dataesr/scientific-paragraphs-categorization huggingface dataset:
To generate random bipartite networks constrained by degree distribution, we use the curveball algorithm recently developed by Strona et al. 57  (see Appendix 1, Supplementary information). This algorithm randomly selects pairs of species within a given trophic level, looks for exclusive interacting partners of each species and exchanges them between these species. After this procedure has been repeated many times, the randomized set of species per interacting partner lists are used to recompile the new presence-absence matrix. Application: the co-structure of plant-lepidopteran networks. Data set. In this study, we used two large bipartite networks of interactions between Lepidoptera and plant species from the state of Baden-Württemberg (Germany). The dataset has been compiled and studied by F.
Altermatt and I. Pearse  5, 37, 39, 58. Lepidoptera species represented >90% of all resident Macrolepidoptera species that have ever been recorded in the Baden-Württemberg state  55. Information on plant species with which Lepidoptera species interact was obtained from published work  55. We considered all terrestrial vascular plant species found in Baden-Württemberg as potential hosts, based on the published plant database http://www. floraweb. de/  58. The dataset used herein is based on an extensive compilation of semi-quantitative estimates of plant-insect interactions, which were assembled by hundreds of entomologists in Baden-Württemberg (for details see Ebert  59  ). The data set is based on in total of 2,149 million adult individuals (nectaring visits) and 2,342 million larval individuals (herbivory records) recorded for all these Lepidoptera species (excerpt of the database of Ebert  59, 60  ; see also Altermatt & Pearse  5  ).
Thereby, it may be one of the most complete plant-insect interaction datasets. Information on the magnitude/abundance of an interaction is on an ordinal scale, and the dataset is especially complete for rare species, given that those have often received disproportionate attention by entomologists. Thus, rarity of a species does not drive a species degree distribution, but the degree distribution is especially well resolved for rare species (for details see also  5, 37, 39, 58  ). Furthermore, the use of host plants is especially well resolved for specialist species, while the interactions for the trophically most generalist species are not completely covered. The dataset is highly robust to rarefaction/subsampling. For example, Pearse & Altermatt  37  showed that the removal of up to 80% of the observed interactions does only marginally reduce the predictability of novel host plants uses.
Acknowledgements We thank  Carlos Melián ,  Paulo R. Guimarães Jr. ,  Nicolas Verzelen ,  Pierre Barbillon  and  Mathieu Thomas  for early discussions on the ideas of this manuscript. JA thanks  FAPESP  for financial support (Grant  2011/09951-2  and Grant  2012/04941-1 ). FA thanks the  Swiss National Science Foundation  for financial support (grant no. PP00P3_150698 ). FM thanks the  CNRS  and the  ANR  ( ARSENIC  project, grant no. 14-CE02-0012 ) for financial support. JA is a researcher of  CONICET (Argentina).
The current data from Fig. 3a  and the distances measured in Fig. 4a  have been normalized and plotted together. As the gure shows, the two normalized datasets are in excellent agreement. Last, the device was tested with a diluted sample of a commercial fruit juice as described in ESI. † The amount of glucose present in this sample was previously determined using a commercial glucometer (CardioCheck, Novalab ES) leading to a value of 7. 6 AE 0. 2 mM. The interpolation of the length of the colour change in the strip when the sample was let to ow yielded a glucose concentration of 7. 3 AE 0. 4 mM, demonstrating the value of this instrumentation-less device to quantify the concentration of analyte in a sample easily and without additional instrumentation.
Acknowledgements This work was partly funded by the  Spanish Ministry of Economy  through the  DADDi2  project ( TEC2013-48506 ). MK acknowledges funding through the  Beatriu de Pinós program  ( BP-DGR-2013 ), supported by the  Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia  and the  Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme  of the  European Union.
This effect was likely due to leaching of Fe(II) from the solid phase. The bicarbonate buffer equilibrated magnetite samples were characterized using ferrozine  [36] , µXRD and TEM (Table  1 ). The BET analysis was conducted on samples stored in anoxic Milli-Q and the surface area was analysed with a Micromeritics ASAP 2000 instrument and ASAP 2010 software. The final magnetite concentration in the experiments was ca. 4 mM Fe 3 O 4 or about 1 g/L.
