dataesr/scientific-paragraphs-categorization huggingface dataset:
In the next step, we have performed frequency calculations on each optimized geometry using the same level of theory as used for the geometry optimization. The absence of any imaginary frequency proved the true variational minima of the ground state electronic wave function. The geometry optimization and the frequency calculations have been done in the Gaussian 09 suite of programs. On these optimized geometries of native D-luciferin (Figure  1 ), we have performed linear and quadratic response theory calculations for evaluating one-and two-photon absorption parameters using Dunning's cc-pVDZ basis set and B3LYP functional. The response calculations have been performed within non-equilibrium PCM  [32, 33] , as implemented in the DALTON code  [34, 35]. The PCM-based response calculation in DALTON uses a spherical cavity model in which the solvent cavities are considered as an interlocking sphere.
Acknowledgments:  Mausumi Chattopadhyaya  and  Md Mehboob Alam  both acknowledge the  University of Calcutta , in particular  Swapan Chakrabarti  of  Department of Chemistry  for allowing the cluster-time for the calculations. Mausumi Chattopadhyaya  also aknowledges Alexander von Humboldt (AvH) foundation for her fellowship. Md Mehboob Alam  acknowledges financial support from the  LABEX "Chemistry of Complex Systems"  and the  ANR  ( MCFUNEX  project).
Further, mice only develop a subset of the histopathology associated with DS and AD, and then only when engineered to contain mutations that are strongly predisposing. Larger models allowing more refined behavior analysis, better access to anatomical structures and an additional perspective of how to understand the relevance of animal pathology to human conditions would be highly valuable. With the advent of CRISPR-Cas9 technology, it may be possible to develop models of DS and of DS-AD in the rat and in small primates (e. g. , marmoset). In the translational interface, the current trials of Basmisanil (CLEMATIS NCT02024789 in adult and adolescents with DS and NCT02484703 in children 6-11 years with DS) and BTD-001 (Balance Therapeutics, ACTRN12612000652875 on the Australian-New Zealand clinical trial registry) are based substantially on findings in the Ts65Dn mouse model, established from extensive behavioral, electrophysiological, and biochemical assessments.
ACKNOWLEDGMENTS MC has received funding from the program, " Investissements d'avenir "  ANR-10-IAIHU-06  and from the  Fondation Jérôme Lejeune. RR's work is supported in part by the  Lumind-RDS Foundation  and by  NIH  awards  5 R01 HD038384-16 ,  1R01HL124836-01 , and  1R21HD082614-01. He is a coPI at the Johns Hopkins site of the Roche clinical trial, CLEMATIS NCT02024789 in adults and adolescents with DS.
| Study population Consecutive adults admitted to the Intensive Care Unit following onset of Guillan-Barré syndrome were screened for inclusion. Patients requiring intubation on admission were excluded. Respiratory function, swallowing capacity, and tongue strength were evaluated at inclusion. Twenty healthy volunteers were recruited as a control group for swallowing and tongue strength parameters. The study was approved by the local ethics committee (Comité de Protection des Personnes Ile de France XI). All participants gave written informed consent. ClinicalTrials. gov Identifier: NCT01024088.
| Swallowing and tongue strength assessment Swallowing was assessed clinically by a bedside drinking test (100 ml of water). The swallowing maneuver was also evaluated instrumentally: activity of the swallowing muscles was recorded using surface electromyography, laryngeal movements were recorded with an accelerometer and respiration was recorded simultaneously by inductance plethysmography, as previously described  (Terzi et al. , 2007). Data were recorded on an analogdigital acquisition system (MP100; Biopac System, Santa Barbara, CA, USA) and analyzed by measuring the duration of the oropharyngeal phase and the number of swallowing movements with increasing fluid quantities (5, 10, and 20 ml). For each fluid quantity, the mean value of 4 trials was calculated. The test was interrupted if signs of swallowing impairment were present.
| Statistical analysis Statistical analysis was conducted using SAS 9. 3. statistical software (SAS Institute Inc, Cary, NC, USA). Chi square and Wilcoxon tests were used to compare subgroups. Univariate logistic regression analysis was performed to identify predictors of intubation and to estimate the strength of association based on the odds ratio (OR). It was not possible to fit a multivariate model, because of the large number of candidate variables compared to few outcomes and the presence of multiple correlations between the predictors. Statistical significance was established at p <. 05.
