dataesr/scientific-paragraphs-categorization huggingface dataset:
Right wings from N = 38 WT males, N = 39 WT females and N = 48 SM9 males were dissected under a binocular microscope and placed on a microscope slide using double-sided tape. Pictures were taken under a Zeiss SteREO binocular microscope with X-Cite Xylis engine (Excelitas technologies) and analysed using ImageJ software  46  to measure wing length as a proxy for body size  27. Male flight ability was assessed using a Flight Test Device as previously described, with N = 3 replicates of 82 ± 13 males for each treatment  30. Relative fertility was calculated by comparing the number of live larvae in the progeny from 30 SM9 pseudo-males crossed with 60 wild-type females vs. 30 wild-type males crossed with 60 wild-type females. N = 3 independent replicates of this test were performed for each strain.
Acknowledgements This study was funded by  EU ERC  CoG-682387 REVOLINC to J. B. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission. Mosquito production and insectarium operation were supported by  Agence Nationale de la Recherche  grant # ANR-11-EQPX-0022. Part of the technical work was funded by  ANR  grants # ANR-19-CE35-0007  GDaMO and #  18-CE35-0003-02 BAKOUMBA  to E. M.
Data availability Plasmid sequences have been deposited on Addgene (https://www. addgene. org/) under reference numbers: #173505, #173665, #173666, #173667 for Nix-expressing piggyBacbased transgenesis plasmids and #173496 for piggyBac vector backbone. All plasmids and mosquito strains described in this paper are available upon request to E. M. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information.
At each site, researchers typically placed 40 blocks (mean = 34. 3 + 14. 6 (1SD)) with 20 per treatment per harvest at 20 stations with stations spaced >5 m apart (see Methods, Supplementary Table  S1 ). In total, we monitored decay in 8,869 wood blocks of a common substrate, Pinus radiata (or in a few cases closely related Pinus species; see Methods), wrapped in ne mesh with and without larger holes to allow or exclude termites for up to 48 months. Our focal wood species, P. radiata, was novel at all locations allowing us to disentangle substrate-decay agent history  35. Wood-feeding termites were largely, but not solely, a tropical phenomenon. Termite discovery (i. e. , percentage of wood blocks with evidence of termites per site and time point) was greatest, but also highly variable, at low latitudes and elevations and where temperature and precipitation were high (Figure  1A, B , Extended Data Figure  1 ; Extended Data Table  1 ).
All wood blocks were covered with 70% green shade cloth to reduce solar radiation degradation of the mesh bags. For each lot of initial wood samples, 3-5 blocks were sent to the University of Illinois. Sawdust samples were ground and analyzed for % nitrogen (N) and C content from individual blocks using an elemental analyzer (Costech, Valencia, CA, USA) (Supplementary Information Table  S1 ). Average elemental %N and %C per lot were used to represent variation within and across wood species as wood chemistry typically is a strong predictor of decomposition rates  47, 48  (Extended Data Tables  6 7 8 9 10 ). Wood blocks were harvested at ~6 months (n = 739), ~12 months (n = 4517), ~24 months (n = 3488), ~36 months (n = 125) and ~48 months (n = 54) after deployment. Once collected, wood blocks were assessed for termites.
We assessed termite discovery and decay following a two step method. First, we ltered to those sites where site researchers recorded termite presence. Second, for those sites with termites, we recorded blocks as discovered when they were noted as having termites, mudding (i. e. , imported soil), and/or damage (e. g. , internal chambering, external surface scoring, or removal) in wood blocks (Extended Data Figure  3 ). When wood blocks were found to be damaged but this damage was not attributable to termites (e. g. , small holes, non-termite larvae, etc. ), these blocks were recorded as undiscovered by termites; few blocks had macrofauna damage not attributable to termites (termite discovery was 2. 7× higher than discovery by other macrofauna), supporting the importance of termites as the main macrofauna decaying wood.
Microbial-driven ML was calculated for blocks in the microbes treatment, while microbial and termite-driven ML was calculated for blocks that were discovered by termites. Additionally, decomposition was calculated assuming an exponential steady-state of decay using percentage mass loss and time since deployment (i. e. k = -log(Final mass/Initial mass)/time). Data were weighted by the number of wood blocks in each discovery category (discovered and not discovered by termites) at each site as appropriate. Similar to the discovery models, we ran two sets of two-tailed analyses to understand how both microbes only and termite discovered decomposition rates (k) varied across geographic and climatic space. First, we ran a series of bivariate regressions, examining how individual spatial (Absolute (Latitude) and elevation) and climatic (MAT and MAP) predictors estimated k for each treatment.
