Background
Recently, the incidence of cholangiocarcinoma (CCA) has gradually increased. As CCA has a poor prognosis, the ideal survival rate is scarce for patients. The abnormal expressed tsRNAs may ...regulate the progression of a variety of tumors, and tsRNAs is expected to become a new diagnostic biomarker of cancer. However, the expression of tsRNAs is obscure and should be elucidated in CCA.
Methods
High‐throughput RNA sequencing technology (RNA‐seq) was utilized to determine the overall expression profiles of tsRNAs in three pairs CCA and adjacent normal tissues and to screen the tsRNAs that were differentially expressed. The target genes of dysregulated tsRNAs were predicted and the biological effects and potential signaling pathways of these target genes were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to validate 11 differentially expressed tRFs with 12 pairs CCA and adjacent normal tissues.
Results
High‐throughput RNA‐seq totally demonstrated 535 dysregulated tsRNAs, of which 241 tsRNAs were upregulated, such as tRF‐21‐YLKZKWE5D，tRF‐16‐9NF5W8B，tRF‐27‐78YLKZKWE52，tRF‐19‐RLXN48KP，tRF‐33‐IK9NJ4S2I7L7DV，tRF‐19‐F8DHXYIV, and 294 tsRNAs were downregulated (tRF‐20‐739P8WQ0, tRF‐34‐JJ6RRNLIK898HR, tRF‐17‐VL8RPY5, tRF‐23‐YP9LON4VDP, tRF‐39‐EH623K76IR3DR2I2, tRF‐17‐18YKISM, tRF‐19‐Q1Q89PJZ, etc.) in CCA compared with adjacent normal tissues (|log2 fold change | ≥ 1 and p value <0.05). GO and KEGG enrichment analyses indicated that the target genes of dysregulated tRFs (tRF‐34‐JJ6RRNLIK898HR, tRF‐38‐0668K87SERM492V, and tRF‐39‐0668K87SERM492E2) were mainly enriched in the Notch signaling pathway, Hippo signaling pathway, cAMP signaling pathway and in growth hormone synthesis, secretion and action, etc. qRT‐PCR result showed that tRF‐34‐JJ6RRNLIK898HR/tRF‐38‐0668K87SERM492V/tRF‐39‐0668K87SERM492E2 was downregulated (p = 0.021), and tRF‐20‐LE2WMK81 was upregulated in CCA (p = 0.033).
Conclusion
Differentially expressed tRFs in CCA are enriched in many pathways associated with neoplasms, which may impact the tumor progression and have potential to be diagnostic biomarkers and therapeutic targets of CCA.
RNA‐seq totally demonstrated 535 dysregulated tsRNAs, of which 241 tsRNAs were upregulated and 294 tsRNAs were downregulated in CCA compared with adjacent normal tissues. Enrichment analyses indicated that the target genes of dysregulated tRFs were mainly enriched in the Notch signaling pathway, Hippo signaling pathway, cAMP signaling pathway, etc. qRT‐PCR result showed that tRF‐34‐JJ6RRNLIK898HR/tRF‐38‐0668K87SERM492V/tRF‐39‐0668K87SERM492E2 was downregulated (p = 0.021), and tRF‐20‐LE2WMK81 was upregulated in CCA (p = 0.033).
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•Multi-objective optimization in DG planning.•Time sequence characteristics of loads and DGs.•Wind turbine generation and Photovoltaic combination.•Improved Non-dominated Sorting Genetic Algorithm ...II.
With the consideration of time sequence characteristics of load and distributed generator (DG) output, a novel method is presented for optimal sitting and sizing of DG in distributed system. Multi-objective functions have been formulated with the consideration of minimum investment and operational cost of DG, minimum purchasing electricity cost from main grid and minimum voltage deviation. To solve the multi-objective optimization problem, an improved Non-dominated Sorting Genetic Algorithm II has been proposed. The compromised solution is extracted from the Pareto set using the fuzzy theory method. Several experiments have been made on the modified PG&E 69-bus and multiple actual test cases with the consideration of multiple DGs. The computational result and comparisons indicate the proposed method for optimal placement and sizing of DG is feasible and effective.
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The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of ...providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.
Rapamycin is a promising agent for treating tumors, but clinical applications of rapamycin are limited due to its poor water solubility and low bioavailability. This paper constructs a liposome ...delivery system for rapamycin to improve the effect in treating colorectal cancer.
