dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This research was supported in part by  US Office of Naval Research  grants  N00014-10-1-0516  and  N00014-12-1-0286  to  Duke University  and to the  University of St Andrews. This work benefited from discussions with participants in a working group supported by  Office of Naval Research  grants  N00014-09-1-0896  to the  University of California, Santa Barbara  and  N00014-12-1-0274  to the  University of California, Davis. Funding was also received from the  MASTS pooling initiative  (The  Marine Alliance for Science and Technology for Scotland)  and their support is gratefully acknowledged. MASTS is funded by the  Scottish Funding Council  (grant reference  HR09011 ) and contributing institutions. We thank  Michele Thums ,  Steve Wall  and  Corey Bradshaw  for field assistance.
Battery-powered PDT irradiation device. The prototype battery-powered 635 nm irradiation device used in this study is built in an aluminium box with breadboard slot for electronics and a battery pack for 3 AA batteries as shown in Supplemental Figure  1. The light source used was a single high-output light emitting diode with peak emission at 635 nm and FWHM of 40 nm, built in a TO-39 housing (OptoDiode OD-624L-ND). To manage thermal energy dissipated in the diode a TO-39 star heat sink was coupled to the LED housing with thermal compound (Wakefield Thermal Solutions, Pelham, NH). Battery power supply and internal electronics were evaluated as reported in the Results. All treatment response data was obtained using 3 AA alkaline batteries connected in series to power the LED, though some validation tests were run using either a 9 V alkaline battery or an external power supply (Keithley, 2401 Source Meter).
Measurements were obtained by centrally positioning the light source to the photo-detector and horizontally scanning the full width of the beam using a BSC203 -Three-Channel apt TM Benchtop Stepper Motor Controller. Due to random noise, data points were collected in 5 -10 second intervals by averaging the stabilized min and max. Light source and electronics performance and power measurements. LED characterization was performed using a Keithley 2401 voltage (1 μV -20 V and 10 pA -1 A) source meter to ramp current at controlled voltage. Output power was measured by a Melles-Griot 13PEM001 broadband (400 nm to 2 um) power/ energy meter (10 μW to 2 W) mounted vertically above the light source using a lab stand while LED temperature was monitored using a thermocouple in contact with the LED throughout irradiation.
Briefly, multiwell cultures were labelled with fluorescent vital dyes Calcein AM and Ethidium Bromide (Life Technologies, Carlsbad, CA). 5X mosaic images of 36 individual microscope fields were obtained from the central region of each well and stitched using a Zeiss AxioObserver Z. 1 microscope with automated motorized stage and standard fluorescence filter cubes (Carl Zeiss) and equipped with a cooled, high-resolution monochromatic 14-bit digital camera (AxioCam, Carl Zeiss). Cultures incubated with ALA but not exposed to light were used as controls. Each individual dose group was repeated three to six times. Image data was batch processed using custom Matlab software routines adapted from a protocol previously reported to quantify viability based on ratiometric analysis of cleaved calcein and intercalated ethidium bromide fluorescence signals  32.
A431 cultures were prepared for smartphone imaging by incubating an entire T75 culture flask in 1mM ALA for four hours prior to harvesting and centrifugation to form a pellet approximately 1 cm in diameter. ALA-treated pellets and identically prepared and harvested controls with no ALA were then imaged in the same manner as the tissue phantoms. The cell phone was secured in place so as to maintain the same object distance for each measurement. Images were analysed using custom routines developed in the MATLAB image processing toolbox.
Acknowledgments We would like to thank  Dr. Jonathan Rochford  and  Stephanie Bellinger-Buckley  for providing solubilized protoporphyrin IX for imaging studies and  Jeffrey La  for assistance with LED characterization. We gratefully acknowledge productive conversations with  Drs. Stephen Bown  and  Colin Hopper  regarding clinical PDT for oral cancer, and discussions with  Dr. Brett Bouma  about LED-based devices. This work was funded by the  National Cancer Institute , of the  US National Institutes of Health :  R00CA155045  (to JPC) and  P01CA084203  (to TH) and  UH2CA189901  (to TH and JPC).
