dataesr/scientific-paragraphs-categorization huggingface dataset:
;  Biogen Idec Inc. ;  Bristol-Myers Squibb Company ;  Eisai Inc. ;  Elan Pharmaceuticals Inc. ;  Eli Lilly and Company; F. Hoffmann-La Roche Ltd  and its affiliated company  Genentech, Inc. ;  GE Healthcare; Innogenetics , N. V. ;  Janssen Alzheimer Immunotherapy Research & Development, LLC. ; Johnson & Johnson Pharmaceutical Research & Development LLC. ; Medpace, Inc. ;  Merck & Co. , Inc. ;  Meso Scale Diagnostics, LLC. ;  Novartis Pharmaceuticals Corporation ;  Pfizer Inc. ; Servier;  Synarc Inc. ; and  Takeda Pharmaceutical Company. Thanks to  Chiara Barattieri di San Pietro  for editing the manuscript. Disclaimer: Data used in preparation of this article were obtained from the  Alzheimer's Disease Neuroimaging Initiative  (ADNI) database (adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.
G. B. Frisoni  and  D. Manset  are funded by  FP7  neuGRID4You funded by the  European Commission  ( FP7/2007-2013)  under grant agreement no. 283562. Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ). Canadian Institutes of Health Research  is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the  Foundation for the National Institutes of Health (NIH)  (www. fnih. org). The grantee organization is the  Northern California Institute for Research and Education , and the study is coordinated by the  Alzheimer's Disease Cooperative Study at the University of California, San Diego. This research was also supported by  NIH  grants  P30 AG010129 ,  K01 AG030514 , and the  Dana Foundation.
2002). This also ensured that a sufficiently high number of phytomers completed their growth during the experiment. Each day, the distance between the tip of a phytomer and the next older visible ligule, and also the distance between successive visible ligules was measured to the nearest 0. 5 mm. Our preliminary data showed that sheath elongation stopped with the emergence of the ligule (data not shown). Thus, phytomer elongation was calculated as the change of distance from phytomer tip to the next older visible ligule. A phytomer was considered fully expanded when the distance from its tip to the next older visible ligule had ceased to change. In most cases, tips of phytomers 12 and 13 appeared (leaf tips emerged) and terminated elongation during the observation period between 24 and 37 d after imbibition.
These data were used to calculate 'visible growth duration' and the visible time course of phytomer elongation. Phytomer tip emergence was defined as the moment a blade tip had grown past the highest visible ligule of the preceding phytomers. However, as elongation measurements were performed only once a day, a phytomer tip could have emerged before our first observation on that phytomer. Therefore, the time lag between tip emergence and our first observation on that phytomer was estimated as the distance between the tip of the phytomer in question and the next older visible ligule divided by the elongation rate measured just following tip emergence; and this time lag was accounted for in the calculation of days after tip emergence. The phyllochron was calculated as the time interval between the appearances of successive phytomers.
Assessing coordination within a phytomer and between phytomers The length of successive, fully expanded phytomers (obtained from destructive measurements) was used to determine ontogenetic changes in the final length of phytomers, i. e. the relationship between final phytomer length and phytomer rank. By extrapolating this relationship, the final length of still growing phytomers (immature ones) was predicted. That prediction was performed for each individual tiller, using the data from that same tiller. The fraction of final phytomer length (f L ) for immature phytomers was calculated as: f L ¼ L a =L P (1) where L a is the actual phytomer length at a given time, L p is predicted final phytomer length. Accordingly, the fraction of final phytomer length is 1 for a fully expanded phytomer. In an analogous manner, the fractional contribution of components (blade, sheath, internode) to final phytomer length was calculated as the ratios of actual blade-, sheath-and internode-length to predicted final phytomer length.
Constructing the time course of phytomer development A young phytomer grows within the sheath tube of the preceding phytomer before tip emergence. Therefore, the complete time course of the development of a phytomer includes a visible phase (tip emergence À full expansion) and an invisible phase (initiation À tip emergence). These time courses were all established by plotting the fraction of final phytomer length against their ages (days after tip emergence). The visible time course of phytomer development was established using the data of phytomers (ranks 12 and 13) on which the elongation rate had been monitored from tip emergence to full expansion. From the dissection of plants we obtained the final length of those phytomers, and the actual length of those phytomers on each day during expansion was calculated retrospectively using the measured daily elongation rate.
