dataesr/scientific-paragraphs-categorization huggingface dataset:
8 9  Acute exacerbations of ILD in patients with RA may also lead to worse outcomes in patients with RA-ILD. 10 11  However, data on the natural course and treatment patterns of RA-ILD are limited, with no currently established guidelines for screening, monitoring and WHAT IS ALREADY KNOWN ON THIS TOPIC ⇒ Interstitial lung disease (ILD) occurs in up to 10% of patients with rheumatoid arthritis and causes significant mortality. While some patient baseline factors such as age, male sex and the rs35705950 variant in the MUC5B promoter gene, are known to be associated with development of ILD, it is not currently possible to predict which patients will develop ILD. RMD Open RMD Open RMD Open treatment. Treatment data come from small open-label studies or case studies, with almost no longitudinal studies to provide an evidence base.
Publication database search criteria Searches were conducted by Whitney Townsend (Librarian, University of Michigan) in three databases (PubMed, Embase and Scopus) between 1 January 2011 and 12 July 2021 on the following review question: what patient-level factors are more likely to be present in patients with RA who go on to develop ILD than those who do not? The search terms are provided in online supplemental online supplemental appendix.
Data extraction and quality assessment Data from the publications that met entry criteria were extracted and entered into spreadsheets circulated to each author. A separate sheet was prepared for each potential prognostic factor. In some studies, factors such as age and sex could not be assessed for prognostic value because patient populations were matched or analyses were already adjusted for these factors. The authors assessed against the Oxford grading criteria for prognostic studies. 24  For grading of evidence, the Oxford system incorporates seven questions: 1. Was the defined representative sample of patients assembled at a common (usually early) point in the course of their disease? 2. Was patient follow-up sufficiently long and complete? Rheumatoid arthritis Rheumatoid arthritis Rheumatoid arthritis 3.
While some studies used a single cut-off for age (eg, 60 years), there is no clear rationale for any specific cut-off point and therefore, consideration of different age categories may be more useful. Possibly due to the variety of ways in which smoking status may be assessed, there was wide variation in the reported predictive ability of smoking status for development of ILD. In this analysis, we included a variety of measures of smoking (pack-years, never/ever smoking and current/past/never smoking). Pack-years may be the most useful parameter, providing the most information regarding exposure to tobacco smoke, but data may not always be available, and even then may not be reported accurately by the patient. Categories of never/ past/current smoker may be more useful in considering risk of ILD.
Funding This review was supported by  Boehringer Ingelheim International GmbH  (BI). The authors meet criteria for authorship as recommended by the International Committee of Medical Journal Editors (ICMJE). The authors did not receive payment for the development of the review. Writing, editorial support and formatting assistance was provided by  John Carron  of  Nucleus Global, UK , and was contracted and funded by BI. BI was given the opportunity to review the article for medical and scientific accuracy as well as intellectual property considerations.
Materials and Methods Since 1975, we collected a large number of papers published after 1900. The papers were sourced from books, proceedings and repository sites such as Treesearch, TreeCD, Agricola, Google Scholar. We excluded papers about angiosperms and pseudoreplication and retained papers that contained survival data for pine seedlings grown in nurseries in North America, Canada, Europe and Africa. A few references to survival of Sitka spruce (Picea sitchensis (Bong. ) Carr. ) and Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) were included. Many papers involved seedling quality but these were excluded as these effects were adequately discussed in other unsystematic reviews. Papers were retained if they involved factors that killed pine seedlings after they passed the nursery gate.
To keep citations to a minimum, a small portion of the total available papers ended up as citations. This review does not address a specific research question and it does not cite all published literature on pine survival. As a result, it is not a systematic review of all literature pertaining to a single question. Powerpoint software was used to construct graphs to illustrate treatment effects on survival of planted stock. Among the 23 graphs, about half present survival data for both the Y-axis (treatment) and X-axis (control). This graphical technique was utilized since it is relatively easy for landowners to understand. It also illustrates that treatment response is often greater when mean survival is less than 80%.
