dataesr/scientific-paragraphs-categorization huggingface dataset:
LID's Performance in Virtual Screening It is observed that ligand pose prediction performance by molecular docking was already improved with LID. At this point, another question arises as to whether LID is also capable of efficiently discriminating between true active compounds of a given protein and their chemically similar decoys or not. To answer this question, a retrospective virtual screening challenge using a set of DUD-E targets was carried out, with LID employed as docking-assistant tool. Only eight protein targets included in both the DUD-E and the LID datasets as described above were investigated. These include: the aldo-keto reductase (ALDR), BACE1, CAH2, CDK2, the estrogen receptor alpha (ESR1), GRIA2, HSP90A, and LKHA4 (Table  1 , Table  S1 ). A rigid docking protocol using a unique representative 3D structure for each target's binding site was implemented.
This observation became more obvious when a docking process using multiple structures of a single protein was carried out. To be more specific, 10 structures were used as input for each of the two most flexible protein targets HS90A and GRIA2, with the aim of taking into account the most diverse structures of the ligand-protein binding site. A docking-based virtual screening process was conducted for each structure. The average recorded calculation time for GRIM rescoring was approximately 58 h per protein structure, while that with LID was only 10 min (noteworthy, GRIM was coded in C++, while LID was coded with Python). Nevertheless, the time required to align all protein structures for LID to function properly has to be considered. In terms of overall performances, the multitarget approach using LID was observed to give notably better results in HS90A and GRIA2, with an improvement of 9.
30 and 19. 0 in true positive percent at 5% decoys, respectively (Table  S5 ). The DUD-E datasets have long been employed for a benchmarking of novel structure-based methods in computer-aided drug design. However, the real value of these datasets has been subject to much debate, primarily due to serious drawbacks in compound selection, e. g. , the structural biases that led to artificial enrichment and an overestimation of virtual screening performance, or the fact that the potency of the decoys always remained unknown  [24]. We therefore put LID into another retrospective virtual screening challenge using a dataset whose active and inactive compounds along with their potency were already verified by confirmatory dose-response biological assays. This dataset, ESR1, was prepared from the results of a high-throughput screening of small molecule antagonists of the estrogen receptor alpha that can be accessed on the website of PubChem BioAssays (for more details concerning the compound selection, see  [19] ).
The dataset employed in this study comprises 1589 compounds, 59 of which are actives with potency values ranging from 3. 9 nM to 9. 6984 µM; the rest (1530 compounds) were confirmed as inactives. As already observed with DUD-E, the ChemPLP scoring function gave an extremely poor performance in the screening challenge with ESR1. LID and GRIM did not give good performances like in the cases of DUD-E datasets (the two approaches had more difficulty in distinguishing between true actives and true inactives than in separating true actives from their chemically similar decoys); however, both methods were capable of selecting true actives among the early hits, with 18. 6% and 16. 9% of active compounds retrieved at a constant false positive rate of 5%, respectively (Figure  6b ). The DUD-E datasets have long been employed for a benchmarking of novel structure-based methods in computer-aided drug design.
However, the real value of these datasets has been subject to much debate, primarily due to serious drawbacks in compound selection, e. g. , the structural biases that led to artificial enrichment and an overestimation of virtual screening performance, or the fact that the potency of the decoys always remained unknown  [24]. We therefore put LID into another retrospective virtual screening challenge using a dataset whose active and inactive compounds along with their potency were already verified by confirmatory dose-response biological assays. This dataset, ESR1, was prepared from the results of a high-throughput screening of small molecule antagonists of the estrogen receptor alpha that can be accessed on the website of PubChem BioAssays (for more details concerning the compound selection, see  [19] ).
The dataset employed in this study comprises 1589 compounds, 59 of which are actives with potency values ranging from 3. 9 nM to 9. 6984 µM; the rest (1530 compounds) were confirmed as inactives. As already observed with DUD-E, the ChemPLP scoring function gave an extremely poor performance in the screening challenge with ESR1. LID and GRIM did not give good performances like in the cases of DUD-E datasets (the two approaches had more difficulty in distinguishing between true actives and true inactives than in separating true actives from their chemically similar decoys); however, both methods were capable of selecting true actives among the early hits, with 18. 6% and 16. 9% of active compounds retrieved at a constant false positive rate of 5%, respectively (Figure  6b ).
