Identifying invariant solutions of wall-bounded three-dimensional shear flows using robust adjoint-based variational techniques

Invariant solutions of the Navier-Stokes equations play an important role in the spatiotemporally chaotic dynamics of turbulent shear flows. Despite the significance of these solutions, their identification remains a computational challenge, rendering many solutions inaccessible and thus hindering progress towards a dynamical description of turbulence in terms of invariant solutions. We compute equilibria of three-dimensional wall-bounded shear flows using an adjoint-based matrix-free variational approach. To address the challenge of computing pressure in the presence of solid walls, we develop a formulation that circumvents the explicit construction of pressure and instead employs the influence matrix method. Together with a data-driven convergence acceleration technique based on dynamic mode decomposition, this yields a practically feasible alternative to state-of-the-art Newton methods for converging equilibrium solutions. We successfully converge multiple equilibria of plane Couette flow starting from inaccurate guesses extracted from a turbulent time series. The variational method significantly outperforms the standard Newton-hookstep method, demonstrating its superior robustness and suggesting a considerably larger convergence radius

Predicting chaotic statistics with unstable invariant tori

It has recently been speculated that statistical properties of chaos may be captured by weighted sums over unstable invariant tori embedded in the chaotic attractor of hyperchaotic dissipative systems; analogous to sums over periodic orbits formalized within periodic orbit theory. Using a novel numerical method for converging unstable invariant 2-tori in a chaotic PDE, we identify many quasiperiodic, unstable, invariant 2-torus solutions of a modified Kuramoto-Sivashinsky equation exhibiting hyperchaotic dynamics with two positive Lyapunov exponents. The set of tori covers significant parts of the chaotic attractor and weighted averages of the properties of the tori -- with weights computed based on their respective stability eigenvalues -- approximate statistics for the chaotic dynamics. These results are a step towards including higher-dimensional invariant sets in a generalized periodic orbit theory for hyperchaotic systems including spatio-temporally chaotic PDEs

Predicting chaotic statistics with unstable invariant tori

It has recently been speculated that long-time average quantities of hyperchaotic dissipative systems may be approximated by weighted sums over unstable invariant tori embedded in the attractor, analogous to equivalent sums over periodic orbits, which are inspired by the rigorous periodic orbit theory and which have shown much promise in fluid dynamics. Using a new numerical method for converging unstable invariant two-tori in a chaotic partial differential equation (PDE), and exploiting symmetry breaking of relative periodic orbits to detect those tori, we identify many quasiperiodic, unstable, invariant two-torus solutions of a modified Kuramoto–Sivashinsky equation. The set of tori covers significant parts of the chaotic attractor and weighted averages of the properties of the tori—with weights computed based on their respective stability eigenvalues—approximate average quantities for the chaotic dynamics. These results are a step toward exploiting higher-dimensional invariant sets to describe general hyperchaotic systems, including dissipative spatiotemporally chaotic PDEs

Quantifying environmental risk factors for multiple sclerosis in discordant monozygotic twins: a case report

Relative contribution of genetic and environmental risk factors in complex disorders is widely explored through discordant identical twins. Multiple sclerosis is a demyelinating disease of the central nervous system in which the interplay of genetic and environmental risk factors define the disease pathogenicity. Robust epidemiological studies in different populations suggested that active levels of serum vitamin D and viral load implicate in MS pathogenicity and severity. In order to refine non-shared components of susceptibility factors in MS, we investigated the role of serum 25-hydroxyvitamin D and viral infection in a pair of identical twins remained discordant for MS during the course of 5 years follow up. Here we report serological finding regarding the viral load and serum 25-hydroxyvitamin D level in a pair of discordant monozygotic twins. Based on our observation, lower levels of serum 25-hydroxyvitamin D and higher anti-viral IgG titre was consistent with the disease statues in the affected sib

A monte carlo approach to calculate the production prerequisites of124I radioisotope towards the activity estimation

