dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 4. 0) using the restricted maximum-likelihood estimator model. Random-effects P values obtained from each of these methods were corrected for multiple hypothesis testing using the Benjamin-Hochberg procedure. For methods requiring count data (i-v and vii), absolute raw counts were estimated from species-level MetaPhlAn 3 relative abundances by multiplying these values by the total number of reads for each sample. The following methods were based on an assessment of statistical methods to detect differentially abundant features in microbiome data  56  and are available at https://github. com/mcalgaro93/sc2meta: i. DESeq2 (v. 1. 30. 0)  57  with the poscounts estimator (DESeq2_poscounts); ii. DESeq2 with the poscounts estimator and a zero-inflated negative binomial model (DESeq2_poscounts_zb), and observational weights were computed using the zinbwave package (v.
1. 12. 0)  58  and supplied to the DESeqDataSet class object to account for zero inflation; iii. DESeq2 with trimmed mean of M values (TMM; DESeq2_TMM), where normalization factors were calculated using TMM normalization, and a negative binomial generalized log-linear model was fit to the read counts of each feature using the glmFit function and the edgeR (v. 3. 32. 0) 59 package; iv. limma (v3. 46. 0)  60  with TMM values (limma_voom_TMM) (the limma package includes a voom function that transforms previously normalized counts to log counts per million, estimates a mean-variance relationship and uses this to compute appropriate observational-level weights); and v. limma with TMM values and a zero-inflated negative binomial model (limma_voom_TMM_zb) (to adapt the limma-voom framework to zero inflation, zinbwave weights were multiplied by voom weights); Other methods not originally considered in this assessment were also included in the meta-analysis: vi.
Cross-validation. A nested cross-validation procedure was applied to calculate within-cohort accuracy (cells on the diagonal in Fig. 2b ) by splitting data into training and test sets for 100-times repeated, fivefold-stratified cross-validation (balancing class proportions across folds). For each split, an L1-regularized (Lasso) logistic regression model was trained on the training set, which was then used to predict the test set. The lambda parameter was selected for each model to maximize the AUC-ROC under the constraint that the model contained at least five nonzero coefficients. Cross-study validation. Metagenomic classifiers were trained on a single cohort and their performance was externally assessed on all other cohorts (off-diagonal cells in Fig. 2b ), which were normalized for comparability in the same way as the training dataset.
All 500 models derived from the cross-validation on the training dataset (100-times-repeated fivefold cross-validation) were applied to the hold-out dataset, and median predictions were taken from all models. Leave one dataset out. Data from one cohort were set aside as an external validation set, whereas data from the remaining cohorts were pooled as a single training set on which we implemented the same procedure as above for 100-times-repeated fivefold-stratified cross-validation. Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by the  Seerave Foundation. The work was also supported by the  European Research Council  ( ERC-STG  project  MetaPG  to N. S. );  MIUR 'Futuro in Ricerca'  (grant  RBFR13EWWI_001  to N. S. ); the  European H2020 program  ( ONCOBIOME-825410  project and  MASTER-818368  project to N. S. ); the  National Cancer Institute of the National Institutes of Health  (grant  1U01CA230551  to N. S. and L. W. ); the  Premio Internazionale Lombardia e Ricerca 2019  (to N. S. );  Spanish Fondo de Investigaciones Sanitarias  (grants  PI15/00716  and  PI15/00956 ) of the  Instituto de Salud Carlos III, Spain , co-financed by  European Development Regional Fund  "A way to achieve Europe" to ERDF; the  Wellcome Trust  (grant  100282/Z/12/Z  to N. C. -L. ); Cancer Research UK  (grants  A27412  and  A22902  to N.
Data availability The metagenomes and main metadata relevant to the analyses are deposited in the European Nucleotide Archive under accession number PRJEB43119. The four publicly available datasets were downloaded through the Sequence Read Archive using the accession numbers SRP197281, ERP104610, SRP116709 and SRP115355. All MetaPhlAn 3 and HUMAnN 3 profiles are available within the latest version of curatedMetagenomicData (https://bioconductor. org/packages/ curatedMetagenomicData/).
