dataesr/scientific-paragraphs-categorization huggingface dataset:
matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 pigments chromatography (HPLC) concentration Water Total carotenoids x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Total chlorophylls x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Total diagnostic x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 pigments chromatography (HPLC) concentration Water Total pheophorbide a x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi.
matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Total pheophytin a x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Total pigments x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Violaxanthin x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Zeaxanthin x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi.
matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Deuterium excess x x x x ‰ Calculated with δ 1 O and δD Bennet Juhls bennet. juhls@awi. de Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 Water δ 1 O x x x x ‰ vs. Finnigan MAT Delta-S mass Bennet Juhls bennet. juhls@awi. de Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 SMOW spectrometer Water δD x x x x ‰ vs. Finnigan MAT Delta-S mass Bennet Juhls bennet. juhls@awi. de Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 SMOW spectrometer Sediment/Pore Anions (PO 3 , NO, x NA NA x NA Ion Chromatography Raoul-Marie Couture raoul. couture@chm. ulaval. ca In progress NA water NO, SO 2 , Cl, F) Sediment/Pore Dissolved organic x NA NA x NA Total Organic Carbon Analyzer Raoul-Marie Couture raoul.
To evaluate the superiority of k-QSM, comparisons with several state-of-the-art methods are performed in terms of QSM artifacts removing, anisotropy preserving, generalization ability, and clinical applications. Compared to existing methods, the k-QSM achieves a 22. 31% lower normalized root mean square error, 10. 30% higher peak signal-to-noise ratio (PSNR), 33. 10% lower high-frequency error norm, and 1. 06% higher structural similarity. In addition, the orientation-dependent susceptibility variation obtained by k-QSM is significant, verifying that k-QSM has the ability to preserve susceptibility anisotropy. When the trained models are tested on the dataset from different centers, our k-QSM shows a strong generalization ability with the highest PSNR. Moreover, by comparing the susceptibility maps between healthy controls and drug addicts with different methods, we found the proposed k-QSM is more sensitive to the susceptibility abnormality in the patients.
Note that, in all the convolutional layers, the kernel size = 3, stride = 1, and padding = 1. In the LeakyReLU layers, we set negative slope = 0. 1. In the ResBlocks, the introduction of Dropout layer can prevent overfitting  (Srivastava et al. , 2014)  and enhance the generalization ability  (Dahl et al. , 2013)  of our network; therefore, we set drop rate = 0. 2. c = iFFT (DCNN θ (D, X TKD )) QSM = abs (c) × sign(real(c)) (6 To train the k-QSM model, two loss functions were designed to optimize the network over the training dataset. The first was the mean squared error (MSE) to fix the ill-signals in TKD compared to COSMOS as defined by: loss MSE = 1 N N i=1 X label i -Xi 2 2 (7) where N is the size of the training set, X label i is COSMOS in k-space, • 2 2 is the squared L2 norm, and Xi is the output of the DCNN.
Data Acquisition and Processing The proposed k-QSM model was trained and tested on data from the F. M. Kirby Research Center for Functional Brain Imaging, which provides 36 MR phase measurements at 7T Scanner (Philips Achieva) with a voxel size of 1 × 1 × 1mm 3 on eight healthy subjects, including five orientations for four subjects with FOV = 224 × 224 × 100mm 3 , TR = 28ms, TE 1 / TE = 5/5ms, and 5 echoes; four orientations for three subjects with FOV = 224 × 224 × 100mm 3 , TR = 45ms, TE 1 / TE = 2/5ms, and 9 echoes; and four orientations for one subject with FOV=224 × 224 × 100mm 3 , TR=45ms, TE 1 / TE=2/5ms, and 16 echoes. Six subjects were used for training, and the training dataset was also augmented by exerting different dipole kernels on convolution with COSMOS QSM to obtain local field maps.
The remaining one subject was used for validation to prevent overfitting, and the other one subject was used for performance testing and measurement of magnetic susceptibility anisotropy. To verify the robustness and generalization ability of the trained k-QSM model, multiple healthy and pathological datasets from different centers were used. Data on healthy subjects were in vivo brain data provided for the 2016 QSM Reconstruction Challenge  (Langkammer et al. , 2018) , including 12 orientation measurements at 3T scanner (Siemens) on one healthy subject with voxel size = 1. 06 × 1. 06 × 1. 06mm 3 , FOV = 170 × 170 × 170mm 3 , TR = 25ms and TE = 35ms. Patient data were cited from the study of  Feng et al. (2021) , including data of patients with multiple sclerosis and intracranial hemorrhage.
