dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure5. Sedimentological units, lithology(Andrén et al. , 2015a), and position of 14 C ages (Van Helmond et al. , 2017)  vs. depth and IODP Site M0059 temperature proxies and related datasets plotted vs. age (extrapolated below ∼ 48. 64 m c. d. ). (a) Environmental zones (EZs) based on diatom data. (b) Pollen-inferred climate reconstructions based on the modern analogues technique, with the shaded area reflecting the range of the best eight analogues. Distmin1 and Distmin2 are the squared chord distance between fossil sample and the best to 8th-best modern analogues, indicating similarity between fossil and modern samples. (c) Methane and branched isoprenoid tetraether (BIT) indices, TEX H 86 -based subsurface water temperature, TEX H 86 -based surface water temperature, TEX L 86 -based surface water temperature from the Gotland Deep (Kabel et al.
Acknowledgements. This investigation used samples from IODP Expedition 347, and we are very grateful to IODP and ESSAC for funding and organizing the expedition. Stephen Obrochta ,  Sofia Ribeiro ,  Ingo Feeser , and  Walter Doerfler  are gratefully acknowledged for insightful discussions and comments on the age model, dinoflagellate cyst taxonomy and ecology, and the Lake Belau pollen record. We thank the crew and scientific team of IODP Expedition 347 as well as the technical staff at  MARUM, Bremen , for their support during the expedition's off-and onshore phases. We are grateful for technical support from the different laboratories involved in the study. We thank  Audrey Limoges ,  Filipa Naughton , and the anonymous referee for their detailed input to the discussion paper, and we thank  Markus Kienast  for editing.
gov (AACT) and identified rare disease trials by cross-referencing MeSH terms in AACT with the list from Orphadata. We examined the effects of prevalence and phase of study in a multiple linear regression model adjusting for other statistically significant trial characteristics. Results: Of 186941 ClinicalTrials. gov trials only 1567 (0. 8%) studied a single rare condition with prevalence information from Orphadata. There were 19 (1. 2%) trials studying disease with prevalence <1/1,000,000, 126 (8. 0%) trials with 1-9/ 1,000,000, 791 (50. 5%) trials with 1-9/100,000 and 631 (40. 3%) trials with 1-5/10,000. Of the 1567 trials, 1160 (74%) were phase 2 trials. The fitted mean sample size for the rarest disease (prevalence <1/1,000,000) in phase 2 trials was the lowest (mean, 15. 7; 95% CI, 8.
Deviation from the perceived gold standard RCT is uncommon. This statement is supported by Buckley  [4] , who presented a short summary of clinical trials of drugs for rare diseases approved by the European regulator between 2001 and 2007. Some of these studies had as few as 12 patients and some several hundreds. For example, the marketing authorisation of carglumic acid for hyperammonaemia due to N-acetyl glutamate synthase deficiency was supported by one pharmacokinetic study with 12 patients and one retrospective study with 20 patients. In contrast, the marketing authorisation of sorafenib tosilate for renal cell and hepatocellular carcinomas was supported by one phase III renal trial with 903 patients and one phase III hepatic trial with 602 patients. Bell and Tudur Smith compared the characteristics of rare and non-rare disease clinical trials registered in ClinicalTrials.
Aggregate analysis of clinicaltrials. gov database (AACT) The database from ClinicalTrials. gov, Aggregate Analysis of ClinicalTrials. gov (AACT), is comprised of clinical studies registered up to 27 September 2015  [7, 9]. A comprehensive documentation of definitions of all variables is available on the ClinicalTrials. gov Protocol Registration System  [10]. Each study in AACT may have information on the study characteristics such as types of study (interventional, observational, patient registry, or expanded access), phase of investigation (phase 1, 2, 3 or 4), design features of the study such as the intervention model (crossover, factorial, parallel or single group assignment), masking (double blind, single blind or open label), allocation (randomized, non-randomized), primary endpoint (e.
