dataesr/scientific-paragraphs-categorization huggingface dataset:
291294 ). We acknowledge fundings from the  ENIGMASS French LabEx (R. A. and F. R. ), the  CNES post-doctoral fellowship program (R. A. ) , the  CNES doctoral fellowship program  (A. R. ) and the  FOCUS   French LabEx doctoral fellowship program  (A. R. ). R. A. acknowledges support from  Spanish Ministerio de Economía and Competitividad (MINECO)  through grant number  AYA2015-66211-C2-2. This work has benefited from the support of the  European Research Council Advanced Grants ORIS-TARS  and  M2C  under the  European Unions Seventh Framework Programme  (Grant Agreement Nos. 291294  and  340519 ). Support for this work was provided by  NASA  through  SAO Award Number   SV2-82023  issued by the  Chandra X-Ray Observatory Center , which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of  NASA  under contract  NAS8-03060.
Widjaja et al. [39] , who found that the B. E. corresponding to PdO gradually increased with decreasing the particle size of PdO supported on alumina. Additionally, the relative surface concentrations of Pd and Ce show a decreased trend with increasing Pd size. This suggests a loss of Pd and Ce dispersion onto alumina at increasing calcination temperature, in line with XRD analysis. Generally, the higher the dispersion of Pd and Ce, the more active sites may be provided for the reaction of CH4 conversion  [40, 41]. To determine the relative concentration of metallic Pd and PdO species, the semiquantitative analysis accounted for a nonlinear Shirley background subtraction with the help of the CasaXPS software. Fig. 7  showed the relationship between the ratio of surface Pd 0 /(Pd 0 + PdO) and Pd particle size.
Acknowledgement This work was supported by the  National Key R&D Program of China  (  2016YFC0204902  ), the  National Natural Science Foundation of China  ( 21673146 ), the  Graduate Student's Research and Innovation Fund of Sichuan University  (  2018YJSY093  ), and the  Doctoral Graduate Students' Academic Visit Fund of Sichuan University. We also thank the  Yunfei Tian of the Analytical & Testing Center of Sichuan University  for XPS work and  Shanling Wang  of the  Analytical & Testing Center (ATC) of Sichuan University  for performing TEM characterization.
Characterization Methods The presence of magnetic molecules on the surface of silica spheres and the correlation between concentration and distribution of molecules on the surface were confirmed by using Transmission Electron Microscopy (TEM). Imaging was carried out by applying the FEI Tecnai G2 20 X-TWIN electron microscope, equipped with emission source LaB6, Charged Coupled Device (CCD) camera FEI Eagle 2 K. The Raman spectra of as prepared samples were recorded with the use of the WITec Confocal Raman Microscope (CRM) alpha 300R which is equipped with an air-cooled solid-state laser (30 mW output power) and a CCD camera. Raman scattering measurements have been performed using 532 nm laser radiation. The laser radiation power at the sample was 5 mW. All recorded spectra were manipulated by peak fitting analysis, baseline correction and cosmic ray removal in the GRAMS software package.
The mixture was shaken with vortex (30 s) and then centrifuged for 5 min (1000 rpm, 10 • C); 1 mL of the supernatant was carefully transferred into a screw cap glass vial for gas chromatography. FAs were quantified as methyl ester (FAMEs). FAMEs were determined using a gas chromatograph (Bruker 436 Scion software Empower (GC)) equipped with 100 m × 0. 25 mm D. I × 0. 20 µm film thickness and a capillary column (BR-2560 Bruker) for the separation of fatty acids. The injector and detector were maintained at a temperature of 260 and 250 • C, respectively. The injection volume was 1. 0 µL, and the split ratio was 1 : 25. The FAME identification was based on retention times of fatty acid patterns of several reference methyl esters: GLC-463 and GLC-538. The desirable fatty acids were calculated as desirable fatty acid (DFA) = MUFA + PUFA + C18:0.