Comparison with Other Methods Using Aligned 3D Structures The first strategies for using crystallographic structures of ligand-protein complexes in docking methods are almost 20 years old  [25, 26]. Different ways of incorporating structural information have been proposed, for example by introducing spatial constraints targeting parts of the ligand that are common with a reference ligand, by guiding the placement of the ligand on reference atom-centered functions or electron density, or by scoring pose by 3D pharmacophore matching similarity  [27] [28] [29]. Okuno et al. suggested to combine multiple ligand-protein crystallographic structure into a reference grid  [30, 31]. The method, called VS-APPLE, aligned the ligand with the reference grid to predict the binding mode. Designed as a screening method, it was able to discriminate active compounds from decoys of the DUD dataset  [32].
The LID and VS-APPLE methods are similar. However, LID is intended exclusively for scoring purposes and is not linked to a particular docking program. LID especially allows to take into account induced fit using a docking program that treats the protein as a rigid body. The method is indeed fast and efficient enough to sort the multiple poses of a ligand docked into an ensemble of 3D structures of its protein site. LID's performance assessment also indicated that the method can be successful using a limited amount of reference information. We estimated that less than ten different ligands are required for effective scoring. This is in line with our recent analysis of binding modes in PDB, which concluded that nine ligands achieve the coverage of interactions formed with the protein pocket  [17].
Pose Prediction Challenge The LID dataset was prepared as previously described  [17]. The 3D structures selected from the PDB are all in high resolution and are completely described. There is no mutation in the binding site. Drug-like ligand complies to the Lipinski's rule of 5, with a maximum of one exception. The molecules of the LID dataset were protonated with the program Protoss v2. 0 (University of Hamburg, Hamburg, Germany)  [33]. Importantly, the cofactors and water molecules were removed from the protein structures.
Retrospective Virtual Screening Challenge Eight protein targets common to the DUD-E and the LID datasets, namely ALDR, BACE1, CAH2, CDK2, ESR1, GRIA2, HSP90A, LKHA4, were used as input and processed as described above. The representative protein structure was determined by fitting and computing RMSD matrix on Cα carbons with the command cealign in PyMOL (The PyMOL Molecular Graphics System, Version 1. 8. 6. 1, Schrödinger, LLC, New York, NY, USA). The structure with the minimal average value was selected. For the selection of 10 structures, an agglomerative hierarchical clustering of all structures was carried out with the scikit-learn v0. 19. 1 Python package, then the most representative of each cluster was kept. Another dataset, ESR1, comprising true actives and true inactives confirmed by a PubChem quantitative high-throughput screening assay of small molecule antagonists of the estrogen receptor alpha was directly employed after preparation steps described by Tran-Nguyen et al.
Properties of Binding Sites A binding site is defined as the consensus residues near the bound drug-like ligands. In each 3D structure, the protein residues with at least one non-hydrogen atom closer than 6. 5 Å to any ligand non-hydrogen atom were identified. The binding site was defined as the ensemble of residues present in more than 10% of the structures  [17]. The RMSD values were computed either on Cα or all non-hydrogen atoms by fitting a structure to a reference (described in LID score subsection) with the command cealign in PyMOL v1. 8. 6. 1. Cavity volumes and the percentage of hydrophobic interactions were determined with the Volsite module in IChem v5. 2. 9 (UMR7200 CNRS-University of Strasbourg, Illkirch, France).
Docking with PLANTS A rigid docking procedure was performed with the PLANTS program v1. 2 using the ChemPLP scoring function and the search speed set at 1 (highest accuracy)  [35]. PLANTS is based on an ant colony algorithm to optimize the placement and the conformation of ligand as well as the positions of the protein hydrogen atoms that form hydrogen bonds with the ligand. PLANTS explores possible torsion angle values of the ligand but does not modify the conformation of rings. In the case of metalloproteins, PLANTS considers geometry parameters related to metal atoms. The cavity center of a protein site was defined from the centroid of all the ligands bound to this protein. The cavity radius was set as the maximum distance between the cavity center and the atoms of all the ligand crystallized in the binding site, plus 2 Å.
