dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell Localization and Cellular Metabolism The cell distribution and localization within the scaffold was tracked using cell tracker staining of the live cells until day 10. Hep G2 and HUVEC live cells were stained with green CMFDA dye (CellTracker TM Invitrogen, C2925) and orange CMRA dye (CellTracker TM Invitrogen, C34551), respectively, and seeded into the scaffold. The fluorescent images of live cells were observed under confocal microscope (FV3000, Olympus, Tokyo, Japan) after day 10 of culture under perfusion. The excitation/emission wavelength of green CMFDA was 492/517 nm and orange CMRA dye was 548/576 nm. Images were processed using the cellSens dimension software suite (Olympus, Tokyo, Japan). In parallel, the cell attachment was also observed under SEM. The samples were prepared by fixing with 4% PFA followed by series of dehydration in different concentration ethanol, namely 25%, 50%, 70%, and 95%, and drying at room temperature for 3 h prior to observation under SEM (TM3030, Hitachi, Tokyo, Japan).
Applications in which researchers aim to extract a single land type from remotely sensed data are quite common in practical scenarios: extract the urban footprint to make connections with socio-economic factors; map the forest extent to subsequently retrieve biophysical variables and detect a particular crop type to successively calibrate and deploy yield prediction models. In this scenario, the (positive) targeted class is well defined, while the negative class is difficult to describe. This one-class classification setting is also referred to as positive unlabelled learning (PUL) in the general field of machine learning. To deal with this challenging setting, when satellite image time series data are available, we propose a new framework named positive and unlabelled learning of satellite image time series (PUL-SITS).
The objective of a PUL framework is to build a binary classification model that can make predictions on unseen data distinguishing between positive and negative samples. With the aim to focus on the analysis of SITS data, in this work, we propose a new framework, named PUL-SITS, to deal with positive and unlabelled learning for land cover mapping from satellite image time series. PUL-SITS involves two different stages: In the first one, a recurrent neural network autoencoder is trained only on positive samples. Successively, the same autoencoder model is employed to filter out reliable negative samples from the unlabelled ones. In the second stage, both labelled (positive and reliable negative) and unlabelled samples are exploited in a semi-supervised manner to build the final binary classification model.
In this stage, the unlabelled samples are employed to force consistency between the prediction of the classification model and a variant of the same network trained on the SITS data reconstructed by the recurrent neural network autoencoder model. To assess the effectiveness of PUL-SITS, we considered two different scenarios involving data coming from a particular study area (Haute-Garonne) located in the southwest area of France. More precisely, the Haute-Garonne study area is heavily characterized by the Cereals/Oilseeds and Forest land cover classes. For this reason, we designed a first scenario in which the Cereals/Oilseeds land cover class has the role of the positive class, while the rest of the land cover classes play the role of the negative class. In the second scenario, the Forest land cover class is targeted as the positive class.
The task of learning from both positive and unlabelled data consists of exploiting both labelled P and unlabelled U samples to learn a binary classification model, allowing the assignment of a binary label (positive or negative) to new, previously unseen, satellite image time series. Regarding PUL-SITS, our framework is constituted of two different stages. The first stage is devoted to selecting a set of reliable negative samples that highly differ from the positive set. This step is addressed via a variational recurrent neural network autoencoder (VAERNN), which is used to model the set of positive samples. In the second stage, PUL-SITS learns a satellite image time series classifier, based on the recurrent neural network (RNN) model proposed in  [17] , to cope with the underlying binary classification task.
