dataesr/scientific-paragraphs-categorization huggingface dataset:
The low number of unique genes detected as being actively transcribed by snRNAseq was used as a diagnostic in assigning identity as early elongating spermatids. However, due to noise, the relatively low signal also made it difficult to assign distinct identities within this broad designation. Both the geography of the testis UMAP and plots of gene expression in nuclei ordered by trajectory inference identified spermatids at the mid-to late elongation stage (Fig. 2C , *4), located in the blunt protruding region at the top of the "shark's head", as these nuclei turn on a coordinated burst of new transcription of a specific set of genes previously identified by the White-Cooper laboratory  (Barreau et al. Development 2008). Trajectory inference analysis of the snRNA-seq data identified a number of additional such genes (152 in all) upregulated in late elongating spermatids, and showed that onset of their transcription is tightly coordinated Figure  6F ).
The FCA data identify many additional genes differentially up or down regulated at specific steps in these stem cell lineages, presenting a unique opportunity for functional tests to identify new players in the transitions from one cell state to the next. As described above, a small cluster of epithelial-like cells termed the hub, located at the apical tip of the testis, is a key player in the organization and function of the testis as a reproductive organ. The hub cells provide close range signals required to maintain stem cell state in the associated germ line and cyst cell lineages, BMP and Upd1 respectively. Represented by only 79 nuclei (Fig. 2C , germinal proliferation center hub) in the snFCA V1 testis dataset, the hub cells express many markers common to epithelial cells, representing their cell biological arrangement and attachments to each other, and to cyst cells, likely reflecting their embryonic co-origin and ability to interconvert with cyst cells after drastic injury  (Hétié et al.
, 2014). The hub cells differ from cyst cells and most other epithelial cells, however, in their diagnostic expression of the cytokine-like ligand Upd1. The UMAP for testis presented in Fig. 2C  is plotted using Relaxed version of the snFCA 10X data because the important hub cell cluster was filtered out by the algorithms used to generate the Stringent FCA datasets, likely due to the expression of many "somatic" transcripts including Upd1 in late spermatocytes. This same expression of "somatic" transcripts also caused many late spermatocytes to be filtered out by the algorithms used to generate the Relaxed FCA dataset. (C) Pieplot showing the 684 marker genes detected in only one cell type. Majority of unique marker genes are unknown CG and CR numbers, while the known marker genes are mostly linked to receptors.
Data integration was performed using Harmony  (Korsunsky et al. , 2019)  using the first 30 PCA dimensions. From this analysis, we were able to identify all previously known cell types in the gut. In addition, interestingly, we 5 were able to characterize more cell types, including visceral muscle cells and 5 subtypes of crop cells. All plots are from UMAP. Three published adult ovarian scRNA-seq datasets are from  (Jevitt et al. , 2020; Rust et al. , 2020; Slaidina et al. , 2020). Datasets were integrated and batch corrected using Seurat v4. 0. 1. Scale bars in G and H depict average expression levels in log2(((UMI + 1)/total UMI)×10^4). Scale bar in I and J, 100 µm. Other Supplementary Materials for this manuscript include the following: Table  S1 : cell type-specific transcription factors for Fig.
(D) tSNE visualization of cells from the Stringent 10x dataset and Smart-seq2 (SS2) cells. 10x cells are from individual tissues. Integrated data is colored by tissue (left) and technology (right). (E) Tissue-level comparison of the number of detected genes between 10x and Smart-seq2 technologies (see Methods). (F) Number of cells for each tissue by 10x and Smart-seq2. Male and female cells are indicated. Mixed cells are from pilot experiments where flies were not sexed. Different batches are separated by vertical white lines. (G) All 10x cells from the Stringent dataset clustered together; cells are colored by tissue type. Tissue names and colors indexed in F.
Data and materials availability All data are available for user-friendly querying via https://flycellatlas. org/scope and for additional custom analyses at https://flycellatlas. org/asap. For each tissue, a CellxGene portal is also available (links can be found on www. flycellatlas. org). Raw sequencing data and count matrices can be downloaded from ArrayExpress (accession number E-MTAB-10519 for 10x dataset, and E-MTAB-10628 for smart-seq2 dataset). Files with expression data, clustering, embeddings, and annotation can be downloaded for each tissue, or all data combined, in h5ad and loomX formats from www. flycellatlas. org.
