dataesr/scientific-paragraphs-categorization huggingface dataset:
4±0. 1. Tagus River SPM. The SPM was obtained from the Tagus Estuary near the mouth of the Tagus River once a month over the course of a year (excluding the month of August). Data from the Tagus River SPM showed that the summed brGDGT and crenarchaeol concentrations in the river SPM varied throughout the year and were on average 45±23 µg gOC -1 , and 9. 8±6. 8 µg OC -1 , (Figs. 3a-b; Table  3 ), respectively, resulting in only small variations in the BIT index (i. e. 0. 8±0. 1; Fig. 3c ; Table  3 ). The distribution of brGDGTs (Fig. 3 ). Tagus River Floodplain sediments. The average crenarchaeol concentration is fairly low in the Tagus River Floodplain sediments, 2. 8±1. 7 µg gOC -1 , conversely, the average sum of the brGDGTs in the sediments, 70±26 µg gOC -1 , is the largest out of the entire transect (Figs.
, 2012; Dirghangi et al. , 2013; Menges et al. , 2013). Our re-analysis of the soils taking into account the novel 6-methyl brGDGTs now provides the possibility to re-evaluate and reconstructed pH (R 2 =0. 89) following the 1:1 line (Fig. 9a ). Differences in soil pH also affect the degree of cyclization of brGDGTs  (Weijers et al. , 2007a; De Jonge et al. , 2014b)  and indeed we find a significant positive correlation between DC' and soil pH (R 2 =0. 74). The effect of MAT is not clearly revealed in the dataset. For the global soil brGDGT dataset a strong relationship exists between MAT and MBT' 5Me  (De Jonge et al. , 2014a). Although we observe substantial variation for MBT' 5Me in soils (i. e. 0. 3-0. 7; Fig. 3f ) for this dataset, we do not observe a statistically significant relationship of MAT with MBT' 5Me.
PC2 also explains a substantial part of the variance in the dataset (25. 6 %, Fig. 6b ) and is correlated with DC' (R 2 =0. 84, Fig. 7b ). Since pH is also the main driver of DC'  (Weijers et al. , 2007a) , it suggests that differences in pH are also responsible for the variance seen in Setúbal Canyon score most positively on PC2. A high degree of cyclization of brGDGTs has been observed previously in marine sediments from a Svalbard fjord and attributed to marine in-situ production in the alkaline pore waters of marine sediments  (Peterse et al. , 2009; Weijers et al, 2014). Re-analysis of the Svalbard sediments for brGDGTs actually showed that this cyclization affects the tetra-and pentamethylated brGDGTs to a much larger extent than that of the hexamethylated brGDGTs (Sinninghe Damsté et al.
BiogeosciencesDiscuss. , doi:10. 5194/bg-2016Discuss. , doi:10. 5194/bg- -146, 2016     Manuscript under review for journal Biogeosciences Published: 30 May 2016 c Author(s) 2016. CC-BY 3. 0 License. these data. It is clear that the fractional abundances of the novel 6-methyl brGDGTs vary to a large extent. The IR II and IR III vary from 0. 1 to 0. 9 (Fig. 4) and some of the soils score very negatively on PC1 (Fig. 6b), which is predominantly determined by the fractional abundance of the 6-methyl brGDGTs. From the global soil brGDGT dataset(De Jonge et al. , 2014a)  it was evident that the main factor influencing the fractional abundance of the 6-methyl brGDGTs is soil pH with an increased abundance in high pH soils. In the Tagus River basin soil pH indeed shows a large variation, i. e. from 5.
Acknowledgements The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement n° [226600]. JSSD is supported by funding from the  Netherlands Earth System Science Center (NESSC)  though a gravitation grant from the  Dutch ministry for Education, Culture and Science. We thank the captain and crew of the  R/V Pelagia  for their support with sampling. We thank  J. Ossebaar  and  E. C. Hopmans  for their help with the HPLC-APCI-MS analysis,  J. Ossebaar  for his help with the element analyzer, and  L. A. C. Rosales  and  A. Mets  for help with sample preparation. Biogeosciences Discuss. , doi:10. 5194/bg-2016-146,2016 Manuscript under review for journal Biogeosciences Published: 30 May 2016 c Author(s)  2016.
