dataesr/scientific-paragraphs-categorization huggingface dataset:
2017 , Pomerantz et al. 2018 , Maestri et al. 2019). During the 2018 trip, we found and sequenced three large Limax specimens, which, based on  Wiktor (1982) , we initially assigned to the well-known, widespread L. cinereoniger Wolf, 1803. This species had been reported from the region earlier  (Bole 1984). Upon comparison of the sequences with those in GenBank, however, it appeared that two distinct species were present. Two individuals indeed belonged to what is generally considered L. cinereoniger. The third individual, however, appeared conspecific with a seemingly undescribed sister species, informally named 'pseudocinereoniger' by Nitz (2013). Our Durmitor specimen had a COI sequence identical to 'pseudocinereoniger' from Biogradska Gora in Montenegro and nearly identical to samples of that taxon from Bulgaria.
2010) , with a GTR substitution model, fixed proportion of invariable sites, four substitution rate categories, estimated gamma distribution and 100 bootstrap replicates. Trees were displayed and manipulated in FigTree v. 1. 4. 2 and Lehmannia marginata (Genbank FJ6064 55) was used as outgroup to root the trees. Large, up to 116 mm long; mantle length up to 37 mm; keel length up to 44 mm (measurements based on alcohol-preserved specimens; living animals can be considerably larger when fully extended). Keel prominent. Colouration monochrome or patterned. Body colour dark brown to black, fading to light brown on the flanks or uniformly light brown (preserved specimens light grey, fading to creamy white on the flanks or uniformly grey to black). Dorsum often darker than the flanks. Keel often (but not always) distinctly brighter than the rest of the dorsal body colour.
(2017)  andMaestri et al. (2019). The sequences (and accompanying photos of live, preserved and dissected animals) were uploaded to the Barcoding of Life Database (BOLD; www. boldsystems. org). They are accessible under numbers TXEX039-19 -TXEX 045-19 and TXEX048-19. To determine the species identities, we obtained all sequences of the cytochrome c oxidase subunit I (COI) barcoding region labelled in Nitz (2013) as ' Limax cinereoniger', 'Limax cf. cinereoniger' and 'Limax " pseudocinereoniger"' from GenBank. All sequences (newly generated as well as mined from GenBank) were aligned.
Acknowledgements This work was carried out under permit numbers  UPI-101-1556/1-02-1101/1  and  02-UPI-662/4  from the  Agencija za Zaštitu Prirode i Životne Sredine, Podgorica, Montenegro. We thank all participants and team members of the 2018 and 2019  Taxon Expeditions  to Durmitor for their help in the field,  Vladimir Damjanović  for organising access to the  Tara Canyon ,  Jan van Tol ,  René Heim ,  Ulrich Schneppat  and  Isabel Hyman  for advice and the staff of  Etno Selo Šljeme  for their hospitality. Barbara Nitz  is thanked for support and discussions regarding her original molecular phylogenetics work on these and other Limax species. The photos in Fig. 1 were made by  Pierre Escoubas. Christine Zorn  of the  Museum für Naturkunde Berlin  is gratefully acknowledged for searching for the type specimens of L.
, 2012). The GoodBerry project (2016-2020) brought together 17 European partners and one each from Chile and China. They conducted multiple field experiments on strawberry, raspberry and blackcurrant, and collected genotypic, phenotypic and sensory data to provide the knowledge and procedures necessary for the development of elite cultivars that maintain high yields and quality in a range of environments  (Woznicki et al. , 2016; Vallarino et al. , 2018; Allwood et al. , 2019; Labadie et al. , 2019; Pott et al. 2019; Vallarino et al. , 2019; Pott et al. , 2020a). Whereas earlier projects targeted key accessions and identified molecular markers to accelerate breeding as well as providing initial methods for sensory analysis and metabolomics, GoodBerry focused mainly on the secondary metabolites responsible for aroma, fruit quality and stress tolerance  (Durán-Soria et al.
