dataesr/scientific-paragraphs-categorization huggingface dataset:
py in Data Citation 2) as the Stop Area was incorrectly defined in the original dataset; vii) Using the hierarchy mentioned above, all Areas include other Areas and non-bus stops Points of lower rank within a distance wd from its Centre (the distance between Areas is defined as the distance between their centres); viii) All remaining non-bus stops Points are 'promoted' to Areas; ix) Rail, Metro and Ferry Areas of same rank are merged if their distance is under wd (Rail) or wd/2 (Ferry, Metro). Here the threshold wd/2 is chosen to avoid joining together consecutive Stops of Tube and Ferry lines in London; x) All Areas can absorb a Bus stop Point if it is within a distance wd/2. In case of conflict, the Point is assigned to the closest Area; xi) Areas containing only one Point are 'declassed' to Points; xii) A stop Point can absorb lower rank Areas/Points if it is within a distance wd/2 and thus becoming an Area (Coach and Bus Stops cannot absorb in this step);.
3 finalformat. py Computes the inter-layer traveltime, corrects the minimal traveltime when 0. The output is the final format of the dataset. The file layers. csv is simply typed in a text editor. Several parameters can be easily modified in this workflow. The walking distance wd is defined in step 1. Walking speed, flight connection times, minimal connection time and the lower threshold to the minimal traveltime are defined in step 3. In addition, the same workflow can be also applied for all years where the NPTDR data are available(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011).
Datalogger According to our criteria, a reliable datalogger for environmental data includes the following features: -at least four ADCs, 100 dB dynamic range at a 1 Hz sampling rate; -a real-time transmission protocol, with an adjustable buffer (1 to 24 h); -a local data archive, recorded in mini-seed, keeping the last 3 months; -a robust and efficient delayed-time transmission protocol; -synchronisation with GPS; -sampling of all ADCs synchronised together; -weak power consumption; and -robust hardware. Published by Copernicus Publications on behalf of the European Geosciences Union. Sampling those data at a frequency higher than 1 Hz may not be necessary, since we are only interested in improving the long-period noise. In order to estimate the needed dynamic range, we used temperature and pressure data from the Geoscope network during the year 2012.
We first calculated the power spectrum density (PSD) of all signals, and then extracted the maximum and minimum PSD shown in Figs. 1  and 2. The Geoscope network includes broadband stations installed over the world, covering a large range of climate conditions. According to Figs. 1 and 2, a dynamic range of about 100 dB is the best performance at a frequency range of 1e-5 to 5e-1 Hz. Those data have to be both sent through the same transmission protocol and locally recorded in the same format as the seismological data, i. e. using seedlink and miniseed, commonly used in seismology. A robust delayed-time protocol has to be implemented in case of loss of transmission, allowing the recovery of the missing data, only, with an optimised algorithm.
Choice of software and hardware We specifically use standard and robust toolkits: a Linux distribution, running gpsd and ntpd for synchronisation with an Usb GPS receiver (XBU-353 from UsGlobalSat), rsync for delayed-time transmission protocol and seiscomp3 for realtime acquisition and transmission through its seedlink protocol (Gempa GmbH, 2014), and finally local archive capacity through its slarchive plugin. In order to study the possibilities of using a commercial embedded computer, more often used in robotics, we used a BeagleBone Black (BBB) Rev C. 1. Its main features are an ARM Cortex A8 (32 bit) processor at 1 Ghz, 512 MB of DDR3 Ram, 4 GB onboard flash, eight ADCs of 12 bits and a dynamic of 0-1. 8 V, and 1 W of power consumption. Other industrial embedded computers are probably better suited for our purpose, but we selected this one on the criteria of price and ease of use, but also because the BeagleBone Black is provided with a Debian 7 operating system, and an active community maintains the needed documentation and libraries.
Loading Linux and softwares on board The board is provided with an already installed Debian 7 system and beagleboard. org provides the latest ready-to-load system image. The methods for loading a Linux operating system are well documented (BeagleBoard. org Foundation, 2014a). We choose to keep this Debian distribution, mainly because it is a well-known robust operating system and because rsync, gpsd and ntpd are packaged, making them easy to install through Debian's utility aptitude.
