Browsing by Author "Moussa, Hanna"
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Item Cellulose and chitin based composites: Preparation and characterization(2017-08) Wansapura, Poorna Tharaka; Abidi, Noureddine; Hequet, Eric; Hamood, Abdul; Mendu, Venugopal; Moussa, HannaNovel cellulose–cadmium-tellurium quantum dot (cellulose-CdTeQD) and chitin–cadmium-tellurium quantum dot (chitin-CdTeQD) hybrid films were prepared via a facile aqueous synthesis route held at room temperature using cellulose, chitin, and CdTe quantum dots (CdTeQDs). Films were characterized by field emission scanning, electron microscopy and energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and thermogravimetric analysis. Antibacterial activity was studied on both gram positive (Staphylococcus aureus) and gram negative (Pseudomonas aeruginosa) bacteria. Antibacterial properties were tested with an agar diffusion testing assay along with confocal laser scanning microscopic analysis. Chitin–CdTeQD film exhibited an excellent antibacterial activity against both gram-positive and gram-negative bacteria. Cellulose–CdTeQD film exhibited an excellent antibacterial activity against only gram-positive bacteria. These films might be a desirable antibacterial material that could have potential use in biomedical applications such as wound dressings, burn treatment, drug delivery systems, ophthalmology, and implants.Item Developing Machine Learning Tools for Quantitative Analyses of Biomedical Images(2023-05) Yosofvand, Mohammad; Moussa, Hanna; Parameswaran, Siva; Maldonado, Victor; Nejat, Ali; Zu, YujiaoBiomedical and pathology images have a significant role in medical and research applications in both diagnosis and disease treatment. With the evolution of digital scanning tools, increasing computing power, and deep learning tools, it is necessary to provide researchers with accurate, fast, and user-friendly tools that can be used in clinical and research applications. In this dissertation, we applied these tools to two biomedical areas and diseases: the first is obesity by studying adipose tissue histology images; the second is breast cancer by studying tumor histology slides. First adipose tissue images were analyzed using the AdipoGauge software that we developed. This software can detect adipose cells, count them, and calculate the area of all cells very accurately. It also can find areas of interest in adipose tissue and calculate the required data for research purposes such as cell size and cell number in different biomedical slides. AdipoGauge contains different analysis tools such as object removal, bordering cell detection, and cell size categorization which makes the analysis process more accurate. We demonstrated that the results from the AdipoGauge software were more accurate than similar software such as ImageJ. Next, the framework of the AdipoGauge was used to develop a new tool for quantitative analyses of breast tumor histology images. Detection of the Tumor Infiltrating Lymphocytes (TILs) in cancer images has gained significant importance as it can be used as a biomarker to guide cancer detection and treatment. Based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. A U-Net deep learning model was employed and trained for different parameters to achieve the highest accuracy possible which is 98% accuracy in the stroma detection task. The segmented stroma images then were used to implement the Mask R-CNN model for the TILs detection task. The R-CNN model detected the TIL cells in different images and was used as the backbone analysis network for the GUI development of the TILs detection tool. In conclusion, we developed sophisticated machine learning tools and software that will assist researchers and clinicians in rapid and accurate analysis of histology and pathology slides, which will help future research and/or treatment strategies.Item Eicosapentaenoic acid regulates brown adipose tissue metabolism in high-fat-fed mice and in clonal brown adipocytes(Elsevier, 2017-01) Pahlavani, Mandana; Razafimanjato, Fitia; Ramalingam, Latha; Kalupahana, Nishan S.; Moussa, Hanna; Scoggin, Shane; Moustaid-Moussa, NaimaBrown adipose tissue (BAT) plays a key role in energy expenditure through its specialized thermogenic function. Therefore, BAT activation may help prevent and/or treat obesity. Interestingly, subcutaneous white adipose tissue (WAT) also has the ability to differentiate into brown-like adipocytes and may potentially contribute to increased thermogenesis. We have previously reported that eicosapentaenoic acid (EPA) reduces high-fat (HF)-diet-induced obesity and insulin resistance in mice. Whether BAT mediates some of these beneficial effects of EPA has not been determined. We hypothesized that EPA activates BAT thermogenic program, contributing to its antiobesity effects. BAT and WAT were harvested from B6 male mice fed HF diets supplemented with or without EPA. HIB 1B clonal brown adipocytes treated with or without EPA were also used. Gene and protein expressions were measured in adipose tissues and H1B 1B cells by quantitative polymerase chain reaction and immunoblotting, respectively. Our results show that BAT from EPA-supplemented mice expressed significantly higher levels of thermogenic genes such as PRDM16 and PGC1α and higher levels of uncoupling protein 1 compared to HF-fed mice. By contrast, both WATs (subcutaneous and visceral) had undetectable levels of these