Data The study area we considered in our research study, namely Haute-Garonne, is located in the southwest area of France (Figure  4a ), and it covers a surface area of 4146. 2 km 2. The satellite data describing the study site consist of a Sentinel-2 (S2) time series involving 31 images acquired over the year 2019 from January to December. The temporal distribution of the satellite image time series is depicted in Figure  5. The Sentinel-2 images were downloaded from the THEIA pole platform (http://theia. cnes. fr, accessed on 10 November 2020) at level-2A in top-of-canopy reflectance values and provided with cloud masks. Only 10 m spatial resolution bands (blue, green, red, and nearinfrared spectrum) were considered in this analysis. Additionally, two common spectral indices were then extracted: the NDVI  [32]  and the NDWI  [33].
e. , Water, Urban, Orchards, etc. ) that are neither Cereals/Oilseeds nor Forest samples. The ground truth (GT) was obtained from various sources: (i) the Registre Parcellaire Graphique (RPG) reference data for 2019 (https://www. data. gouv. fr/en/datasets, accessed on 10 November 2020), which is part of the European Land Parcel Identification System (LPIS) provided by the French Agency for services and payment ; (ii) the French Topographic Database (BD TOPO) (https://geoservices. ign. fr/blog/2020/10/26/Grand_angle_ diffusion_BDTOPO. html, accessed on 10 November 2020), which contains geographical and administrative entities of the national territory; (iii) the French National Forest inventory (BD FORET), which includes information about forest cover at the national scale. The GT was assembled in a Geographic Information System (GIS) vector file, containing a collection of polygons, each attributed to a land cover category (see Table  1 ).
• The FCGRU  [17]  classification model only trained on the positive and reliable negative samples obtained from the first stage of our framework. This ablation did not involve the consistency loss. We named such an ablation PUL-SITS noReg ; • The FCGRU classification model trained on the positive and reliable negative samples obtained from the first stage of our framework. Here, the classifier was not trained on the original data, but on the reconstructed data output by the autoencoder model. We named such an ablation PUL-SITS reco.
We organized the training/test data split procedures in terms of objects with the aim to avoid possible spatial bias in the evaluation procedure  [5] , since, in this way, pixels belonging to the same ground truth object were assigned exclusively to one of the data partitions. In addition, for time series data, all values were normalized per band in the interval [0, 1] using 2% (resp. 98%), rather than the minimum (resp. maximum) value to rescale the data  [8]. Regarding our framework, PUL-SITS, the VAERNN involves an encoder with two levels with 64 and 16 units, respectively, while the decoder is symmetric to the encoder. The FCGRU  [17]  recurrent cell involved in the binary classification process has a number of units equal to 32. Both models were trained for 50 epochs with a batch size equal to 32 and a learning rate of 10e -4 through the Adam optimizer.
For the latter component, we also employed a dropout rate equal to 0. 2. The same learning procedure was employed for the PUL-SITS noReg and PUL-SITS reco ablations. Regarding the competing methods, RF PUL , OCSVM, and ENSEMBLE PUL , they were employed with the default settings. The assessment of the quantitative performances was performed considering the Accuracy, F-Measure, Sensitivity, Specificity, and Kappa metrics  [36]. To reduce the bias induced by the training/test data split, all the results were averaged over ten different random splits. Finally, both the average and standard deviation are reported for each evaluation metric. The experiments were carried out on a workstation with an AMD Ryzen 7 3700X 8-core processor CPU@3. 60GHz with 64 GB of RAM and a GeForce RTX 2080 Ti GPU. All the deep-learning methods were implemented using the Python Tensorflow library.
This fact is illustrated by high Sensitivity (Table  6 ) and low Specificity (Table  7 ) values, which highlight that RF PUL and ENSEMBLE PUL simply label the majority of the data points with the negative class. Concerning the ablations associated with our framework, PUL-SITS noReg and PUL-SITS reco , we can see that both strategies achieved better performances than the competing methods, with PUL-SITS noReg as a clear winner between them, but the PUL-SITS reco component played an important role in supporting the performance of PUL-SITS, underlying the quality of our two-stage methodology. Figure  6  shows the confusion matrices associated with the three direct competing approaches, as well as PUL-SITS. A closer look at these statistics confirms that PUL-SITS was more precise than the competitors.
