dataesr/scientific-paragraphs-categorization huggingface dataset:
All other models performed better than chance, with Riemann clearly leading, followed by SPoC and diag. Those results are consistent with our simulations in Fig. 2(B ) in which the only model violation comes from individual mixing matrices. The performance and ordering of the models in the TUH data is also consistent with the results obtained on the Cam-CAN dataset. This strongly suggests that the loglinear model is more appropriate in this regression problem. It is noteworthy, that the best performance based on the Riemannian model was virtually identical to its performance with MEG on the Cam-CAN data. However, it remains open to which extent the benefit of constrained signal geometry due to fixed sensor positioning is cancelled out by reduced spatial sampling with 21 instead of 306 sensors.
Riemannian methods can also be operated in low-rank settings  (Sabbagh et al. , 2019). However, model order search may be more complicated as the best model may be anywhere in the spectrum. This can lead to increased computation times, which may be prohibitive in realtime settings such as BCI  (Lotte et al. , 2007 (Lotte et al. , , 2018;; Tangermann et al. , 2008). Issues with the numerical rank of the covariance matrix also appear when predicting at the subject-level. The reason for this is fundamentally different and rather unrelated to the quality of covariance estimation. Many modern M/EEG preprocessing techniques focus on estimating and projecting out the noise-subspace, which leads to rank-deficient data. In our analysis of the Cam-CAN dataset  (Shafto et al. , 2014; Taylor et al. , 2017) , we applied the SSS method  (Taulu and Kajola, 2005)  by default, which is the recommended way when no strong magnetic shielding is available, as is the case for the Cambridge MEG-system on which the data was acquired (see also discussion in  Jas et al.
Yet, these findings did not rule out that leadfields themselves were not predictive of the outcome. We, therefore, derived a novel error-decomposition technique from the statistical framework presented in Fig. 1  to estimate the sensitivity of our M/EEG regression models to anatomy, spatially uniform power and topographic details. We applied this technique on the Cam-CAN dataset to investigate the subject-level prediction problem. While all models captured anatomical information and the Riemannian models were most sensitive to it, anatomical information did not explain the performance based on the full data. At the same time, this demonstrated that MEG captures age-related anatomical information from the individual leadfields and raises the question of which aspects of anatomy were concerned.
Conclusion Our study has investigated learning continuous outcomes from M/ EEG signal power from the perspective of generative models. Across datasets and electrophysiological modalities, the log-linear model turned out to be more appropriate. In the light of common empirical model violations and preprocessing options, models based on Riemannian geometry stood out in terms of performance and robustness. The overall performance level is remarkable when considering the simplicity of the model. Our results demonstrate that a Riemannian model can actually be used to perform end-to-end learning  (Schirrmeister et al. , 2017)  involving nothing but signal filtering and covariance estimation and, importantly, without deep-learning  (Roy et al. , 2019). When using SSS, performance improves beyond the current benchmark set by the MNE model but probably not because of denoising but rather due to the addition of gradiometer information.
This could potentially facilitate data collection and shift the strategy towards betting on the law of large numbers: Assembling an MEG dataset in the order of thousands is easier when collecting MRI is not a prerequisite. It is worthwhile to consider important limitations of this study. Unfortunately, we have not had access to more datasets with other interesting continuous outcomes. In particular the conclusions drawn from the comparison between event-level and subject-level regression may be expanded in the future when considering larger event-level datasets and other outcomes for which the linear-in-powers model may be more appropriate. Second, one has to critically acknowledge that the performance benefit for the Riemannian model may be partially explained by increased sensitivity to anatomical information, which might imply reduced specificity with regard to neuronal activity.
Acknowledgements This work was supported by a 2018 "m edecine num erique" (for digital medicine) thesis grant issued by  Inserm  ( French national institute of health and medical research) and Inria (French national research institute for the digital sciences ). It was also partly supported by the  European Research Council Starting  Grant  SLAB ERC-StG-676943. The authors would like to thank  Lucas C. Parra  and  Marijn van Vliet  for critical reviewing and suggestions on earlier versions of the manuscript. The authors would also like to thank  Lukas Gemein  and  Tonio Ball  from the  Neuromedical AI Lab at University of Freiburg  for their help in extracting the age from the  TUH dataset. The authors thank  Sheraz Khan  for help with the Freesurfer segmentation and data management of the Cam-CAN dataset.