Acknowledgements Serge Gofas (Dept. of  Animal Biology, University of Málaga, Spain ),  Luis Sánchez-Tocino  ( Department of Biology, University of Granada, Spain ), and  Mercedes Checa-Ros  ( Department of Mineralogy and Petrology, University of Granada, Spain ) provided shells. Vicente Hernández-García  ( Instituto Universitario EcoAqua, University of Las Palmas de Gran Canaria, Spain ) procured the ethanol-fixed complete specimens. Funding was provided by projects  PID2020-116660GB-I00  of the  Spanish Ministerio de Ciencia e Innovación  ( AGC, CG, CS ), and  B-RNM-265-UGR18  of the  Consejería de Conocimiento, Investigación y Unversidad of the Junta de Andalucía (AC) , the  Unidad Científica de Excelencia   UCE-PP2016-05  of the  University of Granada (AGC, CG) , and the Research Group RNM363 of the  Junta de Andalucía (AGC, CG).
Preparation, Examination, and Study of Specimen The amber piece containing the type specimen of †Valkyritermes inopinatus gen. et sp. nov. was polished with a polisher (Buehler EcoMet 30) by using a thin silicon carbide sanding paper (grit size = 7,000). Then, the fragment was embedded with cedar oil between microscopic slides to minimize light scattering during focus stacking. The specimen of †Valkyritermes inopinatus gen. et sp. nov. was examined with a Leica MZ APO stereomicroscope, and a Nikon SMZ25 stereomicroscope. Photographs were taken using a Canon EOS 5D mark II mounted on a Leica MZ APO stereomicroscope. All images are digitally stacked photomicrographic composites of several individual focal planes, obtained using HeliconFocus 6. 7 software. Figures were composed with Adobe Illustrator CC 2019 and Photoshop CC 2019 software.
FUNDING This work was supported by  French National Research Agency  grant no. ANR-19-CE02-0023  (project  SOCIOGENOMICS ), the  Second Tibetan Plateau Scientific Expedition and Research  ( 2019QZKK0706 ), the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  ( XDB26000000 ), and the  National Natural Science Foundation of China  ( 41925008  and  41688103 ).
Methods The experimental data used to parameterize the model We used data from a greenhouse experiment aimed at testing the combined effects of genetic relatedness and abiotic stress on intraspecific interactions. In this experiment, we set up mini-populations of the annual legume Medicago truncatula, composed of kin or non-kin plants. Medicago truncatula is a widespread annual legume native to the Mediterranean basin. The species reproduces predominantly by selfing  (Bataillon and Ronfort 2006, Jullien et al. 2019) , and has high levels of homozygosity, where maternal progenies share the same genotype (i. e. kin). Because of a mixed dispersal strategy where pods of the legume either can fall close to the maternal plant or be passively dispersed over larger distances by attaching to the hair of small mammals, populations of M.
All calculations were performed using the software Mathematica  (Wolfram 2003). Lastly, we performed an analysis of variance (ANOVA) using a single point variable measured at the end of our experiments as response variable. We used our measures of plant volume from our last date of measurement and analyzed plant volume as a function of relatedness (kin versus non-kin), water availability (high/low) and the interaction between these. Estimates of plant volume was square root transformed prior to analysis to fulfill model assumption. We performed this analysis to relate our growth model to a situation where only single point end measures are used.
Milk production phenotypes Data for six economically important milk production traits were analyzed: milk yield per lactation (MY), fat (FY) and protein (PY) yields per lactation, lactation mean fat (FC) and protein (PC) contents and lactation mean SCS (LSCS). The data were obtained from the official milk records. The following milk recording protocol is applied in the blond-faced Manech breed: milk yield is measured individually 4 to 6 times per lactation and the composition of the milk is analyzed from samples taken at 2 to 4 testdays among the first four test-days during the first lactation. ICAR guidelines were followed  [8]. Lactation performances were estimated from these test-day records as described by Rupp et al. [9]. MY was estimated using the Fleischmann method and adjusted for milking length on a reference period of 160 days  [10].
FC and PC were calculated as the arithmetic mean of test-day records adjusted for days in milk (DIM). FY and PY were computed as the product of MY by FC and PC, respectively. Test-day somatic cell counts (SCC) were log-transformed to somatic cell scores (SCS). LSCS were estimated as the weighted arithmetic mean of test-day SCS adjusted for DIM. Milk recording data for 140,127 dairy ewes in first lactation during the 2009-2018 period were included in the analyses. These 10 years included on-farm performances of females in first lactation that were born from rams phenotyped for resistance to GIN.