, 2021) , while still covering environmental adaptation and genetic resources. Importantly, GoodBerry studied physiological and fruit quality traits in individuals from a biparental population planted in five European countries for a period of 3 years, highlighting the plasticity of strawberry (European Union, 2020). Furthermore, modern highthroughput screening tools were developed to combine omics datasets  (Bolger et al. , 2019; Labadie et al. , 2019; Schwacke et al. , 2019). In the framework of the European Cooperative Programme for Plant Genetic Resources (ECPGR), a working group on berries was established in March 2019 (https://www. ecpgr. cgiar. org/workinggroups/berries; Höfer, 2021). The main goal of the 51 members from 22 European countries is to coordinate activities between the national collections based on continuous long-term network cooperation in Europe to create synergies and establish contact points for berry research projects.
Outside the EU, one of the largest berry-related projects was funded from 2009 to 2019 by the USDA Specialty Crop Research Initiative (SCRI), and focused on the US market. RosBREED  (Iezzoni et al. , 2020)  covered diverse species in the family Rosaceae, including strawberry and raspberry, and published genomic data and some phenotypic data in the Genome Database for Rosaceae (GDR; https://www. rosaceae. org), which is still regularly updated by researchers and breeders, and includes downloadable data and software for data visualization and analysis  (Jung et al. , 2019). The USDAfunded Vaccinium Coordinated Agricultural Project (VacCAP) was launched in 2020 to investigate the genetic basis of blueberry and cranberry fruit quality attributes, develop standard genotyping and phenotyping protocols, and develop or optimize tools and methods applied by the project partners.
Wild accessions and related species were included in the project to develop new pre-breeding material. The project established the Genomic Database for Vaccinium (GDV; https://www. vaccinium. org), which is similar in function to GDR, but focuses on blueberry, cranberry and related berry species. The knowledge from these projects allows the efficient use of genetic resources, and the creation of new breeding material that meets the current demands of growers and consumers. However, the genomic background of certain stress-response traits and the contribution of particular metabolites to the overall sensory qualities of berries remain unknown. Furthermore, a consumer survey across a wide geographic area has not yet been commissioned to inform future breeding objectives.
, 2021)  but lower in a subset from California  (Hardigan et al. , 2018). Studying diverse material representing all stages of the breeding program, including unexploited related species, pre-breeding accessions and advanced breeding material, facilitates population-based studies of breeding germplasm and the diversity within this genepool, allowing genome-wide association studies (GWAS), from which QTLs and genomic prediction models can be derived. GWAS and genomic selection often utilize the open-source software GAPIT  (Tang et al. , 2016)  to detect genomic associations, and statistical tools such as ASReml-R  (Butler et al. , 2017)  or BGLR  (Pérez and de los Campos, 2014)  to develop prediction models  (Davik et al. , 2020; Gezan et al. , 2017; Enciso-Rodriguez et al. , 2018). Such approaches have already been applied successfully to strawberry, for example in the context of stress resistance  (Pincot et al.
This also ensures that germplasm variation identified or generated in research projects can be exploited by research and breeding communities. Guidelines have been established through initiatives such as FAIR, which stipulates that data should findable, accessible, interoperable and reusable in order to facilitate (re)utilization  (Wilkinson et al. , 2016). Generalized repositories for genetic and genomic data are hosted by the National Center for Biotechnology Information (NCBI; https://www. ncbi. nlm. nih. gov), the European Molecular Biology Laboratory -European Bioinformatics Institute (EMBL-EBI; https://www. ebi. ac. uk) and the DNA Databank of Japan (DDBJ; https://www. ddbj. nig. ac. jp). Related initiatives such as the Proteomics Identifications Database (PRIDE) exist for proteins and peptides, and metabolite databases have been established for metabolomic data  (Haug et al.
, 2019). Phenotypic and organoleptic attributes require databases that allow users to query complex characterization and evaluation data, including effective visualization tools that provide a deeper understanding of the data, alongside tools for data export in formats suitable for downstream analysis. Open-source information systems such as Germinate  (Shaw et al. , 2017; Raubach et al. , 2021)  provide such functionality and are used in the soft fruit, stone fruit and cereal communities. The interactive visual interfaces in Germinate provide access to information from plant genetic resources and pre-breeding experiments, and data can be exported as plain text if necessary. Germplasm and field trial sites can be represented using geographical maps. Complex phenotypic data can be displayed in a number of ways, including interactive matrix plots to show correlation, clustering and outliers in datasets comparing traits, or even the combined analysis of genetic and phenotypic data.
