This PDF file contains the front matter associated with SPIE Proceedings Volume 8723, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

Utilizing saliva instead of blood for diagnosis of both local and systemic health is a rapidly emerging field. Recognition of oral-systemic interrelationships for many diseases has fostered collaborations between medicine and dentistry, and many of these collaborations rely on salivary diagnostics. The oral cavity is easily accessed and contains most of the analytes present in blood. Saliva and mucosal transudate are generally utilized for oral diagnostics, but gingival crevicular fluid, buccal swabs, dental plaque and volatiles may also be useful depending on the analyte being studied. Examples of point-of-care devices capable of detecting HIV, TB, and Malaria targets are being developed and discussed in this overview.

Objective: The topological complexity of contact networks in classrooms and the potential transmission of an infectious disease were analyzed by sex and age. Methods: The Tutte polynomials, some topological properties and the number of spanning trees were used to algebraically compute the topological complexity. Computations were made with the Maple package GraphTheory. Published data of mutually reported social contacts within a classroom taken from primary school, consisting of children in the age ranges of 4–5, 7–8 and 10–11, were used. Results: The algebraic complexity of the Tutte polynomial and the probability of disease transmission increases with age. The contact networks are not bipartite graphs, gender segregation was observed especially in younger children. Conclusion: Tutte polynomials are tools to understand the topology of the contact networks and to derive numerical indexes of such topologies. It is possible to establish relationships between the Tutte polynomial of a given contact network and the potential transmission of an infectious disease within such network

Human saliva holds tremendous potential for transforming disease and health diagnostics given its richness of molecular information and non-invasive collection. Enumerating its molecular constituents is an important first step towards reaching this potential. Among the molecules in saliva, proteins and peptides arguably have the most value: they can directly indicate biochemical functions linked to a health condition/disease state, and they are attractive targets for biomarker assay development. However, cataloging and defining the human salivary proteome is challenging given the dynamic, chemically heterogeneous and complex nature of the system. In addition, the overall human saliva proteome is composed of several “sub-proteomes” which include: intact full length proteins, proteins carrying post-translational modifications (PTMs), low molecular weight peptides, and the metaproteome, derived from protein products from nonhuman organisms (e.g. microbes) present in the oral cavity. Presented here will be a summary of communal efforts to meet the challenge of characterizing the multifaceted saliva proteome, focusing on the use of mass spectrometry as the proteomic technology of choice. Implications of these efforts to characterize the salivary proteome in the context of disease diagnostics will also be discussed.

Objective: The objective of this experiment was to compare the salivary protein profiles of saliva specimens from individuals diagnosed with breast cancer with and without HER2/neu overexpression in an attempt to demonstrate how these profiles may be used to study breast cancer progression. Methods: Pooled (n=10 pooled) stimulated whole saliva specimens from women were analyzed. One pooled specimen was from women diagnosed with Stage IIa (T₂N₀M₀) invasive ductal carcinoma (IDC) without positive HER2/neu receptor status. A second pooled specimen was from women diagnosed with Stage IIa (T₂N₀M₀) IDC with a positive HER2/neu receptor status. Experimentally, saliva from each of the pooled samples was trypsinized and the peptide digests labeled with the appropriate iTRAQ reagent. Labeled peptides from each of the digests were combined and analyzed by reverse phase (C18) capillary chromatography on an LC-MS/MS mass spectrometer equipped with an LC-Packings HPLC. Results: The results yielded 34 up-regulated proteins and 37 down regulated proteins that were differentially expressed between HER2/neu receptor positive HER2/neu receptor negative specimens. Conclusions: This pilot study provides support to the novel idea of using salivary secretions to study breast cancer progression.