, 2001; Freschet et al. , 2010) , suggesting the importance of nutrient storage under nutrient-poor environments. SRL generally showed a positive correlation with relative growth rate (RGR)  (Reich et al. , 1998; Comas and Eissentat, 2004) , suggesting that SRL can be related to whole-plant economics  (Kramer-Walter et al. 2016). In contrast, the correlation between RDMC and specific root length (SRL) is more debated in the literature  (Craine et al. , 2001; Kong et al. , 2014). Thanks to all this knowledge and the availability of trait data in databases (e. g. TRY database,  Kattge et al. , 2011) , trait-based approaches are increasing applied in agroecology  (Garnier and Navas, 2012; Milla et al. , 2015; Martin and Isaac, 2015 and 2018) , but are still scarcely used in crop sciences. Wheat represents about 20% of the human food supply  (CGIAR Wheat, 2013)  and had a long history of artificial selection and intentional breeding.
We quantified 18 plant traits (8 belowground traits, 8 aboveground traits, and 2 whole plant traits) and yield for 57 wheat varieties used in France (especially in the Paris Basin),  representing a broad range of modern selection histories, and analyzed trait syndromes among these varieties. We also compared the intraspecific variability of trait values and the trait syndromes observed among wheat varieties to their counterparts observed among Pooideae species using the TRY database records  (Kattge et al. , 2011).
, Canada). Images were analysed with the WinRHIZO TM software to determine root length and average root diameter. Specific root length (SRL, m g -1 ) was computed as the root length-to-dry mass ratio. Following root morphology analysis, the sample was weighted and dried for 48 h at 105°C. Root dry matter content (RDMC, mg g -1 ) was computed as root dry mass divided by water-saturated fresh mass. The foliage of each variety were dried at 105°C for two days to measure above dry masses (DM) and compute the plant Shoot/Root Ratio (SRR). Root aliquots Downloaded from https://academic. oup. com/jxb/advance-article/doi/10. 1093/jxb/eraa491/5932789 by RU Inst Voor Aardwetens Schappen/University Library Utrecht user on 22 October 2020 A c c e p t e d M a n u s c r i p t were milled to fine powder in order to determine Root Nitrogen Content (RNC, %) using an elemental analyser (Flash EA 1112 Series CNS analyser, ThermoFisher Scientific, USA).
Strictly speaking, our data were not sufficient to compute the Relative Growth Rate (RGR, g day -1 ), as RGR often decreases with increasing plant size  (Paine et al. , 2012). Still, we computed the plant dry matter produced at the end of the 58 days growing period for each wheat variety as DM = DMt58 -DMt0; where DMt58 is the total plant biomass after 58 days and DMt0 is the seed mass. This provides a rough but useful index of the biomass build up during the first weeks after germination, which is highly important for plant carbon economy  (Pérez-Harguindeguy et al. , 2013). Seminal root morphological characteristics (seminal root number and seminal root mean angle) were measured following the methodology described in  Planchamp et al. (2013). Briefly, 10 germinated seeds of each variety were grown for 6 days in individual 2D rhizotron made of pouches lined with wet filter paper supplying water.
Use of existing data sets to retrieve Pooideae trait values The comparison of intra-species variation of trait values for wheat with intra-species variation of traits for other Pooideae species was not possible as there is no sufficient data on within-species trait variation in wild species for that many traits. We thus compared the variability of wheat trait values to trait variability between a large panel of grass species, including their wild relatives sensus largo, by focusing on the Pooideae subfamily from the Poaceae family. This subfamily is one of the largest families of Poaceae, which contains most of the cereals, including wheat, oats, barley, rye, and contains a large part of grass species from temperate zone and Eurasia  (Soreng et al. , 2017). The values of the 8 following traits were extracted for Pooideae species from the TRY database  (Kattge et al.
, 2011 ;  this database contains plant trait data collected worldwide): RDMC, RNC, SRL, average root diameter, SLA, LNC, height and seed mass. Values for RDMC, RNC, SRL, average root diameter, SLA, LNC, height and seed mass were available for 11, 37, 21, 32, 154, 14, 289 and 386 species, respectively. When several values were available for a given species, the mean was considered. For NO3 -and NH4 + uptake capacities, we used the dataset described in  Grassein et al. (2015)  which includes values of N uptake capacities for 8 Pooideae species.