LID Score The representative 3D structure of a protein was defined after multiple comparisons of possible binding sites using the Shaper program (minimal average distance of the matrix) (v1. 0, UMR7200 CNRS-University of Strasbourg, Illkirch, France)  [36]. Reference structures were aligned onto the representative with the CE program (v2003. 03. 13, RCSB Protein Data Bank, San Diego, CA, USA)  [37]. The IPAs were obtained from the aligned reference structures with the ints module of IChem v5. 2. 9, the maximal distance to detect π-π stacking interactions was fixed at 5. 0 Å (-D_Ar 5. 0). All IPAs were merged into a mol2 file and were considered as a single pseudomolecule. This file was used for the creation of the LID maps, using the in-house intgrid program, v1. 0. The LID program (v1. 0) issued a LID score for each docking pose.
Acknowledgments: We would like to express our sincere gratitude to  Guillaume Bret  for providing informatics supports,  Jérémy Desaphy  for fruitful discussion, the Calculation Center of the  IN2P3 (CNRS, Villeurbanne, France)  for the allocation of computing time and the  Programme d'Investissement d'Avenir  ( ANR-10-LABX-0034 ) for financial support.
For subject 4 there was a decrease of 41% from DCC to TCC, and 22% from SCC to DCC. EEG characterization. EEG data was acquired from Subject 1 in order to further investigate SCC, DCC, and TCC stimulations. Single channel stimulation was performed for both the median and ulnar nerves, double channel stimulation through one active site of the median nerve and one of the ulnar nerve, and three-channel stimulation with two ASs of the median nerve and one AS of the ulnar nerve. Sub-perceptual threshold (SS) was tested as the control condition for the median nerve stimulation. Figure  5  shows the grand-average somatosensory evoked potentials (SEPs) for subject 1 at late latencies. Scalp topographies for each condition show that 60-80 ms after the onset of the stimulation there was a clear contralateral activation.
The data was analyzed using custom Matlab (R2016b, The Mathworks, US) scripts based on routines from the EEGLAB toolbox  41. Continuous data was processed using a reliable independent component analysis (RELICA) approach  42  to remove artifacts and other non-neural noise sources, without any preliminary data  43  dimensionality reduction. To maximize the stability and dipolarity  44  of independent components (ICs), raw data was high-pass filtered using a zero-phase 1. 2 Hz, 24th order Chebyshev type II filter, low-pass filtered using a zero-phase 45 Hz, 70th order Chebyshev type II filter and resampled at 250 Hz. Channels were removed that had a kurtosis outside five standard deviations with respect to other channels or with prominent prolonged artifacts as confirmed by visual inspection.
Epochs with high-amplitude artifacts or high-frequency muscle noise were also identified by visual inspection and removed. The remaining data was submitted to RELICA with AMICA core  42, 45  , and 100 point-by-point Infomax independent component analysis (ICA)  46  bootstrap repetitions. ICA unmixing weights were then re-applied to the raw data, this time high-passed filtered using a 0. 5 Hz 94th order Chebyshev type II filter and a custom (complex poles only) 50 Hz comb notch filter  47. Dipolar and stable ICs related to stereotyped artifacts such as movement and blinks were then removed from the data. Epochs from -100 ms to 300 ms with respect to the onset of each stimulation pulse were extracted and centered with respect to the pre-stimulus baseline. Similarly to continuous data, noisy epochs were rejected by careful visual inspection.
Statistical analysis. All sensation characterization data was extracted and processed in Matlab (R2016b, The Mathworks, US). The normality of the data was first checked (one-sample Kolmogorov-Smirnov test). Since none of the data was normally distributed, a Kruskal-Wallis test was adopted (instead of ANOVA). When necessary, a Tukey-Kramer correction for multi-group comparison was applied. α was set to 0. 05. In order to compare the occurrences of sensation types, a Fisher's test was run. α was set to 0. 05. Regarding EEG analysis, significance of SEP differences across the stimulation types (SCC-median, SCC-ulnar, DCC and TCC) for selected channels was assessed using Montecarlo statistics with multiple comparisons cluster correction  48  adapted from the FieldTrip toolbox  49.
