dataesr/scientific-paragraphs-categorization huggingface dataset:
5 SDs below the mean. A classification of MCI required impairment in any of the four domains used. Impairment on the memory domain was needed for aMCI, and impairment on any of the other domains (without memory impairment) for naMCI; individuals with data for fewer than three of the non-memory domains were included if impairment was present in one or more domain, otherwise excluded. For other analyses, objective cognitive impairment was defined as an MMSE score from 24 to 27 (inclusive), or as a CDR of 0. 5. An MMSE score of 24-27 has been previously used to define a range of milder forms of cognitive impairment, including MCI  [41, 42] , mild cognitive decline  [43]  and mild AD  [44] , and a CDR of 0. 5 is a commonly-used criteria for MCI (e. g. ,  [45, 46] ).
Statistical analysis The Sydney team performed all analyses of the harmonized and pooled data. Five different MCI classifications were made: MCI and its aMCI, and naMCI subtypes using harmonized cognitive domain scores for determining objective cognitive impairment; and MCI using each of MMSE and CDR scores for this criterion. For each type of classification, only individuals with complete data for all four MCI criteria were included. Crude prevalence was determined for men and women in three age groups (60-69, 70-79 and 80-89 yrs). The Wilson score method described, evaluated and endorsed by Newcombe  [47]  was used to determine 95% confidence intervals (CIs), and chi-square tests were used to make comparisons. Prevalence estimates and CIs directly standardized for age and sex were calculated (for details see S1 Text).
For studies with participants in only one or two of the age groups, data for the remaining age group or groups were generated using a multiple imputation procedure (outlined in S1 Text). Associations between educational level and MCI were investigated with logistic regressions that controlled for age and sex. The two studies that provided results rather than raw data (CFAS and Invece. Ab) used the standard protocols developed for this report. The analyses were done using IBM SPSS Statistics 20, and the imputations done using the R-package mice 2. 21.
681% for the study on at least one harmonized cognitive domain. Estimates were directly standardized for age and sex, with the standard population being the total sample of all studies included in the analysis; data were imputed for missing age ranges within Invece. Ab, PATH and Sydney MAS. SLAS had not previously published prevalence estimates of mild cognitive impairment (MCI), and a published estimate for MoVIES was for amnestic MCI only  [19]. CFAS and ZARADEMP did not have neuropsychological test data from which harmonized cognitive domain scores could be derived. Published prevalence estimates are for baseline, except for PATH (wave 3, the first assessment when all relevant data for classifying MCI were obtained). References for the published estimates shown are: CFAS  [48] ; EAS  [16] ; ESPRIT  [24] ; HK-MAPS  [25] ; Invece.
Note that not all studies contained participants with dementia in their original samples, with some excluding individuals with dementia at the time of recruitment. There being higher prevalence of MCI when less stringent criteria are applied reinforces the need for a harmonized approach. Compared to the -1. 5 SD threshold for cognitive impairment we used, the DSM-5 definition of mild neurocognitive disorder recommends a less stringent criterion of -1. 0 to -2. 0 SD, without specifying how many tests should comprise a domain. Of course, the use of a threshold assumes that normative data are available for each test. We derived norms from the non-demented population of the same study. This approach offers the advantage of removing potential sociocultural and ethnic biases in any external source of normative data uniformly applied across all studies, and a similar approach was used by the 10/66 group  [15].
CFAS and ZARADEMP did not have neuropsychological test data from which harmonized cognitive domain scores could be derived. Published prevalence estimates are for baseline, except for PATH (wave 3, the first assessment when all relevant data for classifying MCI were obtained). References for the published estimates shown are: CFAS [48] ; EAS [16] ; ESPRIT [24] ; HK-MAPS [25] ; Invece. Ab [49] ; PATH [50] ; Sydney MAS [51] ; WHICAP [52] ; ZARADEMP [53]. Note that the HK-MAPS sample was over-represented by individuals considered to be at increased risk of conversion to dementia, and the prevalence estimates for MCI shown are likely to overestimate those for the broader population [25].
