dataesr/scientific-paragraphs-categorization huggingface dataset:
Firstly, assays were carried out with isolated phenotypically well-characterized Enterobacterales resistant to β-lactams to determine growth patterns in the presence of ertapenem of different resistance mechanisms (ESBL, AmpC, Carbapenemases). Then, this culture-based approach is applied to environmental and clinical samples. Results on clinical samples were compared to data of the medical bacteriology laboratory of the Centre Hospitalier Territorial (CHT), the main and reference hospital of the archipelago. Presumptive CP-CRE isolates were finally identified using MALDI-ToF MS, and the presence of a carbapenemase was assessed with a synergy test (EUCAST, 2022) and confirmed with a commercial immunochromatography test (ICT). Finally, based on first results and to improve turnaround time for diagnosis, detection of carbapenemases (using ICT) after only nine hours of incubation in ertapenem-supplemented selective media was performed.
Although regarded as laborious and time consuming, culture-based methods allow the detection of some extremely rare resistance types that may go undetected by other methods  [29]. In this study, hospital effluents are used to evaluate our method on environmental samples. Wastewater analysis for antimicrobial resistance is an innovative approach in New Caledonia. Indeed, works focused on AMR were clinical studies, and these results are the first ones to provide antimicrobial resistance data using water analysis. Our method allows the quantitative detection of β-lactams resistance in Enterobacterales in a high bacterial load sample. As reported in other studies  [30, 31] , a majority of presumptive non-CRE were detected in our hospital effluents. In our study, 18 CP-CRE were isolated from hospital effluents.
Species Identification A rapid identification of the microorganisms based upon protein profiles was performed on the MALDI Biotyper system with MALDI-ToF Mass Spectrometry (Infectio MALDI, microflexLT-MS, Bruker, Billerica, MA, USA). Selected bacterial colonies were directly placed on the MALDI-ToF target (MSP 96 target polish steel BC, microscout Target), followed by addition of CHCA matrix solution (1 µL α-cyano-4-hydroxycinnaminic acid 50% acetonitrile-2. 5% trifluoro acetic acid). It was then dried at room temperature to allow co-crystallisation. The plates were then subjected to MALDI-ToF MS measurement. The mass spectra obtained from each sample were imported into the Bio-Typer software (Bruker daltonics) and analyzed by standard pattern matching. Results of the pattern-matching were expressed with scores ranging from 0 to 3.
Data and statistical analyses The rodent communities of each site were characterised by their species richness (S), the relative abundance of each species (Pi) and their species diversity. Species diversity at each site was calculated using the Shannon-Weaver index H'. Then, the two species that dominated the different populations of the small rodent communities of the Gabonese savannas were selected. A molecular analysis of these species was carried out, using supplemental data from other localities in West Central Africa.
Time of divergence Using the clades identified in the phylogenetic analysis, a Bayesian approach was applied to calculate the time to the most recent common ancestor (TMRCA) of all L. striatus and M. minutoides populations from the study sites. TMRCA can be used as a proxy for ancestral population age. Clades were dated using the BEAST v1. 4. 6 program  (Mboumba et al. , 2011). For this last analysis, two calibration points derived from paleontological data were used to estimate the time of divergence based on cytochrome b sequences, namely the Mus/Rattus lineage split estimated to have occurred 11-12. 3 million years ago (Mya)  (Benton and Donoghue, 2007) , and the divergence between the 'big' Apodemus mystacinus and all the 'small' Sylvaemus (A. flavicollis, A. sylvaticus, A. alpicola, A.
DISCUSSION Data about the structure of small rodent savanna communities in Gabon illustrate the low species richness of Gabonese savannas. The highest diversity was recorded in peripheral savanna areas (south-western and south-eastern peripheral savannas). M. munitoides and L. striatus were the two most represented species, also found in all the savannas of West Central Africa. This result revealed an old connection between the Gabonese savannas and the other savannas of West Central Africa. The data also revealed a species with austral affinities known Zambesian savannas: Pelomys campanae was present only in the savannas of southern (Ngounié/N'yanga) and southeastern (Batéké Plateaux) Gabon. This result may be due to i) an ancient contact between the southern Gabon savannas and the more austral ones, or ii) recent colonisation via an ancient savanna corridor located between the southern savannas of Gabon and the austral ones.