Acknowledgements The Authors are deeply grateful to the four subjects who freely donated weeks of their life for the advancement of knowledge and for a better future of people with hand amputation. The Authors are also grateful to  Prof. Fernandez  for the surgical implantation of the TIMEs. The  EU  Grant  FP7-611687   NEBIAS (NEurocontrolled BIdirectional Artificial upper limb and hand prosthesiS ) and the  EU  Grant  FP7-HEALTH-1602547   EPIONE (Natural sensory feedback for phantom limb pain modulation and therapy ) supported this study. Dr. Artoni 's contributions were supported by the  European Union 's  Horizon 2020 Research and Innovation Programme  under  Marie Sklodowska Curie Grant  Agreement No. 750947  (project  BIREHAB ). The funder had no role in the.
[4] This full-dimensional IPES was fitted to reproduce available benchmark calculations of the interaction energy D e  [5]  and employed for diffusion Monte Carlo calculations of the vibrational zero-point energy of (H 2 O) 2 to predict a dissociation energy D 0 of 1103 cm À 1. An alternative indirect approach was demonstrated by Kollipost et al. [6]  for an even larger system, the doubly hydrogen-bonded dimer of formic acid (HCOOH) 2 , based on the macroscopic dissociation equilibrium constant and the rich rovibrational spectroscopic datasets available for this strongly bound system. After an extensive far-infrared jet spectroscopic characterization of largeamplitude hydrogen bond vibrational modes,  [6, 7]  the combination of room temperature dissociation equilibrium constants and statistical treatments of the rovibrational partition function involving the complete set of altogether 24 vibrational fundamental transitions enabled the determination of an experimental D 0 -value of 59.
5(5) kJ mol À 1. The present work demonstrates an indirect semi-experimental strategy for the homodimer of HCN, where an extensive rovibrational dataset for the basically complete set of thirteen fundamental transitions is now available. ] [13] [14] [15] [16] [17]  Subsequently, complementary high-resolution infrared  [18] [19] [20] [21] [22] [23] [24]  and Raman  [25, 26]  spectroscopic studies employing a combination of static cryogenic long-path absorption cells and supersonic jet expansions have provided accurate hydrogen bond induced spectral shifts and (partly) resolved the rovibrational structures of the more or less perturbed intramolecular CH (ν 1 and ν 2 ) and CN (ν 3 and ν 4 ) stretching bands of both the hydrogen bond donor and acceptor moieties. The dedicated line shape analysis and extracted line width parameters from the high-resolution infrared spectra of the hydrogen bond acceptor and donor CH stretching bands have provided crucial information about pre-dissociation lifetimes and indirectly the coupling between these intramolecular vibrational modes and the large-amplitude intermolecular hydrogen bond modes of (HCN) 2.
The observed rotational structure of the band was analyzed employing a standard semi-rigid polyatomic linear molecule Hamiltonian as implemented in the PGOPHER software package developed by Western  [44]. This semi-rigid linear molecule Hamiltonian is based on the rovibrational energy expressions for the vibrational ground state E'' and the vibrational excited state E' given in eqs. (  1 ) and (  2 ) including the rotational constants B'' and B', the quartic centrifugal distortion constants D'' J and D' J together with the l-type doubling constant q for the degenerate level associated with the n  1 6  transition. E 00 ¼ B 00 JðJ þ 1Þ À D 00 J J 2 ðJ þ 1Þ 2 (1) E 0 ¼ B 0 JðJ þ 1Þ À D 0 J J 2 ðJ þ 1Þ 2 � 1 2 qJðJ þ 1Þ (2) The P-and R-branch transitions occur to the lower components of the doublets with an effective rotational constant (B'À 1 2 q) according to the general symmetry selection rule, whereas the Q-branch transitions occur to the upper components of the doublets with an effective rotational constant (B' + 1 2 q).
Results and discussion At first, we present a test case of vortex-induced vibration of cylinder at Reynolds number based on cylinder diameter Re D = 100. The computations are performed in two-dimensional configuration for various structural-to-fluid mass ratios, and a ROM is constructed for the predictions at off-reference values of the mass ratio. Secondly, a test case of panel flutter induced by shock wave boundary layer interaction is considered in order to demonstrate the applicability of the proposed method for three-dimensional and highly non-linear fluid-structure interaction problems. For this case, we consider an existing high-fidelity simulation database from Shinde et al. [27] , where an incoming laminar boundary layer at Mach 2 and Reynolds number base on panel length Re a = 50000 interacts with an incident shock at the shock angle of 35 • and shock strength of 1.
