dataesr/scientific-paragraphs-categorization huggingface dataset:
We built the collaborative CADRE (Coeur, Artères et DREpanocytose) study as a large cohort of patients with SCD in five countries of West and Central Africa (Cameroon, Gabon, Ivory Coast, Mali and Senegal) to gather reliable data on SCD-related organ damage. The main objectives of the CADRE cohort were (1) to estimate the incidence of microand macrovascular complications of SCD and (2) to identify predictive factors of these complications in these countries. Between 2011 and 2013, 3627 patients, 3 years of age and over, were included and investigated in the five centres. 14, 15  Our project combines an epidemiological approach to identify specific patterns of organ damage with experimental studies. For the 5-year follow-up of the CADRE patients, we designed a cross-sectional study, BIOCADRE, nested in cohort CADRE, based on severe phenotypes of SCA in two African countries, Mali and Senegal.
Patient selection and biological analysis BIOCADRE is a cross-sectional study nested in the CADRE cohort. Two centres participating in the CADRE cohort were selected for the BIOCADRE study: the "Centre National de Transfusion Sanguine" in Dakar, Senegal and the "Centre de Recherche et de Lutte contre la Drépanocytose" in Bamako, Mali. Dakar and Bamako were chosen among the five participating countries for practical reasons. First, BIOCADRE required local medical doctors or scientists ready to be trained in France for a few weeks on the special experimental techniques which had to be carried out in the field. Second, the laboratory where the experiments took place had to be close to the clinical setting so that the blood samples could be processed very quickly. The CADRE inclusion database contained 699 patients from Bamako, including 377 with a SS phenotype and 719 patients from Dakar, including 662 with a SS phenotype.
Echocardiography was also performed using a predefined protocol. Retinopathy was evaluated based on dilated fundoscopy in Bamako only, because of the lack of ophthalmologists available in Dakar to participate in the study. The leg ulcer and priapism diagnosis were based on clinical inquiry and examination. Osteonecrosis was suspected in the case of chronic articular pain and confirmed by radiological criteria (Arlet and Ficat classification). Patients were categorized in the "no complication" group only if they had no missing data regarding the five complications of selection. Local investigators were trained by the French teams to measure blood viscosity and study microcirculation function. Blood viscosity was measured with fresh blood samples using a cone-plate viscometer (Pro DV-II+, with CPE40 spindle; Brookfield) at native haematocrit, 37°C and several shear rates (4.
R E SU LTS We recalled 240 patients selected in the inclusion CADRE database: 17 were not reachable, and 21 were deceased. Among the 202 patients who could attend the BIOCADRE visit, 62 had developed at least one additional complication (Table  S1 ). In particular, among the oldest 37 patients of the CADRE cohort with no complications who were investigated after the initial selection, 31 had developed one of the six studied complications between the inclusion and BIOCADRE visits. Therefore, we recalled 56 other adult patients without any complications mentioned in the CADRE inclusion database and 36 were eventually able to join the control group of patients. The oldest patient without any complication was 43 years old in Bamako and 53 years old in Dakar. Finally, 235 SCA adult patients were investigated, 107 from Bamako and 128 from Dakar.
). They also have to be cheap, which reduces considerably the number of good candidates. Moreover, given the absence of a clear pattern of complications combinations, it is unlikely that one marker will be able to predict the global severity of SCD. Therefore, several markers specific to one complication pathological process probably have to be combined to build a global prognostic score. Since some determinants of SCA severity may be specific to the African context, we would also like to highlight the importance of acquiring datasets with comprehensive phenotypes of patients living in Africa in addition to the existent Caribbean, European and American cohorts.
Grant/Award Number:  ANR-11-LABX-0051 ;  Institut National de la Santé et de la Recherche Médicale ;  French National Research Agency , Grant/Award Number:  ANR-11-IDEX-0005-02  This work (clinical trials. gov identifier  NCTO3114137 ) was supported by the  Institut National de la Santé et de la Recherche Médicale (Inserm) , the  Agence National pour la Recherche (ANR)  and the  Laboratory of Excellence GR-Ex , reference  ANR-11-LABX-0051. The labex GR-Ex is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. Abdoul Karim Dembélé  was supported by a doctoral fellowship from the  Fondation Pierre Fabre. C ON F L IC T OF I N authors declare no conflict of interest. datafile of the study is available upon request (to the cor-).