The developed middleware was also in charge of utilizing the real-time decision from the sensor systems (weeding quality analytics system for the weeder and crop perception unit (PU) for the sprayer) for the application. Besides that, dispatching the task data to the farming controller (FC), which assists in making guidance for navigating the robot or tractor, was established through the middleware. Eventually, the evaluation of the in-field performance of the developed weeder and the sprayer, in terms of the weeding quality and as-applied accuracy information, was carried out. The novelty of the present work remains in (1) the approach to developing a realistic middleware that incorporates industry standards (ISO 11783 and J1939) into  (2)  the communication between the horticultural implements (weeders and sprayers) with real-time decisionmaking systems and advanced mobile robots as well as retrofitted tractors with cognitive capabilities.
Therefore, the architecture defined the need for the middleware to establish three communication networks: (1) the J1939 network, which introduced the robot CAN bus, in terms of the required messages from the AGC-Box to the implement bus; (2) the ISO 11783 network for the implement bus, which specified a virtual ECU of the weeder as well as a physical connection with the ISOBUS virtual terminal (VT); (3) the virtual CAN network, which established communication with external components such as the FC and the Analytics software.
Decision-making system for weeding The decision-making system used in the weeding application was the so-called "Analytics", as mentioned in Section 2. 1. 1. The Analytics software was synchronized with the virtual network of the middleware and operated on the same rugged PC (Karbon 700-X2, OnLogic, USA) as the middleware. The final decisions such as the quantified weeding quality and capacity were transferred to the ISOBUS VT through the simulated weeder ECU on the ISO 11783 network of the middleware. As it can be seen from Table  1 , the algorithm performs data acquisition and processing from the sensors and implements with various functionalities. It starts by importing the necessary libraries and setting up the file path based on the operating system. Then, it loads a CAN database file and establishes communication with a serial port and CAN bus.
It also initializes a connection to a Robot Operating System (ROS) framework. Within the main loop, the code reads sensor data, processes it, and sends the processed data to relevant ROS topics. The software calculates the frequency of sensor value changes, updates the quality metric based on the changes, and publishes the frequency and quality values. Further, it also checks a buffer condition and takes appropriate action. Overall, the code provides real-time monitoring of sensor data, communication via CAN bus, and integration with the ROS framework for data publication and control. The algorithm also includes the collection of raw data from the sensors and encoders that capture the full information on the spins and rotations of each weed-removing metallic disk. This information specified the irregularity of the disk spins, in terms of the heartbeats of the sensors.
The "heartbeat" is a value or impulse that was calculated based on the sensor type and the probability of the sensor having irregularities (a factor extracted from the sensor website) and priority (in case many sensors are connected). If the examination of the sensors reveals inconsistencies, the algorithm organizes an analysis of how much the "heartbeat" hasn't been heard by setting a secondary timer. Then, the algorithm also computationally reflects the effect of the portion of the unheard heartbeats on the "weeding quality" parameter. The Analytics software also specifies the frequencies of the spinning disk to define the blockage in the frequency domain. If the "heartbeat" of the specific sensor is totally inaudible for a specific time, the algorithm immediately logs in GPS-based distance and the time of the sensor being inactive to determine the area of missed weeding.
On the implement side, the sprayer is designed with a real implement bus and an ECU to integrate the ISO 11783 standard. Besides that, the PU (see Section 2. 1. 4) is directly connected to the implement bus. Although the sprayer is fully ISOBUS compliant, the developed middleware was essential for communication with the tractor. This is because the retrofitted tractor does not designate the tractor CAN bus (J1939 protocol), in terms of the ISOBUS stack. The virtual network of the middleware was also used to exchange data with the FC.
Annotation of the depth data was performed by field experts that categorized each image into the corresponding class, using the Point Quadrant measurement technique and domain knowledge where necessary. As a result, the PU could modulate the default/maximum tree row spraying volume based on color and depth information. If no canopy was detected, the spraying rate was set to 0. If a sparse canopy was identified, then the spraying rate was set to 50 % of the maximum rate. Finally, if a dense canopy was detected, a maximum spraying rate of 100 % was recommended. Since the device  was mounted in the front of the tractor/robot, an offset distance and the operation speed were taken into account to compensate for the delay in generating each iterative spraying recommendation and sending it to the implement bus at the correct time.
