dataesr/scientific-paragraphs-categorization huggingface dataset:
These graphs provide users with a basis for visualizing ecological networks and characterizing their functional properties by means of connectivity metrics. Given their need for few input data, they have been increasingly used for 20 years in ecology, biological conservation, and landscape planning  [5]. Landscape graphs have given rise to several software applications, including mainly Conefor Sensinode  [6]  and Graphab  [7]. While Conefor Sensinode is mostly for calculating connectivity metrics, Graphab was designed as an integrated tool covering all the modeling steps, from graph construction with a wide range of parameters to the implementation of spatial analyses for specific operational applications. As Graphab has significantly progressed since its first online release, we propose in this paper to review its specific advanced functions and its thematic applications.
First, command-line facilities are available and documented to ease batch processing. Similarly, Graphab functions have recently been included in the R software environment  [10] , which is frequently used in ecological research, and supplemented by specific functions for landscape genetic analyses through the release of the graph4lg package  [11]. Finally, a QGIS  [12]  plugin has been developed to make basic functionalities available to the wider community of users of this free GIS software. This connection could also make it possible to set modeling outputs against other geographical information layers for interpretation.
Limitations and potential improvements The wide range of Graphab users and their regular feedback have contributed to the diversification of the software functions. Yet, Graphab is not without its limitations and several improvements can already be outlined. Graphab inherits one of the problems of landscape graph modeling, namely the assignment of cost values. Except for the case of input data derived from a species distribution model  [66] , such assignment is usually derived from expertise, raising the question of its validation. This refers to the broader need for validating landscape graph parameters with biological data  [69]. From this perspective, specific procedures could be added to help users validate their choices. When field data such as geolocated points characterized by species presence or community richness are loaded, statistical tests could be set up to provide a validity level of the model and a rapid sensitivity analysis.
, 2018). Hierarchical clustering of bacterial communities correlated with disease association have been extensively used to attempt identification of important bacterial taxa  (Jackson et al. , 2018). Correlation with taxon presence or absence is often confirmed using metrics such as Jaccard's index: between two species, this index is defined by the ratio between the number of samples where both species are present out of the number of samples tested. The values of this index range between 0 and 1, from no correlation to a strong correlation, respectively,  (Mainali et al. , 2017). Nevertheless, an extensive study on microbial community modeling showed that indexes selected to evaluate correlations between species should be adapted to the specific dataset being studied. It is noteworthy that Jaccard index has been reported to have a relatively low sensitivity compared to other metrics such as the Pearson index when applied to cooccurrence networks  (Berry and Widder, 2014).
Therefore, even if the use of Jaccard's index is reliable to identify the correlations using taxon presence or absence, the use of Pearson or Spearman indexes should be preferred to assess correlations when using network-based methods. To overcome the issues associated with taxon-based correlation analysis that may lead to conflicting results due to spurious associations, it is noteworthy that Wu and coauthors have recently proposed to exploit the concept of bacterial guild to reduce metagenomic data dimensionality into ecologically meaningful functional units  (Wu et al. , 2021). Although this approach is still limited by the use of relative abundance data, it proposes an interesting approach to refine data analysis of 16S-based datasets to identify relevant disease associations.
After demonstrating that correlations in species abundance were not predictive of interactions between species, they used this simulated dataset to prove that LIMITS, an algorithm that uses sparse linear regression corrected by bootstrap aggregation, can be relevant to identify keystone species in two time-series samples from the gut microbiome  (Fisher and Mehta, 2014). They concluded that B. stercosis and B. fragilis showed more interactions than other bacteria and that these interactions were mainly beneficial, since the growth of these two bacteria has been correlated with an increased abundance of B. thetaiotaomicron and E. rectale, the latter being a well-known butyrate producer  (Fisher and Mehta, 2014). These results are coherent with other studies identifying Bacteroides sp. as key members of the gut microbiome  (Loftus et al.
