dataesr/scientific-paragraphs-categorization huggingface dataset:
trichocarpa, and B. pendula coding sequence databases downloaded from CoGe, using E-value cutoff 1 × 10 -10. The 10 topmost hits from each database were translated and aligned together using MUSCLE. The alignments were reverse translated, and ambiguous regions were removed using Gblocks, with stringency parameters to allow smaller blocks, gap positions within the final blocks, and less strict flanking positions. Phylogenetic analyses based on the alignments were performed using RAxML-HPC with the GTR substitution model (Supplementary Data Set 3). To analyze whether a sweep could be explained by drift and population structure, a PCA of each of the sweep regions and 2-kb flanking regions was carried out for SNPs called by ANGSD. The principal components of the sweep regions were computed in R using data from ANGSD.
Additionally, Bayes-empirical-Bayes analyses were conducted to estimate individual amino acids that might have evolved under positive selection (Supplementary Table  20 ). Data availability. OpenStreetMap data are available under the Open Database License (http://www. openstreetmap. org/copyright). The genome assembly is available from Ensembl Plants (http://plants. ensembl. org/) and the CoGe comparative genomics platform (https://genomevolution. org/ CoGe/GenomeInfo. pl?gid=35079 and https://genomevolution. org/CoGe/ GenomeInfo. pl?gid=35080). All sequencing data have been deposited in the European Nucleotide Archive (ENA) under accession code PRJEB14544.
ACKNOWLEDGMENTS We thank  A. Korpijaakko ,  A. Korpilahti ,  S. Koskela ,  K. Lipponen ,  P. Laamanen ,  H. Kangas ,  M. Rantanen , and  E. -M. Turkki  for excellent assistance, and  L. Schulman  and  L. Junikka  ( University of Helsinki Botanical Gardens ) for providing birch species samples. Birch sequencing was supported by a  Finnish Technology Development Agency (TEKES)  grant to J. K. , Y. H. , and P. A. J. K. and Y. H. were supported by the  Finnish Centre of Excellence in Molecular Biology of Primary Producers  ( Academy of Finland  CoE program 2014-2019, decision 271832). Y. H. was funded by the  Gatsby Foundation  and the  European Research Council  Advanced Investigator Grant SYMDEV. V. A. A. acknowledges support from the  US National Science foundation  ( 0922742  and  1442190 ).
RETScreen RETScreen is a free clean energy management software system for co-generation project feasibility analysis, and energy performance evaluation. The Ministry of Environment of Canada has released it. The first version was introduced for on-grid applications, the RETScreen PV model was currently upgraded to deal with off-grid applications. These include stand-alone, hybrid and water pumping systems. The software guides the users in the design of their systems, by giving initial estimates of an array, battery, or pump size. By modifying few of the system parameters, users have the ability to quickly screen the most helpful technology and system size depending upon the load, weather conditions, and season of use. It has capabilities for evaluating both financial and environmental costs, assists in the decision, determine, and make the most of the advantages of renewable energy technologies for any location around the world  [11] [12] [13].
RETScreen has several worksheet for carrying out detailed project analysis, including energy modeling, cost analysis, emission analysis, financial analysis and sensitivity and risk analyzes sheets. It has a global climate data database of over 6000 ground stations (month wise solar irradiation and temperature data for the year), hydrology data, energy resource maps (such as wind maps), product data like solar photovoltaic panel information and wind turbine power curves. It also offers a link to the climate database of NASA. it is used for the analysis of various types of energy-efficient and renewable technologies (RETS) dealing with mainly energy production, life-cycle costs, and greenhouse gas emission reduction. RETScreen Plus is based upon an energy management software tool to study the energy performance.
iHOGA iHOGA (Improved Hybrid Optimization by Genetic Algorithms) is a software formerly known as HOGA (Hybrid Optimization by Genetic Algorithm), developed for the simulation and optimization of hybrid renewable energy systems. It has been developed at the University of Zaragoza (Spain)  [19] [20] [21]. It is a single-purpose or multi-optimization software target hybrid renewable energy systems. It uses genetic algorithms to optimize the control strategies of hybrid systems consisting in PV panels, wind turbines, Diesel generators, batteries, hydraulic turbines, H 2 tanks, electrolyzers, fuel cells, rectifiers, and inverters. The simulation is performed using one-hour intervals, during which all parameters remain constant. Optimization is obtained by minimizing the total cost of the system throughout its lifetime.
