dataesr/scientific-paragraphs-categorization huggingface dataset:
Using these GEMs together, we reconstruct an in-silico co-culture constraint-based model (CBM). The model is assembled through a bespoke computational workflow that integrates publicly available transcriptomic data sets (retrieved from the NCBI Gene Expression Omnibus -GEO), using the mCADRE algorithm, to a thermodynamically constrained version of the latest global knowledge base of metabolic functions categorised for the human genome: the Human Recon3D; using a subroutine of the redHuman algorithm (Fig. 2 )  [37] [38] [39] [40]. Our computational co-culture was further constrained to mimic the in-vitro growth medium, RPMI, using the metabotools MATLAB routine. We validate the resulting model against its experimental counterpart, where we found it successfully recapitulated the observed in-vitro growth and respiration rates.
As a result, each metabolite is associated with its Gibb's free energy (∆G). Then the ∆G of a given reaction is estimated from the thermodynamic properties of its reactants and products. Following the incorporation of this data, the model was then primed for thermodynamic flux balance analysis (tFBA) and thermodynamic flux variability analysis (tFVA) using the prepModelforTFA. m routine from redHuman  [40]. A readily available annotated matrix and a MATLAB script are available in the matTFA package by  [61]. We, however, repeated the protocol due to updates in the MetaNetX archive (with the latest update occurring in March 2022). The model was verified for consistency by running the redHuman's matTFA subroutine and comparing its results to the thermodynamically annotated Recon3D version generated by the redHuman algorithm package  [40, 61].
Database mining and cell-specificity (Step 2) To determine which reactions must be removed from the now thermodynamically constrained Recon3D matrix to generate our cell-specific models, we used transcriptomic datasets from the Gene Expression Omnibus database (GEO)  [37, 39]. In this study, we adhere to the data sets based on the Affymetrix Human Genome U133 Plus 2. 0 Array platform for the bone marrow mesenchymal stem cell model and the malignant plasma cell  [63]. The choice was driven by full support from Affymetrix via the availability of the. cdf file (a file that describes the layout for an Affymetrix GeneChip array) and its compatibility with the latest MATLAB version  [63]. Those sets for generating the bone marrow mesenchymal stem cell model reconstruction were September 12, 2022 6/30 obtained from healthy human early bone marrow passage samples and gene expression data from mesenchymal stem cells cultured in MSCGM (mesenchymal stem cell growth medium).
These were chosen in line with an early model that uses a legacy version of Recon3D, namely Recon1, to generate a genome-scale reconstruction-iMSC1255  [64, 65]. Their accession ID: GSM184636, GSM184637, GSM184638, GSM194076, GSM194077, GSM194078, GSM194079, GSM764199, GSM797497, GSM797498, GSM920586, GSM920587, and GSE80608  [66]. In particular, the GSE80608 data set includes newer datasets gather from gene expression of bone marrow mesenchymal stem cells grown out from bone marrow aspirates for multiple patient passages  [30]. For the myeloma cell model, we have used those datasets from a series of pre-treatment bone marrow aspirates derived from multiple myeloma patients. These consist of exclusively CD138 + expressing plasma cells from monoclonal gammopathy of unknown significance (MGUS) and multiple myeloma bone marrow samples from newly diagnosed and previously untreated patients.
Their accession ID: GSM50986, GSM50987, GSM50988, GSM50989, GSM50990, GSM50991, GSM50992, GSM50993, GSM50994, GSM50995, GSM50996, and GSM50997  [67]. Using the thermodynamically constrained Recon3D (Step 1) and the microarray datasets above, we executed the mCADRE routine. Briefly, as a first step, mCADRE scores those genes included in the Recon3D matrix according to their presence in the transcriptomic dataset. These scores are then attributed to reactions according to the initial model's annotated gene-protein-reaction (GPR) relations. Next, the topology of the metabolic network is considered to update these scores. Once the procedure is complete, reactions with scores below a certain threshold are removed to complete the reconstruction of the cell-specific metabolic network. We refer our reader to  [38]  for a detailed explanation of the algorithm.
