From Multiview Image Curves to 3D Drawings

Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting - without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity - most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an overview of the supplementary material available at multiview-3d-drawing.sourceforge.ne

A measure of individual role in collective dynamics

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures describe a node's importance by its position in a network. The key issue obviated is that the contribution of a node to the collective behavior is not uniquely determined by the structure of the system but it is a result of the interplay between dynamics and network structure. We show that dynamical influence measures explicitly how strongly a node's dynamical state affects collective behavior. For critical spreading, dynamical influence targets nodes according to their spreading capabilities. For diffusive processes it quantifies how efficiently real systems may be controlled by manipulating a single node.Comment: accepted for publication in Scientific Report

Quantifying the Dynamics of Coupled Networks of Switches and Oscillators

Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's evolution. We, therefore, introduce a new modeling framework that describes the dynamics of networks composed of both oscillators and switches. Both oscillator synchronization and switch stability are preserved in these heterogeneous, coupled networks. Furthermore, this model recapitulates the qualitative dynamics for the yeast cell cycle consistent with the hypothesized dynamics resulting from decomposition of the regulatory network into dynamic motifs. Introducing feedback into the cell-cycle network induces qualitative dynamics analogous to limitless replicative potential that is a hallmark of cancer. As a result, the proposed model of switch and oscillator coupling provides the ability to incorporate mechanisms that underlie the synchronized stimulus response ubiquitous in biochemical systems

Pulmonary Abnormalities in Mice with Paracoccidioidomycosis: A Sequential Study Comparing High Resolution Computed Tomography and Pathologic Findings

Paracoccidioidomycosis (PCM) is a fungal infection caused by the dimorphic fungus Paracoccidioides brasiliensis. It occurs preferentially in rural workers in whom the disease is severe and may cause incapacitating pulmonary sequelae. Assessment of disease progression and treatment outcome normally includes chest x-rays or CT studies. Existing experimental PCM models have focused on several aspects, but none has done a radiologic or image follow-up evaluation of pulmonary lesions considered as the fungus primary target. In this study, the lungs of mice infected with fungal conidia were studied sequentially during the chronic stage of their experimental mycosis by noninvasive high resolution medical computed tomography, and at time of sacrifice, also by histopathology to characterize pulmonary abnormalities. Three basic lung lesion patterns were revealed by both techniques: nodular-diffuse, confluent and pseudo-tumoral which were located mainly around the hilus thus accurately reflecting the situation in human patients. The experimental design of this study decreases the need to sacrifice a large number of animals, and serves to monitor treatment efficacy by means of a more rational approach to the study of human pulmonary diseases. The findings we are reporting open new avenues for experimental research, increase our understanding of the mycosis pathogenesis and consequently have repercussions in patients' care

Structural and Topographic Dynamics of Pulmonary Histopathology and Local Cytokine Profiles in Paracoccidioides brasiliensis Conidia-Infected Mice

Paracoccidioidomycosis (PCM), an endemic fungal infection of pulmonary origin resulting in severe disseminated disease, occurs in rural areas of most South American countries and presents several clinical forms. The infection is acquired by inhalation of specific fungal propagules, called conidia. Considering the difficulties encountered when studying the infection in humans, this work was done in mice infected by inhalation of infective fungal conidia thus mimicking the human natural infection. The lungs of mice were sequentially studied by histopathological and multiplex cytokine methods from 2 h to 16 weeks after infection to verify the course of the disease. The mycosis presented different morphologic aspects during the course of time, affecting several pulmonary compartments. Otherwise and based on the analysis of 30 cytokines, the immune response also showed heterogeneous responses, which were up or down regulated depending on the time of infection. By recognizing the different stages that correspond to the evolution of pulmonary lesions, the severity (benign, chronic or fibrotic) of the disease could be predicted and the probable prognosis of the illness be inferred

Alternative Oxidase Mediates Pathogen Resistance in Paracoccidioides brasiliensis Infection

