Interaction of Retinol with HSA using Spectroscopic Techniques

The interaction between retinol and HSA has been investigated using UV-absorption spectrophotometry, fluorescence spectroscopy and Fourier Transform Infrared (FT-IR) spectroscopy.UV-absorption spectrophotometry showed an increase in the absorption intensity with increasing the molecular ratios of retinol to HSA, it is found that the value of the binding constant is estimated to be1.7176×102 M-1. FTIR spectroscopy is used in the mid infrared region with Fourier self deconvolution, second derivative, difference spectra, peak picking and curve fitting were used to determine the effect of Retinol on the protein secondary structure in the amides I, II and Ill regions. Analysis of FTIR absorbance spectra is found that the intensity of the absorption bands increased with increasing the molecular ratios of retinol, however from the deconvoluted and curve fitted spectra found that the absorbance intensity for α-helix decreases relative to β-sheets, this decrease in intensity is related to the formation of H- bonding in the complex molecules

Comparative studies on the interactions between human serum albumin, bovine serum albumin and cholesterol: ftir and fluorescence spectroscopy

The interaction of the human serum albumin (HSA), bovine serum albumin (BSA) with cholesterol has been investigated. The basic binding interaction was studied by FTIR and fluorescence spectroscopy. From spectral analysis cholesterol showed a strong ability to quench the intrinsic fluorescence of HSA and BSA through a static quenching mechanism. The binding constant (k) between HSA and cholesterol is estimated to be K=2.14 × 103 M-1 at 293 K while between BSA and cholesterol is estimated to be K=.1.12 × 103 M-1 at the same temperature. FTIR spectroscopy with Fourier self-deconvolution technique was used to determine the protein secondary structure and cholesterol binding mechanisms. The observed spectral changes indicate a higher percentage of H-bonding between cholesterol and -helix compared to the percentage of H-bonding to cholesterol and -sheets.This work is supported by the German Research Foundation DFG grant No. DR228/24-

Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms

Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site