This consideration emerges from the fact that the corresponding heat map (Figure  6d ) has a more visible diagonal structure (the dark green blocks concentrated on the diagonal). This is not the case for the other methods, where the majority of the samples are associated with the negative (zero) class. Tables 9-13 resume respectively the results in terms of the F-Measure, Accuracy, Sensitivity, Specificity, and Kappa of the different competing methods when the Forest land cover was considered as the positive class. In this case also, PUL-SITS outperformed all the other approaches, while RF PUL and ENSEMBLE PUL simply labelled all of the data points as the negative class (as highlighted by the high values of the Sensitivity and the low values of the Sensibility they achieved). OCSVM exhibited the best performance among the state-of-the-art competitors.
Concerning the ablations, PUL-SITS noReg did not always achieve better performance over OCSVM, while PUL-SITS reco did. This phenomena clearly underlines the importance of exploiting the available unlabelled data and the appropriateness of the consistency-based procedure in order to regularize the training stage of the final classification model. Figure  7  shows the confusion matrices, which confirm that PUL-SITS achieved a better predictive performance w. r. t. the other approaches, i. e. , it has a more visible diagonal structure.
Conclusions 391 In this research work, we have introduced a novel framework, namely PUL-SITS, to 392 deal with the positive unlabelled learning analysis of satellite image time series data for the 393 task of land cover mapping. Our approach is based on a two steps strategy: in the first one 394 a recurrent neural network autoencoder is trained only on positive samples. Successively, 395 the same autoencoder model is employed to filter out reliable negative samples. In the 396 second stage, both labelled (positive and reliable negative) and unlabelled samples are 397 exploited in a semi-supervised manner to build the final binary classification model. To 398 assess the performances of PUL-SITS, we have compared it with state of the art methods 399 for positive unlabelled learning considering two different scenarios: a first one in which 400 the Cereal/Oilseeds land cover class has the role of positive class while the rest of the land 401 cover classes plays the role of the negative class and, a second scenario where the Forest 402 land cover class is targeted as positive class.
Conclusions In this research work, we introduced a novel framework, namely PUL-SITS, to deal with the positive unlabelled learning analysis of satellite image time series data for the task of land cover mapping. Our approach was based on a two-step strategy: in the first one, a recurrent neural network autoencoder was trained only on positive samples. Successively, the same autoencoder model was employed to filter out reliable negative samples. In the second stage, both labelled (positive and reliable negative) and unlabelled samples were exploited in a semi-supervised manner to build the final binary classification model. To assess the performances of PUL-SITS, we compared it with state-of-the-art methods for positive unlabelled learning considering two different scenarios: a first one in which the Cereal/Oilseeds land cover class has the role of the positive class while the rest of the land cover classes play the role of the negative class and a second scenario where the Forest land cover class was targeted as the positive class.
, 2008; Bailey et al. , 2014; Huon et al. , 2015) ; or at the population scale  (Jones et al. , 2015)  pooling multiple datasets. In this study, we combined for the first time several datasets for the assessment of foraging habitat selection across the wider population, but at local scale. Analyzing the data in this way enables new insights into gray and harbor seal's ecology across the Northeast Atlantic. We aimed at 1) studying the foraging habitat selection of gray and harbor seals at a colony (i. e. , local) scale and 2) investigating the relationship between population trends and physical habitat features on the seals' spatial patterns and foraging habitat selection.
GPS locations were attempted every 20 min resulting in irregular location intervals when seals were underwater or in areas of poor satellite coverage. Diving was recorded when a seal reached a depth greater than 1. 5 m, with start and end time of dive, maximum depth, dive duration, and nine intermediate dive points recorded for each dive. The location of each dive was spatially interpolated from the true GPS locations. The recorded data were relayed through onboard mobile phone with GSM modem (Sea Mammal Research Unit, St Andrews University 1 ). Some of these data were already used in previous publications (Table  1 ).