markers with no up regulation by EPA. HIB 1B cells treated with EPA showed significantly higher mRNA expression of PGC1α and SIRT2. EPA treatment significantly increased maximum oxidative and peak glycolytic metabolism in H1B 1B cells. Our results demonstrate a novel and promising role for EPA in preventing obesity via activation of BAT, adding to its known beneficial anti-inflammatory effects.Item Estimation of absorbed dose to adipose tissue from full field digital mammogram using Monte-Carlo simulation(2018-12) Rashid, Al Maqsudur; Moussa, Hanna; Moussa, Hanna; Kumar, Golden; Parameswaran, Siva; Abidi, Noureddine; Ramalingam, LathaRecent study has found influential role of inflamed adipose (fat) tissue in breast cancer due to low dose radiation. Various inflammatory factors secreted from these irradiated adipose cells might stimulate surrounding tissues including dormant cancer cells. Previously, only glandular tissues were considered most radio-sensitive organ in breast hence no dosimetry studies exist on breast adipose tissues. The objective of this study was full field digital mammogram (FFDM) radiation dosimetry in adipose tissue region of the breast. The average energy deposition in the adipose tissue was simulated as a function of different target-filter materials, quality of x-ray beam (half value layer), percentage of adipose tissue, compressed breast thickness, peak tube voltages (kVp), and tube current-time products (mAs). These variables were taken from recommended imaging techniques published in the technique charts by the manufacturer corresponding to patient’s anatomy and actual mammogram experiment data of anonymous patient’s courtesy. The absorbed doses were simulated and estimated using Monte Carlo N-Particle (MCNP 6) transport codes developed by Los Alamos national laboratory. The obtained dose information’s could be helpful for breast cancer research and provide data necessary for epidemiological studies to improve various risk models such as those presented in BEIR (biological effects of ionizing radiation) or ICRU (International Commission on Radiological Protection) reports.Item Exploring Potential Applications of Fourier Transform Infrared Microspectroscopy Imaging(2017-11-27) Liyanage, Sumedha priyadarshani; Abidi , Noureddine; Hequet, Eric; Moustaid-Moussa, Naima; Mendu, Venugopal; Moussa, HannaFourier Transformed Infrared microspectroscopy (FTIR) Imaging is a reliable tool to investigate biochemical changes in biological samples. The FTIR image is composed of a grid of infrared spectra, where each pixel has an individual spectrum that reveals the chemical composition of a given pixel. Each vibration in an IR spectrum is assigned to a chemical functional group. The location, intensity, and shape of IR vibrations provide information regarding the concentration, the molecular structure, and the chain conformation of biomolecules present in biological samples. The functional group distribution maps can be used for spatial characterization of different chemical functional groups without sample staining. Although the FTIR imaging has been increasingly used for analyzing biological tissues, the main application has been shifted towards cancer investigations. Only limited studies were reported on FTIR imaging of plant cells and tissues. In this study, a diverse range of biological tissues was analyzed using FTIR imaging to explore the potential uses of FTIR imaging. The FTIR images can produce inaccurate results due to several reasons (ex. sample contaminations and uneven sample thickness). Therefore, sample preparation, data collection, and analysis were optimized for each study. First, the biochemical composition of individual plant cells was investigated using onion epidermal cells as model plant cells. The compositional variability within individual onion tissues and between immature and mature onion cells were identified. Second, the distribution of cellulose in individual cotton fibers at different phases of fiber maturity, and the continuous biochemical changes associated with fiber development in individual cotton fibers were investigated. Next, cryo-sectioning of different mouse tissues was optimized, and high-fat diet induced biochemical changes in liver and adipose tissues (brown and white adipose tissues), harvested from mice fed either a low-fat or a high-fat diet, were investigated. Future target of this study is to investigate the beneficial effects of different plant extracts, such as dietary fibers (ex. guar gum), antioxidants (ex. grape), and polyunsaturated fatty acids (ex. olive oil), to reduce obesity-associated metabolic disorders. Although, the study of induced changes in biomolecules of biological samples, in plants and animals is not a simple task, cryosectioning followed by FTIR imaging could provide new insight to understand those changes.Item FTIR microspectroscopy study of compositional changes in biological samples(2017-08) Parajuli, Prakash; Abidi, Noureddine; Hequet, Eric; Moussa, Hanna; Ramalingam, LathaFourier Transform Infrared (FTIR) microspectroscopy has emerged as a powerful technique for analyzing biological samples. Obtaining spatially resolved molecular information from the samples is one of the major advantages of this technique. Qualitative, as well as quantitative analysis of the sample, is possible with the use of this technique. It gives information regarding the intrinsic molecular chemistry of the