The Influences of Key Factors on the Consequences Following the Natural Gas Leakage from Pipeline

AbstractThe effects of the environmental dispersion (i.e. atmospheric stability, wind speed, temperature, humidity and ground roughness) and source release factors (i.e. pipeline diameter, length, pressure and release opening area) on the suffocation distance, flammable vapor cloud distance, overpressure distance and thermal radiation distance after the natural gas released from pipeline were evaluated and analyzed. The results show that all the environmental dispersion factors except humidity have an effect on the flammable vapor cloud distance. The more stable atmospheric condition, lower wind speed and smaller ground roughness lead to the longer flammable vapor cloud distance. The atmosphere temperature has a very limited influence on the flammable vapor cloud distance. The higher ambient temperature and larger humidity result in the longer downwind thermal radiation distance, while the atmospheric stability, wind speed and ground roughness nearly does not. All the four source release factors significantly influence the flammable vapor cloud distance and thermal radiation distance, which is due to the different release amount, release rate and initial momentum

Genetic polymorphism in HTR2A rs6313 is associated with internet addiction disorder

IntroductionInternet addiction disorder (IAD) has grown into public health concern of global proportions. Previous studies have indicated that individuals with IAD may exhibit altered levels of serotonin and dopamine, which are known to play crucial roles in depression, anxiety, impulsivity, and addiction. Therefore, polymorphisms in the receptors that mediate the effects of serotonin and dopamine and affect their functional states as well as their activities are suspect. In this study, we aimed to investigate the association between IAD and rs6313 (T102C) polymorphism in the serotonin 2A receptor (5-HT2A) gene, (HTR2A).MethodsTwenty patients with IAD and twenty healthy controls (HCs) were included in this study. Young’s Internet Addiction Test (IAT), Self-Rating Anxiety Scale, Self-Rating Depression Scale, Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), Barratt Impulse Scale, Pittsburgh Sleep Quality Index (PSQI), and Social Support Rating Scale (SSRS) were used to assess the severity of internet addiction, mental status, impulsive traits, sleep quality, and social support. Genotyping was performed to identify rs6313 polymorphisms in the HTR2A gene of all participants.ResultsThe frequencies of the C and T alleles of HTR2A T102C were 28% and 72% in the IAD group and 53% and 47% in the HCs group, respectively, indicating that the differences between these two groups were significant. No significant difference was observed in the distribution of the CC, CT, and TT genotypes of HTR2A gene T102C between the IAD and the HCs groups. Additionally, there was no difference in the distribution of the frequencies of the HTR2A gene T102C CC and CT+TT genotypes between the two groups. However, the distribution between the TT and CC+CT genotypes showed an apparent statistical difference in the HTR2A gene T102C between the two groups. Correlation analysis indicated that the IAT score was positively correlated with the Y-BOCS and BIS scores for the CC+CT genotype in patients with IAD. Moreover, the IAT score was positively correlated with the PSQI score in patients with IAD carrying the TT genotype.ConclusionThe present study demonstrates that rs6313 in HTR2A is associated with IAD, and that the T allele of rs6313 in HTR2A may be a risk factor for IAD

Clinical characteristics and prognosis of basaloid squamous cell carcinoma of the lung: a population-based analysis

