dataesr/scientific-paragraphs-categorization huggingface dataset:
2017b ). However, the third isolate clustered within the other VNB African isolates, suggesting a more recent intercontinental exchange. In that case, it is important to note that this last strain has been isolated in the environment. In 2017, Casadevall and colleagues suggested that the separation of C. neoformans complex species and C. gattii complex species occurred in the middle of the Cretaceous period  (Casadevall et al. 2017). This geological period was marked by the breakup of the supercontinent west Gondawa which encompassed Africa and South America. This modern continent formation ended much before the global dispersion of major Cryptococcus spreader birds  (Prum et al. 2015)  which occurred 40 Â 10 6 years after the end of the Cretaceous period. Although our data cannot reliably date the separation between the C.
For example, BCPA has been used to identify the timing of animal movements  [21, 22]  and to quantify animal behavioural shifts from seasonal environmental changes  [23, 24]. Secondly, unsupervised machine learning is a statistical approach that can identify hidden patterns present within datasets and has the potential to cluster, and therefore define, movements. These clustering algorithms group datapoints into distinct collections based on any latent structure present within data  [25, 26]  and have been used to identify patterns in neuronal ensembles in the brain  [27]  and to identify subgroups within patient populations  [28]. However, BCPA and unsupervised machine learning are yet to successfully classify insect behaviour from movement trajectories alone. Their application has been restricted in this context, as path segmentation can only identify changes in behaviour rather than behaviours themselves  [18] , while clustering requires a sufficiently high signal-tonoise ratio to be successful (something raw movement trajectories do not possess)  [26].
Data acquisition, cleaning and assessment Activity rates, based on observations from the raw data, were found to be much higher around an untreated net, with the number and length of movements significantly lower when an ITN was used (Table  1 ). When the autocorrelation of the datasets was assessed, it was found that movement velocity was positively autocorrelated through 50 time-lags in both the untreated (Fig 2A ) and treated (Fig 2B ) data. Accordingly, the data were taken to be suitable for analysis. This association becomes weaker as the lag increases (from 0. 7 at lag 1 to 0. 3 at lag 50).
gambiae s. s. , in the presence of both baited untreated bednets and baited ITNs. As the study replicated previous findings, the method is deemed to be an innovative, validated and productive approach that improves and expands the existing toolkit available to vector biologists. Furthermore, the method is repeatable, as any individual with the same dataset will produce the same behavioural tokens and behavioural types; it is generalisable, as it is not limited to a single domain, but can be applied to any vector in any setting; and its foundation in mathematical processes ensures it is immune to observer bias. The accuracy of the new method is confirmed using both internal and external validation  [37, 38]. The former ensures the correct algorithm and initial parameters are applied, while the latter tests the accuracy of the approach itself.
As the key difficulty here is the frequency of lost frames (caused by the recording system momentarily losing track of the small vector), a method that can handle an irregular dataset is required. As BCPA is a likelihood-based form of path segmentation, which sweeps an analysis window over an entire movement path to identify significant shifts in a parameter value, it provides a robust method for dissecting vector activity into behavioural tokens that can account for irregular temporal measurement intervals and does so without any a priori assumptions  [19, 20]. Although offering several advancements, the method presented here is subject to its own limitations. One such constraint concerns the clarity of the silhouette created by any grouping of behaviours. That is, as behavioural units are nebulous concepts, any silhouette of their classification will be equally unclear and datapoints from different behaviours will not necessarily have a high separateness  [51] [52] [53].
Resources The workflow presented here is implemented using a combination of R and Python. R is used for preprocessing, utilising the BCPA package built for that language. Python, through a Jupyter notebook, is used at the clustering stage to exploit the scikit-learn library. We recommend that the Anaconda platform be used to access RStudio and JupyterLab, as up-to-date installations for Windows, Linux and Mac can all be found in that single distribution. Code, and further details, needed to run the workflow can be accessed through a public GitLab repository: https://gitlab. com/MTFowler/lstm_flightcluster. All analysis found here was performed on a ThinkPad X1 Carbon, using an Intel i7-7500u CPU. All procedures associated with the collection of mosquito flight data are as described in  [9, 11, 55, 56].