Sampling ADCs through seiscomp3: bbb_plugin In the standard distribution, the ADCs are readable in a simple system file. However, standard drivers are not able to handle a continuous analogue application at a high sample rate (1 kHz maximum). Moreover, one of the advantages of the BeagleBone Black is a sub-module of its processor (AM335x from TI) called PRU, supporting that kind of "fast" analogue application. We then wrote a seedlink acquisition plugin filling those roles: 1. drive the PRU to launch sampling; 2. copy data in a buffer; and 3. send buffer to seiscomp server. Each library necessary to write this program is available on the Internet. We used the libpruio library, specifically written to manage the PRU on BBB (BeagleBoard. org Foundation, 2014b), and the development file provided with the seis-comp3 package.
We called this simple plugin bbb_plugin. Since seiscomp3 is able to manage the mini-seed conversion and the numerical decimation/filtering (see Sect. 7) through its StreamMonitor (Gempa GmbH, 2014), we used the send_raw_depoch() function in bbb_plugin in order to avoid the coding of the mini-seed conversion. Our code is available at https://github. com/mbdb/bbb_plugin. Figure  3  shows a diagram of the seedlink acquisition.
Digital considerations A basic concept of analogue-digital conversion is related to the data oversampled at the highest possible frequency, and then digitally decimated to the usable frequencies  (Asch, 2003). The quantisation noise of a datalogger is uniformly distributed between DC and the Nyquist frequency and is independent of the sampling rate. Therefore the effective noise power density due to the quantisation at the band of interest is lower at the highest frequency rates. Since it is difficult to estimate the noise reduction (a new filter design would be required at each sampling rate and a large number of data needed), we used the decimation option within the seiscomp3 StreamMonitor module and measured the highest frequency acceptable in terms of resource consumption. We therefore chose a sampling rate at 10 kHz.
). These tests suggest as well that this instrumentation could fulfill a central role in a station. Up to now, the BBB has only been considered as a secondary source of data at a station, which presents a problem, because the data centre receives two distinct sources of data, and has to associate different streams for each station. To overcome this limitation, another strategy has been tested: (1) we loaded a BBB with a seiscomp3 server and set up the acquisition of the seismological datalogger on it; and (2) we also installed our own software (bbb_plugin) for the acquisition of its ADCs, and set up the server to mix the streams. This latter configuration has advantages: -the data centre communicates with a unique source of data per station; and -the unique model of the source permits a total abstraction of the local instrumentation, allowing a standardisation of transmission protocols and local archive structure for every station.
Introduction Rheumatic heart disease (RHD) is the chronic consequence of an aberrant immune response to infection by the bacterial pathogen Streptococcus pyogenes that results in permanent scarring of the heart valves. [1]  This process, which may manifest clinically as heart failure, stroke and early death,  [2]  remains a major public health problem in developing countries. [3] [4] [5]  Despite this, efforts to measure the disease burden and institute control strategies are impeded by the lack of up-to-date epidemiologic data from endemic areas. [4, 6, 7]  Although current global estimates assert there are approximately 275,000 deaths due to RHD each year,  [8]  deriving such figures has been problematic. [4, 7]  In 2005, a WHO report found mortality estimates based on either vital registration data or verbal autopsy techniques to be unreliable, largely because of the difficulty distinguishing RHD-attributable death from other causes of cardiac death.
Design We established a new national and historical cohort of RHD patients in Fiji by probabilistic record-linkage, using diagnostic information and outcome events ascertained from routine clinical and administrative records for the period 2008-2012 (Fig  1 ). We used relative survival methods to estimate and examine RHD-attributable deaths in persons aged 5-69 years. [13]  We then used census data to calculate crude, age-specific and age-standardised RHD-attributable death rates for the wider population, as well as years of life lost (YLL) from which we estimate the cost to the economy. Finally, we compared our RHD mortality estimate with vital registration data for 2011-2012.