Background This study analyzed the clinical features and prognosis of basaloid squamous cell carcinoma of the lung (BSC), and constructed a nomogram to predict the prognoses of patients. Methods The information of pure BSC patients was obtained in the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Then, it was evaluated, and compared with the data of lung squamous cell carcinoma (SCC), lung large cell carcinoma (LCC) and lung adenocarcinoma (LAC) patients. Subsequently, we used univariate and multivariate analyses to investigate the independent factors related to the prognoses of patients with BSC and constructed a nomogram to verify the prognoses. Results A total of 425 patients diagnosed with BSC were enrolled. Compared with patients with SCC, LCC and LAC, the mean survival time of BSC patients was better than all of them. Compared with SCC, there were significant differences between the characteristics of grade (P < 0.001), total stage (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), M stage (P < 0.001), surgery (P < 0.001), radiotherapy (P < 0.001), and chemotherapy (P < 0.001), while BSC also had significantly different clinical characteristics from LCC and LAC. Univariate and multivariate survival analyses showed that age (P < 0.001), T stage (P < 0.001), N stage (P = 0.009), M stage (P < 0.001), and surgery (P < 0.001) were independent prognostic factors of BSC. The survival of patients undergoing lobectomy was significantly better than sublobar resection, with an OR of 0.389 (0.263–0.578). We constructed a nomogram with a C-index of 0.750 (95% confidence interval) based on the results of multivariate analysis. The calibration curves based on nomogram scores indicated that the nomogram could accurately predict the prognosis of patients. Conclusions BSC had unique clinical and prognostic features. T stage, N stage, M stage, age, and surgery were independently associated with overall survival (OS). Lobectomy was a relative ideal choice for patients with BSC. The nomogram effectively predicted the OS at 1-, 3-, and 5-years

Direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network

Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level, leading to higher image-reconstruction quality compared with their natural counterparts. However, prevailing design methods of CAHMs are based on Huygens-Fresnel theory, meta-atom optimization, numerical simulation and experimental verification, which results in a consumption of computing resources. Here, we applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design. A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory, which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images. The training results show that the normalized mean pixel error is about 3% on dataset. As verification, the metasurface prototypes are fabricated, simulated and measured. The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field, which demonstrates the effectiveness of our design. Encouragingly, this work provides a monolithic field-to-pattern design method for CAHMs, which paves a new route for the direct reconstruction of metasurfaces

Discovery of a high-altitude ecotype and ancient lineage of Arabidopsis thaliana from Tibet

Arabidopsis thaliana (A. thaliana) has long been a model species for dicotyledon study, and was the first flowering plant to get its genome completed sequenced [1]. Although most wild A. thaliana are collected in Europe, several studies have found a rapid A. thaliana west-east expansion from Central Asia [2]. The Qinghai-Tibet Plateau (QTP) is close to Central Asia and known for its high altitude, unique environments and biodiversity [3]. However, no wild-type A. thaliana had been either discovered or sequenced from QTP. Studies on the A. thaliana populations collected under 2000 m asl have shown that the adaptive variations associated with climate and altitudinal gradients [4]. Hence a high-altitude A. thaliana provides a precious natural material to investigate the evolution and adaptation process. Here, we present the genome of a new ecotype of A. thaliana collected in the Gongga County, Tibet (4200 m asl) (Fig. 1a), to demonstrate its evolutionary history and adaptation to highaltitude regions

MicroRNA-29 specifies age-related differences in the CD8+ T cell immune response

MicroRNAs (miRNAs) have emerged as critical regulators of cell fate in the CD8+ T cell response to infection. Although there are several examples of miRNAs acting on effector CD8+ T cells after infection, it is unclear whether differential expression of one or more miRNAs in the naive state is consequential in altering their long-term trajectory. To answer this question, we examine the role of miR-29 in neonatal and adult CD8+ T cells, which express different amounts of miR-29 only prior to infection and adopt profoundly different fates after immune challenge. We find that manipulation of miR-29 expression in the naive state is sufficient for age-adjusting the phenotype and function of CD8+ T cells, including their regulatory landscapes and long-term differentiation trajectories after infection. Thus, miR-29 acts as a developmental switch by controlling the balance between a rapid effector response in neonates and the generation of long-lived memory in adults

Multiple Strategies Based Salp Swarm Algorithm for Optimal Operation of Multiple Hydropower Reservoirs