The crystalline minerals in the samples were identified via comparison with reference samples from the International Center for Diffraction Data database. The average crystallite sizes were calculated using the Scherrer equation  [40]. For each sample in the series, 57 Fe Mössbauer spectra were obtained at 140 K with additional spectra recorded at 77 K for the 7 nm samples. Samples were prepared inside an anoxic glovebox (100% N 2 ) by filtration (0. 45 µm mixed cellulose esters). The filter papers loaded with sample were sealed anoxically between two layers of Kapton tape and kept in anoxic bottles until measurement. Samples were loaded into a closed cycle exchange gas cryostat. The Mössbauer spectrometer (WissEL) was operated in transmission mode, with a 57 Co/Rh source driven in constant acceleration mode and calibrated with a 7 µm thick α-57 Fe foil measured at room temperature, which was also used to determine the half width at half maximum (fixed to 0.
Recently published results indicated that the atrophy of NbM may precede and predict atrophy of the entorhinal cortex in relation to different stages of AD pathophysiology  26. These evidences were in line with previous findings highlighting the existence of a correlation between BFCS atrophy and cortical amyloid burden in asymptomatic at-risk and pre-dementia stages of AD  [27] [28] [29]. Based on the data previously reported by the Hippocampus Study Clinical Trial (NCT00403520)  13, 16  and the recent literature results suggesting an early involvement of BFCS in AD pathophysiology  26, 28, 30  , the objective of this explorative study is to further investigate the effect of one-year Donepezil treatment on the rate of BFCS atrophy in prodromal AD patients.
Results Study design and clinical results. This study included 332 patients with prodromal AD that participated in the multi-centre double-blind, randomized, placebo-controlled trial with a Donepezil treatment period (10 mg/ day) of 12 months (Clinical Trial. gov Number: NCT00403520, Submission Date: November 21, 2006)  13. From the per-protocol population, we selected the participants with imaging scans with enough quality for the data analysis (Placebo N = 88, Donepezil N = 75). No difference between Donepezil and Placebo groups was present in terms of sociodemographic features (age, gender and education), clinical features (depressive symptoms), global cognition and memory performances (Table  1 ). Finally, no differences were found in the time period of MRI scans and in the frequencies of scans executed with 3 Tesla MRI machine between groups (.
Methods Study Design. Multi-centre double-blind, randomized, placebo-controlled trial with a treatment period of 12 months. The study started in November 2006 and was concluded in August 2010 (NCT00403520), for details on the selection of population, randomisation and sample size calculation please see our previous publication by Dubois and colleagues  13. Patients were randomly assigned to either active or Placebo treatment group (1 capsule of 5-mg Donepezil daily for Weeks 0 to 6, then 2 capsules of 5-mg Donepezil [i. e. , 10 mg] daily from Week 6 to Month 12 for double-blind treatment; or 1 placebo capsule daily for Weeks 0 to 6, then 2 capsules daily from Week 6 to Month 12 for double-blind treatment, respectively). The study protocol was approved by the institutional review board from each site and by the Ethic Committee of the Coordination Centre at La Pitié-Salpêtrière Hospital, Paris, France (Reference Number: E2020-E033-41, Ethic Committee Approval: November 8, 2006).
The expert neuroradiologist in charge of quality control verified artifacts of movements, ringing, wrap around and metal artifacts. Moreover they attested that the sequences were acquired according to protocol parameters. Due to gross MRI artefacts or MRI quality issues, which might interfere with data analysis measurement of BFCS volume, 10 MRI images were excluded. The final sample considered for the study was composed of 163 subjects (Placebo N = 88 and Donepezil N = 75).
MRI processing. Main processing steps and computational analyses employed in the present study followed the longitudinal processing stream implemented in SPM12  63  , adapted to the analysis of serial MRI scans used for BFCS volume  23. MRI data were processed by the statistical parametric mapping (SPM8 and SPM12, Wellcome Trust Center for Neuroimaging) and the VBM8-toolbox (http://dbm. neuro. uni-jena. de/vbm/) implemented in MatLab R2013a (MathWorks, Natick, MA). First, we applied the symmetric diffeomorphic registration for longitudinal MRI developed in SPM12  63. In particular, this algorithm rests on an intra-subject modelling framework that combines nonlinear diffeomorphic and rigid-body registration and further corrects for intensity inhomogeneity artefacts. The optimization is realized in a single generative model and provides internally consistent estimates of within-subject brain deformations that may have occurred during the study period.
