Background There is valid evidence that air pollution is associated with respiratory disease. However, few studies have quantified the short-term effects of six air pollutants on influenza-like illness (ILI). This study explores the potential relationship between air pollutants and ILI in Jinan, China. Methods Daily data on the concentration of particulate matters < 2.5 μm (PM 2.5), particulate matters < 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) and ILI counts from 2016 to 2017 were retrieved. The wavelet coherence analysis and generalized poisson additive regression model were employed to qualify the relationship between air pollutants and ILI risk. The effects of air pollutants on different age groups were investigated. Results A total of 81,459 ILI counts were collected, and the average concentrations of PM2.5, PM10, O3, CO, SO2 and NO2 were 67.8 μg/m3, 131.76 μg/ m3, 109.85 μg/ m3, 1133 μg/ m3, 33.06 μg/ m3 and 44.38 μg/ m3, respectively. A 10 μg/ m3 increase in concentration of PM2.5, PM10, CO at lag0 and SO2 at lag01, was positively associated with a 1.0137 (95% confidence interval (CI): 1.0083–1.0192), 1.0074 (95% CI: 1.0041–1.0107), 1.0288 (95% CI: 1.0127–1.0451), and 1.0008 (95% CI: 1.0003–1.0012) of the relative risk (RR) of ILI, respectively. While, O3 (lag5) was negatively associated with ILI (RR 0.9863; 95%CI: 0.9787–0.9939), and no significant association was observed with NO2, which can increase the incidence of ILI in the two-pollutant model. A short-term delayed impact of PM2.5, PM10, SO2 at lag02 and CO, O3 at lag05 was also observed. People aged 25–59, 5–14 and 0–4 were found to be significantly susceptible to PM2.5, PM10, CO; and all age groups were significantly susceptible to SO2; People aged ≥60 year, 5–14 and 0–4 were found to be significantly negative associations with O3. Conclusion Air pollutants, especially PM2.5, PM10, CO and SO2, can increase the risk of ILI in Jinan. The government should create regulatory policies to reduce the level of air pollutants and remind people to practice preventative and control measures to decrease the incidence of ILI on pollution days.

Replication technology is commonly used to improve data availability and reduce data access latency in the cloud storage system by providing users with different replicas of the same service. Most current approaches largely focus on system performance improvement, neglecting management cost in deciding replicas number and their store places, which cause great financial burden for cloud users because the cost for replicas storage and consistency maintenance may lead to high overhead with the number of new replicas increased in a pay-as-you-go paradigm. In this paper, towards achieving the approximate minimum data sets management cost benchmark in a practical manner, we propose a replicas placements strategy from cost-effective view with the premise that system performance meets requirements. Firstly, we design data sets management cost models, including storage cost and transfer cost. Secondly, we use the access frequency and the average response time to decide which data set should be replicated. Then, the method of calculating replicas’ number and their store places with minimum management cost is proposed based on location problem graph. Both the theoretical analysis and simulations have shown that the proposed strategy offers the benefits of lower management cost with fewer replicas.

Financial fraud has extremely damaged the sustainable growth of financial markets as a serious problem worldwide. Nevertheless, it is fairly challenging to identify frauds with highly imbalanced dataset because ratio of non-fraud companies is very high compared to fraudulent ones. Intelligent financial statement fraud detection systems have therefore been developed to support decision-making for the stakeholders. However, most of current approaches only considered the quantitative part of the financial statement ratios while there has been less usage of the textual information for classifying, especially those related comments in Chinese. As such, this paper aims to develop an enhanced system for detecting financial fraud using a state-of-the-art deep learning models based on combination of numerical features that derived from financial statement and textual data in managerial comments of 5130 Chinese listed companies' annual reports. First, we construct financial index system including both financial and non-financial indices that previous researches usually excluded. Then the textual features in MD&A section of Chinese listed company's annual report are extracted using word vector. After that, powerful deep learning models are employed and their performances are compared with numeric data, textual data and combination of them, respectively. The empirical results show great performance improvement of the proposed deep learning methods against traditional machine learning methods, and LSTM, GRU approaches work with testing samples in a correct classification rate of 94.98% and 94.62%, indicating that the extracted textual features of MD&A section exhibit promising classification results and substantially reinforce financial fraud detection.

In the cloud storage system, data sets replicas technology can efficiently enhance data availability and thereby increase the system reliability by replicating commonly used data sets in geographically different data centers. Most current approaches largely focus on system performance improvement by placing replicas for an independent data set, omitting the generation relationship among data sets. Furthermore, cost is an important element in deciding replicas number and their stored places, which can cause great financial burden for cloud clients because the cost for replicas storage and consistency maintenance may lead to high overhead with the number of new replicas increased in a pay-as-you-go paradigm. In this paper, we propose a combination strategy of real-replicas and pseudo-replicas (by computation from its provenance) from cost-effective view in order to achieve the minimum data set management cost, not only for the independent data sets but also for related data sets with generation relationships. We first define cost models that fit into the cloud computing paradigm, including data sets storage, computation and transfer costs, and then develop a new data set management cost model, helping to achieve a multi-criteria optimization of data set management. After that, a minimum cost benchmarking approach for the best trade-off between real-replicas and pseudo-replicas is proposed once decision to add a replica has been made. Then, a more practical and reasonable genetic algorithm as an alternative procedure for generating optimal or nearoptimal solution is given in order to identify the suitable replicas storage places. Finally, we present simulations setups and results that provide a first validation of our strategy. Both the theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications have shown efficiency and effectiveness of the improved system brought by the proposed strategy in cloud computing environment.

The autoimmune disease multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE), is characterized by an ascending paralysis that is characterized by extensive infiltration of the central nervous system by inflammatory cells. Although several studies to some extent uncover the cellular mechanisms of microglia that govern EAE pathogenesis, the molecular mechanisms that orchestrate the movement of microglia remain unknown, and potential novel therapeutic strategies are still required. In this study, we report that dexmedetomidine, an alpha 2a adrenergic receptor agonist, attenuates the clinical severity of EAE with less infiltration of microglia. During EAE, dexmedetomidine inhibits SDF-1- and I-TAC-induced chemotaxis of microglia mediated by CXCR7 but not CXCR4 or CXCR3. Most importantly, the alpha 2a adrenergic receptor is essential in dexmedetomidine-induced CXCR7 desensitization in microglia. Further experiments confirmed that CXCR7 desensitization required atypical protein kinase C ζ activation, while conventional and novel protein kinase C isoforms were not involved. Altogether, our data elucidate the mechanism of dexmedetomidine-induced CXCR7 desensitization in microglia and amelioration in EAE, which might lead to a better understanding of the therapeutic effects of dexmedetomidine as well as its implications for CXCR7 desensitization in autoimmune disease.