Participants This study included women with singleton pregnancies participating in a prospective casecontrol study comparing lean (BMI ≤25 kg/m 2 ) and SO (BMI ≥40 kg/m 2 ) women in Edinburgh, Scotland, UK. As previously described, SO women were recruited from a specialist antenatal metabolic clinic for women with BMI>40 kg/m 2 ) whilst lean women were recruited from community antenatal clinics  (Mina et al. , 2015). Maternal body composition, maternal and pregnancy factors were characterised  (Mina et al. , 2015). All samples and data were collected with ethical approval (references 08/S1101/39 and 13/ES/0126), and with fully informed and written consent from all women.
5x TBE buffer to assess probe specificity. For absolute QRTPCR, all samples were assayed in triplicates using FAM hydrolysis thermal cycling set-up in LightCycler 480 QRTPCR system (Roche), and housekeeping genes assay was performed for each batch of cDNA synthesis. A standard curve was prepared by pooling 1μl of each cDNA sample, which was diluted 1:4 with RNase-free water, and then serially diluted 1:2 to produce 8 standards (1:4-1:512). The QRTPCR reaction mixture was prepared following PerfeCTa® QRTPCR Fastmix® II (Quanta Biosciences, VWR, Leicestershire, UK) protocol, with final primer concentration = 250 nM, probe = 100 nM and cDNA per reaction = 25 ng. The datasets were analysed with LightCycler 480 QRTPCR software using E-Methods, and only those with standard error ≤0. 05 and efficiency ≥1.
Statistical Analyses Statistical analyses were performed using SPSS 19 (IBM, New York, USA). P≤0. 05 was used as a cut-off of statistical significance. Prior to any analyses, data distribution was determined by Q-Q Plot and by histogram visualisation. The gene expression data were first normalised using an average of YWHAZ and TBP housekeeping genes and then log-transformed. For the distress questionnaires there were missing data due to either improper completion of the questionnaire or failure to complete an entire scale. Overall there were 2. 3 % missing distress assessment data in lean and 12% in the SO group. Missing data imputation was performed on these data using Markov Chain Monte Carlo (MCMC) algorithm. Pearson's correlations were used to test intra-and inter-correlation among the genes.
Atmospheric Density The atmospheric model was designed to determine the altitude at which the Cassini spacecraft starts to tumble due to atmospheric drag during its last orbit. We use the model in the range where it is supposed to be valid: Tumbling should occur around 1. 02R S. Koskinen et al. [2013]  used EUV occultation data to determine the exobase at 1. 05R S and the H 2 density up to about 1. 06R S. The engineering model uses an upper limit of the H density. Despite being a small fraction of the number density near Saturn  [Koskinen et al. , 2013] , H becomes highly dominant outside of 1. 05R S due to its slower falloff with radial distance. If we assume instead that H is always negligible, I increases by a factor of 2. The overall error of the engineering model near the equator is estimated to be smaller than a factor of 2.
The model result shown in Figure  A1  roughly agrees with the measured spectrum. The gradient change below 100 keV results from the drop of the charge exchange rate v around that energy. The second gradient change at slightly higher energies results from the shape we gave the source spectrum S. While we can reproduce the proton spectra using plausible assumptions, this does not work for the oxygen ion spectra shown in Figure  A1 , right. The charge exchange rate of oxygen ions is approximately constant over Intensities measured throughout the Cassini mission within ±10 ∘ latitude are linearly averaged after applying a median filter. Data were taken by the CHEMS and LEMMS instruments. Smooth curves show polynomial fits to the data that were used to determine the radial intensity peak (black solid diamonds) for each energy.
Acknowledgments Cassini/MIMI data are available online through NASA's planetary data system (PDS). The JHU/APL authors were partially supported by the  NASA Office of Space Science  under task order 003 of contract  NAS5-97271  between  NASA/GSFC  and  JHU. The MPS authors were partially supported by the  German Space Agency (DLR)  under contract  50 OH 1502 , by the  Max Planck Society , and by the  International Max Planck Research School for Solar System Science (IMPRS)  at the  University of Göttingen, Germany  and at the  Max Planck Institute for Solar System Research (MPS). The authors like to thank  D. Strobel  ( JHU ) for developing the engineering atmosphere model,  M. M. Hedman  ( Cornell University ) for his analysis on the D ring,  A. Lagg  ( MPS ) for analysis software support, and  J.
