dataesr/scientific-paragraphs-categorization huggingface dataset:
In the fusion centre, information of the network is received, and measures of each sensor are stored to improve the final prediction: I t ij : Information from Sensor i of platform j of type t. (3) Sensors 2019, 19, 286 5 of 22 That could be seen as a sequence of values registered in a database, defining each register as a vector with the corresponding values (time stamp, platform j, sensor i, type of sensor t, value). These vectors would be stored in a database, as shown in Table  1. The final prediction could be based on the information of only one sensor of the platform, for example, tracking a target using the radar of one platform. In this problem, we have a primary source, that is composed of a deployment with platforms of soft sensors located in each local market. Each platform is defined by the market data: position, number of products, number of sellers, quantity of population that uses the local market, etc.
, x n }, the variogram can be estimated considering the separation vector h as: γ(h) = 1 2 E [Z(x + h) -Z(x)] 2 (11) In the case that γ(h) is isotropic (identical in all directions of the space), it does not depend on the angle of vector h, but only on its magnitude, |h|. In the general case, it is difficult to obtain the experimental variogram from data, given the scarce distances and directions, and usually a theoretical model must be adjusted with the available dataset. A quite generic model for variogram considers growing from the origin until a distance for stabilization, around a plateau, so that the random variables Z(x) and Z(x + h) are correlated when the length of the separation vector h is lower than a certain distance, the zone of influence, and beyond |h|= a the variogram keeps constant (the plateau).
Each association rule should be evaluated to assess each quality, and evaluation is based on three common metrics: support, confidence, and lift  [21] : • Support (of a rule) is evaluated as the number of instances (register in the data set) the rule covers related to the whole set (of registers in the dataset). sup a (x) = |x|, sup r (x) = |x| |D| , ( 23 ) where D is the total set of transactions. If antecedent A and consequent B are considered, the support is the intersection set: tecedent A and consequent B are considered, the support is the intersection set: sup a (A⇒B) = sup a (A B) (24) • Confidence (of a rule) is evaluated as the number (percentage) of times that consequent B appears among the instances that are selected by the antecedent A. The meaning of this concept is the accuracy of its prediction; it is defined as: conf(A ⇒ B) = sup a (A ⇒ B) sup a (A) = |A B| |A|.
( 25 ) • Lift (of a rule) is evaluated as the ratio of observed support considering that A and B were independent: lift = sup(A ⇒ B) sup(A) x sup(B) , (26) There are several algorithms that extract association rule from a database. The most representative algorithm for this task is the Apriori algorithm  [22]. Explanations about this algorithm in  [23, 24]  clarify that Apriori algorithm finds trends using performance parameters (support, confidence, and lift) evaluated on "a priori" frequent sets (prior knowledge). The algorithm is composed of the following steps: (1) Generate all item sets L with a single element; this set is used to form a new set with two, three, or more elements. All possible pairs are taken so that their support equals minsup (2) For every frequent item set L' found: For each subset J, of L' Determine all association rules of the form: If L'-J→J Select those rules whose confidence is greater than or equal to minconf Repeat 1, including next element into L As explained, all item sets that satisfy a threshold of minimum support are searched.
However, looking for all subsets would not be possible for the exponential size of search space of potential item sets to analyze. The Apriori algorithm prunes candidates with an infrequent subset before counting their supports. This is a Bread-First Search (BFS) process; it ensures that the support values of all subsets of a candidate are known in advance. All candidates of a cardinality k are counted in each scan in order to prune the branches below the support threshold, and then the search descends along the rest in the tree. A possible alternative approach could be using Depth-First Search (DFS), expanding the candidate sets from the item sets of one of the nodes of the tree. Obviously, scanning the database for every node would result in tremendous overhead, so counting occurrences in a DFS mechanism is not practical.
This kind of problem should be formulated as discovering patterns from dataset considering temporal attributes and try to model how they vary with time. Many algorithms for finding temporal patterns in sequence databases are listed in the bibliography; these algorithms are usually based on sequence mining techniques (or frequent patterns search) and, at the same time, temporal data association. There are some extensions of the Apriori algorithm that consider lists of ordered objects using time as items. Then, searched result is the associations of items in the form of sequences of items. Some examples of these algorithms derived from Apriori are Generalized Sequential Pattern (GSP) for spatiotemporal associations  [27] , Sequential PAttern Discovery using Equivalence classes (SPADE)  [28] , and Sequential PAttern Mining (SPAM)  [29].