Farming controller The FC platform was designed to help farmers plan and manage their farming activities. More specifically, the FC offered farmers the ability to configure, execute, and monitor agricultural tasks by selecting the field of operation. This would be possible for the FC by establishing a seamless communication of all resources and sensors with a digital visualization of the field under a common framework. The preliminary approach to data interpretation within the FC is illustrated in Fig. 4. The Smart Implement retrieved data from the tractor and certain sensors that tracked farming activities, such as weed quality or spraying rate. A script then serialized this information into a JSON file and sent it to the FC via a web socket opened by the ROS bridge server. The FC then deserialized the JSON file back into the pre-defined variables and processed them according to the required activity.
Middleware and data collection It can be noticed in Sections 2. 1. 1 and 2. 1. 3 that the middleware was supposed to be developed for two different cases of vehicle-implement combination: (1) the CEOL robot and weeder; and (2) the retrofitted tractor and sprayer. The CEOL robot and weeder: the main assignment of the middleware lies on the ISO 11783 network  [26] , as shown in Fig. 5. The ECU of the implement  [11]  is virtually configured on the ISO 11783 network by setting the application layer of implement messages  [12]. The application layer of the implement messages is then identified by an interaction node layer of the CANoe v15 (Vector Informatik GmbH, Stuttgart, Germany). Activating the interaction layer in the CANoe allows the use of ISOBUS-dedicated Communication Access Programming Language (CAPL) functions.
In both cases, the FC was in charge of collecting, storing, and representing the data. However, the exchange of information between the implements and the FC was limited to the prescribed and applied data, including geolocation and speed information, due to their priority in guiding the vehicle and the interest of the farmers. Therefore, in addition to the FC, the data logging and storage in the middleware were configured to capture the full CAN/ISOBUS data of the vehicleimplement setup. For the weeding application, the data acquisition was performed at the field of a vineyard (47 • 23′04. 1″N 0 • 29′02. 5″W) in TERRENA, Ancenis-Saint-Géréon, France. For the spraying application, it was carried out in apple orchards of SERRATER (42 • 09′41. 8″N 3 • 05′37. 6″E), Girona, Spain.
It also acted as a real interface between the robot and the attached implement since it complied with the CANopen protocol of  the Controller Area Network (CAN). The output of the AGC-Box could also be monitored and regulated from a distance using smartphones/ tablets. There was another frame developed for the weeder to carry the Fusebox (Fig. 6b ). The Fuse-box consisted of the rugged PC running the middleware and the Analytics software, and all the necessary cables for the robot-implement communication as well as power supply. The Fusebox was connected to the AGC-Box via ISOBUS Break-Away Connector/ Cable (IBBC, Erich Jaeger GmbH, Friedberg, Germany). On the implement side, the IBBC was connected to one of the two channels of the VN1610 CAN interface (2 x CAN high-speed transceiver, Vector Informatik GmbH, Stuttgart, Germany) using a Y-Cable (Vector Informatik GmbH, Stuttgart, Germany).
The use of the robot battery to power the rugged PC was controlled by a microcontroller built into the Fuse-box. This has been done to allow the robot to be switched on and off at will and to avoid the rugged PC's power supply being cut off abruptly. The analytics software checked the depth and color information based on data from two stereo cameras (OAK-D-PoE) connected to the rugged PC via PoE, while other sensors such as a ZF Terental sensor (GS100701) and an encoder (Joy-it COM-KY04ORE) detected the presence of the magnetic field and rotation respectively to check for blockages on the discs.
Verification of the implement performances 2. 3. 1. The correctness of weed detection The data collected from the external sensors on the weeding machine were reflected in the frequency domain to define the critical frequency of the spinning disk for the blockage and the weeding quality (see Section 2. 1. 2). Therefore, the pattern agreement between the frequencies of the sensors' impulse and the computed weeding quality verified the correctness of the weed detection. The frequency falling below a certain threshold (in this case, 5 Hz) indicated the blockage on the disk. The algorithm re-evaluated the raw data, and if there were no changes detected, it initiated a reduction in the quality parameter. The rate at which the quality parameter decreases was determined by a customizable or hyper-parameter.
