dataesr/scientific-paragraphs-categorization huggingface dataset:
In this context, BRCA1/2-deficient tumors, which lack the ability to accurately repair DSBs, can be treated with single strand break (SSB) repair inhibitors, such as inhibitors of the base excision repair-mediators PARP1/2  [259]. Four such agents, olaparib, rucaparib, niraparib and talazoparib, are clinically available for the treatment of pretreated advanced breast, ovarian and fallopian tube or primary peritoneal cancer patients harboring BRCA1/2 mutations (this restriction does not apply to niraparib), either germline or/and somatic  [260] [261] [262] [263] [264]. Several more PARPis are in clinical development with three of them, fluzoparib (NCT03863860), pamiparib (NCT03519230) and veliparib (NCT02470585, NCT02163694 and NCT02152982), currently being tested in phase III trials. In addition to the BRCA1/2m-PARPis couple, increasing evidence demonstrates that synthetic lethality may also take place following ATR inhibition in HDR-deficient tumors carrying inactivated ATM  [265] [266] [267] , with active ongoing phase II clinical trials testing two such agents: berzosertib (NCT02567409, NCT03517969 and NCT02595892) and ceralasertib (NCT03330847, NCT03328273, NCT03787680 and NCT02937818), in a variety of human cancers.
Currently, the first strategy has come to the fore, being the only approach to include compounds under clinical evaluation. Thus, two SF3B-complex (a component of U2 snRNP) inhibitors, E7107 (NCT00459823 and NCT00499499) and H3B-8800 (NCT02841540) are currently being tested in phase I trials for their safety and anti-tumor activity, against solid and hematological malignancies, respectively.
Funding: This study was co-funded by the  European Regional Development Fund and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation , under the call  Research-Create-Innovate  (project code:  T2EDK-03266 , project acronym and title: " BIOKARETRA-Identification of genomic and transcriptomic prognostic bio-signatures in head and neck cancer "); and BV was also supported by the  European Regional Development Fund-Project ENOCH  (No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000868 ) and by the  Ministry of Health Development of Research Organization, MH CZ-DRO  (MMCI,  00209805 ).
Methods: PubMed, PEDro, and Cochrane electronic databases were searched in July 2021 for relevant original articles, with no limits on publication dates. Studies were included if they were original articles, targeted adults with a diagnosis of axSpA, and reported wearable device-measured physical activity or sedentary behavior among patients with axSpA. Data regarding the study's characteristics, the sample description, the methods used for measuring physical activity and sedentary behavior (eg, wearable devices, assessment methods, and outcomes), and the main results of the physical activity and sedentary behavior assessments were extracted. Results: A total of 31 studies were initially identified; 13 (13/31, 42%) met the inclusion criteria, including 819 patients with axSpA. All the studies used accelerometer-based wearable devices to assess physical activity.
Data Sources and Search Strategy In July 2021, we conducted searches with no date restrictions in 3 electronic bibliographic databases (PubMed, PEDro, and Cochrane). The Boolean operators AND and OR were used to combine keywords relevant to the population, wearable devices, and the outcomes of physical activity or sedentary behavior and were searched in all fields. The detailed search strategy is presented in the review protocol recently published in JMIR Research Protocols  [32].
Acknowledgments This work was partly supported by the  French National Research Agency  within the framework of the  Investissements d'avenir program  ( ANR-10-AIRT-05  and  ANR-15-IDEX-02 ). The sponsors had no involvement in the study design, collection, analysis, and interpretation of data or in writing the manuscript. This study also forms part of a broader translational and interdisciplinary GaitAlps research program (NV).
We first evaluate the performance of ELMv1-CNP using the ILAMB benchmarking system  (Collier et al. , 2018) , which has been widely used in the evaluation of land surface models and ESMs  (Lawrence et al. , 2019; Bonan et al. , 2019; Zhu et al. , 2019; Friedlingstein et al. , 2019). We then evaluate ELMv1-CNP simulated N and P pools and fluxes with an observation-based dataset. Realizing that the static benchmarking may not help constrain future model projections, we further evaluate ELMv1-CNP using experimental manipulations of atmospheric CO2. Finally, we take advantage of the P-enabled capability in ELMv1-CNP to quantify the effect of P dynamics on the simulated ecosystem responses to increasing atmospheric CO2, increasing N deposition, LULCC, and climate change on the global scale.
