dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary With the release of the IDP2014, GEOTRACES seeks to promote intensified collaboration within the marine geochemical community and beyond. The availability of a large integrated and quality controlled dataset, such as the IDP2014, will allow a much wider range of studies than would be possible with individual cruise data alone. Examples of such research include basin-wide tracer budgets and quantification of sources and sinks on large scales. The new 3D visualisation techniques and the strong inter-linkage of sections and 3D scenes provided by the eGEOTRACES Atlas aids the scientific interpretation of TEI data, but also facilitates outreach to a wider community, including not only scientists from different disciplines but also the general public and policy makers. In early 2015, the GEOTRACES Data Management Committee will formally seek feedback from the community on IDP2014, with an email sent out to those who provided data, those who have downloaded the product and also those on the GEOTRACES mailing list, directing them to an online survey.
The IDP2014 is the first in a series of intermediate data products planned to be produced at regular intervals with the next scheduled for release at the Goldschmidt Conference in 2017. These future data products will extend the geographical coverage by including data from new GEOTRACES cruises in the Mediterranean Sea, Pacific, Atlantic and Indian Oceans, as well as containing additional data from existing cruises for parameters that take longer to measure and complete. User feedback from the survey will help make the next IDP an even more useful product.
Acknowledgements We gratefully acknowledge financial support by the  Scientific Committee on Oceanic Research (SCOR)  through grants from the  U. S. National Science Foundation , including grants  OCE-0608600 ,  OCE-0938349 , and  OCE-1243377. Financial support was also provided by the  UK Natural Environment Research Council , the  Ministry of Earth Science of India , the  Centre National de Recherche Scientifique, l'Université Paul Sabatier de Toulouse , the  Observatoire Midi-Pyrénées Toulouse , the  Universitat Autònoma de Barcelona , the  Kiel Excellence Cluster The Future Ocean , the  Swedish Museum of Natural History, The University of Tokyo , The  University of British Columbia, The Royal Netherlands Institute for Sea Research , the  GEOMAR-Helmholtz Centre for Ocean Research Kiel , and the  Alfred Wegener Institute.
Repression of SRIs Dissemination The SetR Repressor The dominant regulatory state of SRIs is the quiescent state in which the element is integrated into the chromosome and the genes associated with recombination and transfer are silent  (Beaber et al. , 2004; Poulin-Laprade et al. , 2015). In this dormant state, very few genes are transcribed, including genes independently regulated belonging to cargo DNA (e. g. , antibiotic resistance genes) and setR. The setR gene is located at the rightmost end of the integrated ICE (Figure  1 ). SetR is an acronym for SXT excision and transfer repressor. setR mRNA transcript is leaderless, expressed from the P R promoter, and codes for a 215-amino acid residue protein with a DNA binding helixturn-helix motif (HTH_3, PF01381) in its N-terminal moiety and a C-terminal LexA-like autoproteolysis motif (Peptidase_S24, PF00717).
Repression of ACPs While no SetR homolog has been found in ACPs, their regulatory module codes for two repressors named Acr1 and Acr2 (IncA/C repressor 1 and 2;  Carraro et al. , 2014a). acr1 codes for a 90-amino acid Ner-like protein that is mainly composed of a helix-turnhelix DNA binding domain (HTH_35, PF13693). Acr1 directly represses its own expression from the constitutive promoter P acr1 (Figure  2 ). This promoter drives the expression of acr1 and also the expression of acaC and acaD, which code for the activator complex AcaCD. Acr2 is a 139-amino acid H-NS-like repressor (Histone_HNS, PF00816) that also directly represses P acr1  (Carraro et al. , 2014a,b). H-NS proteins are known to globally repress expression of horizontally acquired DNA by binding AT-rich sequences  (Dorman, 2004 (Dorman, , 2014;; Navarre et al.
Alteration of P traI is associated with a poorer affinity for SetCD as determined by ChIPexo, which could contribute to the lower transfer and replication of SXT compared with R391  (Carraro et al. , 2015b; Poulin-Laprade et al. , 2015). In ACPs, AcaCD activates the expression of traIDJ (mob2) from a unique promoter (Figure  1 ;  Carraro et al. , 2014a Carraro et al. , , 2015a)). No ACPs available to date in the Genbank database harbor a P traI promoter altered by insertion of cargo DNA  (Carraro et al. , 2014a). Surprisingly, no AcaCD binding-site was detected upstream of mobI A/C (formerly known as vcrx001,  Carraro et al. , 2014a,b). As for MobI of SRIs, MobI A/C is essential for conjugative transfer of ACPs  (Carraro et al. , 2014b). The impact of such subtle differences on the regulation of conjugative transfer of SRIs and ACPs need to be experimentally addressed.