In order to accurately predict the metabolic capabilities of the communities, both available nutrients and genome information (from reference databases or metagenomic samples) are combined to predict minimal communities of bacteria that can produce target metabolic compounds. The bacteria encountered in all predicted minimal communities are considered essential symbionts, whereas bacteria encountered in at least one of the predicted minimal communities, but not necessarily all of them, are considered alternative symbionts  (Belcour et al. , 2019). This method has been applied to a set of over 1,500 reference genomes from the human gut, allowing the identification of 11 essential symbionts, namely Propionibacterium sp. KPL2009, Paenibacillus polymyxa, Bacillus licheniformis, Lactococcus lactis, Enterococcus casseliflavus, E.
faecalis, Hungatella hathewayi, Dorea longicatena, R. torques, Burkholderiales bacterium, and Citrobacter portucalensis, and 194 alternative symbionts  (Belcour et al. , 2019). As the reference genomes were assembled from 155 fecal samples, bacterial communities from each individual were mixed, maybe explaining the large amount of alternative symbionts  (Zou et al. , 2019). In addition, the fairly reduced number of predicted keystone species compared to the input dataset of genomes could be due to the non-exhaustive list of initial nutrients and target metabolic compounds that need to be provided to the software in order to predict minimal communities. Nonetheless, this tool is the first tool to the best of our knowledge that is specifically designed to identify both putative keystone species and their associated metabolic functions in complex microbial communities.
Despite this limitation, NGS methods and bioinformatics have been effectively supporting the search for keystone species in the gut microbiome. The rapid improvement of NGS techniques that generates increasingly large datasets allowing for deep characterization of the gut microbiome community also calls for new bioinformatics tools to analyze NGS datasets in a more effective and complete way. For metagenomics studies and as summarized in Figure  2 , two methodology decisions drastically influence the results: (i) the DNA sequencing technology and (ii) the bioinformatic methods that will be applied to analyze these datasets. The choice of a DNA sequencing strategy determines the information retrieved from a sample. Amplicon sequencing, especially 16S rRNA gene sequencing, allows to have an overview of the bacterial content of the samples by assigning the obtained reads to taxonomies.
For the assignment of 16S rRNA genes, two factors strongly influence results. First, there is high reliance on the diversity contained in the reference catalog and second, the amplified regions can skew the identification of OTUs. Indeed, 16S rRNA gene catalogs are often built from complete 16S sequences whereas the amplification and sequencing of the 16S rRNA gene usually targets hypervariable regions (V-regions), leading to biased performances and challenges when comparing different studies. To overcome this hurdle, a new tool called OTUX has been proposed, that combines several custom datasets of OTUs defined by 16S rRNA V-regions retrieved from full-length 16S rRNA sequences  (Yadav et al. , 2019). This new method was challenged against conventional full-length 16S databases and revealed an improved assignment of reads for all of the V-regions targeted, except for the V1-V3 region  (Yadav et al.
, 2019). For the assignment of shotgun reads, using MAGs could significantly improve identification accuracy of bacterial species. Recent research efforts produced a comprehensive catalog containing more than 200,000 reference genomes from the human gut microbiome referenced as the UHGG database  (Almeida et al. , 2020). This work also revealed that 81% of the species in the catalog lacked a cultured representative, indicating the huge potential for discovering new keystone species in the future  (Almeida et al. , 2020). Another critical parameter in the analysis of microbial communities using NGS is the sequencing depth. Several keystone species from the human gut microbiome, such as C. minuta, which presence in stool samples is significantly associated with BMI, are low abundance microbes that can be missed when using a reduced sequencing depth  (Waters and Ley, 2019).
Thus, an increased depth is required to ensure that the whole diversity of species from the gut microbiome is identified  (Berry and Widder, 2014). Considering data processing using bioinformatic tools, one of the main issues regarding the use of sequencing data to identify correlations between taxa within the gut microbiota is the compositional nature of these datasets. The analysis of read counts begins with their normalization by the total number of sequenced reads. As explained by Friedman and Alm, the estimation of correlation between parameters (e. g. , species) is biased by the relationship between the fractions: because they must sum to 1, they are not independent  (Friedman and Alm, 2012). Thus, a study can be artificially biased by the disappearance of highly abundant species due to technical or assignment issues: the relative abundance of low-abundant species will increase as the variables are dependent even if their absolute abundance is constant.