| Breeding and molting period During the breeding and molting periods, seals tend to strongly reduce their foraging activities, increasing the amount of time spent hauled out, or staying close to their haulout sites  (Boness, 1984; Caudron et al. , 2009; Lidgard et al. , 2003). We aimed to focus on foraging activities outside these periods when seals need to replenish their body reserves and are less constrained to haulout sites. Data obtained during the breeding (September to December for gray F I G U R E 1 Map of the different study areas for gray seals (in orange): Irish Continental Shelf (ICS), Irish Sea, Firth of Tay (FoT), Iroise Sea, and Eastern English Channel (EEC); and harbor seals (in blue): Kenmare Bay, Inner Hebrides, Firth of Tay (FoT), Baie du Mont Saint michel (BdM), Baie des Veys (BdV), and Baie de Somme (BdS); including the tagging sites (black stars) seals, June to July for harbor seals) and early molting (January to February for gray seals and August to September for harbor seals) periods were excluded from the analyses.
| Explanatory variables Based on seals' benthic foraging behavior  (Bjørge et al. , 1995; Hindell et al. , 1991; LeBoeuf et al. , 1988; Thompson et al. , 1991) , we used three environmental variables to identify habitat (Appendix S2). Bathymetry was obtained from the European Marine Observation and Data Network (EMODnet), with a grid size resolution of 0. 125 × 0. 125 min. 2 Sediment data were obtained from the MESH_ EUNIS model (Mapping European Seabed Habitat project), which predicts habitat types with a spatial resolution of 300 m. Sediment types were based on a simplified FOLK classification system  (Folk, 1954)  and limited to the most dominant types: rock, mud, sand, gravel, coarse, and mixed sediments. We used different tidal current datasets for Irish, Scottish, and French areas, scaled to similar resolutions for inter-site comparison.
Datasets for the French study areas were obtained from Previmer  (Lecornu & De Roeck, 2009)  for the tracking period. These were created from the MARS 2D model with a resolution of 250m and were available at an hourly scale. The Irish Marine Institute provided tidal current data for the Irish Continental Shelf and the Irish Sea 3. Data were obtained from a numerical model with a spatial resolution varying between 1. 2 Km and 1. 5 Km and corresponded to surface tidal current at 3 hr interval. This dataset did not cover the Kenmare bay (for which no tidal current data were available). We averaged model datasets for the French and Irish areas, respectively, in order to represent the tidal strength in space irrespective of instant tidal phases (ebb, slack, or rising tide). Tidal current data for Scottish sites were obtained from the Web vision renewable website and were calculated from the ABP mer model (Atlas of UK marine Renewable Energy Resources 2008 4 ).
The multicollinearity between covariates was assessed using the VIF value  (Kutner et al. , 2004 ). The best model was selected using the AIC  (Akaike, 1973). Firstly, we fitted one model per site for each species to focus at the local scale. Secondly, to highlight the importance of modeling habitat selection around colonies, particularly when local habitat characteristics differ, we also fitted a global model for each species using pooled data from all colonies and included "site" as a random factor. We did not include Kenmare for harbor seals' global model, as tidal current dataset was not available for this area. However, as we had problem of convergence when running global model, we only included one diving point on three. For models fitted for each colony, we calculated the importance of each covariate using the prediction function of the GAMM, providing an index of the relative importance of each covariate in the chosen model.
| D ISCUSS I ON This study highlights the importance of local-scale effects in understanding the relationship between animals and their environment, particularly in the case of metapopulations where local population trends and physical habitat features vary regionally. By incorporating tracking data from multiple colonies in the Northeast Atlantic (including core population and limit range), our study provides new knowledges on the foraging habitat selection and spatial distribution of gray and harbor seals. One hundred and two individuals of both species were tracked by telemetry from different colonies from the limit of their range (France and Ireland) to their core population (Scotland), thus depicting habitat selection similarities and differences at the metapopulation level. This represents one of the most extensive datasets available for these species and covers colonies in the core and distributional limits.
