dataesr/scientific-paragraphs-categorization huggingface dataset:
The 'median percentage for a network' refers to the median of the percentage of MOD across all trials of the network. Overall, 37% of the 539 trials in our dataset had low MOD, followed by 30% with moderate and balanced MOD, 18% with moderate and unbalanced MOD, 9% with large and unbalanced MOD, and 6% with large and balanced MOD. Almost half of the networks were classified as having moderate and balanced MOD (Table  1 ), followed by low MOD (41%). Overall, three networks were found to be more problematic in terms of MOD: two networks of moderate and unbalanced MOD, and one network of large and unbalanced MOD. None of the networks was classified as having large and balanced MOD.
Model implementation and presentation of results Both approaches were implemented in JAGS via the Rpackage R2jags  [27]  (statistical software R, version 3. 6. 1  [28] ). Technical details on the specification of the models (i. e. prior distributions, convergence inspection, 0. 02 (0. 00, 0. 24)  [10]  0. 03 (0. 00, 0. 57)  [12]  Moderate and balanced median (minimum, maximum) 0. 12 (0. 00, 0. 62)  [8]  0. 09 (0. 00, 0. 37)  [6]  0. 11 (0. 00, 0. 62)  [14]  Moderate and unbalanced median (minimum, maximum) 0. 18 (0. 00, 0. 45)  [1]  0. 09 (0. 03, 0. 27)  [1]  0. 15 (0. 00, 0. 45)  [2]  Large and unbalanced median (minimum, maximum) -0. 30 (0. 03, 0. 87)  [1]  0. 30 (0. 03, 0. 87)  [1]  Factors that affect within-trial normal approximation Trial sample size median (minimum, maximum) 204  (12, 18, 201)  364 (74, 8240) 262  (12, 18, 201)  Event risk median (minimum, maximum) 0.
An agreement was inferred when the points were aligned with the diagonal line. To quantify the agreement, we used the concordance correlation coefficient (CCC)  [29]  via the Rpackage epiR  [30]. The R-package ggplot2 was used to draw the scatterplots  [31]. The dataset and the code to perform the empirical study are available online at https://github. com/LoukiaSpin/One-stage-vs-two-stage-PM-models. git.
05 (frequent events and small τ 2 ) under the two-stage PM approach (Supplementary Fig. 3 , Additional file 1). Therefore, Fig. 2  Dot plots on the bias of posterior mean of NMA log OR for all pairwise comparisons under one-stage and two-stage approaches while accounting for the degree of missing outcome data (moderate, large) being unbalanced in the compared arms, the size of trials (small, moderate), the event frequency (low, frequent) and small τ 2. MOD, missing outcome data increasing the amount of MOD increased the bias in both basic parameters, under the two-stage PM approach. Note that in each network of our database, the percentage of MOD (%MOD) was ranging from very low levels (indicating low attrition bias; %MOD < 5) to moderate or large levels (indicating serious attrition bias; %MOD > 20) across the trials.
g. there are outlying trials in the synthesised dataset  [15] ), may compromise the validity of the results  [17]. Using a simulation study to compare seven models for random-effects meta-analysis in the frequentist framework, Jackson et al. [18]  demonstrated that both the binomial-normal (one-stage approach) and normal-normal (two-stage approach) models performed poorly overall. The authors suggested alternative model parameterisations (models 4, 6 and 7, there) especially when the event is low or there is considerable statistical heterogeneity according to visual inspection of the forest plot. Extending these models to incorporate the pattern-mixture model in the Bayesian framework may be proper alternatives to the current one-stage and two-stage PM approaches. We did not perform sensitivity analysis to different assumptions about the missingness mechanisms in the compared interventions, as we were interested in investigating the performance of the competing models in the presence of MOD, rather than inferring on the relative effectiveness of the compared interventions in the studied networks.
Acknowledgements Chrysostomos Kalyvas is employed by  Merck Sharp & Dohme. Katerina Papadimitropoulou  is a PhD candidate at the  Department of Clinical Epidemiology of Leiden University Medical Center  and an employee of  Danone Nutricia Research in Utrecht, The Netherlands. The authors alone are responsible for the views expressed in this article, and they should not be construed with the views, decisions, or policies of the institutions to which they are affiliated.
