dataesr/scientific-paragraphs-categorization huggingface dataset:
Aerosol models The size distribution of the aerosol models is bi-modal and includes an accumulation (fine) and a coarse mode. The refractive index of the accumulation mode is defined as an internal mixture between the refractive index of a scattering (sulfate) and an absorbing aerosol (soot) from the OPAC database  (Hess et al. , 1998). Prior information for the aerosol models is given in Table  1. The prior aerosol models and associated uncertainties are established following the literature review presented in section 2. The literature review proposes a range of aerodynamic diameters for the accumulation modes and partial information on the width of the size distribution. The aerodynamic diameter is set to 0. 22 µm, 0. 67 µm and 0. 60 µm for the flaring, sinter plan, and coal-fired plant respectively.
Introduction In many fields of science and industry (climate, health, economics, artificial intelligence, etc. ), High-Performance Computing (HPC) is an essential element to solve complex problems and to process the ever-increasing amount of data being generated. Informally, HPC consists of utilizing highly parallel and distributed computing platforms. We have reached an extreme-scale of such platforms, with ranks such as the Top500  [21]  listing supercomputers with millions of interconnected processors. Among the many important problems that arise at such a scale, resource management is a critical problem that needs to be solved efficiently for a proper use of such platforms. Resource management involves assigning when and where HPC applications (hereafter referred to as jobs) will be processed in an HPC platform.
Results and Discussion Within this section, we present the primary outcomes attained through our research. A training data set with (p j , q j , r j , score(j)) values was created and used to evaluate the effectiveness of the regression-obtained scheduling heuristics. The simulations were conducted on an Amazon EC2 c5a. 2xlarge instance equipped with a second-generation AMD EPYC 7002 processor with up to 3. 3 GHz frequencies, 8 virtual CPU cores, and 16GB of RAM. All simulations, including obtaining the training data set and evaluating the scheduling policies, were performed using the SimGrid  [9]  simulation software. The job characteristics were obtained from a trace generated with the Lublin and Feitelson  [20]  workload model, with set sizes of |S| = 16 and |Q| = 32. While obtaining the training data set, we had to determine the maximum number of trials that we could perform with a pair (S, Q) in the c5a.
Does the effectiveness of regression-based scheduling heuristics increases as a function of polynomial size? To address this question, we employed the methodology detailed in Section 4. Specifically, we used multiple linear regression on the functions Lin, Qdr, Cub, and Qua; using a dataset comprising of 14,081 jobs and their corresponding score(j). Our analysis was conducted on a synthetic homogeneous platform with 256 processors. The complete list of coefficients θ, obtained through regression, is presented in Table  3. Below, we provide a qualitative description of these coefficients. Table  3 : Features of the four defined functions, their optimal coefficients, and their Variance Inflation Factor (VIF). We observed instabilities in the features' coefficients between Lin, Qdr, Cub, and Qua.
In this work, we conducted an experimental analysis to investigate how the performance of regression-obtained scheduling heuristics can be improved. Initially, we created a data set of scheduling observations by conducting quick scheduling simulations for various scheduling contexts. Subsequently, we utilized a multiple linear regression model to learn the optimal parameters of the proposed functions. Finally, we evaluated the scheduling performance of these heuristics as scheduling policies. Our study revealed that the scheduling performance of the aforementioned policies highly depends on the parameters (coefficients) obtained from the linear regression. We further discovered a multicollinearity effect that had a detrimental impact on the stability of the coefficients of the scheduling policies, ultimately leading to unsatisfactory scheduling outcomes.
We performed an experimental simulation campaign with workload data spanning 19 years, encompassing numerous platform and workload generations. We assessed the scheduling efficiency of the Lin and F2 functions by comparing them against other scheduling policies. Our results demonstrate that the regression-obtained scheduling heuristics can deliver stable and efficient scheduling performance across numerous HPC platforms and workloads without the need to readjust their coefficients over time. We also observed that regression-obtained scheduling heuristics that combine the characteristics p, q, and r in more unusual ways, like the policy F2 [6], possibly lead to slightly better performances than Lin. This slight performance increase comes with the drawback of lesser interpretability, as it is harder to interpret why the square roots and logarithms in F2 lead to better scheduling.
