dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths of the model One of the key strengths of this model, and the results presented herein, is the extent to which these results can be validated by empirical data. These data were primarily obtained from our own work but we also used findings by others in the public domain. In addition, we used a realistic model of the human body, based on the success of PBPK analysis of drug AMDE  [14]. In these models, and in the variation adopted by us, the body is represented as a network of intercommunicating compartments; each organ has an adjustable volume to accommodate anatomical variations, and the organs are interconnected by a realistic vascular tree with arteries and veins. However, the capillary compartment is not included. The main strength and uniqueness of this approach resides in the clinical data we used to model permeability of the blood-brain barrier.
0 ng/ml, which resulted in a serum concentration of 0. 115 ng/ml. This implies that any change in a subject's skin pigmentation (e. g. , tanning) will increase levels of S100B. This was experimentally confirmed in b showing the results of a comparative analysis on the effects of exposure to sun. Note the significant increase in S100B after sun exposure regardless of whether dark skinned (Latinos, African-American subjects) or light skinned individuals were studied blood testing by contrast-enhanced CT scans. Figure  2b  shows the quantitative relationship between radiological measurements of BBBD and associated changes in blood S100B. Please note that because of the clinical nature of this trial and the large number of subjects enrolled, the data are not as clear-cut as one desires. Human studies were still utilized over available data from animal studies, however, due to increased translatability and clinical relevance.
This will be ARCHLAB, a totally original concept which demonstrates that research on cultural heritage materials is a long term endeavor and that it must take into account the results achieved in the past, recent or distant, and all the historical depth of knowledge gained by the study of works of art 4. As for the access to FIXLAB and MOLAB, access to ARCHLAB is obtained through calls for proposals which are submitted to an expert panel who judge their scientifi c quality and feasibility. During the four years of the project, 273 projects have been carried out, corresponding to 1390 access days for 621 users from over 20 different countries. For FIXLAB accesses, archaeometric studies dominate, on a variety of materials but with a majority of them on metals and minerals 5. For MOLAB, studies on paintings largely top the accesses with 66% of the projects, followed by those on manuscripts 6.
Data analysis. We used a combination of Excel 2007 and IBM SPSS Statistics version 22. 0, which are mainly descriptive due to the rather small sample and not having other samples to compare our results with. The purpose of our analysis at this stage is to identify potential trends and potential flaws in our evaluation process and to be able to propose future improvements for better data accuracy and reliability. For the PANAS scale the Positive Affect -PA was observed through four variables and the Negative Affect -NA was also observed through four variables. For analysis purposes we added a derived variable PA-NA which for each patient is represented by the difference between the sum of the PA variables and the sum of the NA  [29]. Even if the difference between the two sums does not represent a certainty for the positive or the negative mood of the patient, we take into consideration the descriptive value of the PA-NA for the tendency of the patient's mood during the procedure.
For the purpose of the paper and for the e-DENT project, which will facilitate dental consultation access for these patients, we consider our approach as being the best possible approach as patient-nurse relationship is a reality for most of the cases. For further research, as a response to the potentially unreliable data collected from one of the sites, we suggest an improvement of the process. The observation form will become digital and it will become part of the tele-consultation software, in which once a tele-consultation is finalized the corresponding observation form will no longer be editable, in this way the nurses will fill in the observation form after each tele-consultation, increasing data accuracy and reliability. To validate our proposal would be the next logical step. To achieve this we are envisioning two separate paths.
In primary screening, a threshold of 80% reduction of neuraminidase activity was chosen to discriminate compounds with an IC 50 above 100 μM. Out of 1280 drugs, three inhibitors of the hPIV-3 neuraminidase activity were identified and confirmed in a secondary screening (data not shown): suramin (7, Fig. 2a ), Evans blue and erythrosine sodium, with a neuraminidase inhibition of 94 ± 0. 2%, 79. 4 ± 1. 8% and 92. 8 ± 0. 9%, respectively. As Evans blue and erythrosine sodium are dyes, their quenching effect on fluorescence was evaluated by adding the compounds at the end of the enzymatic reaction. No decrease in fluorescence was observed, showing no quenching effect of the dyes. However, due to the nature of the compounds, they were not further evaluated as inhibitors of hPIV-3 in this study.