Statistical analyses All statistical analyses were carried out using JMP Pro17 (SAS Software). To test the level of trait variability for RDMC, RNC, SRL, average root diameter, SLA, LNC, Height, Seed mass and N uptake capacities, among wheat varieties and among Pooideae species, the coefficients of variation (i. e. standard deviation divided by the mean) were computed and the minimum and maximum trait values for wheat varieties and Pooideae species were used. As Pooideae traits retrieved from TRY database vary in sample size, from 11 to 368 species and to avoid bias of the sample size in trait variability analyses, we measured the coefficients of variation on data from a random sampling repeated 10 times for each trait studied, in order to have the same number of values for species and varieties.
For instance, when values for a given plant trait were available for n species in the TRY database with n<57, random sampling for n varieties was performed on our wheat dataset. When TRY database included more than 57 species for a trait, random sampling of 57 species was performed on the TRY database data. We thus computed coefficient variations with same "n" between wheat varieties and Pooideae species (n = 11, 37, 21, 32, 57, 14, 57, 57 and 8, respectively for RDMC, RNC, SRL, average root diameter, SLA, LNC, Height, Seed mass and N uptake capacities). Given that the level of trait variability can strongly differ between traits, we also compared the variability among wheat To test possible trait differences between the three modern selection histories, a oneway variance analysis (ANOVA) was used.
ACKNOWLEDGEMENTS The authors thank the  ANR (French Agence Nationale pour la Recherche)  for funding allocation to the project  Wheatamix , and the  INRAE meta-programme EcoServ  for funding to the project  SolFaMi. We also acknowledge the use of data retrieved from the database of the TRY initiative on plant traits [http://www. try-db. org]. The TRY initiative and database is hosted, developed and maintained by  J. Kattge And G. Bönisch  ( Max Planck Institute For Biogeochemistry, Jena, Germany ). TRY is currently supported by  DIVERSITAS/Future Earth  and the  German Centre For Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. We thank the "Serre et chambres climatiques" platform ( Université Lyon1, FR BioEnviS ) for growing the plants for the pot experiment. We thank  Mégane Yslan  and  Elise Lacroix  for their help during experiments conducted in Lyon and  Joëlle Messoud  and  David Cormier  for the experiments conducted in Clermont-Ferrand.
DATA ACCESSIBILITY Data available from the TRY database: Kattge, J. , Bönisch, G. , Günther, A. , Wright, I. ,   Zanne, A. , Wirth, C. , Reich, P. B. and the TRY Consortium (2011)  TRY -Categorical Traits Dataset. Data from: TRY -a global database of plant traits. TRY File Archive https://www. try-db. org/TryWeb/Data. php#3. Downloaded from https://academic. oup. com/jxb/advance-article/doi/10. 1093/jxb/eraa491/5932789 by RU Inst Voor Aardwetens Schappen/University Library Utrecht user on 22 October 2020.
data set (n = number of Pooideae species considered). from https://academic. oup. com/jxb/advance-article/doi/10. 1093/jxb/eraa491/5932789 by RU Inst Voor Aardwetens Schappen/University Library Utrecht user on 22 Oct Height Ear/Plant SeedMass YRresist Unit Ears plant cm g % -1 Meaning Mean height of the main shoot Mean number of ears per plant Thousand Kernels Weight/1000 a n u s c r i p t Stage/age of the plant Grain filling Harvest Harvest surface non-attacked M Sensitivity to yellow rust, percentage of the leaf Tillering to flowering Compilation of data from ARVALIS, and Growth conditions Site: Le Moulon Field conditions, 100 plants m -2 , Site: Le Moulon Field conditions, 100 plants m -2 , Site: Le Moulon Field conditions, 100 plants m -2 , Site: Le Moulon the ECOGER and ECOSYS laboratories Comparison with… TRY database (n= 289) TRY database (n= 386) - a) Below-ground traits RDMC mg g RNC % SRL m g -1 -1 RootDiameter mm NO 3 -uptake mg g -1 min -1 NH 4 + uptake mg g -1 min -1 RootNumber No dimension Sresist % c) Whole-plant traits SRR No dimension DM g A c c e p t e d Sensitivity to septoria, percentage of the leaf Root Dry Matter content Root Nitrogen Content Specific Root Length Mean root diameter NO 3 -uptake per root unit NH 4 + uptake per root unit Mean root number surface non-attacked Shoot root ratio Plant dry matter produced Yield g.
