dataesr/scientific-paragraphs-categorization huggingface dataset:
| Histology Electrode placements were verified via electrophoretic ejection of Chicago Sky Blue dye (Sigma-Aldrich) at the recording site. Rats were killed with a lethal dose of pentobarbital (182. 2 mgÁkg À1 , IP), and brains were removed. The tissue was fixed in 8% paraformaldehyde for at least 48 h and transferred to a 25% sucrose solution for cryoprotection. Once saturated, the brains were frozen and sliced coronally at 60 μm thick using a cryostat (Thermo Fischer Scientific™ Cryostat CryoStar™ NX70, Illkirch, France) and mounted onto gelatin-coated slides. Tissue was stained with a combination of neutral red and cresyl violet. Only rats with verified electrode placements for both recording and infusion sites were included in the data analysis.
| Statistical analysis The manuscript complies with the recommendations of the British Journal of Pharmacology on experimental design and analysis in pharmacology  (Curtis et al. , 2022). The statistical analysis was performed only where each group size was at least n = 5. Group mean values and statistical analysis used independent values and n is number of animals. The sample size and animal numbers were determined by power analysis of pre-existing data  (Belujon et al. , 2016; Belujon & Grace, 2014; Chang & Grace, 2014; Salin et al. , 2021). Data were analysed with the statistical program GraphPad Prism (Version 8. 0. 1; GraphPad Prism, San Diego, California, USA, RRID: SCR_002798). All the results are expressed as the mean ± SEM. Results from individual neurons were used as replicates in calculating the mean value ± SEM per rat.
ACKNOWLEDGEMENTS This work has benefited from the facilities and expertise of PREBIOS platform ( University of Poitiers ). This work was supported by the  Institute National de la Santé et de la Recherche Médicale , the  Centre National pour la Recherche Scientifique , the  University of Poitiers ,  CHU of Poitiers ,  SFR FED 4226 , and the  Agence Nationale de la Recherche  ( ANR JC ,  ANR-15-CE37-0010  to P. B. ). The authors thank  Catherine Le Goff  and  Eric Balado  for technical support.
Acknowledgments First and foremost, we would like to thank  Johanne Saint-Charles  who has collaborated on every course since the beginning. Without her insight and direction, the course would be only a shadow of what it is. We would especially like to thank the late  Bruce Hunter ,  Donna Mergler ,  David Waltner-Toews , and  Dominque Charron  whose work and collaborations laid the foundation for  Canadian Community of Practice in Ecosystem Approaches to Health (CoPEH-Canada ). Our authorial team also thanks others who have contributed substantially to the development and teaching of the course along the way, in alphabetical order:  Ben Brisbois ,  Chris Buse ,  Donald Cole ,  Myriam Fillion ,  Marc Fraser ,  Sherilee Harper ,  Karen Houle ,  Mélanie Lemire ,  Sandra Martin Harris ,  Karen Morrison ,  Suzanne McCullagh ,  Audrey Simon ,  Jerry Spiegel ,  Céline Surette ,  Jennifer Vansteenkiste ,  Robert Woollard , and  Annalee Yassi.
There are basically three main techniques that rely on RSSI. The proximity technique is used to assess the closeness to a sensor or an emitter. In the trilateration or multilateration the positions are estimated using signal-to-distance relations. This latter technique is fast and easily trained, but depend on the signal-to-distance mapping models that are easily disrupted by the natural characteristics of the electromagnetic signals like the multipath  [10]. The fingerprinting-based techniques perform significantly better, but the required labor and time makes them inherently disadvantageous  [11]. In fingerprinting, data should be captured for long durations to catch the most available variability in terms of the signal parameters. Moreover, the area in question should be covered as densely as possible in order to approximate the true mapping as accurately  [12].
A. Parametrizing Fingerprints We build the PRM estimates using fingerprinting, that is, taking the snapshot of the map in terms of RSSI values on RPs. We assume to have access to timestamped RSSI values captured by multiple BLE sensors. These streaming RSSI values are compiled into histograms for each RP for each sensor, which are called the raw histograms. As it is impractical to collect data on a dense grid of map positions, the data are collected on a relatively small but evenly distributed set of RPs. We interpolate (or extrapolate) these histograms on a dense grid of positions in order to construct a PRM estimate of the target area. The resulting high resolution PRM can then be used to relate the RSSI values to the positions. We believe that the raw histograms describe the map too locally because they also include the RSSI data resulting from location specific signal reflections and interferences.
