dataesr/scientific-paragraphs-categorization huggingface dataset:
Care should therefore be taken when applying e. g. the Redfield ratio to ensure state variables are presented in the correct units. ERSEM adopts the modular and scalable structure of the Framework for Aquatic Biogeochemical Models (FABM: https://github. com/fabm-model/fabm, last access: 9 February 2023). In NEMO the coupling happens within the TOP_SRC routines via the FABM driver, with the necessary steps and changes being documented in the wiki of the NEMO4. 0-FABM repository (https: //github. com/pmlmodelling/NEMO4. 0-FABM/wiki, last access: 9 February 2023). Worked examples for setting up ERSEM for the SANH and SEAsia domains are given in the linked repositories (SANH-BGCsetup: https://github. com/dalepartridge/SANH_BGCsetup, last access: 8 February 2023; SEAsia-BGCsetup: https://github.
Optional parameterisations can be activated to control the complexity of iron chemistry or the description of particulate organic materials. Examples where PISCES has been used to study specific regions include the Peru upwelling  (Echevin et al. , 2020) , the Indian Ocean  (Resplandy et al. , 2012)  and the Mediterranean  (Richon et al. , 2018). The PISCES community (https://www. pisces-community. org, last access: 8 February 2023) offers training materials that are suitable for beginner and advanced users. To explore a NEMO-PISCES configuration, a 1D worked example (https://doi. org/10. 5281/zenodo. 7139521, Éthé et al. , 2022)  is given in the form of a demonstrator targeted at beginners. The demonstrator gives an introduction to PISCES and describes the steps to install and run the code for the BATS station (which can easily be changed) along with a description of the expected outputs.
Though MEDUSA was conceived as an efficient biogeochemistry model for global applications in NEMO, it has also been used in a regional configuration. A worked example for setting up NEMO-MEDUSA in a western Indian Ocean domain is given in the linked repository (https:// github. com/NOC-MSM/Regional-NEMO-Medusa/, last access: 9 February 2023). https://doi. org/10. 5194/gmd-16-1481-2023 Geosci. Model Dev. , 16, 1481-1510, 2023.
g. Lewis et al. , 2019a, and Hashemi et al. , 2015). This cost can be mitigated by the level of coupling used: if the waves and the ocean do not significantly affect each other, then the components can be run independently, or if adequate products exist and the ocean component does not significantly affect the waves, then data could be downloaded from e. g. the CMS catalogue. Cost can also be mitigated in coupled ocean-wave configurations by decreasing the rate at which variables are exchanged between modules. Having determined whether or not the wave field needs to be simulated, the level of coupling can then be chosen. There are three "zones" where coupling can be implemented. 1. At the air-sea interface, a. waves act as a buffer to momentum exchange between the atmosphere and the ocean, for example reducing the surface stress experienced by the ocean when waves are growing.
Sea ice (SI3) Sea ice is represented using the Sea Ice modelling Integrated Initiative (SI3) module. SI3 is a European collaboration to pool resources and develop a unified NEMO sea ice model for regional and global applications. This module specifically targets ice dynamics, thermodynamics, brine inclusions and sub-grid-scale thickness variations and is designed for an effective resolution of 10 km or coarser. SI3 is designed in close harmony with NEMO and ships with the NEMO code base. Three data categories are required: ice thickness, snow thickness and ice fraction (or ice "concentration"). These are, of course, also required for the initial conditions. Sea ice at the boundaries depends on whether the ice flows into the regional domain or towards the outside. If the flow is strictly outward, then the ice at the boundary takes the characteristics of the closest grid point inside of the domain.
If the flow is inward, then boundary conditions are read from a BDY file. Boundary data can be obtained from global simulations or reanalyses. Only three fields are required: ice fraction and ice and snow thicknesses. Additional fields can be provided for better accuracy: ice and snow temperature, surface temperature, ice salinity and melt pond concentration and thickness. If not provided, these optional fields are set to constant values determined in the namelist. The treatment of temperature is a bit more subtle. For instance, ice and snow temperatures are derived from the surface temperature if the latter is the only field provided. On integration, the boundary fields can be set to the initial field or be allowed to vary if the data exist. Only the FRS boundary condition scheme is implemented for the tracer fields, but you can use "None" if there is no sea ice at the boundary.