Furthermore, although BCPA accounts for semi-regular sampling  [19, 20] , allowing for some irregularity in the dataset, movement tracks were removed from the analysis if they contained two datapoints at the same time or if they had especially large time gaps (i. e. , greater than 10 seconds). Finally, as path segmentation assumes that all time series data displays serial dependence, it was confirmed that the dataset was autocorrelated (i. e. , that the velocity of each datapoint is statistically correlated with its recent past)  [20]. This was accomplished in R using the 'Autocorrelation and Cross-Correlation Function Estimation', ACF().
Path segmentation With a correctly formatted dataset, that had been cleaned and assessed, BCPA was applied. BCPA is a form of path segmentation that identifies changes in animal behaviour, at the path-level, based on significant shifts in a parameter value of an organism's movement trajectory. As BCPA accepts movement paths as sequentially ordered step lengths, turning angles and velocities, rather than the spatial identifiers collected by tracking technology, spatial values were converted into the required variables using the GetVT() function from R's BCPA package  [57]. Within BCPA there are four user defined parameters: (1) the 'Parameter Value' (the response time-series variable in which significant changes will identify a behavioural change point); (2) the 'Window Size' (the number of datapoints the window will capture when sweeping); (3) a sensitivity parameter 'K'; and (4) the 'Cluster Width.
Clustering To determine the optimal unsupervised learning algorithm and initial parameters for clustering, internal validation was undertaken. Following  [37, 38] , the form of internal validation used was silhouette scores  [58, 59]  and visual inspection of t-SNE plots  [39] [40] [41]. A silhouette score measures how well data had been grouped, comparing each object's similarity to others within its own cluster (group tightness) and those from other clusters (group separation) and was calculated using Python's silhouette_score() function from the metrics module of the scikit-learn library. This measure gives a score between -1. 00 and +1. 00, with a silhouette value below 0. 20 showing no structure is present in the data and the grouping is invalid; a figure over 0. 70 representing a strong structure and a valid grouping; and a silhouette score around 0.
manifold module found within Python. When performing t-SNE, the user needs to define the perplexity (an estimate number of nearest neighbours for each datapoint), with larger datasets requiring a larger perplexity  [41]. Consequently, perplexity was fine-tuned to show global geometry. Once the optimum algorithm and parameters were determined by silhouette score and inspection of t-SNE plot, findings were applied to the BCPA output. Python's scikit-learn library was used as it is an efficient means of building standard machine learning models  [60]. (Other packages, such as R's class, are available when implementing unsupervised machine learning, however different packages may produce different results. ).
External Validation To ascertain the accuracy of the new method, its performance was externally validated by comparing results concerning the difference in flight patterns of An. gambiae s. s. around human-baited insecticide-treated bednets (ITNs) and untreated bednets. Findings generated by the new, computation-derived, method were contrasted with those from a previous study that employed the existing, expert-defined, method for behavioural classification. Although a standard method of external validation is through comparison against an a priori dataset, testing whether a known, true, clustering can be recreated  [38] , this was not possible in this instance. There is no known, incontrovertibly true, grouping for this An. gambiae s. s. behaviour and therefore no such comparison can be made.
Bioinformatic Analysis All bioinformatic analyses were performed using a standardized pipeline  (Brandt et al. , 2021) , available on Gitlab, 1  on a homebased cluster (DATARMOR, Ifremer). First, sequence files were renamed from their Genoscope identifiers to more explicit names. Due to non-directional adapter ligation, inserts were sequenced in different orientations. We thus used Cutadapt v1. 9  (Martin, 2011)  to identify the primer sequence in each read and sort them according to two criteria: forward or reverse primer and forward or reverse sequencing. Data for each sample was thus split into 4 sequence files (R1F, R1R, R2F, R2R). Cutadapt then removed the identified primer sequences and BBMAP repair  (Bushnell, 2014)  was used to ensure that reads were still paired by sorting reads using the information present in their description line and removing unmatched reads.