Co-Culture assembly and objective function To model the experimental cell culture conditions, we must define an in-silico culture medium based on those used in the in-vitro counterpart. A correct definition of the medium in the model is fundamental for adequately representing intracellular metabolism. Using a combination of the redGEMX (part of redHuman) and the metabotools MATLAB package routine setMediumConstraints, we could guarantee that the reduced model has all the feasible pathways that consume and produce the components of the extracellular environment of the medium  [40, 70, 72]. The redGEMX subroutine finds those pathways from the model needed to connect the extracellular metabolites to the core network defined above. We also employed constraints on the exchange fluxes to the extracellular medium in both cells by using data related to RPMI media composition.
Multi-objective function Our two cells residing within the microenvironment (i. e. , the extracellular media) have different functions, each needing a different objective in a simulation. In a multi-objective paradigm, we assume that both cells, even under controlled environments, address various biological tasks (i. e. , objectives) to sustain life. To obtain an optimal solution, we use the concept of "Pareto-optimality". Briefly, a Pareto-optimal solution is one where the performance of one task would diminish the ability to achieve one or more other tasks. As we assume, for simplicity's sake, that regardless of the biological functions of the two cell types, in our simulation, the biomass function carries sufficient knowledge about the biological system  [74] [75] [76]. Our simulations implemented multi-objective optimisation using the MOFA routine for the COBRA package in MATLAB  [77].
The confidence intervals act as lower and upper bounds in the CBM generated from steps 1 through 4. The result is the incorporation of experimental data and further modelling constraints. Consequently, we allow each cell in the model to acquire the phenotype displayed, or extrapolated, from the co-culture in-vitro setup. Although many routines can perform 13 C-MFA, we have chosen the MATLAB package for isotopomer network compartmental analysis (INCA)  [42]. Briefly, INCA uses the elementary metabolite unit (EMU) framework to simulate isotopic labelling in any arbitrary biochemical model. The routine is equipped with a Montecarlo parameter estimation routine, which we used to obtain confidence intervals for each predicted flux  [41, 42, 83]. The selection of isotopic tracers in this study was 13 C 6 -Glucose and 13 C 5 -Glutamine.
C-MFA Mass and isotopomer balances were simulated for Myeloma cell and Bone Marrow Stem Cell, HS-5 and JJN-3 cell lines. Isotopic steady state was verified by measuring mass isotopomer distributions (MIDs) equilibration at the time points analysed. The isotopic steady state is reached when the change in isotopic enrichment over time falls within the measurement uncertainty range. We implemented the 13 C 6 Glucose and 13 C 5 Glutamine datasets in parallel datasets by regressing simultaneously to yield one complete metabolic flux map for each medium formulation. Glucose uptake flux was calculated to be 0. 45 µmol/10 6 Cells/hr, and the measured glutamine was 0. 63 µmol/10 6 Cells/hr -these values were calculated according to the protocol outlined in  [41]. All fluxes were simulated in the INCA routine for MATLAB using a minimum of 100 unique restarts from random initial values to ensure a global minimum was found  [42].
Acknowledgements The authors would like to acknowledge funding from  Cancer Research UK  to D. A. T. , E. V. -S. , and C. E. -G. ( C42109/A26982  and  C42109/A24747 ). Figs. 1, 2 and8 were created with BioRender. comunder an academic license. Finally, we would like to acknowledge the support and resources of the Birmingham Metabolic Tracer Analysis Core.
Materials and methods In this study the bio-geochemical models CERES-EGC  (Gabrielle et al. , 2006)  and the model framework LandscapeDNDC  (Haas et al. , 2013)  with its two biogeochemical models MeTrx and DNDC were applied to a gridded regional dataset of the EU-27, which includes information on arable land management, soil properties and climate for past, current and future conditions. Specifics of the models, model initialization data and driving information is provided in the following.
MeTrx Recent developments of LandscapeDNDC have focused on an advanced photosynthesis based plant growth model for grassland and arable crops as well as an advanced process description of the soil bio-geochemistry, which resulted in the development of an own plant growth module and the MeTrx biogeochemical module  (Kraus et al. , 2014a). The photosynthesis plant growth module on the basis of the approaches used by  Farquhar et al. (2001)  and  Ball et al. (1987)  was developed by  Kraus et al. (2014a)  and further refined and extensively tested by  Molina-Herrera et al. (2017)  and  Petersen et al. (2021). Simulated crop/plant species essentially share an identical process description and are solely distinguished by speciesspecific parameters  (Petersen et al. , 2021). MeTrx distinguishes the four plant compartments leaf, stem, roots and storage.
