Search

Abstract Hepatocellular carcinoma (HCC) is a global health concern, ranking as the fourth leading cause of cancer-related deaths worldwide. However, the role of piwi-interacting RNAs (piRNAs) in HCC processes has not been extensively explored. Through small RNA sequencing, our study identified a specific piRNA, pir-hsa-216911, which is highly expressed in HCC cells. This overexpression of pir-hsa-216911 promotes HCC cell invasion and inhibits cell death, particularly pyroptosis. Knocking out pir-hsa-216911 led to increased cell pyroptosis activity, resulting in the activation of caspase-1 and GSDMD. Further analysis revealed that pir-hsa-216911 targets and suppresses TLR4, a key gene associated with pyroptosis in HCC. In the Huh7 cell line, pir-hsa-216911 knockout confirmed its role in suppressing the TLR4/NFκB/NLRP3 pathway by silencing TLR4. Knocking out pir-hsa-216911 significantly inhibited the formation of Huh7 xenograft tumor. In HCC patients, pir-hsa-216911 was highly expressed in HCC tumor samples with steatosis, suppressing TLR4 expression and inhibiting GSDMD activation. This study introduces pir-hsa-216911 as a new high-expressing piRNA in HCC, which inhibits pyroptosis by silencing TLR4 to suppress GSDMD activation. These findings have significant implications for HCC molecular subtyping and as a potential target for cancer therapy.

In person reidentification (PReID) tasks, challenges such as occlusion and small object sizes frequently arise. High-precision object detection methods can accurately locate small objects, while attention mechanisms help focus on the strong feature regions of objects. These approaches mitigate the mismatches caused by occlusion and small objects to some extent. This paper proposes a PReID method based on object detection and attention mechanisms (ODAMs) to achieve enhanced object matching accuracy. In the proposed ODAM-based PReID system, You Only Look Once version 7 (YOLOv7) was utilized as the detection algorithm, and a size attention mechanism was integrated into the backbone network to further improve the detection accuracy of the model. To conduct feature extraction, ResNet-50 was employed as the base network and augmented with residual attention mechanisms (RAMs) for PReID. This network emphasizes the key local information of the target object, enabling the extraction of more effective features. Extensive experimental results demonstrate that the proposed method achieves a mean average precision (mAP) value of 90.1% and a Rank-1 accuracy of 97.2% on the Market-1501 dataset, as well as an mAP of 82.3% and a Rank-1 accuracy of 91.4% on the DukeMTMC-reID dataset. The proposed PReID method offers significant practical value for intelligent surveillance systems. By integrating multiscale attention and RAMs, this method enhances both its object detection accuracy and its feature extraction robustness, enabling a more efficient individual identification process in complex scenes. These improvements are crucial for enhancing the real-time performance and accuracy of video surveillance systems, thus providing effective technical support for intelligent monitoring and security applications.

Abstract Background The harmonious cognitive alignment among various netizen groups is pivotal for the spread and amplification of online rumors. This alignment, characterized by shared cognitive inclinations, fosters uniformity in attitudes and perspectives, thereby precipitating synchronized engagement in the dissemination of such rumors. Notably, discernible disparities emerge in group cognition as different types of rumors pertaining to the same event propagate. This research endeavors to dissect the roles of netizen groups through the lens of cognitive variance, thereby attaining a more profound comprehension of the distinctive traits and behavioral dynamics of various netizen factions in the context of online rumor dissemination. Methods By integrating Bloom’s taxonomy and crafting a survey questionnaire, this study captured the cognitive responses of netizens to various online rumor themes across two critical dimensions: (1) Information Cognition: exploring cognitive processing levels from basic recall to application and analysis and (2) Attitude Change: evaluating higher-order cognitive processes such as evaluating and creating in response to complex rumor scenarios. The decision tree classification algorithm was meticulously applied to dissect the catalysts behind the cognitive shifts among netizens. Additionally, the K-Means clustering algorithm was effectively utilized to categorize netizen groups along thematic lines, offering a nuanced view of their cognitive engagement. Results The initial impression of a rumor significantly influences netizens’ final cognitive perceptions. Twelve characteristics were observed in netizen groups during the dissemination of rumors on different themes, and these groups were classified into four categories: knowledge-oriented, competition-oriented, social-oriented, and entertainment-oriented, based on their cognitive differences. Conclusions Throughout the lifecycle of online rumors, from inception to dissemination, diverse netizen groups assume distinct roles, each exerting a unique influence on the spread and reception of information. By implementing tailored governance strategies that are sensitive to the characteristics of these groups, it is possible to attain substantially more effective outcomes in managing the propagation of online rumors. This nuanced approach to governance recognizes the heterogeneity of the online community and leverages it to enhance the efficacy of interventions.

Abstract Background Delayed epistaxis after endoscopic transnasal pituitary tumor resection (ETPTR) is a critical complication, tending to cause aspiration or hemorrhagic shock. This study assessed clinical characteristics, risk factors, and provide treatment and prevention advice of this complication. Methods This was a retrospective monocentric analysis of 862 patients who underwent ETPTR. Statistical analyses of clinical data revealed the incidence, sources and onset time of delayed epistaxis. Univariate analysis and binary logistic regression were used to identify risk factors. Results The incidence of delayed epistaxis was 2.78% (24/862), with an average onset time of 20.71 ± 7.39 days. The bleeding sources were: posterior nasal septal artery branch of sphenopalatine artery (12/24), multiple inflammatory mucosae (8/24), sphenopalatine artery trunk (3/24) and sphenoid sinus bone (1/24). Univariate analysis and binary logistic regression analysis confirmed that hypertension, nasal septum deviation, chronic rhinosinusitis and growth hormone pituitary tumor subtype were independent risk factors for delayed epistaxis. Sex, age, history of diabetes, tumor size, tumor invasion and operation time were not associated with delayed epistaxis. All patients with delayed epistaxis were successfully managed through endoscopic transnasal hemostasis without recurrence. Conclusions Delayed epistaxis after ETPTR tends to have specific onset periods and risk factors. Prevention of these characteristics may reduce the occurrence of delayed epistaxis. Endoscopic transnasal hemostasis is recommended as the preferred treatment for delayed epistaxis.

Abstract Backgrounds Long nonconding RNAs (lncRNAs) have been found to be a vital regulatory factor in the development process of human cancer, and could regarded as diagnostic or prognostic biomarkers for human cancers. Here, we aim to confirm the expression and molecular mechanism of RP11-171K16.5 (lnc171) in hepatocellular carcinoma (HCC). Methods Screening of differentially expressed lncRNAs by RNA sequencing. Expression level of gene was studied by quantitative real-time PCR (qRT-PCR). The effects of lnc171, mir-873-5p, and ethanol on migration and invasion activity of cells were studied used transwell assay, and luciferase reporter assay was used to confirm the binding site. Results RNA sequencing showed that lnc171 was markedly up-regulated in HCC. siRNA-mediated knockdown of lnc171 repressed the migration and invasion ability of HCC cells. Bioinformatic analysis, dual luciferase reporter assay, and qRT-PCR indicated that lnc171 interacted with mir-873-5p in HCC cells, and Zin-finger E-box binding homeobox (ZEB1) was a downstream target gene of mir-873-5p. In addition, lnc171 could enhance migration and invasion ability of HCC cells by up-regulating ZEB1 via sponging mir-873-5p. More interestingly, ethanol stimulation could up-regulate the increase of lnc171, thereby regulating the expression of competing endogenous RNA (ceRNA) network factors which lnc171 participated in HCC cells. Conclusions Our date demonstrates that lnc171 was a responsive factor of ethanol, and plays a vital role in development of HCC via binding of mir-873-5p.

ObjectiveTo investigate the diagnostic value of metagenomic next-generation sequencing (mNGS) in detecting pathogens from joint infection (JI) synovial fluid (SF) samples with previous antibiotic exposure.MethodsFrom January 2019 to January 2022, 59 cases with suspected JI were enrolled. All cases had antibiotic exposure within 2 weeks before sample collection. mNGS and conventional culture were performed on SF samples. JI was diagnosed based on history and clinical symptoms in conjunction with MSIS criteria. The diagnostic values, including sensitivity, specificity, positive/negative predictive values (PPV/NPV), and accuracy, were in comparison with mNGS and culture.ResultsThere were 47 of the 59 cases diagnosed with JI, while the remaining 12 were diagnosed with non-infectious diseases. The sensitivity of mNGS was 68.1%, which was significantly higher than that of culture (25.5%, p

IntroductionDuring public health emergencies, online rumors spread widely on social media, causing public information anxiety and emotional fluctuations. Analyzing the co-evolution patterns of online rumor themes and emotions is essential for implementing proactive and precise governance of online rumors during such events.MethodsRumor texts from mainstream fact-checking platforms during the COVID-19 pandemic were collected and analyzed in phases based on the crisis lifecycle theory. The LDA topic model was applied to analyze the distribution of rumor themes at different stages. The Baidu AI Sentiment Analysis API was used to study the emotional tendencies of rumors at different stages. Line graphs were utilized to analyze the co-evolution characteristics of rumor themes and emotions.ResultsDuring the COVID-19 pandemic, the themes of online rumors can be categorized into five types: epidemic prevention and control, panic-inducing, production and livelihood, virus dissemination, and social figures. These themes exhibited repetition and fluctuation at different stages of the pandemic. The emotions embedded in pandemic-related online rumors evolved with the progression of the pandemic. Panic-inducing rumors co-evolved with negative emotions, while epidemic prevention and control rumors co-evolved with positive emotions.ConclusionThe study results help to understand the public’s focus and emotional tendencies at different stages of the COVID-19 pandemic, thereby enabling targeted public opinion guidance and crisis management.

Objective Significant progress has been made in recent years in the diagnosis of periprosthetic joint infections (PJI). However, the lack of a gold standard test for the diagnosis of PJI remains a challenge.The aim of this study was to evaluate the diagnostic values of the albumin/fibrinogen ratio (AFR), the C‐reactive protein/albumin ratio (CAR), and the levels of fibrinogen (FIB) and albumin (ALB) in the diagnosis of PJI. Methods The medical records of 158 patients who had undergone hip or knee revisions from January 2018 to May 2022 were retrospectively analyzed. Of these patients, 79 were diagnosed with PJI, while 79 were diagnosed with aseptic loosening (AL). PJI was defined using the Musculoskeletal Infection Society criteria. The plasma levels of C‐reactive protein (CRP), ALB, and FIB; the erythrocyte sedimentation rate (ESR); and the AFR and CAR in the two groups were recorded and analyzed. The receiver operating characteristic curve was used to calculate the sensitivity and specificity of each indicator; the diagnostic value for each indicator was calculated as the area under the curve (AUC). Results The ESR, CRP, FIB, and CAR values in the PJI group were significantly higher than those in the AL group, and the ALB and AFR values were significantly lower than those in the AL group (p

IntroductionAutoimmune thyroid diseases (AITDs) are prevalent disorders, primarily encompassing Graves’ disease (GD) and Hashimoto’s thyroiditis (HT). Despite their common occurrence, the etiology of AITDs remains elusive. Th9 cells, a new subset of CD4+T cells with immunomodulatory properties, have been linked to the development of various autoimmune diseases. However, research on the role of Th9 cells in AITDs is limited. MethodsWe investigated the expression of Th9 cells,their functional cytokine IL-9, and transcription factor IRF4 in peripheral blood mononuclear cells (PBMCs) and plasma of AITD patients and healthy controls. Additionally, we explored the genetic association between four loci polymorphisms (rs31564, rs2069879, rs1859430, and rs2069868) of the IL-9 gene and AITDs.ResultsWe reported, for the first time, that refractory GD patients exhibited elevated mRNA levels of IL-9 and IRF4 in PBMCs, increased IL-9 protein levels in plasma, and a higher proportion of Th9 cells in peripheral blood when compared to normal controls. Furthermore, human recombinant IL-9 protein was found to enhance IFN-g secretion in PBMCs from both GD patients and normal controls. At the genetic association level, after adjusting for age and sex, the rs2069879 polymorphism exhibited a significant association with AITDs under an additive model (P

The study on neuromorphic oscillation behaviors and their oscillators have been considered as one of the most straightforward approaches to mimic some biological neurons via the configured nonlinear memristive equivalent circuits. Also, neuromorphic oscillation is one classical oscillation phenomenon, both the frequency response and zero-pole analysis could be clearly regarded as a complete description of the sinusoidal oscillation behavior for a nonlinear circuit. As one of the most classical memristors, Chua corsage memristor (CCM) is so famous to exhibit chaotic oscillation and neuromorphic dynamics due to its locally active and edge of chaos. However, the parasitic phenomena in the practical circuits are unique and tiny but existed as one of the inherent characteristics, which could lead to some unexpected results. Even a pretty small perturbation may heavily affect the quality of the entire system. In this paper, one modified CCM with the parasitic parameter (named $g_{p}$ ) is proposed. Then, some unique and unusual phenomena are captured and analyzed. Moreover, the analysis on the distributions for the locally-active domains (LADs) and edge of chaos are presented. Furthermore, one small-signal equivalent circuit with a positive capacitance is introduced, as well as its impedance and admittance functions. Also, both types of neuromorphic oscillators are captured and observed via external inductor and capacitor, respectively. Finally, the applications in neural networks are explored, which herald the proposed model could be more suitable to transmit the mental, physical fatigue, memory load and closer to simulating the actual neuromorphic systems.

Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles’ past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed.

With the rapid development of 3D printing technology, porous titanium scaffolds have provided a new restoration method to repair bone defects. Compared with the traditional body-centered cubic (bcc) dot matrix structure with a simple arrangement and repetitive structure, the topology-driven properties of triply periodic minimal surfaces (TPMS) can offer a continuous surface and smooth curvature, an excellent platform for cell proliferation. In this study, we used reverse engineering techniques to model the mandible. Sheet and solid networks of gyroid structure, the most common type of TPMS, were selected for porous design and then molded using metal 3D printing technology. At the same time, the surface treatment parameters of sandblasted, large-grit, and acid-etched (SLA) were optimized by orthogonal experimental design. Then, the optimized SLA parameter was used to treat the gyroid with 70% porosity. The result showed that reverse engineering reconstructed the TPMS-based mandibular model had good formability. Furthermore, the best surface morphology, wettability, and roughness were obtained for 3D printed Ti6Al4V under the treatment of 80 mesh Al2O3, blasting distances of 4 cm, and a 1:1:2 acid ratio. Moreover, the mechanical properties of Sheet-Gyroid and Solid-Gyroid were significantly different at 70% porosity. The porosity of the scaffolds was close to the design porosity after SLA treatment. However, no significant changes were found in its mechanical properties, all matching the mandible’s mechanical properties to meet the implantation conditions.

ObjectivePost-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.MethodsFive-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.ResultsThe DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.ConclusionWe developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

According to laboratory tests of soil samples of Wangyan highway landslide, the mechanism of formation of landslide and the degree of sensitivity of various factors to slope stability are analyzed. It is found that the landslide is destroyed by the properties of expansive soil, the existence of water in the cracks, the loading of artificial filling, and the traction of old landslide. The soil samples were tested for SEM and MIP tests by lyophilization, and the particle morphology, the mode of combination, porosity, and connectivity of the sliding zone soil were observed by a scanning electron microscope. Results show that the sliding zone developed many microfissures and the clay minerals arranged tightly, the pore of sizeslope angle>highway load>filling weight. The results of research can provide a scientific basis for the treatment, protection, and reinforcement of the Wangyan highway landslide.

Background: Graves’ disease (GD) is a common autoimmune disease, and its pathogenesis is unclear. Studies have found that the occurrence of GD is related to the immune disorder caused by the interaction of genetic susceptibility and environmental factors. The CD4+ T cell subset is closely related to the immune disorder of GD. LncRNAs are RNA molecules with a length of more than 200 nt and are involved in a variety of autoimmune diseases. However, the roles of lncRNAs in recurrent GD are still elusive. The purpose of this study is to identify lncRNA and mRNA expression profile in relapsed Graves’ disease.Method: CD4+ T cells from 12 recurrent GD and 8 healthy controls were collected for high-throughput sequencing. The gene-weighted co-expression network analysis (WGCNA) was used to construct the co-expression module relevant to recurrent GD, and the key genes in the module were verified by RT-PCR.Results: There are 602 upregulated lncRNAs and 734 downregulated lncRNAs in CD4+ T cells in recurrent GD patients compared with the healthy controls. The module most relevant to GD recurrence was constructed using WGCNA, and the key genes in the module were verified by RT-PCR. We found that the expression of RPL8, OAS2, NFAT5, DROSHA, NONHSAT093153.2, NONHSAT118924.2, and NONHSAT209004.1 was significantly decreased in GD group (p < 0.001, p < 0.001, p < 0.01, p < 0.05, p < 0.001, p < 0.05, and p < 0.01, respectively).Conclusion: LncRNAs are closely related to the recurrence of GD. For the first time, we constructed the expression profile of lncRNAs and mRNAs in CD4+ T cells in recurrent GD patients.

Our study is to explore the expression profiles and potential functions of circRNAs in prostate cancer (PCa). A total of 95 circRNAs and 830 mRNAs were screened to be significantly differentially expressed in PCa tissues by microarrays. Co-expression and competitive endogenous RNA (ceRNA) network were constructed to reveal the potential regulatory mechanisms of circRNAs. Three circRNAs, hsa_circ_0001206, hsa_circ_0001633, and hsa_circ_0009061 were validated to be down-regulated in PCa by quantitative real-time PCR (qRT-PCR) and hsa_circ_0001206 as well as hsa_circ_0009061 was significantly associated with clinical features of PCa patients. Meanwhile, Receiver Operating Characteristic (ROC) curves showed their good diagnostic value as biomarkers for PCa. The down-regulation of hsa_circ_001206 was partly because of the regulation of DExH-Box Helicase 9 (DHX9). Moreover, overexpression of hsa_circ_0001206 inhibited PCa cell proliferation, migration, and invasion in vitro and prevented tumor growth in vivo. Dual-luciferase reporter assays showed hsa_circ_0001206 could directly bind to miR-1285-5p. The expression of Smad4, a well-known suppressive gene in PCa, can be increased by overexpression of hsa_circ_0001206 and this effect could be partly reversed by co-transfection of miR-1285-5p mimic. The study revealed expression profiles and potential functions of circRNAs and demonstrated hsa_circ_0001206 played a suppressive role in the pathogenesis of PCa.

Abstract Background Metastatic prostate cancer is a fatal disease despite multiple new approvals in recent years. Recent studies revealed that circular RNAs (circRNAs) can be involved in cancer metastasis. Defining the role of circRNAs in prostate cancer metastasis and discovering therapeutic targets that block cancer metastasis is of great significance for the treatment of prostate cancer. Methods The circSOBP levels in prostate cancer (PCa) were determined by qRT‐PCR. We evaluated the function of circSOBP using a transwell assay and nude mice lung metastasis models. Immunofluorescence assay and electron microscopic assay were applied to determine the phenotypes of prostate cancer cells’ migration. We used fluorescence in situ hybridization assay to determine the localization of RNAs. Dual luciferase and rescue assays were applied to verify the interactions between circSOBP, miR‐141‐3p, MYPT1, and phosphomyosin light chain (p‐MLC2). Results We observed that circSOBP level was significantly lower in PCa specimens compared with adjacent noncancerous prostate specimens, and was correlated with the grade group of PCa. Overexpression of circSOBP suppressed PCa migration and invasion in vitro and metastasis in vivo. CircSOBP depletion increased migration and invasion and induced amoeboid migration of PCa cells. Mechanistically, circSOBP bound miR‐141‐3p and regulated the MYPT1/p‐MLC2 axis. Moreover, the depletion of MYPT1 reversed the inhibitory effect of circSOBP on the migration and invasion of PCa cells. Complementary intronic Alu elements induced but were not necessary for the formation of circSOBP. The nuclear export of circSOBP was mediated by URH49. Conclusion Our results suggest that circSOBP suppresses amoeboid migration of PCa cells and inhibits migration and invasion through sponging miR‐141‐3p and regulating the MYPT1/p‐MLC2 axis.

In order to study the bearing characteristics and failure mechanism of the rigid antislide pile under horizontal load, the stress of rigid antislide pile under transverse axial large displacement load is analyzed by using elastic-plastic theory, finite element analysis, and model test. The theoretical formula of the proximal plastic earth pressure near the pile with the depth of soil under the horizontal force is obtained. The results show that the standard is insensitive to the variation of soil parameters and the influence of soil parameters on allowable soil resistance in front of pile should be considered. With the increase of the horizontal force of the pile top, the soil near the pile is destroyed in this process gradually, which is the decline of the cross section of the maximum soil resistance of the pile. When the horizontal displacement of pile top is 20 mm and 70 mm, the soil resistance value and the ultimate soil resistance value in front of the pile can be selected, respectively. The plastic zone develops to the front and bottom of the pile at the same speed, at an angle of 45° with the direction of gravity. When the displacement reaches 34 mm, the plastic zone develops to the deeper depth obviously. The results can provide a theoretical basis for the design and application of antislide piles during the process of slope protections.

This study investigated the corrosion damage and bearing characteristics of bridge pile foundations under a dry-wet-freeze-thaw cycle of composite salt in an alpine salt marsh area using an in situ test, laboratory test, and numerical simulations. The in situ test showed that the dry-wet-freeze-thaw cycle has little effect on the quality of the concrete specimens and rebar. The area of the rebar at a depth of 0.25 m had the highest corrosion rate of 91%. The application of epoxy resin on the surface improved erosion resistance. After one year of outdoor dry-wet-freeze-thaw cycle test, due to the interaction of compound salts, the quality of specimens is reduced under the denudation of chloride ions, while the mass of specimens is increased by the corrosion products formed under the joint action of carbonate and sulfate, resulting in less obvious change of specimen quality, the antierosion coefficient of the specimens decreased, the maximum loss rate of compressive strength was 38.2%, and the pile foundation began to deteriorate. The laboratory test showed that expansive substances, such as Friedel salt, appeared in the concrete specimens of pile foundation during 225 cycles of dry-wet-freeze-thaw cycles, the relative dynamic elastic modulus was reduced by 60.9%, the antierosion coefficient was reduced to 0.51, and the compressive strength loss rate was 65.9%. As such, the pile foundation was seriously damaged. The numerical simulation shows that, with an increase of the peeling thickness and the corrosion depth, the bearing capacity of the pile foundation will gradually decrease after 8 years. Without maintenance, the bearing capacity of a pile foundation will decrease by 34.45% in the 20th year.

As a malignant tumor of the central nervous system, glioma exhibits high incidence and poor prognosis. Circular RNA HIPK3 (circHIPK3) is a circular RNA (circRNA) related to cancer progression. However, the role of circHIPK3 in gliomas remains unclear. The purpose of this study was to investigate the role of circHIPK3 in gliomas and its mechanism. The qRT-PCR method was used to determine the expression pattern of circHIPK3 in glioma cell lines. CCK-8 assay was used to detect cell proliferation. Cell migration and invasion were evaluated using the Transwell assay. Our results showed that circHIPK3 expression was significantly up-regulated in glioma tissues and cell lines. In vitro, the down-regulation of circHIPK3 significantly inhibited the proliferation, migration and invasion of glioma cells. Besides, we demonstrated that circHIPK3 acted as a sponge to absorb miR-124 and promoted CCND2 expression. In summary, our results indicated that circHIPK3 had carcinogenic effects by regulating the expression of CCND2 in glioma by sponging miR-124. These findings provided favorable evidence to reveal the role of circHIPK3 in the development of gliomas.

With the rapid development of the global economy, air pollution, which restricts sustainable development and threatens human health, has become an important focus of environmental governance worldwide. The modeling and reliable prediction of air quality remain substantial challenges because uncertainties residing in emissions data are unknown and the dynamic processes are not well understood. A number of machine learning approaches have been used to predict air quality to help alleviate air pollution, since accurate air quality estimation may result in significant social-economic development. From this perspective, a novel air quality estimation approach is proposed, which consists of two components: newly-designed dendritic neural regression (DNR) and customized scale-free network-based differential evolution (SFDE). The DNR can adaptively utilize spatio-temporal information to capture the nonlinear correlation between observations and air pollutant concentrations. Since the landscape of the weight space in DNR is vast and multimodal, SFDE is used as the optimization algorithm due to its powerful search ability. Extensive experimental results demonstrate that the proposed approach can provide stable and reliable performances in the estimation of both PM2.5 and PM10 concentrations, being significantly better than several commonly-used machine learning algorithms, such as support vector regression and long short-term memory.

Purpose: Sjögren’s syndrome (SS) is an autoimmune disease characterized by dry mouth and eyes. To date, the exact molecular mechanisms of its etiology are still largely unknown. The aim of this study was to identify SS related key genes and functionally enriched pathways using the weighted gene co-expression network analysis (WGCNA).Materials and Methods: We downloaded the microarray data of 190 SS patients and 32 controls from Gene Expression Omnibus (GEO). Gene network was constructed and genes were classified into different modules using WGCNA. In addition, for the hub genes in the most related module to SS, gene ontology analysis was applied. The expression profile and diagnostic capacity (ROC curve) of interested hub genes were verified using a dataset from the GEO. Moreover, gene set enrichment analysis (GSEA) was also performed.Results: A total of 1483 differentially expressed genes were filtered. Weighted gene coexpression network was constructed and genes were classified into 17 modules. Among them, the turquoise module was most closely associated with SS, which contained 278 genes. These genes were significantly enriched in 10 Gene Ontology terms, such as response to virus, immune response, defense response, response to cytokine stimulus, and the inflammatory response. A total of 19 hub genes (GBP1, PARP9, EPSTI1, LOC400759, STAT1, STAT2, IFIH1, EIF2AK2, TDRD7, IFI44, PARP12, FLJ20035, PARP14, ISGF3G, XAF1, RSAD2,LY6E, IFI44L, and DDX58) were identified. The expression levels of the five interested genes including EIF2AK2, GBP1, PARP12, PARP14, and TDRD7 were also confirmed. ROC curve analysis determined that the above five genes’ expression can distinguish SS from controls (the area under the curve is all greater than 0.7). GSEA suggests that the SS samples with highly expressed EIF2AK2 or TDRD7 genes are correlated with inflammatory response, interferon α response, and interferon γ response.Conclusion: The present study applied WGCNA to generate a holistic view of SS and provide a basis for the identification of potential pathways and hub genes that may be involved in the development of SS.

Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mechanism-based BSO (ABSO) algorithm based on the chaotic local search in this study. The adjustment of the search space using the local search method based on an adaptive self-scaling mechanism balances the global search and local development performance of the ABSO algorithm, effectively preventing the algorithm from falling into local optima and improving its convergence accuracy. To verify the stability and effectiveness of the proposed ABSO algorithm, the performance was tested using 29 benchmark test functions, and the mean and standard deviation were compared with those of five other optimization algorithms. The results showed that ABSO outperforms the other algorithms in terms of stability and convergence accuracy. In addition, the performance of ABSO was further verified through a nonparametric statistical test.

In view of the current increasing new energy installed capacity and the frustration in outputting clean electricity due to limited channel capacity, the new energy intelligence operation system based on big data platform technology, joint power monitoring technology and large-scale energy storage power station integrated with control technology is adopted through unified modeling and communication protocols, so as to solve the problems in information interaction and unified controlling for manufacturers of multiple wind turbine, PV, storage equipment ,and varieties of equipment types.So, by structuring the power-grid friendly wind power plant, photovoltaic power plant and the energy storage power plant, and taking the "five ascension" measures can greatly reduce the workload of the staff, improving the working efficiency and the economic benefits of the enterprise greatly, meanwhile it also provide new methods, new measures and new ideas for other new energy power plants, to realize the improving of the comprehensive benefits and social value.

A microfluidic lab-on-chip device was developed to automatically and selectively manipulate target cells at the single cell level. The device is composed of a microfluidic chip, mini solenoid valves with negative-pressurized soft tubes, and a LabView®-based data acquisition device. Once a target cell passes the resistive pulse sensing gate of the microfluidic chip, the solenoid valves are automatically actuated and open the negative-pressurized tubes placed at the ends of the collecting channels. As a result, the cell is transported to that collecting well. Numerical simulation shows that a 0.14 mm3 volume change of the soft tube can result in a 1.58 mm/s moving velocity of the sample solution. Experiments with single polystyrene particles and cancer cells samples were carried out to demonstrate the effectiveness of this method. Selectively manipulating a certain size of particles from a mixture solution was also achieved. Due to the very high pressure-driven flow switching, as many as 300 target cells per minute can be isolated from the sample solution and thus is particularly suitable for manipulating very rare target cells. The device is simple, automatic, and label-free and particularly suitable for isolating single cells off the chip one by one for downstream analysis.

Motion planning for autonomous vehicles relies heavily on perception and prediction results to find a safe, collision-free local trajectory that adheres to traffic rules. However, vehicle perception is frequently limited by occlusion, and the generation of safe local trajectories with restricted perception poses a significant challenge in the field of motion planning. This paper introduces a collision avoidance trajectory planning algorithm that considers potential collision risks, within a hierarchical framework of sampling and optimization. The primary objective of this work is to generate trajectories that are safer and align better with human driver behavior while considering potential collision risks in occluded regions. Specifically, in occlusion scenarios, the state space is discretized, and a dynamic programming algorithm is used for a sampling-based search to obtain initial trajectories. Additionally, the concept of a driving risk field is introduced to describe potential collision risk elements within the human-vehicle-road environment. By drawing inspiration from graph search algorithms, potential collision risk areas are accurately described, and a cost function is proposed for evaluating potential risks in occluded regions. Drivers typically exhibit conservative and cautious driving behavior when navigating through occluded regions. The proposed algorithm not only prioritizes driving safety but also considers driving efficiency, thereby reducing the vehicle’s conservativeness when passing through occlusions. The research results demonstrate that the ego vehicle can actively avoid blind spots and tends to move away from occluded regions, aligning more closely with human driver behavior. [ABSTRACT FROM AUTHOR]