Top Inset represent sampling effort for each country: mean number of samples per site + S. E. Number by the ISO code is the number of sites sampled. Map created with QGIS 3. 4. 5 (https ://www. qgis. org/fr/ site/). pollutants  20, 21. In recent years, the level of CD was further explored in several parts of Europe and has dramatically increased, establishing G. fossarum as a complex of highly divergent lineages that may represent cryptic species  [55] [56] [57] [58] [59]. These studies revealed a substantial amount of CD occurring even at small geographical scales. Intriguingly, the number of lineages seems to be the highest in south-eastern Europe, but the highest phylogenetic diversity recorded so far is in central Europe  59. However, the extent of diversity in G. fossarum is currently inadequately known and most likely underestimated at the continental level, because of numerous and wide sampling gaps.
Only 35 of the 146 BINs have already been known from literature or derived from the sequences mined from GenBank by BOLD, resulting in 111 new BINs in the present study, a fourfold increase. In addition, eight short sequences (397-468 bp) derived from the literature, divergent enough from the existing BINs not to be ascribed to any of them, but too short to be attributed their own BINs (following BOLD standard), should be considered as eight extra BIN-level MOTUs, extending the total to 154 BIN-equivalent MOTUs. Both BIN-and ABGD-MOTUs, shared a bell-shaped distribution ofK2P distances centred on 0. 22, illustrating that most MOTUs are highly divergent (Fig. 3b, c ). However, while the lowestK2P value among ABGD-MOTUs was 0. 05, this value for BIN-MOTUs was occasionally down to < 0. 01. In 62 instances, MOTUs based on BIN, bPTP and ABGD methods were concordant.
First inset: Image of a male G. fossarum sensu stricto, i. e. Type A which occurs at the type locality (Photograph: Denis Copilaş-Ciocianu). Second inset: Lineages through time (LTT) plot. Analysis was performed on the dataset reduced to BINs. Grey lines represent 95%HPD. Tree was generated using BEAST 2. 4. 8 (https ://www. beast 2. org/). Geographic overlap and co-occurrence of BIN-, ABGD-MOTUs and major clades. We found a regional co-occurrence of BIN-MOTUs, ABGD-MOTUs and major clades (Figs. 4, 5 ). To further quantify within-site co-occurrence, sites with two or more sampled individuals (321 out of 498 sites) were used. Withinsite co-occurrence of at least two BIN-MOTUs, ABGD-MOTUs and highly divergent clades was observed in 32. 4%, 17. 8% and 7. 2% of the sites, respectively. Also, at least three BIN-MOTUs co-occurred in 19 locations and at least three ABGD-MOTUs in four sites (Table  S1 ).
Sympatry between these different genotypes is therefore quite frequent. Sampling size is likely to impact the ability to detect co-occurrence, particularly if MOTUs are unevenly distributed, which seems a regular feature for G. fossarum (e. g. 55 ). For example, in our dataset, rare BIN-MOTU had a frequency of no more than 0. 2 in 73. 6% of the sites with more than ten individuals sampled, and no more than 0. 1 in 49. 1% of such sites. Geographic hotspots of divergence. The landscape genetic map (Fig. 6a ) of molecular divergence showed that the Carpathian, Dinaric and Pannonian regions exhibit the highest levels of lineage divergence. This pattern holds regardless if one or more of the six major clades are present in a given region. If highly divergent endemic Carpathian, Dinaric and Pannonian lineages are excluded (i.
Methods Data overview. Sequences for COI were derived from a total of 4926 individuals collected from 498 sites (Fig. 1 , Table  S1 ). The sampling included 19 countries, covering the whole range of G. fossarum in continental Europe as well as two sites from United Kingdom, an area considered as recently invaded  40. The collected data included: (1) original data (206 sites, 2071 sequences), (2) data derived from the co-authors' previously published studies  [57] [58] [59] 91  (198 sites, 682 sequences, although 463 sequences from  54  were initially unreleased in GenBank) and (3) data derived from other published papers  20, 40, 55, 56, 63, 92, 93  (94 sites and 2173 sequences, although 2086 of these sequences 56 originated from a very restricted area in Germany). Al  sequences retrieved from GenBank were checked to be phylogenetically part the Gammarus fossarum species complex using both BLAST (https :// blast.
bolds ystem s. org/)  112. In this algorithm, the newly submitted COI sequences are aligned and compared pairwise and also to each sequence already deposited in BOLD. Then, the initial single-linkage clustering is performed on the aligned sequence data in such a way that each cluster is allowed for a maximum intra-cluster distance of 0. 022, and its distance to any other cluster is more than twice the threshold (> 0. 044). In the subsequent step, the clusters are refined in the sense that clusters whose members show high sequence variation, but lack discontinuity remain as a single MOTU, while those in which sequence variation shows clear internal partitions are assigned to two or more MOTUs, even if their separation is less than 0. 022. Then, finally, each refined cluster is assigned a globally unique and specific identifier.
