dataesr/scientific-paragraphs-categorization huggingface dataset:
From those discussions a concept emerged, to bring together additional partners with similar study systems, to expand the datasets further. This led to the establishment of LFTG, with a series of annual workshops funded by ALTER-NET and partly supported by the ETN EU COST Action program. LFTG workshops (organized in 2018, 2019 and 2022) each involved 30-34 participants, mainly research groups from Europe, and beside the groups working on lacustrine telemetry systems, they also involved several groups working in rivers and saltwater systems. The primary goal of the workshops was to develop joint collaborative projects with empirical data, but the workshops were designed to also jointly develop conceptual work, and to share knowledge and experience. Topics covered included winter ecology, circadian rhythms, 3D analysis, high-throughput movement ecology, the importance of lakes, the history of positioning and fine-scale fish tracking, and trends in telemetry in lakes.
In this way, each group was eased into the workshop and had time to understand where their work could fit in with others. Thirdly, many joint projects were proposed, some of which were successes and many of which fell flat, or require more time to completion; focusing on a larger number of potential projects increased the likelihood that certain subset would become successful. The initial objective of the LFTG was to combine, scale up and synthesize empirical datasets. However, the output of the LFTG to date have been mainly conceptual papers and reviews. This speaks to the difficulty of jointly performing empirical analyses. In our experience, writing is easily a very collaborative effort, particularly now with shared online text processing tools such as google docs. By contrast, data analysis often landed in the hands of the leader of the joint project.
The analysis of fine-scale data can be a cumbersome task even for a single study site, and the joint project can require considerable additional efforts. The leader has to synchronize formats of multiple datasets, solve problems related to diverse study designs, such as inconsistent tracking durations, burst rates, system errors, and fish traits (e. g. , size), and ensure greater computation power. For future similar endeavors, more effort up front to build a shared database, with standard data formats would have reduced major efforts later in starting projects. Moreover, we recommend the use of collaborative online scripting tools (e. g. , jupyter notebooks, observablehq) and github repositories to share the effort of analysis and synergize the data analysis knowledge of the participants.
Two reviews address particular issues from the movement ecology toolbox, the use of accelerometer transmitters and space-use estimators. Lennox et al. [16]  review the use of accelerometers in aquatic sciences. The review includes 53 articles, covering a wide range of aquatic species, including fish, reptiles and invertebrates. The main applications of accelerometers so far included analyses of activity and survival, exercise physiology, response to and recovery from stressors, energetics, conservation planning, and behaviour identification. Their analysis reveals an important problem of a lack of standardization among studies, with a wide variation in the sampling frequencies and windows used that may negatively affect the utility of data and the potential for comparisons across studies. Kraft et al.
They found that the aggregations are maintained for up to two months during winter, with a strong site fidelity, and they are more stable when temperatures decrease. Larger fish tends to leave aggregations more frequently, while individual differences in aggregation preference reveal potential presence of distinct behavioural types. Beside ecological insights in fish behaviour, the study also provides practical information for population management, particularly for control measures in areas where the species represents an invasive alien species. Blanchfield et al. [20]  assess seasonal drivers of activity, habitat use and diet of lake trout (Salvelinus namaycush) in a subarctic lake in Canada. They estimate fish home ranges, movement rates, tail beat activity, depth use, and seasonal nearshore habitat use, coupled with data on seasonal diet and abiotic conditions.
Fish tracking data are used to compare catchability, finescale movement, emigration from the lake and natural mortality of triploid and regular diploid fish, and coupled with data from traditional stock assessments. The study confirms the relevance of triploid trout for fisheries, and provides valuable insights into the movement patterns of stocked fish to inform management and help maximize catch rates. Zdasiuk et al. [27]  examine the effects of hydrological alterations on movement of the threatened grass pickerel (Esox americanus vermiculatus) in an agricultural drain, through a combination of markrecapture surveys and passive tracking. While there is a lack of clear relationship between movement and habitat characteristics, results indicate the presence of a densitydependent movement, with more mobile fish in areas with a higher abundance, and a high level of individual variation in mobility.
Funding Authors acknowledge support from the project " Multi-Lake Research of Fish Ecology and Management using High-Resolution 3D Telemetry Systems ", funded by ALTER-Net within the  Multi-Site Research  (MSR) initiative, as well as support by the  European Aquatic Animal Tracking Network COST-Action  ( CA18102 ) -a project supported by  COST (European Cooperation in Science and Technology)  as a part of the  Horizon Europe program.