Saliva is a protein-rich oral fluid that contains information about systemic and oral-specific disease pathogenesis and diagnosis. Technologies are emerging to improve detection of protein components of human saliva for use not only in biomarker discovery, but also for the illumination of pathways involved in oral disease. These include the optimization of liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analysis of saliva in health and disease. Downstream of saliva component identification and validation comes the complex task of connecting salivary proteomic data to biological function, disease state, and other clinical patient information in a meaningful way. Augmentation of database information with biological expertise is crucial for effective analysis of potential biomarkers and disease pathways in order to improve diagnosis and identify putative therapeutic targets. This presentation will use LC-MS/MS analysis of saliva from chronic Graft-versus-Host disease (cGVHD) patients to illustrate these principles, and includes a discussion of the complex clinical and diagnostic issues related to proteomics and biomarker research in cGVHD.

Traumatic brain injury (TBI) is common and especially with military service. In Iraq and Afghanistan, explosive blast related TBI has become prominent and is mainly from improvised explosive devices (IED). Civilian standard of care clinical practice guidelines (CPG) were appropriate has been applied to the combat setting. When such CPGs do not exist or are not applicable, new practice standards for the military are created, as for TBI. Thus, CPGs for prehospital care of combat TBI CPG [1] and mild TBI/concussion [2] were introduced as was a DoD system-wide clinical care program, the first large scale system wide effort to address all severities of TBI in a comprehensive organized way. As TBI remains incompletely understood, substantial research is underway. For the DoD, leading this effort are The Defense and Veterans Brain Injury Center, National Intrepid Center of Excellence and the Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury. This program is a beginning, a work in progress ready to leverage advances made scientifically and always with the intent of providing the best care to its military beneficiaries.

Nearly 2 million Traumatic Brain Injuries (TBI) occur in the U.S. each year, with societal costs approaching $60 billion. Including mild TBI and concussion, TBI’s are prevalent in soldiers returning from Iraq and Afghanistan as well as in domestic athletes. Long-term risks of single and cumulative head impact dosage may present in the form of post traumatic stress disorder (PTSD), depression, suicide, Chronic Traumatic Encephalopathy (CTE), dementia, Alzheimer’s and Parkinson’s diseases. Quantifying head impact dosage and understanding associated risk factors for the development of long-term sequelae is critical toward developing guidelines for TBI exposure and post-exposure management. The current knowledge gap between head impact exposure and clinical outcomes limits the understanding of underlying TBI mechanisms, including effective treatment protocols and prevention methods for soldiers and athletes. In order to begin addressing this knowledge gap, Cleveland Clinic is developing the “Intelligent Mouthguard” head impact dosimeter. Current testing indicates the Intelligent Mouthguard can quantify linear acceleration with 3% error and angular acceleration with 17% error during impacts ranging from 10g to 174g and 850rad/s² to 10000rad/s², respectively. Correlation was high (R² > 0.99, R² = 0.98, respectively). Near-term development will be geared towards quantifying head impact dosages in vitro, longitudinally in athletes and to test new sensors for possible improved accuracy and reduced bias. Long-term, the IMG may be useful to soldiers to be paired with neurocognitive clinical data quantifying resultant TBI functional deficits.

Traumatic brain injury (TBI) is a significant biomedical problem among military personnel and civilians. There exists an urgent need to develop and refine biological measures of acute brain injury and chronic recovery after brain injury. Such measures “biomarkers” can assist clinicians in helping to define and refine the recovery process and developing treatment paradigms for the acutely injured to reduce secondary injury processes. Recent biomarker studies in the acute phase of TBI have highlighted the importance and feasibilities of identifying clinically useful biomarkers. However, much less is known about the subacute and chronic phases of TBI. We propose here that for a complex biological problem such as TBI, multiple biomarker types might be needed to harness the wide range of pathological and systemic perturbations following injuries, including acute neuronal death, neuroinflammation, neurodegeneration and neuroregeneration to systemic responses. In terms of biomarker types, they range from brain-specific proteins, microRNA, genetic polymorphism, inflammatory cytokines and autoimmune markers and neuro-endocrine hormones. Furthermore, systems biology-driven biomarkers integration can help present a holistic approach to understanding scenarios and complexity pathways involved in brain injury.

