dataesr/scientific-paragraphs-categorization huggingface dataset:
The results showed that ICEEMDAN has the best decomposition performance. Seismic data were processed in  [8] , where the authors demonstrated that the ICEEMDAN-based approach is a more suitable filtering method than the CEEMDAN-based method for removing high-oscillating components. In the bearing fault diagnosis field, ICEEMDAN showed higher diagnosis ability than EEMD  [9]. Ren et al. [10]  compared some combinations of EMD-based (EMD, EEMD, CEEMD, and CEEMDAN) algorithms, the artificial neural network, and the support vector regression (SVM), for forecasting wind speed time series. CEEMDAN-SVM showed the best performance concerning the CPU time and the number of decomposed subseries. Liu et al. [1]  tested and compared the decomposition accuracy of EMD, EEMD, CEEMD, and ICEEMDAN using an artificial signal.
To compare the four noise-assisted EMD algorithms fairly, we had to set their parameters consistently. According to results in  [3] [4] [5] 7] , we adopted the following setting for these algorithms. For EEMD, CEEMDAN, and ICEEMDAN, the amplitude of the white noise was set to 0. 2, and the number of ensemble trials was 100. For CEEMD, the amplitude of the white noise was also set to 0. 2. However, the number of ensemble trails was set to 50 because there are twice the decomposition procedures in each trial  [4]. The number of iterations for the four algorithms was 10. Numerical simulations were conducted using Matlab 2016a. The rest of this paper is organized as follows. Section 2 briefly introduces EMD, EEMD, CEEMD, CEEMDAN, and ICEEMDAN. Section 3 shows a metric for assessing the mode mixing alleviation performance.
EMD The EMD algorithm decomposes the non-linear and non-stationary signal into a set of IMFs adaptively. However, the mode mixing phenomenon may appear in the decomposition process when the decomposed signal contains intermittent components  [2]. EMD is a well-known signal processing method. Studies on this algorithm can be found in large amounts in the literature  [2]. In addition, its Matlab code is available online. Thus, we will not illustrate the EMD algorithm in detail. To illustrate the mode mixing phenomenon caused by intermittent components, we considered the first two components of the simulated signal used in  [4]. Therefore, the simulated signal used in this paper, denoted x(t), is composed of an intermittent signal x 1 (t) and a sinusoidal signal x 2 (t). The intermittent component is a sinusoidal signal with amplitude α 1 and frequency f 1 , which appears at some durations.
Background: Multi-omics experimental approaches are becoming common practice in biological and medical sciences underlying the need to design new integrative techniques and applications to enable the holistic characterization of biological systems. The integrative analysis of heterogeneous datasets generally allows us to acquire additional insights and generate novel hypotheses about a given biological system. However, it can often become challenging given the large size of omics datasets and the diversity of existing techniques. Moreover, visualization tools for interpretation are usually non-accessible to biologists without programming skills. Results: Here, we present MiBiOmics, a web-based and standalone application that facilitates multi-omics data visualization, exploration, integration, and analysis by providing easy access to dedicated and interactive protocols.
Background The holistic characterization of biological systems is extending our knowledge about the functioning of organisms and natural ecosystems. Today, their multi-omics characterization is becoming standard, thus novel methodologies and easily accessible tools are required to facilitate the study of associations and interactions within and across omics layers (e. g. (meta-)genome, (meta-)transcriptome, metabolome) and scales (e. g. cells, organs, holobionts, communities). The analysis of single omics datasets has helped to identify molecular signatures associated to phenotypes of interest. However, it usually does not allow to predict mechanisms underlying phenotypic variabilities. Although multi-omics information is not sufficient to identify causes and consequences of a biological process, it can contribute to delineate key players sustaining it  [1].
