Neuropsychiatric Lupus Erythematosus: Future Directions and Challenges; a Systematic Review and Survey

This study aimed to systematically review neuropsychiatric lupus erythematosus (NPSLE) and establish a simplified diagnostic criterion for NPSLE. Publications from 1994 to 2018 in the database (Wanfang data (http://www.wanfangdata.com.cn/index.html) and China National Knowledge Internet (http://www.cnki.net)) were included. In total, 284 original case reports and 24 unpublished cases were collected, and clinical parameters were analyzed. An attempt was made to develop a set of simplified diagnostic criteria for NPSLE based on cases described in the survey and literature; moreover, and pathophysiology and management guidelines were studied. The incidence rate of NPSLE was estimated to be 12.4% of SLE patients in China. A total of 408 NPSLE patients had 652 NP events, of which 91.2% affected the central nervous system and 8.8% affected the peripheral nervous system. Five signs (manifestations, disease activity, antibodies, thrombosis, and skin lesions) showed that negative and positive predictive values were more than 70%, included in the diagnostic criteria. The specificity, accuracy, and positive predictive value (PPV) of the revised diagnostic criteria were significantly higher than those of the American College of Rheumatology (ACR) criteria (w2=13.642, 15.591, 65.010, po0.001). The area under the curve (AUC) for revised diagnostic criteria was 0.962 (standard error=0.015, 95% confidence intervals [CI] =0.933–0.990), while the AUC for the ACR criteria was 0.900 (standard error=0.024, 95% CI=0.853–0.946). The AUC for the revised diagnostic criteria was different from that for the ACR criteria (Z=2.19, po0.05). Understanding the pathophysiologic mechanisms leading to NPSLE is essential for the evaluation and design of effective interventions. The set of diagnostic criteria proposed here represents a simplified, reliable, and costeffective approach used to diagnose NPSLE. The revised diagnostic criteria may improve the accuracy rate for diagnosing NPSLE compared to the ACR criteria

Molecular management for refining operations

Molecular management targets the right molecules to be at the right place, at the right time and at the right price. It consists of molecular characterisation of refining streams, molecular modelling and optimisation of refining processes, as well as overall refinery optimisation integrating material processing system and utility system on the molecular level. The need to increase modelling details to a molecular level is not just a result of political regulations, which force refiners to managing the molecule properly, but also seems to be a very promising to increase the refining margin. In this work, four aspects of molecular management are investigated respectively. Molecular Type Homologous Series (MTHS) matrix framework is enhanced on both representation construction and transformation methodology. To improve the accuracy and adequacy of the representation model, different strategies are formulated separately to consider isomers for light and middle distillates. By introducing statistical distribution, which takes the composition distribution of molecules into account, the transformation approach is revolutionised to increase the usability, and tackle the challenge of possibly achieving significantly different compositions from the same bulk properties by the existing approaches. The methodology is also enhanced by applying extensive bulk properties. Case studies demonstrate the effectiveness and accuracy of the methodology. Based on the proposed characterisation method, refining processes are modelled on a molecular level, and then process level optimisation is preformed to have an insight view of economic performance. Three different processes, including gasoline blending, catalytic reforming, and diesel hydrotreating, are investigated respectively. Regarding gasoline blending, the property prediction of blending components, and the blending nonlinearity are discussed. To tightly control on the property giveaway, a molecular model of gasoline blending is developed, and then integrated into the recipe optimisation. As for the conversion processes, catalytic reforming and diesel hydrotreating, reactions and reactors are modelled separately, and then followed by the consideration of catalyst deactivation. A homogeneous rigorous molecular model of a semiregenerative catalytic reforming process, considering pressure drop, has been developed. In addition, a multi-period process optimisation model has been formulated. Regarding diesel hydrotreating, a molecular model of reactions with a three-phase trickle-bed reactor has been developed. The concept of reaction family is successfully applied. A structural contribution approach is used to obtain kinetics and adsorption parameters. A series of procedures are developed to solve the complex problem. Thereafter, a process optimisation model has been developed with the consideration of catalyst deactivation, with a new strategy on the division of catalyst life. Finally, a two-level decomposition optimisation method is extended to incorporate molecular modelling into the overall refinery optimisation, and then applied in two aspects. Firstly, with the integration of the process and the site-level models, a better perspective is obtained with regard to a material processing system. By molecular modelling of refining streams and processes, the integrated approach not only controls the molecules in products properly, but also increases the overall performance. In the second application, a framework integrating a hydrogen network with hydroprocesses is developed to target the maximum profit, rather than saving hydrogen. It allocates hydrogen on the hydrogen network level and utilise hydrogen efficiently on the process level by optimising operating conditions. Consequently, the extent of achieving the maximum profit could be fully exploited with optimal hydrogen utilisation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Correlation and scaling behaviors of fine particulate matter (PM2.5) concentration in China