, 1997; Singh and Benbi, 2020; Tang et al. , 2006). The structure of the SOC module in LandscapeDNDC is based on the arable DNDC model and the sub-module MeTrx has been successfully used to simulate long-term SOC dynamics of rice-maize cropping systems  (Kraus et al. , 2016). Therefore, we consider that the LandscapeDNDC can be used to accurately simulate SOC stocks of European cropping systems.
Regional input data For this study we developed a gridded regional dataset of arable land management for terrestrial ecosystem modelling for the EU-27. The underlying raster grid was derived from the regional climate projection and is based on a domain decomposition into 133 × 168 grid raster in 0. 25°resolution (29. 0°to 71. 5°Latitude and -24. 0°to 45. 5°Longitude) with 8861 grid cells containing arable land. Each grid cell is associated with its arable land use share derived from the CORINE land use dataset, illustrated in Fig. 1a ).
Soils Soil data (soil type, texture, physico-chemical and hydrological profile properties) were obtained from the European Soil Database (ESDB; Hiederer, 2013) in 1 km × 1 km raster resolution contrasting topsoil (0 to 30 cm) and subsoil (30 cm to maximum simulated soil depth of 1 m) data of clay, silt, sand, gravel, and soil organic carbon (SOC) content, bulk density and maximum rooting depth. Soil pH for the topsoil was derived from the ESDB dataset provided by  Reuter et al. (2008). The soil properties were reprojected onto the coarse spatial simulation grid of 0. 25°l atitude/longitude resolution, thereby using the dominating soil property representing arable soils (e. g. bulk density, SOC content, texture class, or pH) for each simulation grid cell. Numerical discretization of the vertical soil profile (individually given by the soil database) for the three models was done by expert knowledge of the investigators.
The unified soil datasets were used to construct the required specific inputs for the models such as SOC and texture profiles (initial conditions). Organic soils with SOC content over 20 kg-C m -2 (corresponding to approx. 5% SOC content or higher) were excluded from simulations (3. 4% of the total simulation units) as simulation of organic soils requires a different parameterization of the model C dynamics. Ceres-EGC and LandscapeDNDC initialize their internal carbon and nitrogen pools and compartments for each numerical soil layer according to the supplied SOC initial condition per strata for each grid cell. The distribution of SOC initial condition (topsoil 30 cm) is shown in Fig. A1  (supplementary material). The models do not perform a spin-up run of several thousands of years to build up the soil for a given climate such as e.
Sources and distribution of organic and mineral fertilizers N fertilization for EU 27 was provided with the crop cultivation dataset provided by the framework of the GHG-Europe project (EU FP7;  Carozzi et al. , 2021). These data are based on the statistical crop distribution of Eurostat (European Statistical Office, Eurostat, 2019), which uses NUTS 2 regional scale data, and the simulations of the CAPRI model  (Britz and Witzke, 2008; Leip et al. , 2008). Information on application of mineral N fertilizer were as well available at NUTS 2 scale and this information was used to estimate crop specific fertilization rates  (Carozzi et al. , 2021). Information on the use of organic N fertilizer use was derived from FAOSTAT (2020). The N was then spatially distributed to the grid cells according to the livestock density for cattle/swine on basis of the database "Gridded Livestock of the World -Latest -2010 (GLW 3)", https:// dataverse.
Management of arable land incl. residue management Crop cultivation and management data were obtained from a combination of the statistical crop distribution in EU-28 (Eurostat, 2019) and modelling (CAPRI model,  Leip et al. , 2008). The dataset contains statistical distributed crop rotations for EU-27 on a 1 km resolution  (Carozzi et al. , 2021). Crop successions were available for the period 1976 to 2010. From this 1 km × 1 km crop distribution, we selected for each grid cell the two most frequent crop successions (named primary successions) as the most representative rotations containing up to 93% of the various crops of the grid cell. Based on this aggregation, the simulated crops across EU-27 were: summer/spring soft wheat, winter soft wheat, durum wheat, summer/spring barley, rye, oats, grain maize, fodder maize, rapeseed, sunflower, soya, pulses, potato and sugar beet.
