dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to the high number of threads on Phi, getting sufficient loop counts requires the domain size along the OpenMP parallelized dimensions to be large enough. The part of the kernel presented in Figure  9  corresponds to the part executed by a single thread where the parallelism comes for SIMD vectorization. The loop along r has been blocked with the iteration along r blocks appearing at line 3 so as to help vectorization by having the innermost loop the same size as that of a processor vector. This vectorization is further favored by iterating over small arrays the size of a vector such as psum (line 16) in the code to accumulate results inside a loop. The upper bound of the loop along r blocks (line 3) is fixed at compile time which accelerates computations on Phi a lot (not true on Sandy).
The performance in GFLOPS is decreased for smaller domain sizes. This dependency on input parameters is not as sensitive on Sandy. The pressure and the constraints on the computing units and the cache hierarchy seem to be higher on Phi than on Sandy and a small imbalance can lead to severe performance issue on Phi. Finally, additional issues come from the compiler version 8. From one version to another, the Intel compiler does not employ the same SIMD instructions and optimizations for a given code. We have noted that a well optimized code can suffer a large slowdown (factor 3 has been observed) when changing the compiler version. This sensitivity clearly advocates for writing the kernel so as to guide more the compiler on the optimizations to apply, for example with Intel intrinsics. This has several drawbacks but avoids such dependency on the compiler.
gysela mini-app By adding the actual computation of the feet of the characteristics and the spline coefficients, the code becomes comparable to the gysela mini-app on Phi presented in section 3. The performance of this optimized version are improved by an impressive factor 14 on medium test cases compared to the initial version (domain size 128 × 128 × 64 × 32) and by a factor 8 compared to the top-down version of section 3. 3. A single advection step takes 3. 2s now (45s initially, 25s after top-down enhancements). Compared to top-down version, the bottom-up approach exhibits several benefits. Firstly, building the kernel step-by-step enables to benchmark tiny/elementary parts of the code and to control the compiler in order to trigger the usage of deisred vectorized instructions. Secondly, identifying block sizes for cache optimization, prefetch parameters and optimizing data loading with indirections is much easier in this context.
This gain is even larger for large domain sizes. On the one hand, the Nosplit case exhibits much more compact kernels in term of the number of code lines. This is definitely an advantage from the software engineering point of view. On the other hand, the Nosplit case also has drawbacks. First, it requires temporary buffers to store the feet of the characteristics (extra memory consumption). Second, tiling sizes have to be specifically tuned for each new machine in order to benefit from maximal cache effects. Considering the communication costs of the NoSplit case in a future application using a MPI parallelization, as the parallel algorithm is not yet known, it is difficult to make accurate predictions. Nevertheless, the constraints and data dependencies are quite similar to those of the Split case, therefore we do not expect that the amount of communications will be completely different.
Conclusion In this paper, we have presented the optimization of a Semi-Lagrangian Vlasov solver for the Intel Xeon Phi architecture. We started this work by running several memory and network micro-benchmarks to get familiar with this new hardware and to set up the kind of performance we should expect from it. Then, starting from the production version of gysela code, several simplified versions were derived in order to explore different implementation strategies with more flexibility. When only the heart of the numerical method (the advection) was remaining and the expected performance could not be reached, it has been decided to start again with very simple interpolation kernels. By increasing incrementally the code complexity, the full 4D spline interpolation kernel could be implemented.
The final obtained performance was satisfactory as the computing time was twice as fast on Xeon Phi as on a dual-socket Sandy bridge node. This kernel has been successfully integrated back into the gysela mini-app. But because of the other parts of the advection (feet and spline coefficients computations), the code still runs slower by a factor 2 on Xeon Phi compared to Sandy Bridge. This is because these other parts are also difficult to vectorize/optimize/parallelize for the Xeon Phi. Complementary work still has to be done to recover the missing factor 4 on the execution time on Xeon Phi. To port the whole production version of gysela code on such architecture remains a long term objective. All along this optimization process, performance evaluation have been performed on both the Sandy Bridge and Phi computing units.
