dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 3: Run LPJ-GUESS We ran LPJ-GUESS with a reference dataset (CRUJRA reanalysis), the full raw CMIP6 ensemble (which includes the skilled, independent and bounding models) and additionally with the bounding models (see section 3. 1. 3) after they were bias corrected according to the methods 3. 2. 1-3. 2. 6. LPJ-GUESS  (Smith et al. , 2014, Lund-Potsdam-Jena General Ecosystem Simulator;  ) is a widely used dynamic global vegetation model for climate-carbon studies  (Sitch et al. , 2003; Smith et al. , 2014). LPJ-GUESS simulates the exchange of water, carbon and nitrogen through the soil-plant-atmosphere continuum  (Smith et al. , 2014)  by accounting for resource competition for light and space between plants. We adopted the global configuration of the model that uses 12 plant functional types (PFTs), simulating differences in growth form (grasses, broadleaved trees or deciduous trees), photosynthetic pathway (C3 or C4), phenology (evergreen, summer green or rain green), tree allometry, life history strategy, fire sensitivity, and bioclimatic We use LPJ-GUESS version 4.
Step 4: Ensemble averages After running LPJ-GUESS with either the raw or corrected climate data (step 3), the final step was to calculate an ensemble average of the resulting carbon fluxes. We focussed on the total carbon storage (C Total ) and foliar projective cover (FPC) over Australia at annual timesteps, and the gross primary productivity (GPP) at seasonal timesteps. We explored three different approaches based on the full ensemble or the selected models (see section 3. 1).
Skill and independence Following  Bishop and Abramowitz (2013) , we calculated weights based on both independence and skill. We here chose the carbon variables resulting from the reference LPJ-GUESS run (driven with the CRUJRA reanalysis) as the target variable, and the carbon variables resulting from the LPJ-GUESS runs forced with the CMIP6 as the predictor variables. This method accounts for both the performance differences and their error dependencies. In a first step, the bias with respect to observational data is calculated. The method then uses the error correlation coefficient as a metric for error dependencies. This method derives the linear combination of the CMIP6 members to minimise the mean square difference to the results from the reanalysis runs following: where j represent the grid cells, and k is the number of the ensemble members.
Consequently, x j k is the value of the k th bias-corrected model (i. e. , after subtracting the mean error from the dataset) at the j th grid cell. The weights (w T ) provide an analytical solution to the minimization of C j w = w T x j = K k=1 w k x j k ( J j=1 (C j w -x j obs ) 2 (6) when subject to the constraint that the sum of the weights (w k ) always adds up to 1. The solution can be expressed as: w = A -1 1 1 T A -1 1 (7) where 1 T = k elements [1, 1,. , 1] and A is the K × K difference covariance matrix.
The arithmetic approach based on the independent GCMs produce the best match compared to LG CRUJRA. In contrast to the two tree groups, the median C3 grass FPC based on the CMIP6 forcing tends to be lower than that based on LG CRUJRA (see fig. LG EC Earth3 Veg a) LG LG KIOST ESM c) LG MPI ESM1 2 HR d) N D J F M A M J J A S O 0. 0 0. 1 0. 2 0. 3 GPP [PgC mon 1 ] LG NorESM2 MM e) LG CRUJRA Raw ensemble Spread Raw Scaling MAV QM CDF-t R2D2 dOTC N D J F M A M J J A S O Ensemble average f) ENS Arithmetic, Full ENS Weighted ENS RF Figure 8. Seasonal cycle of gross primary productivity for C4 grasses. The different panels show the seasonality when LPJ-GUESS is forced with the bounding five bounding models (a-e). The different colors show the unconstrained model climate forcing (blue), or after bias correcting the data following the scaling (orange), the mean and variance (green), the quantile mapping (red), the CDF-t (purple), the dOTC (brown) and the R2D2 (grey) method.
The foliar projective cover gives an indication of the fidelity of vegetation cover. We found that temperate trees and C4 grasses in particular can vary strongly in dominance and relative cover depending on the GCM used as the input forcing and bias correction applied. For example, the distribution of C4 grasses based on the MPI-ESM1-2-HR climate forcing does not overlap with any of the other unconstrained models. Only the two multivariate approaches adjust the distribution so that it is more comparable to the reference dataset and the other ensemble members. This implies that both the model selection as well as the bias correction method can lead to small but potentially important differences in composition of vegetation distributions across the landscape. Models that show large differences in the vegetation distribution are also sensitive to the bias correction for seasonal GPP.
Given the heterogeneity of climate and large area of the Australian continent, we did not attempt correcting the spatial scales given limitations in computation time but this would be worth exploring in future work. In summary, within a framework of testing bias correction methods on the five models spanning the CMIP6 model spread, we found that the bias correction methods successfully reduced the bias to the reference dataset for averages over time and space (C Total ) but show limited impact for other temporal properties (such as the change over time; e. g. Hagemann et al. , 2011; Maurer and Pierce, 2014; Cannon et al. , 2015; François et al. , 2020). For example,  Hagemann et al. (2011)  found that bias correction does not necessarily lead to a more realistic climate change signal. In a different study focusing on precipitation,  Maurer and Pierce (2014)  demonstrated that long-term changes in simulated precipitation can artificially deteriorate following quantile mapping.
Further,  Cannon et al. (2015)  find that quantile mapping approaches can inflate relative trends in precipitation extremes projected by GCMs. The lack of skill in correcting temporal properties was also demonstrated for multivariate bias correction approaches  (François et al. , 2020). Using single models or even a subset of the ensemble may therefore not inform trends and processes on short timescales for studies exploring the future carbon cycle. Despite the demonstrated limited impact of bias correction on temporal and spatial scales, correcting the driving forcing is still preferable to using raw climate forcing. DGVMs largely rely on bioclimatic limits that define where specific types of vegetation can grow. Relying on a biased climate forcing dataset might therefore result in a misrepresentation of the vegetation.
Sensitivity to ensemble averaging methods and model selection methods We also tested the commonly used arithmetic ensemble average, a weighted averaging approach following  Bishop and Abramowitz (2013) , and a random forest regression approach. We found that the weighted average and the random forest approach outperform the arithmetic ensemble average for average C Total , and seasonal GPP with results very similar to the reference dataset. The random forest approach produces a small error magnitude when spatial dimensions are explored (see fig. 5 ) while for the arithmetic and weighted ensemble average, systematic biases persist. While the FPC of tropical trees and C3 grasses seems to be broadly captured by all averaging methods, C4 grasses shows a strong bias where only the random forest approach achieves a median value within the IQR of the LG CRUJRA run.
We attempt to account for this in the model selection methods by applying the respective metrics on monthly and annual timescales. In addition, five models for all selection methods may seem like a small subset. However, earlier studies (e. g. Pierce et al. , 2009)  found that the multi-model ensemble mean tends to converge towards a similar value after including five models. We therefore conclude that five models was a sufficient number in our testing framework. We further chose a relatively short calibration time period  to allow sensitivity tests with multiple reanalysis datasets. While these 22 years may not cover decadal variability, we assume it is sufficient to account for interannual variability https://doi. org/10. 5194/egusphere-2022-623 Preprint. Discussion started: 18 July 2022 c Author(s) 2022.
CC BY 4. 0 License. such as the El Niño Southern Oscillation, the Indian Ocean Dipole, and Southern Annual Mode which have been shown to be important influences on the Australian carbon cycle (e. g. Cleverly et al. , 2016). Other areas of uncertainty may include the sensitivity of the methods to the reference dataset. Several studies have discussed that both bias correction methods (e. g. Iizumi et al. , 2017; Famien et al. , 2018; Casanueva et al. , 2020) , and weighted ensemble averaging methods (e. g. Merrifield et al. , 2020)  depend on the observation dataset they are calibrated on. Casanueva et al. (2020)  demonstrate that precipitation in particular is sensitive to the choice of reference dataset. We therefore repeated the bias correction and chose ERA5 as a second dataset. We found high correlation coefficients between LPJ-GUESS runs that are based on GCMs corrected to CRUJRA and LPJ-GUESS runs that were based on GCMs corrected to ERA5 for C Total (0.
