dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Pr Patrice Courvalin ,  Antimicrobien Agents Unit, National Reference Center (NRC) ,  Pasteur Institute of Paris  for the kind provision of reference strains used for the specificity assays and  Dr Frederique Randrianirina ,  Clinical Center of Biology, Pasteur Institute of Madagascar, Antananarivo, Madagascar , for the kind provision of the clinical isolates and specimens used for the evaluation test in this study. We specially thank all staff of  Bacteriology Experimental Unit, Pasteur Institute of Madagascar ,  Antananarivo Madagascar.
Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The sequences for the analysis were obtained from GenBank NCBI www. ncbi. nlm. nih. gov/ (Accession number ADK56074. 1, HM146786. 1, HM146785. 1, HQ25931. 1, HQ25930. 1, KQ960958. 1).
N-body simulations We calibrate our predictions for the number of substructures in a WDM model using high-resolution DM-only N-body simulations of cosmological volumes. The Copernicus Complexio (COCO) suite consists of two zoom-in simulations: one of ΛCDM that we refer to as COCO-COLD  [32] , and the other of 3. 3 keV thermal relic WDM, hereafter COCO-WARM  [33]. These two versions differ only in the matter power spectra used to perturb the simulation particles in the initial conditions. Both COCO-COLD and COCO-WARM are simulated in periodic cubes of side 70. 4 ℎ -1 Mpc using the 3 code that was developed for the Aquarius Project  [24]. The high-resolution regions correspond approximately to spherical volumes of radii ∼18 ℎ -1 Mpc that each contain ∼1. 3 × 10 10 DM particles of mass,   =1.
First, ref. [90]  use an abundance matching technique extrapolated to very faint magnitudes to populate substructure with galaxies. Such techniques adopt a model to describe the relationship between the DM structure and the luminous component; however, they may not capture the full complexity of galaxy formation physics at the faint end  [14]. Semi-analytic models like the one used in this work are physically motivated and fare better at modelling the baryonic processes taking place on small scales, encapsulating more of the complexities of galaxy formation in this regime; however, they are not entirely free of simplifying assumptions. Secondly, the ref. [90]  results are based on a combination of Pan-STARRS and DES data whereas our analysis uses satellite galaxy data from SDSS and DES. The Pan-STARRS survey data are not as deep as those from SDSS, particularly at the faint end of the satellite galaxy luminosity function.
Acknowledgments The authors thank the anonymous referee for valuable comments on the manuscript. We also thank  Piotr Oleśkiewicz  and  Andrew Griffin  for their help to understand and modify the code, and  Sownak Bose  for his assistance with using COCO simulation data. ON thanks  Steven Gillman  for his hospitality during the final stages of this work. ON was supported by the  Science and Technology Facilities Council (STFC)  through grant  ST/N50404X/1  and acknowledges support from the  Institute for Computational Cosmology (ICC) PhD Scholarships Fund  and thanks the benefactors who fund it. ON also acknowledges financial support from the  Project IDEXLYON at the University of Lyon  under the Investments for the  Future Programme  ( ANR-16-IDEX-0005 ) and supplementary financial support from  La Région Auvergne-Rhône-Alpes.
MC, ARJ and CSF were supported by  STFC  grant  ST/L00075X/1. MC and CSF were supported by the  ERC Advanced Investigator  grant, DMIDAS [GA 786910]. MC acknowledges support by the  EU   Horizon 2020 research and innovation programme  under a  Marie Skłodowska-Curie grant agreement   794474  (DancingGalaxies). This work used the DiRAC@Durham facility managed by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). The equipment was funded by  BEIS  capital funding via STFC capital grants  ST/K00042X/1 ,  ST/P002293/1 ,  ST/R002371/1  and  ST/S002502/1 ,  Durham University  and  STFC  operations grant  ST/R000832/1. DiRAC is part of the  National e-Infrastructure.
Statistics Data were analysed using Prism 8 (version 8. 4. 3) GraphPad Software (GraphPad Software, San Diego, CA, USA). Data are presented as mean ± standard deviation. The data were checked for normality of distribution using the D'Agostino and Pearson test. Differences amongst means were tested using a one-way ANOVA followed by the Bonferroni's multiple comparison test. Statistical difference was set at P < 0. 05. In order to test the independence of USI from sex of the young controls and of the elderly participants, the unpaired, Student's t-test was used separately for the two populations. Moreover, independence of USI values of young and elderly participants with height and body mass was tested using the Pearson's correlation. USI score of each patient was compared with the USI mean value of the young reference group (YC), and a Z-score was calculated as USI Z score ¼ USI value À mean USI YC SD USI YC Pearson's correlation (r) and simple linear regression were calculated to determine relationships between the USI and the SMI data.
