dataesr/scientific-paragraphs-categorization huggingface dataset:
2 mL • min -1 with a Na 2 CO 3 (4. 5 mM) and NaHCO 3 (1. 4 mM) buffer as eluent. Data were collected and processed using the Chromeleon 6. 50 software (Dionex Corp. ). For quantification, calibration ranges were carried out using NaCl, KCl, NaNO 3 , NaH 2 PO 4 , and (NH 4 ) 2 SO 4. The results were expressed in µmol • g -1 DM.
Cell Wall Sugar Composition Plant cell wall material was prepared from 150 mg of frozen shoot powder as described by Duran  Garzon et al. (2020). Dry cell wall material was digested with amylase according to  Fleischer et al. (1999). After digestion, the dry cell wall (2 mg) was hydrolyzed, and the samples were injected onto a CarboPac-1 column (Dionex) HPAEC, separated, and detected by PAD. Data were collected and processed using the Chromeleon 6. 50 software (Dionex Corp. )  (Duran Garzon et al. , 2020).
Proteomics Analysis Proteins were extracted from the shoots using a global protein extraction method adapted from Ma et al. (  2016 ) and described by  Leschevin et al. (2021). Proteins were identified and quantified using a Tandem Mass Tags (TMTs)-based comparative proteomics analysis method  (Leschevin et al. , 2021). These experiments were performed on three biological replicates. Data were processed using Proteome Discoverer 2. 4 (Thermo Fisher Scientific) before being run against The Arabidopsis information Resource (TAIR10_pep_20101214), as well as against Swissprot 1. Proteins were annotated using the ProtAnnDB  (San Clemente et al. , 2009)  and Uniprot 2  databases. The MS/MS data (raw data, identification, and quantification results) are available at ProteomeXchange with identifier PXD022441 3.
Quantitative Proteomics Data Related to the Studied Traits in Col-0 and Ws Shoots A TMTs-based comparative proteomic analysis was performed on total soluble proteins from the shoots of control and salttreated plants. All the data described in this paragraph are available in Supplementary Tables  3, 4. Altogether, upon salt stress exposure, there were 542 DAPs in Ws and 558 in Col-0. The distribution of overaccumulated (316 Ws vs. 331 Col-0) and underaccumulated proteins (226 Ws vs. 227 Col-0) was similar in both ecotypes (Figures  5A, B ). There were common 75 underaccumulated and 159 overaccumulated proteins between Ws and Col-0 (Figures  5C, D ). Based on our physiological and biochemical data, we focused the following analysis on DAPs involved in photosynthesis, primary and secondary metabolisms, ROS detoxification, and cell wall.
ACKNOWLEDGMENTS We thank  Hervé Demailly  and the  CRRBM (Centre de Ressources Régionales en Biologie Moléculaire, Amiens, France)  for the use of plant growth chambers. EJ and HS wish to thank the  Centre National de la Recherche Scientifique (CNRS)  and the  Paul Sabatier-Toulouse 3 University  for supporting their work. We thank  Carine Froment  for stimulating discussions and  Daniel Cahill  for his proofreading. We also thank the  French Ministry of Higher Education, Research and Innovation  for the funding of ML's Ph. D.
To this end, the authors decided to start analyzing a State-of-the-Art (SoA) technology called MPEG Depth Estimation Reference Software (DERS), able to generate high-quality depth maps from YUV images at the cost of a huge processing time. Then, this tool was GPU accelerated to prove its viability in producing high-quality depth maps in an operating room at real time. As this goal was achieved, the software was updated to use HS images as input while keeping the real time constraints fulfilled. In order to test the application, a laboratory test HS multiview sequence was employed in this work, focusing on the restrictions set by the brain cancer detection application: (i) the definition of real time to a few seconds; (ii) the target depth range, from around 0. 5 m to around 1 m; and (iii) the need for a detailed depth map.
3. A comparison between DERS and GoRG YUV in terms of objective and subjective quality and processing time. The rest of the paper is structured as follows: Section 2 presents the current SoA related to HS depth estimation as well as an introduction to GPU-accelerated depth estimation algorithms. Sections 3 and 4 include the background required to explain the proposal in this work, mainly focusing on the analysis of DERS, one of the SoA highly detailed depth estimation algorithms. Section 5 introduces GoRG, a GPU-accelerated software based on DERS able to process YUV or HS images and the proposal of this work. In Section 6, the test material and the methodology to test the proposal are explained. Section 7 includes (i) the results during the development stage of GoRG, (ii) the final results for GoRG YUV, and (iii) the final results for GoRG HS.