Time-calibrated phylogenetic tree. The time-calibrated phylogenetic tree was reconstructed using Bayesian inference (BI) in BEAST 2. 4. 8 (https ://www. beast 2. org/)  114. A Gammarus roeselii sequence (KP789695) was used as an outgroup for the analysis. Two sets of data were used, one at the haplotype level the other at the BIN level. The outgroup was removed after the analysis. Priors for the substitution models were selected using bModelTest (https ://githu b. com/BEAST 2-Dev/bMode lTest /)  115. The best-fitting model of substitution in both cases was the transversion model (TVM) with gamma-distributed rate heterogeneity (G) and a given proportion of invariable sites (I). The log-normal relaxed clock with the Birth-Death speciation model was set as priors following the results of path sampling selection  116.
4. 8 (https ://www. beast 2. org/)  114. In order to provide additional support for the BI topology, we have also reconstructed a phylogeny using the Maximum Likelihood approach (ML) in RAxML HPC 8. 2. 9 (https ://cme. h-its. org/exeli xis/web/softw are/ raxml /)  113. The best-scoring ML tree was searched under the GTR + G substitution model which was applied to each codon partition. Statistical support was estimated with the GTR CAT model of rate heterogeneity and 1,000 rapid bootstrap (RBS) replicates  119. The history of diversification was explored with the help of a Lineage Through Time (LTT) plot, generated with Tracer 1. 7  118  , using 1,500 post burn-in trees that resulted from the BEAST analysis on the BIN dataset. Definition of major clades. We defined the major clades following studies from Copilaş-Ciocianu and Petrusek  58  and Copilaş-Ciocianu et al.
Geographic patterns of genetic diversity. The spatial diversity pattern of G. fossarum in Europe was illustrated with genetic landscape shapes generated with Alleles in Space (AIS; https ://www. marks genet icsof tware. net) software  120. The genetic landscape visualizes the abrupt transitions between populations and groups of populations characterized by divergent haplotypes. The analysis followed the approach already applied to amphipods (e. g. 5  ), i. e. converting the genetic distances between sites into a hypsometric map. First, using the AIS software, the genetic distances between sampling sites were calculated based on the COI sequences longer than 496 bp (in order to obtain a gap-free alignment) and connected into a network based on the Delaunay Triangulation. The genetic distance values were set in the midpoints of each connection in the network.
The raw genetic distances acquired from the program were interpolated afterwards and the matrix of the ' elevation' values, with their respective latitude and longitude coordinates, was then imported into QGIS (https ://qgis. osgeo. org) software to produce a genetic divergence surface image using the Triangulated Irregular Network (TIN) algorithm. The resulting image was plotted onto a relief map of Europe to create a final map in which the hypsometric tints reflect the genetic distance between population pairs. Two maps were constructed, one that included all the sampling sites (498) and all haplotypes (691) and another one which excluded the highly divergent lineages endemic to the Carpathian, Dinaric and Pannonian regions (the hotspots of the most ancient divergences, see "Results"), to better illustrate the level of genetic divergence in the remaining area.
Three ABGD-MOTUs, 1, 2 and 6, corresponding to types A, B and C as defined byMüller et al. (2000), respectively are specified. Six major clades were distinguished and highlighted by the following colours: SEE (South-Eastern Europe, purple), FRA-CE A (France and Central-European A, yellow), EE Q (Eastern-Europe Q, dark blue), CWE (Central-Western Europe), CE A (Central-EuropeanA, yellow) and CEE (Central-Eastern Europe). First inset: Image of a male G. fossarum sensu stricto, i. e. Type A which occurs at the type locality (Photograph: Denis Copilaş-Ciocianu). Second inset: Lineages through time (LTT) plot. Analysis was performed on the dataset reduced to BINs. Grey lines represent 95%HPD. Tree was generated using BEAST 2. 4. 8 (https ://www. beast 2. org/).
