dataesr/scientific-paragraphs-categorization huggingface dataset:
The encoding of M1 and M2 as relational context families (RCF) consists in converting each data class as a formal context (object-attribute context) and each arrow as a relational context (object-object context). To measure the effect of the encodings on the resulting conceptual structures, two encodings of the M1 and M2 arrows are considered: the "original" encoding implements the arrows presented in Figure  3 , while the "enhanced" encoding includes the arrows and their opposite (making a sort of symmetric closure at the model level). M1 and M2 encodings are evaluated on a dataset reduced to the descriptions associated to six protecting plant species, i. e. Cymbopogon citratus, Hyptis suaveolens, Lantana camara, Moringa oleifera, Ocimum gratissimum, and Thymus vulgaris. These plants have been selected by domain experts of the Knomana project for their first investigations, according to these plant efficacy in contrasted situations, diversity of applications, and high presence in most of the West African territories.
The dataset comprises 225 descriptions composed of 16 pieces of information: the protecting plant (name of the species, genus and family), the plant origin (territory, part of continent, and continent), the targeted organism (name of the species, genus, family, and reign), and the protected system. The latter is described using the protected organism (name of the species, genus, family, and reign), the production step (e. g. crop, or cattle) and the treatment place (e. g. field, or stock). Table  3  presents the number of objects and attributes of the formal contexts of M1 and M2 on the reduced dataset, and details the number of binary attributes generated for each data type of the model. RCAExplore software enables to evaluate four kinds of conceptual structures, and algorithms that allow to build them: concept lattices built with addIntent/addExtent  [16]  (fca), AOC-posets built with Ares  [4]  (ares), and Iceberg lattices  [21]  for support 30 and 40 (iceberg30, iceberg40).
An AOC-poset is a restriction of the concept lattice to the concepts introducing objects or attributes. Iceberg is a restriction of the concept lattice to the concepts having a minimal support (i. e. extent size), here to concepts with a minimal support of 30% and 40%. In the next section, we conduct an evaluation of the dataset along two dimensions: quantitatively, by assessing the possibility of the conceptual structures and their size, if appropriate, and qualitatively, by analyzing the ability of M1 and M2 to answer a specific case of the following generic biological question Q1 raised by our domain experts: "Given a plant able to protect an organism against an aggressor, which other plants can alternatively be used with the same benefits?". This question corresponds to a so-called "replacement" scenario: replace a plant by another one with supposed similar ability to deal with the observed aggressor on the attacked organism.
Six Key Plant Dataset: Conceptual Structure Variants Tables  4  and 5  respectively show for model M1 and model M2 the numbers of concepts that were built for each algorithm, and for the enhanced case (where we take the symmetric closure of the model) and the original cases (as shown in Figure  3 ). Tables  6  and 7  respectively show for model M1 and model M2 the numbers of relational attributes that were built. Step numbers until RCA stops and execution times are compared in Table  8. As a first remark, some computations failed (see italics figures between parentheses) on a laptop in the enhanced case. For enhanced M1 (resp. enhanced M2), concept lattices and Iceberg30 (resp. concept lattices) could not be computed because of lack of memory. Computing AOC-posets and Iceberg40 was always possible, but Iceberg40 shows very few concepts in the original models, thus we suspect it will not be very useful for experts in this case.
We also observe a significant difference between Iceberg30 and Iceberg40 in M2. Table  8  shows the running times and the step numbers. The step numbers are similar in original M1 and M2. Computing concept lattices for original M2 needs more steps due to the existing circuit and the creation of many nonintroducer concepts. The 16 steps for obtaining the AOC-poset of enhanced M1 are noticeable and correspond to a running time relatively high, compared to the others. The different running time for original M1 and M2 (from 127ms to about 9s) allows to envisage online work for experts. For enhanced models (AOC-posets), it can be preferable to compute them offline. In the light of the above evaluation, AOC-poset and Iceberg40 are appropriate for the dataset on both M1 and M2 original models. They will be used in the next section on a real question raised by the experts.
