dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS Christophe Arpin  warmly thanks  Maxime Lepetit  and  Matteo Bouvier  for their help in efficient coding. The authors wish to thank  Drs. John T Chang  (Kurd et al. , 2020) and  Chi Zhang  (Alghamdi et al. , 2021) for providing helpful information on their data and algorithm, respectively. We gratefully acknowledge support from the  Pôle Scientifique de Modélisation Numérique of the Ecole Normale Supérieure de Lyon , especially  Loïs Taulelle , and the  Institut Français de Bioinformatique  for the computing resources. We also thank the bioinformatic hub of the  Laboratory of Biology and Modeling of the Cell (LBMC) , especially  Laurent Modolo , for their teaching and help. We thank the  BioSyL Federation  and the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the  University of Lyon  for inspiring scientific events.
Flux gap filling and statistic tools A random forest model was calculated to gap-fill our EC dataset. Random forest is a supervised machine learning technique proposed by  Breiman (2001)  that can model a non-linear relationship with no assumption about the underlying distribution of the data population. This method has been shown to be particularly suited to gap-fill EC data  (Kim et al. , 2020; Cui et al. , 2021). Random forest builds multiple decision trees, each of which are based on a bootstrap aggregated data sample (i. e. bagging of the EC data) and a random subset of predictors (i. e. the selected environmental data). The random forest model was built with environmental predictors that have been identified in the literature to control CO2 fluxes in salt marshes and which were available during the gaps (PAR, air temperature, water level height and relative humidity) and with measurements recorded between 2019 and 2020.
Each random forest model was built from a trained bagging ensemble of 400 randomly generated decision trees  (Kim et al. , 2020)  with the "randomForest" package in the R software  (Liaw and Wiener, 2002). Each tree was trained from bagged samples including 70% of the initial dataset. The remaining 30% of the data were used to estimate the fit of each random forest model. The model used was then able to explain 88% of the variability in the test data. Daytime data were better explained than night-time ones (59% vs. 38%), with light being the main parameter of the model. Using a partial dependence analysis and an ondelette analysis, we concluded that the relationships and temporal dynamics modelled were sufficiently consistent to fill the gaps in our dataset. However, the model reduced the influence of certain variables on CO2 flux during high PAR levels (> 1000 µmol m -2 s -1 ).
Acknowledgements I would like to thank  Ifremer  (the  French research institute  for exploitation of the sea) for financing my PhD thesis (2020)(2021)(2022)(2023). We are grateful to our colleagues who contributed to the fieldwork carried out during this study. This work is a contribution to the  Jérémy Mayen 's PhD thesis and the  ANR-PAMPAS project  ( Agence Nationale de la Recherche « Evolution de l'identité patrimoniale des   marais des Pertuis Charentais en réponse à l'aléa de submersion marine » ,  ANR-18-CE32-0006 ). The proofreading of the manuscript and the correcting of the English content were carried out by  Sara Mullin  (PhD; freelance translator).
Escherichia coli example We now apply the tools we have developed to an example based on the model e coli core in the BiGG database  [37] , which corresponds to Escherichia coli str. K-12 substr. MG1655. The model has 95 reactions and 72 metabolites. We take all parameters for the FBA routine (7) from the COBRA Toolbox  [12, 13]. We augment the model with Michaelis-Menten kinetics for the reactions listed in Table  1. The values of K m and V max for each reaction were taken from Table  1  in  [38]. All optimizations were carried out in Python using CVXPy  [39]  and the solver Gurobi  [40]. All figures were made with Matplotlib  [41].
CFBA We first apply CFBA. We solve the alternative formulation described in Section 3. 2. 1, which is identical to  (9)  except in two respects. • The objective is -1 z, which corresponds to minimizing the sum of the concentrations of the metabolites in Table  1. • We add the constraint v b ≥ Ξ. v b is the flux of the biomass reaction, which is BIOMASS Ecoli core w GAM in the BiGG database. Here, instead of maximizing the biomass as in FBA, we constrain it to be above Ξ > 0. The SOCP for CFBA took 0. 08 seconds to solve. For comparison, the LP for FBA took 0. 007 seconds, roughly an order of magnitude less. Figure  1  shows the sensitivities of the optimal objective to V max and K m for three of the reaction in Table  1  for Ξ = 0. 1. Given primal and dual CFBA solutions, the 480 sensitivities are computed via  (11).
