dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative assessment-Dynamic tools A case study comprising a dataset published by Hanly and Henson  [73]  was used to validate and quantitatively evaluate the dynamic tools. The experimental setup consists of a bioreactor operating in batch mode with a co-culture of E. coli and S. cerevisiae designed to study the efficient aerobic consumption of glucose/xylose mixtures. Each microbe utilizes a specific substrate. S. cerevisiae only consumes glucose, whereas the engineered E. coli strain ZSC113 only consumes xylose. This was done to prevent diauxic growth shown in monoculture for S. cerevisiae. The individual biomasses of the two microbial species as well as the concentration dynamics of the substrates glucose and xylose, were studied. Moreover, ethanol concentration dynamics were also measured because S.
cerevisiae cannot be modeled by simple optimization as done by FBA. dFBAlab predicted the magnitude and kinetic characteristics most accurately, while μbialSim and MMODES gave an underestimation for the ethanol concentrations and reflected linear profiles (Fig  6C ). Overall, DFBAlab achieved the best predictions for glucose, xylose, and ethanol kinetics based on the experimental data with R 2 values of 0. 902, 0. 978, and 0. 440, respectively (for all R 2 values, see S1 Data). ). From the qualitative assessment of all the spatiotemporal tools, none of the tools received a perfect score. Many tools were highly rated in many categories, while several were also deficient in many categories (see  Fig 7)  and S3 Fig). For example, COMETS, BacArena, CROMICS received excellent scores for software availability and being open-source because these tools and their source codes are readily available via a GitHub repository or a maintained website, whereas MatNet received an inadequate score because neither the tool nor its source code can be easily accessed online.
When evaluating the software maintenance, user support, and ease of installation of the software, two tools stood out as excellent: COM-ETS and BacArena. In contrast, many of the tools, including IndiMeSH, MiMoSA, ACBM, and CROMICS were merely rated as being satisfactory to poor for software maintenance and user support (Fig  7 ). However, except for MiMoSA, they were regarded as good for simple installation because of the accompanying instructions within the software download. Most of the tools received a good or excellent score for some features. For instance, for reproducibility, scalability, and visualization of results, all tools scored good to excellent, except ACBM and MatNet (for reproducibility), MiMoSA and MatNet (for scalability), and MiMoSA and MatNet (for visualization of results) (  Fig 7).
Quantitative assessment-Spatiotemporal tools A case study comprising a dataset published by Harcombe et al. [33]  was used to validate and quantitatively evaluate the spatiotemporal tools. The experimental setup consists of a twomember consortium of two mutant strains of E. coli K-12 and S. enterica LT2, designed to study the syntrophy between the two strains. E. coli K-12 is deficient in methionine production, so it relies on methionine production from S. enterica LT2. Conversely, S. enterica LT2 relies on the secretion of acetate by E. coli K-12 because S. enterica LT2 cannot uptake lactose under microaerobic conditions. This experiment created the optimal condition to observe a mutualistic relationship where neither species can grow without the other being present. The two-species consortium was grown as a lawn on a Petri dish.
Consistent with what was observed experimentally  [33] , the simulations using COMETS, IndiMeSH, and CROMICS showed similar degrees of decreased growth as they were initiated further apart (see Zenodo repository: https://doi. org/10. 5281/zenodo. 8074832 for output data for all the tools). However, the BacArena tool could not capture any variation in spatiotemporal growth as observed during the experiment.
In this study, the assessment was based on the modeling of two-species co-cultures, and therefore, the results could have varied when the tools were applied to larger communities, or to alternative systems. In addition, the limited availability of experimental data affected the selection of case studies, which might have an effect on the assessment of tools. By having more publicly available datasets, GEM modeling tools would be able to be more robustly evaluated on a quantitative level in terms of their scalability and numerical stability. In general, the performance of these tools would be enhanced if the quality of the GEMs or model architecture was improved, and more constraints were applied to fit the experimental studies or to compute fluxes. However, we ran the tools without augmenting them and following the reported standards used to avoid an unbiased evaluation.
