dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment This study was financially supported by the  Czech Science Foundation  ( GACR  (  21-26883S ,  GACR 21-04987S ) a longterm research development project no. RVO 67985939  ( Czech Academy of Sciences) ,  Biology Centre CAS (Institute of Hydrobiology and Soil Biology) & SoWa  ( MEYS  projects  LM2015075  and  EF16_013/0001782 -Soil  and  Water Ecosystems Research ). We would like to thank  Vilem Ded  for assistance with graphical data analysis in R,  Iva Tomkova  510 for analyzing soils for TP and  Lenka Capkova  for technical help with enzyme activity measurements in the laboratory for Ecosystem Biology, at the  University of South Bohemia and Institute of Hydrology in Slovakia  for the analyses of stable isotopes in samples from glacier snout and glacier stream water. We are grateful to  Madalina Jaggi  and  Stefano Bernasconi  for the analysis of oxygen isotopes in soil water at the Stable Isotope Laboratory of the Geological Institute of the ETH Zurich.
Acknowledgments: The members of the research team deeply thank all those who have contributed to the success of this project: Kefraya domain (Edward Kosremelli and Fabrice Guiberteau), the Lebanese Ministry of Industry, the Association of Lebanese Industrialists, the Institut Français de la Vigne et du Vin, and the Doctoral School of Science and Technology, Research Platform for Environmental Sciences, (EDST-PRASE), Lebanese University.
| Whitefly colonies used in the crosses Whitefly colonies with similar genetic background but differing only by Arsenophonus infection status were developed in these experiments and belonged to the sub-group 3 haplotypes of the SSA1 (SSA1-SG3A+ and SSA1-SG3A-) and SSA2 (SSA2A+ and SSA2A-) species (accession numbers KM377902 and KM407142)  (Ghosh et al. , 2015). These two colonies were prepared from isofemale lines which have been maintained in NRI insectary for 20 years. Briefly, one female and two males were collected from core field colony and enclosed on an eggplant for 7 days to mate and oviposit. Parents were screened for their sequenced mtCO1 marker and endosymbiont composition as described in  Ghosh et al. (2015). Both populations were confirmed to be free of all other known symbionts infecting B.
| Data analysis Whitefly adult emergence time distributions were compared using Anderson-Darling test. The proportion of eggs, nymphs, sex ratio, hatching rate, and survival rate was compared between the experimental and control crosses using a MANOVA test against treatment and then Tukey test per variable. To investigate the differences in bacterial diversity and communities within and between SSA1-SG3 and SSA2, the colonies SSA1-SG3A-, SSA1-SG3A+, SSA2A+, and SSA2A-were filtered out from Arsenophonus. One-way ANOVA test was also carried out to investigate bacterial diversity parameters such as Simpson index and observed OTUs. These were screened to investigate bacterial diversity across B. tabaci species generated from isofemale lines. Bray-Curtis dissimilarities between all pairwise combinations of whitefly samples were ordinated following a non-metric multidimensional scaling (nMDS).
| Confirmation of the status of Arsenophonus Over 9 million (9,325,004) clean reads were generated from Illumina HiSeq platform for the 29 whitefly samples. After filtering both lowquality sequences and those that belonged to Portiera, chloroplast, and mitochondria, a total of 7,639,071 were assigned to the bacterial community and 1,685,933 reads were assigned to S-endosymbionts. The overall recovered clean reads were different for each B. tabaci species (Table  S2 ), with an average read of 282,576 obtained per sample with an average sequence length of 377 bp. Finally, a total of 80,765 reads were unassigned to any of the previously known OTUs from Silva database. The presence of Arsenophonus and other bacteria was examined in parents and progeny of the species SSA2A+, SSA1-SG3A+, and SSA1-SG3A-.
Bacterial composition (a, b and c) detected in whitefly colonies. Heat map of log-reads of most abundant bacteria detected in whitefly colonies including those used in the crossing trials (a). Some of them were identified at the family level, whilst the majority were at the genus level (a). Bacterial composition was shown in nMDS graph for SSA1-SG3 (b) and SSA2 (c). *others = all non-abundant OTUs aggregated together, Unassigned = all OTUs not assigned with any OTU in the database factors, such as the different genetic background of.
F Bacterial composition (a, b and c) detected in whitefly colonies. Heat map of log-reads of most abundant bacteria detected in whitefly colonies including those used in the crossing trials (a). Some of them were identified at the family level, whilst the majority were at the genus level (a). Bacterial composition was shown in nMDS graph for SSA1-SG3 (b) and SSA2 (c). *others = all non-abundant OTUs aggregated together, Unassigned = all OTUs not assigned with any OTU in the database factors, such as the different genetic background of.
