dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis The impact of aphids on plant growth was assessed by calculating the slope of the linear regression fitted on the total leaf area accumulated through time. The distribution of growth slopes of infested and non-infested plants was then compared running a simple ANOVA (with plant treatment as factor). The variables related to gas exchange (Amax, Tr, gsmax, and Ci), leaf dry mass, leaf surface, LMA, N and C contents, and N/C ratio were tested using an analysis of covariance (ANCOVA) with these response variables as dependent variable to compare between infested and non-infested plants (treatment as factor) with both leaf age and growth rate of individual plants as covariates. Normality and homogeneity of the variances were checked using the Lilliefors and the Levene's test, respectively (transformation of the data was not necessary).
e. the number of eggs per clutch). The second one, the method we are proposing here, is based on the embryonic stage information. Firstly, the distribution of the clutch size was plotted against the week of the year, i. e. against the week of collecting, according to the embryonic stage ("A" or "B", see above). Then, the number of clutches per week was plotted for each embryonic stage and a corresponding smoothing curve, using the loess method, was drawn (package "ggplot2": Wickham 2016). These last plots were used to split potential clutch phases without invoking a separation based on the clutch size. All analyses were performed with the software R v. 3. 6. 2 (R Core Team 2019).
ACKNOWLEDGMENTS We thank the  Arctic Research Centre at Aarhus University  and  ECOBIO  from the  University of Rennes 1  for supporting this project, and the  Natural History Museum Aarhus  for access to laboratory facilities and for storing samples. NV acknowledges funding from  Erasmus+ program  and financial support from ECOBIO. We also thank our reviewers  Yuri Marusik  and  Matthias Foellmer  for helpful feedback on this manuscript.
, 2017). To further improve existing prediction models, the integration of multiple approaches and data sets to obtain more accurate functional miRNA-mRNA interactions has been proposed  (Oliveira et al. , 2017). These integrative approaches have successfully improved the precision of predictions, but often at the cost of discarding a large number of functional interactions  (Davis et al. , 2017; Id et al. , 2018; Liu and Wang, 2019). We hypothesize that the inability of current miRNA target prediction models to provide a full and reliable view of functionally relevant miRNA interactions could be partially attributed to the lack of incorporating important context-specific factors. In this study, we performed an integrative analysis combining OMICs data from a unique series of miRNA-deficient mouse Embryonic Stem Cell (mESC) lines generated in the same genetic background  (Drosha, Dgcr8, Dicer, Ago2&1 knockouts)  with other publicly available datasets (prediction models, AGO-bound miRNAs) to determine global and accurate direct functional miRNA interactions in mESCs.
Combination of DEG from several RNAi_KO mESC lines is a key feature for the identification of functional miRNA targets Mouse ESCs have a specific miRNA expression pattern  (Ciaudo et al. , 2009; Houbaviy et al. , 2003) , with over 50% of all expressed miRNAs originating from four genomic clusters  (Calabrese et al. , 2007). Indeed, the miR-290-295 cluster is the most strongly expressed cluster in our dataset and almost half of our predicted miRNA target genes are predicted to be targeted by at least one member of this cluster (324 out of 707 genes, Figure  3B ). MiR-290-295 has previously been shown to be involved in pluripotency maintenance and self-renewal of mESCs through several molecular processes  (Yuan et al. , 2017). We therefore complemented our predictions with experimental data by deleting the miR-290-295 cluster in the same mESC background as the RNAi_KO mESCs, using a similar paired CRISPR/Cas9 approach (Figure  S3B )  (Wettstein et al.