Data are presented as mean values ± SD. Groups were compared using 2 × 2 Fisher's Exact test. h Dot plot presenting the clustering of eART (blue) and lART (red) based on blood SIgA level and frequency of immunoglobulin sequences shared between the blood and the intestinal mucosa. Source data are provided as a Source Data file. speculate that SIgA immune complexes may be the predominant form of SIgA translocation in the blood. In that regard, while our assays were not sensitive enough to detect serum SIgA binding to commensals, we found a correlation between SIgA levels and total serum IgA reactivities against half of the bacteria strains tested. Chronic immune activation related to circulating microbial products is typically associated with the progression of HIV-1 infection and predicts disease outcome  30.
Accordingly, we found a higher proportion of circulating mucosa-derived gp140-reactive CD27 -β7 + CS B cells in lART, possibly arising from GC-independent reaction  81, 82. Of note, however, comparative blood B-cell analyses between lART and eART by flow cytometry were based on a limited number of samples, which warrants additional studies on larger sized-groups to substantiate these findings. Our data also showed that a substantial fraction of commensalsbinding antibodies circulating in the blood are polyreactive. Systemic antibody polyreactivity in HIV-1-infected individuals described 35-years ago is now well documented  83, 84  , and was proposed to be partly due to infection-induced polyclonal B-cell activation  85  , including via stimulating bacterial products such as LPS. Hence, we speculate that circulating antibodies originating from the GALT in response to translocating bacteria are an important source for the HIV-1-featured sero-polyreactivity.
Sera were collected and analyzed for a total of 38 eART and 40 lART. Paired PBMC, sera and colon biopsies (10 rectal biopsy specimens of ∼2 mm 3 were collected at 10-15 cm from the anal margin) we obtained for 3 eART (E1-E3) and 3 lART (L1-L3). Intestinal biopsy (n = 8, E4-E7 and L4-L7) and PBMC (n = 3, E5, E8 and L8) samples were also obtained from additional donors (Supplementary Table  1 ). The clinical research protocol received approval from the Comité Consultatif pour le Traitement de l'Information en matière de Recherche dans le domaine de la Santé (CCTIRS) on December 12th 2013, the Commission Nationale de l'Informatique et des Libertés (CNIL) on August 8th 2014 and the Comité de Protection des Personnes de Tours (CPP Région Centre-Ouest 1) on December 17th 2014. All donors gave written consent to participate in this study, and data were collected under pseudo-anonymized conditions using subject coding.
001 Miltenyi Biotec, 1:100), Integrin β7 BUV395 (FIB504, #744014, BD Biosciences, 1:100) and streptavidin R-PE conjugate (#SA10041, Invitrogen, Thermo Fisher Scientific, 1:1000). Single mucosal CD19 + IgA + and CD19 + IgG + classswitch memory B cells (CD27 + CD38 -) and antibody-secreting cells (CD27 + CD38 + ) from e-ART (n = 3) and l-ART (n = 3) donors were singlecell sorted into 96-well PCR plates using a FACS Aria II sorter (Becton Dickinson) as previously described  88. Single-cell cDNA synthesis using SuperScript IV reverse transcriptase (Thermo Fisher Scientific) followed by nested-PCR amplifications of IgH, Igκ and Igλ genes, and sequences analyses for Ig gene features were performed as previously described  88. Purified digested PCR products were cloned into human Igγ 1 -, Igκ-or Igλ-expressing vectors (GenBank# LT615368.
114. 001, Miltenyi Biotec, 1:100), Integrin β7 BUV395 (FIB504, #744014, BD Biosciences, 1:100) and streptavidin R-PE conjugate (#SA10041, Invitrogen, Thermo Fisher Scientific, 1:1000). Cells were then washed and resuspended in FACS buffer. Following a lymphocyte and single-cell gating, live cells were gated on CD19 + B cells. FACS analyses were performed using a BD LSR Fortessa instrument (Becton Dickinson) and FlowJo software (v10. 3, FlowJo LLC). Polyreactive cells were defined as B cells binding to fluorescently labeled Insulin among those reactive to both RNA polymerase α and LPS (polyR; Insulin + RNA pol α + LPS + ). To ensure binding specificity to gp140, gp140 + cells were gated on non-polyR B cells (Insulin -RNA pol α -LPS -).