The Tier1 computing centres are supported by IN2P3 (France), KIT and BMBF (Germany), INFN (Italy), NWO and SURF (The Netherlands), PIC (Spain), GridPP (United Kingdom). We are indebted to the communities behind the multiple open source software packages on which we depend. We are also thankful for the computing resources and the access to software R&D tools provided by Yandex LLC (Russia). Individual groups or members have received support from EPLANET, Marie Sk lodowska-Curie Actions and ERC (European Union), Conseil général de Haute-Savoie, Labex ENIGMASS and OCEVU, Région Auvergne (France), RFBR (Russia), XuntaGal and GENCAT (Spain), Royal Society and Royal Commission for the Exhibition of 1851 (United Kingdom).
Docking simulations Rigid body docking of B-form DNA to the filament (receptor) was performed using either Autodock Vina  (36)  or the ATTRACT program  (29, 37)  in its PTools/PyAttract implementation  (25, 38). These simulations explored the possible mode of association of oligonucleotides to the filament as a gateway to filament incorporation. In both cases, the B-DNA structure (ligand) was docked on a protein nucleofilament composed of five RecA monomers bound to ssDNA (PDB code 3CMW) (6) (receptor). Autodock Vina considers the association partners in atomic representation and uses docking scoring function developed based on X-Score  (39). For this simulation, the whole filament surface was selected as putative binding pocket on the receptor surface. The ATTRACT docking simulations in coarse-grained representation followed the same protocol as detailed in reference  (25) ; however, the simulations done here include the L2 loops that had been omitted in that previous work.
Two out of the three docking protocols started by a rigid body docking run as detailed above. In the ATTRACT protocol, the docked ligand was a curved (dT. dA) 30 DNA built from the structure of SRY-bound DNA (PDB code 1HRY) (described in (  40 )), and the simulations targeted the whole filament surface, again in the presence of the L2 loops. The best interacting patches on the protein surface were situated in a cleft between the L2 loop and the filament interior, containing the positively charged protein residues K198, K216, R222, R226, R227, R243 and K245 associated to site II in the literature  (6). In the Autodock Vina protocol, the docked ligand was a short DNA fragment composed of a 3base-pair (bp) long nucleotide phosphate-deoxyribose, and the simulations targeted the putative site II basic patches described above.
Alternatively, a whole (dT. dA) 9 DNA oligomer obtained by truncating the site I-RecAbound dsDNA structure in the 3CMX, already presenting 50% stretching and 40% unwinding deformations with respect to B-DNA, was fitted to the site II patches identified in the first step. To this aim, harmonic restraints were applied to the phosphate atoms of thymine residues 1, 2, 4, 5, 7, 8 during energy minimization with the Jumna software  (29)  to radially pull these phosphate atoms to the favorable positions identified in site II under constant helical symmetry conditions (i. e. conserving the global stretching and winding characteristic of the binding to the site I). This process was applied to DNA in the absence of the protein to capture the contribution of the DNA internal mechanics to the protein-DNA association geometry.
Molecular dynamics simulations Molecular dynamics (MD) simulations were performed using the NAMD 2. 9 package (41) with the CHARMM 27 force field including CMAP correction  (42). Solvation of the simulated structures used TIP3P water model placed within a 125 Å × 120 Å × 170 Å box, subject to periodic boundary condition. Physiological concentration of Na + and Cl -ions of 0. 15 mol/l were added to each simulation to maintain electro-neutrality (Supplementary Figure  S1A ). As a whole, the simulated systems contained over 260 000 atoms. We used 2-fs time steps, and the bond lengths involving hydrogen atoms were constrained using the SHAKE method. We utilized particle-mesh Ewald method for long-range electrostatics calculation, and van der Waals interactions were smoothly switched off at 10-12 Å by a force-switching function.
Non-specific binding of B-form dsDNA to the RecA filament The structure of the non-specific binding intermediate with unaltered DNA structure (class 2) was modeled as the result of rigid body docking of B-DNA to about one turn of the presynaptic active filament using either the Autodock Vina in all-atom representation or the ATTRACT program in coarse-grained representation (see 'Materials and Methods' section). The two protocols converged towards structures where the DNA is partially inserted in the filament groove and where at least four DNA phosphates strongly interact with residues K280, K282, K286, and K302 through the formation of salt bridges. In these structures, which remained stably assembled during 20 ns of MD simulations (Supplementary Figure  S2 ), the interacting phosphates are distributed in the two DNA strands while the basic protein residues belong to two consecutive or near-consecutive Cterminal domains (CTD) of RecA.