The beam dimensions during the first alignment run were σ x × σ y = 0. 7 × 1. 4 mm 2. During the second alignment run they were 0. 9 × 0. 9 mm 2. The latter beam aspect ratio corresponded closely to the nominal beam spot used for data taking runs at full intensity. These alignment procedures have been repeated whenever the target has been moved or replaced. The method takes advantage of the facts that the proton beam is attenuated and generates additional radiation as it passes through material, and that the RMS width of the beam increases due to multiple scattering. Variation of the intensity and spatial distributions measured in the hadron monitor and loss monitors associated with the horn crosshairs are used to determine the positions of the elements being studied. The measured data are correlated to the proton beam position and are fit to a set of empirical functions to obtain quantitative determination of device positions and angles.
The Fermilab control system known as ACNET  [51] , using various plotting programs, was used to obtain and record the readings of various monitors described in the previous section. These graphs become a permanent record of the experiment. The main function of the work in the MINOS Control Room was to monitor and keep record of the performances of the two detectors. Single events from the two detectors were displayed in real time to give visual confirmation that systems were live. In addition, the JAVA Analysis Studio 3 (JAS3), an object based data analysis package developed at Brookhaven National Laboratory  [52]  for the analysis of particle physics data, was used to show the most important beam information such as the beam intensity (number of protons per spill), the beam spot size, and beam timing information.
In total, there were 61 million horn pulses during the experiment. The standard LE configuration data sets corresponded to an exposure of 10. 71 × 10 20 POT obtained in ν beam mode, and an exposure of 3. 36 × 10 20 POT obtained in the ν beam mode. Including the short special runs such as medium or high energy, horn off runs or calibration runs, the total exposure in POT in the experiment was 15. 6 × 10 20 POT. The time distribution of different runs and the intensity as a function of time are illustrated in Fig. 35. As can be seen, the intensity has gradually increased over the course of the experiment due partially to incremental improvements in the operation of the Booster and the Main Injector, as well as the initiation of the slip-stacking in the latter part of the experiment. The primary proton beam line functioned without any serious problem during the whole run.
However, the other targets have not shown deterioration even though some were irradiated beyond the level where the onset of deterioration was seen in NT02. So one may speculate that some other factor such as gas contamination may have contributed in that case. The spectrum is zoomed into the peak region and is broken down by MINOS official runs and time within those runs. The solid line is the POT-weighted average data spectrum over the whole data taking period, while the points represent the data for specific MINOS runs or shorter time periods. The significant drop in runs II and III is due to NT02 target degradation. Furthermore, from run III onwards, helium was added to the NuMI decay pipe, which also leads to a few-percent level loss in neutrino events in the peak of the spectrum. Runs V and VI had new targets, however, these were slightly skewed with respect to the z-axis.
Post-MINOS Plans and Developments After a long accelerator shutdown following the completion of the MINOS experiment, the NuMI beam was restarted in September 2013 for two new experiments, NOνA and MINOS+, and for the continuation of MINERνA data taking. NOνA is an off-axis long-baseline neutrino experiment. The off-axis location means that the NOνA experiment is able to utilize a medium energy NuMI beam in order to observe a narrow band beam centered around approximately 2 GeV, at the neutrino oscillation maximum, and thus maximize the experiment's neutrino oscillation sensitivity. To generate the required medium energy beam, the Horn 2 location was optimized and is further away from Horn 1 than it was for the MINOS experiment run. Horn 2 was moved to its present location of 19. 2 m downstream of Horn 1 during the long shutdown.
Figure36: All-Time MINOS LE ν Beam Mode Reconstructed Neutrino Spectrum. The spectrum is zoomed into the peak region and is broken down by MINOS official runs and time within those runs. The solid line is the POT-weighted average data spectrum over the whole data taking period, while the points represent the data for specific MINOS runs or shorter time periods. The significant drop in runs II and III is due to NT02 target degradation. Furthermore, from run III onwards, helium was added to the NuMI decay pipe, which also leads to a few-percent level loss in neutrino events in the peak of the spectrum. Runs V and VI had new targets, however, these were slightly skewed with respect to the z-axis. Run X used a target which did not show any degradation through to the end of the run, but is nevertheless lower in the peak than run I due to the helium in the decay pipe.
