dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of high potential areas In animal health programs, land-use planning, or management of conflicts between farmers and herders, it is essential to be able to prioritize intervention zones. To do so, we need to spatially aggregate the information contained in the maps of potential paths for livestock movements in order to identify high potential areas. In this study, we spatially aggregated the maps of potential paths at regional level for Senegal, Gambia and Mauritania, using data downloaded from the GADM web platform (https://gadm. org/ index. html, last accessed 18/02/2020). We thus obtained a distribution of values informing us about the level of activity within each administrative unit based on the potential for each 500 × 500 m 2 pixel to be crossed during a livestock movement.
Consequently, the maps obtained in this study do not take into consideration the need to pass through watering points, especially during the dry season. This is an important drawback counterbalanced by the fact that Senegal and Mauritania have a very dense grid of boreholes. Another difficulty is the reliability of the mobility data. Mobility data were collected using two different approaches in Senegal and Mauritania. For the Mauritanian case, a synthetic survey was conducted by the National Livestock and Veterinary Research Centre (CNERV) and compared with health certificates collected by Veterinarian Offices. In the case of Senegal, paper copies of sanitary movement permits (LPS) were collected by ad-hoc activities. These certificates provided information about origins and destinations, and we do not know if the composition of the herd changed during the journey due to animal sales.
ACKNOWLEDGEMENTS ML thanks the  French National Research Agency for its financial support  (project  NetCost ,  ANR-17-CE03-0003  grant). IS thanks the  Veterinary Services in Senegal for the financial support (Government of Senegal Budget). The work of AA is partially funded by the  EU  grant  H2020-727393   PALE-Blu. This project has received funding from the  European Unions   Horizon 2020 research and innovation programme  under grant agreement MOOD No 874850. The contents of this publication are the sole responsibility of the authors and donot necessarily reflect the views of the  European Commission. A special thank goes to  Peter Biggins  for correcting English.
github. io/forestatrisk for more details on model specifications). To forecast the forest cover in 2050 for our three study areas, we removed the 2010 forest pixels with the highest probability of deforestation following the annual deforestation rate of the two intensity scenarios considered (conservative scenario S1 and worst-case scenario S2). As a rough validation of our projections, we computed the percentage of observed deforestation in the period 2010-2017 included in the predicted deforestation in the period 2010-2050.
Cash crops and unregulated market In the study areas we surveyed, peanuts (Fig. 3 ) was cultivated as a cash crop. Peanuts are consumed as whole seeds or transformed into peanut oil (Ministère de l'Agriculture, de l'Elevage et de la Pêche de  Madagascar, 2004 ). According to FAOSTAT data (Tab. 3), the area harvested for peanut crop in Madagascar has increased of 50% in 7 years, from 52,000 ha in 2010 to 78,426 ha in 2017. At the same time, according to UN Comtrade data, peanut exports have also dramatically increased, from 1,233 T in 2010 to 27,370 T in 2017 (Tab. 3 and Appendix 3). In 2017, about half of the national peanut production was exported, mainly at the destination of the Asian market (Vietnam and Pakistan, in particular). During our stay in the field in June 2016, which took place in the middle of the peanut harvest, we observed an uninterrupted parade of trucks arriving empty in the villages of Lambokely and Kirindy (Fig.
2a ) and leaving loaded with peanut bags. Our surveys indicated that maize (Fig. 3 ) was cultivated for auto-consumption (about 30% of the production) and as a cash crop (70%). FAOSTAT and UN Comtrade data indicate that only a small part of the national maize production was exported (about 5,000 T/yr out of a total production of about 200,000 T/yr), most part of the production being at the destination of the domestic market (Tab. 3 and Appendix 3). Several people we interviewed said that the maize production was sold to the Star company to brew the THB national beer. We computed that about 2,471 ha of maize are necessary to produce the annual volume of THB of 840,000 hL (Appendix 4). This is a relatively small area compared to the 249,186 ha of maize harvested annually in Madagascar (Tab. 3).