S17 † ) show rather dense particles with no visible sign of mesopores (pore diameters between 2 and 50 nm). Regarding the possible presence of micropores (pore diameters below 2 nm), this cannot be visualized by TEM. In this purpose, carbon dioxide sorption at 273 K was performed for each sample. The unusual shape of the as-obtained isotherms, with no adsorption at low relative pressures (Fig. S18 † ), strongly suggests the absence of micropores. One may, however, note that the total pore volumes of Bm-P900 and (Bn)m-P900 (ca. 0. 03 cm 3 g -1 at p/ p 0 = 0. 029) are three times higher than the one obtained for (Bn) 2 -P900 (ca. 0. 01 cm 3 g -1 ). a Data obtained from combustion elemental analyses. b Data extracted from combustion elemental analyses by difference, considering that boron is the only element present in addition to carbon, hydrogen, nitrogen and oxygen.
Acknowledgements SA is grateful to  Lebanese University  for nancial support. Several characterization experiments were performed with the support of the " Balard Plateforme d'Analyses et de Caractérisation" (PAC Balard ). The authors would like to thank  Amine Geneste  for the DSC experiments,  Emmanuel Fernandez  for the solid-state 1 H NMR experiments,  Erwan Oliviero  ( MEA platform, Université de Montpellier ) for HR-TEM analyses,  Bernard Fraisse  for the XRD analyses and  Lucie Koeller  for the ICP-OES analyses.
Acknowledgements. We warmly thank  R. Redmer  and  A. Bergermann  for sharing their EOS data. We also thank the referee for constructive comments which improved the quality of the manuscript. We thank the  Juno Interior Working Group  and particularly  R. Helled ,  S. Müller  and  N. Nettelmann  for useful discussions and comments. This research was carried out at the  Observatoire de la Côte d'Azur  under the sponsorship of the  Centre National d'Etudes Spatiales.
Assessment of benthic cover At the beginning of each recording, we placed a 1 x 1 m quadrat in front of the cameras. We quanti ed the substrate cover of each quadrat by taking a long shot photograph. These images were uploaded to the program SketchAndCalc Version 1. 1. 2 (iCalc Inc), in which the 1 x 1 m quadrat was calibrated so each transformed image contained roughly the same number of cells. This equated to ~1000 cells per image, each being around 5 cm². The images with the canvas imprinted upon them were subsequently exported and annotated with each form of substratum having a corresponding colour. We counted the annotated cells and calculated relative substrate cover (%).
AI driven tracking of coral reef shes In this study, we aimed to achieve AI driven sh detection, identi cation and tracking from stereo-video by performing several steps. Firstly, we calibrated the system in Matlab (TheMathWorks) using a checkboard pattern recorded with both cameras. Next, we performed stereorecti cation using OpenCV (Open Source Computer Vision Library) to locate pixels in both images and triangulate the depth of the scene. Using this method of calibration we obtained an overall mean [±SD] absolute reprojection error of 0. 9 [±1. 9] mm which corresponds to 0. 45% of the true value. For object detection, we employed the You Only Look Once (YOLO) convolutional neural network (CNN)  (Bochkovskiy et al. 2020 ), which we retrained with background images from the recorded videos to improve its performance.
We then used the bounding boxes produced by the detection algorithm as input data for the classi er and stereo matching. To classify the detected sh species, we utilized science-grade location invariant images of identi ed sh species from iNaturalist (www. inaturalist. org) to train the CNN  (Van Horn et al. 2018 , Shepley et al. 2021 ). However, the iNaturalist dataset had limited images, and therefore we employed transfer learning using weights computed from a previously recorded dataset from Mayotte, Indian Ocean, as a starting point  (Villon et al. 2018). Finally, we implemented the Deep SORT framework -an enhanced version of the Simple Real Time Tracker (SORT) algorithm -for multi-object tracking  (Wojke et al. 2017). This framework tracked each bounding box in both the left and right videos.
