dataesr/scientific-paragraphs-categorization huggingface dataset:
GECKO simulation utilities. As ecModels are defined in an irreversible format and incorporate additional elements such as enzymes (as new pseudo-metabolites) and their usages (represented as pseudo-reactions), they might sometimes not be directly compatible with all of the functionalities offered by currently available constraint-based simulation software  44, 45, 50, 54, 55. We therefore added several new features to the GECKO toolbox that allow the exploration and exploitation of ecModels. These include utilities for: (1) basic simulation and analysis purposes, (2) accessible retrieval of kinetic parameters, (3) automated generation of condition-dependent ecModels with proteomic abundance constraints, (4) comparative flux variability analysis between a GEM and its ecModel counterpart, and (5) prediction of metabolic engineering targets for enhanced production with an implementation of the FSEOF method  56  for ecModels.
Detailed information about the inputs and outputs for each utility can be found on their respective documentation, available at: https://github. com/SysBioChalmers/GECKO/tree/master/geckomat/utilities. All of these utilities were developed in MATLAB due to their dependency on some RAVEN toolbox functions  50. Predicting microbial proteome allocation in multiple environments. In order to test the quality of the phenotype predictions of an ecModel automatically generated by the ecModels container pipeline, batch growth under 11 different carbon sources was simulated with eciML1515 for E. coli. Figure  3a  shows that, for all carbon sources, growth rates were predicted at the same order of magnitude as their corresponding experimental measurements, with the most accurate predictions obtained for growth on Dglucose, mannose and D-glucosamine.
This shows that the new version of the GECKO toolbox ensures the generation of functional ecModels that can be readily used for simulation of metabolism, due to its systematic parameter flexibilization step, which reduces the need of extensive manual curation for new ecModels. Furthermore, iML1515 is a model available as a static file at the BiGG models repository  57  ; therefore, its integration to the ecModels container for continuous update demonstrates the flexibility of our pipeline, regarding compatibility with original GEM sources, which can be provided as a link to their git-based repositories or even as static URLs. Proteomics constraints refine phenotype predictions for multiple organisms and conditions. The previously mentioned module for integration of proteomics data generates a conditiondependent ecModel with proteomics constraints for each condition/replicate in a provided dataset of absolute protein abundances [mmol/gDw].
Even though absolute quantification of proteins is becoming more accessible and integrated into systems biology studies  [58] [59] [60] [61] [62]  , a major caveat of using proteomics data as constraints for quantitative models is their intrinsic high biological and technical variability  63  , therefore some of the incorporated data constraints need to be loosened in order to obtain functional ecModels. When needed, additional condition- dependent exchange fluxes of byproducts can also be used as constraints in order to limit the feasible solution space. A detailed description of the proteomics integration algorithm implemented in GECKO is given in the supplementary methods section in the Supplementary Information File 1. The new proteomics integration module was tested on the three ecModels for budding yeasts available in ecModels container (ecYeastGEM, eciYali, eciSM996).
Additionally, the incorporation of individual protein abundance constraints improves even further the predictive accuracy of gaseous exchanges, for 5 out of the 11 evaluated cases (Fig. 4a-c ). Although only a trend and not a significant improvement, it would be of interest, in the future, to run further analyses that include more proteomics datasets. The impact of incorporating enzyme and proteomics constraints on intracellular flux predictions was further assessed by mapping all condition-dependent flux distributions from the tested ecModels to their corresponding reactions in the original GEMs. In general, metabolic flux distributions showed high similarity when comparing ecModel to GEM predictions (Supplementary Fig. 1 ), as 70-90% of the active reaction fluxes were predicted within the interval of 0.
