dataesr/scientific-paragraphs-categorization huggingface dataset:
The application of a randomization procedure to accommodate gaps in the reference database can also affect the estimations of phylogenetic and functional diversity components, which we assessed in our study. Building on increasing evidence of the monitoring capacity of eDNA metabarcoding  (DiBattista et al. , 2017; Polanco Fernández et al. , 2021; West et al. , 2021) , our study illustrates how this technique could evolve toward a general approach for the monitoring of fish communities on coral reefs. Functional and phylogenetic characteristics are expected to offer higher dimensions of information to describe and manage ecosystems  (Strecker et al. , 2011). Coupling ecological indices with eDNA can provide more complete ecosystem information for coral reefs  (Aglieri et al. , 2021; Marques et al.
That said, key sources of uncertainty still exist in using eDNA to assign species and a better coverage within reference databases will yield more information on the taxonomic units recovered from eDNA  (Valdivia-Carrillo et al. , 2021) , to the point where generating MOTUs as a species proxy will become unnecessary if almost all regionally occurring species are genetically referenced. Increasing evidence suggests that eDNA metabarcoding offers higher species detection abilities compared with traditional surveys  (Polanco Fernández et al. , 2021; Valdivia-Carrillo et al. , 2021) , which was confirmed in our study with the greater number of MOTUs detected with eDNA (129 MOTUs) than fish species in UVC (120 species). We found overlap in species composition between eDNA and UVC, but also differences.
| Genome assemblies and gene prediction Raw reads were processed using tools from the smrtanalysis suite 2. 3. 0 (https://github. com/Pacif icBio scien ces/Genom icCon sensus) as the previously published genomes  (Badouin et al. , 2015; Branco et al. , 2017 Branco et al. , , 2018)). We converted the bax. h5 files from the same sequencing run into one fastq file using pls2fasta. We generated the assembly using canu v. 1. 8 (Koren et al. , 2017)  with the parameters "genomeSize=30m" and "-pacbio-raw". We used pbalign (version 0. 3. 0) with the blasr algorithm  (Chaisson & Tesler, 2012)  to realign the raw reads onto the assembly (indexed with samtools faidx;  Li et al. , 2009)  and then used the output bam file into quiver  (Chin et al. , 2013)  to polish the assembly basecalling. Default parameters were used when not detailed in the text.
See Table  S1  for assembly statistics. As for the previously published Microbotryum high-quality genome assemblies  (Badouin et al. , 2015; Branco et al. , 2017 Branco et al. , , 2018;; Carpentier et al. , 2019) , the protein-coding gene models were predicted with EuGene 4. 2a  (Foissac et al. , 2008) , trained for Microbotryum. Similarities to the fungal subset of the uniprot database  (Consortium TU, 2011)  plus the M. lychnidis-dioicae Lamole proteome  (Badouin et al. , 2015)  were integrated into EuGene for the prediction of gene models.
| Transposable element detection and annotation De novo detection of transposable elements (TEs) was done using LTRharvest  (Ellinghaus et al. , 2008)  from GenomeTools 1. 5. 10, performing long-terminal repeat (LTR) retrotransposons detection and RepeatModeler 1. 0. 11 (Smit & Hubley, 2008)  combining results from three other programs, RECON  (Bao & Eddy, 2002) , RepeatScout  (Price et al. , 2005)  and Tandem Repeats Finder  (Benson, 1999). The TE detection was enriched by BLASTn 2. 6. 0+ (Altschul et al. , 1990)  using the genomes as a database and the previously detected TE models as queries. To fulfill the repetitivity criterion, a TE sequence detected by RepeatModeler or LTRHarvest and their BLAST hits were retained only if the query matched three or more sequences in the same species with an identity ≥0.