South Africa In order to estimate population differentiation and confirm species identity, two workers per nest were sequenced for a fragment of the cytochrome oxidase 1 marker (COI) using the LF1 and LR1 primer pair  (Hebert et al. , 2004; Smith et al. , 2005). PCR products were purified using the EXOSAP-it PCR purification kit (Applied Biosystems) and sequenced with the ABI BigDye Terminator v. 3. 1 cycle sequencing kit (Applied Biosystems). Base calling and sequence pairing were performed using CodonCode Aligner (CodonCode Corporation, Dedham, MA, USA). Fifty-four new sequences were generated for our samples and combined with 27 additional sequences of Melissotarsus species obtained from GenBank [Melissotarsus insularis, Melissotarsus weissi, Melissotarsus beccarii and Melissotarsus emeryi  (Smith et al.
population and Colony struCture Allele frequencies, observed and expected heterozygosity, and F-statistics were assessed using FSTAT  (Goudet, 1995). Population and colony structure were determined using only worker genotypes. For each locality, genotypic frequencies were compared between every pair of nests using log-likelihood (G)-based tests of differentiation using GENEPOP ON THE WEB  (Rousset, 2008) , to determine whether they belonged to the same colony. A Bonferroni correction was applied to account for multiple comparisons of all pairs. The genetic clustering of individuals within nests and populations was visualized by plotting individuals on a Principal Component Analysis (PCA) using the Adegenet R package  (Jombart, 2008). The clustering of nests into distinct colonies was also assessed by Bayesian assignments of individuals into genetic clusters using STRUCTURE v.
2. 3. 4  (Pritchard et al. , 2000). For each locality, the most likely number of genetic clusters (K) was estimated using simulations run with values of K ranging from one to the total number of nests encountered within each data set and repeated ten times for each value of K. Each run included a 5 × 10 4 burn-in period followed by 1 × 10 5 iterations of the MCMC. The most likely number of genetic clusters was evaluated using the ΔK method  (Evanno et al. , 2005)  implemented in Structure Harvester v. 0. 6. 8  (Earl & von Holdt, 2012). Different nodes of clustering were determined using CLUMPAK  (Kopelman et al. , 2015). PCA and STRUCTURE analyses were first performed at the entire population scale (i. e. when all localities were grouped together) and then run for each locality separately.
In addition, the phylogenetic relationship among mtDNA haplotypes was investigated using Maximum Likelihood implemented in the PhyML online web server  (Guindon & Gascuel, 2003). Nodal support was assessed by bootstrap resampling (1000 pseudoreplicates). Trees were visualized using FigTree v. 1. 3. 1 (http://tree. bio. ed. ac. uk/software/figtree).
The reproductive contribution of multiple queens was deduced when at least one worker per colony could not be unambiguously assigned to a single queen. The number of matings per queen was determined for each monogyne colony based on mother-offspring analyses using the maximum-likelihood method implemented in the software COLONY v. 1. 2 (Wang, 2004). This analysis infers the number of males from the workers' genotypes and assigns each worker to a given patriline. The probability that additional patrilines were not detected due to two fathers sharing the same alleles at all loci was calculated for each population studied  (Boomsma & Ratnieks, 1996). For each monogyne colony, the effective mating frequency (M ep ) was calculated for each queen following  Nielsen et al. (2003). This estimator takes into account potential unequal contribution of the different fathers to offspring production.
The effective number of patrilines equals the absolute mating frequency when all males contribute equally. For all colonies, relatedness coefficients (r) among nestmate workers were calculated using the program COANCESTRY v. 1. 0 (Wang, 2011) , according to the algorithm described by  Queller & Goodnight (1989). Relatedness coefficients were weighted equally and standard errors (SE) were obtained by jack-knifing over colonies. Relatedness coefficients were calculated separately for each locality to account for the strong genetic structure (and differences in allele frequencies) between populations (see Results).