Suramin (7) inhibits the hPIV-3 HN neuraminidase activity via a non-competitive mechanism. Although suramin (7) has been shown to inhibit some neuraminidases, its mechanism of action remains unknown  [31] [32] [33]. To understand the mode of inhibition of the drug on HN, we conducted an enzyme kinetics analysis of the neuraminidase activity in presence of suramin (7) and the substrate MUN (6). In the setup of the neuraminidase activity inhibition experiment, we evaluated the initial velocity v i of the neuraminidase at several substrate concentrations ([S]). At each of these [S], we challenged the enzyme with a dilution range of inhibitor concentrations ([I]). Linear regressions of data points from Lineweaver-Burk plots using Equation (1) were undertaken for each concentration of suramin (7, Fig.
Kinetic analysis. The inhibition mechanism of hit compounds was determined by enzyme kinetics experiments on the hPIV-3 HN protein using a modified protocol of Ryu et al. 48 , Nguyen et al. 49  and Mishin et al. 50. The neuraminidase activity of HN was measured every 2 min over a period of 20 min, at 5 concentrations of MUN (6) [S]: 2, 4, 8, 10 and 20 mM, and 3 concentrations of inhibitor [I]: 0, 10 and 16 μM. The initial velocity v i of the enzyme for each [S]-[I] combination was determined automatically by the software provided with the Varioskan Flash Multimode Reader. A Lineweaver-Burke plot was represented for all [I], by plotting =             v f S 1 1 [ ] (1) i and applying a linear regression using the software GraphPad Prism version 5 (GraphPad Software, La Jolla California USA).
Virus-infected cells were stained by addition of TrueBlue Peroxidase Substrate (KPL, Gaithersburg, MD) until appearance of dark blue foci. The concentration of compound resulting in 50% inhibition (IC 50 ) of virus binding (4 °C) or adsorption (37 °C) was determined by foci counting, while the IC 50 for virus replication (post-adsorption) was determined by measurement of foci size. IC 50 values were determined using non-linear regression analysis with the software GraphPad Prism (GraphPad Software, La Jolla California USA). Foci counts and size measurements were done using the software Fiji  51. Briefly, the backgrounds of 1200 dpi colour images from scanned infection plates were subtracted using a rolling ball radius setting of 10 pixels with the sliding paraboloid option. Colour channels were split and red channels (channels containing the darkest foci) smoothened before applying the threshold function to convert to binary images and the watershed function to split merged foci.
For STD-NMR, the on-resonance frequency was set to -1. 00 ppm and the off-resonance frequency to 300 ppm. The virus was saturated with 60 Gaussian soft pulses of 50 ms, each resulting in a total saturation time of 3 s. The residual water signal was reduced by applying the WATERGATE sequence. Molecular docking simulations. Simulations were carried out using the software Autodock Vina  53. The apo and zanamivir (3)-bound structures of HN were taken from the Protein Data Bank, accession numbers 1V3B and 1V3E, respectively. Water molecules were removed, as well as B-chains to keep only one monomer. The half-suramin molecule (suramin h, (8)), truncated after the urea moiety (NHCONH 2 -term), and suramin (7) were optimised through the PRODRG2 server  54. Zanamivir (3) was extracted directly from the crystal structure 1V3E.
Acknowledgements The authors thank  Dr Thomas Haselhorst  and  Jeffrey C. Dyason  for useful discussions concerning the NMR experiments and in silico docking simulations, respectively. This work was funded by  The Australian Research Council  ( DP1094549 , M. v. I and P. G. ) and the  National Health and Medical Research Council  ( ID1047824 ,  ID1071659 , M. v. I). We gratefully acknowledge the award of  Griffith University  and  Institut Pasteur International Network (Calmette Program )  Postgraduate Scholarships  to B. B.
