Using full-text content to characterize and identify best seller books: A study of early 20th-century literature.

Artistic pieces can be studied from several perspectives, one example being their reception among readers over time. In the present work, we approach this interesting topic from the standpoint of literary works, particularly assessing the task of predicting whether a book will become a best seller. Unlike previous approaches, we focused on the full content of books and considered visualization and classification tasks. We employed visualization for the preliminary exploration of the data structure and properties, involving SemAxis and linear discriminant analyses. To obtain quantitative and more objective results, we employed various classifiers. Such approaches were used along with a dataset containing (i) books published from 1895 to 1923 and consecrated as best sellers by the Publishers Weekly Bestseller Lists and (ii) literary works published in the same period but not being mentioned in that list. Our comparison of methods revealed that the best-achieved result-combining a bag-of-words representation with a logistic regression classifier-led to an average accuracy of 0.75 both for the leave-one-out and 10-fold cross-validations. Such an outcome enhances the difficulty in predicting the success of books with high accuracy, even using the full content of the texts. Nevertheless, our findings provide insights into the factors leading to the relative success of a literary work.

Network Analysis and Natural Language Processing to Obtain a Landscape of the Scientific Literature on Materials Applications.

Recent progress in natural language processing (NLP) enables mining the literature in various tasks akin to knowledge discovery. Obtaining an updated birds-eye view of key research topics and their evolution in a vast, dynamic field such as materials science is challenging even for experienced researchers. In this Perspective paper, we present a landscape of the area of applied materials in selected representative journals based on a combination of methods from network science and simple NLP strategies. We found a predominance of energy-related materials, e.g., for batteries and catalysis, organic electronics, which include flexible sensors and flexible electronics, and nanomedicine with various topics of materials used in diagnosis and therapy. As for the impact calculated through standard metrics of impact factor, energy-related materials and organic electronics are again top of the list across different journals, while work in nanomedicine has been found to have a lower impact in the journals analyzed. The adequacy of the approach to identify key research topics in materials applications was verified indirectly by comparing the topics identified in journals with diverse scopes, including journals that are not specific to materials. The approach can be employed to obtain a fast overview of a given field from the papers published in related scientific journals, which can be adapted or extended to any research area.

Robustness modularity in complex networks.

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.

Dynamic Gene Network Analysis of Caco-2 Cell Response to Shiga Toxin-Producing Escherichia coli-Associated Hemolytic-Uremic Syndrome.

Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some strains may cause hemolytic-uremic syndrome (HUS). In Brazil, these strains are commonly found in cattle but, so far, were not isolated from HUS patients. Here, a system biology approach was used to investigate the differential transcriptomic and phenotypic responses of enterocyte-like Caco-2 cells to two STEC O113:H21 strains with similar virulence factor profiles (i.e. expressing stx2, ehxA, epeA, espA, iha, saa, sab, and subA): EH41 (Caco-2/EH41), isolated from a HUS patient in Australia, and Ec472/01 (Caco-2/Ec472), isolated from bovine feces in Brazil, during a 3 h period of bacteria-enterocyte interaction. Gene co-expression network analysis for Caco-2/EH41 revealed a quite abrupt pattern of topological variation along 3 h of enterocyte-bacteria interaction when compared with networks obtained for Caco-2/Ec472. Transcriptional module characterization revealed that EH41 induces inflammatory and apoptotic responses in Caco-2 cells just after the first hour of enterocyte-bacteria interaction, whereas the response to Ec472/01 is associated with cytoskeleton organization at the first hour, followed by the expression of immune response modulators. Scanning electron microscopy showed more intense microvilli destruction in Caco-2 cells exposed to EH41 when compared to those exposed to Ec472/01. Altogether, these results show that EH41 expresses virulence genes, inducing a distinctive host cell response, and is likely associated with severe pathogenicity.

Dynamic gene network analysis of Caco-2 cell response to Shiga toxin-producing Escherichia coli-associated Hemolytic–Uremic Syndrome

Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some strains may cause hemolytic–uremic syndrome (HUS). In Brazil, these strains are commonly found in cattle but, so far, were not isolated from HUS patients. Here, a system biology approach was used to investigate the differential transcriptomic and phenotypic responses of enterocyte-like Caco-2 cells to two STEC O113:H21 strains with similar virulence factor profiles (i.e., expressing stx2, ehxA, epeA, espA, iha, saa, sab, and subA): EH41 (Caco-2/EH41), isolated from a HUS patient in Australia, and Ec472/01 (Caco-2/Ec472), isolated from bovine feces in Brazil, during a 3 h period of bacteria–enterocyte interaction. Gene co-expression network analysis for Caco-2/EH41 revealed a quite abrupt pattern of topological variation along 3 h of enterocyte–bacteria interaction when compared with networks obtained for Caco-2/Ec472. Transcriptional module characterization revealed that EH41 induces inflammatory and apoptotic responses in Caco-2 cells just after the first hour of enterocyte–bacteria interaction, whereas the response to Ec472/01 is associated with cytoskeleton organization at the first hour, followed by the expression of immune response modulators. Scanning electron microscopy showed more intense microvilli destruction in Caco-2 cells exposed to EH41 when compared to those exposed to Ec472/01. Altogether, these results show that EH41 expresses virulence genes, inducing a distinctive host cell response, and is likely associated with severe pathogenicity.

