dataesr/scientific-paragraphs-categorization huggingface dataset:
For 234 samples with low DNA yield, DNA was amplified and genotyped as previously described. 19 Genotyping was performed using the MassARRAY System and iPLEX Gold chemistry (Sequenom, San Diego, CA) with standard protocol. Genotype clusters were manually reviewed using Typer 4. 0 software (Sequenom), and genotype calls were corrected where necessary. Genotyping success rate was >95% for all but 1 SNP (rs2022309, 91. 3%), with an average success rate of 95. 7%. No discrepancies were identified among a total of 1,256 successful blind duplicate genotype pairs. Cox regression analysis adapted for the casecohort data was used to assess the association between the genotypes and ischemic stroke in the MORGAM cohorts, assuming an additive genotypic effect. The analysis was stratified by cohort and sex.
Statistical Analysis Stage 1. We used multiple linear regression models to assess association between genotypes and phenotypes, using age as a covariate. The phenotypes examined in the TUK cohort were inverse-normal transformed to satisfy the assumption of normality of trait distribution of the linear models. Association analysis was carried out using Merlin  22  to control for family structure within the dataset. Independence of the effects conferred by SNPs in the same region was assessed by means of a backward stepwise regression analysis on the trait with which they were associated. This yielded 23 statistically independent significant SNPs (p < 5 3 10 28 ), associated with at least 1 quantitative outcome, which were taken forward for examination in the clinical groups at Stage 2. This stage of the analysis was performed using Stata for Windows version 10 (Stata-Corp, College Station, TX) with adjustment for the twins' relatedness.
Stage 3 We examined the association of the 4 ABO SNPs in the large Meta-Stroke dataset (Table  5 , with genotyping details in Supplementary Table  3 ) and further explored their relationship with stroke subtype in Meta-Stroke and WTCCC2. Results for ischemic stroke overall are shown in Table  6  (positive results) and are illustrated by a forest plot (Fig  2 ). The results show an association for lead SNP rs505922 C allele with ischemic stroke (odds ratio [OR] 5 1. 07, 95% confidence interval [CI] 5 1. 03-1. 11, p 5 0. 0006). Two other ABO SNPs also showed significant association: rs643434 (for A allele, meta-analysis logistic regression OR 5 1. 06, 95% CI 5 1. 02-1. 11, p 5 0. 002) and rs651007 (C allele, OR 5 1. 07, 95% CI 5 1. 02-1. 12, p 5 0. 007; see Table  6 ). Analysis by stroke subtype for SNP rs505922 showed association with cardioembolic stroke (OR 5 1.
Potential Conflicts of Interest F. M. K. W. : grants/grants pending, Wellcome Trust Fellowship 2007-12. M. D. : board membership, Boehringer Ingelheim, Ever Pharma; consultancy, Bayer Vital, Boehringer Ingelheim, Biol Heilmittel Heel, Bristol-Myers Squibb; grants/grants pending, EU FP7, DFG/DLR, Fondation Leducq, Vasc. Dem. Res. Foundation, Jackstaedt Foundation, Corona Foundation; paid manuscript preparation, Georg Thieme Verlag, UpToDate, W. Kohlhammer Verlag. P. A. : board membership, Fondation Plan Alzheimer; consultancy, Servier, Total, Alzprotect; grants/ grants pending, Ipsen, Sanofi-Aventis, AstraZeneca; stock/stock options, Genoscreen. P. -G. W. : board membership, Boehringer-Ingelheim. K. St. : board membership, stock/stock options, DeCODE Genetics. B. P. : Yale University, Service on Steering Committee for YODA funded by Medtronic; University of San Francisco, Service on DSMB for a clinical trial funded by Zoll LifeCor.
