dataesr/scientific-paragraphs-categorization huggingface dataset:
Independently, we propose a set-based sensitivity method, named C-EDGE (C), solved as a single-objective parallel genetic algorithm (soPGA), selecting k-uples of ε-expressed and KO genes, suggesting single genes (C1) and groups of genes (C2) with toxic or beneficial effect on the biomass. Finally, we apply our methods to the study of cancer metabolism (D). We assess cell-specific gene effects in cancer cells, and we use C-EDGE to compare nine tissue-specific normal and cancer cells. cancer cell lines, mRNA and protein levels have been found to be positively correlated  [18]. More specifically, transcriptomic data is mapped to the Recon model using three maps with real-valued domain and range, defined by three rules that allow us to further constrain the model  [19]. Each reaction in the model is controlled by a single enzyme, by two or more enzymes (enzymatic complex, represented by a Boolean AND relation), or by different but equivalent enzymes (isoenzymes, represented by a Boolean OR relation).
Inconsistencies, duplications of metabolite names, charges, and annotations were corrected manually. We also replaced three reactions to satisfy stoichiometric balance, following PSAMM  [25]. The list of the replaced reactions is available in S9 Table. The Matlab file of the resulting metabolic model is available as Supplementary Information. In the proposed model, we focus on biomass as the first (inner) objective (i. e. , u v in Eq. (  3 )), and we study separately high and low biomass scenarios, corresponding to fast-and slow-growing cells. For both scenarios, we consider the phosphoglycerate dehydrogenase (PHGDH) as a second (outer) objective (i. e. , t v in Eq. (  3 )). We select this objective as PHGDH is the enzyme catalyzing reactions in the serine synthesis pathway, together with phosphoserine aminotransferase (PSAT), and phosphoserine phosphatase (PSPH).
Then, we modify the population of individuals through mutations and cross-over using the PGA to run again the FBA and generate another Pareto optimal front under the new gene expression values. In this way, we can analyse how the gene expression profiles affect the rates of biomass and PHGDH as shown in Figure  2 , exploring the transcriptomic search space by mapping each gene expression profile generated by the PGA onto a 2D metabolic objective space. We set the initial population equal to 128 individuals and the maximum number of populations generated equal to 384 (see the Supplementary Information for more details). More details on the PGA settings are provided as Supplementary Information. The full Matlab code and the steps to execute it are at https://github. com/claudioangione/PGA and C-EDGE/.
C-EDGE: effects of single-gene perturbations C-EDGE enables exact tests of toxicity on pairs, triplets (or larger sets) of genes. A gene is neutral if the cellular objective is constant regardless of the gene being KO or forced to be ε-expressed. It is beneficial if the cellular objective is reduced when the gene is knocked out, while it is toxic if the cellular objective is increased when the gene is knocked out. We take into account the biomass as the cellular objective. ε is an infinitesimal perturbation but it cannot be arbitrary due to the finite precision of the floating-point representation in Matlab. We took ε = 10 -2 as a gene perturbation, which in turn produces a perturbation in the order of 10 -6 for the flux bounds according to Eq. (  3 ). By perturbing one gene at a time, and then running the model using Eq.
In Figure  3 , we show the behavior of the first pair of genes (DTYMK, SLC25A19) in the human metabolic pathways, extracted from the BiGG database  [42]. An ε-gene expression of both genes causes a decrease in the biomass. This interaction is due to the fact that dTDP needs to be produced and then transported into the mitochondrion from the cytosol. If both reactions are impaired, the metabolism is not able to compensate and the production of mitochondrial DNA is impaired, causing a decrease of biomass to 0. 61 h -1. In Figure  4 , we show the behavior of the pair (GUK1, SLC25A19). The interaction between these genes is due to the shared metabolite dGDP. The lack of both enzymes also affects mitochondrial DNA, in a more severe way with respect to the first pair. The biomass is decreased to 0.