Thus, the fraction of final phytomer length of those phytomers was calculated using Equation (1) (using the actual measured final length as denominator) for each day from tip emergence to full expansion. The initial phase of phytomer development was established using the data of younger phytomers which were not fully elongated (immature) at the time of dissection. For these phytomers, their fractions of final phytomer length were determined using Equation (1). As their ages (the corresponding days after their tip emergence) were not known, these had to be estimated. In each tiller, an immature phytomer was selected that had reached 60-80 % of its final length (typically the second oldest immature phytomer). Using the visible time course of phytomer development (see previous paragraph) the time after tip emergence of this particular phytomer was estimated.
The data were analysed with a linear model using the generalized least squares method. This was implemented with the generalized least squares (gls) function by maximizing the restricted log-likelihood (REML) method in the 'nlme' package  (Pinheiro et al. 2014) , and the chamber effect was also included in the model. None of the analyses yielded a significant chamber effect. For the developmental pattern of phytomers, 95 % confidence bands of regressions were fitted using Sigmaplot (Systat Software, San Jose, CA) and used to test for treatment effects.
Practical settings For the generation of the multivariate test functions we use the Sobol decomposition of a d- variate function f ∈ L 2 [0, 1] d into 2 d orthogonal summands of growing dimensions, f (x 1 ,. , x d ) = d u=1 i 1 <•••<iu f i 1. iu (x i 1 ,. , x iu ). (17) In some cases, the 'interaction' terms can be taken just as products of univariate functions. Hereafter we list the test functions used in the numerical experiments, most of them are standard test functions for univariate function estimation in the wavelet literature  (Antoniadis et al. , 2001). When a full interaction is specified, it is basically a full tensor product model, i. e. , the interactions are obtained as a weighted product of the univariate, marginal, functions. A: 2d we consider two standard images. The first one is a picture of John Lennon furnished in R package Wavethresh  (Nason, 2013) , the second one is an image of a house, exhibiting stronger contours, furnished in the Matlab package Threshlab (Jansen, 2015), B: 3d full interactions: f 1 : 'parabolas', f 2 : 'wave', f 3 : 'bumps', C: 3d full interactions: f 1 : 'blip', f 2 : 'wave', f 3 : 'step', D: 3d f (x 1 , x 2 , x 3 ) := f 1 (x 1 ) + f 2 (x 2 ) + f 3 (x 3 ) + f 123 (x 1 , x 2 , x 3 ) , f 1 : 'blocks', f 2 (x 2 ) = 0.
Acknowledgements We thank the reviewers of this paper for their helpful comments. We acknowledge the support of the  Fund for Scientific Research Flanders, KU Leuven  grant  GOA/12/14  and of the  IAP Research Network   P7/06  of the  Belgian Science Policy. Jean-Marc Freyermuths  research was supported by the  Engineering and Physical Sciences Research Council  [ EP/K021672/2 ].
6 NA, Ph1). Microscopic images were processed with ImageJ software version 1. 42q (NIH, Bethesda, USA). 33 Briefly, microscopic images were first converted to 8-bit grayscale images. Subsequently, the images were calibrated using the "Set Scale" command. An intensity-based threshold was applied manually to delineate the antibody aggregates. A mask was then computed the following magnitudes in the mask were collected with "Analyze Particles" command: number of aggregates, area and mean fluorescent intensity of aggregate. Data were presented as mean ± standard deviation (SD) from five independent experiments. Fluorescence measurements. Herceptin ® sample aliquots were placed in 96-well plates and the fluorescence was measured by a FLUOstar OPTIMA (BMG Labtech GmbH, Ortenberg, Germany). The antibody aggregation was determined by measuring the intensity of Nile Red fluorescence (Excitation wavelength: 550 ± 10 nm; Emission wavelength: 615 ± 10 nm).
Cells were then washed twice and incubated in the dark for 30 min at 4°C under gentle agitation, with a FITC-conjugated secondary antibody diluted in PBS-2% FCS. Subsequently, cells were washed twice and resuspended in 0. 6 mL PBS-2% FCS containing TO-PRO ® -3 (1 μM; Life Technologies™) used to localize cell nuclei. The chamber was mounted on the Zeiss LSM 510 livecell imaging station stage equipped with a 32 PMT meta-detector (Carl Zeiss, MicroImaging GmbH, Gottingen, Germany) and Plan-Apochromat 63× Zeiss objective (1. 4 NA, oil immersion). Fifteen different images per experimental condition were acquired (Zen 2008 software, Carl Zeiss) to observe FITC-labeled plasma membrane (excitation laser: 488 nm; emission filter: BP 505-530 nm) and TO-PRO ® -3 stained nuclei (excitation laser: 633 nm; emission filter: LP 650 nm).