Data Preparation The dataset of products that are considered in the analysis is listed in Table  2. In this table, names in Spanish (the original dataset) appear in the first column, their corresponding scientific names appear in the second column, and their corresponding translations into English appear in the third column. The process starts validating the original data provided by the governmental office. Non-significant information is deleted, and product names and units of measure are standardized. The dataset provided contains data stored weekly in local fairs of CIALCO (related to the provinces of Chimborazo and Tungurahua, which belong to the central area of the Andes in Ecuador). The initial dataset is subjected to processing for homogenization data (cleansing data). This cleaning mainly affects the names of products, values for unit sales, and the deletion of products that are not relevant for this work.
Spatial Data Table  2  is complemented in Table  3 , with seventh and eighth columns that integrate the X and Y coordinates of each transaction. X-Y coordination is related to the commercialization area that corresponds to the provinces of Tungurahua and Chimborazo in the month of July 2014. X and Y represent the latitude and length of local fairs (a position for each local fair) that participate in this work, respectively. Additionally, a fourth column is integrated corresponding to sales values that reflect the behavior of commercialization of kidney tomato. After this process, the dataset is transformed converting sales data into spatial type structures using latitude and longitude, as shown in Figure  3. After this process, the dataset is transformed converting sales data into spatial type structures using latitude and longitude, as shown in Figure  3.
The variation of the climatic variables (Figure  5 ) does not present a representative slope; the one that contributes for this study is the humidity, with a correlation with the tomato variable. Another important source of information is population. Population dataset is public information that is provided by the Ecuadorian Institute of Statistics and Censuses. In particular, population information related to the cantons considered in this work can be found in  [35]. Finally, Table  4  is generated using the information of the presented datasets where coordinates are transformed to common references. Table  4  is a unified file where sales information, population, and climate zones are integrated to build the multivariable prediction model. Another important source of information is population.
Association Rules Mining As mentioned, the initial file with transactions was analyzed in order to search significant association rules. For each transaction in the available dataset, each agricultural product was checked to determine whether it took part in the sale or not ("t" denotes True and "f" denotes False). However, the negative cases were removed from training set in order to concentrate only on "positive" rules, i. e. , relations among products sold together, and avoid rules including absent products in the relationships. After that, association rules were searched with algorithms available in Weka platform  [36]. With a preprocessed training set containing 549 transactions and subsets of 31 items purchased was analyzed to search association rules, setting as parameters a support value greater than or equal to 0.
The predictions for tomato sales were computed with a fusion model considering the other variables available in the study, such as population, meteorological variables, as well as the set of products present in the strongest association rules. LOOCV was applied to compare the different predictions (using the residuals). LOOCV has minimum bias because the training set contains almost the whole dataset. As discussed in  [38] , LOOCV can achieve the minimum variability both in bias and variance with stable learning schemes as linear regression, and it is usually the selected choice for comparative analysis when the available dataset has limited size.
Conclusions The focus of this paper was centred on the use of transactional information about market sales of small farmers to analyze dependencies among products in order to improve the accuracy of predictions. Dependency was learned from individual sales and recorded and stored in a database to mine association rules that measure the dependency among products. These rules show which products should be considered jointly to predict future values in an extended model. Additionally, contextual information was added and fused with the previous information to analyze the improvement of predictions. The research was focused on a target area for analysis located in two provinces of Ecuador, with available data from alternative marketing circuits. Besides, a general fusion methodology based on variable selected from association rules mining was applied to improve the predictions.
Drug-target binding kinetics are suggested to be important parameters for the prediction of in vivo drug-efficacy. For G protein-coupled receptors (GPCRs), the binding kinetics of ligands are typically determined using association binding experiments in competition with radiolabelled probes, followed by analysis with the widely used competitive binding kinetics theory developed by Motulsky and Mahan. Despite this, the influence of the radioligand binding kinetics on the kinetic parameters derived for the ligands tested is often overlooked. to address this, binding rate constants for a series of histamine H 1 receptor (H 1 R) antagonists were determined using radioligands with either slow (low k off ) or fast (high k off ) dissociation characteristics. A correlation was observed between the probe-specific datasets for the kinetic binding affinities, association rate constants and dissociation rate constants.