From this perspective, it is mandatory for plant breeders worldwide to develop new strategies to deliver crop varieties at a faster rate, i. e. , increase the genetic gain for each crop. Here, we describe the OMICAS alliance and its commitment to the design, development, validation and deployment of an interdisciplinary panomics strategy and tool set to address the sustainability of productive agricultural systems and global food security. OMICAS was selected in 2018, as the sole program in the Food category of the Scientific Colombia ecosystem. Its name, was inspired from the Spanish acronym for Optimizacioń Multiescala In-Silico de Cultivos Agrı ́colas Sostenibles that translates into English as In-Silico Driven Observed area-weighted global yield 1961-2008 shown using closed circles and projections to 2050 using solid lines for maize, rice, wheat, and soybean.
For the countries that count on a value added basis, including Colombia, the gross value added at factor cost is used as the denominator. Source: World Bank National Accounts Data and OECD National Accounts Data Files. The application of omics technologies for the improvement of plant traits has enabled significant advances in recent years, as summarized in different reviews;  (Großkinsky et al. , 2017; Zander et al. , 2020; Jamil et al. , 2020; Yang et al. , 2021)  which describe, for the most part, partial integrative approaches, the application of different omics levels to address specific plant models and traits  (Rasheed et al. , 2013; Singh et al. , 2020; Yadav et al. , 2022) , or the use of modeling and simulation to drive discovery and optimization  (Matthews and Marshall-Coloń, 2021; Marshall-Colon, 2022).
Development and implementation of an integrated lowcost, high-throughput, geographically-distributed multimodal phenotyping platform (fixed, mobile and aerial) that integrates soil-plant-atmosphere variables during meristematic, elongation, and maturation phases of plant growth, 5. Development of computational models and data visualization tools for in-silico analysis and optimization of crops, based on graph theory and big data analytics algorithms, aimed at gene annotation, identification of gene and metabolic circuits associated with productivity and tolerance to stress conditions. 6. Applying the methods and tools developed by P1-P5 to the identification and annotation of genes, the The OMICAS program consists of seven highly-coupled projects, each identified in the figure with a number, the first four build the omics characterization layer of the program, the fifth integrates the characterization data through in-silico models for systematic big data analysis, and projects six and seven use the structure-function relationships obtained from the rest of the projects to develop new varieties with improved traits (validated on rice and sugar-cane models).
OMICAS program architecture, its macroprojects and their parameter-driven couplings. This figure depicts a sub-set of the basic data couplings in the OMICAS strategy. For example, P1 receives plant tissues from P6 and P7, and outputs DNA/RNA sequences for the modeling efforts in P5; P2 validates sensor technologies on metabolomics and ionomics characterized in P3; P3, in turn, contributes metabolomic and ionomic data for the high-throughput phenotyping effort in P4, and for the development of predictive models in P5; P4 uses molecular and elemental information from P2 and P3, and produces continuum-level phenotypic data for the models and codes developed in P5; while P5, integrates the experimental (physical and computational) multi-omics characterizations from P1-P4 into complex models derived from graph-and network theory, machine learning, and other mathematical and computer science methods to produce predictive tools for in-silico analysis and breeding optimization; P6 and P7 contribute breeding of new plant varieties with improved agronomic traits, based on the in-silico results from P5, and feeds tissues, soil and other environmental samples into the characterization and modeling that occur within P1-P5.
This alliance is committed to contributing basic, measurable, and transferable solutions to these problems, including but not limited to: new omics characterization and analysis techniques and tools, candidate genome sequences, candidate quantitative trait locus (QTLs) and genes, and optimized germplasm. A key element in OMICAS is the integration of an in-silico and physical experimental optimization cycle, based on epigenomic, genomic, metabolomic, and proteomic data and its correlation with phenomic expression to enable elucidation of complex genotype-phenotype relationships. The in-silico components are meant to improve breeding throughput, and to reduce the cost and time involved in traditional methods. The 'omic' characterization layer allows for multiobjective optimization of agricultural traits, such as, resource use efficiency, nutrient sink-source translocation efficiency, resistance to different biotic and abiotic stresses, and minimization of the environmental footprint.
