dataesr/scientific-paragraphs-categorization huggingface dataset:
Renal function was found to be comparable between the two arms at 12 months post-transplant, with an acceptable tolerability profile. Here, we report a 5-year, prospective, non-interventional follow-up study of the ADHERE patient cohort. The primary objective of this follow-up study was to evaluate long-term graft survival in kidney transplant recipients, treated with once-daily prolongedrelease tacrolimus-based therapy. Secondary objectives included evaluation of the long-term impact of prolonged-release tacrolimus on patient survival, renal function, and acute rejections (ARs). Patients and methods ADHERE (ClinicalTrials. gov identifier: NCT01363752) was a multicenter, randomized, open-label, parallelgroup, 52-week, Phase IV study that evaluated renal function in de novo adult kidney transplant patients treated with once-daily prolonged-release tacrolimus (Advagraf â ; Astellas Pharma Europe BV, Leiden, the Netherlands) plus MMF or sirolimus  [14].
In the follow-up study, patients received prolongedrelease or immediate-release tacrolimus. A total of 5 years of follow-up post-transplant data are presented here: 1 year of the primary ADHERE study and a 4-year non-interventional follow-up period. The tacrolimus dose was administered by the investigator according to standard clinical practice. In this study, start of treatment was defined as the time when the first prolonged-release tacrolimus dose was administered during the ADHERE study period.
Additional secondary endpoints included: concomitant immunosuppressant regimen, dose, and formulation (in addition to or instead of tacrolimus); tacrolimus dose, formulation and trough levels; medical conditions of interest (malignant diseases, diabetes mellitus, and cardiovascular events); and infections of interest [cytomegalovirus (CMV) and BK virus]. Safety data [adverse events (AEs) and adverse drug reactions (ADRs)] were also reported. Investigators provided a causality assessment for AEs potentially related to tacrolimus. ADRs were AEs considered to be possibly or probably tacrolimus-related.
Statistical analysis The enrolled patient set (EPS) comprised all patients who enrolled in the primary ADHERE study and received a kidney transplant. The treatment arm (TA)-EPS comprised patients in the EPS who received randomized treatment with prolonged-release tacrolimus. The follow-up patient set (FPS) comprised patients in the EPS who consented to the follow-up study or who died before they could give consent. Patients in the FPS who received randomized treatment with prolonged-release tacrolimus were included in the TA-FPS. Patients who died before starting the follow-up study had only their date of death and ADHERE data included in these analyses, provided the date of death was available in the public domain. The main analyses of the primary endpoint (graft survival) and all the secondary time-to-event endpoints (overall patient survival, AR and BCAR) were based on the EPS and the TA-EPS.
This was done to ensure that all events that occurred during ADHERE were taken into account in time-to-event analyses in order to reduce bias in the analysis. Thus, the analysis included ADHERE data from patients who died or experienced graft loss before entering the follow-up study. Likewise, patients who were not approached for the follow-up study, who were approached but did not provide informed consent, or who were lost to follow-up between the end of the ADHERE study and the start of the follow-up study, had their ADHERE data included in the analyses up to the date of their discontinuation from the primary ADHERE study. The Kaplan-Meier method was used for the analyses of these endpoints. Patients without graft loss were censored at the EOS date or last evaluation date. A post hoc analysis of death-censored graft survival (graft survival censored for death with a functioning graft) was also performed using the Kaplan-Meier method.
Discussion In this long-term, 5-year follow-up of the patient cohort from the 12-month primary ADHERE study, follow-up data were obtained for 587 (70%) of the 838 patients  originally enrolled. Overall graft survival was high at 5 years post-transplant (84. 0%), as was patient survival (90. 8%). Renal function remained stable over 5 years post-transplant. Kaplan-Meier estimates of AR and BCAR events were low, with most occurring within the first 6 months post-transplant. The mean overall prolonged-release tacrolimus dose and trough levels decreased from 1 to 5 years post-transplant. Overall, 16 patients (2. 7%) had ADRs that were considered probably tacrolimus-related during follow-up. These safety findings are consistent with those from the primary ADHERE study  [14]  and no new safety signals beyond the established safety profile for tacrolimus  [19]  were detected in this kidney transplant patient population.
