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In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference.

Pure self-management model, pure aggregation business model and Self-support + aggregation model are three commonly used business modes on ride-hailing platforms. We use an analytical model to study these three business models and give the optimal business model decision of the platform. The research shows that the heterogeneity ratio of drivers, the cost of the platform under the Self-support model, the franchise fee received by the platform under the aggregation model and the dissatisfaction of the original users on the platform play a key role in the selection of the platform’s business model. When the difference between the franchise fee under the aggregation mode and the platform cost under the Self-support mode fails to generate positive feedback on the platform profit, the platform should choose the pure Self-support mode. When riders are more sensitive to the heterogeneity of service quality of the platform and user stickiness can be ensured, the platform should choose the pure aggregation business model. When user stickiness can be guaranteed and the cost of the platform under the self-run model is controllable, the platform should choose the Self-support + aggregation business model.

Background Chronic kidney disease (CKD) is one of the most significant cardiovascular risk factors, playing vital roles in various cardiovascular diseases such as calcific aortic valve disease (CAVD). We aim to explore the CKD-associated genes potentially involving CAVD pathogenesis, and to discover candidate biomarkers for the diagnosis of CKD with CAVD. Methods Three CAVD, one CKD-PBMC and one CKD-Kidney datasets of expression profiles were obtained from the GEO database. Firstly, to detect CAVD key genes and CKD-associated secretory proteins, differentially expressed analysis and WGCNA were carried out. Protein-protein interaction (PPI), functional enrichment and cMAP analyses were employed to reveal CKD-related pathogenic genes and underlying mechanisms in CKD-related CAVD as well as the potential drugs for CAVD treatment. Then, machine learning algorithms including LASSO regression and random forest were adopted for screening candidate biomarkers and constructing diagnostic nomogram for predicting CKD-related CAVD. Moreover, ROC curve, calibration curve and decision curve analyses were applied to evaluate the diagnostic performance of nomogram. Finally, the CIBERSORT algorithm was used to explore immune cell infiltration in CAVD. Results The integrated CAVD dataset identified 124 CAVD key genes by intersecting differential expression and WGCNA analyses. Totally 983 CKD-associated secretory proteins were screened by differential expression analysis of CKD-PBMC/Kidney datasets. PPI analysis identified two key modules containing 76 nodes, regarded as CKD-related pathogenic genes in CAVD, which were mostly enriched in inflammatory and immune regulation by enrichment analysis. The cMAP analysis exposed metyrapone as a more potential drug for CAVD treatment. 17 genes were overlapped between CAVD key genes and CKD-associated secretory proteins, and two hub genes were chosen as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning. Furthermore, SLPI/MMP9 expression patterns were confirmed in our external cohort and the nomogram could serve as novel diagnosis models for distinguishing CAVD. Finally, immune cell infiltration results uncovered immune dysregulation in CAVD, and SLPI/MMP9 were significantly associated with invasive immune cells. Conclusions We revealed the inflammatory-immune pathways underlying CKD-related CAVD, and developed SLPI/MMP9-based CAVD diagnostic nomogram, which offered novel insights into future serum-based diagnosis and therapeutic intervention of CKD with CAVD.

Unresolved inflammation can lead to tissue fibrosis and impaired organ function. Macrophage–myofibroblast transition (MMT) is one newly identified mechanism by which ongoing chronic inflammation causes progressive fibrosis in different forms of kidney disease. However, the mechanisms underlying MMT are still largely unknown. Here, we discovered a brain-specific homeobox/POU domain protein Pou4f1 (Brn3a) as a specific regulator of MMT. Interestingly, we found that Pou4f1 is highly expressed by macrophages undergoing MMT in sites of fibrosis in human and experimental kidney disease, identified by coexpression of the myofibroblast marker, α-SMA. Unexpectedly, Pou4f1 expression peaked in the early stage in renal fibrogenesis in vivo and during MMT of bone marrow-derived macrophages (BMDMs) in vitro. Mechanistically, chromatin immunoprecipitation (ChIP) assay identified that Pou4f1 is a Smad3 target and the key downstream regulator of MMT, while microarray analysis defined a Pou4f1-dependent fibrogenic gene network for promoting TGF-β1/Smad3-driven MMT in BMDMs at the transcriptional level. More importantly, using two mouse models of progressive renal interstitial fibrosis featuring the MMT process, we demonstrated that adoptive transfer of TGF-β1-stimulated BMDMs restored both MMT and renal fibrosis in macrophage-depleted mice, which was prevented by silencing Pou4f1 in transferred BMDMs. These findings establish a role for Pou4f1 in MMT and renal fibrosis and suggest that Pou4f1 may be a therapeutic target for chronic kidney disease with progressive renal fibrosis.

Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree-based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes.

