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Aldehyde dehydrogenase 2 (ALDH2), a pivotal enzyme in the metabolism of toxic aldehydes produced by oxidative stress, has been demonstrated to play a cardioprotective role in cardiovascular diseases. Antrodia cinnamomea triterpenoids (ACT) is a medicinal mushroom with anti-inflammatory and antioxidant properties, and our previous study found that ACT can exert anti-fatty liver effects by regulating ALDH2. This study aimed to elucidate the impact of ACT and its monomer on cardiac hypertrophy and investigate the relationship between its pharmacological mechanism and ALDH2. Through examining cardiac morphology and expression levels of hypertrophic biomarkers, ACT significantly reduced myocardial hypertrophy induced by angiotensin II (Ang II) and transverse aortic constriction (TAC)surgery in wild-type mice, but not in ALDH2 knockout mice. In vitro, ACT and its monomeric dehydrosulphurenic acid (DSA) inhibited the hypertrophic phenotype of Ang II-stimulated neonatal cardiac myocytes (NRCMs) in an ALDH2-dependent manner. Regarding the pharmacological mechanism, it was observed that ACT and DSA restored ALDH2 expression and activity in myocardial tissues of WT-Ang II/TAC mice and Ang II-induced NRCMs. Furthermore, it inhibited oxidative stress and improved mitochondrial quality control (MQC) homeostasis in an ALDH2-dependent manner. We screened SNW1, a transcriptional coactivator, as a DSA-binding protein by “target fishing” and cellular enthusiasm transfer assay techniques and validated that SNW1 promoted ALDH2 transcription and translation levels through synergistic interaction with the transcription factor RXR. In conclusion, the findings demonstrate that ACT/DSA upregulates ALDH2 expression via regulating SNW1/RXR, thereby inhibiting oxidative stress and maintaining MQC homeostasis, and then protects against cardiac hypertrophy.

Abstract Background CDC6 is an oncogenic protein whose expression level fluctuates during the cell cycle. Although several E3 ubiquitin ligases responsible for the ubiquitin-mediated proteolysis of CDC6 have been identified, the deubiquitination pathway for CDC6 has not been investigated. Methods The proteome-wide deubiquitinase (DUB) screening was used to identify the potential regulator of CDC6. Immunofluorescence, protein half-life and deubiquitination assays were performed to determine the protein stability of CDC6. Gain- and loss-of-function experiments were implemented to analyse the impacts of OUTD6A-CDC6 axis on tumour growth and chemosensitivity in vitro. N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN)-induced conditional Otud6a knockout (CKO) mouse model and tumour xenograft model were performed to analyse the role of OTUD6A-CDC6 axis in vivo. Tissue specimens were used to determine the association between OTUD6A and CDC6. Results OTUD6A interacts with, depolyubiquitinates and stabilizes CDC6 by removing K6-, K33-, and K48-linked polyubiquitination. Moreover, OTUD6A promotes cell proliferation and decreases sensitivity to chemotherapy by upregulating CDC6. CKO mice are less prone to BCa tumorigenesis induced by BBN, and knockdown of OTUD6A inhibits tumour progression in vivo. Furthermore, OTUD6A protein level has a positive correlation with CDC6 protein level, and high protein levels of OTUD6A and CDC6 are associated with poor prognosis in patients with bladder cancer. Conclusions We reveal an important yet missing piece of novel DUB governing CDC6 stability. In addition, our findings propose a model for the OTUD6A-CDC6 axis that provides novel insights into cell cycle and chemosensitivity regulation, which may become a potential biomarker and promising drug target for cancer treatment.