ILAMB We used the International Land Model Benchmarking system  (Collier et al. , 2018; Luo et al. , 2012; Hoffman et al. , 2017)  to assess the model performance. The ILAMB package is a powerful tool for systematic evaluation of land model performance through comparison with observational data for biogeochemistry, hydrology, radiation and energy, and climate forcing. It was designed to use a wide array of observational data to constrain model results, including various land carbon pools and fluxes, inferred CO2 concentration variability, and functional relationships. For each variable, ILAMB scores model performance for period mean, bias, root-mean-square error (RMSE), spatial distribution, interannual coefficient of variation, seasonal cycle, and long-term trend. These scores are aggregated into an overall score representing multiple aspects of model performance for each variable.
These aggregated absolute scores are then used to calculate the relative score, which indicates the relative performance of each model with respect to other models used in the same analysis. ILAMB offers a variety of graphical diagnostics and tabular data to assist the user in understanding when, where, and to what degree model results deviate from observational data. The observational datasets used for the evaluation of carbon cycle in ILAMB are listed in Table  S4. In order to understand how the implementation of P cycling dynamics affects model performance, we evaluated the performance of both ELMv1-CNP and ELMv1-CN. In order to provide a context in terms of model performance in ILAMB, we provide the ILAMB evaluation of several other land models included in the Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) as part of CMIP6 (https://www.
For example, Quesada et al. (2012) found that the decreasing west-east gradient in productivity is mostly related to total soil P. to east across the Amazon basin. The problem is that this productivity gradient across the Amazon basin is not captured in ILAMB benchmark data so the "failure" of a CN model would not be captured by ILAMB. We show that the model performance generally improved with realistic P availability through the implementation of a prognostic P cycle in ELM. Compared to ELMv1-CN, ELMv1-CNP simulated biomass has lower bias across the tropical regions as P limitation leads to lower productivity and hence lower biomass. ELMv1-CNP produces better ILAMB scores on the functional relationships between GPP, LAI and other forcing variables, mainly due to improved estimate of GPP and LAI in tropical regions.
, 2005; Huete et al. , 2002; Kobayashi and Dye, 2005). Another example is the observational data for biomass. There are significant differences between the "tropical" and "GlobalCarbon" datasets and the "GeoCarbon" dataset for tropical biomass, but they were given about the same default weight in the ILAMB scoring system. Mitchard et al. (2014)  investigated the marked differences between different estimates of Amazon biomass and suggested the regional biases in some remote sensing products might be due to the lack of consideration of ecological variation in tree wood density and allometry. Further investigation of these datasets is needed to ensure the quality of biomass benchmarking data. as shown here (section 4. 2) and in other studies  (Wieder et al. , 2019; Davies-Barnard et al. , 2020).
Conclusions In this study, we provide an evaluation of ELMv1-CNP using the ILAMB benchmarking We highlight the data needs for global land model evaluation, particularly the need for more synthesis datasets on nutrient pools and fluxes, as well as observations from manipulation experiments that provide additional benchmark data for nutrient cycle evaluation. This need is becoming increasingly pressing as more land models are including N and P cycle dynamics and C-N-P interactions. We also identify challenges in constraining P cycle dynamics and point to the need for soil P measurements. Our simulations suggest, probably not surprisingly, that in general P is the more limiting nutrient in the tropical regions while N is more limiting in the middle to high latitudes. However, our results also suggest widespread N and P colimitation, even in the tropical regions where P limitation is more dominant.
https://doi. org/10. 5194/bg-2022-130 Preprint. Discussion started: 17 June 2022 c Author(s) 2022. CC BY 4. 0 License. The current version of ILAMB includes analysis of 28 variables using more than 60 datasets or data products. None of these variables, however, are directly related to nutrient cycles. As more land surface models are implementing N and P dynamics, it is becoming increasingly important to include metrics for nutrient stocks and fluxes. Davies-Barnard et al. (2020) assessed five nitrogen-enabled land surface models in CMIP6 and called out the need to have better constraints of nitrogen cycle processes. The need is equally urgent, if not more, to synthesize more observations to better constrain the P cycle processes, as less synthesized data are available for P. Encouragingly, recent studies have started to develop observational datasets based estimate of N and P cycling on the global scale for model evaluation, such as the GOLUM-CNP dataset we used in this study.
