New trends in the treatment of bone metastasis

Bone metastasis is often the penultimate harbinger of death for many cancer patients. Bone metastases are often associated with fractures and severe pain resulting in decreased quality of life. Accordingly, effective therapies to inhibit the development or progression of bone metastases will have important clinical benefits. To achieve this goal understanding the mechanisms through which bone metastases develop and progress may provide targets to inhibit the metastases. In the past few years, there have been advances in both understanding the mechanisms through which bone metastases develop and how they impact bone remodeling. Additionally, gains in promising clinical strategies to target bone metastases have been developed. In this prospectus, we will discuss some of these advances.     

Research on WNN Greenhouse Temperature Prediction Method Based on GA

Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally optimize the initial weights of the original model, which improved the insensitivity of the model to the initial weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results for the greenhouse showed that the model training speed, prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.     

Comparative transcriptome analysis reveals the regulation network for fiber strength in cotton

Biotechnology Letters - Determine the effect of secondary cell wall (SCW) thickness and microcrystalline cellulose content (MCC) on mature fiber strength (FS) and reveal through comparative...     

Characterization of Agrobacterium-mediated co-transformation events in rice using green and red fluorescent proteins

Molecular Biology Reports - Biotechnologists seeking to develop marker-free transgenic plants have established co-transformation methods. For co-transformation using mixed Agrobacterium strains,...