495 (GLCpts) and 17 (O2t), which depend on glucose and oxygen, indicating that an increase in K m for either reaction will significantly increase the amount of metabolite needed to keep the biomass reaction flux at Ξ. This indicates that at the optimal solution, Reactions   Table  1 : Michaelis-Menten parameters. The units of K m and V max are mM and mmol/g dw/h, and the values are from  [38]. The metabolites and reactions are listed along with their ID in the BiGG database  [37]. Menten function. The concentration of oxygen increases least rapidly because, as described above, a slight increase dramatically increases the reaction rate. These concentrations are consistent with reported ranges. When Ξ = 0. 5, the predicted concentration of citrate is 520 1. 17 mM, within the range of 1. 1 to 3.
CMCS analysis 580 We compute CMCSs of our example by solving  (18) , an MISOCP, and (  22 ), an MILP. In addition to the setup at the start of this section, we must also specify target sets, T v and T z , in the form of (15a) and (15f). Table  2  lists reaction 'knockouts' and the correspond- ing CMCSs found by (  18 ) and (  22 ), specified by their IDs in the BiGG database  [37]. The target set in each case consists of constraining the flux to be greater than one. Note that we only list the first CMCSs found by the solver, that one target set can have many CMCSs, and that a given 590 CMCS can be a solution for both  (18)  and  (22). Target CMCS from (18) CMCS from (  22  Table  2 : Target knockout reactions and the CMCSs found via  (18)  and  (22). Whereas an MCS contains only critical reactions, the CMCSs in Table  2  consist of reactions, metabolites, or combinations of both.
The double blind design will be maintained by a specific blinding of study treatments, which will be described in the study operations manual and site blinding plan to ensure an unbiased safety and efficacy evaluation. The double blind design will be maintained until the primary safety and secondary efficacy endpoints have been assessed (i. e. at Day 90). The study participants (Sponsor, investigators and site team members) will be unblinded after all Day 90 data have been collected. The study will permit concomitant SoC treatment of the sCABP including antibiotic and standard therapy in the ICU, in an add-on design. Stratification will be considered based on CABP severity criteria at inclusion: -shock requiring vasopressors, or respiratory failure requiring invasive mechanical ventilation, or both.
16. Chronic granulocytopenia, not thought to be due to sepsis, as evidenced by an absolute neutrophil count <500 per μL >21 days prior to onset of pneumonia symptoms. 17. Subjects who received stem cell therapy, or allogenic transplantation (organ or bone marrow transplant) within the past 6 months. 18. Subjects receiving treatment with a biological agent (e. g. antibodies, cells), immunotherapy or plasma exchange treatment within the last 8 weeks. 19. Subjects currently receiving, or having received another investigational medication within 90 days prior to start of the study (or 5 half-lives of the investigational compound, whichever is longer). EudraCT No. : 2015-002994-39  21. Subjects with a known liver function impairment associated with liver cirrhosis (Child Pugh C) or known oesophageal varices.
ENDPOINTS: Primary Safety Endpoints by Day 90 Safety measured throughout the study by the incidence of treatment emergent adverse events (TEAEs) judged related or not to study treatment, focussing on any adverse event of special interests (AESIs). Safety analyses will be performed based on the Safety Population. An independent Data Monitoring Committee (DMC) will review safety data on a regular basis and ad hoc if needed. This DMC will be composed of a Chairman, expert in stem cells and former Chair of the Safety Committee of the completed CELLULA phase I trial, at least two Intensivists and an Independent Statistician. Membership, roles, responsibilities and operating procedures for the DMC will be specified in a separate independent DMC charter. Subjects will be continuously monitored during and after treatment for: ! Frequency, duration, severity, seriousness, relatedness to study treatment, actions taken and outcome of adverse events (AEs), from time of signature of informed consent until Visit 11 (Day 90) or study discontinuation.
