dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein Profile Analysis by SDS-PAGE Whole cell proteins were analysed by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) according to the method described by Laemmli  [38]. Electrophoresis were performed in 5% stacking gel and 10% separating gel in Tris-Glycine-SDS buffer (25 mM Tris-HCl pH 8. 6; 192 mM glycine; 0. 1% sodium dodecyl sulphate) with using PROTEAN System (Bio Rad Laboratories Inc. , Hercules, CA, USA) with a constant voltage of 20 V/cm. The gels were stained with Coomassie Brillant Blue R-250 (0. 25% Coomassie Brillant Blue R-250; 10% acetic acid; 45% methanol)  [39]. The molecular weight of the proteins and the pixel intensity of the protein bands were analysed with the SigmaGel software (Jandel Scientific, San Rafael, CA, USA). PageRuler Protein Ladder (Thermo Fisher Scientific, Waltham, MA, USA) was used as a size standard for protein electrophoresis.
Service fees were used as a proxy for treatment cost. To validate the modeled cost data, empirical cost data were collected at a second private facility: an urban teaching hospital. At the urban hospital, financial records were available. Records were searched for all patients presenting with a snakebite diagnosis from 2008 to present. Seventy-one records were identified, and cost data were recorded as the direct cost to the patient, categorized by service provided. All costs were collected in Indian rupees and converted to 2015 USD using the commercial market conversion rate in May 2015. 34 No corresponding medical outcomes were available. These data were used to calculate a mean cost of snakebite treatment, which was inflated to account for a 20. 4% deficit between patient payments and total costs at the urban hospital.
Acknowledgements: We thank the helpful staff and faculty at  PSG Hospitals, Tamil Nadu, India , and  TCR Multispeciality Hospital, Krishnagiri, Tamil Nadu, India. Additionally , the  Andrew and Marina Lewin Family Foundation  provided funds supporting  Ben Herzel 's thesis project and funds to make this article available by open access. Disclosure: MRL, SPS, and TCB have stock or stock options in Ophirex,  Inc. , a public benefit corporation developing snakebite antidotes. BJH,  CSR , and JGK have no competing interests to declare.
Statistical analysis A two-way ANOVA was used followed by Tukey HSD post hoc test. For sugar and lignin data, the first factor accounted for different treatments, while the second factor was the analytical series, which impacted significantly the results. For all other data, the first factor was the treatments and the second factors the field plot. Principal component analysis (PCA) was carried out for soil parameters with package FactoMineR. The PCA function of the FactoMineR package automatically transforms data to a standardized normal distribution.
Principal component analyses To investigate the parameters most affected by land use legacy, we carried out a PCA (Fig. 3 ). Based on Pearson correlation coefficients among variables of the dataset, it appeared that C, N, MB-C, lignin, sugar concentration and k were positively correlated (all r > 0. 57 and p < 0. 001) and all were negatively correlated to the C6/C5 sugar ratio (p < 0. 01). Soil P was positively correlated only with Co/V (r = 0. 61) and S/V (r = 0. 51) lignin ratios; S/V itself correlated negatively with sugar (r = -0. 53) and POM (r = -0. 64). PD was positively correlated with the S/V lignin ratio (r = 0. 54; p = 0. 05). The first two dimensions of the PCA explained 73,17% of the variability. The first dimension, representing 52. 15% of the variability, was strongly and positively (r > 0.
Acknowledgements This study was carried out under the framework of the  AEGES  project funded by  ADEME. A. C. is grateful to the  European commission  ( EXPEER  project [grant number  262060 ]) and the  region Poitou-Charente  for joint funding of a PhD grant. Xavier Charrier  and the  SOERE ACBB  are acknowledged for providing access to an excellent field experiment. Potential denitrification assays were performed on the AME plateform (LEM,  UMR1418 ).
9 and 7. 6 cm from the beam axis and covering |η| < 2 and |η| < 1. 4, respectively. The V0 detector, consisting of two arrays of scintillator counters covering 2. 8 < η < 5. 1 and -3. 7 < η < -1. 8, is used for triggering and evaluation of collision centrality. Finally, the Zero Degree Calorimeter, placed at 112. 5 m from the interaction point along the beam line, is used to reject electromagnetic interactions  [42]. The dataset used in this analysis consists of Pb-Pb events at √ s NN = 5. 02 TeV selected with a dimuon trigger that requires the coincidence of a minimum-bias (MB) trigger and a pair of tracks with opposite sign in the muon spectrometer, each with p T 1 GeV/c. The MB trigger is defined by the coincidence of the signals from both arrays of the V0. The MB trigger is fully efficient for events within the 0-90% centrality interval, which are used in this analysis.