The present study has several limitations. Owing to the limited amount of data available for analysis in real-world practice, data on some endpoints were not collected. For example, neither adherence nor variability in exposure was assessed despite being potentially key factors associated with long-term graft survival. Although the patients who entered the follow-up study were representative of the original ADHERE patient population, these results should be interpreted with caution given that not all the patients in the EPS (n = 838) from the initial 1-year ADHERE study were included in the TA-FPS (n = 539) and had 5-year follow-up data. It is important to note that safety data in postmarketing observational studies are subject to underreporting bias  [25, 26]. Therefore, the safety findings reported in the current study should be interpreted with caution.
Acknowledgements The authors would like to thank all centers participating in this study. This study was sponsored by  Astellas Pharma Europe, Ltd. Paola Accalai  and  Fernando Gibson , from  Cello Health MedErgy , assisted in drafting the manuscript under the direction of the authors. Editorial support was provided by  Cello Health MedErgy  throughout the development process. Editorial support was funded by  Astellas Pharma, Inc.
These metadata can be efficiently used to establish correlations with other media analysis results like social networks or events generated by cyber security monitoring systems. The applied bottom-up Knowledge Management approach combined with knowledge modelling allows easy extension to the others system components of global security, i. e. social networks analysis and cybersecurity, especially in the era of ubiquitous IoT and drones equipped with facial recognition capabilities. After the general introduction and pointing out the challenge, this paper describes our research method, followed by the state of the art in the fields covered. The explanation on the role of Knowledge Management in this research is provided before we focus on the first system component of this researchhybrid decision support system for real-time video analysis.
Evolution of video surveillance Learning from human generated annotations, the machine has shown a capacity to generalize, identifying objects and generating text sentences describing image and scenes. In 2017, this effort is extended to video with the DAVIS challenge on video object segmentation  (davischallenge. org, 2018). The downside of this approach, is that human contribution is highly necessary to generate meaningful datasets. Initiatives to deploy crowd-annotations platform have been recently undertaken to improve and speed-up the ground truth collection from users on the Web  (Kavasidis, 2014) , fostering the need for creation of machine learning datasets. It is notable that image segmentation, object recognition, video labelling creates a leap frog in surveillance applications and human interface.
Highly focused European FP7 2010-2013 research project, VANAHEIM  (Odobez, 2012)  has been developed in the context of two metro stations and revealed the difficulty to use inputs from video analytics modules to automate the displays on video walls. Nevertheless this project has been pivotal in demonstrating the huge potential of unsupervised video analysis and the detection of abnormality into long recordings. Since 2010, a revolution has begun in video analytics. Thanks to Convolutional Neural Networks, Deep Learning techniques, object recognition, image segmentation and labelling has shown impressively efficient, up to the point where the machine, using a software built on top of GoogLeNet has demonstrated in 2015 an ability to identify objects in still images that is almost identical to humans.
This was made possible thanks to the availability of a very large image dataset called Imagenet  (Russakovski, 2015)  (over 1,4 million images from over 1000 classes) manually annotated and a challenge that took place annually between 2010 and 2015, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). In 2015, the Chinese company BAIDU also claimed actual superiority of machine image recognition compared to human on the same image dataset  (Wu, 2015).
It is important to note that annotations as well as any other indexing data may be stored and preserved much beyond the limits of video retention periods, as advised for instance by the European Data Protection Supervisor (EDPS). This means that real-time automated labelling data are key to provide large datasets of surveillance contexts without the burden of keeping the video they originated from. VORTEX is an attempt to rationalize the capture of human feedback in surveillance and crisis context. Putting face to face these data with large volume of sensors data enable the System to correlate and generalize on a rich dataset and leads to the emergence of predictive alerts and prescriptive surveillance actions that increase considerably the situational awareness in operations centres. We are confronted with several difficulties, notably in scene description and modelling.
Performance and Interpretation of GH Stimulation Tests GH stimulation tests should be performed in the fasting state after adequate replacement of other hormone deficiencies (hypothyroidism and hypogonadism). Multiple GH secretagogues  [9]  have been used in GH stimu-lation tests, but there are no data to demonstrate that one is better than another. GHRH is not appropriate for use as a stimulant in children as theoretically it will not diagnose GHD of hypothalamic or pituitary stalk origin. Stimulation tests in current use include the insulin tolerance test, and tests using glucagon, arginine, clonidine, L-dopa, and GH-releasing peptide-2 (GHRP2)  [42]. Failure to respond to 2 provocative stimuli is needed to diagnose GHD, which limits false-positive results while not eliminating these completely.