, 2016). We generated two independent cell lines and confirmed the integrity of the deletion at DNA and RNA levels (Figures  S3C, D ). We next profiled the transcriptome of the miR-290-295_KO mESCs and performed a DE analysis (Table  S6 ). Surprisingly, miR-290-295_KO mESCs exhibited a strongly misregulated transcriptome with 1265 up-and 828 downregulated genes, similar to the Dgcr8_KO mESC lines (Figures  1B, 3C ). We then specifically looked at the DE of predicted miR-290-295 target genes in the miR-290-295_KO mESC lines. As expected, predictions from the integrative analysis (324 predicted miR-290-295 targets) showed a strong enrichment for upregulation, with 70% of predicted genes (226 of 324) exhibiting a positive log2FC (pink curve in Figure  3D , Table  S6 ). We used the DE analysis to estimate the predictive contribution of each individual dataset used in our integrative analysis.
Depletion of the miR-290-295 cluster combined with predicted functional interactions identifies novel key transcription factors regulated by miRNAs Out of the 324 predicted miR-290-295 targets, 92 showed a statistically significant upregulation in the miR-290-295_KO mutant (Table  S6 , Figure  S4A ). Interestingly, 13 of these were annotated as transcription factors (TFs) by the mTFkb database  (Sun et al. , 2017)  of which three of them, Tfap4, Dazap2 and Mycn, had previously been implicated in stem cell functions including pluripotency  (Chappell and Dalton, 2013; Papathanasiou et al. , 2021; Sugawara et al. , 2020)  (Figure  S4A ). TFs are proteins that primarily bind to promoter regions to modulate gene transcription  (Spitz and Furlong, 2012) , therefore potentially contributing to the observed DGE that cannot be explained by the depletion of miRNAs alone.
These data strongly suggest that Tfap4 is regulated by these two members of the miR-290-295 cluster. We further hypothesized that a substantial number of misregulated genes observed in the miR-290-295_KO cell line (Figure  3C ) may be in part a result of increased TF levels. To understand the transcriptional contribution of TFAP4 in mESCs, we attempted to rescue (i. e. , downregulate) its expression level in miR-290-295_KO using a pool of small inhibitory RNAs (siPOOL) targeting Tfap4 mRNA (Figure  S4C ). We monitored TFAP4 levels 36h after transfection by WB and observed that TFAP4 was indeed expressed at near WT levels in the miR-290-295_KO mESCs (Figure  4C ). We then sequenced the transcriptome of the siPOOL-transfected cells and assessed the DGE (Table  S7 ), which also confirmed near-WT Tfap4 RNA levels (Figure  4D ).
Limitations of the study A substantial part of our integrative analysis was based on transcriptomics data, and while we showed that predicted targets are also affected at the protein level, we cannot exclude that some miRNA targets are only affected at the RNA level and therefore not detected by our approach. Nevertheless, our datasets demonstrate a strong correlation between Ribo-seq and RNA-seq in mESCs as previously described in a different cellular context  (Guo et al. , 2010). Another aspect to take into account is that not all miRNAs require the full set of RNAi genes for their biogenesis  (Bodak et al. , 2017). Our approach is partially robust against this phenomenon, as we considered genes to be potential miRNA targets even if they are upregulated in only a subset of the RNAi_KO mutants (i.
Proteome analysis by SWATH-MS The MS data acquisition (SWATH-MS and DDA mode) was performed on TripleTOF 5600 mass spectrometer equipped with a NanoSpray III source and operated by Analyst TF 1. 5. 1 software (AB Sciex). The samples were injected onto a C18 nanocolumn packed in-house directly in a fused silica PicoTip emitter (New Objective, Woburn, MA, USA) with 3-μm 200 Å Magic C18 AQ resin (Michrom BioResources, Auburn, CA, USA) and reverse phase peptide separation was performed on a NanoLC-Ultra 2D Plus system (Eksigent-AB Sciex, Dublin, CA, USA). The total acquired data were analyzed using a pipeline configured on the euler-Portal platform at ETH Zurich.