Acknowledgements The authors thank the  French National Agency for Research for funding  ( ANR-13-BSV7-0004   ANR MULTIST-RESS ;  ANR-14-CE21-0006 PROTEOGAM ), the  Adour-Garonne water agency (program Dylaq)  and the  French Office for Biodiversity (OFB)  for funding. Study was financially supported by the  Polish Ministry of Science and Education , grant number  N N303 579439  and partially from the internal funds of the  University of Lodz. Additional financing for MJ's stay in Poland was provided by the  Short-Term Scientific Mission (STSM)  of  COST Action   CA15219 -Developing new genetic tools for bioassessment of aquatic ecosystems in Europe-DNAqua-Net  (Grant no. 39779 )".
Data availability All newly produced COI sequences are released in GenBank under accession numbers: MT978656-MT980726. The 463 sequences from Lagrue et al. 57  which were initially unreleased in Genbank are now available under accession numbers: MT411018-MT411480. Sequences and metadata are available in BOLD dataset DS-GFOSCDEU (dx. doi. org/10. 5883/DS-GFOSCDEU). All the new material used in this study has been stored in the permanent collection of either Biogeosciences Laboratory, University of Burgundy-Franche-Comté or the Department of Invertebrate Zoology and Hydrobiology, University of Lodz, Poland.
Acknowledgments We thank two anonymous reviewers for their valuable feedback that helped improving this manuscript. We are grateful to all modeling centers for contained. We thank the  WCRP-CliC Project  for supporting the  SIMIP  project. E. Blockley  was supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). J. B. Debernard is supported by the  Research Concile of Norway  through  INES  ( 270061 ). E. Dekker is supported by the  Arctic Across Scales Project  through the  Knut and Alice Wallenberg Foundation  ( KAW2016. 0024 ). P. DeRepentigny is supported by the  Natural Sciences and Engineering Council of Canada , the  Fond de recherche du Qubec-Nature et Technologies , and the  Canadian Meteorological and Oceanographic Society  through PhD scholarships and  NSF-OPP  Award  1847398.
D. Docquier  is funded by the  EU   Horizon 2020 OSeaIce project , under the  Marie Sklodowska-Curie Grant Agreement 834493. J. Dörr  is funded by the  German Ministry for Education and Research  through the project " Meereis  bei  +1. 5  • C. " N. S. Fučkar  acknowledges support of  H2020 MSCA IF (Grant ID 846824 ). E. Hunke is supported by the  Regional and Global Modeling and Analysis program  of the  Department of Energy's Biological and Environmental Research division. A. Jahn 's contribution is supported by  NSF-OPP  Award  1847398. F. Massonnet is a F. R. S. - FNRS Research Fellow. D. Notz  is funded by the  Deutsche Forschungsgemeinschaft  under  Germanys Excellence Strategy EXC 2037  ' CLICCS -Climate, Climatic Change , and Society' Project Number:  390683824 , contribution to the  Center for Earth System Research and Sustainability (CEN) of Universität Hamburg.
Background: Hippocampal atrophy is associated with cognitive decline. Determining the clinical features associated with hippocampal volume (HV)/atrophy may help in tailoring preventive strategies. Objective: This study was aimed to investigate the association between HV (at visit 2) and vascular status (both at visit 1 and visit 2) in a cohort of individuals aged 60+ with hypertension and without overt cognitive impairment at visit 1 (visit 1 and visit 2 were separated by approximately 8 years). Methods: Hippocampal volume was estimated in brain MRIs as HV both clinically with the Scheltens' Medial Temporal Atrophy score, and automatically with the Free Surfer Software application. A detailed medical history, somatometric measurements, cognitive tests, leukoaraiosis severity (Fazekas score), vascular parameters including pulse wave velocity, central blood pressure, and carotid artery plaques, as well as several biochemical parameters were also measured.
Although the exact underlying mechanisms remain unclear, vascular lesions may activate pathways leading to neurodegenerative lesions  [5]. The aim of the present study was to investigate the association between vascular status and HV (between two visits separated by approximately 8 years), in older hypertensive subjects without overt cognitive impairment. HV was measured both clinically by Scheltens' Medial lobe Temporal Atrophy (MTA) score, and automatically with the use of the Free Surfer Software (FSS).