Orphadata Orphadata is a database of rare diseases compiled by the 40-country consortium Orphanet coordinated by the French National Institute of Health and Medical Research (INSERM) team  [11]. It gives an inventory of rare diseases comprised of the typology of the disease and crossreferencing with external classifications such as the 10 th International Classification of Diseases (ICD-10), Online Mendelian Inheritance in Man (OMIM), United Medical Language System (UMLS), MeSH and Medical Dictionary for Regulatory Activities (MedDRa). Orphadata, also contains epidemiology data such as type of prevalence (point prevalence, birth prevalence, lifetime prevalence, incidence, or the number of cases/families, see Posada de la Paz et al. for definition of types of prevalence  [12] ) by geographical area (e.
g. , country, continent), average age of onset, clinical signs, and for some rare disorders, the associated genes and their influences in the pathogenesis of the disease  [8]. Prevalence data for rare conditions in Orphadata are classified into six possible classes, namely, <1/1,000,000, 1-9/ 1,000,000, 1-9/100,000, 1-5/10,000, 6-9/10,000, >1/1,000, along with "not yet documented" and "unknown". These data were obtained from either published literature or registries. Some information from published literature were yet to be validated by experts and so the entry was recorded as "Not yet validated". For our work, we focus on the epidemiological data such as type and class of prevalence data and geographical area. Figure  3  (see Appendix 1) shows that there were 9199 rare diseases in Orphadata but only 5029 (55%) had prevalence information.
Merging of AACT and Orphadata AACT and Orphadata were downloaded on 9 May 2016. A technical description of the merging of AACT and Orphadata is given in Appendix 2. For our analysis, we identified trials in AACT by matching the MeSH terms in AACT with those in Orphadata. Trials in AACT with MeSH terms not in Orphadata were declared as trials not studying nonrare diseases. In our work, focus was restricted to interventional phase 2 and/or 3 trials in a single rare disease with treatment as the primary purpose conducted in the US only, EU only (member states of the EU and associated countries) or in both US and EU. We restricted trials to these countries only because we believe that rare disease prevalence was well estimated and homogeneous in these countries whereas the prevalence for some diseases varies very considerably between US/EU and some other countries.
See Appendix 2 for details on the merging of diseases and their class of prevalence. Note that if only the number of cases/families was recorded, the prevalence was assumed to be <1/1,000,000. Figure  4  (see Appendix 3) shows that there were 186941 trials in ClinicalTrials. gov and of these 28547 were interventional phase 2 and/or 3 treatment trials conducted in the US and/or EU. There were 2136 trials that studied rare conditions only and of these 2019 studied one rare condition only. Of the 2019 trials of a single disease in the Orphadata database, 415 were excluded from analyses; 16 because they had prevalence greater than 5/10,000 (because a rare disease is defined to affect less than 5/10,000) and 399 because they had no prevalence information. An additional thirty seven trials studied conditions with prevalence recorded as "Unknown".
Statistical analyses The characteristics of trials of diseases in each prevalence category were summarised, either as frequencies and percentages for categorical data or means and standard deviations for continuous data. In addition to the characteristics listed in Section Aggregate analysis of clinicaltrials. gov database (AACT) above, the duration of collection of primary outcome were calculated, from the date that enrolment to the protocol begins to either the actual completion date or anticipated date where trials were ongoing. Phase 2/3 trials were grouped with phase 3 trials; these will be collectively referred as phase 3 henceforth. Analysis of variance and linear regression models were used to investigate the association between prevalence and trial characteristics and the sample size.
The increase was noticeably larger in phase 3 trials compare to phase 2. However, due to small number of trials in some classes, it is difficult to make comparisons. The generalisability of the results obtained in this study rely on the extent to which trials included in the database are representative. Although institutions such as the International Committee of Medical Journal Editors (ICMJE) require certain studies to be registered either in Clinical-Trials. gov or other equivalent registries  [14, 15] , it seems likely that certain types of trials are more likely to be registered, especially, efficacy trials in serious or life-threatening diseases with investigational new drugs regulated by the FDA and EMA. This is a strength of this research as we concentrated on interventional phase 2 and/or 3 trials where there would be better coverage.