8, a sequence length >100bp and a coverage (defined as the query alignment length with removed gaps divided by the query length) ≥0. 8. When these criteria were met, the other query matches were retained for the following parameters: identity ≥0. 8, sequence length >100bp, e-value ≤5. 3e-33 and coverage ≥0. 8. TE annotation  (Wicker et al. , 2007)  was performed using the fungal Repbase database 23. 05  (Bao et al. , 2015)  based on sequence similarity. First, a BLASTn search and a BLASTx search were performed using the fungal Repbase DNA and protein sequence databases, respectively, and TE sequences as queries. For each of these similarity-based searches, the minimum e-value score was set at 1e-10 and minimum identity at 0. 8. Then, protein domain detection was performed using pfam_scan. pl (ftp://ftp.
ebi. ac. uk/pub/datab ases/Pfam/Tools/) on TE sequences and compared to protein domain detection in the fungal Repbase database. Matches with e-values lower or equal to 1e-5 were kept. The annotation found by RepeatModeler was also considered. When no annotation was found, the TE sequence was annotated as "unclassified"; such annotations were discarded when overlapping with predicted genes. Annotation was performed using a python script. TE copies were removed from the predicted genes in further analyses. The pipeline and python scripts used are available at https:// gitlab. com/marine. c. duham el/microtep.
intermedium or M. lagerheimii, used as a proxy for the ancestral state as done previously  (Branco et al. , 2017 (Branco et al. , , 2018)). Synonymous divergence (d S ) was calculated from the alignment of a 1 and a 2 allele sequences using MUSCLE  (Edgar, 2004)  implemented in TranslatorX v1. 1  (Abascal et al. , 2010). Synonymous divergence (d S ) and its standard error were calculated using the yn00 v4. 9f program of the PAML package  (Yang, 2007)  and plotted using the ggplot2 library of R  (Wickham, 2009). Non-recombining regions were identified as non-collinear regions with non-null synonymous divergence using the a 1 -vs-a 2 circos and d S plots. Pseudo-autosomal regions were identified as the remaining regions, being collinear and with null synonymous divergence, as expected in these highly selfing fungi.
Introduction The digital soil database, S-map (https://smap. landcareresearch. co. nz;  Lilburne et al. , 2004; McNeill et al. , 2018) , with a coverage of 37% of New Zealand in 2021, provides the  Kosugi (1994 Kosugi ( , 1996) )  soil water retention θ(ψ), and the unsaturated hydraulic conductivity K(θ)  (Pollacco et al. , 2013b (Pollacco et al. , , 2017)) , for up to six functional horizons to a maximum depth of 1 m, on a 150 m × 150 m spatial grid. This soil hydrological property database is termed 'Smap-Hydro'. However, most hydrological models are not designed to accommodate multiple layers and therefore require soil hydrological characterization at a coarser vertical resolution (e. g. , one, two or three layers). Here we determine how to upscale hydraulic parameters to multiple soil layers, replacing a heterogeneous domain by a less heterogenous domain  (Vereecken et al.
Weighted objective functions of the multistep optimization The global optimizer searches for the optimal hydraulic parameters by minimizing a weighted objective function (WOF). We selected the robust global optimizer BlackBoxOptim v0. 6. 1 (https://github. com/ro bertfeldt/BlackBoxOptim. jl) written in Julia  (Bezanson et al. , 2017)  and selected the adaptive_de_rand_1_bin_radiuslimited method. For every optimization step, the maximum evaluation functions that are allowed to be optimized are controlled by MaxFuncEvals, which was set to 100. We designed the novel WOF to address the issue that observed θ, θ obs [L 3 L -3 ], in deeper layers (below the root zone), contains greater uncertainties compared to measurements in the root zone. It is also important that simulated θ, θ sim [L 3 L -3 ], gives more accurate predictions in the root zone than below the root zone.
, 2018) , and a positive linear relationship between K C and pasture growth. Allen and Pereira (2009)  showed that K C is a function of the fraction of ground cover and crop height, which is also related to pasture growth. Pasture growth (kg DryMatter/ha/day) data were provided by DairyNZ for the Waikato region (https://www. dairynz. co. nz/media/ 5793237/average-pasture-growth-data-waikato-2020-v1. pdf) with the following normalized monthly values [0-1] from January to December: 0. 48, 0. 24, 0. 15, 0. 23, 0. 34, 0. 14, 0. 00, 0. 23, 0. 65, 1. 00, 0. 79, and 0. 72. The range of K C , 0. 8 to 0. 95, was taken from rotated grazing pasture according to FAO irrigation paper 56  (Allen et al. , 1998) , and the range of the LAI, 0. 19 to 5. 10, was taken from Van Housen (2015). Normalized LAI and K C are calculated as: normalisedvalue = value -value min value max -value min (9) where the corresponding value of the vegetation parameters, LAI min , LAI max , K Cmin , and K Cmax , as well as the maximum saturated storage capacity of a wet canopy (Sint sat  [L] ), are optimized after multistep Opt_1 and reoptimized after multistep Opt_9 or Opt_7, depending on whether the number of soil layers is five or four.
