Do Double-Edged Swords Cut Both Ways? The Role of Technology Innovation and Resource Consumption in Environmental Regulation and Economic Performance

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is one of the significant regions with the strongest economic vitality in China. This study focuses on environmental regulation in the eleven Greater Bay Area cities to explore the relationship between it and economic performance for the period 2000–2016. In doing so, we employ spatial panel models (including the spatial instrumental variable method) to investigate the nonlinear relationship between economic growth and environmental regulation. We confirm the existence of a U-shaped relationship between economic growth and environmental regulation in the Greater Bay Area. In the first half of the inverted U shape, the higher the economic development level, the stronger the environmental regulation strength; however, the latter begins to decrease after the peak point. The doubled-edged sword does not cut both ways. This paper verifies that technology innovation and resource consumption are two important mechanisms. Further, we find that both economic growth and environmental regulation have negative spatial externalities; innovation has a positive impact on the environmental regulation of the local city as well as surrounding cities, while resource consumption is on the contrary. In conclusion, this paper provides policy recommendations to further promote economic growth and environmental technologies, and to enhance energy efficiency in GBA.

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

A Novel Position-Specific Encoding Algorithm (SeqPose) of Nucleotide Sequences and Its Application for Detecting Enhancers

Enhancers are short genomic regions exerting tissue-specific regulatory roles, usually for remote coding regions. Enhancers are observed in both prokaryotic and eukaryotic genomes, and their detections facilitate a better understanding of the transcriptional regulation mechanism. The accurate detection and transcriptional regulation strength evaluation of the enhancers remain a major bioinformatics challenge. Most of the current studies utilized the statistical features of short fixed-length nucleotide sequences. This study introduces the location information of each k-mer (SeqPose) into the encoding strategy of a DNA sequence and employs the attention mechanism in the two-layer bi-directional long-short term memory (BD-LSTM) model (spEnhancer) for the enhancer detection problem. The first layer of the delivered classifier discriminates between enhancers and non-enhancers, and the second layer evaluates the transcriptional regulation strength of the detected enhancer. The SeqPose-encoded features are selected by the Chi-squared test, and 45 positions are removed from further analysis. The existing studies may focus on selecting the statistical DNA sequence descriptors with large contributions to the prediction models. This study does not utilize these statistical DNA sequence descriptors. Then the word vector of the SeqPose-encoded features is obtained by using the word embedding layer. This study hypothesizes that different word vector features may contribute differently to the enhancer detection model, and assigns different weights to these word vectors through the attention mechanism in the BD-LSTM model. The previous study generously provided the training and independent test datasets, and the proposed spEnhancer is compared with the three existing state-of-the-art studies using the same experimental procedure. The leave-one-out validation data on the training dataset shows that the proposed spEnhancer achieves similar detection performances as the three existing studies. While spEnhancer achieves the best overall performance metric MCC for both of the two binary classification problems on the independent test dataset. The experimental data shows that the strategy of removing redundant positions (SeqPose) may help improve the DNA sequence-based prediction models. spEnhancer may serve well as a complementary model to the existing studies, especially for the novel query enhancers that are not included in the training dataset.

Evaluation of water security based on capacity for socio-economic regulation

From the perspective of social and economic regulation, the development trend of regional water security in different periods and under different regulation strength is evaluated by using fuzzy set pair analysis method. The results show : the degree of regional water resources security changes from insecurity in the pilot period of water-saving society construction to basic security, and then to the security in the planning period, with the enhancement of economic and social regulation. The order of importance of each index is C19 > C1 = C5 > C18 > C14 > C10 > C13 > C3 = C4 > C8 > C7 > C15 > C16 > C12 > C2 > C11 > C17 > C6 > C9, the long-term shortage of water resources is the most important factor, the contribution rate is 60%, but the index weight of economic and social coordination ability is large, and its rapid change causes the improvement of water resources security; the contradiction between the regulation intensity of social and economic indicators in the planning year and the demand for water resources is not matched, and the degree of water security is mainly restricted by the shortage of regional water resources.

Regulation strength and technology creep play key roles in global long-term projections of wild capture fisheries

Many studies have shown that the global fish catch can only be sustained with effective regulation that restrains overfishing. However, the persistence of weak or ineffective regulation in many parts of the world, coupled with changing technologies and additional stressors like climate change, renders the future of global catches uncertain. Here, we use a spatially resolved, bio-economic size-spectrum model to shed light on the interactive impacts of three globally important drivers over multidecadal timescales: imperfect regulation, technology-driven catchability increase, and climate change. We implement regulation as the adjustment of fishing towards a target level with some degree of effectiveness and project a range of possible trajectories for global fisheries. We find that if technological progress continues apace, increasingly effective regulation is required to prevent overfishing, akin to a Red Queen race. Climate change reduces the possible upper bound for global catches, but its economic impacts can be offset by strong regulation. Ominously, technological progress under weak regulation masks a progressive erosion of fish biomass by boosting profits and generating a temporary stabilization of global catches. Our study illustrates the large degree to which the long-term outlook of global fisheries can be improved by continually strengthening fisheries regulation, despite the negative impacts of climate change.

An Order Independent Algorithm for Inferring Gene Regulatory Network Using Quantile Value for Conditional Independent Tests

Background: In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring structure of Gene Regulatory Networks (GRNs). Bayesian network is one of the popular methods in this field, however, still has many drawbacks and there is still a great space to be improved. For example, the Path Consistency (PC)-based algorithms as Bayesian network methods are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. Results: We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different order of CMI test. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while others well-known PC-based methods only considers the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with others well-known PC-based methods. Conclusions: According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported by Zhang et al are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, and especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

How and when environmental regulation induces middle managers’ proactive behavior

Purpose The purpose of this paper is to explore how middle managers respond to the career challenges caused by environmental regulation. In particular, this paper examines whether environmental regulation strength is positively related to middle managers’ openness toward change, and whether middle managers’ openness toward change is positively related to proactive behavior. Furthermore, the moderating role of top managers’ bottom-line mentality in these two relationships is examined. Design/methodology/approach Cross-sectional survey research (n=155) was conducted. During a training program, data were collected from 155 middle managers from a listed company that manufactures primary products. With these data the authors examined the main relationship and also explored the moderating effect of top managers’ bottom-line mentality. Findings Analysis of the findings indicates that perceived environmental regulation strength influences middle managers’ openness toward change and consequently their proactive behavior. In addition, top managers’ bottom-line mentality moderates both the link between environmental regulation strength and openness toward change and the link between openness toward change and proactive behavior. Originality/value The findings of this study reveal how environmental regulation induces middle managers’ proactive behavior, and the influence of top managers’ mentality on how middle managers respond to environmental regulation both cognitively and behaviorally.