This limitation has usually been overcome with incorporation of measured exchange fluxes as constraints. However, these data are typically sparse in the literature. Previous studies explored the drastic reduction in flux variability ranges of ecModels for S. cerevisiae and 11 human celllines when compared to their original GEMs due to the addition of enzyme constraints  1, 2, 38. However, the irreversible format of ecModels (forward and backwards reactions are split in order to account for enzyme demands of both directions) hinders their compatibility with the flux variability analysis (FVA) functions already available in COBRA  44  and RAVEN  50  toolboxes. As a solution to this, an FVA module was integrated to the utilities repertoire in GECKO, whose applicability has been previously tested on studies with ecModels for S.
cerevisiae 1 and human cell lines 2. This module contains the necessary functions to perform FVA on any set of reactions of an ecModel, enabling also a direct comparison of flux variability ranges between an ecModel and its GEM counterpart in a consistent way (supplementary methods section in the Supplementary Information File 1). The FVA utility was applied on three different ecModels of microbial metabolism and their correspondent GEMs (iML1515, iYali4, and iSM996). In all cases the FVA comparisons were carried out for both chemostat and batch growth conditions in order to span different degrees of constraining of the metabolic networks (0. 1 h -1 dilution rate and minimal glucose uptake rate fixed for chemostat conditions; biomass production fixed to experimental measurements of μ max and unconstrained uptake of minimal media components, for batch conditions).
To overcome these limitations, we generalized the code with the aim of making GECKO a model-agnostic tool. The development of a procedure for automatic curation of kinetic parameters enabled the generation of functional ecModels with minimal requirements for experimental data. Recently, ecModels for 11 human cancer celllines were generated with this automated procedure, using Human1 as a model input and RNAseq datasets together with the tINIT algorithm  10  to generate cell-line specific networks 2. These ecModels were used for the prediction of cellular growth and metabolite exchange rates at different levels of added constraints, resulting in remarkable improvements in accuracy when compared with predictions of their original GEMs. This highlights one of the main advantages of ecModels: their capability of yielding biologically meaningful phenotype predictions without an excessive dependency on exchange fluxes as constraints.
This result suggests the relevance of metabolic robustness in contrast to optimal protein utilization for microbial growth under environmental stress and nutrientlimited conditions. Incorporation of proteomics data allows the use of ecModels as scaffolds for systems-level studies of metabolism, providing a tool for uncovering metabolic readjustments induced by genetic and environmental perturbations, which might be difficult to elucidate by purely data-driven approaches, specially at conditions of relatively low changes at the transcript  69  and protein levels  64. For all studied stress conditions in this study, we identified upregulated proteins (increased abundance) that are needed to operate at high saturation levels in stress conditions, while showing low usage at reference conditions, creating lists of potential gene amplification targets for enhancing stress tolerance in three industrially relevant yeast species (Source Data: Data Source File 2).
In contrast, the new version of the GECKO toolbox provides a systematic and robust parameterization procedure, leveraging the vastly accumulated knowledge of biochemistry research stored in public databases, ensuring the incorporation of biologically meaningful kinetic parameters even for poorly studied reactions and organisms. The applicability of these other simple modeling formalisms to models for diverse species is limited as none of these methods has been provided as part of a generalized model-agnostic software implementation. Recently, a simplified variant of the MOMENT method (sMOMENT) was developed and embedded into an automated pipeline for generation and calibration of enzymeconstrained models of metabolism (AutoPACMEN)  70. The pipeline was tested on the generation of an enzyme-constrained version of the iJO1366 metabolic reconstruction for E.
Methods Automation pipeline and version-controlled hosting of the ecModels container. The ecModels repository is used to version-control the pipeline code and the resulting models. The pipeline is restricted to 2 short Python files, whose role is to decide when models need to be updated based on a configuration file config. ini, and to consequently invoke the use of GECKO for each model. Updates are deemed necessary when either the underlying dependencies (i. e. , GECKO, RAVEN and COBRA toolboxes, the Gurobi solver, and libSMBL) or the source GEMs are independently updated to a new version (release) in their respective repositories. The pipeline is designed be automatic and to not require supervision. It was developed to work with both version-controlled GEMs and GEMs downloadable from a URL, updating the version in the configuration after a new ecModel is obtained.
