dataesr/scientific-paragraphs-categorization huggingface dataset:
Visualising human, mosquito and intervention characteristics The AnophelesModel package can provide visualisations of the entomological characteristics of mosquito species at different locations and model how these impact various vector control interventions. One resource included in the package is a readily available database encompassing human activity patterns, mosquito biting patterns, mosquito entomological characteristics and intervention characteristics. The user can directly access the various data types through dedicated data objects. A detailed description of these data objects is provided in the package documentation.
The AnophelesModel package is flexible beyond the provided database, allowing the user to plug in new data and parameters and model intervention effects for a custom setting. The package database is not exhaustive and does not account for seasonal variation or variation by human age or occupational group. The package is a powerful tool for exploring how the impact of vector control interventions changes following the observed variation in input mosquito biting and human behaviour patterns. Planned developments of the AnophelesModel package include extension of the database of mosquito, human behaviour and intervention characteristics through systematic reviews, including more recentlygenerated data and intervention models. Currently three interventions are modelled within the package, namely IRS, LLINs and house screening, but other interventions such as spatial repellents and attractive toxic sugar baits will be added in the future.
Availability and Future Directions The AnophelesModel R package source code and data are publicly available online in a dedicated GitHub repository at https://github. com/SwissTPH/AnophelesModel. A user-friendly website available at https://swisstph. github. io/AnophelesModel/index. html provides package installation instructions, comprehensive descriptions of functions, parameters and data, and detailed examples of use-cases. A systematic tutorial and documentation of the different package functions are provided at https://swisstph. github. io/AnophelesModel/articles/AnophelesModel. html. Furthermore, all code used for the examples presented in this paper and for generating the corresponding figures is available at https://github. com/SwissTPH/AnophelesModel/tree/main/extdata. This include the XML files and scripts used for OpenMalaria simulations.
D The package database can be queried, for example to analyse how An. gambiae, among the dominant malaria vectors in sub-Saharan Africa (55) , differs from An. farauti, a major vector in Papua New Guinea (PNG) (Fig2). The two species are different not only in their bionomics, but also in terms of their biting patterns. An. gambiae has higher parous rates, sac rates, and human blood index, and is more endophagic than An. farauti (Fig2A). Furthermore, An. gambiae preferentially bites indoors during the night, while An. farauti also bites outdoors, especially in the early evening (Fig2B). These differences all affect the modelled impacts of interventions such as LLINs. In the following example, we demonstrate how AnophelesModel can be used to compare the impacts of LLINs for these two species in their respective settings mainly relying on the data present in the package database, and incorporating new, recently published data on human behaviour for a PNG-like setting (56) (Fig2C).
2012; Isidro y Sánchez and Akdemir 2021). Different optimization criteria have been proposed to define the TRS  (Rio et al. 2022b). Rincent et al. (2012)  proposed optimizing the TRS by maximizing the mean of the coefficient of determination (CDmean) of contrasts between each unphenotyped PS individual and the target population mean. Numerous studies have shown that building the TRS using the CDmean significantly increases the accuracy of GS models relative to random sampling  (Isidro y Sánchez and Akdemir 2021; Rio et al. , 2022; Fernández-González et al. 2023). In a breeding program, where genomic prediction is applied routinely, a large dataset from previous years of phenotyping is available for model training. One can wonder which phenotypic data from the previous generation(s) should be included in the TRS and which additional hybrids should be phenotyped to complete the existing TRS and achieve the highest prediction accuracy for the new generation with a given phenotyping effort.
Acknowledgments We thank  Lidea ,  Limagrain Europe ,  Maïsadour Semences ,  Corteva ,  RAGT 2n ,  KWS  and  Syngenta Seeds  grouped in the frame of the  ProMais SAM-MCR  project for the funding, inbred lines development, hybrid production, and phenotyping. We are also grateful to scientists from these companies and scientists of the  INRAE-CIRAD "R2D2 " network for helpful discussions on the results. A. L. PhD contract was funded by  RAGT 2n  and  ANRT  contract n°  2020/0032 , with the contribution of all  SAM-MCR  project partners. GQE-Le Moulon  benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Funding Lidea ,  Limagrain Europe ,  Maïsadour Semences ,  Corteva ,  RAGT 2n ,  KWS  and  Syngenta Seeds  grouped in the frame of the ProMais association funded the  SAM-MCR  project. A. L. PhD contract was funded by  RAGT 2n  and  ANRT  contract n°660  2020/0032 , with contributions of all  SAM-MCR  project partners. GQE-Le Moulon  benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Consequently, to avoid multiple analyses, keep a reasonable number of loci and for the sake of consistency, population genetics data analyses were undertaken with females only, at the eight loci that were successfully amplified over all the dataset (see Table  S1 ): X55-3, XpGp13, pGp24, A10, B3, XB104, C102, and GPCAG. Unless specified otherwise, the global dataset (File S1) was processed with Create  (Coombs et al. , 2008)  to produce datasets in different formats, depending on the kind of analyses to be undertaken.