Aspergillus Attack: Answering a Concrete Replacement Scenario To assess the pertinence of our approach, we have selected a smaller dataset from Knomana base and have explored it with both models M1 and M2 with AOC-posets. This smaller dataset contains the same 6 plants, but only targeted organisms of Aspergillus family. The aim is then to answer an instantiation of general question Q1: "knowing recognized benefits of Hyptis suaveolens in the management of Arachis hypogaea against Aspergillus parasiticus, which other plants could alternatively be used?". Figure  4  gives a simplified version of an excerpt from the AOC-posets built from BioPesticide and ProtectionSystem contexts, according to M1 model. In this figure, arrows represent the navigation links (the relational attributes) which allowed to find and highlight the concepts and the hierarchy we want to give to our domain expert to explore data around their question.
The attributes leading to TargetedOrganism concepts reveal, by looking at the most specific concepts (number 1, 2 and 0), that the biopesticides produced from Hyptis suaveolens and Ocimum gratissimum are used to fight against Aspergillus ochraceus, Aspergillus parasiticus and Aspergillus flavus. The attributes leading to ProtectedSystem concepts allow to find again Arachis hypogaea that is introduced by the most specific among the targeted concepts, concept 0, thanks to relational attribute PeO 0. To summarize, for the case study of this small dataset, both models M1 and M2 allow to find Ocimum gratissimum as an alternative plant to Hyptis suaveolens for protecting Arachis hypogaea against Aspergillus parasiticus. In addition, a query will not give more information, contrary to RCA. The formed concepts not only give one or more answers to the initial question, but they also show how these answers are classified, and which additional (not included in the initial question) description they share.
Related Work As for any data analysis method, studying the data encoding for Formal Concept Analysis and its impact on the analysis results is an essential phase. In our case, we need to take into account two specific features of our dataset: multi-relational information and ternary relations. Multi-relational information can be encoded through different and complementary schemes, according to the envisaged analysis. In the FCA domain, several approaches highlight the graph nature of relational data  [12, 15] , and pattern structures  [8]  are used to classify graphs describing objects (or tuples). Other approaches  [1, 7]  rely on logical formula for relational data encoding, providing features equivalent to the RCA scaling quantifiers. With the RCA scheme, the objective is to classify the objects themselves in several conceptual structures (one per object category), according to the relations that the objects of one category have with objects of another (or the same) category.
To facilitate this interpretation,  [19]  proposed to synthesize the concepts of a main lattice and their related concepts from the other lattices within a hierarchy of closed partially-ordered (sequential) patterns, i. e. directed acyclic graphs. This idea has been generalized by  [6] , where a family of concept lattices built by RCA is summarized through a hierarchy of concept graphs. Each concept graph is a set of concepts (potentially coming from several lattices) whose intents are mutually dependent, allowing to highlight relational patterns. Concept graphs are then organized according to the specialization order between concepts they include. Overlays on RCAExplore have been proposed in  [20]  to help the analyst choices, e. g. by forecasting the number of concepts and rules resulting from a relational concept family and a quantifier or an algorithm, and several configurations are studied on an environmental dataset.
Graph signal and weighted graph A dataset consisting of N elements with a known relationship between these elements can be represented by a graph G = {V, A, W}, where V, A and W stand for the nodes set, the adjacency matrix and the weight matrix of graph G, respectively. Each element in set V corresponds to a node in graph G while each W i,j in W reflects the degree of relationship in-between the two nodes v i and v j. In the general case, G can have directed or undirected edges (i. e. arcs) and W i,j can take arbitrary real or complex values. For a denoising image, the direction of edge is meaningless and, considering one node v i and nodes connected to it, one can construct the neighborhood of v i as N i = {j|W i,j 6 ¼ 0}. The graph signal of G is defined as a map from the nodes of G into the real number set <, V ! < v i ! f ðv i Þ; ð3Þ where f is a real value function and f(v i ) is the graph signal on vertex v i.
