A study on possible interactions between biomolecules and nanoparticles

Along with the rapid growth of the nanotechnology, nanoparticles (NPs) have found many applications in commercial products. However, there are only a few studies on the toxicity and the environmental effects of NPs in biological systems. In the study described in this thesis, I have used water-soluble Au NPs that were synthesized using the Brust method and then modified by small molecules. I explored the interactions of these modified Au NPs with self-assembled monolayer films on gold surfaces.Three types of self-assembled monolayer (SAM) modified gold surfaces were used in this study. The surfaces had SAMs that could be positively or negatively charged or carry no charge, or be able to engage in hydrogen bonding. Cyclic voltammetry (CV) was used to characterize SAMs of disulfide-glycine conjugate, disulfide-aspartic conjugate, and 11-mercaptoundecanoic acid (MUA) on gold surface electrodes. The possible interactions of Au NPs with the disulfide-aminoacid conjugates and alkanethiol modified surfaces were evaluated by cyclic voltammetry and by electrochemical impedance spectroscopy (EIS). An apparent decline in current density observed in CV along with an electron transfer resistance increase in EIS measurements upon exposure of the films to the MUA-modified anionic Au NPs clearly indicate interactions of the NPs with the films. Likewise, upon exposure of the films to cationic NPs, electron transfer resistance decreases dramatically in EIS experiments. In addition, the current increase in CV measurements provided further evidences for the interactions. The interactions between modified Au NPs and the SAMs were investigated in more detail by infrared spectroscopy and by employing quartz crystal microbalance. These studies clearly showed that upon exposure of these SAM films to the water-soluble Au NPs, significant changes occur. As would be expected for the adsorption of the Au NPs onto the SAMs, the weight of the film increased due to the addition of the NPs on the surface. Moreover, there are significant increases in the carbonyl stretching vibration at 1735 cm-1 along with the appearance of the amide hydrogen stretching band, between 3160-3380 cm-1, which indicate the adsorption of Gly-CSA modified Au NPs onto the MUA film

Numerical Study of Flow Characteristics Over Pivot Weirs

Pivot weirs have a lifting mechanism to change the weir angle relative to the channel bed. These are installed across the waterways in the form of multiple weirs in a row. The water level will be adjusted by changing the weir angle. In this study, the flow over the pivot weirs was simulated with different flow discharges and angles using Ansys CFX model to investigate the flow characteristics. The model was evaluated using USBR experimental data. The standard K-ε turbulence model was considered as the best model for numerical analysis. According to the results, discharge coefficient increases with the inclination angle up to 1.076. The results showed a slight difference in comparison with the previous studies where values of 1.121, 1.110 and 1.082 were presented. The discharge coefficient equations were developed for the weirs. The equations for various hydraulic parameters, including upstream water depth, water head on the crest, the ratio of water head over the crest to the weir height, and weir angle were developed. Based on the developed equations, the operation of the weirs was analyzed during flood events

A distributed wind downscaling technique for wave climate modeling under future scenarios

The aim of this study is to develop a Weibull-based distributed downscaling technique for wind field as forcing for the wave models to investigate the wave climate under future scenarios. For this purpose, the statistical downscaling approach modifies Weibull distribution parameters of the global circulation model wind speeds based on the corresponding features of wind data of ECMWF (European Center for Medium-Range Weather Forecasts). The proposed technique has the advantage of modifying the wind components in each grid point based on the corresponding values in the same grid point of ECMWF wind field. Hence, it is superior to other existing models due to considering the spatial variation. The previous models using inverse distance weighting suffer from heterogeneity and ignoring spatial variation in areas with high gradient of wind speed. Moreover, the Weibull-based technique outperforms the existing statistical downscaling techniques in terms of accuracy. Prior to investigate future distribution of wave characteristics, performance of the selected GCM was evaluated and compared against the corresponding models obtained from the available regional climate models. Future projections of wind fields (RCP4.5, RCP8.5) were downscaled for the period of 2081 to 2100 with the proposed model as driving force for wave modeling in the Persian Gulf. To investigate the impacts of climate change on wave characteristics, results of the wave simulations from a third generation wave model (SWAN) for future scenarios are compared with those of the historical period (1981–2000) in monthly, seasonal, and annual scales. Generally, for RCP8.5, the results indicate a decrease in future significant wave height and peak wave period about 15% and 5%, respectively. However, the change of wave direction is marginal. Moreover, wave models forced with RCP4.5 wind data provide slightly higher average values in terms of wave height and peak wave period compared to those of RCP8.5

Reduced levels of hydroxylated, polyunsaturated ultra long-chain fatty acids in the serum of colorectal cancer patients: implications for early screening and detection

