dataesr/scientific-paragraphs-categorization huggingface dataset:
At the same time, comparisons with simpler systems such as binary alloys with similar bulk Cr concentrations are critical to identify more complex interactions. To explore these effects further, the corrosion protectiveness of the formed passive films must be studied for varying alloy Cr content. This requires a complex set of analytical datasets, including valence states present in the passive film, elemental composition, and cation distribution in conjunction with its electrochemical corrosion behavior. Complementary techniques, including XPS and atom probe tomography (APT), are ideal in this context to provide quantitative analyses of nanoscale elemental segregation and partitioning, localized enrichment/depletion, and chemical states. In this study, a series of non-equimolar, single phase, solid solution Ni 38 Fe 20 Cr x Mn 21- 0.
003 detected ions/pulse detection rate. Data were reconstructed and analyzed using the Interactive Visualization and Analysis Software (IVAS), version 3. 8. 8 by CAMECA. Mass spectrum peak ranging is defined in supplemental Figures  S1-S3. Elemental composition profiles were determined using the proximity histogram method across O isoconcentration surfaces (located at the approximate base alloy/passive film interface, which varied in at. % for each alloy), with a bin width of 0. 2 nm. The extracted concentration profiles were normalized to consider only alloy principal elements (i. e. , excluding O or other trace species) to enable more direct interrogation of enrichment or depletion relative to the base alloy composition. Definitions of base alloy (metal)/passive film, and passive film/Cr cap interfaces are given in supplemental Table  S4.
Acknowledgements This work was supported as part of the  Center for Performance and Design of Nuclear Waste Forms and Containers , an  Energy Frontier Research Center  funded by the  U. S. Department of Energy (DOE), Office of Science, Basic Energy Sciences  under Award # DE-SC0016584. PHI VersaProbe III system was supported by  NSF  Award  162601 ,  MRI Acquisition of an X-Ray Photoelectron Spectrometer for Chemical Mapping of Evolving Surfaces: A Regional Instrument for Research and Teaching. DKS and LJK acknowledge support from the  U. S. DOE, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division  for performing.
Test sites The evaluation of the GPP models was performed using in situ observations from eddy covariance stations. Test sites were selected from the FLUXNET2015 dataset  (Pastorello et al. , 2020)  and the ICOS "2018 drought initiative" dataset  (Drought 2018 Team and ICOS Ecosystem Thematic Centre, 2019). It was ensured that the sites had a homogeneous land cover, which could be captured by the remote sensing products. A site was considered homogeneous when in 1 km × 1 km area surrounding the station location was dominated by a unique vegetation type (i. e. grassland, deciduous forest, evergreen forest). The site homogeneity was visually evaluated using high-resolution satellite images in Google Earth. Additionally, the sites were required to have a minimum of 3 years of GPP data since 1 January 2007 (i.
e. the start of the SIF time series). This resulted in a selection of 61 sites, listed in Table  1. The dataset contained 461 years worth of GPP data, in which evergreen needleleaf forest (ENF) and the mid-latitude temperature-driven hydroclimatic biome (MidL_T;  Papagiannopoulou et al. , 2018)  were dominantly represented. All data were pre-processed with the ONEFLUX pipeline  (Pastorello et al. , 2020). The observed net ecosystem exchange was partitioned into the ecosystem respiration and GPP components using the daytime fluxes and a constant friction velocity threshold across years (labelled as GPP_DT_CUT in the database). Depending on site data quality, the reference GPP (GPP_DT_CUT_REF) or mean GPP (GPP_DT_CUT_MEAN) method was selected. Daily data with a quality flag indicating poor gap filling (QF < 0.
Meteorological data Incoming short-wave radiation, long-wave radiation and precipitation data, required by the meteo-driven and hybrid GPP models, were taken from the half-hourly tower observations. Due to large gaps in the atmospheric humidity time series, ERA5 was used as an alternative source for air temperature, atmospheric humidity, wind speed and atmospheric pressure  (Hersbach et al. , 2020). It was verified that the impact of the use of ERA5 instead of local observations was limited for these variables (Fig. S2  and Tables  S3  and S4  in the Supplement). The forcing from ERA5 (hourly resolution) was linearly interpolated to match the 30 min temporal resolution from the tower observations. The atmospheric CO 2 concentration was taken from the TRENDY time series  (Sitch et al. , 2015 ; https://sites.