AGTV algorithm AGTV algorithm was proposed for tomographic data reconstruction. The whole algorithm can be divided into five steps: (i) Project input data into the sinogram space to obtain a filtered back projection (FBP). (ii) Construct a patch graph from the FBP. (iii) Formulate an objective function that takes graph total variation and adjoint operator of the wavelet transform as regular terms. (iv) Solve the objective function with the forward-backward primal dual (FBPD) algorithm. (v) Repeat step (ii) to (iv) until convergence. Although the AGTV algorithm can perform well on the tomographic data denoising, there still exist some shortages that hinder it from being applicable to the natural image denoising problem. First, only image intensity is taken into consideration during the graph construction while ignoring the patch location information.
Experiments This section presents the experimental results we obtained by applying our method to the classic benchmark images considered in the literature. Performances of our scheme are also compared to several state-of-the-art denoising methods. Tests have been conducted using Graph Signal Processing MATLAB toolbox (GSPBox) on a PC with an Intel 4. 0 GHz CPU and 16 GB of memory. In the following examples, images were contaminated by additive independent and identically distributed zero-mean Gaussian noise (AIIDZMGN) with the standard deviation σ. To investigate the effect of the denoising process, as in  [1]  [4] and  [8] , we choose the standard Baboon, Boat and Pepper images. The top row of Fig  4  highlights the denoising results obtained on the standard Baboon image with NPGTV considered an AIID zero-mean Gaussian noise of 10 dB.
Funding: This work was supported by the  National Key R&D Program of China  ( 2017YFC0107900 ), by the  National Natural Science Foundation of China  (nos. 61876037 ,  61271312 ,  61401085 ,  31571001 ,  31800825 ,  31400842 ,  61572258 ,  11301074 ), by the  Natural Science Foundation of Jiangsu Province  under grant  BK20150650 , by the  Qing Lan Project  and the '333' project ( BRA2015288 ), and by the  Short-term Recruitment Program of Foreign Experts  ( WQ20163200398 ).
, 2017). The database thus generated can be readily integrated into a 3D model  (Williams and Richardson, 2004). Thus, it is necessary to develop simple and effective characterisation methods of the distinct processing products/residues. This is particularly true when it comes to industrial minerals such as kaolin for which the final product is a mineral concentrate rather than a mono-element product  (Glass, 2016). Geometallurgy can be broadly split into two key levels: strategic geometallurgy, which focuses on the whole orebody and long-term life-of-mine scale and tactical geometallurgy which relates to the short-to medium-term scale during daily operation  (McKay et al. , 2016). Such a strategicn approach is fundamental for the unconventional 'resource' investigated in this feasibility work, not only because of its nature but also because the target metals must be considered as by-products.
Applying a geometallurgical approach to this case study is rather challenging for several reasons. First, there are no a priori certainties on the economic feasibility of metal by-product recovery and there are no known specifications for a marketable product consisting of REE. Next, if the operation is already in production, there is no processing on site for metal recovery from micaceous residues. This influences the development of a hypothetical geometallurgical model: most geometallurgical programs are applied to more conventional deposits already in operation for which a significant amountthere is no historical process of data are available, with large numbers ofnor a geometallurgical variables database allowing the use of multivariate modelling  (Boisvert et al. , 2013). Finally, given the complexity of the investigated deposits, with multiple granite-types each one having its own accessory mineral assemblage, this approach could not consider the whole St Austell granite but will have to focus on a specific granite type, ore-type or location.
Core samples All the core sample data used in this work are from a sonic drilling campaign undertaken in 2011 by Imerys Minerals Ltd, UK. The dataset selected for this study covers only the Melbur-Virginia and Wheal Remfry pits (referred as pit A and pit B hereafter) located at the western part of the St Austell granite (see Figure  2Figure  2) and comprise around 1200 core sample collected every 3 meters over 100 drill holes.