The quantitative evaluations should be taken with a degree of awareness about their generality and projection to other types of systems. Furthermore, the quantitative evaluations here performed should be carefully considered as indications of tool performance rather than absolute measures of performance. They represent the behavior of the tools when confronted with specific datasets with defined biological characteristics. The syntrophic relationship established in the system used to test the static tools is different from the alleviation of toxicity observed in the dataset used for the dynamic tools. We cannot rule out that tool performance is affected by the nature of the interactions. The current status of the array of static, dynamic, and spatiotemporal tools for GEM modeling microbial consortia presents a substantial barrier to entry for researchers unfamiliar with specific requirements and idiosyncrasies that may arise within CBM frameworks.
Dynamic tools should allow the possibility of including inhibitory effects to better predict community behavior. Tools oriented for modeling small and big communities should incorporate sufficient series of examples featuring a range of application scenarios from small to big communities. Tools could incorporate case studies with GEMs of non-model organisms since many case studies are typically proven on communities of several E. coli species that use the same GEM. Other tools are meant to work with models retrieved from a specific database and defined namespace (e. g. , AGORA  [87] ). The applicability of these tools to non-model organisms is critical for designing and optimizing synthetic communities. In the past decades, many synthetic communities have been established that do not depend on model organisms and have shown a high potential for producing commodity chemicals  [4, [88] [89] [90] [91].
Below, we describe the specific methods applied to run every tool. The links to access the tools repository can be found in S1, S5 and S9 Tables. OptCom. OptCom was run using the OptCom function found within the MICOM module called 'micom. optcom' instead of using the code from the original publication. Therefore, we first installed MICOM 0. 32. 2 using pip upon installation of Python 3. 7 and COBRApy 0. 24. 0. 'OSQP' was installed by default with MICOM and used as solver. The community model was created using the 'Community' function. The function requires a 'taxonomy' table as an input parameter, which contains information about the species, models, and species relative abundance (biomass fraction). In addition, we included the mass as the input parameter (0. 22 g). OptCom function was run with the generated community model as input, selecting 'original' as the strategy parameter and setting a min_growth of 0.
We used the same community model without adding the extra reactions described in the latter publication. The species biomass reactions were constrained to the relative abundance multiplied by the growth rate (g h -1 ), and the same as the community growth rate (0. 021 h -1 ). The solution space was computed using the 'sample' function in the flux_analysis submodule found in COBRApy. The results shown here are the average and standard deviation based on 10000 samples generated under the specified condition. SteadyCom. MATLAB 2022b was installed using an academic license, and COBRA Toolbox version 3. 33 was installed following the installation instructions specified in the GitHub repository of COBRA Toolbox. SteadyCom was run using the 'SteadyCom' subroutine in COBRA Toolbox and 'GLPK', the LP solver installed when installing COBRA Toolbox.
The community model was built using 'createMultipleSpeciesModel. ' The names and IDs for metabolites and exchange reactions in the shared compartment of the community [u] were added into the community model as 'infoCom' and 'indCom' fields using 'getMultiSpeciesMo-delId. ' The bounds of the reactions defined in the [u] compartment (e. g. , 'autoIEX_MG[u]tr, 'EX_MG[u]') were imported from the Excel file 'Bounds_steadycom. xlsx'. SteadyCom was run using the built community model and the following options as input parameters: GRguess = 0. 5; algorithm = 1; feasCrit = 1 and BMweight = 0. 2. The community growth rate was maximized, and fluxes were computed. Microbiome Modelling Toolbox (MMT). MATLAB 2022b was installed using an academic license, and COBRA Toolbox version 3. 33 was installed following the installation instructions specified in the GitHub repository of COBRA Toolbox.
