dataesr/scientific-paragraphs-categorization huggingface dataset:
Can These Pockets Tie Up Small Drug Molecules? The six potential targets contain "SL + Invariant" positions predicted to be good spots to avoid resistance. To be of therapeutic interest, these targets should constitute binding sites, meaning pocket-shaped locations and composed of atoms that a drug-like molecule (small molecule) can bind. From the PDB three-dimensional HA structure 1RU7, Fpocket software determines protein regions that can form binding pockets. To select less escape-prone targets, pockets composed of at least five residues positioned in one of the targets previously described, were retained. As our aim was to determine pockets binding small molecules, we also restricted pocket volumes to a range between 60 Å 3 and 500 Å 3 and set the Fpocket parameters to a minimum radius of alpha spheres at 2.
After applying to our dataset the χ 2 , BLD and dissimilarity tests, we were able to describe two sub-graphs which draft two potential targets (Figure  4A', B' ). Target A' is composed of  23 residues (124, 126, 128, 129, 131, 132, 133, 156, 157, 158, 159, 160, 163, 165, 167, 187, 188, 189, 192, 193, 197, 198, and 199)  and Target B' is formed of eight residues  (137, 140, 141, 142, 143, 144, 145, and 224). The detection of these two targets corroborates our initial results, which predicted that SL groups would be limited in number because there was little covariance in general. The first target H3N2 (Figure  4A' ) is in fact the conjunction of two H1N1 targets (Figure  4C, E ) and the second target is included in the proposed D target for H1N1 (Figure  4B' ). These results bear the thesis of a shared evolution in terms of function despite the primary sequence of these two proteins.
This tripeptide could therefore bind F570 AAs of loop B and K575 and E578 of zone C to maintain these two secondary structures in a position that does not allow the virus to fuse to the cell membrane. The choice of a peptide rather than another small molecule is important to ensure the non-toxicity of the possible future drug. We first used the Avogadro software to find the most stable conformer of the KFE tripeptide using the universal force field. The energy of this conformer is 300 kJ/mol. We generated a new conformer of equivalent energy whose structure is closer to that required to bind AAs F570, K575 and E578 as they are positioned in the 1RU7 structure. Using the Autodock suite (Autodock tool and Autodock vina), we performed docking experiments between this conformer and the 1RU7 protein with the following parameters: the tripeptide can adopt a flexible structure as well as the three AAs F570, K575, and E578, the rest of the protein bearing a rigid structure.
Acknowledgments: We thank  P. Le Chien  for his encouragement since 1993,  Lucie Dubrunfaut  and  Florence Jornod  for their professional and friendly support,  Michel Petitjean  for his flawless coaching for many years,  Anne-Claude Camproux , for her advice especially on statistical studies and  Anne-Lise Haenni  for the care she took in proofreading and correcting the manuscript. This work is dedicated to Lou, 5 years old, who died of AIDS in 1997.
Context. We study the distribution of galaxies in nearby clusters to shed light on the evolutionary processes at work within clusters and prepare for a full dynamical analysis to be conducted in forthcoming papers of this series. Aims. We use the Wide-field Nearby Galaxy-clusters Survey (WINGS) database complemented with literature data. We assign galaxy membership to individual clusters, then select a sample of 67 clusters with at least 30 spectroscopic members each. 53 of these clusters do not show evidence of substructures in phase-space, as measured by the Dressler-Shectman test, while 14 do. We estimate the virial radii and circular velocities of the 67 clusters by a variety of proxies (velocity dispersion, X-ray temperature, and richness) and use these estimates to build stack samples from these 53 and 14 clusters, that we call 'Reg' and 'Irr' stacks, respectively.
5, with a decreasing fraction of S0 and an increasing fraction of S with z  (Dressler et al. 1997; Fasano et al. 2000; Postman et al. 2005; D'Onofrio et al. 2015) , but then this evolution slows down at higher z  (Desai et al. 2007 ). This change is stronger in lower-mass clusters  (Poggianti et al. 2009 ). The cluster velocity dispersion is mildly dependent on the morphological fraction  (Desai et al. 2007) , not only in low-z but also in high-z clusters, up to z ≈ 1 at least. Fasano et al. (2015)  have recently revisited the morphologydensity paradigm, using the WIde-field Nearby Galaxy-cluster Survey (WINGS) database of galaxies in nearby clusters. They considered 5504 spectroscopically confirmed members of 76 nearby clusters, down to an absolute magnitude limit M V = -19. 5. Fasano et al.