Some of them were also tested on GPU accelerators, obtaining processing times at around tens of milliseconds  [27, 28]. In this domain, global methods or semi-global methods were also employed to improve the quality of results at the cost of spending more time processing. One example is the work of  Chai et al. [29] , with a semi-global stereo matching algorithm based on minimum spanning tree that achieves better quality results than other SoA algorithms except for graph cuts  [30]. Although their algorithm is less complex than graph cuts, they did not include a processing time comparison. Considered the algorithm with the best quality depth map generation, graph cuts were implemented in DERS to fulfill the quality requirements of immersive video applications. The quality and processing time needed by this software were studied in another work  [31] , showing that for generating these highly detailed depth maps, the processing time needed oscillates between hundred and thousand of seconds.
Background: CUDA Cuts CUDA Cuts  [33]  is a GPU-accelerated graph cuts considered one of the most important GPU implementations in the SoA. Based on the push-relabel algorithm, this parallel implementation makes use of shared memory to solve up to 60 graph cuts per second for 1024 × 1024 pixel images (15 ms period). These results, compared to the Kolmogorov library (the one used in DERS), are between 40 and 60 times faster, although for obtaining this acceleration, some losses are introduced  [34]. For this reason, one implementation of CUDA Cuts (https://github. com/metthueshoo/graphCut, accessed on 10 June 2021) was studied and incorporated into the proposal of this work. This implementation is intended for the segmentation of an image to a binary image (foreground and background) by creating a graph with a certain weight for every pixel and connections with its neighbors.
Proposal: Graph Cuts Reference Depth Estimation in GPU (GoRG) Graph cuts Reference depth estimation in GPU (GoRG) is a GPU-accelerated application intended to obtain depth maps either from YUV or HS images. This application was conceived as an accelerated software based on DERS able to obtain similar results in terms of quality using a video synthesis processing chain while obtaining processing times under tens of seconds. For this reason and prioritizing the acceleration, GoRG includes two functional differences to DERS that produce quality losses: (i) interpolation is not included in GoRG, as the quality-time trade-off is not considered beneficial (further explained in Section 5. 4) and (ii) CUDA Cuts library introduces appreciable quality losses at the cost of accelerating graph cuts algorithm (see Section 4).
Read Cameras In this stage, the central image along with the four nearest images from the multiview dataset are incorporated into the application, either they are YUV or HS, and are able to work in these two different modes. To avoid unnecessary data movements, these images are transferred to the GPU memory at this point, as all of the following computations do not make use of any other large CPU data. In this way, only two CPU-GPU data transactions are performed per frame: the input images and the retrieval of the final depth map. Intrinsic and extrinsic camera parameters are read as well.
YUV Material For testing GoRG in YUV, a set of eight multiview sequences from MPEG-I is used  [37, 38]. Their identifiers and features are introduced in Table  1. Quality and processing time results are compared to DERS in Section 7. 2. These sequences do not count using depth map ground truths, hindering its quality assessment. For these reason and taking into account that they are intended for immersive video applications, their quality is evaluated through a complete immersive video chain. In this chain, the multiview dataset is used (i) to obtain all of the depthmaps associated with every real camera and (ii) to synthesize, using a video synthesizer, a virtual camera in the same position of the real cameras, using information of depth and color from its surrounding real cameras. This is depicted in Figure  12 , illustrating the process for camera 4.
After this chain, a synthesized image, completely dependent on the depth maps, is generated in exactly the same position as a real camera. This can be compared pixel by pixel to the real camera image, obtaining an objective metric for quality assessment. In this work, the image synthesizer software employed is Versatile View Synthesizer (http://mpegx. int-evry. fr/software/MPEG/Explorations/6DoF/VVS, accessed on 10 June 2021)  [39] , and the metrics employed for objective quality are Weighted Spherical PSNR (WS-PSNR)  [40] , Immersive Video PSNR (IV-PSNR)  [41] , and Video Multimethod Assessment Fusion (VMAF)  [42].