Statistical analyses Two computational methods were used to estimate the genetic correlations between resistance to GIN and milk production traits. A first method included data resistance to GIN collected on the males and data on milk traits collected on the females, whereas the second method included daughter yield deviations (DYD) of the males for milk traits. The genetic and residual variances were computed in bivariate analyses by average information restricted maximum likelihood. The analyses were performed with the AIREMLF90 program  [11].
Method 1 Heritabilities and genetic correlations were estimated for each measure of FEC (one after each of two successive experimental infections) and the six milk traits using bivariate analyses, which exploit the close pedigree relationship between the rams measured for resistance to GIN and the ewes measured for milk traits. FEC were fourth-root transformed in order to normalize the data before subsequent analyses (root_FEC_inf1 for FEC at the end of the first infection and root_FEC_ inf2 for FEC at the end of the second infection). Fourthroot transformation was selected because it results in the best normalization of the data. The model for FEC traits was: where y ij is the trait (root_FEC_inf1 or root_FEC_ inf2), µ is the population mean, cg i is the contempo- rary group i (combination of the year of measure and dose effects, 9 levels: year (2008) × doses (5000-5000); year (2008) × doses (7500-7500); year (2009) × doses (3500-5000); year (2011) × doses (3000-3000); year y ij = µ + cg i + age j + a j + e ij , (2013) × doses (3500-5000); year (2015) × doses (3500-5000); year (2016) × doses (3500-5000); year (2017) × doses (3500-5000); year (2018) × doses (3500-5000)), age j is the fixed effect of the age of individual j at the time of sampling (4 levels: 1 year; 2 years; 3 years; 4 years and older), a j is the additive genetic random effect of individual j following a normal distribution with mean 0 and variance σ 2 u and e ij is the random residual effect.
Method 2 In this method, additive genetic effects were fitted for the candidate rams only. For resistance to GIN, the same dataset and model as for Method 1 were used. For milk production traits, DYD were computed using data from the national genetic evaluation and the models presented in Method 1. DYD correspond to the average performance of the female offspring of a ram corrected MY ijklm = µ + hy i + age j + ml k + int l + a n + e ijkln , FY ijmn = µ + hy i + age j + c m + a n + e ijmn , PY ijmn = µ + hy i + age j + c m + a n + e ijmn , FC ijmn = µ + hy i + age j + c m + a n + e ijmn , PC ijmn = µ + hy i + age j + c m + a n + e ijmn , LSCS ijkn = µ + hy i + age j + ml k + a n + e ijkn , for environmental effects and the genetic value of the dams  [12].
The motility reduction was produced by comparison of the motility levels of the drug treated group to the negative control groups. The EC 50 values were calculated using a sigmoidal interpolation in Graph Pad Prisme 7 data analysis software. The test drug is considered active when a motility reduction of ≥50% is observed by comparison to the untreated controls. In addition, biochemical evaluation of O. gutturosa adult worm viability was assessed using MTT/formazan colorimetry. The MTT assay was carried out after the last motility reading (120 h). Single intact worms were placed in each well of a 48well plate (Fisher Scientific, UK) containing 0. 5 mL of 0. 5 mg/mL MTT (Sigma, UK) in PBS solution, and then incubated for 30 min at 37 • C. The worms were removed, blotted carefully, and individually transferred to separate wells of a 96-well microtiter plate, each containing 200 μL of DMSO to solubilize the formazan.
Mouse pharmacokinetics All studies were conducted in fed animals (male CD-1 mice) by Charles River Laboratories (Wilmington) INC. Oral dosing (at 30 mg/kg, 10 mL/kg) was as a suspension in 0. 5% carboxymethylcellulose and 0. 25% Tween 80 in water. Plasma samples (at 0. 5, 1. 5, 3, 5, 8 h) were analyzed by LC-MS/MS, and the PK parameters were calculated using Phoenix WinNonlin software.
gutturosa ex vivo data generated for these series. Ligand efficiency (LE, the measure of potency per atom) and ligand lipophilicity efficiency (LLE, the measure of potency relative to lipophilicity) were assessed to determine the relative efficiency for the various series based on ex vivo data. Based on evaluation of these parameters, two distinct chemical series, A and B, emerged in desirable chemical space and LE > 0. 35 and ligand LLE >3 (Fig. 5 ). Assessment of the filarial parasite screening results and evaluation of the drug-like properties of the clustered compound series was based on overall ex-vivo potency and compound properties. Compounds in series A and B that showed activity across the multiple parasites were further progressed into ADME pharmacokinetic (PK) assays in order to assess pharmacokinetic properties such as stability, absorption, distribution, metabolism, and excretion (ADME) and in vivo viability.
