Do not be afraid of local minima: affine shaker and particle swarm

Stochastic local search techniques are powerful and flexible methods to optimize difficult functions. While each method is characterized by search trajectories produced through a randomized selection of the next step, a notable difference is caused by the interaction of different searchers, as exemplified by the Particle Swarm methods. In this paper we evaluate two extreme approaches, Particle Swarm Optimization, with interaction between the individual "cognitive" component and the "social" knowledge, and Repeated Affine Shaker, without any interaction between searchers but with an aggressive capability of scouting out local minima. The results, unexpected to the authors, show that Affine Shaker provides remarkably efficient and effective results when compared with PSO, while the advantage of Particle Swarm is visible only for functions with a very regular structure of the local minima leading to the global optimum and only for specific experimental conditions

A Network Mdoel to Investigate Robustness of Gene Expressions

Correlation networks are ideal to describe the relationship between the expression profiles of genes. Gene expression is a characteristic exhibited by a particular gene. Our body has thousands of genes; each of them expresses differently, and each one of them has a particular function associated with them. When genes corresponding to a particular part of the body becomes non-functional, i.e., not expressed, then the function corresponding to that part of the body does not happen, thereby causing impairment or mutations. Co-regulation is a method involved in clustering analysis to find genes that perform similar functions. We want to identify genes that are co-regulated or expressed in concert to be able to identify defective cellular programs. By understanding this co-regulation, different ways for the healthy development of a cell can be identified and even changes leading to disease can be detected. However, this concept is not yet fully applied due to reasons such as a lack of benchmarking studies that support the global acceptance of these networks, the volume of data available, and the presence of coincidental noise or extra inconsequential relationships. In my project, I propose to explore the robustness of the gene expressions by comparing structural similarities of commonly developed networks using big data infrastructures. Further, I will work on forming a theory about the structure of correlation networks which supports their conceptual usability in biomedical big data. The proposed research will also provide an ideal software pipeline which can supply valid, reproducible and reliable correlation networks

Analysis of effective mechanical properties of thin films used in microelectromechanical systems

This research aims at analyzing the effective mechanical properties of thin film materials that are used in MEMS. Using the effective mechanical properties, reliable simulations of new or slightly altered designs can be performed successfully. The main reason for investigating effective material properties of MEMS devices is that the existing techniques can not provide consistent prediction of the mechanical properties without time-consuming and costly physical prototyping if the device or the fabrication recipe is slightly altered. To achieve this goal, two approaches were investigated: soft computing and analytical. In the soft computing approach, the effective material properties are empirically modeled and estimated based on experimental data and the relationships between the parameters affecting the mechanical properties of devices are discovered. In this approach, 2D-search, Micro Genetic Algorithms, Neural networks, and Radial Basis Functions Networks were explored for the search of the effective material properties of the thin films with the help of a Finite Element Analysis (FEA) and modeling the mechanical behavior such that the effective material properties can be estimated for a new device. In the analytical approach, the physical behavior of the thin films is modeled analytically using standard elastic theories such as Stoney’s formulae. As a case study, bilayer cantilevers of various dimensions were fabricated for extracting the effective Young’s modulus of thin film materials: Aluminum, TetraEthylOrthoSilicate (TEOS)-based SiO2, and Polyimide. In addition, a Matlab® graphical user interface (GUI), STEAM, is developed which interfaces with Ansys®. In STEAM, a fuzzy confidence factor is also developed to validate the reliability of the estimates based on factors such as facility and recipe-dependent variables. The results obtained from both approaches generated comparable effective material properties which are in accord with the experimental measurements. The results show that effective material properties of thin films can be estimated so that reliable MEMS devices can be designed without timely and costly physical prototyping