Assessment of Energy Expenditure from 3D sh trajectories To quantify EE during foraging in individual sh we used acceleration data obtained via the AI generated sh trajectories on the basis of stereo-video footage  (Krohn & Boisclair 1994). We computed their velocity (cm s -1 ) and acceleration (cm s -2 ) by measuring the differences in X, Y, and Z coordinates between consecutive video frames. To estimate EE based on ODBA, we utilized the following equations  (Gleiss et al. 2011) : Eq. 1 ODBA = (|acceleration X m s -2 |) + |acceleration Y m s -2 |) + |acceleration Z m s -2 |) We calculated ODBA using net acceleration, where negative values indicated deceleration or changes in direction as the sh slowed down. We used allometric scaling to account for body mass and calibrate our ODBA data to estimate EE during foraging, avoiding the need for costly respiratory measurements in the laboratory  (Chakravarty et al.
2023). For each frame, we computed EE (W) using the equation: Eq. 2 EE = (K * mass * ODBA E ) In this equation, mass is expressed in kilograms (kg), and ODBA is measured in meters per second squared (m s - 2 ). The constant K represents a correction factor, while the exponent E is derived from the species-speci c relationship between mass and metabolism. We followed the methodology of  Chakravarty et al. (2023)  and used functionally similar surrogate species, Ctenochaetus striatus and Zebrasoma scopas, to obtain standard metabolic rate (SMR)-mass relationships for A. nigrofuscus and Z. xanthurum, respectively, These data were obtained from  Schiettekatte et al. (2022)  (https://github. com/nschiett/activity_rate_ shes/blob/master/data/data_respirometry. csv). The exponent E was obtained from the slope of the log-log regression line between log SMR and log mass, while the correction constant K was derived by raising 10 to the power of the intercept of the regression line.
For C. striatus: SMR = 0. 004 x mass ^ 0. 7789 For Z. scopas: SMR = 0. 003 x mass ^ 0. 6958 To calculate EE, we extracted these metrics from a subgroup of individuals whose automatically measured lengths fell within the manually determined length frequency distribution (Appendix Fig. 4 ; Appendix Table  1 ). These individuals had automatically generated tracks that were precisely cut down to 700 frames, ensuring a standardized and consistent observation period. We consequently estimated the mean EE across a timeframe of 11. 6 s of continuous foraging behaviour in three-dimensional space. Statistical Analysis Analyses and visualizations of feeding traits at the community level and surgeon sh feeding preferences were executed using JMP Pro 16. 0. 0 (SAS Institute Inc). Spearman Rank Sum tests were utilized to identify correlations within the quadrat benthic cover composition.
Analysis of 3D sh trajectories To optimize the quality of our 3D sh trajectory data, we implemented a systematic three-step process: 1. Outlier Removal: Recognizing the sensitivity of the Kalman lter to outliers, due to its Gaussian-distributed measurement noise assumption, we initiated our process with the IQR method for outlier detection and removal. This ensures that the lter operates optimally, delivering robust performance even when conditions deviate from the norm  (Kassam & Poor 1985 , Kautz & Esko er 2015). 2. Running Median Smoothing: We used the zoo package to apply a 5-point running median lter (Zeileis et al.
2023 ). The choice of a 5-frame lter size, given our dataset's 60 Hz acquisition rate, adeptly balances noise reduction and the preservation of intrinsic data features, all while achieving our targeted minimum resolution of 10 Hz. 3. Kalman Filtering: Building upon the median-smoothed data, we employed Kalman ltering, as suggested by  Kalita & Lyakhov (2022). This step was facilitated by the KFAS package, which hinges on the Gaussian distribution assumption of measurement noise  (Helske 2017). The culmination of this data processing protocol enabled calculations of mean velocity and EE for each sh (https://github. com/Knochen sch/Functional-and-Metabolic-Traits-of-Surgeon shes/blob/dff466118eec84146cc2e32171289c5a57f05516/output/surgeon sh_3D_trajectories. html). To ascertain signi cant differences in velocity and EE across the studied species, we employed Wilcoxon rank sum tests.
However, we expect that the classi cation accuracy will improve as the iNaturalist dataset is enriched with more robust and varied images of these species, speci cally including low-resolution identi ed images. Additionally, examining functional traits and EE within the same individuals, incorporating other ecological variables such as competition and predator-prey dynamics, and obtaining direct SMR measurements for our focal species will contribute to a more nuanced understanding of herbivore foraging behaviors and their impact on coral reefs. As AI and tracking technologies continue to advance, they will become integral to understanding ecological processes and ecosystem resilience  (Besson et al. 2022 ). Our approach offers potential for studying movement and energy budgets of keystone species across ecosystems of differing health, rapidly assessing metabolic traits in entire communities  (Nathan et al.
