Proof of Concept: Examining Characteristics of Roadway Infrastructure in Various 3D Visualization Modes, 2015

Utilizing enhanced visualization in transportation planning and design gained popularity in the last decade. This work aimed at demonstrating the concept of utilizing a highly immersive, virtual reality simulation engine for creating dynamic, interactive, full-scale, three-dimensional (3D) models of highway infrastructure. For this project, the highway infrastructure element chosen was a two-way, stop-controlled intersection (TWSCI). VirtuTrace, a virtual reality simulation engine developed by the principal investigator, was used to construct the dynamic 3D model of the TWSCI. The model was implemented in C6, which is Iowa State University’s Cave Automatic Virtual Environment (CAVE). Representatives from the Institute of Transportation at Iowa State University, as well as representatives from the Iowa Department of Transportation, experienced the simulated TWSCI. The two teams identified verbally the significant potential that the approach introduces for the application of next-generation simulated environments to road design and safety evaluation.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Modulatory effects of acid-sensing ion channels on action potential generation in hippocampal neurons.

Extracellular acidification has been shown to generate action potentials (APs) in several types of neurons. In this study, we investigated the role of acid-sensing ion channels (ASICs) in acid-induced AP generation in brain neurons. ASICs are neuronal Na(+) channels that belong to the epithelial Na(+) channel/degenerin family and are transiently activated by a rapid drop in extracellular pH. We compared the pharmacological and biophysical properties of acid-induced AP generation with those of ASIC currents in cultured hippocampal neurons. Our results show that acid-induced AP generation in these neurons is essentially due to ASIC activation. We demonstrate for the first time that the probability of inducing APs correlates with current entry through ASICs. We also show that ASIC activation in combination with other excitatory stimuli can either facilitate AP generation or inhibit AP bursts, depending on the conditions. ASIC-mediated generation and modulation of APs can be induced by extracellular pH changes from 7.4 to slightly <7. Such local extracellular pH values may be reached by pH fluctuations due to normal neuronal activity. Furthermore, in the plasma membrane, ASICs are localized in close proximity to voltage-gated Na(+) and K(+) channels, providing the conditions necessary for the transduction of local pH changes into electrical signals.

Immunobiology of human papillomavirus infection and vaccination - implications for second generation vaccines.

Prophylactic human papillomavirus (HPV) L1 virus like particle (VLP) vaccines have been shown, in large clinical trials, to be very immunogenic, well-tolerated and highly efficacious against genital disease caused by the vaccine HPV types. However these vaccines, at the present, protect against only two of the 15 oncogenic genital HPV types, they are expensive, delivered by intramuscular injection and require a cold chain. The challenges are to develop cheap, thermo-stable vaccines that can be delivered by non-injectable methods that provide long term (decades) protection at mucosal surfaces to most, if not all, oncogenic HPV types that is as good as the current VLP vaccines. Current approaches include L1 capsomers, L2 protein and peptides, delivery via recombinant L1 bacterial and viral vectors and large-scale VLP production in plants. Rational design and successful development of such vaccines will be based on an understanding of the immune response, and particularly the 'cross talk' between the innate and adaptive responses. This will be central in the development of adjuvants and vaccine formulations that induce the response to provide effective protection.

An Evaluation of the Second Generation Consolidation Monitoring Device, HR-1013, 1984

The objective was to evaluate the usefulness, accuracy, precision, and reproducibility of the second generation CMD for PC concrete under production conditions.