This cut removes 88% of all tracks for events in the 0-90% centrality interval, while retaining all signal candidates with negligible residual background contribution. with the result of a Monte Carlo (MC) simulation obtained using the POWHEG  [44]  event generator paired with PYTHIA 6. 4. 25  [45]  for the parton shower. The propagation of the particles through the detector is simulated with the GEANT3 code  [46]. The isospin of the Pb-nucleus is accounted for by a weighted average of neutron and proton interactions, but no modification of the nucleon PDF was applied to account for nuclear effects. The simulations account for variations in the detector response with time and in-situ alignment effects. A data-driven description of the muon momentum resolution is also implemented (see Ref. [32]  for details).
The uncertainties in the track resolution and alignment are estimated by comparing the A • ε values obtained with two different simulations. In the full simulation, the alignment is measured using the MILLIPEDE  [52]  package and the residual misalignment is taken into account. In the fast simulation, the tracker response is based on a parameterisation of the measured resolution of the clusters associated with a track  [32]. The resulting systematic uncertainty is 3. 5%. N part N coll N part N coll 0-90% 6 The total systematic uncertainty in the yield and R AA are determined by summing in quadrature the uncertainty from each source, listed in Table  2. All uncertainties except those due to T AA and the centrality bin boundaries are independent of collision centrality. Correlations in centrality or rapidity of the uncertainties of different sources are indicated in Table  2.
Background: Colorectal cancer in pregnancy is rare, with an incidence of 0. 8 per 100,000 pregnancies. Advanced disease (stage III or IV) is diagnosed more frequently in pregnant patients. We aimed to review all cases of colorectal cancer in pregnancy from the International Network on Cancer, Infertility and Pregnancy database in order to learn more about this rare disease and improve its management. Methods: Data on the demographic features, symptoms, histopathology, diagnostic and therapeutic interventions and outcomes (obstetric, neonatal and maternal) were analysed. Results: Twenty-seven colon and 14 rectal cancer cases were identified. Advanced disease was present in 30 patients (73. 2%). During pregnancy, 21 patients (51. 2%) received surgery and 12 patients (29. 3%) received chemotherapy.
Introduction Colorectal cancer (CRC) in pregnancy is rare, with an incidence of 0. 8 per 100,000 pregnancies  [1]. The diagnostic and therapeutic management of pregnant patients with CRC is especially difficult because it involves two individuals, the mother and the foetus. Locally advanced or metastatic CRC is diagnosed more often in pregnant than in non-pregnant patients because the presenting signs and symptoms of CRC are easily attributable to pregnancy  [2]. Presentation with advanced CRC during pregnancy is thought to be a result of delayed diagnosis  [3]. In this study, we analyse all the cases of CRC during pregnancy from the International Network on Cancer, Infertility and Pregnancy (INCIP) database  [4]  with specific focus on the treatment modalities given during pregnancy and the obstetric, neonatal and maternal outcomes.
Materials and methods For this international observational cohort study, we identified all patients diagnosed with CRC during pregnancy from the INCIP registration study, registered with ClinicalTrials. gov (NCT00330447). Patients diagnosed with a recurrence of CRC were excluded. Data on demographic features, symptoms, histopathological characteristics, diagnostic and therapeutic interventions and outcomes (obstetric, neonatal and maternal) were collected. The INCIP started in 2005 and collects oncological, obstetric and perinatal data related to the pregnant patients diagnosed with all types of cancer. At the time of case inclusion, it contains information reported by 70 doctors from 62 medical centres in 25 countries. Reporting of patients occurs on a voluntary basis by the doctors affiliated to the INCIP, all of whom work in the specialized hospitals where patients with cancer during pregnancy are treated.
