dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Our study evaluates how well TRENDY models reproduce variables that drive the terrestrial carbon sink. A particular focus was to quantify what level of agreement between model output and reference data should be expected given that reference data are imperfect. Our approach accounts for observational uncertainties using two sets of skill scores. Model scores summarize the similarity between model output and reference data across multiple statistical metrics, including the bias, the centralized root mean square error, time lags of seasonal maxima or minima, inter-annual variability, as well as spatial variability and correlation. Scores range from zero to unity, where unity implies perfect agreement. Using the same statistical framework we then compute benchmark scores that quantify the similarity between independently derived reference data, which serves as an approximation of observational uncertainty.
If model scores reach benchmark scores, then models perform sufficiently well, given how uncertain reference data are. For instance, comparing modeled against reference GPP from FluxCom yields a maximum model score of 0. 70, suggesting that model performance is modest. However, comparing remotely sensed GPP (GOSIF) against FluxCom yields a benchmark score of 0. 72, which suggests that model performance is reasonable given how uncertain reference data are. Our results show that the disagreement between independently derived reference data are much larger than expected, with benchmark scores ranging between 0. 33 for soil organic carbon, to 0. 82 for annual streamflow. Comparing model scores against benchmark scores shows that both scores are positively correlated, suggesting that low model scores is often a sign of large observational uncertainty rather than poor model performance alone.
For instance, model and benchmark scores are both relatively low for NBP (0. 51 and 0. 55, respectively) and relatively high for streamflow (0. 71 and 0. 82, respectively). The larger the gap between model scores and benchmark scores, the greater the potential for model improvement. For instance, this applies to LAI, with a model score of about 0. 49 and a benchmark score of about 0. 66 for globally gridded data. We further conclude that the lower the benchmark score, the greater the need to reduce observational uncertainty. This applies in particular to gridded reference data for soil organic carbon and inversion-based estimates for NBP. Considering these findings, can we conclude that TRENDY models are fit for simulating the terrestrial carbon sink? Let us recall that the terrestrial carbon sink, which is here defined by the term S LAN D in equation 1, represents the natural carbon sink under present-day conditions for atmospheric CO 2 and climate, but pre-industrial land cover (S2 simulation).
Given the counter-factual nature of S LAN D , we can only evaluate it indirectly by assessing NBP, and the processes that drive it, in the S3 simulation where CO 2 , climate, and LULCC forcings all vary in time. The better a model performs for those variables, the greater the likelihood that its estimate of S LAN D is reliable. In the best case, all models, or at least the multi-model mean, would reach benchmark scores for all variables assessed in this study. While this is clearly not the case, for multiple variables (NBP, vegetation carbon, LAI, latent heat flux) there is at least one model that reaches the benchmark. In the case of GPP, none of the models reach the benchmark manuscript submitted to Global Biogeochemical Cycles remains large, in particular in the boreal regions. We conclude that the performance of the TRENDY ensemble is encouraging, but that ample potential for improvements remains.
For instance,  Seiler et al. (2021)        manuscript submitted to Global Biogeochemical Cycles Table  1. TRENDY (v9) terrestrial biosphere models, their horizontal resolution in terms of degrees longitude and latitude, and whether models include representations of processes required for simulating carbon cycle dynamics related to (i) carbon-nitrogen (C-N) interaction, (ii) wetlands, (iii) peatlands, and (iv) permafrost. Zhang, Y. , Xiao, X. , Wu, X. , Zhou, S. ,  Zhang, G. , Qin, Y. , & Dong, J. (2017, October). A global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Scientific Data, 4 (1), 170165. -.
Model scores are calculated by comparing model output against observation-based reference data (Figure1). Benchmark scores are computed by comparing multiple reference datasets of a variable among each other. The purpose of benchmark scores is to quantify the similarity between equally plausible reference datasets, which indicates what level of agreement between model output and reference data can be expected, given how uncertain reference data are. For instance, consider the three inversion-based NBP reference datasets CAMS, CT2019, and CarboScope. Comparing CT2019 using CAMS as a reference yields an overall score (S overall ) of 0. 57. Comparing CarboScope using CAMS as a reference yields an S overall value of 0. 56. The benchmark score is then chosen to equal the minimum of both scores (0.
All three reference datasets show a very similar global seasonal cycle, with a net carbon source during the NH winter and a net carbon sink during the NH summer. While the seasonal cycle of the multi-model mean is in reasonable agreement with the reference data, the inter-model spread can be large. For instance, model values in the boreal region range between 0 and 2 gC m -2 day -1 during summer (Figure6a and g). Multi-model mean scores (0. 50-0. 53) and benchmark scores (0. 52, 0. 56) are similar, with six models reaching benchmark scores (IBIS, ISAM, ISBA-CTRIP, ORCHIDEE, ORCHIDEEv3 and VISIT; Figure4).
Table2). The variable NEE is defined as RECO minus GPP, such that negative NEE values imply a net land carbon sink. In situ observations that fell into the same model grid cell were averaged prior to the comparison against model output. In situ reference data are compared against model output at the grid cell level. An evaluation that accounts for the presence of particular plant functional types at a site would have been desirable, but most model data were reported on a grid cell level only. All comparisons are conducted for locations and time steps that models and reference data have in common. Time-invariant reference data (vegetation carbon) were compared against model output averaged from 1980 to 2019. Details on each in situ reference dataset are provided next.
Acknowledgments The authors wish to thank all groups that provided public access to the reference data listed in Table 2. The eddy covariance data that are shared by the  FLUXNET community include the networks AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly ,  CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia , and  USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and  Fluxdata  project of FLUXNET, with the support of  CDIAC  and  ICOS Ecosystem Thematic Center , and the  OzFlux, ChinaFlux  and AsiaFlux offices. ORNL is managed by  UT-Battelle, LLC , for the DOE under contract  DE-AC05-1008 00OR22725. EJ acknowledges the European Union's  Horizon 2020 research and innovation program  under grant agreement no.
101003536  ( ESM2025 -Earth System Models  for the Future). Libo Wang  compiled  LAI  from  MODIS  and  Brianna Wolfe  compiled  LAI  from  Copernicus , as well as aboveground biomass in situ measurements. Mike Brady ensured that  AMBER  and its dependencies can be deployed across Linux platforms. Roland Séférian  provided comments on an earlier version of the text. The data, scripts, code, computational environment, and instructions required for reproducing the results presented in our paper can be downloaded from https://doi. org/10. 5281/zenodo. 5670387. The full set of Figures produced by  AMBER  for this study can be accessed at https://cseiler. shinyapps. io/AmberTrendy2020/(last visited on November 22, 2021).
Table2). The variable NEE is defined as RECO minus GPP, such that negative NEE values imply a net land carbon sink. In situ observations that fell into the same model grid cell were averaged prior to the comparison against model output. In situ reference data are compared against model output at the grid cell level. An evaluation that accounts for the presence of particular plant functional types at a site would have been desirable, but most model data were reported on a grid cell level only. All comparisons are conducted for locations and time steps that models and reference data have in common. Time-invariant reference data (vegetation carbon) were compared against model output averaged from 1980 to 2019. Details on each in situ reference dataset are provided next. located in forests where no disturbance occurred over the last 50 years as documented in Besnard et al.
(2018) and for months that have ≥ 95% of high quality data. The first and second version of this reference dataset is here referred to as NEE-FLUXNET and NEE-FLUXNETB, respectively. Aboveground biomass measurements were obtained from two datasets. The first database consists of 1974 measurements that were compiled from literature by Xue et al. (2017). The second database consists of 1645 measurements from 274 sites provided by the Forest Observation System (Pastorello et al. , 2020)includes 204 eddy covariance sites with measurements made sometime during the 1997-2014 period(Pastorello et al. , 2020)(Table2; Figure Appendix B1a). The corresponding variables are GPP, ecosystem respiration, NEE, and latent heat flux. Only sites with at least 3 years of data were considered. We assessed NEE using two versions of the FLUXNET2015 database.
ESSOAr | https://doi. org/10. 1002/essoar. 10508892. 1 | CC_BY_4. 0 | First posted online: Wed, 24 Nov 2021 10:06:07 | This content has not been peer reviewed. manuscript submitted to Global Biogeochemical Cycles gression Splines, and Random forest), and the energy balance closure correction (none, The first dataset provided by FluxCom covers the period 2001-2013 (Jung et al. , 2019). As for GPP, Flux- Com upscales FLUXNET observations, where remote sensing data and meteorological data serve as global predictors. Our study uses median values from 36 FluxCom ensem- ble members that vary with respect to the employed meteorological forcing (Climate Re- search Unit National Centers for Environmental Prediction version 8, WATCH Forcing Data ERA Interim, the Global Soil Wetness Project 3, and Clouds and the Earth's Ra- diant Energy System in combination with the Global Precipitation Climatology Project), the machine learning algorithm (Artificial Neural Network, Multivariate Adaptive Re- -8-.
show that the global mean biases of seven out of 19 vari-Our benchmark approach is not limited to TBMs or the AMBER or ILAMB statistical framework, but can be applied to any geophysical model that is evaluated against observations. We hope these results will stimulate model development that aims at reducing the uncertainties of processes that drive terrestrial carbon, water, and energy fluxes. ESSOAr | https://doi. org/10. 1002/essoar. 10508892. 1 | CC_BY_4. 0 | First posted online: Wed, 24 Nov 2021 10:06:07 | This content has not been peer reviewed. ESSOAr | https://doi. org/10. 1002/essoar. 10508892. 1 | CC_BY_4. 0 | First posted online: Wed, 24 Nov 2021 10:06:07 | This content has not been peer reviewed. manuscript submitted to Global Biogeochemical Cycles TRENDY TBMs Reference Datasets AMBER AMBER Model Scores Benchmark Scores Benchmarking Figure 1.
Conceptual diagram of benchmarking Terrestrial Biosphere Models (TBMs) us- ing the Automated Model Benchmarking R package (AMBER). Model scores are computed by comparing model output against reference data. Benchmark scores are computed by comparing multiple reference datasets against each other. Benchmarking consists of comparing model scores against benchmark scores. ables switches sign when forcing CLASSIC with different meteorological datasets. Such results suggest that robust model development must consider multiple forcing datasets to avoid tuning models towards a particular forcing dataset. Future evaluations of TRENDY models would benefit from having access to above- ground vegetation carbon model output, which is currently available for some models only. Evaluating above ground rather than total vegetation carbon is an advantage be- cause below ground vegetation carbon is difficult to measure.
Review of these individuals helped refine which features would be most specific to include as clinical diagnostic criteria for AKS and helped exclude features that were variably present (see Table  S2  and Figure  S1 ). The diagnostic criteria were then tested on a larger set of individuals with clinical features overlapping AKS and/or presence of an HNRNPK variant. This set included a previously published individual with a known LoF variant (AKS10),  8  a published individual with an LoF variant (AKS8), an unpublished individual with an intronic variant confirmed to have a splice effect (N1, unpublished data from UDN), and individuals with missense variants affecting a recurrent residue in multiple probands (P6, 7, and 11). Four clinicians (P. Y. B. A. , A. M. I. , A. D. K. , and V. M. ) rated this cohort independently to determine whether the clinical score was reproducible amongst clinicians.
Quality control and normalization The raw IDAT files were converted into b values, which represent DNAm levels as a percentage (between 0 and 1), with the minfi Bioconductor package in R. Data preprocessing included filtering out non-specific probes (44,135 probes); probes with detection p value > 0. 05 in more than 25% of the samples (771 probes); probes located near single-nucleotide polymorphic sites (SNPs) with minor allele frequencies above 1% (n ¼ 29,958); probes with raw beta ¼ 0 or 1 in >0. 25% of samples (n ¼ 21); non-CpG probes (n ¼ 2,932); and X and Y chromosome probes (n ¼ 19,627) for a total of 91,379 probes removed and a total of n ¼ 774,480 probes remaining for differential methylation analysis. Standard quality control (QC) metrics in minfi were used, including median intensity QC plots, density plots, and control probe plots: all samples passed quality control and were included in the study.