Remote sensing data The simplest models considered were the linear regressions based on remotely sensed proxies of GPP, including VI and SIF. Remote sensing data were gathered from SPOT Vegetation+PROBA-V (SPV) for each tower location (the nearest pixel). This data product has a 10 d interval and 1 km resolution. The SPV decadal synthesis product is derived using the "maximum value composite" procedure after quality check of SPV native data and gives the best reflectance value on the 10 d time interval. Though daily data are available, they were not used here. The use of daily data would introduce gaps and noise in the SPV time series (in case of cloudy conditions at satellite overpass time, for instance) while not adding significant information on the vegetation status throughout the study period.
, 2022). It was calcu-Table  2. Overview of the RS-driven, hybrid and meteo-driven GPP models used in this study. The following modelling methodologies are used: quantile regression (QR), machine learning (ML), light-use efficiency (LUE) models and dynamic global vegetation models (DGVMs). The remote sensing (RS) sources are SPOT-Vegetation+PROBA-V (SPV), GOME-2, MODIS and Copernicus Global Land Service (CGLS) products. The short-wave radiation (SWrad) and other meteorological data were obtained from in situ tower observations, ERA-5 and GEOS-5. lated as follows:.
RS-based regression models The simplest models considered were the linear regressions based on remotely sensed proxies of GPP. A robust linear regression model of the RS data versus the daily GPP was constructed using quantile regression  (Koenker and Hallock, 2001). The complete dataset was used to obtain a model for each proxy. The use of daily or 16 d average GPP did not have a strong impact on the results. Only NIRvP, which used in situ incoming short-wave radiation observations, had a significantly steeper slope using the daily resolution GPP (see Fig. S1 ). Note that the training data used here were also used in the evaluation of the model performance. Furthermore, most models in this study were directly or indirectly trained with data from eddy covariance towers (FluxCom  (Jung et al.
Machine learning models The FluxCom dataset consists of up-scaled FLUXNET observations, using machine learning, remote sensing data and meteorological data  (Jung et al. , 2020). In this study, we considered the FluxCom RS GPP product (0. 0833 • grid, 8 d resolution), which relies on MODIS observations and the FluxCom RSMet GPP product (0. 5 • grid, daily resolution), which incorporates supplementary ERA5 meteorological data. Notably, a basic soil water balance model is used to derive the water availability index from the meteorological data and ingest it in the FluxCom RSMet machine learning algorithm  (Tramontana et al. , 2016). For each tower location, the closest pixel was extracted from the database.
GPP = APAR (5) This approach forms the basis of the MODIS MOD17 GPP product  (Running et al. , 2004)  and the LSA SAF GPP product  (Martínez et al. , 2020). The algorithm behind MOD17 is a fairly simplistic formulation, where is linearly dependent on air temperature and vapour pressure deficit. Atmospheric forcings for this product are taken from the GMAO/NASA daily global meteorological reanalysis dataset, generated by GEOS-5 (Goddard Earth Observing System-5). Soil moisture is not considered in the MOD17 model  (Running et al. , 2004). Conversely, in the LSA SAF model depends on the ratio between the actual and potential evapotranspiration. Consequently, the impact of soil moisture is indirectly considered. For MOD17, the closest pixel was extracted for each tower site (MOD17 GPP is available at 1 km resolution with 8 d interval).
Analysis To evaluate the performance of the models to capture the temporal variability, the time series in the dataset were decomposed in two ways: (1) by separating the inter-site variability, seasonal variability, and variability of seasonal anomalies and (2) by separating daily, weekly, monthly, seasonal, and interannual components with singular spectral analysis (SSA). The performance at these timescales was evaluated by comparing the simulated variability (quantified by the standard deviation, σ ) in observations and simulations and by computing the Pearson correlation (r). Additionally, the covariance (cov) between GPP and its driver variables was used to assess the sensitivity of GPP to these variables. It was evaluated whether the models reproduce the observed patterns. Finally, the accuracy of the simulated carbon phenology was evaluated by comparing the timing of the simulated seasonal GPP cycle with observations.