Processing Speed Comparison The average-bin-width and the matrix calibration methods lead to better results in terms of noise than the bin-by-bin method. The drawback is a more complex signal pro cessing which includes more multiplications and data access. As mentioned before, the average-bin-width calibration and the matrix calibration are post-processing on the TDC raw data. Moreover, the bin-by-bin method for asynchronous TDCs is very complicated to implement for online calibration, and it is easier to be performed as post-processing Thus, all the studied calibration methods were implemented as post-processing. The SoC FPGA kit used in experiments integrates a hard processor system (HPS), namely the ARM Cortex A9 processor, with a Cyclone V FPGA fabric and provides a high-speed interface for the data transfer between these two parts.
Processing Speed Comparison The average-bin-width and the matrix calibration methods lead to better results in terms of noise than the bin-by-bin method. The drawback is a more complex signal processing which includes more multiplications and data access. As mentioned before, the average-bin-width calibration and the matrix calibration are post-processing on the TDC raw data. Moreover, the bin-by-bin method for asynchronous TDCs is very complicated to implement for online calibration, and it is easier to be performed as post-processing. Thus, all the studied calibration methods were implemented as post-processing. The SoC-FPGA kit used in experiments integrates a hard processor system (HPS), namely the ARM Cortex A9 processor, with a Cyclone V FPGA fabric and provides a high-speed interface for the data transfer between these two parts.
Introduction Climate change has been disturbing the hydrological cycle globally, leading to more extreme weather events like heavy precipitation  (Sugiyama et al. , 2010; Kao and Ganguly, 2011; Muller and O'Gorman, 2011; Diffenbaugh et al. , 2017). Several studies based on observational datasets have indicated that heavy precipitation has increased in many regions of the world (e. g. ,  Asadieh and Krakauer, 2015). These events can cause natural disasters such as landslides and flooding  (Debortoli et al. , 2017)  and are threatening climate-sensitive sectors like agriculture, human health, urban and rural development, hydroelectric power generation, public infrastructure, and watershed management  (Odoulami and Akinsanola, 2017; Papalexiou and Montanari, 2019). For example, in the health sector, heavy precipitation can increase the microbial contaminants in drinking water and is a key factor in water-borne disease outbreaks  (Papalexiou and Montanari, 2019).
Thus, understanding how climate change will affect heavy precipitation in this region is critical for risk mitigation, management, and adaptation planning  (Mortazavi-Naeini et al. , 2015). Several studies using different observational datasets have focused on trends in mean precipitation and observed a decreasing trend in longterm precipitation over equatorial Africa (e. g. ,  Malhi and Wright, 2004; Yin and Gruber, 2010; Samba and Nganga, 2012; Dezfuli, 2017; Cook et al. , 2020). Additionally,  Diem et al. (2019)  used four satellite-based datasets and found a decrease of 20% in mean precipitation over equatorial Africa between 1950 and 2014. However, only a few studies have addressed indices of extreme rainfall. For example,  Aguilar et al. (2009)  reported a decreasing trend in station-based heavy rainfall in western central  Africa between 1955 and 2006.
Additionally, we aim to project these rainfall indices for the mid (2035-2065) and the end (2070-2 100) of the twenty-first century, using the high emission scenario RCP8. 5 (Representative Concentration Pathways,  Van Vuuren et al. , 2011). This paper is organized as follows: Section 2 presents the methodology and datasets used in this study. Section 3 focuses on the evaluation of RCM runs, specifically examining their ability to represent mean and heavy rainfall. In Section 4, we present and discuss the future projections of heavy rainfall. The final section is dedicated to drawing conclusions based on the findings of the present study.
Observation data For model validation, we utilized the Climate Hazards Group InfraRed Rainfall with station data  (CHIRPS v2. 0; Funk et al. , 2015). CHIRPS is a dataset that combines station and satellite data, serving as a valuable resource in regions where station data is sparse or discontinuous, such as the DRC. It has demonstrated excellent performance in capturing mean rainfall features in Africa compared to other available datasets  (Cattani et al. , 2018; Dinku et al. , 2018; Gebrechorkos et al. , 2018; Ayugi et al. , 2019). Notably, CHIRPS offers a high spatial resolution of 0. 05 • × 0. 05 • , incorporating information on local-scale features such as topography. Furthermore, CHIRPS has exhibited strong agreement with station data in equatorial Africa  (Cattani et al. , 2018; Kimani et al.
