dataesr/scientific-paragraphs-categorization huggingface dataset:
| Data analysis Baseline characteristics of all patients enrolled in the study were compared across two study sites, Yangon and Dawei. Thaketa and Insein clinics were merged into one site category, Yangon. Where appropriate, differences in proportions were calculated using Pearson's χ 2 test and differences in medians were measured using Mann-Whitney U test. A two-sided p < 0. 05 was considered statistically significant. Comparison of baseline characteristics between the patients included in the study and those who did not.
Ethical considerationsEthical approval to conduct the study was given by the MSF Ethics Review Board (MSF ERB ID 1523) in June 2015 and by the Department of Medical Research, Myanmar Ministry of Health and Sports (MoHS) in October 2016 (Ethics/DMR/2016/139). Before the study enrollment, written informed consent was obtained from all patients. All biological and clinical data were anonymized with only the program committee and the investigators having access to the coded databases.
ACKNOWLEDGMENTS We would like to acknowledge our colleagues at  MSF HIV clinics  and patients for their participation in the study. We also thank the  National Hepatitis Control Program  at the  Myanmar Ministry of Health and Sports  for their guidance and collaboration. The study was funded by  UNITAID  grant number  SPHQ14-LOA -217. The funders had no role in study design, data collection, analysis, publication decision, or writing the manuscript.
2. 7 | 7 Ethical considerationsEthical approval to conduct the study was given by the MSF Ethics Review Board (MSF ERB ID 1523) in June 2015 and by the Department of Medical Research, Myanmar Ministry of Health and Sports (MoHS) in October 2016 (Ethics/DMR/2016/139). Before the study enrollment, written informed consent was obtained from all patients. All biological and clinical data were anonymized with only the program committee and the investigators having access to the coded databases.
Plants and virus inoculation The Florida CTV isolates T36 (GenBank accession number: AY170468) and T68-1 (GenBank accession number: JQ965169) used in this study have been maintained in C. macrophylla plants under greenhouse conditions. For each treatment (Mock, T36, and T68-1), three biological replicates, in the form of 2 year-old C. sinensis cv. Madam Vinous seedlings, were graft-inoculated with tissue from the source plants infected with either T36  (Karasev et al. , 1995; Satyanarayana et al. , 1999) , T68-1  (Folimonova et al. , 2010; Harper, 2013) , or healthy tissue. The inoculated plants were maintained under greenhouse conditions.
Small RNA analysis The Illumina generated sRNA reads were trimmed to remove NEXTflex sequencing adapters using cutadapt 1 and mapped to the reference sequences of the CTV T36 (GenBank accession number: AY170468) and T68-1 (GenBank accession number: JQ965169) variants and C. sinensis cv. Valencia nuclear (GenBank assembly accession: GCA_022201045. 1), chloroplast (GenBank accession: DQ864733. 1), and mitochondrial (GenBank accession: MG736621. 1) genomes with Burrows-Wheeler Aligner (BWA). 2 The BWA alignment files (. bam) were used as inputs to generate CTV consensus sequences for each dataset with MISIS-2  (Seguin et al. , 2016). Both viral and plant sRNA reads mapped without mismatches to the CTV consensus sequences and C. sinensis nuclear, chloroplast, or mitochondrial genomes, respectively, were sorted according to their size class (15-34 nt), polarity, and 5′-nucleotide identity and counted (Supplementary Dataset S1).
The read counts in each library were normalized in reads per million (RPM) of total (15-34 nts) reads. The distribution of viral sRNA reads along the CTV genomes and their 5′-nucleotide identity profiles were analyzed and visualized with MISIS-2  (Seguin et al. , 2016 ; Supplementary Dataset S1 and Supplementary Figure  S1 ). Single nucleotide resolution maps of the sRNA reads mapped to the CTV genomes with zero mismatches are provided in Supplementary Dataset S2.
In addition, the eight most up-regulated and seven most down-regulated DEGs identified upon the T36 infection were also identified with the T68-1 infection (Figure  2A , dashed boxes). Hence, the T36-specific DEGs were few and not very strongly altered. Overall, these data clearly demonstrate a much stronger alteration of the host transcriptome by the T68-1 infection, compared to that of T36. A full list of T36-and T68-1specific DEGs is presented in Supplementary Table  S2. Some of the proteins encoded by the DEGs did not have any significant homology with proteins in the annotation databases and were classified as uncharacterized proteins (Supplementary Table  S2 ). The high fold change in the expression of some of these DEGs suggests that they are heavily involved in the virus-host interactions.
