dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods This cross-sectional study was conducted at a primary care clinic on the East Coast of Peninsular Malaysia. This clinic provides a structured programme for patients with T2DM run by a trained diabetes team. It is the referral centre for fundus camera examinations from other clinics under the jurisdiction of the District Health Office of Administration. Prior to the data collection period (October 2011 until January 2012), there were 1,722 registered diabetes patients in this clinic. These registered patients were randomly selected through a computer-generated list of random numbers. Their diabetes records were retrieved, and their criteria for participation multiple linear regressions was done to determine the predictors that significantly influenced the respondents' QoLs.
Quality of Life Determinants The independent variables in this study included the respondents' sociodemographic factors (age, gender, educational level, monthly family income and living status, i. e. , living alone or with others). The other independent variables were clinical factors, including the type of treatment received (non-insulin therapy or insulin therapy), duration of diabetes (< 5 years, 5-10 years or > 10 years), diabetes control (HbA1C ≤ 7% or > 7%), number of diabetesrelated complications (0, 1 or ≥ 2) and the presence of diabetes-related hospitalisation. These data were retrieved from the respondents' medical records.
Statistical Analysis The data analysis was performed using the IBM SPSS Statistics version 19. A bivariate analysis was done using the Spearman correlation test (for the numerical independent variables), as well as the independent t-test and one-way analysis of variance (ANOVA) (for the categorical independent variables). Subsequently, a multivariate analysis using The independent variables included in the analysis were those found to have P values of < 0. 250 (age, gender, education level, living status, type of treatment, number of diabetesrelated complications and presence of diabetesrelated hospitalisation). Only the age (adjusted B = 0. 05, P = 0. 004) and insulin use (adjusted B = -0. 84, P = 0. 011) were found to be predictors of the ADDQoL-18 AWI score after controlling for the gender, educational level, living status, number of diabetes-related complications and presence of diabetes-related hospitalisation (Table  4 ).
EMISSION SPECTROSCOPY Emission spectra are recorded during combustion of propellants in order to identify the main emitting species. Figure  2  (in Table  1 , Runs 1a, 1b) shows emission spectra recorded at 0. 3 MPa in the flames of AP/HTPB propellants doped with Fe-C nanoparticles. Fe spectrum is calculated using the Kurucz database  [25]  and is shown in Fig. 2 ; it takes into account 101 lines involving the lower levels between 0 and 8155 cm -1 and upper levels ranging from 25900 to 36000 cm -1. The intensity distribution is calculated assuming thermal equilibrium and a temperature of about 2500 K according to vertical spatial averaging in AP/ HPBT flame  [26]. The population in each excited state is calculated from the Boltzmann distribution. In Fig. 2(a) ,O H (A 2 Σ -X 2 Π) bands at 309 nm appear on a large continuum background, with a signal-to-noise (SNR) ratio of 2.
Laser ionization is neglected because the energy of the laser can only induce one photon transition. The absorption/ stimulated emission rates are calculated from the expression of Hilborn  [32]  using the transition strengths extracted from the Kurucz database  [25]. Even the levels that are not populated by radiative transitions can be populated by collisional energy transfers. Accordingly, calculation has to properly simulate these collisional transfers responsible for the distribution of the population density over all levels. One advantage of the model is that it is not necessary to specify which levels are involved in the spectrum because it is able to determine the levels populated by collisions.
Alligator clips are then connected to the left ventricle guide wire (negative clip) and to the needle (positive clip) following insertion of the delivery system close to the aortic valve. The rapid pacing is then tested using maximal output and minimal sensitivity (Figure  3 ). Valve implantation is then carried out under rapid pacing. In the presence of high-degree conduction disturbance, stimulation can be performed with this technique while a temporary pacemaker is inserted through a venous access, more frequently through brachial vein access to limit femoral vascular complications. This new technique has been shown to be feasible and safe, allowing stable stimulation with a low rate of complications and a potential reduction in procedural time. A randomized trial comparing left ventricle guide wire rapid pacing to conventional pacing (Easy TAVI) is ongoing in France (NCT02781896).