Model skill score metrics and statistical analysis We applied skill score metrics to assess the performance of each RCM and their ENS. Skill score metrics for rainfall extreme indices were summarized in the domain, as it would be impractical to plot maps for all indices. We therefore used the spatial correlation of climatological rainfall indices between RCMs and CHIRPS, and the standardized standard deviation of RCMs relative to CHIRPS. These two metrices have been chosen because they are not prone to model biases and are typically represented in a Taylor diagram, not plotted here due to the large amount of data involved (RCMs, seasons, and indices). For indices that follow a normal distribution (PRE, Rx1day, SDII, and R95p), we calculated the Pearson spatial correlation. For non-normally distributed indices (RR20mm and CWD), we computed the Spearman spatial correlation.
This is consistent with the majority of studies using both large ensembles of GCMs and RCMs  (Pinto et al. , 2016; Dosio et al. , 2019). Despite our confidence in these projections, it is important to interpret this study cautiously due to its major limitations: (i) Model performance assessment relies on the quality of observational datasets, which may contain biases compared to station datasets  (Harrison et al. , 2019; Alexander et al. , 2020). This is also true for CHIRPS, despite being one of the best satellite rainfall products available  (Toté et al. , 2015; Ayehu et al. , 2018) ; (ii) the magnitude of changes may be influenced by a relatively small number of simulations (ten) in the ENS, which could be sensitive to outlier simulations; (iii) the limited number of GCMs used as lateral boundary conditions for RCMs can introduce systematic biases inherited from their parent GCMs.
Conclusions This study has demonstrated that most of the CORDEX-CORE RCMs can simulate the spatial patterns of rainfall indices in DRC reasonably well. The evaluation of the correlation patterns is meaningful as it assesses the actual model performance rather than the performance of a bias correction or spurious effects arising from multi-decadal variability. However, we want to point out that when applying the model data in impact models or studies, a thorough bias correction is necessary to remove systematic biases  (Maraun, 2013; Maraun and Widmann, 2018). Among the ten simulations considered, REMO2015-MPI-ESM is identified as the best-performing one based on the spatial correlation and spatial standard deviation. It shows comparable performance to that of ENS. However, it tends to overestimate rainfall in the western parts of the country, which are characterized by complex atmosphere-vegetation interactions.
However, these short and long-term change patterns for all rainfall indices vary spatially and seasonally. Despite the strong agreement of models in the direction of changes, it is essential to approach these results with caution due to its limitations. These include the reliance on observational datasets for model performance assessment, a relatively small number of simulations (ten) in the ensemble, and the definition of robustness which may be subjective (threshold and statistical testing). The predicted patterns can provide valuable insights for the design and management of climate-resilient infrastructure and socioeconomic activities under future climate change, which is crucial for the developing countries. For the case of DRC, projected increase in heavy rainfall can lead to challenges for water treatment and sewage systems, agricultural practices, and the outbreak of waterborne diseases.
The severity of these impacts varies across regions. However, future developments of this study must address performances uncertainties in the projections in more detail. This includes using a larger number of ensemble members when available and validating the hindcast model performance against various observational datasets, as rainfall observations differ substantially across the African continent. Given the significant role of vegetation in DRC's local climate, the use of coupled RCMs with interactive  (Rai et al. , 2022; Giorgi et al. , 2023)  or even dynamic vegetation processes  (Drüke et al. , 2021)  is recommended. Additionally, realistic land use change scenarios should be considered, as they are particularly important for addressing biases in the western part of the country, which is characterized by complex atmosphere-vegetation interactions and ongoing transformation of natural vegetation.