Non-specific dsDNA binding to the filament site II The presence of bulky L2 loops folded upon the ssDNA in site I of the RecA filament (residues 198-206 shown in yellow in Figure  2B ) only offers two possibilities for the dsDNA alignment in parallel with the ssDNA, either on the ssDNA side of the L2 loops or on the opposite side (Supplementary Figure  S4 ). The first possibility has been explored in former theoretical work  (25)  which concluded that at least one L2 loop needs to be displaced for each ds-DNA triplet to reach alignment with the incoming strand. In the present work, we explore the second route. The Cterminal side of the L2 loops forms a strongly electronegative cleft when intersecting with the protein core (Supplementary Figure  S5 ), which can accommodate a DNA single strand as was confirmed here using interactive docking simulations with the BioSpring software (see 'Materials and Methods' section).
FUNDING Research was supported by  NIH  [ NIH/NIGMS-R01-GM025326  to M. P. ];  Nancy Kleckner ;  National Science Foundation Graduate Research Fellowship  [ DGE-1144152  to D. Y. ];  Initiative d'Excellence program  from the  French State [DYNAMO ,  ANR-11-LABX-0011-01  to C. P. ]; Stampede at the  Texas Advanced Computing Center (TACC)  under an XSEDE startup allocation [TG-MCB130076];  Centre Informatique National de l'Enseignement Supérieur (CINES)  [DARI x2015077438]. Funding for open access charge:  NIH [NIH/NIGMS-R01-GM025326 ].
Spatial filters, obtained by diagonalization of data covariance matrices, enhance the differences between variances of signals of different classes/tasks. They are efficient on clean datasets obtained from strongly constrained environment. However they are sensitive to artifacts and outliers  [13, 19]. Working directly on covariance matrices is advantageous: it simplifies the whole BCI system  [21] , avoiding the alignment of two learning steps (spatial filters and classifiers) that might lead to overfitting. Covariance matrices being Symmetric and Positive-Definite (SPD), they are best handled by tools provided by Riemannian geometry. Classification in the space of SPD matrices eliminates the need of spatial filters and improves the system robustness  [5, 7, 21]. A classification technique referred to as minimum distance to Riemannian mean (MDRM) has been recently introduced to EEG classification  [5].
SSVEP Dataset The experimental study is conducted on multichannel EEG signals recorded during an SSVEP-based BCI experiment  [11]. EEG are recorded on C = 8 channels (i. e. Oz, O1, O2, PO3, POz, PO7, PO8, PO4) from 12 subjects. The subjects are presented with F = 3 visual target stimuli blinking respectively at 13Hz, 17Hz and 21Hz. It is a K = 4 classes setup combining F = 3 stimulus classes and one resting class (no-SSVEP). In a session, 32 trials are recorded: 8 for each visual stimulus and 8 for the resting class. The number of sessions recorded per subject varies from 2 to 5. For each subject, a test set is made of 32 trials while the remaining trials (which might vary from 32 to 128) make up for the training set.
Conclusion Riemannian approaches have been successfully applied on EEG signals for brain computer interfaces. Straightforward algorithms, such as Minimum Distance to Mean, provide competitive results with state-of-the-art methods, without requiring meticulous parametrization or optimization. Working on covariance matrices in Riemannian spaces offers a wide choice of distances, embedding desirable invariances: it is thus possible to avoid the computation of user-specific spatial filters which are sensitive to artifacts and outliers. Nonetheless, the estimation of the Riemannian geometric mean has a strong impact on the classifier accuracy. This study investigates the performance of several distances and divergence on a real EEG dataset in the context of BCI based on the SSVEP paradigm. The experimental results indicate that the α-divergence yields the best accuracy after the selection of the best α values, but the Bhattacharyya distance has the lowest computational cost while providing decent accuracies.
