Alzheimer’s disease (AD), a neurodegenerative disease, is characterized by the presence of extracellular amyloid-β (Aβ) aggregates and intracellular neurofibrillary tangles formed by ...hyperphosphorylated tau as pathological features and the cognitive decline as main clinical features. An important cellular correlation of cognitive decline in AD is synapse loss. Soluble Aβ oligomer has been proposed to be a crucial early event leading to synapse dysfunction in AD. Astrocytes are crucial for synaptic formation and function, and defects in astrocytic activation and function have been suggested in the pathogenesis of AD. Astrocytes may contribute to synapse dysfunction at an early stage of AD by participating in Aβ metabolism, brain inflammatory response, and synaptic regulation. While mesenchymal stem cells can inhibit astrogliosis, and promote non-reactive astrocytes. They can also induce direct regeneration of neurons and synapses. This review describes the role of mesenchymal stem cells and underlying mechanisms in regulating astrocytes-related Aβ metabolism, neuroinflammation, and synapse dysfunction in early AD, exploring the open questions in this field.
The rapid spread and high death rates of the COVID-19 pandemic resulted in massive panic and anxiety all over the world. People rely heavily on media for information-seeking during the period of ...social isolation. This study aimed to explore the relationship between media exposure and anxiety, and highlighted the underlying mechanisms mediated by the media vicarious traumatization effect. A total of 1118 Chinese citizens participated in the online survey, who were from 30 provinces in mainland China. Results showed that all four types of media (official media, commercial media, social media, and overseas media) cause vicarious traumatization to their audiences to different degrees. It was also found that the impact of media exposure on anxiety was mediated by media vicarious traumatization: there were full mediation effects for commercial media exposure and overseas media exposure, while there were indirect-only mediation effects for official media exposure and social media exposure. Audiences staying in cities with a relatively severe pandemic were more susceptible to the vicarious traumatization caused by commercial media compared to those staying in Hubei. This study expanded the concept and application of vicarious traumatization to the mediated context, and the findings provided insightful advice to media practitioners in the face of major crisis.
Tremendous amounts of execution data are collected during software execution. These data provide rich information for software runtime behavior comprehension. The unstructured execution data may be ...too complex, involving multiple interleaved components and so on. Applying existing process discovery techniques results in spaghetti-like models with no clear structure and no valuable information that can be easily understood by end users. In this article, we start with the observation that a software system is composed of a group of components, and we use this information to decompose the problem into smaller independent ones by discovering a behavioral model per component. To this end, we first distill a software event log for each component from the raw software execution data. Then, we construct the hierarchical software event log by recursively applying caller-and-callee relation detection. Next, component behavioral models, represented as hierarchical Petri nets, are discovered by recursively applying existing process discovery techniques. To measure the quality of discovered models against the execution data, we transform hierarchical Petri nets to flat ones, and the quality metrics, e.g., fitness, precision, and complexity, are applied. All proposed approaches have been implemented in the open-source process mining toolkit ProM. Through the experimental evaluation using both synthetic software systems and open-source software systems, we illustrate that the proposed approach facilitates the discovery of more understandable and high-quality software behavioral models. Note to Practitioners -Software execution data record rich information on the runtime behavior of software systems. Discovering an overall behavioral model for the whole software system typically results in an extremely complicated model that hinders further comprehension and usage. With the observation that a software system is composed of a set of interacting components, this article considers the problem of discovering a behavioral model per component. The proposed techniques are implemented in the open-source process mining tool ProM, and experimental evaluations using both synthetic and real-life software systems have indicated their applicability. The proposed approaches are readily applicable to industrial-size software behavior comprehension.
The existing literatures mainly focus on the relationships between ICT and CO2 emissions in developed countries from the perspective of technology, but little attention has been paid to China. ...Concerning regional differences in China, this paper investigates the impact of ICT industry on CO2 emissions at the national and regional levels using the STIRPAT model and provincial panel data during the period 2000–2010. The results show that ICT industry contributes to reducing China’s CO2 emissions and the impact of ICT industry on CO2 emissions in the central region is greater than that in the eastern region, while that in the western region is insignificant. The findings not only contribute to advancing the existing literature, but also deserve special attention from policymakers.
Since the industrial revolution, it has been assumed that fossil-fuel combustions dominate increasing nitrogen oxide (NO
) emissions. However, it remains uncertain to the actual contribution of the ...non-fossil fuel NO
to total NO
emissions. Natural N isotopes of NO
in precipitation (δ
N
) have been widely employed for tracing atmospheric NO
sources. Here, we compiled global δ
N
observations to evaluate the relative importance of fossil and non-fossil fuel NO
emissions. We found that regional differences in human activities directly influenced spatial-temporal patterns of δ
N
variations. Further, isotope mass-balance and bottom-up calculations suggest that the non-fossil fuel NO
accounts for 55 ± 7% of total NO
emissions, reaching up to 21.6 ± 16.6Mt yr
in East Asia, 7.4 ± 5.5Mt yr
in Europe, and 21.8 ± 18.5Mt yr
in North America, respectively. These results reveal the importance of non-fossil fuel NO
emissions and provide direct evidence for making strategies on mitigating atmospheric NO
pollution.