We prepared the rapamycin liposomes using the ethanol injection method. The cellular uptake and biodistribution were detected by LC-MS and in vivo imaging system. MTT assay, transwell migration experiment, flow cytometry, and Western blot analysis evaluated the antitumor effect of rapamycin liposomes in vitro. Furthermore, HCT-116 tumor-bearing mice were used to assess the therapeutic efficacy of rapamycin liposomes in vivo.
The prepared rapamycin liposomes had a particle size of 100±5.5 nm and with a narrow size distribution. In vitro cellular uptake experiments showed that the uptake of rapamycin liposomes by colorectal cells was higher than that of free rapamycin. Subsequently, in vivo imaging experiments also demonstrated that rapamycin liposomes exhibited higher tumor accumulation. Therefore, the ability of rapamycin liposomes to inhibit tumor proliferation, migration and to induce tumor apoptosis is superior to that of free rapamycin. We also demonstrated in vivo good antitumor efficacy of the rapamycin liposomes in HCT-116 xenograft mice. In addition, rapamycin liposomes and 5-FU can synergistically improve the efficacy of colorectal cancer via the Akt/mTOR and P53 pathways.
Collectively, rapamycin liposomes are a potential treatment for colorectal cancer, as it not only improves rapamycin's antitumor effect but also synergistically enhances 5-FU's chemotherapy effect.
Automation and accurate fault detection and diagnosis of HVAC systems is one of the most important technologies for reducing time, energy, and financial costs in building performance management.In ...recent years, data-driven fault detection and diagnosis methods have been heavily studied for fault detection and diagnosis of HVAC systems.However, most existing works deal with single systems and are unable to perform cross-system fault diagnosis.In this paper, a federal learning-based fault detection and diagnosis method is proposed, which uses convolutional neural networks to extract information features, aggregates features using special-designed algorithms, and perform cross-level and cross-system fault detection and diagnosis via federal lear-ning.For multi-fault level fault detection and diagnosis, federal learning is performed using data from four fault levels of chillers.Experimental results show that the average F1-score of the fault detection and diagnosis effect of the four-fault levels is close to 0.97
A novel pattern-reconfigurable dielectric resonator antenna (DRA) with compact structure and high efficiency is proposed in this letter by introducing a pair of switchable directors. The switchable ...directors are arranged on both sides of an omnidirectional DRA, which operates in the TE01δ mode. Their directing function can be controlled by switching the on / off status of the p-i-n diodes so that one omnidirectional radiation pattern and two unidirectional endfire patterns are provided in the azimuthal plane. To verify this idea, a prototype of the proposed antenna is fabricated and measured. Good agreement between the simulated and measured results can be observed. The peak gain in endfire status reaches 4.5 dBi and the front-to-back ratio is more than 12 dB. Meanwhile, the gain variation of the omnidirectional pattern in the azimuth plane is less than 2 dB, which shows good omnidirectional radiation performance.
Studies on large‐scale geographic patterns of aquatic plant diversity can promote research on the generality of macroecological patterns in different ecosystems. Here, we compiled a checklist of 889 ...aquatic angiosperms in China, including 738 helophytes (emergent and marshy plants) and 151 hydrophytes (submerged, free‐floating, and floating‐leaved plants). We explore the geographic patterns and environmental correlates of aquatic plant diversity based on six metrics including species richness (SR), weighted endemism (WE), phylogenetic diversity (PD), phylogenetic endemism (PE), the standardized effect size of phylogenetic diversity (PDses), and the standardized effect size of mean phylogenetic distance (MPDses). Our results show that the diversity of aquatic plants in China is extremely uneven, with high diversity in southeastern China and low diversity in northwestern China, and the geographic patterns of taxonomic and PD are generally consistent. The pattern of helophytes differs from that of hydrophytes. Notably, the wavy‐shaped pattern of aquatic plant diversity (especially SR and PD for hydrophytes) across the latitude observed in this study is not consistent with those previously observed for aquatic plants in other continents. Climatic variables and water environmental variables are the main drivers of aquatic plant diversity in China; however, the effects of individual variables differ between helophytes and hydrophytes. Water environmental variables have a greater impact on PDses and MPDses of hydrophytes than those of helophytes. Overall, our work provides insight into understanding the large‐scale patterns of aquatic plant diversity and is a critical addition to previous studies on the macroecological pattern of terrestrial organisms.