Acknowledgment None of the funding bodies given below played any role in the design, writing, or decision to publish this article. TwinsUK acknowledges the  Wellcome Trust  funding of TwinsUK as well as Intermediate Fellow F. M. K. W. ,  EU Framework 6 support for funding EuroCLOT  ( QLRT-2001-01254 ), and genotyping by  Wellcome Trust Sanger Centre and Sequenom/Gemini genomics, Cambridge. TwinsUK is also supported by an  National Institute of Health Research (NIHR) Biomedical Resource Centre  grant to Guy's and  St Thomas' National Health Service (NHS) Foundation Trust and King's College London. M. Boothby  and  J. Surr  from  The Division of Cardiovascular and Diabetes Research in Leeds ,  United Kingdom  are thanked for their contributions to this program of work. The principal funding for the WTCCC2 stroke study was provided by the  Wellcome Trust , as part of the  WTCCC2  project ( 085475/B/08/Z ,  085475/Z/ 08/Z , and  WT084724MA ).
For MORGAM, we thank  M. Suvela  and  A. Vikman  for MassARRAY SNP genotyping, the DNA core of the  National Institute for Health and Welfare  for DNA sample handling, and the  Wellcome Trust   Sanger Centre for Metabochip genotyping. The  Stroke Association provided additional support for collection of some of the St George 's, London cases. Collection of the Munich cases and data analysis were supported by the  Vascular Dementia Research Foundation. The Oxford cases were collected as part of the  Oxford Vascular Study , which is funded by the Medical as part of the  MetaStroke  project were supported by grant  HL093029  to  M. Fa. The authors thank the staff and participants of the  Atherosclerosis Risk in Communities study  for their important contributions. The  Australian Stroke Genetics Collaborative (ASGC) GWAS  was funded by grants from the  Australian National and Medical Health Research Council (NHMRC Project  Grant  ID: 569257 ), the  Australian National Heart Foundation  ( NHF Project Grant ID: G 04S 1623 ), the  University of Newcastle , the  Gladys M Brawn Fellowship scheme  and the  Vincent Fairfax Family Foundation in Australia.
Australian population control data was derived from the  Hunter Community Study. We thank the  University of Newcastle  for funding and the men and women of the Hunter region who participated in this study. EGH is supported by the  Australian NHMRC Fellowship scheme. Bio-Repository of DNA in Stroke (BRAINS) is an international study recruiting highly phenotyped patients with stroke. For the purposes of the current work, all patients were Caucasians. Diagnosis of stroke was confirmed using positive imaging (MR imaging or CT). BRAINS is partly funded by a  Senior Fellowship  from the  UK Department of Health  to  Dr Pankaj Sharma , the  Henry Smith Charity , and the  UK-India Education  Research Institutive from the  British Council. The  Besta Cerebrovascular Diseases Registry (CEDIR)  was supported by the  Italian Ministry of Health , years 2007-2010 (Annual Research Funding; Grant Numbers:  RC 2007/LR6 ,  RC 2008/LR6 ;  RC 2009/LR8 ;  RC 2010/ LR8 ).
Overview of the Dataset Our dataset consists of messages posted on the Twitter social network, crawled by means of the Application Programming Interface (API) made available by the service itself. We collected tweets sent between 00:00 AM, 1 st July 2012 and 11:59 PM, 7 th July 2012 containing at least one of the following keywords or hashtags: lhc, cern, boson, higgs (see Methods). The final amount of tweets we analyzed was 985, 590. Hence, we built the corresponding social network of the authors of the tweets: the resulting graph is composed of 456,631 nodes and 14,855,875 directed edges. Nodes correspond to the authors of the tweets and edges represent the followee/follower relationships between them. We discarded 70,838 users from the original dataset containing 527,469 users because of the non accessibility of the list of their followees and followers due to privacy settings.
Modeling the Dynamics of User Activation without De-activation. As the first step, we do not consider the influence of the structure of the network on the process. We define a node as active at time t if he or she has tweeted at least once about the Higgs boson within that instant of time. In the following, we indicate with A (t) and a (t) the number and the fraction of active users at time t, respectively. Hence, the number A (t) of active users is expected to be a monotonic increasing function of time. We divide the whole period of data taking into four temporal ranges of interest, corresponding to periods I, II, III and IV, previously described. In Fig. 7  we show for each period the observed evolution of the fraction a (t) = A (t)/N of infected users versus time, where N is the total number of users in the dataset in the data collection period.