As a consequence, the SparCC method was developed to estimate the linear Pearson correlation between transformed variables: the log transformation of the ratio of abundances between a pair of OTUs because the ratio between abundances is independent from other OTUs included. Using SparCC, it was demonstrated that the bias observed in standard correlation studies that is induced by dominant species, is greatly reduced in simulated data of varying diversity. Applied to the HMP dataset, SparCC revealed new positive correlations between highly abundant and low abundant species, instead of the spurious negative correlations usually observed when using standard correlations  (Friedman and Alm, 2012). Recently, another method was developed by Li et al. , to overcome the compositional bias when using generalized Lotka-Volterra (gLV) models.
As explained above, gLV models are one of the most common approaches for modeling microbial interactions. These models also suffer from the compositional bias induced by the relative abundances. Indeed, absolute biomass values are needed in order to accurately fit the gLV differential equations model of each organisms' growth rate to the data  (Li et al. , 2019). Therefore,  Li et al. (2019)  developed an algorithm called BEEM that estimates biomass in silico before inferring interactions when total biomass cannot be estimated experimentally. This algorithm introduces relative abundances in the equation modeling the growth rate of each species, resulting in two parameters that can be estimated using longitudinal datasets. This method was applied to diverse synthetic or existing datasets and accurately estimated biomass and gLVM parameters.
In this paper, Macop for Minimalist And Customisable Optimisation Package, is proposed as a discrete optimisation Python package which doesn't implement every algorithm in the literature, but provides the ability to quickly develop and test your own algorithm and strategies. The main objective of this package is to provide maximum flexibility, which allows easy implementation when experimenting new algorithms. Based on a common interaction loop (see Figure  1 ) of all the algorithms, Macop wants to allow users to quickly focus on one of the main parts of this loop.
Statement of Need Most of the operational research libraries developed in Python offer users either problems and algorithms where it is possible to choose parameters to obtain optimal (or near optimal) results such as proposed in  (Maher et al. , 2016) , or, libraries targeted to a specific problem or algorithm such as  (Perry, 2019). Another package is proposed in  (Soni, 2017)  which is a comprehensive gradient-free optimization framework written in Python. It seems very similar to Macop. However, hiearchic dependencies between algorithms, the notion of callbacks and adaptive operator selection are proposed within Macop. On the other hand, available libraries  (Hart et al. , 2017; Perez et al. , 2012)  in the literature did not allow to attach custom evaluation function to each algorithm used in this hierarchy of algorithms.
Indeed, it is sometimes possible that the main algorithm manages local searches. Each local search may evaluate the solution differently using a different evaluation function of the parent algorithm (the main algorithm). Such as example, using a surrogate mathematical model  (Leprêtre et al. , 2019)  with a quick-to-evaluate function if the real evaluation function is very expensive in time. This is why in Macop, each algorithm can have its own mechanism (or partially), i. e. its evaluation function, its operators for obtaining new solution, as well as its solution update policy. This is independent of the parent algorithm to which it is linked. This means that only the results (solutions found) are exchanged. Hence, motivation behind Macop is a flexible discrete optimisation package allowing a quick implementation of problems.
Hence, this algorithm aims at decomposing the multi-objective problem into µ single-objective problems in order to obtain the Pareto front  (Kim & De Weck, 2005)  where single-objective problems are so-called child algorithms linked to the multi-objective algorithm. The main purpose of these developed algorithms is to show the possibilities of operational search algorithm implementations based on the minimalist structure of the library. In macop. solutions module: Currently, only combinatorial solutions (discrete problem modelisation) are offered, with the well-known problem of the knapsack as an example. Of course, it's easy to add your own representations of solutions. Solutions modeling continuous problems can also be created by anyone who wants to model his own problem. In macop. operators and macop.
