulting iCell networks. Then, these topological changes were further characterised by identifying the genes that change or conserve their local topology between each pair of conditions. We related the identified most rewired genes and the least rewired ones to MSigDB hallmarks by performing an Over-Representation Analysis (ORA) (see Methods for details).
Finally, interactions common to the two cell lines IPC298 and SKMEL30 were used. D RNA-seq data for SKMEL30 cell line as log fold changes overlayed onto the resistant miRNA network. Selected interactions for further validation by qPCR are represented with thick lines and the corresponding mRNAs and miRNAs with bolded borders. E Relative expression of LGALS3BP, TXNIP and TYRP1 mRNAs assessed by qPCR across three NRAS and two BRAF mutant melanoma cell lines. F Relative expression of miR-211-5p across the same cell lines as determined by qPCR. G Expression of miR-211-5p in the small RNA-seq. activation of these two pathways can be concomitant and their relative importance may be cell-type specific. This phenomenon, termed "viral mimicry", has been extended to other types of retroelements and it has been proposed to be integral part of carcinogenesis in a rheostat model  [37].
Total RNAseq Transcriptome sequencing was performed using total RNA. Libraries were prepared using "TruSeq Stranded Total RNA Library Prep Gold" Kit (Illumina, USA) and sequenced as 2x75bp on a NextSeq 550 (Illumina, USA). The sequencing was carried out at Luxgen platform, LNS. Data were aligned to the reference genome (hg38) using STAR (v 2. 7. 9a) and differential expression analysis was performed using R (v 4. 2. 2) and DESeq2 (v 1. 34. 0). This analysis was automated in a snakemake workflow (v 0. 3. 2)  [80]  (https:// gitlab. lcsb. uni. lu/aurelien. ginolhac/snakemake-rna-seq) using docker file (v0. 6) (https://hub. docker. com/r/ginolhac/snake-rna-seq/tags). Mixing all cell lines into one analysis masked the differences of each time course. Therefore, a subset per cell line was created and the differential gene expression was performed independently.
Gene-list based pathway enrichment Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) were performed using R (v 4. 2. 2) and the package "clusterProfiler" (v 4. 0. 5). The Hallmark Gene Set Collection from MSigDB were used as reference. Differentially expressed genes used for GSEA were ranked according to the π value, which takes into consideration both the statistical significance and the biological relevance through the product of the p-value and the fold change  [81]. The results were represented using the package "pheatmap" (v 1. 0. 12) using the default "Ward. D2" clustering algorithm. ORA results were displayed using the "ggplot2" (v 3. 3. 6) package.
Fig. 4 ). Image processing was achieved using the instrument manufacturer's Bionavigator software before applying COMBAT normalization and performing Upstream Kinases Analysis (UKA) using a multiple hypothesis testing approach. This identified responsible kinases for the observed phosphorylation status with a median final score for the likelihood of the kinase being responsible for observed phosphorylation to which a cutoff of 1. 2 was applied and a median kinase statistic indicating the degree of and direction of kinase activity change between compared conditions. The results were compiled to construct representative kinome trees with an adaptation of the CORAL tool  [82]  in R (v 4. 2. 0).
Network-based pathway enrichment To identify biological pathways enriched in a protein list, we applied a network-based protein set enrichment analysis approach using Enrich-Net  [83]. The inputs are a list of the PamGene protein identifiers and the database of interest from which reference protein sets will be extracted. The databases include (KEGG, BioCarta, WikiPathways, Reactome, PID, and GO. Using a genome-scale molecular interaction network, we map the target and reference datasets and then perform a network analysis, which involves two steps: i) the use of a random walk with restart (RWR) algorithm to score the distance between the mapped target protein set and reference datasets in the network. ii) the comparison of the calculated scores against a background model. Extensive information on how the RWR method for node relevance scoring was developed is found in  [83].
A network similarity score (XD-score) measures how close the protein set of the PamGene data is to the pathways in the databases. The XD score is compared to a classical enrichment analysis score (pvalues) which measure the significance of the overlap between pathways and the protein set. A similarity ranking table of the reference datasets is generated, including their network-based association scores (measured using the XD-score). The table ranks the pathways from a chosen database to the protein set of the PamGene data. Using an interactive graph-based visualization, we generate interoperable interaction networks for each pathway. This network (XML-based) highlights the proteins that overlap in pathways, illustrating their interactions.