Today, more than 90% of cases are performed without predilatation. Only very complex anatomies or highly calcified valves are predilated before valve deployment (5-10%). Post dilatation is performed mainly after self-expandable valve deployment in the presence of significant paravalvular leak or transvalvular gradient (10-15%). Thus, avoiding balloon predilatation may reduce complication rates, decrease the need for permanent pacemaker and reduce procedural time. A large randomized trial  (37)  with the Sapien 3 valve is on-going in France (NCT02729519). After TAVI, systematic close monitoring with special attention to hemodynamic and cardiac rhythm is mandatory to allow early detection of periprocedural complications. In many centers, monitoring is performed for at least 12 to 24 h in the ICU before transferring the patient to a CCU after clinical and paraclinical status re-assessment.
Acknowledgments We thank  Drs. Arash Bohloul  and  Carolina Avendaño  for providing the patterned nanodisk substrates. E. C. P. acknowledges financial support from the  Merle & Helen Converse Scholarship , the  Ethel W. Bloom Scholarship , and the  Carlos & Malú Alvarez Special Opportunities Fund. This work received computational support from  UTSA 's HPC cluster Shamu, operated by the  Office of Information Technology , and  HPC center CALMIP at the University of Toulouse.
Funding Programme Investissements d' Avenir  ( ANR-11-IDEX-0002-02 ,  ANR-10-LABX-0037-NEXT );  Welch Foundation  ( C-1716 );  Airforce Research Office  ( FA9550-14-1-0268 );  Office of Naval Research  ( N00014-17-1-3024 ) via a subaward from  Northwestern University  ( SP0042269-PROJ0011928 );  Merle & Helen Converse Scholarship ;  Ethel W. Bloom Scholarship ;  Carlos & Malú Alvarez Special Opportunities Fund.
Ansys CFX 17. 1 Ansys CFX is a CFD commercial software. It employs a finite volume method to solve the Navier-Stokes equations  [23]. However, thanks to its multiphase flow model, the code is well-adapted to model sediment-laden currents such as turbidity currents. Both the Eulerian-Eulerian and Lagrangian methods are available in the software's code. For practical reasons, the Eulerian-Eulerian approach was chosen and sediment transport equations, represented by interfacial area density and interphase transfer between the water and sediment phases were solved using the Particle Model  [23]. Regarding the turbulence model, a sensitivity analysis was done leading to the selection of the Shear Stress Transport (SST) model. For more information, all equations used in Ansys CFX code can be found in the program's user's guide  [23].
An interesting aspect of Ansys CFX is that it allows the user to introduce equations to the code in the form of expressions, thus providing a better control and adaptation for each particular phenomenon simulated. In the case of the present work, the settling velocity as proposed by  [24]  was implemented in the code using the following equation: V s V 0 = (1 -C) m (2) where V s and V 0 are the particle's settling velocities in a suspension having a volumetric concentration C and in clear water respectively. m is a constant ranging between 2. 25 and 7 according to  [25]. Based on a sensitivity analysis, m was set to 2. 5. The definition used for V 0 can be found in  [26]. Additionally, an expression was added for the drag coefficient following  [27]. Finally, since Ansys CFX does not include expressions for the simulation of erosion and deposition of suspended sediments, an equation proposed by  [22]  was used and adapted.
Telemac 3d In this case, a 2D unstructured mesh with cell size 0. 01 m and 60 planes irregularly spaced on the vertical are used leading to 36'180 triangular elements and 2'170'800 3D elements. The time step of the calculation is 0. 1 s. The Manning-Strickler friction coefficient is 50 m 1/3 s -1 and sediment have a median diameter of 132. 8 µm with a settling velocity of 1. 5 10 -3 ms -1. Dynamic characteristics of the current were analyzed, this paragraph focus on the bed evolution calculated by Telemac 3d. The simulated deposition height are compared with the measurements in Figure  6. The evolution of the deposits is well captured by the numerical code even if it smoother and small discrepancy can be observed. In the experiment, the gate opening causes an initial acceleration and very high turbulence causing erosion, before the formation of the current.
