dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010; Morgans et al. , 2018 Morgans et al. , , 2021)). A university animal hospital participated in an intervention as an actor that could change its prescription practices  (Hubbuch et al. , 2021). Organizations of producers (including the different actors in production, processing and marketing) (n i =15) and organizations of veterinarians (n i =9) were major actors and supported the research in the recruitment of participants in intervention studies. They could also initiate and fund interventions in collaboration with governmental authorities or academics. They collected useful data on livestock, veterinary infrastructure and practices and generated useful outputs for the implementation of strategies (diagnosis, courses, technical advice, guidelines). They could also be involved in facilitation  (Morgans et al.
Limitations Our scoping review has some limitations. To make our review more feasible, we only included references in open access and published between 2000 and 2022. It would be interesting to complete this scoping review by using the same method but without the restriction to open access references. Furthermore, only one author performed the whole study selection and data charting process. There are potential biases and subjectivity related to qualitative data analysis and interpretation  (Bumbuc, 2016). Authors minimized this effect by defining, testing and validating the variables together. Our review allowed to identify what works but not to what extent. Indeed, we analyzed success of interventions through the number of interventions reporting positive, null or negative measured outcomes and impacts but a more refined meta-analysis is needed to quantify the success of interventions, taking into account the scale and not only the number and type of impacts and outcomes generated.
However, it is unclear whether IDO1 expression by tumor cells specifically impacts the response to PD1/PD-L1 blockade. To decipher the effect of IDO1 expression on the outcome of NSCLC patients treated with immunotherapy, we first analyzed the largest NSCLC gene expression dataset worldwide, which includes whole-transcriptome profiles of 891 pretreatment tumors from the POPLAR study (phase 2  12  ) and the OAK study (phase 3  2  ), two randomized studies assessing the efficacy and safety of atezolizumab versus the cytotoxic agent docetaxel in NSCLC. We then complemented this transcriptomic analysis with multiplex immunofluorescence (mIF) and digital pathology analyses to validate our findings at the protein level and to investigate the correlation between the expression of IDO1 by tumor cells and the spatial distribution of CD8+ T cells.
Patient population Gene expression data were collected from patients included in the open-label, randomized phase 2 POPLAR (NCT01903993) and phase 3 OAK trials (NCT02008227), which evaluated atezolizumab versus docetaxel in patients with NSCLC who progressed following platinum-based chemotherapy  2, 12  and enrolled 287 and 1225 patients, respectively. Patients in both trials received either 1200 mg atezolizumab intravenously (IV) every 3 weeks (q3w) until disease progression or loss of clinical benefit or 75 mg/m 2 docetaxel IV q3w until progressive disease (PD). The POPLAR and OAK trials showed significant improvement in OS with atezolizumab vs. docetaxel, regardless of PD-L1 expression  2, 12. No crossover was allowed, and OS was the primary endpoint. mIF analysis was performed on tumor samples from patients included in an institutional molecular profiling program (BIP, sponsor: Institut Bergonié, Bordeaux, France, NCT02534649).
RNAseq data analysis FASTQ files generated during the POPLAR (GO28753) and OAK (GO28915) clinical trials were downloaded from the European Genome-Phenome Archive (EGAC00001002120). Raw data were first processed through fastp software (v0. 32. 2)   to remove reads containing adapters, poly-N sequences and reads with low quality. Paired-end clean reads were mapped to the human reference genome (GRCh38 -Gencode release 39) and counted using STAR software (v2. 7. 10a). Differentially expressed genes were tested using the DESeq2 R package (v1. 36. 0), and gene signature enrichment analyses were performed using the fsgea R package (v1. 22. 0) with GO-BP terms. Immune cell estimations were performed and TLS signature scores were calculated by the ssGSEA framework using the consensusTME R package (v0.
0. 1. 9) with the Bindea et al. 13  gene set and the Hennequin et al. 14  gene signature, respectively. Patients were further classified as TLS-high or TLS-low based on the optimal cutoff point with PFS as the outcome (survminer R package V. 0. 4. 9). To impute the PDL1 status for all the patients treated with atezolizumab, an optimal cutoff point was defined to classify the patients as PDL1-high (≥1%) or PDL1-low (<1%) using the cutpointr R package (v1. 1. 2), and the maximize metric method was used for the patients with PDL1 22C3 measurements. ROC curves and volcano plots were drawn using the ROCit R package (v2. 1. 1) and EnhancedVolcano R package (v1. 14. 0), respectively.
