dataesr/scientific-paragraphs-categorization huggingface dataset:
(3) Some translational exceptions might be due to taxonomic attributions of the genetic codes that are too coarse-grained. (4) Specifics of the mitochondrial translational system, in particular non-canonical start and incomplete stop codons, are not represented in the codon tables. Representing such properties in the binary genetic code tables might be a non-trivial task. (5) Due to the binary nature of the code tables, the actual prevalence of the usage of start and stop codons is not represented. Rather the mere possibility that a certain codon might be a start is indicated. Using start and stop codons as defined by the NCBI code tables for annotation, as it was the case in the original implementation of MITOS, is thus not sufficiently accurate. At the same time, our results show that additional efforts should be undertaken to identify the start and stop codons of metazoan mitogenomes.
This could be achieved by different strategies: (1) Start and stop codons could be determined by wetlab experiments modifying the primary sequence. This might be feasible and sufficient for a sample of well chosen key taxa. (2) RNA-Seq or EST data are available for many different species. Previous studies on drosophilids and sauropsids have shown that this data can be used to reliably identify gene boundaries  (27, 28). (3) Alternatively, the frequencies of annotated start and stop codons can be used for a systematic computational prediction. In this respect the presented study is a first step in this direction. Precise annotation of PCG boundaries. The method presented here identifies start codon positions based on empirical evidence. This also includes non-canonical start codons because they are currently a well-accepted hypothesis  (7).
While the occurrence of out-of-frame start codons might be plausible, they seem to appear only rarely. Currently, not much is known about the underlying mechanism. Thus, for now, considering them in an automated context would only significantly increase the number of putative start codon positions that need to be checked. Furthermore, the computational prediction of frameshifts close to the 3 end of PCGs is not a trivial task  (20). In summary, we suggest that translational exceptions (non-canonical start and incomplete stop codons) should be handled with caution, unless accompanied by direct ex-perimental evidence. The possibility of gene overlaps often offers a plausible alternative. The implementation of our new method takes this into account and allows for large overlaps. Similar to any reference-based method, also MITOS might propagate systematic errors that might be present in its reference database RefSeq.
Generally, utilities companies use 2D maps for inspection of the underground assets, involving the tedious task of locating them correctly. Today, with the digital revolution, advanced systems for integrated management of buried networks could be used. These systems offer the possibility to conduct collaborative work for the design, construction and management of urban underground utilities. Tools for computer-aided design (CAD) are generally used in the design phase. However, these tools are not updated during the operating phase. We have also some difficulties related to crack detection to access the exact depth of some objects  [2]. An integrated system for buried networks requires a client-server architecture. It should be able to extend the geographic information systems (GIS) features in terms of spatial databases and data analysis to data visualization objects and ubiquitous access.
Marker-based AR Called also image recognition AR, Marker-based augmented reality anchors a computer-generated virtual contents to some forms of visual marker of the real life. A marker can be an image 2D or a Fudicial marker. A fudicial marker can be black and white images that consist of 2D image, barcodes or "quick response" (QR) codes  [5]  as shown in (Figure  1 ). Marker-based AR uses a camera with an AR software to detect AR markers as the location for virtual objects. The marker should be kept in front of the camera, once the marker is recognized by the camera frame, a virtual computer-generated content can be viewed on a screen and digital assets are placed into the scene at the location of the markers  [6]  (Figure  2 ).
The use of physical targets allows device's orientation with an accuracy below centimeter. The difficulty of this technique is related to the necessity to figure out the target points, which is very complex in dense cities. [8]  Proposed an AR system that allows creating, reading, updating, and deleting data from a geospatial database through a mobile interface which is connected to Internet by means of a client-server architecture. This system can be deployed easily using only underground infrastructures data in CAD format. Chicago city has deployed a system for both city mapping and assessing of underground infrastructure  [19]. Some utility companies use the Ground-penetrating radar (GPR) as a remote non-destructive sensing technique for both underground infrastructure imaging and assess buried pipe condition assessment  [20, 21, 22].
