From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population–weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3–70.0) to 42.0 µg/m3 (95% CI: 35.7–48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9–7.1), 4.4- (95% CI: 3.8–4.9), 2.8- (95% CI: 2.5–3.0), and 2.2- (95% CI: 2.0–2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35–0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China’s recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Upland cotton (Gossypium hirsutum) produces the most widely used natural fibers, yet the regulatory mechanisms governing fiber cell elongation are not well understood. Through sequencing of a cotton fiber cDNA library and subsequent microarray analysis, we found that ethylene biosynthesis is one of the most significantly upregulated biochemical pathways during fiber elongation. The 1-Aminocyclopropane-1-Carboxylic Acid Oxidase1-3 (ACO1-3) genes responsible for ethylene production were expressed at significantly higher levels during this growth stage. The amount of ethylene released from cultured ovules correlated with ACO expression and the rate of fiber growth. Exogenously applied ethylene promoted robust fiber cell expansion, whereas its biosynthetic inhibitor L-(2-aminoethoxyvinyl)-glycine (AVG) specifically suppressed fiber growth. The brassinosteroid (BR) biosynthetic pathway was modestly upregulated during this growth stage, and treatment with BR or its biosynthetic inhibitor brassinazole (BRZ) also promoted or inhibited, respectively, fiber growth. However, the effect of ethylene treatment was much stronger than that of BR, and the inhibitory effect of BRZ on fiber cells could be overcome by ethylene, but the AVG effect was much less reversed by BR. These results indicate that ethylene plays a major role in promoting cotton fiber elongation. Furthermore, ethylene may promote cell elongation by increasing the expression of sucrose synthase, tubulin, and expansin genes.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Esophageal cancer is the sixth leading cause of death from cancer and one of the least studied cancers worldwide. The global microRNA expression profile of esophageal cancer has not been reported previously. Here, for the first time, we have investigated expressed microRNAs in cryopreserved esophageal cancer tissues using advanced microRNA microarray techniques. Our microarray analyses identified seven microRNAs that could distinguish malignant esophageal cancer lesions from adjacent normal tissues. Some microRNAs could be correlated with the different clinicopathologic classifications. High expression of hsa-miR-103/107 correlated with poor survival by univariate analysis as well as by multivariate analysis. These results indicate that microRNA expression profiles are important diagnostic and prognostic markers of esophageal cancer, which might be analyzed simply using economical approaches such as reverse transcription-PCR.