BackgroundThe continuous emergence and spread of polymyxin-resistant Acinetobacter baumannii pose a significant global health challenge, necessitating the development of novel therapeutic strategies. Aloe, with its long-standing history of medicinal use, has recently been the subject of substantial research for its efficacy against pathogenic infections.MethodsThis study investigates the potential application of anthraquinone components in aloe against polymyxin-resistant A. baumannii by liquid chromatography-mass spectrometry, in vitro activity assessment, and construction of animal infection models.ResultsThe findings demonstrate that aloe emodin, emodin, rhein, and their mixtures in equal mass ratios (EAR) exhibit strain-specific antibacterial activities against polymyxin-resistant A. baumannii. Co-administration of EAR with EDTA synergistically and universally enhanced the antibacterial activity and bactericidal efficacy of polymyxins against polymyxin-resistant A. baumannii, while also reducing the frequency of polymyxin-resistant mutations in polymyxinssensitive A. baumannii. Following toxicity assessment on human hepatic and renal cell lines, the combination therapy was applied to skin wounds in mice infected with polymyxin-resistant A. baumannii. Compared to monotherapy, the combination therapy significantly accelerated wound healing and reduced bacterial burden.ConclusionsThe combination of EAR and EDTA with polymyxins offers a novel therapeutic approach for managing skin infections caused by polymyxinresistant A. baumannii.

Abstract Overexpressed pro-survival B-cell lymphoma-2 (BCL-2) family proteins BCL-2 and BCL-XL can render tumor cells malignant. Leukemia drug venetoclax is currently the only approved selective BCL-2 inhibitor. However, its application has led to an emergence of resistant mutations, calling for drugs with an innovative mechanism of action. Herein we present cyclic peptides (CPs) with nanomolar-level binding affinities to BCL-2 or BCL-XL, and further reveal the structural and functional mechanisms of how these CPs target two proteins in a fashion that is remarkably different from traditional small-molecule inhibitors. In addition, these CPs can bind to the venetoclax-resistant clinical BCL-2 mutants with similar affinities as to the wild-type protein. Furthermore, we identify a single-residue discrepancy between BCL-2 D111 and BCL-XL A104 as a molecular “switch” that can differently engage CPs. Our study suggests that CPs may inhibit BCL-2 or BCL-XL by delicately modulating protein-protein interactions, potentially benefiting the development of next-generation therapeutics.

In recent years, the scale of China's power grid has been expanding, and the electricity consumption load has been rising year by year. Load forecasting plays a crucial role in ensuring the efficient coordination of power generation, transmission, and distribution in intelligent power systems. It holds immense significance in the planning, operation, control, and scheduling of new power systems. However, many current forecasting models do not take into account the temporal relationship of electricity consumption data, and therefore the models do not perform very well. In order to improve the accuracy of electricity consumption prediction, a Long Short Term Memory neural network model is proposed. We collect a series of electricity consumption data based on a fixed time interval, and use the daily collected data as a time series, and use Long Short Term Memory to build a simulated ensemble for the time series. Considering the impact of different acquisition intervals on the prediction results, we conducted experiments on load prediction with different sampling intervals. Our experiments were all performed on the data provided by the test questions of the 9th Electrician Mathematical Modeling Contest 2016.

With the rapid development of the power industry and the widespread adoption of smart meters, the occurrence of smart meter failures has also become more frequent. Consequently, the classification of smart meter faults has become a crucial task to ensure quality assurance in the power industry. Accurately determining the fault types of smart meters and improving maintenance efficiency are of utmost importance to ensure their safe and stable operation. Traditional methods for classifying smart meter faults primarily rely on manual inspection and testing, which suffer from issues such as low classification efficiency, high cost, susceptibility to missed detections, and false detections. In recent years, machine learning methods have demonstrated advantages in this field. This paper proposes an ensemble learning method for the multi-classification of smart meter faults to enhance the efficiency and accuracy of fault classification. Firstly, various data preprocessing techniques are employed to clean and extract features from a real-world dataset, thereby enhancing the data quality of the smart meter fault types. Secondly, a selection process is conducted to screen classical machine learning algorithms, resulting in the choice of three algorithms: K Nearest Neighbors (KNN), Random Forest (RF), and Xtreme Gradient Boosting (XGBoost). These algorithms are then utilized to classify the fault types of smart meters. Finally, a multi-classification ensemble learning method is introduced to combine the results from multiple classifiers, thereby improving the accuracy and robustness of smart meter fault classification. Experimental results demonstrate that the proposed method exhibits high accuracy and robustness in fault classification, offering promising applications and value for widespread adoption.

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

Whether hypoxia-inducible factors (HIFs) can be directly regulated by endogenous small molecules is a long-standing question. Here authors identified the metabolite oleoylethanolamide as a HIF-3α ligand and further revealed its mechanism of action.