m -2 Seed yield at harvest 8 weeks 8 weeks 8 weeks 8 weeks 8 weeks 8 weeks 6 days Tillering to flowering Compilation of data from ARVALIS, and Greenhouse conditions in sand and hydroponic solution, Site: Lyon Greenhouse conditions in sand and hydroponic solution, Site: Lyon Greenhouse conditions in sand and hydroponic solution, Site: Lyon Greenhouse conditions in sand and hydroponic solution, Site: Lyon Greenhouse conditions in sand and hydroponic solution, Site: Lyon Greenhouse conditions in sand and hydroponic solution, Site: Lyon Hydroponic growth in a 2D rhyzotron, Site: Clermont the ECOGER and ECOSYS laboratories 8 weeks Greenhouse conditions in sand and hydroponic solution, Site: Lyon 8 weeks Greenhouse conditions in sand and hydroponic solution, Site: Lyon Site: Le Moulon Harvest Field conditions, 100 plants m -2 , TRY database (n= 11) TRY database (n= 37) TRY database (n= 21) TRY database (n= 32) Grassein et al.
2015 (n= 8) Grassein et al. 2015 (n= 8) ----- RootAngle Degree Mean root angle 6 days Hydroponic growth in a 2D rhyzotron, - Site: Clermont b) Above-ground traits SLA cm². g -1 Flag leaf area per flag leaf dry mass Flowering Field conditions, 170 plants m -², TRY database (n= 154) Site: Grignon LNC % Flag leaf nitrogen content Flowering Field conditions, 170 plants m -², TRY database (n= 14) Site: Grignon Flowering days Flowering date Flowering Field conditions, 100 plants m -2 , - d.
Observational Cohort Study The study protocol of the BEYOND-SWIFT registry has been registered (NCT03496064), and details have been published previously. 18 n brief, the registry is a retrospective, international, multicenter, nonrandomized observational study set up to investigate the safety and efficacy of a market-release neurothrombectomy device (applied as initial devices) in AIS patients. Table II in the online-only Data Supplement gives an overview of the patients included, availability of follow-up data for each center, and ethical approval procedure. For the primary analysis, we included all patients from BEYOND-SWIFT (Bernese-European Registry for Ischemic Stroke Patients Treated Outside Current Guidelines With Neurothrombectomy Devices Using the Solitaire FR With the Intention for Thrombectomy) for whom information on OAC therapy before stroke onset was available.
Variables and Image Analysis Local investigators collected data on OAC medication before stroke onset and records of coagulation assays, which were performed according to local standards. We defined tandem occlusion as the presence of an intracranial large vessel occlusion and >90% extracranial cervical stenosis or occlusion. We defined successful recanalization as reperfusion of at least 50% of the initially occluded target territory, according to the modified treatment in cerebral ischemia score (≥2b). 27.
The rationale for this model was the combination of known predictors of sICH following EVT  [29] [30] [31] [32] [33] [34]  and baseline differences between patients experiencing sICH and those who did not (Table V in the online-only Data Supplement). Patients with missing data items were excluded from the multivariable analysis. For the sensitivity analysis, the same model was used to assess the association of INR (continuous) with sICH in VKA patients. No corrections for multiple testing were made for secondary and sensitivity analyses. For the prognostic postinterventional model, the following additional confounders were included: reperfusion success (categorical), use of balloon guide catheter (categorical), maneuver count (ordinal, aOR per one maneuver), time from groin puncture to reperfusion (continuous), and compound complication (categorical).
Systematic Review and Meta-Analysis We performed a systematic review and meta-analysis conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement  35  and the Meta-Analysis of Observational Studies in Epidemiology checklist  36  (for further details, search strategy, and statistics, see the online-only Data Supplement). The systematic review and meta-analysis was registered at the International Prospective Downloaded from http://ahajournals. org by on January 31, 2020 Register of Systematic Reviews (CRD42019127464). Briefly, prevalences of sICH and mortality in patients undergoing EVT according to whether patients had previously been prescribed DOAC, VKA, or no OAC were used to calculate summary estimates of effect sizes (summarized odds ratios [sORs])  37  using the inverse variance heterogeneity model assuming heterogeneous populations and accounting for multiple true effect sizes.