Infection by the T68-1 variant up-regulated expression of genes coding for papain-like cysteine proteases (PLCPs) In the T68-1-infected trees, genes encoding six papain-like cysteine proteases (PLCPs) were some of the most strongly induced genes (Figure  4A ). Upon the analysis of their amino acid sequences on the NCBI BLASTP platform  (Altschul et al. , 1990)  using the non-redundant UniProtKB/SwissProt database, the two highest scored hits for all six PLCPs were found to be the A. thaliana senescence-specific cysteine protease (AtSAG12) and xylem cysteine peptidase 1 (AtXCP1). Both SAG12 and XCP1 belong to the C1A family of vacuolar PLCPs. Even though the closest ortholog was AtSAG12 for all PLCPs, the percentage identity was only between 48. 4 and 53. 7%. It should be noted that Cs2g27820 and Cs2g27810 have 99.
, 2010). It is intriguing that both CLas and T68-1 induce CsSAG12s, and both cause stunting of the affected trees. The CsSAG12s only have about 50% amino acid sequence identity with the AtSAG12 protein. Although this homology is considerable, it is not sufficient to decisively classify them as the SAG12 family proteins. The protein with the second highest BLASTP score according to the UniProtKB/SwissProt database (also with ~50% amino acid identity to the PLCPs up-regulated by T68-1) is AtXCP1. XCP1 is localized to the vacuoles of differentiating tracheid elements (TEs). Ectopic expression of XCP1 in A. thaliana resulted in decreased plant size and chlorophyll levels, which correlated with increased XCP1 peptidase activity  (Funk et al. , 2002). TE autolysis is the programmed cell death of maturing xylem cells to transform into empty TEs.
, 2021) ; and CaRLPs were involved in the pepper immune response against TMV and pepper mottle virus  (Kang et al. , 2022). Furthermore, induction of R-related genes has been previously reported in plantvirus interactions, particularly, when the infection was accompanied by severe symptoms or the hypersensitive response was triggered  (Li et al. , 2018; Kalapos et al. , 2021). Interestingly, in some cases, viral proteins recognized by plant NLRs triggering ETI serve as pathogenicity determinants and suppressors of RNA silencing  (Zvereva and Pooggin, 2012). Given the importance of these proteins in host-virus interactions, DEGs identified with both CTV variants were analyzed for resistance (R) gene-related domains and motifs using DRAGO 3 (Disease Resistance Analysis and Gene Orthology) pipeline of plant resistance gene database (PRGDB) (Calle  García et al.
The profiles of both viral and host sRNA reads in the T36-and T68-1-infected samples were similar. No substantial differences were observed in the number, size, polarity, or 5′-nucleotide identity of sRNAs derived from the C. sinensis genome (Supplementary Figure  S1  and Supplementary Dataset S1). Furthermore, for both virus variants, the most abundant viral sRNAs of both sense and antisense polarities belonged to the 21 and 22 nt classes (Figure  7B ; Supplementary Figure  S1 ; Supplementary Dataset S1). These are the expected sizes of small interfering RNAs (siRNAs) produced by the Dicer-like (DCL) enzymes DCL4 and DCL2, which are primarily responsible for the host RNA silencing-based antiviral response  (Pooggin, 2018; Ramesh et al. , 2021). The siRNAs were produced from all areas throughout the entire genomes of T36 and T68-1, with a large proportion of those corresponding to the 3′-terminal regions (Figure  7C  and Supplementary Dataset S1).
This was in agreement with earlier reports assessing RNA silencing in citrus hosts infected with various CTV isolates  (Ruiz-Ruiz et al. , 2011; Folimonova et al. , 2014). No substantial differences were observed in the 5′-nucleotide identity profiles of siRNAs derived from T36 and T68-1 (Supplementary Dataset S1), suggesting that Argonaute proteins sorting viral siRNAs mostly based on size and 5′-nucleotide identity  (Pooggin, 2018)  may not be differentially affected by the two CTV variants. Overall, no significant differences were found in the citrus RNA silencing-based responses against T36 and T68-1, suggesting that activation of this host antiviral defense mechanism may not have contributed to the observed differences in the symptoms induced by the two variants.