A Life Cycle Assessment (LCA) will also be undertaken on a subset of material in field trials to assess agronomic performance and sustainability. The LCA analysis will be performed using the standardized software Simapro v. 9 with associated databases, and methodology based on ISO 14040 and ISO 14044. The development of genomic tools for improved berry breeding efficiency will be facilitated through three approaches that will be extensively validated. Transfer of promising pre-breeding accessions, genomic tools and technologies to berry breeders outlines the strong focus of BreedingValue towards innovative technologies and will have a strong impact on European berry breeding. Firstly, previously published markers for commercially important Mendelian traits strawberry will be made publicly available for utilization by the breeding and pre-breeding communities.
Markers linked to fruit quality traits (such as aroma, color, soluble acids and sugars, and anthocyanin content), flowering traits (everbearing), and disease resistances will be prioritized. A low-density SNP array for marker assisted selection and cultivar identification will be developed by utilizing Fluidigm ® SNP Type™ technology, which will allow for high-throughput, cost-effective and reproducible analysis of polyploid strawberry genotypes. Leaf samples of the BreedingValue strawberry germplasm collection extended by further accessions from commercial breeders (obtained through targeted calls) will be genotyped on the array, with data linked to phenotypes for those selections. The second approach will employ GWAS and genomic prediction models to target commercially important polygenic traits with complex inheritance.
The wide range of plant material from sources within and outside the project (obtained through targeted calls) will facilitate the model development. GWAS and genomic prediction models will mainly be applied to material defined in the BreedingValue germplasm collection, and those available from related studies. Genotyping of strawberry will be performed on the Axiom 50K SNP array  (Hardigan et al. , 2019) , with powdery mildew resistance chosen as the candidate trait. A 9K axiom is available for raspberry  (Jibran et al. , 2018) , but the results of a preliminary study will show if the marker density will be sufficient for our purposes. Alternatively, a genotyping-by-sequencing (GBS) approach might be employed, with resistance to late leaf rust chosen as candidate trait. In addition, phenotypic data collected across the project for strawberry and raspberry will be utilized to identify genomic associations.
The third approach to develop genetic resources involves an allelic diversity study to assess genetic in strawberry material of the BreedingValue germplasm collection and further accessions provided by participants of targeted calls from Plant Categories 1, 2, and 3. Genotyping will be performed using the Axiom 50K SNP array. Genetic differentiation between the three categories can be assessed using the multiallelic extension of Wright's fixation index (F ST ), while phenotypic data can provide insight into the strength of selection on quantitative traits and the prevalence and effects of inbreeding. Extending the project dataset with historic or open-source data will additionally enable elucidating the achieved breeding progress in the different cultivar categories and the underlying factors  (Tollenaar and Lee, 2002).
Accurate and efficient phenotyping is essential for cultivar improvement, and for developing markerassisted selection and genomic prediction tools. The phenotyping work will focus on image analysis related to yield traits and the analysis of sensory attributes and quality traits of ripe fruits using multiplatform metabolomics techniques. This will provide valuable information on the phenotypic properties of the BreedingValue germplasm collection, and investigate best methods for screening architectural, ripe fruit quality, and post-harvest traits in large germplasm collections and pre-breeding material. Nearinfrared spectroscopy (NIR) will be used to image a subset of previously-phenotyped genotypes. This data will be used to establish protocols and predictive models for early screening of fruit ripening, fruit quality features, and biotic and abiotic stresses.
Similarly, hyperspectral and 3D imaging of phenotyped strawberry, raspberry, and blueberry germplasm will be analyzed to link imaging data to fruit quality traits and stress. Image analysis will be optimized to study plant architectural traits that correlate with yield, allowing the high-throughput selection of cultivars with better yields. A novel 3D plant model, calibrated on phenotypic data, will be developed to define ideotypes under various environmental conditions, in the open-source OpenAlea platform  (Pradal et al. , 2008). Imaging and metabolite analysis will be performed on a subset of genotypes across two growing seasons. Data will be used to assess whether imaging techniques can be utilized as non-invasive and non-destructive alternatives to infer metabolite levels in fruit. Metabolic analysis will include quantification of folic acid and vitamin levels (particularly vitamin C).