To further evaluate our proposed method in practical applications, we tested it on in-house dataset acquired from 15 patients with heroin addiction and 15 age-and sex-matched healthy volunteers using a 3T GE scanner with voxel size = 1. 06 × 1. 06 × 1. 06mm 3 , FOV = 256 × 256 × 136mm 3 , TR = 28ms, TE 1 / TE = 3. 276/2. 352ms and 16 echoes. Signed informed consent was obtained from all the participants. This dataset was used to verify the variation in susceptibility among different gray matter regions between heroin addicts and healthy individuals. Multiple steps were required to preprocess phase images, including Laplacian-based phase unwrapping  (Schofield and Zhu, 2003) , brain extraction with FSL BET  (Smith, 2002) , V-SHARP for background field removal  (Özbay et al. , 2017) , echo averaging, and conventional QSM reconstruction using the STAR-QSM  (Wei et al.
Generalization of the k-QSM The generalization ability of a deep learning model on different datasets is crucial to its practical application. The trained k-QSM model was tested for the generalization ability on the data of healthy subjects (2016 QSM Reconstruction Challenge) and patients with MS and hemorrhage (cited from the study of  Feng et al. , 2021) , which were not used for training. Figure  6  displays the results of different deep-learning-based QSM reconstruction methods tested on the data of healthy individuals from three orthogonal views. The k-QSM 0. 1 achieves better results than k-QSM 0. 2. The proposed k-QSM outperforms the others in visualizing zoomed-in details (cyan boxes) and error maps (red boxes) related to COSMOS, with red arrows indicating lower errors of the k-QSM 0.
6% lower) and GP (47. 5% lower). In contrast, the LP-CNN overestimates the susceptibility with values 26. 4% higher than that of COSMOS. Statistical metrics are shown in Table  3  of the MAE  (Willmott and Matsuura, 2005) , RMSE, and Pearson's correlation coefficient (PCC) between a deep-learning method and COSMOS. Our k-QSM 0. 1 achieves the best performance with MAE and RMSE values 75. 68% better than MoDL-QSM, 50. 74% better than LP-CNN, and 62. 27% better than QSMnet + on average, and with PCC values 28. 57% better than MoDL-QSM, 2. 06% better than LP-CNN, and 1. 02% better than QSMnet +. Figure  8  shows the results of TKD, STAR-QSM, QSMnet + , MoDL-QSM, and k-QSM on the data of patients with MS and hemorrhage. All these QSM reconstruction methods showed the ability to detect the lesion regions of MS (red boxes) and hemorrhage (blue boxes).
Practical Application of the k-QSM To further evaluate our proposed method for clinical applications, we compare different QSM reconstruction methods on in-house dataset to explore whether the QSM reconstructed by different methods can distinguish drug addicts (DA) and healthy normal controls (NC). 51. 62% shrinkage (p = 0. 0014) in the ACC, 52. 62% decrease (p = 0. 0059) in the CN, and 119. 54% increase (p = 0. 025) in the putamen. Meanwhile, the results of STAR-QSM showed significant differences in insular (p = 0. 00039), STC (p = 0. 020), ACC (p = 0. 00070), and CN (p = 0. 0095), while the results of MoDL-QSM showed significant differences in insular (p = 0. 021), hippocampus (p = 0. 0058), ACC (p = 0. 012), and CN (p = 0. 018). Note that the magnetic susceptibility level should be compared with the absolute value.
There are differences among the results of different methods on the same data in one brain region. For the NC group, the results of k-QSM 0. 1 were significantly different from those of STAR-QSM only in the ACC (p = 0. 017). For the DA group, only in the hippocampus dose k-QSM 0. 1 showed no significant difference (p = 0. 66) with STAR-QSM, which means that the above methods show different performances on the data of drug addicts. As previously mentioned, the results of MoDL-QSM are too smooth to reveal richer details, with the standard deviation 10. 52% less than the results of k-QSM 0. 1 and STAR-QSM on average. According to previous studies  (Franklin et al. , 2002; Matochik et al. , 2003; Thompson et al. , 2004; Avants et al. , 2007; Groman et al. , 2013) , the proposed k-QSM can detect the expected magnetic susceptibility changes between healthy subjects and drug addicts in specific brain regions.