SWATH assay library generation The samples were recorded in data-dependent acquisition (DDA) mode to generate a mouse SWATH assay library, which is used for targeted data extraction from SWATH-MS recorded data. 15 mESC samples recorded in DDA mode were combined with 65 available DDA files originating from fractionated mouse liver peptide digest to create a common mouse assay library. The nanoLC gradient used for all acquired DDA data was linear from 2 to 35% of buffer B (i. e. , 0. 1% formic acid in ACN) over 120 min at a 300 nl/min flow rate. Electrospray ionization was performed in positive polarity at 2. 6 kV, and assisted pneumatically by nitrogen (20 psi). Mass spectra (MS) and tandem mass spectra (MS/MS) were recorded in "high-sensitivity" mode over a mass/charge (m/z) range of 50 to 2000 with a resolving power of 30,000 (full width at half maximum  [FWHM] ).
DDA selection of the precursor ions in a survey scan of 250 ms was as follows: the 20 most intense ions (threshold of 50 counts) corresponding to 20 MS/MS-dependent acquisitions of 50 ms each, charge state from 2 to 5, isotope exclusion of 4u, and precursor dynamic exclusion of 8 s leading to a maximum total MS duty cycle of 1. 15 s. External mass calibration was performed by injecting a 100-fmol solution of β-galactosidase tryptic. Raw data files (. wiff) were centroided, and converted into mzXML as a final format using openMS. The converted data files were searched in parallel using the search engines X! TANDEM Jackhammer TPP (2013. 06. 15. 1 -LabKey, Insilicos, ISB) and Comet (version "2016. 01 rev. 3") against the ex_sp 10090. fasta database (reviewed canonical Swiss-Prot mouse proteome database, released 2017.
SWATH-MS measurement and data analysis Reverse phase peptide separation during SWATH-MS acquisition was performed with linear nanoLC gradient from 2 to 35% of buffer B (0. 1% formic acid in ACN) over 60 min at a 300 nl/min flow rate. Quadrupole settings in SWATH acquisition method were optimized for the selection of 64 variable wide precursor ion selection windows as described earlier  (Röst et al. , 2014 ). An accumulation time of 50 ms was used for 64 fragment-ion scans operating in high-sensitivity mode. At the beginning of each SWATH-MS cycle, a TOF MS scan (precursor scan) was also acquired for 250 ms at high resolution mode, resulting in a total cycle time of 3. 45 s. The swaths overlapped by 1 m/z, thus covering a range of 50-2000 m/z. The collision energy for each window was determined according to the calculation for a charge 2+ ion centered upon the window with a spread of 15.
Ribosome Profiling Data Analysis Initial analysis, including mapping and quantification of mRNA and footprint abundance, were performed as previously described  (Castelo-Szekely et al. , 2017). Briefly, purity-filtered reads were adapters and quality trimmed with Cutadapt v1. 8  (Martin, 2011). Only reads with the expected read length (16 to 35 nt for the ribosome footprint and 35 to 60 nt for total RNA) were kept for further analysis. Reads were filtered out if they mapped to Mus musculus ribosomal RNA (rRNA) and transfer RNA (tRNA) databases (ENSEMBL v91,  (Cunningham et al. , 2019) ) using bowtie2 v2. 3. 4. 1  (Langmead and Salzberg, 2012). The filtered reads were aligned against Mus musculus transcripts database (ENSEMBL v91) using bowtie2 v2. 3. 4. 1. Finally, remaining reads were mapped against D.
melanogaster transcript database (ENSEMBL v78). Reads mapping to transcripts belonging to multiple gene loci were filtered out. Reads were then summarized at a gene level using an in-house script and mouse samples were then normalized by using corresponding fly spike-in read counts. Differential ribosome occupancy was defined by DESeq2 with an absolute fold change > 0. 5 and FDR (adjusted p-value) < 0. 05. The spike-in normalized counts and differential expression data are available as excel files (Table  S5 ).