sample. It could be applied for the analysis of wide range of samples. In this study, we used FTIR microspectroscopy to investigate the changes in the chemical composition of mouse adipose tissue upon oxygen plasma treatment and cellulose substrate upon plasma treatment and grafting of vinyl laurate. The first chapter describes the instrumentation and principle of operation of FTIR microspectroscopy and microwave plasma. Moreover, it provides a description of adipose tissue and cellulose. The chapter also covers reactive oxygen species (ROS), their functions and their role in oxidative stress. In the second chapter, the effect of ROS on lipids and proteins of mouse white adipose tissue was studied using FTIR microspectroscopy. Microwave plasma was used to induce oxidative damage to the biomolecules of white adipose tissue (WAT) of mouse through insitu production of ROS. The analysis of the IR spectra extracted from the infrared (IR) images of adipocyte and ECM of the mouse white adipose tissue showed a significant effect of ROS on lipids. Mainly, unsaturated lipids were found to be highly affected by ROS as a drastic decrease in the area of the band attributed to the olefinic (=CH-) vibration from lipid was observed. Similarly, changes associated with saturated lipids were also observed. Along with the decrease in the area of bands assigned to lipids, significant increase in the area of carbonyl (C=O) band was observed. However, amide bands from proteins did not change significantly, indicating that proteins are comparatively more resistant to ROS than lipids. Chemimaps and band ratio images were developed from the FTIR image recorded after each treatment. Chemimaps clearly showed decreasing concentrations of olefinic group and increasing concentration of the carbonyl group. Similarly, band ratio images showed decreasing intensity of olefinic/lipid ratio and increasing intensity of carbonyl/lipid ratio. In the third chapter, grafting of vinyl laurate monomer on the regenerated cellulose film and cotton fibers was studied using FTIR microspectroscopy. The grafting of monomer on the cellulose films and cotton fibers was initiated by means of microwave plasma treatment. The analysis of spectra extracted from the FTIR images of the monomer grafted samples showed the presence of additional peaks at 1735, 2925 and 2855 cm-1. Comparison of correlation coefficient between the spectra of the control and those of treated samples showed significantly higher correlation in treated samples compared to control samples. Chemical distribution maps showed non-uniform distribution of vinyl laurate on the cellulose film as indicated by a non-uniform distribution of carbonyl group on the FTIR image of the cellulose film. In the final chapter, a brief summary of three chapters is included. It also describes the importance of FTIR imaging and future directions of the projects described in chapters 2 and 3. Overall, the results showed that FTIR imaging is effective in analyzing animal tissue as well as plant materials. This shows a wide range of application of FTIR imaging and could be used as a rapid and sensitive technique to monitor subtle changes in biomolecules. The simplicity of the sample preparation, non-destructiveness, and ease in obtaining results has made it an excellent tool for the analysis of biological samples. Overall, the present study is further important as it sheds light on the compositional changes occurring in the macromolecular content of biological samples, which could be useful for further studies.Item Radiation Exposure Estimates for Deep Space Missions Revisited(47th International Conference on Environmental Systems, 2017-07-16) Townsend, Lawrence; de Wet, Wouter; Zaman, Fahad; McGirl, Natalie; Heilbronn, Lawrence; Moussa, HannaVarious studies of potential exposures to the space radiation environment for astronauts on missions beyond low-Earth orbit (LEO) have been carried out and the results published in conferences and journals. These studies involved estimating radiation exposures from galactic cosmic rays and/or solar energetic particle events for missions to the moon, Mars and beyond. Estimating potential risks usually involved comparing the calculated doses and effective doses to NASA limits published in NASA Standard 3001. These radiation limits consist of: (1) short-term limits imposed to prevent early effects such as nausea, vomiting and lethality; (2) non-cancer lifetime limits are imposed to prevent late term degenerative effects in the lens of the eye, central nervous system, cardiovascular system; and (3) career effective dose limits to prevent a 3% excess risk of exposure induced death (REID) from cancer at the 95% confidence limit. Specific effective dose values for the career effective dose limits depend on the particulars of the specific mission, including the gender of the crew member, the expected environments, and the length of the mission. Many previous studies compared the estimates of effective doses for mission crew members to a table in NASA Standard 3001 that listed values for the 3% excess cancer risk. These values did not include the 95% confidence limits, which would have reduced those effective doses by an approximate factor of 3. Hence, some studies suggested that the estimated effective doses would be below the career limits, when in fact they would exceed those limits. In this work, we revisit some of these deep space mission analyses