Glutamate is the primary excitatory neurotransmitter used by the central nervous system (CNS) for synaptic communication, and its extracellular concentration is tightly regulated by glutamate transporters located on nearby astrocytes. Both animal models and human clinical studies have demonstrated elevated glutamate levels immediately following a traumatic brain event, with the duration and severity of the rise corresponding to prognosis. This rise in extracellular glutamate likely results from a combination of excessive neurotransmitter release from damaged neurons and down regulation of uptake mechanisms in local astrocytes. The immediate results of a traumatic event can lead to necrotic tissue in severely injured regions, while prolonged increases in excitatory transmission can cause secondary excitotoxic injury through activation of delayed apoptotic pathways. Initial TBI animal studies utilized a variety of broad glutamate receptor antagonists to successfully combat secondary injury mechanisms, but unfortunately this same strategy has proven inconclusive in subsequent human trials due to deleterious side effects and heterogeneity of injuries. More recent treatment strategies have utilized specific glutamate receptor subunit antagonists in an effort to minimize side effects and have shown promising results. Future challenges will be detecting the concentration and kinetics of the glutamate rise following injury, determining which patient populations could benefit from antagonist treatment based on their extracellular glutamate concentrations and when drugs should be administered to maximize efficacy.

Brain diseases such as cancers, neurodegenerative disorders, or trauma are frequently diagnosed with imaging modalities and sometimes with intracranial biopsies. Treatment response is similarly monitored, along with clinical indications. While these technologies provide important windows into the disease state, they fail to provide us a detailed molecular portrait of the disease and of the changes taking place during therapy. Exosomes are virus-sized nanovesicles derived from the endosomal system and are released extracellularly from essentially all cell types. Exosomes contain intracellular entities (proteins, nucleic acids, metabolites), membrane proteins and lipids, and even extracellular proteins bound to them. Exosomes may be considered as mini-surrogates of their cells of origin, with some content common to all cells/exosomes, but some of the content would be cell-specific. These vesicles are found in all biofluids in humans, and are thus accessible to “liquid biopsy” with harvest of vesicles from such fluids. Current challenges are to identify disease-related markers or panels of markers to distinguish the disease state. Here we will show examples of brain tumor markers found in/on exosomes from cell culture and patient sera, and we will suggest that aspects of the biology of disease may have a relevant place in the search for biomarkers.

Polycythemia is a universal lowlander response to altitude; healthy Andean high-altitude natives also have elevated [Hb]. While this may enhance O₂ transport to tissues, studies have shown that acute isovolumic changes in [Hb] do not affect exercise capacity. Many high-altitude Tibetans have evolved sea-level values of [Hb], providing a natural opportunity to study this issue. In 21 young healthy male Tibetans with [Hb] between 15 and 23 g/dl, we measured VO₂MAX and O₂ transport capacity at 4200m. VO₂MAX was higher when [Hb] was lower (P<0.05), enabled by both higher cardiac output and muscle O₂ diffusional conductance, but neither ventilation nor the alveolar-arterial PO₂ difference (AaPO₂) varied with [Hb]. In contrast, Andean high altitude natives remain polycythemic with larger lungs and higher lung diffusing capacity, a smaller exercising AaPO₂, and lower ventilation. The challenges now are (1) to understand the different adaptive pathways used by Andeans and Tibetans, and (2) to determine in Tibetans whether, during evolution, reduced [Hb] appeared first, causing compensatory cardiac and muscle adaptations, or if enhanced cardiac function and muscle O₂ transport capacity appeared first, permitting secondary reduction in [Hb]. For (2), further research is necessary to determine the basis of enhanced cardiac function and muscle O₂ transport, and identify molecular targets of evolution in heart and muscle. Putative mutations can then be timed and compared to appearance of those affecting [Hb].

