Adenosine, a widespread and endogenous nucleoside that acts as a powerful neuromodulator in the nervous system, is a promising therapeutic target in a wide range of conditions. The structural similarity between xanthine derivatives and neurotransmitter adenosine has led to the derivatives of the heterocyclic ring being among the most abundant chemical classes of ligand antagonists of adenosine receptor subtypes. Small changes in the xanthine scaffold have resulted in a wide array of adenosine receptor antagonists. In this work, we developed a QSAR model for the [Formula: see text] subtype, which is, as yet, not well characterized, with two purposes in mind: to predict adenosine [Formula: see text] antagonist activity and to offer a substructural interpretation of this group of xanthines. The QSAR model provided good classifications of both the test and external sets. In addition, most of the contributions to adenosine [Formula: see text] receptor affinity derived by subfragmentation of the molecules in the training set agree with the relationships observed in the literature. These two factors mean that this QSAR ensemble could be used as a model to predict future adenosine [Formula: see text] antagonist candidates.
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http://dx.doi.org/10.1007/s11030-015-9608-0 | DOI Listing |
Sci Rep
January 2025
Faculty of Computers and Information, Minia University, Minia, Egypt.
This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency's Downscaler Model (DS) to predict Particulate Matter ([Formula: see text]) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time.
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January 2025
Department of Applied Physics, School of Engineering Sciences, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691, Stockholm, Sweden.
Non-trivial band topology along with magnetism leads to different novel quantum phases. When time-reversal symmetry is broken in three-dimensional topological insulators (TIs) through, e.g.
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January 2025
Changchun Automobile Economic & Technological Development Zone Employment Service Bureau, Jilin City, China.
The permanent magnet synchronous motor control system is characterized by its nonlinear and strongly coupled complexity, presenting significant challenges for control performance optimization. To address these challenges, a Fuzzy adaptive fractional order [Formula: see text] control strategy based on torque observation compensation is proposed. The parameters of the fractional order [Formula: see text] controller are optimized real time using fuzzy logic reasoning, in order to enhance the speed of parameters tuning, a graphical design method of the fractional order [Formula: see text] controller parameters based on frequency domain performance indicators is proposed to obtain the initial values of the fuzzy adaptive fractional order [Formula: see text] controller parameters graphically and intuitively.
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January 2025
TH-PPM Group, Physics Department, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt.
A wealth of details regarding an individual's state of health, like a person's respiratory and metabolic functioning, can be studied by analyzing the volatile molecules and atoms in human exhaled breath. Besides, the salinity of seawater is a crucial factor in understanding its characteristics because any variation in the salinity of seawater represents the variations in the hydrological, biological, and chemical distributions. In this paper, a symmetrical one-dimensional phononic structure is theoretically designed using two symmetrical crystals separated with a defective cavity.
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January 2025
Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, Riyadh, 11543, Saudi Arabia.
Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. By addressing existing localization challenges, the algorithm minimizes localization errors, energy consumption and Time costs while significantly enhancing localization accuracy to 99.
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