Thermally dimorphic pathogenic fungi are responsible for potentially life-threatening diseases of immunocompetent and immunocompromised individuals. These microorganisms grow as conidia-producing mycelia in the environment, which when inhaled by the host convert to the pathogenic yeast form at 37°C. During adaptation and growth, fungi interact with host immune cells and must cope with defense mechanisms such as imposed-oxidative stress (e.g., reactive oxygen species; ROS). Alternative oxidase (AOX) is an enzyme recently implicated in the reduction of ROS production by the mitochondria when triggered by external stimuli, such as temperature and ROS. During this work we have evaluated the relevance of AOX during infection with Paracoccidioides brasiliensis, the etiological agent of one of the most prevalent mycoses in Latin America, paracoccidioidomycosis. We show that PbAOX gene expression is stimulated after interaction with alveolar macrophages or in the presence of H2O2 and is essential for survival against fungicidal activity of both the immune cells and the ROS compound. Moreover, decreasing PbAOX gene expression in P. brasiliensis led to increased survival of infected mice. Altogether, our data supports a relevant role for AOX in the virulence of P. brasiliensis

Statistical modeling of ground motion relations for seismic hazard analysis

We introduce a new approach for ground motion relations (GMR) in the probabilistic seismic hazard analysis (PSHA), being influenced by the extreme value theory of mathematical statistics. Therein, we understand a GMR as a random function. We derive mathematically the principle of area-equivalence; wherein two alternative GMRs have an equivalent influence on the hazard if these GMRs have equivalent area functions. This includes local biases. An interpretation of the difference between these GMRs (an actual and a modeled one) as a random component leads to a general overestimation of residual variance and hazard. Beside this, we discuss important aspects of classical approaches and discover discrepancies with the state of the art of stochastics and statistics (model selection and significance, test of distribution assumptions, extreme value statistics). We criticize especially the assumption of logarithmic normally distributed residuals of maxima like the peak ground acceleration (PGA). The natural distribution of its individual random component (equivalent to exp(epsilon_0) of Joyner and Boore 1993) is the generalized extreme value. We show by numerical researches that the actual distribution can be hidden and a wrong distribution assumption can influence the PSHA negatively as the negligence of area equivalence does. Finally, we suggest an estimation concept for GMRs of PSHA with a regression-free variance estimation of the individual random component. We demonstrate the advantages of event-specific GMRs by analyzing data sets from the PEER strong motion database and estimate event-specific GMRs. Therein, the majority of the best models base on an anisotropic point source approach. The residual variance of logarithmized PGA is significantly smaller than in previous models. We validate the estimations for the event with the largest sample by empirical area functions. etc

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure

The impact of prior outpatient ACE inhibitor use on 30-day mortality for patients hospitalized with community-acquired pneumonia

BACKGROUND: Recent studies suggest that angiotensin-converting enzyme (ACE) inhibitors may have beneficial effects for patients at risk for some types of infections. We examined the effect of prior outpatient use of ACE inhibitors on mortality for patients hospitalized with community-acquired pneumonia. METHODS: A retrospective cohort study conducted at two tertiary teaching hospitals. Eligible subjects were admitted with a diagnosis of, had a chest x-ray consistent with, and had a discharge ICD-9 diagnosis of pneumonia. Subjects were excluded if they were "comfort measures only" or transferred from another acute care hospital. Subjects were considered to be on a medication if they were taking it at the time of presentation. RESULTS: Data was abstracted on 787 subjects at the two hospitals. Mortality was 9.2% at 30-days and 13.6% at 90-days. At presentation 52% of subjects were low risk, 34% were moderate risk, and 14% were high risk. In the multivariable conditional logistic regression analysis, after adjusting for potential confounders, the use of ACE inhibitors at presentation (odds ratio 0.44, 95% confidence interval 0.22–0.89) was significantly associated with 30-day mortality. CONCLUSION: Prior outpatient use of an ACE inhibitor was associated with decreased mortality in patients hospitalized with community-acquired pneumonia despite their use being associated with comorbid illnesses likely to contribute to increased mortality. Confirmatory studies are needed, as well as research to determine the mechanism(s) of this protective effect