Acknowledgements We would like to acknowledge  Dr Line Garnero  who deceased during the study, without her support and experience this study would never have been undertaken. HH is supported by the  AXA Research Fund , the  Fondation Université Pierre et Marie Curie  and the  Fondation pour la Recherche sur Alzheimer, Paris, France. Ce travail a bénéficié d'une aide de l' Etat « Investissements d'avenir »   ANR-10-IAIHU-06  (HH). The research leading to these results has received funding from the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche -10- IA Agence Institut Hospitalo-Universitaire-6) (HH).
More details on this calculation are provided in  Stopelli et al. (2015). Wind speed was measured at Jungfraujoch by MeteoSwiss, and data were stored as 10 min averages. Univariate and non-parametric statistics were carried out with PAST software version 2. 17  (Hammer et al. , 2001). Concentrations of total bacterial cells in the 56 samples collected over the course of 18 months (excluding the winter period between 2013 and 2014) were mostly between 3. 0 × 10 3 (10th percentile) and 3. 9 × 10 4 mL -1 (90th percentile), a range which is coherent with previous observations carried out on precipitation and cloud water samples collected at several places around the world (Fig. 1 ;  Bauer et al. , 2002; Joly et al. , 2014; Šantl-Temkiv et al. , 2013; Sattler et al. , 2001; Vaïtilingom et al. , 2012).
Acknowledgements. We thank the  International Foundation High Alpine Research Station Jungfraujoch and Gornergrat (HFSJG)  for making it possible for us to conduct our measurements at Jungfraujoch; Urs and  Maria Otz  as well as Joan and  Martin Fisher  are acknowledged for their support on site. Corinne Baudinot  measured the abundance of INPs in snow samples during the second year of observations. Thomas Kuhn  and  Mark Rollog  analysed the stable isotope ratio in our snow water samples. We thank  MeteoSwiss  for providing data on meteorology at Jungfraujoch. The work described here was funded by the  Swiss National Science Foundation (SNF)  through grant nos. 200021_140228  and  200020_159194. Edited by: G. Wohlfahrt Reviewed by: two anonymous referees.
Tyrode: n = 291, glycine: n = 110, D-serine: n = 103, glycine in NMDA: n = 268, D-serine in NMDA: n = 233 spine and shaft clusters; tyrode p=0. 4234, r 2 = 0. 0001, glycine p=0. 3650, r 2 = 0. 0011, D-serine p=0. 0255, r 2 = 0. 0368, glycine in NMDA p=0. 0949, r 2 = 0. 0065, D-serine in NMDA p=0. 4348, r 2 = 0. 0001, Paired t-test, one-tail, before and after, *p<0. 05. Data are represented as mean ± s. e. m. (E) Schematic representation of the c-terminus tails of the NMDAR in basal conditions (tyrode, (i) or in the presence of NMDA (ii), the co-agonists alone (iii, iv) or in activating conditions (co-agonists together with NMDAR, (v, vi). DOI: 10. 7554/eLife. 25492. 006 The following figure supplement is available for figure  4 : Figure supplement 1. Lifetime measurement of GluN1-GFP co-transfected 14 div hippocampal neurons, with GluN1-mCherry, and Flag-GluN2B (1:3:2), at 10 div.
DOI: 10. 7554/eLife. 25492. 007 D-serine remained without effect on Ro25-6981-mediated inhibition in this experiment indicating that the effect of glycine did not result from the mere recruitment of unsaturated GluN2B-NMDAR. In addition, we previously demonstrated that such application of co-agonist did not alter AMPA receptor synaptic transmission  (Papouin et al. , 2012). In young slices, the change in Ro25-6981mediated inhibition after D-serine (Figure  5I ) was not accompanied by a concurrent increase of zinc inhibition (Figure  5G ). This may be due to the presence of other NMDAR subunits (e. g. GluN2D, GluN3A/B) and/or GluN1/2A/2B triheteromeric receptors, which have different pharmacological responses than diheteromeric receptors  (Zhu and Paoletti, 2015). Collectively, these data represent of 'proof of concept' that fluctuations in co-agonist levels, and in particular in the nature of the prevailing co-agonist, markedly alter the NMDAR synaptic content.
Discussion Understanding the molecular mechanism by which glutamatergic synapses adjust their GluN2A/B-NMDAR signaling has captured a lot of attention over the last decades. Here, we provide evidence that co-agonist availability plays a direct role in the synaptic composition of NMDAR subtypes through the regulation of GluN2B-NMDAR surface dynamics. These data fuel a model in which the developmental up-regulation of D-serine availability at hippocampal synapses reduces the basal surface trafficking of GluN2B-NMDAR, favoring a higher GluN2A/GluN2B-NMDAR ratio at maturing synapses (Figure  6 ). Therefore, we postulate that the NMDAR co-agonists are key regulators of the receptor surface trafficking at hippocampal synapses. In the central nervous system, the synaptic ratio between GluN2A-and GluN2B-NMDAR is not uniform but varies between developmental stages, brain regions, axonal inputs and sensory experience  (Lau and Zukin, 2007; Yashiro and Philpot, 2008).