It will also enable the identification of key metabolites as selection markers and permit metabolomic data to be used for the certification of different berry species. The fruit quality and sensory attributes of the diverse germplasm under investigation will also be characterized by trained panels, and the metabolomic data will be linked to these sensory profiles as the basis for automated sensory analytics. User-friendly and multilingual harmonized sensory analysis tools will be developed to describe the sensory properties which are important to consumers. Sensory profiles of a subset of diverse accessions will subsequently be obtained by panel testing. A broad consumer survey will be prepared to gain a high-resolution picture of consumer preferences relating to strawberries, raspberries and blueberries across the continent.
In these laboratory conditions, spectra are constituted of many wavelengths possibly from the visible and near infrared (400 to 2500 nm approximately), constituting a dataset of hundreds of variables  [9]. With the rise of high-throughput phenotyping, spectrum measurements have benefited from technological developments which enables the direct collection of spectra in the field possibly at several time points, like hyperspectral imaging from Unoccupied Aerial Vehicle (UAV) or direct measurements of fresh material with portable (micro-)spectrometers. Hyperspectral imaging takes images with several wavelengths for each pixel, possibly at multiple time   2019 ), one per flying date. When several methods were tested, the one with the highest predictive ability was kept for each modality. When the prediction was made in several environments, years or on different tissues for PS, the range of predictive ability is indicated in brackets points in the visible and in a small portion of the NIR spectrum.
Recently, Zgouz et al. [27]  have reported a dataset of spectra collected on 60 sugarcane samples with 8 visible/ NIR spectrometers including handheld micro-spectrometers. Such a dataset is very useful to compare different tools, although results might be context dependent, i. e. , the most accurate model for different traits and species might be obtained with different spectrometers. Still, quantifying the gain or the loss of predictive ability for each technique will be helpful to guide in using one technique rather than another for a specific objective. Other techniques could be used to facilitate measurement, for example to combine hyperspectral images and laboratory spectra. Instead of using a spectrometer to measure samples one by one, hyperspectral images can measure several samples at the same time.
Making Use of Historical NIRS Data in Prediction In GS, the enrichment of the training set with historical data from multiple environments can improve predictive ability by increasing the size of the training set and by limiting the effect of atypical years that are difficult to predict with reduced and traditional datasets  [43]. The use of historical data might be more complicated with PS, as NIR spectra are likely to be more specific to the environment in which they are measured. To estimate the NIR similarity matrix (H) between historical varieties characterized with NIRS over successive years (the varieties changing from 1 year to another), the effect of environment and GEI should be accounted for and corrected from the spectra. One option would be to use check varieties for which NIR spectra would be collected each year to determine the transformation from the spectra collected in one environment to the spectra collected in another environment.
Funding: The preparation of this chapter was partly supported by: the project OASIs funded by the French Ministry of Agriculture and Food ("fond CASDAR, appel aprojets Semences et selection vegetale 2020, projet n C-2020-5"), the project SelGenVit funded by the French National Research Agency (ANR-19-ECOM-0006), and the BreedWheat project funded by the French National Research Agency under Investment for the Future (ANR-10-BTBR-03). Partial funding of Charlotte Brault's PhD was provided by the Association Nationale de la Recherche et de la Technologie (ANRT, grant number 2018/0577), IFV and Inter-Rhone. Pauline Robert's PhD was jointly funded by Agri-Obtentions, Florimond-Desprez, and the Association Nationale de la Recherche et de la Technologie (ANRT, grant number 2019/0060).