Differential DNAm analysis The analysis was performed with our previously published protocol. 12 Differential DNAm analysis between AKS and controls was performed at 774,480 CpG sites with beta scores, representing DNAm levels as a percentage (between 0 and 1). The b value from each sample at the remaining 774,480 CpGs was used for downstream analysis and generation of a DNAm signature. b values were logit transformed to M values with the following equation: log2(b/(1 À b)). We used a linear regression modeling by using limma package  24  to identify the differentially methylated probes. We estimated blood cell counts by using Houseman's method implemented in minfi and FlowSorted. Blood. EPIC Bioconductor packages to generate the proportions of CD8 þ T cells, CD4 þ T, natural killer, B cells, monocytes, and granulocytes (mainly neutrophils [Neu]).
A classification model was developed on the basis of scoring each new DNAm sample with the HNRNPK score: a test sample with a positive score is more similar to the AKS reference profile based on the signature CpGs and is therefore classified as ''AKS'', whereas a sample with a negative score is more similar to the control-blood reference profile and is classified as ''not-AKS. '' To test specificity, we scored and classified EPIC array data from 172 additional neurotypical controls. To test sensitivity, we scored and classified the validation cohort of three additional unrelated AKS individuals with HNRNPK pathogenic variants.
For the individuals in the test cohort with missense variants, intronic variants, and the individual with no HNRNPK variant, 11/22 met diagnostic criteria for having AKS on the basis of clinical features alone. Facial scores for the individuals with missense variants were extremely variable, ranging from 0 to 6 (see Table  1 ). 7/17 of the individuals with missense variants met diagnostic criteria for AKS (see Table  1 ). Of note, facial scores could be variable even for individuals with recurrent variants, e. g. , individuals with the recurrent HNRNPK variant: c. 253G>A (p. Glu85Lys) (GenBank: NM_002140. 4) scored either ''possible AKS'' with 4/5 (P8, 13, and 11) or as ''AKS'' with >5/6 (P6); however, for some individuals there was insufficient information (photograph or documented exam) to fully score the tongue.
Functional enrichment of the HNRNPK DNAm signature We investigated the DNAm signature for its potential to elucidate the molecular pathophysiology of AKS. Gene set enrichment analysis was performed with Metascape. The top ten pathways that reached statistical significance (p < 0. 05) are shown in Figure  4A. For the GO analysis, the enriched genes function mainly in neuronal projection morphogenesis, synapse organization, and tissue/skeletal development as well as intracellular signaling. Unbiased tissue-specific enrichment analysis (TSEA) (Figure  4B ) and cell-type specific expression analysis (CSEA) (Figure  4C ) show AKS-significant genes are most strongly enriched in transcripts expressed in the brain, pituitary, uterus, blood vessels, and specifically in the developing cerebellum. Analysis of the genomic locations of the CpG sites in the signature showed that CpGs were over-represented in CpG island shores (defined as 0 to 2 kb upstream of CpG island) and DNase hypersensitive sites (Figure  4D ).
The other four individuals with missense variants and an intermediate DNAm signature (P8, 11, 13, and 21) had only facial scores of 4 and were considered as ''possible AKS'' by diagnostic criteria; however, they were highly suspected of having AKS as they had de novo missense variants affecting residues that are recurrently affected in our test cohort, Glu85 or Tyr225. These missense variants may be considered as likely pathogenic by ACMG criteria,  22  as they affect recurrent residues in HNRNPK seen in other individuals who meet diagnostic criteria for AKS (see Table  1 ) (P5 and 6). Given the above observations, we reclassified any variant of uncertain significance associated with either a positive or intermediate AKS DNAm signature to likely pathogenic. All three individuals (P6, 8, and 13) in the methylation cohort with the recurrent HNRNPK variant, GenBank: NM_002140.
4 c. 253G>A (p. Glu85Lys) (GenBank: NM_ 002140. 4),were intermediate for the AKS DNAm signature (Figure  3  and Table  1 ). P2 (facial score 5) had a recurrent HNRNPK missense variant, c. 137G>T(p. Arg46Leu) (GenBank: NM_002140. 4), and had a positive DNAm signature; however, P7 (facial score 5), who had the same variant, was intermediate. Six individuals with VUSs in HNRNPK who did not meet the criteria for a clinical diagnosis of AKS (P14, 15, 16, 17,  18, and 19) were all negative for the AKS signature. These individuals all had de novo variants, with the exception of P16, for whom the inheritance was unknown as the maternal sample was not available. The individual with no HNRNPK variant (P20) who was a ''possible AKS'' on the basis of a facial score of 4 was also negative for the AKS signature.