A three-dimensional HNcaNNH spectrum  (41)  was additionally used in the assignment. Three-dimensional HNCACB experiments were acquired to confirm the assignment of the Tau residues in 13 C, 15  M). The one-dimensional 19 F spectra were recorded at 293 K with 1024 scans on a Bruker 600MHz Avance NMR spectrom-eter equipped with a 5-mm CPQCI 19 F/ 1 H-13 C/ 15 N cryogenic probehead (Bruker). The chemical shifts in one-dimensional 19 F spectra were referenced by using the fluor resonance of external Trifluoroacetic acid at Ϫ76. 55 ppm. NMR Data Processing-Spectra were processed with Bruker TopSpin 3. 1 software. Data analysis, peak picking, and calculation of peak volumes were done with Sparky 3. 114 software (T. D. Goddard and D. G. Kneller, SPARKY 3, University of California, San Francisco).
* This work was supported by  TGE RMN THC  ( FR-3050 , France) and  FRABio (Lille University, CNRS ,  FR 3688 ) and also by a grant from the  LabEx (Laboratory of Excellence) ,  DISTALZ  ( Development of Innovative Strategies  for a Transdisciplinary approach to Alzheimer's disease), and in part by the  French government funding agency Agence Nationale de la Recherche TAF. This work was supported by  National Institutes of Health  Grant  R01 GM081578  (to S. P. and J. G. ). The authors declare that they have no conflicts of interest with the contents of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
While full elucidation of Unc5B signalling is beyond the scope of the current work, the data shown here demonstrate that endothelial ROBO4 targets VEGFR2 Y951 via the UNC5B UPA domain, thereby counteracting Y951-mediated vessel permeability and angiogenesis. Selectively enhancing VEGFR2 Y951 activation by blocking ROBO4 function targets revascularization without affecting neovascular tuft formation. The reason for this differential behaviour might be because tuft formation involves proliferation, which is not regulated by Y951 but via the VEGFR2 1175 residue. Taken together, we propose that Robo4 blockade might represent an opportunity to accelerate revascularization in retinopathy of prematurity and wound healing in diabetic patients.
The data were first normalized to GAPDH, and the relative expression levels of different genes were calculated. Signalling studies. HUVECs were transfected with 20 pmol siRNA per well in 6-well plates using RNAiMax transfection reagent (Invitrogen, #13778) according to the manufacturer's instruction. Infection with GFP, Robo4 or Unc5B adenovirus was done one day after siRNA transfection. After 48 h, cells were starved 10 h to overnight in EBM-2 supplemented with 0. 2% FBS and treated with 1 mg ml À 1 (6 nM) recombinant Slit2 or 25 ng ml À 1 (3 nM) VEGF-A. For MLEC signalling experiments, primary MLECs were starved 10 h to overnight in DMEM containing 2% FBS and stimulated with 5 nM VEGF-A. Cell lysates were collected at the indicated time points and subjected to 4-15% SDS-PAGE followed by immunoblotting using appropriate primary and second Abs.
Acknowledgements This project was supported by grants from  NHLBI  ( 1R01HLI125811 ),  NEI  ( 1R01EY025979-01 ) and the  Agence Nationale de la Recherche  ( ANR-GANDHI ,  ANR-NOVA). Feng Zhang  was supported by a T32 grant from  NHLBL  ( 5T32HL007950-15 ). We thank  Federico Corti ,  Jinah Han ,  Roxana Ola ,  June-Hee Park  and  Georgia Zarkada  for technical support and discussion.
partial support from the COST Short Term Scientific Mission MP1210-32976, and thanks the Mathematical Institute at Oxford University for hospitality during part of the preparation of this paper. XD and EES would like to thank the Department of Physics and Astronomy at Uppsala University for its hospitality when part of the work was finalised. The work of EES, made within the Labex Ilp (reference Anr-10-Labx-63), was supported by French state funds managed by the Agence nationale de la recherche, as part of the programme Investissements d'avenir under the reference Anr-11-Idex-0004-02. The work of XD is supported in part by the EPSRC grant BKRWDM00. Proof. β = -β ψ =⇒ -2 β ab = β cd ψ cd ab. We use this to prove both identities. For the first one, we contract with ϕ as follows: β ad ϕ d bc = - 1 2 β ef ψ ef ad ϕ d bc = 3 β ef δ [b [e ϕ c] f d] g da = 2 g da β ef δ [b e ϕ c] f d = -β bd ϕ d ca -β cd ϕ d ab.
