dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Modifications The regulation network applied in Orth et al. [44]  was examined for refinement. A modification to formate metabolism was made based on experimental data. Formate has been measured in E. coli cultures in the presence of O 2. The pyruvate formate lyase (PFL) enzyme, which produces formate, is O 2 sensitive, but activity is possible when dissolved O 2 concentrations are low, as occurs when cells grow rapidly or in high density cell cultures  [14, 15, 45, 46]. In the regulation network, the PFL enzyme was disabled in the presence of O 2 by transcriptional regulators ArcA and FNR. Removing this regulation rule for formate metabolism resulted in a ∼10-fold increase in the number of total EFMs (Table  1 ) and a slightly different Pareto front, which predicted formate production at low O 2 availability (Figure  2 ), consistent with experimental data and previous EFM analyses  [9, 14, 15, 46].
The current standard for metabolic models is to link genes to reactions through Boolean associations  [50]. clingo[LP] is a very efficient tool for solving these Boolean constraints while still representing the syntax in a readable format; and thus, many models found on the BiGG database  [51] , could be analyzed with our tool using only a reasonable number of additional constraints. The computation time could be further improved via network reduction and using multi-thread computation routines. The ASP-based implementation with clingo[LP] does not currently use multi-threading, so computing EFMs on a server would have minimal benefit in terms of computing time. The method is compatible with network reduction techniques such as the enzyme subsets' (i. e. , groups of enzymes that operate together in fixed flux ratios at steady state) computation as described in  [1, 52] , although in this case, only the reduced reactions and metabolites should be used as the input metabolic network.
Answer Set Programming Answer Set Programming (ASP) is a declarative approach oriented toward knowledge processing with a logic programming approach. Problems are formulated according to first-order propositional logic in order to facilitate the problem modeling. A logic program in ASP is a finite set of rules of the form: To state that an atom should be present in the solution, the body is omitted. This is called a fact. Alternatively, to state integrity constraints on body atoms, the head atom is omitted. A typical ASP tool is composed of two parts: the grounder which handles predicate variables and the solver which finds stable sets of atoms satisfying the logic program. For a complete formal introduction to answer set programming, we refer the reader to  [53]. The software clingo from the University of Potsdam performs ASP grounding and solving.
These program rules and the metabolic networks are expressed in ASP using the predicates presented in Appendix E. The problem formulation is reminiscent of the k-shortest EFMs method. In the MILP problem, on top of these rules, the solver is given the task to minimize the sum of indicator variables, thus returning the shortest flux modes. In our method, such a minimization was not considered. Instead, clingo allows us to set heuristics to enumerate answer sets that are a subset minimal in regards to the indicator variables  [55]. This gives us flux solutions with subset minimal support or elementary flux modes. In summary, we are able to enumerate the EFMs of a given input metabolic network by translating the network and the rules presented above into a clingo[LP] logic program and by using clingo heuristics.
Supplementary Materials: The following are available online at http://www. mdpi. com/2227-9717/8/12/1649/s1, File S1: Pareto optimal pathways of the E. coli core, File S2: E. coli core Biomass modifications, File S3: Pareto optimal pathways of the E. coli core with the adjusted biomass, File S4: ASP programs, File S5: Additional Python code.
These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g. , BioPAX and SBML) and between tools supporting SBGN (e. g. , CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn. org. With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.
■ The support of colors and other annotations through extensions enables the storage of rendering information and biological annotations (e. g. , database identifiers). The definition of the model description language presented here does not specify how programs should communicate or read/write SBGN. We assume that for diagram editing software to communicate a model encoded in SBGN, the program will have to translate its internal data structures to and from SBGNML, use a suitable transmission medium and protocol, and to provide any further necessary infrastructure. However, these issues are outside the scope of this document. The software library libSBGN  (van Iersel et al. , 2012)  was developed for reading, writing, and manipulating SBGN maps stored in SBGNML format. A broad set of software tools support SBGNML, including modeling software CellDesigner  (Balaur et al.
, 2020) , SBGN editors Newt  (Sari et al. , 2015) , Krayon for SBGN 3  , and SBGN-ED  (Czauderna et al. , 2010). STON  (Touré et al. , 2016)  and ySBGN 4  provide conversion between SBNGML and GraphML/Neo4j, respectively. The software EscherConverter provides an SBGN viewer and a bidirectional converter for metabolic maps in JSON format and SBGNML  (King et al. , 2015). Numerous databases (Reactome  (Croft et al. , 2011) , Panther Pathways  (Mi et al. , 2017) , Pathways Commons  (Rodchenkov et al. , 2020) , PathWhiz  (Pon et al. , 2015) , Path2Models  (Büchel et al. , 2013) , MetaCrop  (Schreiber et al. , 2012)  and Atlas of Cancer Signaling Networks  (Kuperstein et al. , 2015) ) provide SBGNML export.