However, this software allows for multi-objective optimization, where additional variables can also be minimized, such as the equivalent CO 2 emissions or unmet load (energy not served). Since all these variables (costs, emissions or unmet load) are mutually against-productive in many cases, more than one solution is provided. Some of these solutions show better performance when applied to emissions or unmet load while other solutions are better suited for costs. It is used for controlling system components. Indeed, the command with iHOGA is limited to energy flow management strategies  [12, 21, 22].
). The maximum iteration number is 100. Then,  (16)  and position are carried out with other parameters values as data in Table  5. Position and velocity will therefore be updated at each time, safeguarding best values and neglecting the worst ones. The  search process will be terminated if the number of iterations reaches a final value or if the value of optimal solution reaches the tolerance value, which was considered in this paper equal to 0. 0001. The final optimum solution is delegated to the system designer depending on its experience, development and expansion of a pre-existing system, or creating a new system hybrid system. In this study, The PSO algorithm is implemented to achieve the optimal solution for the different following scenarios and constraints. Scenario 1: This scenario considers a hybrid renewable system including all the available renewable sources and the batteries to obtain the minimum COE and the lowest TNPC.
In a relational database context, fuzzy quantified queries have been long recognized for their ability to express different types of imprecise and flexible information needs. In this paper, we introduce the notion of fuzzy quantified statements in a (fuzzy) RDF database context. We show how these statements can be defined and implemented in FURQL, which is a fuzzy extension of the SPARQL query language that we previously proposed. Then, we present some experimental results that show the feasibility of this approach.
I. INTRODUCTION The Resource Description Framework (RDF)  [17]  is the standard data model promoted by the W3C 1 for representing information about resources available on the Web. SPARQL  [16] , is the official W3C recommendation for querying these data in a crisp way. In a previous work  [13] , we proposed a first extension of SPARQL (called FURQL) aimed to support flexible querying of crisp and fuzzy RDF databases. In a relational database context, fuzzy quantified statements have been long recognized for their ability to express different types of imprecise information needs  [1]. Recently, such statements started to attract attention of many researchers  [20] ,  [4] ,  [5] ,  [14]  in a graph database context. However, in the specific RDF/SPARQL setting, the current approaches from the literature that deal with quantified queries consider crisp quantifiers only  [3] ,  [6].
In the present paper, we intend to integrate fuzzy quantified statements in a FURQL query addressed to a fuzzy RDF database. We use as a starting point our previous work  [14]  where we extended the CYPHER language, used for querying crisp graph databases, with fuzzy quantified structural queries. The remainder of this paper is organized as follows. Section II presents background notions. In Section III, which is the core of the contribution, we introduce the syntactic format for expressing fuzzy quantified statements in the FURQL language defined in  [13]  and we describe their interpretation. Section IV deals with query processing and discusses implementation issues. In Section V, some experimental results showing the feasibility of the approach are presented. Related work is discussed in Section VI.
B. SPARQL and FURQL SPARQL  [16]  is the standard query language promoted by the W3C for querying RDF Data. It is a declarative query language based on graph pattern matching, in the sense that the query processor searches for sets of triples in the data graph that satisfy a pattern expressed in the query. Example 2: Listing 1 gives an example of a SPARQL query that retrieves every artist such that the albums he/she recommends have a rating under 4 and were created by another artist connected by a path of friend links to him/her.
Listing 1. A SPARQL query The language FURQL (Fuzzy RDF Query Language) defined in  [13] , uses fuzzy extensions of the SPARQL graph patterns introduced in  [12]  in order to express fuzzy preferences on the entities of an F-RDF graph (through fuzzy conditions) and on the structure of the graph (through fuzzy regular expressions). A fuzzy graph pattern considers the following binary operators: AND (SPARQL concatenation), UNION (SPARQL U N I O N), OPT (SPARQL O P T I O N A L) and FIL- TER (SPARQL F I L T E R). Syntactically, FURQL extends SPARQL by allowing the occurrence of fuzzy graph patterns in the W H E R E clause and the occurrence of fuzzy conditions in the F I L T E R clause. A fuzzy regular expression is close to a property path, as defined in SPARQL 1. 1  [8] , and may involve fuzzy structural properties (e.
