dataesr/scientific-paragraphs-categorization huggingface dataset:
In that case, non-parametric methods can be helpful. Villa-Vialaneix et al. (2012)  showed that metamodelling methods based on machine learning have good results for medium and large data sets. This is particularly true for Random Forests  (Breiman, 2001)  which provide the best trade-off between speed and accuracy. Moreover, non-parametric methods can tolerate heterogeneous data sets. This is crucial as FLORSYS with its numerous inputs precludes building a suitable experimental design as the one needed for the present approach. The global emulation of FLORSYS will be a necessary step to make the model accessible for farmers and crop advisors, particularly for a use in participatory workshops  (Colas et al. , 2020). Indeed, none of the avenues proposed in Table  6  will be fast enough or compatible with computers used in such as situation.
Acknowledgements The present work was funded by INRAE (EA and MIA divisions), the French project CoSAC (ANR-14-CE18-0007) and the Burgundy Region. The cropping-system trials were conducted at the INRAE experimental station of Dijon-Époisses, and the weed observations were carried out by Dominique Meunier and colleagues (INRAE Agroécologie Dijon). The paper was greatly improved thanks to the comments of an anonymous reviewer on an earlier version of the manuscript. Table  1 : Compilation of different sensitivity analysis methods for independent variables depending on complex model's proprieties. From  (Tenenhaus, 1998; Harrington et al. , 2000; Bizouard, 2012; Faivre et al. , 2013; Gauchi et al. ,.
Study Design This was a 12-week, prospective, parallel-group, randomized controlled trial. Individuals were recruited from consultations of the Physiology Department of the University Hospital of Montpellier (France) and from the general population with media advertisements. After a screening period of 12 months, interested patients were contacted by email or phone and were registered on the Aviitam health platform. They were scheduled for half-day baseline assessments. All participants provided written informed consent. The study was conducted in accordance with the CONSORT (Consolidated Standards of Reporting Trials) ethical guidelines and the CONSORT of Electronic and mHealth Applications and Online Telehealth checklist  [26]. The study was approved by the ethics committee (CPP Nord-Ouest IV, France; ClinicalTrials.
gov identifier: NCT03396666). Patients were admitted to the Physiology Department of the University Hospital of Montpellier (France) between January 2018 and November 2018. Baseline assessments included physical examination, bioimpedance, blood test, effort calorimetry, 6-minute walk test (6MWT), and self-questionnaires. Once baseline assessments were completed, participants were randomized to either a 12-week TR program or usual care. The randomization sequence was computer-generated using random blocks in an order unknown to investigators. The list was established by a statistician and was only accessible by the personnel in charge of randomization. Although participants could not be blinded to their treatment, both programs were presented as active interventions. All tests and evaluations of the study were performed at the same place for each group under the same conditions and with the same devices.
Intervention and Control Groups Patients from both groups had a specialized medical consultation with cardiopulmonary exercise testing and prescription of an adapted PA program at an intensity that elicited maximal lipid oxidation (called LIPOXmax). The patients in the usual care group (UCG) were advised to carry out their sessions independently, focusing on endurance PA sessions such as brisk walking, cycling, or swimming. Moreover, these patients received a booklet with different exercises and tips on PA and nutrition management. All patients were registered on the Aviitam website before and during the trial. Aviitam is a highly secure health record that allows the centralization, protection, and sharing of medical data with doctors. No restrictive diet was prescribed in either group. The TR program is a multicomponent intervention available on smartphones (Figure  1 ) and the website (Figure  2 ).
Patients received a package containing a smartphone (Archos with Android operating system) on which the TR Telemouv app was installed. A pedometer (Care Trackfit) and a heart rate monitor (Polar H7) were connected to the smartphone via Bluetooth. The patients received secure access codes for the app and website. They were trained in the use of the program and connected objects by a technician and received an instruction booklet to guide their first steps in the TR program. Telemouv solution contains 3 components: PA, nutritional, and educational programs. After 1 week, a PA teacher went to the patients' homes to install a connected bicycle ergometer (Care Fitness) and performed the first supervised session. The exercise training program included endurance sessions, muscle reinforcement, and posture and balance exercises.