2A ). In average, about 80% of the differentially expressed genes (DEGs) per comparison are supported by all four DEG tools. As mentioned in the methods section, the final DEG list is determined by deriving the intersection of DEGs reported by all four tools. In the further analysis, we focused on DEGs with absolute log 2 fold change (log 2 FC) values greater than or equal 1. 5. Using GO Slim, we searched for enriched GO Biological Processes in the three strains at both time points. These data are summarized in Fig. 2B. Up-regulation of genes belonging to the category Carbohydrate Metabolic Process is evident for the wild type and to a more moderate extent for the Δskn7 mutant. A further analysis using the GO category Cellular Component revealed that the wild type is enriched for up-regulated cell wall-related genes.
Both strains show a similar down-regulation of genes belonging to the categories Ribosome Biogenesis, Cytoplasmic Translation, Mitochondrial Organization, Protein-containing Complex Assembly, Nucleocytoplasmic Transport and Metal Ion Homeostasis. Three-times more genes of the category Ribosome Biogenesis were downregulated in the wild type compared to the Δskn7 mutant (Fig. 2B ). The category Lipid Metabolic Process showed an inverse regulation in the wild type and the Δskn7 mutant and this included several genes involved in sterol biosynthesis that were up-regulated in the wild type, but down-regulated in the Δskn7 mutant. In a control experiment, we analysed five genes by qPCR that according to the RNA-seq data were differentially expressed in the wild type. Four genes of these genes were up-and one was down-regulated.
Data Raw data were pre-processed using the RNA-seq preprocessing pipeline "GEO2RNAseq" package  [50]  in R version 3. 2. 0. Briefly, low-quality reads and adapters were removed using Trimmomatic version 0. 36  [51]. rRNA reads were removed using SortMeRNA version 2. 1  [52]. High-quality reads were mapped against the A. fumigatus Af293 reference genome (version s03-m05-r11) obtained from the Aspergillus Genome Database using HISAT2 version 2. 1. 0  [53]. Read counts were estimated using Rsubread's feature Counts version 1. 20. 6  [54] ). The "Median Ratio Normalization" (MRN) method was employed to normalize read counts across all samples. Principal component analysis (PCA) was performed using "prcomp" functions from the "stats" package in R (R core team, 2015). DEGs were calculated for 15 comparisons focussing on differences in gene expression between time points and between strains.
The significant DEGs were tested by four different packages in R including DESeq version 1. 22. 1, DESeq2 version 1. 10. 1  [55] , edgeR version 3. 12. 1  [56] , and limma version 3. 26. 9  [57]. An absolute log 2 FC of 1. 5 and an FDR-adjusted p-value of 0. 01 were as cut-off. The final DEG list is determined by deriving the intersection of significant DEGs reported by all four aforementioned tools. The RNA-seq data of this study are available at the database repository Gene Expression Omnibus (GEO) with the accession number GSE224696. The data set was further analyzed with the Gene Ontology Enrichment tool using GO Slim at https:// fungi DB. org. Enriched GO terms of the ontology "Biological process" were searched using a p-value threshold of 0. 05. Gene sets were compared using Venny 2.
4, 500 mM NaCl and 1 mM TCEP was used for all samples. Prior to each run the column was equilibrated with at least 3 column volumes of buffer and the baseline monitored. All data from the run were collected using a sample to detector (Pilatus 2M Dectris) distance of 2. 81 m corresponding to an q range of 0. 008-0. 45 Å -1. Due to column separation of the sample, some dilution effects will occur prior to measurement. 1800 frames (2 sec/frame) were collected. Initial data processing was performed automatically using the Dahu pipeline, generating radially integrated, calibrated and normalised 1-D profiles for each frame  62, 63. All frames were then further processed using Scatter IV  64  , briefly, the frames were dropped into the software and 50 -100 frames selected for the background buffer.
Multi angle laser light scattering experiments 50 µl of ELF3 PrD Q0, Q7 or Q20 at a concentration of ~4-7mg/ml were loaded onto an S200 Increase size-exclusion column (Superdex 200 Increase10/300 GL, GE Healthcare) at a flow rate of 0. 5 ml min-1. The column was pre-equilibrated with 50 mM Bis Tris propane at pH 9. 4, 1 M NaCl, 1 mM TCEP and connected to a Hitachi Elite LaChrom UV detector and LAChrome Pump L-2130, a multi-angle laser light-scattering detector (DAWN HELEOS II, Wyatt Technology Corporation) and a refractive-index detector (Optilab T-rEX, Wyatt Technology Corporation). The data were processed with the ASTRA 6. 1. 7. 17 software (Wyatt Technology Corporation).
