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The ongoing obesity epidemic has raised awareness of the complex physiology of adipose tissue. Abnormal adipocyte differentiation results in the development of systemic metabolic disorders such as insulin resistance and diabetes. The conjugation of NEDD8 (neural precursor cell expressed, developmentally downregulated 8) to target protein, termed neddylation, has been shown to mediate adipogenesis. However, much remains unknown about its role in adipogenesis. Here, we demonstrated that neddylation and its targets, the cullin (CUL) family members, are differentially regulated during mouse and human adipogenesis. Inhibition of neddylation by MLN4924 significantly reduced adipogenesis of 3T3-L1 and human stromal vascular cells. Deletion of NAE1, a subunit of the only NEDD8 E1 enzyme, suppressed neddylation and impaired adipogenesis. Neddylation deficiency did not affect mitotic cell expansion. Instead, it disrupted CREB/CEBPβ/PPARγ signaling, essential for adipogenesis. Interestingly, among the neddylation-targeted CUL family members, deletion of CUL3, but not CUL1, CUL2, or CUL4A, largely replicated the adipogenic defects observed with neddylation deficiency. A PPARγ agonist minimally rescued the adipogenic defects caused by the deletion of NAE1 and CUL3. In conclusion, our study demonstrates that neddylation and its targeted CUL3 are crucial for adipogenesis. These findings provide potential targets for therapeutic intervention in obesity and metabolic disorders.

Heart development is a spatiotemporally regulated process that extends from the embryonic phase to postnatal stages. Disruption of this highly orchestrated process can lead to congenital heart disease or predispose the heart to cardiomyopathy or heart failure. Consequently, gaining an in-depth understanding of the molecular mechanisms governing cardiac development holds considerable promise for the development of innovative therapies for various cardiac ailments. While significant progress in uncovering novel transcriptional and epigenetic regulators of heart development has been made, the exploration of post-translational mechanisms that influence this process has lagged. Culling-RING E3 ubiquitin ligases (CRLs), the largest family of ubiquitin ligases, control the ubiquitination and degradation of ~20% of intracellular proteins. Emerging evidence has uncovered the critical roles of CRLs in the regulation of a wide range of cellular, physiological, and pathological processes. In this review, we summarize current findings on the versatile regulation of cardiac morphogenesis and maturation by CRLs and present future perspectives to advance our comprehensive understanding of how CRLs govern cardiac developmental processes.

As the largest tissue in the body, skeletal muscle has multiple functions in movement and energy metabolism. Skeletal myogenesis is controlled by a transcriptional cascade including a set of muscle regulatory factors (MRFs) that includes Myogenic Differentiation 1 (MYOD1), Myocyte Enhancer Factor 2 (MEF2), and Myogenin (MYOG), which direct the fusion of myogenic myoblasts into multinucleated myotubes. Neddylation is a posttranslational modification that covalently conjugates ubiquitin-like NEDD8 (neural precursor cell expressed, developmentally downregulated 8) to protein targets. Inhibition of neddylation impairs muscle differentiation; however, the underlying molecular mechanisms remain less explored. Here, we report that neddylation is temporally regulated during myoblast differentiation. Inhibition of neddylation through pharmacological blockade using MLN4924 (Pevonedistat) or genetic deletion of NEDD8 Activating Enzyme E1 Subunit 1 (NAE1), a subunit of the E1 neddylation-activating enzyme, blocks terminal myoblast differentiation partially through repressing MYOG expression. Mechanistically, we found that neddylation deficiency enhances the mRNA and protein expressions of class IIa histone deacetylases 4 and 5 (HDAC4 and 5) and prevents the downregulation and nuclear export of class III HDAC (NAD-Dependent Protein Deacetylase Sirtuin-1, SIRT1), all of which have been shown to repress MYOD1-mediated MYOG transcriptional activation. Together, our findings for the first time identify the crucial role of neddylation in mediating class IIa and III HDAC co-repressors to control myogenic program and provide new insights into the mechanisms of muscle disease and regeneration.

Cardiovascular disease is the leading cause of death in the United States, accounting for nearly one in every seven deaths. Over the last decade, various targeted therapeutics have been introduced, but there has been no corresponding improvement in patient survival. Since the mortality rate of cardiovascular disease has not been significantly decreased, efforts have been made to understand the link between heart disease and novel therapeutic targets such as non-coding RNAs. Among multiple non-coding RNAs, long non-coding RNA (lncRNA) has emerged as a novel therapeutic in cardiovascular medicine. LncRNAs are endogenous RNAs that contain over 200 nucleotides and regulate gene expression. Recent studies suggest critical roles of lncRNAs in modulating the initiation and progression of cardiovascular diseases. For example, aberrant lncRNA expression has been associated with the pathogenesis of ischemic heart failure. In this article, we present a synopsis of recent discoveries that link the roles and molecular interactions of lncRNAs to cardiovascular diseases. Moreover, we describe the prevalence of circulating lncRNAs and assess their potential utilities as biomarkers for diagnosis and prognosis of heart disease.

Protein-coding genes account for only a small part of the human genome; in fact, the vast majority of transcripts are comprised of non-coding RNAs (ncRNAs) including long ncRNAs (lncRNAs) and small ncRNAs, microRNAs (miRs). Accumulating evidence indicates that ncRNAs could play critical roles in regulating many cellular processes which are often implicated in health and disease. For example, ncRNAs are aberrantly expressed in cancers, heart diseases, and many other diseases. LncRNAs and miRs are therefore novel and promising targets to be developed into biomarkers for diagnosis and prognosis as well as treatment options. The interaction between lncRNAs and miRs as well as its pathophysiological significance have recently been reported. Mechanistically, it is believed that lncRNAs exert “sponge-like” effects on various miRs, which subsequently inhibits miR-mediated functions. This crosstalk between two types of ncRNAs frequently contributes to the pathogenesis of the disease. In this review, we provide a summary of the recent studies highlighting the interaction between these ncRNAs and the effects of this interaction on disease pathogenesis and regulation.

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error sensitivity analysis on FPGAs can help target the more sensitive areas for detection and correction, thereby improving the efficiency of soft error repair. Firstly, in accordance with the dual-layer architecture of SRAM-based FPGAs, methods for the soft error sensitivity analysis of FPGA application layer resources and configuration bitstreams are reviewed. Subsequently, based on the analysis results, it also covers corresponding application layer memory scrubbing and configuration scrubbing techniques. A prospective look at emerging soft error mitigation technologies is discussed at the end of this review, supporting the development of highly reliable aerospace-grade SRAM-based FPGAs.

Safety is a primary concern for the civil aviation industry. Airlines record high-frequency but potentially low-severity unsafe events, i.e., incidents, in their reports. Over the past few decades, civil aviation security practitioners have made efforts to analyze these issues. The information in incident reports is valuable for risk analysis. However, incident reports were inefficiently utilized due to incoherence, large volume, and poor structure. In this study, we proposed a technical scheme to intelligently classify and extract risk factors from Chinese civil aviation incident reports. Firstly, we adopted machine learning classifiers and vectorization strategies to classify incident reports into 11 categories. Grid search was used to adjust the parameters of the classifier. In the preliminary experiment, the combination of the extreme gradient boosting (XGBoost) classifier and the occurrence position (OC-POS) vectorization strategy outperformed with an 0.85 weighted F1-score. In addition, we designed a rule-based system to identify the factors related to the occurrence of incidents from 25 empirical causes, which included equipment, human, environment, and organizational causes. For cause identification, we used rules obtained through manual analysis with keywords and discourse. F1-score above 0.90 was obtained on the test set using the causes identification model derived from the training set. The proposed system permits insights into unsafe factors in aviation incidents and prevents reoccurrence. Future works can proceed on this study, such as exploring the causal relationship between causes and incidents.

In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.

Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson’s correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.

In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.