and compare the estimated doses and effective doses to the correct limits which include the 95% confidence limits.Item Size-effects in deformation behavior of metallic glasses(2019-08) Meduri, Chandra Sekhar; Blawzdziewicz, Jerzy; Kumar, Golden; Kim, Jungkyu; Moussa, Hanna; Qiu, Jingjing; Cong, WeilongMetallic glasses are metal alloys with disordered atomic structure. Due to their amorphous structure, they exhibit a unique set of properties that are ideal for wide range of applications including electrical transformers, sporting goods, fuel cells, precision gears for micromotors etc. The near-theoretical strength (1-3 GPa), exceptionally high elastic limit (2-3%), and excellent formability (down to nanoscale) of these materials are desirable in structural applications, micro and nanodevices in particular. On the downside, the amorphous structure also results in zero tensile ductility at room temperature. The plastic strain localizes in narrow shear bands (~20-40 nm in thickness) at low temperatures and high stresses. The nucleation and propagation of shear bands depends on multiple parameters such as, the elastic constants, the sample size and processing, and the testing conditions (temperature, strain-rate, and loading geometry). Studying the effects of these variables and linking them to a unifying flow model is critical for fundamental understanding and improving the intrinsic ductility of metallic glasses. While many of these aspects are well documented, the sample size dependence has been poorly understood, and even hotly debated. This study investigates the sample size and testing temperature effects on shear banding process through tensile and bend tests. Under tensile loading, sample size and temperature effects on fracture morphologies and deformation modes were explored in Pt-based metallic glass. Thermoplastic drawing procedure was utilized to fabricate ASTM grade tensile specimens with diameters ranging from 500 µm to 150 nm. Constant strain-rate (10-2 s-1) tensile tests at different temperatures ranging from cryogenic to glass transition temperature were conducted using a customized setup. Analysis of fracture morphologies from these high-throughput tests show a gradual transition from catastrophic shear bands to slow shear bands, to shear bands plus some distributed plastic flow, and eventually necking to a point flow was observed as sample size was reduced. In addition, it was observed that a decrease in sample size has a similar effect as a decrease in testing temperature on the deformation behavior both in shear localization, and homogeneous/necking regime. In the shear localization regime, an increasing contribution of thermal softening (through shear offset) and decreasing contribution of defect development (through coalescence of nanovoids and formation of microcracks) to the final fracture was observed as sample size and/or temperature decreases. In the homogeneous regime, the shear band stability and thus the ductility increased with decreasing sample size and/or testing temperature. In addition, bend tests were conducted on Zr- and Pt-based metallic glasses in the temperature range (0.1Tg-0.8Tg). The results show an increase in bending strain, shear band density, and critical shear offset with decreasing temperature. The observed size-temperature equivalence in both bending and tension was discussed based on fundamental plastic flow units, Shear Transformation Zones (STZs). In an attempt towards a unified flow theory to describe the fracture of metallic glasses, the results obtained in this study were analyzed using the existing plastic flow models.Item Solar Particle Event Dose Forecasting Using Regression Techniques(44th International Conference on Environmental Systems, 2014-07-13) Moussa, Hanna; Townsend, Lawrence W.Doses from solar particle events can be a serious threat to the wellbeing of crews traveling through space. Therefore predicting the time that such event will take place, forecasting the dose buildup over time, and the total dose from such event is needed to enable crews to take actions to mitigate the effects by entering a shielded area designed for their protection. Earlier work developed methods that used neural networks and Bayesian methods to forecast the total dose and dose versus time profile from an event. Subsequently, Locally Weighted Regression (LWR) and Kernel Regression (KR) techniques have been investigated to forecast the total dose. In this work, Kernel Regression methods are used to train and dose forecasting software using the dose rate and total accumulated dose. After training, the software predicts the dose buildup over time and the total dose for the test event. In the current research we have divided all of the events in our database into eight groups and use KR to train each group separately. We then test them to determine if the percentage differences between the dose forecast predictions for the test events and the actual event data, for each event in the group, are less than a 15% target value within 4 hours of the onset of the event. Results for the current dose forecasting system are presented.Item The cellular growth analyzer: a simpler and more comprehensive scratch assay analyzing program(2018-05) Lovelace, Alan; Moussa, Hanna; Kumar, Golden; Ramalingam, LathaA scratch, or wound, assay is a low-cost and simple method to measure cells migration; it is also an easy way to measure the growth rate