Null allele frequencies were also estimated with the EM algorithm  (Dempster et al. , 1977)  implemented by FreeNA  (Chapuis & Estoup, 2007). For these analyses, missing genotypes (true blanks) were coded as null homozygotes for allele 999, as recommended  (Chapuis & Estoup, 2007). The goodness of fit of observed blank genotypes, with the expected number of missing data, was assessed with a one-sided exact binomial test with R (command "binom. test"). If null alleles and random mating fully explain the data, there should be at least as many blank genotypes as expected. For this test we computed the square of null allele frequency of each locus in each subsample and multiplied it by the subsample size. To increase statistical power, we summed these expected number of null homozygotes over all subsamples (NExp-b) and compared those to the observed number of missing genotypes for each locus, over all the considered dataset.
We then also undertook the regression FIS~pn, where pn is the global null allele frequency. Stuttering detection and correction followed the procedure described elsewhere (De  Meeûs et al. , 2021; De Meeûs & Noûs, 2022 ) over all subsamples and for each locus, using the associated template "TestStutterDioecious-n1000N100-1-10%Stuttering. xlsx" available at https://zenodo. org/record/7029324, exact binomial tests and adjustment of pvalues with Benjamini and Hochberg (BH) procedure with R (command "p. adjust"). Short allele dominance (SAD) was looked for with the correlation method between FIT and the size of the alleles  (Manangwa et al. , 2019)  using a one-sided (negative relationship) Spearmans rank correlation test. In case of doubt, we also undertook the regression FIS~Allele size, weighted with pi(1-pi), and where pi was the frequency of the allele (De  Meeûs et al.
Acknowledgements We thank  Stéphane Verger ,  Alice Malivert  and  Olivier Hamant  for providing the ktn1, csi1-3, prc1-1 and spr2-2 mutants and the p35S::TUA6-GFP, pPDF1::mCitrine-MBD and pPDF1::CFP-N7 reporters,  Miguel Botella  for providing the ttl1 ttl3 mutant,  Frederic Berger  for providing the iku2-2 mutant,  Marilyn Vantard  for providing the p35S::MAP65-1-RFP reporter and  Marie-Cecile Caillaud  for providing the MAP65-1-pENTR-L1-L2 plasmid. We also thank  Olivier Hamant ,  Marie-Cécile Caillaud ,  Magalie Uyttewaal ,  Olivier Ali , and  Charlotte Kirchhelle  for helpful discussion and comments on the manuscript;  Audrey Creff ,  Vincent Bayle ,  Olivier Ali ,  Claire Lionnet  and  Corentin Mollier  for technical assistance with experiments and analysis; the  PLATIM  ( SFR128 Biosciences Lyon ) and the  Bio21 Advanced Microscopy Facility (University of Melbourne)  for technical assistance with microscopy;  Alexis Lacroix ,  Patrice Bolland  and  Justin Berger  for technical assistance with plant cultivation;  Isabelle Desbouchages  and  Hervé Leyral  for technical assistance regarding molecular biology work;  Cindy Vial ,  Laureen Grangier ,  Nelly Camilleri  and  Stéphanie Maurin  for administrative assistance.
Biosensor FRET activity Images were acquired with a confocal microscope (Leica SP8 inverted) with the pinhole opened to the maximum in order to increase slice thickness and the signal to noise ratio for the analysis. We used two Photo Multiplier Tubes (PMTs) as detectors. Bandwidths were chosen as follow: 472nm-512nm for mTFP1 (CFP analog, donor) and 523nm-544nm for Venus (YFP analog, acceptor). mTFP1 was excited by an argon laser at 468nm with 15% of power and emission for mTFP1 (donor) and Venus (FRET) were simultaneously acquired by the two PMTs. Images were then corrected and processed with Biosensor Package from Danuser Lab (available on the following link: https://github. com/DanuserLab/Biosensor). Because it is a unimolecular biosensor, no bleedthrough correction was done. Prior analysis, histograms were manually resliced in order to remove high and close-to-zero boundary artefacts.
