dataesr/scientific-paragraphs-categorization huggingface dataset:
Characteristics of the indica subpanel Structure was run on the 114 indica accessions with a set of 840 SNP markers. Six populations were detected and confirmed by a DAPC analysis (Additional file 1: Table  S1 ). The populations are represented in Figure  2. The passport information (province and ecosystem) and phenotyping data (maturity time, grain shape and endosperm type) enabled us to characterize these populations (Table  1 ). Population I1 (11 accessions) included mostly shortduration improved irrigated accessions from the Mekong River delta, all possessing long and slender grains that were generally non-glutinous. Population I2 (26 accessions) included almost exclusively long-and very longduration rainfed lowland accessions also from the Mekong delta, with a non-glutinous grain type but a large diversity of shapes.
Even with the choice of a rare-cutter enzyme, the threshold of less than 20% missing data that we applied to determine whether to keep a marker in the dataset led us to remove a large number of markers and implied imputation for the remaining scattered missing data. Imputation is considered an efficient and cost-effective way to deal with missing data ? the alternative being sequencing at a higher depth ? but, to be truly accurate, it supposes that the LD spans a long distance, the marker order is correct, the marker density is sufficiently high and that all haplotypes that need to be imputed are captured in the initial dataset  [54]. All these conditions appear to be reasonably fulfilled in our panel. Even if the marker distribution, determined by the repartition of the restriction enzyme cut sites, still left a few missing segments in the genome, as a whole, the panel is covered by a large number of well distributed markers.
Acknowledgements Nhung Thi Phuong Phung  was supported by a  Global Rice Research Partnership [GRiSP] PhD fellowship  and  Nhung Kim Ta  by a  GRiSP  and a Vietnamese 322 PhD fellowships. This work was supported by the  Biotechnology program  of the  Ministry of Agriculture and Rural Development of Vietnam. The authors thank  T. M. H Truong  from  AGI  for helping with the collection of data in the phenotyping experiment and  Emilie Thomas  from  Cirad  for her help in the use of the image analysis software. The authors thank  American Journal Expert  for the English edition of the manuscript.
System Description The Mentor system includes a training phase where it builds a detection model using a training set of known malicious and benign domains. It further applies this model during detection to unqualified C&C blacklists in order to remove benign false positives and keep only confirmed malicious domains for detection. As in figure  1 , the training phase implements a crawler system that builds a comprehensive set of features using the initial ground truth dataset. Features extracted by the crawler are further used as input to a supervised learning system. It implements machine learning techniques in order to build a classifier model that is further used during detection. This section provides the details of our features and describes the process that we use to build our detection model.
Domain Features. Describe empirical time-based observations about a domain name extracted from the public whois database. We consider that benign professional domain names are usually not changed and they are expected to remain accessible during the lifetime of their associated business. However, C&C domains are less static in nature. In order to enhance their resilience against detection and takedown, botnet herders frequently modify their callback domains using techniques such as DGA and domain flux. In fact, using the same C&C domain name during an extended period of time adds single nodes of failure in the botnet architecture. Therefore, botnet herders usually register short-lived domain names, along with short TTL values in order to frequently switch for other C&C domains, adding another level of complexity for botnet takedown.
In order to build our domain classification system, we tested multiple supervised learning algorithms, including selective bayesian classifiers  [14] , SVM, J48 and C4. 5 decision trees  [8, 18]. First of all, decision trees offer a way to express structures in data. They provide a classic way to represent information from a machine learning algorithm. Besides, SVM provides an extension to nonlinear models that is based on the statistical learning theory. On the other hand, Bayes models provide a probabilistic classifier based on the Bayes Theorem. In the context of this paper, we evaluated the detection rates, including false positives and negatives, that we obtained by applying each of these learning algorithms, through application to our labeled ground truth dataset. We obtained a higher accuracy using the Bayesian classifier, and therefore we use this algorithm to build our domain classification model.
Experiments This section provides the details of our experiments, including the dataset that we used in order to build and evaluate our system. First, we build a ground truth dataset of malicious and benign domain names that we process, using our crawler system, in order to train our classifier model. Then we evaluate the contribution of our features towards detection, and we adopt a cross-validation process in order to evaluate the accuracy of our system. Last of all, we tested Mentor against real public botnet C&C blacklists in order to evaluate the performance of our system for different malware families, as well as its ability to characterize unknown domain names by the time of building our system.
Ground Truth Dataset Mentor applies machine learning to a training set of malicious and benign domain names in order to build a classifier model. The quality of our classifier strongly depends on the coverage of the initial training set and the accuracy of the ground truth labels associated with this training set. In fact Mentor actively connects to the domain names in the training set and builds detection features on the fly before these can be used as input to the training model. Hence, we need a valid blacklist of botnet C&C domains that are all active by the time of building our model. Yet we want to ensure that our blacklist only includes malicious domains and is clear of misclassified benign domain names. (b) Cross-validation results.
Domain features, including information collected from the whois database, provide lower detection accuracy compared to content features. As shown in figure  2a , most of our malicious C&C domains still have lower TTL values than benign domains. However, TTL values for malicious C&C domains have a large standard deviation, which leads to overlap between our set of malicious and benign domains, thus reducing the overall detection accuracy of those features. Last of all, popularity features also provide an overall good detection accuracy, and so they clearly separate malicious and benign domains. According to our learning set, C&C domains have much less incoming and outgoing links compared to other benign domains. On the other hand, and as shown in figure  2a , features describing the popularity on social networks provide a lower separation between malicious and benign domains in our training set.
Cross-Validation. We first evaluate the Mentor detection system by crossvalidating our classifier model against the ground truth dataset at our disposal. We performed multiple experiments, each time randomly splitting our dataset into 80% of data that we used for training, and 20% that we used for evaluation. Then we used our training set as input to the selective Bayesian learning model in order to build our classifier. We further tested this classifier against the remaining evaluation set in order to evaluate the accuracy of our system. The table of figure  2b  summarizes the results of our cross-validation process. Mentor correctly classifies 99. 02% of benign domains, with almost 0. 98% misclassification rate regarding benign domains. On the other hand, it correctly classifies 95. 32% of malicious domains, with an overall classification accuracy of 97.
Conclusion This paper presented a new system called Mentor, which implements positive DNS reputation to identify benign domains within a list of malicious C&C domain names. Positive DNS reputation measures the likelihood of a given domain name being innocuous, as opposed to negative DNS reputation that rather observes malicious artifacts for the same domain. Mentor describes a given domain name using three sets of features, including popularity, content and domain-based features. It implements an active crawler system that collects artifacts and content features from the remote domain itself and the public whois database. It groups all features for a given domain within a single vector and further applies machine learning techniques in order to separate benign and malicious domains. Mentor completes current negative DNS reputation systems; it processes domain blacklists generated by these systems and reduces their high false positives rate.
Introduction Exquisite specificity and high binding affinity, the hallmarks of the antibody (Ab) response, make Abs excellent tools for biotechnology and biomedical applications. During the past decade, achievements in the field of Ab engineering have markedly expanded the range of these applications. A number of engineering strategies have been applied to modify the functionality of therapeutic monoclonal Abs according to the requirements of the particular biological mechanism to be treated  (Kaneko & Niwa, 2011; Klohn et al. , 2013; Lu et al. , 2012; Vincent & Zurini, 2012). The development of methods for the humanization and functional expression of Abs and their fragments, together with the emergence of powerful techniques for screening combinatorial libraries and the expansion of structure-function databases aided by refined X-ray analysis, has opened unlimited opportunities for the engineering of Abs with tailor-made properties for specific applications.
The elbow angle, or elbow bend, defined as the angle between the pseudo-twofold axes relating V L to V H and C L to C H1 , is a highly variable parameter in antibodies, and its role in the antigen-binding capacity has been speculated on  (Huber et al. , 1976; Landolfi et al. , 2001). It is still unknown how far the changes that a hapten induces in an Ab structure can extend. An analysis of Protein Data Bank (PDB) depositions has shown that in some cases significant differences exist between the elbow angles of liganded and unliganded Fabs  (Stanfield, Zemla et al. , 2006; Stanfield, Gorny et al. , 2006). Dramatic changes in the domain structure of NC6. 8 (an Ab directed against the compound NC174) caused by small ligand binding were revealed by X-ray structure analysis followed by various molecular-dynamics simulations  (Guddat et al.
Crystallization and data collection Crystals of FabA17 were grown using the hanging-drop vapour-diffusion method by mixing equal volumes of protein (7 mg ml À1 in 50 mM Tris-HCl buffer pH 7. 4) and precipitant solution [0. 25 M ammonium sulfate and 20%(w/v) polyethylene glycol 5000 monomethyl ether in 0. 1 M 2-(Nmorpholino)ethanesulfonic acid (MES) pH 6. 5]. Rod-shaped crystals of approximately 0. 4 Â 0. 1 Â 0. 1 mm in size were obtained after 3-4 d and X-ray data were collected on EMBL beamline P14 at the PETRA III storage ring (DESY, Hamburg, Germany) at a wavelength of 1. 2234 A ˚using a MAR CCD 225 mm detector. The data were collected at a cryogenic temperature of 100 K, and the mother-liquor solution supplemented with 20%(v/v) PEG 400 was used as a cryoprotectant. Images of 0. 25 oscillation were collected over a total rotation of 85 using 2 s exposure per image.
Structure solution and refinement The A17 structure was solved by molecular replacement using MOLREP  (Vagin & Teplyakov, 2010)  with the heavy chain of the FabA17 structure (PDB entry 2xza;  Smirnov et al. , 2011)  as a search model. Attempts to use the whole Fab structure as a model were unsuccessful, probably owing to the difference in the overall shape of the molecule. The partial structure solution was followed by location of the V L domain, which is identical in the and variants, and building of the rest of the Fab molecule was achieved using the ARP/ wARP program  (Langer et al. , 2008). A -sheet of the C L domain could not be built automatically and was added using several manual interventions with Coot  (Emsley & Cowtan, 2004)  and PHENIX  (Afonine et al. , 2012) , including simulated annealing.
PDB entries 3mly and 4evn;  Jiang et al. , 2010; Lingwood et al. , 2012). Thus, solving the A17 structure with MOLREP, we first located the V H domain using the V H domain of the 2xza or the 3mly structure as a model and then the C H1 domain of the same structures. Thereafter, the V L and C L domains were located using as models the homologous domains from the 2xza and 4evn structures, respectively. Similar results were obtained by first locating the domains of the light chain and then those of the heavy chain or first the two variable domains and then the constant domains of the Ab. By carrying out molecular replacement with  Phaser (McCoy et al. , 2007) , it was also possible to locate the four domains of the Fab molecule by using individual domains of 2xza or 3mly for V H and C H1 and of 4evn for V L and C L as search ensembles.
Molecular-dynamics simulation The GPU-accelerated GROMACS 4. 6. 3 software package  (Pronk et al. , 2013)  was used for the simulation and analysis of MD trajectories using the Amber ff99SB-ILDN force field  (Lindorff-Larsen et al. , 2010). Explicit solvent simulations were performed at a temperature of 300 K with a time constant for coupling of 0. 1 ps under the control of a velocity rescaling thermostat and isotropic constant-pressure boundary conditions under the control of the Berendsen algorithm of pressure coupling with a time constant of 5 ps and application of the particle mesh Ewald method for electrostatic interactions (PME). A triclinic box of TIP3P water molecules was added around the protein to a depth of 20 A on each side of the solute. Charges were neutralized by the addition of chloride ions.
Additional NaCl was added to the systems to a final concentration of 0. 14 M. In each of the simulations, there were two temperature-coupling groups, the first consisting of protein and the second consisting of water with Na + and Cl À ions. The time step for integration in all simulations was 2 fs. Coordinates were written to output as a trajectory file every 10 ps and the total time of simulation was 250 ns. All simulations were performed on a Lomonosov supercomputer provided by the SRCC of Moscow State University. Analysis of the trajectories was also performed using the GROMACS 4. 6 software package.
Stopped-flow measurements with fluorescence detection were made using an SX. 18MV stopped-flow spectrometer (Applied Photophysics, England). All experiments were carried out in 20 mM sodium phosphate buffer pH 7. 4 with 150 mM NaCl at different temperatures (280-293 K). The Trp fluorescence was excited at 290 nm and monitored at >320 nm. Each trace in the diagrams is the average of no fewer than four individual recordings. The concentration of Fabs in all experiments was 10 mM and the concentrations of phosphonate and paraoxon were varied from 5 to 300 mM. Kinetic parameters were calculated as described by  Smirnov et al. (2011)  using the DynaFit software  (Kuzmic, 1996)  (for details, see Supporting Information xS1). Thermodynamic parameters (rate constants k 2 and equilibrium constants K d ; Supplementary Table  S1 ) for the interaction of phosphonate X with A17 were determined.
Determination of denaturation temperature The denaturation temperatures (T d ) of A17 and A17 were measured in a VP-DSC microcalorimeter (MicroCal, USA) in 0. 5 ml cells at a heating rate of 1 K min À1 as described in  Mitkevich et al. (2003). Test solutions contained 0. 6-1. 5 mg ml À1 protein in 50 mM sodium phosphate buffer pH 7. 4. The accuracy of the measurements was AE0. 1 K. To analyze functions of excess heat capacity, the Origin-DSC program package was used.
Surface plasmon resonance (SPR) analysis The surface plasmon resonance experiments were performed using a Biacore T200 system (GE Healthcare) equipped with a research-grade SA sensor chip. Chemically synthesized peptides (oxidized pep50C and pep54C, reduced pep50L and pep54L and a control peptide) were immobilized according to the manufacturer's protocol. Flow cell 1 was left blank as a reference surface. To collect kinetic binding data, A17, A17 and control Abs were injected at a range of concentrations into the four flow cells at a flow rate of 10 ml min À1 at 298 K. Ab-peptide association and dissociation were each monitored for 300 s. The surfaces were regenerated by a 100 s injection of 100 mM glycine-HCl pH 2. 0. Data were collected at a rate of 1 Hz and fitted with a 1:1 binding model using the affinity-analysis option available within the BIAevaluation software.
As calculated by the PISA program  (Krissinel & Henrick, 2007) , the interface area between the heavy and light chains in A17 is 1785 A ˚2 and there are 18 hydrogen bonds, one salt bridge and one disulfide bond (Supplementary Table  S2 ). This interface is comparable to others reported for Fab structures, but is more extended than that in the A17 structure (PDB entry 2xza; 1527 A ˚2 with 12 hydrogen bonds and one salt bridge). An important feature of the A17 structure and the key difference between the two variants is a large interface between the two variable domains of the Fab molecule, where interactions take place between the CDR loops located close to the A17 active centre, as well as Tyr-H53 from H-CDR2 (Fig. 4a ). They include strong direct interactions between residues of the H-CDR3 loop with L-CDR1 and L-CDR2, between H-CDR2 and L-CDR3 (Supplementary Table  S2 ), as well as contacts via water molecules and a bound MES molecule.
1 versus 21. 5 A ˚2), the ADPs of the A17 CDR loops are much lower (17. 0 versus 25. 4 A ˚2) (Supplementary Table  S3 ). Furthermore, the electron density in this region is very well defined (Fig. 4b ), in contrast to the A17 structure, in which the electron density of the CDR loops is relatively poor (Fig. 4c ). Comparison of the normalized ADP for residues of CDR loops for A17 (PDB entry 2xza), phosphonylated A17 (PDB entry 2xzc) and A17 (PDB entry 3zl4) demonstrated that the ADP values for all CDR loops are generally higher for the two A17 structures compared with A17, with no considerable dependence on the crystal contacts taking place in this loop (Supplementary Table  S4  and Fig. S3 ). This in turn means that the active centre of the A17 variant is more rigid than that of A17. The reported A17 structure is characterized by marked differences in the ADPs between the variable and constant domains of the chains.