Acknowledgments The  GRS  and all the authors of this report would like to thank the following Workshop participants from regulatory agencies and industry for their invaluable and unrestricted sponsorship, comments, and perspectives. From regulatory agencies:  Kolbeinn Guðmundsson  (EMA) and  Marina Zemskova  (FDA), and from industry:  Nicola Ammer  ( AEterna Zentaris ),  Jonathan Day  ( BioMarin Pharmaceutical Inc. ),  Roy Gomez  (Pfizer),  Rick Hawkins  ( Lumos Pharma ),  Michael Højby  ( Novo Nordisk A/S ),  Lei Jin  ( Gene-Science Pharmaceutical ),  Roberta Luzzi  ( Sandoz Biopharmaceuticals ),  Rudolf Schemer  ( Immunodiagnostic Systems -IDS ), and  Aimee Shu  (Ascendis Pharma A/S). Judith Andersen  is thanked for her undaunted assistance in arranging the Workshop.
Disclosure Statement G. A. , M. B. , P. T. C. , C. G. , Y. H. , P. L. H. , R. H. , V. K. , and X. L. have no conflicts of interest to declare; P. F. B. was a consultant for  Novo Nordisk  and received research funding from  Novo Nordisk, Ipsen , and Opko; B. M. K. B. was a principal investigator of research grants to the  Massachusetts General Hospital  from  Novo Nordisk and Opko , and a recipient of consulting honoraria from  Merck Serono ,  Novo Nordisk ,  Pfizer , and Strongbridge; M. C. S. B. received speaker fees from  Pfizer ; C. S. Y. C. is an investigator for studies funded by  Opko  and  Merck Serono ; L. E. C. is an investigator for studies funded by Ascendis and Opko; P. C. was a consultant for  Ascendis, Genexine, GenSci , and Opko; P. F. C. -S. received travel grants and speaker fees from  Pfizer ,  Merck-Serono ,  Novo Nordisk , and Sandoz; A.
Model evaluation The purpose of this sensitivity study is not to specifically improve the skill of the biogeochemical model at representing the Fe and SChl distributions in the SO but to investigate the uncertainties associated with the external source of Fe from the AIS. However, in order to confirm that large biases are not introduced by the implementation of the new iron source in the biogeochemical model, we perform a statistical model-data comparison for Fe and SChl over the SO, south of 50 • S. For Fe, we compare the model experiments to a global database constructed by  Tagliabue et al. (2012)   S1  and S2  in the Supplement). The biases are relatively small, ranging from -0. 07 to 0. 02 nmol L -1 for Fe and -0. 13 to -0. 07 mg m -3 for SChl. The main difference is the increase in the mean Fe and surface chlorophyll concentrations showing a better agreement with observations, such as in the SOLUB5 experiment for Fe (Table  S1 ) and in the SOLUB10 and ICB-SURF experiments for SChl (Table  S2 ).
Conclusions We implement in the biogeochemical model NEMO-PISCES  (Aumont et al. , 2015)  the external source of iron from the Antarctic Ice Sheet based on recent estimates of Antarctic meltwater fluxes from icebergs and ice shelves  (Depoorter et al. , 2013; Merino et al. , 2016). The modeled Fe fluxes from the AIS are in the range of previous modeling studies  (Death et al. , 2014; Laufkötter et al. , 2018)  and in the lower range of recent estimates from data  (Raiswell et al. , 2016). The potential indirect supply of Fe by the ice shelf melt-driven circulation, i. e. , the meltwater pump  (St-Laurent et al. , 2017 , 2019) , is also represented by using the parameterization of  Mathiot et al. (2017). We investigate the impacts of different sources of uncertainties related to the AIS iron source on Fe and surface chlorophyll distributions: the solubility of Fe, the vertical distribution of the iceberg source, and its seasonal variability.
Acknowledgements. This work was performed using HPC resources from  GENCI-IDRIS  (grant  2018-A0040107451 ). The corrected ocean color product was retrieved from  Australia 's Integrated Marine Observing System (http://imos. org. au/, last access: 18 September 2019). The dissolved iron database is available on the  GEOTRACES International Data Assemble Centre  (https:// www. bodc. ac. uk/geotraces/data/historical/, last access: 18 September 2019). We would like to thank  Robert Raiswell  and two anonymous referees for their helpful comments on the paper. Financial support. This research has been supported by the  ANR SOBUMS  (grant no. ANR-16-CE01-0014 ) and the  EU-H2020-CRESCENDO  (grant no. 641816 ). Review statement. This paper was edited by  Tina Treude  and reviewed by  Robert Raiswell  and two anonymous referees.