Inter-site, seasonal and anomaly components The variability of the simulated and observed GPP was decomposed into the inter-site (i. e. spatial) component, seasonal component and the component associated with the anomalies. If we concatenate the GPP time series from all sites into one array, we can decompose it as follows: where X all is the full dataset, X site contains the mean GPP of each site, X seas contains the mean seasonal cycle of each site (after subtracting the mean of the site) and X anom contains the resulting anomalies. An illustration of this decomposition is given in Fig. 1. The mean seasonal cycle was obtained by subtracting the time series mean and computing the smoothed (20 d moving average) mean annual cycle. The accuracy of the models to capture each of these components was evaluated using the metrics given further below.
This was achieved using weights proportional to the lower-frequency limit of each bin. An example of the analysis is shown in Fig. 2. The benefit of SSA compared to other spectral disaggregating methods (e. g. the Fourier transformation) is that it is less sensitive to gaps in the dataset and that it can handle datasets with a lower sampling frequency (e. g. the NDVI time series with 8 d resolution). Consequently, datasets with lower sampling frequency have no signal at the daily timescale. The SSA was applied to the observed and simulated GPP, allowing evaluation at each timescale. The evaluation was performed using the metrics given below.
Performance metrics The daily GPP estimations from the various models were compared to the observed GPP at the eddy covariance stations (Table  1 ). The variability of the (decomposed) time series was quantified using the standard deviation of the data (σ ). The relative variance (rel. σ 2 ) of a time series component was calculated as rel. σ 2 = σ 2 comp σ 2 all , (7) where σ comp and σ all are the standard deviations (σ ) of the component and the full dataset, respectively. This calculation assumes all components to be independent (as the covariance is ignored). It was verified that the covariance of the components is negligible compared to the variance. Detailed results are given in Tables  S8  and S9. Furthermore, the performance of the models was quantified using the Pearson correlation r: r = n o (y * -y * )(y o -y o ) n o (y * -y * ) 2 n o (y o -y o ) 2 , ( 8 ) where y * and y o are the predicted and observed values, respectively; y represents the mean of y; and n o represents the number of observations.
Inter-site and seasonal variability A comparison of the variability of GPP in observations and simulations is given in Table  4. The overall observed variability of σ = 4. 18 g C m -2 d -1 was underestimated in all models, except LSA SAF. After decomposing the observed GPP dataset, the inter-site variance represented 18 % of the total variance, the seasonal cycles represented 62 % and the anomalies represented 24 % (the sum of these fractions is larger than 100 % due to covariances; see Tables  S8  and S9 ). This partitioning was not well represented in the NDVI, EVI or NIRv time series, where a large fraction of the variance (> 30 %) was attributed to the inter-site component and a very small fraction (< 12 %) to the anomalies. In the NDVI observations, the inter-site variance was even larger than the seasonal variance.
In SIF, the contribution of the spatial and seasonal components was reasonably accurate, but the relative variance of the anomalies was too low (10 %). The relative variance of the seasonal pattern was strongly overestimated in the FluxCom products (∼ 80 %), whereas the contribution of the anomalies was the lowest of all datasets (∼ 5 %). The closest match with the observed variance partitioning was found in NIRvP, MOD17, LSA SAF and the DGVMs. To ensure that these results were not affected by the temporal resolution of the time series, the same analysis was performed after downsampling to a 10 d interval. This did not result in substantial changes in the variability or its partitioning (Table  S6 ). Depending on the land cover type, the variability and its partitioning between different components varied (Table  5 ).
The correlation of the simulated GPP was substantially reduced after subtracting the mean seasonal cycle. For NDVI, EVI, NIRv and SIF, r anom was smaller than 0. 2 (median for all sites). LSA SAF and ISBA were the only models with r anom > 0. 5 (median for all sites). The performance of FluxCom to estimate the anomalies was similar to the NDVI-, EVI-and NIRv-based models. Though FluxCom RSMet achieved r anom = 0. 43 (median for all sites), the variability of the anomalies was strongly underestimated. A notable difference emerged in the anomalies simulated with NIRvP and SIF. While both datasets showed a similar performance in the full GPP time series, SIF performed much poorer than NIRvP in the anomalies. The RS-driven models, which relied purely on RS observation of the vegetation state, had a significantly lower σ anom (Wilcoxon p < 0.