Time of execution of multistep optimization. The yearly average execution time for each site of performing the multistep optimization by running HyPix model is described in Table  10. HyPix is run with Julia 1. 7. 3 on a laptop equipped with 64 GB RAM and Intel® Xeon® E-2186M Fig. 2. Simulated water balance components with HyPix for the experimental sites and simulated and measured θ during the calibration and validation periods for the experimental sites. As explained in the text, there was no sufficient data to validate Waihou site. Plots include ΔPr precipitation, throughfall precipitation ΔPrThrough, ponding ΔHpond, drainage ΔQ, potential evapotranspiration ΔPet, sink term ΔSink, evaporation ΔEvap, and measured (continuous line) and simulated (dashed line) θ at depths corresponding to measurements for each site.
Modelled heterogeneity compared to pedological description Table  11  classifies the result reported in Table  9  and shows the minimum number of layers to accurately model each site. The classification derived from inverse modelling is compared to the soil characteristics derived from the soil database S-map. Despite the limited number of experimental sites included in this study, Table  11  shows there is a clear relationship between the number of layers required for adequate representation of the soil hydraulic parameters and the heterogeneous soil description derived from S-map. Both the soil heterogeneity and the drainage class from S-map can provide an indication of the vertical discretization required to compute the soil water balance components. For example, well-drained soils can generally be adequately described by a relatively small number of layers.
Acknowledgements This research was supported by the  New Zealand Ministry of Business, Innovation and Employment  throught the ' Winning Against Wildings ,  C09X1611 ' and ' Next Generation S-map ,  C09X1612 ' research programmes. Mathieu Sellier  from  Mechanical Engineering of the University of Canterbury, New Zealand  is thanked for reviewing the manuscript. Funding for open access was provided by  University of Granada/CBUA, Spain.
2018; Kuceyeski et al. 2013). Disconnection analyses consider a probability of dysfunctions in other brain areas. For example, if a patient has a lesion in a subcortical region (e. g. posterior limb of the internal capsule), the disconnection analysis will provide us with a probability of interruption of the white matter connection (e. g. CST) that links the subcortical (internal capsule) and cortical regions (e. g. primary motor cortex). When comparing those methods, the disconnection analysis often explains more variance in data than lesion load analysis  (Corbetta et al. 2015; Hope et al. 2016). The disconnection analysis  (Conrad et al. 2022; Hajhajate et al. 2022; Forkel et al. 2022; Souter et al. 2022; Silvestri et al. 2022; Sperber et al. 2022 ) is different from the lesion network symptom mapping methods that estimate from a normative dataset the functional connectivity impaired by a lesion  (Boes et al.
Generation of disconnection maps Methodological details are available from Thiebaut de  Schotten et al. (2020). In brief, each lesion serves as the input for the BCBtoolkit's Disconnectome tool that computes maps of white matter pathway disconnection probabilities and its impact on loss of function  (Foulon et al. 2018 ; http:// toolk it. bcblab. com). Probabilities of white matter pathways were derived from a normative population of 163 healthy controls (44. 8% males) using a diffusion-weighted imaging dataset acquired on a 7T scanner as part of the Human Connectome Project  (Vu et al. 2015). The pattern of brain areas that were disconnected in each stroke was subsequently characterised by measuring the average level of disconnection in subcortical areas and areas derived from a multimodal atlas of the brain surface  (Glasser et al.
Behavioural components To summarise behavioural measurements while keeping as much variability as possible and minimising noise, behavioural components were extracted as described in  Corbetta et al. (2015). In brief, an oblique rotation principal component analysis was applied to the motor scores obtained with different neuropsychological tests as described above. Two components (left and right side of the body) resulted from the analysis explaining 77% of the observed variance in the data. The analysis was computed in Matlab (MathWorks Inc. ).