These are critical to ultimately understand gene expression. P1 has established an experimental platform supported by the implementation of computational tools for the analysis of massive omics characterization data. The project integrates a physical layer for handling and processing experimental tissue samples, and a complementary computational highperformance computing (HPC) infrastructure (a GPGPU cluster set up at the alliance's anchor institution) for the storage and analysis of omics data generated. This data will be released to the public domain as the infrastructure grows. Three major computational-experimental efforts are under way between P1 and other projects in OMICAS, one (with P6) meant to identify epigenetic cues associated to reducing the effect of abiotic stresses (specific case of Al 3+ toxicity from acid soils), a second (again with P6) meant to uncover the genotypic and phenotypic variations underlying sucrose production, and a third (with P5) meant to systematically annotate genes from genome-phenome data using machine learning methods.
Correlations are obtained by comparing the estimations against an assembled ground-truth dataset with biomass and SPAD readings directly measured at ground-level. On average, we have obtained biomass correlations of r=0. 9568 with R 2 =0. 9154, whereas r=0. 986 with R 2 =0. 97 for leaf nitrogen. These are promising results towards the autonomous estimation of rice canopy AGB and N, with the aim of enabling highresolution genome trait mapping for genomic selection models for plant improvement against abiotic stresses. The OMICAS alliance has developed, validated and is currently deploying three High-Throughput Phenotyping (HTP) strategies and integrated platforms: aereal, terrestrial-fixed and terrestrial-mobile. From the aereal, drone-based, multispectral imaging platform we are now able to predict leaf-N, NDVI, and other crop data; from the fixed ground-based systems we obtain soil-based nutrients, plant indices (including metabolite profiles) and atmospheric state variables (including greenhouse gas footprints from crops, primarily N 2 O and CH 4 ).
In-silico strategies for improved crop breeding Plant breeding efforts generally require intensive labor as well as long optimization cycles that can last up to 12 years. Figure  9  illustrates how the in-silico approach strategy in the OMICAS program accelerates traditional approaches, by narrowing down potential candidate species from a large set, based on fitness functions associated to one or more agronomic trait. This reduces the time and cost of experimental breeding and selection. Different components that may complement traditional approaches to plant breeding are grouped together inside the blue box in Figure  9. They take into account omic data representations, mathematical models and optimization algorithms to facilitate the identification of critical features that are present in populations with one (or more) desirable traits.
Our goal in OMICAS has been to apply big data and machine learning algorithms on omics data characterized over multiple scales, in order to explore and ultimately uncover the key variables that intervene in stress-response and productivity. For example, an in silico approach may implement a computational environment to simulate critical optimization routes and explore a more ample and complete state/search space at a fraction of the time and cost. In P5, genetic, metabolic, protein, and cellular networks are used to supplement phenotypic traits associated to stress response, and to understand complex interactions and correlations upon which predictions can be based. Our in-silico approach in OMICAS builds on epigenetic, genetic, metabolic and cellular regulation network models, characterized via results from P1-P4 to elucidate some of these complex interactions and correlations.
, 2020; Riccio et al. , 2020). In  (Riccio et al. , 2020) , for example, we proposed both a generalization and an extension of the original WGCNA, which is applied to rice (Oryza sativa. The proposed in silico approach identifies a group of 19 genes which are relevant in the response to salt stress. Such genes are considered target genes for experimental efforts to improve salinity tolerance in rice. To identify the target genes, the approach relies on the idea of defining specific overlapping network 'communities' of In-silico characterization strategy in OMICAS. genes, which are assumed to underlie the co-expression gene network. In other words, a key hypothesis of the proposed approach is that the overlapping nature of the systems' regulatory domains that generate co-expression can be identified by applying an algorithm that detects modules of overlapping network communities.
