dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  MeteoSwiss  for providing global radiation data from Egolzwil and  Mosen  used in this study and  Rüdiger Schander ,  Peter Nyfeler , and  Michael Schibig  for their technical assistance at Beromünster. AP was supported by computational and storage resources through the ECMWF special project spatvojt. AP and ML were supported by the  Swiss National Science Foundation  project  SNF 172550. We further thank the members of the Flexteam (flexteam. univie. ac. at) at the  University of Vienna  for the valuable discussions, their support, and the great working environment - Katharina Baier ,  Lucie Bakels  (proof reading),  Silvia Bucci  (technical help),  Marina Dütsch  (proof reading),  Ioanna Evangelou ,  Petra Seibert ,  Andreas Stohl ,  Rakesh Subramanian ,  Daria Tatsii , and  Martin Vojta.
Code and data availability. EddyPro Software v7. 0. 6 is a commercial software available from LI-COR Biosciences (https://www. licor. com/ env/support/EddyPro/software. html); REddyProc v1. 2 (Wutzler et al. , 2018)  is freely available as an online tool here: https://www. bgcjena. mpg. de/REddyProc/brew/REddyProc. rhtml; A collection of the data used for the analysis in this study is available here: https://doi. org/ 10. 25365/phaidra. 361.
and 6 indicate an above average number of high observations in the respective subsets (compared to the statistics of the entire dataset; Fig. A1; see definitions in Sec. 2. 3. 1). https://doi. org/10. 5194/egusphere-2022-1019 Preprint. Discussion started: 22 December 2022 c Author(s) 2022. CC BY 4. 0 License.
Assessment of genomic status Germline and somatic genomic statuses were assessed using whole exome sequencing (WES), see online supplemental methods. TIB, defined as the total number of indels that are commonly called by two variant callers, Strelka  30  and TNscope,  31  and TMB, defined as the total number of somatic missense mutations,  32  were assessed. HLA genotyping was performed on HLA loci A, B, and C, as previously described. 14 Patients were classified as heterozygous if all three loci were heterozygous and homozygous if one or more loci were homozygous. Mutation status WES data were used to categorize patients as wild type (WT) or mutant (MUT) for all protein-coding genes, based on somatic variant calls of single-nucleotide variants and indels. See online supplemental methods for more details.
Gene set enrichment analysis A Cox proportional-hazards model was used to estimate the association of each gene's expression with PFS and OS as a continuous variable in RNA-seq-evaluable patients within each treatment arm. Reverse signed Wald test Z scores from the Cox proportional-hazards models were ranked and then evaluated with the 'GSEA' algorithm (Bioconductor V. 3. 8)  33 n the HALLMARK gene sets from the Molecular Signatures Database. 33 34 atistical analyses All analyses were conducted using SAS (SAS Institute) and R V. 3. 6. 1 unless otherwise stated. Patient characteristics and clinical outcomes were compared between the biomarker-evaluable population and the intent-totreat population using frequency statistics and descriptive statistics. Survival analyses were conducted using the R survival (V.
2. 44-1. 1) and survminer (https://github. com/kassambara/survminer) (V. 0. 4. 6) packages. HRs and their 95% CIs for associations with PFS or OS in patient subgroups were assessed by Cox proportionalhazards models and were illustrated with forest plots and Kaplan-Meier plots. Descriptive p values were calculated from Wald tests unless otherwise stated and adjusted for multiple hypothesis testing using the Benjamini-Hochberg Procedure  35  ; all comparisons were exploratory, and p values should be interpreted with caution. All comparisons for continuous variables use the two-sided Mann-Whitney test for two groups.
RESULTS In this exploratory analysis of 1082 treated patients from CheckMate 214, 992 patients were evaluable for PD-L1 expression by IHC, 481 patients were evaluable by WES, and 213 patients were evaluable by RNA-seq (online supplemental tables 1 and 2). Baseline characteristics were similar between each category of biomarkerevaluable patients and the entire treated population (online supplemental table 2). PFS and OS were similar across biomarker-evaluable and treated populations with nivolumab plus ipilimumab and sunitinib (online supplemental figures 1 and 2). Sarcomatoid RCC was identified in 143 patients (13%): 5 with favorable risk and 138 with intermediate/poor risk. 23 Of note, the success rate of RNA analysis was low. Factors influencing the availability of RNA-seq data included the age of the tissue (the median sample age at which RNA-seq data passed quality control was 104 days, while 335 samples that failed had a median age of 219 days from sample procurement), the availability of tumor tissue for biomarker analyses allowed by patient informed consent, the amount of tissue available, and tissue quality.