Acknowledgments We acknowledge the  World Climate Research Programme's Working Group on Regional Climate , and the  Working Group on Coupled Modelling , former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups for producing and making available their model output. Finally, we acknowledge the  Earth System Grid Federation infrastructure , an international effort led by the  U. S. Department of Energy 's  Program for Climate Model Diagnosis and Intercomparison , the  European Network for Earth System Modelling  and other partners in the  Global Organisation for Earth System Science Portals (GO-ESSP ). We thank the anonymous reviewers for their comments on this paper.
Numerical simulations ZFS simulation of the defect under external strain and electric field perturbations is calculated in a home-built Python code. We iterate over the magnitude of the perturbation in 1000 steps for the simulation range. In each iteration, we adaptively sample (scaled by the magnitude) the random configurations of effective perturbation vectors ε = (ε xx , ε yy ) and E ¼ ðE x ; E y Þ for the normal strain and electric fields, respectively. The component are sampled from uniform random distribution and the vector is normalised. In the ODMR simulation, we sample a uniform random distribution of positions inside a sphere, representing parasitic charged defects around the target boron-vacancy defect. To this end, we sample the elements of the position vector from standard normal distribution and multiply with d ¼ r ffiffi ffi x 3 p random distance, where x is sampled from a uniform random normalised distribution and r is the simulation sphere radius.
ACKNOWLEDGEMENTS This work was supported by the  National Excellence Programme  for the project of  Quantum-coherent materials  ( NKFIH Grant  no. KKP129866 ), the  Ministry of Culture and Innovation  and the  National Research, Development and Innovation Office within the Quantum Information National Laboratory of Hungary  (Grant no. 2022-2. 1. 1-NL-2022-00004 ), the  French Agence Nationale de la Recherche  under the programme  ESR/EquipEx+  (Grant no. ANR-21-ESRE-0025 ), the  Institute for Quantum Technologies in Occitanie  through the project  BONIQs and Qfoil. Support for hBN crystal growth is provided by the  Office of Naval Research , awards numbers  N00014-22-1-2582  and  N00014-20-1-2474. The neutron irradiation was supported by the  U. S. Department of Energy, Office of Nuclear Energy  under  DOE Idaho Operations Office  Contract  DE-AC07-051D14517  as part of a  Nuclear Science User Facilities experiment.
2018 , Doorenweerd et al. 2020). The contradictory and unclear results may be explained by differences in taxonomic coverage and intraspecific sampling effort, in particular of the target species, and by identification errors, in particular when DNA entries from public databases are used (e. g. ,  Asadi et al. 2019). As a consequence, B. dorsalis identifications could possibly be validated by combining the mtDNA barcode with independent nDNA markers. Finding appropriate nuclear barcodes is challenging, however, since nuclear genes often lack sufficient specieslevel diagnostic variability. In this context, IPPC (2019) recommends using the nuclear Internal Transcribed Spacer 1 (ITS1)  (Boykin et al. 2014) , which discriminates B. dorsalis from B. carambolae (only) via length polymorphism. Furthermore, Plant Health Australia (2020) provides reference sequences for 4 nuclear markers, including the Eukaryotic translation Initiation Factor 3 subunit L gene (EIF3L), developed from anchored hybrid enrichment research and which seems the most promising for discriminating the species closest to  -Thin "T" pattern on abdomen.
(2015) , we considered B. invadens, B. papayae, and B. philippinensis as synonyms of B. dorsalis. We used nondestructive DNA extraction protocols on whole specimens to conserve morphological characters, as detailed in Supplementary Table  S1. The 658 bp 5' standard "barcoding region" of the COI gene  (Hebert et al. 2003)  and the 581 bp of the EIF3L gene (Plant Health Australia 2020) were amplified using polymerase chain reactions (PCRs) performed as detailed in Supplementary Table  S2  and sequenced by Eurofins Genomics. All nucleotide sequences were checked for ambiguous bases, the presence of frame shift mutations and stop codons, edited using CodonCode Aligner v. 10. 0. 2 and aligned using Seaview v5. 0. 5  (Gouy et al. 2021). PCR products of the ITS1 marker  (Boykin et al. 2014 ), obtained as detailed in Supplementary Table  S2 , were run in 2.