The data 2. 1. The WINGS database WINGS  (Fasano et al. 2006 ) is an all-sky (|b| > 20) survey of a complete, X-ray selected sample of galaxy clusters in the redshift range 0. 04 -0. 07. The goal of the WINGS project is the systematic study of the local cosmic variance of the cluster population and of the properties of cluster galaxies as a function of cluster properties and local environment. The core of the WINGS project is the optical (B, V) imaging survey (called WINGS-OPT;  Varela et al. 2009 ). It provides photometric data for large samples of galaxies (∼550,000) and stars (∼190,000) in the inner field (34 ′ × 34 ′ ) of 76 nearby galaxy clusters, as well as structural and morphological information for a subsam-A. Cava et al. : Structural and dynamical modeling of WINGS clusters. ple (∼40,000) of relatively bright galaxies  (Pignatelli et al.
2006; Fasano et al. 2012; D'Onofrio et al. 2014). Additional photometric information comes from follow-up imaging surveys in the near-infrared (NIR) J and K bands (WINGS-NIR, 28 clusters;  Valentinuzzi et al. 2009 ) and the U Johnson band (17 clusters;  Omizzolo et al. 2014). Currently, the photometric data set is being expanded by very wide field observations (four times the original WINGS area) taken with the VLT Survey Telescope (VST) OmegaCAM in the usual B and V Johnson bands  (Gullieuszik et al. 2015)  and in the Sloan u band  (D'Onofrio et al. , in prep. ). The WINGS database is made available to the community through the Virtual Observatory  (Moretti et al. 2014). WINGS is currently the only data set providing a homogeneous database of detailed photometric, morphological, and spectroscopic characteristics for several thousand galaxies in galaxy clusters.
(2009, hereafter  Paper I) for a detailed description of the WINGS-SPE spectroscopic survey, including a comparison with literature data. In this work we exploit the full dataset, WINGS and literature data, to perform a detailed dynamical analysis of the whole WINGS-OPT sample, including 76 galaxy clusters (cluster A3562 is excluded because V-band images were obtained under poor seeing conditions). In Paper I we have already presented a detailed data quality analysis and comparison with literature data, detailing the method for including literature data in the WINGS-SPE catalog (see also  Moretti et al. 2014). After updating the catalog with the Sloan Digital Sky Survey (SDSS) DR7 and the latest NASA/IPAC Extragalactic Database (NED) data, our final catalog includes ∼ 10, 000 galaxies with redshift determinations (∼ 60% from WINGS only), increasing the global spectroscopic completeness level (defined as the number counts of galaxies with redshifts to the number counts of galaxies in the photometric parent sample in magnitude bins, see Paper I) for our sample of galaxy clusters from ∼ 50% to ∼ 80% for galaxies below magnitude ∼ 18, and reaching 50% completeness at a magnitude of ∼ 19.
In the following we concentrate on the subsample of 73 clusters with ≥ 10 galaxies with measured spectroscopic redshifts each, for a total of 10,277 galaxies. We complement our sample with the photometric and morphological information from the WINGS database  (Moretti et al. 2014). Galaxy morphologies have been determined using the MORPHOT automatic tool of  Fasano et al. (2012) , which is optimized for the WINGS survey. It applies maximum likelihood and neural network methods to derive the best set of morphological parameters, and the equivalent morphological index, T M (see Table  1  in  Fasano et al. 2012  for a comparison between MOR-PHOT and visual classification indices). Adopting the MOR-PHOT classification, we define the three subsamples of ellipticals ('E'), with T M ≤ -4, lenticulars ('S0'), with -4 < T M ≤ 0, and spirals + irregulars ('S'), with T M > 0.