Brain MRI imaging analysis Brain imaging analysis was performed using brain MRI obtained with a 1. 5 Tesla system (Signal General Electric; GEMS), performed in the axial and sagittal planes with a slice thickness of 5-mm. HV was estimated by two different methodologies, manually by an expert radiologist based on the MTA scale, known as Scheltens' score, and automatically with FSS  [10]. Grading of the Scheltens' score includes a 5-stage escalation (0: no atrophy, 1: only widening of choroid fissure, 2: also widening of the temporal horn of lateral ventricle, 3: moderate loss of HV (decrease in height), and 4: severe loss of HV)  [11]. Free Surfer software version 5. 2 (http://surfer. nmr. mgh. harvard. edu/) was applied on high-resolution 3D-T1-weighted MR images to obtain HV for all subjects using an observerindependent approach.
ACKNOWLEDGMENTS We thank  Mr. Pierre Pothier  for the editing of the manuscript. This study was funded by grants from the  Programme Hospitalier de Recherche Clinique  (PHRC national, N •  2010-A01399-30 ). This work was supported by the  "Federation Hospitalo-Universitaire on Cardiac and Arterial Aging -FHU CARTAGE ). AB, JPF and PR are partly supported by the  French PIA  projects: " Lorraine Université d'Excellence " (reference:  ANR-15-IDEX-04-LUE ) and  RHU Fight-HF  (reference:  ANR-15-RHU-0004 ). Both public grants are overseen the  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" programme. Authors' disclosures available online (https:// www. j-alz. com/manuscript-disclosures/19-0842r3).
Here, we acquired MEG data while each participant performed three different standard NT tasks on three sensory modalities with the aim of characterizing supramodal brain mechanisms of conscious perception. In the first experiment we show how neural patterns related to perceptual consciousness can be generalized over space and time within and-most importantly-between different sensory systems by using classification analysis on sourcelevel reconstructed brain activity. In an additional control experiment, we replicate the main findings and exclude the possibility that our observed patterns are due to response preparation/ selection despite the need to report stimuli detection on every trial in our experiments.
Decoding and Multivariate Searchlight Analysis across Time and Brain Regions. We investigated the generalization of brain activation over time within and between the different sensory modalities. To this end, we performed a multivariate analysis of reconstructed brain source-level activity from the initial experiment. Time generalization analysis presented as a time-by-time matrix between 0 and 500 ms after stimulus onset shows significant decoding accuracy for each condition (Fig. 3A ). We train and test our classifiers on 50% of the dataset with the same proportion of trials coming from each separate run of the acquisition compensating for potential fatigue or habituation effect along the course of the experiments. As can be seen on the black cells located on the diagonal in Fig. 3A , cross-validation decoding was performed within the same sensory modality.
Materials and Methods Participants. Twenty-five healthy volunteers took part in the initial experiment conducted in Trento and 21 healthy volunteers took part in the control experiment performed in Salzburg. All participants presented normal or corrected-to-normal vision and no neurological or psychiatric disorders. Three participants for the initial experiment and one participant for the control experiment were excluded from the analysis due to excessive artifacts in the MEG data leading to an insufficient number of trials per condition after artifact rejection (fewer than 30 trials for at least one condition). Additionally, within each experiment six participants were discarded from the analysis because the false alarms rate exceeded 30% and/or the near-threshold detection rate was over 85% or below 15% for at least one sensory modality (due to threshold identification failure and difficulty in using response button mapping during the control experiment, also leaving fewer than 30 trials for at least one relevant condition in one sensory modality: detected or undetected).
The remaining 16 participants (11 females, mean age 28. 8 y; SD, 3. 4 y) for the initial experiment and 14 participants (9 females, mean age 26. 4 y; SD, 6. 4 y) for the control experiment reported normal tactile and auditory perception. The ethics committee of the University of Trento and University of Salzburg, respectively, approved the experimental protocols that were used with the written informed consent of each participant. Stimuli. To ensure that the participant did not hear any auditory cues caused by the piezo-electric stimulator during tactile stimulation, binaural white noise was presented during the entire experiment (training blocks included). Auditory stimuli were presented binaurally using MEG-compatible tubal in-ear headphones (SOUNDPixx; VPixx Technologies). Short bursts of white noise with a length of 50 ms were generated with Matlab and multiplied with a Hanning window to obtain a soft on-and offset.