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.

Astroglial Hmgb1 regulates postnatal astrocyte morphogenesis and cerebrovascular maturation.

Astrocytes are intimately linked with brain blood vessels, an essential relationship for neuronal function. However, astroglial factors driving these physical and functional associations during postnatal brain development have yet to be identified. By characterizing structural and transcriptional changes in mouse cortical astrocytes during the first two postnatal weeks, we find that high-mobility group box 1 (Hmgb1), normally upregulated with injury and involved in adult cerebrovascular repair, is highly expressed in astrocytes at birth and then decreases rapidly.

Automatic detection of the parasite in blood smears using a machine learning approach applied to mobile phone images.

Chagas disease is a life-threatening illness caused by the parasite . The diagnosis of the acute form of the disease is performed by trained microscopists who detect parasites in blood smear samples. Since this method requires a dedicated high-resolution camera system attached to the microscope, the diagnostic method is more expensive and often prohibitive for low-income settings.

Unbiased analysis of mouse brain endothelial networks from two- or three-dimensional fluorescence images.

A growing body of research supports the significant role of cerebrovascular abnormalities in neurological disorders. As these insights develop, standardized tools for unbiased and high-throughput quantification of cerebrovascular structure are needed. We provide a detailed protocol for performing immunofluorescent labeling of mouse brain vessels, using thin ( ) or thick (50 to ) tissue sections, followed respectively by two- or three-dimensional (2D or 3D) unbiased quantification of vessel density, branching, and tortuosity using digital image processing algorithms.

Maternal high-fat diet in mice induces cerebrovascular, microglial and long-term behavioural alterations in offspring.

Various environmental exposures during pregnancy, like maternal diet, can compromise, at critical periods of development, the neurovascular maturation of the offspring. Foetal exposure to maternal high-fat diet (mHFD), common to Western societies, has been shown to disturb neurovascular development in neonates and long-term permeability of the neurovasculature. Nevertheless, the effects of mHFD on the offspring's cerebrovascular health remains largely elusive.

Gland context networks: A novel approach for improving prostate cancer identification.

Prostate cancer (PCa) is a pervasive condition that is manifested in a wide range of histologic patterns in biopsy samples. Given the importance of identifying abnormal prostate tissue to improve prognosis, many computerized methodologies aimed at assisting pathologists in diagnosis have been developed. It is often argued that improved diagnosis of a tissue region can be obtained by considering measurements that can take into account several properties of its surroundings, therefore providing a more robust context for the analysis.

Publisher's Note: Entropy of weighted recurrence plots [Phys. Rev. E 90, 042919 (2014)].

This corrects the article DOI: 10.1103/PhysRevE.90.

Spacing ratio characterization of the spectra of directed random networks.

Previous literature on random matrix and network science has traditionally employed measures derived from nearest-neighbor level spacing distributions to characterize the eigenvalue statistics of random matrices. This approach, however, depends crucially on eigenvalue unfolding procedures, which in many situations represent a major hindrance due to constraints in the calculation, especially in the case of complex spectra. Here we study the spectra of directed networks using the recently introduced ratios between nearest and next-to-nearest eigenvalue spacing, thus circumventing the shortcomings imposed by spectral unfolding.

A biochemical network modeling of a whole-cell.

All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks.

Characterization and comparison of large directed networks through the spectra of the magnetic Laplacian.

In this paper, we investigated the possibility of using the magnetic Laplacian to characterize directed networks. We address the problem of characterization of network models and perform the inference of the parameters used to generate these networks under analysis. Many interesting results are obtained, including the finding that the community structure is related to rotational symmetry in the spectral measurements for a type of stochastic block model.

Vascular contributions to 16p11.2 deletion autism syndrome modeled in mice.

While the neuronal underpinnings of autism spectrum disorder (ASD) are being unraveled, vascular contributions to ASD remain elusive. Here, we investigated postnatal cerebrovascular development in the 16p11.2 mouse model of 16p11.

Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity.

As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.

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

Shiga toxin-producing (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.

Clustering algorithms: A comparative approach.

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios.

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.

Morphological Neuron Classification Based on Dendritic Tree Hierarchy.

The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons.

Gene regulatory and signaling networks exhibit distinct topological distributions of motifs.

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

Hyperfiltration in ubiquitin C-terminal hydrolase L1-deleted mice.

Neuronal ubiquitin C-terminal hydrolase L1 (UCHL1) is a deubiquitinating enzyme that maintains intracellular ubiquitin pools and promotes axonal transport. deletion in mice leads to progressive axonal degeneration, affecting the dorsal root ganglion that harbors axons emanating to the kidney. Innervation is a crucial regulator of renal hemodynamics, though the contribution of neuronal UCHL1 to this is unclear.

The aPKC-CBP Pathway Regulates Post-stroke Neurovascular Remodeling and Functional Recovery.

Epigenetic modifications have emerged as attractive molecular substrates that integrate extrinsic changes into the determination of cell identity. Since stroke-related brain damage releases micro-environmental cues, we examined the role of a signaling-induced epigenetic pathway, an atypical protein kinase C (aPKC)-mediated phosphorylation of CREB-binding protein (CBP), in post-stroke neurovascular remodeling. Using a knockin mouse strain (CbpS436A) where the aPKC-CBP pathway was defective, we show that disruption of the aPKC-CBP pathway in a murine focal ischemic stroke model increases the reprogramming efficiency of ischemia-activated pericytes (i-pericytes) to neural precursors.

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.

Effects of threshold on the topology of gene co-expression networks.

Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection.

Analysis of Scanning Electron Microscopy Images To Investigate Adsorption Processes Responsible for Detection of Cancer Biomarkers.

Adsorption processes are responsible for detection of cancer biomarkers in biosensors (and immunosensors), which can be captured with various principles of detection. In this study, we used a biosensor made with nanostructured films of polypyrrole and p53 antibodies, and image analysis of scanning electron microscopy data made it possible to correlate morphological changes of the biosensor with the concentration of cells containing the cancer biomarker p53. The selectivity of the biosensor was proven by distinguishing images obtained with exposure of the biosensor to cells containing the biomarker from those acquired with cells that did not contain it.

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.

Mechanosensing is critical for axon growth in the developing brain.

During nervous system development, neurons extend axons along well-defined pathways. The current understanding of axon pathfinding is based mainly on chemical signaling. However, growing neurons interact not only chemically but also mechanically with their environment.

Topic segmentation via community detection in complex networks.

Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects.