dataesr/scientific-paragraphs-categorization huggingface dataset:
Obtained data are saved and organized, after that, into a centralized database. We were able to assimilate the various objects in an unknown indoor environment with a MRS, collect enough information about this environment and update the centralized database after each new detection. As perspectives of this work, we envisage to apply the proposed approach on real RFID hardware in different indoor environments so that each robot will be equipped with a real RFID reader while taking into consideration the nature of tagged objects. The proposed approach will also be applied on other applications such as MRS localization and mapping, etc. VI.
Intervention Both the eHealth and the mHealth versions of the intervention were developed using the TailorBuilder software (OverNite Software Europe, Geleen, the Netherlands). Both interventions had exactly the same content. The mHealth intervention was specifically developed for use with a mobile phone, while the eHealth version was developed for use with a computer. Therefore, the intervention within the eHealth condition was delivered via email, whereas in the mHealth condition advice was delivered via short text messages (short message service; SMS). Questionnaires for both groups were sent via email; however, participants allocated to the mHealth group were requested to complete this questionnaire via their mobile phone. Participants in the control condition received an email to inform and remind them that they could assess a questionnaire.
Statistical Analyses All statistical analyses were performed using IBM SPSS Statistics version 20 (IBM Corporation). We used multiple imputation with 25 iterations to replace missing values on sociocognitive and outcome variables at T0. Additionally, we replaced missing values on BMI and physical activity at T4. Descriptive statistics and frequencies described the characteristics of the study population. We analyzed differences at baseline using analyses of variance (ANOVAs) with Tukey post hoc tests for continuous variables and chi-square tests with Bonferroni correction for categorical variables. We analyzed attrition using logistic regression, with attrition at follow-up (T4) as the outcome variable (0=not completed; 1=completed whole intervention), and intervention condition and all baseline variables (ie, age, sex, educational level, BMI, baseline physical activity, and baseline sedentary behavior) as predictors.
However, we found the same results regardless of whether we performed the analyses with the multiple imputation or with the completers-only dataset. The third limitation is that our process analyses were not accompanied by qualitative measurements. For example, by asking participants why they found messages less clear, we could have gained insight into whether the difference between groups was based on technical difficulties only or could be attributed to the other factors. The fourth limitation is that, because the tested intervention was message based, the results are difficult to generalize to the broader field of mHealth and eHealth. The fifth limitation is that participants were all recruited from an online panel and were a random sample from the panel. This might make it difficult to generalize the findings from participants who are used to participating in scientific research to the broader population.
Results Protein surface topography provides a rational framework for the design of peptides with increased activity. To date, there is a considerable body of results on α -conotoxins blocking the α 7 nAChR. These data are a firm basis for structure-function analysis aiming to map the ligand pharmacophores and to choose substitutions that would modify their activity in a desirable way. In this work, our aim was to design a novel α 7 nAChR ligand with enhanced affinity. The computational strategy comprised several steps (summarized in Fig. S1 ): 1. Establishing a database of α -conotoxins with known α 7 nAChR blocking activity and ascribing them to three groups: "good", "average" and "bad" (see Methods). 2. Creating a structural database: 3D models of all relevant α -conotoxins were either obtained from PDB or built by homology.
The area of maximum difference between our current best blocker and two other toxins is denoted with a dashed black box; it encloses residue 9, where substitution to Arg is shown to be important (this work). The figure was prepared with our in-house Protein Surface Topography software  13  , which is currently available only on request. A similarly potent inhibition was also observed for acetylcholine-binding proteins from Lymnaea stagnalis and Aplysia californica (Ls-and Ac-AChBPs), which are excellent spatial homologs of the ligand-binding domain of the α 7 nAChR and are often used in parallel with full-size nAChRs. The calculated binding parameters derived from two independent experiments performed for each analog and each target are provided in Table  1. According to the competitive radioligand assay, the most potent α 7 nAChR inhibitor was the PnIA[R9, L10] analog with IC 50 = 270 ± 10 nM and a Hill coefficient of 0.
3B ). The calculated IC 50 value for "replaceable" binding at the orthosteric site was 11 ± 3 nM and its Hill coefficient was 1. 0 ± 0. 3. On the other hand, the displacement of the [ 125 I]-PnIA[R9, L10] by the same non-radioactive analog constitutes 100% (Fig. 3B ). The calculation of the data obtained with the OriginPro 7. 5 program revealed the clear two-site nature of the binding inhibition: the IC 50 value for the high-affinity site was 15 ± 6 nM (see Table  1 ), which practically coincides with that for α Ctx (11 ± 3 nM), whereas the affinity for the low-affinity ("allosteric") binding site of the radioligand was about 1 μ M. It is worth mentioning that two other α -conotoxin PnIA analogs also completely (although with lower affinity) displaced the [ 125 I]-PnIA[R9, L10] from the α 7 nAChR (Fig.
One should also bear in mind that the α 7 nAChR has 5 orthosteric binding sites for agonists/competitive antagonists,   4 ) and in three independent experiments for (1) and (  2 ). The curves were calculated from the means ± s. e. m. using the ORIGIN 7. 5 program (see Methods). The IC 50 value (mean ± s. e. m. ) for the α -cobratoxin was 11 ± 3 nM from 75% binding sites, and the respective values of complete inhibition for α -conotoxins are provided in Table  1. and that radioligand analysis monitors the displacement of the α Bgt from all of them. On the contrary, to block the α 7 nAChR function in electrophysiology tests, one molecule of the antagonist is sufficient  49. In accordance with earlier findings  35  , the binding parameters obtained in the present communication from electrophysiology experiments on the α 7 nAChR expressed in Xenopus oocytes (Fig.
Methods Database of conotoxins that act on the α7 nAChRs. To perform the structure-function analysis, we established a database of 4/7 α -conotoxins and their mutants that inhibit the α 7 nAChRs (Table  S1 ). According to this inhibiting activity (IC 50 ), the ligands were divided into three groups: "good" (IC 50 < 16 nM), "average" (39 nM < IC 50 < 390 nM), and "bad" (IC50 > 390 nM). This database consisted of 39 α -conotoxins collected from literature.
Molecular dynamics simulations. The discovery of structure-function relationships requires 3D structures of all α -conotoxins, and it will benefit from taking into account their behavior in molecular dynamics (MD) simulations. Several conotoxins' structures (PnIA, Pdb ID: 1PEN; GID, 1MTQ; Vc1. 1, 2H8S; and MII, 1MII) were obtained from PDB, others were modelled using MODELLER 8. 2 software  55  starting from these templates. MD simulations were performed with GROMACS 4. 5. 2 56 using Gromos96 45a3 parameters set  57. The conotoxins' structures were solvated inside a (3. 5-5 nm) 3 box; a SPC water model  58  was used; the number of water molecules was 1600-3600. Counterions (Na + or Cl -) were added to maintain electroneutrality (0-4 ions). Simulations were carried out with a time step of 2 fs, imposing 3D periodic boundary conditions, in the isothermal-isobaric (NPT) ensemble using a Berendsen barostat  59  (pressure of 1 bar) and a V-rescale thermostat  60  (temperature of 37 °C).
Protein surface topography (PST). In the PST method  13  , the protein surface is transformed into a sphere, and the sphere-distributed properties, like hydrophobicity in the form of Molecular hydrophobicity potential  61  (MHP) or electrostatic potential (ELP), are presented as regular spherical projection maps. MHP and ELP were calculated with PLATINUM  62  and APBS 63 software, respectively. The regularity of interpolated data allows for simple mathematical operations on the maps, such as summation, subtraction, averaging, etc. Thus, sampling the frames extracted from the MD trajectories (each 100 ps) and spatially superimposing them on the starting structure yields a series of MHP/ELP maps, and averaging results in "dynamic maps" (some of them are provided in Fig. S1 ) encompassing the dynamic mobility of the toxins' side chains.
The filtration on GF/C filters was performed as mentioned above. The equilibrium binding data were fitted using ORIGIN 7. 5 to a one-site model according to the Equation: B(x) = B max /(1 + K d /x), where B(x) is the radioligand specifically bound at free concentration x (determined by subtracting the amount of bound and adsorbed radioligand from the total amount added to the incubation mixture), B max is the maximal specific bound radioligand, and K d is the dissociation constant. To evaluate the dissociation kinetics of the [ 125 I]-labeled PnIA[R9, L10] from the α 7 nAChR, the binding of the 0. 4 nM radioligand was allowed to reach equilibrium (2 h incubation), after which a saturating concentration of the α -cobratoxin (20 μ M) was added to prevent any re-association of the radioligand to the receptor.
The reaction was terminated by rapid filtration on the GF/C filters as mentioned above for different time intervals (2 to 120 min). Analysis of a-Conotoxin PnIA Analog Interaction with the a 7 -nAChR via Two-Electrode Voltage Clamp Electrophysiology. Oocytes were prepared from mature female Xenopus laevis by following the standard procedure described elsewhere  50. After a rat α 7 nAChR cDNA injection, the oocytes were incubated at 18 °C for 48-72 hours and analyzed via electrophysiology measurements. The membrane potential was clamped at -70 mV using a turbo TEC-03X amplifier (npi electronic, Germany). The data were collected and handled using either Patch Master or WinWCP software. Acetylcholine applications (20 s) were performed every 5 minutes. To test activity, the conotoxin analogs were applied 4 minutes before acetylcholine application.
Intracellular calcium concentration [Ca 2+ ] i measurements were performed in an external buffer containing 140 mM NaCl, 2 mM CaCl 2 , 2. 8 mM KCl, 4 mM MgCl 2 , 20 mM HEPES, 10 mM glucose, pH 7. 4 at 25 °C. The cells were incubated with the PnIA[R5, R9, L10, R14] analog (0. 55 μ M) for 20 min at room temperature before adding acetylcholine iodide (Sigma, Germany). Changes in the fluorescence of the calcium sensor Case12 (ex/ em = 491/516 nm) were detected with the microplate reader HidexSence (Hidex, Turku, Finland) every 2 s for three minutes. The responses were measured as peak intensity minus basal fluorescence level. The data files were analyzed using HidexSence software (Hidex, Turku, Finland) and OriginPro 7. 5 software (OriginLab, MA, USA).
(E,F) Maps for PnIA[R5, D7, L10, R14] and PnIA[R5, D7, L10], respectively, which were synthesized and tested previously 35  , and do not exhibit high inhibiting potency. Panels D-F are colored by ELP according to the scale on the bottom right. The area of maximum difference between our current best blocker and two other toxins is denoted with a dashed black box; it encloses residue 9, where substitution to Arg is shown to be important (this work). The figure was prepared with our in-house Protein Surface Topography software 13  , which is currently available only on request.
Acknowledgements The authors are grateful to  Prof. A. B. Smit  ( VU University, Amsterdam, the Netherlands ) for AChBPs; to  EliLilly (London)  for GH 4 C 1 cells transfected with human α 7 nAChR; to  Michael Hollmann  and his lab ( Ruhr University, Bochum, Germany ) for their help with electrophysiology; to  R. J. Lukas  ( Division of Neurobiology, Barrow Neurological Institute, Phoenix, AZ, USA ) for the α 7 nAChR clone; to  Veit Witzemann  ( Department of Molecular Neurobiology, Max Planck Institute for Medical Research, Heidelberg, Germany ) for the muscle nAChR clones. We thank  Ms. Anastasia Efremova  for editing the manuscript. This work was carried out with support from the  Russian Scientific Foundation  (project No. 14-24-00118 ). V. I. T. and R. G. E. are grateful to the " Molecular and Cellular Biology" program.
3 ). These images also show hotspots to the west of the vent where the land was later inundated by the lava flow  17. Digital terrain model. We used high-resolution digital elevation data purchased from the Astrium company to quantify the uplift of the post-eruption land surface in a 100 km 2 area around the vent. A post-eruption DTM was constructed using stereo optical images dated 13 March 2014 and collected by the Pleiades satellite constellation. The DTM has a lateral and vertical resolution of 4 m. We assessed the amount of uplift at Cordon Caulle by subtracting the ASTER global DTM, which predates the eruption (April 2011), from the Pleiades DTM, using Generic Mapping Tools and QGIS software. This operation created a difference map from which net topographic and volume changes could be assessed.
6 ). Deformation modelling. We used the numerical code of Galland and Sheibert 20 to simulate ground deformation above an inflating axisymmetric sill and infer, from the model solution fits to natural topography, the intrusion pressure, size and depth. The model, implemented with Matlab R2014b for Macintosh, generates 2D deformation profiles that are readily comparable to the natural surface topography. Thus, we used two orthogonal directions in the deformed region (Fig. 4 ) as modelling targets to estimate the laccolith's diameter, pressure and depth. We investigated the effect of profile position on the resulting solutions and found that the shortest and longest dimensions of the deformation field produced the biggest differences in laccolith depth (Boverburden thickness) and radius; oblique sections produced intermediate values of depth and size compared with the two extreme sections (A-A' and B-B' of Fig.
Acknowledgements We are grateful for the constructive comments from reviewers  Helge Gonnermann ,  Steffi Burchardt  and  Alessandro Bonforte. We thank  Mats Landgren  and the  Geoeye foundation for providing imagery of Cordon Caulle. J. M. C. was supported by the  VAMOS research centre at the University of Mainz. C. I. S. acknowledges  Victoria University FSRG  grant number  205424 , a  Royal Society of New Zealand Cook Fellowship  awarded to  C. J. N. Wilson. H. T. is supported by a  Royal Society University Research Fellowship.
These changes may promote floods, threaten, destabilize hydraulic structures and disrupt their operations  (Soualmia and Gharbi, 2014). In a second step, this current research considers a two dimensional model using TELEMAC 2D code coupled with SISYPHE code for analyzing the hydrodynamic and sediment transport mechanisms and predicting their effects. The 2D hydraulic model includes a significant improvement in calculating the variation of hydraulic variables, and also the delineation of flood zones  (Dusty, 2012). Whereas, the sediment transport model provides us about the river morphological changes occurred during extreme events. To conclude, a comparative study of the results of both 1D and 2D models will be conducted in order to determine the advantages and weaknesses of these models for predicting the sediment transport capacity.
SISYPHE is the state of the art sediment transport and bed evolution module of the TELEMAC modeling system. SISY-PHE can be used to model complex morphodynamic processes in diverse environments, such as coastal, rivers, lakes and estuaries, for different flow rates, sediment size classes and sediment transport modes  (Tassi and Villaret, 2014). In SISYPHE, sediment transport processes are grouped as bed load, suspended load or total load, with an extensive library of bed load transport relations. SISYPHE is open source software; it is a horizontal two dimensional morphodynamic model. SISYPHE can be internally coupled to the module TELEMAC 2D, in order to take in account the evolution of the bottom in calculating hydraulic parameters (Fig. 3 ). For the evaluation of sediment transport rates, unlike the usual models which take into account only one median diameter, SISYPHE provides a non-uniform pattern (multiple-size).
We will therefore assume that the vertical variations of flow and sediment are small enough; variations in the horizontal plane can be described by Saint-Venant equations. Solving these equations by TELEMAC 2D module is performed on a mobile bed, where the bottom evolution is calculated by the SISYPHE module. The times and distances should be sufficient for the moving particles can be considered as a continuous mass  (Recking, 2013). For our case study, we dispose of the necessary information for the model implementation to quantify the materials transported by rivers. Below, we present the various available data for the implementation of a 2D model for the calculation of sediment transport rate; in fact, we need granulometric curve, rating curve, water depth, and flood hydrographs. It should be noted that all measurements were made directly in the Medjerda River.
The grain size distribution of sediments in the Medjerda River is obtained by a sampling companion, where sediments were collected at several stations. The particle size analysis is obtained by the technique of sieving and sedimentation in the laboratory of the General Directorate of Water Resources (DGRE). The available data set provides information about the particles diameter repartition of deposit samples. All these information are required for the analysis of sediment distribution in river. The granulometric fraction transported by the flows is composed essentially of clay and fine silt 66% at Slouguia, with a predominance of fine silt of about 35% at the Slouguia station.
RESULTS AND DISCUSSION Let us summarize our results concerning the modeling of the Medjerda River performed by TELEMAC 2D coupled with SISYPHE. Initially, we started by the model calibration by adjusting the friction coefficient to approximate the measured data. However the Strickler coefficient is about Ks = 25 m 1/3 /s, it is deemed satisfactory because it corresponds to a river with abundant vegetation. We also present the evolution of sediment transport rate carried by the Medjerda. We were interested on the study of the morphological changes occurred after the passage of the spectacular flood of January 2003. The last part is dedicated to a comparative study between simulated results of 1D and 2D models. The aim is to determine the benefits and limitations of the 1D and 2D models, and to propose eventually the most suitable software for accurate modeling for the Medjerda River.