FigureA1: Spatial probability of deforestation for the year 2010. Source: https: /bioscenemada. cirad. fr/forestmaps. The spatial probability of deforestation is modelled as a function of the altitude, the distance to forest edge, the distance to main town, the distance to main road, the protected areas, and the distance to past deforestation. These variables describe the accessibility, the land policy and the historical deforestation. The deforestation model also includes spatial random effects at the regional scale to account for the residual variability in the deforestation process which is not explained by the environmental variables (see https://ghislainv. github. io/forestatrisk for more details on model specifications).
Acknowledgements This study took form in the framework of the  BioSceneMada  project (https://bioscenemada. cirad. fr)and the  Roadless  forests project (https://forobs. jrc. ec. europa. eu/roadless). The  BioSceneMada  project was funded by  FRB  ( Fondation pour la Recherche sur la Biodiversité ) and the  FFEM (Fond Français pour l'Environnement Mondial)  under the project agreement AAP-SCEN-2013 I. The  Roadless  forests project was funded by the  European Commission. We thank all the people who welcomed us and kindly provided us with useful information during our field trip, both institutional people (from  DREF , MNP, WWF and Fanamby) and villagers. The authors declare no conflict of interest.
They do not have enough data to compare the three filtration technologies defined in this study in terms of fractions compositions, operating variables and design of the plant. This lack of data makes the optimisation of given specifications impossible regarding the choices of the optimal membrane technology, operating variables and process design. In the scientific literature, optimal operating conditions are often identified empirically in experiments that reveal the influence of one variable on a group of chosen variables  (Adams et al. , 2015; Gésan-Guiziou et al. , 1999 , 2000; Jørgensen et al. , 2016; Tremblay-Marchand et al. , 2016; Zulewska and Barbano, 2013, 2014). Other studies, such as that of  Astudillo-Castro (2015) , modelled the MF process. Each of these experiments and models assessed only one membrane technology to determine processing conditions that increase the yield of serum protein recovery in the permeate and/or improve permeation flux.
gigas  33  , and the other by also using DESeq2 as performed in the sea anemone Aiptasia diaphana  34. Both approaches gave similar results in terms of dominant cycles and number of rhythmic transcripts. We present the results obtained with the DeSeq2 normalisation here while the results gained with the downsampling procedure are given in the Supplementary Fig. 1. Putative cycling transcripts were explored using two methods that can detect nonsymmetric wave forms in short datasets, the non-parametric method RAIN  35  and the Bayesian algorithm ABSR  36. In situ, 7. 4% of the transcriptome (i. e. , 2502 transcripts) oscillated in the range 10. 4-14. 4 h, and 1. 1 % of the transcriptome (376 transcripts) showed a period of 12. 4 h (Fig. 3a, b ). The sampling site was clearly under tidal influence during the sampling period (Fig.
Methods General guidelines. The experimental procedures comply with French law and Ifremer institutional guidelines. No statistical method was used to predetermine sample size, which was chosen as a compromise between deep-sea technical constraints and number of replicates. In this manuscript, in situ refers to samples gathered and fixed directly on the seafloor, here at deep-sea hydrothermal vent, to allow a realistic environmental approach. Behavioural analysis of Bathymodiolus azoricus in situ. Behavioural data were recorded with the ecological observation module TEMPO, which is part of the EMSO-Azores observatory and allows the direct observation of a Bathymodiolus azoricus mussel assemblage at the base of the Eiffel Tower edifice (LS vent field, MAR). The module is built around an aluminium frame equipped with a handle and two adjustable feet that hosts an autonomous video camera (720 × 576 pixels) and two 35 W white LED projectors protected by an anti-fouling system for light  71.
The data analysed in this study were collected from 27 July 2014 to 31 August 2014, during which the camera recorded 2-min video sequences four times a day (i. e. , 0, 6, 12, and 18 h UTC). Thirty one mussels could be tracked over the entire period. For each video, a snapshot extracted at the 25th second was analysed to determine if each individual mussel was open (coded as 1) or closed (coded by 0) using Image J©  72. The proportion of opened mussels was then calculated for each observation date and the resulting time series was submitted to a Dutilleul MFPA to identify the dominant periods in the opening and closing of mussels. This method estimates significant periodic fractional frequencies and computes the corresponding Rsquare of the explained variance for each corresponding period  73.