Acknowledgments We are thankful to  A. Lavigueur  for critical reading of the manuscript. We are thankful to  D. Matteau  for its contribution to Figure 1. This work was supported by a  Discovery Grant and Discovery Acceleration Supplement  from the  Natural Sciences and Engineering Council of Canada  ( 326810  and  412288  to VB). VB holds a Canada Research Chair in molecular bacterial genetics. DPL was supported by a scholarship from the  Fonds de Recherche du Québec-Nature et Technologies (FRQNT).
Methods In 2010, an international multidisciplinary working group was established to undertake a systematic review of the literature pertaining to the development, investigation and management of SCCs in people with EB, and to develop clinical recommendations based on the literature and their own expert opinions. This group included the following specialties: dermatology, plastic surgery, medical oncology, surgical oncology, dermatopathology, specialist nursing and palliative care. The systematic literature searches were conducted using Medline, CINAHL, Allied and Complimentary Medicine Database, British Nursing Index, Embase and PsychINFO using the search terms 'epidermolysis bullosa', 'cancer' and 'squamous cell carcinoma'. The search was limited to articles about humans in English or French with no other restrictions.
Ideally, the patient and/or family/carers should be involved in deciding how and when to carry out dressing changes. Urinary catheterization and giving nasogastric or subcutaneous fluids towards the end of life in EB may be beneficial in some cases if they outweigh discomfort. Similarly, patients may be more comfortable having less frequent dressings or even forgoing dressing changes completely at this stage. The patient and family/carers should be central to this decision making. (Strength of recommendation GPP. ) or lay input to these guidelines. All authors volunteered their time and no source of funding was provided for any aspect of this guideline.
Search strategy for the identification of studies Electronic searches of the literature published until September 2014 were conducted using PubMed and the Cochrane Collaboration Database to identify articles evaluating the ONJ as a side effect in women receiving zoledronic acid every six months for the prevention of aromatase inhibitor associated bone loss (AIBL) in patients with early stage breast cancer. Only randomized and non-randomized controlled clinical trials were included. We excluded studies enrolling less than 500 patients. The duration of follow-up was not among the exclusion criteria. Search strategies employed included key words and Boolean operators described as follows: 'zoledronic acid', 'early breast cancer' and 'aromatase inhibitors'. This search was augmented by a hand search of the reference lists of relevant articles included in the literature review.
Selection criteria Types of studies: Randomized controlled trials were included. The language of publication of eligible studies was restricted to English. Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/jbo Types of Participants: Included studies were limited to postmenopausal women receiving zoledronic acid every six months with aromatase inhibitors to prevent AIBL. Studies presenting data on premenopausal patients or receiving tamoxifen were excluded. Selection of Studies: The articles were first selected individually on the basis of their titles by two of the authors. Then, the abstracts of the available articles were examined. Our searches yielded 81 study reports from the PubMed database. All the review articles and case reports were excluded. Finally, four reports were considered illegible for our analysis  [6] [7] [8] [9].
A total of nine potential ONJ events from seven patients were reported in this study after 60 months follow up. Each event was independently adjudicated by an external panel that confirmed ONJ occurrence in three cases, deemed possible for insufficient data in two cases, and excluded the remaining cases  [7]. Therefore, in the ZO-FAST trial, ONJ occurred in 0. 28-0. 47%. In the E-ZO-FAST trial, 527 postmenopausal women receiving aromatase inhibitors were randomized to either immediate or delayed zoledronic acid treatment, at 4 mg every 6 months. Two reported cases of ONJ of the immediate zoledronic acid group were confirmed by the adjudication committee. At diagnosis, patients had received 3 and 6 doses of zoledronic acid. Treatment was discontinued in both patients but ONJ only resolved in one patient  [8].
An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required.
Application to climate model evaluation As alluded to in the introduction, Feldhoff et al. [8]  have demonstrated the complex networks approach to climate model evaluation, comparing the performance of two regional climate models (RCMs) over South America. Specifically, the authors constructed networks of 2m temperature, precipitation, 500hPa geopotential height and sea level pressure time series from hindcast climate simulations and ERA-Interim reanalysis data  [46]. The GHD between model and reanalysis networks was employed as the error measure, with respect to which the statistical model STARS outperformed the dynamical model CCLM for all variables except the 500hPa geopotential height. The ERA-Interim reanalysis data are the result of a 12-hourly assimilation of historical meteorological observations and their integration by a numerical weather prediction (NWP) model.
The data quality varies from variable to variable and depends on the relative influences of the observations and the NWP model  [47] [48] [49]. Among the variables considered by Feldhoff et al. [8] , precipitation is the most uncertain one as it is difficult to model and since no precipitation measurements are included in the assimilation of ERA-Interim. Therefore, in the following, we reevaluate the quality of networks of precipitation time series modeled by CCLM and STARS, this time with respect to networks of observed precipitation data, and using our new local network difference measures. Before discussing results, we briefly introduce the models and the observational dataset, specify the precipitation climatology to be modeled, and describe our network construction methodology.