Document conventions We use Unified Modeling Language (UML) version 2. 0  (Dennis et al. 2015)  class diagram notation to define the constructs provided by this package. We first provide an overall view of the various data types and constructs along with their relationships, followed by a more local view of the constructs and their relationships in associated sections. In this section, we define the syntax and semantics of the Systems Biology Graphical Notation -Markup Language. We expound on the various data types and constructs defined in this package, then in Section 3 on page 24; we provide complete examples of using the constructs in sample SBGN models. Section 2. 3 Primitive data types.
C Acknowledgments Here we acknowledge those people and organizations that assisted in the development of version 0. 3 release of the SBGNML specification. We acknowledge contributors that attended workshops and forum meetings or, in some other way, provided input to previous revisions to this effort. Then, we acknowledge the bodies that provided financial support for the development of the standard. The following list includes members of the SBGN community that have contributed to the development of SBGNML in the past. Their efforts helped shape what SBGNML is today: ■ Mirit I. Aladjem.
The partitioned features are visualized using the defined shape color wheel according to the invariance classes that they belong to. Experimental results show that the proposed method can achieve not only appropriate partitioning but also accurate invariance class recognition for geometrical product specifications and verification. It performs well for both nominal models and real parts. In addition, it also shows good robustness for partitioning low-density data and noisy data. Therefore, it is believed that this research can help the exploration and standardization of partitioning operations spearheaded by the ISO/TC 213/AG 12 (Mathematical Support Group). The remainder of this paper is organized as follows: Section 2 summarizes the existing point cloud and mesh partitioning methods. Section 3 introduces the invariance classes defined in ISO GPS and the related partitioning methods.
Heart-rate and respiratory-rate variability Our semiautomated processing approach, previously developed using Matlab R2013a software (The MathWorks Inc. , Natick, MA, United States), was applied on the ECG and respiratory signals  (Al-Omar et al. , 2018). Each 6-h recording was automatically processed to extract all the 2-min stationary periods, which were considered to reflect the changes in autonomic state, according to previously proposed criteria  (Borgnat et al. , 2010). QRS complexes were then automatically detected as the maximum above a manually fixed threshold. The quality of each RR (cardiac-cycle length) series obtained was checked manually and corrected when necessary. Heart-rate variability. Heart-rate variability reflects the heart's adaptation to internal and external stimuli and is measured by the variation in RR intervals from the ECG signal.
Data analysis Data collection was performed over 10 s immediately before each stimulation (baseline) and continued over the next 60-s period. The following inhibitory cardiorespiratory responses to each stimulation were computed: (1) the percent decrease in HR; (2) the number of cardiac decelerations (defined as a decrease in HR of at least 30% for <5 s); (3) the number of bradycardias (defined as cardiac deceleration lasting at least 5 s; (4) the total duration of cardiac inhibition (including cardiac decelerations and bradycardias); (5) the number and total duration of apneas (defined as at least two missed breaths), and (6) the percent decrease in O 2 hemoglobin saturation.
18 µm. The images were preprocessed to subtract background and remove speckles with ImageJ. Following qualitative assessment of the number and morphology of microglial cells, a quantitative 3D-morphological analysis of microglial cells using the 3DMorph Automatic Analysis Software, a MATLAB-based script (MathWorks, Natick, MA, United States) was performed. Output data of this analysis included individual cell volume, total territorial volume, and branch length  (York et al. , 2018).
All analyses were performed with SPSS Statistics for Windows (version 25, Armonk, NY, United States). In addition, mixed models were used to evaluate the association between independent variables (recording-time point, temperature, and LPS injection) and each outcome variable (respiratory frequency, heart rate, and mean arterial pressure). Since multiple measurements were made in each lamb, a random effect on intercept and a first-order autoregressive structure of the residuals were specified in the covariance structure of the mixed models. Linear, quadratic, and cubic associations were studied for time and temperature, considering each day separately, and included in the multivariable models if significant. Models were obtained from PROC MIXED in SAS version 9. 3 (SAS Institute Inc. , Cary, NC, United States).
FUNDING PP received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 689260  ( Digi-NewB  project). ST received funding from the  Scottish Rite Charitable Foundation of Canada  and the  Canadian Institutes of Health Research. J-PP  received funding from the  Canada Research Chair in Neonatal Respiratory Physiology  and is a member of the  University of Sherbrooke Hospital Research Center.
The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons.