A. Syntax of a Fuzzy Quantified Query in FURQL In the following, we consider fuzzy quantified statements of the type "Q B X are A" over fuzzy RDF graph databases, where the quantifier Q is represented by a fuzzy set and denotes either a relative quantifier (e. g. , most) or an absolute one (e. g. , at least three), B is the fuzzy condition "to be connected to a node x", X is the set of nodes in the graph, and A denotes a fuzzy (possibly compound) condition. An example of such a statement is: "most of the recent albums that are recommended by an artist, are highly rated and have been created by a young friend of this artist". The general syntactic form of fuzzy quantified queries in the FURQL language is given in Listing 3. D E F I N E. S E L E C T ?res W H E R E { B(?res,?X) G R O U P B Y ?res H A V I N G Q(?X) A R E ( A(?X) ) } Listing 3.
IV. IMPLEMENTATION The evaluation strategy we propose for processing these queries consists of a software add-on layer over a standard classical SPARQL engine. This software, called SURF 3 (Sparql with fUzzy quantifieRs for rdF data), is implemented within the Jena Semantic Web Java Framework 4  for creating and manipulating RDF graphs. SURF evaluates FURQL queries that contain fuzzy quantified statements whose syntax was presented above. It basically consists of two modules. 1) A pre-processing module, the query compiler, produces (i) the query dependent functions that allow us to compute µ B , µ A and µ, for each returned answer, according to the chosen interpretation, and (ii) a (crisp) SPARQL query R flat , which is then sent to the SPARQL query engine for retrieving the information needed to calculate µ B and µ A.
Finally, we get a set of answers ranked in decreasing order of their satisfaction degree. In order to demonstrate the performances of our approach in the case of selection SPARQL graph pattern queries, we run some experiments. We considered a set of fuzzy quantified queries divided in two types: Q crisp and Q fuzzy where Q crisp (resp. Q fuzzy ) is a fuzzy quantified query involving crisp conditions (resp. fuzzy conditions). We processed four crisp and four fuzzy queries by changing each time the nature of the patterns corresponding to condition B and A from simple to complex ones. A complex pattern differs from a simple one by the number and the nature (including structural properties) of its statements. These queries are summarized in Table  1. Our RDF data is inspired by Musicbrainz linked data (which is originally crisp), and for representing fuzzy information, we used the reification mechanism that makes it possible to attach fuzzy degrees to triples, as proposed in  [22].
For these experiments, we used four different sizes of fuzzy RDF datasets containing crisp and fuzzy triples (DB 1 of 11796 triples, DB 2 of 65994 triples, DB 3 of 112558 triples and DB 4 of 175416 triples. The results according to Yager's OWA-based interpretation are depicted in Figure  4. Figure  4  presents the execution time in milliseconds of the processing of the fuzzy quantified queries involving fuzzy conditions from Table  1  over the considered RDF datasets (due to space limitations, results for queries involving crisp conditions are note given but are quite similar). All experiments were carried out on a computer running Windows 7 (64 bits) with 8GB of RAM. An important result is that, for all the fuzzy quantified queries involving fuzzy and crisp conditions presented in Table  1 , the processing time of the overall process is proportional to the size of the dataset.
It is especially striking to see that the processing time taken by the compiling step and the score calculation step, which are directly related to the introduction of flexibility into the query language, are very strongly dominated by the time taken by the SPARQL evaluator (which includes the time for executing the query and getting the result set). Moreover, The FURQL compiling step takes so little time compared to the other two steps that it cannot even be seen in Figure  4. This time remains almost constant, and is independent on the size of the dataset while slightly increasing with complex patterns or fuzzy conditions. As to the score calculation step, it represents around 10% of the time needed for evaluating a crisp SPARQL query. The time used for calculating the final satisfaction degree is of course dependent on the size of the result and the nature of the patterns.