However, inducing delivery to start chemotherapy for fear of adverse fetal effects may not be in the best interest of the unborn child, especially when 5-FU based regimens are of first choice. Evidence based guidelines on CRC during pregnancy are lacking. This study is one of the largest series that describes the management and outcome for CRC during pregnancy and adds important information for this rare patient population. As a result of the rarity of CRC during pregnancy, our patient sample size is relatively small. A limitation of our study is that registration of these cases is voluntarily based and we cannot estimate the completeness of the database and it is therefore likely that not all cases of CRC during pregnancy are included in this analysis. Since all hospitals included all their cases, and not just the advanced stage of disease cases, we do not expect a selection bias on stage of disease at diagnosis.
Acknowledgments We want to acknowledge all members of the writing committee on this manuscript;  Kristel Van Calsteren M. D. , Ph. D. ;  Department of Obstetrics and Gynaecology, University Hospitals Leuven, Belgium and Department of Reproduction and Regeneration, KU Leuven, Leuven, Belgium. Karina Dahl Steffensen M. D. , Ph. D. ;  Department of Clinical Oncology, Vejle Hospital, Vejle ,  Denmark and Institute of Regional Health Research, University of Southern Denmark, Odense C, Denmark. Charles Honor e M. D. and  Alexander Mare M. D. ;  Department of Surgery, Gustave Roussy Cancer Campus ,  114 , rue Edouard Vaillant, 94805  Villejuif Cedex ,  Universit e Paris Sud, France. Mina Mhallem Gziri M. D. , Ph. D. ;  Department of Obstetrics, Cliniques Universitaires St. Luc, Brussels, Belgium.
Significant overall effects of treatment type were further explored using a post-hoc Tukey test. All the statistical tests were evaluated using α = 5%. Mixed effects modelling was performed using the lme function of the nlme package  (Bates et al. , 2015) , and the Tukey test using the multcomp package  (Hothorn et al. , 2008). Structural equation modelling was performed to test whether the EFB application influenced soil biota and the soil ecosystem functions by direct provision of food resources, or via modifying soil abiotic properties. This approach allows for evaluating pre-existing theories using observational data, with a focus on exploring possible mediation pathways  (Eisenhauer et al. , 2015; Grace et al. , 2012). Among the ten indicators, soil aggregate stability was not included in the structural equation modelling as it positively correlated with soil bulk density.
ACKNOWLEDGMENTS We are grateful to Ristek Indonesia for research permissions. This study was supported by the Department of Zoology of University of Oxford and the SMART Research Institute (SMARTRI). We thank SMARTRI soil laboratories for soil nutrient analysis and following researchers for the assistance in field sampling: Resti Wahyu, Rudi Harto Widodo, Tony Irawan, Suhardi, Pujianto, and Ribka Sionita Tarigan. We thank Dr Matthew Shepherd for the assistance on mite identification and Dr Samuel Wooster James for earthworm identification. We thank Dr Emma Sayer and Dr Lénaïc Pardon for commenting on the early draft. HHT was supported by Taiwanese Ministry of Education Scholarship, JLS by a EPSRC grant (EP/M013200/1) and NERC El Nino grant (NE/P00458X/1), and EMS by a NERC HMTF grant (NE/K016261/1).
Patient selection Patients diagnosed with endometrial cancer who underwent EBRT and VBT from January 2000 to December 2014 were retrospectively considered for this study. All data were selected from the intranet hospital multidivisional electronic database, Spider's Net  [8]  in the frame of the COBRA system  [9]. Inclusion criteria were histologically proven diagnosis of endometrial cancer, EBRT > 40 Gy, VBT boost, and follow-up > 6 months. In all cases, the indication for the treatment was discussed in a multidisciplinary gynecologic tumor board. All patients provided informed consent.
Acknowledgments M. Ü. thanks  Nick Manton  for discussions. We thank the  KITP at UC Santa Barbara  for its hospitality during the program " Resurgent Asymptotics in Physics and Mathematics " where some of this work was done. Research at KITP is supported by the  National Science Foundation  under Grant No. NSF   PHY-1125915. This material is based upon work supported by the  U. S. Department of Energy, Office of Science, Office of High Energy Physics  under Award Number  DE-SC0010339  (GD), and by the  U. S. Department of Energy, Office of Science, Office of Nuclear Physics  under Award Number  DE-FG02-03ER41260  (AB,TSc,M Ü).
