dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors gratefully acknowledge the contributions of all the participants in NeT Task Group 4. Without their hard work and commitment the project could not have succeeded. Table 1 lists both the contributors to TG4, and the work that they contributed. The support of the  UK Engineering and Physical Sciences Research Council  for  M C Smith  is also acknowledged, via the  New Nuclear Manufacturing Programme Grant ,  EP/J021172/1 , and the Fellowship in Manufacturing "A whole-life approach to the development of high integrity welding technologies for Generation IV fast reactors",  EP/L015013/1. Collecting and collating the data generated in TG4 were large tasks and key to the success of the project. One of the authors,  Ann Smith , had the unenviable responsibility of both managing (aka "cat-herding") and documenting  TG4 , a task which she discharged with quiet competence, commitment, and unfailing good humour.
Material and methods We collected data with a standardized search of two literature databases (Web of Science and the first 10 pages or 50 accounts on Google Scholar, whichever came first). We used the search string 'disturbance type' (listed in the previous paragraph) AND 'recov*' OR 'restor*' OR 'resilience', and searched abstracts for relevance, finding 972 manuscripts from 1900 to May 2013. The disturbance is defined as an event with the potential to impact ecosystems to which researchers measured a response. We estimated how close response variables were to either a pre-disturbance (n ¼ 1092 variables) or nearby undisturbed reference system (n ¼ 4001 variables), through measuring response ratios (to indicate recovery completeness; hereafter termed recovery completeness) and the rate of recovery (percentage improvement per year; figure  1 ).
For the 49 variables that had both types of references (i. e. those studies that used before-after-control-impact designs), we used the pre-disturbance reference to calculate recovery completeness. This yielded 400 studies that were identified by the lead author (electronic supplementary material, figure  S11 ). We considered two different variables to measure restoration success: recovery completeness and recovery rate. Recovery completeness is measured as the response ratio, a commonly used metric in meta-analysis  [19 -21] , and we use the term recovery completeness to refer to our response ratio calculations throughout the main text, while using response ratio when referring to this metric in reference to meta-analysis generally. While other metrics are also commonly used in meta-analysis, those metrics require variance or sample size data, which was not commonly reported in our dataset (electronic supplementary material).
(a) Datasets Our data analysis involved two datasets. The first dataset contains data only from studies in which analyses of both an actively restored site(s) and passively recovered site(s) in a single study were performed, hereafter referred to as the passive-active dataset. Because this first dataset represented a very small portion of the total number of studies (8. 5%, 436 out of 5142 response variables, 16 studies) from our literature search, we also used a second dataset containing data from all studies in the literature search, hereafter referred to as the allstudies dataset. We removed category levels with fewer than.
(b) Models We used the multivariate model function rma. mv from the metafor package to construct general linear mixed models  [22]  in R v. 3. 0. 1  [23]. Recovery rate values were inverse hyperbolic sine transformed prior to analysis to improve normality and homoscedasticity. Outliers remained after the transformation, and we identified and removed these outliers via the interquartile range method  [24]. The study was included as a random factor to account for non-independence of multiple response variables in a single study. The absolute value of latitude also was included as a random factor to account for climatic effects dependent on geographical location. Because our models were random-effects models, tests for publication bias via funnel plots or other means would not be instructive because they assume a fixed-effect model structure  [25].
We created three sets of model structures to address our questions about the effects of restoration (electronic supplementary material, table  S4 ). The first model structure included one of the five categorical variables (type of disturbance, ecosystem, metric, trophic level and organism; see electronic supplementary material, table  S3 ) and the additive effect of recovery type as moderator variables. The results of these models are shown in figures 2 and 3, and electronic supplementary material, figures S2, S3 and S6. The second set of models included only one of the five categorical variables listed above as a main effect. The third model structure was our most inclusive and included only recovery type as the main effect (electronic supplementary material, figure  S10 ). We used the first model structure with both the all-studies and passive-active datasets to build models with recovery completeness and recovery rate as dependent variables.