VI. RELATED WORK In a graph database context, there have been some recent proposals for incorporating quantified statements into user queries. In  [3] , Bry et al. propose an extension of SPARQL (called SPARQLog) with first-order logic (FO) rules and existential and universal quantification over node variables. This query language makes it possible to express statements such as: "for each lecture there is a course that practices this lecture and is attended by all students attending the lecture". More recently, in  [6] , Fan et al. introduced quantified graph patterns (QGPs), an extension of the classical SPARQL graph patterns using simple counting quantifiers on edges. On the other hand, quantified graph patterns make it possible to express numeric and ratio aggregates, and negation besides existential and universal quantification.
The authors also showed that quantified matching in the absence of negation does not significantly increase the cost of query processing. However, to the best of our knowledge, there does not exist any work in the literature that deals with fuzzy quantified patterns in the SPARQL query language, which was the main goal of our work. Fuzzy quantified queries have been long studied in a relational database context, see e. g. [1]  whose authors distinguish two types of fuzzy quantification: horizontal quantification  [9]  used for combining atomic conditions in a where clause and vertical quantification for which the quantifier appears in a having clause in order to express a condition on the cardinality of a fuzzy subset of a group. This is the type of use we make in our approach. In a graph database context, fuzzy quantified queries have an even higher potential since they can exploit the structure of the graph, beside the attribute values attached to the nodes or edges (see,  [20] ,  [4] ,  [5] , and  [14] ).
The work the most related to that presented here is  [14] , where we considered a particular type of fuzzy quantified structural statement in the general context of fuzzy graph databases. An example of such a statement is: "most of the recent papers of which x is a main author, have been published in a renowned database journal". We showed how this statement could be expressed in the language FUDGE (which is a flexible extension of the CYPHER query language) that we previously proposed  [15].
VII. CONCLUSION In this paper, we have investigated the issue of integrating fuzzy quantified queries of the type "Q B X are A" into a language aimed to query fuzzy RDF graph databases. We have defined the syntax and semantics of an extension of the FURQL query language, that makes it possible to deal with such queries. A query processing strategy based on the derivation of non-quantified fuzzy queries has also been proposed, and we performed some experiments in order to study its performances. The results of these experiments show that the extra cost induced by the fuzzy nature of the queries remains very limited, even in the case of rather complex fuzzy quantified queries such as those considered in this approach. As a future work, we plan to study other types of (more complex) fuzzy quantified queries, in particular, those that aim to find the nodes x such that x is connected (by a path) to Q nodes reachable by a given pattern and satisfying a given condition C.
2. 1 In silico studies 2. 1. 1 Molecular modeling. The in silico experiments were carried out using the Schrödinger molecular modeling soware package installed on a workstation running on a T7400-Dual Intel Xeon X5482 (3. 20 GHz, 1600 FSB, 2x6MB, Quad Core) and using Ubuntu 14. 4 LTS as the operating system. 2. 1. 2 Ab bril structure preparation. The X-ray crystal structure of a portion of the Ab bril (PDB code: 2BEG)  18  was retrieved from the Protein Data Bank  19  and prepared using the Protein Preparation Wizard. 20 This was then pre-processed by verifying the bond orders, adding hydrogens, and lling in the missing loops and side chains using Prime. 21 Water molecules were deleted beyond 5 Å from the ligand and ionization/ tautomeric states were generated at pH 7. 0 AE 0. 5 using Epik.
22 ubsequently, the Ab brils were rened by optimizing the hydrogen bonds (H-bonds) and the sample water orientations. Finally the Impref-minimization was carried out using the OPLS 2005 force eld. 20. 1. 3 Ligand preparation. The structures of the compounds recorded in the database were built in the Maestro 9. 3 panel. 23 igPrep 24 was used to produce low energy 3D structures of the database compounds. The ionization/tautomeric states were generated using Epik. 22 The chirality of the compounds was retained from the original state. All of the conformations were minimized using the OPLS-2005 force eld and the most likely 32 conformations per ligand were generated. 2. 1. 4 Induced t docking. All of the docking calculations were performed via XP-Glide  25  and run in the Virtual Screening Workow framework.