Gene expression signatures are related to tumor characteristics in pretreatment biopsies In total, 109 patients in the nivolumab plus ipilimumab arm and 104 patients in the sunitinib arm were RNAseq-evaluable. RNA-seq data were analyzed to establish scores for published gene expression signatures, and MCP-counter was used to predict the abundance of cell populations in each sample. Clustering of all 213 RNA-seq samples based on their signature score revealed overlap in sample classification by three gene expression signatures (JAVELIN Renal 101 Immuno signature (JAVELIN), TIS, and IMmotion150 Teff) despite minimal overlap in gene content (online supplemental figure  4 ). In addition, lower Angio signature scores were associated with sarcomatoid RCC samples (p=0. 02), while no significant relationship was observed between presence of sarcomatoid features and Myeloid (p=0.
12 15-17 Similarly, our results are consistent with other studies that reported no association between VHL, PBRM1, BAP1, and MTOR mutation status and outcomes in patients with RCC treated with first-line PD-L1 inhibitor monotherapy or in combination with TKIs. 12 15 Our findings that are suggestive of prolonged PFS with nivolumab plus ipilimumab in patients with PBRM1 WT RCC are in contrast to other reports of improved clinical benefit with PD-1 or PD-L1 inhibitor monotherapy or in combination with anti-cytotoxic T lymphocyte antigen-4 inhibitors in patients with PBRM1 MUT RCC. 16 19 Differences in the reported associations between the mutation status of PBRM1 and treatment efficacy may be influenced by processes within the TME such as T-cell infiltration or immunosuppression. RNA-seq data from CheckMate 214 were analyzed to evaluate the association between previously published gene expression signatures and clinical benefit with nivolumab on June 24, 2022 by guest.
Acknowledgements The authors would like to thank the patients and families who made this study possible and the clinical study teams who participated, and  Dako , an  Agilent Technologies, Inc company , for collaborative development of the PD-L1 IHC 28-8 pharmDx assay. Professional medical writing and editorial assistance were provided by  Katerina Pipili, PhD , and  Jay Rathi , MA, of  Spark Medica , funded by  Bristol Myers Squibb. Contributors RJM, TC, DM, TP, MBH, PR-M, and MW-R contributed to the design of the study; RJM, TC, TP, MBH, and PR-M contributed to the acquisition of the data set; RJM, DM, TP, JY, CH, RA, SP-C, MBH, PR-M, and MW-R contributed to the analysis of the data and RJM, TC, DM, TP, YV, SG, JY, CH, SP-C, SSS, MBH, PR-M, and MW-R contributed to their interpretation. All authors reviewed and revised the work and approved the final draft for submission.
Open access ipilimumab and the sunitinib arms, 146 and 116 patients were categorized as having high TIB (≥median) while 116 and 103 had low TIB (<median), respectively. TIB was not associated with either PFS or OS with nivolumab plus ipilimumab (PFS: HR, 1. 14 (95% CI, 0. 85 to 1. 53); p=0. 38; OS: HR, 1. 35 (95% CI, 0. 93 to 1. 97); p=0. 12). However, TIB was positively associated with both PFS and OS with sunitinib (PFS: HR, 0. 63 (95% CI, 0. 46 to 1. 87); p<0. 01; OS: HR, 0. 70 (95% CI, 0. 49 to 1. 01); p=0. 05) (figure 2A,B). The association between TIB and PFS with sunitinib was maintained after adjustment for multiple hypothesis testing (adjusted p=0. 01) and in multivariate analyses accounting for age, sex, International Meta- static RCC Database Consortium criteria, and presence of sarcomatoid features (p=0.
Acknowledgements We would like to thank the members of the  Norms and Network Cluster at the department of sociology at the University of Groningen  for their helpful feedback on earlier versions of the paper. Special thanks to  Marion Hoffman  for her suggestions on the implementation of homophily in the model. Marijn Keijzer  acknowledges IAST funding from the  French National Research Agency (ANR)  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ).