Cardiovascular adjustments accompanying exercise in high ambient temperatures are likely responsible for diminished aerobic capacity and performance in such conditions. These adjustments include a phenomenon known as cardiovascular drift in which heart rate rises and stroke volume declines progressively over time during constant-rate exercise. A variety of factors modulate the magnitude of cardiovascular drift, e.g., elevated core and skin temperatures, dehydration, and exercise intensity. Regardless of the mode of manipulation, decreases in stroke volume associated with cardiovascular drift result in directionally and proportionally similar decreases in maximal aerobic capacity. Maximal aerobic capacity is determined by maximal heart rate, maximal tissue oxygen extraction, and maximal stroke volume. Because maximal heart rate and maximal tissue oxygen extraction are unaffected during exercise in the heat, decreased stroke volume associated with cardiovascular drift likely persists during maximal efforts and explains the decrease in maximal aerobic capacity. Decreased maximal aerobic capacity results in a greater perceptual and physiological strain accompanying any given level of work. Therefore, sustaining and enhancing performance involves sophisticated monitoring of physiological strain combined with development of countermeasures that mitigate the magnitude of deleterious phenomena like cardiovascular drift.

This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.

Bioluminescent imaging is an emerging biomedical surveillance strategy that uses external cameras to detect in vivo light generated in small animal models of human physiology or in vitro light generated in tissue culture or tissue scaffold mimics of human anatomy. The most widely utilized of reporters is the firefly luciferase (luc) gene; however, it generates light only upon addition of a chemical substrate, thus only generating intermittent single time point data snapshots. To overcome this disadvantage, we have demonstrated substrate-independent bioluminescent imaging using an optimized bacterial bioluminescence (lux) system. The lux reporter produces bioluminescence autonomously using components found naturally within the cell, thereby allowing imaging to occur continuously and in real-time over the lifetime of the host. We have validated this technology in human cells with demonstrated chemical toxicological profiling against exotoxin exposures at signal strengths comparable to existing luc systems (~1.33 × 10⁷ photons/second). As a proof-in-principle demonstration, we have engineered breast carcinoma cells to express bioluminescence for real-time screening of endocrine disrupting chemicals and validated detection of 17β-estradiol (EC₅₀ = ~ 10 pM). These and other applications of this new reporter technology will be discussed as potential new pathways towards improved models of target chemical bioavailability, toxicology, efficacy, and human safety.

CdSe/ZnS semiconductor quantum dots (QDs) are ideal materials for biological sensing and cellular imaging applications due to their superior photophysical properties in comparison to fluorescent proteins or dyes and their ease of conjugation to biological materials. We have previously developed a number of in vitro FRET based biosensors in the laboratory for detection of proteases and biological and chemical agents. We would like to expand these biosensing capabilities into cellular systems, requiring development of QD cellular delivery techniques. Peptide mediated cellular delivery of QDs is ideal as peptides are small, easily conjugated to QDs, easily manipulated and synthesized, and can be designed with “handles” for further chemical conjugation with other cargo. Here we discuss four cell delivery peptides that facilitate QD uptake in live cells. Understanding these peptides will help us design better nanoparticle cellular delivery systems and advance our capabilities for in vivo biosensing.

Single domain antibodies (sdAb) are variable domains cloned from camel, llama, or shark heavy chain only antibodies, and are among the smallest known naturally derived antigen binding fragments. SdAb derived from immunized llamas are able to bind antigens with high affinity, and most are capable of refolding after heat or chemical denaturation to bind antigen again. We hypothesized that the ability to produce heterodimeric sdAb would enable reagents with the robust characteristics of component sdAb, but with dramatically improved overall affinity through increased avidity. Previously we had constructed multimeric sdAb by genetically linking sdAb that bind non-overlapping epitopes on the toxin, ricin. In this work we explored a more flexible approach; the construction of multivalent binding reagents using multimerization domains. We expressed anti-ricin sdAb that recognize different epitopes on the toxin as fusions with differently charged leucine zippers. When the initially produced homodimers are mixed the leucine zipper domains will pair to produce heterodimers. We used fluorescence resonance energy transfer to confirm heterodimer formation. Surface plasmon resonance, circular dichroism, enzyme linked immunosorbent assays, and fluid array assays were used to characterize the multimer constructs, and evaluate their utility in toxin detection.