A minimum of 300 tests were counted in each sample. Based on previous research, species were categorized into three groups: (chilled) Atlantic water indicators, Arctic water indicators, and those with no apparent specific environmental preference ("indifferent"). We use these previously proposed categorizations to explore whether these groupings are also evident in our dataset (i. e. whether species with similar environmental preferences display similar variability in Upernavik Fjord). It should be noted that for some species, different environmental preferences have been suggested in the literature (Table  1 ). In those cases, we categorize the species according to the most recent literature and in accordance with sites most comparable to our study but also list the contradicting references (Table  1 ).
PCA analysis Principal components 1 and 2 (PC1 and PC2) explain 28. 6 % and 16. 8 % of the variance in the dataset, respectively. The loadings plot reveals a clustering of three main groups of foraminiferal species (Fig. 6 ). The first group is characterized by strongly negative PC1 loadings (group 1). This group consists of calcareous species, both (chilled) Atlantic water and Arctic indicators. The second group consists of coldwater agglutinated species, characterized by strongly positive PC1 loadings (group 2). The third group consists mostly of species that are not indicative of a specific water mass (group 3). These species have low loadings for PC1 and strongly negative loadings for PC2. These results show that variability within the dataset can be predominantly explained by shifts between calcareous vs.
, 2012a; Seidenkrantz et al. , 2007). An important factor controlling the abundance of calcareous foraminifera is the concentration of HCO 2- 3 in the ambient water. In cold waters the calcification process may be limited by a lack of HCO 2- 3 , while warmer waters promote calcification. In addition, and perhaps more importantly, the preservation of calcareous specimens can be influenced by post-mortem dissolution. Colder waters contain more CO 2 and are thus less alkaline than warmer waters; this promotes dissolution of calcareous tests. Test dissolution is common in Arctic waters and appears similarly important in Upernavik Fjord, implied by the strong variation in the total calcareous abundance and an interval dominated by agglutinated fauna (7-22 cm). The fact that post-mortem dissolution plays an important role in explaining the patterns of our dataset is further supported by the inverse relationship between the percentage of calcareous species and foraminiferal test linings (Fig.
Acknowledgements. We thank the captain and crew of  R/V Porsild. The two reviewers are thanked for insightful comments and suggestions relating to a previous version of the paper. Financial support. This study is a contribution to the  VILLUM FONDEN  project " Past and Future Dynamics of the Greenland Ice Sheet : what is the ocean hiding?" (grant no. 10100 ). Marit-Solveig Seidenkrantz  was supported by the  Independent Research Fund Denmark  (grant no. 7014-00113B/FNU ). Review statement. This paper was edited by Alberto Reyes and reviewed by two anonymous referees.
Data availability. Foraminiferal abundances and age models are available from the Pangaea data archive (https://doi. org/10. 1594/PANGAEA. 896945). The supplement related to this article is available online at: https://doi. org/10. 5194/cp-15-1171-2019-supplement. www. clim-past. net/15/1171/2019/ Clim. Past, 15, 1171-1186, 2019 Supplement.
1 This was subsequently investigated in a pediatric patient population. The Study of Bevacizumab (Avastin) in Combination with Temozolomide and Radiotherapy in Paediatric and Adolescent Participants with High-Grade Glioma (HERBY) (BO25041; clinicaltrials. gov NCT01390948) was a Phase II, open-label, randomized, multicenter, comparator study set up to establish the efficacy and safety of the addition of BEV to RT and TMZ in patients between 3 and 18 years of age with newly diagnosed non-brain stem high-grade glioma (HGG). 2  The radiologic aspects of the HERBY trial were expanded compared with AVAglio in a number of aspects. In HERBY, the.
RANO and Multimodal Imaging The number of times that radiologic evaluation of perfusion or diffusion data changed the EFS time point (ie, the EFS incorporating diffusion and/or perfusion) was calculated to determine the potential influence of adding these imaging findings to the RANO criteria. In addition, a subjective score of the multimodal imaging influence in each case was recorded by each reader on a scale of 1 to 5, with the highest score indicating the most influence in the decision.