1  H NMR spectra were recorded on a Bruker (600 MHz or 400 MHz) using the stated solvent. Chemical shifts (δ) in ppm are quoted relative to CDCl 3 (δ 7. 26 ppm) and DMSO-d 6 (δ 2. 50 ppm). Liquid chromatography-mass spectrometry (LC-MS) data was collected using a Waters Alliance LC (Waters Corporation, MA, USA) with Waters ZQ mass detector. Analytical HPLC data was recorded using Agilent 1200 HPLC system with a β-Ram Flow Scintillation Analyser, using the following conditions: Waters Sunfire C 18 , 3. 5 µm, 4. 6 × 100 mm column at 40 °C, eluting with 5% acetonitrile/water +0. 1% TFA to 95% acetonitrile/water +0. 1% TFA over a 32 minute gradient. Specific activities were determined gravimetrically with a Packard TriCarb 2100CA Liquid Scintillation Analyser (Packard Instrument Company Inc. , IL, USA) using Ultima Gold TM cocktail.
Data analysis. Analysis of saturation binding experiments, competition binding experiments and associa- tion binding experiments are fully described elsewhere  [29] [30] [31]. The kinetic experiments were analyzed by GraphPad Prism (GraphPad Software, Inc. , La Jolla, CA, USA) using non-linear regression of the data to pharmacological models that assume a one-step binding of the ligand to the receptor. Competitive association -Motulsky-Mahan model = ⋅ + = ⋅ + = - + ⋅ ⋅ ⋅ ⋅ = + + = + - = ⋅ ⋅ - = ⋅       - + - - -       -⋅ -⋅ ⁎ ⁎ ⁎ ⁎ K k L k K k I k S K K k k L I K K K S K K K S Q B L k K K RL Q k K K K K k K K e k K Ks e [ ] [ ] ( ) 4 [ ] [ ] ( ) 2 ( ) 2 [ ] ( ( ) (1) A B A B F A B S A B max F S F S F S F F K t S K t 1 2 3 4 2 1 3 1 4 4 ( ) 4 ( ) F S = ⋅ - ⁎ ⁎ RL equation e ( 1 ) (2).
Probe dependent differences in binding characteristics. The observed association binding data of both radioligands (Fig. 4a-f ) agreed well with the fitted non-linear regression lines based on the Motulsky-Mahan model from which binding rate constants (k on and k off ) of the unlabeled ligands could be calculated (Table  2 ). Thus, two datasets were obtained with the binding rate constants of unlabeled ligands that were determined by using either [ 3 H]mepyramine or [ 3 H]levocetirizine as competitive probe. The measured binding rate constants correlated well between datasets as is depicted in Fig. 5a  (k on values: R 2 = 0. 80, P < 0. 001) and in Fig. 5b  (k off values: R 2 = 0. 77, P = 0. 002). However, the regression lines (solid lines) deviate from unity (dashed line) and some unlabeled ligands showed larger differences in binding kinetics between the two datasets than others.
For example, more than 10-fold differences in the k on and k off values were observed for VUF14454 and VUF14544 between both datasets (with the K d,kin , calculated as the k off /k on , deviating less than 2-fold). The differences in the k on values between datasets were largest for ligands with a relatively high k off value (Table  2 ). Additionally, a probe-dependent difference for the range in k off values was observed, with [ 3 H]mepyramine discriminating unlabeled ligands over a range with higher k off -values (Fig. 5b , logk off -2. 2 and 0. 1) and [ 3 H]levocetirizine discriminating unlabeled ligands over a range with lower k off -values (Fig. 5b , logk off -3. 2 and -0. 7). These data suggest that the [ 3 H]mepyramine-based assay better distinguishes fast dissociating unlabeled ligands(high k off values), whereas the [ 3 H]levocetirizine-based assay better distinguishes slow dissociating unlabeled ligands (low k off values).