We present the results of a search for cold gas at high redshift along QSO lines-of-sight carried out without any a priori assumption on the neutral atomic-hydrogen content of the absorbers. To do this, we systematically looked for neutral-carbon (C i) λλ1560,1656 transition lines in 41 696 low-resolution QSO spectra (1. 5 < z em < 4. 46) from the SDSS-II -Data Release seven -database. C i absorption lines should indeed probe the shielded gas in the neutral interstellar medium of galaxies more efficiently than traditional tracers such as neutral atomic-hydrogen (H i) Damped Lyman-α (DLA) and/or Mg ii systems. We built up a sample of 66 C i absorbers with redshifts in the range 1. 5 < z < 3. 1 and rest-frame equivalent widths 0. 1 < W r (λ1560) < 1. 7 Å. The completeness limit of our survey is W r,lim (λ1560) ≃ 0.
We therefore embarked in a systematic search for C i absorption in QSO spectra from the SDSS-II -Data Release seven (hereafter DR 7) -database. In this paper, we present the results of this search and the basic properties of the detected C i absorbers. Note that here we will equally refer to QSO absorption-line systems detected through C i absorption as "C i systems" or "C i absorbers". In Sect. 2, we describe our selection and identification of C i absorbers. We discuss the properties of the sample in terms of intervening C i-absorber number per unit redshift, proximate systems, and C i rest-frame equivalent widths, in Sects. 3. 3, 3. 4 and 3. 1, respectively. We then assess the impact of the C i absorbers on their respective background QSOs both from the observed QSO optical colours (Sect.
While H i column densities derived from UVES spectroscopy are usually more accurate, the last two columns of Table  1  show that they confirm those derived directly from SDSS spectra as testified by the five systems at z abs > 2. 2 where this measurement could be done from both datasets. For this reason, we here complement our UVES measurements with the values we derived using SDSS spectra for the three systems at z abs > 2. 2 for which high-resolution spectroscopic data are not available because the background QSOs are too far North for the VLT to observe them. The H i sub-sample thus comprises a total of 21 systems. In Fig. 9 , we compare the observed H i column-density distribution of C i-selected absorbers with that of H i-selected DLAs (from SDSS DR 7 as well;  Noterdaeme et al. 2009b).
g. ,  Rafelski et al. (2012) ). This should be confirmed by accurate measurements of metal column densities. In Fig. 11 , we display the relation between E(B-V) and log N(H i) among the C i absorbers from the H i sub-sample. Here again, the data points are highly scattered. Most of the systems are associated with low albeit consistently non-zero QSO reddening. Since most of the N(H i) values are relatively low, the measured amounts of reddening, with median E(B-V) ∼ 0. 045, are actually remarkable. This departs from what is observed in the overall DLA population where the reddening is usually negligible (see, e. g. ,  Vladilo et al. 2008; Khare et al. 2012). The latter authors have shown that DLAs at z abs ≈ 2. 8 typically induce a reddening E(B-V) ∼ 5 × 10 -3 mag. Apart from a few outliers, most of the C i systems have reddening properties consistent with those of the Galactic ISM.
Conclusions In this work, we presented a new population of QSO absorbers selected directly from the properties of the shielded gas, namely the strongest C i absorbers, detected in low-resolution QSO spectra from the SDSS-II DR 7 database. These C i absorbers, with W r (λ1560) ≥ 0. 4 Å, are more than one hundred-times rarer than DLAs at z abs = 2. 5. Their number per unit redshift is increasing significantly below z abs = 2, probably coupled to an increase in the star-formation efficiency at these redshifts. Gupta et al. (2012)  reported a similarly high detection rate of 21-cm absorbers towards even lower redshifts among strong Mg ii systems, which they argued must be related to the evolution of the CNM filling factor in the latter absorbers. The H i column-density distribution of C i-selected absorbers is flatter than that of H i-selected absorbers.
High-resolution spectroscopic followup observations of the present sample therefore opens up the door to systematic searches for carbon monoxide (CO; see  Noterdaeme et al. 2011 ) and molecules like CN and CH as well as diffuse interstellar bands at high redshift. Such a spectroscopic campaign will be presented in a companion paper. The typical reddening induced by C i absorbers along with the relation between reddening and shielded-gas column density imply that the extinction could be high in some DLAs with C i absorption. If strong dusty DLAs exist, they probably have been missed in the current magnitude-limited QSO samples (see also  Boissé et al. 1998; Vladilo et al. 2008). Some of the QSO lines-of-sight identified here, as well as those which may be found by extending the present survey to even larger databases 3  , will result in exceedingly long integration times on high-resolution spectrographs installed on 8-10 m class telescopes.