Background & Aims: The ligand-activated transcription factor, aryl hydrocarbon receptor (AHR) can sense xenobiotics, dietary, microbial, and metabolic cues. Roles of Ahr in intestinal epithelial cells (IECs) have been much less elucidated compared with those in intestinal innate immune cells. Here, we explored whether the IEC intrinsic Ahr could modulate the development of alcohol-related liver disease (ALD) via the gut–liver axis. Methods: Mice with IEC specific Ahr deficiency (AhrΔIEC) were generated and fed with a control or ethanol diet. Alterations of intestinal microbiota and metabolites were investigated by 16S ribosomal RNA sequencing, metagenomics, and untargeted metabolomics. AHR agonists were used to evaluate the therapeutic potentials of intestinal Ahr activation for ALD treatment. Results: AhrΔIEC mice showed more severe liver injury after ethanol feeding than control mice. Ahr deficiency in IECs altered the intestinal metabolite composition, creating an environment that promoted the overgrowth of Helicobacter hepaticus and Helicobacter ganmani in the gut, enhancing their translocation to mesenteric lymph nodes and liver. Among the altered metabolites, isobutyric acid was increased in the cecum of ethanol-fed AhrΔIEC mice relative to control mice. Furthermore, both H. hepaticus and isobutyric acid administration aggravated ethanol-induced liver injury in vivo and in vitro. Supplementation with AHR agonists, 6-formylindolo[3,2-b]carbazole and indole-3-carbinol, protected mice from ALD development by specifically activating intestinal Ahr without affecting liver Ahr function. Alcoholic patients showed lower intestinal AHR expression and higher H. hepaticus levels compared with healthy individuals. Conclusions: Our results indicate that targeted restoration of IEC intrinsic Ahr function may present as a novel approach for ALD treatment.

The COVID-19 pandemic has spread rapidly and significantly impacted most countries in the world. Providing an accurate forecast of COVID-19 at multiple scales would help inform public health decisions, but recent forecasting models are typically used at the state or country level. Furthermore, traditional mathematical models are limited by simplifying assumptions, while machine learning algorithms struggle to generalize to unseen trends. This motivates the need for hybrid machine learning models that integrate domain knowledge for accurate long-term prediction. We propose a three-layer, geographically informed ensemble, an extensive peer-learning framework, for predicting COVID-19 trends at the country, continent, and global levels. As the base layer, we develop a country-level predictor using a hybrid Graph Attention Network that incorporates a modified SIR model, adaptive loss function, and edge weights informed by mobility data. We aggregated 163 country GATs to train the continent and world MLP layers of the ensemble. Our results indicate that incorporating quantitatively accurate equations and real-world data to model inter-community interactions improves the performance of spatio-temporal machine learning algorithms. Additionally, we demonstrate that integrating geographic information (continent composition) improves the performance of the world predictor in our layered architecture.

Blood–brain barrier (BBB) damage after ischemia significantly influences stroke outcome. Compound LFHP-1c was previously discovered with neuroprotective role in stroke model, but its mechanism of action on protection of BBB disruption after stroke remains unknown. Here, we show that LFHP-1c, as a direct PGAM5 inhibitor, prevented BBB disruption after transient middle cerebral artery occlusion (tMCAO) in rats. Mechanistically, LFHP-1c binding with endothelial PGAM5 not only inhibited the PGAM5 phosphatase activity, but also reduced the interaction of PGAM5 with NRF2, which facilitated nuclear translocation of NRF2 to prevent BBB disruption from ischemia. Furthermore, LFHP-1c administration by targeting PGAM5 shows a trend toward reduced infarct volume, brain edema and neurological deficits in nonhuman primate Macaca fascicularis model with tMCAO. Thus, our study identifies compound LFHP-1c as a firstly direct PGAM5 inhibitor showing amelioration of ischemia-induced BBB disruption in vitro and in vivo, and provides a potentially therapeutics for brain ischemic stroke.