Data sampling Between 2016 and 2019, we sampled the phenotype and genotype information of 244 honey bee colonies, the majority of the samples belonging to A. m. mellifera (MEL) from Switzerland. Our data collection included 146 MEL colonies belonging to a selection programme (SL_CH), sampled at different test apiaries in Switzerland, as well as 70 MEL colonies from two conservation areas located in Switzerland (CS_CH, 45 colonies) and France (CS_FR, 25 colonies). We also included 28 A. m. carnica colonies (CAR) to identify putative outliers within our MEL sample and to verify the identified QTL in another honey bee subpopulation. For each colony, we collected approximately 500 workers using a standardised sampling method: the volume corresponding to 500 workers had previously been determined and was used to select a jar of corresponding capacity for sampling.
DNA extraction The approximately 500 workers per colony were shredded in a DNA extraction solution containing 4 M urea, 10 mM Tris-HCl pH 8, 300 mM NaCl, 10 mM EDTA, using a blender. The resulting mix was centrifuged for 15 min at 3500 g, and 200 µl of supernatant was used for DNA extraction. 0. 5 mg proteinase K and 15 µl of DTT 1 M were added before incubation overnight at 56 °C. The lysate is then used for DNA extraction with a QIAsymphony instrument and DNA Mini Kit (Qiagen). The final elution volume is 100 µl. Pair-end sequencing was performed on a Illu-minaTM HiSeq 3000 or a NovaSeq 6000 platform. The number of samples per lane was optimised to target approximately 309 raw sequencing data per sample.
Sequence alignment Raw reads from pool sequencing of the 244 colonies were aligned to the honey bee reference genome Amel_HAV3. 1, Genebank assembly accession GCA_003254395. 2 (Wallberg et al. , 2019), using  BWA-MEM (v0. 7. 15; Li, 2013) , and duplicates were marked with Picard (v2. 18. 2; https:// broadinstitute. github. io/picard/, last accessed 5 March 2021). Samples sequenced in two runs were merged with SAMTOOLS (v1. 8; Li et al. , 2009).
Files produced by mpileup were interpreted by the PoPoolation2 utility mpileup2sync  (Kofler et al. , 2011)  for the Sanger Fastq format, with a minimum quality of 20. Finally, sync files were converted to a depth file containing a sequencing depth value for each SNP and count files summarising reference and alternative allele counts for each SNP, using a custom-made Python script  (van Rossum & Drake, 2009).
Genome-wide association studies and candidate gene mapping Genome-wide association studies were performed on SNP dosage data using a linear regression model as implemented in PLINK  (Purcell et al. , 2007). We adjusted the model for covariates capturing population stratification (PCA) and significant effects (e. g. apiary) on the traits. These effects were identified by ANOVA on a linear regression model containing apiary, hive type, evaluator, and year. Significant associated SNPs were determined based on a 1% genome-wide Bonferroni-corrected threshold. The GWAS results were visualised using Manhattan and quantilequantile plots with the R package qqman (Turner, 2014). Furthermore, the effect and the allele frequency of the bestassociated SNP for each trait and subpopulation (including CAR) were calculated and visualised.
Population stratification of the sampled colonies The first principal component (PC1), accounting for 98% of the total variance, clearly separated the CAR from MEL colonies and simultaneously highlighted the existence of five outliers within the MEL colonies (one SL_CH and four CS_FR; Fig. 2 ). The second principal component (PC2), accounting for 1% of the total variance, illustrated that CS_FR gradually overlapped with SL_CH colonies, whilst CS_CH colonies tightly clustered. After this result, the five MEL outliers were removed from the dataset, and the PCA was re-calculated without CAR colonies to account for the population stratification of the 211 purebred MEL colonies in the GWAS analyses (not shown). The GWAS analyses were performed on 1 326 634 genome-wide SNPs.
