Compensatory mechanisms of neuroprotection by PKD signaling against oxidative damage in experimental models of Parkinson\u27s disease (PD): Relevance to PD drug discovery strategies

Oxidative stress is a key pathophysiological mechanism contributing to the selective degeneration of dopaminergic neurons in Parkinson\u27s disease. Unraveling the molecular mechanisms underlying various stages of oxidative neuronal damage is critical to better understanding the diseases and developing new treatment modalities. In this study, we identified that protein kinase D1 (PKD1) functions as a key anti-apoptotic kinase to protect neuronal cells against early stages of oxidative stress. Blockade of PKCδ cleavage, PKCδ knockdown or overexpression of a cleavage-resistant PKCδ mutant effectively attenuated PKD1 activation, indicating that PKCδ proteolytic activation regulates PKD1 phosphorylation. We also identified that phosphorylation of S916 at the C-terminal is a preceding event required for PKD1 activation loop phosphorylation. Further PKD1 signal transduction was characterized in a pre-clinical model of Parkinson\u27s disease. Exposure of dopaminergic neuronal cells or primary mesencephalic neurons to MPP+ induced PKD1 activation. PKD1 was not activated in the nigral dopaminergic neurons of PKCδ knock-out (PKCδ -/-) mice exposed to acute MPTP treatment. Earlier we reported that Fyn kinase regulates PKCδ in dopaminergic cells. PKD1 was not activated in nigral dopaminergic neurons of Fyn knockout (Fyn -/-) mice exposed to acute MPTP treatment. Further, dopaminergic neurons co-treated with the PKD1 inhibitor kbNB 142-70 and exposed to MPP+ exacerbated neuronal death, confirming the survival role of PKD1. Having confirmed that positive modulation of PKD1 can be a novel neuroprotective strategy, we took a translational approach by developing PKD1 activator and characterizing the protective function in pre-clinical models of Parkinson\u27s disease. Peptides were rationally designed and screened for their ability to activate PKD1 using various screening methods. Peptide AK-P4 was identified to activate PKD1 specifically and protect against MPP+ and 6-OHDA in both N27 cells and primary mesencephalic neurons. Further, AK-P4 tagged with the TAT sequence (AK-P4T) delivered using intravenous injections activated PKD1 in mice. Co-treatment with AK-P4T restored the neurotransmitter levels and the behavioral and locomotor activities of the MPTP treated mice significantly. Overall, positive modulation of PKD1 suggests its promise as a potential therapeutic strategy in PD

Shor’s Algorithm: How Quantum Computing Affects Cybersecurity

Honorable Mention Winner Almost all of today’s computer security relies on something known as the RSA cryptosystem. This system relies on a mathematical, specifically number theory, problem known as prime factorization, where a composite number is broken down into its two prime number factors. This in an ideal method for encryption because it is easy to multiply two numbers, encoding the data, but it much harder to determine which numbers were originally multiplied together, thus hard to decode the data. If this composite number is sufficiently large, there is no known algorithm for efficiently breaking it down – at least not in classical computation. Peter Shor developed an algorithm in 1994, however, which can factor integers very efficiently and thus break down RSA encryption by employing some mathematical principles of quantum mechanics, specifically quantum parallelism, which allows for an exponential speedup with some quantum algorithms. The main goal of this research is to implement these quantum principles, as well as some necessary classical components, to demonstrate Shor’s algorithm and its superior time complexity. To do this, we needed to build Shor’s algorithm in the form of a quantum circuit, which can be done using python and employing the libraries of Qiskit, a quantum computing simulation program developed by IBM. The goal of this program is to show that Shor’s algorithm successfully returns the factors of some integer and compare it to classical computation

Analysis and Implementation of Quantum Computing Algorithms

In this research, we investigate the relationships between classical and quantum computing, and the superior time complexity and memory allocation proposed theoretically for quantum algorithms. This is accomplished by building quantum circuits to represent algorithms and test in a quantum computer simulation. Classical circuit components have been continually reducing in size to the point where they are now being impacted by quantum properties, resulting in the need to investigate quantum computing. The inherent parallelism of quantum computing also allows us to solve problems for which classical computers are inept. The class of intractable problems in computing where the solution can only be found through exhaustive search is where we observe quantum computing supremacy. It is important to explore and advance our knowledge of quantum computing so we are prepared for when it becomes a reality, and so that in the future our understanding of encryption is deepened and new quantum-proof methods can be developed. Although Google and IBM have developed physical quantum computers, there is still a large deficit in knowledge of how they may be utilized. There is also a gap between proposed superior quantum solutions and problems demonstrably solved using quantum algorithms. Among the problems classically considered intractable is one extremely relevant to cybersecurity, known as Shor’s algorithm, that being an efficient algorithm for integer factorization, breaks down our well-established methods of cryptography. The aim of this research is to show the differences and advantages of quantum computing, and to specifically demonstrate how we can use Shor’s algorithm in a quantum system