Disconnection map components The Disconnectome consists of 46 components, where 33 components have already been shown to capture more than 90% of the variance in the distribution of disconnection maps in stroke (Thiebaut de Schotten et al. 2020). These components were derived from an independent normative dataset of 1333 disconnection maps of ischemic stroke patients (M = 63. 89, SD = 15. 91, range 18-97 years; 56. 1% males) fully described in  Xu et al. (2018)  and Thiebaut de  Schotten et al. (2020).
Statistical analysis The analyses for the anatomical prediction of the motor outcome were carried out in  RStudio (v. 1. 3. 1093; RStudio Team, 2020). Backward/forward hierarchical linear regressions, backward/forward stepwise regressions, ridge and lasso regressions were performed and the results between different approaches were compared. Each regression used the patients' estimated scores of each component (i. e. beta coefficients from the linear regression) to predict two principal components of motor scores (the left and right side of the body, n = 2) at two weeks, three months, and one year after the stroke (n = 3 time points) resulting in 6 regression models per regression type. This procedure is available as supplementary code with the manuscript (see https:// github. com/ lidul yan/ Hiera rchic al-Linear-Regre ssion-R-and https:// github.
Lesion characteristics (location, overlay) Patients' lesions and disconnection maps from the dataset (N = 62) were distributed bilaterally with 67% of lesions in the right hemisphere (Fig. 1 ). The most common site of damage was observed in the right subcortical regions (n = 27; other areas n < 15), including the thalamus, putamen, caudate, pallidum, hippocampus, amygdala, nucleus accumbens, insula, subcallosal cingulate, paracingulate, and parahippocampal areas. The disconnection maps generated.
Prediction of the motor outcomes Linear regressions used the patients' estimated scores of each component to predict motor scores' components (the left and right side of the body, n = 2) two weeks, three months, and one year after the stroke (n = 3 time points). We performed different linear algorithms (backward/forward hierarchical regressions, backward/forward stepwise regression, lasso and ridge regressions) to select the most important components that are able to explain as much variance in new data as possible. Each regression was repeated 1000 times (5000 times in the case of the hierarchical regressions) with different training and testing dataset splits. In comparison to other algorithms, the predictions of the ridge regression are not superior at any time point (2 weeks, 3 months, 1 year) for the left-side motor impairments.
This diminishes the role of interindividual variability  (Forkel et al. 2021) , and inference about an individual based on group-level data analysis leads to a potential error, known in the literature as "ecological fallacy"  (Portnov et al. 2007; Robinson 1950). Therefore, predictions based on these two approaches could be misleading. With the availability of big datasets and the development of new statistical tools, it became possible to advance from single cases and group-level studies toward an integrative approach that accounts for interindividual variations but generalises to the group-level. This integration allows researchers to make personalised and individual predictions like in the present study. Preliminary attempts used different machine-learning approaches to predict motor impairment.
However, most of the studies fall into one of the following pitfalls. The models are not validated on an independent dataset. They also focus on the lesion location, statistically ignoring the remote effects of a lesion on distant brain regions. Finally, they only consider a few aspects of motor functions independently rather than the entire pattern of motor abilities. Akin to  Salvalaggio et al. (2020) , we considered these issues that may downplay the potential power of prediction motor symptoms based on neuroimaging data. We applied the resampling method to validate the results. We used behavioural data that includes excellent variability of neuropsychological tests that capture motor disorders from different angles. We applied disconnection analysis that is a more accurate statistical measure of the theoretical concept of diaschisis.
However, in contrast to Salvalaggio and colleagues' (2020) work, we used the Disconnectome (Thiebaut de Schotten et al. 2020) developed on an independent and much larger cohort of stroke patients dataset (n = 1333) to model the profile of disconnection of individual patients. The Disconnectome allowed increasing the predictive power for motor impairments from 37 to 49% (right hemisphere lesion) and from 42 to 81% (left hemisphere lesion) at 2 weeks. These progresses are indicative of the importance of the disconnectome in the prediction of neuropsychological impairment and a significant step forward personalised prognosis in clinical neuropsychology. Additionally, we compared the results of our approach with a previously established method such as lesion load analysis of cortico-spinal tract and our algorithm proved to explain more variance (see Fig.