Acknowledgements We are grateful to  Feiran Li ,  Raphaël Ferreira ,  Jonathan Robinson , and all the GECKO users that have provided feedback for improving our toolbox and extending its range of applications and to the  CHASSY  project consortium for having motivated and supported this work. This project has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and   Innovation-Grant  Agreements No. 720824  to I. D. , A. M. O. , C. H. , V. S. , and J. P. M. 686070 to B. S. and  760798  to M. A. This work was also supported by the  Knut and Alice Wallenberg Foundation  and The  Novo Nordisk Foundation -Grant no. NNF10CC1016517  to J. N.
Data availability Mass spectrometry raw data that support the findings of this study have been deposited in PRIDE database  74  with the dataset identifier PXD012836. The processed proteomics datasets are available in our GitHub repository at: https://github. com/SysBioChalmers/ GECKO2_simulations/tree/v1. 0. 1/data/proteomics. All collected kinetic data for the study presented in Supplementary Information File are available at: https://github. com/ SysBioChalmers/Enzyme-parameters-analysis/tree/master/data. The generated computational models used for this study are available at: https://github. com/ SysBioChalmers/ecModels/tree/v1. 0. 0. Data for reproduction of all main and supplementary figures are provided in the Sournce Data: Data Source file 1, Data Source File 2, and Data Source File 3.
Code availability The source code of the updated GECKO toolbox is available at: https://github. com/ SysBioChalmers/GECKO/releases/tag/v2. 0. 2  75. The source code for ecModels container can be accessed at: https://github. com/SysBioChalmers/ecModels/tree/v1. 0. 0  76. All custom scripts for simulations included in this study can be found at: https://github. com/ SysBioChalmers/GECKO2_simulations/releases/tag/v1. 0. 1  77. All the necessary scripts for reproducing the k cat parameters analysis in the Supplementary Information File 1 are available at: https://github. com/SysBioChalmers/Enzyme-parameters-analysis/releases/ tag/v1. 0. 0  78. All of these repositories are public and open to collaborative continuous development.
Methods Forty-one international or national level short-course 50-meter (n = 14), 100-meter (n = 14) and 200-meter (n = 13) breaststroke races were recorded. There were no significant differences between the groups according to the international point system for performance (FINA-points): 674. 93 ± 92. 23, 695. 50 ± 86. 03 and 627. 38 ± 93. 29 points for 50-, 100-and 200-m, respectively. The study was approved by the local ethical committee and the National Data Protection Agency for Research in accordance with the Declaration of Helsinki. Participants were informed about procedures, benefits and potential risks of the study orally and in writing. Thereafter, participants or the legal guardian (for minors) provided written informed consent prior to participation. Each race was recorded by the AIM race analysis system (AIMsys Sweden AB, Lund, Sweden) synchronized with an electronic Omega timing system (Swiss Timing, Bienne, Switzerland).
The system does not require swimmers to wear any device besides a yellow silicon cap (used for movement recognition). Each race was captured using ten stationary cameras, whereof five were placed above and five underneath the water surface alongside the 25-meter swimming pool. The first camera was placed 2. 5 m from the starting wall, and thereafter each camera was placed 5 m apart. The sampling frequency was 50 Hz. Calibration resulted in a mean error of 2. 0 pixels, corresponding to a 0. 025°a ngular and 0. 6 cm linear error. On race completion, the automatic postprocessing produced a panning video file (MPEG-4 Part 14) and a synchronized Excel sheet containing the center of the head displacement data relative to the global coordinate system, which were used for further analysis. For more details regarding the set-up, calibration and post-processing, see Olstad et al.