We also used the first model structure with the all-studies dataset to build models with disturbance magnitude as the dependent variable. Because the data requirements were relatively high for the finer scale analysis, we only used the all-studies dataset to build models with recovery completeness and recovery rate as dependent variables for those model structures. We used the second model structure to build model sets for both the all-studies and passive-active datasets for the purpose of determining the 'average' effects of the five categorical variables listed above for recovery completeness, recovery rate or disturbance magnitude as dependent variables (grey bars in figures 2 and 3; electronic supplementary material, figures S2, S3, S6 and S7). We used the third model structure to build model sets for the all-studies to determine the relationship between active and passive recovery with no other moderating variables and recovery completeness and recovery type as dependent variables.
One potential explanation is that aquatic systems undergoing eutrophication often experience many other concurrent pressures and non-point sources of pollution that may prevent full recovery or even a complete end to disturbance in those systems  [15]. It could be that the lack of complete recovery is due to an insufficient amount of time to detect ecosystem recovery. However, we found recovery completeness did not vary with time since restoration (p ¼ 0. 73), which was surprising. A previous study found a similar near-universal lack of complete recovery using a subset of the current dataset (n ¼ 89 studies versus the 400 in the current study) and when viewing recovery through an ecosystem services lens  [21]. Our results expand those findings to a broader range of ecosystems and geographies, and, together with previous work  [29] [30] [31] [32] , suggest the majority of ecosystems have not yet recovered fully following disturbance and may not in the future.
However, we found faster recovery for variables with shorter times since recovery began (electronic supplementary material, figure  S4 ), with recovery rate decreasing by 0. 026% per year since recovery began. Given that half our dataset had no more than 10 years to recover, the recovery rates we estimate may be optimistic, especially for systems in the later stages of recovery. Recovery trajectories may slow in later stages of restoration, while the species, functions and interactions that are most difficult to restore remain elusive. Nonlinear recovery trajectories could explain why we find potential for complete recovery, but our results rarely show it. Indeed, recovery trajectories can be nonlinear for ecosystems recovering from major disturbances  [33]. For example, before restoration is undertaken, ecosystems may be stuck in an alternate stable state, with hysteresis preventing linear recovery  [33] , and in systems with episodic recruitment, recovery will necessarily be punctuated  [34].
(c) Recovery progress and restoration goals Restoration efforts often aim to recover the biodiversity and ecosystem functions that were lost with disturbance  [8]. The fact that our study often finds a lack of complete recovery but progress towards a reference, suggests that restoration goals may need to be modified so they are more realistic to actual ecosystems' ability to withstand/recover from damage. Many in the field of restoration ecology have called for a move away from historical reference goals to consider other endpoints that might be more attainable, such as contemporary reference sites that exist today but have not experienced the focal disturbance  [36] [37] [38] [39]. Our dataset reflects that shift, with 79% of variables compared against contemporaneous rather than historical reference values.
One potential explanation for why we did not find consistently faster or more complete recovery in actively restored ecosystems is that managers may correctly choose which ecosystems are not recovering on their own and require active restoration to improve recovery outcomes relative to passively recovering systems, resulting in the similar rates and levels of recovery found here. Such choices would mean that actively restored sites begin with slower rates/levels of recovery than those that are passively recovering. Then, as restoration proceeds, recovery rates and levels speed up to approximate, but not exceed, recovery rates and levels in passively recovering systems. We cannot test this with our dataset given how few studies (n ¼ studies and 436 response variables from the passive-active dataset) compared both passive and active restoration strategies in the same system, which highlights the need for future studies to do so.
(Online version in colour. ) disturbance 1. end disturbance (e. g. cease logging) A. full recovery of pre-disturbance time 2. reconnect waterways restoration project state or undisturbed reference B. incomplete recovery of undisturbed reference Figure 1. rspb. royalsocietypublishing. org Proc. R. Soc. B 285: 20172577 ecosystem functioning/configuration six studies in our datasets on a per model basis to keep a minimum sample size.