This system provides an ergonomic easy to use interface (UI). The user could interact with the system and getting the results displayed on his device screen. The system allows bi-directional interactions between the GIS database, the 3D objects database and users. All changes on the GIS database would be automatically translated into 3D objects and synchronized with GIS databases and 3D objects database (figure  4 ). Users have informations precisely about the device position and orientation and the surrounding underground utilities.
(Bush, 1989b (Bush, , 1989a;; Bush & Jacoby, 2010; Bush et al. , 1995). Significant progress in sequencing technologies led to a large number of class A β-lactamases in sequence database, in some cases being deposited under inappropriate classification  (Philippon, Slama, Dény, & Labia, 2016). The class A enzymes can be further classified in subclasses A1 and A2 based on phylogenetic clustering  (Philippon et al. , 2016). Functional class B β-lactamases, which appeared more than two billion years ago, suggest that resistance to antibiotics long predates their use in medicine  (D'Costa et al. , 2011). The functional classification of β-lactamase and its correlation with their distinct molecular structure has been described by  Bush et al. (1995) , and the functional evolution of superfamily class B β-lactamase has been studied by Alderson et al  (Alderson, Barker, & Mitchell, 2014).
| Data collection The β-lactamase amino acid sequences were retrieved from the Antibiotics Resistance Gene-ANNOTation (ARG-ANNOT) database  (Gupta et al. , 2014). A total of 1,147 sequences from the four molecular classes (A-D) were recovered. All sequences were checked in the NCBI PubMed database (http://www. ncbi. nlm. nih. gov/pubme d/) to obtain the publication in which the sequence was described, from which we collected minimum inhibitory concentration (MIC) values of the respective β-lactamase enzymes and β-lactam antibiotics, whenever this information was available. Two types of MIC value were retrieved, with and without β-lactamases, and we then calculated the fold change (MIC value of with β-lactamase was divided by without β-lactamase MIC) between them. A total of 218 sequences were selected with these MICs against different β-lactam antibiotics.
These MICs and fold change values are also available in the β-lactamase database  (Keshri et al. , 2019). Further, the fold change MIC values were normalized on a log scale followed by convergent evolution study. The remaining sequences were excluded from the study because the sequences were published on the basis of homologous sequences. Some sequences were characterized on the basis of kinetic parameters, while others did not report MIC. Further information such as original published article, collected MIC values (with and without β-lactamase), and other related information can be accessed in a β-lactamase database  (Keshri et al. , 2019). The MICs of the same species-strain, same experiment technique/conditions, and specific sufficient data are not available in the scientific community for a particular species-strain and experiment convergent evolution study.
Therefore, it is not possible to analyze data in species and/or strain and expression system specific. Generally, for comparison of MICs between different experiments, it is also necessary to perform experiment in the same laboratory with similar experimental protocols for removing the technical variability. Unfortunately, this kind of specific datasets is not available. Because of these difficulties, we initiated to analyze all MIC data (regardless of specific species, strain, techniques, etc) of a particular class of βlactamase enzymes and correlate with their phylogenetic trees (each molecular class separately).
| Multiple sequence alignment and phylogenetic inference The multiple sequence alignment and phylogenetic tree reconstructions were conducted in MEGA6 version 6. 05  (Tamura, Stecher, Peterson, Filipski, & Kumar, 2013). Sequences were aligned using the MUSCLE algorithm  (Edgar, 2004 ) with default settings. The phylogenetic trees were constructed with the maximum-likelihood method under a WAG + G+I amino acid substitution model (estimated to be the best substitution model by maximum-likelihood-based model comparison, also in MEGA6). Node support was estimated using 1,000 bootstrap replicates. The phylogenetic trees were midpoint-rooted, and branch lengths of the phylogenetic trees were converted to relative time ("dated" with the total length of the tree equal to 1) using maximum penalized likelihood as implemented in the chronos function in the ape package of R 3.
| Convergent amino acid residue identification We investigated whether the convergent functional evolution we observed at the intraclass level of β-lactamase superfamilies was mediated by underlying convergence at the molecular sequence F I G U R E 4 Class C (Cefoxitin): The midpoint-rooted phylogenetic tree was constructed by maximum-likelihood method based on the alignment. Bootstrap values are shown on each node. The phylogenetic tree contains class C β-lactamases. The color of the branch (also in scale bar) indicates functional activity of enzymes against cefoxitin β-lactam antibiotic. Blue through red color indicated susceptible to resistance functional activity level (i. e. , whether the same mutations conferred resistance). We attempted to identify amino acid residues that may be responsible for functional changes using a python script (provided in Appendix S1) which looks for convergent mutations that co-occur on the phylogeny with each similar change in the functional activity.