, 2007) , to sexual hormone estrogen  (Potier et al. , 2016) , or to strong synaptic activation  (Dupuis et al. , 2014). These paradigms have in common to be potent regulators of NMDAR-dependent synaptic maturation and plasticity in the hippocampus. Deciphering the molecular mechanism by which D-serine selectively impacts GluN2B-NMDAR conformation, surface trafficking and synaptic retention is a major challenge. Since there seems to be no difference between the binding of glycine and D-serine onto the co-agonist site of NMDAR as far as affinity and efficacy are concerned, one can rule out the possibility that GluN2B-NMDAR use D-serine as a preferred co-agonist. Interestingly, our data favor a model in which the binding of D-serine induces a rapid conformational change of the receptor C-terminus domain and a consequent alteration of the interactions between GluN2B-NMDAR and PDZ scaffold partners.
This is further supported by the observations that the activation of NMDAR with the glutamate-site agonist NMDA also induces a conformational change of the receptor that reduces the interaction between the C-terminus and PSD-95 or with protein phosphate 1 (PP1,  Doreet al. , 2014; Aow et al. , 2015; Dore et al. , 2015). Noteworthy, the conformational changes specifically induced by D-serine do not alter the ion flux through the receptor, reminiscent of a metabotropic-like effect  (Nabavi et al. , 2013; Dore et al. , 2015). In-depth investigations of the co-agonist impact on GluN2A and GluN2B subunit conformations will be necessary to shed light on this process. Our data support a model in which an increase in D-serine levels reduce GluN2B-NMDAR mobility, favor their internalization and down-regulate their synaptic content (Figure  6 ).
Acknowledgements This work was supported by grants from  INSERM, Agence Nationale pour la Recherche ,  Conseil Regional d'Aquitaine, NARSAD ,  Fondation pour la Recherche Medicale, Federation pour la Recherche sur le Cerveau , and the  Human Frontier Science Program. TP, LL and VCL were recipients of studentships from the  Ministere de l'Enseignement Superieur et de la Recherche. JSF was supported by a grant from  FCT  and  POPH/FSE , Portugal. SS and LP were supported by a grant from  Fondo di Ateneo per la Ricerca  ( Universitadegli Studi dell'Insubria ). We thank the laboratory members for the fruitful discussions. The microscopy was done in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure France BioImaging.
Participants Participants belonged to the Whitehall II Imaging Sub-study, and the study protocol and MRI pre-processing pipeline has been described in detail previously  (Filippini et al. , 2014). Briefly, participants were drawn from the Whitehall II study, a cohort of 10 308 British Civil Servants established in University College London in 1985 and followed-up for over 30 years across 12 waves. For the Whitehall II Imaging Sub-study, 550 participants were randomly selected from the parent study; a battery of cognitive tests was administered followed by an MRI scan at the FMRIB Center, Oxford between 2012 and 2015. rs-fMRI data from 497 participants were used in this analysis. Exclusion criteria were incomplete or poor quality MRI data and/or structural abnormalities on the MRI scan (e. g.
Verbal memory scores Cognitive tests were administered by trained psychology graduates and psychiatrists and in the following order: Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT-A and TMT-B), Lexical (letter: "F") and Semantic Fluency (category: "Animals"), Rey-Osterrieth Complex Figure (ROCF) copying, RCF immediate recall, Hopkins Verbal Learning Test (HVLT-R) immediate recall, Boston Naming Test, Digit Span (forward, backward, ascending sequence) and Digit Coding (from the Wechsler Adult Intelligent Scale-IV), HVLT-R delayed recall, RCF delayed recall and Test of Premorbid Function (TOPF)  (Filippini et al. , 2014). All tests were included in a factor analysis (except the MoCA which is a screening tool for cognitive impairment). Factor analysis was performed using an oblique rotation (direct oblimin in SPSS 21) to allow for correlation between factors.