The main objective of this paper is thus to investigate future wind speed and wind power changes by the year 2100 over Cape Farewell by analysing trends over the study area from projections built with MAR forced with an ensemble of CMIP6 ESMs. Prior to this analysis, an evaluation of hourly MAR wind speed outputs over the Greenlandic tundra will be carried out by comparing the model outputs with observation data, including a new set of data acquired during the Belgian KATABATA expedition (link to https://www. katabata-project. uliege. be/). Such an evaluation will provide good insights into whether or not the MAR-modelled wind speeds are reliable before performing the long-term wind speed analysis. This paper is structured as such: Section 2 is a brief description of the wind regime in South Greenland.
Section 3 includes a description of the study area (3. 1), the observation data (3. 2), the MAR model (3. 3), simulations and forcing fields (3. 4) used in this study. This sections also includes a summary of the MAR outputs evaluations (3. 5) which is further detailed in the Supplement and a description of the methodology (3. 6) used for the long term wind speed and power change analysis. Finally, results and discussion can respectively be found in Sections 4 and 5 while Section 6 gives the conclusions and perspectives.
| Observation data The observation data used for the evaluation of MAR are in situ measurements obtained from multiple weather stations located in the study area. These stations are displayed in Figure  2  and are from three different databases: the Danish Meteorological Institute (DMI), the Program for Monitoring of the Greenland Ice Sheet (PROMICE) and the KATABATA project carried out by the University of Liège (Belgium). Details about the station locations are given in Table  1. The KATABATA project was developed and conducted by two research units, SPHERES and MONTE-FIORE, from the University of Liège (ULiège) and aims at increasing the availability of in situ observations of katabatic winds at the southern tip of Greenland (https://www. katabata-project. uliege. be/). Therefore, three Finnish-made Vaisala automatic weather stations were installed in the ice-free area near Cape Farewell in September 2020.
These stations have been established so as to be directly located in front of the prevailing winds, where previous MAR test simulations have identified wind speed maxima  (Radu et al. , 2019). The stations measure temperature, humidity and horizontal wind speed with readings transmitted every 20 min by satellite connection. The wind speed is measured at 10 m above ground level (a. g. l. ) and the temperature at 2 m a. g. l. The datasets from these stations range from September 2020 to the end of January 2021 for KAT 6640, the end of August 2021 for KAT 0460 and the end of February 2021 for KAT 0680. The time period differs from stations because some experienced a wind sensor failure during the winter of 2020/2021, but these problems have been resolved thanks to a maintenance visit in July 2021.
The KATABATA project has been implemented in the context of the "global grid" and the potential connections that could be made between West Europe and South Greenland in terms of renewable energy supply. Although the KATABATA stations have been installed specifically to assess the katabatic wind potential of Cape Farewell and to enable the evaluation of model performances to simulate these winds, their time series are relatively short and representative of a narrow area to evaluate our MAR climate model in depth. This is why, in addition to time series from the KATABATA project, PROMICE and DMI data have been used in the evaluation of MAR hourly wind speed outputs. F I G U R E 2 The PROMICE stations are located directly on the GrIS and measure a series of variables including horizontal wind speed, sensor height and GPS location.
The climatic variables are measured every 10 min and thereafter transmitted as hourly averages. The GPS data are recorded every 6 hr. Because the stations are located on the ice sheet where the snowfall accumulation is high, the height above the surface at which the wind speed is measured is variable, as the base of the station is gradually buried under snow during winter. Theoretically, the height measured by the sensor would be 3. 1 m if the stations were not to sink into the snow  (Fausto et al. , 2021). Measured heights above ground, recorded every 6 hr, are provided in the PROMICE datasets. The PROMICE data used in this study are the third version of the hourly datasets of stations QAS_L, QAS_M and QAS_U for the years 2016, 2017 and 2018, except for QAS_M which has only been active since the 11th of August 2016 and which has a very incomplete dataset for 2017.