, 2007)  available in DADA2 v. 1. 10, using the Silva v138 reference database  (Quast et al. , 2013)  and a bootstrap threshold of 80. The ASV and taxonomy tables produced by this pipeline were then combined in a phyloseq object  (phyloseq v1. 28. 0, McMurdie and Holmes, 2013)  in an R v3. 6. 1 environment. Reads from the same amplicon library, but originating from different Illumina runs, were merged under the same sample name before removing sequences from unwanted taxa (Eukaryota, Chloroplast and Mitochondria affiliated sequences). Data was decontaminated using extraction, PCR and field controls using the decontam package (v1. 4. 0,  Davis et al. , 2018) , or handpicking in the case of the ASV dominating control libraries reads (see reproducible workflow on github). Samples totaling less than 40,000 reads after decontamination were removed, the appropriate metadata added (Supplementary Table  1 ), and the final object saved as a phyloseq object for further analysis in R.
Sediments Characterization Characterization of the sediment samples was carried out by Filab S. A. S (Dijon, France). Granulometry values were obtained using wet Malvern laser scattering together with humidity level and loss on ignition at 550 • C (see Supporting Information for more details on the methods used). Temperature for each sampling station was extrapolated when possible (MEDWAVES expedition) from CTD data from the same sampling stations  (Orejas et al. , 2017). When this data did not exist, it was set to average temperature recorded in the ocean basin at the depth considered  (Mantyla and Reid, 1983; Pierre, 1999; Martín-Cuadrado et al. , 2007).
Statistical Analysis All subsequent statistical analyses were done in R v3. 6. 1, using phyloseq (v1. 28. 0, McMurdie and Holmes, 2013 ), vegan (v2. 5. 7, Oksanen et al. , 2020 ), and ggplot2 (v3. 3. 0, Wickham, 2016)  packages to compute alpha diversity, beta diversity, and produce taxonomy barplots. A map of the sampling stations was generated using ggplot2 (v3. 3. 0, Wickham, 2016). Description of the sampling sites was based on available metadata using the principal components analysis (PCA) from package FactomineR  (Lê et al. , 2008). Environmental parameters considered for each sample were ocean depth at sampling station, distance from shore, sediment horizon (depth in the sediment core), temperature above seafloor, and sediment characteristics, namely mean organic matter content by station and horizon, mean humidity level by station and horizon, mean granulometry (µm), and heterogeneity of particle size.
Visualization of the DDRs relied on community similarity computed using a Bray-Curtis index after normalization of the dataset using cumulative sum scaling with the metagenomeSeq package  (Paulson et al. , 2013). Geographic distance was measured using a "Vincenty" (ellipsoid) great circle distance to take into account Earth curvature, relying on packages enmSdm, and geosphere (v1. 5. 10, Hijmans, 2019). Environmental similarity between samples was estimated with euclidean distances on the centered and scaled environmental data table, using the R base scale function. The statistical difference between the slopes of the models fitted to the DDRs was tested using function diffslope2 from package simba  (Jurasinsk and Retzer, 2012). Beta-diversity variation partitioning analysis were computed using function varpart in the vegan package.
Ordinations of the microbial data were visualized as nMDS using phyloseq, and environmental data were fitted to the ordinations using the envfit function in package vegan. Permutational multivariate analyses of variance were performed when appropriate with the adonis function of package vegan, after checking the homogeneity of group dispersions using function betadisper. Finally, biomarker detection was done with the DESeq2 package (1. 24. 0, Love et al. , 2014). The fully reproducible workflow for statistical analysis is available at: https://loimai. github. io/ABYSS_16S/.
16S rRNA Gene Amplicon Processing A total of 230 sample libraries were built and sequenced, producing 195,470,177 raw sequences of the 16S rRNA gene's V4-V5 region, with a mean of 849,870 reads by library. A total of 17,366,268 reads were recovered from the additional 24 control libraries that were constructed and sequenced simultaneously, originating from sampling (empty storage bags conditioned onboard research vessels at the end of some of the sampling sessions), extraction (empty kit processed through all extraction steps together with the samples) and PCR (ultrapure water) blanks (Supplementary Table  1 ). After processing with DADA2 the dataset included 123,454,421 sequences for a total of 265,198 ASVs. Of those, 1,223 (about 0. 5%) were found in control samples, and 728 ASVs were specific to the control libraries.