INTRODUCTION During the last decade, progress in phenotyping methods have given ground for the development of many high-throughput phenotyping (HTP) platforms  (Berger et al. , 2010; Tisne et al. , 2013; Cabrera Bosquet et al. , 2015; Vadez et al. , 2015) , established across the globe to support rapid screening of plant phenotypes. These platforms generate large-scale phenotypic datasets that are complex to handle, process, and interpret. An important aspect that contributes to the complexity of HTP data handling is the presence of exogenous effects, which primarily include systemgenerated noise and fluctuations in environmental conditions. This is particularly the case in HTP platforms characterizing phenotypes in open-environments or under non-controlled conditions like the LeasyScan, the PhenoField  (Barker et al.
, 2016) , or the Field scanalyzer  (Virlet et al. , 2017)  platforms. These effects can result in large heterogeneity besides erroneous and missing observations. Hence, the data treatment from outdoor platforms requires a systematic approach to deal with such artifacts and facilitate routine usage of HTP data  (Dupuy et al. , 2017; Namin et al. , 2018; Tello et al. , 2018). There are few existing automated procedures (pipelines) to process and analyze HTP data  (Klukas et al. , 2014). In addition, existing ones largely use image-based analysis of plant phenotypes  (Hartmann et al. , 2011; Artzet et al. , 2019) , and some are even platform or trait-specific  (Galkovskyi et al. , 2012; Faroq et al. , 2013; Hasan et al. , 2018). This paper presents an automated data analysis pipeline called SpaTemHTP that processes and analyzes large temporal HTP data taking into consideration the specificity of data generated on outdoor platforms.
The need to adjust phenotypic data for heterogeneity due to field variation is known since many years  (Gilmour et al. , 1997). So far, spatial adjustment was generally done by a sequential procedure of model fitting and diagnostics steps, which was difficult to implement in an automated way. More recently,  Rodríguez-Álvarez et al. (2016)  introduced the SpATS model, a twodimensional (2D) P-spline approach for mixed model spatial adjustment. Velazco et al. (2017)  demonstrated that the SpATS model could be successfully adapted for routine applications. Therefore, the SpATS model gave us the opportunity to perform spatial adjustment for genotype adjusted mean computation in an automated way on large HTP time series data. To the extent of our knowledge, the routine application of spatial adjustment to obtain genotype adjusted mean time series from outdoor platform HTP data has not yet been done.
Here, we used a change point analysis to identify the portion in the temporal dataset where the probability to capture genotypic variance was maximized. Therefore, the second important contribution of this study was the use of genotype adjusted means time series and heritability estimates (obtained after spatial correction) to enable the systematic identification of critical growth phase(s) having maximum genotypic variance. In the following sections, SpaTemHTP is illustrated by applying it on a wide range of scenarios varying in terms of species (chickpea, sorghum), phenotypic traits (leaf area, projected leaf area, plant height), and experiment replications. Using real-data analyses and simulations, we evaluated the different components of SpaTemHTP (outlier detection, missing value imputation, and spatial adjustment) to estimate their relative contribution in the quality of the temporal series of genotypic estimates.
Outlier Detection The first step in preprocessing was the detection of outliers, which are the extreme values that occurred mostly due to measurement errors. Outliers in the raw data were detected for each day using boxplots  (Sun and Genton, 2011)  of the phenotypic value distribution per day, i. e. , the distribution included all genotype and replicate values of a specific day. The 25% quantile (QR1), 75% quantile (QR3), and 50% interquantile range (IQR) were calculated, and the observations below QR1 -1. 5 * IQR or above QR3 + 1. 5 * IQR were considered outliers. Those outliers were replaced by missing values and then imputed in the next step along with the already existing missing values in the dataset.
Missing Value Imputation The second step in preprocessing was the imputation of missing values. According to  Huque et al. (2018) , the use of multiple imputations (MI) on longitudinal data can improve the accuracy of mixed model estimates. This can be due to the capacity of MI to borrow information on the past and future points of the time series to identify genotypes with similar growth pattern and fill the gap of the missing genotypes with similar observed values. We performed MI using the predictive mean matching (PMM) method from the R package "mice"  (Buuren and Groothuis-Oudshoorn, 2010). PMM is a method that was developed to reduce the bias by drawing real values sampled from the observed data  (Rubin and Schenker, 1986). Therefore, The duration of data collection, the minimum and maximum recorded temperatures (MinT, MaxT) in degree Celsius ( • C), and the average relative humidity (RH%) during the experiments (E) are shown for each crop (chickpea, sorghum) and experiment data, along with the minimum (Min), maximum (Max), and mean percentage of daily amount of missing data points.