sRNA-seq analysis Reads were trimmed using Cutadapt 1. 13  (Martin, 2011)  with adapter TGGAATTCTCGGGTGCCAAGG and arguments "-m 14 -M 40" and aligned to the mouse genome (GRCm38 primary assembly, annotation: GENCODE vM20) using STAR 2. 4. 2a (Dobin et al. , 2013)  with arguments "--outFilterMismtachNoverLmax 0. 05" to allow for 0 mismatches for reads < 20bp. Next, reads were counted using subread-featureCounts 1. 5. 0  (Liao et al. , 2014)  with arguments "-f -O -s 1 --minOverlap 17" for 3 different annotations (miRbase v21  (Griffiths-Jones et al. , 2006) , GENCODE vM20 and GtRNAdb2 mm10  (Chan and Lowe, 2016)  to collect gene counts for the following groups of small RNAs: miRNAs, small nuclear RNAs, small nucleolar RNAs, mitochondrial tRNAs and tRNAs. To take the global reduction of small RNAs in RNAi_KO mESC lines into account, miRNA expression was normalized by small nuclear RNA, small nucleolar RNA, mitochondrial tRNA and tRNA expression using estimateSizeFactors from DESeq2 1.
RNA-seq analysis Raw and normalized read counts for both genes and transcripts were computed by the RNA-seq pipeline from snakePipes 2. 3. 1  (Bhardwaj et al. , 2019)  using the ENSEMBL GRCm38. 98 primary assembly and annotation. The following command line arguments were passed "--trim --trimmer trimgalore --trimmerOptions '--illumina --paired' --mode 'alignment,alignment-free,deepTools_qc -fastqc". The RNA-seq pipeline was further run for each RNAi_KO mESC line with an according sample sheet and the "--sampleSheet" option to perform differential gene expression (DGE) analysis. Briefly, the pipeline employs TrimGalore/Cutadapt  (Martin, 2011) , STAR  (Dobin et al. , 2013) , featureCounts  (Liao et al. , 2014)  and DESeq2  (Love et al. , 2014)  to produce read counts and DGE data on a pergene basis.
AGO2 binding data analysis AGO2 binding peaks were downloaded from GEO (GSE139345) as provided by  (Li et al. , 2020)  and miRNA seed matches (7merA1, 7merm8 and 8mer; 6mers were discarded) in peaks were identified for all mouse miRNAs (miRBase v21  (Griffiths-Jones et al. , 2006) ). Seed matches were then mapped to gene regions to further associate them with a gene ID (if applicable) and the region type (5'UTR, CDS or 3'UTR).
KO mESC lines AGO2 binding data and TargetScan scores were preprocessed as explained above and reduced such that there was only a single entry for each (gene, region_type, miRNA, seed_match_type)-tuple. In the rare cases of duplicates, the associated scores (peak size for AGO2 binding data and context++ score for TargetScan interactions) were computed as the exponentially decaying weighted sum (e. g. 1 ‫כ‬ max_value ଵ ଶ ‫כ‬ second_value ‫כ‬ ଵ ସ ‫כ‬ third_value). Next, prepared TargetScan scores were joined on the set of unique keys (gene, region_type, miRNA, seed_match_type) such that data entries without a match in one of the datasets were kept and missing data fields were set to 0 (as in an outer-join operation). The result was stored as integrated, unfiltered set of interactions.
Acknowledgments We would like to thank the members of the  Ciaudo lab  and  Dr. Tobias Beyer  for fruitful discussions and the critical reading of this manuscript. We are grateful to  Bulak Arpat  for help at the beginning of computational analyses of the Ribo-seq datasets. This work was supported by the  Swiss National Science Foundation  (grants  31003A_173120  and  310030_196861 ) to C. C. C. C, H. W and D. G were supported by the  NCCR RNA and Disease. We also want to thank the  Functional Genomics Center Zurich (FGCZ)  for their support with the preparation of RNA-seq libraries and sequencing.
Studies looking at nutrient enrichment or deposition were not included. Studies had to report observed or applied pollutant concentrations at the studied sites (e. g. concentrations in the soil, pesticide application rates) either in the paper itself, or in a previously-published study. Although our search strategy was not limited to papers published after 1990, the final included papers were mostly published after 1995. This result may be due to the database (Web of Science) and strict inclusion criteria that we used to compile the list of studies. Indeed, older papers tended to lack a DOI, their full texts were more difficult to retrieve, and they were often not meeting our criteria regarding contaminant quantifications as well as means and standard deviations for soil fauna data. It is unlikely that our conclusions regarding knowledge gaps and future research directions would change by adding older publications.