INTRODUCTION The fine-tuning of antiparkinsonian treatment in Parkinson's disease (PD) is often a challenging task for clinicians. In fact, these medications need to be prescribed at their minimal efficient doses to optimize mobility while minimizing undesirable side effects  [1]. In routine practice, the adjustment of antiparkinsonian therapy relies on a combination of objective data derived from clinical examination, and subjective data from the patient's interview. The Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a helpful, validated tool for a structured patient interview. It focuses on the week prior to the visit  [2]. Motor symptoms are assessed by parts II (motor experiences of daily living) and IV (motor complications). However, retrospectively-assessed subjective information might be biased by the subjects' recall capability  [3].
e. "slight" to "severe" problem): PD phenotype, presence of hallucinations and psychosis, depressed mood, anxious mood, apathy and features of dopamine dysregulation syndrome. The correlation between quantitative demographic parameters, and both agreement and positive/negative thinking were calculated using Kendall's (τ) correlation coefficient (using the "cor. test" correlation of the R statistics package  [15] ). Correlations were considered significant if the p-value was <0. 05 and relative achieved power >80%. This coefficient is suitable for the measurement of a monotonic association of non-normally distributed data. Correlation between categorical demographic variables, and both agreement and positive/negative thinking was assessed with the classical Wilcoxon tests ("wilcox. test f" function in the R statistics package [R]), equivalent to a paired t-test for non-parametric distributions.
ACKNOWLEDGMENTS We are particularly grateful to all the patients and their families who participated in this project. This study was conducted in the framework of the " Sleep, Awake & Move " project, which was financed by  Swiss Parkinson 's Association 2015 research award and by a 2015 research grant from the  Advisory Board for scientific research of Ente Ospedaliero Cantonale (ABREOC). We also acknowledge the  DRCI (Délégation de la Recherche Clinique et de l'Innovation) of University Hospitals of Martinique  for contributing to the publication fee of this article. We thank  Prof. Moustapha Dramé  for his advice in manuscript revision. Many thanks to  Dr. Stephany Fulda  for her support on the first conception of the "Sleep,  Awake & Move " research project, to  Miss Simona Bonoli ,  Miss Lucia Guglielmetti  and  Mr.
Contribution of Earth observation to Indian agrohydrology The Indian space program provides several high-resolution satellite missions (1 to 56 meter spatial resolution) that have been used to measure seasonal land cover. In 2016, the National Information System for Climate and Environment Studies (NICES) project released a national database of seasonal net sown areas (http://bhuvan. nrsc. gov. in/data/download/index. php) which highlights the high spatio-temporal heterogeneity of land cover and its temporal dynamics. This dataset is published as a fraction of crop sown at 5km resolution by the National Remote Sensing Center (NRSC), part of the Indian Space Research Organization (ISRO). A similar program, called Water Bodies Fraction (WBF) and based on the same optical data, produces the areal fraction of surface water every 15 days since 2012.
Objectives of the study over Hyderabad region The aim of this paper is to present the methodology, based only on those new observations from the Sentinel-1 and Sentinel-2 satellites for monitoring the essential agro-hydrological variables of the Indian context. It presents an update of a previous work where synergy and spatial and spectral resolution of Sentinel-1&2 products were evaluated regarding the irrigated crop detection accuracy for 3 growing seasons 2016 and 2017  (Ferrant et al. 2017). Following the conclusions of this study, we used the only 10m resolution Sentinel-1&2 bands and ratios. The main difference in this study is the use of surface reflectance Sentinel-2 produced from the MAJA workflow (MACCS ATCOR joint Algorithm, O. Hagolle, https://github. com/olivierhagolle/Start_maja) which produces accurate cloud mask based on image time series.