Acknowledgements Open access funding provided by  University of Graz. The research leading to this paper was partially funded by the french " ANR blanche " project  Kibord :  ANR-13-BS01-0004. K. F. was partially supported by  NAWI Graz  and acknowledges the kind hospitality of the  ENS Cachan. The research leading to this paper was also funded by  Université Sorbonne Paris Cité , in the framework of the " Investissements d'Avenir ", convention  ANR-11-IDEX-0005. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Data analysis for estimation of Cl int The in vitro intrinsic clearance (Cl int , mL/min mg protein) was determined by substrate depletion (where substrate concentration gave the maximal clearance), using the following equation: Cl int ¼ Slope Â Vol prot ð3Þ where ''Slope'' is the elimination rate constant (min À1 ) for exponential substrate loss, ''Vol'' is the incubation volume (mL), and ''prot'' is the microsomal protein (mg) in incubation.
Statistical analysis Quantitative data were expressed as means ± standard error to the mean (SEM). They were statistically analyzed using Student's t-test. Criteria of significance were: * for p 5 0. 05; ** for p50. 01; *** for p50. 001. All statistical analyses were performed using GraphPad Prism software 5. 0 (GraphPad Software, Inc. , San Diego, CA). The accuracy of the prediction was assessed and compared from root mean-squared error (RMSE) and average fold error, calculated with the following equations  (Obach et al. , 1997; Sheiner & Beal, 1981) : RMSE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 N X ðpredicted À observedÞ 2 r ð9Þ AFE ¼ 10 1 N AE log predicted observed ð Þ j j ð10Þ.
Acknowledgements We are grateful to  L. Ruby  for providing such a good atmosphere during the preparation of this work. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
, 2008). A recent comparative study on ratites using up-to-date statistical assessments has detected a significant phylogenetic signal in their bone histology  (Legendre et al. , 2014). This study included most available extant and extinct ratite taxa. Most of them, such as the ostrich, moa and kiwi, have already been described histologically  (Amprino and Godina, 1947; Bourdon et al. , 2009b; Chinsamy, 1995; Enlow and Brown, 1957; Turvey et al. , 2005)  but some species included in the new material have never been described histologically. Among these are sections from the famous subfossil 'elephant bird' Aepyornis from Madagascar. The purpose of the present study is to provide a preliminary qualitative description of Aepyornis bone histology, as a complement to the quantitative data computed in  Legendre et al.
Acknowledgments Pr. Daniel Goujet  ( MNHN ) and  Dr. Claire Sagne  (MNHN) kindly provided material of Aepyornis for this histological study. Hayat Lamrous (UPMC) prepared excellent thin sections of Aepyornis bones. This work was funded by operating grants of the UMR 7193 (ISTeP) to AdR, LJL and JC by the grant  CGL2012-34459  of the  Spanish Government  (to J. C. ), and by a  Carlsberg grant  ( Carlsbergfondet , No. 2013 01 0480 ) to EB. We thank  Pr. Éric Buffetaut ,  Dr. Koen Stein  and an anonymous referee for constructive comments on the manuscript.
The matrix with entries |X i -X j | β needs only to be stored once in memory, and all standard mathematical software (such as Matlab and R) have efficient functions for finding its largest m eigenvalues (the eigs function in Matlab and the eigs function in the R package rARPACK). Each iteration of the algorithm's simulation step requires summing the columns of an m × T matrix of standard normal variables, where m is the number of λ i retained and T the number of grid points over which we approximate the Brownian bridge processes between 0 and 1. For m = 50 and T = 1 000 it takes about one second to perform this R = 499 times, and is independent of the number of points in the signal. In contrast, the E-divisive method takes about ten seconds for n = 1 000, one minute for n = 2 000, eight minutes for n = 5 000, etc.