6  Maximum Likelihood distance network with admixture analysis of invasive and native Spodoptera frugiperda populations. Maximum likelihood (ML) distance network with admixture analysis inferred from five genetic clusters (K = 5) presented as pie charts for each individual analysed. The network was drawn using the plotAdmixture function in the R package NetView  120, 121  , and is based on a ML distance matrix calculated from the IQ-Tree shown in Fig. 3. using the R package ape  122. The four major clusters, I-IV, correspond to those shown in the phylogenetic tree (Fig. 3 ). Individuals are identified by country codes as follows: China XP (CX), China YJ (CY), China CY (CC), China CH06 (CH), India (IN), Uganda (UG), Tanzania (TZ), Malawi (MW), Benin (BE), Brazil CC (BC), Brazil rCC (BR), Peru (PE), French Guiana (GF), Mexico (MX), Guadeloupe (GP), Puerto Rico (PR), USA-Florida (UF), and USA-Mississippi (UM).
pest in the production areas around Hanoi since 2008  70. We also provided evidence for multiple introductions of this agricultural pest into Africa, demonstrating conclusively that the Malawian FAW population has a distinct genomic signature different from Chinese populations. The pre-border interceptions of FAW larvae (identified via morphological characters) that originated from countries outside of the Americas since prior to 2014  71  (although molecular diagnostics of these suspect FAW larvae will be required to provide definitive confirmation of such non-native range interceptions), the early detections and report of FAW in Asia/S. E. Asia (e. g. , CH06 12 (GenBank MT897262); 2008 Hanoi outbreaks  69  ), and the complex pattern of multiple introductions including potential North American origins for various Chinese FAW populations (e.
10)  102  to obtain a whole-genome SNP panel. Variants were filtered to remove SNPs with minimum allele frequency of 0. 01, any missing data and linkage disequilibrium (LD) pruned with stringent parameters (-indep-pairwise 50 kb 10. 000001) using Plink2. 0 103 < http://pngu. mgh. harvard. edu/purcell/plink/> to obtain 870 unlinked SNPs across all individuals. analyses. Unrooted phylogenies based on trimmed partial mitochondrial DNA genomes of 15,059 bp and from genome-wide SNPs were individually inferred using IQ-Tree <http://iqtree. cibiv. univie. ac. at>  104. For the nuclear SNPs, the panel of 870 SNPs from each individual in fasta format was uploaded to the IQ-Tree web server and selecting the automatic substitution model option with ascertainment bias correction (+ASC) model  105. For the mitochondrial DNA genome maximum likelihood (ML) phylogeny was inferred with edge-linked partition for the 13 protein-coding genes and excluding all four regions of low complexity (best substitution models identified by the IQ-Tree automatic model selection option for the mitochondrial genomes as: HKY + F + I (COI), HKY + F + I (COII), TPM3u + F (ATP8),F81 + F (ATP6), HKY + F (COIII), HKY + F + I (ND3), HKY + F (ND5), HKY + F + I (ND4), HKY + F + I (ND4L), HKY + F + I (ND6), HKY + F (CYTB), HKY + F + I (ND1), HKY + F + I (ND2); ML phylogeny Log-likelihood: -17149.
23-3. 56 Mya;  30, 66  ) the inclusion of an outgroup could obscure the recent migration signals (e. g. , see  108  ). Genetic diversity and neutrality tests. Observed (H obs ) and expected (H exp ) heterozygosity were calculated for each population using the populations program in Stacks  109  and the Adegenet package in R  110, 111. The number of loci departing significantly from Hardy-Weinberg equilibrium (HWE) in the global population and individual populations was assessed using PLINK 2. 0 112 and VCFtools 113. To test for neutrality, Tajima's D  114  and Fu and Li's D*  115  were calculated for each population using the PopGenome package in R  116. Nucleotide diversity (π) and Wright's inbreeding coefficient, F IS 117 , were calculated using the populations program in Stacks. Pairwise comparisons of weighted F ST values between populations were calculated using Genepop (v4.
7. 5)  118  and differentiation between populations tested for significance using the exact G test. Population structure and migration. Principal component analysis (PCA) was performed using PLINK v1. 9  103. The admixture was estimated using Admixture v1. 3. 0  119. For the plotting of networks, the R package NetView  120, 121  was used. The network drawn using the plotAdmixture function in this package is based on a maximum likelihood (ML) distance matrix calculated from the IQ-Tree phylogeny, using the R package 'ape'  122. To estimate directional gene flow between the populations, as well as the relative magnitudes of these flows, the function divMigrate in the R package diveRsity  123  online version was used <https://popgen. shinyapps. io/divMigrateonline/> 124. Gene flows between all sites were calculated and then normalized to obtain relative migration rates (between 0 and 1).