96-0. 98; see appendix fig. B3-B7 ). We conclude that our results were robust to the choice of reference dataset. Another concern frequently discussed is impact of the mismatch in spatial resolution (high resolution reanalysis product vs. low resolution GCM output). A solution to reduce the mismatch in spatial resolution might be to use dynamically downscaled datasets, such as CORDEX. However,  Casanueva et al. (2020)  find the impact of the horizontal resolution on the bias correction results to be small in comparison to the impact of bias correction method. Given dynamically downscaled products were only available for older CMIP generations (CORDEX is based on CMIP5, NarCLIM on CMIP3) or contained a small subset of GCMs only (ISIMIP), and we expected the uncertainty associated with the spatial mismatch to be small, we chose the state-of-the-art CMIP6 GCM output.
Correcting biases may also lead to different outcomes relying on thresholds of absolute values when applied to individual GCMs, such as for climate threshold studies exploring tipping points. Importantly, bias correction methods do not correct temporal (such as IAV or trend) and spatial properties, unless the methods are specifically designed and set-up to do so. We found that using corrected GCM output can even increase the distance in change compared to our reference dataset. Future studies of ecosystem/carbon cycle impacts based on GCM climate forcing should therefore carefully choose a subset of models that is representative of the ensemble uncertainty, and do not rely on using a single GCM. f) ACCESS-CM2 ACCESS-ESM1-5 BCC-CSM2-MR CanESM5 CESM2-WACCM CMCC-CM2-SR5 EC-Earth3 EC-Earth3-Veg GFDL-CM4 GFDL-ESM4 INM-CM4-8 INM-CM5-0 IPSL-CM6A-LR KIOST-ESM MIROC6 MPI-ESM1-2-HR MPI-ESM1-2-LR MRI-ESM2-0 NESM3 NorESM2-LM NorESM2-MM.
Acknowledgements. The research was funded by the  ARC Centre of Excellence for Climate Extremes  ( CE170100023 ) and by the  New South Wales Department of Planning, Industry and Environment. MDK and AJP acknowledge support from the  ARC Discovery Grant  ( DP190101823 ). MDK acknowledges support from the  NSW Research Attraction and Acceleration Program   (RAAP). AMU acknowledges support from the  Australian Research Council  ( DE200100086 ). We are grateful to the  National Computational Infrastructure  at the Australian.
Code and data availability. The CMIP6 output used in this study is available via the Earth System Grid Federation (ESGF). The CRUJRA reanalysis dataset is accessible via https://catalogue. ceda. ac. uk/uuid/7f785c0e80aa4df2b39d068ce7351bbb (last access: March 2021). The analysis code can be found on https://github. com/lteckentrup/CMIP6_australia.
115 aligned to temperature, cloud fraction, vapour pressure and precipitation in CRU TS (v4. 03), respectively. The CRUJRA dataset spans the years 1901-2018 on a 6 hour timestep which we aggregated to a daily temporal resolution, at a 0. 5 • spatial resolution. https://doi. org/10. 5194/egusphere-2022-623 Preprint. Discussion started: 18 July 2022 c Author(s) 2022. CC BY 4. 0 License.
Zebrafish are genotyped by PCR followed by Sanger sequencing. Primers used for genotyping are in Supplementary Data 1. CRISPR-mediated mouse knockout. All mice procedures were approved by the Institutional Animal Care and Use Committee of Biological Resource Centre, A*STAR, Singapore (IACUC #201555). All mice used were on the C57BL/6 background. The Smchd1 Δ6nt mouse was generated by CRISPR/ Cas9 injection into the pronucleus of 1-cell zygotes. Guide RNA against exon 3 was used with the following targeting sequence, 5ʹ-gagcaggttacttacgcaa-3ʹ, which was previously used in Shaw el al. (2017)  8. Founders were backcrossed with C57BL/6. Genotyping primers are listed in Supplementary Data 1. Western blot and antibodies. Zebrafish embryos at sphere stage were dechorionated using 1 mg/ml pronase and deyolked as previously described  37.
To enrich for nuclear proteins, embryos were lysed in hypotonic lysis buffer (10 mM Tris pH7. 5, 10 mM NaCl, 3 mM MgCl 2 , 0. 3% NP40, 10% glycerol, 1x Protease inhibitor) for 10 min on ice and centrifuged 800 g for 8 min. The supernatant containing cytoplasmic fraction was discarded. The pellet containing nuclear proteins was resuspended in MWB (10 mM Tris pH: 7. 5, 300 mM NaCl, 4 mM EDTA, 1% NP40, 10% glycerol, TURBO DNase, 1x Protease Inhibitor). Nuclear proteins from 300 embryos were loaded per lane. Human fibroblasts and mouse embryo heads were lysed in RIPA buffer (250 mM Tris, pH: 7. 5; 150 mM NaCl; 1% NP-40; 0. 5% Na deoxycholate, Halt protease inhibitors [Thermo]). Immunoblotting was performed using the following antibodies: anti-SMCHD1 (Atlas HPA039441, 1:500), anti-Histone H3 (abcam ab1791, 1:1000), anti-GAPDH (Santa Cruz SC47724, 1:1000).
In situ hybridization Probes for in situ hybridisation were cloned from zebrafish embryo cDNA using primers in Supplementary Data 1. Zebrafish whole mount immunofluorescence. 33 hpf embryos were dechorionated and fixed for 2 h at room temperature in 4% paraformaldehyde in PBS. The embryos were stored in methanol at -20 °C and then rehydrated in a gradient of PBS/methanol. The embryos were blocked in PBDT (PBS, 1% BSA, 1% DMSO and 1% Triton X-100) with 5% goat serum at room temperature for 1 h. Embryos were incubated with anti-GnRH antibodies (Sigma G8294, 1:500) overnight at 4 °C and washed in PBDT. Secondary antibody used was Alexa fluorophore-labelled antirabbit (1:500, Molecular Probes). Embryos were mounted with 2% low melting agarose on slides and imaged with Olympus FV1000 confocal microscope.
Terminal nerve length was measured using ImageJ. qPCR. RNA from 30-50 zebrafish embryos at the designated stages or human fibroblast was extracted using RNeasy Mini Kit (Qiagen) with DNase digestion on column. cDNA was synthesized from 1 μg of total RNA using the iScript cDNA Synthesis Kit (BioRad). qRT-PCR was performed with Power SYBR Green PCR Master Mix (Thermo Fisher) on QuantStudio 7 (Thermo Fisher). Gene expression was normalized to actin for zebrafish samples and HPRT for human samples. Data represent mean ± SD of biological replicates. A list of primer sequences can be found in Supplementary Data 1. miRNA expression analysis. RNA from 30-50 zebrafish embryos at the designated stages was extracted using Trizol (Thermo Fisher). cDNA was synthesized using Mir-X miRNA First Strand Synthesis kit (Takara Bio) and qPCR performed using TB Green Advantage qPCR Premix (Takara Bio).
16 clones were picked per genotype and sequenced with T7 primer. Efficiency of bisulfite conversion was over 99% as observed by the conversion of non-CpG cytosines. Zebrafish odour tests. In total, 2-4 weeks old larval fish were gently introduced into the observation tank (75 mm × 30 mm x 30 mm (L x W x H) with 40 ml of water. Video recordings started after 3-5 min of acclimation. After 5 min of video recording, 0. 5 ml of 100 µM glycochenodeoxycholic acid (GCDA) at one end of the tank and a similar volume of tank water from the other end were delivered. Recording continued for the next 5 minutes. Fish were tracked online and tracked data was analyzed as described previously  39. In total, 20% area of the tank from the delivery port was defined as the stimulus zone. Schreckstoff preparation and behavioural data analysis was performed as described previously  40  with minor modifications.
6. 3. Automated algorithms were used to compute all parameters described from the XY position data of the subject. 5-Aza-dC treatment. 8-cell stage embryos were treated with various concentrations of DNA methylation inhibitor 5-Aza-2′-deoxycytidine (5-Aza-dC) for 48 h then washed to remove the inhibitor. Embryos were observed for survival and developmental defects. Dead embryos were removed daily. Cell culture. Written informed consent of individuals were received prior to collection of samples and subsequent analysis for research purposes according to the ethical approvals of the local Institutional Review Boards in Singapore (A*STAR IRB 2019-087, NUS IRB N-20-054E) and France (La Timone Children's Hospital). All skin biopsies except for one control (foreskin) were taken from the forearms of individuals.
Acknowledgements We are grateful to all members of the Reversade and  Xue lab  for support. We thank  Keerthika ,  Lydia Ng ,  Glenn Koh  for technical assistance,  IMCB-MMD (formerly IMB-KOre unit)  for generating the knockout mice and  P. Ingham  for feedback. B. R. is indebted to  Davor Solter , for his mentorship, acumen and inspiration. This work was supported by  NMRC /OFYIRG/0062/2017  and  NUS   PYP startup grant  to  S. X. Work  in A. S. M's lab was supported by the  Ministry of Education, Singapore (MOE)  and  Yale-NUS College  through  IG19-BG106 ,  MOE-T2EP30220-0020 , and an SUG. Work in F. M. 's lab was funded by " Association Française contre les Myopathies " (AFM; TRIM-RD, MoThARD) and  Fondation Maladies Rares. B. R. is an investigator of the  National Research Foundation  ( NRF, Singapore ),  Branco Weiss Foundation (Switzerland)  and an  EMBO Young Investigator , and is supported by an  inaugural Use-Inspired Basic Research (UIBR)  central fund from the  Agency for Science & Technology and Research (A*STAR ) in Singapore.
Reporting and statistical analysis All respondents were included in the analysis. Data are presented as the number followed by the percentage. Data from five-point Likert scales (with anchors strongly disagree to strongly agree, very negatively impacted to very positively impacted, or not helpful to very helpful) are also reported as the number followed by the percentage. Neutral (or no impact) components of the Likert scales are not reported, and only the positive and negative components of the Likert scales are reported, where the extent of each component is specified (i. e. agree and strongly agree). Data are presented using descriptive statistics and processed using R Studio v. 1. 4. 1103.
Philip Robinson has received consulting fees, speaking fees, honoraria from Abbvie, Atom Biosciences, Janssen, Lilly, Novartis, Pfizer, Roche, UCB (less than $10 000), all unrelated to this work. Philip Robinson has also received other research grants from Janssen, Novartis, Pfizer and UCB, all unrelated to this work. Emily Sirotich has received non-financial support from Canadian Arthritis Patient Alliance, outside the submitted work. Emily Sirotich is a board member of the COVID-19 Global Rheumatology Alliance and has received honoraria from the COVID-19 Global Rheumatology Alliance (all less than $10 000). Jinoos Yazdany has received consulting fees, speaking fees, honoraria from Pfizer, Astra Zeneca, Eli Lilly and Aurinia (less than $10 000), all unrelated to this work. Jean Liew has received research grant funding from Pfizer (more than $10 000), unrelated to this work.
In fact, experimental models have shown that restriction of dietary isoleucine led to reduced hepatic lipid deposition and smaller lipid droplets  [8, 9]. There is now accumulating evidence showing the E3 ubiquitin ligasesda large and diverse group of proteins with multiple binding domains to interact with ubiquitin, E2 enzymes, and substrate proteinsdplay a critical role in the tight regulation of metabolic and inflammatory processes in various contexts, including obesity [10e13]. Here, the analysis of liver transcriptomes and metabolomes from obese women with steatosis and data obtained from different mouse models of steatohepatitis and hepatocellular carcinoma (HCC) show a novel functional role for itchy E3 ubiquitin-protein ligase (ITCH) in the hepatic regulation of BCAA metabolism.
Transcriptomics data A pilot microarray study was performed using Human Genome U133 Plus 2. 0 Array (Affymetrix) (accession number GSE177050). Collection and processing of the FLORINASH expression data have been described previously  [2]. The normalized data, available from ArrayExpress (accession E-MTAB-4856), were used in this study. Microarray data were analyzed using R and the BioConductor package LIMMA (Linear Models for Microarray Data)  [14] , with the modifications for single channel data implemented  [15]. An adjusted P value (false discovery rate) of less than 0. 05 was used as a cut-off for identifying significantly differentially expressed genes (DEGs)  [16]. Lists of DEGs, based on comparisons of ITCH low and high quartiles (1817 genes) and NAS 0 vs. NAS 3 (117 genes) were generated.
Enrichr analysis was used to identify over-represented KEGG pathways and over-represented gene ontologies within the dataset  [17, 18]. 2. 1. 3. Metabolic phenotyping of plasma by 1 H nuclear magnetic resonance (NMR) spectroscopy Plasma samples were characterized as previously described  [2]. Briefly, plasma samples were centrifuged at 12,000 g at 4 C for 5 min, then 350 mL of sample was mixed with 150 mL of buffer before centrifugation at 12,000g at 4 C for 5 min. Aliquots (600 mL) were transferred into a tube for automated acquisition on an NMR spectrometer (Bruker) operating at 600. 22 MHz 1 H frequency as previously described  [2]. The 1 H NMR spectra were pre-processed in MATLAB using in-house routines, including automatic phasing, baselining and referencing to the a-anomeric glucose doublet at 5.
To gain mechanistic insight on the role of ITCH in the NAFLD phenotype, we next investigated its expression in the FLORINASH hepatic whole-genome transcriptome database (n ¼ 88, 32 men and 56 women) in relation to steatosis grade  [2]. We confirmed that ITCH is among the 499 significantly differentially expressed genes (p-FDR < 0. 05) when comparing steatosis degree (Steatosis 0 versus 3) in the whole FLORINASH liver transcriptome dataset (p-FDR ¼ 0. 019, mean expression 8. 76, log 2 fold change À0. 30) (Table  S1 ). Immunohistochemical analysis of FLORINASH liver biopsies showed that ITCH protein abundance was negatively associated with steatosis (Figure  1A  and Figure  S2 ). We confirmed through linear models (adjusted for age, sex, and BMI) that low ITCH expression, assessed by qPCR, was significantly associated with ultrasound-assessed steatosis (Figure  1B ) in the FLORINASH liver mRNA biobank (n ¼ 129, clinical data are reported in Table  S2 ).
When the adjusted P threshold was increased to 0. 1 (2,765 significantly differentially expressed genes), five additional genes (ABAT, ACAA2, ALDH9A1, AOX1, and DLD) belonging to the pathway were significantly differentially expressed (Figure  2  and Table  S4 ). We then analyzed plasma metabolites profiled by 1 H NMR spectroscopy and built multivariate O-PLS analyses to integrate functional datasets of steatosis with ITCH qPCR data. Remarkably, the steatosis and ITCH metabolic signatures depicted in the O-PLS model coefficient plots presented reciprocal inverse patterns suggestive of mirrored metabolic signatures for blood plasma metabolites (Figure  3A, B ). Using permutation testing with 10,000 iterations, we confirmed the opposite trends between ITCH and steatosis for 1 H NMR resonances corresponding to eCH 3 and eCH 2 -from lipoproteins, lactate, glucose and BCAAs.