Data were normalized based on the weights of macerated tissues from the wild-type strain. Chicory leaf assays were performed as described in the Materials and methods section with an incubation time of 18 h, and weights of macerated tissues were measured. The experiments were repeated three times with ten leaves tested in each replicate. Non-capital letters a and b are presented above the boxplots. Groups with the same letter are not detectably different (are in the same set) and groups that are detectably different get different letters (different sets). Note that if the groups have the same letter, this does not mean they are the same, just that there is no evidence of a difference for that pair (P < 0. 05, Wilcoxon Mann-Whitney test). any macerated tissue (p-value = 5. 7e-4). Consequently, we did not weigh any macerated tissue in the hfq mutant.
In contrast, overexpression of proQ from the episome slightly decreased the biofilm formation capacity of   (B, C ) Impact of the hfq and proQ mutations on biofilm formation. Assays were carried out in multi-well plates. Data were normalized relative to the adherence of the wild-type strain. The effect of heterologous complementation is also showed. Quantification of the cells present in the aggregates and in the planktonic fractions for the different strains. Experiments were repeated at least three times. Non-capital letters a, b, and c are presented above the boxplots. Groups with the same letter are not detectably different (are in the same set) and groups that are detectably different get different letters (different sets). Note that if the groups have the same letter, this does not mean they are the same, just that there is no evidence of a difference for that pair (P < 0.
7 ). For a depth of 15 m, which corresponds to the average depth of P. oceanica meadows in the Mediterranean Sea (mean extension between -2 and -28 m, in UNEP/MAP-RAC/SPA, 2015), annual primary leaf production (blades and sheaths) is estimated at 343 g DM. m -2. Linéaire (Port-Cros) Linéaire (Corsica) Linéaire (Ischia) Linéaire (Rukavac) R 2 =0. 78 R 2 =0. 94 R 2 =0. 99 R 2 =0. 85 Figure  7 : Leaf annual primary production of P. oceanica meadow (g DM. m -2 ) according to depth in the Mediterranean Sea (data from:  Wittmann & Ott, 1982; Bedhomme et al. , 1983; Bay, 1984; Thelin & Giorgi, 1984; Wittmann, 1984; Esteban 1989; Romero, 1989; Esteban et al. , 1990; Buia et al. , 1992; Pergent-Martini et al. , 1994; Pergent et al. , 1997; Torricelli & Peirano, 1997; Zupo et al. , 1997; Boumaza & Semroud, 2000; Guidetti, 2000; Guidetti et al.
, wetlands (13-73 kg C. m -2 ; Laffoley & Grimsditch 2009) and the boreal forests (9-34 kg C. m -2 ; Serrano et al. 2014) and Mediterranean forest (5. 7 kg C. m -2 ; FAO & Plan Bleu, 2018). Acknowledgments This work would not have been possible without the financial support of the Agence Française pour la Biodiversité, the Collectivité de Corse (PADDUC-CHANGE programme), the Uffiziu di Ambiente di a Corsica. A special thanks to Maria Cristina Buia (Stazione Zoologica Anton Dohrn, Ischia -Italy) and Sylvie Gobert (University of Liège) for providing raw data and Michael Paul for proof-reading the manuscript. References Akçalı B. , Kaboğlu G. , Guçlusoy H. , 2019. A review on Posidonia oceanica (Linnaeus) Delile coverage along the Turkish coasts until 2019. J. Black Sea/Mediterranean Environment, 25(1): 115-124.
(2012b)  for Cytb and COI mtDNA fragments and by  Jeyaprakash and Hoy (2002)  for the 12S rRNA fragment. After DNA extraction, voucher specimens were retrieved as described in  Tixier et al. (2010b)  to confirm molecular assignment. For the COI and CytB fragments, a preliminary analysis was conducted to check for the absence of stop codons. The sequences were analysed both strands (forward and reverse). The consensus sequences obtained were compared to those included in the NCBI GenBank database to detect possible contaminations. DNA sequences were aligned (using ClustalW) and analysed using MEGA 6. 0. 6® (Tamura et al. 2013). Genetic distances (using the Kimura 2 parameter) were calculated for comparing DNA sequences to references (sequences published in Genbank). Neighbour-Joining phylogenetic trees were built to assess relationships (i) between T.