During the execution of a software system, tremendous amounts of data are recorded, and such data provide valuable information on software runtime behavior analysis. This paper presents an approach ...on how to utilize process mining as an enabler to discover software behavioral models. To achieve this, we formally define the software event log and its transformation from original software execution data. Essentially, a software event log consists of a set of cases that each is a manifestation of an independent software run. A case is represented as an ordered sequence of events that each refers to a method call. Given the observation that software usually has a hierarchical structure, we first propose an approach to construct a hierarchical software event log from the original flat one by the recursively applying method calling relation detection. Next, using extended process discovery techniques, we discover a software behavioral model, which is represented as a kind of hierarchical Petri net with components, from the hierarchical software event log. We have implemented the proposed approach in the open source process mining toolkit ProM . By using two synthetic software event logs, we show that our approach can deal with infrequent behavior. Moreover, a validation with one real-life software event log shows that our approach can help visualize actual software runtime behavior in an easy-to-understand manner. Note to Practitioners -Software data analysis techniques play an increasingly important role for understanding the real behavior of software systems. This paper addresses the issue of discovering behavioral models from real-life software execution data. A kind of hierarchical Petri nets is defined to represent the discovered software behavior. All proposed concepts and algorithms are supported by open source tools, and experiments over both synthetic and real-life software execution data have shown their applicability. The proposed methodology is readily applicable to industrial-size software behavior discovery problems.
The contribution of this paper focuses on the development of a security-based methodology for the solution of short-term SCUC when considering the impact of natural gas transmission system. The ...proposed methodology examines the interdependency of electricity and natural gas in a highly complex transmission system. The natural gas transmission system is modeled as a set of nonlinear equations. The proposed solution applies a decomposition method to separate the natural gas transmission feasibility check subproblem and the power transmission feasibility check subproblem from the hourly unit commitment (UC) in the master problem. Gas contracts are modeled and incorporated in the master UC problem. The natural gas transmission subproblem checks the feasibility of natural gas transmission as well as natural gas transmission security constraints for the commitment and dispatch of gas-fired generating units. If any natural gas transmission violations arise, corresponding energy constraints will be formed and added to the master problem for solving the next iteration of UC. The iterative process will continue until a converged feasible gas transmission solution is found. A six-bus power system with seven-node gas transmission system and the IEEE 118-bus power system with 14-node gas transmission system are analyzed to show the effectiveness of the proposed solution. The proposed model can be used by a vertically integrated utility or the ISO for the short-term commitment and dispatch of generating units with natural gas transmission constraints.
Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past ...twenty years; however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model (MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets (MPNs) that are an extension of Petri nets with distinguishable tokens. Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used. The proposed discovery approach is properly implemented as plugins in the ProM toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-the-art process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.
Soil organic carbon (SOC) in coastal wetlands, also known as “blue C,” is an essential component of the global C cycles. To gain a detailed insight into blue C storage and controlling factors, we ...studied 142 sites across ca. 5000 km of coastal wetlands, covering temperate, subtropical, and tropical climates in China. The wetlands represented six vegetation types (Phragmites australis, mixed of P. australis and Suaeda, single Suaeda, Spartina alterniflora, mangrove Kandelia obovata and Avicennia marina, tidal flat) and three vegetation types invaded by S. alterniflora (P. australis, K. obovata, A. marina). Our results revealed large spatial heterogeneity in SOC density of the top 1‐m ranging 40–200 Mg C ha−1, with higher values in mid‐latitude regions (25–30° N) compared with those in both low‐ (20°N) and high‐latitude (38–40°N) regions. Vegetation type influenced SOC density, with P. australis and S. alterniflora having the largest SOC density, followed by mangrove, mixed P. australis and Suaeda, single Suaeda and tidal flat. SOC density increased by 6.25 Mg ha−1 following S. alterniflora invasion into P. australis community but decreased by 28.56 and 8.17 Mg ha−1 following invasion into K. obovata and A. marina communities. Based on field measurements and published literature, we calculated a total inventory of 57 × 106 Mg C in the top 1‐m soil across China's coastal wetlands. Edaphic variables controlled SOC content, with soil chemical properties explaining the largest variance in SOC content. Climate did not control SOC content but had a strong interactive effect with edaphic variables. Plant biomass and quality traits were a minor contributor in regulating SOC content, highlighting the importance of quantity and quality of OC inputs and the balance between production and degradation within the coastal wetlands. These findings provide new insights into blue C stabilization mechanisms and sequestration capacity in coastal wetlands.
● China’s coastal wetlands had large spatial heterogeneity in SOC density of the top 1‐meter ranging from 40 to 200 Mg C ha‐1.
● Vegetation type influenced SOC density, with P. australis and S. alterniflora having the largest SOC density. SOC density increased in salt marsh but decreased in mangroves following S. alterniflora invasion.
● Edaphic variables controlled SOC content, with soil chemical properties explaining the largest variance in SOC content. Climate did not control SOC content, but had a strong interactive effect with edaphic variables.
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