Passenger Car Unit Estimation at Signalized Intersection for Non-lane Based Mixed Traffic Using Microscopic Simulation Model

In India, traffic on roads is mixed in nature with widely varying static and dynamic characteristics of vehicles. At intersections, vehicles do not follow ordered queue and lane discipline. Different vehicle types occupy different spaces on the road, move at different speeds, and start at different accelerations. The problem of measuring volume of such mixed traffic has been addressed by converting different vehicles categories into equivalent passenger cars and expressing the volume in terms of Passenger Car Unit (PCU) per hour. The accurate estimation of PCU values for different roadway and traffic conditions is essential for better operation and management of roadway facilities. Hence, the objective of the present study is to estimate the PCU values at signalized intersection in mixed traffic and to study the influence of traffic volume, traffic composition and road width on PCU values.For this purpose, a mixed traffic simulation model developed specifically for a signalized intersection was used. The model was calibrated and validated with the traffic data collected from a signalized intersection in Chennai city. Simulation runs were carried out for various combinations of vehicular composition, volume levels and road width. It was observed that presence of heavy vehicles and increase in road width affects the PCU values. The obtained PCU values were statistically checked for accuracy and proven to be satisfied. The PCU values obtained in this study can be used as a guideline for the traffic engineers and practitioners in the design and analysis of signalized intersections where mixed traffic conditions exist

Involvement of Src family of kinases and cAMP phosphodiesterase in the luteinizing hormone/chorionic gonadotropin receptor-mediated signaling in the corpus luteum of monkey

Background: In higher primates, during non-pregnant cycles, it is indisputable that circulating LH is essential for maintenance of corpus luteum (CL) function. On the other hand, during pregnancy, CL function gets rescued by the LH analogue, chorionic gonadotropin (CG). The molecular mechanisms involved in the control of luteal function during spontaneous luteolysis and rescue processes are not completely understood. Emerging evidence suggests that LH/CGR activation triggers proliferation and transformation of target cells by various signaling molecules as evident from studies demonstrating participation of Src family of tyrosine kinases (SFKs) and MAP kinases in hCG-mediated actions in Leydig cells. Since circulating LH concentration does not vary during luteal regression, it was hypothesized that decreased responsiveness of luteal cells to LH might occur due to changes in LH/CGR expression dynamics, modulation of SFKs or interference with steroid biosynthesis. Methods: Since, maintenance of structure and function of CL is dependent on the presence of functional LH/CGR its expression dynamics as well as mRNA and protein expressions of SFKs were determined throughout the luteal phase. Employing well characterized luteolysis and CL rescue animal models, activities of SFKs, cAMP phosphodiesterase (cAMP-PDE) and expression of SR-B1 (a membrane receptor associated with trafficking of cholesterol ester) were examined. Also, studies were carried out to investigate the mechanisms responsible for decline in progesterone biosynthesis in CL during the latter part of the non-pregnant cycle. Results and discussion: The decreased responsiveness of CL to LH during late luteal phase could not be accounted for by changes in LH/CGR mRNA levels, its transcript variants or protein. Results obtained employing model systems depicting different functional states of CL revealed increased activity of SFKs pSrc (Y-416)] and PDE as well as decreased expression of SR-B1correlating with initiation of spontaneous luteolysis. However, CG, by virtue of its heroic efforts, perhaps by inhibition of SFKs and PDE activation, prevents CL from undergoing regression during pregnancy. Conclusions: The results indicated participation of activated Src and increased activity of cAMP-PDE in the control of luteal function in vivo. That the exogenous hCG treatment caused decreased activation of Src and cAMP-PDE activity with increased circulating progesterone might explain the transient CL rescue that occurs during early pregnancy