Nevertheless, we need to know that both of them play an important role in predicting motor impairments and by dropping one of them will make our interpretation of the brain region involvement incomplete. By contrast, ridge regression appeared to be the most reliable and suitable method to predict motor outcomes in our dataset since (1) it accounts for multicollinearity, (2) it did accurately predict longitudinal right motor impairments better than other methods, and (3) it demonstrated the consistency of the important components with Lasso regression and across time (2 weeks, 3 months, 1 year). For example, out of the top 20 important components in the ridge regression model, 16 components were coinciding with the components retained in the lasso regression for the right motor impairment at 2 weeks (see Fig.
5  from the Supplementary materials). Another possible reason for the inconsistency in the retained components is the small sample size. This has been one of the major problems in the application of machine learning algorithms on small datasets. However, the nested cross-validation technique must have diminished the biased effect and led to a higher consistency across the results  (Vabalas et al. 2019). Another potential limitation of the study is that the disconnection maps of 6 haemorrhagic stroke patients (~ 10% of the dataset) were described with the Disconnectome that was developed only on ischemic stroke patients. However, the Disconnectome was able to explain the variance in those 6 patients' disconnection maps as well (R2: M = 0. 983, SD = 0. 011). Thus, the inclusion of 6 patients with haemorrhagic strokes did not influence our results.
Acknowledgements We thank the  University of Bordeaux  and  CNRS  for the infrastructural support. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 818521 ), the  Marie Skłodowska-Curie programme  (grant agreement No. 101028551 ),  The Erasmus+ for Traineeships Programme , and the Donders Mohrmann Fellowship No. 2401515  ( SJF, NEUROVARIABILITY ). Funding Funding was provided by  European Research Council  (Grant no  818521 ),  H2020 Marie Skłodowska-Curie Actions  (Grant no  101028551 ),  Erasmus+ , and the  Donders Institute  (Grant no  2401515 ). This study was conducted in the framework of the  University of Bordeaux 's  IdEx "Investments for the Future" program RRI "IMPACT " that received financial support from the  French government.
Data availability The forward/backward hierarchical linear regression, backward/forward stepwise regression, lasso and ridge regressions were carried out in RStudio (v. 1. 3. 1093; RStudio Team, 2020). The code is freely available (https:// github. com/ lidul yan/ Stepw ise-Lasso-Ridge and https:// github. com/ lidul yan/ Hiera rchic al-Linear-Regre ssion-R-). The code for the estimation of the component's score is freely available (https:// github. com/ lidul yan/ Estim ate_ CompS core_ lm).
Methods BuscoPhylo is developed in PHP, Python and Bash scripts as backend. HTML5, CSS3 and JavaScript are used as front-end programming language. The storage of the inputs uses MySQL. BuscoPhylo receives FASTA les (one genome per le) as an input (Fig. 1 ) with a minimum of 4 genomes sequences, once a project is created. The pipeline proceeds to run BUSCO software v5. 4. 1  [12]  for each genome sequence using the appropriate lineage and mode. The next step consists in retrieving the shared protein Single Copy Busco genes (SCBg) between queried genome sequences and creating a multi-FASTA le for each BUSCO gene family. Then, individual alignments of protiens sequences are performed using muscle v3. 8. 31  [13]. Subsequently, the alignments are trimmed using trimAl 1. 2rev59  [14]  then concatenated into one super-matrix alignment using seqkit v0.
Interface was developed with Graphical User Interface (GUI) that could be executed using any modern internet browser without installation of any tool or software. BuscoPhylo enables the user to sign up for a personal account facilitating the management of the project submissions. The project manager implemented helps the user to check the project's history and progress, reexport data and to avoid redundancy in projects running. To tackle issues of storage capacities, each project is stored for a period of 7 days.
Webserver and Stand-alone version We deployed BuscoPhylo at https://buscophylo. inra. org. ma/ which enabled its use online without installation. Users are encouraged to install and run BuscoPhylo on personal computers very easily with required software installed. Advanced users can deploy BuscoPhylo on local or public servers to provide the online use to other users. Advanced users can also contribute to the development of BuscoPhylo, as its source code is available in GitHub (https://github. com/alaesahbou/BuscoPhylo). Copy genes (BSCg) are extracted, and individual alignments are performed for each family gene before generating the super-matrix alignment. (6) ML phylogenomic tree is inferred. Finally, the graphical visualisation of the tree is displayed in addition to the overall results les.