Sources of data The routine data used in this study was obtained from four sources: an electronic patient information system, a database of death certificates, a disease control register, and echocardiography clinic registers. Patient information system. Fiji's Ministry of Health uses a national hospital patient information system ("PATIS") based on a purpose-built Structure Query Language database. [16]  The database contains identifiers and basic demographic information, indexed by a nine- Death certificate database. Fiji's Ministry of Health receives reports of more than 90% of deaths because submission of a medical certificate is mandatory before burial or cremation. [17, 18]  There were a total of 30,257 deaths in people aged over four years for the period 2008-2012. Control Programme register.
Record-linkage Information that referred to the same person was amalgamated from the four datasets using identifier fields. [19]  We designed and calibrated a probabilistic record-linkage procedure using 1,406 known duplications in the patient information system from which we calculated the sensitivity and specificity (S1 Box). In its final configuration, our procedure identified the known duplications with sensitivity of 91. 4% and specificity of 99. 9% with record pairs considered a match if they achieved a posterior probability of over 50%. Stata 1 code for the procedure is available for download at: http://users. ox. ac. uk/*clme1250/data_linkage/linkage. html. Our first step was to find at least one match in the patient information system for each record available from the death certificate database, the control programme register and the echocardiography clinic registers.
Cohort and outcomes We focused on 2008-2012 because the most complete data were available for this period. The study was restricted to people aged 5-69 years because RHD is expected to cause very few deaths before five years of age and cause-of-death information can be unreliable in old age. Based on diagnostic information in control programme records, echocardiographic data, hospital discharges and death certificates, any individual with follow-up at ages 5-69 years who had a least one record of a diagnosis of either RHD or its precursor acute rheumatic fever (ARF) was eligible for inclusion (S2 Box). We assumed the onset of disease was in childhood irrespective of the date the individual became known to clinical services, circumventing potential bias due to late presentation. The primary outcome was the time to all-cause death defined by a date of death in either the patient information system or the death certificate database.
The secondary outcomes were cause-specific death defined: 1) Narrowly, where the underlying cause-of-death was ascribed to an International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD10) code pertaining to RHD or ARF; and 2) Broadly, where the death certificate listed an ICD10 code pertaining to either RHD, an alternative description of valvular heart disease, or a cardiac complication thereof (i. e. heart failure, stroke, infective endocarditis, arrhythmia) as an immediate or underlying cause-of-death, without listing ischaemic heart disease. We surmised there was no loss to follow-up because the linked patient information system and the death certificate database would include most deaths in the country during 2008-2012. Deaths in the cohort that went undetected due to, for example, emigration, would have led us to under-estimate RHD-attributable mortality.
Statistical methods Data were inspected for missing and outlying points, categorical variables were tabulated and continuous variables were summarised in histograms. The cohort data were expanded into thirteen five-year age categories and standardised mortality ratios (SMR) and relative survival rates were calculated by applying the background age category, gender and ethnicity specific death rates to the cohort. [13]  Background death rates were calculated using the count of deaths in the patient information system and death certificates after duplicates had been removed divided by estimates of population using data from the Fiji Bureau of Statistics. [14]  Before application these rates were checked for adherence with expectations by performing of standard quality control checks such as log rate versus age category.
Results In total, 1,773,999 records were available, including 34,773 records that pertained to a death. Links were identified in the patient information system for 87. 1% of control programme records, 85. 3% of echocardiography clinic records and 66. 0% of death certificates (S3 Table ). After selecting eligible individuals with an RHD or ARF diagnosis, a cohort of 2,619 individuals remained for analysis (Fig  2 ). Of these, 1,038 (39. 6%) were present in more than one database (S1 Fig). Characteristics are summarised in Table  1 ; the person-time observed totalled 11,537. 5 person-years. During follow-up, 430 of the 2,619 (16. 4%) RHD/ARF patients were linked to a death in the patient information system, the death certificate database or both. This equated to 2. 1% of the 20,796 deaths in the general population in the same age bracket during this time.