The Monte Carlo simulation code MCNPX has been used to simulate the production of124I by124,125Te(p, xn) and123,124Te(d, xn) reactions to form high activity124I. For this reason, the TALYS-1.8 and ALICE/ASH codes were used to calculate the reaction cross-section. The optimal energy range of projectile is selected for this production by identifying the maximum cross-section and the minimum impurity due to other emission channels. Target geometry is designed by SRIM code based on stopping power calculations with identical dimensions as the experimental data. The thick target yield of reactions is predicted because of the excitation functions and stopping power. All of the prerequisites obtained from the above interfaces are adjusted in MCNPX code and the production process is simulated according to benchmark experiments. Thereafter, the energy distribution of proton in targets, the amount of residual nuclei during irradiation, were calculated. The results are in good agreement with the reported data, thus confirming the usefulness and accuracy of MCNPX as a tool for the optimization of other radionuclides production. Based on the results, the124Te(p, n)124I process seems to be the most likely candidate to produce the124I in low-energy cyclotrons. © 2018, Vinca Inst Nuclear Sci. All rights reserved

AUTOMATIC ROAD CRACK RECOGNITION BASED ON DEEP LEARNING NETWORKS FROM UAV IMAGERY

Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition

Biological and Clinical Relevance of Long Non-Coding RNA PCAT-1 in Cancer, A Systematic Review and Meta-Analysis

Long non-coding RNA (lncRNA) prostate cancer associated transcript 1 (PCAT-1) has been identified as a potential biomarker for the diagnosis and prognosis of various cancers. We performed this systematic review and meta-analysis to evaluate the role of dysregulation as well as the biological and clinical significance of lnc-PCAT-1 for predicting the malignancy status in several cancers. Two independent reviewers conducted an extensive search in electronic databases of Medline, Embase, Scopus, Web of Science and PubMed until the December of 2017. Five articles investigating the clinical significance of lncRNA PCAT-1, including 996 patients, were analyzed. Our results revealed that the increased PCAT-1 expression was related to overall survival (OS) (HR = 1.9, 95%CI: 1.13-3.18, P=0.015). Also, pooled results of the diagnostic data analysis demonstrated that PCAT-1 has a sensitivity of 0.59 and specificity of 0.66 for cancer diagnosis. Moreover, pooled area under curve was 0.62 (95% CI: 0.58–0.69). This meta-analysis revealed that lncRNA PCAT-1 could be served as a potential diagnostic and prognostic biomarker in various solid tumors

Electrically conductive nanomaterials for cardiac tissue engineering

© 2019 Elsevier B.V. Patient deaths resulting from cardiovascular diseases are increasing across the globe, posing the greatest risk to patients in developed countries. Myocardial infarction, as a result of inadequate blood flow to the myocardium, results in irreversible loss of cardiomyocytes which can lead to heart failure. A sequela of myocardial infarction is scar formation that can alter the normal myocardial architecture and result in arrhythmias. Over the past decade, a myriad of tissue engineering approaches has been developed to fabricate engineered scaffolds for repairing cardiac tissue. This paper highlights the recent application of electrically conductive nanomaterials (carbon and gold-based nanomaterials, and electroactive polymers) to the development of scaffolds for cardiac tissue engineering. Moreover, this work summarizes the effects of these nanomaterials on cardiac cell behavior such as proliferation and migration, as well as cardiomyogenic differentiation in stem cells

Plasticity of the human visual system after retinal gene therapy in patients with Leber's congenital amaurosis.

Much of our knowledge of the mechanisms underlying plasticity in the visual cortex in response to visual impairment, vision restoration, and environmental interactions comes from animal studies. We evaluated human brain plasticity in a group of patients with Leber's congenital amaurosis (LCA), who regained vision through gene therapy. Using non-invasive multimodal neuroimaging methods, we demonstrated that reversing blindness with gene therapy promoted long-term structural plasticity in the visual pathways emanating from the treated retina of LCA patients. The data revealed improvements and normalization along the visual fibers corresponding to the site of retinal injection of the gene therapy vector carrying the therapeutic gene in the treated eye compared to the visual pathway for the untreated eye of LCA patients. After gene therapy, the primary visual pathways (for example, geniculostriate fibers) in the treated retina were similar to those of sighted control subjects, whereas the primary visual pathways of the untreated retina continued to deteriorate. Our results suggest that visual experience, enhanced by gene therapy, may be responsible for the reorganization and maturation of synaptic connectivity in the visual pathways of the treated eye in LCA patients. The interactions between the eye and the brain enabled improved and sustained long-term visual function in patients with LCA after gene therapy