Sentinel-1 and 2 pre-processing The Sentinel-2 dataset downloaded in this study is the surface reflectance produced by PEPS web platform and Theia land data center (Level-2A). It provides a cloud mask and a surface reflectance corrected from the atmospheric effects, based on the MAJA workflow  (Hagolle et al. 2015). In this semi-arid Indian area, this correction is crucial as the seasonal variations of aerosol concentrations in the form of dry dust is high  (Sivaprasad et Babu 2014)  and alters the reflectance. The Sentinel-1 dataset comprises Level-1 Ground Range Detected (GRD) data in Interferometric Wide swath mode (IW) consisting of focused SAR data that has been multi-looked and projected to ground range using the WGS84 Earth ellipsoid model. The resulting images in dual polarization (VH and VV) have a dimension of 270x270km and a resolution of 10 m.
The terrain correction was then applied to geocode the images by correcting SAR geometric distortions (foreshortening, layover and shadow) using the digital elevation model from the Shuttle Radar Topography Mission, producing ground-projected images. To do this, the Orfeo ToolBox (OTB) was used. Each image was cropped and superposed on the Sentinel-2 tiles. A multi-temporal speckle filter  (Bruniquel et Lopes 1997)  was preferred to the more classical spatial filter in order to preserve spatial resolution and the fine structure of Sentinel-1 images. As shown previously in  (Mermoz et Le Toan 2016; Mermoz et al. 2014) , this method produces images with reduced speckle effects from the whole sentinel-1 acquisition time series (30 dates since February 2015) and multi-polarized (VH and VV) images.
Sentinel Bands and derived indexes selection We selected Sentinel-2 bands at 10 m: the blue (B2 = 490 nm), green (B3 = 560 nm), red (B4 = 665 nm), and near infra-red (B8 = 842 nm) bands and have computed the NDVI for each images (Eq. 2). VV and VH backscatters from Sentinel-1 IW acquisition mode and the ratio based on VV and VH (Eq. 3) were computed at 10 meter resolution and both Sentinel bands and indexes were stacked together by growing seasons. The sizes of these stacks of data obviously vary with the number of images in a season. (Eq. 2) (Eq. 3) Acquisition dates were selected using a number threshold of valid pixels, based on merge edge mask and cloud mask. Each acquisition dates which are not covering 90% of the pixel zone are not used for the classification. The number of bands and sampling dates varies highly from a season to another, with both the cloud mask between Kharif and Rabi seasons, and with the increase of Sentinel acquisitions from 2016 to now.
Land cover surveys for learning samples Six seasonal surveys of land cover were carried out in February and October, from 2016 to 2018, corresponding to 428 plots of non-irrigated areas: bare ground or natural vegetation cover, rainfed and tree plantations (eucalyptus and teak), 1455 irrigated areas: orchards (mainly mango trees), maize crops and vegetables (beans, lentils) and 192 flooded rice crop areas. The dataset was especially scrutinized for rice as the main waterconsuming crop. A USB GPS receiver (G-STAR IV) was connected to GIS software (QGIS) on a laptop to position the operator in the landscape. The operator used the latest Sentinel-2 acquisition to delineate polygons containing both homogeneous land cover and reflectance observed in the field and on the Sentinel-2 image. The dataset created from the field surveys was used to create six seasonal datasets: perennial land cover such as orchard, forest and natural land use are preserved across the three datasets together with the irrigated, flooded and rain-fed crop fields observed during one season.
The accuracy of the land cover map produced is evaluated with the precision, recall and F-score of the targeted irrigated and inundated crop class from the confusion matrices. F-score is the harmonic mean of the user's accuracies and the producer's accuracies. The best value of the F-score is 1, and the worst 0. We use the inundated and irrigated area estimates to compare the methods and seasons. The overall land-cover accuracy is given by the Kappa index for all classes. The size of each satellite time series dataset analyzed is also given, to illustrate the basic informatics settings required by this method.
Nevertheless, the K2017 shows really low F-Scores for irrigated crops and high confusions between rice and other crops. As for other Kharif period, only one cloud free Sentinel-2 is available, yet in 2017, this image comes very late in the season, at the end of November. Sentinel-1 time series, used alone for this season, gives the same low F-scores. For this season, Sentinel acquisitions do not sample properly the cropping pattern reflected by the seasonal field data set. On the contrary, the relatively high Kappa indices (from 0. 58 to 0. 92) are consistent with a good accuracy over natural land use, urban areas and surface water. For instance, Water F-scores are above 0. 9 for all seasons, even K2017.