Crop rotations included also rice which was generally substituted with soft wheat in the raw data as our focus is on upland systems. CERES-EGC does not provide a parameterization for rye and potato, such that the later were substituted in the CERES-EGC simulations with soft wheat for rye and with sugar beet for potato. LandscapeDNDC had no parameterization for durum wheat which has been replaced by winter wheat and pulses were simulated as beans. Temporal analysis of the primary crop successions revealed that different fertilization levels of the crops in the successions (e. g. maize versus cereals) can lead to oscillations in the time series of N fertilization levels, and create artefacts in the simulation results (e. g. temporal dynamics of the soil N 2 O emission strengths). To overcome such artefacts, for each grid cell, both primary successions were duplicated and shifted one year into the future to counterbalance the variations in fertilizer input levels leading to a smooth fertilization time series.
Climate input and climate scenarios In our study we used historical climate data in combination with climate projections as input at grid cell scale. The two climate projection scenarios (Representative Concentration Pathways, RCP) RCP4. 5 and RCP8. 5 from the fifth Assessment Report (AR5,  IPCC et al. , 2013)  were used in our study, which also included historical data. The RCPs RCP4. 5 and RCP8. 5 have a forcing value in 2100 of 4. 5 W m -2 and 8. 5 W m -2 , respectively, and are considered as a mild and a very high GHG emission scenario. Increasing CO 2 concentration time series with up to 537 and 926 ppm in 2100 were used for RCP4. 5 and RCP8. 5. Climate data were provided by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP;  Warszawski et al. , 2014)  based on global climate simulations with the Earth System model HadGEM2-ES and subsequently downscaled to a spatial resolution of 0.
Result aggregation and Ensemble dataset To reduce the size of the individual datasets, results of the model simulations were analyzed on a yearly basis. The inventory calculation involves in the first step result aggregation across the four crop rotations by the plain arithmetic mean resulting in one inventory dataset for each of the two climate scenarios (RCP4. 5 and RCP8. 5) and four residue management scenarios (Baseline, Exported, Surface and Buried). This is done for each of the three models. In the second step, the inventory results from the three models were aggregated into one ensemble inventory dataset. The ensemble building was done by applying the simple arithmetic mean for any datapoint in space and time. Missing datapoints due to model failures on some grid cells were gap filled by the respective mean values of the representative NUTS-2 area.
Potential of residue management to improve the GHG balance of cropping systems To assess the potential of residue management to improve the GHG balance of EU-27 cropping systems, we compared the net GHG balance of the three residue change scenarios (Exported, Surface, Buried) with the Baseline scenario. 6 ) reach a maximum of approx. 33 t CO 2 e ha -1 for Surface and approx. 13 t CO 2 e ha -1 for Buried management scenarios. After 2045, the Buried scenario turns back into a net GHG source and by the end of 2100 a resulting CNGB of -6. 30 ± 24. 85 and -3. 35 ± 24. 81 t CO 2 e ha -1 was reached for RCP4. 5 and RCP8. 5, respectively. All CNGB data are summarized in Table  A10  in the supplementary material. The high standard deviations originate from heterogenic CNGB trends and levels as shown as example in Fig.
Crop yields Even though several European and international projects actions such as MACSURE & AgMIP (comparing crop model estimates at site and regional scale), ISIMIP & GGCMI (comparing crop models on the global scale) are assessing the potential of crop models to simulate crop yields at continental to global scale, our simulation represent for the first time such    A8. an approach for EU-27. On basis of FAO and the EUROSTAT yield datasets, Ciais et al. (  2010 ) estimated mean crop yields for the period 1990-1999 at the scale of EU-25 as 275 (FAO) and 239 (EUROSTAT) g C m -2 yr -1 corresponding to 6. 1 and 5. 3 t DM ha -1 yr -1 , respectively. In our study we calculated a model ensemble mean for crop yields for EU-27 of 4. 41 ± 1. 85 t DM ha -1 yr -1 for the period 1990-1999 (data not shown).