Detection of eSPWs, MEC bursts and myoclonic movements Wideband recordings were preprocessed using custom-written functions in MATLAB (MathWorks, USA). For a given recording session, eSPWs were detected semi-automatically from downsampled (1000 Hz), bandpass filtered (0. 1-100 Hz, Chebyshev Type 2 Filter) LFPs on each channel. All events reaching an absolute value greater than 80-100 μV were first considered as putative eSPWs. To discard movement and static artifacts, LFP segments were taken from -0. 5 to 1 s around the eSPW peak negativity for each channel and visually inspected. Time = 0 for the eSPWs was defined as the time of maximal LFP negativity of the eSPW. MEC bursts were detected using a complex parameter of the power in beta-gamma frequency (15-90 Hz) multiplied by the MUA density.
(2005). Units with an ID < 5 were discarded from the dataset. Only potential units with the following properties were kept for analysis: waveforms had to be detected on ≤3 electrode contacts; their amplitude had to be higher than 50 μV; their refractory period had to be ≥2 ms and they had to fire more than 50 action potentials in a 10-min session. Single unit isolation and clustering in pups was more complex than in adult rodents. Single unit features in MEC and hippocampus and their firing in relation to the eSPWs, MEC bursts and spontaneous movements are summarized in Supplementary Table  4. Of the 159 CA1 units detected by the klustasuite pipeline (n = 26 rats), only 36 reached our selection criteria (n = 12 rats). These proportions were also small in CA3 (34/82 Units; n = 12 and n = 13 rats), DG (30/120 Units) or MEC L3 (58/153 Units, n = 11 and n = 18 rats) likely due to weaker amplitude of extracellular spikes of immature neurons, their low firing rate and the differential damage to cells layers made by the linear shank in its path (shallower structures being more damaged by the electrode body).
001). There was no significant difference between CA1, CA3 and DG units in terms of firing rate and width. Thus, single units fired less frequently and their duration was longer than in adults. Also, based on their firing properties we could not separate pyramidal cells from fast-spiking interneurons that is in keeping with the results of previous developmental studies  (Weir et al. 2014). Unit PETHs were performed by binning spike trains (5 ms bins) in a ±2 s perievent time window (eSPWs, MEC burst, myoclonic movement). Such binned data was then convolved with a 10-bin Gaussian window and averaged across trials. To estimate PETH significance, z-scores were estimated on the basis of a shuffled surrogate dataset. For each trial, spike trains were randomly shuffled in time and a PETH was computed for the whole surrogate session.
Statistics Statistical analysis was performed using the Graphbad PRISM 6 software, Origin and MATLAB Statistics toolbox. Group comparisons were done using Student's t-test and one-or two-way ANOVA if homogeneity of variance between groups was respected. Otherwise, non-parametric Kruskal-Wallis tests were performed. When global statistical differences were found, post hoc comparisons (Tuckey or Dunn multiple comparison tests for parametric or non-parametric data, respectively) were performed. To compare proportions of single units activated during sharps, chi-square or Fisher exact tests were used, depending on the number of samples considered. Degrees of freedom, tand P-values are reported as df, t, and P, respectively. P-value 0. 05 was considered significant. Correlations between variables were estimated using either the Pearson (r) or Spearman, non-parametric, rank-based (ρ) correlation coefficients, depending on whether data were linearly correlated and normally distributed around the regression line.
Single unit behavior during MEC L3 bursts and eSPWs We then investigated whether activity of single units in MEC L3 and CA1 pcl was modulated in relation to MEC L3 bursts (Fig. 3 ; see Materials and Methods and Supplementary Fig. 4  for the single unit detection procedure and Supplementary Table  4  for unit features and relation to population events). This analysis was restricted to sessions with simultaneous CA1 and MEC L3 recordings (n = 4 pups). To assess single unit modulation by MEC L3 events, we performed PETHs aligned to MEC L3 bursts. Significance was estimated by expressing peak firing rates as z-scores with respect to shuffled surrogate data (see Materials and Methods). All units in CA1 (n = 9) and MEC L3 (n = 53) showed a significant firing increases during MEC L3 events (Fig. 3A, B ), as characterized by remarkably high Z-scores (CA1 Z = 13.