Core sub-sampling In order to control the accuracy of the XRF data for the LREE and other metals a set of 30 GT53MR180 samples from the set of core samples covering the western part of the biotite granite, i. e. kaolin pits A and B, have been selected to be analysed by ICP-MS for calibration and by QEMSCAN ® for mineral characterisation. All these samples correspond to relatively-high to high grade kaolin ore, i. e. completely altered granite. The linear regression models for Nb and LREE prediction, obtained with process samples in  Dehaine and Filippov (2015) , suggest that TiO2 can be used as a "tracer" for Nb and LREE. Moreover, TiO2 is generally well quantified by XRF analysis. Hence, sub-samples have been selected among all the available core samples following a procedure based on their TiO2 content to have ato ensure that the obtained sub-set representative sub-set displays the same variability as the original dataset  (Dehaine, 2016).
As the samples were well mixed and carefully prepared, the 5000 particles provided a representative sub-population of each sample. Data measurement used iMeasure 4. 2 SR1 and data processing used iDiscover software 4. 2SR1 and 4. 3. Data processing involved checking the data, customizing the database to the match samples, adding and improving SIP (database) categories as required, whilst considering the details of the technique outlined in  Rollinson et al. (2011). Modal mineralogy, mineral association, average grain size, liberation data and images were output for the samples. Additional textural observations were performed on a Hitachi FEG S4800 scanning electron microscope at the Service Commun de Microscopie Electronique et Microanalyses (SCMEM-UL, GeoRessources, Nancy, France) with an imaging voltage of 20 kV.
Methodology The purpose of this work is to investigate and develop preliminary geometallurgical tools for byproduct resource estimation of critical metals within the St Austell kaolin deposits with a focus on LREE. The methodology used in this study follows three main steps which aim to populate the core sample database: (1) to characterise the GT53MR180 core samples through QEMSCAN analysis, to control, and calibrate if necessary, the trace element grades using duplicate ICP analyses (not presented here, see  Dehaine (2016)  for details), then (2) to integrate metallurgical parameters through regression models (e. g. spiral performance prediction models) applied to the GT53MR180 trace element data. Finally (3), to use the core samples data along with the regression models to propose a simple preliminary geometallurgical classification scheme to help by-product resources estimation of critical metals.
65 respectively. All these properties are known to have a positive influence on gravity concentration performance, i. e. , coarse and liberated monazite preferentially report to the concentrates (Figure  14Figure  13 and Figure  15Figure 14 ). The fact that there are all positively correlated is an indication that the LREE feed grade could potentially be used to predict spiral concentration performance. The influence of the LREE grade in the feed material on the spiral concentration efficiency has been tested using the spiral setup in scavenging configuration. To reflect the representativeness of the spiral feeds in terms of grade, the LREE feed grade (  ) is normalised using the minimum and maximum LREE contents found in the core sample database as follows: 𝐺 𝐹 * = 𝐺 𝐹 -𝑚𝑖𝑛 𝑚𝑎𝑥 -𝑚𝑖𝑛 (1).
𝐺 𝐹 * can therefore vary between 0 and 1, these two extreme values corresponding to the case where   * equals respectively to the minimum or the maximum LREE content in the core sample database. Figure  16Figure  15 shows the influence of the normalised LREE grade (  * ) in the feed material on the performance of spiral concentration of LREE. The results show that the normalised feed grades are well distributed over the [0;1] interval which suggests that they cover almost the whole range of LREE values displayed in the GT53MR180 core samples data. As expected, spiral performance is found to improve with increasing feed grade. Indeed, LREE recovery and concentrate grade increase with LREE content in the feed, with a maximum recovery and concentrate grade of 55% and 1000 ppm respectively. However, LREE enrichment degree reaches a plateau at 4.
All performance indexes trend lines suggest that below 0. 2 LREE normalised feed grade (around 60 ppm LREE), no separation should be expected (Figure  16Figure  15). Hence, this value could be considered as the minimum grade of the GT53MR180 fraction that should be considered for LREE recovery. The aforementioned results suggest that the regression models proposed to predict spiral performance for LREE can reasonably been applied to GT53MR180 core sample data. In addition, the range of LREE feed grade used to calibrate the models, as mentioned before, covers almost the whole range of GT53MR180 LREE grades (from 0. 25 to 0. 95 normalised feed grade) therefore suggesting a good representativeness of the results. Hence, prediction of spiral performance using a core sample database can be realised using the following equation (with coefficient values given in Table  5Table  5): y = 𝑎 + 𝑏 𝑒𝑥𝑝(𝑐 𝐺 𝐹 ), for 𝐺 * 𝐹 ≥ 0.