Model description The 3D-CMCC FEM  (Collalti, 2011; Collalti et al. , 2014)  (source code and executables are available upon request to the corresponding authors and downloadable at http://dev. cmcc. it/git/3D-CMCC-FEM-git) is hybrid between an empirical and a process-based model relying on the concepts of the LUE approach at canopy level for carbon fixation. The 3D-CMCC FEM is designed to simulate forest ecosystems at flexible scale (from hectare to 1 km per 1 km) and on a daily time step. The model simulates tree growth as well as carbon and water fluxes, at species level, representing ecophysiological processes in heterogeneous forest ecosystems including complex canopy structures. The 3D-CMCC FEM v. 5. 1 uses daily meteorological data, site-specific data and ecophysiological data (e. g.
maximum canopy conductance, specific leaf area; see Table  S3  and  Collalti et al. , 2014)  to simulate forest processes. The model code architecture allows aggregating trees into representative classes, each characterized with its variables (e. g. carbon pools, leaf area index, tree height) based on their ages, species-specific and structural traits. These variables are identified by the model through four indices: i. e. species (x index), diameter class (Diameter at Breast Height, DBH) (y index), height class (z index), and age cohort (k index); such indices represent the main state variables considered by the model in distinguishing ecosystems across sites. To deal with forest heterogeneity within and across different ecosystems, 3D-CMCC FEM v. x. x (all model versions follow the same architecture) uses a speciesspecific parametrization for each species simulated.
Data description Model validation has been performed for 10 different forest sites (Table  1 ) included in the European EC fluxes database cluster (URL: http://www. europe-fluxdata. eu). For each site, 3D-CMCC FEM v. 5. 1 simulations were performed averagely for 10 years, forced with gap-filled daily meteorological data, according to the available time series. The selected sites cover a wide range of European forest ecosystems across different latitudes, landscapes and three climatic zones: temperate, Mediterranean and subalpine. For all sites, daily time series of meteorological variables (maximum and minimum air temperature, precipitation, vapour pressure deficit and incoming solar global radiation) were used as drivers, while GPP was used for model output validation. The GPP derives from Net Ecosystem Exchange (NEE) measurements that have been previously quality checked and processed including storage correction, spike detection and low turbulence condition (u * ), filtering according to the method in  Papale et al.
(2006)  and gap filled using the Marginal Distribution Sampling method (MDS;  Reichstein et al. , 2005). The GPP is not directly measured by the eddy covariance technique but it is estimated using a par-Table  1. Main characteristics of the study sites. Sites were classified according to the IGBP (International Geosphere Biosphere Program) legend as in the FLUXNET database: MF = mixed forest; DBF = deciduous broadleaf forest; EBF = evergreen broadleaf forest; ENF = evergreen needle leaf forest. Year of simulation starting and ending depend on available time series of observed data. In the rest of the paper, we will refer to these data as "measured" or "observed" GPP for simplicity but it is important to highlight that they are obtained using a modelling approach (although strongly based on direct measurements).
Model and experimental set-up Site data needed for model initialization concerned information on forest structure (DBH, tree height, age and density), its species composition and soil characteristics (e. g. soil depth, texture and bulk density). These data were used for each site to initialize the model, i. e. to describe soil characteristics and the initial forest conditions at which the model starts to simulate forest processes. Initialization data were taken from the BADM (Biological, Ancillary, Disturbance, Metadata) files, available at http://www. europe-fluxdata. eu, for each of the selected sites, and complemented by a literature review and personal contacts with the sites' Principal Investigators. Length of model simulations, basic sites description and forest attributes used for model initialization are shown in Table  1.