A homology search was performed with BLAST software from the National Center for Biotechnology Information homepage. 1 The sequence data obtained were aligned by ClustalW software 2 with the sequences of Naegleria species are presented in Supplementary Table  S2  and were also deposited in GenBank. 3   1 http://www. ncbi. nlm. nih. gov/ 2 http://www. nig. ac. jp 3 http://www. ncbi. nlm. nih. gov/ For whole genome sequencing purposes, NF_NF1 (5 biological replicates), NL_Lova6 and NL_Lova7 were cultured in axenic culture during at least 5 passages in tissue culture flasks using the SCGYEM conditions  (De Jonckheere, 1977). Trophozoites (1 × 10 6 Naegleria per strain) cultivated in axenic culture conditions were scraped from T-flasks and centrifuged at 1,000g for 10 min at room temperature; after supernatant removal, 200 μL of T1 buffer was added.
Whole-genome SNP-based phylogenetic analysis A whole-genome phylogenetic analysis based on SNPs was performed for N. fowleri and N. lovaniensis. For the comparison of N. fowleri strains, we compiled our 11 samples (including the 5 biological replicates of NF_NF1) with the published raw data publicly available from SRA (N = 49; Supplementary Table  S1 ;  Joseph et al. , 2021). For N. lovaniensis, we used our 3 samples plus the reads from the sequencing projects of the strains NL_ATCC 30569  (Liechti et al. , 2018) and NL_76-15-250 (Joseph et al. , 2021). After a cleaning of raw reads using Cutadapt, Illumina raw reads were first mapped either against the N. fowleri TY "close-tocomplete" genome  (Ali et al. , 2021)  or the N. lovaniensis ATCC 30569, using BWA-MEM (version 0. 7. 17-r1188) software  (Li, 2013).
BAM mapping files were then converted to pileup format using SAMtools  (Danecek et al. , 2021)  and SNP calling was performed using VarScan  (Koboldt et al. , 2012)  for each sample, using a minimum read coverage of 8X with a Phred quality score of at least 15. Genetic variations and alleles were then compiled into a global SNP matrix file (VCF file), using a home-made script. SNP-based phylogenetic tree was then generated using the SNiPlay web application  (Dereeper et al. , 2015). Finally, the phylogenetic tree was displayed using the iTOL v6 online application  (Letunic and Bork, 2021). In addition, N. fowleri samples were assigned to internal transcribed space (ITS) genotype, using a home-made script searching for ITS genotyping loci directly from raw reads.
Genome assembly In order to estimate the conservation of scaffolds between genomes from different species, we first aligned the published scaffolds of N. lovaniensis (ATCC 30569)  (Liechti et al. , 2018)  against the recently published genome of N. fowleri TY  (Ali et al. , 2021) , which consists of 37 well defined pseudo-chromosomes. For this, we used the D-genies online application  (Cabanettes and Klopp, 2018) , taking advantage of minimap2 alignment software  (Li, 2018). Except for minor rearrangements detected in only one pseudo-chromosome, a good collinearity was observed between N. lovaniensis scaffolds and N. fowleri pseudo-chromosomes (Supplementary Figure  S1 ), suggesting that scaffolds from the 2 species can be simply ordered according to their matching positions in pseudo-chromosomes.
This data manipulation prevents the generation of heterogeneous outputs for the different genomes and will facilitate thereafter the comparison of genomes and their graphical representation, notably for synteny analysis. The 6 new Naegleria genomes were assembled using the same strategy. Briefly, after a cleaning step by cutadapt, filtered reads were de-novo assembled using SPAdes  (Prjibelski et al. , 2020) , to generate scaffolds independently of the respective reference genome and ensure subsequent microsynteny analysis. In a second step, scaffolds were anchored, ordered and oriented, using the 37 chromosomes of N. fowleri TY by running the RaGOO program  (Alonge et al. , 2019).