Convergence and future prospects The OMICAS program brings a trans-disciplinary approach into crops breeding. It couples theory, lab, field and computational experiments within a multiscale omics characterization strategy that enables breeding and validation of new varieties with improved agronomical traits; with higher precision, and in a cost-effective and timely manner. Within three years of its launch, the OMICAS team has, among others: • uncovered epigenetic differences from four commercial rice cultivars and two accessions of wild rice associated to Al 3+ toxicity tolerance, • developed and validated novel graph-theory and machine learning tools to annotate genes using topological properties from co-expression networks, Identified 13 SNP biomarkers and 20 candidate genes associated to sucrose production in sucarcane, • designed, developed and validated new nano-sensors for the detection and quantification of biomarkers (primary and secondary metabolites) related to a plant organism's health, heavy metals (Al 3+ ) that compromise nutrient absorption, and gases (methane and nitrous oxide) that contribute to the greenhouse footprint of agriculture, • predicted the tertiary structure of a key membrane protein for stress signaling in plants, from which we are currently studying the plausible signaling pathways through two plant hormones (Abscisic acid [ABA] and gibberellins [GA1]), • designed, developed and validated a high-throughput phenotyping platform that integrates real-time data from fixed, mobile terrestrial with aerial devices to characterize soil, plant, atmosphere and crop variables, • identified different rice genes that confer tolerance to RHBV, high nocturnal temperatures, low-radiation, and to aluminum toxicity, • applied gene-editing technologies (mainly site-specific nuclease (SSN) with CRISPR/Cas) to produce experimental rice crops with enhanced stress response to RHBV, and improved resource use efficiency (Nitrogen and water) and higher yields for both rice and sugarcane, and • quantified and mapped the emission of nitrous oxide, methane and carbon dioxide from commercial sugarcane production in contrasting environments in Colombia.
Acknowledgments The authors would like to recognize all the students and technical staff sponsored by the program, from the different institutions involved, for their dedication and contributions to the work reported here, including:  Juan M. Marmolejo ,  Jhonattan de la Roche ,  Mauricio Peñuela ,  Jenny Gallo ,  Chrystian Sosa ,  Gustavo Lara ,  Sammy Perdomo ,  Pedro M. Hernańdez ,  David Jimenez ,  Miguel A. Romero ,  Nicolaś Loṕez ,  Camila Riccio ,  Sandra Loaiza ,  Manuel Valencia ,  Vanessa Reyes , and  Juliana Chaura. The authors would also like to thank  Dr. Edison Suarez ,  Ana Claudia Gordillo , and  Sandra Losano  from  Minciencias , father  Luis F. Gomez ,  S. J. ,  Carlos Monthermoso ,  Ingrid Schuler ,  Jaime Aguilar ,  Leidi Rojas ,  Yaneth Rodrıǵuez ,  Alejandra Catano , and  Michael Hernańdez  from the  PUJC , and the alliance's board members  Joe Tohme  from  CIAT-CGIAR ,  Rodomiro Ortiz  from the  Swedish University of Agricultural Sciences ,  JoséH.
Funding This work was partially funded by the " OMICAS program :  Optimización Multiescala In-silico de Cultivos Agrıćolas Sostenibles  ( Infraestructura y validacioń en Arroz y Caña de Azúcar )" Scientific Ecosystem belonging to the  Scientific Colombia Program , sponsored by The  World Bank , The  M i n i s t ry o f S c i e n c e , T ec h n o l o gy a n d I n n o vat i o n (MinCiencias ),  ICETEX , the  Colombian Ministry of Education  and the  Colombian Ministry of Commerce, Industry and Turism , under GRANT ID:  FP44842-217-2018 , Award ID:  792-61187.
Lattice and Core Test Cases Description Simplified core models have been defined within the CAMIVVER Project (Ref. [1] ) for investigating the advantages of 3D neutronics/thermal-hydraulics-coupled calculations while keeping the computational time reasonable. The size was chosen for favorizing the deployment of an advanced coupling based on APOLLO3 ® /CATHARE3 (proof of concept under development during the CAMIVVER Project) and the comparison with High-Fidelity Monte Carlo-based solutions coupled with subchannel codes  [5]. The choice of reduced size cases has also been made to open the discussion on code capabilities for treating different core sizes and innovative small size reactors as the Small Modular Reactor (SMR) concepts as indicated in other Projects  [17].