Several authors have discussed the use of PS analysis in some extreme situations such as small sample size  [22]  or rare outcome of interest  [23] [24] [25]. But the use of PS analysis is also challenging in the case of rare exposure. This situation could frequently be encountered in pharmacoepidemiologic observational studies, particularly when study design does not require a high prevalence of exposure (for example, studies performed on electronic healthcare data, databases constituted with a nonspecific objective or analyzed for a different purpose than initially defined, evaluation of newly marketed drugs  [26] ). In this setting, the first step of PS analysis (i. e. conditional probability of treatment estimation) can be problematic, due to separation issues with the logistic model used for PS estimation, unless a large sample size is available.
Although some recommendations encourage the use of alternative methods like disease risk score (DRS) in this setting  [27, 28] , to our knowledge, no study specifically assessed the effect of infrequent exposure on PS analysis. Even among the recent literature comparing DRS and PS based methods  [29, 30] , no article has explored the infrequent exposure setting. Therefore, our objective was to evaluate the performance of PS-matching and PS-weighting to estimate the marginal hazard ratio associated with a rare exposure in the context of an observational study. An illustration is also provided from a real observational dataset, assessing the association between thiazolidinedione use and major cardiovascular outcomes.
A Monte Carlo simulation study We used Monte Carlo simulations to examine the ability of some PS methods to estimate the marginal hazard ratio (HR) associated with a binary treatment in the context of rare exposure. They consisted in: 1. randomly generating independent datasets with several settings defined by exposure prevalence, covariates effect on exposure allocation and on outcome of interest, number of covariates, censoring rate, and exposure effect on outcome of interest (section 'Data-generating process'); 2. applying each analytical method to analyze representative samples of each data set (section 'Statistical analyses in simulated data sets'); 3. computing several criteria to evaluate and comparing the performance of each method (section 'Performance criteria').
Performance criteria We performed 5000 simulations per scenario. Results were assessed in terms of the following: • Bias of the exposure effect estimation: E( γ -γ ). • Root mean squared error (RMSE) of the exposure effect estimation, defined as: E(( γ -γ ) 2 ). • Variability ratio of the exposure effect, defined as: 1 5000 5000 i=1 ŜE( γi ) 1 4999 5000 i=1 γi -γ 2 , where ŜE( γi ) is the estimated standard error of exposure effect γ in the simulation i. A ratio > 1 (or < 1) suggests that standard errors overestimate (or underestimate) the variability of the estimate of exposure effect  [25, 37]. • Coverage: proportion of times γ is enclosed in the 95 % confidence interval of γ estimated from the model. The mean sample size n were also computed for each scenario. The data-generating algorithm used in this simulation study allows to generate data with a desired level of ATE.
Real observational dataset illustration To illustrate these results, we applied the PS methods described above in an already published real observational study  [39]. The objective of this study was to compare the occurrence of death, non-fatal myocardial infaction, and congestive heart failure in patients with diabetes, according to the use of thiazolidinedione (TZD), in the REACH (REduction of Atherothrombosis for Continued Health) Registry, an international prospective cohort of patients with either established atherosclerotic arterial disease or at risk for atherothrombosis  [40] [41] [42] [43]. Patients were enrolled in 44 countries between December 2003 and December 2004. In each country, the protocol was submitted to the institutional review boards according to local requirements, and signed informed consent was obtained for all patients.
From the REACH Registry, we selected 28,332 patients with type 2 diabetes and available data on TZD use. This population (mean age 68 years, standard deviation 9. 6 years, 61 % of male) has been previously described, and The list of co-variables used to calculate the propensity score was the same as in the original publication, and included age, geographic region of enrolment, height, body mass index, smoking status, atrial fibrillation/flutter, history of congestive heart failure, treated hypertension, use of lipid-lowering agents, anti-platelet agents, oral anti-coagulants, non-steroidal anti-inflammatory agents, diuretics, cardiovascular agents, peripheral arterial claudication medications, insulin, and use of other anti-diabetic agents. Before the use of PS methods, some known risk factors of cardiovascular events were imbalanced between TZD users and non-users, according to their absolute standardized differences (ASD) (Fig.