Role of the funding source Part of the work described in this paper was funded by former Celgene, currently Bristol Myers Squibb (BMS) and New England Biolabs. NAH, SC, and VK are former employees of BMS. GH is an employee of BMS. JZ is a former employee of Celgene. ZL and CC are employees of New England Biolabs. Work by SG, ST, AE, MK, N L-V, CM, AH, SS, and MH was funded by the Drugs for Neglected Diseases initiative through funding provided by USAID. The Drugs for Neglected Diseases initiative (DNDi) is grateful to the following donors for funding part of this work: the Bill & Melinda Gates Foundation, USA (Grant no. OPP1111431) and the US Agency For International Development (USAID). The contents are the responsibility of the Drugs for Neglected Diseases initiative and do not necessarily reflect the views of the donors.
FA methyl esters were prepared by acid-catalyzed transmethylation of total lipids using boron trifluoride (BF 3 ) in methanol (14%) according to the Shantha and Ackman method  [34]  and analyzed in a Varian 3900 gas chromatograph (Varian, les Ulis, France) equipped with a fused silica DB wax capillary column (30 m x 0. 25 mm internal diameter, film thickness 0. 25 μm; JW Alltech, France). Injection volume was 1 μl, using helium as a carrier gas (1 ml/min). The temperatures of the injector and the flame ionization detector were 260 and 250 °C, respectively. The thermal gradient was as follows: 100-180 °C at 8 °C/min, 180-220 °C at 4 °C/min and a constant temperature of 220 °C for 20 min. FA were identified with reference to a known standard mixture (Sigma, St Louis, MO, USA) and peaks were integrated using Varian Star Chromatography Software (Star Software, version 5).
Therefore, both krt8b, 18 s and eef1α were chosen for normalization in tongue tissue (Figure  3 ). Relative expression of the target genes was determined by the ΔΔCT (Cycle Threshold) method  [44]. Mean ± S. E. M. values for each group are expressed in fold changes relative to organ (tongue for Figure  4 ) or C diet (Figures  3  and 5  and supplemental information 2). The mRNA sequences of RT used in this study are available in the NCBI and Ensembl genome browser. By comparing other species (teleosts, rodents and humans), we found the available sequences of genes in RT with variant paralogs and accession numbers (Table  4 ). Mammalian species were chosen because their fatty acid receptors in particular have been characterized and studied. The zebrafish (danio rerio) model (the main fish specie model in the world, Relative gene expression measured by RT-PCR of ffar1, ffar2a, ffar2b/e, ffar2c/f, ffar2g, ffar3a, ffar3b, gnat2, gpr84, gpr119, cd36, tas1r1a, tas1r1b, tas1r2 tas1r3, casr and gpr92/93 in the tongue, lip, palate, pharynx, gill filament, liver, intestine, stomach and heart of RT fed for 30 days with the C diet.
Values are expressed as group mean ± SEM; fold change v. tongue for all genes (n = 6). One-way ANOVA, Tukey post hoc. 1 2 3 4 5 6 7 8 9 1 0 ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ ⁎ (b) 11 Aquaculture Nutrition where the genome and receptors have already been studied and quantified) allowed us to compare the RT receptors with another fish species (and to quantify the differences between species (between fish species or fishes v. mammals). All gene sequences of RT used were identified by in silico analysis from the Genomicus software program, version 100. 01 (http://www. genomicus. biologie. ens. fr) and Ensembl (http://www. ensembl. org, Ensembl Release 102; November 2020, RT genome available). They were queried against the human, rodent and zebrafish genomes using the BLAST tool in Ensembl and in NCBI (https://blast.
ncbi. nlm. nih. gov/ Blast. cgi) to confirme gene identification (Table  5 (a)). For paralog genes (such as ffar2), the percentage of similar sequences was determined by alignment of the mRNA of RT genes using MUSCLE software (http://www. ebi. ac. uk/ Tools/msa/muscle) and compared to other species (Table  5 (b)). In the case of genes that possesed numerous paralogs, when some of them presented high similar sequences (> 95%), we designed primer pairs to amplify paralogs together in order to not distinguish the expression of each of them (ffar2 for paralog b and e, c and f). 2. 8. Statistical Analysis. Statistical analyses were performed using R software (v3. 5. 2)/R Commander package. Data were tested for normality of distribution and homogeneity of variance using the Shapiro-Wilk test and Bartlett's test, respectively.
