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. Uchl1 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. Immunofluorescence revealed significant neuronal UCHL1 expression in mouse kidney, including periglomerular axons. Glomerular filtration rate trended higher in 6-week-old Uchl1-/- mice, and by 12 weeks of age, these displayed significant glomerular hyperfiltration, coincident with the onset of neurodegeneration. Angiotensin converting enzyme inhibition had no effect on glomerular filtration rate of Uchl1-/- mice indicating that the renin-angiotensin system does not contribute to the observed hyperfiltration. DCE-MRI revealed increased cortical renal blood flow in Uchl1-/- mice, suggesting that hyperfiltration results from afferent arteriole dilation. Nonetheless, hyperglycemia, cyclooxygenase-2, and nitric oxide synthases were ruled out as sources of hyperfiltration in Uchl1-/- mice as glomerular filtration rate remained unchanged following insulin treatment, and cyclooxygenase-2 and nitric oxide synthase inhibition. Finally, renal nerve dysfunction in Uchl1-/- mice is suggested given increased renal nerve arborization, decreased urinary norepinephrine, and impaired vascular reactivity. Uchl1-deleted mice demonstrate glomerular hyperfiltration associated with renal neuronal dysfunction, suggesting that neuronal UCHL1 plays a crucial role in regulating renal hemodynamics.

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.

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. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.

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.

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

Significance: 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. Aim: We provide a detailed protocol for performing immunofluorescent labeling of mouse brain vessels, using thin ( 25 µ m ) or thick (50 to 150 µ m ) 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. Approach: Mouse brain sections were immunofluorescently labeled using a highly selective antibody raised against mouse Cluster of Differentiation-31 (CD31), and 2D or 3D microscopy images of the mouse brain vasculature were obtained using optical sectioning. An open-source toolbox, called Pyvane, was developed for analyzing the imaged vascular networks. The toolbox can be used to identify the vasculature, generate the medial axes of blood vessels, represent the vascular network as a graph, and calculate relevant measurements regarding vascular morphology. Results: Using Pyvane, vascular parameters such as endothelial network density, number of branching points, and tortuosity are quantified from 2D and 3D immunofluorescence micrographs. Conclusions: The steps described in this protocol are simple to follow and allow for reproducible and unbiased analysis of mouse brain vascular structure. Such a procedure can be applied to the broader field of vascular biology.

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

Chagas disease is a life-threatening illness caused by the parasite Trypanosoma cruzi. 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. Here, we present a machine learning approach based on a random forest (RF) algorithm for the detection and counting of T. cruzi trypomastigotes in mobile phone images. We analyzed micrographs of blood smear samples that were acquired using a mobile device camera capable of capturing images in a resolution of 12 megapixels. We extracted a set of features that describe morphometric parameters (geometry and curvature), as well as color, and texture measurements of 1,314 parasites. The features were divided into train and test sets (4:1) and classified using the RF algorithm. The values of precision, sensitivity, and area under the receiver operating characteristic (ROC) curve of the proposed method were 87.6%, 90.5%, and 0.942, respectively. Automating image analysis acquired with a mobile device is a viable alternative for reducing costs and gaining efficiency in the use of the optical microscope.

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. Here, we sought to address this knowledge gap by using a translational mouse model of mHFD exposure. Three-dimensional and ultrastructure analysis of the neurovascular unit (vasculature and parenchymal cells) in mHFD-exposed offspring revealed major alterations of the neurovascular organization and metabolism. These alterations were accompanied by changes in the expression of genes involved in metabolism and immunity, indicating that neurovascular changes may result from abnormal brain metabolism and immune regulation. In addition, mHFD-exposed offspring showed persisting behavioural alterations reminiscent of neurodevelopmental disorders, specifically an increase in stereotyped and repetitive behaviours into adulthood.

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

This corrects the article DOI: 10.1103/PhysRevE.90.042919.

Regulation of radial glial motility by visual experience.

Radial glia in the developing optic tectum express the key guidance molecules responsible for topographic targeting of retinal axons. However, the extent to which the radial glia are themselves influenced by retinal inputs and visual experience remains unknown. Using multiphoton live imaging of radial glia in the optic tectum of intact Xenopus laevis tadpoles in conjunction with manipulations of neural activity and sensory stimuli, radial glia were observed to exhibit spontaneous calcium transients that were modulated by visual stimulation. Structurally, radial glia extended and retracted many filopodial processes within the tectal neuropil over minutes. These processes interacted with retinotectal synapses and their motility was modulated by nitric oxide (NO) signaling downstream of neuronal NMDA receptor (NMDAR) activation and visual stimulation. These findings provide the first in vivo demonstration that radial glia actively respond both structurally and functionally to neural activity, via NMDAR-dependent NO release during the period of retinal axon ingrowth.

Estimating complex cortical networks via surface recordings- a critical note.

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites. Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers. A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites. We used various complex network models for reference and tried to classify sampled versions of the "brain-like" network as one of these archetypes. It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes. For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network.