Acknowledgements Fieldwork was conducted within the framework of  Leibniz Centre for   Tropical Marine Research (ZMT)  " European Scienti c Diver " training course 2017/2018 and facilitated by the  Eilat Leibniz IUI Center (ELIC). We would like to express our gratitude to  Christian Brandt  for his help in building the stereo-video setup. We thank everyone.
Data Availability Statement The datasets generated during this study are available through PANGAEA (https://doi. pangaea. de/10. 1594/PANGAEA. 957634). Scripts used for the statistical analysis of functional feeding traits and metabolic traits derived from AI derived 3D sh trajectories can be found at https://github. com/Knochen sch/Functional-and-Metabolic-Traits-of-Surgeon shes. git.
This population-based cancer registry has a high-level of data quality, guaranteed by data quality assessment performed by national and international agencies for cancer control. Martinique has the lowest overall incidence for all cancers, compared to French Guyana and Guadeloupe. However, Martinique has the highest number of thyroid cancer diagnosis with an average cumulate of 30 cases, 6 men and 24 women, per year in 2007-2014  [6]. This study aims to describe overall survival of thyroid cancer patients diagnosed between 2008 and 2018 in Martinique.
Data collection Data were included in the MCR database according to the international standards laid down by the International Agency for Research on Cancer (IARC), taking to account the French FRANCIM network, and the recommendations of the European Network of Cancer Registries (ENCR). The MCR Registry procedures have the approval of the French National authority for the protection of privacy and personal data. The registry is cooperating with different local organizations to ensure an exhaustive data collection process. Data Quality check was performed in accordance with international recommendations for cancer registries. We extracted from the MCR database the sociodemographic variables: sex, birth date, zone of residence at diagnosis. Clinical variables analyzed in this study were vital status, tumor size, histology, stage at diagnosis (TNM), date of diagnosis, date of last news or death.
Clinical stage at diagnosis was classified following American Joint Committee on Cancer (AJCC)/ TNM risk score from I to IV following 7 th edition scoring. Addition staging was performed dividing patients into locally advanced (T1-T4 N0M0), versus node (N + /M-), and metastatic group (M +). Additional variables were extracted from Multidisciplinary Teams (MDT) reports and medical records archived at the University Hospital of Martinique. The following variables were: modality of cancer discovery, tumor site, surgery (lobectomy or total thyroidectomy), lymph node dissection, use of radioiodine, immunotherapy or chemotherapy. The availability of numerous archived digital MDT reports allowed us to largely implement our database with therapeutic characteristics. We collected data regarding deaths from the French epidemiological center on medical causes of death.
The log-rank test was performed to assess the statistical differences of the observed survival curves by each socio-demographic and clinical variable. Prognostic factors for OS were assessed using a multivariable Cox model for censored. Variables with a p-value < 0. 20 in the Univariate analysis were included in the model for multivariable analysis. A p-value < 0. 05 was considered statistically significant. All our analyses were performed using SAS version 9. 4. (SAS Institute Inc. , Cary, NC, USA).
Discussion This study is the first survival analysis of thyroid cancer patients in Martinique and in the Caribbean. It was supported by high-quality data from a population-based cancer registry with analysis of stage at diagnosis and socio-demographic characteristics. We showed that our patients had a very good prognosis, with a 91% 5-years survival rate in this territory over 2008-2018. The added  value of this study was to provide patterns of care in thyroid cancer patients. Very few studies reported data regarding thyroid cancer survival in the LAC area; most studies provided insight on incidence or mortality data  [7]. Thyroid cancer predominantly affects women; in Martinique 81% of thyroid cancer patients were women. There was a statistical difference between men and women survival with a better survival among women (94% OS at 5-years versus 78% in men).
In Martinique, the University Hospital is located in the center of the island but also has a center in the North-Atlantic, where specialized clinical explorations of the thyroid gland are conducted. The North Atlantic area undergoes specific exposure to toxic environmental factors, such as sargassum and chlordecone. Variations in geographical distribution of thyroid cancer can therefore be hypothesized in terms of care services supply and environmental exposure. Therapy data such as levothyroxin treatment could have been useful for explanation of thyroid cancer survival. Unfortunately, this data wasn't available in the cancer registry database and would have required specific data collection using the integrated national health insurance database. This information could have been useful for a better assessment of observance and tolerance of that treatment.