Nonribosomal lipopeptides contain an acyl chain important for bioactivity, but its incorporation into the peptidyl backbone, mediated by the starter condensation (Cs) domain of nonribosomal peptide synthases, is not fully understood. Here, the authors show that acyl chains of different lengths can be obtained by engineering Cs domains and identify residues that determine the selectivity for acyl chains.

This paper investigates resource modeling and management for a base station (BS) providing mobile edge computing (MEC) service. In the proposed modeling, BS is recognized as a queueing network consisting of multiple multi-type servers. The uplink transmission users, downlink transmission users, and MEC users with different priority levels are jointly considered. It is assumed that their service-requests arrive dynamically and are also served dynamically. With such a general resource modeling, the interaction among these users can be analyzed based on the queueing network theory. The average delay of each service-type with different priority levels is derived. Based on the derived results, two resource management optimization problems are formulated and solved from the perspective of a service provider. The revenue brought by MEC services is first maximized by doing user admission control while provisioning the quality-of-service (QoS) of all admitted users with the given amount of communication and computation resources. Then, the capital expenditure of resource deployment is minimized by satisfying the QoS of all users. It is formulated as an integer programming problem. An algorithm is developed to solve it, which can help service providers to determine the optimal amount of communication and computation resources to be placed in a BS to guarantee QoS for all users at a minimal total capital expenditure. Computer simulations are done to validate all analysis and comparisons are made with BS serving multi-type users of single priority level. Through comparison, an insight is gained that service providers can obtain more revenue or spare less capital expenditure by differentiating the user priority levels.

Nuclear receptors (NR) are multidomain proteins, which makes their crystallization challenging. Here the authors present the crystal structure of the retinoic acid receptor β–retinoic X receptor α (RARβ–RXRα) heterodimer bound to DNA, ligands and coactivator peptides, which shows that NR quaternary architectures are variable.

This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem (MS-SHM-Hadoop). By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System (HDFS) and high-performance parallel data processing engine called MapReduce programming paradigm, MS-SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge serviceability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method (e.g., association rule learning) or structural mechanics modeling and simulation.

Battery packs with a large number of battery cells are becoming more and more widely adopted in electronic systems, such as robotics, renewable energy systems, energy storage in smart grids, and electronic vehicles. Therefore, a well-designed battery pack is essential for battery applications. In the literature, the majority of research in battery pack design focuses on battery management system, safety circuit, and cell-balancing strategies. Recently, the reconfigurable battery pack design has gained increasing attentions as a promising solution to solve the problems existing in the conventional battery packs and associated battery management systems, such as low energy efficiency, short pack lifespan, safety issues, and low reliability. One of the most prominent features of reconfigurable battery packs is that the battery cell topology can be dynamically reconfigured in the real-time fashion based on the current condition (in terms of the state of charge and the state of health) of battery cells. So far, there are several reconfigurable battery schemes having been proposed and validated in the literature, all sharing the advantage of cell topology reconfiguration that ensures balanced cell states during charging and discharging, meanwhile providing strong fault tolerance ability. This survey is undertaken with the intent of identifying the state-of-the-art technologies of reconfigurable battery as well as providing review on related technologies and insight on future research in this emerging area.

Monitoring human bodies and collecting physiological information are the major healthcare applications for wireless body area networks (WBANs). Due to the limited energy resource of body sensors, their energy depletions will cause severe network performance degradation, such as latency and energy efficiency. The heterogeneity of body sensors can result in different sensor energy consumption rates. Besides, the important degrees of monitored physiological data could vary hugely. Targeting at the above-mentioned problems, an energy-efficient data forwarding strategy (EDFS) is proposed in this paper to balance sensor energy consumption and improve network lifetime and collaborative operations of heterogeneous WBANs. Our major contributions include: 1) the original physiological data are processed by compressed sensing to reduce the data size to be transmitted and 2) the remaining energy levels, sampling frequency, and sensor importance are jointly considered by EDFS for the optimal relay sensor selection. With the EDFS, the energy-efficiency and reliability of WBAN data transmission can be improved. In addition, our simulation results demonstrate that the proposed EDFS can effectively deal with the frequently changing WBAN topologies while providing balanced energy consumption and energy efficiency.