Acknowledgements Financial support for this study was provided by  Bundesamt f€ ur Landwirtschaft BLW (Swiss Federal Office for Agriculture FOAG) , grant No. 627000708 , and by  Labogena and FranceAgriMer  ( Programme d'Investissements d'Avenir ). We thank  Lucie Genestout  ( Labogena ) for coordinating the  Beestrong  project,  Christina Sann  and  Yannick Poquet (Labogena)  for organising the sampling in Savoie, and  Alice Revel (Agroscope)  for her help in organising the sampling in Switzerland. We thank  Rachid Mahla  ( Labogena ) for DNA extraction and  Olivier Bouchez  ( GeT-PlaGe ) for sequencing. Sequencing was performed in collaboration with the  GeT platform, Toulouse (France) , a partner of the National Infrastructure France Genomique, thanks to support by the  Commissariat aux Grands Investissements  ( ANR-10-INBS-0009 ).
Solvent Fluoroflex's solubility parameter was calculated from the swelling data collected to estimate polymer-solvent interactions of solvents not tested. However, a singlecomponent, Hildebrand solubility parameter model was found to be insufficient in describing the polymersolvent interactions observed. For example, acetone and dichloromethane both have a Hildebrand solubility parameter of 20. 2 joule 1/2 cm -3/2 , but acetone is one of the highest swelling solvents (S = 1. 43) whereas dichloromethane swells Fluoroflex by only a minimal amount (S = 1. 03). It is clear that a more descriptive model is necessary to describe the observed swelling behavior of Fluoroflex. Charles Hansen's three-component solubility parameter provided greater accuracy in describing solubility interactions, accounting for separate contributions of atomic dispersion forces, permanent dipole-dipole (polar) forces and hydrogen bonding to the overall cohesive energy density of a material.
Fluorescence spectroscopy Autofluorescence measurements (λ exc = 250-600 nm; λ em = [λ exc +30]-800 nm) were conducted on pristine Fluoroflex samples using an Edinburgh FLS908 spectrometer (Edinburgh Instruments Ltd. , Livingston, UK). A MATLAB script (The MathWorks, Inc. , Natick, Massachusetts, USA) was used to plot an autofluorescence excitation/emission heat map across the range of wavelengths tested.
Atomic force microscopy Fluoroflex sheets of 1 mm thickness were hot embossed between two silicon wafers and topographic images of 6 × 6 µm, 4 × 4 µm and 2 × 2 µm of the sheets were recorded in tapping mode with a PicoSPM 5100 scanning probe microscope (Agilent Technologies, Santa Clara, CA, USA). Additional imaging was performed on sTPE sheets after acetone swelling and de-swelling, as well as one of the silicon wafers used for hot embossing. Measurements were taken in ambient conditions using silicon cantilevers (AC160TS-R3, Olympus Corporation, Tokyo, Japan) with resonance frequency around 300 kHz and spring constant around 26 N m -1 , and subsequent roughness analyses were conducted with WSxM 5. 0 software. [95] A one-way analysis of variance (ANOVA) was conducted to evaluate statistically significant variation in surface roughness between pristine and acetone-swelled sTPE samples, where variation was considered significant when P < 0.
Ethics statement The study was approved by the Comitede Protection des Personnes Sud-Ouest et Outre Mer III (IRB 2014-A01504-43) and is registered with ClinicalTrials. gov (NCT 02553369) (see S1 Protocol). Both oral and written information explained the objectives and characteristics of the study to patients and an informed consent was signed by each participant. All patient personal data and information were de-identified.
Study design In the French Caribbean Islands of Guadeloupe and Martinique, people living with HIV (PLWHIV) are cared for in the Department of Infectious Diseases of the University hospital of each island. We conducted a cross-sectional study within the cohorts of PLWHIV and cared for in these two hospitals, which accounted for 997 and 950 patients, respectively, in January 2015. These cohorts of PLWHIV are registered with ClinicalTrials. gov (NCT02898987). Each cohort is maintained using the same electronic medical record system (Nadis). All collected data are subject to quality control, which is done once yearly, independently of this study  [3]. Eligible for selection in this study were adults (> 18 years old) followed for HIV infection at the two investigators centers (Martinique and Guadeloupe, French West Indies) living in these territories.