Most of the contamination was dominated by a specific ASV that accounted for 99% of reads in negative control libraries. This ASV, affiliated with partial 16S sequences of Sphingobium strains, is a recognized contaminant of Taq-Phusion reagents  (Salter et al. , 2014). After bioinformatic processing, taxonomic refining and decontamination, the dataset comprised a total of 210 libraries including 66,826,975 sequences representing 260,567 ASVs (min 40,076 sequences, max 847,227 sequences) (Supplementary Table  2 ). Rarefaction curves (Supplementary Figure  1 ) confirmed that sequencing and sampling efforts captured most of the sample diversity.
Linear regressions of the DDRs showed that the highest rate of decrease in community similarity with distance occurred in the transition zone (slope: -0. 00342), followed by the Mediterranean basin (slope: -0. 00134), and the Atlantic basin (slope: -0. 000715). At inter-regional scale, the slope of the DDR was steepest between the Atlantic and Transition regions and the Mediterranean and Transition regions (Figure  2B ). It was 0. 5 times less steep in the Atlantic-Mediterranean comparison, likely due to the most distant samples originating from the deepest stations in the dataset (water depth). In addition, we observed a generally positive correlation between community similarity and environmental similarity, with a modeled regression slope at least four times higher at intra-regional scale (Figure  2A , slope: 0.
16-0. 62) (Figure  2A ). Variation partitioning analysis showed that a larger fraction of variation in data was attributed to the horizon effect (29. 6-32. 7%) than to site effect, conflating spatial distance and variation in depth and temperature (9%), or sediment composition (1. 8%) (Figure  5C ). Biomarker analysis strengthened this observation (Figure  5D ). We split our samples between surface horizons [0-10 centimeters below the seafloor (cmbsf)] and subsurface horizons (10-40 cmbsf), based on the findings of  Petro et al. (2019)  highlighting a shift in community composition at the bottom of the bioturbation zone. We then determined three sets of site-specific surface biomarker ASVs, together with one set of general surface biomarkers, and one set of general subsurface biomarkers. Represented in Figure  5D  is the contribution of each set of biomarkers to the communities at each site, in the surface (left) and subsurface (right) horizons.
Site-specific biomarkers made up a small fraction of the community (<1. 8%) while surface and subsurface ones accounted for 36. 9-45. 6%. As seen in the taxonomic profiles, the surface biomarkers were assigned to a variety of taxonomic groups, including classes Nitrososphaeria, Alpha-and Gamma-proteobacteria, while the subsurface biomarker ASVs mostly belonged to  In plot (B), the blue line illustrates the linear regression for the Alborán Sea samples (p < 2. 2e -16 ), and the red line the regression for the complete dataset (p < 2. 2e -16 ). (C) Variation partitioning analysis for the Alborán Sea samples. (D) Relative composition of communities of the three sites of the Alborán Sea (Western Mediterranean Sea) based on the five sets of biomarkers identified: overall surface and subsurface biomarkers, and site-specific biomarkers (considering only surface horizons).
, 2017; Petro et al. , 2019; Lloyd et al. , 2020). We took advantage of the longitudinal extent of our dataset to investigate what processes might be at play in the assembly of microbial communities in and just below the bioturbation zone. On the Importance of Water Depth: An Environmental and Biogeographic Boundary? The transition between the upper and lower continental slope, around 800-1,000 mbsl, is often associated with sharp local changes in sea bottom and water column conditions. It delineates the upper bathyal zone, found below the mesopelagic waters between 200 and ∼1,000 mbsl, and the lower bathyal zone (1,000-3,500/4,000 mbsl)  (Watling et al. , 2013; Costello et al. , 2017). Our results indicate that this transition is also associated with marked changes in benthic microbial community structure as shown in the multidimensional analysis (Figure  4B ).