Terrestrial ecosystems are more directly influenced by climate, whereas the water environment is more important to aquatic organisms, thus the geographic patterns of aquatic plant diversity might differ from the general pattern observed for most terrestrial groups. Here, we compiled a checklist of 889 aquatic angiosperms in China, based on several floras, monographs, research papers, and online sources, and explored the geographic patterns and environmental correlates of the taxonomic and phylogenetic diversity of these aquatic plants. We obtained some interesting results, for example, the wavy‐shaped pattern of aquatic plant diversity across latitudes observed in this study is not consistent with that previously observed for aquatic plants on other continents. The shoreline complexity could affect aquatic plant diversity. Climatic variables and water environmental variables are the main drivers of the aquatic plant diversity in China; however, the effects of individual variables differ between helophytes and hydrophytes. Water environmental variables have a greater impact on the diversity of hydrophytes than that of helophytes. Our work provides insight into understanding the large‐scale pattern of aquatic plant diversity, and is a critical addition to previous studies on the macroecological pattern of terrestrial organisms.
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Nitrogen-doped graphene (N-graphene) has attractive properties that has been widely studied over the years. However, its possible formation process still remains unclear. Here, we propose a highly ...feasible formation mechanism of the graphitic-N doing in thermally treated graphene with ammonia by performing ab initio molecular dynamic simulations at experimental conditions. Results show that among the commonly native point defects in graphene, only the single vacancy 5-9 and divacancy 555-777 have the desirable electronic structures to trap N-containing groups and to mediate the subsequent dehydrogenation processes. The local structure of the defective graphene in combining with the thermodynamic and kinetic effect plays a crucial role in dominating the complex atomic rearrangement to form graphitic-N which heals the corresponding defect perfectly. The importance of the symmetry, the localized force field, the interaction of multiple trapped N-containing groups, as well as the catalytic effect of the temporarily formed bridge-N are emphasized, and the predicted doping configuration agrees well with the experimental observation. Hence, the revealed mechanism will be helpful for realizing the targeted synthesis of N-graphene with reduced defects and desired properties.
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BACKGROUND Epithelial-mesenchymal transition (EMT) plays an important role in cancer tumorigenesis. Transforming growth factor β1 (TGF-β1) can induced EMT, which could increase tumor migration and ...invasion. Moreover, recent studies have been proven that mammalian target of rapamycin (mTOR) is a critical regulator of EMT. We investigated the mechanisms of mTOR in transforming growth factor β1 (TGF-β1)-induced EMT in cervical cancer cells. MATERIAL AND METHODS HeLa and SiHa cells were treated with 10 ng/ml TGF-β1 to induce EMT. Then, they were treated with or without rapamycin. CCK8 assay was performed to determine cell proliferation. Cell migration was detected by wound-healing assay; apoptosis was analyzed by flow cytometry; mTOR inhibitors inhibited mTOR pathway to assess the expression of E-cadherin, Vimentin STAT3, Snail2, p-p70s6k, and PKM2 expression. RESULTS TGF-β1 promoted proliferation and migration, and attenuated apoptosis in cervical carcinoma cells. Rapamycin abolished TGF-β1-induced EMT cell proliferation and migration and reversed TGF-β1-induced EMT. E-cadherin were suppressed, whereas Vimentin and PKM2 were increased in HeLa and SiHa cells after stimulation with TGF-β1. Moreover, mTOR was activated in the process of TGF-β1-induced EMT. Rapamycin inhibited the phosphorylation of p70s6k. Furthermore, inhibition of the mTOR pathway decreased PKM2 expression. CONCLUSIONS Inhibition of the mTOR pathway abolished TGF-β1-induced EMT and reduced mTOR/p70s6k signaling, which downregulated PKM2 expression. Our results provide novel mechanistic insight into the anti-tumor effects of inhibition of mTOR.
With the rapid development of solar energy plants in recent years, the accurate prediction of solar power generation has become an important and challenging problem in modern intelligent grid ...systems. To improve the forecasting accuracy of solar energy generation, an effective and robust decomposition-integration method for two-channel solar irradiance forecasting is proposed in this study, which uses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a Wasserstein generative adversarial network (WGAN), and a long short-term memory network (LSTM). The proposed method consists of three essential stages. First, the solar output signal is divided into several relatively simple subsequences using the CEEMDAN method, which has noticeable frequency differences. Second, high and low-frequency subsequences are predicted using the WGAN and LSTM models, respectively. Last, the predicted values of each component are integrated to obtain the final prediction results. The developed model uses data decomposition technology, together with advanced machine learning (ML) and deep learning (DL) models to identify the appropriate dependencies and network topology. The experiments show that compared with many traditional prediction methods and decomposition-integration models, the developed model can produce accurate solar output prediction results under different evaluation criteria. Compared to the suboptimal model, the MAEs, MAPEs, and RMSEs of the four seasons decreased by 3.51%, 6.11%, and 2.25%, respectively.
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