For a subset (50 clusters) of the WINGS parent cluster sample, also X-ray temperatures, T X , are available (extracted from the BAX 1 database). We provide alternative estimates of the virial radii using the mass-temperature scaling relation obtained from X-ray observations by  Arnaud et al. (2005). We call r 200,X these virial radius estimates. Yet another possibility is to use a richness-based estimate of the virial radius (named 'Num';  Mamon et al. , in prep. , see, Old et al. 2014). This is obtained by selecting cluster members in a rectangular area of projected phase space within a projected radius of 1 Mpc and an absolute line-of-sight velocity of ±1333 km s -1 from the cluster center in projected space and velocity. The richness is defined as the total number of members within this rectangular section of PPS, corrected for radial incompleteness.
1 BAX is the X-ray galaxy clusters database: http://bax. ast. obs-mip. fr/. On the stack sample we run again the Clean procedure for interloper removal. This is required because the Clean procedure can fail to reject some interlopers when the size of the individual cluster sample is not large enough. These remaining interlopers are, however, very few; less than ∼ 1% of the initially selected members are identified as interlopers by running the Clean procedure on the stack sample. We do not consider all WINGS clusters in the stack, since some of them do not have a sufficient number of members (N m hereafter) for a reliable r 200 estimate. To choose the N m limit for inclusion of a cluster in our sample, we have compared the values of r 200,σ , r 200,X , and r 200,N. Decreasing N m reduces the accuracy by which we can estimate r 200,σ and r 200,N.
the relations of  Bell & de Jong 2001). The sample we use for the Σ(R) determination is ∼ 1 mag fainter that of  Postman & Geller (1984) , corresponding to a stellar mass log M ⋆ ∼ 9. 1. The field morphological fractions change significantly with stellar mass, but not below log M ⋆ ∼ 9. 5, so the morphological fractions of  Bamford et al. (2009)  are applicable to our sample. The statistics are good enough to allow fitting 47/42/33 of the 67 individual cluster E/S0/S surface number density profiles. Using the Bayesian information criterion BIC = -2 ln L max + (ln N d ) N p , (1) where N d , N p are the number of data and free parameters, respectively, we establish that the projected NFW model provides a better fit than the King model to 84, 78, and 67% (respectively 80, 63, and 56%) of the E, S0, and S galaxy surface density profiles for the Reg (respectively Irr) cluster set.
Acknowledgements. We wish to thank the anonymous referee for her/his useful comments that have helped to improve this manuscript. The work of AC is supported by the  STARFORM Sinergia Project  funded by the  Swiss National Science Foundation. This research has made use of the X-Rays Clusters Database (BAX) which is operated by the  Laboratoire d'Astrophysique de Tarbes-Toulouse (LATT) , under contract with the  Centre National d'Etudes Spatiales (CNES). This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. AB acknowledges the hospitality of the  Institut d'Astrophysique de Paris. The work of AB has been financially supported in part by the  PRIN INAF 2014 : " Glittering kaleidoscopes in the sky: the multifaceted nature and role of Galaxy Clusters ", P.
We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes. Unlike most navigation datasets, the lack of rotation implies an easier structure from motion problem which can be leveraged for different kinds of tasks such as depth inference and obstacle avoidance. We also propose an architecture for end-to-end depth inference with a fully convolutional network. Results show that although tied to camera inner parameters, the problem is locally solvable.
, 2015)  that would greatly lower autonomy. In addition, along with some being unable to operate under sunlight (e. g. IR and ToF), most RGBD sensor suffer from range limitations and can be inefficient in case we need long-range trajectory planning  (Hadsell et al. , 2009). The faster an UAV is, the longer range we will need to efficiently avoid obstacles. Unlike RGB-D sensors, depth from motion is robust to high speeds since it will be normalized by the displacement between two frames. Given the difficulty of the task, several learning approaches have been proposed to solve it. A large number of datasets has been developed in order to propose supervised learning and validation for fundamental vision tasks, such as optical flow  (Geiger et al. , 2012 , Dosovitskiy et al. , 2015 , Weinzaepfel et al.
, 2013)  stereo disparity and even 3D scene flow  (Menze and Geiger, 2015, N. Mayer et al. , 2016). These different measures can help figure up scene structure and camera motion, but they remain low-level in terms of abstraction. End-to-end learning of a certain high semantic value such as three dimensional geometry may be hard to compute on a totally unrestricted monocular camera movement. We focus on RGB-D datasets that would allow supervised learning of depth. RGB pairs (preferably with the corresponding displacement) being the input, and D the desired output. Our choice today to learn depth from motion in existing RGB-D datasets is either unrestricted w. r. t. ego-motion  (Firman, 2016, Sturm et al   Figure  1. Camera stabilization can be done via a) mechanic gimbal or b) dynamic cropping from fish-eye camera, for drones or c) hand-held cameras 2012), or a simple stereo vision, equivalent to lateral movement  (Geiger et al.