Acknowledgments We thank  J. Van Unen  and  M. Inamdar  for discussions and feedbacks. We also thank  A. Honigmann  for kindly sharing the ZO1-GFP MDCK cell line, the  Imaging Platform of IGBMC , and the  Riveline Lab. for help and discussions. S. L. V. is supported by the  University of Strasbourg  and by la  Fondation pour la Recherche Médicale. D. R. , M. S. and L. N. acknowledge supports from  Idex Unistra  and from the  Cell Physics Master at the University of Strasbourg. O. P. and D. R. thank funding from  SNF Sinergia. This study has been also supported by a  French  state fund through the  Agence Nationale de la Recherche  under the  frame program Investissements d'avenir labeled   ANR-10-IDEX-0002-02. D. R. and O. P. acknowledge funding from a  Swiss National Science Fundation Sinergia  grant.
Improved nitrogen utilization of dairy production systems should improve not only the economic output of the systems but also the environmental metrics. One strategy to improve efficiency is through breeding programs. Improving a trait through breeding is conditional on the presence of exploitable genetic variability. Using a database of 1,291 deeply phenotyped grazing dairy cows, the genetic variability for 2 definitions of nitrogen utilization was studied: nitrogen use efficiency (i. e. , nitrogen output in milk and meat divided by nitrogen available) and nitrogen balance (i. e. , nitrogen available less nitrogen output in milk and meat). Variance components for both variables were estimated using animal repeatability linear mixed models. Genetic variability was detected for both nitrogen utilization metrics, even though their heritability estimates were low (<0.
Data The data from grazing dairy cows used in the present study were already described in detail by  Tavernier et al. (2023a). In summary, routinely recorded milk yield (daily), milk composition (weekly), BW (every 2 wk), and feed intake (sporadically per lactation; on average, twice per lactation) were available on 2,241 lactations from 1,291 dairy cows of multiple breeds and crossbreds namely Holstein-Friesian (896 cows), Jersey (61 cows), their cross (181 cows), and "others" (153 cows). Grass dry matter intake (GDMI) of the grazing animals was estimated using the n-alkane technique  (Mayes et al. , 1986). With this procedure, cows were dosed twice daily with C32, an artificial even-chain alkane, for 12 d. The n-alkane of individual cow fecal samples taken twice daily from d 7 was quantified as was that from a representative sample of grass.
These fecal samples were bulked per animal across all 6 d of sampling and GDMI was then estimated using the formula of  Mayes et al. (1986). Accompanying these data was information on the CP content of both the grass and the concentrate offered to the cows. All data were collected from experiments conducted between the years 2008 and 2018 on 4 experimental dairy farms of the Teagasc Dairy Research Centre, Moorepark, Ireland.
Sources and Sinks of Nitrogen As in  Tavernier et al. (2023a) , the 2 sources of available nitrogen to dairy cows considered were nitrogen intake (N intake ) and nitrogen mobilized, with their sum representing the total nitrogen available (N avail ). Nitrogen intake was the sum of the CP intake from both the grass and the concentrate converted to nitrogen equivalents  (Jones, 1931). To compute the nitrogen mobilized from the cow's reserve, the energy balance of the cows (i. e. , energy intake less the energy used by the cows expressed in Unité Fourragère Lait) was computed as described in detail for this dataset by  Tavernier et al. (2023a) , based on the approach proposed by  Sauvant et al. (2018a)  for dairy cows. When the energy balance was positive, the nitrogen available from mobilization of the dairy cow's reserves was equal to 33/6.
(2023a)  for the dataset used in the present study. In summary, a population-wide quadratic polynomial across age (i. e. , a fixed effect), along with animal-specific coefficients (i. e. , random effects) representing intercept and linear deviations from the population-wide growth profile was fitted to 283,709 BW records corrected for the pregnancy for all cows. This enabled the estimation of cow BW for every day of age, and the difference between consecutive days was used to calculate BW change. Nitrogen used for the conceptus was 4/3 times the estimated nitrogen in the fetus; the estimated fetus weight was computed as in  Agabriel and de la Torre (2018) , who used the number of days in gestation and calf weight at birth to estimate the weight gain of the fetus. Finally, the quantity of nitrogen stored in body reserves was computed from the derived energy balance of the animal; the nitrogen stored in body reserves was equal to 33/6.
