Mr. Tingting Dong Profile

Tingting Dong

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Proceedings Article | 5 March 2015 Paper

Low level light in combination with metabolic modulators for effective therapy

Tingting Dong, Qi Zhang, Michael Hamblin, Mei Wu

Proceedings Volume 9309, 93090A (2015) https://doi.org/10.1117/12.2079363

KEYWORDS: Brain, Hypoxia, Traumatic brain injury, Acquisition tracking and pointing, Tissues, Cell death, Oxygen, Neurons, Glucose, Injuries

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Vascular damage occurs frequently at the injured brain causing hypoxia and is associated with poor outcomes in the clinics. We found high levels of glycolysis, reduced ATP generation, and increased formation of reactive oxygen species (ROS) and apoptosis in neurons under hypoxia. Strikingly, these adverse events were reversed significantly by noninvasive exposure of injured brain to low-level light (LLL). LLL illumination sustained the mitochondrial membrane potential, constrained cytochrome C leakage in hypoxic cells, and protected them from apoptosis, underscoring a unique property of LLL. The effect of LLL was further bolstered by combination with metabolic substrates such as pyruvate or lactate both in vivo and in vitro. The combinational treatment retained memory and learning activities of injured mice to a normal level, whereas those treated with LLL or pyruvate alone, or sham light displayed partial or severe deficiency in these cognitive functions. In accordance with well-protected learning and memory function, the hippocampal region primarily responsible for learning and memory was completely protected by a combination of LLL and pyruvate, in marked contrast to the severe loss of hippocampal tissue due to secondary damage in control mice. These data clearly suggest that energy metabolic modulators can additively or synergistically enhance the therapeutic effect of LLL in energy-producing insufficient tissues like injured brain. Keywords:

Proceedings Article | 14 November 2007 Paper

Quantitative research on soil erosion based on BP artificial neural network

Tingting Dong, Zengxiang Zhang, Lijun Zuo

Proceedings Volume 6790, 67901E (2007) https://doi.org/10.1117/12.748770

KEYWORDS: Artificial neural networks, Soil science, Error analysis, Analytical research, Neurons, Statistical analysis, Process modeling, Remote sensing, Applied research, Roads

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It's well known that soil erosion is a complicated phenomenon. It's hard to express it with a uniform equation, however BP artificial neural network has great advantages of solving non-linear problems, so it can use BP artificial neural network to research on soil erosion quantitatively. In this research it lays out experiment in the east and west of Liaoning province. It measures 4 factors which mainly influence soil erosion except quantity of soil erosion. They are rainfall erosivity, slop, soil water content before rainfall and crop coverage. These data are composed of 85 samples in total. This paper builds double-layer BP artificial neural network for east region and west region respectively. It uses some samples to train BP artificial neural network and others to verify it. Research results show that judging from the errors these two BP artificial neural networks can be applied to research on soil erosion quantitatively. Simulative results can be used to confirm the rank of soil erosion. Comparing with the results of multi-factor orthogonal regression analysis using BP artificial neural network is much more approaching the real value. Besides it discusses the problems on BP artificial neural network application.

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