Acknowledgments The members of the contributing COSMIC studies are: CFAS: CB (study leader), FEM, BCMS; EAS: RBL, MJK (study leaders); ESPRIT: KR, MLA (study leaders), IC; HK-MAPS: LCWL (study leader), CHYW, AWTF; Invece. Ab: AG (study leader), RV, AD; MoVIES: MG (study leader), HHD, TH; PATH: KJA (study leader), NC, PB;  SLAS I & II: TPN (study leader ), QG; Sydney MAS: PSS, HB (study leaders), DML, NAK, JDC, AT, GA, SR; WHICAP: NS (study leader), JM, YS; ZARADEMP: AL (study leader), RLA, JS. The authors of particular studies acknowledge: EAS: the contributions of  Molly Zimmerman  and Carol Derby; HK-MAPS: the contributions of Grace T. Y. Leung ,  Wai Chi Chan ,  Department of Health of Hong Kong SAR , the participants and the social centres for their assistance in the assessment, and thank  Novartis  and  Astra Zeneca  for their sponsorship of souvenirs for the participants in the baseline study; (Invece.
The composed system, referred as the BLSTM-CTC, has shown very impressive results on challenging databases  (Graves, 2008; Graves et al. , 2009; Grosicki and Abed, 2009; Menasri et al. , 2012). In this paper, we present three baseline systems using the same architecture, built around the BLSTM-CTC network. These systems only differ by the features that are extracted using a sliding window method. These baseline systems enable a direct comparison between the three sets of features, which enables us to prefer one set of features over the other. However, it is well known that combining systems or features may improve the overall results  (Menasri et al. , 2012; Gehler and Nowozin, 2009). In this paper we explore different ways of combining an ensemble of F BLSTM-CTC. We explore different levels of information combination, from a low level combination (feature space combination), to mid-level combinations (internal system representation combinations), and high level combinations (decoding combinations).
BLSTM Network The BLSTM is a complex memory management network, it is in charge of processing a N dimensional input signal to produce an output signal that takes into account long term dependencies. In order to do this the BLSTM is composed of two recurrent neural networks with Long Short Term Memory neural units. One network processes the data chronologically while the other processes the data in reverse chronological order. Therefore at time t a decision can be taken by combining the outputs of the two networks, using past and future context. For handwriting recognition having a certain knowledge of previous and future possible characters is important since in most cases characters follow a logical order induced by the underlying lexicon of the training database. Instead of modeling this order at a high level using N-grams they are integrated at a low level of decision inside the BLSTM networks.
Database and Task Description In order to compare our results we use the RIMES database. This database consists in handwritten letters addressed to various administrations and registration services. From these letters, single words were isolated and annotated. The database is divided in three different subsets: training, validation and test. Each set contains respectively 44197 images, 7542 images and 7464 images. The RIMES database was built to be a low noise database, i. e. the background and foreground pixels are easily identifiable. However, the intraword variation is very important since 1300 different writers participated for writing the letters. The word length distribution among the three sets is very similar. It has to be stressed that the words with three and less characters contribute to more than 40% of the database.
This will affect the HMM lexicon correction since it is harder to correct short words. We compare our results on the ICDAR 2009 WR2 task  (Grosicki and Abed, 2009). This task consists in finding the correct transcription of a word using a lexicon of 1600 words. The measure used is the Word Error Rate (WER) on the Top 1 outputs and Top 10 outputs. In order to learn the system we use the whole training database to train each part of the system. This may cause some overfitting, however splitting the training base in order to train each system on separate bases causes some global loss on the learning performances.
Results We now present the results of the various combination schemes on the RIMES database for BLSTM-CTC feature combination. Table  1  presents the results of the different combinations by displaying the top 1 and top 10 Word Error Rate (WER). We also provide the raw WER output of the last CTC layer prior to the HMM layer, i. e. the system without the lexicon decoding. Combination 3. 2 an 3. 5 do not outperform the single features, all other prove to be better strategies than single feature combination. The best combination is the low level feature combination. The reason is that the BLSTM-CTC is able to model very efficiently the data. In a very similar fashion to deep neural networks, it models very well In order to combine several features using a BLSTM-CTC it is therefore preferable to directly combine the features.