Figure37: All-Time MINOS LE ν Beam Mode Reconstructed Anti-Neutrino Spectrum. The spectrum is zoomed into the peak region and is broken down by MINOS official runs and time within those runs. The solid line is the POT-weighted average data spectrum over the whole data taking period, while the points represent the data for specific MINOS runs or shorter time periods. The drop at the end of run VII was caused by a water leak in the target, and the drop in event rate was visible in both the MINOS Near Detector and the muon monitors further upstream. The lower rate in run IX was caused by the necessity to reuse the older NT02 target, which showed target degradation.
Acknowledgments In a very real sense, it takes a whole community of thought, planning, and effort to carry out an enterprise of the scope of the NuMI beam line. Our acknowledgement and thanks go out first of all to the support groups of the  Fermilab Accelerator Division , including the Mechanical Support Department -and in particular the  Target Hall Support Group , the  Electrical Engineering Support Department , the  Controls Department  and the  Instrumentation Department. We recognize and gratefully acknowledge the support of the  Fermilab Particle Physics Division , who provided crucial design and support via its Mechanical Engineering and Alignment and Metrology Departments. The beamline could never have been completed or operated without the help of the  Fermilab Facilities Engineering Services Section (FESS) ; we recognize their contributions with thanks.
Microscopy and quantitative analyses Nissl-stained slices were observed with a visible light optical microscope (Leica DM 4000B, Microvision, Evry, France) and slices stained for BrdU, fluorojade C or cleaved caspase-3 detection were analyzed with a fluorescence microscope (Zeiss, Axiophot, Germany). Histolab software (Microvision Instruments, Evry, France) was used to analyze and quantify the staining. Quantitative analyses were performed in cortical, hippocampal and dentate neuroepithelia of brains from embryos from GD13 and fetuses from GD18 and in the DG of hippocampus from PND0 and PND5. At PND21, analyses were focused on the granular cell layer of the DG. From each developmental stage, DU-exposed animals were compared with controls. We measured the BrdU-stained area/mm 2 on each structure in embryos GD13, fetuses GD18 and pups from PND0 and PND5.
At birth, the CA1, CA2 and CA3 regions are well organized in layers. However, the DG is immature and 85% of cells will be generated between PND0 and PND21  (Altman and Bayer, 1990). Cell death and cell proliferation were thus investigated in the DG during neonatal and postnatal development. We observed an increase of cell death by fluorojade staining in the dentate gyrus of PND0 and PND5 exposed pups associated with a significant increase of apoptotic cell number in PND5 exposed pups. At PND0, our data show also an increase of apoptotic cell number in exposed pups but it was not significant. This increase of the cell death has been observed only at the highest dose of DU exposure. Therefore, our results suggest that apoptosis seems to be one of the pathways of cell death affected by DU, but it could also disturb cell death by acting on different pathways during postnatal development  (Oppenheim, 1991).
Acknowledgments: We would like to thank any staff involved in fighting malaria in Cambodia, especially the village malaria workers, staff in health centers, provincial hospitals, and staff of the  National Center for Parasitology, Entomology and Malaria Control (CNM) in Phnom Penh. We are grateful to  Chansuda Wongsrichanalai  for her help in compiling data, writing, and revising the manuscript. Authors' addresses:  Sovannaroth Siv ,  Seshu Babu Vinjamuri ,  Dysoley Lek , and  Rekol Huy ,  National Center for Parasitology, Entomology and Malaria Control (CNM) ,  Phnom Penh , Cambodia, E-mails: sivsovannaroths@gmail. com,vinjamuriv@wpro. who. int,soleycnm@ gmail. com,and kolhuy@gmail. com. Arantxa Roca-Feltrer  and  Ngau Peng By ,  Malaria Consortium Cambodia ,  Phnom Penh , Cambodia, E-mail: a.