There is a clear need for accessible (web) tools to facilitate the integration, analysis and representation of multi-omics datasets through an intuitive and guided approach. MiBiOmics aims to provide established and novel techniques to reveal robust signatures in high dimensional datasets  [7]  through a graphical user interface allowing to perform widely applicable multi-omics analyses for the detection and description of associations across omics layers. Available as a web-based and a stand-alone application, it gives access to several R packages and tools to help users who are not familiar with programming to load and explore their data in a simple and intuitive way. MiBiOmics allows the parallel study of up to three omics datasets, as well as the in-depth exploration of each single dataset.
Data Upload Within MiBiOmics, the user can upload up to three omics datasets, allowing the data exploration and network analysis of a single-or multi-omics dataset. There must be common samples between omics datasets in order to perform all analyses provided by the application. An annotation table describing external parameters (e. g. pH, site of extraction, physiological measures) needs to be provided. These parameters may be quantitative or qualitative, and available for each sample. An additional taxonomic annotations table can be uploaded when one omics table corresponds to microbial lineages (e. g. as Operational Taxonomic Units (OTUs) or Amplicon Sampling Variants (ASVs)). Following data upload, the user can filter, normalize and transform each data matrix using common methods, such as the center log ratio (CLR) transformation to deal with the compositional nature of sequencing data, or filtration based on prevalence.
In this section, it is also possible to detect and remove potential outlier samples. To allow new users to easily test the functionality of MiBiOmics, we provide two example datasets: the breast TGCA datasets from The Cancer Genome Atlas  [8]  allows to explore assocations between miRNAs, mRNAs and proteins in different breast cancer subtypes; and a dataset from the Tara Oceans Expeditions  [9, 10]  to explore prokaryotic community compositions across depth and geographic locations.
Data Exploration In this section, several ordination plots (Principal Component Analysis (PCA), Principal COordinates Analysis (PCoA))  [11]  are dynamically produced to visualize and explore relationships between samples, and to identify main axes of variation in each dataset. When OTUs or ASVs are uploaded with their taxonomic annotations, it is possible to obtain a relative abundance plot describing the proportion of lineages at a given taxonomic level (e. g. Phylum, Family, Genus or Species) in each sample.
Multi-Omics Analysis Here, MiBiOmics allows users to detect and study associations across omics datasets. Ordination methods including Procrustes analysis  [11]  and multiple coinertia  [14]  are useful to compute and visualize the main axes of covariance, and also to extract multi-omics features driving this covariance. This central section of MiBiOmics implements an innovative approach for detecting robust links between omics layers. All modules delineated within each omics-specific network are associated to each other by directly correlating their eigenvectors. Here, the dimensionality reduction of each omics dataset through module definition ensures a small number of correlations, thereby increasing the statistical power for detecting significant associations between omics layers. For visualization, a hive plot helps summarizing significant associations between each module as a multi-layer network integrating links between omics-specific modules as well as their association to contextual parameters (traits or phenotypic characteristics).
Results and Discussion MiBiOmics enables the exploration, integration, analysis and visualization of up to three omics datasets. Through the primary exploration of a dataset, the inference of biological networks and the extraction of multi-omics associated features, the application provides a ready-to-use analysis pipeline to interactively explore sources of variability and variables of interest in a given biological dataset, as well as associations between multi-omics features in holistic studies. The inference of networks from omics features is useful to represent and model the complex architecture of putative interactions in biological systems. In addition, networks provide a way to reduce the dimensionality of a dataset by delineating cohesive groups of co-varying, often functionally related features, that can then be associated to contextual or phenotypic characteristics of interest  [1].
A key functionality of MiBiOmics is the multi-omics adaptation of WGCNA  [12]  to explore association across omics datasets via a network-based approach. As shown in Figure  2A , the interface provides the ability to interactively probe associations across omics layers of different breast cancer subtypes  [8]  within each network and their association to patient parameters. The original WGCNA outputs are provided by the application to deepen the analysis between modules and external parameters (Figure  2B ). In addition, we provide the user with the possibility to perform an OPLS regression for modules of interest to evaluate the robustness of these variables to predict a given trait or phenotype. Figure  2C  is an example of an OPLS regression using WGCNA module variables as features. On the x-axis the features of the red module are ordered according to their VIP score (their importance for the module), and on the y-axis according to their correlation to the subtype parameter.