Ploidy and heterozygosity analyses based on SNPs An estimate of ploidy level was performed for each of the newly sequenced genomes, based on the ratio of alleles of SNPs. For this purpose, cleaned reads were mapped back against the corresponding assembly of the genome with BWA mem, and SNPs were called with the same approach as previously with the combination SAMtools mpileup/VarScan. A home-made script was developed for evaluating the level of heterozygosity, and for extracting the information of allele depth ratio (i. e. , between reference and alternate allele) at each heterozygous position. Allele ratios were then reported along the chromosomes using Circos visualization  (Krzywinski et al. , 2009)  and the distribution of ratios was calculated for each genome. This technique allows to evaluate the global level of ploidy as well as its variation along the chromosomes and thus to detect potential abnormality in specific chromosomes or genomic regions.
Functional annotation Gene functions were assigned according to the best alignment of predicted protein sequences using BlastP (default values: E-value 1E-03) to the Uniprot database (including the SWISS-PROT and TrEMBL databases). We used InterProScan program  (Jones et al. , 2014)  to assign conserved protein motifs (PFAM, InterPro). An additional assignation of predicted proteins to specific COG (Clusters of Orthologous Groups) was performed independently using the "COG assignation" Galaxy wrapper available in Galaxy KaruBioNet, 4 (Couvin et al. , 2022), based on rpsblast and cdd2cog Perl script.
Pangenome analysis To perform the pangenome analysis of Naegleria genus, we used a dataset of 14 Naegleria genomes with clinical and environmental isolates from distinct geographic location, 8 genomes of N. fowleri  (NF_ATCC 30894, NF_ATCC 30863, 10. 3389/fmicb. 2022. 1056418 Frontiers in Microbiology 06 frontiersin. org NF_V212, NF_986, NF_TY, NF_NF1, NF_AR12 and NF_PA34), 5 genomes of N. lovaniensis (NL_ATCC 30569, NL_76-15-250, NL_F9, NL_Lova6 and NL_Lova7) and one of N. gruberi (strain NEG-M-ATCC 30224; Table  1 ). For this, published protein FASTA files containing predicted protein sequences were downloaded either from the AmoebaDB resource 5 (release 53) or from the NCBI FTP server, and were compiled together with the MAKER2 output files generated for our 6 new genomes to perform a genomic and protein comparison.
The pangenome analysis was conducted using OrthoFinder software  (Emms and Kelly, 2019 ) with all-versus-all BLAST strategy to define the orthogroups among the 14 genomes. The resulting presence/absence matrix was analyzed with an in-house developed Perl script to extract and classify genes as core-genes (genes present in all Naegleria sp. ) and accessory genes (genes present at least once in 1 or more but not all Naegleria species). In this latter category, we emphasize on species-specific genes which can be subdivided in 2 gene sets: species-core specific genes (gene shared by all strains within one Naegleria species) and speciesspecific accessory genes (genes present at least once in 1 or more strains of one Naegleria species). To determine whether the pangenome can be considered closed or open, we calculated the alpha parameter using the MicroPan R library  (Snipen and Liland, 2015).
Synteny analysis on Naegleria core genome Chromosomal synteny was estimated by connecting coregenes between two representative genomes, NF_NF1 and NL_ Lova7 for N. fowleri and N. lovaniensis, respectively. A homemade Perl script was used to extract core-gene locations and identify (i) specific links that connect core-genes located in different chromosomes between the two species, and (ii) links that connect core-genes that physically distant more than 50 kb between homologous chromosomes to highlight insertion or inversion. Syntenic regions shared between pairs of homologous chromosomes were visualized using both Circos  (Krzywinski et al. , 2009)  and Mauve Viewer. 6.
Protein-protein interaction network Schematic information on Naegleria biological pathways is mainly available for N. gruberi. 7 However, based on our pangenome results, several genes present in NG are absent in NF and NL (and vice-versa) and many Naegleria genes are of unknown function. Herein, in attempt to bring new insights on Naegleria biological pathways, we used Cytoscape v3. 8. 0 software platform  (Su et al. , 2014 ) combined with StringApp (Doncheva et al. , 2019)  plugin. STRING is a database of quality-controlled proteinprotein association networks and enables researchers to construct 5 https://amoebadb. org/amoeba/app/ 6 https://github. com/PATRIC3/mauveviewer 7 Namely at https://www. genome. jp/kegg a functional association network of uploaded genes/proteins of an organism based on three aspects: computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases  (Szklarczyk et al.