Past environmental information is typically inferred from proxy data contained in accretionary sediments. The validity of proxy data and analysis workflows are usually assumed implicitly, with systematic tests and uncertainty estimates restricted to modern analogue studies or reducedcomplexity case studies. However, a more generic and consistent approach to exploring the validity and variability of proxy functions would be to translate a sediment section into a model scenario: a "virtual twin". Here, we introduce a conceptual framework and numerical tool set that allows the definition and analysis of synthetic sediment sections. The R package sandbox describes arbitrary stratigraphically consistent deposits by depth-dependent rules and grain-specific parameters, allowing full scalability and flexibility.
However, the general challenge remains. An alternative to this geometric and parametric reductionist approach is a model not at the scale of its discrete constituents but one with model-wide rules that describe the properties of potential constituents at any given location within the sedimentary deposit. Here, we introduce the R package sandbox, a novel framework to create virtual sediment deposits. We explain the concept and structure of sandbox along with a step-bystep description of how to map a "real-world" loess section into a model. We illustrate different potential applications using simple examples, acknowledging that more realistic representations are possible with additional parameterisation efforts. While we focus on geochronometric data, various other applications can be pursued.
The Supplement contains an extensive tutorial to the package, elaborated examples on how to implement more realistic deposition effects, and all code used to create the figures of this article. 2 Philosophy and structure of sandbox sandbox is a free and open framework to build and analyse virtual sediment sections in R (R Development Core  Team, 2021). The package  (Dietze and Kreutzer, 2021 ) is available at the Comprehensive R Archive Network (CRAN). The current developer version is available on GitHub (https: //github. com/coffeemuggler/sandbox/, last access: 19 May 2022). The term framework implies that sandbox is not tailored to a specific task but instead provides methods to use the tool in different scenarios. Specifically, sandbox does not impose any default physical rules to mimic sedimentation processes.
For this, the function requires the arguments book (the rule book used to define the sediment section), depth (defining the centroid depth where the sample is created), and geometry (defining the geometrical shape of the sample container). Currently, two types of sample containers are implemented: "cuboid" and "cylinder". Depending on which of the two container shapes is used, further input is needed regarding height, width, length, and radius. The function output is a data. frame object with all grains, each described by the parameters contained in the rule book. The following code snippet creates a 1 cm 3 large sample cube: ## assign packing rule sample_1 <-make_Sample( book = book, depth = 1, geometry = "cuboid", height = 0. 01, width = 0. 01, length = 0. 01) With prepare_Sieving(), one can simulate the physical sieving of a sample.
The loess deposit Gleina We use a 17 m thick real-world loess deposit from a former brickyard in the Saxonian Loess Region, eastern Germany  (Meszner et al. , 2011 (Meszner et al. , , 2013;; Meszner, 2015). For this site, we can access a detailed granulometric data set  (Meszner et al. , 2021)  along with a geochronological framework  (Zech et al. , 2017) , both suitable in the context of this study. The grain-size distribution of all 42 samples of the loess section were measured with a Horiba LA950 laser particle sizer, providing 98 grain-size classes. About 0. 5 to 1. 5 mg air-dried and homogenised material were treated with 10 % HCl for 24 h and subsequently treated with 40 % H 2 O 2 for 72 h. Each sample was measured for 5 s with ultrasonic excitation for 10 s in the device to disaggregate particles mechanically.
EMMAgeo as auxiliary tool To convert the quasi-continuous grain-size distributions into discrete populations, i. e. parametric descriptions (mean and standard deviation) of grain-size rules for sandbox, we unmixed the data set using the R package EMMAgeo v0. 9. 6  (Dietze and Dietze, 2016, 2019). This package allows endmember modelling analysis (EMMA) of grain-size data sets; it describes grain-size distributions as a linear combination of end-member loadings and scores. Loadings are the fundamental, genetically interpretable grain-size distributions inherent to all grains. They can be interpreted in terms of discrete sediment sources, transport pathways, and/or transport processes. Scores depict the contribution of each loading to each sample and can be considered as a description of the relevance of a transport process for a given sample.
Structure and implementation of sandbox The proposed structure of sandbox, consisting of grains, populations, parameters, rules, and functions, allows consistent definition synthetic sections as virtual twins of sediment deposits. Further extension of a rule book is possible by adding populations, grain parameters, and rules. The available distribution functions to describe parameters and rules cover a significant range of use cases. Adding other functions would require updating the R code of the package, either by a new package release or by editing the functions manually. The fundamental assumption of sandbox is a valid, true age-depth relationship. At face value, this assumption implies that a sediment section fulfills the stratigraphic principle. However, this enforced principle is implemented through a spline function.