For reasons of comparability, in these test sites the model has been forced with the same meteorological input data, and eco-physiological species-related parametrizations, i. e. only model initializations data, related to stand attributes, differ. These data are based on different sources: site measurements and/or literature data and/or experimental settings. In the case of BE-Bra we initialized the model with nearly all possible combinations of initialization data sets. The first simulation (BE-Bra P_Q-3L) has explicitly taken into account the site heterogeneity (vertical and horizontal) (following  Gielen et al. , 2013 , and ancillary data sources) consisting in mixed species composition at a different canopy coverage rate of Quercus robur (Q) and Pinus sylvestris (P) (20 and 80 %, respectively), with two cohorts (oaks and pines, 65 and 72 years old, respectively) and three forest layers.
Validation approach In order to analyse model performance, we used daily (X daily ), monthly (X monthly ) and annual (X annual ) time series for modelled and observed GPP values, which were compared at the different timescales. At first, we conducted a comparison via appropriate performance indices on longterm annual average (i. e. over the full series of all the available years), then we evaluated how the model performed in the different seasons aggregating values for months of the same season. In addition, to avoid misleading results in the daily and monthly signal comparisons due to the strong seasonality for both daily and monthly signals, we followed the decomposition technique proposed by  Zhao et al. (2012). To partially remove the seasonal cycle signal, we build a new daily (Y daily ) and a new monthly (Y monthly ) data set for both observed and modelled data, respectively.
The Y daily is created by subtracting the daily time series from the daily mean of the month, and the Y monthly by substracting the monthly time series from the annual mean (see Table  S1b  in the Supplement). For both X and Y data sets we firstly adopted the Pearson coefficient of correlation (r). Then, we calculated the Normalized Root Mean Square Error (NRMSE)  (Anav et al. , 2010; Keenan et al. , 2012)  as a standardized index of error. The NRMSE reports the mean difference between observed and modelled GPP values (GPP EC and GPP MD , respectively) normalized on the variability in the GPP EC , in order to have an indication of the average distance between GPP MD and GPP EC , comparable among the different sites. NRMSE was quantified as NRMSE GPP = 1 N N i=1 GPP EC i -GPP MD i 2 σ GPP EC i , (8) where i represents the day (or month), and σ (GPP EC ) is the standard deviation of the full daily (or monthly) series of observed GPP consisting of N records.
89) and closer position in terms of normalized standard deviations units from the reference point even if the other indices are a little bit far from the average values of the other sites. To reduce the effects of seasonality, we also examine model performance using the decomposition method (Sect. 2. 5). In the daily time step, the overall model performance is much lower in Y data set (Fig. 1c  and Table  S1b ) than in X data set, that is, r = 0. 51, MEF = -0. 43, NRMSE = 1. 18 and MABstd = 0. 8 in Y data set vs. r = 0. 82, MEF = 0. 57, NRMSE = 0. 63 and MABstd = 0. 44 in X data set. The large model errors at synoptic scale have been well recognized by previous studies  (Dietze et al. , 2011; Zhao et al. , 2012). The model shows to be a good predictor for DE-Tha and FR-Pue and to be less predictive for DK-Sor and FR-Hes with respect to the X data set.
Accordingly, for FR-Pue comparisons between X and Y data sets show that this site is less affected by seasonality while DK-Sor is the most affected one. As expected, in the monthly time step, the decomposition technique returns more similar results between X and Y data sets. Worst results are for IT-Cpz while best results are for DE-Hai, DK-Sor, DE-Tha and IT-Ren (see Table  S1b  and Fig. 1d ). Overall, after smoothing the seasonality the model shows to be slightly better predictive with average values among sites consistent with observed data (r = 0. 94, MEF = 0. 85, NRMSE = 0. 36 and MABstd = 0. 27). Comparison between X and Y data sets shows that DE-Hai is less affected by seasonality and IT-Cpz is the most affected one. In brief, comparison between X and Y data sets shows similar reconstruction ability in the monthly time step, but very different in the daily time step because X data set contains the feature of large seasonality.