Nitrogen Utilization Metrics The information on the nitrogen sinks and sources was used to define 4 different nitrogen utilization metrics representing a subset of the 9 traits described in detail by  Tavernier et al. (2023a)  based on the same dairy cow dataset used in the present study. The 4 nitrogen utilization metrics included 2 nitrogen use efficiency metrics (i. e. , ratio traits of inputs and outputs) along with 2 nitrogen balance metrics (i. e. , calculated as the difference between input and outputs). Nitrogen use efficiency of milk (NUE milk ) represents the nitrogen intake partitioned toward milk production and is a commonly used definition  (Powell et al. , 2010; Gourley et al. , 2012). In the present study, NUE milk was the sum of the true protein yield, converted to nitrogen equivalents, and MUN, all divided by the nitrogen intake: NUE = N N milk milk intake , where N milk was the nitrogen output in milk and N intake the nitrogen intake.
Validation of Genetic Evaluations For validation of the genetic evaluations, only the pure Holstein-Friesian (i. e. , the most dominant breed) were considered (i. e. , crossbreds and Jersey cows were not considered). The dataset was divided into a calibration set and a validation set with no cow present in both subsets. The calibration dataset comprised 3,577 nitrogen utilization records from 1,076 dairy cows. The validation dataset consisted of records of pure Holstein-Friesian between the years 2017 and 2018 from one of the research farms. The validation dataset comprised 154 Holstein-Friesian dairy cows with 422 nitrogen utilization and intake records, as well as 4,848 BW records and 2,913 BCS records. The average nitrogen utilization metrics, as well as the performance of the full, calibration, and validation datasets are detailed in Supplemental Table  S1  (https: / / figshare.
com/ articles/ figure/ Sup _table _1 _genet _Nit _E _Tavernier _pdf/ 24015909;  Tavernier et al. , 2023b). Variance components were estimated using the already described mixed model applied to the calibration dataset. These estimated variance components were then used for a genetic evaluation using the calibration dataset in the Mix99 software suite  (Lidauer and Strandén, 1999). The pedigree of all calibration and validation animals were included enabling the estimation of breeding values for the validation animals despite not having any phenotypes in the genetic evaluation. To quantify the differences in performance between animals divergent on EBV for nitrogen utilization, the validation animals were stratified by EBV into 3 equal groups to generate low-, middle-, and high-EBV groups. The predicted marginal means per group for each of the efficiency and performance traits were computed using the same statistical model as that used to estimate the variance components except that the EBV group was included as a categorical fixed effect; the contemporary group was considered as a random effect, and the additive genetic and permanent environmental effects were collapsed into a single random cow effect.
RESULTS Average daily milk yield of the cows in the dataset was 19. 3 (SD = 6. 2) kg with an average intake of 16. 6 (SD = 2. 7) kg DM for the reference cow. The average nitrogen intake was 556 (SD = 114) g of N/d, resulting in an average Nbal tot of 441 (SD = 100) g of N/d and a NUE tot of 0. 220 (SD = 0. 003) for the reference cow (Table  1 ).
Sequence data analysis The FROGS pipeline was used to perform the initial analysis of the sequence data  [34]. The forward and reverse reads of each sample were merged, the adapter sequences were trimmed, and the sequences with long ambiguous bases (N) were removed (18. 74 % sequences withdrawn). After the quality lter steps, the resulting in 12,770,918 sequences, representing 81. 26% of the initial input sequences were used for the downstream analysis. To group together amplicons with a maximum of one nucleotide difference between two amplicons, the clustering swarm protocol was used  [35] , resulting in the creation of 1,081,234 clusters. Clusters having an abundance less than 0. 005% and not present in at least 4 samples were removed. This step resulted in 385 clusters. The PhiX database was used to remove the sequences corresponding to chloroplast and mitochondria  [36].
The taxonomic a liation was made with the silva138. 1 pintail 100 16S reference database  [37]. Finally, the samples had an average of 130,308 ± 24,495 sequences (minimum: 38,085; maximum: 194,676 sequences) with 239 OTUs (operational taxonomic units), ranging from 63 to 136 OTUs per sample. Microbiota data were all rare ed to 38,085 sequences/sample.