The number and percentage of subjects (in each treatment group) prematurely discontinuing study treatment due to a TEAE will be tabulated by SOC and PT. Safety laboratory data will be presented by absolute and changes from baseline values by visit. Shift tables for each assessed laboratory parameter will be presented to summarise the change from Low, Normal, High values at baseline to 'on treatment' visit (Day 2) and post-treatment visits (Days 4, 7, 14, 29 and 90). The number and percentage of subjects with laboratory, vital signs, ECG, or physical examination abnormalities at baseline, subsequent visits, or ET from the study will be tabulated by treatment group. The results of all laboratory test results, physical examination findings, ECGs, and vital signs will be presented in data listings.
Efficacy analyses Efficacy and biomarker analyses will be descriptive summaries in the safety population at the scheduled visits. The change from baseline in SOFA scores and all other secondary efficacy and biological variables will be summarised using descriptive statistics. All data, including derived values that are computed for use in summaries or analyses, will be listed. Baseline values will be defined as the last value obtained prior to first dosing and the value used in the baseline summary will also be used as the baseline value for purposes of computing change from baseline. Data might be treated or transformed, as appropriate (e. g. log-transformed or ranked). Details of any transformed data will be specified on the Statistical Analysis Plan (SAP). Summary tables will indicate the number of patients with complete data for each measurement, event, or outcome.
In general, no imputation of missing data will be made, and any exception on efficacy data would be detailed on the SAP. The following subgroup analyses will be performed but not limited to: severity score, bacterial load, initial adequate or inadequate antibiotic treatment, concomitant treatment, sCABP clinical response (cure or non-response i. e. failure) or indeterminate, antimicrobial resistance profile, previous vaccination, and presence or not of bacteraemia. A thorough review will be performed when data for the primary endpoint are available and before database lock and unblinding, by a blinded Adjudication Committee (AC) of experts to assess subject evaluability and agree on sCABP clinical response assessment and patient assignment process e. g. validation based on the dose, the bacterial susceptibility profile to administered antibiotics and duration of antibiotics, molecular identification data, etc.
In each stratum, patients will be randomised 1:1 to Cx611 or placebo. The Screening duration will be around 18 hours, treatment duration will be three days and the total study duration per subject will be two years. There will be a total of 12 visits per subject: Screening visit and on Days 1, 2, 3, 4, 5, 6, 7, 8-10, 14, 29 and 90. For all visits scheduled after hospital discharge, costs of travel to hospital (such as taxi) will be reimbursed by the Sponsor. The double blind design will be maintained until the primary safety and secondary efficacy endpoints have been assessed (i. e. at Day 90). The study participants (Sponsor, investigators and site team members) will be unblinded after all Day 90 data have been collected. to three phone call attempts will be done and if not successful, a contact will be established with the patient's General Practitioner (GP) or family doctor.
! both In each stratum, patients will be randomised 1:1 to Cx611 or placebo. During Screening, after confirmation of inclusion/exclusion criteria, all eligible patients will be randomised via IRT to one of the treatment arms. After performing the screening visit and verifying inclusion and exclusion criteria and subject´s eligibility, the investigator will place a screening and approval call to the physicians to be provided with a randomisation authorisation number. No patient will be randomised without this authorisation number. personnel will also be available for consultation in case of screening doubts and for medical advice of subject´s management. The will keep record of the conversations with investigators in a dedicated database and available for review on a monthly basis at least.
Infants should be followed until 3 months after to detect any complication or developmental impairment. The Investigator is encouraged to provide outcome information of the pregnancy of the female partner of a male subject, if this information is available to the Investigator and the female partner gives her permission. The statistical analysis will be performed by the Department of Biostatistics of using SAS ® Version 9. 3 or later. A summary of the analysis to be detailed in the Statistical Analysis Plan (SAP) is presented here. A complete SAP will be written and finalized before data base closure.
Subjects who experienced multiple adverse events within the same PT will be counted once for the summaries, using the worst severity. The number and percentage of subjects in each treatment group reporting at least 1 occurrence of a SAE for each unique SOC and PT will be tabulated. SAEs will also be tabulated by severity and by the relationship to study medication in treatment groups as assessed by the Investigator. Listings of patients with SAEs, Deaths and AEs leading to discontinuation and individual narrative summaries for these cases will be provided. The number and percentage of subjects (in each treatment group) prematurely discontinuing study treatment due to a TEAE will be tabulated by SOC and PT. Safety laboratory data will be presented by absolute and change from baseline values by visit.