STRUCTURAL, FUNCTIONAL AND EVOLUTIONARY STUDIES OF ANTIMICROBIAL PEPTIDES

Antimicrobial peptides represent a heterogeneous group that displays multiple modes of action such as bacteriostatic, microbicidal and cytolytic properties that are sequence and concentration dependent. Life threatening infectious disease is now a worldwide crisis and treating them effectively is becoming difficult day by day, due to the emergence of antibiotic resistant strains at alarming rates. Hence, there is an urgent need for new class of antibiotics and, antimicrobial peptides (AMPs) are an ideal candidate for this job. AMPs are gene encoded short (<100 amino acids), amphipathic molecules with hydrophobic and cationic amino acids arranged spatially which exhibit broad-spectrum antimicrobial activity. AMPs form an ancient non-specific type of innate immunity found universally in all living organisms and used as the principal first line of defense against the invading pathogen. AMPs have been in the process of evolution, as have the microbes, for hundreds of years. Despite the long history of co-evolution, AMPs have not lost their ability to kill the microbes totally nor have the microbes learnt to avoid the lethal punch of AMPs. Based upon accumulating positive data, we are encouraged to believe that antimicrobial peptides have a great potential to be the next breakthrough and first novel, truly biological in nature, class of antibiotics. The purpose of this study was twofold; primarily to elucidate the factors involved in governing the peptide activity and toxicity against membranes, and secondly to design a simple approach where we can boost and spread the spectrum of antimicrobial activity against pathogens such as S. aureus and P. aeruginosa for a peptide that is otherwise non-lethal to the bacteria. Results presented in this thesis show that antimicrobial domains of the anaphylatoxin C3a are structurally and evolutionary conserved. Moreover antimicrobial activity is not governed by a single factor, but instead by a combination of net charge, amphipathicity and helicity. By utilizing a low number of amino acid substitutions at strategic positions in an antimicrobial peptide derived from C3a, CNY20, we were able to develop peptides, which exert a significant activity on both S. aureus and C. albicans in contrast to the parent peptide. Although, antimicrobial activity is not governed by single parameter, the activity can still be boosted by end-tagging of a peptide with hydrophobic oligopeptide stretches. This modification promotes peptide binding to bacteria and subsequent cell wall rupture, but does not increase the toxicity or the protease susceptibility of the peptide. It is noteworthy that end tagging of ultra short peptides spanning 5-7 amino acids with hydrophobic amino acids enhances bactericidal activity, while preserving low toxicity and protease resistance

Design and fabrication of structured roughnesses in microchannels with integrated MEMS pressure sensors

As the demand for cooling is increased for high-density powered electronic devices, researchers have proposed solutions to improve the efficiency of the microchannel liquid cooling system to meet these requirements. Introducing roughness in the microchannels is one of the approaches to enhance the heat transfer. The fluid flow behavior needs experimental investigation before exploring the heat transfer efficiency. A test structure consisting MEMS pressure sensors were fabricated along the length of the microchannel. Smooth microchannels and rough microchannels with structured roughness were fabricated. A fabrication process flow has been developed to fabricate the microchannel and the pressure sensor on the same silicon wafer. The fabrication process challenges were solved to achieve the required test structure design. The process characterization of Microspray(TM) SU-8 has been one of the key features to define uniform coating on the silicon wafer. A packaging technique was developed on the fabricated test structures and was successfully implemented in some cases. An experimental setup was designed to test the fabricated test structure. The fabricated MEMS pressure sensors were calibrated and the experimental setup was validated using a smooth microchannel

Thermal management of the LSU micro gas chromatograph

Gas chromatography is a technique widely used for the separation and analysis of gas samples. Gas chromatographs are used for environmental maintenance, monitoring sophisticated biological analyses, and to separate components from a mixture of gases for mass spectrometer analysis. There has been a tremendous interest in miniaturization of gas chromatograph systems because of the potential for portability, faster response time, lower dead volume, lower power consumption, and lower cost of operation. Conventional gas chromatography keeps the column at a constant temperature during separation, which is called isothermal analysis. Temperature programming is a mode of gas chromatography in which the column temperature is raised progressively during the course of analysis. Temperature programming facilitates separation of a wider range of components, when compared to isothermal analysis, in less time. No miniaturized gas chromatograph systems with temperature programming capability have been reported to date. A temperature programming cycle was implemented for the LSU microGC. The thermal behavior of the device was modeled using an energy-based approach to determine the thermal power requirements. Two heaters were designed, one heater gave uniform temperature distribution over the LSU microGC column, and the other gave a linear temperature gradient along the length of microGC. The heaters were fabricated by electrodepositing Ni-Cr (97.5-2.5) alloy on silicon substrates. The heaters were integrated with test microGC. A commercial PID controller was integrated with the heater and fan to direct the temperature programming for the LSU microGC. Heating and cooling ramp rates of more than 2.46 oC/sec were obtained

Cognitive Video Streaming

Video-on-demand (VoD) streaming services are becoming increasingly popular due to their flexibility to allow users to access their favorite video contents anytime, anywhere from a wide range of access devices such as smart phones, computers and TV. The content providers rely on highly satisfied subscribers for revenue generation and there has been significant efforts in developing approaches to “estimate” the quality of experience (QoE) of VoD subscribers. But a key issue is that QoE is not defined, appropriate proxies needs to be found for QoE, via the streaming metrics (the quality of service (QoS) metrics) that are largely based on initial startup time, buffering delays, average bit rate and average throughput and other relevant factors such as the video content and user behavior and other external factors. The ultimate objective of the content provider is to elevate the QoE of all the subscribers at the cost of minimal network resources, such as hardware resources and bandwidth. We propose a cognitive video streaming strategy in order to ensure the QoE of subscribers while utilizing minimal network resources. The proposed cognitive video streaming architecture consists of an estimation module, a prediction module and an adaptation module. Then, we demonstrate the prediction module of the cognitive video streaming architecture through a play time prediction tool. For this purpose, the applicability of different machine learning algorithms such as k-nearest neighbor, neural network regression and survival models are experimented with; then, we develop an approach to identify the most relevant factors that contributed to the prediction. The proposed approaches are tested on data set provided by Comcast Cable