Bioinformatics pipeline and analyses Forward and reverse reads with 10 overlapping nucleotides minimum were merged using PEAR software  94  with the following parameters: minimum length n = 300, quality threshold q = 20, and minimum length after trimming low-quality parts t = 300. Assignment of immunoglobulin germline genes was performed using IgBlast  95  (https:// www. ncbi. nlm. nih. gov/igblast/). Isotype definition was made using a BLAST procedure. A database was built using an inhouse program and sequences presenting low-quality sequence features (Stop codon, V-gene E-value > 10 -3 , undefined CDR H 3 ending motif WGXG, and subject alignment start >9) were removed. We then used CD-HIT  96  (http://weizhongli-lab. org/cd-hit/) to cluster similar V-REGION sequences sharing a 99% identity to remove redundancy and only the representative sequence was used.
Protein microarray binding analyses All experiments were performed at 4 °C using ProtoArray Human Protein Microarrays (Thermo Fisher Scientific). Microarrays were blocked for 1 h in blocking solution (Thermo Fisher), washed and incubated for 1 h 30 min with IgG antibodies at 2. 5 µg/ml as previously described  25. After washings, arrays were incubated for 1 h 30 min with AF647-conjugated goat anti-human IgG antibodies (at 1 µg/ml in PBS; # A-21445, Thermo Fisher Scientific), and revealed using GenePix 4000B microarray scanner (Molecular Devices) and GenePix Pro 6. 0 software (Molecular Devices) as previously described  25. Fluorescence intensities were quantified using Spotxel® software (SICASYS Software GmbH), and mean fluorescence intensity (MFI) signals for each antibody (from duplicate protein spots) was plotted against the reference antibody mGO53 (non-polyreactive isotype control) using GraphPad Prism software (v8.
Statistics The numbers of V H , Vκ and Vλ mutations were compared across groups of antibodies using unpaired two-tailed Student's t test with Welch's correction. Bivariate correlations were assayed using twotailed Pearson correlation test. Antibody reactivities (sera and monoclonals) and levels of serum markers were compared between groups using two-tailed Student's t test with Welch's correction. Frequencies of blood B-cell sub-populations were compared between groups using two-tailed Mann-Whitney test. Cumulative distributions of antimicrobial reactivities between gut-derived monoclonal and serum IgG antibodies were compared using the Kolmogorov-Smirnov unpaired test. Statistical and analyses were performed using GraphPad Prism software (v8. 2, GraphPad Prism Inc. ). Volcano plots comparing gene features (n = 206 parameters) of intestinal B cells was also performed using GraphPad Prism software (v8.
2, GraphPad Prism Inc. ). The y axis indicates the statistics expressed as -log 10 (p values) and the x axis represents the differences between the group means for each parameter. Principal component analysis (PCA) was performed using the Qlucore Omics Explorer bioinformatics software. The Barnes-Hut implementation of t-distributed stochastic neighbor embedding (t-SNE) was computed using FlowJo software (v10. 3, FlowJo LLC, Ashland, OR) with 2000 iterations and a perplexity parameter of 200. Colors represent density of surface expression markers or cell-populations varying from low (blue) to high (red). The correlation plot between anti-p24 and -g140 antibody reactivities (Fig. 2a ) was generated using FlowJo software (v10. 3; FlowJo LLC) after transforming for all final sequences in a given library, (x, y) coordinates-containing text file into a FCS format file using DISCit software 101.
The correlation matrix of clinico-virological and serological values was generated with R software (v4. 1. 3) using rcorr() function from Hmisc package (v4. 7, https://CRAN. R-project. org/package=Hmisc). The correlogram was generated using the corrplot() function from corrplot package (v0. 92, https://CRAN. R-project. org/package=corrplot). Cells are colored according to the correlation values between two parameters (blue colors: positive correlations, white: no correlation and pink: negative correlations). Principal component analysis (PCA) was performed with R software (v4. 1. 3) using the PCA() function from FactoMineR package (v2. 4, https://CRAN. R-project. org/ package=FactoMineR). PCA plot [fviz_pca_biplot()], was generated using the factoextra package (v1. 0. 7, https://CRAN. R-project.