Conclusion and Perspectives Modeling of turbidity currents is complex. It should take into account dynamic stratified flows with the difficulty of modeling sediment transport. The challenges of modeling vary depending on the context of the modeling, the aspects to study and the precision needed. Good modeling requires knowledge of theoretical, experimental and numerical literature supporting the user and improving the quality of the simulation. In this paper, one theoretical and one experimental cases have been simulated using Telemac 3d and Ansys CFX showing good agreement with theory or experimental data. Modeling real reservoirs requires to account for other effects such as surface elevation variations, complex topography, several sediment grain sizes, etc. This paper underlines the capacity of both software to reproduce specific aspects of turbidity currents dynamics.
Ansys is a user-friendly software with the possibility to adapt the code with user's expressions. Its main disadvantage is the absence of a deposition/erosion model for multiphase flows. Telemac 3d is an open source code which can be easily modified, the erosion/deposition model allows to simulate the bed evolution. The 3D mesh made of horizontal layers could make complex geometries such as dam structures difficult to integrate in the model. The next step of this work would be to compare both software on a real reservoir case.
The diffusivity is then identified by fitting a mathematical model that is based on Fick's second law to the experimental sorption kinetics. The diffusivity value is the one that minimizes the sum of squared error between the two datasets. Water vapor sorption kinetics could be obtained by gravimetrical methods, either discrete methods, e. g. , isopiestic methods, or continuous ones, e. g. , dynamic vapor sorption (DVS), intelligent gravimetrical analyzer (IGA), thermogravimetric analysis (TGA), or quartz crystal microbalance (QCM). While a numerical approach is often required for complex situations, analytical solutions exist for simple geometries and well-defined initial and boundary conditions  [18]. These analytical solutions are strongly related to the geometry of the system. Consequently, a lack of knowledge or too strong hypotheses on the system geometry can lead to a poor, even false, estimation of the diffusivity.
Particle Morphology Optical microscopy. Images of particles were obtained by using a macroscope Nikon Multizoom AZ100 (Tokyo, Kantô, Japan) equipped with a RGB DS-Ri1 camera (Tokyo, Kantô, Japan) in light transmission mode. 40 µL of a suspension of cellulose in absolute ethanol at a concentration of 0. 35 g L -1 was dropped on a microscope glass slide. Ethanol was then evaporated under vacuum. The magnification was set to ×25 by adjusting the lens AZ-Plan at ×5 and setting the optical zoom to ×5. To avoid unclear particles, a vertical displacement (z axis) was added by seven steps of 4 µm. Mosaic images were assembled by reconstructing 5 × 5 images. Nikon software NIS-Elements (Tokyo, Kantô, Japon) operating with the Multizoom AZ100 system was used to combine these images. This microscope allowed for distinguishing all particles of area greater than five pixels, i.
e. , greater than 4. 2 µm 2. Image analysis. Mosaic images obtained by optical microscopy were analyzed by using the ImageJ software (North Bethesda, Maryland, United States). Major axis (length of the inertia ellipse) and minor axis (height of the inertia ellipse) were determined for each particle. Image processing was performed on an increasing number of particles until that the cumulative average of the determined indicator (major axis or minor axis) was stable. Three different mosaic images were used containing a total of 3300 particles. Particles were assumed to have cylindrical shapes. The cylinder volumes were obtained from the major and minor axis values.
The following methodology was applied. The population was sorted according to the diameter and length of particles. Five squared classes of decreasing side were used: 20 µm (18 classes), 10 µm (72 classes), 5 µm (288 classes), 2. 5 µm (1152 classes), and 1 µm (7200 classes). For each class, the volume fraction linked to the mass fraction of particles was calculated. The sub-population of each class was then assumed to be a homogenous population of diameter and length, which were determined by using the three following indicators: center, mean and median. The resulting 2D particle size distributions (PSD) were then used in Equation  (10)  to estimate apparent diffusivity, and they were denominated as PSD-center, PSD-mean, and PSD-median respectively. Parameters estimation. Numerical simulations were realized by using the Matlab ® (Natick, MA, USA) software and the lsqnonlin function to estimate two parameters: apparent diffusivity D of water vapor in cellulose and equilibrium mass of sorbed water vapor M ∞.