Differential analysis of nine tissue-specific cancer models In order to test C-EDGE on various cancer models, we use our framework to perform comparative metabolic analysis in the same tissue and across different tissues for cancer and normal cells. We use the genome-scale models of nine tissuespecific cancer models by Nam et al. [43] , obtained from Affymetrix and Illumina Hiseq RNA-seq platforms, where the affy package and GIMME were used to generate each model. Table  S10  reports the details of each model including the number of reactions, metabolites and genes. By considering tissue-specific models as a case-study of C-EDGE, we provide hypotheses on the molecular basis of cancer for nine tissue-specific metabolic models, obtained using gene expression profiles of primary cancer cells and the corresponding normal cells  [43].
g. ,  Rayner et al. , 2005) , to reconcile the roles of specific datasets in constraining paramet-ric uncertainty (e. g. ,  Keenan et al. , 2013) , and more  (Scholze et al. , 2017). Here we use an MDF system called the CARbon DAta MOdel fraMework, or CARDAMOM  (Bloom and Williams, 2015; Bloom et al. , 2016) , chosen because of its high customizability. The structure of its underlying ecosystem carbon model, DALEC  (Williams et al. , 2005; Bloom and Williams, 2015) , can be easily adjusted to become more simple or detailed (e. g. , by changing the number of carbon pools or by modifying the functional representations of certain carbon fluxes). Various combinations of observational and functional constraints can also be tested in the assimilation process, along with different assumptions on the amount of error inherent to each assimilated dataset (the characterization of which is an ongoing challenge for the modeling community;  Keenan et al.
Suite of carbon cycle models (DALEC variants) The Data Assimilation Linked Ecosystem Carbon (DALEC) model suite includes 16 related intermediate-complexity models of the terrestrial carbon cycle. Each model variant tracks the state and dynamics of both live and dead carbon pools, their interactions, and responses to meteorology and disturbance such as fire or biomass removals. From an initial DALEC model  (Williams et al. , 2005) , we produced alternate structures that either aimed to reduce complexity by focusing on core variables/processes and removing others or aimed to increase complexity by including hypothesized missing carbon pools or improving on over-simplified processes. Accordingly, the DALEC suite spans a range of model structures (i. e. , number of carbon pools, carbon pool connectivity) and process representations (component sub-models of varying complexity) related to different simulations of photosynthesis, plant respiration, decomposition, and water cycle feedbacks.
Site selection COMPLEX uses information from six globally distributed eddy covariance sites participating in  FLUXNET (Pastorello et al. , 2020)  (Table  2 ). Our site selection procedure aimed to maximize biogeographical spread and diversity of natural ecosystems while fulfilling specific data requirements. These constraints collectively yielded a series of site selection criteria that are described in detail in Appendix C. As an example, the sites must not be dominated by the C 4 photosynthetic pathway, nor arable agriculture nor intensively grazed grassland. Additionally, we required that the range of time series observations to be used for model calibration and validation  spanned at least a decade. Data collated at each site are described below (see Sect. 2. 3).
Experimental design We performed a factorial experiment such that each of the 16 structurally distinct carbon cycle models was run within CARDAMOM under all possible combinations of sites, observational and functional constraints, and assumptions on data uncertainties. These scenarios represented differing degrees of certainty with which parameter distributions were determined. Specifically, we considered (a) six sites; (b) six options for assimilated data, including one for which no data were ingested into the model; (c) four options for the magnitude of error assumed on the assimilated datasets (represented by scalar multipliers on the prescribed nominal uncertainties); and (d) two options for EDC state (either present or absent) (Table  3 ). In total, this factorial approach yielded 4032 unique model runs (16 models × 6 sites × 21 data scenarios × 2 EDC states).
Using a high number of factorial model runs both added robustness to our interpretation and allowed for consideration of each factor's influence across a range of background conditions. Figure  3  shows examples of three model analyses at the FR-LBr site, highlighting the range in NEE prediction performance across different model structures and data scenarios. Each model run contains a calibration period (the first 5 years of the site record; shown in white) during which optimized parameters were derived and a forecast period (the remaining years of the record, which always spanned at least 5 years because no site contained fewer than 10 years of data; shown in gray) during which fluxes and pools were predicted with the optimally parameterized model. In the scenario presented, model S2 is highly constrained by multiple datasets (Fig.
