We report the generation of both polarization-locked vector dissipative soliton and group velocity-locked vector conventional soliton in a nanotube-mode-locked fiber ring laser with large normal ...dispersion, for the first time to our best knowledge. Depending on the polarization-depended extinction ratio of the fiber-based Lyot filter, the two types of vector solitons can be switched by simply tuning the polarization controller. In the case of low filter extinction ratio, the output vector dissipative soliton exhibits steep spectral edges and strong frequency chirp, which presents a typical pulse duration of ~23.4 ps, and can be further compressed to ~0.9 ps. In the contrastive case of high filter extinction ratio, the vector conventional soliton has clear Kelly sidebands with transform-limited pulse duration of ~1.8 ps. Our study provides a new and simple method to achieve two different vector soliton sources, which is attractive for potential applications requiring different pulse profiles.
With the continuous development of technology, intelligent production, which brings huge changes to social welfare, has gradually become the main trend in the economic development of many countries. ...Does intelligent production featuring robot adoption help improve the environment and solve energy problems? In this study, we first construct a theoretical model at the micro-level to analyze how robot adoption affects a firm’s energy efficiency. Then, based on Propensity Score Matching-Difference in Differences (PSM-DID) method, we use the data of China’s firms from 2001 to 2012 to identify the causal relationship between robot adoption and firm’s energy efficiency. We find that the adoption of robots in production can significantly increase firms’ energy efficiency. Further mechanism tests show that the increase of productivity is an important factor through which adopting robots can improve a firm’s energy efficiency. In addition, the increase in the firm’s energy efficiency is mainly due to the increase in the firm’s output rather than the decrease in total energy consumption. Altogether, this study provides the first micro evidence on the relationship between robot adoption and energy efficiency, providing significant implications for the world’s sustainable development.
•Robot adoption affects firm’s energy efficiency and thus improve environment.•A theoretical model at micro-level analyzes the issue.•Based on PSM-DID, China’s firm-level data is used to identify causal relationship.•Improvement is due to increase in output, instead of decrease in energy consumption.
Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the ...most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had Formula: see text pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.
1,8-Cineole (also known as eucalyptol) is mostly extracted from the essential oils of plants, which showed extensively pharmacological properties including anti-inflammatory and antioxidant mainly ...via the regulation on NF-κB and Nrf2, and was used for the treatment of respiratory diseases and cardiovascular, etc. Although various administration routes have been used in the application of 1.8-cineole, few formulations have been developed to improve its stability and bioavailability. This review retrospects the researches on the source, biological activities, mechanisms, and application of 1,8-cineole since 2000, which provides a view for the further studies on the application and formulations of 1,8-cineole.
In recent years, bike-sharing has experienced rapid development; however, controversies about the externalities of bike-sharing programs have arisen as well. While bike-sharing programs have impacts ...on traffic, the environment, and public health, the social impacts, the management, and sustainable development of bike-sharing has also been of interest. The debate regards whether there are externalities, as well as whether and how such externalities can be determined. Based on the rapidly diffused bike-sharing in China, this paper quantitatively explores bike-sharing externalities. Specifically, this paper estimates the impacts of bike-sharing on the economy, energy use, the environment, and public health. The empirical results show that bike-sharing programs have significant positive externalities. The bike-sharing systems can provide urban residents with a convenient and time-saving travel mode. We find that the bike-sharing dramatically decreases traffic, reduces energy consumption, decreasing harmful gas emissions, improves public health generally, and promotes economic growth. This study contributes to a better comprehension of the externalities of bike-sharing and provides empirical evidence of the impacts of bike-sharing. Findings suggest that bike-sharing can play a critical role in the process of urban transportation development and provide information useful for urban transportation policies.
In China, green credit aims to guide the flow of credit funds by regulating the quantity and price of credit, so as to support the development of green industries and curb the emission behavior of ...polluting enterprises. Based on the behavior of microeconomic entities we construct a Dynamic Stochastic Genernal Equilibrium (DSGE) model to analyze the output and welfare effect of green credit in China. Specifically, the incentive green credit is taken as an example to carry out an empirical study, and we further quantitatively measure the output and welfare effect of green credit under different environmental regulations. We find that both price-based and quantity-based green credit have obvious output, environment, health and utility welfare effect, which is conducive to the green upgrading of industrial structure and can achieve a win-win situation of output and environment in China. However, there are some differences in transmission mechanism and utility welfare. In addition, in the short term, the environmental tax regulation inhibits the economic expansion effect of green credit, and in the long term, this inhibitory effect gradually disappears. The DSGE comprehensive evaluation framework constructed in this paper expands the application scope of DSGE model, and provides a quantitative theoretical model for the mechanism analysis of China’s green credit policy and similar policies. Furthermore this study provides theoretical and quantitative basis for the formulation of green credit policies and the improvement of macro-control policies in China.
Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of ...nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM's mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called "iNitro-Tyr" was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.
Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential ...biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential.
Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611.
We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.
Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters ...with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.
Although processing export accounts for a great proportion of Chinese exports, Chinese processing trade is always different from the traditional trade in many aspects. Under the background of Chinese ...energy problems becoming energy problems of the world, it is critically significant to analyze Chinese energy efficiency performance from the perspective of processing export. Our research examines how firms’ processing export behaviors affect their energy efficiency. Our results illustrate that energy efficiency performance of China’s processing exporters is worse than that of non-processing exporters. Besides, there is an obvious inverse U-shaped relationship between processing export intensities and energy efficiency. When enterprises engage in processing exports, the promotional effects on energy efficiency are constantly improving when processing export intensity is low. However, when firm’s processing export intensity is high, the promotional effects of processing export activities on energy efficiency decline rapidly. Our results also suggest that the increased levels of participation in processing exports can promote firms’ energy efficiency through innovation effect when processing export intensity is low, while the increased participation in processing exports will decrease energy efficiency through cost reduction effect when firms’ processing export intensity is high. This research is the first attempt to examine the impacts of firms’ processing export activities on their energy use performance and provides significant policy advisories on transformation of China’s trade mode.
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