, 2012, Scharstein and Szeliski, 2002). We thus propose a new dataset, described Part 3, which aims at proposing a bridge between the two by assuming that rotation is canceled on the footage that contains only random translations. This assumption about videos without rotation appears realistic for two reasons : 1. Hardware rotation compensation is mainly a solved problem, even for consumer products, with IMU-stabilized cameras on consumer drones or hand-held steady-cam (Fig  1 ). 2. this movement is somewhat related to human vision and vestibulo-ocular reflex (VOR)  (De Nó, 1933). Our eyes orientation is not induced by head rotation, our inner ear among other biological sensors allows us to compensate parasite rotation when looking at a particular direction. This assumption allows to dramatically simplify links between optical flow and depth and leverage much simpler computation.
The main benefit being the camera movement's dimensionality, reduced from 6 (translation and rotation) to 3 (only translation). However, as discussed in Part 4, depth is not computed as simply as with stereo vision and requires being able to compute higher abstractions to avoid a possible indeterminate form, especially for forward movements. Using the proposed dataset, we then show that depth can be learned as an end-to-end problem just like other usual Deep Learning problems. With a trained artificial neural network, we perform much better depth accuracy than flow based methods and are confident this will be efficiently leveraged for sense and avoid algorithms.
, 2016 , Mur-Artal and Tardos, 2016 , Klein and Murray, 2007)  primitives number 20 texture ratio 0. 5 size range of meshes (m) [0, 2] distance range of meshes (m)  [0, 25]  displacement 10cm length (frames) 10 nominal shift 3 speed equivalent (for 30fps) 9m. s -1 Table  2. datasets parameters very sparse point-cloud based 3D maps and require heavy calculation to keep track of the scene structure and align newly detected 3D points to the existing ones. SLAM is not widely used for obstacle avoidance, but more for off-line 3D scan. Our goal is to compute a dense (where every point has a valid depth) quality depth map using only two images, and without prior knowledge on the scene and movement, apart from the lack of rotation and the scale factor.
STILL BOX DATASET For our dataset we used the rendering software Blender to generate an arbitrary number of random rigid scenes, composed of basic 3d primitives (cubes, spheres, cones and tores) randomly textured from an image set scrapped from Flickr (see Fig  2 ). These objects are randomly placed and sized in the scene, so that they are mostly in front of the camera, with possible variations including objects behind camera, or even camera inside an object. Scenes in which camera goes through objects are discarded. To add difficulty we also applied uniform textures on a proportion or of the primitives. Each primitive thus has a uniform probability (corresponding to texture ratio) of being textured from a colorramp and not from a photograph. Walls are added at large distances as if the camera was inside a box (hence the name).
Why not disparity ? Flow Estimation and disparity (which is essentially magnitude of optical flow vectors) are problems to which exist a lot of very convincing methods  (Ilg et al. , 2016 , Kendall et al. , 2017). Knowing depth and displacement in our dataset, we could be able to easily get disparity and train a network for it using existing methods. We consider a picture with (u, v) coordinates, and optical center at P0 = u0 v0 Definition 1 Disparity is defined by the norm of a flow vector flow(P) = du dv of a point P = u v. ∀P = u v , disparity(P) = flow(P) Definition 2 Focus of Expansion is defined by the point FOE where each flow vector flow(P) = du dv of a point P = u v is headed from. Note that this property is true only when considering no rotation and a rigid scene. One can note than for a pure translation, FOE is the projection of the displacement vector ∀P = u v , -----→ P -FOE.
Dataset set augmentation The way we store data in 10 images long videos, with each frame paired with its ground truth depth allows us to set a posteriori distances distribution with a variable temporal shift between two frames. If we use a baseline shift of 3 frames, we can e. g. assume a depth three times as great for two consecutive frames (shift of 1). In addition, we can also consider negative shift, which will only change displacement direction without changing speed value compared to opposite shift. This allows us, given a fixed dataset size, to get more evenly distributed depth values to learn, and also to de-correlate images from depth, preventing any overfitting during training, that would result in a scene recognition algorithm and would perform poorly on a validation set.