Spatial transcriptomic data analysis Spatial transcriptomic data were acquired as previously described  15. In brief, tumor slides from 6 patients were analyzed on the Nanostring GeoMX Digital Spatial Profiler platform using the Whole Transcriptome Atlas panel (Nanostring). Regions of interest were drawn on the TLS structures, which were further segmented into CD20+ and CD20-areas of interest (AOIs). Raw counts were normalized using full quantile normalization from the limma R package (v3. 52. 4), and the estimation of Treg and pDC abundances was performed using the SpatialDecon R package (v1. 6. 0) and safeTME data as a cell-profile matrix.
2. 60; Akoya Biosciences). Tumor areas of each tissue slide were delineated by a pathologist using PhenoChart (Akoya Biosciences), and the annotated sections were analyzed using inForm software (v. 2. 6. 0). Tissues were first segmented into "tumor" vs. "stroma" areas based on PanCK staining, and cells were segmented based on DAPI and fluorescent membrane signals. Mean fluorescent marker intensities for each cell were extracted, and signal intensities were normalized using the GaussNorm function from the flowStats R package (v4. 8. 2).
After 6 days, supernatants were collected, and soluble IgG was quantified using the Human IgG HTRF kit (Cisbio) according to the manufacturer's instructions. Wilcoxon and chi-squared tests were used where indicated. For survival analyses, the log-rank test was used to compare Kaplan-Meier survival curves (survival R package v3. 3. 1). The Cox proportional hazards regression model was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Patients were classified as IDO1-high or IDO1-low based on an optimized threshold obtained using the maximally selected rank statistics from the maxstat R package and using PFS as the optimal outcome (survminer R package v0. 4. 9). TLS+ patients were classified as TLS IDO1-high or TLS IDO1-low if they displayed at least one TLS with 10% IDO1+ cells.
Data availability Raw and processed transcriptomic data and limited clinical data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGA: EGAS00001005013. Additional clinical data is available via request from https://vivli. org/ourmember/roche/. The immunofluorescence datasets are not publicly available due to information that could compromise research participant consent. According to French/European regulations, any re-use of the data must be approved by the ethics committee. Each request for access to the immunofluorescence dataset (including the images) will be granted after a request sent to the corresponding author (A. I. ) and approval by the ethics committee. illustrated in Fig. 2A  and B. Gene Ontology-Biological Process (GO-BP) enrichment analysis revealed that several immune pathways, such as the B-cell receptor and T-cell receptor signaling pathways, as well as immune response-regulating pathways, were enriched in the group with high expression of IDO1 (Fig.
Downloaded We complemented this transcriptional approach by performing mIF analysis with a 7-color panel (CD8/CD163/IDO1/PD1/PDL1/PanCK) on an independent series of 53 NSCLC tumors treated with ICIs and included in an institution molecular profiling program (Bergonie Institute Profiling, BIP, NCT02534649) (Supplementary Fig. 2 ). The characteristics of these patients are presented in Supplementary Table  2. As previously described  16  , CD163+ tumor macrophages were found to be associated with a poor prognosis in NSCLC, whereas tumor PDL1 expression and stromal infiltration by CD8+ cells were found to be correlated with a better outcome (Supplementary Fig. 3 ). IDO1 expression by tumor cells was also associated with a trend toward higher long-term clinical benefit (36. 4% vs. 15%, p=0. 09) and with significantly improved PFS (median: 5.
Data collection process Average rural prices (ARP) of Agave Mezcalero are given in Mexican pesos from a time series that was obtained from SIAP (AgriFood and Fisheries Information Service) (SIACON-SADER, 2020) for 1999 to 2018. These prices are the dependent variable in the model. The following variables that influence the average rural price of Agave Mezcalero were identified as yield in tons per hectare of Agave Mezcalero (YAM), yield in tons per hectare of Agave Tequilero (YAT), average rural price in pesos of Agave Tequilero (ARPAT), new planted area of Agave Tequilero Ciência Rural, v. 53, n. 2, 2023. Cruz-Ramírez et al. in hectares NPAAT t-6 , new planted area in hectares of Agave Mezcalero NPAAM t-6 , volume of exports of Agave Tequilero in thousands of pesos (EXPT), Agave consumption for the tequila industry (ACI), harvested area of Agave Mezcalero in hectares (HAAM), harvested area of Agave Tequilero in hectares (HAAT), total planted area of Agave Tequilero in hectares (TPAAT) and total planted area of Agave Mezcalero in hectares (TPAAM).