The docking grid was generated by Glide using the Ab brils' 3D-space extended to 10 Å in each dimension. The compounds were exibly docked using penalization for non-planar amide bond conformations. The docking poses with the best scoring results are kept. We combine in an iterative way the ligand docking techniques with those for modelling receptor conformational changes. The Glide docking program is used for ligand exibility, while the renement module in the Prime program is used to account for receptor exibility; the side chain degrees of freedom are mainly sampled, while minor backbone movements are allowed through minimization. The major feature of the side chain prediction algorithm is the sampling performed in a dihedral angle space; in fact, although the algorithm uses the same type of force eld-based energy functions used in Molecular Dynamics (MD), the small movements are replaced with large ones in the dihedral angle space.
Small angle X-ray scattering Small Angle X-ray Scattering (SAXS) experiments were carried out at the BM29 beamline in ESRFthe European Synchrotron Radiation Facility in Grenoble, France. 31 Protein samples were carried in ice, and freshly prepared with the desired amount of curcumin-like compound, as described in Section 2. 3. Ab 1-40 concentration was spectrophotometrically checked to be 270 mM. Each sample was measured at 37 C. SAXS patterns were recorded using a bidimensional Pilatus 1 M detector. The sample cell used is a 1. 0 mm diameter quartz capillary, with a few tens of microns wall thickness. On BM29, data collection on protein solutions is possible in a wide temperature range via an automated sample changer. Aer each measurement protein samples are stored at 37 C, without stirring.
To minimize the dose and the consequent radiation damage, protein solutions were only irradiated during data collection using a fast experimental shutter located 4 m upstream of the sample, thus controlling the acquisition time. The transmitted intensity is monitored with a diode integrated in the beamstop and the intensity measured during data acquisition is used for normalization. The modulus Q of the scattering vector Q is dened as Q ¼ 4p sin(2Q)/l, where 2Q is the scattering angle and l ¼ 0. 8 Å, the X-ray wavelength. Since the sample-detector distance was xed at 2. 867 m, the Q values ranged from 0. 01 to 0. 45 ÅÀ1. SAXS macroscopic differential scattering cross sections dS/dU(Q) (briey referred to as scattering intensities) were obtained on an absolute scale (cm À1 ) by calibrating via a water sample, subtracting the proper buffer contribution for each investigated condition, and correcting for the protein volume fraction, as previously described.
Biological evaluation The cytotoxicity of the compounds and their effect on Ab The Ab amyloid aggregation kinetics were followed at a controlled temperature (37 C) and under stirring (200 rpm) at 50 mM. The samples for cytotoxicity assays were collected at the beginning, aer 30 minutes, and aer two hours of kinetics experiments, and were diluted in a medium at 5 mM of Ab. To assess cell viability aer 24 h of treatment, the MTT (3-(4,5dimethylthiazol-2-yl-2,5-diphenyl tetrazolium bromide)) assay (Sigma-Aldrich) was performed as previously reported. 34. 6. 1 Statistical analysis. Statistical analysis was performed using the statistical soware package GraphPad PrismTM 4. 0. 35 Comparisons were carried out using one-way analysis of variance. If a signicant difference was detected by the ANOVA analysis, this was further evaluated using the Bonferroni post hoc test.
Results and discussion The in silico study was performed by replacing the central diketonic core of the lead-compound curcumin with various aromatic portions, in order to discover novel scaffolds able to target Ab oligomers. In particular, the database of compounds was built with structures endowed with a more stable and planar heterocycle. Thus, a large library with novel curcuminlike structured compounds was generated (see Fig. 1 ). The virtual screening was accomplished using the soware suite Maestro Schrödinger. In particular, the rst approach was focused on the calculation of molecular descriptors able to highlight the drug-like prole of the newly designed molecules. 20 The selection was based on Lipinski's rules (rule of ve) and by taking into account the molecular descriptors such as log BB, which allows the evaluation of BBB permeation ability.