dataesr/scientific-paragraphs-categorization huggingface dataset:
Model calibration We tested two approaches for model calibration. We considered first the binary variable corresponding to the presence (y i =1) or absence (y i =0) of at least one TreM on tree i. The (eq. 17) We tested three hypotheses concerning the shape of the hazard rate function We fitted the three models on our data set with a Bayesian approach using the "runjags" package  (Denwood, 2016)  in the R software (R Development Core Team, 2005). The code is provided in Appendix 1. We combined all the trees of the six forest sites without modelling any site effect. For the three models, we chose the parameter  to be a gamma prior distribution with a mean of 100 and a variance of 500:. For the Weibull model, we chose the parameter k to be a gamma prior distribution with a mean of 2 and a variance of 4:.
Application: predicting the rate of TreM formation at the forest stand level To analyse the rate of formation of TreMs at the stand level, new simulations were carried out using the observed DBH of each tree in the data set as an initial state. For this section, only the Rayleigh model was used because of its good predictive performance and simplicity (see § 3. 2. ). We sampled 1000 estimates of parameter  in the MCM chain and made 1000 predictions of TreM formation during an annual diameter increment for each tree. Taking into account the sampling area corresponding to the DBH of each tree (constant angle sampling method, see § 2. 1. ), we calculated a predicted distribution of TreM formation rates per year and per hectare for each of the six forests. We then used linear regression to analyse the relation between the forest's TreM formation rate and the forest's TreM density or TreM bearing tree density.
Substantial differences in TreM density have been shown for example between Mediterranean, montane and lowland forests  (Bouget et al. , 2014; Regnery et al. , 2013; Remm et al. , 2006; Vuidot et al. , 2011). These effects could partly be explained by methodological differences among studies but have also been reported by authors analysing a range of sites in a single study  (Bouget et al. , 2014; Vuidot et al. , 2011). All these perspectives require large data sets. TreM data have been gathered by authors in several countries (France, Germany, Ukraine, Sweden, Switzerland, the United States, Canada and Australia), and the constitution of collaborative databases is a key to future progress. As the interest in TreMs increases, repeated observations on permanent plots should become available in the future  (Lindenmayer et al.
Acknowledgements: We are grateful to  Valentine Lafond  and  Guillaume Lagarrigues  for their assistance. This study was supported by the agreement no. 2101870336  ( 2016-2017 ) for the management of habitats and biodiversity between the  French Ministry of Environment, Energy  and the  Sea and Irstea  and by a PhD grant from the  Community of Academic Research Rhône-Alpes  (ARC 3 Environment, 2015) for A. Letort. EMGR is part of  Labex OSUG@2020  ( ANR10 LABX56 ).
Development of 'regular expressions' In the context of a systematic review of studies describing animal models of psychosis, we collected phrases associated with the reporting of randomization, blinded assessment of outcome and sample size calculations and used these to develop target regular expressions ("RegEx"s). These were informally tested through application to a second dataset of 1173 full-text publications describing in vivo research where risks of bias had been previously extracted  [13] , and no changes were made. We then applied these tools to the converted full-text of stroke publications using the Grepl function in R, resulting in a call of 'true' or 'false' for each of the measures to reduce risk of bias. We then conducted manual ('gold standard') ascertainment of reporting of measures to reduce risk of bias for each publication.
To compare the text analytic with human 'gold standard' , we calculated sensitivity (effectiveness of the tool in identifying positives), specificity (effectiveness of the tool in identifying negatives) and accuracy (number of publications correctly labelled as a proportion of the total number of publications). The R code is available at GitHub (link provided on publication). We present the prevalence of reporting of randomization of animals to experimental groups, blinded assessment of outcome and sample size calculation, along with exact binomial confidence intervals (derived using cii in STATA) for each dataset and, for the MCAO literature, in quartiles of PUBMED accession date and by journal. Least squares linear regression was used to understand the correlation between time of publication and the reporting of measures taken to reduce the risk of bias.