MRI analysis T1-weighted structural MRI (multi-echo MPRAGE sequence with motion correction) and multiband echo-planar imaging rs-fMRI scans (voxel ¼ 2 mm isotropic, TR ¼ 1. 3 s, acquisition time ¼ 10 min 10 s, multi-slice acceleration factor ¼ 6, number of volumes ¼ 460) were acquired. Participants were scanned on a 3T Siemens Magnetom Verio (Erlangen, Germany) scanner with a 32-channel head coil, at the FMRIB Center, Oxford. Data were pre-processed using FSL tools  (Jenkinson et al. , 2012; Smith et al. , 2004)  as described in  Filippini et al. , (2014). Network modelling was performed using FSLNets  (Smith et al. , 2013).
Pre-processing and group-ICA T1 images were bias field corrected, brain extracted and segmented into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using FSL-FAST. Rs-fMRI data were pre-processed (motion correction, brain extraction, high-pass temporal filtering at 100s, field-map correction) using FSL tools. FIX (FMRIB's ICA-based X-noisefier) was used to remove the artefactual components that reflected non-neuronal fluctuations  (Griffanti et al. , 2014; Salimi-Khorshidi et al. , 2014). FIX was trained using the WhII_MB6. RData trained-weights file, which was generated from hand-labelling 25 participants from this study. This training file is available online (http://www. fmrib. ox. ac. uk/datasets/ FIX-training/) and described, including the leave-one-out classification accuracy results, in  Salimi-Khorshidi et al.
(2014). The pre-processed and cleaned rs-fMRI scans were registered to standard space using FNIRT and spatially smoothed using a Gaussian kernel of 6 mm full width at half maximum. MELODIC group-ICA with a dimensionality of 100 was used to generate the group-level spatial maps (or nodes) for 500 participants who met the inclusion criteria. Small dimensionalities (<30) typically provide an estimate of whole resting-state networks whereas larger dimensionalities (>70) are used to define smaller nodes which can be used in network modelling analyses  (Abou-Elseoud et al. , 2009). As dimensionalities >100 show decreasing ICA repeatability, we chose 100 ICs, which is in line with recent studies adopting similar high-dimensionality ICA parcellations in multiband rsfMRI datasets  (Miller et al. , 2016; Smith et al.
01 in Ridge Regression option in FSLNets) that were converted from Pearson correlation r-values into z-statistics with Fisher's transformation  (Smith et al. , 2011). The 58 nodes were reordered according to a hierarchical clustering of the group-average full correlation netmat using Ward's method implemented in Matlab to generate the functional connectome. Each subject's partial correlation netmat was then unwrapped into a single row and combined across subjects to create a Subject x Edges matrix, which represents all the participants' connectomes. Each column (i. e. a given edge, estimated for all subjects) in this matrix was correlated with verbal and visuospatial memory scores using a linear regression in FSLNets. Given the complex interdependence between age and memory  (Fjell et al. , 2014; Nyberg et al.
Funding sources Work on the  Whitehall II Imaging Sub-study  was mainly funded by the " Lifelong Health and Wellbeing" Programme Grant : " Predicting MRI abnormalities with longitudinal data of the Whitehall II Substudy " ( UK Medical Research Council :  G1001354 ). Sana Suri , NF, EZs, and AM are funded by the  HDH Wills 1965 Charitable Trust  (Nr:  1117747 ). AT is supported by  UK Medical Research Council : ( G1001354 ) and  Oxford University Clinical Academic Graduate School. ASM receives research support from the  US National Institutes of Health  ( R01AG013196 ,  R01AG034454 ). MK is supported by the  UK Medical Research Council  ( K013351 ) and  NordForsk , the  Nordic Programme on Health and Welfare. CEM  and CES are supported by the  National Institute for Health Research (NIHR) Oxford Health Biomedical Research Centre  based at  Oxford University Hospitals NHS Trust  and  University of Oxford.
Acknowledgements We would like to thank all participants and to the  Whitehall  staff at  UCL , who so helpfully collaborated with us. We are grateful to all FMRIB staff, in particular radiographers  Jon Campbell ,  Michael Sanders ,  Caroline Young  and  David Parker. We thank  Prof. Martin Turner  and his colleagues for advising on incidental findings and taking over clinical responsibility for such participants. The Multi-Echo MPRAGE sequence is a Works-in-Progress package, developed by  Siemens  in collaboration with the  Athinoula A. Martinos, Center for Biomedical Imaging, Massachusetts General Hospital. We are grateful for provision of the multiband pulse sequence and reconstruction algorithms to the  Center for Magnetic Resonance Research, University of Minnesota, USA.