Funding Sources This project received support from the  ANR  ( ANR-19-CE20-0021 ) and  GRAL , a program from the  Chemistry and Biology Health Graduate School of the University Grenoble Alpes  ( ANR-17-EURE-0003 ). GRAL support for the µLife imaging facility was provided. This work benefited from access to the  MX-Grenoble , an Instruct-ERIC centre within the  Grenoble Partnership for Structural Biology (PSB). The X-ray diffraction and SAXS dilute and condensed phase experiments were performed on beamline ID23-2 and BM29 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. The SAXS temperature ramps experiments were performed at  Diamond Light Source  B21. Financial support was provided by  Instruct-ERIC  ( PID 13317 ). This work used the platforms of the Grenoble Instruct-ERIC center (ISBG; UAR 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ), supported by  FRISBI  ( ANR-10-INBS-0005-02 ).
The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation Recherche Medicale (FRM) , the fonds  FEDER  and the  GIS-Infrastructures en Biologie Sante et Agronomie (IBiSA). IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble  (IRIG, CEA). L. C. and P. E. M. acknowledges support from  CNRS Momentum program  (2017) and from the  Plan Cancer Equipment 2016. The CBS is a member of the  France-BioImaging (FBI) , national infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04-01 ) and of the  GIS IBISA (Infrastructures en Biologie Santé et Agronomie). J. R. K is supported by an  MRC Career Development Award  ( MR/W01632X/1 ).
The time-resolved analysis of heart rate (HR) and heart rate variability (HRV) is crucial for the evaluation of the dynamic changes of autonomic activity under different clinical and behavioral conditions. Standard HRV analysis is performed in the frequency domain because the sympathetic activations tend to increase low-frequency HRV oscillations, while the parasympathetic ones increase high-frequency HRV oscillations. However, a strict separation of HRV in frequency bands may cause biased estimations, especially in the low frequency range. To overcome this limitation, we propose a robust estimator that combines HR and HRV dynamics, based on the correlation of the Poincaré plot descriptors of interbeat intervals from the electrocardiogram. To validate our method, we used electrocardiograms gathered from different open databases where standardized paradigms were applied to elicit changes in autonomic activity.
A. Datasets description Twenty-four adults were recruited for the cold-pressor test. A total of 18 subjects were included in this study (six of them were discarded because of missing data in their ECG). Three trials of the cold pressor test were performed. Each trial consisted in a 5-minute resting period, followed by a 3-minute immersion of the hand in ice water (0°C) and a 2-minute recovery through immersion of the same hand in warmer water (32°C). In this study, the first trial was considered. Trials were considered in the -120 to 120 s with respect the onset of the cold-pressor test. ECG was measured using Finapres NOVA system (Finapres Medical Systems, Amsterdam, The Netherlands) with a sampling frequency of 200 Hz. The study was approved by the local ethical committee (Radboud University, ECS17022 and REC18021).
3%; 58/406) in the 'BRC-TP' sampling. The molecular diversity of yam-infecting members of the family Betaflexiviridae was also investigated. For this, 31 amplification products  (18, 11 , and 2 from the 'Guadeloupe', 'BRC-TP', and 'CNRA' samplings, respectively; Supplementary Table  S1 ) were cloned in pGEM-T Easy Vector (Promega, Charbonnières, France) according to the manufacturer's protocol. One to six clones per amplification product were randomly chosen and sequenced (Genewiz, Leipzig, Germany), resulting in a total of 80 nucleotide sequences of 310 nt length after primer sequences were removed: 50, 22 and 8 sequences originated from the 'Guadeloupe', 'BRC-TP', and 'CNRA' samplings, respectively (GenBank accession numbers: OP495428-OP495507). This dataset was completed with nucleotide sequences of reference isolates for each of the 15 recognized genera of the family Betaflexiviridae, banana virus X (BVX-Som; AY710267; unassigned Betaflexiviridae) and YLV-SG1 (KJ789130), sequences available for YVY isolates YVY-Mak and YVY-Dan (MK782910 and MK782911), identified in Nigeria by high-throughput sequencing (HTS)  [7] , and for dioscorea virus A (DioVA-PE5.
