dataesr/scientific-paragraphs-categorization huggingface dataset:
5b-e ). Only the RDP trainset v16 achieved about 25% of correct species annotations, while the other databases only classified to genus rank. Although the RDP trainset v16 performed best among all universal databases, annotation accuracy was below the accuracy values assessed in previous studies  [48]. Universal databases achieved highest annotation accuracy with V4, with exception to Silva, which performed best on V2 (Fig. 5 ). Generally, the difference in annotation accuracy was stable through all HVRs with exception to the Silva database, where bigger oscillations were observed between the HVRs showing a clear drop for V6 and V7 (Fig. 5e ). All HVRs taken together, the DAIRYdb achieved a significantly better taxonomy annotation accuracy (adjusted p-value <0. 001) of average 88. 9% ± 5. 5 as compared to the universal databases tested at any taxonomic rank, but particularly at order to species ranks (Fig.
6b-c ). The average accuracy value for correct species annotation of all HVR pairs with the DAIRYdb was over 94% ± 2. 8 (Fig. 6f  ). Only species annotation with the RDP trainset v16 achieved 25% of correct annotations. The BLAST16S database was shown to obtain genus accuracy values ∼50% for V4, which improved with increasing length to ∼60% with V3-V5 and ∼70% with full-length 16S  [52]. As expected, the increasing number of HVR increases the confidence in taxonomy annotation. Results with Blast+ and Metaxa2 on HVR pairs are available in Additional file 3. Different taxonomy predictors using the DAIRYdb showed similar performances and only little difference between single HVRs (Additional file 3: Figures S11) or HVR pairs (Additional file 3: Figure  S12 ). Annotation accuracy performance varied more between different databases than between different classification predictors.
The results confirm that OTUs annotation is primarily influenced by the selection of the database, by the HVR, and only at last by the taxonomy predictor (Additional file 3: Figures  S1-S10 ). A comparison of the three classification predictors, Blast+, Metaxa2 and SINTAX with the DAIRYdb confirmed that HVR pairs could be more accurately assigned to the correct species than single HVR (Fig. 7 ). Among all tools, Blast+ and SINTAX were slightly better than Metaxa2. Since Metaxa2 uses more stringent parameters, as it only assigns the taxa if in agreement with Blast+, the lower performance of Metaxa2 with respect to Blast+ alone is therefore not surprising. Moreover, Metaxa2 performance is strongly dependent on the ANI thresholds used, which were set according to previous studies  [83]. On the other hand, the more stringent parameters of Metaxa2 reduced the number of over-classified sequences.
Generally, taxonomy annotation results are most robust whilst using different classification predictors with the DAIRYdb. We therefore recommend to use both, Metaxa2 with integrated Blast+ and SINTAX to obtain taxonomy annotations closest to the ground truth. Although a lower SINTAX cutoff of 0. 6 increases the risk of over-classification, it is justified by the better quality of the DAIRYdb and the comparison with Metaxa2 for definitive taxonomy annotation (more details on the recommended usage on real samples are described on https://github. com/marcomeola/DAIRYdb). The main scope of the DAIRYdb is to improve accurate species classification in dairy products. Beyond this, it covers a considerable diversity in agreement with the diversity detected in dairy products so far. However, the DAIRYdb does not necessarily perform better than universal databases on a set of sequences from environments other than dairy, such as the human gut.
A B Fig. 7  Comparison of the overall annotation accuracy of the three algorithms, Blast+, Metaxa2 and SINTAX for all single HVR (a) and HVR pairs (b). Although Blast+ presented a slightly better performance over SINTAX and Metaxa2, it was not statistically significant. All three classification tools assigned more than 75% of the sequences using the DAIRYdb as a reference for all HVR pairs 8 Taxonomy annotation accuracy test on sequences from the HITdb. Comparison of the taxonomy annotation accuracy at species rank between the DAIRYdb, RDP, Silva and Greengenes on type strain sequences present in the HITdb  [50]. On sequences from origin other than dairy products, the DAIRYdb performs better than Silva and Greengenes, but not better than the RPD trainset v16 in the Human Intestinal Tract database (HITdb) showed that the DAIRYdb performed comparably well to the RDP trainset v16 and better than Silva and Greengenes (Fig.