Results In 2259 studies reporting animal models of psychosis, the RegEx had performed above our pre-defined performance criteria (sensitivity of 0. 80 and specificity of 0. 91 for randomization, sensitivity of 0. 83 and specificity of 0. 95 for blinding, and sensitivity of 1. 00 and specificity of 0. 96 for sample size calculation). In a second dataset of in vivo studies from leading U. K. institutions  [13] , the tool was somewhat less efficient in calling papers as randomized (sensitivity of 1. 00 and specificity of 0. 62), but blinding and sample size calculation ascertainment were still above our successful performance criteria level of 0. 80 (sensitivity of 0. 88 and specificity of 0. 98 for blinding, and sensitivity of 0. 88 and specificity of 0. 97 for sample size calculation). For the MCAO search we identified 1311 publications, of which 1152 met our inclusion criteria.
In journals represented in the dataset by five publications or more, reporting of randomization ranged from 33% to 100% (median 76%), of blinded outcome assessment from 17% to 100% (median 70%), and of sample size calculations from 0% to 17% (median 0%). Overall, reporting of these measures to reduce risk of bias showed substantial improvements from those observed in a selection of stroke systematic reviews reported in 2007. In the 50 publications carried in the journal Stroke, the performance in 2014-2016 was comparable with that reported from 2013 by Minnerup. For the lacunar search we identified 492 publications, of which 61 met our inclusion criteria and of which we were able to retrieve full-text for 46. This lower rate of inclusion (12% vs 87% for MCAO) reflects the reduced sensitivity of the search terms used, reflecting in turn the heterogeneity in the terms used to describe animal experiments modelling lacunar stroke.
Finally, the performance of the RegEx for randomization and for sample size calculations did not perform as well for stroke studies as had been the case in other datasets. Problems related mainly to 'overcalling' of blinding and randomization (low specificity) and 'undercalling' of sample size calculations (low sensitivity). It may be that there are domain specific differences in language used, and these will be incorporated into further iterations of the RegEx used. Future work will include the integration with text mining tools which support screening (such as app. syrf. org. uk) and reduce the amount of annotations required by using topic analysis and active learning  [16, 17] ; and crowdsourced annotations performed in the context of reviews using the SyRF platform will contribute to the pool of annotated data available for tool development.
ANTARES The ANTARES neutrino telescope has been continuously operating since 2008. Located deep (2500 m) in the Mediterranean Sea, 40 km from Toulon (France), it is a 10 Mtscale array of photosensors, detecting neutrinos with energies above O(100) GeV. Based on the originally communicated locations of the GW signal and the GRB detection, high-energy neutrino candidates were initially searched for in the ANTARES online data stream, relying on a fast algorithm which selects only up-going neutrino track candidates  (Adrián-Martínez et al. 2016b). No up-going muon neutrino candidate events were found in a ±500 s time window centered on the GW event time -for an expected number of atmospheric background events of ∼ 10 -2 during the coincident time window. An extended online search during ±1 h also resulted in no up-going neutrino coincidences.
As it subsequently became clear, the precise direction of origin of GW170817 in NGC 4993 was above the ANTARES horizon at the detection time of the binary merger (see Fig. 1 ). Thus, a dedicated analysis looking for down-going muon neutrino candidates in the online ANTARES data stream was also performed. No neutrino counterparts were found in this analysis. The results of these low-latency searches were shared with follow-up partners within a few hours for the up-going search and a few days for the down-going search  (Ageron et al. 2017a,b). Here, ANTARES used an updated high-energy neutrino follow up of GW170817 that includes the shower channel. It was performed with the offline-reconstructed dataset, that incorporates dedicated calibration in terms of positioning, timing and efficiency  (Adrián-Martínez et al.
2012; Aguilar et al. 2011; Aguilar et al. 2007 ). The analysis has been optimized to increase the sensitivity of the detector and extended to the longer time window of 14 days. The search for down-going neutrino counterparts to GW170817 was made feasible as the large background affecting this dataset can be drastically suppressed by requiring a time and space coincidence with the GW signal. It was optimized, independently for tracks and showers, such that a directional coincidence with NGC 4993 within the search time window of ±500 s would have 3σ significance. Muon neutrino candidates were selected by applying cuts on the estimated angular error and the track quality reconstruction parameter. While ANTARES is sensitive to neutrino events with energy as small as O(100 GeV), the energy range corresponding to the 5% -95% quantiles of the neutrino flux for a E -2 signal spectrum is equal to  [32 TeV; 22 PeV].
2. In the central range from 10 TeV to 100 PeV, the upper limit for an E -2 power-law spectral fluence is F (E) = 0. 19 (E/GeV) -2 GeV -1 cm -2. Both the through-going track selection and the starting event selection were applied to data collected in the 14day period following the time of GW170817. Because of IceCube's location at the South Pole and 99. 88% on-time during the 14-day period, the exposure to the source location is continuous and unvaried. No spatially and temporally coincident events were seen in either selection during this follow-up period. The resulting upper limits are presented in Fig. 2. At most energies these are unchanged from the short time-window. At the lowest energies, where most background events occur, the analysis effectively requires stricter criteria for a coincident event than were required in the short time window; the limits are correspondingly higher.
ACKNOWLEDGMENTS (ANTARES) The ANTARES authors acknowledge the financial support of the funding agencies:  Centre National de la Recherche Scientifique (CNRS) ,  Commissariat  à l'énergie atomique et aux énergies alternatives (CEA),  Commission Européenne (FEDER fund and Marie Curie Program) ,  Institut Universitaire de France (IUF) ,  IdEx program and Uni-vEarthS Labex program  at  Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ),  Labex OCEVU  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ),  Région Île-de-France (DIM-ACAV) ,  Région Alsace  (contrat CPER),  Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seyne-sur-Mer, France ;  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Italy ;  Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO) , the  Netherlands; Council of the President of the Russian Federation  for young scientists and leading scientific schools supporting grants,  Russia ;  National Authority for Scientific Research (ANCS) ,  Romania ;  Ministerio de Economía y Competitividad (MINECO) :  Plan Estatal de Investigación  (refs.
LABEX ANR-10-LABX-63  within the  Investissements d'Avenir Programme  Grant No. ANR-11-IDEX-0004-02 ;  Germany -Bundesministerium für Bildung und Forschung (BMBF) ;  Deutsche Forschungsgemeinschaft (DFG) ;  Finanzministerium Baden-Württemberg ;  Helmholtz Alliance for Astroparticle Physics (HAP) ;  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ;  Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen ;  Ministerium für Wissenschaft, Forschung und Kunst des Landes Baden-Württemberg; Italy -Istituto Nazionale di Fisica Nucleare (INFN) ;  Istituto Nazionale di Astrofisica (INAF) ;  Ministero dell'Istruzione, dell'Universitá e della Ricerca (MIUR) ;  CETEMPS Center of Excellence; Ministero degli Affari Esteri (MAE) ;  Mexico -Consejo Nacional de Ciencia y Tecnología (CONACYT)  No.
Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the  Council of Scientific and Industrial Research of India , the  Department of Science and Technology, India , the  Science & Engineering Research Board (SERB), India , the  Ministry of Human Resource Development, India , the  Spanish Agencia Estatal de Investigación , the  Vicepresidència i Conselleria d'Innovació ,  Recerca i Turisme  and the  Conselleria d'Educació i Universitat del Govern de les Illes Balears , the  Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana , the  National Science Centre of Poland , the  Swiss National Science Foundation (SNSF) , the  Russian Foundation for Basic Research , the  Russian Science Foundation , the  European Commission , the  European Regional Development Funds (ERDF) , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , the  Hungarian Scientific Research Fund (OTKA) , the  Lyon Institute of Origins (LIO) , the  National Research, Development and Innovation Office Hungary (NKFI) , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  Natural Science and Engineering Research Council Canada , the  Canadian Institute for Advanced Research , the  Brazilian Ministry of Science, Technology, Innovations , and Communications, the  International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR) , the  Research Grants Council of Hong Kong , the  National Natural Science Foundation of China (NSFC) , the  Leverhulme Trust , the  Research Corporation , the  Ministry of Science and Technology (MOST), Taiwan  and the  Kavli Foundation.
Participants The English Longitudinal Study of Ageing (ELSA) is a representative panel study with six waves of data across 2002-13. Men and women aged 50 or more years, selected randomly, and their cohabiting partners were recruited to the first wave of ELSA (2002-03). 11392 adults participated (response rate 67%). To maintain representativeness, refreshment participants were recruited to the study at subsequent waves. The total analytical sample constituted 17 906 people. Constant objective criteria based on cognitive and functional impairment were used to ascertain dementia cases at each wave.
A total of 17 906 participants were recruited to the study between waves 1 and 6 (see supplementary figure  1 ). Wave 7 data (2014-15) was not included in the analysis other than to identify transient impairments in cognition or function. At each wave, participants were interviewed to collect extensive demographic, medical, and lifestyle data. Clinical examinations were conducted at waves 2, 4, and 6. Participants provided written informed consent.
The survival outcome (incident dementia) was dependent on both the current value and the slope of the trajectory of the longitudinal outcome (standardised score on cognitive function tests). Other independent covariates in the model were age at entry, age squared, time since entry in the study (representing the effect of aging), time squared, sex, calendar year, level of education, and midlife history of obesity, hypertension, and diabetes. The longitudinal component allowed random intercepts and random slopes for the effect of age at entry and time since entry to the study. From the joint model we obtained individual level predictions for probability of dementia for ELSA study participants who were alive, including those lost to follow-up. To obtain calendar trends in incidence of dementia, we fitted linear regression models with log odds of incident dementia as the outcome to the data with terms for sex, age, age squared, interaction of sex with age and with age squared, and calendar time.
The validity of this method to obtain predictions of incident dementia was assessed by comparing joint model predictions for future waves with data driven observed incidence rates. We validated the dementia case definition by comparing age specific incidence rates with those obtained from the Cognitive Function and Ageing Study-II. 13  examined the effect of changes in potential vascular and lifestyle risk factors on calendar trends in dementia incidence by entering risk factors as time varying covariates in regression models, with log odds of dementia as the outcome. The method for assessment of risk factors is shown in section 1 of the supplementary material.
We obtained the numbers of the population in England and Wales in each stratum of age and sex at year 2006 (the start of the IMPACT-BAM model iterations) from ONS. Numbers of men and women who reach age 35 and enter the model at each calendar year are also obtained from ONS predictions. The entering cohort of 35 year olds is assumed to be free of cardiovascular disease and cognitive and functional impairment. The baseline age and sex specific prevalence of each health state of the model was calculated using six waves of ELSA data pooled together and attributed to mid-point of the data collection timeframe, in 2006. The values obtained from this method   3 ). 33 Mortality rates from each health state were assumed to change in parallel with overall cardiovascular and non-cardiovascular mortality rates.
We calculated health state specific transition probabilities to cardiovascular and non-cardiovascular mortality by estimating the odds ratios of death from each health state using ELSA data (see supplementary table 1) and using the odds ratios to obtain the probability of deaths from cardiovascular and non-cardiovascular causes from each health state compared with that in the general population. To obtain state specific cardiovascular and non-cardiovascular mortality rates we applied the probability ratios to overall cardiovascular and non-cardiovascular mortality rates shown in supplementary figure  3. The decline in cardiovascular incidence paralleled the decline in cardiovascular mortality in ELSA (see supplementary figure  4 ). Hence we used the annual percentage change in cardiovascular mortality to estimate temporal change in cardiovascular incidence.