Long-Term Evolution (LTE) communication systems feature advanced frequency reuse and interference coordination techniques providing faster and more secured mobile services. However, the network capacity in licensed spectrum is still behind market demands. Dynamic spectrum access or spectrum sharing in other frequently vacant or unlicensed frequency bands is considered an effective means to boost system throughput. Different from operations in licensed spectrum with exclusive access, LTE deployment needs to take into account the distinct regulations on channel access to each shared frequency band, in order to avoid interference to incumbent users, and to maintain fair play with peer operators in heterogeneous networks. This paper presents an overview on LTE spectrum sharing technologies on three popular spectrums, including the TV white-space channels, the frequently unused service-dedicated 3.5-GHz citizens broadband radio service spectrums, and the 5-GHz unlicensed bands. Existing spectrum usage in these frequency bands is analyzed, and the proposed methodologies on compliant operations are discussed for the reference of potential solutions to more efficient spectrum sharing mechanisms in the next generation mobile networks.

The vegetative insecticidal proteins (Vips) secreted by Bacillus thuringiensis are regarded as the new generation of insecticidal toxins because they have different insecticidal properties compared with commonly applied insecticidal crystal proteins (Cry toxins). Vip3A toxin, representing the vast majority of Vips, has been used commercially in transgenic crops and bio-insecticides. However, the lack of both structural information on Vip3A and a clear understanding of its insecticidal mechanism at the molecular level limits its further development and broader application. Here we present the first crystal structure of the C-terminal fragment of Vip3A toxin (Vip3Aa11200–789). Since all members of this insecticidal protein family are highly conserved, the structure of Vip3A provides unique insight into the general domain architecture and protein fold of the Vip3A family of insecticidal toxins. Our structural analysis reveals a four-domain organization, featuring a potential membrane insertion region, a receptor binding domain, and two potential glycan binding domains of Vip3A. In addition, cytotoxicity assays and insect bioassays show that the purified C-terminal fragment of Vip3Aa toxin alone have no insecticidal activity. Taken together, these findings provide insights into the mode of action of the Vip3A family of insecticidal toxins and will boost the development of Vip3A into more efficient bio-insecticides.

The neuronal PAS domain proteins NPAS1 and NPAS3 are members of the basic helix-loop-helix-PER-ARNT-SIM (bHLH-PAS) family, and their genetic deficiencies are linked to a variety of human psychiatric disorders including schizophrenia, autism spectrum disorders and bipolar disease. NPAS1 and NPAS3 must each heterodimerize with the aryl hydrocarbon receptor nuclear translocator (ARNT), to form functional transcription complexes capable of DNA binding and gene regulation. Here we examined the crystal structures of multi-domain NPAS1-ARNT and NPAS3-ARNT-DNA complexes, discovering each to contain four putative ligand-binding pockets. Through expanded architectural comparisons between these complexes and HIF-1α-ARNT, HIF-2α-ARNT and CLOCK-BMAL1, we show the wider mammalian bHLH-PAS family is capable of multi-ligand-binding and presents as an ideal class of transcription factors for direct targeting by small-molecule drugs.

Although cross-layer has been thought as one of the most effective and efficient ways for multimedia communications over wireless networks and a plethora of research has been done in this area, there is still lacking of a rigorous mathematical model to gain in-depth understanding of cross-layer design tradeoffs, spanning from application layer to physical layer. As a result, many existing cross-layer designs enhance the performance of certain layers at the price of either introducing side effects to the overall system performance or violating the syntax and semantics of the layered network architecture. Therefore, lacking of a rigorous theoretical study makes existing cross-layer designs rely on heuristic approaches which are unable to guarantee sound results efficiently and consistently. In this paper, we attempt to fill this gap and develop a new methodological foundation for cross-layer design in wireless multimedia communications. We first introduce a delay-distortion-driven cross-layer optimization framework which can be solved as a large-scale dynamic programming problem. Then, we present new approximate dynamic programming based on significance measure and sensitivity analysis for high-dimensional nonlinear cross-layer optimization in support of real-time multimedia applications. The major contribution of this paper is to present the first rigorous theoretical modeling for integrated cross-layer control and optimization in wireless multimedia communications, providing design insights into multimedia communications over current wireless networks and throwing light on design optimization of the next-generation wireless multimedia systems and networks.