Models The RCMs CCLM  [50, 51]  and STARS  [52, 53]  differ fundamentally in their approach to climate modeling. While the statistical model STARS takes climate data as input and stochastically resamples them such that a prescribed trend of some variable is matched, the dynamical model CCLM is the climate version of a NWP model, i. e. it numerically solves the differential equations governing the dynamics of the atmosphere under prescribed external drivers such as the solar radiative forcing and greenhouse gas concentrations. For detailed descriptions of both models and the experimental setup of the evaluation see  [8]  and references therein. In short, ERA-Interim reanalysis data was used to force both models. With STARS, daily mean value time series from 1979 through 1995 were resampled in order to reproduce the 1996-2011 temperature trends.
CCLM was run on the CORDEX South America domain  [54, 55] , simulating 1979 through 2011 to ensure a proper model spin-up during the first half of the simulation period. The daily mean precipitation time series were then conservatively interpolated  [56]  back to the ERA-Interim grid in order to enable network constructions on a common node set. We also include the ERA-Interim reanalysis data (ERAI) in our reevaluation. While there is only one CCLM run and one reanalysis dataset, with STARS an ensemble of 200 climate realizations was generated. All quantities evaluated in the following are calculated for each realization individually and then averaged across the ensemble, i. e. any STARS result shown represents the respective ensemble average.
Precipitation data and climatology Arguably the best observational precipitation dataset available for tropical South America is the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 daily satellite product, which starts in 1998 and comes at a native resolution of 0. 25°  [57]. The product is based on measurements by radar, infrared and microwave sensors aboard numerous satellites, and calibrated by station data. While it is available up to 50°latitude, the TRMM precipitation radar data only extend to 36°latitude so that we constrain our analysis to latitudes north of 40°S, where the data are most reliable. To facilitate a proper network comparison, the TRMM data are conservatively remapped to the native ERA-Interim grid which has a resolution of about 0. 7°. We concentrate our analysis on the austral summer season DJF, when a monsoon system develops over tropical South America and provides for most of the annual precipitation  [58] [59] [60].
The region under study tightly encompasses the South American mainland and is depicted in Fig. 2 , where we show the DJF mean values and 90th percentiles of daily precipitation amounts as measured by TRMM and modeled by CCLM, ERAI and STARS from 1998 through 2011. The TRMM data exhibit the typical pattern of abundant rainfall in the Intertropical and the South Atlantic Convergence Zone (ITCZ and SACZ, respectively), and along the eastern slopes of the Andes. These main rainfall patterns are replicated by the models but we find substantial differences in intensities. Regarding the seasonal mean values, CCLM mostly underestimates rainfall, while ERAI and STARS are closer to TRMM except along the Andes, where we find strong overestimations. The 90th percentiles, which quantify the intensity of extreme rain events, are mostly and substantially underestimated by all models.
The ES may be used to analyze the spatial synchronicity structure of extreme precipitation events  [27]  which has been done over South America based on the TRMM data used in this study  [64]. We adopt the network construction methodology described in the latter study. It is based on a transformation of precipitation time series to binary extreme event time series as depicted in Fig. 3. At each location, daily precipitation above the 90th percentile of its climatological (DJF 1998-2011) distribution is defined as an extreme event. Grid cells at which the 90th percentile is zero in any of the observed or simulated datasets [hatched in Fig. 2(e-h )] are discarded from the analysis. Between two extreme event time series at different locations, the ES then quantifies the degree of event synchronization, with two events contributing to ES if they can be uniquely associated within a maximum period of 3days.
Network topologies Prior to an application of our novel network difference measures we investigate the individual network topologies starting with mean Fisher transformed rank correlations, f i ¼ 1 N À 1 X j6 ¼i Fðr ij Þ; ð22Þ and simple graph degrees as shown in Fig. 4. The f i field is mostly positive which shows that positive correlations between anomaly time series predominate. The few locations of negative f i differ in position between datasets. Spatial contrasts between f i values are least pronounced for TRMM and most for CCLM. Although SRn and SRp links encode fundamentally different statistical relationships, the degree fields of the respective networks roughly agree in exhibiting and locating three distinct regions of enhanced degree indicated by colored boxes in Fig. 4 (i). All models reproduce this general pattern.