In particular, we focus on the representation of the ice shelf cavities, which can be open (and explicitly represented) or closed (and therefore parameterised). If the cavity is closed, an ice melt flux can be specified. Values can be sourced from an observational study (such as  Rignot et al. , 2013; Depoorter et al. , 2013; Adusumilli et al. , 2020)  or from an ice shelf simulation. Ideally, the basal melt flux is distributed from the base of the floating ice shelf to the grounding bathymetry. This depth range can be extracted from an ice shelf draft dataset (e. g. BedMachine  Morlighem et al. , 2020). A current limitation of the ice shelf module is that one can only specify a constant melt flux. If, on the other hand, the cavity is open, melt rates are computed explicitly by interaction with the circulating flow.
A tool to distribute icebergs randomly is available as part of the NEMO CDFTOOLS toolbox (https://github. com/meom-group/CDFTOOLS, last access: 9 February 2023). Finally, the user can easily define the number of categories, the size of each iceberg category and how to distribute the calved mass across the category in the model namelist. Sensitivities of results to these choices are described in  Stern et al. (2016). In addition to this, it is worth keeping in mind the two major limitations of the Lagrangian iceberg model in the current NEMO version: icebergs are "virtual", and therefore they cannot serve as anchor points for land-fast ice  (Li et al. , 2020) , and there is no fragmentation scheme, so the very large tabular icebergs do not break up and thus have too long a lifetime  (England et al.
Ice process demonstrator To explore a NEMO configuration with a mix of parameterised and explicit ice shelves, tides, sea ice and an iceberg climatology, a Weddell Sea worked example (https://doi. org/10. 5281/zenodo. 6817000, Mathiot and Hutchinson, 2022)  is given in the form of a demonstrator. This is adapted from the WED025 configuration of  Bull et al. (2021). The "expected results" section illustrates the melt rate and barotropic streamfunction under the Filchner-Ronne Ice Shelf in a 1/4 • simulation. In this demonstrator the iceberg melt climatology is specified as an additional runoff source. There is no demonstrator for the Lagrangian iceberg module. As a reference, please see the description of the global GO6 simulation set-up  (Storkey et al. , 2018).
Acknowledgements. At the  National Oceanography Centre (NOC) , formerly the  Proudman Oceanographic Laboratory , this journey started in 2009 with the  Global Coastal Ocean Modelling  project (Holt et al. , 2009), wherein selected authors tiled the global continental shelf seas with the  Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS)  and ERSEM regional models. Milestones in the development include the development of the PyNEMO tool for open-boundary conditions which facilitated the democratisation of the capability under the  UK ACCORD National Capability  project. Engagement with the  NEMO Development Strategy 2023-2027  ( NEMO  Consortium, 2022) helped broaden the international collaborative input for this work, supported by the  Improving Ocean Models  for the Copernicus programme (IMMERSE) Horizons 2020 project.
This work used the  ARCHER  and  ARCHER2   UK National Supercomputing Service  (http://www. archer2. ac. uk, last access:  20 2023 ). The UK coordination of this work is facilitated by the  Joint Marine Modelling Programme , a collaboration between the  Met Office  and  NERC -supported research centres. Financial support. This research has been supported by the  UK Research and Innovation  (grant no. NE/R000123/1  and  Climate Linked Atlantic Sector Science) , the  NERC  (grant no. NE/S009019/1 ), and the  European Union  (grant nos. 821926  and  820575 ). Review statement. This paper was edited by  Olivier Marti  and  David Ham  and reviewed by two anonymous referees.