Therefore, only the 2018 dataset was used for this station. This 2016-2018 time period was chosen because, together with the DMI stations, it has the most complete datasets for the selected stations. The DMI stations (ANG, IKM, IKS, NAR, NUN and UKI; see Table  1 ) are located on the ice-free area surrounding the GrIS. In Figure  2 , some stations seem to be located directly in the ocean. This is due to their location on small islands along the coast that are not resolved by MAR at a 15 km resolution. These stations automatically record variables including wind speed, measured at 10 m a. g. l. every 10 min  (Cappelen et al. , 2001). The datasets in the chosen time period were available for 2016, 2017 and 2018 for ANG, IKM, NUN and UKI, for 2016 and 2017 for IKS and were only available for 2018 for station NAR.
| Evaluation of the MAR wind speed In order to evaluate the performance of MAR when forced by ERA-5 to simulate wind speed above Cape Farewell, the model outputs have been compared to the wind speed observations described in Section 3. 2. A methodology, described in Data S1, Supporting Information, was designed to choose the corresponding MAR grid pixel for each station and to check for potential outliers caused by instrumental problems that could alter the results. Statistics for annual and seasonal time series (winter and summer) were then calculated to evaluate MAR. All the results of this evaluation are listed and described in Data S1. To sum up, it appears that even at 15 km of spatial resolution, MAR correlates well with the observations (R > 0. 70) with relatively small bias and RMSE.
ACKNOWLEDGEMENTS The authors thank the  University of Liège  for having funded the  KATABATA  project,  Alison Delhasse  for proof reading the manuscript and giving writing advice and  David Dundas  for English spell and grammar revision. Clara Lambin  is funded by the  European PolarRES  project. Computational resources used to perform MAR simulations have been provided by the  Consortium des Equipements de Calcul Intensif (C ECI) , funded by  F. R. S. -FNRS  under Grant No. 2. 5020. 11and the Tier-1 supercomputer (Zenobe) of the  Fédération Wallonie Bruxelles  infrastructure funded by the  Walloon Region  under Grant Agreement No. 1117545. This work been supported by the  European H2020  project  PolarRES  under Grant No. 101003590.
DATA AVAILABILITY STATEMENT The main KATABATA hourly measurement as well as the main MAR outputs are available on Zenodo (https:// doi. org/10. 5281/zenodo. 6055786) and the modelled datasets presented in this study are also available from the authors upon request and without conditions. Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) were provided by the Geological Survey of Denmark and Greenland (GEUS) at http:// www. promice. dk and were downloaded on October 25, 2021 from https://promice. org/PromiceDataPortal/ api/download/f24019f7-d586-4465-8181-d4965421e6eb/ v3/hourly/csv. Data from the Danish Meteorological Institute were downloaded from https://dmiapi. govcloud. dk/ on October 20, 2021.
Detailed table of the station locations from Figure2as well as their available datasets time ranges Number Station ID Station name Database Lon. Lat. Elevation (m) Time range 1 ANG Angissoq DMI -45. 15 59. 99 4. 38 2016-2018 2 IKM Ikeramiuarsuk DMI -42. 07 61. 94 39. 59 2016-2018 3 IKS Ikerasassuaq DMI -43. 17 60. 06 88 2016-2017 4 NAR Narsarsuaq DMI -45. 44 61. 16 3. 9 2018 5 NUN Nunarssuit DMI -48. 45 60. 76 32. 6 2016-2018 6 UKI Ukiiviit DMI -50. 41 62. 58 22. 23 2016-2018 7 QAS_L QAS_L PROMICE -46. 85 61. 03 280 2016-2018 8 QAS_M QAS_M PROMICE -46. 83 61. 10 630 2018 9 QAS_U QAS_U PROMICE -46. 82 61. 18 900 2016-2018 10 KAT 6640 AWS 6640 KAT. -ULiège -45. 18 59. 98 36 Sep/2020-Jan/2021 11 KAT 0460 AWS 0460 KAT. -ULiège -45. 07 60. 16 76 Sep/2020-Aug/2021 12 KAT 0680 AWS 0680 KAT. -ULiège -44.