Conclusions Accurate prediction of taxonomy based on the marker gene 16S is a fundamental step in microbial diagnostics and microbial ecology studies. Dairy products, particularly cheeses, are enriched by a few dominant species often belonging to the same genera, such as Lactobacillus spp. , Lactococcus spp. , Streptococcus spp. An automatic and reliable taxonomic annotation to the correct species is pivotal to further routine microbial diagnostics. Different to available universal databases, DAIRYdb achieved correct taxonomy annotation for ∼90% of species names on single HVRs and HVR pairs with 16S sequences present in dairy samples  [52]. The better performance of the DAIRYdb over universal databases can be explained by the overall reduced number of sequences, only 10'290, with no conflicting taxonomy at all taxonomic ranks.
Our results are in disagreement to the recommendation to use the largest and most diverse database possible for 16S classification  [95]. On the opposite, manually curated 16S databases with authoritative full-length 16S sequences dedicated to the studied environment enormously improve classification confidence to the species rank  [48] [49] [50]. Reducing the number of representative sequences to a minimal number in the training set further diminishes the risk of highly similar sequences with conflicting taxonomy, thus lowering the performance of the database used for classification  [48, 49]. We therefore propose the manually curated DAIRYdb as a reference database for 16S microbiome studies on cheese and dairy products. The implementation of a curated database may lead to wider consensus and standardization processes reducing conflicts in literature due to the use of different universal databases integrated in different classification tools  [82, 96].
DAIRYdb construction To retrieve a comprehensive set of near full-length 16S sequences originating from cheese and dairy products, a search was performed against the NCBI Genebank nucleotide database using the command "CHEESE  [  Mostly unpublished 16S sequences from INRA (171'282), as well as some recently published  [97]  (NCBI Bioproject PRJNA421256), and Agroscope (1'559) were also included after clustering at 99% (see below for details). The resulting set of 236'946 sequences constituted the starting pool for building up DAIRYdb. The extracted sequences of the starting pool were matched against the Greengenes v13_5, SSURef_NR99_123. 1_tax_silva_trunc (Silva123) and RDP current_bacteria, RDP trainset v16_022016 16S databases using the usearch_global command of vsearch (v1. 11. 1) with parameters -id 0.
8 and -mid 97. About 34'515 sequences were extracted from Greengenes, 29'181 from Silva123 and 52'686 from RDP, representing all possible full-length 16S sequences associated to the studied environment from the universal repositories prior to OTU clustering. Altogether, the keywords associated sequences (236'946) and their representative database sequences (116'382) amounted to a total of 391'672 16S sequences of different length and quality. The extracted sequences were first dereplicated with priority to full-length sequences (279'737) and minimal length of 300 bp (268'613) if no full-length was present using the derep_fulllength and derep_prefix of usearch (v6. 0. 307_i86osx32), respectively. The remaining 268'613 sequences were divided into "good" sequences, i. e. , >1300 bp (50'309), and "bad" sequences, i.
e. , <1300 bp (218'304), using Prinseq (v0. 20. 4). The "bad" sequences were matched against all databases (Greengenes, Silva and RDP) to obtain reference sequences of better quality and length using the usearch_global command of vsearch (v1. 11. 1) with parameters -id 0. 5 and -maxhits 1. The 146'355 hit sequences were dereplicated using the derep_fulllength and derep_prefix of usearch6. 0. 307_i86osx32 resulting in 92'722 unique 16S sequences that were merged with the initial 50'309 "good" sequences. To remove redundancy, the 143'031 16S sequences were dereplicated again, reducing the total amount to 98'590 sequences and subsequently divided based on the length threshold of 1300 bp using Prinseq (v0. 20. 4) for clustering purposes. The sequences were first subjected to chimeric sequence removal using the 16S reference database available at www.