This estimate remained stable to adjustments in the record-linkage threshold and between the 2008-2010 and 2011-2012 components of the dataset (S7 Table ). Age-specific rates of RHD-attributable death increased throughout life (Fig  3A ). Standardised to the portion of the WHO World Standard Population aged 0-69 years, our primary estimate equates to 9. 9 deaths (95% CI 9. 8-10. 0) per 100,000 person-years (S5 Table ). Additionally, we estimated 323. 3 YLL (95% CI 317. 9-328. 6) per 100,000 person-years, equating to a WHO standardised rate of 331. 0 YLL (95% CI 330. 4-331. 5) per 100,000 person-years (S5 Table ). Agespecific RHD-attributable YLL rates were elevated from late childhood onwards (Fig  3B ). The cost of these deaths for the five-year period was current Fiji Dollar $58,810,903, which at midmarket rates equates to United States Dollar $30,387,153.
Finally, we estimated there were 132 RHD-attributable deaths during 2011-2012 based only on deaths associated with a death certificate, compared to 81 RHD deaths reported in vital registration data (Fig  4 ). Only five other conditions caused more than 132 deaths in the wider population while ten caused more than 81 deaths (S8 Table ). Moreover, only drowning caused more than the 40 deaths attributable to RHD at ages 5-29 years, which was greater than the number attributed to suicide and road injury, both well-recognised causes of death in young people. Using the background death rates in the general population, we were able to estimate RHDattributable mortality by measuring excess mortality. These methods, which are widely used for population-based cancer survival analyses  [13] , are highly applicable to RHD, a disease for which cause-of-death information is often absent or unreliable.
[6, 25]  For example, if our results are compared to underlying cause-of-death classifications in vital registration data for 2011-2012, we find 1. 6-fold more RHD-attributable deaths at ages 5-69 years, a discrepancy that peaked at 3. 8-fold in the 30-49 years age group (Fig  4 ). This finding is consistent with a recent study of mortality amongst RHD patients in Western Australia which, by reviewing death certificates and other clinical data, concluded a third of RHD-attributable deaths were ascribed to other underlying cause-of-death diagnoses. [34]  Few existing data are available for comparison. One recently published study presents RHD-attributable death rates based on vital statistics for South Africa for the period 1997-2012 during which time the crude all-age death rate declined from 1.
Alternatively, we can compare our results to the estimates made by the GBD project (Fig  3 ). [28]  Directly standardised to the population aged 0-69 years, we report higher death and YLL rates than GBD (S6 Table ), the latter amounting to a 2. 6-fold difference in the death rate and 2. 4 in the YLL rate compared to the GBD's developing countries estimate. Thus our data not only have important implications for the Pacific region but also, if generalisable to other developing countries, for global summary estimates. Although the results appear reasonable, they have some limitations. First, there are potential shortcomings to using background death rates to estimate exposure-attributable mortality. If we were wrong to assume time-to-death from RHD was independent of time-to-death from other causes, we may have over-estimated RHD mortality; however, the impact would be small and alternatives such as cause-specific survival remain unsatisfactory.
Acknowledgments We thank the  Ministry of Health  and the  National Disease Control Programme  for providing the datasets as well as their employees who compiled them, particularly those at the  Ministry of Health Health Information Unit. Access  to and use of the data was approved by the  Permanent Secretary for Health in Fiji  and the  Fiji National Health Research Committee. TP thanks  Professor Adrian Hill  for supervision and guidance,  Professor Michael Goldacre  for suggestions on recordlinkage, and  Ms Nia Roberts  for advice on the systematic review, all at the  University of Oxford. An earlier version of this study based on a deterministic record-linkage procedure was presented at the  World Congress of Cardiology 2014. [40].