Images of cells were analyzed with the "Measure" and "Cell counter" functions of ImageJ software. The total number of HER-2 positive breast cancer cells and the associated fluorescence intensity were determined for each image (N = 400 cells/experimental condition). Data were presented as mean ± SD from three independent experiments. Flow cytometry: BT-474 cells were harvested using 2 mM EDTA in PBS, washed with cold PBS solution and incubated in PBS-2% FCS for 20 min at 4°C under gentle agitation. Subsequently, cells were centrifuged (150 g, 5 min, 4°C) and washed twice with PBS-2% FCS. The cells were then incubated in PBS-2% FCS with native or heated Herceptin ® (10 μg/mL) for 1 h at 4°C under gentle agitation. Human IgG1 isotype control (10 μg/mL) was used as nonbinding antibody control.
After two additional washing steps, cells were incubated with FITC-conjugated anti-human IgG1-Fc antibody diluted in PBS-2% FCS in the dark, for 30 min at 4°C under gentle agitation. Cells were then washed twice and resuspended in 0. 4 mL of PBS-2% FCS containing propidium iodide (0. 1 μg/mL; Sigma-Aldrich ® ) to stain dead cells. Dot plots were recorded using the BD FACSCanto II flow cytometer (BD Biosciences, Breda, Netherlands) and analyzed using the BD FACS Diva software (v6. 1. 3. , BD Biosciences) supplied by the manufacturer. A minimum of 10,000 events were analyzed to generate each dot plot. Data were presented as mean ± SD from three independent experiments.
Affinity binding of Herceptin ® to HER-2 receptors BT-474 cells (1. 5 × 10 5 ) were washed with ice-cold PBS and harvested. The cells were then incubated with unconjugated native or heated Herceptin ® (5 × 10 -6 to 5 × 10 -2 mg/mL) in PBS-2% FCS for 30 min at 4°C under gentle agitation. They were then washed twice with PBS-2% FCS, centrifuged (150 g, 5 min, 4°C) and further incubated with FITC-conjugated Herceptin ® or isotype control antibody (5 μg/mL) for 30 min at 4°C under gentle agitation. After two additional washing steps, the samples were resuspended in 0. 4 mL of PBS-2% FCS. Fluorescence intensity was recorded using the BD FACSCanto II flow cytometer and analyzed using the BD FACS Diva software. A minimum of 10,000 events were analyzed to generate each fluorescence histogram. The gate was arbitrarily set for the detection of FITC fluorescence.
The blots were washed thrice for 5 min under gentle rolling agitation. They were then incubated with HRP-conjugated secondary antibody (1:5000 dilution) for 1 h at room temperature under gentle rolling agitation. After additional washing steps, the membranes were developed using Lumi-Light Western Blotting Substrate (Roche Diagnostic Nederland, B. V. Almere, Netherlands) according to the manufacturer's instructions and visualized using the ChemiDoc™ XRS detection system (Bio-Rad Laboratories B. V. , Veenendaal, The Netherlands). Relative densities of HER-2 receptor, phosphorylated HER-2 receptor and ACTIN were measured using densitometric analysis of western-blots with the "Gels" function of ImageJ software. HER-2 receptor and phosphorylated HER-2 receptor were normalized to ACTIN and the fold change in HER-2 receptor and phosphorylated HER-2 receptor over the control condition was determined.
Cell proliferation BT-474 cells were seeded in 96-well culture plates at 1. 5×10 4 cells per well. The following day, cells were incubated with control and heated Herceptin ® (1 × 10 -8 to 5 × 10 -2 mg/mL) for 6 days at 37°C in a humidified atmosphere using a 5% CO 2 incubator. CellTiter 96 ® non-radioactive cell proliferation assay was carried out according to the manufacturer's protocol (Promega Benelux B. V. , Leiden, the Netherlands). Mean relative growth was expressed as a ratio of the control mean relative growth (vehicle-treated cells). Data were presented as mean ± SD from four independent experiments and are fitted with Variable slope model with a 95% confidence interval (GraphPad Prism 6 Software).
