DPOAE in HIV infected adults

HIV infection is associated with impairment of hearing function, at any stage of disease causing complication to the external, middle, inner ear and CNS. Audiological manifestation of HIV is a direct consequence of virus or secondary to the pharmacological treatment or viral complication. \ud Objectives: There is paucity of information pertaining to hearing status in HIV. As the deafness can occur at any stage of HIV with varying degree and people with HIV live longer, there is need to address the hearing problems in these individuals. So this study aimed detecting the outer hair cell functioning by doing DPOAE in normal hearing HIV infected adults.\ud Method: The experimental group comprised of 12 HIV infected (24 ears) within 20 to 40 years. The age matched control group comprised of 15 subjects (30 ears). All the subjects had normal hearing sensitivity. Initially puretone audiometry and immittance was performed for the subject selection. Subsequently DPOAE procedure was done. \ud Results: The DPOAE was abnormal in 50% of the subjects.\ud Conclusion: It can be concluded that the cochlear involvement is a common observation in HIV infected individuals. DPOAE test can be used as a tool for early identification of cochlear pathology in HIV infected

Exponential sensitivity of noise-driven switching in genetic networks

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably

Modeling Evolution of Crosstalk in Noisy Signal Transduction Networks

Signal transduction networks can form highly interconnected systems within cells due to network crosstalk, the sharing of input signals between multiple downstream responses. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk and the emergence of specificity for two parallel signaling pathways that arise via gene duplication and are subsequently allowed to diverge. We focus on a sequence based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. Surprisingly, we find that the two fitness functions lead to very different evolutionary outcomes, one with a high degree of crosstalk and the other without.Comment: 18 Pages, 16 Figure

Hidden long evolutionary memory in a model biochemical network

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.Comment: 20 Pages, 14 Figure

Carbon Nanotube Gas Sensor Using Neural Networks

The need to identify the presence and quantify the concentrations of gases and vapors is ubiquitous in NASA missions and societal applications. Sensors for air quality monitoring in crew cabins and ISS have been actively under development (Ref. 1). In particular, measuring the concentration of CO2 and NH3 is important because high concentrations of these gases pose a risk to ISS crew health. Detection of fuel and oxidant leaks in crew vehicles is critical for ensuring mission safety. Accurate gas and vapor concentrations can be measured, but this typically requires bulky and expensive instrumentation. Recently, inexpensive sensors with low power demands have been fabricated for use on the International Space Station (ISS). Carbon Nanotube (CNT) based chemical sensors are one type of these sensors. CNT sensors meet the requirements for low cost and ease of fabrication for deployment on the ISS. However, converting the measured signal from the sensors to human readable indicators of atmospheric air quality and safety is challenging. This is because it is difficult to develop an analytical model that maps the CNT sensor output signal to gas concentration. Training a neural network on CNT sensor data to predict gas concentration is more effective than developing an analytic approach to calculate the concentration from the same data set. With this in mind a neural network was created to tackle this challenge of converting the measured signal into CO2 and NH3 concentration values