From the determined binding rate constants, the binding affinity (pK d,kin = k off /k on ) and the residence time (RT = 1/k off ), a proposed metric to relate binding kinetics to in vivo drug efficacy  3, [5] [6] [7] [8] [9]  , were calculated (Table  2 ). The pK d,kin values correspond well with the respective pK i values (Fig. 5c ), with a good correlation for both the [ 3 H]mepyramine-dataset (R 2 = 0. 93, P < 0. 0001) and [ 3 H]levocetirizine-dataset (R 2 = 0. 87, P < 0. 0001). Furthermore, the pK d,kin values correlate nicely between the probe specific datasets as well (R 2 = 0. 87, P < 0. 0001, data not shown). Since the K d,kin value is directly derived from the binding rate constants (k off /k on ), these correlations highlight the reciprocal changes in k on -and k off values in both binding assays and suggests that the ratio between the binding rate constants (e.
6a, b ) as is apparent for, e. g. , -logk off values > -1. Additionally, an increase in the relative error on the k off -values (Fig. 6b ), but not on the k on -values (Fig. 6a ), is observed for unlabeled ligands with low mean k off -values as is apparent for, e. g. , -logk off values < -2. Interestingly, Fig. 6  clearly shows a probe-dependent accuracy for the determination of the k on and the k off -values of unlabeled ligands. The k on value for H 1 R binding is generally more accurate when determined in a [ 3 H]mepyramine binding experiment (blue curve, Fig. 6a ), whereas the k off value is more accurate for the [ 3 H]levocetirizine dataset in the case of unlabeled ligands with a logk off < -2 and less accurate for ligands with a logk off > -2 (red curve, Fig. 6b ).
Discussion In GPCR drug discovery, drug-receptor binding kinetics are often quantified using competition association experiments with a radioligand probe. Despite the increased use of this methodology, it is unclear whether the kinetic properties of the probe influence the obtained kinetic binding parameters of unlabeled ligands. Therefore, in this study we employed two radioligand probes, that differ in their H 1 R binding kinetics ([ 3 H]mepyramine and [ 3 H]levocetirizine), to measure the binding rate constants of a diverse set of unlabeled antagonists. The analysis shows that the k on and k off values obtained with each probe correlate between both probe-specific datasets (Fig. 5a, b ). However, large differences in the binding rate constants are observed for some compounds, e. g. , VUF14454 and VUF14544.
Moreover, although more than 10-fold differences in the k off are observed among (S)-cetirizine, triprolidine, mepyramine, VUF14454, VUF14493 and VUF14544 when using [ 3 H]mepyramine as probe, no difference is observed when the k off are measured for the same set of unlabeled ligands with [ 3 H] levocetirizine as probe. The comparison of these two datasets therefore suggests a probe-dependent limit to discriminate binding rate constants of unlabeled ligands. A related probe-dependent effect is apparent in the relative errors of the determined k on (Fig. 6a ) and k off values (Fig. 6b ) of the unlabeled ligands. In our competitive binding experiments, only the binding of the probe can be directly observed. When unlabeled ligands reach a binding equilibrium rapidly, before noticeable binding of the probe, binding kinetics of these unlabeled ligands is masked by the slow onset of probe binding at each time point.
Moreover, in our dataset the k on (Fig. 7a ) and k off (Fig. 7b ) are fitted with a higher accuracy using [ 3 H]mepyramine as probe for unlabeled ligands with a residence time less than 100 min (log k off > -2). In contrast to the determined k on -values, which show a growing inaccuracy upon increases of the linked k off -value (Fig. 6a ), the k off -values are increasingly inaccurate at the lower end of the spectrum as well, i. e. both with low k off as well as high k off values (Fig. 6b ). Interestingly, a probe dependent difference in inaccuracy in these determinations is again apparent. However, for slowly binding unlabeled ligands (residence time of more than 100 min; fitted log k off < -2) a moderately better accuracy is obtained when using [ 3 H]levocetirizine (and not [ 3 H]mepyramine) as probe in competition association experiments (Fig.
8 ). This might be explained (partially) by the fact that all three H 1 R probes have quite similar k off values (Table  1 ). In fact, the correlations for the measured binding rate constants were stronger when comparing the different assays (Fig. 8a, b ) than when comparing the datasets obtained with the different radioligands (Fig. 6a, b ). The notable exception were the k off -values determined with AV082 competition association binding experiments (measured by NanoBRET), which deviated only slightly among unlabeled ligands, suggesting again a probe-dependent limitation for discriminating the binding kinetics of unlabeled ligands. However, this effect is most likely not explained by the binding kinetics of the H 1 R probe as the binding rate constants of AV082 and [ 3 H]mepyramine differed only <3-fold.