While  (Hoffmann et al. , 2015; Zhao et al. , 2016)  reported large variances in model ensemble simulations for crop yields,  Grosz et al. (2017)  and  Kuhnert et al. (2017)  reported large variances between models in simulated SOC dynamics and NPP responses across a 29 year simulation period. This observation compares well to our model ensemble. The more complex the model outcome to simulate (SOC dynamics or soil N 2 O emissions), the larger the model variances in the ensemble become. For EU-27 scale cropland modelling, the provision of agricultural management constitutes by far the largest source of uncertainty as pointed out by e. g. Molina-Herrera et al. (2016a, b). The dataset used in our study  (Carozzi et al. , 2021)  provides crop rotations derived from spatial crop cultivation distributions (NUTS 2) and by crop succession likelihoods on a high resolution scale (1 km), with interpolations between both scales still meeting crop cultivation and N fertilization statistics.
Soil N 2 O emission summary, mean and standard deviation (std) of annual N 2 O emissions (data: all grid cells 2000-2099 of ensemble dataset); %-changes to Baseline: comparing calculated means for Exported, Buried and Surface scenario versus Baseline; %-changes 2100 vs 2000: comparing mean for 2080-2099 versus 1980-1999; testing for statistical significance of time series per grid cells across climate change and residue management scenarios reveal a high level of statistical significance (more than 90% grid cells with p < 0. 01, data not shown); (all raw data provided in supplementary material).
Please provide details of author contributions here. The authors declare no conflict of interest. ing on the location of the source  (28) , as early recognized by  (30) , thus can be used to infer source location or navigate to it  (31) (32) (33) (34) (35). Here we ask what salient features of turbulent odor signals best predict the location of the odor source and specifically compare quantities related to intensity vs timing of odor encounters. We first compose a dataset of realistic odor fields at scales of several meters using accurate state-of-the-art fluid dynamics simulations. We then develop machine learning algorithms that predict source location based on these synthetic odor fields. We find that measures of odor temporal dynamics based on a short memory span (down to about 1 second) hold information about source location.
Close to the source or close to the substrate, measures of intensity predict distance better than measures of timing; but this ranking is reversed at further distance from the source or from the substrate. Pairing the two kinds of measure improves dramatically the quality of the prediction robustly across all datasets, whereas pairing two measures of intensity or two measures of timing is either useless or detrimental. Our results demonstrate that timing and intensity are complementary attributes of odor dynamics and are most effective in more dilute and concentrated conditions respectively. These different conditions exist in different portions of space because odor gets transported, mixed and diluted by the fluid. As a result, the spatial range of operation of a living organism constrains the solutions it may evolve to make predictions with turbulent odors.
Note that depending on the sampling location, odor may be more or less sparse (compare for example Figure  1e  left and right). All parameters and methods are summarized in Table  1  and Materials and Methods. Do odor cues bare information about source location meters away from the source? To answer this question, we develop supervised machine learning algorithms that learn the relationship between the input (odor) and the distance from the source (output) from a large dataset of examples. In order to dissect what are the best predictors of source location and how ranking depends on the statistics of the odor, we need to detail more specifically the input and output of the algorithm. To design the input we start with the odor concentration field c(z, t) which varies stochastically in space and time as a result of turbulent transport.
From a modeling perspective, a flexible nonlinear/nonparametric approach based on kernel methods is contrasted and shown to be superior to a simpler linear model (Supplementary Figure  S1 ). A careful protocol based on hold-out cross-validation is used to select the hyper-parameters of the considered learning models. We refer to Materials and Methods for all details. To illustrate the results we pick the two dimensional plane at height H/4 that contains the source. The first result is that individual features (d = 1) bare useful information for twodimensional source localization even at several meters from the source. Performance is quantified by the normalized squared error averaged over the Nt points in the test set χ = N t i=1 [yi - f (xi)] 2 / N t i=1 [yi -ȳ] 2. For this dataset, intensity features rank higher than timing features (Figure  2b-c ), consistent with previous work  (30)  and predictions are more accurate in the crosswind than in the downwind direction (compare with Figure  S2 ).