Intelligent manufacturing and green innovation: Quasi-natural evidence from China.

Intelligent manufacturing is an important driving force for improving quality and efficiency and promoting green innovation. Based on the data of Chinese listed companies and taking the Chinese intelligent manufacturing pilot demonstration projects as a quasi-natural experiment, this paper constructs a difference-in-differences (DID) model to explore the effect and mechanism of intelligent manufacturing on enterprise green innovation. The results show that intelligent manufacturing has significantly promoted green innovation in China, and this effect is still valid after considering various robustness tests. Heterogeneity analysis shows that in areas with a good green development foundation and poor information infrastructure, the impact is more obvious. In non-state-owned enterprises and mature enterprises, the impact is more obvious. Mechanism analysis indicates that intelligent manufacturing enhances green innovation through cost management effects, efficiency improvement effects, and employment structure optimization effects. The conclusions provide clear policy implications for developing countries to promote intelligent manufacturing practices and green high-quality development.

Environmental regulation, foreign investment, and green innovation: a case study from China.

Green innovation (GI) has the dual attributes of green development and being innovation driven, and it has become an inevitable choice for solving the prisoner's dilemma of environmental protection and economic development under the action of the concept of sustainable development in the new era. This paper aims to clarify how environmental regulation (ER) can achieve a win‒win situation of GI and environmental protection by using data from prefecture-level cities in China and creating a dynamic panel model, quantile model, spatial econometric model, and panel threshold model to empirically analyze the dynamic effect and spatial effect of ER on GI as well as the nonlinear characteristics of the relationship between them and to examine the moderating effect of foreign direct investment (FDI). The results show that ER significantly promotes the development of the GI level and that FDI can play a positive moderating role. The impact has regional heterogeneity, time period heterogeneity, and resource endowment heterogeneity. After several robustness tests, the empirical conclusions are still credible. Based on the empirical conclusions, this paper makes policy suggestions on ER, foreign investment introduction, and the coordinated development of regional GI.

The effect of the industrial structure and haze pollution: spatial evidence for China.

Based on provincial data pertaining to China from 2003 to 2018, this paper empirically analyzes the impact of the industrial structure on haze pollution by constructing static and dynamic spatial econometric models. The marginal contribution of this paper lies in the analysis based on two indicators: the upgrading and rationalization of the industrial structure. The results indicate that: at the overall level, haze pollution in China exhibits a significant positive spatial correlation and remains relatively stable, upgrading and rationalization of the industrial structure can significantly reduce haze pollution, the control variables of technological progress and trade openness yield obvious haze reduction effects, and the market-oriented haze reduction effect is better that of the government behavior. In terms of the robustness, the effect of industrial structure upgrading is not obvious in the eastern regions and even aggravates haze pollution in the central and western regions, while industrial structure rationalization can play a role in haze reduction in all regions. Industrial structure upgrading and rationalization achieve better effects in the southern region but can aggravate haze pollution in the northern region. Based on the results of the time period test, the effect is very obvious at the first stage but not that at the second stage because of the diminishing marginal effect. The robustness results of the replacement of the core variables and dynamic spatial Durbin model further validate the empirical results in this paper. Finally, according to the empirical results, we propose corresponding policy implications.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

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.

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.

BACKGROUND: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. RESULTS: Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. CONCLUSION: We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Defining and using microbial spectral databases.

This work shows how fingerprints of mass spectral patterns from microbial isolates are affected by variations in instrumental condition, by sample environment, and by sample handling factors. It describes a novel method by which pattern distortions can be mathematically corrected for variations in factors not amenable to experimental control. One uncontrollable variable is "between-batch" differences in culture media. Another, relevant for determination of noncultured extracts, is differences between the cells' environmental experience (e.g., starved environmental extracts versus cultured standards). The method suggests that, after a single growth cycle on a solid medium (perhaps, a selective one), pyrolysis MS spectra of microbial isolates can be algorithmically compensated and an unknown isolate identified using a spectral database defined by culture on a different (perhaps, nonselective) medium. This reduces identification time to as few as 24 h from sample collection. The concept also proposes a possible way to compensate certain noncultured, nonisolated samples (e.g., cells concentrated from urine or impacted from aerosol or semi-selectively extracted by immunoaffinity methods from heavily contaminated matrices) for identification within half an hour. Using the method, microbial mass spectra from different labs can be assembled into coherent databases similar to those routinely used to identify pure compounds. This type of data treatment is applicable for rapid detection in biowarfare and bioterror events as well as in forensic, research, and clinical laboratory contexts.

ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.

The mapping of the human genome and the determination of corresponding gene functions, pathways, and biological mechanisms are driving the emergence of the new research fields of toxicogenomics and systems toxicology. Many technological advances such as microarrays are enabling this paradigm shift that indicates an unprecedented advancement in the methods of understanding the expression of toxicity at the molecular level. At the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration, core facilities for genomic, proteomic, and metabonomic technologies have been established that use standardized experimental procedures to support centerwide toxicogenomic research. Collectively, these facilities are continuously producing an unprecedented volume of data. NCTR plans to develop a toxicoinformatics integrated system (TIS) for the purpose of fully integrating genomic, proteomic, and metabonomic data with the data in public repositories as well as conventional (Italic)in vitro(/Italic) and (Italic)in vivo(/Italic) toxicology data. The TIS will enable data curation in accordance with standard ontology and provide or interface a rich collection of tools for data analysis and knowledge mining. In this article the design, practical issues, and functions of the TIS are discussed through presenting its prototype version, ArrayTrack, for the management and analysis of DNA microarray data. ArrayTrack is logically constructed of three linked components: a) a library (LIB) that mirrors critical data in public databases; b) a database (MicroarrayDB) that stores microarray experiment information that is Minimal Information About a Microarray Experiment (MIAME) compliant; and c) tools (TOOL) that operate on experimental and public data for knowledge discovery. Using ArrayTrack, we can select an analysis method from the TOOL and apply the method to selected microarray data stored in the MicroarrayDB; the analysis results can be linked directly to gene information in the LIB.

Development of public toxicogenomics software for microarray data management and analysis.

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.