Code and data availability. The version code of the NEMO model, including PISCES-v2, used for this study is freely available at https://www. nemo-ocean. eu/ (last access: 18 September 2019). To access the NEMO svn repository, users should register on the NEMO website at https://forge. ipsl. jussieu. fr/nemo/register (last access: 18 September 2019). Model data are available at https://doi. org/10. 5281/zenodo. 2633097 (Person, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-16-3583-2019-supplement. Author contributions. The author contributions to this paper are as follows. RP and OA developed the conceptualization. RP conducted the formal analysis. OA and LB acquired funding. All authors con-Biogeosciences, 16, 3583-3603, 2019   www.
25 mm i. d. , 0. 25 mm film thickness) using a temperature gradient: 50 °C (5 min) to 100 °C at 4°C/min, 100 to 250 °C at 30 °C/min. Helium flowing at 1 mL/min was used as carrier gas. MS inlet line was set at 250 °C and Electron Ionization source at 200 °C. Full scan monitoring was performed from 33 to 500 m/z. All data was collected and processed using Turbomass 6. 1 (Perkin Elmer, Courtaboeuf, France). Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, USA). Chemical identifications were validated with both Reverse and Forward scores above 800. Retention indexes were used to confirm the identifications. The area under the peak curve was then collected for each identified peak. The measurement of a blank sample was carried out in parallel, consisting of a non-inoculated blood culture bottle containing the same culture medium as the inoculated bottles.
Helium flowing at 1 mL/min was used as carrier gas. MS inlet line and Electron Ionization source were set at 200 °C. To insure compound selectivity, Selected Ion Recording (SIR) was performed after a 4. 5 min solvent delay with the following masses: 43 m/z (isobutanoic acid), 60 m/z (acetic, butanoic, pentanoic, isopentanoic, hexanoic and heptanoic acids) 74 m/z (propanoic acid), 88 m/z (2-ethylbutyric acid, IS). All data was collected and processed using Turbomass 6. 1 (Perkin Elmer, Courtaboeuf, France). Quadratic internal calibration was calculated for each acid using the peak areas from the associated SIR chromatograms. Coefficients of determination were all above 0. 999. Back-calculated standards and calculated quality controls (0. 5 and 5 mmol/L) all showed good accuracy with deviations below 15%.
5 minutes, 20 to 80% during 4 minutes followed by a 1. 5 minutes wash step a 98% and then a 3. 5 minutes equilibration step towards initial conditions. Total run time was 15 min. Compounds were ionized in a Zspray ESI source using a default "Labile Tune" to reduce compound fragmentation before QTOF monitoring. Ionization parameters were set as follow: 0. 8/2. 0 kV for positive/negative modes, source/desolvation temperatures = 120/600 °C, cone/desolvation gaz = 100/600 L/h. Ions were monitored using a Sensitivity HDMSe data independent analysis method between 50 and 1000 m/z as follow: scan time 0. 15 s, collision energy ramp 20-40 eV, automatic mass correction during survey (using Lockmass 554. 2620 m/z from a Leucine Enkephalin solution). All ions produced in-source were alternatively analyzed at low/high fragmentation energy to collect parent/fragments masses throughout the analysis.
Data processing Raw data was then processed by UNIFI software to collect "components" described by a retention time, a drift time and parent/fragment masses. Automatic parameters were set to detect 4D peaks above 150 and 75 counts for low and high energy scans. Once all components were listed, a marker table was generated with the following parameters: 0. 2-12 minutes retention time window, intensity above 2000 counts, automatic mass/ drift time/ retention time tolerances (14. 2 ppm/ 0. 47 ms / 0. 03 min for positive experiment and 16. 2 ppm/ 0. 54 ms/ 0. 03 min for negative experiment). The marker table was sent to the EZinfo multivariate statistics software (Umetrics, Umea, Sweden) for data exploration by PCA then data classification according to both anaerobic and aerobic groups using an OPLS-DA model.