This figure highlights how central features of a WGCNA module relate to an external parameter. The exploratory multi-omics analysis allows to study the main axes of covariance across omics profiles and give the ability to discover and select variables implicated in an association between omics datasets. The concomitant application of (multiple) co-inertia (Figure  3A ) and/or Procrustes ordination techniques with the exploration of multi-omics correlations between WGCNA modules of distinct omics layer (Figure  3B ), provides a complementary vision of multi-omics relationships. The MiBiOmics interface allows to explore WGCNA modules of interest to directly infer significant associations between features from distinct omics layers (Figure  3C ). In a multi-omics adaptation, WGCNA can be used to delineate a group of modules associated together and to a parameter to interest and extract features of different omics nature but related to each other.
While an interactive version of WGCNA already exists  [15] , MiBiOmics goes beyond by providing a multi-omics strategy to identify correlated modules across omics layers and generate novel hypotheses. Associating modules across different datasets has already been performed in the original WGCNA article  [12]  and reproduced in several studies. For example, the overlap of modules between transcriptional profiles of different tissue  [16]  was assessed, as well as a comparison between proteomics and gene expression profile of modules in a cohort of alzheimer patients  [17]. In both cases, the association between modules was determined by overlapping identical features (e. g. same genes in a given reference genome) within each module, a method which is not applicable when omics datasets do not contain similar data types or refer to the same biological system.
In MiBiOmics, we enable the inference of relationships between omics layers within an entire biological system (e. g. holobiont) or ecosystem (e. g. the plankton), which makes it more widely applicable and especially suited for omics-based environmental studies. While there is a growing need for multi-omics analysis tools, a few applications have been developed to fulfill the need of integrating and mining these complex and heterogeneous datasets. Several methods such as MONGKIE  [18] , are based on prior knowledge and integrate data by projecting them on known metabolic networks and biological pathways. More generally, existing multi-omics pipelines are focusing on certain data types (Metabolomics with MetaboAnalyst  [19] ) or on disease-related mechanisms (MergeOmics  [20] ). More widely applicable methods exist, such as the R package mixOmics  [21]  that provides several semi-supervised methodologies often based on ordination techniques.
We compared methods integrated in MiBiOmics (see Supplementary information for details) to the mixOmics DIABLO methodology  [22]. Only few multi-omics features associated to breast cancer subtype in the TGCA dataset were extracted by all three methods (n=32, Figure  4A  and Supplementary table  1 ). Both methods integrated in MiBiOmics (i. e. multiple co-inertia and multi-omics WGCNA) and DIABLO extracted mostly distinct features (Figure  4A ) underlying the probable complementarity of these multi-omics integrative strategies. Scores attributed by each method to the common set of extracted features were also dissimilar (Figure  4BCD  and Supplementary table  1 ). This may be explained by the fact that these methods implement fundamentally different approaches to features extraction and selection, which confirms the complementary nature of each analysis.
Conclusion MiBiOmics is an interactive web-based (and standalone) application to easily and dynamically explore associations across omics datasets. Through an innovative network-based integrating strategy, it can help biologists to identify potential mechanisms of interactions and generate novel hypotheses. The core of the application lies behind the reduction of dimensionality across omics datasets to efficiently link them at the molecular level, and to identify biomarkers associated with a given trait or phenotype. The MiBiOmics pipeline facilitates the exploration, integration and analysis of multi-omics datasets to a broad audience by providing scientists a powerful way to predict and explore putative molecular mechanisms underlying complex phenotypes across a wide range of biological scales and systems.