Results The Phytoseiidae species identified during the survey are listed below. Measurements of morphological characters are provided when they were necessary for species identification and comparison with morphologically close species. Table  1  presents the localities where species were retrieved and Genbank accession numbers of the DNA sequences obtained. When available, biological information is provided, especially for traits relevant to biological control. Data on the known distribution of the species is retrieved from the online Phytoseiidae catalogue  (Demite et al. 2021).
Six DNA sequences (three sequences for CytB mtDNA, and three sequences for the COI mtDNA) were obtained from three specimens. The COI sequences were blasted in the Genbank database and were clearly assigned to A. eharai. Table  4a  shows the COI genetic distances between the Georgian specimens and Amblyseius largoensis (Muma), A. herbicolus and A. eharai. The COI mtDNA sequences of the three Georgian specimens are identical (0%). They differ from A. largoensis and A. herbicolus sequences in Genbank by high genetic distances (27. 2 and 29. 2%, respectively), and from the 15 DNA fragments of A. eharai retrieved from Genbank by very low distances (2. 1-3. 2%), corresponding to intraspecific variation. The phylogenetic tree also illustrates that the Georgian specimens belong to the clade including the 15 DNA sequences retrieved from Genbank and assigned to A.
eharai (JX080331-JX080345) (figure  1 ). No CytB mtDNA sequence of A. eharai is available in Genbank, whereas they are for A. herbicolus and A. largoensis (Supplementary Table  S1b ). The CytB genetic distances between the specimens herein considered and (i) A. herbicolus range between 39. 1% and 40. 3%, and (ii) A. largoensis range between 42. 7% and 44. 5%, clearly showing that Georgian specimens do not belong to these two latter species. Thus, based on morphological characteristics and molecular data, we conclude that the Georgian specimens belong to the species A. eharai. It is the first time that this species is reported from this country and outside eastern Asia. In this survey, it was reported on Rubus sp. and Citrus trifoliata (L. ) Rafinesque both at Lanchkhuti (near the Black Sea coast).
(2016)  for specimens collected from Morocco and with those of the original description (Table  2 ). The molecular distances range from 0 to 0. 3 % between the four CytB mtDNA sequences and are null between the three COI mtDNA sequences. Only one 12S rRNA sequence was obtained. The eight DNA sequences for the three molecular fragments are now included in the Genbank database and will serve as references for further molecular identification of this species. Very few studies refer to the biology of N. umbraticus. This species seems able to feed on T. urticae and Thrips tabaci (Lindeman)  (Sengonca and Dresher 2001, Kazak et al. 2002). Wainstein and Vartapetov (1973)  reported that this species feeds on P. citri and T. urticae, and that it tends to prefer humid areas. Transeius wainsteini  (Gomelauri)  Amblyseius wainsteini Gomelauri 1968: 518.
Remarks. This species was known from Georgia, reported by  Wainstein (1958)  on Salvia nemorosa L. (Lamiaceae), and is commonly found in the West Palearctic region, especially on plants of the family Lamiaceae  (Tixier et al. 2020a). Two DNA sequences (one of the CytB and of the COI fragment) were obtained. CytB genetic distance between the Georgian specimen and the 54 specimens collected in South of France and Italy was 5. 7% (4. 7% -14%)  (Tixier et al. 2020b). Genetic distances among the COI sequences, ranged from 2 to 2. 2% between the Georgian specimen and the four reported in Genbank (MT828361-364, from France and Italy). This differentiation can be due to population isolation or result from adaptation to climatic conditions  (Tixier et al. 2020b; Queiroz et al. 2021). Typhlodromus (Anthoseius) halinae  (Wainstein & Kolodochka)  Anthoseius (Amblydromellus) halinae  Wainstein & Kolodochka (1974) : 629.
Genu II with seven setae (2-2/0, 2/0-1), Genu III with seven setae (1-2/0, 2/1-1). Chelicera. Fixed digit 25 long; and movable digit 23 long. Dentition not visible because chelicerae closed. Spermatheca. Spermatheca with cervix 15-18 long (on the two sides) and 10 wide, with an atrium inserted in the cervix. Remarks. The specimen studied is morphologically close to Typhlodromus (Anthoseius) rhenanus  (Oudemans) , even if some differences are observed in spermatheca shape (Table  3 ). Molecular comparisons with specimens from our own database show high CytB distances between the sequences herein obtained and those referring to T. (A. ) rhenanus (19. 6% -20. 3%). Similar high distances have been previously observed at the intraspecific level (i. e. Tixier et al. 2017 Tixier et al. , 2019)). However, for the COI fragment, 22% divergence was observed between T.