Bioelectrical Impedance Analyses Participants' body composition was assessed by bioimpedance analysis with a 6 terminal impedance plethysmograph (Biacorpus RX4000 software,  BodyComp 8. 4). This device measures the total resistance of the body to an alternating electric current of 50 kHz  [27, 28]. Body FM and FFM were calculated for each segment of the body according to the manufacturer's database-derived disclosed equations and total water with published equations using the classical cylindrical model and Hanai mixture theory  [29]. FM, FFM, and muscular mass were expressed in kilograms and as a percentage of total body mass. Muscle mass index was calculated as muscular mass/height² and expressed in kg/m².
Actigraphy Participants wore a GT3X accelerometer (ActiGraph) on the nondominant wrist, programmed to collect data from the vertical axis in 15-second epochs and initialized using a normal filter (AGNorm). Accelerometers were worn for 7 days during all waking hours and removed for sleeping and during water-based activities. The minimum wearing criteria was ≥4 days, with ≥8 hours of wearing time each day  [38]. In addition to the daily steps, daily sedentary time in minutes and daily time spent in moderate to very vigorous activity were extracted from actimetry according to the manufacturer's specifications using the Freedson cutoff and the software (Actilife) provided by the company.
In case of significant interaction effect, false discovery rate-adjusted post hoc tests were performed. Linear mixed effects assumptions were tested before each test. Per-group analysis of the effect of the intervention was performed in the TRG group using paired t tests. The effect size was also calculated using Cohen d. Spearman rank order or Pearson correlations, depending on the data distribution, were used to determine associations between continuous variables. The data were analyzed using R software (R 4. 0. 3; R Foundation for Statistical Computing) and plotted using Prism Software. Statistical significance was set at P<. 05.
Study Limitations One limitation of the study was the lack of sufficient objective monitoring of the intervention in the TRG. Patients experienced difficulties, mainly secondary to connectivity defects with Bluetooth, to use heart rate monitors, pedometers, and cycle ergometers. Therefore, too few data were collected to monitor the intervention correctly. However, our study was a pragmatic trial, and all analyses were intention-to-treat analyses, which means that the impact of these missing data on the results was limited. Nonetheless, monitoring the intervention would have allowed for a better understanding of the effects of the TRG in patients with obesity, particularly in terms of the patients' phenotype in response to the intervention. In addition, the information technology firm that codeveloped the TR solution with our team did not have the opportunity to implement push notifications and provide pertinent feedback to patients.
Modeling and Simulation Workflow A modeling and simulation workflow was established (Figure  1 ). Computed tomography (CT) data were used to develop a patient-specific model of the knee structures and associated implant. Segmentation and 3D reconstruction of the geometrical models of the femur, tibia, and implant were performed. Then, meshed models were generated. A two-step (initiation and propagation) crack simulation was applied. Finally, the obtained outcomes were analyzed and evaluated to investigate the age-related effects.
Patient Data and Finite Element Model Set-Up The patient data from the Knee Grand Challenge  [28]  was acquired and used in this present study for making 3D tibia bone modeling. Computed tomography (CT) scan data were acquired for a patient (male, 88 years old, 168 cm body height, 66. 7 kg body mass, and 23. 6 kg/m 2 Body Mass Index (BMI)) with a knee implant instrumented on his right knee side (Figure  2a ). Semi-automatic segmentation was applied on the CT scan data to segment different knee structures (femur and tibia bones) and TKR implant, using the 3D Slicer software. A thresholding process was performed to segment the bone tissues and the associated implant from surrounding tissues. A threshold value of 810 was used for cortical bone and implant segmentation and a threshold value of 560 was used for cancellous bone segmentation.