drive5. com/uchime/rdp_gold. fa with usearch (v6. 0. 307_i86osx32) and subsequently clustered to OTUs separately to a similarity threshold of 97% in steps of 0. 5% using the cluster_otus command in usearch (v6. 0. 307_i86osx32). The resulting 18'457 OTUs were matched against a database composed of full-length 16S sequences of type strains from Silva126 using the parameters -id 0. 5 -mid 97 -maxhits 1. The sequences with no match were matched again, however, using less stringent parameter -id 0. 5 -maxhits 1. Finally, the resulting 14'468 sequences were dereplicated, clustered at 97% and cleaned by removing eukaryotic, chloroplastic and mitochondrial sequences, Pintail value <75 and <1200 bp. The intermediate version of the DAIRYdb contained 9'739 representative 16S sequences. Species whose sequences were present before the clustering at 97% and absent after clustering have been reintroduced if identical sequence with synonymous taxonomic identity was absent in the DAIRYdb (see List of Prokaryotic Names validly published of the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ)).
This step was iterated several times until all possible sequences could be assigned to a species group. The sequences with no unequivocal annotation where identified with LPT followed by an underscore and respective taxonomic rank (e. g. , XXX_Family, Lactobacillus_Species). At least one type strain for each bacterial species present in the DAIRYdb was added if available in the DSMZ database bacdive. dsmz. de/ (https://bacdive. dsmz. de/). After manual curation the 4'545 CN reference sequences with authoritative taxonomy were removed. The final DAIRYdb. v1. 1 contains a total of 10'290 16S sequences.
Annotation accuracy was assessed using 4 universal databases: Greengenes v13. 5 (1'262'986 sequences)  [45] , SILVA v128 SSURef Nr99 (645'151 sequences)  [40] , LTP v123 SSU (11'939 sequences)  [42] , RDP training set v16 (13'212 sequences)  [43]  and the DAIRYdb (10'290 sequences). Correct annotation at all taxonomic ranks was assessed comparing the resulting assignment with the reference DAIRYdb. Pairwise comparisons using Wilcoxon rank sum test were used to highlight significant differences between database assignment accuracy. The p-values obtained were adjusted using Bonferroni correction. For more details on the statistical evaluation process see Additional file 4.
Venn diagram A Venn diagram to determine the potential origin of each sequence composing the DAIRYdb was generated with the web app Jvenn  [100]. The global alignment -usearch_global module of vsearch to per a global alignment between the 10'290 sequences of the DAIRYdb and each raw database corresponding to keywords: dairy, cheese, starter, whey, milk. These raw databases were previously dereplicated. We used the parameter -strand both -id 0. 9 -maxaccepts 100 -maxrejects 100. For each keywords, the matching sequence identifiers were used as input in Jvenn to generate the Venn diagram.
Acknowledgements Eric Dugat-Bony  ( INRA Grignon, GMPA ) improved the completeness of the DAIRYdb by providing some additional 16S sequences from his own studies related to cheese rind bacteria. We are grateful to the  Genotoul bioinformatics platform (Toulouse, Occitanie , https://www. genotoul. fr/)and  INRA MIGALE bioinformatics platform (Jouy-en-Josas, Ile-de-France , http://migale. jouy. inra. fr) for providing computing and storage resources.
, South Plainfield, NJ, USA). Briefly, 50 ng DNA was used to generate amplicons that cover V3 and V4 16S rDNA gene hypervariable regions of bacteria and Archaea. Indexed adapters were added to the ends of the 16S rDNA amplicons by limited cycle PCR. Sequencing libraries were validated using an Agilent TapeStation (Agilent Technologies, Palo Alto, CA, USA), and quantified by Qubit and real time PCR (Applied Biosystems, Carlsbad, CA, USA). DNA libraries were multiplexed and loaded on an Illumina MiSeq instrument according to the manufacturer's instructions (Illumina, San Diego, CA, USA). Sequencing was performed using a 2x250 paired-end (PE) configuration; image analysis and base calling were conducted by the MiSeq Control Software (MCS) on the MiSeq instrument. Three technical replicates were performed and a total of 164,205 +/-52,440 reads were obtained for each sample.