2  A descriptive analysis (mean and one standard deviation) was calculated for all start performance variables, time gaps and phases. The different time gaps and phases were normalized to the duration and distance of the active underwater sequence. A Shapiro-Wilk test was used to assess the normality of the distribution for all variables. A oneway ANOVA with Bonferroni post-hoc correction was used to assess differences between the time gaps and phases for the 50, 100 and 200meter event. Significance level was p <. 05. All statistical analyses were conducted using the Statistical Package for Social Sciences (SPSS) version 24. 0 (IBM Corp, Armonk, NY, USA) and Microsoft Excel 2010 (Microsoft Corp, Redmond, WA, USA).
RNA was extracted from hairy root lines 24 hours upon the induction of NEROSs-3xHA, NEROSscTPmut-3xHA, or eGFP-3xHA (Supplementary Fig. 4b ). Then, RNA was sequenced using Illumina high throughput sequencing technology. Analysis of differentially expressed genes (DEG) identified 308 up-and 402 down-regulated genes for NEROSs compared to eGFP control lines, or 934 up-and 558 down-regulated genes for NEROSs compared to NEROSscTPmut (Supplementary Table  3  and 4 ). qRT-PCR on seven randomly selected (but with a similar trend in both comparisons) up-regulated and downregulated genes showed an appropriate correlation with the transcriptome data (Supplementary Fig. 5a, 5b , and Supplementary Table  5 ). When NEROSs was compared to the eGFP control, gene ontology (GO) term analysis highlighted an overrepresentation of downregulated genes involved in "protein folding or unfolding under stress conditions", as well as "response to hydrogen peroxide and ROS" (Fig.
In silico sequence analysis. Using Meloidogyne INRAE Blast (https://meloidogyne. inrae. fr/), we found the closest homolog of Mi-4D01 (Accession number AF531162)  5  to be Mjav1s02606g024064 (M. Javanica Scaff2606g024064), here referred as Mj-NEROSs. SignalP 5. 0 was used to predict signal peptides. For all the experiments, LOCALIZER  63  was used to predict the protein localization and chloroplast localization signal. Motif scan and NCBI Conserved Domain Search were used to predict motifs and domains in the proteins. Plasmid and strain construction. All plasmids used in this study are listed in Supplementary Table  2  and constructed as indicated in Supplementary Table  2. Standard molecular cloning methods were used, and DNA assembly was performed using Gateway cloning, Gibson assembly or site directed mutagenesis.
After that, they were fixed and destained in a destaining solution (absolute ethyl alcohol: acetic acid, 3:1 v/v) for 24 h followed by 1 h 70 % ethanol, 1 h 50 % ethanol 1 h 10 % NaOH at 37°C. Transparent leaf segments were washed three times in 150 mM K2HPO4 (pH 9. 6) and stained with 0. 02 % aniline blue (cat. no: B8563-500ML; Sigma) in 150 mM K2HPO4 (pH 9. 6). The pictures were taken using a wide field epifluorescent microscope (Nikon Instruments Inc. ). Images were acquired with DAPI filter (excitation at 387/11 nm and emission 447/60nm). Callose deposits were analyzed in fields of 8. 4 mm 2 using ImageJ 80 software and normalized to 1 mm 2. RNA-seq sample preparation. Seeds of tomato (S. lycopersicum cv Moneymaker) were surface sterilized in 70% (v/v) ethanol for 10 min and then rinsed three times 5 min with sterile water.
Genome and annotation files were assessed from EnsemblPlants (https://plants. ensembl. org/index. html)  93. RT-qPCR for confirmation of RNA-seq results. RNA was extracted using the RNeasy Plant Mini Kit (Qiagen), and cDNA was synthesized using Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Fisher Scientific). Primers were designed using QuantPrime  94. RT-qPCRs were performed with four biological replicates. The qPCR conditions consisted of initial denaturation at 95 °C for 10 min followed by 50 cycles of 95 °C for 25 s, 58 °C for 25 s, and 72 °C for 20 s (all primers were designed using QuantPrime  94  and are listed in the Supplementary Table  2. Expression data were normalized using three reference genes  95  and analyzed using REST2009  96. a NEROSs suppresses the electron transport rate (ETR) in N.