RESULTS population and Colony struCture Overall, eight new haplotypes were found on the 622 bp fragment of the COI mitochondrial marker for the 54 samples sequenced in this study. GenBank samples were included, although the species identifications may be ambiguous (Melissotarsus alpha taxonomy awaits a thorough revision). The overall data set comprised 81 sequences for which 200 nucleotide positions were variable (175 parsimony-informative). Surprisingly, samples from different localities clustered into clearly separated clades (Supporting Information, Fig. S2 ). The Cederberg population clustered with the GenBank samples identified as M. emeryi. However, the localities of Eastern Cape and St Lucia segregated away from M. emeryi, separated by one putative species (M. insularis), and the locality of uMkhuze clustered away from M.
Figure2. Graphical representation of STRUCTURE results determining the number of genetic groups in the overall dataset for different values of K. Each genetic group is characterized by a colour; and each individual is represented by a vertical bar according to its probability of belonging to each group. Distinct simulations were subsequently run for the four populations, separately. In each population, grey bars below the plot indicate different colonies assigned to a single genetic group by STRUCTURE (only the pairs EC1/2 and CEc/ma are not significant using the G-test of differentiation).
ACKNOWLEDGEMENTS This paper is dedicated to the last author,  Christian Peeters , who passed away suddenly on 1 September 2020. He had an extraordinary career as a myrmecologist with a vast knowledge of ant natural history. Much of his work over the past several years focused on the biology and natural history of Melissotarsus ants. C. P. thanks  Peter Hawkes ,  Peter Goodman ,  Robin Crewe  and  Bennie Bezuidenhout  for logistical help in South Africa. Nigel Gericke  and  Peter Goodman  helped with plant identification. Brigitte Church , iSimangaliso  Wetland Park Authority and Ezemvelo KZN Wildlife. We also thank  Adam Cronin  and  Kazuki Tsuji , as well as four additional anonymous reviewers who provided valuable comments on the manuscript. Funding for P. -A. E. and E. L. V. was provided by the  Urban Entomology Endowment at Texas A&M University.
NSFigure 2. 2 Figure2. Graphical representation of STRUCTURE results determining the number of genetic groups in the overall dataset for different values of K. Each genetic group is characterized by a colour; and each individual is represented by a vertical bar according to its probability of belonging to each group. Distinct simulations were subsequently run for the four populations, separately. In each population, grey bars below the plot indicate different colonies assigned to a single genetic group by STRUCTURE (only the pairs EC1/2 and CEc/ma are not significant using the G-test of differentiation).
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Acknowledgments The authors would like to acknowledge the following persons for providing supplementary details on the how their competition is run:  Christine Cheylan ,  Jury President of Olive d'Or competition ;  Sandro Fulzi , Director of JOOP;  Maria Paola Gabusi ,  Organizer of Leone d'Oro International Olive Oil Competition ;  Jean-Emmanuel Jourde , general secretary of AVPA;  Maria Katsouli , Manager of ATHIOOC;  George Kouvelis , General Manager of London Olive Oil;  Antonio G. Lauro , President EVO IOOC;  Gilbert Moity of OLéOMONDO  website; the support team of  NYIOOC  and  Marco Oreggia , curator and editor of  Flos Olei.
Material and methods The recently published updated list of the mean flowering cycle of 85 bamboo species was used in this study  (Zheng et al. , 2020)  (Supplementary Table  S1 ). Data for the species Phyllostachys bambusoides from a previous list were added  (Janzen, 1976). When the range of values for a cycle was lower than ten years, the mean value of the cycle was used. Otherwise, two or more values were taken for the species when the range was higher than ten years, thus indicating variability in the flowering cycle within the species  (Alam, 2008). Thus, 108 mean flowering cycles observed in 86 species were analysed. The shift in the sun and moon phases after exactly one year was computed using a year duration of 365. 2563631 days (International Earth Rotation and Reference System Service, 2010) and a mean synodic lunar cycle of 29.