DISCUSSION In this study, a deep-learning method, k-QSM, was developed to perform ill-conditioned dipole inversion of QSM reconstruction. The proposed k-QSM measures high-quality COSMOS-like QSM (Figure  3  and Table  1 ) and shows the ability to preserve the anisotropy of tissue susceptibility in WM (Figure  5 ). It achieves better performance with truncated value of 0. 1 than 0. 2 regarding the ability of generalization and anisotropy preserving. The trained models of k-QSM and other deep-learning methods were tested on the datasets acquired from different centers, and the trained k-QSM shows the potential of generalization, achieving a balance between reconstructing richer high-frequency details and suppressing artifacts. The k-QSM can also detect the expected variation in susceptibility from healthy to drug addicts in specific regions of the brain.
Dmitriy et al. inferred that the biological tissue structure anisotropy influenced the calculation of susceptibility and the MSA of tissue in WM, and the MSA provided a quantitative interpretation of data from MR phase imaging of WM diseases  (Yablonskiy and Sukstanskii, 2017). Thus, the ability to maintain the MSA of k-QSM is important for the clinicopathologic diagnosis of WM diseases. Learn Less means that the k-QSM only learns to fix the ill-posed signals in TKD around zero values of the dipole kernel and Infer More indicates that the results of k-QSM are both COSMOS-like and anisotropic in tissue susceptibility. Additionally, the proposed k-QSM demonstrates strong generalization ability on datasets not used in training, including healthy and patient datasets from different acquisition sites.
The healthy dataset was measured by 3T scanner (Siemens) and the patient dataset included data of patients with multiple sclerosis and hemorrhage acquired with a 3T GE HDxt MR scanner. Even though the k-QSM model is trained on the 7T dataset, it still works well on these 3T datasets. The utilization of the dropout layer in the deep neural network makes it possible for k-QSM to prevent overfitting  (Srivastava et al. , 2014)  and enhance the ability of generalization  (Dahl et al. , 2013). Besides, as mentioned above, k-QSM keeps the correct signals in the Fourier domain from TKD. In the meanwhile, k-QSM inherits generalization ability from TKD, which works well for all datasets as an analytical solution with numerous correct signals, and, thus, k-QSM 0. 1 shows stronger generalization ability than k-QSM 0.
2. What is more, the k-QSM method is trained in k-space directly, and expresses the field-susceptibility relationship as Equation (2) described, thus, the trained k-QSM model can be applied to almost arbitrary dataset to process QSM dipole inversion. Furthermore, the proposed k-QSM can also reconstruct QSM on dataset of drug addiction and reveal the impairment of drug administration through susceptibility changes in brain tissues between DA and healthy NC groups. Consistent with the reported decreases in gray matter concentration in the anterior cingulate, superior temporal cortices and anteroventral insular of cocaine abusers  (Franklin et al. , 2002; Matochik et al. , 2003) , smaller hippocampal volumes of MA abusers  (Thompson et al. , 2004) , and smaller caudate volumes of cocaine abusers  (Avants et al.
The OYT6 line list has been generated using empirically refined ˜ X 2 + and ˜ A 2 state potential energy surfaces, high-level ab initio transition dipole moment surfaces, and a rigorous treatment of both Renner-Teller and spin-orbit coupling effects, which are necessary for correctly modelling the CaOH spectrum. Post-processing of the CaOH line list has been performed so as to tailor it to high-resolution applications, i. e. by replacing calculated energy levels with more accurate empirically deri ved v alues (where available), hence improving the accuracy of the predicted line positions in certain regions. The OYT6 line list is available from the ExoMol data base at www. exomol. com and the CDS astronomical data base.