4). Compared to NIRv (and SIF), NIRvP captured anomalies in GPP more accurately, in particular at the weekly timescale (Figs. 3  and 5 ). Alternatively, light-use efficiency models ingest more meteorological variables, such as VPD and TA, in addition to SWrad and vegetation state variables. Consequently, the quality of the simulated GPP strongly depended on the quality of the meteorological forcings. The MOD17 product relied on the coarse GMAO/NASA reanalysis dataset for the meteorological forcing and failed to achieve a better performance than the VI-based models (Fig. 3 ). The LSA SAF GPP model, here forced by in situ SWrad observations, excelled in the simulation of temporal variability at all timescales and in all domains. Although there were other factors that impact the performance (e.
Acknowledgements. The research presented in this paper is supported by  BELSPO (Belgian Science Policy Office)  in the frame of the  STEREO III programme  -projects  ECOPROPHET  and  ECOPROPHECIES. We thank the countless contributors behind the scenes of the FLUXNET2015 dataset (Pastorello et al. , 2020) and the ICOS "2018  drought initiative "  dataset (Drought 2018 Team  and  ICOS Ecosystem Thematic Centre , 2019). These publicly available datasets are the keystone of this study. Financial support. This research has been supported by the  Belgian Federal Science Policy Office (ECOPROPHET  (grant no. SR/00/334 ) and  ECOPROPHECIES  (grant no. SR/34/211 )). Review statement. This paper was edited by  Paul Stoy  and reviewed by two anonymous referees.
Code and data availability. The dataset is published at https://doi. org/10. 5281/zenodo. 7928514  (De Pue et al. , 2023). It contains GPP from all sources plus in situ radiation, temperature, vapour pressure deficit and ERA5 soil moisture. The scripts used in this study are freely available upon request to the authors. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-20-4795-2023-supplement.
acuminata ssp. malaccensis accessions that form part of the core Musa collection used in a diversity study  [59] ; c improved diploids and a selected number of breeding lines from IITA, Uganda. Musa balbisiana (BB genome) served as a negative control for A-genome-specific amplification of 29730. In the Foc-STR4 and Foc-TR4 columns, resistances were generally defined as R: resistant, SS: slightly susceptible, S: susceptible, and n/a: data not available. Phenotypic data was referenced from multiple studies performed as either pot or field trials. ITC numbers are indicated on accessions where available, while other numbers correspond to accessions in their respective germplasm collections (MMC-NARO, Uganda; MRF-Maroochy Research Facility, QLD, AUS; and MUSA-INIVIT, Cuba). To further test this marker and aid the marker-assisted selection of Foc-STR4and Foc-TR4-resistant lines, we screened 72 accessions from the IITA collection (Uganda) and 46 accessions from the IITA's M.
In Arabidopsis, FLAGELLIN SENSING2 (FLS2) recognises an elicitor epitope from the bacterial flagellin  [85] , and PEP RECEPTOR 1 (PEPR1) and PEPR2 recognise plant elicitor peptides, or peps, to activate a defense against Pythium irregulare  [86, 87]. In rice, LRR RK Xa21 recognises a highly conserved protein, RaxX, from Xanthomonas species to trigger immune responses  [88]. Overall, there are multiple resistance genes differentially expressed between the S and R banana progenies with similar temporal expression profiles. All of them are indicative of a rapid response in the induction of resistance gene transcripts at the onset of STR4 infection. This suggests that these genes may act in close proximity to one another or even belong to the same gene network. Co-expression gene networks will be constructed from RNA sequencing data to identify co-expression modules.