53058886 days  (Seidelmann, 1992). The computation started at dayi of year 0 and was yearly run. A list of 19 years over a period of 160 years when the moon phase at dayi differed by less than two days from the original phase was established, and two additional values with differences less than 3. 2 days, at 8 and 16 years, were added. Automatic clustering of the 108 mean observations was performed using the MODECLUS procedure (k=4) in SAS  (SAS, 2012). A second supervised clustering was performed in Excel by aggregating each observed flowering cycle to the closest value among the values on the list of 21 years with appropriate moon phasing. Only basic statistics can be done on such a dataset, in which sampling is not expected to follow a specific distribution. The cluster means were regressed against the observed values with the SAS procedure REG to calculate the R² with the model probability.
Along with 60S ribosomal protein L8 (RPL8) (AAEL000987), CYP6BB2 (AAEL014893) and CYP9J26 (AAEL014609) compiled Detox (A), GSTD4 (AAEL001054) and CCEae3a (AAEL023844) Detox (B), CYP9J28 (AAEL014617) and CYP9M6 (AAEL001312) Detox (C), and CYP9J32 (AAEL008846) Detox (D). The RPL8 gene was included in all four Detox assays, thus allowing robust normalization of each reaction independent of sample volume and other possible reaction variations. Primers and probes for the multiplex TaqMan qPCR assays were designed de novo (S3 Table ) taking into account the standard guidelines for qPCR assays enhanced with the following criteria: i) Primer(s) in exon junction to avoid DNA amplification and ii) applicability for multiplexing. For the latter criterion, testing of potential primer dimers was performed via the Multiple Primer Analyzer software (Thermo Scientific).
Statistical analysis Mosquito mortality was evaluated 24h post insecticide exposure and the susceptibility status was determined according to WHO criteria  [26]. Mortality rates � 98% indicated susceptibility, mortality rates between 90% and 97% indicated suspected resistance or tolerance and mortality rates less than 90% indicated resistance to the tested insecticide. When mortality in the control was between 5% and 20%, the mortality rates in test samples were corrected using the Abbott formula  [34]. The test result was omitted if the mortality in control was more than 20%. For pyrethroids and DDT, the knock-down times for 50% (KdT 50) and 95% (KdT 95) of tested mosquitoes were calculated using log-probit model with R-Studio software  [35]. Calculation of fold-changes, 95% CIs and statistical significance was performed according to the Pfaffl method  [36].
Acknowledgments The authors would like to thank  Abdou Karim Bodian  for his technical assistance in the insectary,  Alassane Mbengue ,  PhD Research Associate in Institut Pasteur of Dakar  and all the populations from the localities investigated for their collaboration. The following reagent was provided by the  NIH/NIAID Filariasis Research Reagent Resource Center for distribution through BEI Resources ,  NIAID ,and  NIH: Uninfected Aedes aegypti, Strain Black Eye Liverpool (Frozen) ,  NR-48920. We thank the  Liverpool Insect Testing Establishment (LITE)  for providing us with the  Aedes aegypti New Orleans , and Cayman strains and the  Swiss Tropical and Public Health Institute (Swiss TPH)  for providing us the Aedes aegypti Rockefeller strain.
, 2018). More recently, studies are emerging that seek to characterize resilience to environmental perturbations  (Ben Abdelkrim et al. , 2019; Adriaens et al. , 2020)  and to relate variation in these resilience measures to frequency of health events and productive longevity  (Berghof et al. , 2019). Poppe et al. (2020) , building on work of  Elgersma et al. (2018)  and using the residual variance in milk yield from daily milk records as a resilience measure, found favorable genetic correlations between resilience and health, fertility, and longevity. Taken together, this literature suggests that short-term efficiency is unfavorably correlated with resilience, and that resilience contributes positively to a longer productive lifespan, at least in adult producers. From the dairy cattle data of the DEFFILAIT project, we have recently been able to look at the relation between RFI and resilience, measured as the accumulated loss in milk yield during perturbations (based on 237 lactations; unpublished).