Direct Numerical Simulations of Turbulent Autoigniting Hydrogen Jets

University of Minnesota Ph.D. dissertation. November 2015. Major: Aerospace Engineering and Mechanics. Advisor: Krishnan Mahesh. 1 computer file (PDF); ix, 122 pages.Autoignition is an important phenomenon and a tool in the design of combustion engines. To study autoignition in a canonical form a direct numerical simulation of a turbulent autoigniting hydrogen jet in vitiated coflow conditions at a jet Reynolds number of 10,000 is performed. A detailed chemical mechanism for hydrogen-air combustion and non-unity Lewis numbers for species transport is used. Realistic inlet conditions are prescribed by obtaining the velocity eld from a fully developed turbulent pipe flow simulation. To perform this simulation a scalable modular density based method for direct numerical simulation (DNS) and large eddy simulation (LES) of compressible reacting flows is developed. The algorithm performs explicit time advancement of transport variables on structured grids. An iterative semi-implicit time advancement is developed for the chemical source terms to alleviate the chemical stiffness of detailed mechanisms. The algorithm is also extended from a Cartesian grid to a cylindrical coordinate system which introduces a singularity at the pole r = 0 where terms with a factor 1/r can be ill-defined. There are several approaches to eliminate this pole singularity and finite volume methods can bypass this issue by not storing or computing data at the pole. All methods however face a very restrictive time step when using a explicit time advancement scheme in the azimuthal direction ($\theta$) where the cell sizes are of the order \Delr\Del\theta. We use a conservative finite volume based approach to remove the severe time step restriction imposed by the CFL condition by merging cells in the azimuthal direction. In addition, fluxes in the radial direction are computed with an implicit scheme to allow cells to be clustered along the jet's shear layer. This method is validated and used to perform the large scale turbulent reacting simulation. The resulting flame structure is found to be similar to a turbulent diusion flame but stabilized by autoignition at the flame base. Mass-fraction of the hydroperoxyl radical, HO2, peaks in magnitude upstream of the flame's stabilization point indicating autoignition. A flame structure similar to a triple-flame, with a lean premixed flame and a rich premixed flame flanking a thick diffusion flame is identified by the flame index. Radicals formed in the shear layer ahead of ignition and oxygen from the coflow do not get fully consumed by the flame and are transported along the edges of the flame brush into the core of the jet. Ignition delays from a well-stirred reactor model and an autoigniting diffusion flame model are able predict the lift-off height of the turbulent flame. The local entrainment rate was observed to increase with axial distance until the flame stabilization point and then decrease downstream. Data from probes placed along the flame reveals a highly turbulent flow field with variable composition at a given location. In general however, it is observed that the turbulent kinetic energy (TKE) is very high in cold fuel rich mixtures and is lowest in hot fuel lean mixtures. Autoignition occurs at the most-reactive hot and lean mixture fractions where the TKE is the lowest

ANTINUCLEAR ANTIBODIES IN PATIENTS WITH UNEXPLAINED RECURRENT ABORTIONS

Objective:This study aims to determine the prevalence of antinuclear antibodies in pregnant women with bad obstetric history (BOH) against healthy multigravid women in and around Tirunelveli districtMethods: This is a case-control study comprising 60 antenatal women with BOH against 60 multiparous pregnant women with no history of abortions. Antinuclear antibodies (ANA) were detected using indirect immunofluorescence with Hep-2 cell substrate, and enzyme-linked immunosorbent assay (ELISA).Results: Among BOH cases 19 (82.6%), 18 (78.26%) were positive by ELISA and indirect fluorescence antibody test (IFAT) method, respectively. Among controls, 4(17.39%) and 5(21.73%) individuals were positive by ELISA and IFAT methods, respectively. Of the 18 positives, homogenous pattern was most common followed by anticentromere pattern, fine speckled and coarse speckled patternConclusion: IFAT is considered to be gold standard in the diagnosis of autoimmune disorders, but ELISA appears to be a suitable simple alternative for testing rheumatological disorders

Data Mining Pipeline for Performing Decision Tree Analysis On Mortality Dataset With ICD-10 Codes

Modernization of the healthcare sector has led to the introduction of wider and newer varieties of medical devices in hospitals. Consequently, there are increasing numbers of infectious complications related to medical devices. However, managing and monitoring the risk of medical devices are difficult and costly. The hospitals and the healthcare device service providers require effective means to manage the healthcare device maintenance to provide better patient care. To address this issue, we propose a data mining pipeline to classify medical devices based on mortality rates and ICD-10 codes. We utilize the decision tree grouping method to build a connection between the mortality dataset and ICD-10 codes. We anticipate that the results of this study will assist with healthcare providers identify risks associated with medical devices based on how many deaths are caused due to the improper use or use of faulty medical instruments during the treatment