This periodogram is particularly useful when the sampling step does not allow to discover useful periods that are whole multiples of the sampling step. However, plotting the periodogram requires a very large number of fractional values on the abscissa and would be impossible to represent graphically. We thus also show the results of the Whittaker-Robinson-type periodogram that finds a fixed set of whole periods contained in the sampling frequency. The analyses were performed with the Rpackage adespatial (v 0. 3-7). Environmental data. Temperature and pressure data were recorded every 2 min by the probe SBE 53 Bottom Pressure Recorder (Sea-bird Scientific) 26 s/n located on the LS vent field at a depth of 1726 m. The probe was located at a distance of 745 m from our sampling site. Sampling procedure for the in situ experiment.
De novo transcriptome and quantification. RNA-seq reads quality checking was performed manually by analyzing the fastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/ version 0. 11. 2) and contamination alignment results. Then, as there is currently no reference genome available for B. azoricus, reads have been assembled de novo to produce a reference transcriptome. Using too many reads in transcriptome assembly can lead to spurious contigs  77  ; the following approach was therefore adopted: the assembly was tested with four small files from the in situ experiment. They were assembled individually with DRAP  78  (version 1. 91) and then meta-assembled with the same software package to obtain a unique reference transcriptome. All the other read files, from both the in situ and the laboratory experiments, were then aligned to this reference.
7. 12-r1039, standard parameters). The alignment files then were compressed, sorted and indexed with SAMtools 80 view, sort and index (version 1. 3. 1, standard parameters). Mitochondrial and 18S and 28S ribosomal reads then were filtered out and all samples were randomly down-sampled  51  to the smallest library size (43. 10 6 pairedreads). Quantification was then realised with SAMtools idxstats (version 1. 3. 1, standard parameters) and result file composition was performed with unix cut and paste. Rhythm analyses. Transcripts rhythmicity was investigated with an exploratory strategy: there was no standard approach to analyse these data, and authors often used previous knowledge and known profiles for specific rhythmic genes to determine which method fits with expected results  51. We had no such knowledge for this work, we did not know whether deep-sea mussels still possess a biological clock and, if any, if the clock(s) is/are still functional in this peculiar environment.
We considered both the tidal period 12. 4 h and the circatidal range (10. 4-14. 4 h) for the analysis of tidal transcripts, and both the daily period 24. 8 h (determined by the sampling interval) and the circadian range (20-28 h reduced here to 20-24. 8 h because we sampled over 24. 8 h only) for the analysis of daily transcripts. First, because one data point is one outbred animal, and we observed a high inter-individual variability in our datasets. Secondly, current algorithms do not provide a confidence interval for a specific period to be tested. Finally, in the darkness of the deep sea, a possible circadian clock at work in B. azoricus would be free-running while a putative circatidal clock would be freerunning in the laboratory under L:D cycles without tides. RAIN and ABSR both detected rhythmic transcripts.
05. Heatmaps were drawn with pheatmap (v 1. 0. 12) on median-normalised  54  transcripts expression. Global gene ontology analyses of rhythmic transcripts were performed using Blast2GO 82 (v. 5. 2. 5); and gene enrichment analysis with the topGO package  83  (v. 2. 34. 0). Identification of canonical circadian clock gene candidates. Putative B. azoricus circadian clock gene sequences were deposited in Genbank. These sequences were taken from our reference transcriptome using nucleotidic and proteic BLAST (Basic Local Alignment Search Tool) with a selection of known circadian clock genes as queries (Supplementary Data 4a). While building the de novo transcriptome, about 3% of the reads were not included in the assembly because their expression was too low to pass the quality thresholds. A specific assembly, distinct from the reference transcriptome, was built and scanned as well as for the candidate circadian clock genes.
In addition, non-published sequences were also analysed (A. Tanguy, co-author). Candidate sequences (Supplementary Data 4b-d) were confirmed manually by alignment with reference sequences and using Pfam conserved domains  84. Deduced amino acid sequences were used for phylogenetic analysis. Isoforms were detected manually with MAFFT (multiple alignment fast Fourier transform) alignments  85  and only the longest isoforms were incorporated into phylogenetic analyses. Candidate circadian clock transcripts originating from the present sequencing data were included in the reference transcriptome for quantification; sequences provided by A. Tanguy were only used to increase the resolution of the phylogenetic analyses. All trees were constructed with the same procedure. Sequences were aligned with MAFFT alignment algorithm (v7.