Taking advantage of recent advances in high-throughput analytical techniques, many studies integrate the use of different 'omics platforms on the same samples, which makes it possible to have a set of data at the genome scale, while being able to measure biomolecules at the different functional levels of the studied organisms  [9, 10]. This 'omics data fusion allows us to handle a biological system as a whole to gain insight into its complex functioning through the identification of more informative models  [11]. The main statistical methods used to integrate several 'omics datasets are the penalized canonical correlation analysis (CCA)  [12, 13]  and self-organizing map (SOM)  [14, 15] , used to compute correlations between blocks, and the multi-block generalizations of partial least squares (PLS) to fit a model between a biological factor and 'omics profiles  [16] [17] [18] [19] [20] [21] [22] [23].
This study aimed to assess whether the NMR-based metabolomic modeling of DON exposure can be enriched by integrating complementary information contained in transcriptomic data, based on a combination of sparse CCA and SOM analysis, to select withinand between-blocks-correlated features, and the O2-PLS to model the selected features. Furthermore, we aimed at assessing the correlations between these two functional levels to identify genes and metabolites as markers of exposure to the mycotoxin. To this end, we generated transcriptomic and metabolomic datasets from pig intestinal explants exposed to a similar and low dose of DON.
Combination of Robust Sparse CCA and O2-PLS Twelve transcriptomic and 12 metabolomic features were selected by rsCCA. An O2-PLS model was then fitted using this set of features. The constructed model explained 60. 4% of the total variability and showed a very low error of prediction (0. 01). Figure  4  presents the projection of individuals along the first latent variable of the fitted O2-PLS-DA model for the transcriptomic (a) and metabolomic (b) datasets, respectively. Both figures showed that the observations were not well discriminated according to the exposure to DON.
Combination of Robust Sparse CCA and O2-PLS Twelve transcriptomic and 12 metabolomic features were selected by rsCCA. An O2-PLS model was then fitted using this set of features. The constructed model explained 60. 4% of the total variability and showed a very low error of prediction (0. 01). Figure  4  presents the projection of individuals along the first latent variable of the fitted O2-PLS-DA model for the transcriptomic (a) and metabolomic (b) datasets, respectively. Both figures showed that the observations were not well discriminated according to the exposure to DON. All the correlated transcriptomic  (12)  and metabolomic  (12)  features were found to be discriminant in the O2-PLS model. Metabolomic features corresponded to six identified metabolites (see Figure  2 ); the aspartate, methanol, and phosphocholine concentrations were lower in the control group, whereas glucose, glutamine, and tyrosine concentrations were higher in the control group.
Combination of Self Organizing Map and O2-PLS A total of 11,069 transcriptomics and 154 metabolomics features were selected by the dissimilarity kernel-based SOM. The adjusted O2-PLS model based on these selected features explained half of the total variability and showed a very low error of prediction (0. 01). Figure  5  presents the projection of individuals along the first latent variable of the fitted O2-PLS model for the transcriptomic (a) and metabolomic (b) datasets, respectively. In the case of the model based on features selected by dissimilarity kernel-based SOM, the observations were well discriminated according to the exposure. All the correlated transcriptomic  (12)  and metabolomic  (12)  features were found to be discriminant in the O2-PLS model. Metabolomic features corresponded to six identified metabolites (see Figure  2 ); the aspartate, methanol, and phosphocholine concentrations were lower in the control group, whereas glucose, glutamine, and tyrosine concentrations were higher in the control group.
Combination of Self Organizing Map and O2-PLS A total of 11,069 transcriptomics and 154 metabolomics features were selected by the dissimilarity kernel-based SOM. The adjusted O2-PLS model based on these selected features explained half of the total variability and showed a very low error of prediction (0. 01). Figure  5  presents the projection of individuals along the first latent variable of the fitted O2-PLS model for the transcriptomic (a) and metabolomic (b) datasets, respectively. In the case of the model based on features selected by dissimilarity kernel-based SOM, the observations were well discriminated according to the exposure. The analysis revealed that 1443 transcriptomic features and 39 metabolites (among which 21 have been identified) were discriminant (see Figure  2 , last line for metabolites).