Dynamic gene network analysis of Caco-2 cell response to Shiga toxin-producing Escherichia coli-associated Hemolytic–Uremic Syndrome

Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some strains may cause hemolytic–uremic syndrome (HUS). In Brazil, these strains are commonly found in cattle but, so far, were not isolated from HUS patients. Here, a system biology approach was used to investigate the differential transcriptomic and phenotypic responses of enterocyte-like Caco-2 cells to two STEC O113:H21 strains with similar virulence factor profiles (i.e., expressing stx2, ehxA, epeA, espA, iha, saa, sab, and subA): EH41 (Caco-2/EH41), isolated from a HUS patient in Australia, and Ec472/01 (Caco-2/Ec472), isolated from bovine feces in Brazil, during a 3 h period of bacteria–enterocyte interaction. Gene co-expression network analysis for Caco-2/EH41 revealed a quite abrupt pattern of topological variation along 3 h of enterocyte–bacteria interaction when compared with networks obtained for Caco-2/Ec472. Transcriptional module characterization revealed that EH41 induces inflammatory and apoptotic responses in Caco-2 cells just after the first hour of enterocyte–bacteria interaction, whereas the response to Ec472/01 is associated with cytoskeleton organization at the first hour, followed by the expression of immune response modulators. Scanning electron microscopy showed more intense microvilli destruction in Caco-2 cells exposed to EH41 when compared to those exposed to Ec472/01. Altogether, these results show that EH41 expresses virulence genes, inducing a distinctive host cell response, and is likely associated with severe pathogenicity.

The dynamics of knowledge acquisition via self-learning in complex networks.

Studies regarding knowledge organization and acquisition are of great importance to understand areas related to science and technology. A common way to model the relationship between different concepts is through complex networks. In such representations, networks' nodes store knowledge and edges represent their relationships. Several studies that considered this type of structure and knowledge acquisition dynamics employed one or more agents to discover node concepts by walking on the network. In this study, we investigate a different type of dynamics adopting a single node as the "network brain." Such a brain represents a range of real systems such as the information about the environment that is acquired by a person and is stored in the brain. To store the discovered information in a specific node, the agents walk on the network and return to the brain. We propose three different dynamics and test them on several network models and on a real system, which is formed by journal articles and their respective citations. The results revealed that, according to the adopted walking models, the efficiency of self-knowledge acquisition has only a weak dependency on topology and search strategy.

Biological network border detection.

Complex networks have been widely used to model biological systems. The concept of accessibility has been proposed recently as a means to organize the nodes of complex networks as belonging to its border or center. Such an approach paves the way to investigating how the functional and structural properties of nodes vary with their respective position in the networks. In this work, we approach such a problem in a biological context applying border detection to Protein-Protein Interaction networks from four organisms of the Mycoplasma genus. We found evidence that the borderness of proteins bears a relation with their spatial organization and molecular function specificity.

Seeking maximum linearity of transfer functions.

Linearity is an important and frequently sought property in electronics and instrumentation. Here, we report a method capable of, given a transfer function (theoretical or derived from some real system), identifying the respective most linear region of operation with a fixed width. This methodology, which is based on least squares regression and systematic consideration of all possible regions, has been illustrated with respect to both an analytical (sigmoid transfer function) and a simple situation involving experimental data of a low-power, one-stage class A transistor current amplifier. Such an approach, which has been addressed in terms of transfer functions derived from experimentally obtained characteristic surface, also yielded contributions such as the estimation of local constants of the device, as opposed to typically considered average values. The reported method and results pave the way to several further applications in other types of devices and systems, intelligent control operation, and other areas such as identifying regions of power law behavior.

Thermodynamic characterization of networks using graph polynomials.

In this paper, we present a method for characterizing the evolution of time-varying complex networks by adopting a thermodynamic representation of network structure computed from a polynomial (or algebraic) characterization of graph structure. Commencing from a representation of graph structure based on a characteristic polynomial computed from the normalized Laplacian matrix, we show how the polynomial is linked to the Boltzmann partition function of a network. This allows us to compute a number of thermodynamic quantities for the network, including the average energy and entropy. Assuming that the system does not change volume, we can also compute the temperature, defined as the rate of change of entropy with energy. All three thermodynamic variables can be approximated using low-order Taylor series that can be computed using the traces of powers of the Laplacian matrix, avoiding explicit computation of the normalized Laplacian spectrum. These polynomial approximations allow a smoothed representation of the evolution of networks to be constructed in the thermodynamic space spanned by entropy, energy, and temperature. We show how these thermodynamic variables can be computed in terms of simple network characteristics, e.g., the total number of nodes and node degree statistics for nodes connected by edges. We apply the resulting thermodynamic characterization to real-world time-varying networks representing complex systems in the financial and biological domains. The study demonstrates that the method provides an efficient tool for detecting abrupt changes and characterizing different stages in network evolution.