Acknowledgments The authors would like to thank  Evi Ceulemans  and  Alain Goossens  ( Goossens Lab ,  VIB Center for Plant Systems Biology, Ghent, Belgium)  for providing plasmids, strain, and protocol for Y2H and for helpful discussions. Maria Aparicio Chacon  and  Sofie Goormachtig  ( Goormachtig Lab ,  VIB Center for Plant Systems Biology, Ghent, Belgium)  for providing the plasmids used for localization studies and helpful discussions. Els Van Damme's Lab  ( Faculty of Bioscience Engineering Ghent University, Ghent, Belgium ) provided plasmids for markers for localization. Jovana Kaljevic  for valuable comments during research design, reading the manuscript, and providing comments and suggestions. The  Special Research Fund of Ghent University  for the  BOF18/GOA/013  project. The visit to INRAE to perform the immunolocalization was funded by  FWO  (Grant for a short study visit abroad, Number  K215922N ) and  EMBO (Scientific Exchange  Grant Application, Number  9604 ).
Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time-and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset.
We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80. 59% and 88. 74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.
Several DL methods have been proposed focused on HN contouring  [19] , predominantly based on convolutional neural networks (CNNs). Many of them are based on 3D U-Net  [20]  and achieve good performance  [3, 10, 13, 21, 22]. Others are based on on a two  [23] [24] [25] [26]  or multi-step  [27]  workflow. The number of structures studied ranges from 4 to 28 OARs. Several factors still limit the clinical implementation of DL-based auto-segmentation  [6, 9, 28] , including a lack of standardization of contouring protocols  [10, 29] , trust among the users, and limited availability of large, labeled databases. Ideally, to train a robust DL segmentation network, an extensive database of patient CT images would need to be labeled, reviewed, and curated by several experts following the same delineation guidelines  [7, 30].
A second labeled database would allow for external validation. Nonetheless, this ideal scenario is highly unfeasible for most medical centers, as labeling large databases is incompatible with the daily clinical workload. On the contrary, clinical centers do have many partially labeled and unlabeled datasets  [7, 9]. In many cases, longitudinally collected data from the treatment is also available. These data can also be exploited with DL and, furthermore, used to optimize individual treatment for patients in an adaptive RT framework  [5]. Curating this data may be necessary to ensure homogeneous learning  [8, 13, 30, 31]. Aiming to exploit the available data in a medical center, Liu et al. [11]  proposed a multiview contrastive representative learning framework. They generated 2D CT slices as the input to a self-supervised 2D CNN outputting three 3D segmentations and using a contrastive loss to select the correlated predictions.
They trained their network with more than 3D 200 CTs from unique patients, but only 56 were labeled. They used the labeled data for initial training and then fine-tuned the network using the unlabeled images, evaluating their results in the labeled dataset, achieving a mean Dice score coefficient (DSC) of 0. 86 over 24 OARs. Although they made direct comparisons with other state-of-the-art methods, they lacked an external validation on an independent test set. Chi et al. [7]  also proposed a solution to train CNNs with limited labels based on a weakly supervised algorithm with a pseudo-contouring generation technique. They generated pseudo-labels with a Demons-based free-form deformable image registration algorithm to transfer contours from labeled data to new images and then trained a recursive ensemble organ segmentation model.
They segmented 5 OARs training their framework with only 29 labeled images, achieving DSCs between 0. 61 and 0. 88. AnatomyNet  [32]  was also trained with inconsistent data annotations with missing ground truth for some anatomical structures. As a solution, they employed a weighted loss function updating and balancing the weights according to the number of missing annotations, segmenting 9 OARs with a mean DSC of 0. 79. In this context, we propose a two-step solution to exploit the available partially labeled data in a medical center and train a robust DL-based auto-contouring framework for HN OAR segmentation. The method takes advantage of the longitudinal character of our database which allows to: (i) generate pseudo-contours for the missing labels; and (ii) improve segmentation accuracy by enriching the framework when incorporating a baseline image from each patient.