1; LC467961), a putative member of family Betaflexiviridae reported in yam in Brazil (Hayashi et al. , unpublished). In addition, sequences from a YVY isolate (IgC10YVYDr) and a BanMMV isolate (IgB10BanM-MVDa) identified in a D. rotundata plant from Benin and in a D. alata plant collected in Guadeloupe, respectively, were  also included. These sequences were obtained from contigs assembled de novo from yam RNASeq transcriptomic Illumina reads generated by the ARCAD project and referenced under numbers SRX1967863 and SRX1967874, respectively, in the SRA GenBank database  [13]. A nucleotide sequence alignment was performed using MAFFT v7. 490  [14]  and checked visually to ensure that none of the introduced gaps would disrupt conceptually translated protein sequences. A maximum-likelihood phylogenetic tree constructed using IQ-TREE v2.
Acknowledgements The authors wish to thank  Chantal Faure  ( UMR BFP, INRAE and University of Bordeaux, Villenave d'Ornon, France ) for help with nucleic acid extraction,  Rose-Marie Gomez  ( UR ASTRO, INRAE, Guadeloupe, France ) for technical help, and  Yacouba Bakayoko  and  Amani Michel Kouakou  for providing samples from the  CNRA  germplasm collection.
Funding This work was funded by the  Agence Nationale de la Recherche (ANR)  and the  Guadeloupe Region  through the  NetBiome program  under project  SafePGR  ( ANR-11-EBIM-0002-06 ) and by the  European Commission  and the  Guadeloupe Region  through the  Malin  project ( 2018-FED-1088 ) under the  EU's Seventh Framework Programme.
Data availability statement The data presented in this study are available in Suplementary Tables  S1-S2. The full dataset is openly available at Recherche Data Gouv, V1, https:// doi. org/ 10. 57745/ GNUXVA, UNF:6:uRiYq5/k7MnOABwsIvhK9A==. The nucleotide sequences reported in this work have been deposited in the GenBank database under accession numbers OP495428-OP495507.
Such information allows the computation of the combined symptom-medication score (CSMS), assessing the daily control of allergic rhinitis. 9  We included the daily monitoring data of European MASK-air® users (i) aged between 16 years (or lower-not below 13 years-for countries with a lower age of digital consent) and 90 years, (ii) with a self-reported diagnosis of allergic rhinitis and (iii) on SLIT for grass pollen. We analysed the data provided during the months of May and June (assuming that they corresponded to the grass pollen season in Europe) from 2015 to 2021. We performed a cross-sectional analysis, in which we studied all days reported between May and June for three different samples: (S1) all users under SLIT (of any type) for grass pollen; (S2) users taking SLIT tablets for grass pollen and (S3) users using SLIT (of any type) for grass pollen and reporting at least 1 day of AIT use during the studied period (to account for non-adherence and potential incorrect reporting of SLIT use).
Additional information about study methods and findings is available in the following repository: https://github. com/Berna rdoSo usaPi nto/Impro vemen t-of-daily -aller gy-contr ol-by-subli ngual -immun other apy-A-MASK-air-study MASK-air® is CE1-registered and complies with the General Data Protection Regulation. All data were anonymised prior to the study. Users agreed to having their data analysed for scientific purposes in the Terms of Use. An independent review board approval was not required for this study. In the cross-sectional analysis, we studied 3968 days from 171 patients in S1. Immunotherapy was reported in 2380 (60. 0%) days.
GM1, GM2, and GM3 were detected as deprotonated ions in the 2:1 extract in negative ion mode after polarity switch in FTMS by scanning m/ z = 1,100-1,650 Da, at Rm/z = 200 = 280,000 with lock mass activated at a common background (m/z = 1,175. 7768) for 30 s. Every scan is the average of two micro-scans, AGC was set to 1E6 and IT was set to 50 ms. PA, PI and PS were detected as deprotonated ions in the 2:1 extract in negative ion mode in FTMS by scanning m/z = 520-1,100 Da, at Rm/z = 200 = 280,000 with lock mass activated at a common background (m/z = 529. 4626) for 30 s. Every scan is the average of two micro-scans, AGC was set to 1E6 and IT was set to 50 ms. All data were acquired in centroid mode.
