dataesr/scientific-paragraphs-categorization huggingface dataset:
Theoretical from NIST database, elemental composition is derived from chemical composition based on Alssabbagh et al (2017). c Theoretical from NIST database, elemental composition is derived from elemental analysis. Gray 0. 354 ± 0. 009 0. 257 ± 0. 006 0. 226 ± 0. 006 Gray_LAB c 0. 346 0. 255 0. 225 5 Tough 0. 348 ± 0. 009 0. 251 ± 0. 006 0. 223 ± 0. 006 6 White 0. 351 ± 0. 009 0. 254 ± 0. 006 0. 226 ± 0. 006 7 NDBase 0. 344 ± 0. 009 0. 250 ± 0. 006 0. 222 ± 0. 006 8 NDC+B 0. 344 ± 0. 009 0. 254 ± 0. 006 0. 220 ± 0. 006 9 NDCast 0. 356 ± 0. 009 0. 257 ± 0. 006 0. 225 ± 0. 006 10 NDSG 0. 342 ± 0. 009 0. 253 ± 0. 006 0. 222 ± 0. 006 11 Hybrid 0. 345 ± 0. 009 0. 250 ± 0. 006 0. 221 ± 0. 006 12 PET-G 0. 351 ± 0. 009 0. 255 ± 0. 006 0. 225 ± 0. 006 PET-G_LAB c 0. 362 0. 263 0. 231 13 ABS 0. 273 ± 0.
Mathematical fitting The binding isotherms were fitted with a MATLAB software (The Math Works, USA), using mathematical binding models (Equation 1-4), which was previously described by Thordarson  [17]. For optimization of the fit conditions, iterations were carried out until the difference in the value of chisquare for successive iterations did not exceed 5 % of the previous value. The fluorescence quantum yields of mTHPC in complexes were determined using mTHPC solution The fluorescence quantum yields of mTHPC in complexes were determined using mTHPC solution , regarded as standard (fluorescence , regarded as standard (fluorescence was excited at 416 was excited at 416 nm. The absolute error in quantum yields values was estimated as ± nm. The absolute error in quantum yields values was estimated as ±.
An equilibration along 500 ps in the NPT ensemble was performed, until reaching a box volume fluctuating around a value corresponding to a cubic box length of 80 Å ( 8 Å) and constant energy. We then carried out a simulation run for a total time of 10 ns using a time step of 1 fs. Coordinates were saved every ps for data analysis. Absorption spectrum was obtained as the convolution of 100 snapshots randomly taken from the MD trajectory. A hybrid QM/MM approach was used with the QM partition constituted by the mTHPC chromophore while CD and water were represented at MM level. Excited state energies were calculated module of Amber14 module of Amber14  [18] [18]. mTHPC force field was specifically parameterized starting from the Generalized Amber Force Field (GAFF) force field was specifically parameterized starting from the Generalized Amber Force Field (GAFF) point charges were obtained using the standard RESP procedure point charges were obtained using the standard RESP procedure  [19] [19]  was used for heptakis  (2,3,6 heptakis(2,3,6  This potential was developed based on the GLYCAM04 parameters This potential was developed based on the GLYCAM04 parameters modified in a consistent way to take into account substitution at the modified in a consistent way to take into account substitution at the Classical molecular dynamic simulations were performed Classical molecular dynamic simulations were performed atm) for a 1:2 mTHPC:CD complex immersed in TIP3P water (about atm) for a 1:2 mTHPC:CD complex immersed in TIP3P water (about the simulation, with long range electrostatics treated by means of the Particle Mesh Ewald (PME) the simulation, with long range electrostatics treated by means of the Particle Mesh Ewald (PME) An equilibration along 500 An equilibration along 500 uctuating around a value corresponding to a cubic box length of 80 uctuating around a value corresponding to a cubic box length of 80 then carried out a simula then carried out a simula saved every ps for data analysis.
Acknowledgments This work was supported by the  Institut de Cancérologie de Lorraine; Contre le Cancer (CCIR-; Belarusian Republican Foundation for Fundamental Research (BRFFR)  [grant numbers  M17MC-028; -002 ; -106]; the  Ministry of Education of the Republic. The authors thank biolitec research  GmbH (Jena, Germany)  for providing with mTHPC and  Alexandre Kriznik  ( Platform of Biophysics and Structural Biology of UMS 2008 IBSLor ,  UL-CNRS-INSERM)  for circular dichroism facilities access.
The uncertainty on the JER has two sources. The first source is the difference between the JER in data and simulation, which is around 15% for all centralities in both pp and PbPb collisions. The associated systematic uncertainty is evaluated by propagating the effects of having a JER that differs by 15% relative to the nominal value. The second source (7%) accounts for the uncertainty in the resolution and the modeling of the JER distributions, and was obtained by considering the differences between the extracted JER in each p gen T bin and the parametrization using Eq. 2, and determining the value at one standard deviation of that distribution, assuming that the differences are normally distributed. Finally, the JES uncertainty arises from three contributions that are added in quadrature for the final value.
The subtraction of the UE in PbPb collisions results in the JES having a larger dependence on the fragmentation pattern than found for pp collisions, since one can only distinguish between soft particles from the jet fragmentation and the underlying event on average. Hence, an additional uncertainty, evaluated using collision data and simulation, is applied in PbPb collisions to account for these fragmentation effects on the JES arising from the subtraction algorithm, underlying event, and quenching. The photon-tagged jet fragmentation function in PbPb data is constructed and fit by a two-component model of the jet fragmentation functions for quark and gluon jets that were obtained from MC simulations. For p γ T > 60 GeV/c, the results show that the fraction of jets originating from gluon fragmentation in data can be constrained to between 0% and approximately 26%, which corresponds to the fraction found in PYTHIA+HYDJET MC samples.
Within FWs, especially in aquatic systems, nestedness appears to be related to body size, because the diets of smaller predators tend to be nested subsets of those of larger predators. There seems to be 2 extremes, these are freshwater FWs that tend to have many generalists and HPWs that tend to have many specialized parasitoids. The nested structure of mutualistic networks is suggested to play a role in network stability. Additionally, recent analyses have shown that ecological networks are also modular and that the modularity co-occurred with nestedness  [85]. Moreover, the correlation between nestedness and modularity depends on network connectance  [56]. Although mathematical and computational analyses have suggested that nestedness increases species richness as well, an empirical analysis of 59 datasets representing mutualistic plant-pollinator networks showed that this statement may be incorrect.
The gene-disease network (diseasome), the archetype of this type of network, is a bipartite graph in which the first set of nodes consists of diseases and the opposing one of diseaseassociated genes  [93]. A disease and a gene are connected by a link only if the gene is implicated in the particular disease. Given a bipartite network, one can construct by projection the human disease network, i. e. , the network of human diseases where diseases with common genetic components are connected, or the human disease gene network, i. e. , the network of human genes where genes participating in common human disorders are connected. The first diseasome was created based on the list of human disorders, disease genes, and associations between them obtained from the OMIM (Online Mendelian Inheritance in Man) database by Goh and coworkers  [94].
When data from genome-wide association studies (GWAS) are used, one is also able to construct a gene-phenotype network linking genetic polymorphisms to intermediate phenotypes, such as cholesterol levels or blood pressure  [98]. Such approaches can be useful, especially in the context of identifying the causal pathways linking genetic variation, intermediate phenotypes, and diseases (Mendelian randomization), but the data on phenotypes are rather sparse. Recently, Kontou et al. [15] , performed a similar analysis by combining data from OMIM and 2 other primary resources containing information of gene-disease associations: the National Institutes of Health's Genetic Association Database (GAD)  [99] , which contains data from genetic association studies that mostly target multigenic diseases of low penetrance, and the National Human Genome Research Institute (NHGRI) catalog of published GWAS  [100] , which includes a manually curated collection of published GWAS, with more than 100 000 assayed SNPs and SNPdisease associations.
GAD and GWAS are, therefore, complementary to OMIM. Moreover, since disease name heterogeneity and ambiguity in all 3 repositories would not allow for a direct data comparison, the naming conventions described in the International Classification of Diseases (ICD-10) were used. Finally, in order to maintain a uniform nomenclature, all genes from the 3 databases were converted to the official Human Genome Organisation (HUGO) Gene Nomenclature Committee  [101]  gene symbols. Such network-based approaches for the discovery of genedisease associations have enabled biomedical researchers to not only investigate the genetic complexity of a particular disease but also the relatedness among apparently discrete disease phenotypes  [92, 102]. Diseases have been found to be highly connected genetically, displaying many connections between both individual disorders and disorder classes.
Further extending diseasome, a large-scale biomedical literature database (including PubMed and National Center for Biotechnology Information's [NCBI's] MeSH terms) was used to construct a symptoms-disease network (Human Symptoms Disease Network) and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions  [110]. In the projected network, the link weight of 2 diseases quantifies the degree of similarity of their respective symptoms. The authors integrated disease-gene association and PPI data and found that the symptom-based similarity of 2 diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a given disease can be related to the connectivity patterns of the underlying PPI network.
Peptide-to-protein assignment modeling by bipartite graphs Any type of high-throughput proteomics experiment that uses MS reports a list of all the detected peptides and a measure of their abundance. The subsequent analysis requires the assignment of each identified peptide to the corresponding protein and an estimate of its abundance. Therefore, as many peptides are assigned to a single protein and many proteins share the same peptides, a bipartite graph between peptides and proteins is constructed to carry out the analysis. This network between peptides and proteins is then processed using a series of algorithms that operate on this bipartite structure to find the most appropriate protein assignment for each peptide. Inferring the correct proteins from these complex bipartite graphs is a difficult problem; therefore, methods based on empirical Bayesian analysis, reverse database search, and calculation of expectation values have been developed.
Other gene expression regulation networks The increasing availability and decreasing price of the highthroughput experiments resulted in the generation of an growing number of datasets that involve different types of biological entities including TFs, mRNAs, proteins, and regulatory sequences (e. g. , enhancers, repressors). Bipartite graphs provide a suitable structure to model and analyze this wealth of data. In a network topology analysis work, the SICORE algorithm has been proposed  [133]  for the identification of regulation as well as coregulation effects using protein arrays, miRNAs, and gene expression. In a similar multidata integrative method, data from the human protein interaction network were combined with those from the transcription regulatory network to characterize coregulatory modules  [134].
The method entailed a probabilistic statistical model that evaluated whether a cluster of coregulated proteins is likely to form a transcriptional regulatory module in an integrated network. A novel class of noncoding RNAs has been discovered recently, the long noncoding RNAs (lncRNAs), more than 200 nucleotides in length, a feature that sets them apart from the other small regulatory RNAs. Evidently, bipartite graphs provide a suitable model to study the structural roles of lncRNAs, as 1 layer of nodes can represent the lncRNAs and the second layer the proteins they interact with. A method termed "lncRNA-protein bipartite network inference" has been developed recently  [135]  that is proposed to be the first to allow the construction of such networks. The method relies on the extraction of characterized lncRNAs-protein interactions from online databases and the usage of a propagation technique to assign each protein a score that is specific for each lncRNA, thereby providing a full set of ranked lists of interacting proteins for every lncRNA.
The generated model was then applied to a series of cancer datasets and was able to robustly reduce the frequently high number of false positives occurring in single DEG experiments. To summarize, bipartite networks are invaluable in modeling and studying biomolecular networks for 2 major reasons. First, they provide a straightforward abstraction as the 2 different layers of nodes correspond directly to 2 different sets of biomolecular entities with distinct properties. Second, there are several powerful analytical methods from graph theory and linear algebra that, by taking into account particular types of bipartite network connectivity, provide solutions to the data representation and complexity reconstruction problems of the multiomics, high-dimensional, high-throughput biological data.
In particular, detailed information from each patient is needed, and the adjacency matrix of the generated bipartite network closely resembles the traditional epidemiological datasets (the rows represent the patients and the columns the diseases). Projection of this bipartite network can also result in a unipartite network with the correlations of various comorbidities, the so-called phenotypic disease network (PDN)  [150]. Large datasets of this type, which could be useful for network analysis, are difficult to be found, in general. However, since worldwide health transaction data are now often collected electronically, disease co-occurrences are currently analyzed quantitatively  [151] , and in some cases, these data cover entire nations  [152]. In the most notable example of PDN, more than 30 million patients' electronic health records compiled from Medicare claims were used.
By analyzing the co-occurrence of diseases and mortality, researchers found that disease progression can be studied using network methods, offering the opportunity to enhance our understanding of the origin and evolution of human diseases. Additionally, the dataset that was made publicly available represents the largest relational phenotypic resource that is publicly available to the research community  [150]. Such data  [153]  can be used in other analytical techniques that are in use in traditional epidemiology (e. g. , in meta-analysis of summary data). Other network analyses, such as analyses of co-morbidities of hip-fractures elderly patients, provided unexpected results that would be difficult to obtain otherwise, since patients with more serious comorbidities seem to have better follow-up that reduces the risk of readmission, whereas those with relatively less-serious specific comorbidities may have less stringent follow-up, leading to unanticipated incidents that precipitate readmission  [154].
Analysis of the gene-disease network As mentioned earlier, network-based approaches for the discovery of gene-disease associations have enabled biomedical researchers to investigate the genetic complexity of a particular disease and the relatedness among apparently discrete disease phenotypes. To illustrate the steps that need to be followed in the analysis of such a network, we used as a test case the bipartite networks that contained the associations between the human diseases and the genes that confer susceptibility to these disease from the study conducted by Kontou et al. [15]. The particular analysis was performed by combining data from OMIM and 2 other primary resources containing information of genedisease associations, the NIH's GAD and the NHGRI catalog of published GWAS. In the original publication, the datasets were combined, but for purposes of illustration, here we used only the GAD dataset in order to avoid confusion.
Bipartite Graphs, the Biomedical Big-Data Era, and Some Hints for the Future Today, we live in the era of big data, where the exponential growth of information in the biosphere is evident. The protein and genome landscapes change continuously as new and hypothetical proteins and genome fragments appear every day. Integrated Microbial Genomes (IMG)  [11]  today includes approximately 6000 bacterial, 1500 archaeal, approximately 300 eukaryotic, approximately 8000 viral isolate genomes, approximately 1200 genome fragments, 6500 metagenomes, and approximately 2000 metatranscriptomes. Based on a very approximate estimate, this corresponds to approximately 70 million proteins coming from the isolate genomes and approximately 4-10 billion proteins coming from the metagenomes and metatranscriptomes.
In addition, the UniProtKB/TrEMBL release of 15 February 2017  [209]  contains approximately 7 750 000 sequence entries. Moreover, Uniparc contains approximately 150 000 000 protein entries. Protein Family Database (PFAM)  [210] , version 31. 0, a database of a large collection of protein families that organizes proteins into families by similar domains, consists of approximately 17 000 entries. Today, NCBI hosts 1 billion sequences corresponding to 2. 2 trillion bases, and RefSeq alone hosts more than 100 million complete accessions. Moreover, PubMed hosts more than 27 million articles today. Also, other databases that host results from high-throughput experiments increase in size every day. Thus, comparative genomics and integrative biology are areas that already have and are expected to experience a boom in the coming years and to dominate other areas within the broader big-data spectrum (e.
g. , internet of things). Another important attribute of the biomedical bipartite networks, which reflect the abstract nature of the entities that they contain, is that they make extensive use of data integration techniques and rely on incorporating data from multiple sources (e. g. , diseases, SNPs, gene expression, PPIs, clinical symptoms, pharmaceutical drugs), contrary to the ecological and molecular networks. This highlights the need for the creation of publicly available biological databases containing high-quality data. Biological databases, in general, play a central role in bioinformatics, since they offer scientists the opportunity to access a wide variety of biologically relevant data  [211]. Furthermore, they are indispensable in the context of network medicine and systems biology and medicine, since the primary data from several databases need to be integrated in order to achieve the desired result  [212, 213].