2. The second camera is a linescan camera, with a row filter pattern and150 spectral bands. This camera needs precise movement through rows, so that the captured object is completely acquired for every spectral band. After the scanning process, images of 2045 × 1085 × 125 (width × height × bands) are generated. This system uses the movement necessary for obtaining high-resolution (both spatial and spectral) HS images with the linescan camera to obtain multiview low-resolution HS images as well. Therefore, only the snapshot HS images are used in this work. The multiview dataset was acquired in a laboratory using the same illumination conditions as the operating room and preprocessed to test GoRG. Preprocessing comprises radiometric calibration, cube generation, spectral correction, and interpolation at a factor of 2, leading to 832 × 448 × 25 images.
The dataset information is depicted in Table  2. It is important to notice the reduced depth range and depth steps, 60 cm and 100 steps, respectively; compared to the YUV sequences, this range is limited. This is designed to emulate functioning in the operating room. As an example, Figure  14  shows a real HS image captured during a surgical operation. This data set consists of a collection of real objects placed in front of the laboratory test acquisition system at different distances and with different textures. In this way, an HS multiview was generated using the HS camera moved across the linear actuator. After the acquisition, a 3D model ground truth was generated using a structured light 3D scanner, depicted in Figure  15. Then, using Blender (https://www. blender. org/, accessed on 10 June 2021), the camera array was simulated to acquire depth maps of the scene in the same positions as the real camera.
GoRG Evaluation Results with HS Images GoRG HS quality results are introduced in Figure  22. These results are expressed using RMSE in meters for every view tested in the dataset. As can be seen, this error oscillates around the average value, which is near 7. 5 cm and depends on the part of the scene captured. This error is not equally distributed within the image; for example, the depth surface of the octopus toy is estimated with an error of 0. 5 cm, the dog is estimated with an error of 1. 5 cm, and the drum is estimated with an error of 2 cm, while the background plain wall has an average error of 10 cm. The subjective results can be observed in Figure  23 , where the ground truth depth map is compared to GoRG results. These results show how GoRG depth maps are well estimated for the objects in the scene, also preserving borders.
Conclusions This works proposes an application-denominated Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated software intended for multiview high-quality depth estimation using HS images as input based on a SoA application named Depth Estimation Reference Software (DERS). GoRG is proposed to obtain high-detailed depth maps for HS images in brain tumor resection operations in real-time conditions. They are used to generate HS 3D models that improve both the classification of HS images and navigation by neurosurgeons. In this work, DERS is analyzed and thoroughly explained stage by stage, serving as a foundation for GoRG. Its results for a varied set of sequences are shown and compared to GoRG, considered as a high-bound quality and processing time reference. In addition, this work includes stage profiling, showing that the processing time is shared between the homography and graph cuts stage.
Using DERS as the background, GoRG is explained in detail, focusing on its contributions: first, its GPU acceleration and, then, the use of HS images. For the former contribution, the acceleration strategies that followed are exposed and their implementation details are introduced. These implementations are then justified by giving the result of different experiments conducted to adjust and optimize the software. For the latter contribution, the approaches to using HS information in a depth estimation algorithm are introduced, using SIDSAM as a cost metric to obtain the initial depth estimation. GoRG YUV, used as a prior step to the HS depth map generation, is compared to DERS, showing that the penalty in quality for using GoRG is, on average, -0. 93 dB, -0. 6 dB, and -1. 96% for WS-PSNR, IV-PSNR, and VMAF, respectively.
The interaction with ligand molecules causes conformational changes which have allosteric effects on the repressor incompatible for DNA binding, inducing the transcription of target genes. Although database searches reveal that rhizobial genomes encode numerous TetR-like proteins [4], only few have been studied. In Rhizobium etli, CasR negatively regulates the expression of casA, which encodes a calmodulin-like protein  [5]. The emrAB genes of Sinorhizobium meliloti, likely to encode an efflux system, are regulated by EmrR  [6, 7]. EmrR was shown to bind to palindromic sequences in the emrAB-emrR intergenic region. Binding was affected by luteolin, which is a known signal molecule in the symbiosis between S. meliloti and alfalfa (Medicago sativa). In Bradyrhizobium diazoefficiens USDA 110 (formerly B.