Air pollution has become a major issue and caused widespread environmental and health problems. Aerosols or particulate matters are an important component of the atmosphere and can transport under complex meteorological conditions. Based on the data of PM2.5 observations, we develop a network approach to study and quantify their spreading and diffusion patterns. We calculate cross-correlation functions of the time lag between sites within different seasons. The probability distribution of correlation changes with season. It is found that the probability distributions in four seasons can be scaled into one scaling function with averages and standard deviations of correlation. This seasonal scaling behavior indicates that there is the same mechanism behind correlations of PM2.5 concentration in different seasons. Further, the weighted degrees reveal the strongest correlations of PM2.5 concentration in winter and in the North China Plain for the positive correlation pattern that is mainly caused by the transport of PM2.5. These directional degrees show net influences of PM2.5 along Gobi and inner Mongolia, the North China Plain, Central China, and Yangtze River Delta. The negative correlation pattern could be related to the large-scale atmospheric waves. Copyright (C) EPLA, 2018Peer reviewe

Eigenstates in the self-organised criticality

We employ the eigen microstate approach to explore the self-organized criticality (SOC) in two celebrated sandpile models, namely, the BTW model and the Manna model. In both models, phase transitions from the absorbing-state to the critical state can be understood by the emergence of dominant eigen microstates with significantly increased weights. Spatial eigen microstates of avalanches can be uniformly characterized by a linear system size rescaling. The first temporal eigen microstates reveal scaling relations in both models. Furthermore, by finite-size scaling analysis of the first eigen microstate, we numerically estimate critical exponents i.e., $\sqrt{\sigma_0 w_1}/\tilde{v}_{1} \propto L^D$ and $\tilde{v}_{1} \propto L^{D(1-\tau_s)/2}$. Our findings could provide profound insights into eigen states of the universality and phase transition in non-equilibrium complex systems governed by self-organized criticality

Evolution mechanism of principal modes in climate dynamics

Eigen analysis has been a powerful tool to distinguish multiple processes into different simple principal modes in complex systems. For a non-equilibrium system, the principal modes corresponding to the non-equilibrium processes are usually evolving with time. Here, we apply the eigen analysis into the complex climate systems. In particular, based on the daily surface air temperature in the tropics (30? S–30? N, 0? E–360? E) between 1979-01-01 and 2016-12-31, we uncover that the strength of two dominated intra-annual principal modes represented by the eigenvalues significantly changes with the El Niño/southern oscillation from year to year. Specifically, according to the ‘regional correlation’ introduced for the first intra-annual principal mode, we find that a sharp positive peak of the correlation between the El Niño region and the northern (southern) hemisphere usually signals the beginning (end) of the El Niño. We discuss the underlying physical mechanism and suppose that the evolution of the first intra-annual principal mode is related to the meridional circulations; the evolution of the second intra-annual principal mode responds positively to the Walker circulation. Our framework presented here not only facilitates the understanding of climate systems but also can potentially be used to study the dynamical evolution of other natural or engineering complex systems. © 2020 The Author(s)