Acknowledgments: The authors would like to express their gratitude to the following:  Rebecca Timmons  ( Alltech Inc. , USA ) for her technical input on microalgae and  Tuoying Ao  ( Alltech Inc. , USA ) for his knowledge of the application of microalgae in broiler nutrition and  Claire Traynor  at the  Mylnefield Institute (James Hutton Ltd. , Dundee, UK ) for her expertise in fatty acid analysis of meat tissue.
In this way, the virtual resources and the services offered by multiple providers can be aggregated by federating them through their respective SOs. Upon a detailed analysis of the existing open source orchestration platforms (like OSM, ONAP and Cloudify), they do not support slicing, federation and MEC, which are the key technology enablers for 5G. 5GEx and 5G-Crosshaul projects explored network slicing and multi-domain aspects, but only limited support was provided, e. g. , 5G-Crosshaul mainly focused on data plane solution to support network slicing, 5GEx was focused on resource federation part. Therefore, in this work the SO advances the aforementioned solutions by providing: i) support for network slicing (mapped to NFV network services) by offering isolated network service instance management along with their lifecycle management and design of novel algorithms for resource allocation and function placement per slice; ii) support for MEC services by extending orchestration for MEC applications; iii) support for federation by developing new functional components for resource and service federation including network service decomposition, service and resource advertisement entity, and interfaces to interact with other administrative domains: iv) support of integrated orchestration of heterogeneous cloud and WAN technologies by developing and integrating a set of new plugins for specific cloud and network technologies.
The pre-phase of the NSaaS consists of business agreement between administrative domains, established "offline" on business meetings. Each domain can offer to other administrative domains a set of agreed and offered NFV-NS referred to as catalogue of services. Once the pre-phase is finished, peering catalogues are shared and stored into the NS Catalogue Database of each administrative domain. Once the instantiation phase of NSaaS is triggered, SO NFVO-NSO checks the NS Catalogue for a desired NFV-NS. A consumer SO decides how to decompose the NFV-NS (i. e. , NSDs) into several segments, for example based on service or resource availability, and for the most suitable provider domain. A request for NFV-NS creation is delivered over the EBI/WBI interface to the chosen provider's SO NFVO-NSO. The chosen provider's SO NFVO-NSO initiates service creation and instantiation procedure to its constituent MTP.
The phases followed by the SO to deploy the service and the time each phase is taking are summarized in Table  1. Results show that the overall service deployment time is about 280s (i. e. , less than 5 minutes). Once the service is delivered, two performance parameters are measured: the data rate (both between the local cache and the enduser and the local cache and the global repository) and the round trip time (RTT) (in the two aforementioned scenarios). The traffic between the end-user and the local cache shows a bursty behavior with peaks of 10Mbps (the video content used has an average bitrate of 2Mbps). When the video is not in the local cache the traffic between this virtual machine and the global repository exhibits the same traffic pattern. On average the measured RTT between the end-user and the local cache is about 40μs (notice that the end user is directly attached to the local cache) while the one between local cache and global repository is about 40ms.
For example, 5G-Crosshaul project [9] delivered an NFVO software prototype able to support the instantiation and termination of Network Services composed of VNFs over a Crosshaul transport network. Other 5G-PPP/H2020 projects like SONATA [10], 5GEx [11] focused on a various operation and use case specific scenarios, like VNF SDK development and multi-domain operation. Service orchestration and automated lifecycle management are mandatory components of the 5G network softwarisation vision. Multiple software implementations of the management and orchestration (MANO) platform are already available both as open source and proprietary solutions. Open Network Automation Platform (ONAP) was founded in 2017 by merging AT&T driven OpenECOMP project and another NFV orchestration project called Open-O [4].