, 2006). Structural comparisons of A17 with A17 (PDB entry 2xza) and A17 phosphonylated by phosphonate X (PDB entry 2xzc) allowed us to conclude that neither the ! light-chain switch nor phosphonylation have any significant effect on the elbow angle (Fig. 7 ). As for the overall structure of the two constant domains (C H1 and C L ) in the Fab molecule, the change in the C L domain of A17 resulted in some differences in its orientation relative to C H1 , as follows from the comparison of the twist/tilt angles between the two domains (176/97 in A17 versus 184/104 in A17) and in the alignment of these modules in the two structures (r. m. s. d. of 3. 2 A ẘhen aligned using the LSQKAB algorithm).
, 2012). This fact may account for the absence of marked differences between A17 and A17 in the kinetic parameters of their interaction with organophosphates. The observed structural divergence of CDRs loops can lead to changes in the antigen-binding properties of Ab molecules. Since A17 was selected as a biocatalyst using artificial chemical substrates, it was relevant to find out whether there is an epitope to which A17 is naturally intended to bind. As we failed to detect such an antigen among nucleic acids, lipids and polysaccharides (data not shown), we focused on epitopes of a protein nature. To address the question of the antigen specificity of A17 and A17, we used a combinatorial approach. 'Epitope mapping' was performed by screening a phage-displayed cyclic heptapeptide library. To facilitate this procedure, scFvA17 was used for peptide-epitope selection.
Isothermal titration calorimetry assays Isothermal titration calorimetry (ITC) experiments were conducted at 298 K on a MicroCal iTC200 instrument (MicroCal, GE Healthcare, UK). The buffer consisted of 25 mM HEPES pH 8, 150 mM NaCl, 0. 2 mM TCEP. To ensure minimal buffer mismatch, protein and DNA samples were dialyzed against the same buffer. The syringe for the ligand contained DNA duplexes in a concentration range between 80 and 100 mM. The thermostatic cell contained BurrH protein in a concentration range between 8 and 10 mM. The corrected binding isotherms were fitted to a multiple but identical sites binding model using a nonlinear least-squares algorithm in the Origin 7. 0 software (MicroCal) to obtain values of the equilibrium binding constant (K a ), stoichiometry (n) and enthalpy changes (ÁH) and the TÁS associated with DNA binding.
Measurements at each protein concentration were repeated at least twice. All responses were doublereferenced. For kinetic analysis, data were globally fitted to a 1:1 interaction model with a correction for mass transport (as provided by the manufacturer's software). For equilibrium analysis, the averaged response during the last 5 s before the injection stop was plotted against the protein concentration and fitted to a simple binding isotherm. All data processing and analysis were performed with the Biacore X100 Evaluation Software (version 2. 0. 1) from GE Healthcare.
Structure determination, model building and refinement The structure of BurrH in the apo form was determined by the single-wavelength anomalous diffraction (SAD) technique using a selenium derivative and a data set at the peak of the Se K absorption edge ( = 0. 98 A ˚). SAD data were collected from cooled crystals at 100 K using a PILATUS detector on the PXI-XS06 beamline at SLS Villigen, Switzerland. Data processing and scaling were accomplished by XDS  (Kabsch, 2010). All methionines were substituted by selenomethionine and the 12 possible Se sites were identified using the SHELX package  (Sheldrick, 2008). Initial phases were calculated at 2. 45 A ˚resolution using the AutoSolve program included in PHENIX  (Adams et al. , 2010). These initial phases were extended to 2. 21 A ˚resolution using the same data set with the PHENIX AutoBuild routine.
1b-1e, Supplementary Fig. S4 ). The kinetic properties of BurrH-DNA interaction are crucial for evaluating its possible genome-modification applications. Surface plasmon resonance (SPR) was employed for this purpose. The target DNA was immobilized on a streptavidin chip (Supplementary Fig. S5a ) and the BuD was assayed for binding (Supplementary Fig. S5b ). Our data confirmed not only that BurrH exhibits a high affinity and specificity for its target but also significantly slower dissociation compared with the AvrBs3 TALE (Fig. 1f  ). Moreover, BurrH does not display binding to other DNA duplexes, in contrast to TALE, which associates with BurrH target DNA (Supplementary Figs. S5c  and S5d ). All of the engineered variants targeting different DNA sequences maintain similar thermodynamic characteristics (Supplementary Fig.