3D models of these structures were saved in STL format for further processing in numerical simulation. Tibia bone with and without the implant were considered in the present study. FEM simulations were performed using the Salome-Meca software (2019. 03 version). The solver used in this platform was code_aster, an open-source finite element solver developed by EDF R&D in France since 1989, and dedicated to thermo-mechanical analyses of structures. All models were meshed with linear tetrahedral elements (see Figure  2b ). Since the crack path was not known a priori, a finer mesh was generated around the crack tip during crack propagation, using automatically adaptive mesh refinement, in order to obtain a good accuracy. All bony solids were modeled using homogeneous, isotropic, and linearly elastic laws.
In this work, C = 10 -8 m cycle 1 (MPa √ m) n and n = 2. 8 were considered from the literature  [31] [32] [33]. The crack propagation was modeled using eXtended Finite Element Method (X-FEM), in combination with the automatic mesh adaptivity through the Salome-Meca software. The eXtended Finite Element Method (X-FEM) is becoming a popular method for modeling cracks in the linear elastic fracture mechanics (LEFM) approach  [18, 25]. In comparison to the classical finite element method where the crack needs to be meshed, the X-FEM method employed an analytical solution to represent the crack in the mesh without an explicit crack mesh. In simple terms, the method stands on enriching the finite element model with the addition of degrees of freedom, which are corresponded to the nodes of the elements that the crack pass-through.
Implementation In our experiment, we simulated each node by a process, where we implemented multi-process programming. In several tests, we created a different number of processes, each of them starting with the creation of a random number blockID ← random(), which is considered as the ID of the candidate block that the process is working on. After that, each process follows the execution of the proposed algorithm (Algorithm 1). Using five rounds and ten processes, the test took about 5 min, and we got the result shown in Figure  2. The execution at University of El Oued was defined in Python using a mainframe made up of 32 dual-processor nodes of 10 cores each. The intensive computing unit has a management node with the following specifications: Processor: Intel Xeon (R) E5-2660 v3 @ 2. 60 GHz x 20, memory: 64 GB of RAM, disk: 2 TB HDD, OS: RedHat Enterprise Linux Server 7.
Stable-isotope labeling experiments are widely used to investigate the topology and functioning of metabolic networks. Label incorporation into metabolites can be quantified using a broad range of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy methods, but in general, no single approach can completely cover isotopic space, even for small metabolites. The number of quantifiable isotopic species could be increased, and the coverage of isotopic space improved, by integrating measurements obtained by different methods; however, this approach has remained largely unexplored because no framework able to deal with partial, heterogeneous isotopic measurements has yet been developed. Here, we present a generic computational framework based on symbolic calculus that can integrate any isotopic dataset by connecting measurements to the chemical structure of the molecules.
As a test case, we apply this framework to isotopic analyses of amino acids, which are ubiquitous to life, central to many biological questions, and can be analyzed by a broad range of MS and NMR methods. We demonstrate how this integrative framework helps to i) clarify and improve the coverage of isotopic space, ii) evaluate the complementarity and redundancy of different techniques, iii) consolidate isotopic datasets, iv) design experiments, and v) guide future analytical developments. This framework, which can be applied to any labeled element, isotopic tracer, metabolite, and analytical platform, has been implemented in IsoSolve (available at https://github. com/MetaSys-LISBP/IsoSolve and https://pypi. org/project/IsoSolve), an open source software that can be readily integrated into data analysis pipelines.
Nevertheless, none of the available methods provides complete coverage of isotopic space, even for small metabolites. Integrating measurements from different approaches should expand the range of quantifiable isotopic species  7  , thus improving the coverage of isotopic space. This is exploited in  13  C-fluxomics studies, where different datasets are frequently integrated using isotopic models of metabolic networks to improve flux quantification  4, [15] [16]. H wever, a major drawback of model-based integrative approaches is their strong dependence on the assumptions and simplifications of the model (e. g. the topology of the metabolic network as defined in the model) and the fact that labeling has to be quantified in several metabolites. As an alternative approach, intuitive reasoning has proven useful in improving the coverage of isotopic space by defining relationships between isotopic measurements directly at the level of the molecule.
