RNAi technology is taking strong position among the key therapeutic modalities, with dozens of siRNA-based programs entering and successfully progressing through clinical stages of drug development. To further explore potentials of RNAi technology as therapeutics, we engineered and tested VEGFR2 siRNA molecules specifically targeted to tumors through covalently conjugated cyclo(Arg-Gly-Asp-d-Phe-Lys[PEG-MAL]) (cRGD) peptide, known to bind αvβ3 integrin receptors. cRGD-siRNAs were demonstrated to specifically enter and silence targeted genes in cultured αvβ3 positive human cells (HUVEC). Microinjection of zebrafish blastocysts with VEGFR2 cRGD-siRNA resulted in specific inhibition of blood vessel growth. In tumor-bearing mice, intravenously injected cRGD-siRNA molecules generated no innate immune response and bio-distributed to tumor tissues. Continuous systemic delivery of two different VEGFR2 cRGD-siRNAs resulted in down-regulation of corresponding mRNA (55 and 45%) and protein (65 and 45%) in tumors, as well as in overall reduction of tumor volume (90 and 70%). These findings demonstrate strong potential of cRGD-siRNA molecules as anti-tumor therapy.

For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brings trouble to the subsequent safety education of workers. Although, many scholars have devoted themselves to the study of person re-identification which neglected safety detection. The study of this paper mainly proposes a method based on deep learning, which is different from the previous study of helmet detection and human identity recognition and can carry out helmet detection and identity recognition for construction workers. This paper proposes a computer vision-based worker identity recognition and helmet recognition method. We collected 3000 real-name channel images and constructed a neural network based on the You Only Look Once (YOLO) v3 model to extract the features of the construction worker's face and helmet, respectively. Experiments show that the method has a high recognition accuracy rate, fast recognition speed, accurate recognition of workers and helmet detection, and solves the problem of poor supervision of real-name channels.

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During their life cycle, buildings not only consume a lot of resources and energy, but also produce a large amount of carbon emissions, which have a serious impact on the environment. In the context of global emissions reduction, the trend has been to low carbon buildings. As a major carbon emitting country, it is urgent to promote emission reduction in the construction industry and to establish a model for carbon emissions and calculation in buildings. To this end, this paper collates life cycle carbon emission calculation methods based on life cycle theory and establishes a mixed life cycle carbon emission calculation model for buildings to provide ideas for low carbon buildings in China. A case study of a hospital in Guangming City, Anhui Province is also conducted to verify the feasibility of the model. The results show that the total carbon emission of the hospital is 43283.66 tCO 2 eq, with the production phase, construction phase, use and maintenance phase and end-of-life phase accounting for 9.13%, 0.35%, 90.06% and 0.46% of the total carbon emission respectively. An analysis of the factors influencing carbon emissions at each stage is presented, and recommendations are given for corresponding emission reduction measures. The carbon emission calculation model based on the hybrid LCA proposed in this study enables a more comprehensive consideration of carbon emissions in the life cycle of a building, and has implications for the study of building carbon emission calculation.