Typhlodromus (Anthoseius) kerkirae Swirski & Ragusa Chelicera. Fixed digit 28 long; and movable digit 23 long. Dentition not visible because chelicerae closed. Spermatheca. Spermatheca with campanulate calyx 13 long and 8 wide, with an atrium incorporated at the basis of the cervix. Remarks. The morphological features reported above are in line with those reported by  Tixier et al. (2019)  for T. (A. ) kerkirae (Table  3 ). Four DNA sequences were obtained for CytB, one for 12S and two for the COI fragments. The Genbank database only includes CytB sequences for T. (A. ) kerkirae (accession number: MK014094), which have 15. 4% -16. 1% divergence with sequences from our specimens from Georgia. In contrast, the mean distance between the four Georgian specimens is 0. 25%. Although the genetic distance between the French and Georgian specimens is high, it is lower than the intraspecific variation already observed for species of the sub-family Typhlodrominae and the genus Typhlodromus (Anthoseius) (i.
Phytoseius finitimus Ribaga Remarks. According to the world database of  Demite et al. (2021) , P. finitimus is not reported from Georgia. However, Phytoseius plumifer (Canestrini and Fanzago) has been reported several times from this country. Because of its long history of misidentification with P. finitimus  (Duso and Fontana 2002) , we can wonder about the specimens reported under the name of P. plumifer from Georgia. Phytoseius finitimus was the most frequent species retrieved herein (41% of the specimens collected). CytB (29), COI (28) and 12S (18) sequences were obtained and compared to those of  Tixier et al. (2017)  for specimens collected on Viburnum tinus (Adoxaceae), Vitis vinifera (Vitaceae) from Italy and Actinidia deliciosa (Actinidiacae) from France. The supplementary table  S4  shows the genetic distances obtained.
Neighbour joining phylogenetic tree including Amblyseius eharai from Georgia, specimens of Amblyseius eharai, Amblyseius largoensis and A. herbicolus from Genbank and Euseius stipulatus (as an outgroup) obtained with COI mtDNA fragment. JX080343 Amblyseius eharai JX080331 Amblyseius eharai JX080336 Amblyseius eharai JX080337 Amblyseius eharai JX080345 Amblyseius eharai JX080335 Amblyseius eharai JX080341 Amblyseius eharai JX080334 Amblyseius eharai JX080340 Amblyseius eharai JX080339 Amblyseius eharai 83 JX080338 Amblyseius eharai JX080333 Amblyseius eharai 48 JX080342 Amblyseius eharai Amblyseius eharai Georgia 100 100 Amblyseius eharai Georgia Amblyseius eharai Georgia JX080332 Amblyseius eharai 99 JX080344 Amblyseius eharai 100 Amblyseius largoensis Amblyseius largoensis 74 Amblyseius herbicolus 100 Amblyseius herbicolus Euseius stipulatus 0.
Mass Spectrometry (MS) Analysis An LTQ Orbitrap Velos mass spectrometer (Thermo-Fisher Scientific, Bremen, Germany) equipped with an HESI source working in positive mode was used for accurate mass measurements. Mass spectra were acquired in profile mode with a setting of 30,000 resolution at m/z 400. Operation parameters were as follows: source voltage, 4 kV; sheath gas, 50; auxiliary gas, 15; sweep gas, 5 (arbitrary units); heat temperature, 250°C; and capillary temperature, 350°C. Default values were used for most other acquisition parameters (FT Automatic gain control (AGC) target 5. 10 6 for MS mode and 5. 10 5 for MS n mode). Methanol extracts were analyzed in full scan mode (m/z range 80. 000-1500. 000) at a resolving power of 30,000 at m/z 400 and data dependent MS/MS events acquired at a resolving power of 15,000.
The most intense ions detected during full scan MS triggered data dependent scanning. Data dependent scanning was carried out without the use of a parent ion list. An isolation width of 2 amu was used and precursors were fragmented by collision-induced dissociation (CID) with a normalized collision energy of 35 V and an activation time of 10 ms. The data analysis was achieved using XCalibur software (Thermo Fisher Scientific). An external calibration for mass accuracy was carried out before the analysis to assure a precision < 5 ppm.