8 ). Compared to the ASD observed in the previous simulations (data not shown), some variables had ASD comparable to the 'weak' confounding condition (like continuous 'age' or binary ' Atrial fibrillation' variables), but also comparable to the 'moderate' (like continuous 'BMI' or binary 'Insulin' variables), or 'strong' confounding condition (like the multimodal 'region' variable). After application of the estimated propensity score to the entire dataset, all variables including those not used in the PS estimation (like formal education and employment) were correctly balanced between TZD users and non-users. In this application, all event types where regrouped into the same composite outcome (time to the occurrence of the first event). An event occurred in 12 % of subjects. TZD effect was estimated with PS-matching and PS weighting approaches.
None of these methods found a significant effect of TZD. No treatment effect heterogeneity was detected (test for homogeneity of the TZD effect across deciles of the PS, p-value = 0. 5425). We then 1) randomly dropped some TZD users to create a new dataset with a pre-specified lower prevalence of exposure 2) applied the three PS-based methods to a representative sample of this new dataset. This two-step process was repeated 2,000 for prevalences ranging from 17 % (real) down to 5 % and increasing sample Fig. 6  Effect of censoring rate. Bias of exposure effect, variability ratio, 1 -coverage and RMSE according to censoring rate (r c ) and mean sample size, for one continuous and one dichotomous confounder, σ U,B = σ U,C = 0. 3, σ B,C = 0, exp(δ B ) = exp(δ C ) = 1. 5, HR = 1 and p = 5 %, with weighting by inverse of PS using ATE and ATT weights and PS-matching sizes (selected according to the number of events in the exposed group, like in our simulations).
We chose to limit the exploration of the real observational dataset to prevalence of exposure higher than 5 %, because event rate was only 12 % in the REACH cohort, and the number of events in the exposed group is then limited. Bias (relatively to the TZD effect estimated by each method applied in the entire cohort) was averaged and drawn on Fig. 9. As demonstrated in the simulation study, we observed that ATE estimations were severely biased compared to TZD effect estimated in the full dataset, particularly for the smallest prevalences, even if a large sample size was analyzed. In contrast, ATT estimations through PSweighting using ATT weights were uniformly less biased, whatever the prevalence and the sample size used. In this application, results observed with PS-matching and PSW-ATT methods seemed superimposed, but this is due to the extremely poor performances of PSW-ATE method, and bias was actually higher with PS-matching than with PSW-ATT.
Our simulation results stress the importance of looking for methods less influenced by exposure prevalence. The concerns with ATE estimation in case of rare exposure were sustained by our real dataset illustration. The number of potential confounders taken into account were high, and some variables had absolute standardized differences comparable to the 'moderate' and 'strong' confounding conditions of the simulations. We assumed from the former simulation results that the high degree of bias observed with PSW-ATE method in the REACH study is due to the strength of confounding and the number of confounders present in the database, which had a large impact on ATE estimates. Hence, results observed in the REACH study were consistent with the simulation results. Pirracchio et al. [22]  concluded from their simulation study that 'even in case of small study samples or low prevalence of treatment, both propensity score matching and inverse probability of treatment weighting can yield unbiased estimations of treatment effect'.
For the estimation of absolute effects, they reported that PS-matching tended to decrease bias compared with PS-weighting approaches. However, all methods compared in this article were applied on simulated cohorts of 10,000 subjects. With fewer subjects, we observed that 1) all methods could be biased, 2) PSweighting using ATT weights outperformed PS-matching for the estimation of ATT, and 3) PS-weighting using ATE Fig. 9  Real observational dataset illustration. Bias of TZD effect estimation in the REACH cohort, using PS-matching and PS-weighting approaches, according to prevalence p and mean sample size weights was the method which performance deteriorates most with the decrease of exposure prevalence. The context of rare exposure is also addressed by authors interested in 'the prognostic analogue of the propensity score' , a.