Funding This research was supported by the  National Science Foundation (NSF)  under Grant Number  MCB-1615723  and in part by the  United States Department of Agriculture (USDA) National Institute of Food and Agriculture ,  Hatch  Project  FLA-PLP-006024. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF and the  USDA. This work of MP's team was supported by the  French National Research Agency (ANR)  project  ROME ANR-18-CE20-0017-01.
Analyzing Mendelian Samplings Although pedigree and performance recording started earlier in some breeds (notably LAC), our analyses begin in 1985, when the dairy sheep selection schemes started to extensively use performance and pedigree recording. The AlphaPart R package  (Obšteter et al. , 2021) , available at http: / / CRAN. R -project. org/ package = AlphaPart, which implements the method of  García-Cortés et al. (2008) , was used for the Mendelian sampling decomposition of EBV and genetic trends. The output of AlphaPart contains for each individual i the EBV (u i ), which is partitioned in 2 different manners. First, the EBV is split into the parent average plus the Mendelian sampling of the individual; in our notation, ˆˆˆ. ˆû PA u u i i i sire i dam i i = + = + + ( ) ( ) φ φ 1 2 1 2 Second, the EBV is split into a series of values (EBV_"category" in the AlphaPart output) per each of the k categories, such that the sum across categories yields the EBV; in our notation, û i = û (i)1 + û (i)2 +.
ACKNOWLEDGMENTS The authors thank the breeders who provided the data and the anonymous referees for useful comments on the manuscript. This study received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 772787  ( SMARTER ), and project  ARDI  (grant agreement  EFA 208/16 ;  Derio [Bizkaia], Spain ) from  POCTEFA  funds. The  GenoToul bioinformatics platform Toulouse Midi-Pyrenees  provided computing and storage resources. The authors have not stated any conflicts of interest.
Similar behavior, wherein increases in sliding velocity result in weakening of the failure surface, has been modeled to occur in fault zones  (Ito and Ikari, 2015). Given that the Chaos Canyon landslide has several years of slow and then accelerating movement, it is a strong candidate for further analysis of potential rate weakening. We cannot, with available datasets, ascertain when this pre-failure movement initiated; however, we can refer to the longterm trend in temperature to evaluate the potential impacts of a warming climate on this alpine landslide. Temperature trends at the top of the landslide, approximated from the Bear Lake SNOTEL, show a clear warming signal across the past three decades (Fig. 11A, B ), with a consistent positive temperature anomaly after 2009. Warming temperatures have been documented to correspond to landslides in both high elevation and high latitude landscapes, particularly where permafrost or ground ice is present (e.
Acknowledgements. The first author would like to thank  Dr. Harrison Gray  ( USGS ) for sending the initial video of this landslide posted 410 to Twitter that spurred this research to action. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). We would also like to thank the support of the  French ANR-PIA   funding program  ( ANR-18-MPGA-0006 ). We also thank the developers of the Digital Image Correlation code we implemented with Google Earth Imagery https://github. com/bickelmps/DIC_FFT_ETHZ.
Code availability. The code developed and associated with this publication is published on Zenodo. https://doi. org/10. 5281/zenodo. 7854068. Other pre-existing and published tools are referenced in the text with sufficient detail for methods replication. Code and data availability. The code developed and associated with this publication is published on Zenodo. https://doi. org/10. 5281/zenodo. 7854068. Video supplement. A short. mp4 video file of Planet images between 2017 and 2022 of the landslide is available in the Supplementary Material.
Data Analysis The statistical analysis was performed in R (version 4. 2. 2, R Foundation for Statistical Computing, Vienna, Austria)  [59]. The fruit set was measured on a binary scale where a flower producing a fruit was given a score of 1, whereas one that failed to produce a fruit was attributed a score of 0. This measure of fruit production is used in pollination studies for various crops, including strawberry  [42, 60]. We used a mixed model approach to carry out our statistics analyses, with location as a random factor in each model. These models were computed using the lme4 R-package  [61]. The MuMin R-package  [62]  was used to test the goodness of fit of mixed models, and the ggeffects R-package  [63]  was used to generate predicted values. A Generalized Linear Mixed Model (GLMM) with binomial error structure was used to test whether the fruit set (response variable) differed between pollination treatments (fixed factor).