From the OPLS-DA model, the VIP and S-plot diagrams were used to select discriminant markers as follow: VIP>1 and S-plot markers >90%. The selected markers were then labelled on the marker table in UNIFI and searched using the elucidate tool against the following structure libraries: metabolite structures (including CCS values, Waters, Saint-Quentin-en-Yvelines, France), then KEGG+HMDB databases (Chemspider access). Potential identities were then added to an inhouse structure library including the structure. mol files from Chemspider (Royal Society of Chemistry, London, United Kingdom) and then targeted on the initial raw data according to the following parameters: 5 ppm mass tolerance, 2 mDa mass tolerance for structure predicted fragments, 5% CCS deviation, multiple adduct finder (+H, +Na, +K, +Li, +NH4) within 5 ppm and 0.
Principal component analysis with the Pearson metrics was performed and biplot were shown to explore the association between strains, atmosphere and metabolites. Statistical tests were two-sided. A p-value  0. 05 was considered significant. These statistical analyses were performed using GraphPad v8. 0 (GraphPad Software, La Jolla, CA, USA) and XLSTAT v19. 1 software (Addinsoft, Paris, France).
Unit B shows the highest mean value, 474 ± 290 ppm (1σ), while the unit SSB exhibits the lowest mean value, 131 ± 124 ppm (1σ). Concentrations in the unit C are similar to those of the unit A, with a mean value of 206 ± 257 ppm (1σ). Only nine samples yielded enough materials for CAS quantification of sulfate contents. The concentrations vary from 23 to 72 ppm with a mean value of 35 ± 17 ppm (1σ). This dataset is restricted and we did not attempt to outline any special trend along the section. The TOC concentrations show variations from 0. 02 to 0. 37 wt. % with a mean value of 0. 114 ± 0. 099 wt. % (1σ) (Fig. 3 , Table  1 ). Units B, SSB and C exhibit similar TOC concentrations with average values of 0. 09 ± 0. 04, 0. 06 and 0. 06 ± 0. 02 wt. % (1σ), respectively. Strongest variations in TOC content are observed in unit A and encompass the highest mean value: 0.
Coupled variations in the carbon and sulfur cycles are suggested and might be traced back using the δ 13 C and δ 34 S records when such paleoenvironmental conditions developed. Covariations between δ 13 C and δ 34 S signals during periods of low seawater sulfate concentration have been hypothesized for the Early Triassic in South China  (Luo et al. , 2010; Song et al. , 2014b) , but also during the Paleozoic  (Gill et al, 2007; Gill et al. , 2011)  and the Cretaceous  (Adams et al. , 2010). Such a relationship between the δ 34 SCAS and δ 13 Ccarb signals cannot be identified in our dataset. Several authors postulated that a gradient in sulfate δ 34 S potentially developed within water column depth during the Early Triassic  (Newton et al. , 2004; Kump et al. , 2005; Riccardi et al. , 2006; Marenco, 2007).
Acknowledgments This work is a contribution to the  ANR  project  AFTER  ( ANR-13-JS06-0001-01 ) and was also supported by the  French "Investissements d'Avenir" program , project  ISITE-BFC  ( ANR-15-IDEX-03 ), and by the  FEDER Bourgogne Franche-Comté. The Mineral Mountains section is located on US public land under the stewardship of the  Bureau of Land Management (BLM) of the US Department of the Interior ; their management and access to this land is greatly acknowledged. Maud Boyet  is warmly acknowledged for her initial work on REE. Rémi Laffont is acknowledged for his work on the statistical analysis of the results.
MRI data acquisition All participants underwent standardised brain imaging on a 3 Tesla Philips Achieva-Tx MR scanner. A 3D-T1-weighted sequence was acquired with a time of repetition (TR): 9. 9ms, echo time (TE): 3. 7ms, flip angle: 7 ο , voxel-size 1×1×1mm, matrix size 244×240, 170 slices. DTI data were acquired with an axial single-shot spin-echo echo-planar imaging sequence with 30 diffusion encoding directions and the following parameters: TR: 7299ms, TE: 68ms, flip angle: 90 ο , field of view: 256×256 mm, voxel size: 2×2×2 mm, 70 slices. FLAIR data were acquired with TR: 11000ms, TI: 2800ms, TE: 125ms, acquisition matrix 384x186, slice thickness 4mm.