Forward and reverse reads were trimmed by 5 and 10 bp, respectively. Error rate convergence values reached 0. 007 and 0. 008 for forward and reverse reads, respectively. A total of 91% of the reads successfully merged. We filtered out sequence variants with more than 95% missing values. The taxonomy was assigned using the SiLVA ribosomal RNA gene database v128. We performed a multiple-alignment using the DECIPHER R package in order to create a phylogenetic tree (Figure  1A ) with the phangorn R by fitting a GTR+G+I (Generalized timereversible with Gamma rate variation) model on a neighbour-joining tree. The count table, the metadata, the sequence taxonomies, and the phylogenetic tree were ultimately combined into a single R object, which was analysed using the phyloseq package  (McMurdie and Holmes, 2013).
Statistical analysis The statistical analysis was performed in R version 3. 4. 1. We have followed the Bioconductor workflow developed by Benjamin Callahan  (Callahan et al. , 2016). The alpha diversity was measured using the Shannon index calculated with the plot_richness phyloseq function, with statistical significance assessed with a Kruskal-Wallis rank sum test. The beta diversity was visualized by a MultiDimensional Scaling (MDS) ordination of the sample Bray-Curtis distances. We investigated the statistical significance of differences in taxa abundance by fitting an ANOVA model using the R lm function, with the sex of the animal as covariate. We investigated the prediction power of the 72 genera on the faecal metabolome. Metabolomics data were mean centred and normalised prior to analysis.
Prior to supervised analysis we conducted an unsupervised principal component analysis (PCA) to identify outliers and potential covariates. We observed that male and female rats had very distinct metabolic profiles as previously reported on this dataset  (Coumoul et al. , 2019). We therefore pursued the analysis separately for male and female animals. Prediction potential of each taxa was assessed individually by O-PLS DA  (Wold et al. , 2004 ) using the metabolomics profiles as a matrix of independent variables and genera as predictors. Models presenting a goodness of prediction coefficient (Q 2 Y) above zero were selected for validation using a thousand permutation. Models were considered as significant if the permutation returned P < 0. 05.
Objectives of the AQUACROSS project The overall aim of the AQUACROSS project has been to support the coordinated implementation of the EU 2020 Biodiversity Strategy and international biodiversity targets, and by doing so to ensure improved functioning of aquatic ecosystems as a whole (from rivers to seas). More specifically, AQUACROSS has had the following research goals: 1) To explore, advance and support the implementation of the EBM concept across aquatic ecosystems in the EU and beyond for the purposes of enhancing human well-being; 2) To specifically identify and test robust, cost-effective and innovative management and business models and tools for seizing all the opportunities offered by aquatic ecosystems services that correspond to the objectives and challenges faced by stakeholders, businesses, and policy makers; and 3) To mobilise policy makers, businesses, and societal actors at global, EU, Member State, and case-study levels in order to learn from real-world experiences, aligned with EU policy implementation, and to co-build and test assessment frameworks, concepts, tools, management approaches, and business models, to ensure endusers' uptake of project results AQUACROSS has focused its research activities on identifying synergies and overcoming barriers between policy objectives, concepts, knowledge, data streams, and management approaches for freshwater, coastal, and marine ecosystems (Fig.
, this issue). Derived causality functions were integrated into the Artificial Intelligence for Ecosystem Services  (ARIES -Villa et al. , 2009; Martinez-Lobez,  this issue) modelling platform, using mapping explicit techniques, to increase forecasting ability of ecosystem services. Additionally, AQUACROSS considered how biodiversity-related causal links are affected during disturbance and recovery. The third research activity, the development of an Information platform (http://dataportal. aquacross. eu/) involved the construction of a software platform based on the Comprehensive Knowledge Archive Network (CKAN) architecture to make possible the cataloguing, interrogation, analysis, and visualisation of diverse datasets on aquatic ecosystems and biodiversity using a range of selection criteria.
Data and information were acquired from within the project and external sources. The platform was implemented as a network of interoperable databases including an ingestion module, in charge of data acquisition from external service providers, e. g. WISE, BISE, OBIS, EMODnet, other EU initiatives (e. g. Freshwater Information Platform) in addition to GEOSS, COPERNICUS and other initiatives led by the European Space Agency (ESA), among others. The open-access information and dissemination platform integrates inputs from the three aquatic realms and contain modules for: (i) overview of data and metadata (including links to data repositories); (ii) AQUACROSS indicators and tools; (iii) technical documentation and guidelines; (iv) geospatial exploration and visualisation of the collected data (e.