Shift tables for each assessed laboratory parameter will be presented to summarise the change from Low, Normal, High values at baseline to 'on treatment' visit (Day 2) and post-treatment visits (Days 4, 7, 14, 29 and 90). The number and percentage of s ubjects with l aboratory, vital s ign, ECG, or physical examination abnormalities at baseline, subsequent visits, or ET from the study will be tabulated by treatment group. The results of all laboratory test results, physical examination findings, ECGs, and vital signs will be presented in data listings. All abnormalities will be assessed for being clinically significant or not as per investigator.
Vital Signs, Electrocardiogram, haematology, coagulation, blood chemistry and urinalysis All vital signs, haematology, coagulation, blood chemistry, urinalysis and ECG data will be listed by subject. All values outside the normal reference ranges will be flagged in this listing. In addition all subjects with clinically significant abnormal values will be flagged as well in this listing and also summarised in separate listings. The number of subjects with at least one clinically significant abnormal value will be summarised. For ECG data, qualitative results will also be listed by time windows. The significant abnormal values will be pre-defined in the SAP before the database closure.
Efficacy and Biomarkers Analyses The efficacy and biomarkers analyses will be descriptive summaries in the safety population at the scheduled visits. The change from baseline in SOFA scores and all other secondary efficacy and biological variables will be summarised using descriptive statistics. All data, including derived values that are computed for use in summaries or analyses, will be listed. Baseline values will be defined as the last value obtained prior to first dosing and the value used in the baseline summary will also be used as the baseline value for purposes of computing change from baseline. Data might be treated or transformed, as appropriate (e. g. , log-transformed or ranked). Details of any transformed data will be specified on the SAP. Summary t ables will indicate the number of patients with c omplete data for each measurement, event, or outcome.
In general, no imputation of missing data will be made, and any exception on efficacy data would be detailed on the SAP. The following subgroup analyses will be performed but not limited to: severity score, bacterial load, initial adequate or inadequate antibiotic treatment, concomitant treatment, sCABP clinical response (cure or non response i. e. failure) or indeterminate, antimicrobial resistance profile, previous vaccination, and presence or not of bacteraemia. A thorough review will be performed by the AC when data for the primary endpoint are available to agree on patient assignment process e. g. validation based on the dose, the bacterial susceptibility profile to administered antibiotics and duration of antibiotics, molecular identification data, etc. Membership, roles and responsibilities and operating procedures for the AC as well as the subject evaluability criteria to be followed will be specified in a separate AC charter.
Determination of Sample Size The results from this study are exploratory in nature, hence there is no hypothesis testing comparing outcomes between treatment arms. Confidence intervals will be constructed to assess between-group differences in treatment effect. As little is known at present about the outcome measures used in the study for stem cells, this study will provide the initial dataset necessary for determining endpoints and making a preliminary estimate of effect size for the design of future efficacy-finding studies of Cx611 for the add-on therapy of severe community-acquired bacterial pneumonia in patients requiring mechanical ventilation and/or vasopressors administration. The number of 180 patients in total (i. e. 90 patients per group) was deemed to be sufficient to fulfil the objectives of this exploratory study.
A legally authorised representative and/or relative is required only if patient is unconscious or incapable to understand the nature of the study. A witness is required only if the participant is unable to write (such as blind or illiterate). If the consent has been given by the legally authorised representative or relative, the patient should be informed about their participation in the study and should have the opportunity to confirm or revoke their consent once they regain consciousness. The consent includes information that data will be recorded, collected, processed and may be transferred to European Economic Area (EEA) or non-EEA countries in accordance with the The Informed Consent and Patient Information will be provided in the local language. A copy of the patient information sheet including the signed consent form will be provided to the patient or their legally authorised representative and/or relative.