To define the lagging variables NPAAT t-6 and NAPAM t-6 in a given year, an adjustment was made to the information presented by the SIAP in accordance with its regulations and estimation procedure (SIAP, 2019). Thus, the total planted area in the previous year was subtracted from the total planted area in the current year. To analyze the impact of this new surface, an average maturation of the planted area was considered for a 6-year harvest (6-period lag). In this way, for example, the impact on the price of 2018 is analyzed according to the new planted area established in 2012. ACI and EXPT were obtained from datasets published by the Tequila Regulatory Council (CRT, 2020) and the remaining independent variables were obtained from SIAP (SIACON-SADER, 2020). both datasets considered information from 1999 to 2018.
When the number of variables that impact the price is very high, the progressive regression method can be carried out, as has been the case in estimating the price of gold  (ISMAIL et al. , 2009)  and more recently of residential property prices (CUI, 2020). In this paper, the number of variables that impact the price was reduced, since 2 independent variables presented correlation issues. In order to correct the multicollinearity problem, two MLRMs were carried out in which these two variables were mutually excluded with the aim of evaluating the performance of each model and selecting the most suitable one. The 3 models were processed in the R statistical package (R DEVELOPMENT CORE TEAM, 2020) version 3. 6. 3. Results reported in models 2 and 3 were processed in a line chart (Figure  2 ) using Microsoft Excel 365.
ACKNOWLEDGEMENTS The authors thank the  Instituto Politécnico Nacional  for the financing granted to the project  SIP-20200681 , as well as the  Consejo Nacional de Ciencia y Tecnología (CONACYT)  for the scholarship granted in cooperation with  Fundação Ezequiel Dias (FUNED)  to  Alberto Gabino Martínez Hernández  to study his master's degree at the  Université Sorbonne Paris Nord , and the scholarship given to  Angel Saul Cruz Ramirez  to pursue his doctorate at the  Instituto Politécnico Nacional.
In Sect. 2, we introduce the high-resolution modeling strategy, the tidal analyses and diagnostics used in this study, and the different datasets or products used to assess our simulation. The model assessment is presented in Sect. 3, where we estimate the model ability to realistically simulate the mean circulation, the mesoscale activity, and the barotropic tides. In Sect. 4, we quantify the energetics of the dominant internal tide in the region around New Caledonia and in the hot spots of internaltide generation. Internal-tide SSH signature is investigated in Sect. 5 in the context of SWOT observability. We finish with a summary and discussion in Sect. 6.
CARS climatology and merged Argo-CARS velocity product The model used in this study is evaluated using climatology and observations. Model stratification and water mass https://doi. org/10. vertical structure are evaluated using climatological hydrography data taken from the CSIRO Atlas of Regional Seas (CARS2009) that provides gridded maps of temperature and salinity by combining a variety of datasets  (Ridgway et al. , 2002 , http://www. marine. csiro. au/~dunn/cars2009/, last access 21 February 2021). The large-scale regional mean circulation in the Coral Sea is compared to the Argo-CARS merged velocity product from  Kessler and Cravatte (2013). This product derives absolute geostrophic currents using climatological hydrographic data from CARS2009 referenced to a level of known motion of 1000 m obtained by drifting trajectories of Argo floats.
Altimetry-derived EKE Surface mesoscale eddy kinetic energy (EKE) during the period 2014-2018 is evaluated using global ocean gridded maps (1/4 • ) of SSH generated and processed by the EU Copernicus Marine Environment Monitoring Service (CMEMS, https://doi. org/10. 48670/moi-00148). In detail, we use the multimission Data Unification and Altimeter Combination System (DUACS) product in delayed time and daily resolution with all satellite missions available at a given time. Mesoscale EKE was derived from the geostrophic velocity field as follows: EKE = 1 2 u 2 g + v 2 g , (17) where u g = -g f ∂η dy , v g = g f ∂η dx with f the Coriolis parameter and η the SSH above geoid (also referred to as absolute dynamic topography), and (u g , v g ) = (u g -u g , v g -v g ) where the overbar denotes the temporal average over the period 2014-2018.