For the best cases of the simple kernels, we managed to reach similar fractions of the peak performance on Phi as on Sandy Bridge. In other cases however, and especially when the code complexity builds up, we did not manage to reach that goal. Regardless the final result, the investment in terms of code engineering to reach these performance levels is very large. The level of parallelism to extract is much higher than on Sandy Bridge: 177 and up to 236 OpenMP threads are required on Phi versus 16 on Sandy Bridge. Extracting this fine grain parallelism is difficult for some kernels, especially for small domain sizes. Many other aspects have to be looked at, including vectorization, prefetching, data alignment, cache locality, cache trashing and memory access patterns while these have much less impact on Sandy Bridge.
In addition, compiler optimizations are very important to get good performance but are very difficult to predict. Some good practice can help such as loop splitting, but no golden rule can be extracted and in practice, one often has to look at the generated assembly code to assess its quality. This problem is further amplified by the fact that the same code can yield very different performance results depending on the compiler version. In order to reduce this cost, we also hope for progress on the Xeon Phi hardware and software side with the next generation of processor: • The hardware architecture and software stack (including MPI) should provide a more homogeneous view of the platform, reducing communication overheads. • An improved compiler that produces performant executables with less programming effort.
Acknowledgements We would like to thank  Thomas Guillet  from  Intel Exascale Labs  for fruitful discussions about optimization on  Xeon Phi. This work has been done with the support of  High Level Support Team  ( IPP Garching ). We have used the computing facility of IFERC-CSC in Rokkasho and in particular the Helios supercomputer. We would like to thank also  LRZ, Cineca ,  Bull R&D  and RZG computing centers for granting us access to their Xeon Phi clusters. Most of this project has been funded by  ANR GYPSI  and  G8-Exascale Nu-FuSE  projects.
The pendent drop method for surface tension measurement consists in analysing the shape of an axisymmetric drop hanging from a capillary tube. This software is an add-on for the public domain image processing software ImageJ which matches a theoretical profile to the contour of a pendent drop, either interactively or by automatically minimising the mismatch. It provides an estimate of the surface tension, drop volume and surface area from the best matching parameters. It can be used in a headless setup. It is hosted on http://fiji. sc/List_of_update_sites with the source code on https://github. com/adaerr/ pendent-drop.
Specialised apparatus performing this drop shape analysis is readily available, generally comprising both acquisition hardware and analysing software. There are however situations in which a commercial solution is not an option. In our case the surface tension measurement bench had to be integrated into a highly space-constrained set-up. As the experiment already relies on ImageJ  [1, 3]  for the image analysis, it was then natural to develop a drop shape analysis as a component that integrates well with the workflow. For a classroom project on surfactants on the other hand the cost of a commercial apparatus may be prohibitive, while a work-bench for roughly one hundred Euro based on a web-cam (cf. Fig. 3 ) in conjunction with the present software already yields a measurement precision around one percent, which is entirely satisfactory in that context.
Internal algorithm The differential equation for the drop shape is integrated using a fourth order Runge-Kutta scheme, except near the tip singularity where we can explicitly write a sufficiently precise approximate solution. The mean square distance between this theoretical contour and the border extracted from the drop image is then minimised by varying the contour parameters along a set of directions in parameterspace updated according to a strategy described by Powell  [10]  until a (local) optimum is found (see Fig. 2 ). More details about both the underlying physics and the algorithm can be found in the PDF article installed along with the software (Plugins->Drop Analysis->About Pendent Drop)  [11].
Similar software (for ImageJ) The Low-Bond Axisymmetric Drop Shape Analysis (LBADSA) method  [5]  uses an image energy approach to fit a first-order approximation of the Young-Laplace equation to the whole image data instead of first detecting the drop contour. A similar approach was taken by the previous version of Pendent_Drop (version 1. x is accessible only via the github repository). It would be interesting to study if this really provides any gain in precision over the computationally much less expensive sub-pixel boundary detection in Pendent_Drop version 2. x. An interesting feature of the cited LBADSA implementation is that contact angles of sessile drops can be precisely estimated by simultaneously fitting both the drop and its reflection in the substrate. DROPFIT  [6, 7, 8]  computes the surface tension of pendent drops by comparison with precomputed profiles.