Code and data availability. This work was underpinned by experiences building a number of regional NEMO configurations. Specific citations in this work include the following. -SANH: worked example in the 1/12 • South Asia domain (https://doi. org/10. 5281/zenodo. 6423211), Jardine et al. (2022) -SEAsia: worked example in the 1/12 • South-East Asia domain (https://doi. org/10. 5281/zenodo. 6483231), Polton et al. (2022a) -SEVERN-SWOT: worked example in the 500 m Severn Estuary configuration (https://doi. org/10. 5281/zenodo. 7473198), De Dominicis et al. (2022) -Belize workshop: workshop material featuring containerisation of an idealised NEMO regional model of Belize to demonstrate the principles of running an ocean model and diagnosing its output (https://doi. org/10. 5281/zenodo. 6417227, with recipes and code), Mayorga Adame et al.
(2022) -Worked example for setting up ERSEM for the SANH domain (https://doi. org/10. 5281/zenodo. 6907302), Partridge (2022b) -Worked example for setting up ERSEM for the SEAsia domain (https://doi. org/10. 5281/zenodo. 6940832), Partridge (2022a) -Worked example in the 1/60 • Sri Lanka domain setting up NEMO and NEMO-ERSEM (https://doi. org/10. 5281/zenodo. 7464071), Rulent (2022) -Worked example for setting up 1D NEMO-PISCES (https://doi. org/10. 5281/zenodo. 7139521), Éthé et al. (  2022 ) -Worked example for setting up NEMO-MEDUSA in the western Indian Ocean (https://github. com/NOC-MSM/ Regional-NEMO-Medusa/, last access: 5 January 2023) -Worked example of the Weddell Sea, Antarctica, with ice capabilities (https://doi. org/10. 5281/zenodo. 6817000), Mathiot and Hutchinson (2022)  Other examples of documented configurations include, with less focus on how-to, -Caribbean (https://doi.
org/10. 5281/zenodo. 3228088), set up with forcing data on JASMIN and provided with scripts designed to auto-build and run clean configurations  (Wilson et al. , 2019) , -BoBEAS (https://doi. org/10. 5281/zenodo. 4014837), containerised and using open MPI (Polton et al. , 2020a), and -AMM7-surge (https://doi. org/10. 5281/zenodo. 4022310), Polton et al. (2020b).
Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests.
Cohort studies can be highly useful for investigating associations between drugs and molecular phenotypes, and can be used to tailor the design of randomized control studies to assess direct causal relationships  8. Common approaches to analysis of cohort data apply univariate statistical methods, linear and logistic regression, dimensionality reduction and clustering analyses. However, when expanding to multi-omics data such analyses are not straightforward and traditional methods of data interpretation are insufficient to exploit the full scope of multi-modality data. Here we investigate vertical data integration, where multiple omics datasets have been generated for the same samples. Challenges that must be overcome include integration of data across multiple continuous and discrete data modalities, efficient handling of missing data or even large missing parts of specific data types, differences in dimensionality, modality-specific noise and how to extract associations across data modalities  [9] [10] [11].
There are several strategies for vertical integration of multi-modal datasets, such as element-wise addition of one dataset at a time, learning individual representations for each dataset before fusion, or multi-dimensional fusion where representations are learned from the input data altogether  9, [12] [13] [14]. Examples are multi-omics factor analysis (MOFA), iCluster, and data integration analysis for biomarker discovery using latent components (DIABLO) implemented https://doi. org/10. 1038/s41587-022-01520-x had up to 24. 7% missingness across the multi-omics data. For the clinical data missingness was higher with a per individual median of 14% and 7% for continuous and categorical clinical data, respectively. We designed the MOVE framework to be flexible in relation to the number of input data types and to be able to handle both continuous and categorical features (Fig.
1a ). To identify the optimal hyperparameters that would capture the structure of the data without losing the ability to generalize on unseen individuals, we initially divided the dataset into training and test sets. We then measured the ability of the models to reconstruct the input as well as the stability when refitting the model to the data several times (Supplementary Figs. 2 3 4 ). The median reconstruction accuracies were between 0. 95-1 and the final models were highly stable when retrained five times with average change of cosine similarities in the latent space of 0. 037. Thus, the VAE models were able to reconstruct the data with high accuracy across the individuals (Supplementary Fig. 5 ).