Results Large-scale SLA variability is investigated by filtering the 2013-2019 daily sea-level product with a 300 km radius Gaussian filter. The error associated with this large-scale SLA product is computed as in  Auger et al. (2022)  by comparing a 2-altimeter product filtered at 300 km, with the along track independent observation from Sentinel-3A data filtered with a 300 km running mean. The error is 5. 7 cm in the permanently ice-covered regions, 3. 0 cm in seasonally ice-covered areas, and 3. 2 cm in the open ocean. The higher errors in the open ocean than in the ice-covered regions stem from the open ocean being more dynamic and therefore having a wider range of SLA values and higher SLA variability. The choice of applying a 300 km low pass filter is motivated by wanting to focus on the larger scale variability of the subpolar Southern Ocean SLA seasonality.
| Study population and design Patients were derived from the Global PBC Study Group database. This database contains individual patient data from longterm follow-up cohorts from 15 liver units across Europe and Northern America. All patients had an established diagnosis of PBC according to the internationally accepted guidelines. 3, 4 In this study, only UDCA-treated patients with sufficient follow-up (≥12 months and ≥2 recorded visits) and known dates of clinical events were eligible for inclusion. Patients with overt overlapping features of auto-immune hepatitis according to the Paris criteria were excluded. 17 e study was conducted in accordance with the Declaration of Helsinki. The protocol was approved by the international research board of the corresponding center and at each participating center, in accordance with their local regulations.
| Data collection Clinical and laboratory data were collected at baseline, which was the start of UDCA treatment, and during follow-up. Clinical data included the following: age, gender, details about PBC diagnosis, baseline anti-mitochondrial antibody (AMA) serological status, liver histology obtained within 1 year from baseline (classified according to Ludwig and Scheuer's criteria  18, 19  ), treatment and clinical outcome (LT or death). Laboratory data included ALP, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, albumin and platelet count and was collected at baseline and after 1 year. Biochemical disease stage was classified according to the Rotterdam criteria. 5 ta of the original cohorts were collected through the end of December 2012. For three centers, which entered the study group more recently, data collection occurred through the end of December 2015.
| Statistical analyses Data are presented as mean with standard deviation (SD), median with interquartile range (IQR) or as proportions. In the current study, patients were categorised according to the age-specific threshold of the GLOBE score. The GLOBE score was considered 'normal' if below the threshold and 'elevated' if above the threshold. At present, patients with a GLOBE score below their age-specific thresholds are considered to have a survival comparable with that of the age-and sex-matched general population, in contrast with those with GLOBE scores above the threshold. 14, 20 For visualisation purposes, patients were stratified according to their age at baseline (<50 years and ≥50 years based on a round cut-off close to the average age in the cohort). For comparisons of categorical and continuous variables the chi-squared test, students' t-test, one-way ANOVA or Kruskal-Wallis test were used, where appropriate.
The LT-free survival of each group was compared with the survival of an age-, sex-and calendar time-matched general population using the life table method and the Wilcoxon (Gehan) test as previously described. 14 Data of this matched population were retrieved from a Dutch registry (Statistics Netherlands, www. cbs. nl). According to the life table data of the World Health Organisation (http://www. who. int/) the median life expectancy of the Dutch population at age 60 was similar to the average median life expectancy of the other represented countries in our global cohort. Missing data at baseline and at year 1 of UDCA therapy was handled by means of multiple imputation using SAS version 9. 4 (SAS Institute Inc. ), as in previous reports from our study group. 16, 20, 22 reto, 10 databases were generated and Rubin's rules were used to obtain pooled parameters and corresponding standard errors.
25, 26 Further clinical studies on the efficacy of farnesoid X receptor agonists and peroxisome proliferatoractivated receptor agonists in halting or reversing hepatic fibrosis are thus awaited. Some limitations to our study should be noted. First, the current analyses were performed in a largely retrospectively constructed dataset. Long-term prospective follow-up studies are, however, unlikely to follow considering the course of UDCA-treated PBC, which is also a relatively rare disease. As a result, adequate data on the cause of death are lacking. Second, the follow-up of the patients in our cohort does not extend beyond 2015. While this is not considered to have had a major influence on the validity of our results, future studies should evaluate our findings in the setting of combination therapy.