However, phase 2 and/or 3 trials taking place in EU site(s) initiated after 2011 may not be registered in ClinicalTrials. gov but in the EU Clinical Trials Register which was launched on 22 March 2011  [16]. A limitation with our study is the potential selection bias because we included only trials conducted in the US and/or the EU. This is a necessary measure to exclude trials studying diseases with low prevalence in the US/EU but high prevalence elsewhere. For example, there was a multi-centre interventional trial on tuberculosis with locations in the US, United Kingdom and Peru. The annual incidence in these countries are 1-9/ 100,000, 1-5/10,000 and >1/1,000, respectively  [8, 17]. Another possible limitation with our study is that we considered a condition to be rare if information on prevalence was listed in Orphadata.
This database is updated on a regular basis and some conditions may have been missed out or with no prevalence information. Table  8  in Appendix 11 provides a list of trials in the AACT database where the conditions studied were listed in Orphadata but for which no value of prevalence is given. Prevalence of some diseases changes over time and because the prevalence information in Orphadata is updated regularly, old prevalence data are not retained. This presents a weakness to the study as trials studying rare diseases prior to 2016 were assumed to have updated prevalence. As explained in the methods section, we have used point prevalence to classify diseases into prevalence classes where this is available. In some cases, some other measure of prevalence has been used. In this project diseases are classified into groups according to their prevalence value and because of categorising continuous variable we have lost some information.
We have focussed attention on the sample size of trials in rare diseases. The AACT database also contains additional data, for example on trial design features such as the intervention model (crossover, factorial, parallel or single group assignment), masking (double blind, single blind or open label), allocation (randomized, nonrandomized), primary endpoint (e. g. , efficacy, safety, pharmacodynamics, pharmacokinetics) and number of interventions in a trial. These might also vary with disease prevalence among rare disease trials. Investigation of such effects could be the subject of further work.
Conclusions This study has investigated sample sizes for clinical trials in rare diseases using data from the AACT database from ClinicalTrials. gov and prevalence data from the Orphadata databases from Orphanet. These databases provide rich resources to understand and characterise clinical trials studying rare diseases or conditions. The inventory of rare disease in Orphanet is updated on a regular basis and the prevalence and other information of the diseases are based on published scientific articles. We have limited our analyses to phase 2, phase 2/3 or phase 3 trials with treatment as the primary purpose conducted in the US and/or EU (member states of the EU and associated countries). We found that where were very few multi-nation trials suggesting that the opportunities to conduct larger or 'adequately' size trials were underused.
Merging of AACT and Orphadata Data from AACT is supplied as delimited text files and Orphadata were downloaded as Extensible Markup Language (XML) files. A database schema was first derived from evaluating the content of each text files in order to identify the database requirements. A SQL Server Integration Services (SSIS) program was then created to map the text files to the database schema and to extract and load the data into the database. A random sample of data from each text file was used to check against the database to verify data integrity. The Orphadata database schema was modified to include two additional tables to store disorder and prevalence data. An Extensible Stylesheet Language Transformation (XSLT) was created to parse the XML to create SQL insert statements for each disorder and prevalence entry.
Data integrity was checked again by taking a random sample from the original XML files to compare against the data inserted into the database. For each trial, the prevalence of the disease in the countries where the trial took place was identified. If there was more than one prevalence entries for the disease in the trial location, the prevalence was used based on the following variables (in decreasing order of preference): 1. Validation status: (i) validated, and (ii) not yet validated. 2. Type of prevalence: (i) point prevalence, (ii) lifetime prevalence, (iii) prevalence at birth, (iv) annual incidence, and (v) cases/families. If no prevalence information was available for the disease in the trial location then the prevalence for a neighbouring country or another country from the same geographic region was used.
For trial conducted in US only, prevalence from that country was used. If there was no data from the US, data from the following was used (in decreasing order): North America, Europe (region), individual European country, Worldwide and other countries. For trials conducted in a single European country, data from the following locations were used (in decreasing order): the European country where the trial was conducted, Europe (as a region), other European countries, US, North America, Worldwide and other countries. For trials conducted in Europe, prevalence from Europe was used. If that was not available data from other locations were used, in decreasing order: other European countries or US, North America, Worldwide and other countries. Finally, for trials conducted in both US and Europe, prevalence data from both US and Europe were used and then ordered by validation status and type of prevalence.