A. Dataset We use a position annotated BLE RSSI dataset to evaluate the proposed PRM generation strategies. The data are collected in a closed office area of size about 364 m 2 , using 12 BLE sensors stationed in the area. A specially designed setup of a BLE beacon and cameras is navigated to collect live BLE RSSI data emitted by the beacon. Each BLE RSSI data point is annotated with the highly precise ground truth positions. The ground truth positions are obtained by a visual position annotation system through the cameras  [7]. The data are compiled into a dataset of several trajectories and RP recordings in the same area. From the dataset, we use four of these trajectories: a zigzag (E 1 ), a rectangular (E 2 ) and two straight trajectories (E 3 and E 4 ), which are formed with the RSSI data captured by the sensors and the high precision position data captured by the visual position annotation system while navigating at a speed of about 0.
2 meters. Each individual experiment is run using the real world BLE RSSI data related to a trajectory from the aforementioned dataset. These RSSI data are also processed on-the-fly by the maximal filter with the same window size as of the PRM generation, and then fed into the particle filter as observations. For the diffusion based transition density, a sensitivity value, ν, is defined, and for the emission density the estimated PRM is employed. Each step of the particle filter yields the weighted mean of the position particles as an estimate for the current position of the tracked object. The accuracy of each position estimate is measured with the Euclidean distance to its ground-truth counterpart. We determine the performance of a configuration by summarizing all of these measured accuracies in all the related experiments into a distribution of errors.
While reporting the results, we focus on the median and mean of these error distributions. The experiments are performed on a high performance computer with two processors of type Intel(R) Xeon(R) CPU E5-2697A v4 2. 60 GHz. The computer can use 64 threaded cores simultaneously. The particle filters to track the positions through trajectories are implemented with Python3. 9 and the batches of experiments are coordinated via Bash scripting (codes available online 1 ). With the availability of multiple cores on the computer, the experiments are run in batches 1 https://github. com/philotuxo/btlocalization python3 of 50 parallel processes for each parameter configuration.
However the main gain from this work is the reduction of required time of the data collection, by decreasing the number of RPs or the data collection duration with no or little change in the positioning accuracy. In the data collection, we assume that we have a 2-minute measurement and calibration overhead due to the replacement of the emitter on each RP. We add this duration on the PRM estimation duration, which basically becomes negligible. In TABLE V, we measure the practicality of the system by comparing different options with the legacy option. The first and the second lines show how dense we select the RPs, and for how long we collect data on each RP. In the original dataset, RSSI data durations of 30 minutes (1800 s) at 81 RPs takes approximately 43 hours (legacy). By reducing the duration on each RP down to 15 seconds (option #1), the same task can be completed in only 3 hours, which may also be considered a long time for fingerprinting.
1: Parametrization system overview and tunable parameters fingerprinting approach. The results show that the addition of the maximal filter and redefining PRM with Gaussian parameters do not only produce smooth PRMs and boost the accuracy, but also increase the practicality by reducing the dedicated time dramatically to build accurate PRMs, (i. e. 6 to 15 seconds per RP). We test our technique using a dataset in which the true trajectories are labeled with the ground truth positions. This setup enables us to evaluate our algorithms exactly and realistically. The experimental results show that by introducing the maximal filter and the subsequent PRM estimation technique, we achieve a general performance boost of about 40%, from 1. 795 meters to 1. 054 meters.
3b ). IFNγ ΔKRKR mice showed no gross abnormalities and developed and bred normally. Histopathology did not identify pathologic changes or signs of neoplasia, degeneration or inflammation in IFNγ ΔKRKR mice compared to wild-type control littermates (IFNγ wt ). Spleen tissue and blood were analyzed to assess the distribution of immune cell populations. No differences in total splenocytes between IFNγ ΔKRKR and IFNγ wt mice were detected (Extended Data Fig. 4a ). Populations of T cells, B cells, monocytes, dendritic cells and natural killer cells were detected with similar frequencies in IFNγ ΔKRKR and IFNγ wt mice (Extended Data Fig. 4b ). IFNγ production after in vitro stimulation of peripheral blood lymphocytes or splenocytes with antibodies to CD3/CD28 was also similar (Extended Data Fig.