Reasonable optimal operation policy for complex multiple reservoir systems is very important for the safe and efficient utilization of water resources. The operation policy of multiple hydropower reservoirs should be optimized to maximize total hydropower generation, while ensuring flood control safety by effective and efficient storage and release policy of multiple reservoirs. To achieve this goal, a new meta-heuristic algorithm, salp swarm algorithm (SSA), is used to optimize the joint operation of multiple hydropower reservoirs for the first time. SSA is a competitive bio-inspired optimizer, which has received substantial attention from researchers in a wide variety of applications in finance, engineering, and science because of its little controlling parameters and adaptive exploratory behavior. However, it still faces few drawbacks such as lack of exploitation and local optima stagnation, leading to a slow convergence rate. In order to tackle these problems, multiple strategies combining sine cosine operator, opposition-based learning mechanism, and elitism strategy are applied to the original SSA. The sine cosine operator is applied to balance the exploration and exploitation over the course of iteration; the opposition-based learning mechanism is used to enhance the diversity of the swarm; and the elitism strategy is adopted to find global optima. Then, the improved SSA (ISSA) is compared with six well-known meta-heuristic algorithms on 23 classical benchmark functions. The results obtained demonstrate that ISSA outperforms most of the well-known algorithms. Then, ISSA is applied to optimal operation of multiple hydropower reservoirs in the real world. A multiple reservoir system, namely Xiluodu Reservoir and Xiangjiaba Rservoir, in the upper Yangtze River of China are selected as a case study. The results obtained show that the ISSA is able to solve a real-world optimization problem with complex constraints. In addition, for the typical flood with a 100 return period in 1954, the maximum hydropower generation of multiple hydropower reservoirs is about 6671 GWh in the case of completing the flood control task, increasing by 1.18% and 1.77% than SSA and Particle Swarm Optimization (PSO), respectively. Thus, ISSA can be used as an alternative effective and efficient tool for the complex optimization of multiple hydropower reservoirs. The water resources in the river basin can be further utilized by the proposed method to cope with the increasingly serious climate change

Operation Rule Derivation of Hydropower Reservoirs by Support Vector Machine Based on Grey Relational Analysis

In practical applications, the rational operation rule derivation can lead to significant improvements in the middle and long-term joint operation of cascade hydropower stations. The key issue of actual optimal operation is to select effective attributions from the deterministic optimal operation results, however, there is still no general and mature method to solve this problem. Firstly, the joint optimal operation model of hydropower reservoirs considering backwater effects are established. Then, the dynamic programming and progressive optimality algorithm are applied to solve the joint optimal operation model and the deterministic optimization results are obtained. Finally, the grey relational analysis method is applied to select more effective factors from the obtained results as the input of a support vector machine for further operation rule derivation. The Xi Luo-du and Xiang Jia-ba cascade reservoirs in the upper Yangtze river of China are selected as a case study. The results show that the proposed method can obtain better input factors to improve the performance of SVM, and smallest value of root mean square error by the proposed method of Xi Luo-du and Xiang Jia-ba are 94.33 and 21.32, respectively. The absolute error of hydropower generation for Xi Luo-du and Xiang Jia-ba are 2.57 and 0.42, respectively. Generally, this study provides a well and promising alternative tool to guide the joint operation of hydropower reservoir systems

CDK14 expression is elevated in patients with non-small cell lung cancer and correlated with poor prognosis

Objective To investigate the clinical significance of cyclin-dependent kinase 14 (CDK14) expression in patients with non-small cell lung cancer (NSCLC). Methods The present prospective observational study included 193 patients diagnosed with NSCLC between January 2010 and December 2014. NSCLC tumor tissues and paired paracancerous normal tissues were obtained from all patients. CDK14, thyroid transcription factor 1 (TTF-1), cytokeratin 5/6 (CK5/6), and Ki67 expression was measured via immunohistochemistry (IHC) Results CDK14 staining was strong (>3) in 129 patients (66.49%) and weak (≤3) in 64 patients (33.16%). The mean IHC scores were markedly higher in tumor tissues than in paracancerous tissues. Pearson’s analysis demonstrated that the IHC scores of CDK14 expression were positively correlated with TTF-1, CK5/6, and Ki67 scores. Kaplan–Meier analysis illustrated that 5-year overall survival was markedly longer in patients with weak CDK14 staining. TNM stage, pleural invasion, lymph node metastasis, CDK14 expression, and Ki67 expression were risk factors for 5-year overall survival in patients with NSCLC. Conclusion CDK14 overexpression portended poor outcomes in patients with NSCLC, and CDK14 expression was correlated with TTF-1, CK5/5, and Ki67 expression