Availability of data and materials The datasets analysed during the current study are available from AACT [http://www. ctti-clinicaltrials. org/what-we-do/analysis-dissemination/stateclinical-trials/aact-database] and Orphadata [http://www. orphadata. org. XML data version 1. 2. 4 / 4. 1. 6 [2016-04-08] (orientdb version), accessed 1 May 2016].
An uncontrolled gas leak from 25 March to 16 May 2012 led to evacuation of the Total Elgin well 25 head and neighbouring drilling and production platforms in the UK North Sea. Initially the atmospheric flow rate of leaking gas and condensate was very poorly known, hampering environmental assessment and well control efforts. Six flights by the UK FAAM chemically-instrumented BAe-146 research aircraft, were used to quantify the flow rate. Where appropriate, two different methods were used to calculate the flow rate: 1. Gaussian plume fitting in the vertical and 2. Direct integration of the plume. When both methods were used, 30 they compared within 6% of each other and within combined errors. Data from the first flight on 30 March 2012 showed the flow rate to be 1. 3±0. 2 kg CH 4 s -1 , decreasing to less than half that by the second flight on 17 April 2012.
The aircraft was equipped with a range of instruments including continuous methane measurement by cavity-enhanced absorption spectroscopy (Fast Greenhouse Gas Analyzer, Los Gatos Research Inc). Whole air grab sampling was carried out by two independent systems: the aircraft's in-built stainless steel flasks sampling facility and also manually into 3l Tedlar bags. Data from 6 flights from 30 th March to 15 th August 2012 are available and presented below. The aircraft data were used to successfully characterise the leaking gas (flow rate and composition), allowing a plan for remedial action at the well head to be implemented. This paper presents the analysis of these data.
Atmospheric measurements CO 2 and CH 4 were measured in situ on the aircraft using a modified Los Gatos Research Inc. Off-Axis Integrated Cavity Output Fast Greenhouse Gas Analyser (FGGA model. This was calibrated in-flight against gas standards certified by the Max-Planck Institute for Biogeochemistry (Jena) as part of the Infrastructure for Measurements of the European Carbon Cycle project (EU 13 IMECC; see http://imecc. ipsl. jussieu. fr/). The stability of these standards was also cross-checked against Royal Holloway laboratory standards. All reported CH 4 mixing ratio data are traceable to the National Oceanographic and Atmospheric Administration NOAA-04 scale  (Dlugokencky et al. , 2005). A technical summary of the FGGA deployed on-board the FAAM aircraft, the calibration system, data analysis and quality control methods developed by the University of Manchester and FAAM is presented elsewhere  (O'Shea et al.
Climatological characteristics of the Pacific coast of Central America Precipitation on the Pacific coast of Central America is characterised by a well-defined rainy season following the annual migration of the Inter Tropical Convergence Zone (ITCZ,  Portig, 1965; Hastenrath, 1967). In Guatemala, this rainy season is from May to October (Figs. 2  and 3 ), ending when the ITCZ migrates south  (Portig, 1965; Hastenrath, 1967). During the Northern Hemisphere summer, easterly waves and tropical cyclones travel across Central America with the north-east trade winds, bringing precipitation to the region   , 1928-2012)  and gridded precipitation across Central America (GPCC V7 reconstruction database; grid size 0. 5 • ;  Schneider et al. , 2015)  averaged for the May-October period and plotted according to the strength of the correlation coefficient.
The  Claxton (1986 Claxton ( , 1998) )  compilation of drought and flood episodes in Guatemala starting from the middle of the 16th century is used in this study to corroborate the information obtained from city and municipal council records and to fill gaps associated with the way in which the information was recorded in the Actas. Claxton, however, compiled his data from a range of sources: discontinuous primary documents, published secondary works and, for some events, personal correspondence. The disparate and discontinuous nature of the material raises the possibility that some information may be missing from the hydrological reconstruction. We have therefore been cautious in our use of the data and have specifically excluded from our reconstruction extreme weather events for which the recording is based on personal correspondence that cannot be cross-checked with other sources.