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41590-023-01420-5. Fig. 4  | IFNγ ΔKRKR mice succumb to viral infections. a, CRISPR-Cas9 approach for the generation of IFNγ ΔKRKR mice; ds, double-strand. b, Scheme of IFNγ ΔKRKR (red) or IFNγ wt (black) mice infected with LCMV-Docile and monitored for 13 d; i. v. , intravenous. c, Weight curves of IFNγ ΔKRKR (red) and IFNγ wt (black) mice following infection. IFNγ ΔKRKR mice deteriorated from days 8 to 9 as determined by weight loss, while IFNγ wt mice recovered.
Mice Mouse experiments were approved by Landesamt für Gesundheit und Soziales Berlin (G-322/10, G-58/16 and G-114/17) and Regierungspräsidium Freiburg (G-15/168). In tumor transplantation experiments, the maximum tumor volume allowed was 15 × 15 × 15 mm. This size was never exceeded in the experiments. Mice were randomly assigned into groups when injected with tumor cells. For adoptive T cell therapy (ATT) experiments, mice were allocated to groups based on equal distribution in tumor size between different groups. Investigators were not blinded, as endpoint criteria of mouse experiments were defined before experiments. No data were excluded from analysis. Data sets were acquired prospectively and analyzed in a retrospective manner. Animals used were group housed with two to five mice in individually ventilated cages (Tecniplast, Green Line or Blue Line) and maintained under identical housing conditions, such as a 12-h light/12-h dark cycle (light cycle 6:30 to 18:30), standard pelleted mouse diet (ssniff, article number v1124-300) ad libitum, free access to water, 22 ± 2 °C room temperature and a relative humidity.
2% Triton X-100 (PanReac AppliChem ITW Reagents) and 0. 2% gelatine from coldwater fish (Sigma-Aldrich) for 1 h at room temperature. Slides were washed three times for 5 min each in PBS. HS was visualized using rat anti-HSPG 2 (clone A7L6, Abcam) in antibody diluent (Dako) overnight at 4 °C in the dark. The primary antibody was detected by a goat anti-rat antibody coupled to Alexa 568 (A11077, Invitrogen) incubated for 2 h at room temperature in the dark, followed by simultaneous CD146 and nuclear staining using a directly labeled CD146-Alexa 647 antibody (clone ME-9F1, Biolegend) and Hoechst 33342 (Sigma-Aldrich), incubating for 2 h at room temperature in the dark. Image acquisition and processing of imaging raw data were performed on a Zeiss LSM 980 AiryScan 2 system (Carl Zeiss Microscopy) using the AiryScan MPLX SR-4Y mode with a final pixel size of 0.
LCMV titers in organ homogenates were quantified using a focus-forming assay  38. For flow cytometry, splenocytes were stained with antibodies for ≥30 min at 4 °C. GP33-specific and NP396-specific CD8 + T cells were detected with PE-labeled H-2D b tetramers from the Tetramer Core Facility at Baylor College of Medicine. For detection of intracellular cytokines, 10 6 lymphocytes were stimulated with 10 -7 M GP33 or NP396 peptide for 4 h, followed by surface staining and intracellular staining for IFNγ using a Cytofix/Cytoperm kit (BD Bioscience). Analyses were performed using an LSR Fortessa cytometer (BD Biosciences) and FlowJo software v8. 8. 7/v10. Histopathological analysis of mice infected with LCMV-Docile was performed at the endpoint (days 10-13 after infection), and organs (liver, lung, brain, spleen and kidney) were taken, fixed in 4% buffered formalin and embedded in paraffin.
Software Data analysis and plotting was performed using Microsoft Excel 2016 and 2019 or GraphPad Prism 9. Statistics were calculated using GraphPad Prism 9 or R software (version 4. 1. 2). Flow cytometry data were acquired using BD Diva or MACSQuantify and analyzed using FlowJo 10. Sequence analysis and cloning procedures were planned in SnapGene 5. f, Representative flow cytometry plots from two experiments of splenocytes from TCR-I x IFNγ ΔKRKR mice, analyzed on the day of transfer. Splenocytes were stained for CD8 and Vβ7 antibodies to determine proportion of CD8 + Vβ7 + T cells per spleen. g, Rag1 -/-mice were treated with 1-2 ×10 6 TCR-I + splenocytes from TCR-I x IFNγ wt donor mice (black lines, n = 11) or TCR-I x IFNγ ΔKRKR donor mice (red lines, right, n = 10) or left untreated (dashed lines, right, n = 4) when 16.