Given that one of the objects of this study focuses on seasonality fluctuation, we mainly show the results based on X data set hereafter without specification. To summarize, although with similar inter-site variability, monthly correlations across different sites are higher than daily ones, with average correlations of 0. 94 for deciduous, 0. 90 for evergreen and 0. 92 for mixed stand (Fig. 1  and Table  S1a ). Daily and monthly NRMSE are usually less than 1. 00. Monthly NRMSE is less than daily NRMSE, 0. 41 vs. 0. 63 on average, respectively (Table  S1a ). These results confirm that the model performs better at a monthly than at a daily timescale (Fig. 1 ), likely because of averaging effects of daily variability in GPP estimation. The same consistency is shown for MEF index that is on average 0.
Differently from other models, 3D-CMCC FEM, both for daily and monthly simulations and for both X and Y data sets, performs better for deciduous species rather than for evergreen, although deciduous species have a more complex phenology and a more pronounced seasonality. Some mismatches in the simulation over the seasons and over the sites still remain, especially during winter and summer. The first reason for these low agreements in winter can be also attributable to errors during the estimation of GPP from NEE and Ecosystem Respiration values from measurements data. The second can be related to the model's lack or simplicity in representation of snow pack dynamics as reported by  Krishnan et al. (2008 Krishnan et al. ( , 2009)) , especially for evergreen sites  (Keenan et al. , 2012). Disagreements in summer could be related to model simplicity in simulating soil drought and, using the  Monteith approach (Monteith, 1977) , to the strong nonlinearity at the daily scale of GPP and PAR, and to the lack of representation of the light saturation processes.
Local learning of sparse image models has proven to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are often clustered using the K-means algorithm with the Euclidean distance as a dissimilarity metric. However, the Euclidean distance may not always be a good dissimilarity measure for comparing data samples lying on a manifold. In this paper, we propose two algorithms for determining a local subset of training samples from which a good local model can be computed for reconstructing a given input test sample, where we take into account the underlying geometry of the data. The first algorithm, called Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme which can be seen as an out-of-sample extension of the replicator graph clustering method for local model learning.
In this work, we study both the adaptive and the nonadaptive settings and propose two different algorithms for geometry-aware local neighborhood selection. We first present an adaptive scheme, called Adaptive Geometry-driven Nearest Neighbor search (AGNN). Our method is inspired by the Replicator Graph Clustering (RGC)  [18]  algorithm and can be regarded as an out-of-sample extension of RGC for local model learning. Given a test sample, the AGNN method computes a diffused affinity measure between each test sample and the training samples in a manner that is coherent with the overall topology of the data graph. The nearest neighbor set is then formed by selecting the training samples that have the highest diffused affinities with the test sample. The evaluation of the adaptive AGNN method in superresolution experiments shows a quite satisfactory image reconstruction quality.
Note that, while the proposed AGNN and GOC algorithms employ similar ideas to those in manifold clustering methods, our study differs from manifold clustering as we do not aim to obtain a partitioning of data. Instead, given a test sample to be reconstructed, we focus on the selection of a local subset of training data to learn a good local model. We evaluate the performance of our methods in image superresolution, deblurring and denoising applications. The results show that the proposed similarity assessment strategies can provide performance gains compared to the Euclidean distance, especially for superresolving images with rich texture where patch manifolds are highly nonlinear. When applying the proposed method in the superresolution problem, we select the NCSR algorithm  [1]  as a reference method, which currently leads the state of the art in superresolution.
II. CLUSTERING ON MANIFOLDS: RELATED WORK As our study has close links with the clustering of lowdimensional data, we now give a brief overview of some clustering methods for data on manifolds. The RGC method  [18] , from which the proposed AGNN method has been inspired, first constructs a data graph. An initial affinity matrix is then computed based on the pairwise similarities between data samples. The affinity matrix is iteratively updated such that the affinities between all sample pairs converge to the collective affinities that consider all paths on the data graph. Spectral clustering is another well-known algorithm for graphbased clustering  [20] ,  [21]. Samples are clustered with respect to a low-dimensional embedding given by the functions of slowest variation on the data graph, which encourages assigning neighboring samples with strong edge weights to the same cluster.