Potential further applications This article's primary purpose is to introduce the synthetic sediment section modelling framework sandbox, particuhttps://doi. org/10. 5194/gchron-4-323-2022 Geochronology, 4, 323-338, 2022 larly with emphasis on luminescence-based age determination. We have demonstrated how the framework can be modified in general. Thus, it is possible and encouraged to apply the package beyond such simple or rather specific examples. sandbox can also be used to pursue questions inherent to other age determination techniques, such as radiocarbon, cosmogenic nuclide, electron spin resonance, palaeomagnetism, and detrital zircon dating, or even varved lake sediments or dendrochronology age models. When parameters are assigned for mineralogical or chemical grain properties, further scientific questions can be approached, for example, from disciplines like provenance analysis (based on detrital zircon age distributions, mineralogical composition, or rare earth element concentrations).
Limitations The structure of the package was designed to allow for flexibility and computational simplicity. This required setting a few fundamental assumptions, which resulted in structural limitations. Some of these limitations may be partly accounted for by workarounds. Most fundamentally, sandbox has no methods to account for erosional processes implicitly. As mentioned above (Sect. 5. 1), the framework is based on a valid and intact age-depth relationship. There is no support for post-depositional modification of grain properties at the moment. Such post-depositional dynamics may be added by defining further rules. For example, pedoturbation may be implemented as the probability to find grains from depths other than the actual sampling depths for each grain in a sample container, i. e.
Conclusions The R package sandbox provides a flexible and scalable framework to tackle research questions emerging from environmental reconstruction and numerical landscape representation. Its structure and available functions allow a virtual twin of given or artificially designed sediment sections to be created focusing on sediment grains and their properties along a depth vector. The current focus on geochronology is a pragmatic one. The framework can be used for numerous further cross-discipline topics, including geochemical analysis, soil formation representation, inverse modelling, and reproducible reference data set generation.
Literature review We began our literature search by inspecting two published reviews on the impact of urbanisation on avian biology  (Chamberlain et al. , 2009; Sepp et al. , 2018). As we were interested in how phenology and reproduction were affected by urbanisation, we identified studies cited in  Chamberlain et al. (2009)  (n = 37) and  Sepp et al. (2018)  (n = 32) that could contain either raw data, or mean and variance estimates for first clutch laying initiation (hereafter laying date), clutch size and number of nestlings fledged per breeding attempt (hereafter number of fledglings), for paired urban and non-urban populations (see details below). Then, we performed four searches of the Web of Science Core Collection on the 27th of October 2020 (databases covered:  SCI-EXPANDED-1900 -present, SSCI-1956 -present, A&HCI-1975 -present, BKCI-S-2005 -present, BKCI-SSH-2005 -present and ESCI-2015-present)  to recover studies published since 1900 and including all languages and all document types.
C. -L. , C. J. B. and D. M. D. ). If the title and/or abstract indicated that the paper could fit our requirements for data collection (see below), we read the study fully, aiming to extract mean, standard deviation (SD) and sample size (n) of our life-history traits of interest for urban and non-urban bird populations. If SD was not available but authors provided SE, the former was calculated as: SD = SE × √ n. Mean and SD were extracted from data quartiles and medians in four effect sizes from two studies following  (Luo et al. , 2016; Shi et al. , 2020). When available, we extracted estimates per breeding season (i. e. papers sometimes reported mean, SD and n for urban and non-urban populations in multiple breeding seasons). If a study reported incomplete information for inclusion in our meta-analysis (e.
When multiple populations were compared along a gradient of urbanisation, we extracted estimates for the two populations at the extremes of the gradient (i. e. most and least urbanised populations). When studies combined estimates across several breeding seasons, we included them in our meta-analysis if urban and non-urban populations had been sampled in the same breeding seasons. All effect sizes were extracted by one author (P. C. -L. ). To validate data extraction, another author (M. J. T. ) checked 15% of the studies included in the meta-analysis, comprising 55 effect sizes (17. 80% of the final dataset; Supplementary Text B). Initially, our dataset contained 443 paired urbannon-urban estimates from 40 bird species and 74 studies. Of these, three observations were removed due to missing sample sizes, 26 observations were removed due to missing SD and 11 observations were removed because their sample size was one (which precludes the calculation of mean and SD).