g. , clinical trial NCT02362529), in order to determine how levels of pro-inflammatory markers before the treatment could predict the clinical response. If the therapeutic effects of minocycline depend on its anti-inflammatory action, one would expect patients with an exacerbated inflammatory profile to be most responsive. Of note, even if minocycline does not target microglia specifically, it could indirectly modulate their physiological functions via astrocytes and vascular endothelial cells. Studies have demonstrated that astrocytes act under the control of reactive microglia in the context of inflammation  (Pascual et al. , 2012; Habbas et al. , 2015; Liddelow et al. , 2017). Animal models, especially microglia-specific gene knockout strategies, have been particularly informative, considering that the readout of microglial pathophysiological alterations is not easily achieved in human subjects.
ACKNOWLEDGMENTS We are grateful to  Drs. Luc Maroteaux  and  Patricia Gaspar , as well as  Prof. Laura Civiero  for their insightful revision of our manuscript. TLT was supported by the  German Research Foundation  (DFG,  TA1029/1-1 ) and the  Ministry of Science, Research and the Arts of Baden-Wuerttemberg  (Research Seed Capital). MKS was supported by excellence scholarships from the  Natural Sciences and Engineering Research Council of Canada (NSERC) ,  Fonds de recherche du Québec-Nature et technologies (FRQNT) , and  Faculty of Medicine of Université Laval. MET was funded by the  Canadian Institutes of Health Research (CIHR , award no. RN283856 -353750 ) and is a Canada Research Chair (Tier 2) in Neuroimmune plasticity in health and therapy. AR is supported by  UPMC  and funded by  BIO-PSY Labex  and  Agence Nationale de la Recherche (ANR).
Acknowledgments  CMIP5  and PDRMIP model output is publicly available for download (see http://pcmdi9. llnl. govand http://www. cicero. uio. no/en/PDRMIP/PDRMIP-dataaccess,respectively). T. B. R. was supported by a  NERC CASE  award in collaboration with the Met Office  NE/K007483/1  and  NERC  grant  NE/N006038/1. P. M. F. was supported by a  Royal Society Wolfson Merit Award  and  NERC  grant  NE/N006038/1. T. A. was supported by the  Newton Fund  through the  Met Office Climate Science for Service Partnership Brazil (CSSP Brazil ). B. H. S. and G. M. were funded by the  Research Council of Norway , through the grant  NAPEX (229778 ). D. S. and G. F. thank  NASA GISS  for funding and acknowledge the  NASA   High-End Computing Program  through the  NASA Center for Climate Simulation at Goddard Space Flight Center  for computational resources.
O. B. acknowledges HPC resources from TGCC under the gencmip6 allocation provided by  GENCI  ( Grand Equipement National de Calcul Intensif ). T. T. is supported by the  NEC SX-ACE  supercomputer system of the  National Institute for Environmental Studies, Japan , the  Environmental Research and Technology Development Fund  ( S-12-3 ) of the  Ministry of Environment, Japan , and  JSPS KAKENHI  grant  JP15H01728  and  JP15K12190. M. K. , D. S. , and A. V. were supported by the  Natural Environment Research Council  under grant  NE/K500872/1  and from the  Grantham Institute at Imperial College. Simulations with HadGEM2 and HadGEM3-GA4 were performed using the MONSooN system, a collaborative facility supplied under the  Joint Weather and Climate Research Programme , which is a strategic partnership between the Met Office and the  Natural Environment Research Council.
Background: The clinical utility of serum procalcitonin levels in guiding antibiotic treatment decisions in patients with sepsis remains unclear. This patient-level meta-analysis based on 11 randomized trials investigates the impact of procalcitonin-guided antibiotic therapy on mortality in intensive care unit (ICU) patients with infection, both overall and stratified according to sepsis definition, severity, and type of infection. Methods: For this meta-analysis focusing on procalcitonin-guided antibiotic management in critically ill patients with sepsis of any type, in February 2018 we updated the database of a previous individual patient data meta-analysis which was limited to patients with respiratory infections only. We used individual patient data from 11 trials that randomly assigned patients to receive antibiotics based on procalcitonin levels (the "procalcitonin-guided" group) or the current standard of care (the "controls").