1 N-glycan and N-glycopeptide site specific compositional analysis Glycopeptide compositional analysis was performed from m/z features using in-house written SysBioWare software  (Vakhrushev et al. , 2009). For m/z feature recognition from full MS scans Minora Feature Detector Node of the Proteome discoverer 2. 3 (ThermoFisher Scientific) was used. A list of precursor ions (m/z, charge and retention time) was imported as ASCII data into SysBioWare and compositional assignment within 5 ppm mass tolerance was performed. The main building blocks used for the compositional analysis were: NeuAc, Hex, HexNAc, dHex and phosphate. The most prominent peptides corresponding to each potential glycosite were determined experimentally by comparing the yield of deamidated peptides before and after PNGase F treatment.
Native MS deconvolution For intact mass analysis, raw spectra were deconvoluted to zerocharge by Intact Mass software (Protein Metrics, CA, United States) using default settings  (Bern et al. , 2018). Glycoproteoforms were annotated by in-house written SysBioWare software  (Vakhrushev et al. , 2009)  using average masses of hexose, N-acetylhexosamine, and the known backbone mass of the naked protein sequence increment (GLA and AGA).
Together, these findings show that repeated dosing of GLA LAGD leads to efficient enzyme uptake and effective substrate clearance in key organs despite the complete lack of M6P moieties. GLA LAGD resulted in enhanced targeting to the heart compared to Fabrazyme at 1. 0 mg/kg, the 'standard' therapeutic dose, although this effect was lost at the lower 0. 3 mg/kg dose. Included are data of single infusion studies at 1. 0 mg/kg that were repeated to confirm findings of our previous study  (Tian et al. , 2019). (B) Gb3 substrate level quantified by shotgun lipidomics MS analysis of the organs of mice mentioned above. (C) Lyso Gb3 level in liver of mice mentioned above quantified by shotgun lipidomics MS analysis.
Outline of this study This study aims to bridge the above-mentioned research gaps by applying agent-based modeling with a synthetic population of Västra Götaland, Sweden. We simulate BEV driving & charging given their planned activities for typical weekdays. We calculate the spatiotemporal patterns of charging demand considering different BEV charging strategies. We compare our results with today's charging infrastructure, highlighting the remaining infrastructure needs to support 100% BEV penetration to better inform future planning. The remainder of this paper is organized as follows. In Methods, we describe the analytic framework including the datasets, simulation modules, and the methods for calculating charging demand. In Results, we present the outcomes of the simulation in three parts: charging demand of individuals; charging infrastructure demand; and a spatial comparison with today's charge points.
Simulating mobility trajectories The two key inputs for MATSim to simulate the synthetic population's mobility trajectories are the car drivers with their daily activity plans, and the road network (detailed below). The configuration follows the benchmark scenario of MATSim 13. 0 2  with minor modifications. The replanning strategy is a combination of BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%), where the percentage in the brackets indicates the share of agents who adopt these strategies. 3  After 200 iterations, we see utility scores stabilize for all agents. We take the output of the 200th iteration as the trajectories for the next module, BEV simulation. Detailed input descriptions of this module are the following.
BEV simulation The BEV simulation concerns charging infrastructure technology, battery energy density, and BEV energy consumption. We combine the current status and near-future projections in designing a future scenario of 100% BEV adoption. From this setup, one can also test different technology development scenarios. The BEV simulation assumes the agents drive BEVs to finish the activities on the simulation day following the trajectories from the MATSim simulation. The inputs are BEV fleet and discharging & charging dynamics, charging strategies, and charging access and initial SOC. Detailed input descriptions of this module are the following.
Acknowledgments This work is supported, in part, by  Thermo Fisher Scientific and Biocartis Inc. , the  "Agence Régionale de la Santé" (ARS) , the " Conseil Départemental 06 ," and the " Fondation ARC" (GENEXPOSOMICS Canc 'air). The content is solely the responsibility of the authors. The authors thank the patients and their families for participating in this study.