This has been demonstrated for the combination of two NMR experiments, ZQF-TOCSY and HSQC, which allowed absolute quantification of 4 of the 8 isotopomers of a block of three carbon atoms  10. A few relationships have also been established between specific MS and NMR datasets  [17] [18]  , but the heterogeneity of the isotopic information obtained by MS and NMR makes integrating measurements from these two platforms difficult. Overall, the lack of a generic integrative framework has meant that the potential expansion of isotopic coverage that could be achieved by combining independent MS(/MS) and/or NMR datasets has remained largely untapped. In this article, we present a computational framework that can be used to integrate any type of isotopic data. We demonstrate how this framework allows isotopic coverage to be clarified and improved, thereby consolidating isotopic measurements.
EXPERIMENTAL SECTION 13 C-labeled standard of amino acids. The reference material we used to evaluate the proposed methodology was a biologically produced sample containing  13  C-labeled amino acids with a controlled and predictable isotopic composition: the isotopic species of each amino acid are forced to all be present at the same concentration. The nature, production, and qualification of this standard sample have been described in detail previously  9, 19. Isotopic analyses. We analyzed the  13  C-labeled reference material by GC-MS  20  , and by HSQC  15  and ZQF-TOCSY  15  NMR experiments, as detailed in the corresponding publications. We also gathered additional (HNCA, HACO-DIPSY, LC-MS) datasets for this reference material from the literature  11, 19. Implementation of the computational framework.
The framework developed in this work was implemented as a Python 3 module named IsoSolve. This can be used both as a command-line tool and as a module imported into Python scripts. The intuitive data input is based on tab separated value (tsv) files. The first (mandatory) input file describes the relationships between the measurements and isotopomers as presented in the Results section. The formalism is universal and can be used for all existing and future types of measurement. The second (optional) input file contains the numerical values of the measurements and their associated standard deviations. If provided, these numerical data are used to consolidate the measurements by solving a non-linear least square problem. The symbolic formulas obtained can be verified by assigning randomly drawn values to isotopomers (and thus to the corresponding measurements) and comparing the randomly drawn and calculated values.
Explicit results and details of the calculation process can be consulted in a user-friendly HTML document and/or as python variables for later programming use. IsoSolve also generates isotopically-enriched InChIs for all isotopic species (https://github. com/MSI-Metabolomics-Standards-Initiative/inchiisotopologue-extension), facilitating its integration into standardized data analysis pipelines. IsoSolve is freely available under open source license (GPL v2) at https://pypi. org/project/IsoSolve. A Jupyter notebook (https://jupyter. org) is also provided at https://github. com/MetaSys-LISBP/IsoSolve_notebook as an introduction to programming applications of the software. This notebook contains the code used to perform all the calculations in this study and generate all the equations and Figures  3 4 5 6.
The rows of A are linearly dependent as the measurements are normalized to add up to 1. Eq 25 can be solved by first reducing A to its echelon form by Gauss-Jordan elimination: 𝐴 = ( 𝑎 11 ⋯ ⋯ ⋯ 𝑎 1𝑝 0 ⋱ ⋮ ⋮ ⋱ 𝑎 𝑟𝑟 ⋯ 𝑎 𝑟𝑝 ⋮ 0 ⋯ 0 ⋮ ⋮ 0 ⋯ ⋯ ⋯ 0 ) (26) The elements in blue can be non-zero while those in black are all 0. The elements on the main diagonal from a11 to arr are strictly nonzero. The number of linearly independent rows defines the rank of the matrix, r. Two situations can arise depending on the datasets considered:  If r < p, the system is under-determined, with p-r free isotopomers. Some of the isotopomers depend only on measurements (we call these defined isotopomers), while others also depend on free isotopomers. It can also happen that some isotopomers are defined by multiple sets of measurements.
This is referred to as measurement redundancy, of which eqs 21a,b and eqs 23a,b are examples;  If r = p, the system is just-or over-determined. All isotopomers can be defined uniquely, or in multiple ways in the case of measurement redundancy. The next step in solving eq 25 is to back-solve the echelon form from xr to x1. During echelon reduction and back-solving, the expressions obtained are simplified at each stage using the fact that the measurements from a given method add up to 1. This property was not included in the standard SymPy  21  module we used to manipulate symbolic expressions so we developed dedicated procedures to solve and simplify these expressions. These can be found in the IsoSolve source code. Once the vector x is obtained as a function of measurements and possibly free isotopomers, measurement redundancy is assessed by substituting isotopomers into the equations defining the measurements, e.