388) using default parameters. Alignment cleaning was performed manually, keeping the sequences included between the first and last conserved domains of the considered clock gene for phylogenetic analyses (Supplementary Data 4e-h). Phylogenetic parameters (i. e. , common substitution model) were set up using IQtree phylogenetic software  86  (v1. 6. 7), and performed with PhyML algorithms (v3. 3. 2), methods and utilities  87. All trees then were constructed with the same parameters: Maximum-likelihood tree using LG substitution model, with tree topology, branch length and substitution rate optimisation, gamma estimation, and branch support values assessed using aLRT. For homogeneity in phylogenetic analyses, the same taxonomic groups were represented: Lophotrochozoa, Insecta, Crustacea, and Euteleostomi (bony vertebrates).
Acknowledgements The authors thank the commandant and crew of the "Pourquoi Pas?"; the team of the Momarsat 2017 cruise; the chief scientist of the Biobaz 2017 cruise Pr. François Lallier; the whole Victor6000 team for sampling under red light on such a precise timing;  Christophe Duchi ;  Philippe Rodier ,  Sandra Fuchs  and  Françoise Lesongeur  for their help in the experimental setting and dissection;  Dr. Ana Colaço  for her help in storing and shipping the samples;  António Godinho  for his help for the Lab Horta experimental setting;  Darryl Perrée  for his help with molecular biology analyses;  Dr. Mathilde Cannat  for providing the field pressure data;  Glyn Orpwood  for correcting the English. The authors acknowledge the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR storage and computational resources (ABSR and BLAST analyses).
Data availability Metadata from the Momarsat 2017 cruise are available under the doi [doi. org/10. 17600/ 17000500]. RNA-seq data were deposited in the ArrayExpress database at EMBL-EBI [www. ebi. ac. uk/arrayexpress] under accession number E-MTAB-8451. The ENA study accession is ERP117902. The data and de novo reference transcriptome are also in the SEXTANT database under the doi [https://doi. org/10. 12770/971d2c1a-51cc-49fd-882c-465970de8ed2]. Circadian clock genes sequences were deposited in Genbank under the following accession: bmal, MN611450; Clock, MN597894; period, MN611455; timeless, MN611456; timeout1, MN611457; timeout2, MN611451; cry1a, MN611458; cry1b, MN611453; cry2, MN611452; 6-4 photolyase, MN611454; cry DASH, MN611459. Source data are provided with this paper.
Mass spectrometry analyses Protein identification in the Q-Sepharose fractions -Proteins contained in each fraction were concentrated in the top of a SDS-PAGE and stained with Coomassie blue. Proteins were treated in gel with dithiothreitol and iodoacetamide before digestion using trypsin. The resulting peptides were extracted and analysed by online nanoLC-MS/MS (UltiMate 3000 and LTQ-Orbitrap Velos, Thermo Scientific) using a 120-min gradient. For this, peptides were sampled on a 300 µm x 5 mm PepMap C18 precolumn and separated on a 75 µm x 150 mm PepMap C18 column (Thermo Scientific). MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Mascot Distiller (Matrix Science) was used to produce mgf files before identification of peptides and proteins using Mascot (version 2. 3) through concomitant searches against TAIR (version 10), classical contaminants database (homemade), and the corresponding reversed databases.
ESI-TRAP was chosen as the instrument, trypsin/P as the enzyme, and 2 missed cleavage allowed. Precursor and fragment mass error tolerances were set respectively at 10 ppm and 0. 6 Da. Peptide modifications allowed during the search were: carbamidomethyl (C, fixed) acetyl (protein N-terminus, variable) and oxidation (M, variable). The IRMa software  37  was used to filter the results: conservation of rank 1 peptides, peptide identification FDR < 1% (as calculated by employing the reverse database strategy), and minimum of 1 specific peptide per identified protein group. The filtered results were uploaded into a relational mass spectrometry identification database and hEIDI  38  was used for the compilation, grouping and comparison of the protein groups from the different samples.