Conclusions Water vapor sorption kinetics in the cellulose particles were recorded at 25 • C by using a quartz crystal microbalance coupled with an adsorption system. Analytical solution of diffusion in a finite cylinder was adapted to diffusion in a heterogeneous population of cylinder particles. For each kinetics, water vapor apparent diffusivity was estimated. In the present work, different shape descriptors, as evaluated by image analysis, have been investigated to estimate apparent diffusivity. The influence of considering a 2D size distribution instead of a single value of shape descriptor was also investigated. Results revealed that the use of an analytical approach when considering the 2D mean-PSD or median-PSD to estimate water vapor apparent diffusivity was the most accurate way to get data at the scale of particle in a polydisperse sample of cellulose particles.
Data preparation An initial 2551 cycloaddition reactions with available experimental values of the rate constant (logk), 1356 reactions with activation energies (Ea, in kJ/mol) and 1237 with pre-exponential factor (logA) values were collected manually from the manuscripts of PhD thesis works of Prof. Konovalov's group from Kazan Federal University, published in 1970-1990. Within the 2551 reactions there were 806 unique structural transformations. The data set contained about 85% of Diels-Alder (4+2) cycloaddition, about 8% (3+2) dipolar cyclisation, and 7% (2+2) cycloadditions. The reactions were carried out in C6H5CH3, CH3COOC2H5, C6H5OCH3, C6H5NO2, CH3CN, C2H5OH, C6H5Cl, THF, CH2Cl2, DMSO, CHCl3, 1,4-Dioxane, C5H11OH, C6H5Br, C6H6, CH3COCH3, C6H12 and CH2ClCH2Cl, at temperatures ranging from 273 to 423 K.
Descriptors The reactions were first rendered as Condensed Graphs of Reaction (CGR), created from the reaction RDF files using the in-house CGR Designer tool and stored in modified SDF format. This was directly processed by the in-house ISIDA Fragmentor software, in order to generate fragment descriptors. The length of monitored fragments varied from 2 to 14 for sequences and from 2 to 6 for atom-centered fragments. The following options were also used at choice: charges on atoms (Formal Charge), accounting for the terminal atoms of a fragment exclusively (Atom Pairs), and exploring all possible paths instead of shortest paths (DoAllWays). An important option regulating the amount of the overall generated CGR fragments is the 'dynamic bond' exclusive inclusion. Toggled on, the option produces the fragments, that contains the bonds forming/breaking while chemical reaction (local fragments) and omits the 'generic' fragments, not assigned to the reaction center.
Building and validation of the models SVR modeling. SVR models were built and validated using the ε-SVR algorithm implemented in the libSVM package  [43]. The modeling was performed using the evolutionary SVR optimizer  [44]  , which can be used to perform both descriptor space selection and optimization of the operational parameters (epsilon, kernel type, cost, gamma) of the SVR method. The procedure, applied to logk as a modeled property generated a total of 6000 models. The descriptor set producing the SVR individual model of maximal robustness (estimated by five-fold cross-validation Q 2 value) was based on atom-centered fragments of the length 2-3. This descriptor space encompassing 688 descriptors (674 of fragments, 14 of the reaction conditions) was used further for SVR and GTM model building and its external validation.
The RBFs determines the probabilities of an object to be mapped into a certain node. Moreover, the data object has non-zero probability over all the nodes. Consequently, the object could be characterized by its probabilities, which are called responsibilities , that represent the degree of association between the object n (in the initial D-dimensional data space) and the node k. Details of the GTM algorithm are given in our publications  [31, [33] [34] [35]  and references therein. GTM property landscapes. Distribution of the property over the chemical space can be visualized as a GTM property landscape, in which the mean property value of each node of the grid defined in the latent 2D space can be computed as: where is the property value of the nth molecule, are responsibilities of the molecules to be located in the node , and N is the number of molecules in the data set.
The database of 1849 reactions of cycloadditions, for which the logk, Ea and logA values have been measured in different solvents and at different temperatures, has been collected from the literature (see 2. 1). The data set has been divided into training and test sets, where the latter has been composed out of 200 reactions picked up randomly from the data set. The remaining 1649 reactions constitute the training set, which has been used to build three individual models, for logk, Ea and logA prediction, respectively. The obtained best SVR and GTM models have been further challenged for the exhaustive external prediction of the logk on the test set of 200 reactions.
