dataesr/scientific-paragraphs-categorization huggingface dataset:
Network Analysis and Identification of Hub Proteins We applied WGCNA to better understand transplantationinduced proteomic changes across tissues. Unlike FC analysis that is limited to top deregulated proteins, WGCNA uses the whole dataset to identify modules of highly interconnected proteins. Nine distinct modules were generated, each represented by a specific color (Figure  5A ), ranging in size from 234 proteins (blue module) to 57 proteins (purple module). Each module was then represented by an eigengene, a synthetic gene, which is the dimension that explains the highest percentage of variance (PC1) from PCA. Hierarchical clustering of module eigengenes (MEs) revealed that many modules were correlated with specific tissues   (Figure  5B ). For instance, the blue, yellow, and brown MEs were highly expressed, respectively, in CPu, Thal, and VM, whereas these same tissues weakly expressed the turquoise, pink, and purple MEs, respectively.
This is consistent with the fact that most variability in the dataset comes from differences between tissues (Figures  1C, D ). Module-trait correlation analysis revealed that, among the nine identified modules, the ME of the red module was negatively correlated with days 1 and 7 compared to sham, but also day 7 compared to day 1 (Figure  5C ). This suggests that the global expression of red module proteins is high in sham but decreases with time after engraftment, as confirmed in Figure  6A. Interestingly, the red ME showed no difference between CPu, MFB, NAcc, Thal, and VM tissues (Figure  6B ). As expected, unsupervised hierarchical clustering revealed that the 132 proteins in the red module displayed lower expression at days 1 and 7 in all tissues, compared to sham samples which clustered together (Figure  6C ).
PPI network analysis, using weighted-correlation data, revealed a set of 48 highly interconnected hub proteins within the red module (Figure  6F ). It is important to note that seven metabolic proteins (bright red nodes) were central in this network and especially involved in mitochondrial function, such as Idh3a, Pdhb, and Sdhb. Finally, targeted molecular and biological pathways' interaction map analysis was performed on the genes from the red module, using Pathway Studio and Ingenuity Pathway analysis (Figure  7 ). Altered differential proteins relevant to transplantation were mapped on the targeted network of BPs and interactions in the DA system (Figure  7A ), neurogenesis, axonogenesis, neurotransmission and neuron development (Figure  7B ), and neuroprotection, nerve cell differentiation, and regeneration (Figure  7C ).
This manuscript presents a fully detailed methodology in order to identify the hydrodynamic parameters of a mini autonomous underwater vehicle (mini-AUV) and evaluate its performance using different controllers. The methodology consists of close-to-reality simulation using a Computed Fluid Dynamics (CFD) module of the ANSYS™ Workbench software, the processing of the data, obtained by simulation, with a set of Savistky-Golay filters; and, the application of the Least Square Method in order to estimate the hydrodynamic parameters of the mini-AUV. Finally, these parameters are considered to design the three different controllers that are based on the robot manipulators theory. Numerical simulations are carried out to evaluate the performance of the controllers.
In this regard, the implementation of Computed Fluid Dynamics (CFD) software and other computational resources has emerged as an alternative to conceive the vehicles at the first stages of research. Recent publications provide evidence of the efforts to improve hydrodynamic parameters estimation techniques, emphasizing the increasing importance of the implementation of computational resources. In  [18] , a maximum likelihood identification algorithm in conjunction with CFD calculations is proposed and validated via experimentation. On the other hand, the hydrodynamic parameters are treated as a function of the angular position in the performance of sharp maneuvers, as exposed in  [19, 20]. A control-oriented modeling approach is proposed for a low-speed complex-shaped semi-AUV at  [21] , and a low-cost, efficient CFD procedure is introduced to compute the parameters.
3. The conception of the control laws and the stability study, including the estimated hydrodynamic parameters of the mini-AUV, through the robot manipulator control theory, in order to validate the behavior of the system. It must be highlighted that, even when Finite Volume Method (FVM) simulations are most commonly adopted for hydrodynamics and fluid applications, this work considers, at the same time, the FEM simulation, as it is more appropriate for structural applications, which include forces definition. The ANSYS CFX module manages both methods, which allows for an accurate simulation of both phenomena  [37, 38]. Nonetheless, recent works on the literature report, for specific cases of study and simulation conditions, slight differences in computation efficiency between the methods mentioned above, and the results do not vary significantly  [39, 40].
Furthermore, the mini-AUV is observed to have no symmetry with respect to the b y b z and b x b y planes (Figure  1 ), which implies that the hydrodynamic parameters for the positive and negative motion must be estimated  [11, 44, 45]. Notice that, in yaw motion, the parameters remain the same for turning right or left due to the symmetry of the vehicle in the b x b z plane. Equation (  18 ) can be expressed in a matrix shape, for n data points, as:        X 1 X 2 X 3. X n        = H   m x X u X |u|u   with H =        u1 u 1 |u 1 |u 1 u2 u 2 |u 2 |u 2 u3 u 3 |u 3 |u 3. un u n |u n |u n        ∈ IR n×3 (19) that fits to Equations (  16 ) and (  17 ) as θLS = m x X u X |u|u T and ϑ = [X 1 X 2 X 3. X n ] T. Next, the conditions that are considered in the software ANSYS CFX to perform the simulations are described.
ANSYS CFX Simulation Results and Data Treatment The information given by the fluid dynamics simulations was processed while using Matlab™. Figure  4  shows that the position, velocity, and acceleration of the mini-AUV, as provided by the ANSYS CFX module, are depicted, highlighting that such results correspond to its motion in b x. Recalling  [11] [12] [13] [14] [15] 44, 45] , the plots can be considered to describe a close-to-reality behavior whose evidence is shown in Figure  5   [11] [12] [13]  (Figures taken from  [11] [12] [13]  for comparison purposes only, no copyright infringement is intended. ). From Figure  4 , it can be concluded that the time step variation of 0. 05 s does not have a notorious impact on the displacement and velocity simulation results. On the other hand, notice that the position and velocities of the system follow the curves that were obtained by real experiments, with the differences due to the design and the dimensions of the vehicles being evident.
The input parameters of the function are: the vector x, which is the signal to be filtered, the polynomial grade k, and the size of the window f > k with this being an odd number  [51, 52]. The data were analyzed with a set of different filter parameters to evaluate the performance of the filter. Moreover, according to the documentation  [51, 52] , the acceleration can also be computed from the velocity by applying the Savitzky-Golay method, which filters the input signal and numerically computes its first derivative. In Matlab™, this method is comprised of the function sgolay(N, F), where N is the polynomial grade, and F the size of the window. In Table  1 , the parameters of the Savitzky-Golay filter and the corresponding case identifier number (label) are established.
ψ d = tan -1 y d -y 0 x d -x 0 ( 42 ) For the simulations, the geometric parameter l y was set to be 0. 16 m, and the estimates of the hydrodynamic parameters, as introduced in Table  4 , were used. Additionally, the gains of the PD, PID, and SM controllers correspond to those in Table  5. Such gains ensure a slow-motion of the vehicle, as it had been assumed in Sections 2 and 4. The simulation was run for a total time tsim = 240 s with a constant sampling time dt = 0. 001 s in Matlab/Simulink™ 2018b while using a computer that was embedded with an 8 GB RAM and an Inter Core™ i5-8250 CPU @ 1. 60 GHz & 1. 80 GHz processor. The initial conditions of the system were set to 0 and the reference point to reach was selected to be at x d = -1 m, y d = 1 m, z d = 1 m. The results of such control studies are exposed through Figures  14 15 16 17.
All eukaryotic cells are covered with a dense layer of glycoconjugates, and the cell walls of bacteria are made of various polysaccharides, putting glycans in key locations for mediating protein-protein interactions at cell interfaces. Glycan function is therefore mainly defined as binding to other molecules, and lectins are proteins that specifically recognize and interact non-covalently with glycans. UniLectin was designed based on insight into the knowledge of lectins, their classification, and their biological role. This modular platform provides a curated and periodically updated classification of lectins along with a set of comparative and visualization tools, as well as structured results of screening comprehensive sequence datasets. UniLectin can be used to explore lectins, find precise information on glycan-protein interactions, and mine the results of predictive tools based on HMM profiles.
Lectins are diverse within and across all species. This particular class of proteins is considered as a major player in many biological processes taking place on the surface of cells. Lectins have been studied for many years using broad technologies ranging from X-ray crystallography to various screening methods, e. g. , microarrays; nonetheless, they are poorly annotated in genomes and no reference database was available before the launching of the UniLectin platform in 2019 (https:// www. unilectin. eu). Structuring lectin knowledge has been challenged by the lack of a reliable classification system that is robust with respect to the continuous production of new sequences and the slow elucidation of corresponding 3D structures. Several criteria for grouping lectins have been proposed based on phylogeny or binding specificity, but none have proven satisfactory  (Bonnardel, Perez, Lisacek, & Imberty, 2020).
A key aspect is that glycan binding relies on multivalence, which gives lectins a unique capacity to interact with surface glycans. β-Propellers in which each propeller contains a glycan-binding site are examples of this situation. The initial content of UniLectin consisted of manually curated lectin 3D structures together with their interacting glycans grouped in the UniLectin3D module  (Bonnardel et al. , 2019). Then, the potential for prediction from sequence data was validated with tandem repeat lectins. As a proof of concept, β-propellers were categorized by their propeller content, and sequence databases were screened with corresponding Hidden Markov Model (HMM) profiles. Prediction was proven reliable and experimentally tested  (Bonnardel et al. , 2019). These results were compiled in the PropLec module.
unilectin. eu/. The homepage displays the modules, each one being dedicated to a specific aspect of lectins. A simple crossmodule search can be launched from the top window; otherwise, each module includes an advanced search tool. UniLectin relies on standard protein cross-references (e. g. , UniProt and PDB) as well as on standard representations of glycans. The latter comply with both the IUPAC condensed and the SNFG (Standard Nomenclature For Glycans) simplified notation  (Neelamegham et al. , 2019). The SNFG symbolism is now popularized in the literature and in the main chemical compound resources. It is implemented in PubChem  (Kim et al. , 2019)  since 2020, and expected in ChEBI in 2022  (Hastings et al. , 2016). In these protocols, usage of the UniLectin3D and LectomeXplore modules is presented.
SEARCHING FOR THE STRUCTURAL DETAILS OF LECTINS BINDING THE O BLOOD GROUP ANTIGEN UniLectin3D is a curated database that classifies and describes 3D structures of lectins by origin and fold, with cross-links to literature and other databases in glycoscience. Among the 2330 entries (sept 2021), more than 60% are complexes between a protein and a carbohydrate ligand, bringing invaluable information on the molecular basis of specificity and affinity. Many lectins recognize glycans present on human tissues, playing a role in cancer, development, immunity, and infection. Structural variations of some of these glycans differentiate individuals, defining for example the basis of the ABO human blood group system  (Marionneau et al. , 2001). Some pathogens, such as Vibrio cholerae or noroviruses, infect sub-populations with given blood group phenotypes more efficiently, and the lectins involved in their recognition have been well studied  (Heggelund, Varrot, Imberty, & Krengel, 2017).
Software Up-to-date web browser such as Chrome or Firefox. Using Safari or Edge may not be as reliable. Access to UniLectin3D and search interface 1. Go to the UniLectin platform webpage: https:// www. unilectin. eu/. 2. Select the UniLectin3D module by clicking on the corresponding blue button on the homepage. This action opens https:// www. unilectin. eu/ unilectin3D/. The UniLectin3D landing page, shown in Figure  2 , offers different search modes (keywords, fields, or glycan ligands). It also displays visual summaries of the module content in the form of sunbursts enabling data browsing. Finally, it shows a hierarchical chart that can be expanded to detail the underlying lectin classification built on folds branching to classes branching to families. In all cases, these charts are interactive and open new windows for accessing the lectin pages.
COMPARING THE LECTOMES OF RELATED ORGANISMS IN DIFFERENT ENVIRONMENTS LectomeXplore is a module of UniLectin dedicated to the exploration of predicted lectins from each of the 109 classes of UniLectin3D. Translated genomes (proteomes) stored in UniProtKB and RefSeq sequence databases have been screened to generate all putative lectins (lectome) for each available species. LectomeXplore can be searched by assessing the extent of a class of lectins in the kingdoms of life. In this protocol, we demonstrate how to analyze the lectome of a specified organism and how to compare it with other species of the same genus, but living in different environments. This is applied to investigating the lectomes of two Pseudomonas species that have been widely characterized, P. syringae, a plant pathogen, and P.
fluorescens, a ubiquitous bacterium present in soil with much less pathogenic behavior. More specifically, P. syringae is a plant pathogen that causes damage in a wide range of plants, for example, with large impact on kiwi fruit production in New Zealand and in Italy  (Donati et al. , 2020). In contrast, P. fluorescens is an environmental bacterium that grows in soil and is in general non-pathogenic. The database also contains strains of P. cichorii, a bacterium first isolated on endives and known to cause green midrib rot of greenhouse-grown lettuces  (Cottyn et al. , 2009). The protocol also shows how to investigate a specific lectin class common to Pseudomonas species. The OAA-like lectins were chosen. These organisms were structurally described in the cyanobacteria Oscillatoria agardhii and considered of pharmaceutical significance due to an antiviral activity related to their high affinity for mannose (Koharudin & Gronenborn, 2011), and later characterized in Burkholderia and Pseudomonas species.
Software Up-to-date Web browser such as Chrome or Firefox. Using Safari or Edge may not be as reliable Access and search LectomeXplore with a query 1. LectomeXplore is accessible via the UniLectin portal via its corresponding button (https:// www. unilectin. eu/ predict/ ). On the LectomeXplore homepage, click on the "search by field" button. The corresponding interface displays search fields spanning lectin characteristics (Fold, class) or phylogenetic properties (Kingdom, species …) to be filled individually or in combination (Fig. 8 ). Note that a second bioinformaticsdedicated box offers the option to query LectomeXplore with protein or protein properties using accession numbers of different databases. Data mining for species-specific genomes 2. Fill in the species field of the search window to launch the P.
Searching for a specific lectin in selected genomes 3. Search can be then focused on a lectin of interest, such as OAA-like lectin that was predicted in both P. syringae and P. fluorescens lectomes (see Fig. 9 ). In the search  window, type OOA-like in the "class" field and Pseudomonas in the "genus" field. This results in the listing of 90 genes, in exactly 90 Pseudomonas genomes in the database, as shown in Figure  10. The monogenic character of this lectin is rather unusual. Interestingly, 20 of these 90 genes are not correctly annotated, and the lectin is not functionally identified in Pfam  (Mistry et al. , 2021). Analyzing the quality of the prediction 4. As mentioned above, 20 out of 90 lectins are poorly annotated. Clicking on the magnifying glass icon next to "uncharacterized protein" will display all corresponding entry summaries.
GUIDELINES FOR UNDERSTANDING RESULTS UniLectin was released for the first time in 2019, and unavoidably still faces teething problems in 2021. The content has grown substantially, and issues tend to be specific to each module gradually added to the platform. The UniLectin3D module is a database of annotated three-dimensional structures of lectins, and, as such, strongly depends on information stored in the PDB. In this context, users should be aware that while crystallography is the main method for structure determination, oligosaccharides are not always treated with the same care as their protein counterparts. Frequent errors in ring shapes and glycosidic linkage conformation have been spotted  (Agirre, Davies, Wilson, & Cowtan, 2015). However, cooperation between glycoscientists and the PDB has been fruitful, and recent structures have been subjected to more stringent controls.
The LectomeXplore module is destined to evolve rapidly. For the time being, lectome prediction is based on 109 classes reflecting currently available structural information. Consequently, prediction misses potential motifs that are present in lectins that have not been structurally characterized. The number of classes should grow with the inclusion of new glycan-binding information that will undoubtedly be generated in the years to come. Furthermore, the identification of putative lectins is based on HMM models, and therefore on sequence similarity over the whole domain of interest, but the definition of this similarity may be hampered by the frequent occurrence of tandem repeats, i. e. , repeats of more or less conserved stretches of 30-60 amino acids. This situation occurs in several families of lectins in which multi-valency is achieved by tandem repeats, probably through a gene duplication process  (Notova, Bonnardel, Lisacek, Varrot, & Imberty, 2020).
COMMENTARY Background Information The UniLectin platform was designed to address the data integration and classification issues encountered by researchers in glycobiology. Several other databases had been previously developed. The Lectin Frontier Database collecting 400 lectins with curated information  (Hirabayashi, Tateno, Shikanai, Aoki-Kinoshita, & Narimatsu, 2015)  is now integrated in GlyCosmos, a newly developed portal for accessing and exploring knowledge in glycobiology  (Yamada et al. , 2020). LectinDB is focused on plant lectins  (Chandra et al. , 2006). SugarBindDB describes pathogen lectins and their corresponding glycan targets  (Mariethoz et al. , 2016). Most recently, ProCarbDB was released with more than 5000 3D X-ray crystal structures of protein-carbohydrate complexes and annotated carbohydrate ligands  (Copoiu, Torres, Ascher, Blundell, & Malhotra, 2020).
Most of these lectin-dedicated databases only partially cover the topic, and, in general, are not based on a solid classification. Lastly, they do not necessarily comply with the latest standards for glycan nomenclature and visualization. We recently demonstrated the efficiency of LectomeXplore for exploring the genomes of organisms in relation to biological questions. The analysis of the vaginal microbiome and its variations that impact women's health demonstrated that vaginal bacterial species associated with infection and inflammation produce a larger variety of lectins  (Bonnardel et al. , 2021). The size of the predicted lectome and the variety of putative specificities correlated with pathogenicity. The UniLectin portal also contains the MycoLec module, a publicly available and searchable database for fungal lectomes, based on genomes available in the JGI MycoCosm  (Grigoriev et al.
, 2014)  and NCBI  (Sayers et al. , 2020)  databases. The lectome content of this module led to correlation of phylogeny and the very diverse lifestyles of fungi, from saprophytes to symbionts and pathogens  (Lebreton et al. , 2021). Curated and predicted lectin data are now available to the community and have already fed several machine-learning approaches to detect the structural basis of specificity or novel lectin function. In particular, the bulk data of UniLectin3D was recently used in a comprehensive analysis of lectin-glycan interactions aiming at revealing the determinants of lectin specificity  (Mattox & Bailey-Kellogg). LectinOracle combined a powerful glycanspecific deep-learning engine  (Burkholz, Quackenbush, & Bojar, 2021)  with a transformer-based protein sequence language model to predict glycan-protein interactions  (Lundstrøm, Korhonen, Lisacek, & Bojar, 2021).
Critical Parameters Except for UniLectin3D, modules of UniLectin were created with HMM profiles for each specific class in each context. A critical parameter is therefore the prediction score, which involves the HMM p-value (<10 -2 ) and the evaluation of amino acid sequence alignment generated by HMMER during the search. More specifically, at each position of the alignment, a cumulative counter is incremented depending on amino acid similarity to set the basis of a normalized score (see  Bonnardel et al. , 2021, for details). Predictions above 0. 5 are highly reliable. Nonetheless, predicted sequences with a score between 0. 20 and 0. 50 can reveal unknown lectins. Further checks are needed, such as the cautious examination of other potentially present domains and amino acid conservation at binding residue positions.
In order to circumvent this reality, researchers have been using MLP, combined with several research techniques and perfected by other analytical tools, to provide a better description of complex transition phenomena in agriculture. El Bilali's research  [31]  has been instrumental in revealing the fragility of the MLP when addressing transition processes in agriculture, mainly when trying to explain governance, anchorage, and political issues related to such processes. In this article, we conducted a literature review that sought to gather information on the specific use of Multi-Level Perspective, namely in studies on the transition towards sustainable agriculture in agroecological contexts. A consultation to Web of Science and Scopus databases has produced a list of 21 articles, published up to December 2019, in which "Multi-Level Perspective" or its acronym "MLP", or even "transition" (in a context of change of the sociotechnical regime), is associated with agroecology.