We report that circACC1, a circular RNA derived from human ACC1, plays a critical role in cellular responses to metabolic stress. CircACC1 is preferentially produced over ACC1 in response to serum ...deprivation by the transcription factor c-Jun. It functions to stabilize and promote the enzymatic activity of the AMPK holoenzyme by forming a ternary complex with the regulatory β and γ subunits. The cellular levels of circACC1 modulate both fatty acid β-oxidation and glycolysis, resulting in profound changes in cellular lipid storage. In a tumor xenograft model, silencing or enforced expression of circACC1 resulted in growth inhibition and enhancement, respectively. Moreover, increased AMPK activation in colorectal cancer tissues was frequently associated with elevated circACC1 expression. We conclude that circACC1 serves as an economic means to elicit AMPK activation and moreover propose that cancer cells exploit circACC1 during metabolic reprogramming.
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•CircACC1, a circular RNA, acts as a component of the AMPK holoenzyme•CircACC1 assembles and stabilizes the AMPK complex and maintains basal activity•CircACC1 functions in metabolic adaptation responses to serum deprivation•Elevated circACC1 expression facilitates tumor development in vitro and in vivo
Li et al. demonstrate the mechanism and action of circACC1, a circular RNA derived from preACC1 mRNA, in directly promoting AMPK holoenzyme stability and activation. CircACC1 contributes to metabolic adaptation during serum deprivation by increasing glycolysis and beta-oxidation. Additionally, increased circACC1 may act as a tumor promoter in colorectal cancer.
The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous ...functions and engineering problems. To make this algorithm work in the binary space, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems, and uses DA to optimize the coefficients of the trigonometric function. Further, to improve the algorithm stability and convergence speed, an improved AMDA, called IAMDA, is proposed by adding one more coefficient to adjust the vertical displacement of the cosine part of the original generating function. To test the performance of IAMDA and AMDA, 12 zero-one knapsack problems are considered along with 13 classic benchmark functions. Experimental results prove that IAMDA has a superior convergence speed and solution quality as compared to other algorithms.
Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance ...of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset.
Effective, safe, and pharmacokinetically suitable drugs are urgently needed to curb the ongoing COVID-19 pandemic. The main protease or 3C-like protease (M
or 3CL
) of SARS-CoV-2 is considered an ...important target to formulate potent drugs corresponding to its crucial role in virus replication and maturation in addition to its relatively conserved active site. Promising baseline data on the potency and safety of drugs targeting SARS-CoV-2 M
are currently available. However, preclinical and clinical data on the pharmacokinetic profiles of these drugs are very limited. This review discusses the potency, safety, and pharmacokinetic profiles of potential inhibitors of SARS-CoV-2 M
and forward directions on the development of future studies focusing on COVID-19 therapeutics.
In the Internet of Things environment, the capabilities of various clients are being developed in the direction of networking and intellectualization. How to develop the clients' capability from that ...of only collecting and displaying data to that of possessing intelligence has been a critical issue. In recent years, machine learning has become a representative technology in client intellectualization and is now attracting growing interest. In machine learning, massive computing, including data preprocessing and training, requires substantial computing resources; however, lightweight clients usually do not have strong computing capability. To solve this problem, we introduce the advantage of transparent computing (TC) for the client intellectualization framework and propose an incremental machine learning framework named transparent learning (TL), where training tasks are moved from lightweight clients to servers and edge devices. After training, test models are transmitted to clients and updated with incremental training. In this study, a cache strategy is designed to divide the training set in order to optimize the performance. We choose deep learning as the performance evaluation case, and conduct several TensorFlow-based experiments to demonstrate the efficiency of the framework.
Succinate dehydrogenase (SDH) is a heterotetrameric enzyme complex belonging to the mitochondrial respiratory chain and uniquely links the tricarboxylic acid (TCA) cycle with oxidative ...phosphorylation. Cancer-related SDH mutations promote succinate accumulation, which is regarded as an oncometabolite. Post-translational modifications of SDH complex components are known to regulate SDH activity, although the contribution of SUMOylation remains unclear. Here, we show that SDHA is SUMOylated by PIAS3 and deSUMOylated by SENP2, events dictating the assembly and activity of the SDH complex. Moreover, CBP acetylation of SENP2 negatively regulates its deSUMOylation activity. Under glutamine deprivation, CBP levels decrease, and the ensuing SENP2 activation and SDHA deSUMOylation serve to concurrently dampen the TCA cycle and electron transport chain (ETC) activity. Along with succinate accumulation, this mechanism avoids excessive reactive oxygen species (ROS) production to promote cancer cell survival. This study elucidates a major function of mitochondrial-localized SENP2 and expands our understanding of the role of SUMOylation in resolving metabolic stress.