EXPERIMENTS A. Datasets As noise-free polarization images cannot be produced using a noisy imaging polarimeter, we instead use a DSLR camera with a rotatable polarizer in front of the lens. This approach to producing imaging polarimetry is one of the earliest  [33]  and has been used by various authors e. g. [6, 34]. For this approach to work, the camera sensor must have a linear response with respect to intensity, that is I measured = kI actual , where I meaured and I actual are the measured and actual light intensities, and k is an arbitrary constant. The linearity can be verified using a fixed light source and a second rotating i ← i + 1 polarizer. As one polarizer is kept stationary, and the other is rotated, the intensity values measured at each pixel will produce a cosine squared curve if the sensor is linear, according to Malus' law  [18].
CONCLUSION Imaging polarimetry provides additional useful information from a natural scene compared to intensity-only imaging and it has been found to be useful in many diverse applications. Imaging polarimetry is particularly susceptible to image degradation due to noise. Our contribution is the introduction of a novel denoising algorithm, 'PBM3D', that when compared to state of the art polarization denoising algorithms, gives superior performance. When applied to a selection of noisy images, those denoised using PBM3D had a PSNR of 4. 50dB greater on average than those denoised using the method of Faisan et al. [16] , and 0. 84dB greater than those denoised using BM3D Stokes. PBM3D relies on a transformation from camera components into intensity-polarization components. We have given two algorithms for computing the optimal transformation matrix, and given the optimal for our system and dataset.
Acknowledgement The authors gratefully acknowledges funding provided by  Zodiac Aerospace and French National Research and Technology Association (ANRT). This work has been partially supported by «  Nouvelle Aquitaine » Region  and by  European Structural and Investment Funds  (ERDF reference:  P-2016-BAFE-94/95 ). Finally the authors would like to thanks  Thierry Rouge-Carassat  for his comment on a present manuscript.
Real-time PCR analysis Total RNA was extracted from muscle with the RNeasy Mini kit (Qiagen), and cDNA was prepared with the SuperScript RT Reagent Kit (Applied Biosystems). Real-time PCR was performed on an ABI Prism 7000 Sequence Detection System. Syber chemistry (Life Technologies) was used to amplify PCR, with the specific primers listed in Table  1. All other Taqman primers and probes came from Life Technologies. Data were analyzed by the 2 -ΔΔCt method, with normalization to the Ct of the GAPDH (glyceraldehyde 3-phosphate dehydrogenase) housekeeping gene.
, 2017; Schr, 2010; Zasloff, 2002). This strategy allowed the emergence of synthetic AMP design by two different approaches. The first was to use natural AMPs as templates and, with small changes considering the optimal properties for a highly active AMP, redesign their sequence, either changing amino acid residues or shortening them  (Cardoso et al. , 2016; Migliolo et al. , 2016 Migliolo et al. , , 2012)). The other approach was to use bioinformatics tools in the design of purely synthetic de novo AMPs  (Hilpert et al. , 2009). The number of AMP databases has increased, which can help researchers in the design process or in the screening of designed sequences  (Seshadri Sundararajan et al. , 2012; Waghu et al. , 2016). More recently, mathematical algorithms that consider desirable antimicrobial peptide properties for improving activity have also been implemented, using for that the sequences already described in the databases  (Melo et al.
At the industrial level, the production costs must be as low as possible. In order to meet this criterion, peptides with short sequences are preferentially selected  (Craik et al. , 2013). The design of peptides with few amino acid residues is challenging and should not compromise their activity. Although there is a high supply of peptide when the production cost is low, some have reported that high production levels can cause a decrease in peptide activity/stability due to aggregation in oral formulations  (Bak et al. , 2015). To overcome such problems, new methodologies should be applied to modify the properties of the peptides. Many approaches have been developed over the years to generate new peptide sequences, for different targets, based on known peptide databases. Computational design is a very powerful tool that has been refined over time, in which a given peptide/protein sequence can be submitted to one of the available web tools in order to be profoundly analysed regarding its physicochemical properties.