Lentinan mitigates pemetrexed chemoresistance by the PI3K/Akt pathway in non-small cell lung cancer.

Pemetrexed is a folate analog metabolic inhibitor that is given for therapy of non-small cell lung cancer (NSCLC). Drug resistance affects the efficacy of pemetrexed in NSCLC. Lentinan is a polysaccharide extracted from Shiitake mushrooms which has antitumor roles in multiple cancers, including lung cancer. However, the effects of lentinan on pemetrexed resistance in NSCLC remain unclear. In present study, The pemetrexed-resistant NSCLC cells were established and exposed to pemetrexed and lentinan. Oxidative stress was investigated via mitochondrial membrane potential (JC-1 staining), levels of MDA and SOD.The phosphorylation and total of PI3K and Akt levels were actuated using specific activator 740Y-P and measured through western blot. We observed that Lentinan decreased IC50 of pemetrexed in resistant NSCLC cells. Lentinan aggravated pemetrexed-induced proliferation inhibition of resistant NSCLC cells via reducing PCNA levels. Lentinan exacerbated pemetrexed-triggered oxidative stress through increasing ROS and MDA levels, and reducing mitochondrial membrane potential and SOD levels. Lentinan inhibited PI3K/Akt signaling activation in pemetrexed-treated cells. Activated PI3K/Akt pathway using activator 740Y-P reversed the effects of lentinan on pemetrexed-mediated proliferation inhibition and oxidative stress. Our findings uncover that Lentinan mitigates pemetrexed resistance in NSCLC through inhibiting cell proliferation and inducing oxidative stress by suppressing PI3K/Akt signaling.

SP3-induced Timeless transcription contributes to cell growth of lung adenocarcinoma cells.

BACKGROUND: Timeless is well-known for its key role in replication checkpoints. Recent studies reveal the involvement of Timeless and specificity protein (SP) 1 in human malignancies. However, no evidence proved the interaction between SP3 and Timeless in lung adenocarcinoma (LUAD). METHODS: The expression and clinical significance of Timeless were analyzed using the LUAD dataset downloaded from the Cancer Genome Atlas (TCGA). Lentivirus-mediated Timeless knockdown in A549 cells was used to examine the role of Timeless in cell proliferation and pemetrexed (PEM) resistance. Transcription factors (TFs) bound to the Timeless promoter were identified by DNA pull-down technology with HPLC-MS/MS analysis and analyzed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Dual-luciferase reporter assay was used to determine the activity of SP3 in Timeless transcription. RESULTS: Timeless was overexpressed in LUAD samples, and it could serve as a potential diagnostic or prognostic biomarker for LUAD patients. shTimeless-mediated knockdown of Timeless reduced cell viability and proliferation and sensitized PEM-resistant A549 cells to PEM. Four fragments (F1: 1-373 bp), (F2: 374-962 bp), (F4: 1274-1645 bp), and (F5: 1646-2000bp) were confirmed as the TF binding profiles of the Timeless promoter. KEGG analysis showed that the TFs bound to the Timeless promoter had relevance to spliceosome, RNA transport, and mRNA surveillance pathways. SP3 promoted the transcription of Timeless via the F2 fragment (374-962 bp) binding motif. CONCLUSION: Upregulation of Timeless mediated by SP3 promotes LUAD cell proliferation, providing evidence to support that targeting the SP3/Timeless axis may be a potential therapeutic strategy against LUAD.

Secure Adaptive Event-Triggered Control for Cyber-Physical Power Systems Under Denial-of-Service Attacks.

Secure control for cyber-physical power systems (CPPSs) under cyber attacks is a challenging issue. Existing event-triggered control schemes are generally difficult to mitigate the impact of cyber attacks and improve communication efficiency simultaneously. To solve such two problems, this article studies secure adaptive event-triggered control for the CPPSs under energy-limited denial-of-service (DoS) attacks. A new DoS-dependent secure adaptive event-triggered mechanism (SAETM) is developed, where DoS attacks are taken into account when designing the trigger mechanisms. Sufficient conditions are derived to ensure the CPPSs to be uniformly ultimate boundedness stable, and the entering time when the state trajectories of the CPPSs are guaranteed to stay in the secure region is also given. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed control method.