A total of 1,200,000 high-quality reference-based SNPs were detected across the 65 Naegleria genomes analyzed. Our results show that (i) additional clades were created when we included our new N. lovaniensis strains and (ii) N. lovaniensis is more diverse than N. fowleri, as 773,266 SNPs were detected within intra-N. lovaniensis species against only 70,026 SNPs within intra-N. fowleri. By restricting the SNP matrix and associated genotyping information (reduced VCF file) to N. fowleri strains only, we could perform a whole-genome SNP-based phylogenetic analysis for comparing the 3 new genomes (NF_AR12, NF_PA34, and NF_ NF1) against 52 strains of N. fowleri (close-to-complete genomes or reads only, Supplementary Table  S1 ) available in online databases at the time of the analysis  (Joseph et al.
Content and organization of the genomes To identify protein coding genes in the newly assembled Naegleria genomes, we performed gene prediction analyses using MAKER2 by providing evidence from transcriptomic datasets collected for each of species. Genomic features collected from the new genomes are presented in Table  1. MAKER2 gene annotation revealed that N. fowleri NF_NF1 and NF_AR12 have approximately the same number of genes as N. fowleri strain TY (N = 9,405), while the number of genes predicted for NF_PA34 (N = 11,036) is close to those predicted for the N. fowleri strains ATCC 30863 and strain 986  (N = 11,499 and 11,599, respectively). For N. lovaniensis, even though complete BUSCO levels are higher, the 3 new genomes present a significantly lower number of predicted genes, but the average length of the genes is higher (~3,000 bp).
, 2022). For instance, A. castellanii encystment is induced by the downregulation of the acetyltransferase-like gene ACA1_384820  (Rolland et al. , 2020). The Naegleria pangenome contains 26 N-acetyltransferase-like genes, 6 in the core and 20 in the accessory genome (3 being specific to N. gruberi; Supplementary Table  S4 ) but none was found to be homologous to gene ACA1_384820. We also identified the presence of 17 genes involved in "starvation" status (and possibly involved in Naegleria Overview of the protein-protein interaction (PPI) and functional modules identified in Naegleria pangenome. PPI network generated with STRING (using a high confidence score of 0. 75 and visualized with Cytoscape) was built using information available in the STRING database. The nodes represent the genes, and the edges represent the protein interactions.
Functional node clusters were created based on the results obtained with the STRING enrichment plugin (with a high confidence score of 0. 75 and based on non-redundant terms of Gene Ontology (GO) term, Kyoto encyclopedia of genes and genomes (KEGG) pathway data and Reactome Pathways Functional Interaction (FI) Network); they highlight major biological processes in Naegleria. Node color code stands as red for NF, pink for genes absent in NF (but present in both NL and NG), green for NL, light green for genes absent in NL (but present in both NF and NG), blue for NG, light blue for genes absent in NG (but present in both NF and NL), black for core genes and gray for accessory (Network figures generated using https://cytoscape. org/). encystment): 11 in the accessory genome, 5 in core genome, and one specific to N.
Funding This work was a part of the  MALIN  project funded by the  European Union  on the Guadeloupe Region under the European Research and  Development Funds (ERDF) 2014-2020 program  [ 2018-FED-1084 ]. C. Fund ,  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA.
Data availability statement The data presented in the study are deposited in the figshare repository https://figshare. com/articles/dataset/NFgwada_ genome_fasta/21603489 and in Genbank under BioSample accession numbers SAMN31682405, SAMN31682406, SAMN31682407, SAMN31682408, SAMN31682409 and SAMN31682410. Naegleria assembled chromosome sequences have been deposited on NCBI and have been assigned to accession numbers from CP113542 to CP113763. Naegleria ITS sequences have been deposited on NCBI and are available under accession numbers: OP867015, OP867016, OP867017 (Supplementary Table  2 ). Home-made scripts used for bioinformatic treatment and statistical analyses are available in a github repository accessible at https://github. com/SouthGreenPlatform/Pan Explorer_workflow/tree/main/Perl/Naegleria.
], and full-length transcriptome prediction based on NGS sequencing. Transcriptomic resources were prepared separately for N. fowleri and N. lovaniensis genomes, from public RNASeq datasets downloaded from European Nucleotide Archive (ENA), PRJNA642022 project, using, respectively, samples NF_ Nelson_medium with NF_PYNH_medium (3 replicates each) and NL_Nelson_medium with NL_PYNH_medium (3 replicates each; Zysset-Burri et al. , 2014; Supplementary Table.