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. Specifically, we characterize the eigenvalue statistics of directed Erdos-Rényi (ER) random networks by means of two adjacency matrix representations, namely, (1) weighted non-Hermitian random matrices and (2) a transformation on non-Hermitian adjacency matrices which produces weighted Hermitian matrices. For both representations, we find that the distribution of spacing ratios becomes universal for a fixed average degree, in accordance with undirected random networks. Furthermore, by calculating the average spacing ratio as a function of the average degree, we show that the spectral statistics of directed ER random networks undergoes a transition from Poisson to Ginibre statistics for model 1 and from Poisson to Gaussian unitary ensemble statistics for model 2. Eigenvector delocalization effects of directed networks are also discussed.

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. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.

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 16p11.2df/+ mouse model of 16p11.2 deletion ASD syndrome. We discover that 16p11.2 hemizygosity leads to male-specific, endothelium-dependent structural and functional neurovascular abnormalities. In 16p11.2df/+ mice, endothelial dysfunction results in impaired cerebral angiogenesis at postnatal day 14, and in altered neurovascular coupling and cerebrovascular reactivity at postnatal day 50. Moreover, we show that there is defective angiogenesis in primary 16p11.2df/+ mouse brain endothelial cells and in induced-pluripotent-stem-cell-derived endothelial cells from human carriers of the 16p11.2 deletion. Finally, we find that mice with an endothelium-specific 16p11.2 deletion (16p11.2ΔEC) partially recapitulate some of the behavioral changes seen in 16p11.2 syndrome, specifically hyperactivity and impaired motor learning. By showing that developmental 16p11.2 haploinsufficiency from endothelial cells results in neurovascular and behavioral changes in adults, our results point to a potential role for endothelial impairment in ASD.

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.

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. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance. We also found that the default configuration of the adopted implementations was not always accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.

Three-dimensional description and mathematical characterization of the parasellar internal carotid artery in human infants.

Inside the 'cavernous sinus' or 'parasellar region' the human internal carotid artery takes the shape of a siphon that is twisted and torqued in three dimensions and surrounded by a network of veins. The parasellar section of the internal carotid artery is of broad biological and medical interest, as its peculiar shape is associated with temperature regulation in the brain and correlated with the occurrence of vascular pathologies. The present study aims to provide anatomical descriptions and objective mathematical characterizations of the shape of the parasellar section of the internal carotid artery in human infants and its modifications during ontogeny. Three-dimensional (3D) computer models of the parasellar section of the internal carotid artery of infants were generated with a state-of-the-art 3D reconstruction method and analysed using both traditional morphometric methods and novel mathematical algorithms. We show that four constant, demarcated bends can be described along the infant parasellar section of the internal carotid artery, and we provide measurements of their angles. We further provide calculations of the curvature and torsion energy, and the total complexity of the 3D skeleton of the parasellar section of the internal carotid artery, and compare the complexity of this in infants and adults. Finally, we examine the relationship between shape parameters of the parasellar section of the internal carotid artery in infants, and the occurrence of intima cushions, and evaluate the reliability of subjective angle measurements for characterizing the complexity of the parasellar section of the internal carotid artery in infants. The results can serve as objective reference data for comparative studies and for medical imaging diagnostics. They also form the basis for a new hypothesis that explains the mechanisms responsible for the ontogenetic transformation in the shape of the parasellar section of the internal carotid artery.

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.

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. As a consequence of enhanced cellular reprogramming, CbpS436A mice show an increased transient population of locally derived neural precursors after stroke, while displaying a reduced number of i-pericytes, impaired vascular remodeling, and perturbed motor recovery during the chronic phase of stroke. Together, this study elucidates the role of the aPKC-CBP pathway in modulating neurovascular remodeling and functional recovery following focal ischemic stroke.

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. Detection was confirmed with cyclic voltammetry measurements, while the adsorption of the p53 biomarker was probed with polarization-modulated infrared reflection absorption (PM-IRRAS) and a quartz crystal microbalance (QCM). Adsorption is described using the Langmuir-Freundlich model, with saturation taking place at a concentration of 100 Ucells/mL. Taken together, our results point to novel ways to detect biomarkers or any type of analyte for which detection is based on adsorption as is the case of the majority of biosensors.

Modifier of cell adhesion regulates N-cadherin-mediated cell-cell adhesion and neurite outgrowth.

Modifier of cell adhesion (MOCA) is a member of the dedicator of cytokinesis 180 family of proteins and is highly expressed in CNS neurons. MOCA is associated with Alzheimer's disease tangles and regulates the accumulation of amyloid precursor protein and beta-amyloid. Here, we report that MOCA modulates cell-cell adhesion and morphology. MOCA increases the accumulation of adherens junction proteins, including N-cadherin and beta-catenin, whereas reducing endogenous MOCA expression lowers cell-cell aggregation and N-cadherin expression. MOCA colocalizes with N-cadherin and actin in areas of cell-cell and cell substratum contact and is expressed in neuronal processes. MOCA accumulates during neuronal differentiation, and its expression enhances NGF-induced neurite outgrowth and morphological complexity. We conclude that MOCA regulates N-cadherin-mediated cell-cell adhesion and neurite outgrowth.