Use of Fiji Ministry of Health data would also be subject to approval by the  Fiji National Health Research Committee. Funding for salaries and research costs was received from the  Medical Research Council UK  (Fellowship G1100449; http://www. mrc. ac. uk), the  British Medical Association  ( Josephine Lansdell Grant ; www. bma. org. uk) and the  Sir Halley Stewart Trust  ( Research Grant ; www. sirhalleystewart. org. uk)awarded to TP. Additional funding for a cost of illness study was received the  World Health Organisation  awarded to TWB, DEB and  ACS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Western blot After neuronal cells lysates obtainment, the following antibodies were used: anti-GluN2A (#AB1555P, Millipore); anti-GluN2B (#05-920, Millipore); anti-β-Actin (#A5441, Sigma) and anti-NMDAR1 (GluN1) (#MAB363, Chemicon International). Western Blots were quantitated using the Image J software.
This protocol is composed of several Matlab scripts and functions that are freely downloadable at the following address: https://github. com/benzekry/fit_tumor_growth. In the following, we detail each step that should be sequentially launched (by typing the script name in a Matlab command window) in order to perform the full task. The associated script is indicated in bold font. We perform these on an illustrative data set example composed of two tumor growth curve groups, each n = 20 mice.
5. Fit the model to the data using a built-in minimization function of Matlab. Options incude: lsqcurvefit, fmincon, and fminsearch. Personal experience suggests a better ability of fminsearch to escape local minima, although no bound constraint on the values of the parameters can be prescribed with this function. This can be circumvented by penalizing the model for parameter values outside the bounds. Alternatively, stochastic algorithms (such as Markov Chain Monte Carlo) or multi-start optimization might also be considered. We recommend fitting each animal growth curve separately, in particular when inter-animal variability is high, as opposed to fitting the average or median growth curve. The script that launches the fit and exports individual plots is launch_fit. m. Individual plots are exported into folders that have user-defined names ('Group1' and 'Group2' in our example).
Acknowledgments This project was supported, in part, by the  National Aeronautics and Space Administration  under NSCOR grants  NNJ06HA28G  and  NNX11AK26G  and by Award Number  U54CA149233  from the  National Cancer Institute , both to L. Hlatky. This study also received support within the frame of the  LABEX TRAIL ,  ANR-10-LABX-0057  with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the "Investments for the future"  Programme IdEx  ( ANR 10-IDEX-03-02 ).
ucsc. edu/) and identified a number of genomic features coincident with each of the four FG-associated variants. Three of these SNPs, upstream of the ABO promoter, reside in a DNase I hypersensitive site with canonical enhancer marks in ENCODE Consortium data: H3K4Me1 and H3K27Ac (Supplementary Fig. 5 ). We analysed all SNPs with similar annotations, and found that these three are coincident with DNase, H3K4Me1 and H3K27Ac values each near the genome-wide mode of these assays (Supplementary Fig. 6 ). Indeed, in haematopoietic model K562 cells, the ENCODE Consortium has identified the region overlapping these SNPs as a putative enhancer  14. Interrogating the GTEx database (N ¼ 156), we found that rs651007 (P ¼ 5. 9 Â 10 À 5 ) and rs579459 (P ¼ 6. 7 Â 10 À 5 ) are eQTLs for ABO, and rs635634 (P ¼ 1.
3 Â 10 À 3 , p WST ¼ 3. 2 Â 10 À 3 vs p SKAT ¼ 0. 6, p WST ¼ 0. 04 using the 10 exome chip or the 26 variants not captured on the chip, respectively, Supplementary Table  16 ). Pathway analyses of FG and FI signals. In agnostic pathway analysis applying MAGENTA (http://www. broadinstitute. org/ mpg/magenta/) to all curated biological pathways in KEGG (http://www. genome. jp/kegg/), GO (http://www. geneontology. org), Reactome (http://www. reactome. org), Panther (http://www. pantherdb. org), Biocarta (http://www. biocarta. com) and Ingenuity (http://www. ingenuity. com/) databases, no pathways achieved our Bonferroni-corrected threshold for significance of Po1. 6 Â 10 À 6 for gene set enrichment in either FI or FG data sets (Supplementary Tables  17  and 18 ). The pathway P values were further attenuated when loci known to be associated with either trait were excluded from the analysis.