Acknowledgement: Authors' contribution:  Dr. Arsène Bikoro Bi Athomo  was the principal investigator of the project. Dr. Carine Arnaudguilhem  helped in chemical analysis. Dr. Péguy Starlin Engozogho participated as a worker in a related field of tropical wood chemistry. Dr. Rodrigue Safou Tchiama  contributed actively as wood scientist and PhD committee's member; he criticized and corrected the final manuscript. Pr. Florent Eyma  and  Pr. Bertrand Charrier  acted as scientific directors. Université de Pau et des Pays de l'Adour  are thanked for the material support and facilities offered by the  ANR-10-EQPX-16  Xyloforest (Xylomat,  Mont de Marsan ). Funding Statement: We gratefully acknowledge the financial support of the  Agence Nationale des Bourses du Gabon (ANBG).
Animal-attached accelerometers have been widely used to monitor species that are difficult to observe, alongside the use of machine learning to identify behaviours from the obtained sequences. Artificial neural networks are powerful supervised learning algorithms that are based on deep learning and have been poorly exploited in movement ecology. Recently, the availability of sophisticated algorithmic architectures via open source libraries facilitates their use. In this study, we adapt a fully convolutional neural network that was originally developed for biomedical 3D image segmentation: the V-net. We test it on a labelled dataset collected from animal-borne video recorders combined with multi-sensors (accelerometers, gyroscopes and depth recorders) deployed on freeranging immature green turtles (Chelonia mydas).
Introduction Machine learning has been widely used in behavioural studies to face the new challenges related to the development of animal-attached sensors  (Valletta et al. , 2017; Wilson et al. , 2018). This technology allows scientists to remotely record a large amount of data on free-ranging animals for which direct observations are impracticable  (Boyd et al. , 2004; Brown et al. , 2013). Their use grew rapidly in the 1990s with the development of microprocessors and the increase in memory capacity, but is now limited by the difficulties in processing the complexity and volume of the recorded data  (Ropert-Coudert et al. , 2010; Wilson et al. , 2018). In this context, machine learning has showed up as a powerful tool to handle large datasets and automatically analyse multi-sensor data.
An emerging field requiring machine learning is the automatic behavioural identification from accelerometer. The accelerometer records the fine scale movements and body posture of the equipped animals from which behaviours can be identified  (Brown et al. , 2013). It is possible to label the acceleration signals with the animal behaviours through a first validation step using visual observations. Then the labelled dataset is used to teach a supervised learning algorithm to automatically identify the behaviours from the acceleration sequences  (Jeantet et al. , 2020a (Jeantet et al. , , 2018;; Nathan et al. , 2012; Shepard et al. , 2008). The best-known supervised learning algorithms used for behavioural identification have mainly been based on decision trees (CART, Random Forest or Extreme Gradient Boosting;  Brewster et al.
, 2018; Graf et al. , 2015) , linear hyperplane separation theorem (Support Vector Machine;  Campbell et al. , 2013; Martiskainen et al. , 2009)  or even the minimum distance between the unknown instance and the training data (K-Nearest Neighbors;  Bidder et al. , 2014). The advantages of these algorithms are that they are easy to implement thanks to the development of free computing libraries based on R and Python programming languages, and they produce a good outcome without requiring advanced skills. However, their effectiveness is still limited in identifying fine behaviours and they do not consider the temporal dimension of the data as they process data in independent segments. Artificial neural networks are other powerful learning algorithms These networks are computational models that translate an input signal through successive layers where linear and non-linear transformation are performed, into an output layer expressing the contained information.
Taking this opportunity, and in order to automatically identify behaviour from multi-sensor records, we propose in this study to test a neural network that was originally developed for biomedical 3D image segmentation: the V-net  (Milletari et al. , 2016). Its good performances on pattern recognition are all the more interesting given that it required few training images despite a highly imbalanced dataset and it performed faster than other methods. Contrary to classical learning algorithms and fullyconnected neural networks, the V-net takes the temporal structure of the data into account as it integrates the notion of neighbourhood. We present here an adaptive version of the V-net for the multi-sensor time series (i. e. 1D signals) to automatically identify the behaviours of green turtles (Chelonia mydas).
, 2000; Howell, 2012). While studying feeding behaviours of green turtles is particularly important to understand their ecology, it is still difficult to identify it automatically from a sensor placed on the carapace. In the attempt to automatically identify the at-sea behaviours of green turtle, with a particular focus on the feeding events, we test the performance of the V-net to classify their behaviours from multi-sensor recordings. We use a labelled dataset collected from animal-borne video recorders combined with multi-sensor recorders (accelerometer, gyroscope and time-depth recorder) deployed on free-ranging immature green turtles in Martinique  (Jeantet et al. , 2020b). The outcome of the V-net could be compared to a method based on classical learning algorithms (such as RF, EGB, CART and SVM) developed from the same dataset in a previous study  (Jeantet et al.