B1 Sox transcription factors are candidate positive regulators of cANE activity Scanning cANE for putative transcription factor binding sites with the Genomatix MatInspector tool  [31]  and the Jaspar database scanning tool  [32]  highlighted three sequences matching binding sites for B1 Sox transcription factors (Figs  3  and 4A ). Within them, a sequence, that significantly matches the composite binding motif for Sox:Oct dimers  [33] , overlaps with Motif1 (Fig  3 ). Several recent published results point at a function of B1 group Sox factors the activation of cyp26a1. First cyp26a1 expression in the ANP is eliminated in zebrafish embryos depleted of all four B1 group Sox factors (Sox2/3/19a/19b)  [13]. Then, anti-Sox2 ChIP-PCR amplifies a 167 bp fragment of the cyp26a1 upstream sequences  [13]  and this fragment overlaps the last 89 bp of cANE (nucleotides 222-310 in Fig  3 ), suggesting that Sox2 binds cANE in vivo.
Acknowledgments The authors thank  A. Bois ,  S. Tronche  and  S. Authier  for excellent fish care;  Martin Catala ,  Guillaume Pezeron ,  Marie Breau  and  Christine Vesque  for critical reading of the manuscript. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santé et de la Recherche Médicale (Inserm) , the  Université Pierre et Marie Curie  ( Sorbonnes Université UPMC Université Paris 06 ), the  Agence Nationale pour la Recherche (ANR)  and the  Fondation ARC pour la Recherche sur le Cancer , and by fellowships from  Mairie de Paris (call "Research in Paris")  to CC and from the  Agence Nationale pour la Recherche  and the  Fondation ARC pour la Recherche sur le Cancer  to AS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Material and Methods To gain insight into the shear stress levels that the STB is expected to experience in vivo, we have formulated three different computational models of varying levels of complexity that reflect different physical representations of the IVS. Computations of the flow fields in all models were performed using the CFD module of the finite element code COMSOL Multiphysics 4. 4. The mean velocity of maternal blood in the IVS during the third trimester was measured in vivo with dynamic MRI (0. 94±0. 14 mm. s -1 ). To investigate if the in silico results are consistent with physiological observations, we studied the cytoadhesion of human parasitized (Plasmodium falciparum) erythrocytes to primary human STB cultures, in flow conditions with different WSS values.
Model 2: Two-dimensional modeling of the intervillous space based on anatomic images In the second model, a 2 cm vertical cross-section image from the chorionic plate to the basal plate of a third-trimester normal placenta was used to represent the villous tree inside the placentone as a realistic, inhomogeneous, anisotropic and rigid medium. The images were scanned and then digitized using Adobe Illustrator CS6 software. In order to obtain a more physiological model, we applied a correction for the placental collapse that occurs after delivery (Fig  2A2 ). This correction consisted of determining the x-and y-coordinates of the centers of gravity of each solid element in the image and subsequently stretching these solid elements by a factor of 2 in the y-direction, which avoided modification of the size and shape of the solid elements.
Model 3: Three-dimensional modeling of a terminal villus in the intervillous space Starting with a scanning electron microscopy image, AutoCAD 18. 0 software (Autodesk, Inc. San Rafael, CA 94903) was used to reconstruct in 3D the terminal villus in the center of the. In this model, the inlet was represented as the top of the cylinder and the outlet as the bottom of the cylinder. No-slip conditions were applied to the surface of the villus, and slip boundary conditions were applied to the side walls of the cylinder to model the larger fluid volume within which the villus would be present in vivo. The Navier-Stokes equations were solved to determine the flow field and the WSS on the villus surface.
Computational Fluid Dynamic Simulations Computations of the flow fields in all models were performed using the CFD module of the commercial finite element code COMSOL Multiphysics 4. 4. In all simulations, flow into the spiral artery is assumed to be steady. In the central cavity of Model 1 and in Models 2 and 3, the flow field is obtained by solving the equations of conservation of mass and linear momentum (the Navier-Stokes equations) as follows:  Mass: r:v ¼ 0 Momentum: r @v @t þ rv:v ¼ Àrp þ mr 2 v where v is the flow velocity vector, ρ is the fluid density, p is the pressure, μis the dynamic viscosity, and r is the del operator. Within the porous media portion of Model 1, we assume that the flow is governed by the Brinkman equation as follows: r φ @v @t þ @v:v φ ¼ Àrp þ m φ r 2 v À mv k À Frjvjv ffiffi ffi k p where F is the Forchheimer coefficient reflecting the influence of the inertia terms at the pore scale, ϕ is the porosity (taken as 0.