11a  with the predominant tidal beams to the north and south of New Caledonia. There is good agreement with the HRET model concerning the spatial representation of the M2 SSH for both mode 1 (Fig. 13c ) and mode 2 (Fig. 13d ). Overall, mode 1 seems to be enhanced in our model, whereas mode 2 is underestimated in some regions. Note that the given differences may be associated with the different time periods the datasets are referenced to as well as the length of the time series for the model (1 year) and altimetry (25 years).
Acknowledgements. Simulations presented in this study were performed at the  Centre Informatique National de l'Enseignement Supérieur (CINES). We thank the  MEOM/IGE (MultiscalE Ocean Modeling/Institut des Géosciences de l'Environnement) group  for allowing us to be part of their allocation of computational resources. We thank  Mathilde Cancet  for the preparation of the bathymetry dataset and  Amélie Ferran  for preliminary work on the modeling and analysis of internal tides around New Caledonia. Further, we appreciate the fruitful discussions with  Jerome Chanut  and  Julien Le Sommer  as well as  Ritabrata Thakur  and  Brian Arbic  with helpful comments on the modeling part. We also thank the  SWOT Science Teams  including the  High-Resolution Ocean Modeling Group  (led by  Brian Arbic ,  Florent Lyard , and  Lionel Renault ) and the https://doi.
Code availability. The tidal analysis was performed using the COMODO-SIROCCO tools, which are developed and maintained by the SIROCCO national service (CNRS/INSU). SIROCCO is funded by INSU and Observatoire Midi-Pyrénées/Université Paul Sabatier and receives project support from CNES, SHOM, IFRE-MER, and ANR. Figures were created using Python 3. 9, Matplotlib 3. 5. 1, and color maps provided by  Thyng et al. (2016)  and van der Velden (2020). Data availability. This study has been conducted using EU Copernicus Marine Service Information CMEMS (https://doi. org/10. 48670/moi-00148, last access: 18 January 2023). Climatological hydrography data were obtained from CARS (http://www. marine. csiro. au/~dunn/cars2009/, last access: 21 February 2021). The Argo-CARS product from Kessler and  Cravatte (2013)  was made available upon request.
Financial support. support This research has been supported by the Université Toulouse III -Paul Sabatier (grant from the Ministère de l'Enseignement supérieur de la Recherche et de l'Innovation, MESRI) carried out within the PhD program of AB at the Faculty of Science and Engineering and the Doctoral School Geosciences, Astrophysics, Space and Environmental Sciences (SDU2E). Lionel Gourdeau and Sophie Cravatte are funded by the Institut de Recherche pour le Développement (IRD); Laurent Brodeau and Aurélie Albert were funded by OceanNext, Grenoble; Michel Tchilibou was funded by the Centre National d'Études Spatiale (CNES); Florent Lyard was funded by the Centre National de la Recherche Scientifique (CNRS); and Clément Vic was funded by the Institut français de recherche pour l'exploitation de la mer (IFRE-MER).
Further, this study has been partially supported through grant EUR TESS no. ANR-18-EURE-0018 in the framework of the Programme des Investissements d'Avenir. This work is a contribution to the joint CNES-NASA project SWOT in the tropics and is supported by the French TOSCA (la Terre, l'Océan, les Surfaces Continentales, l'Atmosphère) program and the French national program LEFE (Les Enveloppes Fluides et l'Environnement).
, 2014; Buss et al. , 2012; Miguel et al. , 2019). Moreover, frequently performed behaviours may alter related brain structures suggesting considerable plasticity (e. g. Maguire et al. , 2000; Pope et al. , 2018; Quallo et al. , 2009). Individual differences emphasise the need for a representative sample of brains from individuals that have lived in highly variable socio-ecological environments, including the natural habitat. To acquire datasets with both suitable brain and behaviour measures, interdisciplinary approaches involving neuroscientists, evolutionary biologists, and ethologists are required. Analysing the brains of living animals has been conducted via capturing electrophysiological signals of brains, or neuroimaging of anaesthetised captive animals  (Martin, 2014). Another invasive approach depends on obtaining post mortem tissue suitable for macro-or micro-structure analyses by killing study animals (e.