For the spatially adjusted model, h 2 is calculated in terms of the effective dimension or the effective degrees of freedom associated to the genetic component in the SpATS model (ED g )  (Rodríguez-Álvarez et al. , 2016). The effective dimension of a model is computed as the trace of the hat matrix H, and Hg is the hat matrix for genotypes. If the number of genotypes = n g and the number of zero eigenvalues of H g = l, then h 2 = ED g /(n g -l). We calculated models with spatial adjustment using the R package "SpATS"  (Rodríguez-Álvarez et al. , 2016)  and the one without spatial adjustment in GenSTAT version 18  (VSN International, 2015). The combination of each of the abovementioned data processing and G-BLUE calculation options: outlier detection (yes/no), missing value imputation (yes/no), and spatial adjustment (yes/no), represents a total of eight strategies (S1-S8) to calculate G-BLUEs from raw data (Table  2 ).
Simulations As suggested by one of the reviewers, we performed simulations based on real data to evaluate the robustness of the methods to manage outliers and/or missing values in the G-BLUE computation. For that purpose, the SGE1 PH data was selected as the most "controlled" dataset, since it had the lowest percentage of missing values (0. 29%), a low percentage of outliers (1. 6%), and a high heritability (around 0. 7) showing the importance of the genetic component compared with other factors. The 15th day of the series was selected as the reference since it contained no missing values and a low number of outliers (1. 4%). The G-BLUEs of that day were calculated and kept as reference. Then some noise was introduced in the time series by adding extreme and/or missing values. We tested three scenarios: (a) addition of x percent of missing values; (b) addition of noise on x percent of the values to generate outliers; and (c) a + b.
, 2004). The Clust-Dist were leveraged as a measurement of genetic diversity change over time. The incorporation of distance between clusters as a measure of genotypic diversity has also been shown in  Tyagi et al. (2015)  and  Sarker et al. (2017). Subsequently, the trait h 2 calculated for each day resulted in a time-ordered set, that was used as the second variable for performing CPA. The E-statistic-based multivariate CPA was implemented to identify the TWs using the R package "ecp"  (Matteson and James, 2014). The utilization of multivariate time series data for detecting and understanding the temporally changing relationships between different variables is also shown in  Cabrieto et al. (2017).
Effect of Data Preprocessing As observed from the simulation results (Table  5 ), the use of a strategy combining outlier detection and missing value imputation (S8) performed the best in most of the cases. Especially in the datasets affected by both missing values and contaminated data, which increased the number of outliers. Such outcomes were also reflected in the real data analyses, but to a lesser extent. For example, the positive effect of outlier detection was noticed only in the CP PH between experiment comparison (Table  4 ). We could also detect a positive effect of missing value imputation on h 2 in the CV performed on dataset CPE1 PH (Table  3 ). Those results were all observed in the chickpea data that contained more missing and extreme values. Therefore, data preprocessing we propose is mostly useful for data characterized by a large amount of noise, which can be characteristics of data generated on outdoors HTP platforms.
All the methods were able to obtain particularly good results even in scenarios  involving up to 50% of missing values. In those scenarios, we could see that strategy using only imputation (S7) was very performant. Such a result supports the idea that PMM is an appropriate method for imputation in HTP time series data. It is similar to previous findings showing the robustness of PMM against large rate of missing values  (Marshall et al. , 2010; Kleinke, 2018). The presence of extreme values in the data is more problematic than the one of missing values. According to the simulations, the procedures could still produce reasonably good results up to 30% of contaminated data if there are no missing values. The presence of missing values along with noise results in reduced performance of the procedures, which becomes critical beyond 20-30% of contaminated and or missing values.
However, currently those analyses are beyond the scope of this work. The simulation results also supported the use of S8 combining outlier detection and missing value imputation in separate steps over S9, which performs imputation during the estimation process and detects outliers based on the mixed model residuals. In all scenarios where noise was added to the data, S8 obtained better results than S9. We could also observe such a result but with a reduced difference in the between experiment comparison (CP PH) and the goodness of fit results of the logistic curve (e. g. , CPE1 PH). From a methodological point of view, the difference between S9 and S8 is due to the fact that S9 uses data only from a single day, while S8 benefits from the information present in the whole time series to impute the missing values.
Thus, by fitting the two-way ANOVA model, we could verify the biological assumption that-most of the phenotypic variation comes from the difference in growth stage (TW), to a reduced but significant extent from the genotypic difference (G c ) and to a lesser extent from difference in genotype modified by the time (G c × TW). However, several studies have reported that large interaction effects that tend to complicate results interpretation, since phenotypic expression (and hence, ranking) of genotypes would differ across environments  (Kaya et al. , 2006; Tulu and Wondimu, 2019). Those studies primarily consider genotypic analysis across environments, and not growth-phases, which could also potentially alter the ranking of genotypes. Therefore, clustering similar genotypes based on the results of CPA was performed, since information contained in temporal datasets is generally non-homogeneous throughout the crop growth stage due to changing patterns of phenotypic development  (Shi et al.