We also tested for a non-linear (quadratic) relationship between the fruit size and the seed set. If it was not significantly different from zero, we removed the quadratic term from the models. For all of the models (GLMMs and LMMs), we computed Type II Wald chi-squared tests to assess the effects of the explanatory variables using the car R-package  [66] , and we used Tukey's post hoc tests to compare treatments using the multcomp R-package  [67].
Acknowledgments: Special thanks go to the association  Veni Verdi  for providing the sand used in the plant potting process. From this association, we would like to thank, in particular,  Thomas Dorey , for help and advice on strawberry plant cultivation. We would also like to thank  Le Paysan Urbain  for supplying us with substrate for our strawberry plants. Special thanks go out to  Jean-Silouane Rebours  for help with field work assistance. We would also like to thank  Pierre-Yves Guilbaud  for tending to the beehives on site. Special thanks also go out to  Est Ensemble ,  Bondy  archery club, and  José Araujo  for granting us access to certain restricted areas within the study site.
Flow cytometry analysis of mouse spleen cell subsets Splenocytes were prepared as described above. Cells were incubated with the appropriate labelled antibody at 4°C for 30 minutes in PBS with 2% FBS. Flow cytometry was performed on a FACS Fortessa II flow cytometer (BD Biosciences) according to standard techniques. For the characterization of splenocytes, the monoclonal antibodies used were: CD3-AF700 (17A2, #100216), CD4-BV510 (GK1. 5, #100449), CD25-PE (PC61. 5, #12-0251-81), B220-Pacific Blue (RA3-6B2, #103230), CD69-PE DAZZLE (H1. 2F3, #104536), CD11b-PerCP Cy5. 5 (M1/70, #101228), F4/ 80-BV711 (BM8, #123147), CD86-BV510 (GL-1, #105039), from eBiosciences (ThermoFisher Scientific, France). Data were analyzed with FlowJo software (Tree Star, Ashland, OR, USA).
When we apply this analysis to one of the marker proteins, Snail, we arrive at a deviation value of 0. 1 (1. 07 in linear scale) in the case of the data set for Toll 10B. This shows that for this protein, the mutants represent the regional distribution in the wildtype very well. With the dataset for spn27A, the deviation value for Snail is 1. 55 (2. 91 in linear scale), which indicates that this mutant genotype overrepresents the ventral population, if used with the assumed weightings for the populations We tested what ratio of the areas of the embryonic regions would give the best fit of the theoretical sum with the data in the wildtype without any prior assumption about their real area in the embryo. A parameter screen of the entire range of possible values (0 to 1 for each mutant population) yielded good fits for a range of combinations.
107 of the resulting set of 109 genes had their proteins detected in our analyses, and 71 had been allocated to one of the DV clusters. 60 were found in clusters that were fully or partially consistent with the reported RNA pattern (Supplementary Table  7 ). Of the 11 proteins among these 71 that show consistent mesodermal upregulation in both ventralizing mutants (DV cluster 5), all are reported as ventrally expressed in BDGP. There is also a database representing an atlas of differential gene expression at single cell resolution for precisely the time window of early gastrulation  [29]  against which we compared the proteomes to regional RNA expression. Filtering out ubiquitously expressed genes left 8924 differentially expressed genes of which 3086 coded for 3120 proteins in our clustered proteome dataset (Fig.
5E, F ), and RhoGEF2 and the myosin phosphatase Mbs were differentially phosphorylated in the DL and D clusters (Fig. 5E ). In summary, we detected most of the elements of a well established pathway required for gastrulation and also identified new candidate regulation nodes within the Rho pathway. To find such networks, we used a diffusion-based algorithm  [32]  on each of the DV clusters. The starting weight of each protein was [based on its ED score and deviation from the linear model. Since these scores existed separately for the two ventralizing mutants, we also had to conduct the analyses twice in each case, i. e once for each dataset. We focussed our further analyses only on those proteins that behaved in the same way for the two ventralising genotypes. Overall, we generated 24 protein networks for the proteome and 24 for the phosphoproteome, i.