We excluded patients who had lived more than 6 months in a risk area for chikungunya virus infection prior to 2014, patients with a chronic rheumatism that had been documented prior to the chikungunya outbreak, patients who had received a blood transfusion during year 2013, and patients who had planned to live outside French West Indies during the follow up period. Within each cohort, eligible patients were sorted by age (18-34, 35-44, 45-54, 55-65, and >65 years) and sex, and assigned one the 10 resulting groups. The study sample was built up by randomly selecting in each of the 10 groups the number of patients that was appropriate to reflect the age and sex distribution of each island population, based on the most recent national population census data (S1 Table ). Subjects identified this way were invited to participate in the study at their subsequent routine HIV outpatient clinic visit, which took place between February and September 2015.
During this visit, patients who accepted to participate were enrolled after they signed an informed consent. They were then invited to answer a questionnaire exploring whether they had developed clinical symptoms consistent with chikungunya at any time of 2014 and, if this was the case, the date and type of clinical manifestations. A 5-ml blood sample was drawn and tested for CHIKV-specific immunoglobulin G (IgG) using a commercially available assay (Euroimmun Chikungunya IgM/IgG ELISA, Luebeck, Germany). Patients' demographics and relevant information were extracted from the Nadis database. A total of 362 subjects were to be enrolled in order to be able to estimate an expected 40% seroprevalence rate with a 95% confidence interval of ±5%. The number of eligible patients was increased by 10% in each age and sex stratum, to take into account the possibility for a subject to decline participating in the study.
Seroprevalence of CHIKV infection was defined by the proportion of positive CHIKV IgG among the number of subjects tested. The proportion of asymptomatic cases was the number of asymptomatic patients divided by the number of subjects that tested positive for CHIKV. CHIKV seroprevalence and proportion of asymptomatic cases were expressed as percentages with 95% confidence intervals. The 95% confidence intervals (CIs) were calculated using the exact (Clopper-Pearson) method. Continuous variables were summarized using median and interquartile range and compared using Mann-Whitney test. Categorical variables were summarized using numbers and percentages and compared using Fisher exact test. Statistical tests were performed with Stata software, version 13 (StataCorp LP, College Station, TX, USA).
Acknowledgments The authors are grateful to the research assistants at the  Centre d'Investigation Clinique Antilles  who helped collect data and deliver information to the participants ( Isabelle CALMONT , Veronique PELONDE-ERIME ´E), to Benoı ˆt TRESSIERES for his support to data management, and to the nurses of our departments ( Francelise COCLES ,  Elisabeth DILOU ,  Marie-Paule FERDINAND ,  Isabelle JEANVILLE ,  Marie-Agnès LAURETTA ,  Astrid ORNEM ,  Sylvie TUR-LEPIN ). They are also indebted to the medical doctors who followed the participants ( Drs Anne-Sophie BATALLA ,  Julien CUZIAT ,  Isabelle FABRE ,  Patrick HOCHEDEZ ,  Isabelle LAMAURY ,  Rachida OUISSA ,  Mathilde PIRCHER ,  Vincent RONIN ,  Benoit ROZE ´, Kinda SCHEPERS ) and to the patients who accepted to participate in this study.
1. Floral resources are a key driver of pollinator abundance and diversity, yet their quantification in the field and laboratory is laborious and requires specialist skills. 2. Using a dataset of 25,000 labelled tags of fieldwork-realistic quality, a convolutional neural network (Faster R-CNN) was trained to detect the nectar-producing floral units of 25 taxa in surveyors' quadrat images of native, weed-rich grassland in the United Kingdom. 3. Floral unit detection on a test set of 50 model-unseen images of comparable vegetation returned a precision of 90%, recall of 86% and F1 score (the harmonic mean of precision and recall) of 88%. Model performance was consistent across the range of floral abundance in this habitat. 4. Comparison of the nectar sugar mass estimates made by the CNN and three human surveyors returned similar means and standard deviations.
Deep learning The term 'deep' defines a subfield of machine learning characterized by hierarchical representations  (Bengio, 2012) , which has recently achieved historic breakthroughs in image classification, speech recog-nition, text-to-speech conversion and board games, by using neural networks to learn features in data. Such models often use hundreds of successive layers of representations learned automatically from training datasets, the retained representations becoming increasingly abstract from the original data and increasingly informative about the target. The learning in such a network therefore is an optimization process in which each layer is adjusted for optimal performance on the training data via a feedback signal (the loss function). Deep learning is within reach of ecologists thanks to powerful open-source software and the availability of graphical processing units (GPUs) as hardware or virtual machines.