of cancer cells in vitro. It does so by removing a strip of cells from a cell culture dish and then measuring the cell migration and cellular growth back into the “scratch” area. The measurement of this migration and cellular growth has traditionally been done using the program, ImageJ. Though this method can work, it is labor intensive and time consuming. We therefore developed a more automated and easier-to-use Java program designed specifically to analyze scratch assays. By contrasting and separating the pixels that formed the scratch and then counting them to provide a numerical result the migration and growth we significantly improved the speed and ease of the analysis while allowing the results to be easily repeatable when compared to ImageJ. Further development, refining and addition of new features to this software will significantly aid researchers, especially in the area of cancer research and in assessing the effectiveness of various treatments on cell migration.Item The influence of loading on crystallization behavior of bulk metallic glasses(2017-05) Davoodi, Elham; Kumar, Golden; Qiu, Jingjing; Moussa, HannaMetallic glasses are amorphous metals with unique properties, such as high strength, elasticity, wear resistance, and thermoplastic processing capability. Thermoplastic forming is enabled by the existence of metastable supercooled liquid state in metallic glasses above the glass transition temperature. The thermoplastic manufacturing critically depends on the crystallization time (processing time window), temperature (viscosity), applied load, and strain-rate. Among these parameters, the effects of crystallization time and processing temperature have been extensively studied in metallic glasses. However, the effects of load and loading rate are generally ignored. Recent studies indicate that the load and loading rate can affect the structures of metallic glass supercooled liquids and hence their crystallization kinetics and thermoplastic processing ability. Here, we systematically study the effects of load on supercooled liquid state of three different metallic glass formers: Pt-based, Zr-based, and Pd-based. The results clearly suggest that load-response of metallic glass supercooled liquids is strongly alloy dependent. The onset of crystallization for the Pt-based metallic glass supercooled liquid is reduced after subjecting it to higher loads whereas the onset of crystallization for Zr-based and Pd-based metallic glasses remains largely unaffected. However, the crystallization peak time is reduced for three metallic glasses. We associate this deformation-induced crystallization to the free volume generated by plastic flow. Further studies on loading rate underway to obtain comprehensive understanding of metallic glass supercooled liquids during thermoplastic embossing.Item Wind Speed Prediction using Classical Time Series and Machine Learning Models: A Comparative Analysis(2022-12) Dhakal, Rabin; Moussa, Hanna; Parameswaran, Siva; Maldonado, Victor; Pol, Suhas; Nejat, AliThe need of delivering future accurate predictions of renewable energy generation has been recognized by stakeholders working in the field of renewable energy. It is the reason for recent improvements in the methods to provide more precise energy generation prediction. Wind power production is dependent on weather pattern variations, particularly wind speed, which are irregular in locations with unpredictable weather. Wind speed prediction in a given location is crucial for the evaluation of the wind power project; the accurate prediction improves the planning, reduces the cost, and improves the use of resources for wind power generation. Models such as Weibull probability density function based wind prediction (WBM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid methods classical time series and deep learning models have been used for accurate prediction of wind speed with different forecast horizons. For short and ultra-short terms that are two to three hours in the future, the ARIMA ensemble with ANN has demonstrated improved performance. For medium-term wind speed predictions, however, SVR, Kalman filters, and ensembles of both have demonstrated good performance. Recurrent neural networks (RNN) in particular have recently reported enormous success in time series forecasts, especially for medium- and long-term predictions. There has been growing interest in the field of deep learning and neural networks for the prediction of wind speed as it can overcome the issue of accurately forecasting the nonlinear patterns of wind speed data using classical time series methods. The main contribution of this dissertation research is the comparative analysis of the performance of each method for accurately predicting wind speed for different time horizons and proposing a Weibull distribution based featured engineered hybrid model for wind speed prediction. In this research, the wind speed generated from the Weibull probability density function is used as a feature in the wind speed prediction model and the prediction model is developed by optimizing the weight function for each model contributed to the hybrid prediction model. The demonstration of the accuracy of the 7 proposed model and comparative analysis of the different model is performed on the five different data set obtained from the National Oceanic and Atmospheric Administration and System Advisory Module (SAM) database.