, 2017; Shultz & Dunbar, 2007). Another advantage of studying primates is the existence of multiple long-term field sites, not only for several species but for different subpopulations living in different habitats. Here, site records offer detailed information about lifetime demography, reproductive and social history. Thus, we can collect data from individuals varying in their habitats, social structures, social networks and strategies, or extractive foraging (including tool use). Modern high-resolution imaging can showcase brain structures and pathways in unprecedented detail, particularly in brains sourced after (natural) death  (Eichner et al. , 2020; Friedrich et al. , 2021) , due to the possibility of using long scanning times. This approach allows for overcoming ethical restrictions when studying the brains of wild and/or endangered animals (almost 60 % of all primate species are threatened with extinction,  Estrada et al.
Diffusion MRI (dMRI) Whole-brain dMRI data were acquired on a preclinical Bruker Biospec 94/30 MRI system at 9. 4 T (Paravision 6. 0. 1), using a 300 mT/m gradient system and a 154 mm transmit-receive quadrature RF coil (Bruker BioSpin). Data were acquired with an isotropic resolution of 500 μm, using a segmented 3D EPI spin-echo sequence (matrix-size:   et al. , 2003). A noise map with matching imaging parameters was recorded for noise debiasing  (Gudbjartsson & Patz, 1995). The total dMRI acquisition time for each brain was approximately 90 h. Diffusion MRI preprocessing included (1) signal debiasing  (Gudbjartsson & Patz, 1995) , (  2.
This study was funded by the Max Planck Society under the interinstitutional funds of the president for the Evolution of Brain Connectivity Project. Nikolaus Weiskopf has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement n° 616905; from the European Union's Horizon 2020 research and innovation programme under the grant agreement No 681094; from the BMBF (01EW1711A & B) in the framework of ERA-NET NEURON. Open Access funding enabled and organized by Projekt DEAL. CO N FLI C T O F I NTE R E S T This work is not subject to any conflict of interest. We note that the Max Planck Institute for Human Cognitive and Brain Sciences has an institutional research agreement with Siemens Healthcare. Nikolaus Weiskopf holds a patent on acquisition of MRI data during spoiler gradients (US 10,401,453 B2).
Furthermore, samples were prepared for fluorescent nuclear staining. After rinsing samples with PBS and fixation using 3. 7% formaldehyde (Boom), Bio-Oss granules were washed with PBS and incubated for 10 min with 4′,6-diamidino-2-phenylindole (DAPI, Sigma-Aldrich, 0. 4 µg/ml) diluted in PBS. After washing, granules were transferred onto microscopy slides (Marienfeld) and mounted  [53]. Similarly, PLGA/PCL samples were stained with DAPI after histological processing as described below. Samples were imaged using the ImagerZ. 2 with the Axiocam 705 microscope camera and ZEN (blue edition) version 3. 3 software (all from Zeiss).
After complete dehydration, sections were incubated in xylene and mounted with DPX. To check for the presence of osteoclasts, sections were stained for TRAP as described afore. Finally, sections were scanned using the Pannoramic 1000 digitalization system at 20 × magnification, and images from scans were prepared using the Case-Viewer version 2. 3 software (both 3DHISTECH).
Results and discussion Presence of A. bombi was detected in nine individuals of Xylocopa spp. : five specimens of X. augusti collected in the provinces of Río Negro (localities of Villa Regina and General Roca), Buenos Aires (Arana and Mar del Plata), and Corrientes (Colonia Pellegrini), three X. atamisquensis [two from Formosa province (Ibarreta and Las Lomitas) and one from Río Negro (General Conesa)], and one X. nigrocincta captured in Formosa (Palo Santo) (Fig. 1A , Table  1 ). Sequencing confirmed that isolates belonged to A. bombi. Two of the isolates were double-sequenced and deposited in Genbank as follows: NCBI accession number MT547746 corresponds to Villa Regina (Río Negro) sampled from X. augusti, and MT547735 to Palo Santo (Formosa) sampled from X. nigrocincta. At the time of collection, the bees did not show any evident sign or symptom of disease [e.
, 2020). Finally, the Pampas region is located in the center-east of Argentina and contains more than one million honey bee hives that generate almost half of the total production in the country (Food and Agriculture Organization Statistical Database, 2019). Detections of A. bombi in Río Negro valley (northern Patagonia), where many beekeepers bring transhumant hives from the Pampas region  (Ugalde and García-Sartor, 2015) , could represent a source of inoculum potentially transmissible to honey bees by sharing floral resources with Xylocopa  (Koch et al. , 2017) , and return to their origin at the end of season. Sanitary status of these hives should be closely monitored to avoid a potential spread of A. bombi in the Pampas.