Dynamic placental MRI The in vivo dynamic MRI measurement was part of a clinical trial (PLACENTIMAGE Study, clinical trials. gov Identifier: NCT01092949). The data from 5 pregnant women who underwent prenatal MRI for fetal malformation without placental pathology between 30+4 and 34 weeks of gestation were used for these analyses. Placental MRI was performed using a 1. 5 Tesla unit (Sigma, GE, Milwaukee, Wis) with a phased-array abdominal coil. The MRI protocol included anatomical sequences of the placenta in the three orthogonal planes (FIESTA 2D sequence: TR = 3. 65; TE = 1. 6; Matrix: 512x512; FA: 65°; Section thickness: 6 mm). We performed a multiphase FSPGR sequence (TR: 2. 2; TE0. 9; Matrix: 256x256; FA: 15°; Section thickness: 5 mm every 5 mm, 20 slices, pixels: 2. 2x1. 1mm 2 , voxels: 2.
Acknowledgments The authors gratefully acknowledge the technical support of  René Lai Kuen  ( Plateau Technique d'Imagerie Cellulaire et Moléculaire, IFR71-IMTCE, Faculté des Sciences Pharmaceutiques et Biologiques, Université Paris Descartes, Paris, France ) for the surface electron microscopy. This work was partially funded by a permanent endowment in Cardiovascular Cellular Engineering from the  AXA Research Fund  (to AIB). MB was supported by an international summer scholarship by  Ecole Polytechnique.
Funding: EL received a grant from  Institut National de la Santé et de la Recherche Médicale (France)  to perform this work. This work was supported by the  PremUP Foundation. This work was partially funded by a permanent endowment in Cardiovascular Cellular Engineering from the  AXA Research Fund  (to AIB). MB was supported by an international summer scholarship from  Ecole Polytechnique. The funders had no role in study design, data collection Results.
INTRODUCTION The development of bioinformatics has been initially driven not only by the enormous quantity of data that the biologist community was able to produce during the last decades, but also by the necessity of finding approaches to organize and better analyze these huge datasets. Although the protein structures constitute small datasets with respect to many other data encountered in biology, they nevertheless represent a challenge for the data analysis, as the relative positions of atomic coordinates in a protein structure take values in the continuous three-dimensional (3D) space. The large variability of protein features is obvious from the variety of physicochemical properties among a given family of proteins. 1 Furthermore, the full understanding of a protein function requires, in addition of the knowledge of its structure, the knowledge of the internal dynamics and thus of the conformational landscape of the protein, which correspond to large datasets.
MATERIALS AND METHODS 2. 1. Molecular Dynamics Simulation. The starting point of the simulations was the X-ray crystallographic structure of the D-Ala:D-Lac ligase (VanA) from Enterococcus faecium BM4147 VanA (PDB ID: 1E4E). 45 The co-crystallized ligands, ADP and phosphinate (1(S)-aminoethyl-(2-carboxypropyl)-phosphoryl-phosphinic acid), located in the active site were removed. The C52-C64 disulfide bridge, observed in the crystal was disrupted to be as close as possible to the physiological state of the D-Ala:D-Ala ligase. 47 he force field CHARMM22 including the correction map (CMAP)  48, 49  was used. The system was neutralized with five Na + counterions. Explicit TIP3P 50 solvent water molecules were added to the systems using a cutoff of 10 Å. The solvated system includes 13585 water molecules.
In that way, we reduce the risk of system instability due to large deviation of the collective variables θ(x) from their target values z. 2. 3. Determination of Contact Communities. The following method has been used to determine the contact communities of VanA along each recorded trajectory. At each trajectory frame, a contact is set up for all α-carbon pairs closer than 12 Å,  58  and the frequency of contacts is calculated along the trajectory. The protein structure is then considered as a graph, where the residues C α constitute the vertices and the edges are weighted by the frequency of contacts between C α atoms along the trajectories. An absence of contact is modeled as a nonexisting edge. The Girvan-Newman algorithm,  59  as implemented in the program Python, allows one to divide, in an iterative way, the graph into contact communities.