In order to model the evolution of the active users over time, we firstly tried to exploit classic susceptibleinfected (SI) models in an unstructured population  [41] , but this led to a very poor fit of the data. For this reason, we developed a new model starting from the observation of the specific characteristics of our dataset. In general, once a user has twitted, we observe that he or she will not probably tweet significantly about the Higgs boson in the near future, according to the bursty behavior shown previously. Therefore, we make the simplifying assumption that he or she will not tweet again after the tweeting activation. In this case, the number of newly active vertices at time t is proportional to the number of users who have not been active before: A (t + ∆t) = A (t) + λ [N -A (t)] ∆t, ( 1 ) where λ is a constant activation rate.
( 8 ) In general, the solution of Eq. (  7 ) and Eq. (  8 ) cannot be obtained analytically because of the complexity of Θ λ(t) (t): therefore, some simplifying assumptions or numerical methods should be adopted instead. Let us focus only on Period IV, i. e. , during and after the main event, from 03:00 AM on 4 th July to the end of the data collection period. The initial fraction of active users is approximately ρ(0) = 0. 1% of the total number of users in our dataset. In order to assess the validity of our analytical model, we perform large-scale Monte Carlo simulations of the spreading dynamics through the network of observed connections among users. More specifically, we consider the case where activation and de-activation rates do not change over time: we vary their values from 0 to 1, independently; for each possible configuration corresponding to the pair (β, λ) we perform 200 random independent realizations of rumor spreading and we calculate the ensemble average at each time step t to obtain an estimation of the expected value of the density ρ(t).
However, such results do not reproduce the observed spreading dynamics, whose density ρ(t) is shown in the third panel of Fig. 8. The data show a quickly increasing number of users within a few hours, with a maximum value reached at the beginning of the International Conference on High-Energy Physics. Such a fast increasing behavior can be explained by tweets related to the excitement for a possible announcement of the discovery of the Higgs boson. In fact, the number of active users in the following hour rapidly decreases by about 40%, staying stable for the subsequent 2 hours and then decreasing again. In case of epidemics with constant activation rate in scale-free networks with a large number of nodes we expect the appearance of an endemic state. However, this is not the case in our dataset.
Methods For each search, Twitter provides information about the number of missing tweets, which is usually negligible. In any case, it worth noting that during this data collection no messages related to missing tweets were received: for this reason, to the best of our knowledge, we might claim that this dataset includes all the tweets satisfying our search criteria. Our original list of relevant keywords was larger, including terms as alice and cms. However, the amount of tweets retrieved using these keywords but not related to the Higgs boson was not negligible and, for this reason, we decided to avoid considering such terms in our analysis. The geographic names contained in the location textfield of Twitter user profiles were converted using the Google Geocoder API. Recent studies make use of the retweet network (i.
e. , who retweets whom) and the mentions network (i. e. , who mentions whom) to investigate, for instance, the patterns of sentiment expression  [43]. However, it is worth remarking that the main source of information consumption on Twitter website and clients is the home timeline, which contains messages from all social contacts, regardless of the number of reciprocal interactions. Therefore, users activity can be triggered (or not) according to their interests. In our study, we prefer to use the follower network to investigate the temporal dynamics of the fraction of people involved in the process of spreading the information about the Higgs boson discovery as a function of time. The social graph was obtained by retrieving the following list for each user participating in the process. We make the reasonable assumption that the time required to collect the data is much shorter than the time required to observe significant changes in the full social graph.
MetaMorph software (Molecular Devices) was used to collect the data. Fluorescence was excited with a 491 nm laser and detected with a 525/39 nm filter. The z-stack acquisition (Fig. 1b ) were acquired using an inverted Leica SP5 with a Leica PL APO 20x/NA50. 7 oil immersion objective. The distance between each plane is 1. 7 mm. Fluorescence was excited with a 488 nm laser and detected from 500 to 550 nm. Quantification of fluorescence intensity and orthogonal projection were performed using ImageJ software. Detection of cell death. The fins were fixed in 4% paraformaldehyde overnight at 4uC, and an In situ Cell Death Detection Kit (Roche) was used for TUNEL assays on whole fins or cryosections. For each fin, the positive cells were counted in ray and inter-ray 2 in all segments. After TUNEL assay on whole fins, the fins were embedded as previously described  34  and sections (20 mm) were cut using a vibratome to analyze the localization of positive cells.