Acknowledgements We thank  A. Schütz  for help with protein production,  C. Friese  and  S. Vucikuja  for technical assistance and  T. Schüler  and  O. Daumke  for discussion. This work was supported by grants from the  Wilhelm Sander Stiftung  ( 2020. 066. 1;T. B. and T. K. ), from the  European Union  ( ERC Advanced Grant   882963 ; T. B. ) and from the  Deutsche Forschungsgemeinschaft   SFB1160 A07  and  B04  (P. A. ) and SFB TR36 (T. B. and T. K. ). This work also used the platforms of the  Grenoble Instruct Centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  French Infrastructure for Integrated Structural Biology  ( ANR-10-INSB-05-02 ) and  Grenoble Alliance for Integrated Structural Cell Biology  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology.
6. 0  63. Differential gene expression and gene ontology (GO) term enrichment analysis. DESeq2 v3. 7  64  was used to calculate differential gene expression between non-inoculated and inoculated conditions. All genes having an adjusted p-value inferior to 0. 01 were considered as significantly differentially expressed. All functional enrichment analysis was performed using g:Profiler (version e95_eg42_p13_f6e58b9) with g:SCS multiple testing correction method applying significance threshold of 0. 01  65, 66. To avoid redundancy between GO terms, we used ReviGO 67 to reduce the number of enriched GO terms as well as replacing terms related to the exact same DEGs lists by the most representative and understandable semantic expression. Reverse transcription and qPCR. cDNA was synthesized from total RNA using Oligo dT (Invitrogen) and the SuperScript III Reverse Transcriptase (Invitrogen) according to manufacturer instructions.
Acknowledgements The authors gratefully acknowledge financial support from the  CGIAR research program  (CRP) RICE as well as from the  French national research agency (ANR)  funding the  BURKADAPT  project ( ANR-19-CE20-0007 ). We thank  Geneviève Conéjéro  ( PHIV platform, Cirad Lavalette, Montpellier, France ) as well as the  MRI imaging facility  (member of the national infrastructure  France-BioImaging , supported by the  ANR-10-INBS-04  grant). EK, AW and LG were supported by fellowships from the  French Ministry of Higher Education, Research and Innovation. MGX  acknowledges financial support from  France Génomique National  infrastructure, funded as part of " Investissement d' Avenir " programme managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The authors would like to thank  Mathilde Hutin  for constructive review of the manuscript.
Background: Low-resolution administrative databases can give biased results, whereas high-resolution, time-stamped variables from clinical databases like MIMIC-IV might provide nuanced insights. We evaluated racial-ethnic disparities in life-sustaining ICU-treatments (Invasive Mechanical Ventilation (IMV), Renal Replacement Therapy (RRT), and Vasopressors (VP)) among patients with sepsis. Methods: In this observational retrospective cohort study, patients fulfilling sepsis-3 criteria were categorized by treatment assignment within the first 4 days. The outcomes were treatment allocations. The likelihood of receiving treatment was calculated by race-ethnicity (Racial-ethnic group (REG) or White group (WG)) using 5-fold sub-sampling nested logistic regression and XGBoost. Results: In 23,914 admissions, 82% were White, 42% were women.
However, most of these studies come from claims-based databases that are prone to variable misclassification or do not contain important timestamped information  [15] [16] [17]. Our group has recently shown that observational studies using low-resolution claims data can introduce substantial bias in effect size estimation  [18]. Our study aims to investigate racial-ethnic differences in the provision of life-sustaining treatments among ICU patients with sepsis using the Medical Information Mart for Intensive Care version 4, or MIMIC-IV database, a de-identified electronic health record database from the Beth Israel Deaconess Medical Center in Boston, Massachusetts  [19] [20] [21]. The high-resolution MIMIC-IV database, which includes time-stamped features, like treatments that enable adjustments for relevant time-varying confounders to investigate disparities in the administration of ICU interventions in a large academic center.