3a ). By contrast, model C2 is moderately constrained (Fig. 3b ), and model G4 is poorly constrained (Fig. 3c ), which is evident by comparing the relative uncertainty of the NEE forecasts (blue shading) for each model. To highlight the effectiveness of the assimilation system, corresponding time series based only on prior parameter distributions are presented in Fig. S1. Accounting for prediction uncertainty -as well as data uncertainty (red shading) -is a key goal of our model skill evaluation approach. Forecast skill for each model run was computed by comparing predictions and observations drawn strictly from the forecast period, using the histogram intersection algorithm (see Sect. 2. 5. 1). The complexity of each run was quantified based on its effective complexity (see Sect. 2. 5. 2).
, 2011)  remains particularly critical for model-data fusion studies. In this analysis, NEE uncertainty was assumed to remain constant both in time (i. e. , for all observations regardless of season or year) and in space (i. e. , across sites), which likely over-generalizes the specifications of individual sensors and the possibility of systematic or increasing biases. These assumptions become even more important to account for when assimilating global datasets, for which retrieval accuracy can vary across land cover types or with atmospheric conditions such as clouds or snow (e. g. ,  Fang et al. , 2013). One benefit of the Copernicus LAI product used here is its explicit, spatially variable quantification of uncertainty, which is still relatively rare for remote sensing datasets. Though the robustness of these uncertainties has been challenged with independent observations in some locations (e.
To assist in this en-deavor, we present the following recommendations for model development and evaluation. 1. Assimilate well-characterized, repeat-observation datasets to constrain model parameters at the scale of model application. 2. Use long time series to undertake independent forecast evaluation studies, and factor observational uncertainty into model evaluation (e. g. , using overlap metrics). 3. Test whether model updates that add complexity lead to forecast improvements (not only calibration improvements), and test for possible model simplification improvements also. 4. Seek to calibrate or optimize model parameters even when data assimilation is not possible (e. g. , using optimality-based approaches;  Walker et al. , 2017; Jiang et al. , 2020). Finally, while beyond the scope of this study, future work will investigate the linkage between specific processes or process representations (e.
Transferability to large-scale models (TBMs) This analysis tested a spectrum of structurally distinct representations of the carbon cycle based on the intermediatecomplexity ecosystem model DALEC, which allowed for coupling with the CARDAMOM model-data fusion system in a computationally tractable manner. Because our findings are not explicitly linked to the roles of specific processes or model features, however, their implications extend beyond the use of DALEC-like models to a wide variety of ecological models, including TBMs. Traditional (based on plant functional type, or PFT) parameter determination in TBMs is far from random. It is informed by data -for example, by hypotheses or generalizations derived from prior literature (e. g. ,  Oleson et al. , 2010; Lawrence et al. , 2011)  or by model calibration at specific locations (e.
This conclusion calls into question the conventional paradigm that greater complexity significantly and consistently improves skill across current TBMs. Earth observation (EO) is one key approach that can provide the high-spatial-and high-temporal-resolution data on carbon cycling needed for more localized calibrations  (Exbrayat et al. , 2019). In COMPLEX, we used Copernicus LAI data, though there are also opportunities to ingest biomass maps from space lidar or radar, estimates of photosynthesis from solar-induced fluorescence (SIF), and satellite-based atmospheric inversions of regional NEE, among others, in future studies. If supplied with appropriate error estimates, these datasets can over time provide powerful constraints for high-resolution carbon cycle analyses with TBMs or DALEC-like models.