Figures Figure  1  MiBiOmics framework. The MiBiOmics workflow can be divided into three main tasks: Data preprocessing, data exploration, and multi-omics integration. The data preprocessing task is dedicated to data upload, data filtration, normalization and transformation. The data exploration task implements classical clustering methods, PCA, PCoA and WGCNA correlation networks that can be applied to each omics dataset separately. Finally, the multi-omics integration task allows the user to perform multi-omics exploration, integration and analyses using ordination techniques (multiple co-inertia and Procrustes analysis), and multi-omics network inference.
The temporal non-stationarity of land use and cover change (LUCC) processes is one of the main sources of uncertainty that may influence the calibration and the validation of spatial path-dependent LUCC models. In relation to that, this research aims to investigate the influence of the temporal non-stationarity of land change on urban growth modeling accuracy based on an empirical approach that uses past LUCC. Accordingly, the urban development in Rennes Metropolitan (France) was simulated using fifteen past calibration intervals which are set from six training dates. The study used Idrisi's Cellular Automata-Markov model (CA-Markov) which is an inductive pattern-based LUCC software package. The land demand for the simulation year was estimated using the Markov Chain method. Model validation was carried out by assessing the quantity of change, allocation, and spatial patterns accuracy.
Introduction Spatially explicit land use and cover change (LUCC) models are developed in order to improve the understanding of the spatial systems dynamic and behavior, develop hypothesis, predict future LUCC, and support policy makers in taking rational decisions  [1] [2] [3] [4] [5] [6]. Several reviews show that these computational models vary widely in underlying the theoretical assumptions and methodological approaches  [6] [7] [8] [9] [10]. Land change modeling approaches can be arranged according to their relative emphasis on pattern versus process and projection versus explanation  [11]. Most LUCC modeling software packages are empirical approaches based on past land change  [12]. They rely on historical data for their calibration to project future changes  [13]. The most popular LUCC models developed to simulate real-world urban processes are cellular automata models  [14].
It seeks to distinguish between the inaccuracies due to the model's behavior and the disagreements resulting from the non-stationarity of input data over the calibration and validation time extent. The application was conducted to Rennes Metropolitan area (France). It consists in simulating future urban development using different calibration intervals based on past LUCC. With five input LULC maps for  2003, 2006, 2010, 2011, 2012, and 2015 , fifteen discrete time intervals were used in order to parameterize the model and then project LUCC for 2018. The simulated LULC maps for 2018 were compared to the actual map based on the quantity, allocation, and spatial patterns accuracy. The study used the CA-Markov software package, which is available in the IDRISI Selva raster-based spatial analysis software  [12, 36].
Many land cover datasets and remote sensing images such as CORINE Land Cover database and large-scale 30-m land cover maps are delivered with a certain spatial and temporal accuracy that is often unsuitable for the temporal slight change analysis in an urban landscape. Consequently, the observed change can be simply due to misclassification errors especially in small zones undergoing slow rate of change as in the current research study area. Accordingly, for temporal land change analysis requirements and modeling purposes over fifteen years from 2003 to 2018, all the input LULC maps are performed using image interpretation. The manual digitization of urban areas is carried out from the ESRI's World Imagery basemap in ArcGIS Online. World Imagery provides satellite and aerial imagery with a ground resolution of 1 m or less, for example, the satellite images used for mapping impervious surfaces in 2018 are provided with an accuracy of 8.
Running CA-Markov module in IDRISI program requires inserting the basis land cover image, the Markov transition areas file, transition suitability image collector, and filter type. Criteria that determine the most suitable areas for projected urban development include the excluded protected zones, slope gradient, proximity to roads, and distance to existing developed areas (Figure  2 ). Proximity to developed areas is calculated for the end point (T 1 ) of the calibration interval [T 0 , T 1 ]. Additionally, the constraints for urban development are provided in a binary map. Zero is assigned to areas of absolute constraints and 1 corresponds to unconstrained areas which have no impact. The transition potentials are multiplied by the constraints map during the change prediction process. Furthermore, input factors are previously standardized using IDRISI Fuzzy module.