1 and 3. 7. Multiple previous analyses, including several completed as part of the GBD collaboration have sought to provide the best possible information about levels and trends in maternal mortality. [8] [9] [10] [11] [12] [13] [14]  In dual recognition of both the importance and difficulty of accurately reporting on maternal mortality in many settings,15,16 each has incorporated increasingly large and geographically precise datasets and used more advanced statistical models. In their latest iteration,12 the WHO methods have also now adopted a single model for all countries and computed statistical uncertainty intervals. Important differences remain, however, between WHO and GBD maternal mortality estimates that at times paint divergent pictures of levels and trends in maternal mortality globally and in many countries.
Overview Maternal mortality is defined as a death that occurs to a woman as a direct result of obstetric complications or indirectly as a result of pregnancy-induced exacerbation of pre-existing medical conditions, but not as a result of incidental or accidental causes. To ensure internal consistency with all other causes of death, maternal mortality was also again analysed as a component of the overall GBD study. Many of the analytical components are therefore shared with other causes, including methods of data source identification and cataloguing, data preparation, modelling platforms, and processing of results. Here, we will focus on parts of the process that are unique, have been updated since GBD 2013, or are especially relevant to our analysis of maternal mortality. Figure  1  illustrates details of the analysis.
General components are described in the appendix (pp , in other GBD 2015 Articles in The Lancet, and have also been published previously. 10, 20, 25  This report follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) guidelines, which recommends documentation of data sources, methods, and analysis. 26  were able to capture trends in quality changes in vital registration with respect to maternal mortality, even in locations where surveillance studies have not been completed. 20  Each code in International Classification of Diseases (ICD)-coded vital registration datasets was uniquely assigned to a corresponding cause in the hierarchical GBD cause list. Codes used in tabular classification systems (eg, ICD-9 basic tabular list, verbal autopsy, maternal surveillance systems) were likewise uniquely matched with a GBD cause.
Confidential enquiries are specialised studies designed to investigate the number and circumstances of maternal deaths. Inclusion required a clear distinction identified between maternal and incidental deaths during pregnancy. As with vital registration systems, HIV-related indirect maternal deaths were excluded from surveillance datasets at this stage (see below for more details) but otherwise were unadjusted. Single-year sibling history and survey data derived from health surveys and censuses was processed as in GBD 2013, using Gakidou-King weights to adjust for survivor bias and only retaining data from older surveys when years of death overlapped (figure  1 , step 3). 27 Our general approach to quantify the role of HIV in maternal mortality is unchanged from GBD 2013 and again involved comprehensive estimation of the population attributable fraction of maternal mortality to HIV10 (figure  1 , step 4).
We processed sibling history and census data to exclude incidental HIV deaths using population attributable fractions calculated above for each geography, age group, and year. This method is analogous to the HIV-correction process used in GBD 2013 except that the correction was done on the data itself rather than the preliminary model results. To ensure consistency between all data sources, we also applied population attributable fractions to all vital registration, verbal autopsy, and surveillance data to add back the corresponding number of HIV-related indirect maternal deaths in each of those sources. Finally, to reduce error introduced by large stochastic fluctuations and upward bias introduced by data that have a value of zero, we processed all data of all specifications using Bayesian noise-reduction algorithms (see appendix  [pp 2-18]  for more details; figure 1, step 5).
We also examined four distinct time windows of maternal death. In addition to late maternal deaths, we estimated deaths occurring during the antepartum period (before onset of labour), intrapartum and immediate post partum (onset of labour up to <24 h after delivery), and early and delayed post partum (24 h to 42 days after delivery). We analysed late maternal death as both a timing category and as a distinct cause because the underlying causes of late maternal deaths are not specified in most data sources. Systematic literature reviews identified studies that examined underlying causes and timing of maternal deaths (figure 1, step 7). We extracted additional information from specialised studies such as confidential enquiries and maternal mortality review boards that were obtained from targeted web searches or from correspondence with GBD collaborators.