METHODS This observational retrospective study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement  [22]. The health equity language, narrative, and concepts of this paper follows the American Medical Association's recommendations  [23]. Data were extracted from the publicly available MIMIC-IV database using SQL via Google's BigQuery using due diligence as suggested by the literature  [24, 25]. The MIMIC-IV database is maintained by the Laboratory for Computational Physiology at the Massachusetts Institute of Technology and shared via the PhysioNet platform  [19]. The dataset has been de-identified, and the institutional review boards of the Massachusetts Institute of Technology (No. 0403000206) and Beth Israel Deaconess Medical Center (2001-P-001699/14) both approved the use of the database for research.
The MIMIC-IV database includes physiologic data collected from bedside monitors, laboratory test results, medications, medical images, and clinical progress notes captured in the electronic health record from patients admitted to the ICU between 2008-2019. Approximately 60,000 de-identified medical records are archived in the MIMIC-IV database. We hypothesized that treatment allocation of ICU interventions, specifically invasive mechanical ventilation (IMV), renal replacement therapy (RRT), and vasopressor use (VP), is not equally distributed across race-ethnicity.
Subsequent admissions to the ICU within the database were excluded from the analysis to avoid repeated measures. We then applied strict treatment eligibility criteria creating three sub-cohorts each, eg, IMV 1, RRT 1, VP 1 for cohort 1 as follows: For IMV, the criteria were adapted from the American Thoracic Society  [28] , and the American College of Chest Physicians  [29]  • p a O 2 /FiO 2 ratio ≤ 300 (average of the day) • p a CO 2 ≥ 60 mmHg (minimum of the day)   • Respiratory rate ≥ 20 bpm (average of the day) For RRT, the criteria were adapted from the Kidney Disease Improving Global Outcomes (KDIGO)  [30, 31]  • Urine output / Weight ≤ 12 L/kg (total of the day) • Potassium ≥ 6. 5 mEq/L (minimum of the day) • pH ≤ 7. 2 and bicarbonate ≤ 12 mEq/L (both maximum of the day) And for VP, the criteria were adapted from the Surviving Sepsis Campaign  [31]  • Mean blood pressure ≤ 65 mmHg (average of the day) • Lactate ≥ 2 mmol/L (minimum of the day) This was done to reflect decision making by the clinicians.
Statistical Analysis Statistical analysis was performed using Python 3. 10. 9. For summary statistics, we used the tableone Python package  [34]. For modelling, penalized linear models -multivariate ridge-penalized logistic regression (LogReg)  [35]  -and non-linear models -XGBoost classifier  [36]  -were used, adjusted for confounders to estimate the likelihood to receive each of the three interventions stratified by race-ethnicity. We report our findings as ORs with 95% confidence intervals (OR 95% CI). White patients were considered as the reference group. Different groupings were considered for the racial-ethnic groups: A) with all REG collapsed into one category, and B) with REG patients disaggregated. The choice of ridge logistic regression was influenced by its ability to handle multicollinearity, thereby stabilizing the model.
Baseline Study Cohort The MIMIC-IV database comprised of 73,140 ICU stays, of which 32,971 met the Sepsis-3 criteria. Following the inclusion and exclusion criteria, the final cohort consists of 23,914 ICU admissions (Figure  1 ). The race-ethnicity distribution is as follows: 81. 8% White, 10. 5% Black, 4. 3% Hispanic, and 3. 3% Asian. The groups differed in age, with White patients being the oldest (68    Variations in chronic medical conditions such as hypertension, congestive heart failure, COPD, and coronary artery disease were observed. For example, hypertension was predominantly prevalent among Black patients (72. 3%), whereas White patients showed a higher prevalence of coronary artery disease (37. 8%). The occurrence of type 2 diabetes was notably higher in Black (40. 5%) and Hispanic patients (37.
DISCUSSION Using the high-resolution MIMIC-IV database, we demonstrated racial-ethnic disparities in the provision of lihood of RRT initiation in the RRT4 sub-cohort, which was significant according to the LogReg model (OR: 1. 2, 95% CI 1. 01 -1. 45). No significant differences in RRT initiation were observed for the Black and Hispanic groups. For VP treatment, we found that REG was generally less likely to receive this treatment with diminishing effect size over the sub-cohorts in both the XGBoost and LogReg analyses. We did not discover relevant differences in VP treatment initiation for the Asian, Black, and Hispanic groups.