20. 6, RMSD on Ca atoms 2. 7 A), QacR  [18]  (PDB ID: 3BQZ, Z-score 15. 4, RMSD on Ca atoms 3. 6 A) and CmeR  [19]  (PDB ID: 2QCO, Z-score 14. 8, RMSD on Ca atoms 3. 1 A). The only difference in the polypeptide fold is found for the C-terminal extension of helix a4. In FrrA, this helix is two turns shorter resulting in an extended conformation for residues 83 -89.
FrrA promoter binding and induction by isoflavonoids The absence of detectable conformational changes in FrrA upon ligand binding suggests that inducers do not alter the free state of the repressor, but act instead by interfering with a structural transition required for DNA binding. Since no structures of FrrA bound to its target site are as yet available, we used DALI  [17]  to search the PDB for closely related folds. The most similar repressor whose complex with DNA has been structurally characterized was found to be QacR, a TetR-like regulator of multidrug resistance from Staphylococcus aureus  [30]. At the sequence level, the two proteins exhibit 18% amino acid identity, while the dequalinium-bound (induced) form of QacR, PDB ID: 1JT6  [31] , can be superposed onto FrrA with RMSD of 3.
2 A on Ca atoms. Figure  5A ,B show a superposition of FrrA onto promoter-bound QacR, PDB ID: 1JT0  [32]. As this comparison demonstrates, a pendulum-like movement of the DNA-binding domains of the FrrA dimer towards each other is required in order to fit the two instances of helix a3, the recognition helix of the HTH motif, into consecutive positions of the major groove on the same face of the double helix. This DNA-binding mode, which is highly conserved in all TetR-like repressors, requires a specific distance between the HTH motifs as well as a precise orientation of the DNA-binding domains in order to align both a3 helical axes with the major groove of the nucleic acid  [13, 14, 33]. Interestingly, the squeezing movement of the DNA-binding domains of the FrrA dimer towards their DNA-binding positions would inevitably push both a6 helices into the inducerbinding pocket, but the inducing flavonoid prop the domains in an open conformation (Fig.
A similar movement has been postulated for QacR  [31]. In several crystal structures of QacR-ligand complexes, the asymmetric unit comprises a remarkably asymmetric ligand-bound QacR dimer, but also a symmetrical dimer of apo-QacR (e. g. PDB ID: 1jt6)  [31]. In relation to the ligand-free QacR, the asymmetric FrrA conformation is favoured in apo-FrrA by crystal packing contacts. Although it seems tempting to speculate that the asymmetric FrrA dimer in our crystals corresponds to the induced state, we do not have any evidence to that effect. Indeed, the asymmetric dimer structure was found to be incompatible with our SAXS data, irrespective of the presence of ligands. Taken together, our crystallographic structures and SAXS measurements in solution demonstrate that the DNA-binding domain (helices a1 -a4) and helix a6 of FrrA have a considerable freedom to move with respect to the ligand-binding core of the repressor dimer.
Scattering of the buffer was subtracted and the resulting curves were scaled to unity protein concentration (1 mgÁmL À1 ). For further data analysis, version 3. 0. 3 of the ATSAS package was used  [41]. Theoretical scattering patterns based on high-resolution structural models were fitted to the experimental data using the program CRYSOL, which accurately accounts for protein surface hydration  [28]. The program CORAL  [20]  was used for SAXS-based modelling of the domain positioning within FrrA as well as for adding dummy residues to represent a short N-terminal segment missing in the crystal structure. In all CORAL calculations, twofold symmetry of the homodimer was imposed. Both, the DNA-binding domain (residues 20-83) and the region comprising helix a6 (residues 111-127) were allowed to move as rigid bodies, while the distance between residues 83 and 84 was restrained to 3 A to favour pendulum-like movements of the former.