El modelo SPHERIMENTAL SPHERIMENTAL es un software propio del CEDEX  [7]. Se trata de un código FORTRAN-CUDA; es la evolución del modelo MDST  [8] , adaptado para computación paralela en tarjetas gráficas. La arquitectura de las tarjetas gráficas permite realizar operaciones con múltiples hilos de ejecución, lo que resulta de gran utilidad en el campo de la supercomputación. El método SPH tiene un gran coste computacional, por lo que esta vía permite abordar problemas de gran tama ño (O•10 6 partículas) con tiempos de cálculo menores que los que se pueden alcanzar en un cluster de CPU. Además, los costes de inversión y consumo de energía son mucho menores. Se han realizado ensayos de rendimiento con un caso de prueba dam break (rotura de presa) con un modelo de 2 millones de partículas, en el que se consiguieron reducir los tiempos de cálculo en 35 veces  [7].
El método de integración empleado es Runge-Kutta de tercer orden tipo total variation diminishing  [12]. Se emplea un paso de tiempo variable que se limita mediante un doble criterio, uno basado en la velocidad del sonido (7) y el otro en la aceleración  (8) , siendo el paso de tiempo el que resulta de la expresión  (9). dt cv = h c s + 0. 6˛c s ( 7 ) dt f = min h dv i (8) dt = min 1. 5dt cv , 0. 3dt f (9) El CEDEX ha realizado con este software diversos trabajos de calibración y asistencia técnica  [13, 14].
En este caso se ha empleado el modelo Iber, que permite reproducir correctamente las pérdidas por fricción en un canal rectangular. En consecuencia, se ha supuesto una hipótesis de funcionamiento en la que el flujo sea en lámina libre y permita, por tanto, realizar esta calibración. Se ha estudiado el canal de descarga del aliviadero con el modelo Iber. Se han realizado diversas simulaciones con diferentes caudales. Como condición de contorno de entrada se ha empleado la velocidad media, medida en el modelo SPH en dicha sección. Se ha empleado un coeficiente de Manning n = 0,016. La figura 7 muestra la superficie libre obtenida con el modelo Iber (a) y con SPHERIMENTAL (b) empleando el coeficiente C rough que mejor ajusta el perfil de velocidades en la rápida como se muestra en la figura 8.
Schematically the guessing process can be split into the following steps. 1. We collected the sequences for time-usage t τ (x) and space-usage s τ (x) from the TM data set as described in Section 2 of this paper. 2. These sequences t τ (x) and s τ (x) are only given for the first 21 different inputs. We used an initial segment of 15 elements of these sequences to guess in an automated fashion the corresponding function that allegedly generates this sequence. In some cases the beginning of the sequence (up to three elements) was removed because the beginning did not match the general pattern that only occurred later on in the sequence. If we would leave the first values, Mathematica was no longer able to find the general pattern. The guessing process was done in Mathematica v. 8 and 9 using the FindSequenceFunction as built-in in this software.
We observe that this is exactly the upper bound as predicted by the Space-Time Theorem. We saw this phenomenon in (3,2) space as well. The only three exponential time performers used linear space so by Lemma 4. 4 we already know that the dimension of those TMs should be one. This has been checked also in Mathematica. The check was not really performed to check our theoretical results, rather the check was used as a test-case for our analyzing software. We saw that a TM in (2,2) space runs in super-polynomial time if and only if its dimension equals 1. This observation is no longer valid in (3,2) space though.
Acknowledgements JJ wishes to thank  José María Amigó García  for his kind support and an invited research stay at the  University of Miguel Hernandez in Elche. JJ received support from the  Generalitat de Catalunya  under grant number  2009SGR-1433  and from the  Spanish Ministry of Science and Education  under grant numbers  MTM2011-26840 , and  MTM2011-25747. We acknowledge support from the project  FFI2011-15945-E  ( Ministerio de Economía y Competitividad, Spain).
Background-We have developed a GWAS analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e. g. single nucleotide polymorphisms, SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalised regression and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis.