The purpose of this study was to explore the influence of different types of social media use on social media addiction and subjective well-being, and the relationship between social media addiction ...and subjective well-being. Using random sampling, we collected a sample of 370 Chinese college students. According to the scores of social media addiction scale, the respondents were divided into addicted group and non-addicted group. On the basis of literature review, a research model was constructed, which was verified by using the data of total students, addicted students and non-addicted students. The results show that social use and entertainment use have different effects on social media addiction and subjective well-being: entertainment use is more likely to lead to social media addiction, and social use tends to improve subjective well-being. Furthermore, social media addiction has a negative impact on subjective well-being, which is supported in the validation of all three groups.
Forest succession is a key driver of plant communities and understanding succession is central to forest restoration. Currently, the information on the response of the microbial community to the ...forest succession process, however, is limited. In the present study, we investigated the dynamics of the soil bacterial community in three forest types undergoing succession caused by pine wilt disease, representing the initial pine forest, gradual mixed pine and broadleaved forest, and eventual broadleaved forest, using Illumina MiSeq coupled with Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis.
The results showed that the soil pH, contents of soil organic carbon (SOC) and soil total nitrogen (TN) increased after the occurrence of initial succession and differed among the forest sites. The mixed pine forest had significantly higher bacteria biomass (p < 0.05), whereas, the total microbial biomass did not differ during the succession. The bacterial community diversity and richness increased significantly following the succession process (p < 0.05). Proteobacteria, Actinobacteria, Acidobacteria and Bacteroidetes were the dominant phyla across the succession, in which the abundance of Bacteroidetes significantly increased (p < 0.05), whereas, Planctomycetes, WPS‐2 and Burkholder decreased in abundance after succession occurred (p < 0.05).
The three forests formed distinct bacterial community structures during the succession (p < 0.05), whereas, only two functional structures were clustered, in which the mixed and pure broadleaved forest did not differ. The dominant functional groups involved in the C cycle in the initial pure pine forest were replaced gradually by the groups involved in N and S cycles following the subsequent succession. The soil pH, soil TN and SOC were the most important factors affecting the bacterial community and functional structures during the succession.
These results indicate that the bacterial community and function shift drastically in the early stages of succession, which reflects the changes in ecological environment caused by succession. The findings provide useful information to better understand the response of microbes to natural forest disturbance and highlight the importance of microbes during forest succession.
A free Plain Language Summary can be found within the Supporting Information of this article.
A free Plain Language Summary can be found within the Supporting Information of this article.
Long noncoding RNAs (lncRNAs) play nonnegligible roles in the epigenetic regulation of cancer cells. This study aimed to identify a specific lncRNA that promotes the colorectal cancer (CRC) ...progression and could be a potential therapeutic target.
We screened highly expressed lncRNAs in human CRC samples compared with their matched adjacent normal tissues. The proteins that interact with LINRIS (Long Intergenic Noncoding RNA for IGF2BP2 Stability) were confirmed by RNA pull-down and RNA immunoprecipitation (RIP) assays. The proliferation and metabolic alteration of CRC cells with LINRIS inhibited were tested in vitro and in vivo.
LINRIS was upregulated in CRC tissues from patients with poor overall survival (OS), and LINRIS inhibition led to the impaired CRC cell line growth. Moreover, knockdown of LINRIS resulted in a decreased level of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2), a newly found N
-methyladenosine (m
A) 'reader'. LINRIS blocked K139 ubiquitination of IGF2BP2, maintaining its stability. This process prevented the degradation of IGF2BP2 through the autophagy-lysosome pathway (ALP). Therefore, knockdown of LINRIS attenuated the downstream effects of IGF2BP2, especially MYC-mediated glycolysis in CRC cells. In addition, the transcription of LINRIS could be inhibited by GATA3 in CRC cells. In vivo experiments showed that the inhibition of LINRIS suppressed the proliferation of tumors in orthotopic models and in patient-derived xenograft (PDX) models.
LINRIS is an independent prognostic biomarker for CRC. The LINRIS-IGF2BP2-MYC axis promotes the progression of CRC and is a promising therapeutic target.
The intelligent recognition systems for sports actions have been a more general demand, so as to facilitate technical analysis of health management. This highly relies on deep analysis towards ...frame-level image data from the perspective of visual knowledge discovery. In recent years, the rapid development of deep learning technology has well boosted a number of technical breakthrough in computer vision. In this context, this work takes aerobics as the main object, and proposes a hybrid deep learning-based intelligent system for sports action recognition via visual knowledge discovery. Specifically, the human skeleton is represented as a graph based on the physical structure of the human body in this paper, and the selective hypergraph convolution network is selected to adaptively extract the multi-scale information in the skeleton. And the selective-frame temporal convolution is specially selected for the situation to construct recognition model. Upon the basis of proper feature extraction, a triple loss-based error measurement method is employed to construct objective function, and a recurrent neural network structure is further developed to model dynamic action sequence characteristics. The data source of this article is mainly the private data compiled by the research group. Finally, experiments are carried out on the CMU motion capture dataset, and the effectiveness of the proposed algorithm is verified by comparing the experimental results with those of the existing algorithms.
The development of mobile devices with improving communication and perceptual capabilities has brought about a proliferation of numerous complex and computation-intensive mobile applications. Mobile ...devices with limited resources face more severe capacity constraints than ever before. As a new concept of network architecture and an extension of cloud computing, Mobile Edge Computing (MEC) seems to be a promising solution to meet this emerging challenge. However, MEC also has some limitations, such as the high cost of infrastructure deployment and maintenance, as well as the severe pressure that the complex and mutative edge computing environment brings to MEC servers. At this point, how to allocate computing resources and network resources rationally to satisfy the requirements of mobile devices under the changeable MEC conditions has become a great aporia. To combat this issue, we propose a smart, Deep Reinforcement Learning based Resource Allocation (DRLRA) scheme, which can allocate computing and network resources adaptively, reduce the average service time and balance the use of resources under varying MEC environment. Experimental results show that the proposed DRLRA performs better than the traditional OSPF algorithm in the mutative MEC conditions.
Although mobile edge computing (MEC), as an extension of the cloud computing paradigm to edge networks, overcomes some obstacles of traditional mobile cloud computing, i.e., the reduced response time ...particularly, it is a nontrivial task to efficiently deploy virtual machine replica copies (VRCs) supporting multiple applications among numerous MEC servers in edge networks. To combat this issue, we are motivated to investigate in detail the optimal placement of VRCs to minimize the average response time (MART) in the MEC architecture with various requests demand among multiple applications and capacity constraints of MEC servers in edge networks. Besides optimal enumeration placement algorithm (OEPA) as benchmark, we design latency aware heuristic placement algorithm (LAHPA) with much lower computation complexity than that of OEPA. To enhance the performance of LAHPA on MART, clustering enhanced heuristic placement algorithm (CEHPA) is proposed, focusing on the optimal VRC placement in each cluster. We also develop substitution enhanced heuristic placement algorithm (SEHPA) to avoid falling into local optimal solutions. As corroborated by extensive simulation results, the performance of SEHPA on MART is very close to that of OEPA compared with LAHPA and CEHPA. Note that CEHPA also outperforms LAHPA, and both are better than a general greedy placement algorithm. Furthermore, we evaluate the normalized total cost for services provision in edge networks, where SEHPA can also get more outstanding results than other algorithms.
Circular RNAs (circRNAs) are a novel type of noncoding RNAs that modulate the pathogenesis of multiple diseases. Nevertheless, the role of circRNAs in diabetic nephropathy (DN) pathogenesis is still ...ambiguous. In the current study, our team aims to investigate the expression profiles of circRNAs in DN and identify the function of circRNA on mesangial cells. CircRNAs microarray analysis revealed dysregulated circRNA in db/db DN mice, and circRNA_15698 was validated to be upregulated in both db/db mice and mouse mesangial cells (SV40‐MES13) that were exposed to high glucose (25 mM) using real‐time polymerase chain reaction. Loss‐of‐functional experiments showed that circRNA_15698 knockdown significantly inhibited the expression levels of collagen type I (Col. I), collagen type IV (Col. IV), and fibronectin. Moreover, the cellular localization of circRNA_15698 was mainly in the cytoplasm. Bioinformatics tools and luciferase reporter assay confirmed that circRNA_15698 acted as a ‘sponge’ of miR‐185, and then positively regulated the transforming growth factor‐β1 (TGF‐β1) protein expression, suggesting a circRNA_15698/miR‐185/TGF‐β1 pathway. Further validation experiments validated that circRNA_15698/miR‐185/TGF‐β1 promoted extracellular matrix (ECM)‐related protein synthesis. In summary, our study preliminarily investigates the role of circRNAs in mesangial cells and ECM accumulation, providing a novel insight for DN pathogenesis.
circRNA_15698/miR‐185/TGF‐β1 promoted the extracellular matrix (ECM) related proteins synthesis.
Polyene polyketides amphotericin B (AMB) and nystatin (NYS) are important antifungal drugs. Thioesterases (TEs), located at the last module of PKS, control the release of polyketides by cyclization ...or hydrolysis. Intrigued by the tiny structural difference between AMB and NYS, as well as the high sequence identity between AMB TE and NYS TE, we constructed four systems to study the structural characteristics, catalytic mechanism, and product release of AMB TE and NYS TE with combined MD simulations and quantum mechanics/molecular mechanics calculations. The results indicated that compared with AMB TE, NYS TE shows higher specificity on its natural substrate and R26 as well as D186 were proposed to a key role in substrate recognition. The energy barrier of macrocyclization in AMB‐TE‐Amb and AMB‐TE‐Nys systems were calculated to be 14.0 and 22.7 kcal/mol, while in NYS‐TE‐Nys and NYS‐TE‐Amb systems, their energy barriers were 17.5 and 25.7 kcal/mol, suggesting the cyclization with their natural substrates were more favorable than that with exchanged substrates. At last, the binding free energy obtained with the MM‐PBSA.py program suggested that it was easier for natural products to leave TE enzymes after cyclization. And key residues to the departure of polyketide product from the active site were highlighted. We provided a catalytic overview of AMB TE and NYS TE including substrate recognition, catalytic mechanism and product release. These will improve the comprehension of polyene polyketide TEs and benefit for broadening the substrate flexibility of polyketide TEs.
Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI ...resistance, but direct clinical evidence is lacking.
Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets.
Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list (P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets.
We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.
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