Small RNAseq Small RNAseq was performed on the same samples used for total RNAseq. Libraries were prepared using "QIASeq miRNA Library kit" (Qiagen, Germany) and sequenced as 1x75bp on a NextSeq 550 (Illumina, USA). The sequencing was carried out at Luxgen platform, LNS. Data were aligned to the miRBase (v 2. 2. 2. 1) and piRBase (regulatoryrna. org, v 2. 0) using bowtie2 (v 2. 3. 4. 1) and differential expression analysis was performed using R (v 4. 2. 2) and DESeq2 (v 1. 34. 0). This analysis was performed using a snakemake workflow adapted from a previous workflow designed by https://github. com/ jounikuj/ for this library preparation. Briefly, the snakemake workflow applies 4 passes of trimming (including UMIs) and map the obtained reads on miRBase using bowtie2 with the parameter -very-sensitivelocal.
Next, the corresponding un-mapped reads are aligned to piRBase and the successfully mapped read coming from these 2 passes of alignment are merged in a unique BAM file per sample. Finally reads are counted, after UMI collapsing (UMI_tools v. 1. 0. 0) using a combination of samtools (v 1. 9) and bash commands. qCLASH qCLASH sample preparations were performed exactly as previously described by Kozar et al. [16]  Briefly, 50 million cells were UV cross-linked at a wavelength of 250nm and 600J/cm2. AGO protein immunoprecipitation was performed using Protein G Dynabeads (Invitrogen), and 10μl 2A8 anti-Ago antibody (Millipore) per sample. qCLASH libraries were sequenced on a NextSeq500 instrument with a read length of 85bp. The raw sequences were pre-processed and analyzed as previously described  [16]  using the bioinformatics pipeline hyb  [84] , and custom scripts available at GitHub (https://github.
com/RenneLab/qCLASH-Analysis). miRNA network construction and representation qCLASH data were analysed using R (v 4. 2. 2) and the "tidyverse" package. Interaction lists were converted to "igraph" objects with the corresponding package (v 1. 3. 4) and further exported to cytoscape using "RCy3" (v 2. 12. 4). Final representations were made using Cytoscape (v 3. 8. 2) and the plugin "Legend Creator" (v 1. 1. 6).
Amplification curves were analyzed using CFX software (BioRad, USA) and expression was normalised using three housekeeping genes (RNU1A, RNU5A, SCARNA17 for miRNA and HPRT, PPIA, TBP for mRNA). Statistical significance was calculated for each condition considering biological replicates (n = 3) using multiple welch's t-test and an FDR of 0. 01.
Single cell RNA-seq analysis Single cell RNA-seq data from  [Randic et al. , in revision]  were reprocessed using Seurat (v 4. 2. 0) and SeuratObject (v 4. 1. 2). Reads were aligned to human genome hg38 using STAR aligner. Transcripts were filtered to be detected in at least 3 cells and to totalise at least 100 counts across cells. Cells were filtered based on mitochondrial transcripts (<=25%), ribosomal transcripts (<= 1%), and a cell complexity (log10GenesPerUMI >=80%). Thresholds for Unique Molecular Identifiers (UMI), genes detected were different between cells lines (for MELJUSO and SKMEL30: 150 and 100, for IPC298: 350 and 150). Finally, mitochondrial, ribosomal and hemoglobin genes were removed, and genes were filtered to have at least 10 counts across the filtered cells. iCell data-fusion framework iCell methodology  [15]  was applied separately to each of the three cell lines (MELJUSO, SKMEL30 and IPC298) at day 0, day 4 and day 33, to obtain integrated networks that encompass the condition-specific (i.
e. , defined by a cell line and day) PPI, COEX, and single-cell RNA-seq data from our lab  [Randic et al. , unpublished]. We used the iCell methodology because it is an intermediate data integration method that uses Non-negative Matrix Tri-factorization (NMTF) to simultaneously decompose multiple relational matrices through the inference of a single joint model  [15, 85]. Therefore, it does not suffer from information loss of early integration approaches that combine all datasets into a single dataset before integration, or late integration methods that first build models for each dataset and then combine them into an integrated model. Additionally, NMTF-based methods are co-clustering methods that encode the high-dimensional input data into three low-dimensional matrix factors  [86]. The clustering information is contained in the so-called cluster indicator matrices that are easily interpretable (unlike the resulting matrices of other artificial intelligence algorithms), making them ideal for mining biological data.