A key research goal is to determine the appropriate model complexity for maximizing the information content of these EO data for robust forecasts and analyses. Alternative methodologies for deriving ecosystem parameters outside the realm of PFTs are also becoming increasingly common  (van Bodegom et al. , 2012; Bloom et al. , 2016; Exbrayat et al. , 2018; Berzaghi et al. , 2020; Fisher and Koven, 2020)  and may represent a way forward in addressing the tradeoff between structural and parametric uncertainty. Recent work has focused on upscaling in situ trait data (e. g. , from the TRY database;  Kattge et al. , 2020)  to yield spatially variable maps of key ecosystem parameters, using modeled relationships with climate or canopy properties (often referred to as environmental filtering relationships, since the environment "filters" the possible distribution of parameters at a given location; e.
g. ,  Verheijen et al. , 2013; van Bodegom et al. , 2014; Butler et al. , 2017) , leaf economics  (Sakschewski et al. , 2015) , or optimality theory (e. g. ,  Smith et al. , 2019). Other studies have investigated how TBM parameters optimized at eddy covariance sites covary with climate (e. g. ,  Peaucelle et al. , 2019; Wu et al. , 2020b). These efforts are not without their challenges, however. The spatial coverage of in situ trait data as well as eddy covariance sites is sparse relative to the large diversity of ecosystem behavior  (Schimel et al. , 2015) , and such datasets also comprise a non-representative sample of species and disturbance histories  (Sandel et al. , 2015). These biases may limit the representativeness of the modeled relationships. Taking a different approach, a small subset of models has also been developed to operate altogether independently from the paradigm of PFTs (e.
A1. 2 Aggregated Canopy Version 1 + cold weather GPP The GPP module also includes an empirical cold-weather GPP limitation sensitivity function. The cold temperature limitation factor (denoted as g) is used as a multiplier on the DALEC GPP function output, to act as a thermostat that regulates evergreen needleleaf carbon uptake. The coldweather factor g is calculated using added model parameters (T minmin and T minmax ) and temperature observations (T min ), such that g = 0 if T min < T minmin , g = 1 if T min > T minmax , and g = (T min -T minmin )/(T minmax -T minmin ) otherwise.
A1. 4 Analytical Ball-Berry For the analytical Ball-Berry GPP module of CARDAMOM, leaf-level GPP and stomatal conductance are calculated using the coupled leaf photosynthesis-stomatal conductance developed by Ball-Berry  (Ball et al. , 1987)  and an analytical solution to the system of equations developed by  Baldocchi (Baldocchi, 1994). This new module serves to calculate both GPP and evapotranspiration coupled through the stomatal behavior. This formulation added the maximum rate of carboxylation (V cmax ), the maximum rate of electron transport (J max ), stomatal slope and intercept, and boundary layer conductance to the set of parameters that were optimized through data assimilation, while removing the explicit water use efficiency (where there is a water cycle in CARDAMOM) and canopy efficiency parameters.
C1 ). The selection aimed to maximize their biogeographical spread and diversity of natural ecosystems while fulfilling specific data requirements. A key DALEC model criterion requires that the sites must not be dominated by C 4 photosynthetic pathway, be arable agriculture or intensively grazed grassland. COMPLEX makes use of a range of time series observations, including LAI, NEE, and wood stock inventory. Furthermore, the experiment uses temporally distinct calibration and prediction periods requiring observational constraints to span both periods. Collectively both scientific and data availability created a series of site selection criteria which are described below. Time series information on leaf area are drawn from the (EO) Copernicus 1 km product derived from Earth observations which provides estimates of LAI magnitude at fine temporal resolution and concurrent location-specific estimates on uncertainty.
Using this EO product and the abovementioned calibration/prediction period constraints requires site data collection periods to be post-1998. Simulation of NEE is a key focus of COMPLEX, making the availability of long-term, temporally consistent, highquality NEE estimated derived from eddy covariance essential (e. g. ,  FLUXNET2015; Pastorello et al. , 2020). The FLUXNET2015 database provides consistent information on data quality (e. g. , observation uncertainty and proportion of model-data gap-filling) that underpins the site selection process. Here, to avoid comparing DALEC-simulated NEE with largely statistically gap-filled observations, only sites with <20 % gap-filled data are used. Hill et al. (2012)  demonstrated that assimilation of NEE observations provides substantial new information up to at least 5 years in duration.