The processes regulating the carbon fluxes and the dimensional growth constitute the core of the HETEROFOR model and are described in  Jonard et al. (in review, 2019). Here, we focus on the description of two modules essential for predicting the impact of climate change on tree growth: phenology and water balance. In addition, we used data from long-term forest monitoring to evaluate the capacity of the model to reproduce key phenological phases (budburst, leaf development, yellowing and fall) and the soil water content dynamics as well as to estimate throughfall and deep drainage. Evaluating each module separately is necessary to ensure the consistency of the whole model and to avoid that different error types compensate each other. Given the number of parameters, good predictions can often be obtained on integrative variables such as the diameter at breast height (dbh) increment but this is not sufficient to guarantee the quality of the model.
As the data used for the calibration represented the phenology of an average tree, the model shifts forward the start of the budburst by half the mean budburst period (extending from the budburst date of the earliest tree to that of the latest) in order 𝑅 𝑙𝑑 = { 𝑇, 𝑇 > 0 0, 𝑇 ≤ 0 ( 5 ) where T is the daily average temperature of the current day (°C). The leaf proportion (leafProp, g g -1 ) is calculated for each day according to 𝑙𝑒𝑎𝑓𝑃𝑟𝑜𝑝 𝑡 = ∑ 𝑅 𝑙𝑑 𝑡 𝑡 2𝑎 𝐿𝐷 * (6) with t, the current day. A constant date, defined according to  Dufrêne et al. (2005) , is considered for the start of the ageing process ( 3 ). This process does not alter leaf quality but is a prerequisite for leaf yellowing (t4a) that is initiated when the cumulated daily ageing rate (Rage) equals the ageing threshold (A*), with 𝑅 𝑎𝑔𝑒 = { 𝑇 𝑏_𝑎𝑔𝑒 -𝑇, 𝑇 < 𝑇 𝑏_𝑎𝑔𝑒 0, 𝑇 ≥ 𝑇 𝑏_𝑎𝑔𝑒 (7) with  _ , the base temperature for ageing (°C).
Parameter determination Most of the model parameters were taken directly from the literature. In addition, an adjustment of some relationships was conducted using available data, which are described hereafter but no overall calibration of the model was performed (Table  1 ). For the hydrological module, the parameters of the Eq. (  54 ) determining the stomatal conductance were determined based on data from Jonard et al. (  2011 ) using a non-linear fitting procedure. For the soil hydraulic properties, the saturation θs was based on the 0. 999 quantile of measured soil water contents (see Sect. 2. 4 for more details). For horizon without soil water content sensor, θs was extrapolated from the closest horizons. Then, the wilting point water content was determined using the obtained saturated water content and the Eq.
(  64 ) with a matric potential, h, of 15000 cm. The parameters of the phenological module used to calculate the start of budburst were determined using observations from the Pan European Phenology dataset (PEP725) which provides data about phenological observations across different European countries, though not in Belgium. We selected 129 sites on the western border of Germany covering the latitudes of our 6 study plots (49. 5-51. 0°N), for which the budburst dates of a representative tree were available at least between 1951 and 2015. The daily minimum, maximum and mean temperatures required to achieve the calibration came from the meteorological stations of the DWD Climate Data Center (Deutscher Wetterdienst). Phenological data from each site were assigned to the nearest meteorological station (5 different stations were sufficient).
The calibration was carried out with the Phenological Modeling Platform software  (Chuine et al. , 2013). This module enables the user to perform a Bayesian calibration procedure using the algorithm of  Metropolis et al. (1953). Some of the parameters can also be fixed. In our case, the chilling starting date of the uniChill and sequential models were fixed to the 1 st of November of the previous year (e. g. ,  Roberts et al. , 2015; Chiang and Brown, 2007)  in order to enhance the effectiveness of the other parameter calibration. The length of the budburst period, the leaf development, yellowing and falling rates were all adjusted from phenological observations made in our study sites.
Climate Even France that are deviated in the Semois valley and gives rise to a precipitation gradient around it. Baileux being closer to the valley and more elevated is subject to a small-scale Foenh effect and receives on average 1075 mm of precipitation each year while only 940 mm are measured in Chimay  (Poncelet, 1956). Finally, Virton was part of the "Basse Lorraine" with elevated annual rainfall (1060 mm) and intermediate average temperature values (9. 9°C) (Table  3 ). For Chimay, Louvain-la-Neuve and Virton, we used data from the meteorological stations of the PAMESEB network. The records covered the 1999-2018 period. A tipping bucket located at 1 m height was used to monitor rainfall. Global radiation was registered with a pyranometer, air temperature with a resistance sensor thermistor, relative humidity with a psychrometer and wind speed with an anemometer.