Mapping the gaps To identify the main knowledge gaps in the research field, we collected meta-data of the 274 relevant papers by screening the full texts for the following information: • Country where the study was conducted, in order to create a map of pollution research and identify areas less well covered. • Soil fauna groups assessed using the global soil biodiversity atlas classification  (Orgiazzi et al. 2016) , with the aim to identify the most and least studied groups. • Main pollutant types (using the classification by  Rodríguez-Eugenio in FAO 2018) , in order to identify the most and least studied chemical stressors. • Human activities causing the pollution (FAO 2018): industrial, agricultural/livestock, mining/smelting, waste/ sewage, natural/geogenic origin (for metals), or other sources (such as accidental spills, or experimental addition of a pollutant to simulate a disturbance or to suppress soil fauna in order to investigate their functional role).
Multiple-driver studies included studies with full factorial designs (where each driver was studied separately and in all possible combinations). Multiple-driver studies also included study designs where the different drivers were studied separately but not in combination, as well as designs addressing only the combined effect of different drivers (separate effects of different drivers cannot be disentangled in this case). We reviewed those papers and counted how many incorporated full-factorial designs enabling investigations of the interactive effects of multiple drivers. The R software was used to create the figures using the R packages 'ggplot2  ', 'ggmap', 'viridis', 'alluvial' and 'patchwork' (Bojanowski and Edwards 2016 , Garnier et al. 2018 , <www. r-project. org>, Kahle et al. 2019 , Wickham et al.
This form of disease typically becomes apparent in adolescence individuals. The kidney problems and corneal crystals are the most developed features of this disease form. The non-nephropathic or ocular cystinosis is caracterized by corneal cystine accumulation leading to photophobia, corneal erosions, and keratopathies  [3]  but usually without the development of kidney malfunction without the other signs and symptoms of cystinosis. The three forms of cystinosis are caused by mutations in CTNS, which encodes lysosmal membran cystinosin. CTNS gene, (OMIM 606272; GenBank NM_004937. 2) is located on the short arm of chromosome 17 (13p) and contains 12 exons that are distributed across ~23 kb of genomic DNA. Cystinosin codes for a 367 amino-acid peptide, predicted to contain seven transmembrane domains (TM) topology and a tyrosine-based GYDQL lysosomal motif.
Molecular modeling The computer-generated three-dimensional structure model of the human cystinosin was constructed by the protein homology modeling server SWISS-MODEL using the protein sequence retrieved from UniProt (UniProtKB id: O60931). The constructed tertiary structure was analyzed by DeepView Swiss-PdbViewer 4. 1 and POV-Ray 3. 6 software  [9]. The transmembrane domains and the inter-transmembrane loops were identi ed and the previously identi ed mutations were localized in these domains. Further Crystallographic analysis and molecular dynamics simulation studies were performed to predict the effect of the novel missense mutations on protein functional and stability.
We have numerically solved the eigenvalue equation derived from linear Cauchy elasticity  23  for the displacement vector field ũk ;i ðrÞ with band index i at wave vector k and for the angular frequency ω i ð kÞ E 2 1 þ ν ð Þ1 À 2ν ð Þ ∇ ∇ Á ũk ;i ðrÞ þ E 2 1 þ ν ð Þ ∇2 ũk ;i ðrÞ ¼ Àρω 2 i ð kÞũ k;i ðrÞ ð7Þ by using the commercial software Comsol Multiphysics, its MUMPS solver, Floquet-Bloch periodic boundary conditions corresponding to the threedimensional geometry shown in Fig. 3  for all three spatial directions, and tractionfree boundary conditions for all interfaces to voids (air or vacuum). E is the Young's modulus, ν the Poisson's ratio, and ρ the mass density of the constituent material. The geometry shown in Fig. 3  has been meshed by about 100 thousand tetrahedra to achieve convergence of the results.