Definition of patient population and trial selection For this meta-analysis focusing on PCT-guided antibiotic management in critically ill patients with sepsis, we updated the database of a previous individual patient data meta-analysis which was limited to patients with respiratory infections only  [20, 21]. Trial selection and data collection were performed following the initial protocol published in the Cochrane Library  [22]  and the report was prepared according to PRISMA individual participant data (IPD) guidelines  [23, 24]. For this analysis, we included all patients residing in an ICU with any type of systemic infection. We thus excluded patients not treated in the ICU and also one trial that focused on ventilator-associated pneumonia because these patients may not have systemic bacterial infections  [25].
Furthermore, pediatric trials and those not using PCT to guide initiation and duration of antibiotic treatment were excluded from this analysis. Given that the definition of sepsis has changed over time  [26] , and is used differently among researchers and clinicians, we decided to focus primarily on trials involving patients presenting with infections to the critical care unit. However, we performed a subgroup analysis on patients meeting the sepsis 3 definition  [26]. We also stratified the analysis based on sepsis severity and type of organ involved. Individual patient data were collected from eligible RCTs which assessed adults meeting these criteria.
Trial search and data collection The search strategy for this review was updated in February 2018 in collaboration with personnel from the Cochrane collaboration and executed in all databases from the date of their inception to February 2018. All references were also screened for eligibility. Databases searched included the Cochrane Central Register of Controlled Trials (CENTRAL; January 2017, issue 1), MEDLINE Ovid (1966 to February 2017), and Embase. com (1980 to February 2017). We applied no language or publication restrictions. Two authors (YW and MAM) independently assessed trial eligibility based on titles, abstracts, and full-text reports, with further information being obtained directly from investigators as needed. Study protocols, case report forms, and unedited databases containing individual patient data were requested from investigators of all eligible trials.
Data from each trial were first checked against reported results and queries were resolved with the principal investigator, trial data manager, or statistician. Data were assessed in a consistent manner across all trials, with standard definitions and parameters, and thus mortality rates differed slightly from previous reports. In accordance with the Cochrane methodology, we used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach to assess risk of selection bias, performance bias, detection bias, attrition bias, reporting bias, and other types of bias  [27]. The grading was performed by two authors (YW and MAM) and, in case of conflicting results, grading was discussed with another author (PS) and within the meta-analysis group.
Study Site and Population This study was conducted within an ongoing randomized trial designed to assess the effectiveness of three prenatal supplements to improve infant immune response to rotavirus vaccination (ClinicalTrials. gov Identifier: NCT02145000)  [24]. The study took place in the Madarounfa Health District within Maradi region, a rural area in south-central Niger along the Nigerian border. Nutritional outcomes in the region are poor. According to the 2012 Demographic and Health Survey (DHS), 17. 9% of women of reproductive age were underweight (body-mass index of <18. 5 kg/m 2 ) and 53. 5% of children under five were stunted (<-2 z-score in height-for-age), both of which were worse than the national average rates for Niger  [9]. Villages in the parent study (n = 53) were randomly assigned in a 1:1:1 allocation to one of the three study arms (IFA, MMN, or LNS).
Study Design and Data Collection This was a two-phase, longitudinal study with emergent design, in which Phase 2 data collection built off findings from Phase 1. We collected quantitative and qualitative data to compare the acceptability and utilization of the three supplement types, as well as to understand the factors influencing acceptability and utilization among participants. The number of interviews, focus groups, and spot checks conducted by participant type, study arm, and study phase are shown in Table  1. Qualitative and spot check data were collected by study staff who were trained in qualitative research methods and not involved in supplement distribution. Semi-structured interview and focus group guides covered supplement acceptability, consumption practices, facilitating factors and barriers, perceived side effects, perceived benefits, support or opposition from household members (e.