, 2012 ). Benefiting from such metabolic mechanism (elaborated in  ( Sage et al. , 2012 ) ), C4 plants such as maize, sorghum, and sugarcane, miscanthus and a range of C4 grass  ( Osborne et al. , 2014 )  demonstrate superior photosynthesis efficiency (approximately 50 % higher) and nitrogen and water use efficiency (WUE)  ( Majeran et al. , 2010 ). Despite only occurring in less than 5 % of the global 250,000 plant species, C4 pathway contributes to 23 % of terrestrial gross primary productivity  ( Sage et al. , 2012 ). Interestingly, a large percentage of grass species use C4 photosynthesis pathways. As shown in a comprehensive grass database covering 99. 6 % of the 11 087 grass species, 42 % of those species use the C4 photosynthetic pathway with remaining 57 % using C3 pathway  ( Osborne et al.
This contributed to the global MSW generation. Out of 0. 12-4. 3 kg per day per capita global MSW, 59 %-68 % is organic fraction (e. g. food waste, paper waste and green waste), varying with the regions  ( Hoornweg and Bhada-Tata, 2012 ). It is estimated to rise to 6. 1 million ton/day by 2025 with over 65 % as organic fraction  ( Hoornweg and Bhada-Tata, 2012 ). A shift in the global MSW generation portfolio has been projected by World Bank, where the low-middle income countries (LMIC) are expected to contribute to 70 % by 2025. Similar in wastewater treatment (WWT) sector, rapid increase in water consumption and discharge (1. 7 times more rapidly than population growth) ( Food and Agriculture Organization of the United Nations, 2017 ) and rising LMIC contribution have been observed. FAO AQUASTAT database showed that out of the currently global freshwater withdrawal (3928 km3/yr), 56 % (2212 km 3 ) is released either as industrial WW, municipal WW or agricultural drainage, of which only 20 % is treated via WWT ( United Nations World Water Assessment Programme, 2017 ; Food and Agriculture Organization of the United Nations, 2017 ).
As a result all alternative reactions that could carry a flux can be sought, giving rise to alternative pathways that still conform to the phenotypic phase plane. A number of computational tools have been produced to analyse the fluxes resulting from a GEM using FBA. Becker et al. (  2007 ) developed a Constraint-Based Reconstruction and Analysis (COBRA) toolbox, a software package running from within the MATrix LABoratory (MATLAB) environment, and is also freely available for the Python and R environments  ( Ebrahim et al. , 2013 ; Conway, 2023 ). COBRA, and COBRApy allows for the quantitative prediction of both steady-state and dynamic optimal growth behaviour cellular flux and remains the most widely used method to model metabolism to date  ( Becker et al. , 2007 ; Jenior et al. , 2023 ).
LCSA methodology and overview. Under the guiding principles (life cycle perspective, transparency and completeness) ( ISO, 2006a ), LCSA consists of four interactive phases (goal and scope definition, inventory analyses, impact assessment and interpretation) ( UNEP/SETAC Life Cycle Initiative, 2011 ), capturing every life stage from raw material acquisition to final disposal. The first LCSA phase scopes the fundamental modelling elements including the system boundaries and the functional unit, and defines the principles and methodologies to be applied. At the inventory stage, data are collected on a unit process basis within the system boundary and assessed against data-quality requirements. Inventories are associated with the impact categories at the assessment stage, which comprises the mandatory element of characterization and possibly other optional ones (normalization, weighting).
g. agriculture residue)  ( Shonnard et al. , 2015 ; Reap et al. , 2008 ; Eranki and Dale, 2011 ). Particularly, spatial LCA models with region-representative data and characterisation indicators have been highlighted in UNEP-SETAC guidelines as a conceptual framework for LCA and ES. This is especially the case for the assessments for land use decision-support at global or regional scales with spatial diversity  ( Koellner et al. , 2013 ). However, in comparison with the large number of LCSA studies adopting country/region/industrial average data inventory (e. g. NREL US LCI databases ( NREL, 2004 ), Plastics Europe  ( Boustead, 2005 ) ), limited spa-tial or dynamic LCSA studies have been published on biorenewable systems so far with focus on the biodiversity, energy and GHG footprints in response to land use change or distributed resource use  ( Arodudu et al.