Comparing those to the expression patterns determined by single-cell RNA sequencing  [29]  reveals that five appear to have little or no dorso-ventral modulation, a result that is also confirmed in the BDGP in situ hybridisation database. Those genes previously identified by genetic or functional studies, and known to be involved in mesoderm development (including marker genes like twist, snail, zfh1, htl etc) show up in all studies. Thus, a comparison of our proteome data to reported RNA expression patterns has to be seen with caution. Nevertheless, such comparisons showed good matches for the abundant, well-studied genes and proteins: We detect the proteins for 13 of the 17 genes that are seen as ventrally upregulated genes in both studies  [11, 29]. Of those, we see all but four as ventrally upregulated, again including known ventral marker genes.
Known biological data thus validate the relevance of this network, which may in turn help to identify the as yet unknown targets for Neur in the mesoderm. Another mechanism for post-transcriptional gene regulation is the differential translation or degradation of mRNAs along the dorso-ventral axis, and we find an enrichment of P-granule-related networks both in the proteome and the phosphoproteome. These networks are enriched within DV clusters with complete or partial ectodermal fate, i. e. the same clusters that show a strong uncoupling between mRNA and protein abundance. Partial agreement between mRNA and protein spatial distribution is not an exclusive feature of the gastrula: it has also been described for larval tissues derived from the ectoderm and neuroectoderm, where nearly all studied genes show mRNA/protein discordance)  [48]  (97.
Calculation of correlation matrices Correlation matrices were calculated using Matlab R2019b on the proteins and phosphosites that were detected in all genotypes and all the replicates. For this, we used the 'corr' function to calculate the Pearson correlation between log2 intensities of proteins or phosphosites in the replicates of all genotypes. The resulting correlation matrix was plotted using the 'clustergram' function by applying the following settings: linkage: average; RowPDist & ColumnPDist: correlation.
t-wtProt X = a * D + b * L + c * V This model requires values a, b, and c for the three regions along the DV axis. For the mesoderm, this has been reported as 0. 2 from measurements on cross-sections  [21] , but we wanted to determine the theoretical optimum for each of the values. The theoretical optimum would be one for which the proportions for the three regions when used in the sum yield a theoretical value t wtProt X that is closest to the experimentally measured value m wtProt X for that protein in the wildtype embryo. To systematically explore the proportions for each region, we tested all possible combinations for a, b and c at 0. 05 steps in the range from 0 to 1 (i. e. : 0, 0. 05, (. ) , 0. 95,1). For each calculated t-wtProt X , we calculated the deviation from the ideal by subtracting the mean linear intensity measured for the same protein in a wild type embryo ( m wtProt X), and transforming this difference between the theoretical and measured wild type to Log2 scale as follows: DeviationProt X = Log2 ( t wtProt X/ m wtProt X) For each possible combination of a, b, and c we obtained distributions of DeviationProt X values, for which we calculated the Interquartile Range (IQR) using the IQR function from Matlab 2019b.
RNA clustering We excluded all genes that were scored as ubiquitously expressed in THE RNA ATLAS  [29]. Of the remaining 8924 genes, we selected those that were also listed in our clustered proteomic dataset (3346 genes coding for 3383 proteins), yielding a list of 3086 non-ubiquitous genes that were present both in the RNA atlas and the clustered proteome. These sorted into classes by comparing the expression pattern of each to that of six reference genes with restricted dorso-ventral expression that represent the six regulation categories D, L, V, DL, DV and VL. We used as reference genes dpp for dorsal, the average between sog and soxN for lateral, twist for ventral, crb for dorsal+lateral, net for dorsal+ventral and neur for lateral+ventral. To compare similarity for each of the 3086 genes we calculated their spatial Pearson correlation to each of the reference genes.
Each gene was then classified as belonging to the category of the reference gene for which the Pearson correlation was the highest. We therefore obtained 6 clusters of genes, which we termed 'DV RNA clusters' each of them with their corresponding maximum Pearson spatial correlation value. To filter out false positives, we selected those genes with the largest similarity to the reference genes, for which we expected strong differential expression along the dorso-ventral axis. We did this by determining the Pearson spatial correlation value corresponding to the 95th Percentile of each dorso-ventral RNA cluster, using the 'prctile' function (Matlab R2019b). Finally, we used the 95th percentile value as a threshold to filter the 5th percentile of genes from each DV RNA cluster. We obtained a list of 155 genes that we used to compare against the proteome clusters (Supplementary Table  8 ), which we termed 'DV RNA Reference Sets'.