5% electrophoresis agarose gel for 90 min to double blind read the fragment size, expected to be 544 bp for B. carambolae and 500 bp for other B. dorsalis complex species. For specimens for which morphological identification was inconclusive, molecular identification was achieved by visualizing the position of their COI and EIF3L sequences in tree graphics relatively to curated sequences of species of the B. dorsalis complex. The reference datasets included 406 (COI) and 309 (EIF3L) sequences from target specimens with unambiguous morphological identifications, specimens of the B. dorsalis species complex published by Plant Health Australia (2020) (Supplementary Table  S1 ), and additional specimens from species morphologically confusable and/or with reported hybridizations with B. dorsalis (see Fig.
1 , Supplementary Tables  S1  and S3  for sample size details). Maximum likelihood (ML) trees were constructed on unique haplotypes, recovered with the sidier package (Muñoz-Pajares 2013), using MEGA v. X  (Kumar et al. 2018)  and the T92+G mutational model  (Tamura 1992). We performed 500 bootstrap replications and condensed the ML trees to show clades supported by bootstrap values over 50%. For the haploid COI barcode, we also constructed a minimum spanning network among haplotypes using Popart v. 1. 7  (Leigh and Bryant 2015).
Results and Discussion Based on morphological keys, we confidently identified 279 specimens as B. dorsalis out of the 289 studied. The 10 remaining specimens, ~3. 5% of the target dataset, possessed character states intermediate between B. dorsalis and other species of the complex, including B. carambolae, B. occipitalis and several little-known species rarely cited in literature and indifferent to ME (but attracted to Cue-lure, except B. irvingiae Drew & Hancock, for which no lure is known), preventing conclusive morphological identification (Table  1 ). All these specimens originated from either Indonesia, Malaysia, or the Philippines, where the rate of ambiguous individuals reaches 47. 6%. The characters that diverged from the conventional morphological description of B. dorsalis were mainly the extent of the wing costal band and apex spot (Fig.
Acknowledgments This work was funded by the  French Agricultural Research Centre for International Development (CIRAD)  and the  French Agency For Food, Environmental and Occupational Health & Safety (ANSES)  (project BACTRACK). We are thankful to the many collaborators named in Supplementary Table S1 for providing us with fruit fly specimens, the phytosanitary border inspectors at Roissy airport, and  Anaïs Chailleux  in particular for coordinating sampling in Africa. We thank the  CBGP (Centre de Biologie pour la Gestion des Populations)  for giving us access to Molecular Biology and Collection platforms. We express our deeply felt gratitude to  Emma Artige  ( INRAE ) for having taken on the colossal administrative workload to obtain the legal agreements between CIRAD and 21 different government agencies required by the  Nagoya Protocol  for the  Convention of Biological Diversity , and  Servane Baufumé (CIRAD)  for her assistance and insightful tips.
Continuous progress in navigation, sensor-based, and GPS technologies have made smart devices essential to our daily lives and many location-based applications. However, the trajectory datasets generated by these applications require the management of large data volumes while preserving their main properties and semantics. One of the most popular methods for compressing trajectory data offline is the Douglas-Peucker (DP) algorithm, but its principles should be applied to a diverse range of contexts when considering real-time trajectory data. This paper introduces a Flexible Douglas-Peucker algorithm (FDP) that takes into account the data's diversity, underlying properties, and semantics. The proposed framework is applied to the Geolife benchmark dataset with a series of different thresholds that reflects different contexts and constraints when performing a trajectory compression process.
PROPOSED METHODOLOGY We introduce the principles of a Flexible DP algorithm (FDP) applied to a multi-modal trajectory, in which appropriate thresholds for each part of a multi-modal trajectory are based on the semantic properties of the associated transportation modes. The main steps of the proposed framework are briefly illustrated in Figure  3  and hereafter described. First, at the pre-processing stage, the raw trajectory is examined to remove outliers and noisy data. At this stage, incomplete data and data without time stamps are removed from the dataset. Then, raw trajectory data is enriched by semantic information related to respective transportation modes. Secondly, and to reflect different contexts, trajectory data are also annotated by specific user data and temporal data (e. g. , day of occurrence).