DepthNet structure parameters (1) where • γs is the weight of the scale, arbitrarily chosen as Ws in our experiments. • (Hs, Ws) = ( 1 /2 n H, 1 /2 n W ) are the height and width of the output. • depths is the scaled depth groundtruth, using average pooling. As said earlier, we apply data augmentation to the dataset using different shifts, along with classic methods such a flips and rotations. We also clamp depth to a maximum of 100m, and provide sample pair without shift, assuming its depth is 100m everywhere. Fig This can be interpreted as 3d shapes identification, along with their magnification : pixels belonging to the same shape are deemed to have close and continuous depth values, resulting in a FOEindependent depth inference.
A batch size of 8 means 8 depth maps are computed at the same time Figure  7. some results on real images input. Up is from a Bebop drone footage, down is from a gimbal stabilized smartphone video 512x512 images, the network fails to converge to better results than with 64x64 images (while better resolution would help getting more precision). However, if we take the converged network and apply a fine-tuning on it with 512x512 images, we get much better results. 4. Fig  7  shows qualitative results from our validation set, and from real condition drone footage, on which we were careful to avoid camera rotation. These results did not benefit from any finetuning from real footage, indicating that our Still Box Dataset, although not realistic in its scenes structures and rendering, appears to be sufficient for learning to produce decent depthmaps in real conditions.
Quality measurement As our network is leveraging the reduced dimensionality of our dataset due to its lack of rotation, it is hard to compare our method to anything else. Disparity estimation is equivalent to a lateral translation that our network has been trained on, and could be used to compare to other algorithms but this reduced context seems unfair compared to methods designed especially for it. Other datasets provide ego motion with 6-DOF on which our network has not been trained and is certain to give poor results. On the other hand, we could test some SLAM methods but they work better when applied to long image sequences and not only image pairs. In short, our method is setting state of the art, but for a very particular problem that we hope will gain interest with time.
UAV NAVIGATION USE-CASE We assumed in learning depth inference from a moving camera, assuming its velocity is always the same. When running during flight, such a system can easily deduce the real depth map from the drone speed Vt, knowing that the training speed was V0 (here 9m. s -1 ) depth(t) = Vt V0 DepthN et(f ramet, f ramet-1) (2) One of the drawbacks of this learning method is that the f value (which is focal length divided by sensor size per pixel) of our camera must be the same as the one used in training. Our dataset creation framework however allows us to change this value very easily for training. One must also be sure to have pinhole equivalent frames like during training.
CONCLUSION AND FUTURE WORK We propose a novel way of computing dense depth maps from motion, along with a very comprehensive dataset for stabilized footage analysis. This algorithm can then be used for depth-based sense and avoid algorithm in a very flexible way, in order to cover all kinds of path planning, from collision avoidance to long range obstacle bypassing. Future works include implementation of such a path planning algorithm, and construction of a real condition fine tuning dataset, using UAVs footages and a preliminary thorough 3D offline scan. This would allow us to measure quantitative quality of our network for real footages and not only subjective as for now. We also believe that our network can be extended to reinforcement learning applications that will potentially result in a complete end-to-end sense and avoid neural network for monocular cameras.
Genetic analyses DNA from collected hair samples was analyzed by haplotype sequencing of 14 variable microsatellites, a marker in the mitochondrial control region and a sex marker in the zink-finger region  (Steyer et al. 2013)  to identify the species, differentiate wildcats from domestic cats, and identify individuals. For microsatellite analyses, we conducted three PCR replicates and calculated error rates and consensus genotypes as described in  Steyer et al. (2016). Genotypes with more than 3 loci missing out of 14 were excluded from further analyses. Sequences were processed in Geneious 7. 1. 8 (Biomatters) and compared to our internal cat haplotype database comprising  3000 control region sequences from European wildcats (< www. wildtiergenetik. de >). Microsatellite genotypes of identified wildcats were assigned to potential source populations using Structure software  (Pritchard et al.