Acknowledgements The authors thank the  FRANCIM Network, Santé Publique France ,  INCa ,  International Agency for Research on Cancer , and all those who contributed to the recording of cancer data in the registries: the  Oncology Haematology Urology Pathology Division of the University Hospital of Martinique , the laboratories and departments of anatomy, cytology, and pathology; the departments of medical informatics of the public and private hospitals; the local offices of the national social security service; and general practitioners and specialists.
Proximity labeling identifies novel PpEC15 interacting proteins To further investigate interactions between PpEC15 and soybean proteins, we performed a proximity labeling (PL) assay by fusing PpEC15wt, PpEC15dm, and an NLS-GFP control to the miniTurbo biotin ligase (Figure  7A )  (Branon et al. 2018; Zhang et al. 2020; Zhang et al. 2019). The fusion proteins were transiently expressed in N. benthamiana. Capture of the biotinylated proteins (Supplemental Figure  S5 ) enabled the identification of the enriched interacting proteins by LC-MS/MS (Supplemental Table  S1 ). Importantly, PpEC15 peptides were detected in the dataset, confirming its expression in planta (Supplemental Table  S1 ). We identified 334 proteins exclusively enriched in the PpEC15wt dataset compared to PpEC15dm, and 44 proteins enriched both in the wild-type and mutated versions compared to the GFP control (Figure  7B ), suggesting that mutation in the catalytic motifs impairs PpEC15 ability to interact with most of its partners, but it does not completely abolish it.
, 2007). It will be interesting to confirm the direct interaction between these novel putative candidates and PpEC15 and to determine if the effector is modulating these immune-related proteins through proteolytic cleavage. Notably, neither GmNAC83 or GmPCRF were identified in our PL assay for several possible reasons, including an inappropriate time-point to capture the interaction, low protein abundance, lowefficiency labeling, lack of surface-exposed lysines, or the lack of a specific stimulus that would trigger the interaction considering that a heterologous plant system was used. Interestingly, approximately 20% (70) of the proteins enriched in our miniTurbo dataset using PpEC15wt contained one of the potential combinations of the preferential cleavage sites between a basic (P1) and a hydrophobic (P1') amino acid  2 , D-E; Supplemental Table  S2 ).
Sequence alignment and protein modeling PpEC15 full-length sequence was used as a query in NCBI BLAST (https://www. ncbi. nlm. nih. gov/). Sequences from PpEC15 homologs were then used in MEGA7 software  (Kumar et al. , 2016)  for multiple sequence alignment. The crystal structure of PpEC15 and DAHP were modeled using the PyMOL Molecular Graphics System.
Callose staining and microscopic analysis Arabidopsis Col-0 leaves were harvested 12 h after bacterial infiltration, cleared, and stained with aniline blue for callose as previously described  (Adam and Somerville, 1996). Leaves were evaluated with a Zeiss Axioplan II microscope with an A4 fluorescence cube. Quantification of callose depositions was determined using ImageJ software (NIH). Six adjacent fields along the leaf length (avoiding the midvein, leaf edge, or syringe-damaged area) were analyzed and averaged.
RNA isolation and reverse transcription real-time quantitative PCR Approximately 100 mg of starting material was used for RNA isolation using the RNeasy Plant Mini Kit (Qiagen) for N. benthamiana or TRIzol reagent (Invitrogen) for soybean according to the manufacturer's instructions. Two µg of RNA and SuperScript III First Strand kit (Invitrogen) for N. benthamiana or Maxima First Strand cDNA Synthesis kit with dsDNase (ThermoFisher) for soybean were used for cDNA synthesis. A quantitative real-time PCR assay was performed on a 7500 real-time PCR system (Applied Biosystems, CA, USA) using iTaq Universal SYBR Green Supermix (BioRad). GmNREG3 (Glyma. 08G211200) or NbActin (GenBank: AY594294. 1) were chosen as internal controls to normalize the data. The transcription level was calculated using the 2 -ΔΔCT method  (Livak and Schmittgen, 2001).