The hydrogen bonds were indexed by protein residue numbers. The Boolean vectors were used as descriptors to train the RF. The predicted value for each vector was the identifier of the kinetic cluster. The RF calculation was performed using the Python package scikit-learn (scikit-learn. org). The number of trees in the forest was set to 10, with a Gini criterion  28  to measure the quality of a split. The number of features used when searching for the best split was set to 40, which is approximately the square root of the length of the Boolean vectors ( 1623 ≈ 40). The trees are expanded until all leaves are pure. Once the training done, the importance of each hydrogen bond to define a kinetic cluster has been computed. 2. 7. Ligand Docking Procedure and GBSA Scoring. The substrates, ATP, D-Ala, D-Lac, D-alanyl-phosphate (D-Ala(P)), the transition-state analogue phosphinate or PHY, the product of the reaction, D-Ala-D-Lac, and the allosteric binder,  65  were formatted in mol2 with Chimera 1.
4  64  and MarvinSketch 5. 1 (www. chemaxon. com/products/marvin/marvinsketch) for docking. UCSF DOCK 6. 5  [66] [67] [68]  was used to perform ligand docking VanA conformations along the opening path obtained as described at the end of the section 2. 5. Chimera  64  was used to add hydrogens, check atom assignment, and assign partial charges consistent with the AMBER-ff99SB force field. 69 Chimera was also used to produce mol2 format files for the ligands and the selected conformations of the receptor. The DMS software program  70, 71  generated the molecular surface of the receptor, using a radius probe of 1. 4 Å. Spheres then were calculated around the receptor with the DOCK 6. 5 command "sphgen" with radius probe values varying between 1. 4 Å and 4 Å. 72 Spheres were selected within a radius of 10 Å around the geometric center defined by the residues E15, K170, R289, N303, E304, N306, which are close to positions observed for the ligands (ADP, phosphinate) in 1E4E.
The grid encoding van der Waals and electrostatic interactions was precalculated with the "grid" tool 72 in a box containing the selected spheres. The DOCK program builds up to 500 flexible ligand docking orientations, on the precalculated "grid" interaction map. The ligand poses were then re-scored with the implementation of the Hawkins Molecular Mechanics Generalized Born Surface Area (MM-GBSA) score,  [73] [74] [75] [76] [77]  implemented in UCSF DOCK 6. 5. The best scoring solution was kept for each protein-ligand pair.
A first series of clusters, represented by clusters 1, 2, and 3, displays significant opening of the ω-loop, with the loop being the most open in clusters 1 and 3. In all of these clusters, the protein internal mobility remains concentrated on the ω-loop (with maximal RMSF values of 13 Å in cluster 1 and 15. 7 Å in cluster 3) and the other regions are much less mobile, except the opposite domain (maximal RMSF value of 8. 0 Å), the other maxima remaining ∼4-5 Å. Thus, after only 30 ns of simulation, the TAMD trajectories have been able to reach conformations displaying a wide opening of the ω-loop. These conformations are similar to the X-ray crystallographic structures published on the TtDdl D-Ala:D-Ala ligase (PDB ID: 2YZG). 47 he second series of clusters, which is represented by clusters 5 and 6, displays conformations with semiopen or semiclosed ω-loop, similar to the X-ray crystallographic structure of the D- Ala:D-Ala ligase in ref 47 (PDB ID: 2ZDG).
Experimental Procedure The material used in this study, AD730 These solutionizing temperatures were selected based on calculations using Thermo-calc® with the TCNI5 database and DTA results. The DTA was carried out using Diamond TG/DTA PerkinElmer with Pt crucible and a flowing Ar atmosphere. DTA samples were heated at the rate of 1 °Cs -1 in order to replicate as close as possible the heating rate applied during the solutionizing process of γ′ particles. Sample size for solution treatment trials was 5×5×5mm 3. Holes of 1. 5mm in diameter were drilled in the center of one face to a depth of 2. 5mm before inserting a K-type thermocouple. All the tests were carried out on samples with thermocouples. Since thermal and thermomechanical processes are performed at various heating rates, two different heating rate ranges (low and high) were selected.