Fuel Assembly PWR Minicore Case The first configuration analyzed is a 32 Fuel Assemblies (FA) PWR minicore case as described in Figure  2. The configuration, based initially on Ref. [18] , has been improved in the framework of the CAMIVVER Project for allowing benchmarks among different codes  [19, 20]. the discussion on code capabilities for treating different core sizes and innovative smal size reactors as the Small Modular Reactor (SMR) concepts as indicated in other Project  [17].
This first investigation has been proposed by Framatome to get confident with hexagonal geometry configurations and to test selected code capabilities on hexagonal cases. These preliminary tests support the analyses foreseen during the benchmark exercise planned in the CAMIVVER project  [19]. This first investigation has been proposed by Framatome to get confid hexagonal geometry configurations and to test selected code capabilities on h cases. These preliminary tests support the analyses foreseen during the b exercise planned in the CAMIVVER project  [19].
At the cell level, Cartesian and hexagonal 2D geometries have been selected, as indicated in Figure  7 , based on Uranium Oxide (UOX) and Uranium-Plutonium Mixed Oxide (MOX) fuel materials. At the assembly level, only PWR configurations have been investigated, as indicated in Figure  8. Indeed, VVER geometry is not mandatory in terms of the CAMIVVER Project work program  [2] , wherein one of the objectives is the development of a multigroup cross-section library generator based on APOLLO3 ® to prepare core data for different reactor types (both PWR and VVER). The first version of the prototype able to treat PWR but also VVER configurations is expected by the end of 2022. PWR applications are already available, while VVER applications are not yet fully ready for carrying out this type of SA analysis.
APOLLO3 ® /CATHARE3 Within the CAMIVVER Project, the newly proposed APOLLO3 ® /CATHARE3 coupling based on the C3PO engine has been developed for benchmarking against existing 3D multiphysics models and High-Fidelity Monte Carlo solutions. This approach was developed to improve the 3D capabilities of the CATHARE3 code in comparison to the multi-1D approach. This coupling is tested under RIA transient within the CAMIVVER project but may be extended to other applications.
C3PO (with Other Codes) C3PO is a Python coupling platform initially developed by the CEA  [27]. The C3PO application in CAMIVVER project aims to increase the robustness of the PWR industrial multiphysics couplings. C3PO enables the interchangeability of core neutronics and thermal-hydraulic codes. To consolidate the preliminary results from CAMIVVER project, an industrial coupling platform, ATHENA has been considered the benchmark in the present paper  [16]. This multiphysics coupling platform is the result of recent developments co-realized by EDF (Electricité de France) and Framatome. It relies on a Python environment to control the execution of the different codes. It allows for a straightforward implementation of the Operator Split couplings between the solvers using their respective Python application programming interface (API).
ATHENA has a modular structure, and it is based on an object-oriented programming approach. Moreover, it is a general-purpose coupling interface, and it uses a series of classes to exchange data among different codes, physical models, and spatial scales, as simplified in Figure 9  [16]. Some open-source Python libraries for data manipulation and interfaces (such as ICoCo API and MEDCoupling) are at the basis of coupling engines, and post-processing may be realized in 3D format for variables of interest. An overview of the 3D coolant temperature pin-by-pin distribution is presented in Figure  10.
In this analysis, Framatome compared the codes used within the CAMIVVER project (APOLLO3 ® /THEDI and APOLLO3 ® /CATHARE3) with the newly developed coupling (ATHENA) that is part of the EDF and Framatome next-generation core physics computer code package ODYSSEE  [16]. ATHENA allows coupling between the core 3D neutronic solver of COCAGNE and the core 3D TH code THYC. The nuclear data for this comparison are produced with the APOLLO2 code  [28]  using the JEFF3. 1. 1 data. The main results on the initial steady-state for Scenario A (initial configuration representatives of all CRs extracted except the one that will be ejected) are given in Table  8. The reactivity of the ejected control rod is about $1. 2 (dollar ($) is the unit of the reactivity normalized to the delayed neutron fraction) for Scenario A.