Experimental Crystal data (i) Àx þ 3 2 ; Ày þ 1 2 ; Àz þ 1. Table  2  Hydrogen-bond geometry (A ˚, ). Data collection: APEX2 (Bruker, 2011); cell refinement: SAINT (Bruker, 2011); data reduction: SAINT; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg & Berndt, 2001)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and publCIF  (Westrip, 2010). D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA N2-H2BÁ Á ÁS1 metal-organic compounds m670.
Computing details Data collection: APEX2 (Bruker, 2011); cell refinement: SAINT (Bruker, 2011); data reduction: SAINT (Bruker, 2011); program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg & Berndt, 2001)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and publCIF  (Westrip, 2010). Hydrogen site location: inferred from neighbouring sites H-atom parameters constrained w = 1/[σ 2 (F o 2 ) + (0. 0156P) 2 ] where P = (F o 2 + 2F c 2 )/3 (Δ/σ) max < 0. 001 Δρ max = 0. 60 e Å -3 Δρ min = -0. 58 e Å -3.
Hierarchical models can make powerful inferences because they account for both observation and process errors, the latter being generally attributed to stochastic environmental variations  [40]. Using yellowfin as a case study, we describe an approach for estimating the individual ages of fish from repeat readings of otoliths that takes account of associated uncertainties. We apply our model to an original age-length dataset collected through large-scale mark-recapture experiments conducted in the IO between 2005 and 2012. Firstly, an ageing error model was developed to explicitly represent the sources of uncertainty associated with age estimation. Developed in a hierarchical Bayesian framework, expert judgment was included in the model through the choice of stochastic error structure and informative prior density functions.
Data collection Otoliths and length data were collected throughout the Regional Tuna Tagging Project (RTTP), a large scale markrecapture program, and the West Sumatra Tuna Tagging Project (WSTTP), a simple capture program, that were carried out by the IOTC. The tagging operations of RTTP were conducted between 2005-2007 on two pole-and-line vessels that were chartered to operate in the Western Indian Ocean. Field operations consisted in catching tunas, tagging them on a vinyl-covered cradle, measuring their fork length (fish length from the front to the fork in the center of the tail; F L ) through marks printed directly on the cradle and finally releasing them at sea  [47]. The tagging operations of RTTP were conducted between 2005-2007 on two pole-and-line vessels that were chartered to operate in the Western Indian Ocean.
Evaluating model performance through simulations Different simulations were performed to test and validate the ageing error model (Fig. S2 ). The first simulation aimed to assess the accuracy of the model in estimating R. For this simulation, two alternative runs with and without individual variability in increment formation were considered, i. e. R fixed to 0. 95 and R varying according to a normal distribution around 0. 95. This simulation was repeated three times using datasets of 25 individuals whose time-at-liberty were generated according to a uniform distribution between 30 and 970 days. Four noisy readings of the number of Im increments were then generated according to Eq. D1 and S1  (2). A second set of simulations was performed using 2 to 5 readings of the same otolith to evaluate the model's ability to accurately estimate fish age and its relevance relative to the traditional method which is based on averaging individual-specific increment counts.
An intermediate method was also considered that based on averaging individual increment counts, but also account for potential bias in the increment deposition. For these simulations, R was fixed to 0. 95. The approach consisted of simulating realistic ages-at-recapture from which increment counts were derived with respect to R, y e , and y n (2 Eq. D4). Noise was then added to the increment counts by randomly generating repeated readings (Eq. S3). Five age classes were considered, from 6 months to 5 years. Three simulations were performed using datasets composed of 500 individuals, i. e. 100 individuals for each age class whose ages were generated according to a uniform distribution between the minimum and the maximum values of the class. The accuracy of age estimates was assessed with the relative root mean square error (RMSE), a normalized indicator that measures the discrepancy between simulated and estimated ages.