Biological databases continue to grow, and the need for data integration techniques, as well as the potential applications in network medicine and systems biology, also increases  [214, 215]. Initiatives for standardization and construction of ontologies is also of paramount importance in this respect. Currently, available and up-to-date databases exist for a large variety of data, including SNPs  [216] , RNAs  [217] , PPIs  [218, 219] , biomolecular pathways  [220] , drugs  [221] [222] [223] , and diseases  [224]. However, the gene-disease relationships, which form the basis of biomedical networks, are considered especially problematic since genetic association studies are characterized by nonreplicability  [225, 226]  and most approaches to collecting data for gene-disease analysis are based on the clearest genedisease associations derived from the literature.
In terms of network analysis, clustering is one of the most active research fields. While a plethora of generic clustering algorithms exist, great efforts have been made in the biomedical area to incorporate such algorithms within established network visualization tools. For example, Cytoscape's ClusterMaker plugin  [230]  includes attribute cluster algorithms such as Auto-SOME clustering  [231] , Eisen's hierarchical and k-means clustering  [232] , as well as topology-based clustering algorithms, such as affinity propagation  [233] , community clustering (GLay)  [234] , MCODE  [235] , MCL  [236] , Spectral Clustering of Protein Sequences  [237] , and transitivity clustering  [238]. While these efforts have proven to be very fruitful, often users misuse these algorithms without taking into consideration the topological characteristics of the network.
Software and tools for bipartite graphs In this section, we discuss software applications and libraries that are available for the analysis and visualization of bipartite and n-partite networks. While tools for analysis and visualization of unipartite biological networks of general use are presented and analyzed elsewhere  [239] [240] [241] [242] , Table  1  summarizes their functionalities, and Figure  10  shows how they can be used for visualizing bi-and n-partite graphs. However, in most cases, specialized software is needed either in the form of a plugin for an existing tool or as a completely different package. Cytoscape  [243]  is an open-source, bioinformatics-oriented software platform mainly implemented to analyze and visualize generic interaction networks. Although it does not specialize in bipartite graphs, some functionality for visualizing and processing such graphs is available through several plugins  [244].
g. , proteomics data), based on biclustering techniques in a single graphical interface. Furthermore, BicAT offers a variety of facilities (e. g. , filtering of biclusters) for data preparation, review, processing, and post analysis. The user is able to choose the optimal/their preferred biclustering algorithm among different algorithms. The program allows the users to install further extensions or algorithms. GeneWeaver  [254]  is an online software package for the integration of functional genomics experiments. It contains a set of interactive tools for analysis and visualization of gene sets, gene set descriptions, and gene set association scores from multiple species. It differs from conventional gene set overrepresentation analysis tools in that it allows users to evaluate intersections among all combinations of a collection of gene sets, including, but not limited to, annotations to controlled vocabularies.
g. , pollination webs or predator-prey webs. Netpredictor  [272]  is an R package (available also as an R Shiny web application) designed for the prediction of missing links in bipartite networks. The package provides a set of tools for calculating missing links in both bipartite and unipartite networks. Also, Netpredictor allows computation of several bipartite network properties, calculation of significant interactions between 2 sets of nodes using permutation-based testing, and visualization of communities for 2 different sets of nodes. biGRAPH  [206]  is an R package extension to the well-known igraph package (which is the method of choice for handling unipartite graphs) that provides a set of methods specifically designed for the analysis of bipartite graphs, including the projection of bipartite graphs handling the problem of information loss.