The energy flux vector averaged over one temporal oscillation period has been evaluated by the formula Ĩi ð kÞ ¼ 1 2 Re À Eν 1 þ ν ð Þ1 À 2ν ð Þ ∇ Á ũk ;i r ð Þ dũ * k;i r ð Þ dt ( À E 2 1 þ ν ð Þ ∇ũ k;i r ð Þ Á dũ * k;i r ð Þ dt þ ũk ;i r ð Þ ∇ Á dũ * k;i r ð Þ dt !) ; ð8Þ where * denotes the complex conjugate. In Fig. 4b , the z-component of this vector, I z , is plotted. This component has been obtained directly from the Solid Mechanics Module of Comsol Multiphysics. Metamaterials for airborne acoustical waves. In the calculations shown in Fig. 6b , we have solved the scalar wave equation  22 with the speed of sound in air v air ¼ 340 m=s, by using the commercial software Comsol Multiphysics. The considered geometry is the complement of the geometry illustrated and defined in Fig. 3. We assume Bloch periodic boundary conditions along all three spatial directions and the walls of all channels as rigid immovable boundaries via Neumann boundary conditions.
Acknowledgements We thank  Tobias Frenzel  (KIT) and  Jörg Schmalian  ( KIT ) for stimulating discussions. Y. C. acknowledges support by the  Alexander von Humboldt Foundation  and by the  National Natural Science Foundation of China  (contract  11802017 ). This research has additionally been funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Germany's Excellence Strategy via the Excellence Cluster "3D Matter Made  to  Order"  ( EXC-2082/1-390761711 ), which has also been supported by the  Carl-Zeiss Foundation  through the " Carl-Zeiss-Foundation-Focus@HEiKA ", by the  State of Baden-Württemberg , and by the  Karlsruhe Institute of Technology (KIT). We further acknowledge support by the  Helmholtz program  " Materials Systems Engineering " (MSE). M. K.
Microscopy Live-cell images were acquired using a wide-field inverted microscope (Leica DMI-6000B) equipped with Adaptive Focus Control to eliminate Z drift, a 100×/1. 4 NA immersion objective with a Prior NanoScanZ Nanopositioning Piezo Z Stage System, a CMOS camera (ORCA-Flash4. 0; Hamamatsu) and a solid-state light source (Spec-traX, Lumencore). The system is piloted by MetaMorph software (Molecular Device). For GFP-mCherry two-colour images, 19 focal steps of 0. 20 lm were acquired sequentially for GFP and mCherry with an exposure time of 100-200 ms using solid-state 475-and 575-nm diodes and appropriate filters (GFP-mCherry filter; excitation: double BP, 450-490/550-590 nm and dichroic double BP 500-550/600-665 nm; Chroma Technology Corp. ). Processing was achieved using ImageJ software (National Institutes of Health).
Acknowledgements We thank  Z. Xu ,  A. Piazza ,  R. Koszul  and  Jamie Phipps  for critical reading of this manuscript and members of the  Marcand and Dubrana laboratories  for stimulating discussions. We thank  Susan Gasser ,  Ann E. Ehrenhofer-Murray ,  Lorraine Symington ,  Rodney Rothstein ,  Laurent Maloisel  and  Florian Roisn e-Hamelin  for sharing plasmids and strains and  Angela Taddei  for the custommade rabbit polyclonal antibodies raised against full-length Sir3. This work was supported by grants from  Fondation pour la recherche m edicale  ( DEP20131128535 ) and from the  European Research Council  under the European Community's  Seventh Framework Program  ( FP7/2007 2013/European Research Council Grant Agreement 281287 ),  Fondation ARC pour la Recherche sur le Cancer  ( PJA-20191209432 ),  CEA Radiation biology programme and EDF ,  Agence Nationale de la Recherche  ( ANR-18-CE12-0003 ).