However, these methods primarily focus on discovering factors or latent variables that can be used for visualization, clustering, or prediction of disease. We have previously developed a deep-learning framework on the basis of variational autoencoders (VAE)  17, 18  for integration and binning of large amounts of unstructured metagenomics data  19. Specifically, a VAE is based on deep neural networks and learns to transform high-dimensional data into a lower-dimensional space, termed a latent representation. During this process the two networks of the VAE learn the structure of input data and associations between the input variables. In our previous study, we found that the VAE could learn to integrate two datasets without any prior knowledge or statistical model  19. Similarly, others have shown the capabilities of VAEs as integrative models for extracting the underlying signal in data for improving clustering and prediction  12, [20] [21] [22] [23]  , as well as for handling large proportions of missing data  24.
We, therefore, speculated that such a model could be used to integrate even deeper cohort-level multi-omics datasets. While previous studies have primarily focused on stratifying patients using the underlying latent representation  22, 25, 26  we were also interested in whether we could acquire insights into the complex relationships that the network learns through data integration. For this purpose, we exploited that the decoder of the VAE is a generative model. Thus, the final trained decoder will be able to generate new examples of data from the learned latent distribution. On the basis of this principle, a variety of generative models have been used to generate new examples of data, such as single-cell RNA data and artificial human chromosomes  27, 28. Additionally, when combined with Bayesian decision theory they have been used for analysis of single-cell RNA data on the basis of variational inference  [29] [30] [31].
These modalities included genomics, transcriptomics, proteomics, metabolomics, and microbiomes as well as data on medication, diet questionnaires, and clinical measurements. Our method was able to integrate multi-omics data with clinical and categorical data and was resistant to systematic biases in the data as well as large amounts of missing data. Using an ensemble of generative VAE models, feature perturbation, univariate statistical methods, and Bayesian decision theory we identify cross omics associations. We compared the drug multi-omics profiles and showed that different drugs are associated with unique clinical and molecular profiles. Our method, multi-omics variational autoencoders (MOVE) is freely available, easily scalable, can integrate any number of categorical and continuous datasets, and able to identify features to multi-omics associations.
Designing a VAE for multi-omics data integration We used a dataset of 789 newly diagnosed T2D individuals with extensive multi-omics characterization (Supplementary Table  1 ). In total the data included 8,807 variables per individual with median missingness within an omics dataset of less than 5% except for metagenomics data where two thirds of the individuals (532) did not have any data (Supplementary Data 1 and Supplementary Fig. 1 ). Therefore, these individuals https://doi. org/10. 1038/s41587-022-01520-x the original data through the model (Fig. 1a ). Because VAE models are stochastic, we used results across an ensemble of models and developed two different approaches to identify significant associations. One approach was based on applying t-tests with Bonferroni correction across four different models, where each model was refitted 10 times (MOVE t-test), while we also, inspired by earlier variational work  [29] [30] [31]  , used Bayesian decision theory and a single model refitted 30 times (MOVE Bayes).
To identify different parameters of the approaches that would allow for comparison across and to standard methods (t-test, analysis of variance (ANOVA)), we applied them to two datasets consisting of randomized clinical, drug and multi-omics data. Our findings showed that MOVE t-test and MOVE Bayes had good performance to identify drug-omics associations compared with t-test and ANOVA at a ground-truth false discovery rate (FDR) of 0. 05 (Supplementary Fig. 12  and Supplementary Table  3  and Methods).