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•Glutamine deficiency decreases SENP2 acetylation by downregulating CBP levels•Mitochondrial-localized SENP2 deSUMOylates the complex II subunit SDHA at K598•The deSUMOylated form of SDHA impairs the assembly and activity of the SDH complex•SENP2 thus serves to dampen TCA cycle flux under nutrient stress conditions
Cancer cells must adapt their metabolism to withstand fluctuating nutrient levels in the tumor microenvironment. Here, Liu et al. show that SENP2 dampens mitochondrial respiration in response to glutamine deprivation. This stress state specifically activates SENP2, which deSUMOylates the SDHA subunit, attenuating the assembly and activity of the SDH complex.
A compact, low profile, multiple-input-multiple-output (MIMO) diversity antenna with super-wideband (SWB) characteristics has been proposed. The proposed antenna comprises four symmetric ...monopole-radiating elements printed on low-cost FR4 substrate with the slotted ground plane. The single antenna of a monopole structure and a quad-port MIMO antenna, with the dimensions of 30 × 20 mm
and 60 × 55 mm
, respectively, are ideal for IoT and high-speed data applications. The proposed MIMO antenna has a high diversity gain and low envelope correlation coefficient (ECC) within the frequency range. Simulated results demonstrate the performance of the MIMO-SWB antenna, which operates from 2.3 to 23 GHz, with a high isolation level over 20 dB in the achieved frequency band. Moreover, the proposed MIMO antenna has been investigated with mirror fashion and orthogonal structure. Both structures provide similar results except for mutual coupling performance. The orthogonal adjustment for high isolation achieves better results with the proposed model. Further, the prototype of the proposed antenna is fabricated and measured effectively. Simulated and measured results show good agreement for super-wideband applications.
Abstract
To better understand the molecular mechanisms of intracranial aneurysm (IA) pathogenesis, we used gene coexpression networks to identify hub genes and functional pathways associated with IA ...onset. Two Gene Expression Omnibus (GEO) datasets encompassing intracranial aneurysm tissue samples and cerebral artery control samples were included. To discover functional pathways and potential biomarkers, weighted gene coexpression network analysis was employed. Next, single-gene gene set enrichment analysis was employed to investigate the putative biological roles of the chosen genes. We also used receiver operating characteristic analysis to confirm the diagnostic results. Finally, we used a rat model to confirm the hub genes in the module of interest. The module of interest, which was designated the green module and included 115 hub genes, was the key module that was most strongly and negatively associated with IA formation. According to gene set variation analysis results, 15 immune-related pathways were significantly activated in the IA group, whereas 7 metabolic pathways were suppressed. In two GEO datasets,
SLC2A12
could distinguish IAs from control samples. Twenty-nine hub genes in the green module might be biomarkers for the occurrence of cerebral aneurysms.
SLC2A12
expression was significantly downregulated in both human and rat IA tissue. In the present study, we identified 115 hub genes related to the pathogenesis of IA onset and deduced their potential roles in various molecular pathways; this new information may contribute to the diagnosis and treatment of IAs. By external validation, the
SLC2A12
gene may play an important role. The molecular function of
SLC2A12
in the process of IA occurrence can be further studied in a rat model.
Reprogramming of adult somatic cells into induced pluripotent stem cells holds great promise in clinical therapy. Increasing evidences have shown that p53 and its target genes play important roles in ...somatic cell reprogramming. In this study, we report that PHLDA3, a p53 target gene, functions as a blockage of iPSCs generation by activating the Akt-GSK3β pathway. Furthermore, PHLDA3 is found to be transcriptionally regulated by Oct4. These findings reveal that PHLDA3 acts as a new member of the regulatory network of somatic cell reprogramming.
In this paper, we propose a spectrum-sharing protocol for a cooperative cognitive radio network based on non-orthogonal multiple access technology, where the base station (BS) transmits the ...superimposed signal to the primary user and secondary user with/without the assistance of a relay station (RS) by adopting the decode-and-forward technique. RS performs discrete-time energy harvesting for opportunistically cooperative transmission. If the RS harvests sufficient energy, the system performs cooperative transmission; otherwise, the system performs direct transmission. Moreover, the outage probabilities and outage capacities of both primary and secondary systems are analyzed, and the corresponding closed-form expressions are derived. In addition, one optimization problem is formulated, where our objective is to maximize the energy efficiency of the secondary system while ensuring that of the primary system exceeds or equals a threshold value. A joint optimization algorithm of power allocation at BS and RS is considered to solve the optimization problem and to realize a mutual improvement in the performance of energy efficiency for both the primary and secondary systems. The simulation results demonstrate the validity of the analysis results and prove that the proposed transmission scheme has a higher energy efficiency than the direct transmission scheme and the transmission scheme with simultaneous wireless information and power transfer technology.
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