This paper presents the design, system structure and performance for a wireless and wearable diet monitoring system. Food and drink intake can be detected by the way of detecting a person’s swallow events. The system works based on the key observation that a person’s otherwise continuous breathing process is interrupted by a short apnea when she or he swallows as a part of solid or liquid intake process. We detect the swallows through the difference between normal breathing cycle and breathing cycle with swallows using a wearable chest-belt. Three popular machine learning algorithms have been applied on both time and frequency domain features. Discrimination power of features is then analyzed for applications where only small number of features is allowed. It is shown that high detection performance can be achieved with only few features.

This paper presents a novel approach to estimate the soil ionic concentration by way of multi-frequency impedance measurements and using the quasi-static dielectric mixing models to infer the various ionic concentrations. In our approach, the permittivity of the soil dielectric mixture is measured using impedance spectroscopy and the results are used as input parameters to dielectric mixing models, combined with the debye-type dielectric relaxation models. We observe that the dielectric mixing models work well for low RF (radio-frequency) range and help in determining the individual ionic concentration in a multi-component soil mixture. Using the fact that the permittivity of a dielectric mixture is proportional to its impedance, we validated our approach by making multi-frequency impedance measurements of a soil mixture at different concentrations of various components. The method provides a good estimate of individual components such as air, water and ions like nitrates. While the paper is written with the perspective of soil constituent concentration determination, the underlying principle of determining individual component concentration using multi-frequency impedance measurement is applicable more generally in areas such as characterizing biological systems like pathogens, quality control of pharmaceuticals etc.

Two-dimensional angle-resolved optical scattering (TAOS) is an experimental method which collects the intensity pattern of monochromatic light scattered by a single, micron-sized airborne particle. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. The solution proposed herewith relies on a learning machine (LM): rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified. The LM consists of two interacting modules: a feature extraction module and a linear classifier. Feature extraction relies on spectrum enhancement, which includes the discrete cosine Fourier transform and non-linear operations. Linear classification relies on multivariate statistical analysis. Interaction enables supervised training of the LM. The application described in this article aims at discriminating the TAOS patterns of single bacterial spores (Bacillus subtilis) from patterns of atmospheric aerosol and diesel soot particles. The latter are known to interfere with the detection of bacterial spores. Classification has been applied to a data set with more than 3000 TAOS patterns from various materials. Some classification experiments are described, where the size of training sets has been varied as well as many other parameters which control the classifier. By assuming all training and recognition patterns to come from the respective reference materials only, the most satisfactory classification result corresponds to ≈ 20% false negatives from Bacillus subtilis particles and ≤ 11% false positives from environmental and diesel particles.

Fine particulate matter measurements (PM_2.5) are essential for air quality monitoring and related public health; however, the shortage of reliable measurmennts constrains researchers to use other means for obtaining reliable estimates over large scales. In particular, model forecasters and satellite community use their respective products to develop ground particulate matter estimations but few experiments have explored how the remote sensing approaches compare to the high resolution models. . In this paper we focus on studying the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellites (GOES) regression based estimates in comparison to more direct bias corrected outputs from the Community Multiscale Air Quality (CMAQ) model, We use a two-year dataset (2005-2006) and apply urban, season and hour filters to illustrate the agreement between estimated and in-situ measured fine particulate matter from the New York State Department of Environmental Conservation (NYSDEC). We first begin by analyzing the correspondence between ground aerosol optical depth (AOD) measurements from an AERONET (AErosol RObotic NETwork) Cimel sun/sky radiometer with both satellite and model products in one urban location; we show that satellite readings perform better than model outputs, especially during the summer (R_MODIS>=0.65, R_CMAQ>=0.37). This is a clear symptom of the difficulty in the models to properly model realistic optical properties. We then turn to a direct assessment of PM2.5 presenting individual comparisons between ground PM_2.5 measurements with satellite/model predictions and demonstrate the higher accuracy from model estimations (R^urban_MODIS ≥ 0.74, R^urban_CMAQ ≥ 0.77; R^non-urban_MODIS ≥ 0.48, R^non-urban_CMAQ ≥ 0.78). In general, we find that the bias corrected CMAQ estimates are superior to satellite based estimators except at very high resolution. Finally, we show that when using both model and satellite approximations as separate estimators merged optimally, our product (PM_2.5 average) becomes closer to real measurements with improved correlations (R_AVE ~ 0.86) in urban areas during the summer.