Site description The experiment was conducted in a mature beech forest (Fagus sylvatica L. ) in Forêt Verte (49°31′12. 6″N 1°07′00. 7″E) close to Rouen University, France. The site has a relatively flat topography and the elevation is about 150 m a. s. l. The climate is "oceanic-temperate" with a mean annual air temperature of 10. 5 °C and the total annual precipitation average of 851. 7 mm, which is distributed relatively evenly over the year (ESM Fig. S1 , climate data at the weather station "Rouen-Boos from 1981 to 2010", data from website Infoclimat: http://www. infoc limat. fr). Spectral irradiance was measured before (February 2017) and after (May 2017) canopy closure at five locations within the study site and compared with an open area nearby. Spectral irradiance was also measured inside the litterbags for each filter treatment to test filter transmittance (Fig.
These photos were used to characterize canopy cover by calculation of the global light index (GLI) and the leaf area index (LAI) with the software "Hemisfer"  (Schleppi et al. 2007; Thimonier et al. 2010). The LAI was estimated to be 0. 895 ± 0. 012 during winter (Dec 2016-Apr 2017) corresponding to a GLI of 50. 5%. On 24th May 2017, when canopy leaves were completely expanded, the LAI reached 2. 930 ± 0. 131 while the GLI dropped to 3. 8%. A time series of modelled daily PAR (Fig. 2  and ESM Fig. S3 ) over the whole experimental period was reconstructed with a library of radiative transfer programs, libRadtran, version 2. 0. 1. (Emde et al. 2016). We used the radiative transfer equation solver DISORT for the simulations to produce spectra of 280-900 nm (based on  Lindfors et al. 2009). Inputs to the model were column integrated water vapour data from AERONET (https ://aeron et.
gsfc. nasa. gov/cgi-bin/webto ol_aod_v3?stage =2&place _code=10&regio n=Europ e&state = Franc e&submi t = Get + AERON ET + Sites ), total ozone column data from the Aura Validation Data Center (AVDC) (https ://avdc. gsfc. nasa. gov/pub/data/satel lite/Aura/ OMI/V03/L2OVP /OMUVB /) and surface type as defined by the International Geosphere Biosphere Programme (IGBP). Modelled above-canopy data were cross-validated against satellite-derived irradiance data provided by SoDa Helioclim-3 and against the spectral irradiance measured with the above-mentioned spectroradiometer. Modelled understorey data (Fig. 2  and ESM Table  S12 ) were calculated by applying the GLI to the above-canopy modelled data  (Canham 1988 ) and were cross-validated against a subset of daily PAR irradiance measured in the understorey on the forest floor, recorded continuously from 25th May to 10th Oct 2017 as 15-min averages with two calibrated quantum sensors (QSO-S, Decagon Devices, Pullman, Washington, USA)  (ESM Fig S2).
The point of abscission was determined as the moment when the leaf would detach without any effort in pulling it away from the branch. Leaves were collected from oak and ash trees in small stands near the University in Rouen (49°27′44. 2″N 1°03′48. 2″E), while the equivalent beech leaves were collected in the Forêt Verte (49°30′17. 0″N 1°06′44. 9″E) close to the study site. The petiole was removed from the leaves before they were weighed and scanned to obtain fresh weight (FW) and leaf area was calculated with the software WinFO-LIA (Image analysis for plant science, Regent Instruments Inc. , Nepean, Canada). Immediately after sampling, both leaf adaxial (upper) and abaxial (lower) epidermal flavonoid content and leaf chlorophyll content were optically assessed using a Dualex Scientific + (ForceA, Paris Orsay, France) device.
Acknowledgements Open access funding provided by  University of Helsinki including Helsinki University Central Hospital. We thank the  ONF (Office National de Forêts)  for the permission to use the study site. We also thank  Paul Barnes  for the valuable comments on the manuscript and advice on the litterbags design,  Saara Hartikainen  and  Santa Neimane  for the hemispherical photo processing and  Philippe Delporte  for help in setting up the field experiment. We would like to acknowledge the  FR-SCALE platform  for the support. We would like to thank the weather station of  Rouen Boos  and  Meteo France  for providing the temperature and precipitation data.