Acknowledgements We are grateful to the genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, doi: 10. 15454/1. 5572369328961167E12) and  Sigenae group  for providing help on computing and storage resources. Thanks to Galaxy instance of the Sigenae group http://sigenae-workbench. toulouse. inra. fr. We thank  Frédéric Terrier ,  Franck Sandres  and  Anthony Lanuque  for the sh rearing at the  Donzacq experimental sh farm (INRAE, France ).
-1. A Voice 200 Ultra SIFT-MS (SYFT Technologies, Christchurch, New Zealand) generating three positive soft reagent ions (H 3 O + , O 2 + , NO + ) and with the nitrogen carrier gas (Air Liquid, Alphagaz 2) was used in this study. Full-scan mass spectra were recorded for each positive precursor ion in a range from 15 to 250 with an integration time of 60 sec and accumulated during 16h. Identi cation was based on speci c ion-molecule reaction patterns of target analytes with the three positive precursor ions described in the literature and in the database from LabSyft software (LabSyft 1. 6. 2, SYFT Technologies). Product ions from ion-molecule reactions of SCFA are summarized in Figure S1. In SIFT-MS analysis, quanti cation is straight forward and requires only measurement of the count rate of the precursor ion [R] and product ions [P].
The Unfolded Protein Response (UPR) is a retrograde, ER-to-nucleus, signalling pathway which is conserved across kingdoms. In plants, it contributes to development, reproduction, immunity and tolerance to abiotic stress. This RNA sequencing dataset was produced from 14-day-old Arabidopsis thaliana seedlings challenged by tunicamycin (Tm), an antibiotic inhibiting Asn-linked glycosylation in the endoplasmic reticulum (ER), causing an ER stress and eventually activating the UPR. Wild-type (WT) and a double mutant deficient for two main actors of the UPR (INOSITOL-REQUIRING ENZYME 1A and INOSITOL-REQUIRING ENZYME 1B) were used as genetic backgrounds in our experimental setup, allowing to distinguish among differentially-expressed genes (DEGs) which ones are dependent on or independent on IRE1s.
VALUE OF THE DATA These data contribute to our molecular understanding of the role of UPR under chronic ER stress, a situation that mimics environmental conditions where plants could be exposed to prolonged stresses, and trigger an acclimation response. This situation is poorly documented in the literature thus far. Our experimental design that consists of separately analyzing shoots and roots also allows for the identification of organ-specific transcriptional responses. In addition to experts working on ER physiology and UPR, our transcriptomic dataset can benefit any researchers interested in plant response to stress, as ER stress is known to be triggered by numerous abiotic and biotic stresses. Since UPR is also invoked in root development and reproduction, our data can be reused by a wider part of the plant biologist community.
DATA DESCRIPTION How the UPR differentially contributes at the organ level (shoots and roots) to plant tolerance under chronic ER stress needs to be documented. Here, we report on an organ-specific RNA sequencing dataset obtained from Arabidopsis WT (Col-0) and double ire1 mutant (ire1dm) seedlings grown in vitro under chemically-induced ER stress condition. ire1dm provided by Pr. Koizumi (Osaka Prefecture University, Japan) were re-genotyped by PCR using left border and gene-specific primers (Fig. 1a-c ). Based on PCR product sequencing (data not shown), T-DNA insertion in IRE1B gene was confirmed to locate upon the 1014 th nucleotide from the start codon (Fig. 1b ), as expected from  [4]. By contrast, insertion in IRE1A gene was relocated to position 2330 (counting from the start codon, Fig.
ACKNOWLEDGEMENTS We wish to warmly thank  Pr. Nozomu Koizumi  ( Osaka Prefecture University, Japan ) for sharing his genetic resources, including the double ire1 mutant. We also warmly thank  Pr. Stephen Howell  ( Iowa State University, USA ) for providing the bzip60-3 mutant. We thank  Dr. Ivan Le Masson  ( UMR Agronomie, INRAe, France ) for protocol and tips about RNA precipitation by lithium chloride. We thank  Pr. Loïc Rajjou  ( AgroParisTech, University Paris-Saclay, France ) for stimulating discussions. We also would like to thank  Pr. Marie-Noëlle Bellon-Fontaine  and  Dr. Aurélie Baliarda  ( AgroParisTech, University Paris-Saclay, France ) for their kind support. RNA sequencing was supported by  AgroParisTech ["Fédérateur "  funding call, grant  AOSV_001_4PBA ].