All these devices were placed at 1. 5 m height. Data were collected at 12 min intervals and were then averaged hourly. For Baileux, an independent meteorological station managed by our laboratory was used to collect meteorological data since 2002. The devices were identical to those described before. Air temperature, relative humidity and rainfall were monitored at 1. 5 m. Wind speed and global radiation were taken at 2. 5 m above the ground.
Water balance Regarding the water balance module, the evaluation was conducted using variables integrating most of the processes described in the model. The observed throughfall, extractable water and deep drainage (considered in the next section) were compared to model predictions. For the evaluation of throughfall predictions, only throughfall data collected in Chimay, Louvain-la-Neuve and Virton between 2000 and 2016 were used as the rainfall partitioning routine was based on the work of  André et al. (2008a André et al. ( , 2008b) )  using data from the Baileux forest. The collecting devices consisted of three long gutters covering each the radius of a crown and connected to plastic barrels. The throughfall volume was measured weekly based on the height of water in the barrels. A log transformation of both the observations and the predictions was necessary to remove the heteroscedasticity.
While the leaf ageing threshold was taken from  Dufrêne et al. (2005) , the yellowing parameter determining the length of the yellowing period was adjusted with the five years of data from Chimay, Louvain-la-Neuve and Virton. Therefore, only the yellowing start was independently evaluated. The prediction of the start of the yellowing displayed a low absolute bias (2. 7 days) and RMSE (7. 0 days). However, a weak correlation (0. 056) was found between predictions and observations (data not shown). For the temporal dynamics of leaf yellowing and leaf fall, the agreement between model predictions and observations was just assessed visually since the parameter regulating these processes (yellowing, falling rate and falling frost amplifier) were adjusted with the same data. The overall agreement was good.
Water balance In a first step, the annual water fluxes predicted by HETEROFOR were compared to measurements and predictions of other studies (Table  4 ). Then, some water fluxes were individually evaluated when data was available. Finally, some potential improvement of the water balance module were discussed. Various studies were taken from the literature to compare our water module predictions with observations. They cover a range of annual rainfall comprised between 425 and 1476 mm (Table  4 ), which is comparable to what can be found in Belgium. The proportions of rainfall converted to stemflow obtained with HETEROFOR (6. 1 to 13. 1%) are within the range reported in the literature (0. 6 to 20. 4%). This large observation spectrum comes from the important seasonal (higher stemflow proportion in winter than during the vegetation period) and species differences (stemflow importance is higher for beech than oak trees), which features are accounted for in HETEROFOR.
Structural and chemical characterizations Power X-ray diffraction was performed at 300 K on a Bruker D8 Advance diffractometer. Lattice parameters were inferred from Rietveld renements against the PXRD data using Fullprof (Fig. S1 -S3 in the ESI †). 44 igh-resolution transmission electron microscopy (HRTEM) and scanning transmission electron microscopy (STEM) experiments were carried out using a JEOL ARM 200Fcold FEG TEM/STEM microscope with an accelerating voltage of 200 keV equipped with double Cs-correctors. The observed thin slice of a sample saturation-annealed at 973 K was prepared by the dual ion beam (FIB)scanning electron microscope system using the in situ li-out technique. STEM images were taken in highangle annular dark-eld (HAADF) and annular bright-eld (ABF) modes. 119 Sn Mössbauer spectra were recorded at 4.
Three decades ago, a faster approach has been proposed by M. J. Sippl  [2] , which consists in constructing scoring functions from interatomic distance (r) distributions observed in a dataset of experimentally determined protein structures, as: ū i , j (r )= -kT ln [ f i , j OBS ( r) f i , j REF (r ) ] (1) where ūi,j(r) is the estimated free energy of interaction between atoms i and j, fi,j OBS (r) is the observed probability (i. e. frequency) of the atoms i and j being separated by a distance r (discretized into bins), fi,j REF (r) is a reference frequency aimed at eliminating the sampling bias, k the Boltzmann constant, and T the temperature. The pseudo-energy of the whole protein is thus computed by summing the ūi,j(r) of every pairwise distance observed in the structure. In this article, for the 30 th anniversary of this knowledge-based approach, we present a new information-theoretic view of its functioning, and propose an improvement in both theory and practice.