For each condition (treated cell lines and time points), we constructed condition-specific Protein-Protein Interaction (PPI) and gene coexpression (COEXP) networks as follows. We collected the experimentally validated protein-protein interactions of Homo sapiens from BioGRID (v 4. 3. 195)  [32] , from which we excluded genes (nodes in the network) that are not expressed in our condition-specific bulk RNA-seq data (zFPKM < -3,  [87] ). To generate the condition-specific COEXP networks, we applied Spearman's correlation between the gene expression profiles from the condition-specific bulk RNA-seq data, using a 0. 9 correlation threshold with a p-value ≤ 0. 01. These two conditionspecific networks were further filtered to exclude genes (nodes in the networks) that are not expressed in any single cell according to the corresponding condition-specific single cell RNA-seq data.
ACKNOWLEDGEMENTS The authors want to thank  L. Sinkonnen  for his suggestions regarding the qCLASH method and its extended applications,  J. P. Wroblewska  for her feedback and her insights regarding the biology of amelanotic melanoma,  A. Ginolhac  for his help in the analysis of our RNA-seq data and his tips regarding R language. Several in silico experiments presented in this paper were carried out using the HPC facilities of the  University of Luxembourg , Varrette et al. 2022, https://hpc. uni. lu.
40-minute precipitation data are obtained from the 10-minute time series available for the stations. The 40-minute time series are aggregated to 80-minute time series to calculate the observed breakdown coefficients W using non-overlapping adjacent pairs of precipitation amounts for this cascade level. The aggregation procedure is repeated until the time series reaches the temporal resolution of 1280 minutes. These different series are used for the estimation of the breakdown coefficients relative to each temporal scale and are also used as a reference for the evaluation of the disaggregation models (see Sect. 2. 6). Note that the 1280-minute aggregated temporal scale does not correspond to the daily resolution of 1440 minutes. Following  Molnar and Burlando (2005) , the first and last 80-minute records of each day were thus discarded from the initial observed times series before aggregations.
https://doi. org/10. 5194/egusphere-2023-544 Preprint. Discussion started: 5 April 2023 c Author(s) 2023. CC BY 4. 0 License. contexts worldwide. Additional applications could help to better characterize its performance, its limitations, and its potential for regionalisation in such other contexts. Code and data availability. The precipitation data used in this study are maintained by Swiss Federal Office of Meteorology and Climatology, MeteoSwiss (MeteoSwiss, 2021). It is available upon request at https://gate. meteoswiss. ch/idaweb/more. do (last accessed 10 December 505 2022). The open-source code with routines allowing fitting and disaggregation of precipitation data based on the four MRC models presented in this study is available as an R package. It can be installed via GitHub: https://github.
De novo genome assembly Bases in raw ONT reads from I. elegans were called using Guppy v 4. 0. 11 (Ao and Io data) and Guppy v 5. 0. 11 (oo data) (https://nanoporetech. com/). Low quality reads (qscore < 7 for v 4. 0. 11 and < 10 for v 5. 0. 11) were subsequently discarded. High quality reads were assembled using the Shasta long-read assembler v 0. 7. 0 81. Each assembly was conducted under four different configuration schemes, which modified the June 2020 Nanopore configuration file (https://github. com/chanzuckerberg/shasta/blob/master/conf/Nanopore-Jun2020. conf) in alternative ways (Table  S3 ). Assembly metrics were compared among Shasta configurations for each morph using AsmQC 82 (https://sourceforge. net/projects/amos/) and the stats. sh script in the BBTools suite (https://sourceforge.