In this study, it is safe to assume that all environmental parameters possibly correlated with geographic distance (temperature, water depth) apply a similar pressure throughout the entire sediment horizons considered. We should thus observe parallel DDR regressions for the different sediment horizons, as community turnover rate would not vary with sediment depth. This is indeed what we observed at local scale (Figure  5A ), suggesting that although the composition of surface communities differed, they changed simultaneously while being buried, most probably due the similar set of environmental conditions encountered. In contrast, we observed a clear increase of the distance-decay rate with sediment depth over the whole dataset (Figure  3  and Supplementary Table  3 ). We thus argue that, in this case, decreasing dispersal toward lower sediment horizons, leading to increasing ecological drift, is most probably responsible for the increase in microbial community differences across deep-sea sites with increasing sediment depth.
ACKNOWLEDGMENTS The samples were collected during research cruises: MEDWAVES, PEACETIME (DOI:  10. 17600/17000300 ), AMIGO 2017,  CanHROV  and  ESSNAUT16  (DOI:  10. 17600/16000500 ). We wish to thank all the participants of the cruises who aided in the sampling efforts for this study, and in particular: the captain and crew of the  R/V Atalante and the Nautile submersible team  for their efficiency, as well as the chief scientist,  Marie-Anne Cambon-Bonavita , and scientific parties of  ESSNAUT16 ,  Cécile Guieu  and  Christian Tamburini  for the sampling on board the  R/V Atalante during cruise PEACETIME, Sophie Arnaud-Haond ,  François Bonhomme  and the crew of R/V Atalante during cruise AMIGO 2017,  Marie-Claire Fabri  and the crew of  R/V Europe and Ariane HROV during cruise CanHROV  and the crew of  R/V Sarmiento de Gamboa  and  Perregrino Cambeiro ,  Joana R.
FUNDING This work was part of the " Pourquoi Pas les Abysses ?" project funded by  Ifremer  and the project eDNAbyss ( AP2016-228 ) funded by  France Génomique  ( ANR-10-INBS-09 ) and Genoscope-CEA. It was also co-funded by a grant from the  University of Western Brittany (UBO)  through the  Ecole Doctorale des Sciences de la Mer et du Littoral  (EDSML). This study was supported by the  European Union 's  Horizon 2020 Research and Innovation Program , under the  ATLAS  project (Grant Agreement No. 678760 ). This output reflects only the author's view, and the  European Union  cannot be held responsible for any use that may be made of the information contained therein.
DATA AVAILABILITY STATEMENT The dataset generated for this study has been submitted to the European Nucleotide Archive (ENA) under the following project: PRJEB33873. Details of the sample correspondence are provided in Supplementary Table  5. Additionally, the full dataset, including raw sequences, processed reads, and ASV tables, as well as bioinformatic scripts are available at: https: //loimai. github. io/ABYSS_16S/.
, 2016). They were also shown to be less robust in regions where forest exhibits strong seasonal variability in the canopy water content  (Hamunyela et al. , 2017). Studies based on the merging of optical and Synthetic Aperture Radar (SAR) datasets, showed a better temporal accuracy in the detection of forest cuts by accounting for the seasonal changes in the vegetation structure but at the costs of a high computational complexity  (Hamunyela et al. , 2020). With the advances in the spatial and temporal resolutions of the satellite observations, the application of change detection algorithms to satellite data requires increasing computational platform performances. Some change detection algorithms such as the RADD and JJFAST alerts  (Watanabe et al. , 2021; FAST, 2021)  are currently operational on powerful platforms such as Google Earth Engine, (see  Reiche et al.
The images are available in several formats: Slant Range, Single Look Complex (SLC), Ground Range, and Multi-Look Detected (GRD). The data used in this study are the GRD products in IW mode at the VV and VH polarizations. The images used in this study were only acquired from Sentinel-1 A on a single descending orbit at the sampling period of 12 days as no Sentinel-1B images were available on the study area. The resulting pixel size is 10 m × 10 m. This product was chosen instead of the SLC as it is a good compromise in terms of possible speckle noise reduction and volume of data to get accurate data but not increasing too much the computational time. A total of 60 images that formed our time-series on the study site between 01/01/2018 and 01/ 01/2020 were provided by the European Space Agency (ESA) at and downloaded using VtWeb (https://visioterra.