Despite the pertinence of this probabilistic framework, the misleading justification based on physics is still recurrent in the literature (e. g. [30] [31] [32] [33] [34] ), presumably because it does not interfere with the practical success of statistical potentials. Here, we propose a new formalism that is conceptually advantageous over the popular 30-year-old statistical potentials, as it is disconnected from any physical interpretation, while being more relevant to probabilistic reasoning. As a proof of concept, we have built two scoring functions, respectively based on the new and the PMF equations, and compared their performance at ranking predicted structures of proteins by their quality. Using the reference dataset 3DRobot (n=60,200 structures)  [35] , we show that the scoring function built with our new formalism is more accurate than statistical PMFs, based on three types of performance evaluation.
Theory There are two critical elements in Eq. 1, the first being how the reference state fi,j REF (r) is defined. The most straightforward way to do so is to calculate fi,j REF (r) as the weighted arithmetic mean of all fi,j OBS (r)  [36]. It is actually the same calculation as for fi,j OBS (r), except that the atom types are indistinct. Formally, the reference state could thus be written as fx,x OBS (r), where x is an atom of any type. Throughout the years, various improvements have been brought to this approach (see  [37]  for a review and comparative test), for example by taking into account the radius of gyration of each native structures, as the size of the proteins included in the training dataset is an obvious bias for the resulting interatomic distance distributions. The other critical part in Eq.
Implementation and training procedure To test the formalism of Eq. 3, we have modified the C++ open source code of MyPMFs  [39] , a computational tool from our previous work, which allows users to generate PMFs from any dataset of protein structures. The source code used for the present article is freely available for download, from the RPBS repository, at https://gitlab. rpbs. univ-paris-diderot. fr/src/ig-score. To build our scoring function, we have defined a set of native protein structures as follows: (i) from the PISCES website (http://dunbrack. fccc. edu/PISCES. php) we have downloaded a precompiled list of 3,768 PDB chains of resolution ≤1. 6 Å (X-ray structures only), R-factor ≤0. 25, and sequence identity ≤20%; (ii) we only kept the 1,973 protein chains of lengths ranging from 80 to 250 residues; (iii) to ensure independence from the benchmark dataset, we have eliminated the 56 protein chains that share more than 20% sequence identity with any of the 200 proteins from the 3DRobot dataset  [35].
Benchmarking procedure Each scoring function has been assessed based on its ability to rank structural models by quality as measured by their TM-score  [43]  to the native structure, which takes values between 0 and 1 (the higher the TM-score, the higher the model quality). As a benchmark, we used the 60,200 structures from the 3DRobot dataset  [35] , which represents 200 non-homologous proteins (48 α-, 40 β-, and 112 α/β-single-domain structures), each having 300 decoys and 1 native conformation. Additionally, we used predicted protein structures from the CASP13 experiment (2018). We selected models corresponding to targets in both the template-based modeling and free modeling categories, taking every model produced by every group. This represents a total of 52,296 models from 133 targets.
Each scoring function was evaluated through a pairwise ranking of the decoys for each of the 200 proteins from 3DRobot, or each of the 133 CASP13 targets. This allowed to calculate the accuracy of each method as the proportion (in %) of correct pairwise rankings. As the difficulty of ranking models may vary depending on their qualities, four subsets of the 3DRobot and CASP13 datasets have been defined, based on the TM-score to the native: "near-native", "good", "medium", and "poor" quality models are defined by three thresholds at 0. 8, 0. 6, 0. 4, respectively. Since comparing two very similar models is pointless, another threshold for the minimal TM-score difference between the compared models has been defined at 0. 1. The other performance criterion used in this study is the average ranking, as predicted by the scoring function, for the aforementioned "nearnative" and "good" categories of models (the higher the rank, the better), as well as for the "poor" ones (the lower the rank, the better).