ACKNOWLEDGEMENTS This work was carried out in the context of the project " Agrohydrology from Space ," with the support of  IRD  and NGRI facilities. We thank  Wajiddudin  for his field expertise and the  Indo-French Center for Groundwater Research (IFCGR)  team for their help and good welcome. This work was possible thanks to Theia land data services infrastructures. We would like to thank  T. Koleck ,  O. Hagolle  for the tools developed for Sentinel download and pre-processing, as well as OrfeoToolBox developer team, used as essential bricks to build the workflow presented in this manuscript.
, 1995), metallo-aminopeptidase PepS (Fernandez-Espla and Rul, 1999) , dipeptidase  (Rabier and Desmazeaud, 1973) , endopeptidase PepO  (Chavagnat et al. , 2000) , and Xaa-Pro dipeptidyl-peptidase PepX  (Tsakalidou et al. , 1998) ] have been characterized biochemically or genetically after either purification or gene cloning. PepX  (EC 3. 4. 14. 11 ) is an enzyme that catalyzes the removal of N-terminal dipeptides (Xaa-Pro) by hydrolyzing the peptide bond implying the carboxylic function of a proline residue and belongs to the MEROPS peptidase family S15 (http: / / www. ebi. ac. uk/ interpro/ entry/ IPR008252). In a previous work we reported the presence of extracellular cell-associated aminopeptidase, carboxypeptidase, peptidyl-dipeptidase, and X-prolyl dipeptidylpeptidase activities in S.
Primers (Table  1 ) were designed using Primer3Plus software  (Untergasser et al. , 2007)  and were purchased from Eurogentec (Serain, Belgium). To create overlap regions between flanked regions of the pepX and specR genes, a short nucleotide sequence homologous to the Up-and Dn-flanked regions of PepX was added to each of the primers used to amplify the specR fragment (Figure  1 ). The PCR amplifications and agarose gel electrophoresis were performed as described previously  (Hafeez et al. , 2013). Overlapping PCR reactions were performed as previously described  (Lecomte et al. , 2014). The PCRamplified fragments were purified using a high pure PCR product purification kit (Roche Applied Science, Meylan, France), and sequence analyses of both DNA strands of PCR fragments were conducted by Beckman Coulter Genomics (Essex, UK).
, 2014). In bacteria, most of the exported proteins display a Sec-signal peptide that target them to the Sec secretion pathway. Other Sec-independent secretion pathways have been described, such as the twin-arginine translocation secretion pathway that was distinguished by the presence of a twin-arginine consensus motif in the signal peptide  (Berks, 1996; Berks et al. , 2000). Nevertheless, some bacterial proteins have been found to be secreted without any apparent signal peptide due to a nonclassical secretion  (Bendtsen et al. , 2005). Concerning S. thermophilus, if the protease PrtS displays a Sec-signal peptide, the sequence analysis of PepX did not reveal any N-terminal signal sequence that could account for translocation from the cytoplasm but highlighted the presence of a galactose-binding domain-like fold (IPR008979).
, 2004 (Bendtsen et al. , , 2005)). Concerning PepX, SecretomeP software (http: / / www. cbs. dtu. dk/ services/ SecretomeP) gives a probability of nonclassical secretion of 0. 449, very close to the threshold set at 0. 5. Thus, this type of secretion cannot be excluded in the case of PepX. Therefore, for all these reasons, we propose that the intracellular PepX might (1) be located in the plasmic membrane with its catalytic domain orientated toward peptidoglycan or (2) cross the cell membrane due to its galactose-binding domain-like fold or by a nonclassical manner and then is buried inside the peptidoglycan. In both hypotheses, β-casomorphin-7 or small peptides would be hydrolyzed after their penetration into the peptidoglycan. To conclude, this study provides the first evidence of the potential implication of intracellular PepX in the hydrolysis of proline-rich peptides extracellularly and that a single PepX might be responsible for the concurrent intra-and extracellular enzyme activity.