For labour-intensive ecological surveys,  Torney et al. (2019)  concluded that their deep learning network of Wildebeest abundance could reduce 18 person-weeks of analysis to 1 day, to within 1% of the estimates of expert human surveyors. In early work,  Chen et al. (2014)  used CNNs for animal identification to a detection accuracy of 0. 38 using a dataset (20,000 images and 20 classes) of a similar scale to our floral units. For comparative purposes in this paper, we use the metrics of precision, recall and F1 score (see Section 3. 2), and the terms F1 and detection accuracy interchangeably.
Training dataset All images were taken from vertical or near-vertical, encompassing approximately 1 m 2 of untrampled ground area, with no extraneous objects (e. g. quadrat frame, litter), of a maximum vegetation height of 1 m, of minimum size 2 MB and in reasonable focus. A Canon Powershot G10 (14. 7 Megapixels) was used to compile these training data. To cover peak annual nectar production, a total of 1997 images were generated in the United Kingdom from May to August inclusive in 2019. Plants were identified using  Stace (2019) , though maintaining the synonym of Jacobaea vulgaris (Senecio jacobaea) for continuity with the UK Pollinator Monitoring Scheme (https://www. growwilduk. com/ blog/help-count-pollinators-science). We estimated that training for each taxon would require around 1000 labelled image tags, and that available time and resources allowed 25 taxa.
Further directions Some additional modelling exercises might achieve small gains in object detection performance. Ensembling of models has been successful elsewhere (e. g. Nobashi et al. , 2020; Priyadarshini & Puri, 2021) , and actually would be fairer in our model tests which effectively pitted a single CNN against an ensemble of human surveyors. Some experimentation with different modelling architectures and other CNN algorithms may reveal one that performs better in this case, and training two models simultaneously on this dataset as generative adversarial networks  (Goodfellow et al. , 2014)  would be an informative test of our choice of deep learning model.
Convolutional neural networks (CNN) are currently one of the most promising deep learning methods for ecological studies. CNNs are designed to learn feature hierarchies and local patterns in a stochastic way, typically learning these in small two-dimensional frames of input images. The building blocks of a CNN are data-processing modules (the convolutional layers), which by matrix arithmetic act as filters, extracting representations from the data to produce a trained network. The numerical arrays of pixel values in the training dataset are inputted to the CNN, then its predictions are iteratively compared to the labelled validation data by backpropagation (see below). As the CNN has not been exposed to the validation data, its performance on this dataset indicates whether the model is underfitting or overfitting, that is how well it is generalizing to unseen instances.
; Trifolium pratense; T. repens; Vicia cracca; V. sepium; yellow composite (Hypochaeris radicata); yellow composite (Leontodon hispidus); yellow composite (Scorzoneroides autumnalis) Flowers of some taxa (e. g. Lathyrus pratensis, Taraxacum agg. ) were represented by fewer tags in the training dataset than the best represented (e. g. Leucanthemum vulgare, Ranunculus spp. ), due largely to population densities in the wild. To minimize the training influence of imbalanced classes, and because we also apply image augmentations downstream (see section 2. 5), we oversampled (e. g. Ferreira et al. , 2019) 104 of the original images once. This increased the training dataset to 1699 images (21. 934 tags. Figure S. 1 in the Supporting Information).
No lower confidence level was enforced on classifications, and an IoU value of 0. 1 (10% overlap) was chosen to generalize to the majority of the taxa. The latter is aimed TA B L E 1 Species and aggregate classes in the training dataset, with derivation of nectar sugar mass per CNN floral unit (far right column). Where the floral unit definitions used in the CNN are the same as those of the original assays, nectar values are equal in the assay and model columns. Where the CNN used larger floral units the nectar values were scaled up. The CNN floral units are illustrated in Figure1.
ii) false positives as those model-detected floral units where there are either none in the image or where a floral unit of a different taxon is present; (iii) false negatives as floral units in the image which are not detected by the model; and (iv) true negatives as an incalculable high number. A confusion matrix was constructed from the 50 test images (Figure S. 4 in the Supporting Information) which illustrates performance per taxon on this dataset. Key performance metrics for the final model are as follows: Precision of floral units = True Positive∕(True Positive + False Positive) = 926∕ (926 + 101) = 0. 90 Recall of floral units = True Positive∕ (True Positive + False Negative) = 926∕ (926 + 152) = 0. 86 Mean F1 score = 2 × (Precision × Recall)∕ (Precision + Recall) = 2 × (0. 90166 × 0.