In addition to the recordings shown above, this dataset included recordings made from dorsal hippocampus without MEC electrode (n = 8 rats; P2-7) and MEC L3 without hippocampal units (n = 1 rat; P5). Altogether, we analyzed the activity of 36 cells in CA1 pyramidal cell layer, 34 cells in CA3 pyramidal cell layer, 30 cells in DG and 58 cells in MEC L3 (see Materials and Methods for cell selection criteria and numbers). As above, we plotted PETHs aligned this time to eSPWs trough in CA1 sl-m. We found that the firing rate of a large proportion of cells increased during eSPWs. There were, however, region-specific differences in the percentage of cells increasing their firing during eSPWs, as well as in the level of modulation of cell firing during eSPWs and the timing of spikes in relation to eSPWs (Figs  4  and 5  and Supplementary Table 4 ).
Research in sub-Saharan Africa highlights other risk factors for UR including lack of prenatal care, limited access to emergency obstetrical care, delays and/or poor management of care  [8] [9] [10] [11] [12]. Yet these studies are small, frequently focus on one institution and often fail to adjust for potential confounding variables such as parity or previous cesarean delivery when characterizing health system risk factors for UR  [8] [9] [10] [11]. Published case reports of UR events provide valuable insights, but may not be applicable to the majority of cases that clinicians and health centers encounter  [13] [14] [15] [16] [17]. Because of the severe consequences of UR, prevention is paramount. Yet, the rarity of UR makes it difficult to study. Therefore, research investigating the factors contributing to UR using large datasets with quality data collection and abstraction of data from medical records is needed.
Data on the women, their pregnancy, labor and delivery were extracted from hospital and medical records into a standard one-page data collection form. Given that data were collected on all women at each participating site, the QUARITE investigators kept the data collection instrument relatively short in order to minimize the burden of the trial on health professionals working at the study sites. Trained midwives collected the data from medical records at each site. National coordinators supervised them and data quality was monitored by random audits  [18]. Complete data on UR, our principal measure of interest, were available for 84,802 women, or 99. 9% of the total number of women who delivered. Records with missing values for UR (n = 122) were excluded from the analysis. The outcome of interest is UR, diagnosed by a health professional.
The definition used for the study was, "occurrence of clinical symptoms (pain, fetal distress, acute loss of contractions, hemorrhage) or intrauterine fetal death that lead to laparotomy, at which the diagnosis of uterine rupture was confirmed; or laparotomy for UR after vaginal birth" (  [21]  p6]). UR has been defined similarly in previous studies  [4]. UR was captured as a dichotomous variable (yes/no) in this dataset, no anatomic details of the rupture (e. g. , total rupture versus dehiscence) were considered for this outcome. The literature identifies multiple risk factors for UR that fall into four categories: maternal characteristics, obstetric, institutional, and geographical factors  [3, [8] [9] [10] [11] [12]. Maternal characteristics include a woman's age at delivery (categorized into < 20, 20-35, > 35 years), and parity for the current delivery (categorized by quartiles into < 1 birth, 2 births, 3-4 births, 5 or more births).
The following obstetric factors were included as dichotomous variables: induction of labor, prior cesarean delivery, comorbidity, obstructed labor (defined as slow or arrest of dilation despite ruptured membranes and oxytocin augmentation, non-engagement of presentation at full cervical dilation, or failed vacuum or forceps at full cervical dilation with engaged head), pre-eclampsia/ eclampsia, hemorrhage, and > 90th centile of birth weight (> 3600 g) in our sample. Comorbidity was defined as having one or more co-occurring diseases or conditions such as HIV/AIDS, malaria, heart or kidney disease, chronic respiratory condition, gestational diabetes, etc. , recorded on the study data collection form. For additional details please see supplementary file (Additional file 1). Mode of delivery was also collected and classified as either spontaneous vaginal delivery, cesarean delivery, and instrumental (forceps or vacuum), breech, or other.
Funding Full funding for QUARITE was provided by the  Canadian Institutes of Health Research (CIHR)  under grant number  200602MCT-1587547-RFA-CFC-100169. CIHR awards grants through an external peer review process that examines scientific quality and merit. CIHR played no role in conducting the research or writing the paper. This secondary data analysis of pre-intervention period data and was supported in part by the  National Institute on Minority Health and Health Disparities  ( U54MD007584 ),  National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIMHD or  NIH.