The equations in this problem are identical to those defining measurements, such as eqs 1-4. The numerical solution minimizes the cost function T(x) defined as the sum of squared residuals. Each residual   () is calculated as the difference between experimental measurement i (   ) and its value calculated from estimated isotopomers Obvious constraints of necessarily non-negative values that add up to 1 are applied to the solution. This NLS problem with inequality and equality constraint is solved using the NLSIC algorithm  23. A chi-square test is performed to determine if the fit is satisfactory (based on a 95 % confidence threshold). Discrepancies indicate inconsistencies between the different datasets. Finally, IsoSolve estimates the precision of the abundance of each isotopomer, cumomer and EMU by propagating measurement uncertainties.
The SDs of x are simply the square roots of the elements on the main diagonal of cov(x). For sake of brevity, the fact that x is constrained to sum to 1 has been omitted from the above description; this constraint is however taken into account in IsoSolve. Clarifying the isotopic coverage of alanine for individual and combined methods. Combining different analytical methods should improve the coverage of isotopic space. As a first step, we used this workflow to clarify the isotopic information provided by combining a broad range of (NMR and/or MS) methods. Based on the literature  10-11, 13, 19-20, 24-25  , we defined a list of eight datasets (D1-D8) that can be obtained for alanine (Figure  2 ). Even though it only contains three carbon atoms, no single method can completely cover alanine's isotopic space by itself.
We evaluated all 255 possible combinations of datasets. The combinations were evaluated based on the following metrics: number of individually quantifiable isotopomers and cumomers, number of EMUs for which all isotopologues can be quantified, number of redundant measurements, and information gainedfrom the proposed integrative framework (i. e. number of additional quantifiable isotopomers). The results are summarized in Figure  3. In most situations, integrating different datasets improves the coverage of isotopic space, though some combinations do not provide any new information (e. g. D2+D3+D4+D5, combination #127). Importantly, 34 % of the combinations (87/255) provide complete coverage of the isotopic space of alanine (i. e. quantify all its isotopomers, cumomers and EMUs), with different degrees of redundancy (from 0 to 10 redundant measurements).
This is only possible if the combined dataset includes both NMR and MS data, highlighting the complementarity of the two techniques. This analysis shows that all the isotopomers of alanine can be quantified with as few as three datasets. As well as improving isotopic coverage, this analysis may thus be used to guide experimental design by identifying the best combination of analytical methods and datasets to quantify a given set of isotopic species. As demonstrated here, the complementarity of any technique is readily evaluated, hence providing guidance for future analytical developments. Isotopic coverage of proteinogenic amino acids. Following the same approach, we used IsoSolve to clarify the current isotopic coverage of proteinogenic amino acids by determining the number of individually quantifiable isotopomers, cumomers and EMUs.
Four amino acids are lost during protein hydrolysis (cysteine, tryptophan, glutamine, and asparagine) and cannot be detected. Data integration was thus carried out for the remaining 16 proteinogenic amino acids. Figure  4  highlights the heterogeneity of isotopic coverage for the different amino acids. While complete isotopic coverage is achievable for amino acids containing up to three carbon atoms (glycine, serine and alanine), the coverage progressively decreases as the number of carbon atoms increases. Isotopomer coverage was 12-31% for C4-amino acids (aspartate, threonine), 6-12% for C5-amino acids (glutamate, methionine, proline, valine), 3-6% for C6-amino acids (arginine, histidine, leucine, isoleucine, lysine), and about 1% for C9-amino acids (phenylalanine and tyrosine). As expected, the coverages are higher for cumomers (100% for C2 and C3, 40-53% for C4, 23-42% for C5, 6-24% for C6, 2-3% for C9) and for EMUs (100% for C2 and C3, 27-40% for C4, 16-32% for C5, 5-21% for C6, 1-2% for C9) than for isotopomers.