Acknowledgements The authors acknowledge  J. B. van den Dikkenberg  and  G. Nadibaidze  for technical assistance,  Dr. M. Bourajjaj ,  Prof. G. Storm  ( Dept. Pharmaceutics ,  Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Netherlands ),  Dr. C. H. Ries  ( Roche Diagnostics GmbH, Penzberg, Germany ),  Dr. A. Kong  ( Dept. Oncology ,  Weatherall Institute of Molecular Medicine, University of Oxford, UK ) and  Dr. N. Heuzé-Vourc 'h (Inserm  U1100 ,  EA 6305 ,  Université de Tours, France ) for fruitful discussions,  C. ten Brink  for her technical assistance (Cell Microscopy Core,  Department of Cell Biology, UMC Utrecht ). Dr. J. Butterworth  professionally proofread the manuscript (Word Perfect Science, http://www. wordperfectscience. com). This work was supported by  Advanced   ERC grant Sound Pharma -268906  (CM).
Enveloped viruses use different types of proteins to induce fusion of the host-cell membrane to their own in order to initiate infection. For coronaviruses, the spike (S) protein is responsible for cell entry  [12] , and was shown to be a major factor of virulence in the central nervous system (CNS) for several coronavirus species, including HCoV-OC43. We previously reported that persistent HCoV-OC43 infections of human neural cell lines led to the appearance of predominant point mutations in the putative receptor-binding domain of the S glycoprotein gene  [13]  and that these mutations were sufficient to significantly increase neurovirulence and modify neuropathology in BALB/c mice  [14]. In order to identify amino acid residues in the S glycoprotein that are involved in viral spread within the CNS, we compared the sequence of the gene encoding the viral S protein in the laboratory reference strain HCoV-OC43 (ATCC VR-759) with sequences of the S gene in viruses detected in clinical isolates from sputum of upper and lower respiratory tract of seven children, aged 3 to 36 months, admitted to the University Hospital of Caen, France, in 2003  [15] , as well as with all S protein sequences found in the NCBI data bank.
RNA concentrations were evaluated in all samples and quantified using a ND1000 spectrophotometer (Nanodrop). Real-time quantitative RT-PCR was performed with the TaqMan-RNAto-CT 1-Step kit (Applied Biosystems/Life Technologies) in a 20 μL reaction mixture with 10 μL of 2x TaqMan RT-PCR Mix (containing ROX as a passive reference dye), 900 nM of forward and reverse primers, and 200 nM of FAM BHQ1-TP probe. Four μL of extracted RNA for supernatant samples and cRNA standards (serial dilutions), or 0. 5 μg of total RNA for cellassociated (LA-N-5 cells infection) were used for the reaction. Amplification and detection were performed in a StepOnePlus Realtime PCR system apparatus and analysis were performed with the StepOne software version 2. 3 (Applied Biosystems).
Damage Identification. To validate the proposed detection procedure, 500 samples of bearing fault data are simulated. Every 50 samples, the amplitude of the fault  2 in (  28 ) is increased, starting from 10 -7 m (reference state) until 10 -6 m (maximum damage). The BCF subspace damage indicator is obtained from the whole set of simulated data. Here, the number of reference data sets is fixed to  ref = 50 and the filter bandwidth is chosen as [BCF ± 0. 1BCF]. The order of the observability matrices is chosen as  = 10. The results are reported in Figure  4  regarding the damage indicator  BSF ; see  (17). It is shown that the damage is identified as soon as the amplitude of the fault increases from 10 -7 m to 2 × 10 -7 m (i. e. , from the undamaged state to the first level of damage). The value of the damage indicator  BSF increases in good agreement with the evolution of the amplitude of the fault, which fully gives credit to the proposed methodology.
Preliminary Comments. The proposed damage identification procedure is applied to an experimental test rig which hosts four bearings on one shaft as shown in Figure  5. The shaft is driven by an AC motor and is connected to rubber belts. A radial load of 6000 lbs is applied to the shaft and the bearing by means of a spring mechanism. A magnetic plug is installed in the oil feedback pipe to collect debris, from the oil, as an evidence of bearing degradation. High sensitive ICP piezoaccelerometers sensors are placed on the bearing housing, in radial horizontal direction as shown in Figure  5. The vibration data are collected every 10 minutes with a sampling rate of 20 kHz. Each data sample contains 20480 points. Data collection is conducted by a NI Labview program and is generated by IMS Center with support from Rexnord Corp.
in Milwaukee, WI  [34]. The 984 data pieces are recorded during 7 days, by considering sample of about 140 data pieces each day, until a significant amount of metal debris is found on the magnetic plug of the test bearing. The test stops until the accumulated debris adherent to the magnetic plug exceeds a certain threshold. At this time, a visual inspection is made. All failures occurred once the designed lifetime of the bearing is reached which is 100 million revolutions. Four Rexnord ZA-2115 double row bearings are mounted on the shaft. According to the geometric parameters of the bearing listed in Table  3 , the nominal BCFs are calculated for a constant rotation speed of 2000 RPM and listed in Table  4.
