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No nationwide studies have examined the associations between mortality risk and PM 1 (PM with an aerodynamic diameter of <1 μm) due to the scarcity of monitoring data of PM 1 . On the basis of newly released national scale PM 1 data, we performed a time series analysis to elucidate the cause-specific mortality risk caused by PM 1 exposure in China. During the period from January 2014 to December 2017, the PM 1 levels in 65 cities of China were on average 37 ± 32 μg/m 3 . Pooled results indicated a 10 μg/m 3 increase in the PM 1 level was associated with a 0.19% [95% confidence interval (CI) of 0.09−0.28%] increased risk in nonaccidental mortality, which was almost the same as that for PM 2.5 (0.18%, 95% CI of 0.08−0.27%) and PM 10 (0.17%, 95% CI of 0.01−0.24%). By comparison, the magnitude increased to 0.29% (0.12−0.47%) in cardiovascular disease for each 10 μg/m 3 uptick in PM 1 , which was significantly higher than that related to PM 2.5 and PM 10 exposure. This nationwide study supported the notion that PM 1 may be a higher risk factor for cardiovascular disease, which suggests rapid action is warranted to put more effort into mitigating the emissions of finer particulate matters.

Rationale: Little evidence from large-scale cohort studies exists about the relationship of solid fuel use with hospitalization and mortality from major respiratory diseases. Objectives: To examine the associations of solid fuel use and risks of acute and chronic respiratory diseases. Methods: A cohort study of 277,838 Chinese never-smokers with no prior major chronic diseases at baseline. During 9 years of follow-up, 19,823 first hospitalization episodes or deaths from major respiratory diseases, including 10,553 chronic lower respiratory disease (CLRD), 4,398 chronic obstructive pulmonary disease (COPD), and 7,324 acute lower respiratory infection (ALRI), were recorded. Cox regression yielded adjusted hazard ratios (HRs) for disease risks associated with self-reported primary cooking fuel use. Measurements and Main Results: Overall, 91% of participants reported regular cooking, with 52% using solid fuels. Compared with clean fuel users, solid fuel users had an adjusted HR of 1.36 (95% confidence interval, 1.32–1.40) for major respiratory diseases, whereas those who switched from solid to clean fuels had a weaker HR (1.14, 1.10–1.17). The HRs were higher in wood (1.37, 1.33–1.41) than coal users (1.22, 1.15–1.29) and in those with prolonged use (≥40 yr, 1.54, 1.48–1.60; <20 yr, 1.32, 1.26–1.39), but lower among those who used ventilated than nonventilated cookstoves (1.22, 1.19–1.25 vs. 1.29, 1.24–1.35). For CLRD, COPD, and ALRI, the HRs associated with solid fuel use were 1.47 (1.41–1.52), 1.10 (1.03–1.18), and 1.16 (1.09–1.23), respectively. Conclusions: Among Chinese adults, solid fuel use for cooking was associated with higher risks of major respiratory disease admissions and death, and switching to clean fuels or use of ventilated cookstoves had lower risk than not switching.

Synthetic genetic circuits are programmed in living cells to perform predetermined cellular functions. However, designing higher-order genetic circuits for sophisticated cellular activities remains a substantial challenge. Here we program a genetic circuit that executes Pavlovianlike conditioning, an archetypical sequential-logic function, in Escherichia coli. The circuit design is first specified by the subfunctions that are necessary for the single simultaneous conditioning, and is further genetically implemented using four function modules. During this process, quantitative analysis is applied to the optimization of the modules and fine-tuning of the interconnections. Analogous to classical Pavlovian conditioning, the resultant circuit enables the cells to respond to a certain stimulus only after a conditioning process. We show that, although the conditioning is digital in single cells, a dynamically progressive conditioning process emerges at the population level. This circuit, together with its rational design strategy, is a key step towards the implementation of more sophisticated cellular computing.

Abstract-Various variants of the well known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) have been proposed recently, which improve the empirical performance of the original algorithm by structural modifications. However, in practice it is often unclear which variation is best suited to the specific optimization problem at hand. As one approach to tackle this issue, algorithmic mechanisms attached to CMA-ES variants are considered and extracted as functional modules, allowing for combinations of them. This leads to a configuration space over ES structures, which enables the exploration of algorithm structures and paves the way toward novel algorithm generation. Specifically, eleven modules are incorporated in this framework with two or three alternative configurations for each module, resulting in 4 608 algorithms. A self-adaptive Genetic Algorithm (GA) is used to efficiently evolve effective ES-structures for given classes of optimization problems, outperforming any classical CMA-ES variants from literature. The proposed approach is evaluated on noiseless functions from BBOB suite. Furthermore, such an observation is again confirmed on different function groups and dimensionality, indicating the feasibility of ES configuration on real-world problem classes.