Information on current and anticipated moisture and thermal condition from satellite data represents a source of affordable yet careful information for malaria forecasters to implement and control of epidemic. During the last decades Orissa state in India suffered from highest level of malaria incidence. This situation requires frequent monitoring of environmental conditions and dynamics of malaria occurrence. During 1985 to 2004 the NOAA AVHRR global vegetation index (GVI) dataset and its vegetation health (VH) have been studied and used as proxy for malaria fluctuation. This paper discusses applications of VH for early detecting and monitoring malaria incidence in Orissa. A significant relationship between satellite data and annual malaria incidences is found at least three months before the major malaria transmission period.

Using computer algebra, the respiratory infection of the histoplasma capsulatum fungus was modeled and analyzed; the effects of the infection could also be described as a change in the lungs capacity to expand (associated with its elastic modulus). A further analysis to the immune system was also done in order to describe and model the way the body can handle those kinds of infections once they are hosted in the body. Using those models we can describe the behavior of the respiratory infection and then how to reduce or control its effects. As an investigation in the medical field, we need to test the models obtained and compare the results with the real infection behavior. The models where made based on the diffusive-convective equation; giving some initial and boundary conditions, we can get to the results obtained, which can describe how the infection is spreading and with a previous study of the immune system, the infection control done by the body can also be modeled.

Maple’s Image Tools package was used to process medical images. The results showed clearer images and records of its intensities and entropy. The medical images of a rhinocerebral mucormycosis patient, who was not early diagnosed, were processed and analyzed using Maple's tools, which showed, in a clearer way, the affected parts in the perinasal cavities.

Polybrominated Diphenyl Ethers (PBDEs) are flame retardants widely used in many commercial products, including building materials, electronics, furnishings, motor vehicles, airplanes, plastics, polyurethane foams, and textiles. Although the specific toxic action of these chemicals is not clear, it is reported that they can cause serious damage to the nervous, reproductive, and endocrine systems. These chemicals are branded as “probable carcinogens” by Environmental Protection Agency (EPA). Therefore, this study is taken up to investigate the expression of genes namely, TP-53, RAD1, CRADD, and ATM, which are involved in apoptosis, DNA repair and cell cycle regulation. For this study human umbilical vein endothelial cells (HUVEC) are exposed to 5 μM of BDE-85 (a penta-BDE) and BDE-209 (deca-BDE). The results of this report reveal significant alteration in all the genes under investigation in BDE-85 and BDE-209 exposed cells. The BDE-85 induced responses are significantly more than BDE-209. These results emphasize the congener specific action of PBDEs on the expression of genes relevant to DNA repair and cell division of HUVEC cells.

Radio wave frequency (RF) radiation emitted from cellular telephones has become increasingly ubiquitous as a result of the popularity of these phones. With the increasing and unavoidable exposure to RF radiation a reality, it is imperative that the effects of such radiation on living tissue be well understood. In particular, it is critical to understand any effects that RF radiation may have as a carcinogen and on embryonic development, as pregnant women are not exempt from such exposure. As a model organism, zebrafish (Danio rerio) have been studied extensively, and their value in studies of gene expression cannot be overstated. This study observed the effects of RF radiation on the embryonic development of zebrafish. The expression of two genes, shha and hoxb9a, that are key to the early development of the fish was examined. Both genes have homologs in humans as well as in other model organisms. Preliminary results suggest that exposure to cell phone radiation might have an effect on the expression of shha in zebrafish embryos, causing under expression. More trials are necessary to validate these results.

