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We report a novel autonomous DNA machine for amplified electrochemical analysis of two DNAs. The DNA machine operates in a two-cycle working mode to amplify DNA recognition events; the working mode is assisted by two different nicking endonucleases (NEases). Two bio-barcode probes, a ZnS nanoparticle (NP)-DNA probe and a CdS NP-DNA probe, were used to trace two target DNAs. The detection system was based on a sensitive differential pulse anodic stripping voltammetry (DPASV) method for the simultaneous detection of Zn(II) and Cd(II) tracers, which were obtained by dissolving the two probes. Under the optimised conditions, detection limits as low as 5.6×10(-17) (3σ) and 4.1×10(-17)  M (3σ) for the two target DNAs were achieved. It has been proven that the DNA machine system can simultaneously amplify two target DNAs by more than four orders of magnitude within 30 min at room temperature. In addition, in combination with an aptamer recognition strategy, the DNA machine was further used in the aptamer-based amplification analysis of adenosine triphosphate (ATP) and lysozyme. With the amplification of the DNA machine, detection limits as low as 5.6×10(-9)  M (3σ) for ATP and 5.2×10(-13)  M (3σ) for lysozyme were simultaneously obtained. The satisfactory determination of ATP and lysozyme in Ramos cells reveals the good selectivity and feasibility of this protocol. The DNA machine is a promising tool for ultrasensitive and simultaneous multianalysis because of its remarkable signal amplification and simple machine-like operation.

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Technical change essentially drives regional social and economic development, and how technical change influences the regional sustainable development of the ecological environment is also of concern. However, technical change is not always neutral, so how does directed technical change affect urban carbon intensity? Is there a spatial spillover effect between these two? In order to answer these above questions, this article first explores the relationship between directed technical change and carbon intensity through the spatial Durbin model; then, it separately analyses whether the relationship between the two in low-carbon and non-low-carbon cities will differ; finally, we performed a robustness test by replacing weights, replacing the explained variable with a lag of one period, and replacing the explained variable. The conclusions are as follows: (1) There is a positive spatial correlation between the carbon intensity of Chinese cities—that is, there is a positive interaction between the carbon intensity of local cities and of neighboring cities. For every 1% change in the carbon intensity of neighboring cities, the carbon intensity of local cities changes by 0.1027% in the same direction. (2) The directed technical change has a significant inhibitory effect on urban carbon intensity, whether in local cities or neighboring cities. However, it is worth mentioning that the direct negative effect is greater in local cities than in neighboring cities. (3) The directed technical change in low-carbon cities has a stronger inhibitory effect on carbon intensity, with a direct effect coefficient of −0.5346 and an indirect effect coefficient of −0.2616. Due to less green policy support in non-low-carbon cities, the inhibitory effect of directed technical change on carbon intensity is weakened; even if the direct effects and indirect effects are superimposed, it is only −0.0510 rather than −0.7962 for low-carbon cities.

High-quality construction of public health system is the key to maintain social economic activities be ordered and stable. The aim of this study is to understand whether public health development can promote economic growth and how. We construct a public health development index combining the variables of local pollution control and health care by EVM method. Based on the panel data of Chinese 283 cities from 2004 to 2017, fixed effect model and two-stage least squares model are used to test the impact of public health development on economic growth. Then, we discuss the heterogeneous impact of public health development on economic growth though the threshold regression model, under different government intervention, employment population scale and urbanization ratio. What’s more, we discuss the possible transmission effect of three human capital mechanisms, namely mortality, consumption rate and employment population size between public health development and economic growth. The conclusions are: (1) For every 0.1 unit increase in public health development, the growth rate of real GDP can be increased by 5 percentage points, which can drive the nominal GDP growth by about 0.17 percentage points. (2) Public health development can promote economic growth regardless of the scale of urban employment population. Only when the urbanization ratio exceeds 70.87 and the government intervention level is lower than 0.058, the development of public health is conducive to the improvement of urban economic development. (3) Public health development helps to promote economic growth, mainly through two paths, reducing mortality and increasing human capital accumulation. And, reducing mortality is more conducive to urban economic growth than increasing human capital accumulation.