Several investigators pointed out the importance of rigid-body motions, which limit the achievable resolution in XRD experiments  [4] [5] [6] [7] [8] [9]. Overall motion is routinely modelled from XRD data using translation-libration-screw (TLS) analyses. However, refined TLS parameters offer only a simplified view of rotational and translational dynamics in the crystal lattice, meaning that some ambiguity remains regarding the physical nature of the modelled motion. Furthermore, diffraction data cannot provide insights into the timescale of motions, making it difficult to distinguish between static disorder and molecular motions. In other words, it is not possible to ascertain that the dynamics modeled from XRD data accurately reflect the overall motion of the molecules in the crystal. Magic-angle spinning (MAS) NMR spectroscopy provides atomic-level-resolution access to crystalline proteins.
These crystals are henceforth referred to as MPD-ub, cubic-PEG-ub and rod-PEG-ub, reflecting the different precipitation agents (methyl-pentanediol (MPD) and polyethylene glycol (PEG), respectively) and the morphology of the crystals. Structures for the three crystal forms have been solved before and correspond to Protein Data Bank entries 3ONS (ref. 16), 3N30 (ref. 17) and 3EHV (ref. 18), respectively. To ensure that our crystals were consistent with the previously reported structures, XRD data were collected on the three crystals. For the two types of PEG crystals, we collected diffraction data at 100 K and solved the structures by molecular replacement, confirming the identity to the two already reported sets of coordinates. Our MPD-ub crystals appeared too thin for conventional structure determination when crystallized under the conditions that yield high-quality MAS NMR spectra.
When applying an overall scaling factor of 1. 04 to the S 2 values from cubic-PEG-ub, the agreement with MPD-ub data is significantly improved (see Supplementary Fig. 6  for details). As discussed further below, this offset can be explained by the rocking motion of ubiquitin within the crystal lattice of cubic-PEG-ub. It has been recently shown that experimental data by MAS NMR and XRD can be successfully reproduced using explicit MD models of protein crystals  [27] [28] [29]. Towards this goal we have recorded 1-ms-long all-atom MD trajectories representing the two different crystal lattice arrangements of ubiquitin. A block of four crystal unit cells (24 ubiquitin molecules) was simulated for MPD-ub, while one crystal unit cell (48 ubiquitin molecules) was simulated for cubic-PEG-ub. The presence of multiple protein molecules in the simulations effectively improves the statistical properties of the MD models.
8 ). These predictions are also borne out by the experimental data, as explained below. Overall rocking impacts resolution in XRD experiments. Both the NMR and MD data indicate that ubiquitin molecules arranged in a crystal lattice experience varying degree of rocking motion at room temperature. But is this rocking motion impacting the XRD data collected at 100 K? Figure  3c  shows that this is indeed the case. The Wilson B-factor in cubic-PEG-ub is almost fourfold higher than in MPD-ub and the resolution is significantly lower, which we propose to arise from differences in the respective rocking dynamics. This correlation between NMR  15  N R 1r relaxation data and XRD resolution is further substantiated by the third crystal form, rod-PEG-ub, which displays lower  15  N R 1r rates, suggesting that rocking motions are of low amplitude (blue bars in Fig.
In this sense, the information content of the TLS parameters is not very different from that of the Wilson B-factor insofar as it is difficult to disentangle libration and translation. Figure  5  | Estimating the timescale of rocking motion from  15  N R 1q measurements. Plotted is the  15  N R 1r relaxation rate constant as a function of the order parameter S 2 and correlation time t that describe the motion of the NH vector. (a) The calculations were conducted using the Redfieldtheory formulas, equations 8 and 18 in ref. 65. (b) Alternatively, the calculations were conducted using a numeric model that is also valid outside the Redfield regime; the geometrical details of this two-site jump model are exactly as described in Fig. 2  of ref. 66, and the simulation was implemented in the program GAMMA  67  , as described before  68.