Conclusion Macop aims to allow the modelling of discrete (usually combinatorial) optimisation problems. It is therefore open to expansion and not closed specifically to a kind of problem. Macop proposes a simple structure of interaction of the main elements (algorithms, operators, solutions, policies, callbacks) for the resolution of operational research problems inside an interaction loop. From its generic structure, it is possible, thanks to the flexible programming paradigm of the Python language, to easily allow the extension and development of new algorithms and problems. Based on simple concepts, this package can therefore meet the needs of the rapid problem implementation.
The model-driven methods take into account the degradation characteristics of PEMFC. However, it is difficult to establish an accurate degradation physical model because the PEMFC is a complex system. For data-driven methods, the degradation trend is learned from recorded data, and the health state of PEMFC is predicted by the learned degradation trend. Common learning algorithms have echo state network  [25] , adaptive neuro-fuzzy inference system  [26] , relevance vector machine  [27] , and group method of data handling  [28]. The data-driven methods predict the health state of PEMFC without degradation model. However, it requires a lot of historical data. For hybrid methods, it combines the advantages of data-driven methods and model-driven methods. The physical degradation model combining with time delay neural network is proposed to predict the degradation of PEMFC  [29].
Degradation test of PEMFC in FCEV under real conditions The European project MobyPost has designed and fabricated 10 FCEVs (Fig. 1 ) to carry out commercial postal delivery services on the real road. The PEMFC stack in FCEV consists of 40 cells with an activation surface of 100 cm 2  [44]. The operating data of PEMFC in FCEV are measured and recorded at a frequency of 1 Hz. The recorded voltage and current data of PEMFC in FCEV are shown in Fig. 2. The load current of PEMFC varies with the load of FCEV, and voltage is affected by operating conditions and degradation conditions.
Degradation test of PEMFC under dynamic load current The degradation test of PEMFC under dynamic load current is made for 550 h in FCLAB, as shown in Fig. 3. The PEMFC stack under dynamic load current consists of 5 cells with an activation surface of 100 cm 2  [45]. The operating data of PEMFC under dynamic load current are measured and recorded approximately every 30 s. In order to better characterize the PEMFC state, the polarization curve test and Electrochemical Impedance Spectroscopy (EIS) test are performed approximately weekly (0 h, 35 h, 182 h, 343 h, 515 h). The recorded voltage and current data of PEMFC under dynamic load current are shown in Fig. 4. From 0 h to 550 h, the overall voltage trend drops due to the global degradation phenomena of PEMFC. After each characteristic test, the reversible phenomena can be observed in PEMFC, and the voltage of PEMFC rises.
Degradation test of PEMFC under stationary load current The degradation test of PEMFC under stationary load current is made for 982 h in FCLAB. The PEMFC stack under stationary load current contains 5 cells with an activation surface of 100 cm 2  [45]. The operating data of PEMFC under stationary load current are also measured and recorded approximately every 30 s. The polarization curve test and EIS test are also performed approximately weekly (0 h, 48 h, 185 h, 348 h, 515 h, 658 h, 823 h). The recorded voltage and current data of PEMFC under stationary load current are shown in Fig. 5. From 0 h to 823 h, the overall voltage trend of PEMFC drops. After each characteristic test, the voltage of PEMFC rises due to the reversible phenomena of PEMFC.
Data reconstruction for PEMFC There are 1 070 370 raw operating data for PEMFC in FCEV under real conditions, 707 508 raw operating data for PEMFC under stationary load current, and 393 138 raw operating data for PEMFC under dynamic load current. The amount of recorded operating data of PEMFC is very large, which will cause a large amount of calculation. The raw operating data of PEMFC are measured and recorded at high frequencies, while the degradation is very slow. In addition, there are some disturbances in the recorded data due to noise. Therefore, the recorded raw data of PEMFC can be smoothed and filtered to improve calculation accuracy and reduce the amount of calculation. The operating data of PEMFC are reconstructed by the Locally Weighted Scatterplot Smoothing (LOWESS). The main principle of LOWESS is that for a length of data, the weighted linear regression is performed at the center point with the weight function  [46].