X-ray diffraction data collection Multiple-wavelength anomalous diffraction (MAD) data sets of SeMet-labelled FrrA crystals were collected at wavelengths near the selenium absorption K-edge (12. 6578 keV, k = 0. 9795 A). Data of peak and inflection points were collected at 100 K at beamline BL14. 1, operated by the Joint Berlin MX-Laboratory at the BESSY II electron storage ring (Berlin Adlershof, Germany) [43] and processed with XDSAPP  [44, 45]. Diffraction data of FrrA in complex with genistein and naringenin were collected at 100 K at beamline P13  [46] , operated by EMBL Hamburg at the PETRA III storage ring (DESY, Hamburg, Germany). Diffraction data were processed with XDS  [47]. Details are listed in Table  1.
Structure determination and refinement Initial phasing of SeMet-labelled FrrA was based on multiple anomalous diffraction of peak and inflection datasets (2WL-MAD) collected at the absorption K-edge of Se  [25]. We used the Auto-Rickshaw server  [48, 49]  of the EMBL/ Hamburg (http://www. embl-hamburg. de/Auto-Rickshaw/), which utilizes programs of the CCP4 suite  [50] , SHELX-C/ D/E  [51] [52] [53] , DM  [54]  and ARP/wARP  [55, 56]. The best solution was identified for space group P4 3 with twelve Se positions with occupancies in the range of 0. 87 to 0. 40 above a noise threshold of 0. 27. According to the selfrotation function, this is in agreement with three SeMet in the FrrA sequence assuming four monomers of FrrA in the asymmetric unit. The same procedure for the alternative enantiomorph space group P4 1 revealed only nine heavy atoms with occupancies 0.
Data accessibility Structure factors and final coordinates of apo-FrrA, the complexes of FrrA with genistein and FrrA with naringenin have been deposited in the Protein Data Bank under accession codes 6G87, 6G8G and 6G8H, respectively. The SAXS data and models of FrrA have been deposited in the Small-Angle Scattering Biological Data Bank (SASBDB http://www. sasbdb. org) with entries SASDKA6 (FrrA), SASDKB6 (FrrA with genistein) and SASDKC6 (FrrA with naringenin).
Norwegian Center for Research Data (NSD), a national archive and center for research data. Data can be accessed without specific permissions by filling in and submitting a Data Access Form at https://nsd. no/nsd/english/order. html, referring to the project ID no. NSD2864 and project name "Determination of entomological indices to assess dengue transmission, predict dengue outbreaks and evaluate vector control interventions, 2019", short name: DENGUE-INDEX.
5mL microcentrifuge tubes at -20˚C and the house number and the location of collection (i. e. , indoor/outdoor) was recorded. Entomological data were used to construct several indices as described in Supporting information S1 Table  and  Aedes indices described hereafter include both Ae. aegypti and Ae. albopictus. At the cluster level, the Container Index (CI c ) was calculated as the proportion of Aedes immature-positive containers per total wet containers inspected in all visited households at the time of survey. The cluster-wide Breteau Index (BI c ) and the House Index (HI c ) were calculated as the proportion of Aedes positive containers per 100 houses and the proportion of positive households visited, respectively. The cluster-level pupal indices, Pupae per House Index (PHI c ) and the Pupae per Person Index (PPI c ), represented the total number of pupae collected per household and per inhabitants in each visited household, respectively.
Three general climatic seasons are defined according to the Thai Meteorological Department [55] with 15-February to 14-May as the hot season, 15-May to 14-October as the rainy (wet) season, and 15 October to 14-February as the cool season. Statistical approach. Data analysis was conducted using R software version 3. 5. 1 (R Core Team, Vienna, Austria) and MASS, Rcmdr, nlme4, and lmerTest packages  [58] [59] [60]. Figures were generated on R using ggplot2 and ggpubr packages  [61, 62]. Maps were built using QGIS software (version 3. 10) and shape files were obtained from the Humanitarian Data Exchange Project  [49]. As the MEI represents the specific exposition to Ae. aegypti, non-responder individuals were considered with a null MEI, thus the MEI was considered as a positive continuous variable (i.
Acknowledgments We thank all the cluster Village Health Volunteers for their diligence in conducting the blood spot collections and the individual questionnaires. We acknowledge the principal governmental officers from the  ODPC7  for the entomological collections, mosquito species identification, and household questionnaire sessions. We thank  Panwad Thongchai  for assistance in DENV detection in mosquito samples. We acknowledge  Franck Remoueand AndreSagna  from the  Institut de Recherche pour le Developpement  for their kind advice and assistance on ELISA experiments.