This was assigned a weakly informative prior gamma distribution with a mean defined from the literature (Eq. P8;  [36;37;32;33] ). The standard deviation of size measurement errors, s e , was determined from the differences in the fork length of RTTP-IO fish released and recaptured several times with time-at-liberty less than or equal to seven days. These individuals were not included in subsequent analyses and therefore constitute an independent data set (Eq. P11). The asymptotic length, L ? , is a particularly important parameter because it determines the shape of the second part of Table  1. Parameters and variables used in the ageing error and somatic growth models.
Ir i Total number of increments for otolith of fish i S3, D4 Ir Ã i,l Total number of increments counted for reading l of otolith for fish i S3 R Ratio between number of increments after OTC mark and time-at-liberty D1, D2, D4 the growth curve. As the dataset included limited information on the asymptotic part of the growth curve, auxiliary information was provided for this parameter consistently with the available knowledge on the biology of the species. An informative prior distribution was defined for L ? using a generalized extreme value distribution (GEV). This allows for the extrapolation of the behavior of distribution tails from the greatest values of a sample and thus estimates the probability of the occurrence of extreme events  [54]. The choice of this distribution is motivated by the fact that yellowfin grow throughout their life, such that the largest observed sizes should correspond to the oldest fish.
The distribution was fit based on size measurement data from fresh fish that were collected during 1952-2011 from the European and Seychelles purse seine fisheries, Maldivian pole and line vessels, and Taiwanese and Japanese longliners. The observed maximum fork length from each measurement platform, i. e. either on vessels board or in cannery, fishery, and year was considered to represent n independent random variables (L 1 ,. ,L n ) with common continuous distribution function F. Asymptotic length L ? was then estimated from the approximation of the upper tail of F (') by using the GEV (m,s,j) distribution: GEV (m,s,j) (')~exp {(1zj '{m s ) À Á{1 j where m is a location parameter, s is the scale parameter (sw0) and j is a tail index (shape parameter). These parameters were estimated using the maximum likelihood method  [55].
Testing the influence of the ageing technique on growth modeling Simulations were performed to evaluate the contribution of the error model on growth parameter estimates and resulting age classification of commercial catches. To illustrate, a simple tagging dataset was simulated and the relationship specified from posterior marginal mode that was obtained with the growth model coupled with the ageing error model (section and Table  3 ) was used as reference growth curve. In this approach, four fork lengths for each 2 cm class, from 20 to 146 cm, were simulated to obtain a data set of 252 fishes. As the VB-logK curve is irreversible, corresponding ages were deduced by minimizing the difference between the simulated and expected length (E1). These ages were converted into numbers of increments from which noisy repeated readings were generated as per the approach previously developed (section and Fig.
S2 ). The growth parameters were then reestimated using the coupled VB-logK model and a classical VB-logK model, which estimates age using the traditional method. As the parameter b is difficult to estimate from a tagging dataset, its value was fixed using the value obtained by fitting the otolith data to the coupled growth model. arg min L Ã {L ? (1{exp({k 2 (A{t 0 ))| ð 1zexp({b(A{t 0 {a)) 1zexp(ba) (k 1 {k 2 ) b 1 A 2 ðE1Þ The mean squared error (MSE) was used to compare the marginal posterior distributions of each parameter with its actual value and evaluate the accuracy of models fits. Accuracy is related to the similarity between the marginal modes and the true values, while precision refers to the uncertainty around the modal value, i. e. the discrepancies between the samples generated by the MCMC simulation.
93 and 20. 8 with k 1 and k 2 respectively; Table  S4 ). These correlations resulted in some model instability and difficulty fitting the posterior distributions of the parameters, particularly in the absence of information from the prior distribution (Fig. S6 ). The expected asymptotic length was estimated at 151. 8 cm F L , which was close to the maximum fork length of the data, 146. 5 cm F L , but comparatively low to the mean asymptotic fork length estimated at about 173 cm from the catch of the purse seiners and longliners and to the maximum lengths of 200 cm that has been observed for yellowfin in the Indian Ocean. In contrast, the data provided more information for the first part of the growth curve (v3 years), where the model fit was good. The uncertainty associated with the mean growth curve that was derived from marginal posterior modes increased as the fish aged.