Evaluation of the dynamic tools Similar to what was done by Hanly and coworkers  [73] , S. cerevisiae S288C (iND750) [103] and E. coli K-12 substr. MG1655's (iJR904) [104] GEMs, acquired from the BiGG database [105], were used to perform the model simulations with DyMMM, DFBAlab, MMODES, and μbialSim. Furthermore, the E. coli model (iJR904) was modified by constraining flux bounds for glucose exchange and glucose kinase at zero to mathematically reflect the associated gene deletions. The simulations were based on an aerobic xylose co-culture of S. cerevisiae and the engineered E. coli strain ZSC113 fermentation experiment. In this experiment, glucose and xylose concentrations are expected to decrease over time, while the ethanol concentration is expected to increase. Simultaneously, S. cerevisiae and E.
coli biomass concentrations are expected to increase over time. For all simulations, any constraints and parameters given in the original tools were modified according to the experimental values from the respective dataset from Hanly and coworkers  [73]. Please see the S5, S6, S7 and S8 Tables for specific inputs as well as summary of boundary conditions, constraints, and parameters for all GEMS for the dynamic modeling approaches. The simulated growth and metabolite concentration curves of E. coli and S. cerevisiae, as well as from glucose, xylose, and ethanol were compared to the experimental measurements. The coefficient of determination, also known as R-squared (R 2 ) was calculated to quantitatively estimate the quality of the model fits and performance with the experimental data as in the work of Montgomery [106] for the dynamic and spatiotemporal cases.
dyMMM. An implementation for this method is provided by the developers on sourceforge. net: https://sourceforge. net/p/dymmm/wiki/Home/. This method requires a working installation of COBRA Toolbox [107] version 3. 33 was used in this study with MATLAB 2022b. The provided formulation allows users to add initial concentrations of limiting or important substrates as well as products of interest. There is also a field to add initial biomass concentrations of microbes. We followed as instructed in the source code by the developers to add the proper inputs regarding initial concentration values of substrates and microbial biomasses, as well as GEMs, where needed. These inputs we added as presented in the supplementary table (S6, S7 and S8 Tables). DFBAlab. An implementation for this method is provided by the developers via the website https://yoric.
mit. edu/software. This method requires a working installation of IBM CPLEX (version 20. 1. 0 was used in this study with MATLAB 2022b) or Gurobi (we used Gurobi version 10. 0). A similar example is supplied in the tutorial to the user as the case study used in this work. Although the original formulation of this method uses values directly from the literature  [62] , in the implementation, the values are not exactly as reported for our chosen case study. Therefore, the initial concentrations of glucose, xylose, and microbial biomasses were adapted S7 and S8 Tables), and the time span of the simulation was modified from 10 to 14 hours. μbialSim. An implementation for this method is supported via the website: https://github. com/fcentler/microbialSim. This method requires a working installation of COBRA Toolbox (version 3.
33 was used in this study with MATLAB 2022b) or CellNetAnalyzer (version 2022. 1 was used in this study). The provided example 2, based on a batch-culture growth of a binary syntrophic community, served as a starting point for employing our dynamic case study conditions. The reactor was defined according to initial substrate concentrations (see S8 Table ). The GEMs (iJR904 and iND750) were loaded and adapted according to the COBRA Toolbox protocol within μbialSim. In addition, reaction indices for the non-growth associate maintenance (NGAM) and biomass reaction were provided. Exchange reactions were defined that link compounds in the reactor to cellular uptake and secretion. The models were parameterized according to the kinetic parameters provided in the literature (S7 and S8 Tables). The simulation duration and time step size chosen were 14 and 0.
coli, so that as cells grew, they produced appropriate amino acid levels. A summary of boundary conditions, constraints, and parameters for all GEMS for spatiotemportal methods can be found in S10 and S11 Tables). COMETS. The in silico experiment was set up as reported in the work of Harcombe et al. [33]  (for more detail, see experimental procedures in the original work). The developers provide an implementation for this method via the website: https://www. runcomets. org/. This method requires a working installation of Java and Gurobi (we used Java 20 in this study as well as Gurobi version 10. 0). The Python version of the COMETS Toolbox (we used Python version 3. 9 in this study as well as COMETS v0. 4. 1) was installed using the Anaconda distribution. BacArena. The developers provide a tutorial to help implement this method via https:// bacarena.