The weight assigned to each driver (distance to urban areas in the basis image: 0. 772; distance to roads: 0. 173; slope: 0. 055) are obtained by using the Analytical Hierarchy Process (AHP) method based on a pair-wise comparison matrix  [45, 46]. The multi-criteria evaluation (MCE) decision support tool is employed to compute transition suitability maps (Figure  4 ) using standard kernel size of 5 × 5 pixels contiguity filter. Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 19 urban development are provided in a binary map. Zero is assigned to areas of absolute constraints and 1 corresponds to unconstrained areas which have no impact. The transition potentials are multiplied by the constraints map during the change prediction process. Furthermore, input factors are previously standardized using IDRISI Fuzzy module.
In parallel to that, a set of seven commonly used spatial metrics is calculated using the FRAGSTATS Spatial Pattern Analysis Program for Categorical Maps  [51]. Spatial metrics are implemented to assess the influence of the calibration dataset variability on the spatial configuration accuracy of the model's projections  [52, 53]. Selected metrics, which are calculated based on the four cells neighborhood rule, comprise the number of patches (NP), patch density (PD), landscape shape index (LSI), mean patch area (Area-MN), mean Euclidean nearest neighbor distance (ENND-MN), large patch index (LPI), and patch aggregation index (AI).
In fact, the effect of the simulation framework related to selected driving forces and change potential evaluation approach should not be neglected. For instance, the current research used the change potential map which was specifically elaborated using a user knowledge-driven approach in order to reproduce the actual dominant edge growth pattern. Regarding spatial allocation, the simulated change using the CA-Markov model was more determined by the last calibration date rather than the transition that has occurred during the calibration interval. Hence, non-stationary change processes cannot be captured based on the CA-Markov empirical approach. Actually, one of the main differences between CA-Markov and other LUCC software packages within the Idrisi program is the change potential map that is used in the spatial allocation of change.
Implications on the Model Performance and Outputs Using appropriate time series input data and calibration intervals is extremely important when the objective is to reproduce past change patterns and validate empirically based LUCC model under the hypothesis of temporal homogeneity. Otherwise, significant errors could be induced, especially when past trend-based models are used in the simulation of path-dependent land use scenarios  [33]. In fact, the results revealed how outcomes and accuracy scores of the Markov chain and CA-Markov models vary significantly with respect to selected calibration dates. The magnitude of measured residues is largely due to the non-stationarity of the observed land change over the calibration and the prediction intervals rather than the inadequacy of Markov model.
Dataset and Land Change Analysis 2. 3. 1. Dependent Variables-Past LUCC Spatiotemporal urban development dynamic from 2003 to 2018 was analyzed at 15 m resolution using LULC maps for 2003, 2006, 2010, 2011, 2012, 2015, and 2018, which were manually performed using image interpretation technique (Figure 2). Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 19.
2. 3. 3 Dataset and Land Change Analysis 2. 3. 1. Dependent Variables-Past LUCC Spatiotemporal urban development dynamic from 2003 to 2018 was analyzed at 15 m resolution using LULC maps for 2003, 2006, 2010, 2011, 2012, 2015, and 2018, which were manually performed using image interpretation technique (Figure 2). Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 19.
Whatever the available method, the measurement of BD is time-consuming and generally requires soil pits to be dug to allow accurate BD measurements. Consequently, BD data are sparse in existing soil databases, which prevent the use of these databases as, for example, inputs for carbon stock calculations  (Walter et al. , 2016). Pedotransfer functions are commonly used for the prediction of BD  (Qiao et al. , 2019; Chen et al. , 2018)  based on relationships between well-characterized soil variables (for example, texture and organic carbon content) and bulk density measurements. The effectiveness of conventional methods used to measure BD at the scale of the soil horizon, such as core sampling and rubber balloon, are also limited by its stone content. The core method is currently considered the standard method for direct BD estimation  (Blake and Hartge, 1986).