This compares favorably with habitat selection and spatial usage studies in seabird species  (Wakefield et al. , 2017)  despite a lack of dedicated program for telemetric monitoring of seals at the metapopulation level in the North-East Atlantic. Previous studies on the ecology of marine mammals, and more specifically pinnipeds, at a comparatively large scale were also based on a compilation of several datasets from different sites to identify habitat use at a global scale (e. g. ,  Baylis et al. , 2018). We recognize that the number of individuals tracked for this study may be low to fully characterize the foraging habitat at the scale of colonies. However, our results provide first interesting results on variations of habitat selection between different sites. The choice of the scale of habitat selection is often defined as a mix between the area considered by biologist and managers, as well as the ecology of the species studied.
The earth harbors trillions of bacterial species adapted to very diverse ecosystems thanks to specific metabolic function acquisition. Most of the genes responsible for these functions belong to uncultured bacteria and are still to be discovered. Functional metagenomics based on activity screening is a classical way to retrieve these genes from microbiomes. This approach is based on the insertion of large metagenomic DNA fragments into a vector and transformation of a host to express heterologous genes. Metagenomic libraries are then screened for activities of interest, and the metagenomic DNA inserts of active clones are extracted to be sequenced and analysed to identify genes that are responsible for the detected activity. Hundreds of metagenomics sequences found using this strategy have already been published in public databases.
INTRODUCTION Microbial ecosystems are unrivaled sources of new protein functions. Thanks to advances in sequencing technologies, microbial ecosystems have been largely explored during the last decades. In the same time, various software packages and analytic pipelines have been developed to assemble, annotate and explore massive sequencing data sets. Everyday, sequence databases are enriched with new metagenomic sequences revealing the tremendous amount of putative functions that can be found in the nonculturable species of microbial ecosystems. However, 20 to 50% of putative genes remain unannotated due to too few homologies with previously characterized proteins, which represents only 2% of putative genes in the trEMBL/RefSeq databases  (Stein, 2001; Aubourg & Rouze, 2001; Pruitt, 2004; Stothard & Wishart, 2006; Harrington et al.
, 2010)  or directly, without marking them  (Lam et al. , 2014), (v)  obtaining DNA sequences in order to identify genes responsible for the screened activity  (Healy et al. , 1995). Using this approach, a protein function can be assessed without any prior information on its sequence. Genes, their genomic context and taxonomical origin are determined in order to obtain new insights into the relationships between gene functions, bacterial taxa, microbiome prevalence and contribution to ecosystems. Finally, it rationalizes sequencing efforts by only focusing on clones exhibiting the targeted activity. A careful analysis of large metagenomic DNA inserts, comprising entire metabolic pathways, is a critical step for functional studies. It requests suitable bioinformatic resources to assemble reads into contigs, remove vectors, detect coding sequences (CDS), translate, align and compare them to reference databases.
To perform these different steps, freely available software packages do exist. Most of them are specific to a single processing step or include different steps which are not specific to cloned DNA fragments analysis such as vector localization and removal. Besides, except the commercial Qiagen CLC Genomics workbench, there is no end-user oriented tool chaining all the steps from read assembly to annotation, providing an easily understandable report including result files. Therefore, we developed the MINTIA software package dedicated to automated analysis of sequences from cloned metagenomic DNA inserts. In this paper, we present MINTIA, results from simulated and real metagenomic data analysis and compare its results with three available open source software packages: fabFos (https://github.
IMPLEMENTATION Description of the MINTIA program MINTIA has been designed to process batches of functional metagenomic DNA fragments raw read files, usually sizing from 35 to 40 Kb, inserted into a fosmid (e. g. , PCC1FOS from Epicentre Technologies) in order to produce assembled and annotated contigs. The 11 steps of the MINTIA workflow were grouped into two modules. MINTIA Assemble module (Fig. 1 ) processes a compressed archive of fastq file(s) (single and/or paired reads) corresponding to distinct DNA inserts and the cloning vector sequence in fasta format. In a preliminary step, read files are cleaned from adapters and reads of poor quality are filtered out using cutadapt  (Martin, 2011). MINTIA Assemble first step counts reads and calculates read set coverage. Then a random read selection is performed to reduce each set to an appropriate user defined coverage (300X by default).