We supplemented aetiology models with cause-specific data from the causes-of-death database. Of note, our criteria for including data from the causes-of-death database was modified from GBD 2013 to include all data from any source where specific subcauses were coded rather than limiting to only those sources where the complete complement of subcauses were included. This change had the effect of substantially increasing the size of our analytical dataset with respect to time and geography. Late maternal death data from the causes-ofdeath database were limited to those location years where at least 0•5% of all maternal deaths in raw vital registration data files were coded to late maternal deaths as this was the lowest proportion reported in any surveillance studies. 34 Only 39 countries met these criteria with variable times in which they began coding late maternal deaths.
Timing models were additionally supplemented with temporal information about pregnancy-related deaths from Demographic and Health Surveys maternal mortality modules. These data only reported on antepartum, intrapartum, and post-partum death. To maximise the volume and geographical distribution of data to inform causal attribution, we again modelled the proportion of deaths due to each cause and timing category using DisMod-MR 2. 1. The exception was HIV-related maternal mortality, for which the proportion was estimated using the population attributable fraction approach described above (figure 1, step 9). All data for cause and timing models for which late maternal death was excluded were statistically crosswalked within DisMod-MR 2. 1 to the reference definition where late maternal death is included.
Age groups and fertility Previous analyses have truncated evaluation of maternal mortality at 15 years to 49 years. Doing so ignores the non-trivial number of pregnancies and deaths occurring in those younger than 15 years and older than 50 years. 35 Deaths in these age groups are routinely coded in our data sources, so for the first time, we have expanded the age range of our maternal mortality analysis to include all 5-year age groups from 10 years to 54 years in GBD 2015. To facilitate calculation of MMR in these age groups, our demographic analysis included expansion of UN Population Division estimates of age-specific livebirths to include 10-14 years and 50-54 years (figure  1 , step 13). The appendix (pp  [49] [50] [684] [685] [686] [687] [688] [689] [690] [691] [692] [693] [694] [695] [696] [697] [698] [699] [700] [701]  provides more detail on fertility estimation in these age groups and a table of age-specific livebirths for all locations.
We have previously discussed some of the important differences between the analytical approaches used by GBD and MMEIG. 65 These included differences in dataset content, data processing methods, all-cause mortality, model specification, quantification of uncertainty, the use of CoDCorrect to ensure consistency between all specific causes of death, and the fact that MMEIG 2013 combined three separate estimation methods for different categories of countries, whereas GBD uses one approach for all countries. MMEIG has made some important modifications to their analysis since 2013-most notably implementation of a Bayesian approach that combines all countries into a single model to estimate maternal mortality cause fractions (the proportion of all deaths in the population that are due to maternal causes).
GBD 2015 estimates of maternal cause fraction estimates remain notably higher in high-income North America, western Europe, and Oceania, although it is likely that much of this is driven by MMEIG 2015 exclusion of late maternal mortality. The total number of sources used by MMEIG 2015 was 203 and by GBD 2015 was 599 (appendix pp 677-83 shows all country-specific sources by type used in each analysis). For a number of populous countries-including China, Ethiopia, Indonesia, and Indiadifferences in maternal cause fraction estimates are largely driven by dataset content. MMEIG 2015 did not include data from Medical Certification of Cause of Death66 or the Survey of Causes of Death67 from India, several years of census and verbal autopsy data from Indonesia and Ethiopia, and maternal mortality surveillance data from China.
MMEIG 2015 similarly did not include vital registration data from Iran and the Dominican Republic or sibling history from Jordan, all of which led to very different estimates of levels and trends of maternal mortality in those countries. In total, 396 sources were excluded by MMEIG 2015. In many cases the MMEIG 2015 documentation does not describe reasons for not including these data. In future iterations of both the GBD and MMEIG estimation, the groups should both work more closely to ensure relevant data sources are included in both analyses. Differences in processing methods of sibling history data are important, especially for countries in sub-Saharan Africa. The nine countries without maternal mortality data in the GBD 2015 analysis were Andorra, Angola, Equatorial Guinea, the Federated States of Micronesia, Marshall Islands, Samoa, Solomon Islands, Somalia, and Vanuatu.