Standard Protocol Approvals, Registrations, and Patient Consents Ethical approval was granted by local ethical standards committees (see Table  II  in the online-only Data Supplement for details of each center). Additionally, ethical approval was obtained for centrally pooling the data in Bern (KEK Bern, Bern, Switzerland, Local Ethics Committee study identifier: 2018-00766). The registry is registered at https://www. clinicaltrials. gov (NCT03496064) and the systematic review and meta-analysis at the Prospective Register of Systematic Reviews (CRD42019127464).
Meta-Analysis The database searches and citation tracking yielded 751 hits. After removing duplicates, 381 abstracts and 45 full-text records were screened as potentially relevant. In total, 14 publications met the inclusion criteria of reporting the risk of sICH after EVT for large vessel occlusion in AIS patients with OAC pretreatment and inclusion of a control group receiving no OAC (13 for VKA and 10 for DOAC; Figure II in the online-only Data Supplement). The definitions of sICH and comparator groups across studies were somewhat heterogeneous (.
Using the proteins enriched in the NF186-BirA* sample (Fig. 1e ), we generated a protein interaction network using Cytoscape and the STRING database of protein-protein association networks  29, 30. This network revealed a module with previously known AIS functions (Ankyrins and Spectrins, yellow), a module not previously described (Septins, red), and many proteins with no previously reported connection to NF186 or AIS (Fig. 1f , white). To confirm potential AIS proteins, we combined results from four independent experiments (Supplementary Data 1). We identified all PSM count ratios > 2 between NF186-BirA* expressing neurons and NF186ΔFIGQY-BirA* expressing neurons (Fig. 2a ). A search of the literature revealed that among the 73 proteins preferentially biotinylated in NF186-BirA* expressing neurons, 33% are scaffolding/cytoskeletal proteins and 14% participate in vesicle trafficking.
Biotinylated proteins were detected using Alexa488-conjugated Streptavidin. Scale bar = 5 μm. c, d The number of peptide spectral matches (PSMs) for each biotinylated protein identified by mass spectrometry and shown at different scales. Known AIS and NF186-interacting proteins are indicated in red. The solid line is the least-squares fit of the biotinylated proteins while the dotted lines parallel to the least-squares fit line represent an arbitrary minimum of ten PSMs confidence interval. PSMs are the average PSM counts for two independent replicates for each chimera. e Rank plot showing the enrichment of NF186-BirA* PSMs over NF186ΔFIGQY-BirA* PSMs. f Protein interaction network generated using Cytoscape and the STRING database of protein interactions. With the exception of Nfasc, the size of each circle is proportional to the ratio of the PSMs count for each protein indicated.
Transfection of hippocampal neurons was performed using Lipofectamine 2000  53. Human embryonic kidney (HEK293T) (Cat#CRL-3216, RRID:CVCL_0063) cells and COS7 Cells (Cat#CRL-1651, RRID:CVCL_0224) were purchased from ATCC and cultured on cell culture dishes at 37 °C in Dulbecco's Modified Eagle Media (DMEM) containing 10% heat-inactivated Fetal bovine serum (GE Healthcare), 50 units/ml penicillin, and 50 μg/ml streptomycin. Plasmid transfection. HEK293T cells and COS7 cells were transfected with plasmid DNA using Lipofectamine 3000 (Invitrogen) according to the manufacturer's instructions. The media was replaced after 24 h of transfection. Primary hippocampal neurons were transfected with Lipofectamine 2000 (Invitrogen). After neurons were washed with neurobasal media three times, the media was replaced after 5 h of transfection.
The proteins in supernatants were denatured in Laemmli sample buffer and then subjected to SDS-PAGE. The electrophoretically separated proteins were transferred to a polyvinylidene difluoride membrane, blocked with 5% skim milk, and immunoblotted with each of the primary antibodies and in turn with peroxidaseconjugated secondary antibodies. Original immunoblots used here are included in the source data file. For α2 spectrin GST fusion protein pulldowns, HEK293T cells were transfected with plasmid DNA (Mical3-myc + α2 spectrin GST fusion protein) using the Lipofectamine 3000 transfection reagent. After 24 h of transfection, cells were lysed in lysis buffer (150 mM NaCl, 20 mM Tris-HCl (ph8. 0), 10 mM EDTA, and 1% Triton X-100) and the lysates were centrifuged at 13,000 × g for 10 min at 4 °C.