( 2021 ). A full-dimensional ab initio spectroscopic model of the ˜ A 2 -˜ X 2 + band system of CaOH has also been reported by the authors  (Owens et al. 2021a  ). High-level ab initio theory was used to construct new potential energy and transition dipole moment surfaces, and both Renner-Teller and spin-orbit coupling effects, which are essential for correctly reproducing the CaOH spectrum, were accounted for in the calculations. Whilst the ground ˜ X 2 + PES was empirically refined to the MARVEL data set of energy le vels, v astly improving its accuracy, the ˜ A 2 state PES was not rigorously refined as further development of the computer program EVEREST  (Mitrushchenkov 2012 )  was necessary. This has now been completed for this work, ultimately leading to considerably more accurate ˜ A 2 state energy levels and ˜ A -˜ X transition wavenumbers MNRAS 516,  3995-4002 (2022)  (up to several orders-of-magnitude).
Extension of the EVEREST code The EVEREST code  (Mitrushchenkov 2012 )  was used in all rotationvibration (rovibrational) and rovibronic line list calculations and for the ˜ A 2 PES refinement. EVEREST is capable of treating interacting electronic states with spin-dependent coupling and Renner-Teller effects in triatomic molecules. The program is general in its design and employs an exact kinetic energy operator. Extending the functionality of EVEREST to be able to perform PES refinement required several developments. The PESs of the ground state V X and excited states V A , V A are represented using the Taylor-type expansion V S = ij k f ( S) ij k ξ i 1 ξ j 2 ξ k 3 , S = X, A , A , (1) where the coordinates ξ 1 = ( r 1 -r eq 1 ) /r 1 , ( 2 ) ξ 2 = ( r 2 -r eq 2 ) /r 2 , ( 3 ) ξ 3 = α -α eq , (4) with the internal stretching coordinates r 1 = r CaO , r 2 = r OH , the interbond angle α = ∠ (CaOH), and the equilibrium parameters r eq 1 , r eq 2 and α eq [see  Owens et al.
2 • for the bond lengths or inter-bond angles, respectively. The implementation of the refinement procedure closely follows a previously developed Fortran wrapper for the triatomic nuclear motion program DVR3D reported in  Polyansky et al. ( 2018 ) , where the underlying ab initio PES was used to constrain the shape of the refined PES via a simultaneous fit to the original ab initio energies. The fitting weights were initially set to be inversely proportional to the experimental (MARVEL) uncertainties and then adjusted via Watson's robust weighting scheme  (Watson 2003 ). The expectation values of the potential expansions are now a part of the EVEREST program. The least-squares fitting procedure was implemented as an external, generalized fitting wrapper written in Fortran 95.
In Fig. 4  , the temperature dependence of the CaOH spectrum is illustrated, and we have simulated absolute absorption crosssections at a resolution of 1 cm -1 using a Gaussian line profile with a half width at half maximum (HWHM) of 1 cm -1. All spectral simulations were done using the EXOCROSS program  (Yurchenko, Al-Refaie & Tennyson 2018b  ). The strongest rovibronic features of the OYT6 line list are around 16 000 cm -1 and even at high temperatures ( T = 3000 K) these still dominate the CaOH spectrum. This region is significant for atmospheric studies of hot rocky super-Earths, a class of exoplanets that are in close proximity to their host star and exposed to extremely high temperatures, e. g. up to 4000 K. Closer inspection of the main infrared part of the spectrum is shown in Fig.
Here, k is the Boltzmann constant, h is the Planck constant, c is the speed of light and T is the absolute temperature. The nuclear spin statistical weight g ns = 2 for CaOH and Q ( T ) is the temperature-dependent partition function. Interestingly, the structure of the bands using the refined ˜ A 2 state PES are quantitatively different to those predicted using the original ab initio PES, see Fig. 5  in  Owens et al. ( 2021a ). This behaviour has been encountered before by the ExoMol project and highlights the fact that rovibronic spectra can be v ery sensitiv e to the quality of the underlying PESs with both line positions and intensities being affected. In Fig. 7  , a CaOH OYT6 stick spectrum at T = 300 K, computed using equation (  11 ), is plotted against pure rotational transitions in the ground ˜ X 2 + state extracted from the Cologne Database for Molecular Spectroscopy  (CDMS; M üller et al.