We thus performed a wider analysis seeking to determine whether there is a correlation between packing density and rocking dynamics (as manifested in XRD resolution and B-factors). A comprehensive search of the Protein Data Bank indeed shows that high solvent content correlates with low resolution and high Wilson B, with correlation coefficients of 0. 39 and 0. 36, respectively (Supplementary Fig. 10a ). As expected, these dependencies are subject to strong scatter, reflecting the intricate and complex nature of the crystallization process and the large diversity of the shapes and properties of the analysed structures  43, 44. We have also repeated this analysis for the subset of crystallographic structures in the Protein Data Bank that have been solved at room temperature. The results prove to be very similar (cf.
Discussion We have shown here that three independent and complementary techniques, NMR, MD and XRD, all provide evidence for an overall rocking motion in protein crystals. The rocking motion is (i) observed by NMR, through the increased R 1r rates, as well as a slight decrease of order parameters; (ii) reproduced by MD in allatom crystal lattice simulations; and (iii) confirmed by XRD through the decreased resolution and increased atomic displacement factors. We have been able to provide for the first time a measure of the timescale at which this motion takes place at room temperature, which turned out to be hundreds of nanoseconds to tens of microseconds. Our data suggest that rigid-body motion is an important determinant for the resolution achieved in X-ray crystallography and may explain at least partly why visually perfect crystals do not always produce high-resolution XRD data  45.
7:1. The reservoir buffer was a mixture of 20 mM citric acid, pH 4. 2 and 2-methyl-2,4-pentanediol (MPD) at a ratio of 40:60. Needle-shaped crystals were obtained at 4 °C after about 1-2 weeks. Cubic-PEG-ub crystals (PDB ID code 4XOL) were obtained with a reservoir buffer of 100 mM 2-(N-morpholino)ethanesulfonic acid (MES), pH 6. 3, 20% PEG 3350 and 100 mM zinc acetate. Cubic-shape crystals were obtained within 1 week at 23 °C. Rod-PEG-ub crystals (PDB ID code 4XOK) were obtained with a reservoir buffer of 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), pH 7. 0, 25% PEG 1500 and 25 mM zinc acetate. Long-rod-shape crystals were obtained after 2 weeks at 23 °C. In addition to these three crystal forms, we also obtained a fourth crystal, from unlabelled ubiquitin. This crystal, rod-PEG-ub-II, (PDB ID code 4XOF) was obtained with a reservoir buffer of 50 mM MES, pH 6.
The resulting curves were fitted to a combination of six exponentials and a constant. The upper bound was imposed on the fitted correlation times: they were not allowed to be longer than the length of the trajectory, that is, 1 ms. The time-modulated portion of the correlation function (that is, the six weighted exponentials) was then used to evaluate the spectral density functions and subsequently calculate the per-residue 15 N R 1 rates  60. The same strategies were used for the other trajectories. XRD data collection and processing. Before being flash frozen in the cryogenic N 2 stream on the beamline, crystals were cryoprotected with a brief soaking in a solution composed of the mother liquor complemented with 20% glycerol. Data were collected at 100 K on the ESRF ID29 (cubic-PEG-ub and rod-PEG-ub) and ID23-2 (rod-PEG-ub II) beamlines.
Model building was carried out with COOT  64. For rod-PEG-ub, unexpectedly high R free and R work values were obtained (0. 325 and 0. 302, respectively). Various refinement strategies were attempted without success (for example, multiple models, TLS refinement, use of a reference model). To validate the correctness of our molecular replacement solution, we carried out a de novo model building, using the autobuild function of PHENIX. The initial map was computed using our experimental data and the refined ubiquitin model obtained under identical crystallization conditions (3EHV). The automated procedure was able to reconstruct 99% of the backbone and 84% of the side chains confirming the correctness of the molecular replacement solution. Cubic-PEG-ub, rod-PEG-ub and rod-PEG-ub-II have been deposited to the Protein Data Bank under the codes 4XOL, 4XOK and 4XOF, respectively.