Nevertheless, in the following we will compare STARS network errors to those of CCLMb and ERAIb only and thereby facilitate a fairer model intercomparison. From the bootstrap network ensemble spread of GCDs and GHDs we can also learn something about the interannual variability present in each dataset. Irrespective of their type, networks from bootstrapped observational data feature spreads about twice as large as those from bootstrapped reanalysis data and even less variability is present in the CCLMb and STARS ensemble data. The SRp graph exhibits the smallest interannual variability of all simple graphs-a persistence that is arguably due to the predominance of short-range links in this network  [73]. Coming back to the SERN model, we observe that its performance varies considerably between graph types.
We observe larger DNDs over land than over sea and find particularly high values in the Guyana Highlands and along the Andes. DNDs between TRMM and TRMMb are much larger for the ESp than for the SRp graph. Climatic uncertainties of extreme event synchronizations are most pronounced in the Amazon basin and along the eastern slopes of the Andes, which is consistent with the outstanding noisiness of TRMM's ESp degree field in those areas [cf. Fig. 4(m) ]. In line with the respective GHDs (Fig. 7 ), the ESp DNDs to TRMM increase slightly from TRMMb to CCLMb to ERAIb to STARS while their spatial patterns do not vary much across datasets. This shows that most of the differences between TRMM and climate model ESp graphs can be explained with interannual climate variability. In this context, note that, generally, the local network difference fields based on the original CCLM and ERAI time series do not differ much from the respective bootstrap ensemble mean fields.
In a sample climate model evaluation we apply the network difference measures to comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. We focus on climate networks constructed from DJF precipitation spacetime series and use satellite data provided by the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 as a reference. We also include the ERA-Interim (ERAI) reanalysis data in the evaluation since they where used to drive the simulations of both the statistical model STARS and the dynamical model CCLM. Different types of networks are constructed to capture distinct aspects of the spatiotemporal precipitation dynamics. Based on spatial rank correlations between anomaly time series we define an edge-weighted graph called SR and two simple graphs with links representing the 2% most positive and the 2% most negative correlations called SRp and SRn, respectively.
As both CCLM and STARS were driven by ERAI data, this suggests that dynamical downscaling may retain value at the resolution of the driving data while statistical resampling can only impair data quality. Interestingly, for the SRp graph, our evaluation against the independent TRMM data yields a relative performance ranking of CCLM versus STARS that is opposite to the one obtained in an evaluation against the driving reanalysis data  [8]. From the ensemble spread of bootstrap network distances to TRMM we infer that ERAI and even more CCLM and STARS underestimate the interannual variability of the precipitation dynamics encoded in our networks. As for the reanalysis, this finding complements documented deficiencies in reproducing the variability of spatiotemporal totals of tropical rainfall over land  [83, 84].
g. volcanic eruptions, bushfires or droughts. Finally, our new local difference measures are employed to study the spatial distribution of network dissimilarities. In doing so, the DND proves to be most useful to identify commonalities across datasets. Larger DNDs over land than over sea are consistent with land surfaceatmosphere interactions being more complex than sea surface-atmosphere interactions, and relatively high DNDs in the Guyana highlands and along the Andes confirm model deficiencies in simulating precipitation over complex terrain  [91, 92]. Using the DND we also reveal that for the given observational record length, differences between ESp graphs are dominated by the interannual variability of extreme events, which prevents a reasonable evaluation of their spatial synchronizations at this point.
This paper was developed within the scope of the IRTG  1740/TRP 2011/50151-0 , funded by the  DFG/FAPESP. JFD thanks the  Stordalen Foundation  (via the PB. net network) and the  German Federal Ministry of Education and Research  (project  GLUES ) for financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Procedure and outcomes Demographic information (ie, age) and clinical data (ie, performance status, albumin level, weight, cancer diagnosis, and metastatic status) were collected. ] [46] [47]  On the day of enrollment, patient assessments included a clinical evaluation, laboratory results, and psychological self-reports (HADS, DT, MT, and GDS-30). In the 10 days after enrollment, a PCI based on MADRS and HDRS questionnaires was conducted. Survival data were collected from the medical chart, starting from the time of enrollment to the patient's death. Patients alive at the time of data collection were included in the analysis as censored cases using the date of last follow-up.
statistical considerations The number of patients to include was defined in the first aim of the GINECO EWOT3 study, a secondary endpoint of which is examined in the current study, using previous findings from our group. 42 A single analysis requires a sample of 105 patients, assuming that depression triples the risk of death (two-tailed test), with a significance level of α=5% and a power of 90% (β=10%). 30 This calculation was based on the assumption, on the basis of results from the EWOT1 and EWOT2 studies, that a death rate of 85% is expected at 2 years (86. 4%, according to EWOT1 and EWOT2). Categorical variables were presented by their frequencies and percentages and continuous variables by their means and standard deviations if they were normally distributed. When not normally distributed, data were reported by their median and interquartile range (Q1-Q3) and analyzed using nonparametric methods.