The calendar trend for cognitive impairment or dementia was obtained from the joint model previously described. The effect of calendar time was imposed on the transition probabilities for 2006 to obtain transition probabilities for future years. The IMPACT-BAM model was implemented in R statistical software and a package specifically written for it by author PB. Stata-14 (StataCorp 2015. College Station, TX: StataCorp LP) was used for data management and regression analysis to derive model inputs. ; and no calendar decline in dementia incidence. We conducted a sensitivity analysis assuming the incidence of cardiovascular disease will no longer decline and will remain stable after 2014.
Validation To validate methods, definitions, and assumptions, we ran the model starting in 2006 to predict prevalence of dementia in 2011. We compared model estimates with the prevalence of dementia observed from the Cognitive Function and Ageing Study-II. 20 Similarly, we compared the prevalence of cardiovascular disease with the health survey for England 2011,  34  and mortality rates with data from the ONS. We also compared model predictions with dementia prevalence at wave 7 of ELSA.
23 The case definition applied in this study, follows DSM-IV and other clinical criteria The case definition for dementia used in this study required moderate to severe impairments in cognition and function to minimise false positives. This results in inclusion of moderate to severe, rather than mild, cases of dementia. The main aim of this exercise is to inform future societal and healthcare needs. To this end, the dementia case definition is relevant to health and social policy as it forecasts numbers of people who would require supportive care owing to moderate or severe cognitive and functional impairment. Although residents of care homes were not included in ELSA, we took account of this group using the statistical joint modelling approach. Furthermore, data from carers and self reported doctor diagnosis of dementia identified cases among those unable to take part in the study.
Conclusion Our novel prediction model integrates recent downward trends in dementia and cardiovascular disease incidence with declining mortality rates in England and Wales. If these trends continue then the number of people with dementia will more than likely increase, from 792 000 in 2017 to more than 1. 2 million in 2040. The projected increase in the burden of dementia, despite the substantial downward trend in age specific incidence, results largely from improvements in life expectancy. The results have important policy implications in terms of care needs and public spending. The findings of our prediction model act as a benchmark to measure the impact of possible dementia prevention initiatives. The prevalence values obtained from the pooled ELSA data matched the prevalence values obtained at the mid-point (2006, wave 3).
Estimates for prevalence of cardiovascular disease are displayed as an example in supplementary figure  2  To improve statistical power, six waves of ELSA data were pooled. Prevalence estimates of cardiovascular disease and cognitive and functional impairment that define the health states were obtained from pooled data and attributed to 2006, which is the mid-point of the ELSA data collection timeframe and the baseline of the model Assumption 4: Prevalence estimates from six pooled waves of data provide a precise and accurate estimate of prevalence at mid-point of the data collection timeframe Epidemiological concept applied to Markov models Assumption 5: The prevalence of each health state at each calendar year from the starting point (2006) onwards, equals the number of people who were in that health state in the previous year, plus new incident cases, minus those who made the transition to another health state or died from any cause.
We thank all ELSA study participants and the study team for their contribution,  Panayotes Demakakos  for help with use of the ELSA dataset, and  Dimitris Rizopoulos  for statistical help. Contributors: All authors made substantial contribution to the conception and design of this study. EJB and MOF developed the original idea for IMPACT-BAM. SAA, MJS,  MGC , and PB analysed and prepared the input data for the model. PB and MGC developed and implemented the model, with input from MOF, SAA, MJS, EJB, and SC. SAA ran the joint model of longitudinal and time to event data, with input from MJS and GMT. SAA wrote the manuscript with coauthors. All authors contributed to interpreting the results, drafting the manuscript, and the revisions. SAA and EJB are the guarantors. Funding: This study was funded by grants from the  British Heart Foundation  ( RG/13/2/30098 ,  RG/16/11/32334 ).