github. io/. This method requires a working installation of R and Sybil (we used R version 4. 2. 2 in this study as well as Sybil version 2. 2. 0). Both GEMs of E. coli (iJO1366) and S. enterica (STM v1 multistrain) were retrieved from the BiGG database [105]. The S. enterica model was modified to ensure methionine production using the method of Harcombe et al. [33]. The biomass reaction was updated to include the production of 0. 5 mmol gDW -1 of excreted extracellular methionine, which was balanced by an equal amount of intracellular methionine consumed. The methionine transporter (METtex, METabcpp) were set to export only (upper bounds set to 0). The E. coli model was modified to block flux through the corresponding reaction, cystathionine γ-synthase (CYSTL). To model metabolic exchanges between the microbes and compare the results of BacArena with the other methods, we performed the simulations with our method using a setup similar to COMETS  [33].
This method requires a working installation of MATLAB (we used MATLAB version 2022b in this study). The in silico experiment was performed to simulate microbial life on a two-dimensional surface in IndiMeSH, a rectangular, saturated pore network (coordination number: 4) was created with the same topology as in the COM-ETS simulations (for details, see S1(A) Fig in the supplementary material of the original paper). Each pore has dimensions of 500x250x70 microns (LxWxH), as specified in the paper. Furthermore, we followed the setup as reported in the work of Borer et al. [38]  (for details, see the Methods section in the original paper). CROMICS. The developers provide an implementation for this method via a GitHub repository: https://github. com/EPFL-LCSB/cromics. This method requires a working installation of MATLAB and CPLEX solver (version 20.
Acknowledgments We thank  Dr. William R. Harcombe  and  Dr. Jeremy Chacon  from the  Department of Biological Sciences at the University of Minnesota  for assistance with the COMETS Toolbox for spatiotemporal modeling and interpretation of some experimental and model results. We thank  Dr. Costas D. Maranas  and  Dr. Mohammad M. Islam  from the  Pennsylvania State University Department of Chemical Engineering  for providing the d-OptCom tool and making suggestions for how to use the software. We thank  Dr. Louca Stilianos  from the  Institute of Ecology and Evolution at the University of Oregon  for correspondence and assistance employing the Microbial Community Modeler (MCM) tool. We thank  Dr. Christian Diener  from the  Gibbons Laboratory at the Institute of Systems Biology  for assistance with MICOM.
⇒ Selected parameters from telemonitored data gathered through patient engagement complement those in the classical electronic clinical research file databases based on medical records. ⇒ The tested domomedicine platform system-based intervention is not compared with standard care within a randomised trial, and the planned study sample is limited to 67 patients. on October 3, 2023 by guest. Protected by copyright. Open access death, with only 6% survivors at 5 years. 1 2  PDAC is most common in men aged 40-85 years. 4] [5]  In a retrospective pooled analysis, the median overall survival (OS) of patients with locally advanced PDAC was 24. 2 months for those receiving FOLFIRINOX vs 6-13 months for those given gemcitabine. 4 The improved survival on mFOLFIRINOX compared with gemcitabine comes at the expense of higher drug-related toxicity, with grades 3-4 adverse events (AEs) affecting up 75% of patients.
17 Disruption of RAR, as measured with I<O<97. 5%, also proved an early indicator of chemotherapy toxicity, which has been significantly associated with lower survival. 18 19 he integration of circadian rhythm data into oncological practice and their combination with current health status indicators in non-hospitalised cancer patients is part of the domomedicine concept, as proposed by the Academy of Technologies of France. 20 n the context of the European project inCASA (FP7-ICT), Lévi et al participated in the conception and development of the first fixed internet e-health platform allowing the distant monitoring of RAR, body weight and self-evaluated symptoms (19 items of an electronic version of the core MD Anderson Symptoms Inventory questionnaire (MDASI)). These three parameters were measured daily for 30-180 days in 31 patients with metastatic or advanced digestive cancer, with individual compliance rates of 60%-90%.