Metallomics Accepted Manuscript Analysis of post-translational modifications in Arabidopsis cells exposed to U stress -Protein bands were cut out before in-gel treatment with dithiothreitol and iodoacetamide and digestion with trypsin. The resulting peptides were extracted and analysed by online nanoLC-MS/MS (UltiMate 3000 RSLCnano and Q-Exactive Plus, Thermo Scientific) using a 40-min gradient. For this, peptides were sampled on a 300 µm x 5 mm PepMap C18 precolumn (Thermo Scientific) and separated on a 75 µm x 250 mm C18 column (Reprosil-Pur 120 C18-AQ, 1. 9 μm, Dr. Maisch). MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). RAW files were processed using MaxQuant 39 version 1. 5. 5. 1. Spectra were searched against the Uniprot database (Arabidopsis thaliana taxonomy, December 2016 version), the frequently observed contaminants database embedded in MaxQuant, and the corresponding reversed databases.
Arabidopsis cells challenged with U. The elemental composition of Arabidopsis cells and protoplasts challenged (U50) or not (U0) with uranyl nitrate is given in Table  1. These data have allowed to calculate the distribution of elements between the cell wall () and the protoplast (), assuming that cell wall proteins account for 0. 5 to 6% of total cellular proteins. 40 Means ± SD are shown (n=6 independent cultures for U0 and U50). A-Resolution of the AEC column as estimated by the number of fractions in which the proteins identified by MS/MS are distributed. More than 55% of the identified proteins are present in one or two fractions eluted from the Q-Sepharose column. B-Hive plot representation of proteins distribution in fractions from the AEC column. The distribution of proteins in the different fractions is represented as a network and was created using the HiveR R package.
62 Linear axes represent the fractions (F0 to F1000); their length is proportional to the number of identified proteins in the fraction and their thickness is proportional to the numbers of proteins specifically present in the fraction. The curved lines connecting two axes symbolize the presence of the same protein in both fractions. The abundance of proteins identified by MS/MS was calculated using weighted spectral count (WSC) values. The higher the WSC, the more proteins were present in a large number of fractions. The elemental composition of Arabidopsis cells and protoplasts challenged (U50) or not (U0) with uranyl nitrate is given in Table  1. These data have allowed to calculate the distribution of elements between the cell wall () and the protoplast (), assuming that cell wall proteins account for 0.
The latter constitutes an optimized methodology that provides guidelines for costly future assessments as part of the site investigation phase in geohazard management. There are no doubts that compared to traditional and conventional surveying methods TLS and Photogrammetry both offer products much faster and with a much higher data density. In the current study, we show that when photogrammetry is applied following a well-defined optimized strategy, it can be potentially an adequate alternative to more costly TLS datasets for mass movement assessment and monitoring purposes.
INTRODUCTION Landslides and rock failures in mountainous environments have serious and dramatic impacts on society and infrastructure, often leading to fatal situations, especially in the Alpine terrain, where high touristic activity takes place throughout the year. Lately, the advancements of geoinformation enabled many researchers to incorporate remote sensing tools for precise visualization, assessment and monitoring of natural processes such as landslide and rockfall phenomena. As a result of the easy transportability and low operational costs compared to laser scanning surveys, close range photogrammetry has shown great adaptability in mountainous environment for collecting detailed 3D datasets. Rockfalls are generally initiated by multivariate climate and morphological factors that change the acting stresses on rock.
Notably, the documented methods are confined to either laboratory conditions or urban environment, where well-defined objects are used for the assessment, which can be positioned anywhere in the scene. However, natural features often represent much more complex setting: using test groups to evaluate each dataset is immaterial, while fixing control points or other objects is not always possible. Aiming for an independent quality assessment procedure that can be used as a preliminary step for geomorphological analysis, we propose an assessment workflow that is not restricted to specific settings, and still maintains the comparative nature of both suggested approaches. All point-clouds are first registered into the same reference system, defined by the most reliable dataset. Then, all of the analyses are carried out both for the entire point-cloud and on selected regions in the cloud, which were chosen arbitrarily and are scattered throughout the scan.