Run-to-Failure Test. From visual inspection, an outer race defect is discovered in bearing 1. A sample of temporal and frequency representations of the data set is shown in Figure  6. Subspace damage indicators are obtained for each accelerometer and each BCF from the whole set of accelerometers data. Here, the number of reference data sets is  ref = 140 and the filter bandwidth is chosen as [BCF ± 0. 1BCF]. The order of the observability matrices (3) is chosen as  = 12 by analyzing the singular values of Γ  (as shown in Figure  7 ). Results of subspace damage indicators  FTF ,  BSF ,  BPFI , and  BPFO are reported in Figures  8, 9 , 10, and 11, respectively. A comparison of results for each BCF and each accelerometer position shows that the first bearing is identified as damaged approximately between two and three days before the experiment is stopped.
Structural determination and refinement Diffraction data sets were collected at the beamline BL41XU in SPring-8 (Hyogo, Japan), and they were processed with the program HKL2000  [31]  and the CCP4 program suite  [32]. The crystal of the FAAP20-UBZ•K63-Ub 2 complex belongs to the space group P2 1 , with unit cell dimensions of a = 59. 8 Å, b = 45. 9 Å, c = 172. 9 Å, β = 98. 1°. The complex structures were determined by molecular replacement using the program MolRep  [33]. The crystal structure of Ub (PDB code: 1ubq) was used as the search model. The atomic model of FAAP20-UBZ was built to fit 2F o -F c electron density map using the program COOT  [34]  with careful inspection. Refinement was carried out using the program Refmac5  [33]  with iterative correction and refinement of the atomic model.
0 ± 2. 2 resonance units (RU), which is significantly higher than the theoretical R max calculated from a 1:1 stoichiometry model (18. 8 ± 0. 4 RU) and slightly lower than that from a 2:1 (UBZ:Ub 2 ) model (37. 6 ± 0. 9 RU). Therefore, two FAAP20-UBZ molecules can simultaneously bind the two Ub moieties of K63-Ub 2 in solution. In addition, we tested the interaction of FAAP20 with K63-, K48-or M1-linked Ub 2 by GST-pulldown assay. As shown in S3 Fig. , both the full-length FAAP20 (1-180) and FAA-P20-UBZ (142-180) can similarly bind these three different diubiquitin chains. Altogether our data indicate that FAAP20 does not display any Ub chain specificity in apparent contradiction with two previous publications, which suggested that FAAP20-UBZ preferentially binds K63 chains over K48 chains  [11, 13].
These results also ensure that binding between FAAP20-UBZ and Ub is physiologically essential for the recruitment of FAAP20 to DNA damage sites. Comparison of FAAP20-UBZ with WRNIP1-, RAD18-, Polη-and NEMO-UBZ The Ub-interacting residues of FAAP20-UBZ are conserved or replaced by functionally equivalent residues among human, mouse, rat, chicken and puffy fish (Fig. 4A ). On the other hand, we could not assess the conservation of these residues among other UBZs, due to their highly divergent amino-acid sequences. Three-dimensional structural information is required to correctly align their sequences. To date, UBZ structures of WRNIP1 and RAD18 have been solved in complex with Ub  [29]. The Ub-interacting residues are conserved between both proteins  [29]. WRNIP1-UBZ (PDB code: 3VHT) and RAD18-UBZ (PDB code: 2MRE) display the canonical ββα fold, in  in Polη (Fig.
4  and S5 Fig. ). The alanine residue corresponding to Ala168 of FAAP20-UBZ seems also missing in other UBZs including WRNIP1 and RAD18 (Fig. 5 ). Altogether, our data suggest the presence of at least three groups of UBZ domains (Fig. 5 ). In the FAAP20/Polη group, the β-loop is absent or too divergent, so that Ile44 interacts with hydrophobic residues in the α-helix. The WRNIP1/Rad18 group interacts with Ile44 of Ub through their β-loop. Finally, the third group of UBZ domains comprises proteins that possess neither the FAAP20-UBZ residues involved in Ub-binding (highlighted in red) nor the RAD18-UBZ residues involved in Ub-binding (highlighted in black), and their mode of interaction should be different from the two other groups. The UBZ domains have been classified, according to the zinc coordinating residues (namely type 3 for CCHH and type 4 for CCHC).