strong ˜ A 2 -˜ X 2 + band around 16 000 cm -1 ( λ = 0. 63 μm) to be of significant interest as it dominates the spectrum at very high temperatures, which will be rele v ant to the conditions found on hot rocky super-Earth exoplanets. Also important is that the OYT6 line list has been adapted to high-resolution applications by replacing computed energy levels with the more accurate empirically derived MARVEL values where available. To generate the OYT6 line list, extension of the computer program EVEREST was necessary such that the ˜ A 2 state PESs of CaOH could be rigorously refined to experimental data, thus considerably improving the accuracy of the computed spectra. The extension of EVEREST will also lend itself to future spectroscopic investigations, for example, the near -ultra violet (UV) spectrum of water which is of considerable interest due to disagreement o v er absorption in this region, see  Wang et al.
AC K N OW L E D G E M E N T S This w ork w as supported by the  STFC  Projects No. ST/M001334/1  and  ST/R000476/1. The authors acknowledge the use of the  UCL Legion High Performance Computing Facility (Legion@UCL)  and associated support services in the completion of this work, along with the Cambridge Service for Data Driven Discovery ( CSD3 ), part of which is operated by the  University of Cambridge Research Computing  on behalf of the  STFC DiRAC HPC Facility  ( www. dirac. ac. uk). The DiRAC component of CSD3 was funded by  BEIS  capital funding via STFC capital grants  ST/P002307/1  and  ST/R002452/1  and  STFC  operations grant  ST/R00689X/1. DiRAC is part of the  National e-Infrastructure. This work was also supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  through Advance Grant number  883830.
The objectives of the study were (i) to investigate how foliar Hg(0) uptake depends on the physiological traits of tree species and (ii) to study how the stomatal response of trees to climate conditions controls foliar Hg(0) uptake. We address these objectives by analyzing a large dataset of foliar Hg uptake rates, tree functional traits, and climate conditions across natural gradients in European forests covering various tree species and climate conditions. 2 Material and methods.
Foliage sampling and dataset description The final dataset for this study comprises Hg concentrations of 3569 foliage samples from 2015 and 2017, of which 2129 samples were provided by 17 participating countries of the United Nations Economic Commission for Europe (UNECE) International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The samples include sun-exposed leaves and needles from the upper third of the tree canopy of five trees (Austrian Bio-Indicator Grid: two trees) of the main species on the plot taken during full development in summer (deciduous species) or at the onset of dormant season in autumn (evergreen species) using harmonized national methods according to the ICP Forests Manual  (Rautio et al. , 2016)  as described, for example, in  Jonard et al.
The combined dataset consists of 3569 foliage samples encompassing 23 species of coniferous and deciduous trees (Table  S1  in the Supplement). The most frequent (number of samples > 100) species within the dataset are Norway spruce (Picea abies; n = 2073), Scots pine (Pinus sylvestris; n = 413), European beech (Fagus sylvatica; n = 372), silver fir (Abies alba; n = 162), sessile oak (Quercus petraea; n = 133), Austrian pine (Pinus nigra; n = 125), and common oak (Quercus robur; n = 101). We pooled individual tree species into groups of tree species genera (e. g. , beech, oak, pine, spruce; see Table  S1 ). Coniferous samples consist of needles of different age classes: most of the needle samples (n = 1958) flushed in the sampling season (current season; y 0 ), 600 samples are 1-year-old (y 1 ), 121 samples are 2-year-old (y 2 ), 125 are 3-year-old (y 3 ), 22 samples are 4year-old (y 4 ), 60 samples are 5-year-old (y 5 ), and 3 samples are 6-year-old (y 6 ) needles.
All data analysis of this study concerning tree species, foliage structure, nutrient contents, and meteorological and site-specific parameters (Sects. 3. 1-3. 6) is based on Hg values of current-season (y 0 ) foliage. Foliage samples originate from 995 European sites: 232 sites are ICP Forests Level II forest monitoring plots, 737 locations are sampling sites of the Austrian Bio-Indicator Grid, and the remaining sites (26) are not classified within the ICP Forests program. See Fig. 1  for a geographic overview of foliage sampling sites from the sampling year 2017. We assembled the foliar Hg concentration dataset including the following metadata: sampling date, needle age class, leaf mass per area (LMA; 19 % of samples), drying temperature, leaf nitrogen (N), and organic carbon (C org ) concentration.