To create a balanced experimental design, COMPLEX sites are required to have a minimum of 10 years of observations (i. e. , 5 years calibration and remainder evaluation). Building on existing analyses with DALEC (e. g. ,  Smallman et al. , 2017) , COMPLEX quantifies the role of woody biomass information on constraining the DALEC models' predictive capacity of NEE. Therefore, multiple wood stock estimates are required spanning both the calibration and prediction periods. As determining the amount and access of inventory data often requires direct contact with site managers, this stage occurs later in the selection process. Collectively, the abovementioned and model process representations formed the basis of a site selection procedure to filter the FLUXNET2015 database. This process ultimately led to the selection of six sites (.
Acknowledgements. This work has made use of the resources provided by the  Edinburgh Compute and Data Facility (ECDF)  (http: //www. ecdf. ed. ac. uk/, last access: 20 May 2020). Part of this work was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration (NASA). Financial support. Caroline A. Famiglietti ,  Gregory R. Quetin , and  Alexandra G. Konings  were supported by  NSF   DEB-1942133  and  NASA NNH16ZDA001N-IDS. Operation of the US-Ha1 site is funded by the  US Department of Energy's Office of Science  ( DE-AC02-05CH11231 ) and  National Science Foundation  LTER funding ( DEB-1832210 ). The Howard Springs site is funded by the  Australian Research Council   FT1110602 ,  DP160101497 , and  Australian Terrestrial Ecosystem Research Network -Ecosystems Process platform.
Aim and Hypothesis Our study's goal was to evaluate the use of Entropy (H) in participants' pseudoword reading aloud responses. Although Entropy has already been used to measure the diversity of vowel pronunciations in German and English children reading aloud pseudowords across grades  [12] , and alternative pronunciations of disyllabic pseudowords in English  [25] , we are the first, to our knowledge, to use it to compare individual responses to pseudowords across age (primary school children and adults) and languages (shallow and deep orthographies), including a consideration for bilingualism (in children). Here, we analyse novel and published pseudoword reading aloud data from different languages (Italian, German and English) which are on different points along the orthographic depth continuum, and we also report data from different age groups.
Materials Participants read aloud pseudowords in their respective language, which were chosen in respect to the size of their body-neighbourhood (see  [28] ). The size of the body-neighbourhood (body-N) for all items was measured thanks to the CELEX database, which is available for both German and English. Here, we analyse only the pseudoword data. The pseudowords were monosyllabic and matched on the number of letters and orthographic neighbourhood  [29] , as well as on body-neighbourhood  [28]. Moreover, all items had consistent bodies (i. e. , while the number of body-neighbours was manipulated, all body-neighbours had the same pronunciation). Altogether, there were 90 English and 90 German pseudowords, half of which contain high-frequency bodies and the other half contain low-frequency bodies.
Procedure Each participant was tested individually in a dimly lit, sound-proof testing booth. In the original experiment, participants read aloud both words and pseudowords (in their respective languages) varying in body-N while being matched across body-N condition on length and orthographic neighbourhood. Each item was shown on the screen for 5 seconds or until the voicekey was triggered, in random order. The items were presented, one at a time, using the software DMDX  [30] , which created audio recordings for each participants and each item. Here, we analyse only the pseudoword reading aloud responses. A native speaker of each language transcribed the participants' responses from the audiofiles previously recorded. We then calculated the Entropy, for each pseudoword, using the formula described in the introduction.
Materials As outlined in the previous section, we chose two subsets of items. For the first subset, we selected a number of words from each language using a frequency-matched design  [34]. We randomly selected words from different frequency bands: 10 words each with a log-frequency between 0 and 0. 5, between 0. 5 and 1, between 1 and 1. 5, and between 1. 5 and 2  [36] [37] [38]. We then created pseudowords for each item in each language, using the software Wuggy  [39]. Wuggy generates pseudowords that are similar to the input words in terms of syllabic structure, bigram frequency, and orthographic neighbourhood. Thus, we obtained 40 pseudowords, based on real words varying in frequency, for each orthography. In the second set, the items were equally easy to pronounce, we assembled 30 pseudowords from simple CV syllables.