The one million hectare reserve passes through the Strait of Gibraltar and includes river basins, coastal and marine waters. The case study identified major drivers and pressures of the study site, which include water management and planning, transboundary fragmentation of water bodies, pollution, water uses, water prices, illegal extraction, and drought and water scarcity. A set of indicators was identified to assess the provision of ecosystem services across the reserve, which can be applied to the 20 diverse natural protected sites in both Andalusia and Morocco and cover the three water realms. Data on case study characterisation and water bodies, statistics, uses, prices, plans and strategies was collected and modelled to forecast the future provision of aquatic ecosystem services over time.
g. hydropower development in the network of tributaries and the conservation and restoration potential of floodplains along the Danube River by considering mechanisms for enhancing the integration between different policies (EU WFD, EU Biodiversity Strategy, Habitats and Birds Directives -HBD) and human activities. A set of indicator species, such as those based on outcomes of historical analyses within the FP7 project MARS, was identified as well as floodplain characteristics, status of protected areas (Natura 2000 sites) and biodiversity indices at different scales. The study assembled data on biodiversity, historical records of indicator species occurrences, and specific data on floodplains as well as analyses on ecosystem services. This data were used to forecast the future development of aquatic ecosystems under changed environmental conditions and management schemes.
This data were then used to develop a Fuzzy Cognitive Map, a qualitative model on the effects of IAS and forecast the potential future changes in the Lough based on the relationships between existing activities and ecosystem components. These forecast models help identify possible opportunities to incorporate EBM approaches within current or emerging plans to address IAS impacts on ecology, social and economic systems in the study area. Case Study 5: Improving integrated management of Natura 2000 sites in the Vouga River, from catchment to coast, Portugal. In the context of environmental (e. g. Natura 2000 network; Biodiversity Strategy) and water related (e. g. WFD; MSFD) policies and the Integrated Coastal Zone Management recommendation, this case study aims to contribute to the improvement of integrated management of aquatic Natura 2000 sites, from catchment to coast, involving the concepts of Science-policy-stakeholders interface.
Initially, the case study promotes actions for engagement of stakeholders at different levels, reviews the current laws and policies governing the environmental management of the area. The study then identifies the main drivers and pressures in the considered area, which include agriculture, fishing, population growth, tourism and recreational activities, uncoordinated management, and associated economic drivers and pressures. A causalities analysis is conducted to explore and identify links to biodiversity, ecosystem functions and services in Natura 2000 aquatic habitats. All data collected on ecosystem service indicators is used in GIS-based models applied to environmental and socio-economic scenario analysis. Lastly, the case study develops innovative management instruments, including participatory initiatives, which set out conservation objectives for biodiversity and preservation of ecosystem services, as well as restoration measures for Natura 2000 sites.
) perspectives. Links between these drivers and pressures and changes in biodiversity and provision of ecosystem functions and services utilise identified indicators, such as habitat characteristics attractive to tourists, support drinking water purification, fisheries, etc. Data is collected on spatial distribution of habitats for water plants, fish and birds; historical land use; fishing pressure; climate change impacts; eutrophication history of the catchment; relevant local and regional policies; socio-economic statistics; and spatial data on ecosystem services perception and use. This information is used in participatory socio-ecological models to specify and explore scenarios of catchment use and restoration, and to address conceptual questions of resilience. Lastly, the study explores possible future trajectories under different management settings through scenario projection and analysis considering responses to climate change, WFD requirements, integrated catchment management and improved (or worsened) water quality.
Brain imaging methods have contributed to shed light on the mechanisms of recovery after early brain insult. The assumption that the unaffected right hemisphere can take over language functions after left perinatal stroke is still under debate. Here, we report how patterns of brain structural and functional reorganization were associated with language outcomes in a group of four-year-old children with left perinatal arterial ischemic stroke (PAIS). Specifically, we gathered specific fine-grained developmental measures of receptive and productive aspects of language as well as standardized measures of cognitive development. We also collected structural neuroimaging data as well as functional activations during a passive listening story-telling fMRI task and a resting state session (rs-fMRI).