MPD-ub crystals grew as sea urchins composed of thousands of extremely thin rods (B100-200 Â 5 Â 5 mm), impossible to isolate and loop individually. We therefore performed a powder diffraction experiment, to confirm that our crystals have the same space group as the previously reported PDB entry 3ONS (which was obtained under identical conditions and comprehensively characterized by NMR). Details of the powder diffraction experiment are reported in the Supporting Information (Supplementary Fig. 11 ). Stereo view images of the electron density maps are provided as Supplementary Fig. 12.
Acknowledgements This work was financially supported by the  European Research Council  ( ERC-Stg-2012-311318-ProtDyn2Function ), the  French Research Agency ANR  ( ANR 10-PDOC-011-01 ), as well as Commissariat al'energie atomique et aux energies alternatives (CEA),  Centre National de la Recherche Scientifique (CNRS)  and  UniversiteGrenoble Alpes. This work used the platforms of the Grenoble Instruct Center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB). Funding from the  National Science Foundation  (Grant MCB  1158347 ) to N. R. S. is acknowledged. We are grateful to the  ESRF  for beam-time under long-term projects  MX722 ,  MX1464  and  MX1583 (IBS BAG ).
In silico approaches predicting ADME and toxicity properties (ACD Percepta v2012, QSAR Toolbox 3. 3 softwares) were used to select the best compounds, while excluding those with low bioavailability and/or high acute or chronic toxicity. In parallel, a bimodulation assay with a highly-active ABCG2 chromone derivative inhibitor (MBL-II-141)  (Honorat, M. , et al. Oncotarget 2014, 5, 11957-11970)  was performed to sensitize tumor growth to the ABCG2 substrate irinotecan. We propose an integrated approach (in silico ADMET/bimodulation) to help the selection of best indenoindole derivatives for further biological explorations.
Acknowledgements: Financial supported by the  Deutsche Krebshilfe  ( 109567 ). We recently described a series of N-benzoylated phenoxazines and phenothiazines as potent antitubulin agents. Herein-as a further development in the course of SAR studies -the synthesis and biological evaluation of heterocycle derived phenylpiperazinylmethanones are reported. The in vitro evaluation of the antitumor activity and effects on the cell cycle were studied on the human chronic myelogenous K562 leukemia cell line. The most active compounds displayed excellent antiproliferative potencies. To gain further insight into the mode of action, particular analogs were assayed for their effects on cell cycle using a K562 (human chronic myelogenous leukemia) Acknowledgments: The members of the scientific committee would like to thank the local organizing committee and all people who worked on preparing the conference for making this event possible.
Hadronization is the non-perturbative process of QCD by which partons become hadrons. It has been studied at high energies through various processes, we focus here on the experiments of lepto-production of hadrons in cold nuclear matter. By studying the dependence of observables to the atomic number of the target, these experiments can give information on the dynamic of the hadronization at the femtometer scale. In particular, we will present preliminary results from JLab Hall B (CLAS collaboration), which give unprecedented statistical precision. Then, we will present results of a phenomenological study showing how HERMES data can be described with pure energy loss models.
This effect is similar to the Cronin effect from heavy ion collisions  [4] , which is believed to be generated by multiple soft scattering. The E665 experiment  [5] , carried at the Fermi National Accelerator Laboratory (FNAL), also provided interesting data. Their study showed the importance of the target fragmentation region and its dominance at intermediate rapidity and subsequently the importance to select properly the current fragmentation in future experiments. They also measured slow protons numbers in the target region, which appeared to give information on the impact parameter of the interaction. The HERMES collaboration published a large set of data  [6, 7]  using the 27. 5 GeV positron beam in DESY. Their results gave new insight on hadronization because of their precision and the identification of individual particles (π + , π 0 , π -, K + , K -, p and p).
Indeed, the partons that go through the longest paths in the nuclear medium are the least likely to produce hadrons and therefore to contribute to the ΔP 2 T measurement. Our result shows that taking this bias into account is necessary to describe HERMES data completely. Figure  5. On the left, multiplicity ratio from HERMES compared with our pure energy loss model  [10]. On the right, the transverse momentum broadening as function of A 1/3 using q = 0. 35 GeV 2 /fm found by matching the simulated ratio to data.