Data Management Data management and handling will be conducted according to the study specific Data Management Plan in accordance with ICH guidelines and standard operating procedures (SOPs), which will be prepared and approved before the end of the experimental phase of the study. Data entry, validation, and data queries will be handled by the Data Management Team. The data will be subjected to validation according to SOPs in order to ensure accuracy in the collected CRF data. Before database closure reconciliation will be performed between the SAEs entered in the safety database and the study database. Any deviations, i. e. discrepancies and additions from the process defined in the Data Management Plan, will be described in a study specific Data Management Report.
Protocol Deviations Deviations to the study protocol will be documented in a Protocol Deviation Log and entered into the study-specific protocol violation database. The classification of patients into protocol violators will be made during a meeting before database lock. The classification will be mutually agreed between the Sponsor and before the database lock. Listings will indicate the allocation of patients by analysis set and the number of patients per analysis set will be recorded in the clinical study report. 13. 12 Amendment of Protocol Any change to a protocol must be considered to be an amendment if the documents have already been submitted to ECs/IRBs or Health Authorities. An amendment could therefore occur before or after the approval of these documents by ECs/IRBs or Health Authorities.
reported in the phase Ib/IIa clinical trial in rheumatoid arthritis (Cx611-0101, EudraCT Number: 2010-021602-37) when up to 4 million cells/kg were administered at an infusion rate of 4mL/min (62), nor in the phase I trial i n healthy volunteers injected with lipopolysaccharide, whose results are presented below (see Section 5. 8) (1). However, in P r o p e r t y o f T a k e d a : F o r.
6. 1 ! 1 Primary Objective ! Investigate the safety profile of two allogeneic Cx611 80 mL infusions administered through a central line within 3 days (on Days 1 and 3) at a dose of 160 million cells each (320 million cells total). To monitor any adverse event and potential immunological host responses against the administered cells during 90 days of followup after the first infusion. Explore the clinical efficacy of Cx611 in terms of a reduction of the duration of mechanical ventilation and/or need for vasopressors and/or improved survival, and/or clinical cure of the sCABP, and other efficacy-related endpoints (see section 10. 1. 3) S. A. U. Version: Final, Version 7 incorporating Amendment 5 Study Code: Cx611-0204 Date: 19 December 2019 EudraCT No. : 2015-002994-39 7 INVESTIGATIONAL PLAN 7.
-term follow-up: Study site personnel will give four follow-up phone calls to the patient, at Months 6 (Day 180 ±30 days), 12 (Day 365 ±30 days), 18 (Day 545 ±30 days) and 24 (Day 730 ±30 days), to assess any spontaneous report of a health event from him/her. Up P r o p e r t y o f T a k e d a : F o r S. A. U. Version: Final, Version 7 incorporating Amendment 5 Study Code: Cx611-0204 Date: 19 December 2019 EudraCT No. : 2015-002994-39 Page 39 of 130.
39  39 S. A. U. Version: Final, Version 7 incorporating Amendment 5 Study Code: Cx611-0204 Date: 19 December 2019 EudraCT No. : 2015-002994-Treatment period (Days 1 and 3) starts with the administration of first dose (Day 1) and ends with the second dose administration of Cx611/placebo (Day 3).  Clinical Response visit (Day 14 ± 2) is the time of the assessment of the clinical response at 14 ± 2 days after the first dose of Cx611/placebo. Clinical response will be also assessed at visits on Days 8-10 and 29 ± 2.  Primary Safety Endpoint visit (Day 90 ± 4) is performed at 90 ± 4 days after the first dose of Cx611/placebo.  Premature discontinuation can occur if the patient withdraws from the study before Day 90, and leads to ET visit.  Long-term safety follow-up: phone calls are performed at Month 6 ±30 days, Month 12 ±30 days, Month 18 ± 30 days and Month 24 ± 30 days after the first dose of Cx611/placebo.
days and Month 24 ± 30 days for long-term safety follow-up. If there is a premature discontinuation for any reason before Day 90, the patient will undergo an ET Visit. P r o p e r t y o f T a k e d a : F o r S. A. U. Version: Final, Version 7 incorporating Amendment 5 Study Code: Cx611-0204 Date: 19 December 2019 EudraCT No. : 2015-002994-39 Confidential Page 42 of 130.