<p>Abstract</p> <p>Background</p> <p>There are currently no accurate serum markers for detecting early risk of colorectal cancer (CRC). We therefore developed a non-targeted metabolomics technology to analyse the serum of pre-treatment CRC patients in order to discover putative metabolic markers associated with CRC. Using tandem-mass spectrometry (MS/MS) high throughput MS technology we evaluated the utility of selected markers and this technology for discriminating between CRC and healthy subjects.</p> <p>Methods</p> <p>Biomarker discovery was performed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). Comprehensive metabolic profiles of CRC patients and controls from three independent populations from different continents (USA and Japan; total <it>n </it>= 222) were obtained and the best inter-study biomarkers determined. The structural characterization of these and related markers was performed using liquid chromatography (LC) MS/MS and nuclear magnetic resonance technologies. Clinical utility evaluations were performed using a targeted high-throughput triple-quadrupole multiple reaction monitoring (TQ-MRM) method for three biomarkers in two further independent populations from the USA and Japan (total <it>n </it>= 220).</p> <p>Results</p> <p>Comprehensive metabolomic analyses revealed significantly reduced levels of 28-36 carbon-containing hydroxylated polyunsaturated ultra long-chain fatty-acids in all three independent cohorts of CRC patient samples relative to controls. Structure elucidation studies on the C28 molecules revealed two families harbouring specifically two or three hydroxyl substitutions and varying degrees of unsaturation. The TQ-MRM method successfully validated the FTICR-MS results in two further independent studies. In total, biomarkers in five independent populations across two continental regions were evaluated (three populations by FTICR-MS and two by TQ-MRM). The resultant receiver-operator characteristic curve AUCs ranged from 0.85 to 0.98 (average = 0.91 ± 0.04).</p> <p>Conclusions</p> <p>A novel comprehensive metabolomics technology was used to identify a systemic metabolic dysregulation comprising previously unknown hydroxylated polyunsaturated ultra-long chain fatty acid metabolites in CRC patients. These metabolites are easily measurable in serum and a decrease in their concentration appears to be highly sensitive and specific for the presence of CRC, regardless of ethnic or geographic background. The measurement of these metabolites may represent an additional tool for the early detection and screening of CRC.</p

Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling

Nowadays, energy crisis is considered an essential active issue for future urbanization in megacities. While the rate of population growth increases, the volume of municipal solid waste production increases significantly. This highlights the need of Sustainable Development Goals (SDGs) for both developed and developing countries. This paper constructs a novel smart framework for supplying biogas energy. Our study is applicable for fields of waste management and energy supply in green buildings. The proposed framework integrates the Response Surface Methodology (RSM), Artificial Intelligence (AI), and Petri net modeling. In this regard, the AI techniques including the Random Tree (RT), Random Forest (RF), Artificial Neural Network (ANN) and, Adaptive-Networkbased Fuzzy Inference System (ANFIS) are employed. In addition, for creating the optimum condition, a dynamic control system using the Petri Net modeling is applied. Among all machine learning methods, ANFIS with 0.99 correlation coefficient had the best accuracy for Accumulated Biogas Production (ABP) based on effective factors. Finally, the main findings of this paper are to introduce a novel framework for addressing different scientific issues such as supplying the clean energy in green buildings, the development of a smart and sustainable biogas production control system, integration of solid waste management with the SDGs in green buildings

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions

This research investigates the application of machine learning to improve the diagnosis of tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers to identify the most effective approach for classifying tinnitus presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, and rigorous model evaluation through performance metrics including accuracy, Area Under the ROC Curve (AUC), precision, recall, and F1 scores. The main findings reveal that the LR model, supported by the ANN, significantly outperformed other machine learning models, achieving an accuracy of 94.06%, an AUC of 97.06%, and high precision and recall scores. These results demonstrate the efficacy of the LR model and ANN in accurately diagnosing tinnitus, surpassing traditional diagnostic methods that rely on subjective assessments. The implications of this research are substantial for clinical audiology, suggesting that machine learning, particularly advanced models like ANNs, can provide a more objective and quantifiable tool for tinnitus diagnosis, especially when utilizing high-frequency audiometry data not typically assessed in standard hearing tests. The study underscores the potential for machine learning to facilitate earlier and more accurate tinnitus detection, which could lead to improved patient outcomes. Future work should aim to expand the dataset diversity, explore a broader range of algorithms, and conduct clinical trials to validate the models' practical utility. The research highlights the transformative potential of machine learning, including the LR model and ANN, in audiology, paving the way for advancements in the diagnosis and treatment of tinnitus

Reduction of novel circulating long-chain fatty acids in colorectal cancer patients is independent of tumor burden and correlates with age

Abstract Background Serum levels of novel hydroxy polyunsaturated ultra long-chain fatty acids (hPULCFAs) have been previously shown to be reduced in pre-treatment CRC patients compared to disease-free subjects, independent of disease stage. However, whether reduced levels of hPULCFAs result from the presence of cancer is currently unknown, as is the distribution of hPULCFAs in the general population. The following studies were carried out to assess whether conventional therapy would result in restoration of systemic hPULCFAs in CRC patients, and to investigate the relationship between hPULCFA levels and age. Methods Tandem mass spectrometry was used to determine serum levels of the 28 carbon-containing hPULCFA C28H46O4 (CRC-446) in the following cohorts: two independent Japanese CRC populations following surgical tumor removal (n = 86), a North American Caucasian CRC cohort (n = 150) following post-surgery combination chemo/radiation therapy, 990 randomly selected anonymized serum samples from subjects ranging between 11 and 99 years of age, as well as longitudinally collected serum samples from healthy normals (n = 8, up to 90 weeks) and stage IV CRC subjects on combination therapy (n = 12, up to 63 weeks). Results Serum CRC-446 levels in CRC subjects were significantly lower than controls (mean of 0.297 ± 0.07 ug/ml in controls versus 0.092 ± 0.03 in CRCs, p 0.05 between pre vs post surgery). CRC-446 levels showed a strong inverse association with age (p Conclusions Our findings show that CRC-446 levels are not affected by conventional CRC treatment and inversely correlate with age, which suggest that reduced serum CRC-446 levels likely exist prior to the development of CRC. Extrapolation of the results to a simple screening scenario showed that, compared to fecal blood testing, pre-colonoscopy screening using serum CRC-446 levels would require 80% fewer colonoscopies, would identify risk in subjects under the age of 50, and would result in increased numbers of early cases detected. The precise role these serum metabolites play in the aetiology of cancer development remains to be determined.</p