Similarly to the scoring functions that we have built here, the only structural features that are quantified by DOPE are the interatomic distances, using the same distance bins and cutoff (0. 5 Å and 15. 0 Å, respectively; see above). GOAP is both distance-and angle-dependent: for each heavy atom in interacting pairs, it uses the relative orientation of the corresponding planes. For the computing fi,j REF (r), DOPE and GOAP take into account either the radius of gyration or the molecular volume of each protein structure from the training set, which eliminates the bias on the interatomic distances-the distance distributions may vary a lot, depending on the sizes of the proteins included in the dataset. Thus, the difference between DOPE/GOAP and PMF/TIG lies in (i) the training datasets, (ii) the calculation of the reference state and (iii), only in the case of GOAP, the dependence on orientation.
Performance: pairwise model ranking The TIG scoring function is supposed to be more accurate than Sippl's statistical potentials (Eq. 1), as it is built on the new approach (Eq. 3). To demonstrate its practical superiority, we have compared its performance with those of our PMF score, through two different tests. The first one evaluates the ability of the method to rank pairs of models taken from the 3DRobot dataset. The results of this benchmarking procedure are presented in Table  1. They include the accuracies of the DOPE, MCK1, and MCK2 scores (for GOAP, see the next paragraph). Overall, it appears that TIG is the best of these five methods, whereas PMF is the worst. To our surprise, the two mock scores systematically outperform PMF, although they were designed only to prove the irrelevance of combining fi,j OBS (r) and fi,j REF (r) as a statistical reference.
On the "Good" category, this difference is still >10%, when taking either the TM-score or the GDT_TS as a reference. On the two other categories of model quality, GOAP is also the most accurate method. This significant superiority is consistent with what has been previously observed on other datasets of decoys  [46] , where GOAP outperformed the OPUS-PSP potential  [47]  by ~15%. The latter was itself reported as more accurate than statistical potentials of lower complexity (i. e. which use less information), such as DFIRE  [48] , RWplus  [49] , and dDFIRE  [50, 51]. Thus, the results obtained with GOAP were expected and can be explained by the greater amount of stereochemical information it uses: the orientation and, in a lesser extent, the volume of the protein molecules. Here, it should be highlighted that the only unbiased comparison-to provide insights into the improvement brought about by the new formalism-is the one between PMF and TIG, as these scoring functions are equally complex.
It is also the case for the two mock scores, which have been specially designed for the sake of fair comparison. The ranking of the methods is similar to the CASP13 benchmark, although every accuracy is higher (Table  S1 ). On this dataset, PMF is systematically outperformed by the two mock scores, which are themselves outperformed by TIG. The latter is not significantly better than DOPE on this dataset, but GOAP is still far more accurate than the other five methods. Compared to 3DRobot, the differences between the methods are aggravated for the poor quality models, whereas they are nonsignificant for the near-native ones. Moreover, the quality of the models appears to have a different influence over the accuracy: (i) the most difficult models to rank are those of near-native and poor quality in 3DRobot versus those of medium quality in CASP13; (ii) the easiest to rank are those of good and medium quality in 3DRobot versus those of near-native quality in CASP13.
Only the performances of GOAP are consistent between the two datasets: the higher the model quality, the higher the accuracy. All these discrepancies presumably arise from the different origins of the two datasets. The 3DRobot set has been specifically designed for benchmarking purposes: for each protein, the native structure has been uniformly altered to generate exactly 300 decoys. Models from CASP13 are produced by different competing research groups, so that there is no control over their quality, nor over their number. Nevertheless, both series of results lead to the same conclusion regarding the improvement brought about the TIG formalism over the statistical PMFs. Finally, although not statistically balanced, the CASP13 dataset provides actual predictions of protein structures, unlike the decoys of 3DRobots, i.
Performance: correlation between predicted and true quality measures To further validate the information gain-based approach, the correlations between the scores produced by PMF or TIG and the corresponding TM-scores have been investigated, for the decoys of 3DRobot. Averaged on the 200 proteins (×300 decoys), the Pearson correlation coefficients are -0. 719 and -0. 782, for PMF and TIG, respectively. This makes TIG equal to dDFIRE, by referring to performances previously reported in the literature  [53]. Like GOAP, the dDFIRE statistical potential is both orientation-and distance-dependent, and uses protein molecular volume in the calculation of the reference state. However, unlike GOAP, it is based on a coarse grained representation of the protein structures. It should be noted here that dDFIRE is the lowestperforming program among those tested in  [53] , where SVMQA is the best, followed by OPUS-PSP, GOAP, and RWplus.