1d ). In addition, the significant associations identified by MOVE were distributed across the drugs (two-sided t-test, P = 0. 016) and not only for the drugs administered to most individuals such as Simvastatin, Atorvastatin, and Metformin. For instance, MOVE identified a median of 20 associations per drug compared to 1 for t-test and 0 for ANOVA, highlighting that our method was more sensitive for extracting associations for drugs given to a smaller number of individuals (Supplementary Tables  5  and 6 ). Among the multi-omics datasets, we found that the largest number of significant drug associations was to the metabolomics, clinical, and transcriptomics data with an average of six associations per drug (Fig. 1e  and Supplementary Fig. 13 ). When normalizing for all possible associations, the highest fraction of asso ciations was to the clinical data (8%) followed by targeted and untargeted metabolomics with an average of 5.
Changes in T2D biomarkers were associated with metformin We then investigated drug and multi-omics interactions (Fig. 2a  and Supplementary Figs. 14-18), and initially focused on expected clinical drug interactions. For instance, for metformin, we identified 88 significant clinical and multi-omics interactions across all the datasets. When investigating associations across the individuals we found low intra-patient variability indicating that the changes were stable (Fig. 2b  and Supplementary Fig. 19 ). We found that metformin was significantly associated with 12 clinical markers of T2D such as insulin clearance, active GLP-1, glucose levels from mixed-meal glucose tolerance test, glucose sensitivity, and blood pressure (Fig. 2a  and Supplementary Data 2-4). The directions of some of the associations were opposite to the expected metformin effects, that is, metformin was associated with decreased glucose sensitivity at baseline (average Z-score change  Effects are given as effect size (z-scaled units) from negative (blue) to positive (red).
Drug polypharmacy and similarity across multi-omics data We then investigated similarities between drugs and their multi-omics associations. Overall, we observed four clusters containing three to six drugs each and found that some of the drugs within a cluster could potentially be associated with polypharmacy (Fig. 3c ). Therefore, we investigated the impact of a drug-drug combination on the associations and found a correlation between overall drug association similarity and the individuals taking the two drugs (PCC 0. 75, P value of 2. 2 × 10 -35 ). This finding indicates possible polypharmacy effects introduced by taking the two drugs together resulting in a higher drug-drug similarity across all clinical and multi-omics changes. However, some of the similarities might to some extent be driven by overlapping patient groups and non-drug-related similarities such as the underlying reason for taking the drug.
The effects of drugs are widespread across the omics data Currently, there are widespread efforts in investigating drugs and gut microbiome interactions suggesting that the microbiome is a potential target and mediator of drug effect  42, 59, 60. As we investigated several multi-omics datasets besides the gut microbiome (metagenomics), we can compare the effect size of the drugs across the omics datasets. Interestingly, we found that the gut microbiome was the dataset with the second fewest number of statistically significant hits across the drugs with 17 significant associations (Supplementary Table  4  and Supplementary Fig. 13 ). Only diet and wearable data had fewer associations (11); transcriptomics, proteomics, targeted, and untargeted metabolomics had between 44-134 significant associations.
We then asked if the effect size of the drugs were different across datasets and determined the cumulative effect size of the drugs in the respective multi-omics datasets. Here we found that the average effect sizes in transcriptomics and metagenomics data were the lowest for all drugs, and that those in the metagenomics dataset were significantly lower compared to all other omics datasets but transcriptomics (ANOVA, Tukey HSD test, adjusted P < 0. 05) (Fig. 3d  and Supplementary Table  9 ). When we subset to significant drug-omics associations, of which the gut microbiome only had two drugs with significant associations (metformin and omeprazole), we found that the effect of these two drugs were similar or lower compared to the effect sizes of the other multi-omics datasets (Supplementary Fig.
22 ). Finally, we investigated if this could be caused by increased uncertainty when learning and reconstructing a given modality but only found small correlations with PCCs of -0. 15 to 0. 16 between modality uncertainty and inferred effect sizes in a modality (Supplementary Table  10 ). Overall, this observation https://doi. org/10. 1038/s41587-022-01520-x implies that the multi-omics response to drug stimuli are not only targeting the gut microbiome and that multiple omics datasets should be included when attempting to understand drug effects.