Hydrological reconstruction for Guatemala City and Antigua Guatemala The books of the Actas de Cabildo and Actas Municipales were analysed chronologically to produce an almost continuous record of hydrological variability affecting Antigua Guatemala and Guatemala City for the period  1640 -1945 (Figs. 4, 5) (Figs. 4, 5). The data as plotted in these figures and extracts of original text are available for download from the NOAA World Data Service for Paleoclimatology archives (https://www. ncdc. noaa. gov/paleo/study/22772). Only 11 years  (1776, 1782, 1785-1786, 1797-1798, 1830, 1855, 1858 and 1861-1862)  could not be reconstructed because the books were missing or damaged. Our analysis revealed 12 years of very wet conditions, 25 years of wet conditions, 34 years of dry conditions and 21 years of very dry conditions.
Reconstruction verification In the overlapping  35-year period (1910 -1945; Fig. 6 ) the driest (1914) and wettest (1933) years of the 20th century recorded by three nearby meteorological stations are correctly classified in the documentary-based hydrological reconstruction (Fig. 6 ). A further two dry years  (1912 and 1920)  and two wet years  (1921 and 1944 ) also match the anomaly recorded from instrumental observations, but two years  (1911 and 1923)  categorised as wetter than average in the index were drier than average according to the El Potrero rainfall observations. In addition, the wetter conditions of 1896-1897 according to instrumental observations from Guatemala City are picked up in the Actas, including the wettest month of the entire 7-year dataset (June 1897; 422. 5 mm; data not included in Fig.
e. 35-year overlap; Fig. 6)  despite the fact that the information inferred from the Actas relates to feedbacks and components of the hydrological cycle and catchment pre-condition rather than simply rainfall volume. A discrepancy arises in the years 1911 and 1923, which were categorised as wet in the index but according to the El Potrero rainfall observations were drier than average. In 1923, the El Potrero monthly rainfall data show that precipitation between May and August was less than half the average for the 1910-1970 period, but rainfall accumulation in October was 3 times (325 mm) the average (119 mm) due to a temporal or storm. Thus, the convective rainfall associated with a temporal, which is a short, high-intensity rainfall event, could induce flooding impacts that were then recorded in the Actas even though the event occurred in a very localised area or during a generally drier wet season.
The weather station measurements used in this study from the Instituto Nacional de Sismología, Vulcanología, Meteorología e Hidrología (the National Institute of Seismology, Vol-canology, Meteorology and Hydrology) in Guatemala City are part of the Global Historical Climatology Network database  (GHCN v2; Peterson and Vose, 1997)  and can be consulted at ftp:// ftp. ncdc. noaa. gov/pub/data/ghcn/v2 (41078640030 EL POTRERO, 41078640000 GUATEMALA OBS, 41078640005 ANTIGUA). The hydrological index (including index values, document dates and references) and the confidence rating developed in this study, as presented in Figs. 4  and 5 , can be downloaded from the NOAA World Data Service for Paleoclimatology archives (https://www. ncdc. noaa. gov/paleo/study/22772). Supplement. The supplement related to this article is available online at: https://doi.
The X-ray structure of ManNOE in complex with BxGH99 reveals a 4 C 1 ground-state conformation mimicking the Michaelis complex, and we conclude that this inhibitor acts primarily to mimic the charge of the transition state. ■ EXPERIMENTAL SECTION X-ray Crystallography. Data reduction was performed using xia2  42  or XDS. 43 Data set HKL index was matched to a previous solution using Aimless  44, 45  software and FreeR set was generated from BxGH99-ManddMan data, and then used for every other data set. Initial polypeptide chain model was obtained from the same previous solution, refined against BxGH99-ManddMan data and used as starting model for other structure solutions. Refmac5 28 with ProSmart was used for restrained refinement and Coot 46 for real-space refinement. During model rebuilding the F obs -F calc difference map was examined at 3σ.