Hexagons represent list of genes enriched in each tissue or cell type going from pSI threshold to include larger but less stringent gene lists to thresholds for the most stringent subsets (smallest, central hexagons). Fisher's exact testing is used to establish p values for each target and each gene list at each threshold, which are corrected by Benjamini-Hochberg for the number of cell types (color bar). (D) Bar chart representing the percentage distribution of the CpG sites according to genomic annotations extracted from the Illumina EPIC array annotation file. The CpG sites that overlap the signature were compared to the distribution of the CpGs within the dataset (background, 774,480 CpGs) for the regulatory feature group: ''relation to CpG island'' and ''DNase hypersensitive sites (DHSs).
Here, we have overcome a major challenge in clarifying the pathogenic significance of these VUSs in HNRNPK by using this functional DNAm assay, which robustly supports variant classification. The highly specific AKS signature allowed reclassification of six individuals with de novo rare missense variants in HNRNPK as not AKS. For some individuals, this suggests that the identified variant may be more likely benign (e. g. , HNRNPK variant, c. 740G>A [p. Arg247His] [GenBank: NM_002140. 4] for P17), as the individual already appears non-syndromic and in silico predictors suggest the variant is not deleterious (Table  S1 ). For other individuals, it is less clear whether the HNRNPK variant is likely benign versus potentially associated with a separate condition related to HNRNPK. The AKS DNAm signature, particularly in combination with the syndrome definition provided by the diagnostic criteria, allows us to report on a large cohort of individuals with missense variants in HNRNPK (n ¼ 13, 11 with AKS DNAm signature plus two additional individuals in the phenotype study with the recurrent p.
Glu85Lys) (GenBank: NM_002140. 4), reported in three AKS-affected individuals, which showed about 15% variation within the three unrelated individuals. In contrast, the difference in the SVM score between P2 and P7 with the recurrent HNRNPK variants, c. 137G>T (p. Arg46Leu) (GenBank: NM_002140. 4),is $40%. Further molecular follow-up will clarify the degree of variation that occurs in DNAm profiles of individuals that carry recurrent sequence variants. The presence of an intermediate DNAm signature suggests possible DNAm signature-phenotype correlation and may provide insight into the pathophysiological origin of the trends noted earlier for LoF and missense variants, where individuals with missense variants often have milder disease severity in comparison to those with LoF variants. The individuals with missense variants are also likely to have an intermediate DNAm score, although it is notable that some individuals with a positive SVM for AKS signature can also have mild phenotypic severity.
Modifiers, either genetic, environmental, and/or agerelated, are most likely influencing both the DNAm signature and clinical phenotype, and these are not yet understood. There can be discordance in the DNAm signature for individuals with the same variant, as previously discussed for P2 (positive) and P7 (intermediate), who both have the same mild severity score of 3. There is also a wide range in severity scores for individuals with loss-offunction variants (ranging from 3 to 9). We highlight two individuals (AKS2 and AKS12) who have the same known HNRNPK splice variant, c. 257G>A (GenBank: NM_002140. 4), but have severity scores of 9 and 3, respectively. Similar observations have been made for other genetic conditions, where individuals with the same pathogenic variant can have extremely variable expressivity.
For example, significant intra-familial variability is seen in neurodevelopmental conditions such as 22q11 deletion syndrome or neurofibromatosis type 1 (MIM: 162200) and in malformation conditions such as branchio-oto-renal (MIM: 113650)  29  or Van der Woude syndromes (MIM: 119300). 30 The degree of variability in the DNAm signature as observed here, in association with the same HNRNPK variant (c. 137G>T [p. Arg46Leu] [GenBank: NM_002140. 4]), has not been reported previously. We expect further multi-omic studies in other tissues, including genome sequencing and RNA and protein analyses, will elucidate these issues. While we speculate with caution that the DNAm signature may become helpful in predicting clinical outcomes for AKS, at this time, it is still challenging to use DNAm as an outcome prediction tool for single unique individuals.