Chemotaxis of Escherichia Coli Towards Norepinephrine Metabolites

The co-existence of ~1014 commensal bacteria and host cells in the human gastrointestinal (GI) tract creates an environment rich in molecules produced by both. The close-proximity of different signals and cells in the GI tract is thought to lead to inter-kingdom (IK) signaling where bacteria and human cells recognize and respond to signals produced by each other. One such IK signaling molecule abundant in the GI tract is Norepinephrine (NE), which is known to increase the virulence and pathogenesis of GI tract pathogen, enterohemorrhagic E. coli (EHEC). It has also been shown that NE is a potent chemoattractant for EHEC as well as for non-pathogenic E.coli. While the effects of NE on virulence are well studied, its role as a chemoeffector is not fully understood. The overall goal of this work is to comprehensively characterize the chemotaxis response of E. coli toward NE and to elucidate the underlying mechanisms. A part of this work is also aimed at developing a probabilistic model to simulate the bacterial migration towards attractants. We showed that attraction of E. coli RP437 towards NE requires prior exposure to a lower concentration of NE during cell growth, and that de novo expression of two enzymes - TynA and FeaB - is required for E. coli chemotaxis towards NE. We discovered that NE is not the actual chemoattractant but the molecule that E. coli RP437 responds to is dihydroxymandelic acid (DHMA) generated from NE. We observed that chemotaxis to DHMA requires the Tsr chemoreceptor and the minimum concentration required for a detectable chemotaxis response was ~5 nM. We also observed that the chemotaxis response to DHMA was significantly reduced at concentrations greater than 50 μM and concluded that negative cooperativity between the two serine-binding sites resulted in attenuation of chemotaxis response. We investigated the molecular mechanism underlying the conversion of NE to DHMA in E. coli RP437, and identified that it requires the QseC histidine kinase and its cognate response regulator QseB, and to a lesser extent, the response regulator QseF. We also determined that the feaR transcription factor is required for tynA and feaB expression. This work is significant as it suggest that host-derived signals such as NE can be converted by commensal bacteria to a potent chemoattractant, which can then recruit pathogens that possess Tsr-like receptors to the site of infection. A probabilistic model was also developed to simulate the chemotaxis behavior of bacteria in microfluidic devices. The time-dependent distribution of bacteria in the chemotaxis chamber was simulated using MATLAB®. We determined that the time dependent bacterial migration in the microfluidic device is influenced by bulk motion of the fluid and existing concentration gradient of chemoeffector. The probabilistic model can be used to reduce the experimental space required to test the response of an unknown chemoeffector in the microfluidic device

Antimicrobial activity of a C-terminal peptide from human extracellular superoxide dismutase

<p>Abstract</p> <p>Background</p> <p>Antimicrobial peptides (AMP) are important effectors of the innate immune system. Although there is increasing evidence that AMPs influence bacteria in a multitude of ways, bacterial wall rupture plays the pivotal role in the bactericidal action of AMPs. Structurally, AMPs share many similarities with endogenous heparin-binding peptides with respect to secondary structure, cationicity, and amphipathicity.</p> <p>Findings</p> <p>In this study, we show that RQA21 (RQAREHSERKKRRRESECKAA), a cationic and hydrophilic heparin-binding peptide corresponding to the C-terminal region of extracellular superoxide dismutase (SOD), exerts antimicrobial activity against <it>Escherichia coli</it>, <it>Pseudomonas aeruginosa</it>, <it>Staphylococcus aureus</it>, <it>Bacillus subtilis </it>and <it>Candida albicans</it>. The peptide was also found to induce membrane leakage of negatively charged liposomes. However, its antibacterial effects were abrogated in physiological salt conditions as well as in plasma.</p> <p>Conclusion</p> <p>The results provide further evidence that heparin-binding peptide regions are multifunctional, but also illustrate that cationicity alone is not sufficient for AMP function at physiological conditions. However, our observation, apart from providing a link between heparin-binding peptides and AMPs, raises the hypothesis that proteolytically generated C-terminal SOD-derived peptides could interact with, and possibly counteract bacteria. Further studies are therefore merited to study a possible role of SOD in host defence.</p