Data generated from individual libraries were used to align to 'DH Pahang' v1 using SOAPaligner v2. 21  [102] , and SNPs were called using SGSautoSNP (Second-Generation Sequencing AutoSNP)  [103]. SNP profiles were visualised in an aligned format using the Integrative Genomics Viewer  [104] , and gene models from 'DH-Pahang' v1 'https://banana-genome-hub. southgreen. fr' (accessed on 5 March 2023) were used to identify genes and SNPs suitable for marker development. Restriction enzyme-cutting sites covering the SNP site were identified using 'NEB cutter v2. 0' 'https://nc2. neb. com/NEBcutter2/' (accessed on 5 March 2023). Enzymes that had multiple restriction sites within a 400 bp region flanking the SNP on each side were avoided. Primers flanking a 344-795 bp amplicon were designed using 'Primer 3'  [105]  and further checked for binding specificity using 'Oligoanalyzer' 'https://sg.
Adaptor sequences and low-quality reads were filtered out using 'Fastp'  [107]. Clean paired-end reads were then aligned to 'DH-Pahang' v4 reference genome using 'STAR' v2. 7. 10a and default parameters for all except '-outFilterMismatchNmax 6' and '-alignIntronMax 10000'  [108]. Non-normalized read counts were tabulated with 'Feature-Counts' software (option: -M -g ID -t gene -p)  [109]  and then normalised to account for differences in sequencing depth among samples using the median-of-ratios method  [110]. This value was calculated as the gene counts divided by a size factor specific to a sample, determined by the median ratio of gene counts relative to geometric mean of the gene counts per gene. DEGs were identified from pairwise comparisons between resistant and susceptible progenies at each time point using the 'DESeq2' R package  [111].
Statistical Analyses The statistical software SPSS v28. 0. 1. 0 (142) (IBM Corp. , Armonk, NY, USA) was used to perform the statistical analysis described in this study. One-way ANOVA was performed in a pair-wise manner, with phenotype set as a dependent variable and marker-defined genotypes (B/H) as factors, to compare the means of STR4 and TR4 sensitivity at these loci. Any 'A' alleles were considered as 'H' for the purpose of statistical analysis, as resistance is completely dominant over susceptibility at this locus. The eta-squared (η 2 ) values on the phenotype were estimated on the basis of the fixed-effects model and reflected the phenotypic variation explained at each marker-defined locus. To analyze the STR4 and TR4 phenotypes of the recombinants, Waller-Duncan's multiple range testing was performed as a post hoc test to separate the means of the recombinants into subsets by least significant difference (LSD).
Acknowledgments: Musa acuminata ssp. malaccensis accessions  'Ma845', 'Ma846', 'Ma848', 'Ma850', 'Ma851' , and 'Ma852' were kindly obtained from  Sharon Hamill  through the Australian in vitro banana cultivar collection that is maintained in the  Quality Banana Approved Nursery (QBAN) scheme accredited Plant Biotechnology Laboratory  located at the  Maroochy Research Facility, Department of Agriculture and Fisheries, Nambour, Queensland ,  Australia. Foc VCG0120  isolates  BRIP63488 ,  BRIP43781 , and  BRIP42331  were kindly obtained from the  Queensland Plant Pathology Herbarium at the EcoSciences Precinct, Department of Agriculture and Fisheries, Brisbane, Queensland, Australia. We thank  Harry Cosgrove ,  Xueying Li ,  Yuhan Liu ,  Amelie Soper ,  Siyuan Gao , and  Angie Mitchell  for technical assistance in plant tissue culture.
Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases.
Using the proposed approach, high F1-scores of 99. 80% and 99. 64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99. 88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99. 39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.
Introduction It is estimated that over 300 million resting ECG tests are performed every year  [1] , to which exercise ECG tests, and a number of procedures involving ECG synchronization must be added. Such procedures may be imaging the heart or ablating electrical pathways. Over the last few decades, ECG has extensively been studied, focusing on detecting QRS complexes, and classifying features  [2]. In an ECG, P-waves indicate atrial depolarization, QRS complexes correspond to ventricular depolarization and T-wave to ventricular repolarization (Figure  1 ). Automated analyses of ECG signals have been accelerated with the public availability of electronic ECG databases  [3]. In ECG, QRS complexes consist of short-time pulses that contain R-peaks with steep slopes and high amplitudes. Algorithms to detect R-peaks and to measure heart rates are ubiquitous in medicine, and automated analyses of ECG are increasingly common.