While this approach preliminary work is promising and inexpensive, it would benefit from being validated by comparing the predicted performances with those obtained after processing micaceous residue samples collected when refining a block of ore for which the GT53MR180 grades are known. This may be very difficult for several technical reasons including ore traceability, contamination, large sample collection, etc. In most geometallurgical approaches, process performance prediction models are built using production data comprising a large dataset collected after months of production. Hence implementation on site of a primary concentration process (spiral concentrators for instance) seems to be the next logical step. Indeed, it would give the opportunity to collect enough data for developing similar process performance prediction models and could also allow assessing the effect of the ore variability in terms of metal grades, alteration grade and lithology (granite type) which is fundamental for the development implementation of a proper tactical geometallurgical modelapproach.
Acknowledgments This work was supported by the European  FP7 project  " Sustainable Technologies for Calcined Industrial Minerals in Europe" (STOICISM)  [grant number  NMP2-LA-2012-310645 ]. The authors also thank the financial support from the  French National Research Agency  through the  'Investissements d'avenir' national research program  LabEx  RESSOURCES21  ( ANR-10-LABX-21-01 ). The authors gratefully acknowledge permission to publish by  Imerys Minerals Ltd, UK , and especially  S. Moradi ,  P. Chauhan  and  A. Coe  for the access to the data and samples. We are also grateful to  R. Joussemet ,  F. Diot ,  C. Gauthier  and  J-M. Fischbach  from the STEVAL pilot plant team of  University of Lorraine  for their support during the pilot tests. B. Simons is thanked for sharing her knowledge and PhD data on geochemistry of Cornubian granites.
The IQ Consortium Nonclinical to Clinical Translational Database and Predictive Value (Thomas Monticello) DruSafe, the Preclinical Safety Leadership Group in the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), has created and analyzed an industry-wide database to determine how safety assessment in animals translates into clinical risk. Mangipudy and colleagues published a commentary addressing the debate on the utility of animal toxicology studies to evaluate human safety and, following a literature review, concluded that animal testing provides value for ensuring patient safety. 9 The same conclusion was reached by the DruSafe analysis of their translational database. 10 he database contained animal toxicology and safety pharmacology data coupled with clinical observations from completed phase I human studies for 182 molecules.
Concordance statistics were performed by organ system and test species. Sensitivity (the proportion of positive clinical findings that had positive nonclinical findings) was 48% with a 43% positive predictive value (PPV; the proportion of positive nonclinical findings that had positive clinical findings). When the same target organ was identified in both the rodent and nonrodent, the PPV increased. Specificity (the proportion of negative clinical findings that had negative nonclinical findings) was 84% with a negative predictive value (NPV; the proportion of negative nonclinical findings that had negative clinical findings) of 86%. If no target organ toxicity was observed in either test species, the NPV increased. The safety pharmacology data (central nervous system [CNS], cardiovascular [CV], and respiratory end points) obtained from the animal studies and phase I clinical trials were categorized by organ system and concordance statistics determined.
11 The PPV for CNS, CV, and respiratory end points were 42%, 41%, and 0%, respectively (with the respiratory PPV attributed to a lack of a respiratory clinical observations). The NPV for CNS, CV, and respiratory findings was 99%, 97% and 100%, respectively. These results support the current regulatory guideline to include CNS and CV assessments in phase I nonclinical packages. However, it was also concluded that a stand-alone safety pharmacology respiratory study may not be needed, due to the lack of impact and low predictive value (a 3Rs opportunity to reduce the animals required). Aligned with other published reports,  12  the clinical adverse event (AE) prevalence in the database was low, which was attributable to earlier attrition of less optimal candidates based on in silico, in vitro, and animal toxicology data.