Activation of Neural Modeling-Related Genes in the Heart of Mice after Gamma Irradiation

Objective. Radiation-induced heart disease (RIHD) is a common sequela of thoracic irradiation. At the same time, nerve remodeling is involved in the progression of heart disease. However, the activation of the nerve remodeling related genes in radiation-induced heart disease is still lacking. Methods. In this study, C57BL/J mice was anesthetized by intraperitoneal injection with pentobarbital sodium (2%, 40 mg/kg), and radiation was delivered using a cobalt-60 (60Co) teletherapy unit (Cirus). When the mice were anesthetized, none of them showed the signs of peritonitis, pain, or discomfort. The mice hearts were exposed to a γ-radiation field of 5 mm × 5 mm . The total dose of γ-radiation was 3 Gy/day for each animal for 5 consecutive days. The mice were executed by severed neck, and its limbs were weak. Quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry were used to explore the possible mechanism of arrhythmia in patients with RIHD. Results. Our results demonstrated that Growth-Associated Protein 43 (GAP43) was increased significantly after radioactive heart injury compared with the control group. Moreover, the protein expression of Tyrosine hydroxylase (TH) and Choline acetyl-transferase (CHAT) was significantly decreased compared with the control group and gradually increased with time rend. The nerve growth factor (NGF) was remarkably increased after radiation-induced heart injury compared with the control group. Immunohistochemistry results indicated that the nerve growth factors GAP43 and NGF were significantly increased after radiation-induced heart injury. Conclusions. Chest radiotherapy could activate the neural modeling related genes in RIHD. This may provide a new treatment plan for the future treatment of heart problems caused by chest radiotherapy.

Quantum inspiration to build a neural model based on the Day-Ahead Market of the Polish Power Exchange

The article is an attempt of the methodological approach to the proposed quantum-inspired method of neural modeling of prices quoted on the Day-Ahead Market operating at TGE S.A. In the proposed quantum-inspired neural model it was assumed, inter alia, that it is composed of 12 parallel Perceptron ANNs with one hidden layer. Moreover, it was assumed that weights and biases as processing elements are described by density matrices, and the values flowing through the Artificial Neural Network of Signals are represented by qubits. Calculations checking the correctness of the adopted method and model were carried out with the use of linear algebra and vector-matrix calculus in MATLAB and Simulink environments. The obtained research results were compared to the results obtained from the neural model with the use of a comparative model.

Method for clustering and identification of objects in laser scanning point clouds using dynamic logic

Today there is a gap between a presence of various new equipment on the market which provides streams of various digital data about the environment, in particular in the form of laser scanning point clouds, and the lack of adequate efficient methods and software for information extraction from such data. A solution to the problem of bridging this gap is proposed on the basis of neural modeling field theory and dynamic logic (DL). We present a DL-based method of extracting and analyzing information from hybrid point clouds, which include not only spatial coordinates and intensity, but also the color of each point, and can be from multiple sources including terrestrial, mobile and airborne laser scanning data. The proposed method is significant for creating a fundamental theoretical basis for new application algorithms and software for many new applications, including building information modeling, “smart city” environment, etc. The proposed method is fairly new to solving various problems related to extracting semantically rich information from a nontraditional type of digital data, especially hybrid point clouds created from laser scanning. This method will allow to significantly expand the existing boundaries of knowledge in the field of extraction and analysis of information from various digital data, because neural modeling field theory and DL can improve the performance of relevant calculations and close the existing gap in analysis of digital images.