Setting of the prognostic method According to the characteristics of recorded operation data of PEMFC, the input layer of WNN contains 5 neurons, which represent time, current, temperature, hydrogen pressure, and relative humidity. The output layer of WNN contains 1 neuron, which represents the voltage of PEMFC. The number of neurons in the hidden layer of WNN is determined by CSA. For CSA, the number of host nests is 20, the probability of host bird discovery is 25%, and the maximum number of iterations is 20. The proposed method is implemented on a commercial computer with an i5-6300 Intel CPU (12 GB RAM and 2. 3 GHz clock). In order to evaluate the computational complexity of different methods, the calculating time is recorded. In order to reduce the impact of randomness, 20 independent runs of each method are performed.
The degradation prognosis for PEMFC based on CSA-WNN The degradation prognosis for PEMFC in FCEV under real conditions is made by CSA-WNN and WNN with the different number of neurons in the hidden layer. The latter WNN is not optimized by CSA. 80% of recorded FCEV operation data are utilized to learn the degradation tendency of PEMFC, and remaining data are utilized to validate the degradation prognosis for PEMFC in FCEV. The degradation prognosis result for PEMFC in FCEV under real conditions is shown in Fig. 10. The accuracy of degradation prognosis and calculating time are shown in Table  1. As shown in Fig. 10. a, the degradation tendency of PEMFC in FCEV under real conditions can be learned and predicted by CSA-WNN and WNN with the different number of neurons in the hidden layer. The number of neurons in the hidden layer optimized by CSA is 7 for the degradation prognosis of PEMFC in FCEV.
From numerical to real-time simulation A simulation platform to enable the grid-edge control solutions, especially for MG applications, can be implemented using either numerical simulation or real-time (RT) simulation approaches. The first approach, i. e. non-RT, is widely used in the early stage, e. g. design, due to ease of implementation, low cost and safety reasons, using simulation software. Several common simulation software applications include MATLAB/Simulink, PSCAD, GAMS and HOMER  [36]. Subsequently, the second approach is used in the next stages, e. g. validation, to further test the proposed works beyond the numerical simulation for solutions before real deployment. In this platform, the RT simulator machine takes the central role as its powerful simulation capability enables the modelled MG to operate closely to realistic manner  [37].
Curvature and Torsion Based of α-Helix and β-Sheet Identification: An Algorithm for Secondary Structure Determination Based on C α Coordinates Secondary structure elements (SSEs) are important descriptors of the native state of proteins  (Konagurthu et al. , 2020)  and of the conformational ensemble of IDP, as shown in the next section for α-syn. The main local structures stabilized by backbone hydrogen bonds are helices (α-, 3 10 -, and π-helices) and β-sheets. Deviations from the canonical definitions of these secondary structures based on Ramanchandran angles are common in the Protein Data Bank. Therefore, a practical and widely used method to assign an SSE to a residue is to apply the DSSP algorithm which is based on the calculation of a simplified energy function describing backbone hydrogen bonds  (Kabsch and Sander, 1983).
Application of DSSP to a protein structure requires the knowledge of the Cartesian coordinates of all backbone atoms. For structures simulated by coarse-grained force fields, like UNRES, only a subset of these coordinates is known, those of the C α atoms. Assignment of the SSE based on C α coordinates can be performed using DSSP but at the extra computational cost of the reconstruction of an all-atom representation of the coarse-grained structure using software like MMTSB  (Feig et al. , 2004)  or PULCHRA  (Rotkiewicz and Skolnick, 2008). Alternatively, the SSE can be defined from the C α coordinates only [P-SEA algorithm  (Labesse et al. , 1997) ]. Here, we have chosen the second faster option and have developed the algorithm CUTABI by analyzing 14,652 experimental structures with less than 40% of sequence identity extracted from the ASTRAL database  (Fox et al.