The data collected were teletransmitted daily by the patients themselves to a server via the internet, using a dedicated platform installed at home (domomedicine). Multidimensional analyses revealed that RAR disruption, jointly with symptoms and body weight loss, predicted emergency hospitalisation due to drug-related toxicity 3 days in advance, with an accuracy of 94%. 21  A recent analysis demonstrated that an I<O cut-off of 96% was more discriminating, with RAR displaying the most prominent weight in the model (unpublished data). In a further study by our group, 11 patients with colorectal cancer or PDAC received the chronomodulated administration of FOLFIRINOX at home, using a programmable-in-time pump. Remote follow-up of these patients using the inCASA platform demonstrated excellent tolerance of this protocol, with few alterations to their daily life, over the 26 treatment cycles administered.
22  However, the inCASA platform was fixed and wire-connected, a single circadian rhythm was measured, and parameters and dynamic changes were not computed and visualised in real time. The PiCADo mobile domomedecine platform system was then co-developed by our team, to respond to these limitations. It consists of a thoracic sensor that measures the number of accelerations per minute, three-dimensional (3D) orientation and chest surface temperature every minute  23  and teletransmits these measurements every 10 min via Bluetooth Low Energy (BLE) to a smartphone-like gateway. The infrared thermal probe measures chest surface temperature, whose rhythm disruption also represents an early marker of poor patient outcomes. 24   25  The gateway Open access also receives body weight measurements taken from BLE-connected scales and teletransmits all sensor data via GPRS to an approved health data hub up to every 6 hours.
The study protocol has been designed in order for the results to be reported according to Standard Protocol Items: Recommendations for Interventional Trials guidelines 2013. It has been registered as www. clinicaltrials. gov NCT04263948 (registered on 11 February 2020) and under registration number ID RCB-2019-A00566-51. The first participant was recruited on 8 June 2021. Study recruitment completion is expected by December 2023.
Patient and public involvement The development of the research question and outcome measures in MultiDom was based on prior extensive experience of individual cancer patients' evaluations throughout the development of earlier versions of the current domomedicine platform. Patients' evaluations and recommendations were gathered through face-to-face interviews by nurses, psychologists and physicians. Anonymised questionnaires regarding technology (Service User Technology Acceptability Questionnaire, SUTAQ) and participation in research experience (Research Participation Questionnaire, RRPQ) have been administered to 68 patients with cancer in three studies  20 21 23 27  and are also collected in MultiDom. Although patients are not involved in recruitment to the study, their engagement is essential since they are providing the daily multidimensional physiological and symptoms data that serve to trigger early warning signals aimed at proactive interventions.
PiCADo mobile platform The PiCADo platform system involves the measurement of multidimensional data from sensors which are transmitted via BLE to a mobile data collection and teletransmission device the size of a smartphone (EeleoCare; Eeleo, Angers, France). The measurements are then teletransmitted via a secure web interface (GPRS) to the Clinikali Platform (Altran, Cap Gemini), which is harboured within the National Health Data Hub. Raw data, automatically computed parameters and their graphical displays are visualised, in near-real time by the study investigators through personalised access to the MultiDom website (figure  1 ). The participants are asked to continuously wear specially designed medical underwear (Thuasne, St Etienne, France) containing a modified telecommunicating chest surface sensor (Move3; Movisens, Karlsruhe, Germany).
Following activation, the sensor automatically measures activity and 3D orientation via an accelerometer, and body temperature via an infrared thermal probe, every min. Medical grade sensor-containing T-shirts and bras have been designed to minimise any skin sensitivity or discomfort, as they are made of cloth material for patients with severe burns. The data are automatically transmitted to the data collection device (EeleoCare) via BLE every 5 min. The patients are asked to weigh themselves daily on a telecommunicating balance (A&D-321-PBT-C) and to selfrate 23 electronic patient-reported outcomes (e-PROs) using a tablet for 1 week before (baseline) and 6 weeks after the first mFOLFIRINOX course. Completion of these questionnaires takes 3-8 min daily. Additional e-questionnaires will also be presented to the patients for self-assessment of their morningnesseveningness orientation (Horne-Ostberg) at inclusion, and their overall sleep quality (Pittsburgh) both at inclusion and at study completion.