The proposed approach in this article applies an efficient and novel statistical technique to accurately describe radiometric data measured by Advanced Very High Resolution Radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration’s (NOAA) Polar Orbiting Environmental Satellites (POES). The corrected data set will then be applied to improve the strength of NOAA Global Vegetation Index (GVI) data set for the 1982- 2003 period produced from AVHRR. The GVI is used extensively for studying and monitoring land surface, atmosphere and recently for analyzing climate and environmental changes. The POES AVHRR data, though useful, cannot be directly used in climate change studies because of the orbital drift in the NOAA satellites over the lifetime of the satellites. This orbital drift causes inaccuracies in AVHRR data sets for some satellites. The main goal is achieved by implementing a statistical technique that uses an Empirical Distribution Function (EDF) to produce error free long-term time-series for GVI data sets. This technique permits the representation of any global ecosystem from desert to tropical forest and to correct deviations in satellite data that are due to orbital drifts and AVHRR sensor degradations. The primary focus of this research is to generate error free satellite data by applying the EDF technique for climatological research.

The substance 1–methyl–4–phenyl–1, 2, 3, 6 tetrahy dropyridine (MPTP) has been studied as a major cause of neurodegeneration dopaminica, which is specifically related to Parkinson's disease. The analysis is in terms of the diffusion of the substance to the mammalian brain, by evaluating the diffusion equation in a spherical coordinate system, being η (collective diffusion term) spatially modulated. Although the progress of the disease with respect to time has not been established with certainty, an attempt to find a stable pattern of the concentration of MPTP and its effects has been made.

Small unmanned aerial vehicles UAVs (SUAVs), micro air vehicles (MAVs), Automated Target Recognition (ATR), and munitions guidance, require extreme operational agility and robustness which can be partially offset by efficient bioinspired imaging sensor designs capable to provide enhanced guidance, navigation and control capabilities (GNC). Bioinspired-based imaging technology can be proved useful either for long-distance surveillance of targets in a cluttered environment, or at close distances limited by space surroundings and obstructions. The purpose of this study is to explore the phenomenology of image formation by different insect eye architectures, which would directly benefit the areas of defense and security, on the following four distinct areas: a) fabrication of the bioinspired sensor b) optical architecture, c) topology, and d) artificial intelligence. The outcome of this study indicates that bioinspired imaging can impact the areas of defense and security significantly by dedicated designs fitting into different combat scenarios and applications.

The unique functional characteristics of nanostructured material are stemming mainly from a large surface-to-volume-ratio and on quantum effects; can yield numerous potential space defense applications. The objective of this study is to explore the polarimetric characterization of polymer nanomaterials, using Mueller matrix and Stokes parameters analysis. Specifically, gold nanoparticles were dispersed within a matrix of two-different polymer domains and their polarimetric response to infrared light was studied.

The objective of the study is to present integrative paradigms highlighting their applicability of polarimetry to multidisciplinary areas such as space defense and bioscience applications. Polarimetric sensing and imaging offer unique advantages for a wide range of detection and classification problems due to the intrinsic potential for high contrast in different polarization components of the backscattered light. Indeed, polarized imaging can yield high-specificity images under high-dynamic range and extreme condition scenarios, in scattering media, or cluttered environments, offering at the same instance information related to the object material composition and its surface characteristics. In this study, a new imaging approach based on polarimetric detection principles will be introduced and the Mueller matrix formalism will be defined, and will be applied for space applications, such as detection of unresolved objects, as well as for early cancer detection. The design principles of the liquid crystal polarimetric imaging system will be introduced and related to operating conditions and system performance metrics. The depolarization, diattenuation, and retardance of the materials will be estimated using Mueller matrix decomposition for different aspect angles.