Adrenergic Receptors as Pharmacological Targets for Neuroinflammation and Neurodegeneration in Parkinson’s Disease

Inflammation is a key component of the dopaminergic neurodegeneration seen in progressive Parkinson’s disease (PD). The presence of activated glial cells, the participation of innate immune system, increased inflammatory molecules such as cytokines and chemokines, and increased oxidative stress and reactive oxygen species are the main neuroinflammatory characteristics present in progressive PD. Therapeutic targets which suppress pro-inflammatory responses by glial cells (mainly microglia) have been shown to be effective treatments for slowing or eliminating the progressive degeneration of neurons within the substantia nigra. In this chapter, we will detail a specific anti-inflammatory therapy using agonists to β2-adrenergic receptors that have been shown to be effective treatments for models of dopaminergic neurodegeneration and that have had efficacy in patients with progressive PD. We will also detail the possible molecular mechanisms of action of this therapeutic in stopping or reversing inflammation within the CNS

Connecticut's Spending Cap: It's History and An Alternative Spending Growth Rule

State spending growth rules and an alternative for Connecticutstate tax policy, spending growth rules, tax and expenditure limits, TELs

Ontology Summit 2008 Communiqué: Towards an open ontology repository

Each annual Ontology Summit initiative makes a statement appropriate to each Summits theme as part of our general advocacy designed to bring ontology science and engineering into the mainstream. The theme this year is "Towards an Open Ontology Repository". This communiqué represents the joint position of those who were engaged in the year's summit discourse on an Open Ontology Repository (OOR) and of those who endorse below. In this discussion, we have agreed that an "ontology repository is a facility where ontologies and related information artifacts can be stored, retrieved and managed." We believe in the promise of semantic technologies based on logic, databases and the Semantic Web, a Web of exposed data and of interpretations of that data (i.e., of semantics), using common standards. Such technologies enable distinguishable, computable, reusable, and sharable meaning of Web and other artifacts, including data, documents, and services. We also believe that making that vision a reality requires additional supporting resources and these resources should be open, extensible, and provide common services over the ontologies

Verification of TFIT Code Numerical Method for Flow Excursion Simulation

This research is aimed towards accurately modeling and predicting the onset of the two-phase flow excursion instability using the code TFIT (Two Fluid Interfacial Temperature). In order to do this we first had to show that the numerical diffusion of the code’s finite difference equations could be reduced to an insignificant level by decreasing the mesh size. Understanding and being able to accurately model flow excursion can help us understand how to prevent the potential negative effects of this instability. We are using a two-fluid model with physics-based closure relations. The results will be validated against the experimental data available in the literature. This research could give us a better understanding of the transition between instabilities and would contribute to the safety measures used in the coolant systems of nuclear power plants to prevent fuel rod burnout. But first it is necessary to test the effect that the mesh size has on the error. We ran a mesh independence test. The results from the independence test showed that decreasing the mesh size did drastically decrease the error but after a certain mesh size, further reduction caused no further decrease in artificial diffusion

Visual Aesthetics of E-Commerce Websites: An Eye-Tracking Approach

This study adopts four facets of visual aesthetics (i.e., simplicity, diversity, colorfulness, craftsmanship) to explain how they relate with users’ gaze patterns, based on how much they fixate on certain points, as well as how fast and how much distance their eyes cover. On a sample of 23 experienced users in online shopping, we collect eye-tracking data while looking at high, neutral, and low appealing websites, and then register their perceptions on visual aesthetics towards those websites. Findings show different patterns of gaze behavior related with users’ perceptions on visual aesthetics. Short fixations with high saccade show high simplicity, while high fixation variance and high backtrack shows high diversity. Short fixations with high backtrack show high colorfulness. Low saccade velocity with high skewness shows high craftsmanship. We contribute towards the need of automatizing the process of understanding users’ perceptions of visual aesthetics, as we might be able to predict the user behavior in real time in the future

Trainability of Dissipative Perceptron-Based Quantum Neural Networks

Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling results are urgently needed for specific QNN constructions to understand which, if any, will be trainable at a large scale. Here, we analyze the gradient scaling (and hence the trainability) for a recently proposed architecture that we called dissipative QNNs (DQNNs), where the input qubits of each layer are discarded at the layer's output. We find that DQNNs can exhibit barren plateaus, i.e., gradients that vanish exponentially in the number of qubits. Moreover, we provide quantitative bounds on the scaling of the gradient for DQNNs under different conditions, such as different cost functions and circuit depths, and show that trainability is not always guaranteed.Comment: 5 + 18 pages, 3 figure

Noise resilience of variational quantum compiling

Variational hybrid quantum-classical algorithms (VHQCAs) are near-term algorithms that leverage classical optimization to minimize a cost function, which is efficiently evaluated on a quantum computer. Recently VHQCAs have been proposed for quantum compiling, where a target unitary U is compiled into a short-depth gate sequence V. In this work, we report on a surprising form of noise resilience for these algorithms. Namely, we find one often learns the correct gate sequence V (i.e. the correct variational parameters) despite various sources of incoherent noise acting during the cost-evaluation circuit. Our main results are rigorous theorems stating that the optimal variational parameters are unaffected by a broad class of noise models, such as measurement noise, gate noise, and Pauli channel noise. Furthermore, our numerical implementations on IBM\u27s noisy simulator demonstrate resilience when compiling the quantum Fourier transform, Toffoli gate, and W-state preparation. Hence, variational quantum compiling, due to its robustness, could be practically useful for noisy intermediate-scale quantum devices. Finally, we speculate that this noise resilience may be a general phenomenon that applies to other VHQCAs such as the variational quantum eigensolver