Comprehensive pan-cancer analysis of role of GPRASP1, associated with clinical outcomes, immune microenvironment, and immunotherapeutic efficiency in pancreatic cancer.

BACKGROUND: GPRASP1 (G-protein-coupled receptor-associated sorting protein 1) plays an important role in tumorigenesis. However, GPRASP1 specific role has not been clearly clarified in cancer, particularly in pancreatic cancer(PC). METHODS: Firstly, we utilized pan-cancer analysis based on RNA sequencing data from TCGA (The Cancer Genome Atlas) to evaluate the expression pattern and immunological role of GPRASP1. Then, through multiple transcriptome datasets (TCGA and Gene Expression Omnibus (GEO)) and multi-omics (RNA-seq, DNA methylation, copy number variations (CNV), somatic mutation data) in-depth analysis, we comprehensively explore the relationship of GPRASP1 expression with clinicopathologic characteristics, clinical outcomes, CNV, and DNA methylation in pancreatic cancer. Additionally, we employed immunohistochemistry (IHC) to further confirm GPRASP1 expression pattern between PC tissues and paracancerous tissues. Lastly, we systematically associated the GPRASP1 with immunological properties from numerous perspectives, such as immune cell infiltration, immune-related pathways, immune checkpoint inhibitors, immunomodulators, immunogenicity, and immunotherapy. RESULTS: Through pan-cancer analysis, we identified that GPRASP1 plays a critical role in the occurrence and prognosis of PC, and is closely related to immunological characteristics in PC. IHC analysis confirmed that GPRASP1 is significantly down-regulated in PC compared with normal tissues. The expression of GPRASP1 is significantly negatively correlated with clinical features (histologic grade, T stage, and TNM stage), and is an independent predictor of favorable prognosis, regardless of other clinicopathological features (HR: 0.69, 95% CI 0.54-0.92, p= 0.011). The etiological investigation found that the abnormal expression of GPRASP1 was related to DNA methylation and CNV frequency. Subsequently, the high expression of GPRASP1 was significantly correlated with immune cell infiltration (CD8 + T cell, tumor-infiltrating lymphocyte(TIL)), immune-related pathways(cytolytic activity, check-point, human leukocyte antigen (HLA)), immune checkpoint inhibitors (CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT), immunomodulators ( CCR4/5/6, CXCL9, CXCR4/5), and immunogenicity(immune score, neoantigen, TMB(tumor mutation burden)). Finally, IPS (immunophenoscore) and TIDE (tumor immune dysfunction and exclusion) analysis demonstrated that GPRASP1 expression levels can accurately predict the immunotherapeutic response. CONCLUSION: GPRASP1 is a promising candidate biomarker that plays a role in the occurrence, development, and prognosis of PC. Evaluating GPRASP1 expression will aid in the characterization of tumor microenvironment (TME) infiltration and orient more efficient immunotherapy strategies.

Secure Control of Networked Control Systems Using Dynamic Watermarking.

We here investigate the secure control of networked control systems developing a new dynamic watermarking (DW) scheme. First, the weaknesses of the conventional DW scheme are revealed, and the tradeoff between the effectiveness of false data injection attack (FDIA) detection and system performance loss is analyzed. Second, we propose a new DW scheme, and its attack detection capability is interrogated using the additive distortion power of a closed-loop system. Furthermore, the FDIA detection effectiveness of the closed-loop system is analyzed using auto/cross-covariance of the signals, where the positive correlation between the FDIA detection effectiveness and the watermarking intensity is measured. Third, the tolerance capacity of FDIA against the closed-loop system is investigated, and theoretical analysis shows that the system performance can be recovered from FDIA using our new DW scheme. Finally, the experimental results from a networked inverted pendulum system demonstrate the validity of our proposed scheme.

Changes in Bulk and Rhizosphere Soil Microbial Diversity and Composition Along an Age Gradient of Chinese Fir (Cunninghamia lanceolate) Plantations in Subtropical China.

Soil microorganisms play key roles in biogeochemical cycling in forest ecosystems. However, whether the responses of microbial community with stand development differed in rhizosphere and bulk soils remains unknown. We collected rhizosphere and bulk soil in Chinese fir plantations with different stand ages (7a, 15a, 24a, and 34a) in subtropical China, and determined bacterial and fungal community variation via high-throughput sequencing. The results showed that soil bacterial, but not fungal, community diversity significantly differed among stand ages and between rhizosphere and bulk soils (p < 0.05). The differences in Shannon-Wiener and Simpson's indices between rhizosphere and bulk soil varied with stand age, with significant higher soil bacterial diversity in rhizosphere than bulk soils in 7a and 34a plantations (p < 0.05), but there were no significant difference in soil bacterial diversity between rhizosphere and bulk soils in 15a and 24a plantations (p > 0.05). Soil microbial community composition varied significantly with stand age but not between the rhizosphere and bulk soil. The dominant bacterial phyla at all ages were Acidobacteria and Proteobacteria, while the dominant fungal phyla were Ascomycota and Basidiomycota in both rhizosphere and bulk soil. They showed inconsistent distribution patterns along stand age gradient (7-34a) in the rhizosphere and bulk soil, suggesting distinct ecological strategy (r-strategist vs. k-strategist) of different microbial taxa, as well as changes in the microenvironment (i.e., nutrient stoichiometry and root exudates). Moreover, bacterial and fungal community composition in rhizosphere and bulk soil were governed by distinct driving factors. TP and NH4 +-N are the two most important factors regulating bacterial and fungal community structure in rhizosphere soil, while pH and NO3 --N, DON, and TN were driving factors for bacterial and fungal community structure in bulk soil, respectively. Collectively, our results demonstrated that the changes in microbial diversity and composition were more obvious along stand age gradients than between sampling locations (rhizosphere vs. bulk soil) in Chinese fir plantations.

Real-Time H∞ Control of Networked Inverted Pendulum Visual Servo Systems.

Aiming at the challenges of networked visual servo control systems, which rarely consider network communication duration and image processing computational cost simultaneously, we here propose a novel platform for networked inverted pendulum visual servo control using H∞ analysis. Unlike most of the existing methods that usually ignore computational costs involved in measuring, actuating, and controlling, we design a novel event-triggered sampling mechanism that applies a new closed-loop strategy to dealing with networked inverted pendulum visual servo systems of multiple time-varying delays and computational errors. Using the Lyapunov stability theory, we prove that the proposed system can achieve stability whilst compromising image-induced computational and network-induced delays and system performance. In the meantime, we use H∞ disturbance attenuation level γ for evaluating the computational errors, whereas the corresponding H∞ controller is implemented. Finally, simulation analysis and experimental results demonstrate the proposed system performance in reducing computational errors whilst maintaining system efficiency and robustness.

Impact of PHEV in active distribution network under gas station network attack.

This paper proposes a novel method to analyze the impacts of plug-in hybrid electric vehicle (PHEV) charging on branch power flows and voltages of an active distribution network under gas station network attack. Specifically, when the gas station network is attacked and cannot provide refueling service, PHEVs running out of gasoline will be only driven in the electric vehicle (EV) mode, which will significantly increase PHEV charging load and lead to branch power flow increment and voltage drop or even voltage collapse in distribution network. To overcome the problem, the switch of PHEV operating mode (i.e., the EV mode and the combustion engine (CE) mode) is first analyzed by considering whether the remaining gasoline can satisfy daily gasoline consumption, and based on that, a novel model of the PHEV charging load is constructed. Then, an integrated approach including Nataf/normalization transformation and elementary transformation (ET) is employed to deal with the general correlation of spatially close distributed generations in the active distribution network. Furthermore, point estimate method (PEM) based probabilistic load flow (PLF) is used to analyze the impacts of PHEV charging on branch power flows and voltages of the active distribution network under gas station network attack. Finally, the proposed method is tested on a real coastal active distribution network, and simulation results verify that PHEV charging could result in continuous branch power flow increase and voltage decrease over a prolonged attack time. Moreover, the higher PHEV operating status (OS) leads to slower branch power flow growth and voltage drop, and a higher PHEV penetration level will exacerbate branch power flow increment and voltage limit violation over with the extension of the attack.

Compact extreme learning machines for biological systems.

In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compact model without significantly increasing the overall computational complexity. This is achieved by associating each model term to a regularized parameter, thus insignificant ones are automatically unselected, leading to improved model sparsity. Experimental results on biochemical data confirm its effectiveness.