Four observations were removed because they reported a SD of zero (these indeed had very low sample sizes: 3, 2, 7, 2 observations). Our final dataset included 399 comparisons between paired urban-non-urban populations from 35 bird species and 68 studies (Figure  1 ; refs. : Antonov & Atanasova, 2003; Bailly et al. , 2016; Baldan & Ouyang, 2020; Beck & Heinsohn, 2006; Berardelli et al. , 2010; Biard et al. , 2017; Boal & Mannan, 1999; Bobek et al. , 2018; Brahmia et al. , 2013; Caizergues et al. , 2018; Capilla-Lasheras et al. , 2017; Cardilini et al. , 2013; Charter et al. , 2007; Conway et al. , 2006; de Satgé et al. , 2019; Dhondt et al. , 1984; Eden, 1985; Evans & Gawlik, 2020; Gahbauer et al. , 2015; Glądalski, Bańbura, Kaliński, Markowski, Skwarska, Wawrzyniak, Zieliński, & Bańbura, 2016; Glądalski, Bańbura, Kaliński, Markowski, Skwarska, Wawrzyniak, Zieliński, Cyżewska, & Bańbura, 2016; Glądalski et al.
Meta-analytic effect sizes We standardised laying date across studies by coding it as the number of days after the 1st of January (January 1st = 1). Mean laying date estimates across habitats always fell within the same calendar year. For each of the three life-history traits, we computed the log response ratio (lnRR) and the log coefficient of variation ratio (lnCVR) to investigate differences in mean values and variability between urban and non-urban populations  (Hedges et al. , 1999; Nakagawa et al. , 2015; Senior et al. , 2020). We calculated lnRR and lnCVR along with their associated sampling variances  (Nakagawa et al. , 2015)  using the R function 'escalc' in the 'metafor  ' R package (v3. 4. 0; Viechtbauer, 2010). Both lnRR and lnCVR were calculated so that positive values meant higher estimates in urban populations compared to their non-urban counterparts.
Quantifying habitat heterogeneity and urban index We calculated habitat heterogeneity from the 3CS LC (Copernicus Climate Change Service Land Cover) and the ESA-CCI LC (European Space Agency-Climate Change Initiative Land Cover) land cover products (ESA. Land Cover CCI Product User Guide, 2017; ESA. 3CS Land Cover Product User Guide 2020). These datasets provide methodologically consistent land cover per year and gridded maps from 1992 to 2019, with a global coverage and a spatial resolution of circa 300 m per pixel (0. 002778° or 10 arcseconds). Each pixel in the products is classified as one of the 22 land cover categories defined by the UN-FAO-LCCS (United Nations Food and Agriculture Organization Land Cover Classification System). From a subset of studies included in our main meta-analysis, we could extract the coordinates of their urban and non-urban populations (26 studies out of 68 provided accurate coordinates of their urban and non-urban populations).
Land cover data were processed and analysed using R (v. 4. 2. 0;  R Core Team, 2022). Geospatial vectorial operations were conducted utilising the 'sf  ' R package (v. 1. 0-7; Pebesma, 2018)  while raster extractions were performed with the 'raster  ' R package (v. 3. 5-15; Hijmans, 2020). All geospatial analyses were performed in the WSG 1984 projected Coordinate Reference Systems, EPSG: 6326. Additionally, we calculated the distance between each urban and non-urban pair of populations using the function 'pointDistance' in the R package 'raster'. We could retrieve location information for 232 urban versus non-urban comparisons for laying date, clutch size and number of fledglings, from 11 species and 26 studies between 1992 and 2017 (land cover data were not available before 1992; see above).
Meta-analyses We handled the datasets, ran all analyses and produced visualisations using R (v. 4. 2. 0; R Core Team, 2022). To evaluate the effect of urbanisation on bird life-history traits, we fitted phylogenetic multilevel (interceptonly) meta-analyses for each response term (i. e. lnRR [Model 1] and lnCVR [Model 3]; Table  1 ) combining the three life-history traits (i. e. laying date, clutch size and Overall meta-analysis. Univariate. Table  S1. Figure  S3  2 lnRR.