This paper proposes a permanent magnet synchronous motor sensorless control strategy based on fuzzy sliding mode controller and fuzzy sliding mode observer. In order to improve the response speed of the system and the anti-interference ability of the system, a sliding mode controller (SMC) is used to replace the PI regulator in the speed loop. In order to suppress the chattering caused by sliding mode variable structure control and reduce the error caused by chattering, the SMC and sliding mode observer (SMO) are improved. An integral sliding mode surface is used in the SMC, and a continuous saturation function is used instead of a discontinuous sign function. The square of the speed error is introduced in the switch term of the exponential approaching law to improve the dynamic quality of approaching motion. The switching function in the SMO also uses a saturation function to suppress chattering caused by switching. Fuzzy rules are established to adaptively adjust the switching gain of the SMO and the parameters in the SMC’s reaching law, which further improves the performance of the control system. Simulations and experiments prove that the proposed control strategy has the characteristics of small chattering, fast response speed, and strong robustness.

The Wnt signaling pathway is necessary for the development of the central nervous system and is associated with tumorigenesis in various cancers. However, the mechanism of the Wnt signaling pathway in glioma cells has yet to be elucidated. Small-molecule Wnt modulators such as ICG-001 and AZD2858 were used to inhibit and stimulate the Wnt/β-catenin signaling pathway. Techniques including cell proliferation assay, colony formation assay, Matrigel cell invasion assay, cell cycle assay and Genechip microarray were used. Gene Ontology Enrichment Analysis and Gene Set Enrichment Analysis have enriched many biological processes and signaling pathways. Both the inhibiting and stimulating Wnt/β-catenin signaling pathways could influence the cell cycle, moreover, reduce the proliferation and survival of U87 glioma cells. However, Affymetrix expression microarray indicated that biological processes and networks of signaling pathways between stimulating and inhibiting the Wnt/β-catenin signaling pathway largely differ. We propose that Wnt/β-catenin signaling pathway might prove to be a valuable therapeutic target for glioma.

Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.

By lacking de novo purine biosynthesis enzymes, Plasmodium falciparum requires purine nucleoside uptake from host cells. The indispensable nucleoside transporter ENT1 of P. falciparum facilitates nucleoside uptake in the asexual blood stage. Specific inhibitors of PfENT1 prevent the proliferation of P. falciparum at submicromolar concentrations. However, the substrate recognition and inhibitory mechanism of PfENT1 are still elusive. Here, we report cryo-EM structures of PfENT1 in apo, inosine-bound, and inhibitor-bound states. Together with in vitro binding and uptake assays, we identify that inosine is the primary substrate of PfENT1 and that the inosine-binding site is located in the central cavity of PfENT1. The endofacial inhibitor GSK4 occupies the orthosteric site of PfENT1 and explores the allosteric site to block the conformational change of PfENT1. Furthermore, we propose a general “rocker switch” alternating access cycle for ENT transporters. Understanding the substrate recognition and inhibitory mechanisms of PfENT1 will greatly facilitate future efforts in the rational design of antimalarial drugs. PfENT1 is a promising antimalarial drug target. Here, authors report cryo-EM structures of PfENT1 that, together with biochemical work, suggests PfENT1 is an inosine transporter and describe the inhibitory mechanism of the endofacial inhibitor, GSK4.

Hafnium (Hf)‐based nanoscale metal‐organic layers (MOLs) enhance radiotherapeutic effects of tissue‐penetrating X‐rays via a unique radiotherapy‐radiodynamic therapy (RT‐RDT) process through efficient generation of hydroxy radical (RT) and singlet oxygen (RDT). However, their radiotherapeutic efficacy is limited by hypoxia in deep‐seated tumors and short half‐lives of reactive oxygen species (ROS). Herein the conjugation of a nitric oxide (NO) donor, S‐nitroso‐N‐acetyl‐DL‐penicillamine (SNAP), to the Hf12 secondary building units (SBUs) of Hf‐5,5′‐di‐p‐benzoatoporphyrin MOL is reported to afford SNAP/MOL for enhanced cancer radiotherapy. Under X‐ray irradiation, SNAP/MOL efficiently generates superoxide anion (O2−.) and releases nitric oxide (NO) in a spatio‐temporally synchronized fashion. The released NO rapidly reacts with O2−. to form long‐lived and highly cytotoxic peroxynitrite which diffuses freely to the cell nucleus and efficiently causes DNA double‐strand breaks. Meanwhile, the sustained release of NO from SNAP/MOL in the tumor microenvironment relieves tumor hypoxia to reduce radioresistance of tumor cells. Consequently, SNAP/MOL plus low‐dose X‐ray irradiation efficiently inhibits tumor growth and reduces metastasis in colorectal and triple‐negative breast cancer models. A nitric oxide‐releasing 2D metal‐organic layer is designed, SNAP/MOL, for overcoming tumor hypoxia and ROS diffusion barriers. Constructed from Hf12 SBUs and 5,5'‐di‐p‐benzoatoporphyrin bridging ligands, the MOL efficiently generates superoxide anion under X‐ray radiation. The spatio‐temporally synchronized generation of superoxide anion and release of nitric oxide facilitate the formation of long‐lived and highly cytotoxic peroxynitrite which efficiently diffuses into the nucleus to increase DNA damage. By alleviating tumor hypoxia, SNAP/MOL effectively enhances the local radiotherapy effect and suppresses distal pulmonary metastasis.