, 2015; Larrieu et al. , 2019). Formerly conducted studies on the IBP aimed to calibrate the index with taxonomic data by quantifying the capacity of each of the factors gathered in the index to co-vary with empirical species richness and composition  (Herrault et al. , 2016; Larrieu et al. , 2019). They used the number of items recorded in the field per factor, e. g. the total number of very large trees per ha, as dependent variables. This study is the first to assess whether changes in IBP scores reflect changes in species richness and therefore used IBP scores as dependent variables. Species of interest included spiders, bats, birds, bryophytes, carabids, fungi, true bugs, lichens, moths, necrophagous beetles, phytophagous beetles, plants, and saproxylic beetles. The research framework provided by the Biodiversity Exploratories provides valuable and unique data to compare the IBP to direct species monitoring.
, 2019) , which is available to the public (Biodiversity Exploratories, 2020) and which builds on the following datasets (IDs): 4141  (Müller et al. , 2009) , 4060  (Boch et al. ) , 15386  (Kahl and Bauhus, 2012) , 16866  (Gossner et al. , 2017a) , 16867  (Gossner et al. , 2017b) , and 16868  (Gossner et al. , 2017c) , 18547  (Blaser and Fischer, 2017 ), 19848 (Jung and Tschapka, 2016a ), 19849 (Jung and Tschapka, 2016b ), 19850 (Jung and Tschapka, 2016c) , 21446  (Renner et al. , 2017a) , 21447  (Renner et al. , 2017b) , 21448  (Renner et al. , 2017c). The different species groups were included in the model function as 13 different levels. Species richness was defined as the total number of species per species group and was used in this study as a surrogate for biodiversity. The forest type classification of the experimental plots was used according to the data set 17706  (Schall and Ammer, 2021).
Time difference between species monitoring and IBP recording Since species richness on the experimental plots was monitored only once between 2007 and 2018, we expected some variation between the year of monitoring and the year 2020, when the IBP was recorded. Three plots that had been affected by storms during that time period were excluded from the analysis. However, very few of the IBP factors are likely to have changed dramatically over a short time. Still, the data on species richness was unique and was the best available for a comparison with the IBP on the same plots. We therefore considered the time difference acceptable and assumed that in those cases where a relationship between species richness and IBP was found, this relationship would have been even stronger if the time difference between recording species and assessing the IBP had been smaller.
Acknowledgements We thank the managers of the three Biodiversity Exploratories,  Konstans Wells ,  Swen Renner ,  Kirsten Reichel-Jung ,  Sonja Gockel ,  Kerstin Wiesner ,  Katrin Lorenzen ,  Juliane Vogt ,  Andreas Hemp ,  Martin Gorke  and  Miriam Teuscher  for their work in maintaining the plot and project infrastructure;  Simone Pfeiffer ,  Christine Fischer  and  Victoria Grießmeier  for giving support through the central office,  Jens Nieschulze ,  Michael Owonibi  and  Andreas Ostrowski  for managing the central data base, and  Markus Fischer ,  Eduard Linsenmair ,  Dominik Hessenmöller ,  Daniel Prati ,  Ingo Schöning ,  François Buscot ,  Ernst-Detlef Schulze ,  Wolfgang W. Weisser  and the late  Elisabeth Kalko  for their role in setting up the  Biodiversity Exploratories  project.
Diagnosis Virus diagnosis is key to efficient disease management, in particular during sanitation programs, which are the backbone of any clean seed production program. Inaccurate diagnosis leads to either false negatives or false positives that can have minor (e. g. , misrepresentation of the distribution of viruses) to major impacts (e. g. , the spread of viruses to new regions through the exchange of infected germplasm). For example, based on electron microscope observations, CMV was reported on D. trifida in Guadeloupe in 1977  [14]. Still, CMV presence has never been confirmed, despite repeated attempts based on large scale samplings and the use of sensitive RT-PCR-based CMV-specific diagnostics  [8]. Additionally, CMV was reported to infect yam in West Africa at low incidences  [15, 16] , but the analysis of high throughput sequencing datasets from D.