Explore the vast range of titles available.

Gliomas represent the most common type of malignant brain tumor, among which, glioblastoma remains a clinical challenge with limited treatment options and dismal prognosis. It has been shown that the dysregulated receptor tyrosine kinase (RTK, including EGFR, MET, PDGFRα, ect.) signaling pathways have pivotal roles in the progression of gliomas, especially glioblastoma. Increasing evidence suggests that expression levels of the RTK MET and its specific stimulatory factors are significantly increased in glioblastomas compared to those in normal brain tissues, whereas some negative regulators are found to be downregulated. Mutations in MET, as well as the dysregulation of other regulators of cross-talk with MET signaling pathways, have also been identified. MET and its ligand hepatocyte growth factor (HGF) play a critical role in the proliferation, survival, migration, invasion, angiogenesis, stem cell characteristics, and therapeutic resistance and recurrence of glioblastomas. Therefore, combined targeted therapy for this pathway and associated molecules could be a novel and attractive strategy for the treatment of human glioblastoma. In this review, we highlight progress made in the understanding of MET signaling in glioma and advances in therapies targeting HGF/MET molecules for glioma patients in recent years, in addition to studies on the expression and mutation status of MET.

Background Glioma is one of the most common type of primary central nervous system tumors. EGFR mutation, a common alteration occurs in various tumors, is not brought to the forefront in understanding and treating glioma at present. Methods In the present study, we demonstrated an immune infiltration related pattern of EGFR mutation in lower-grade glioma. In silico analyses were performed to investigate EGFR mutation and its biological effects and clinical values. GO and GSEA process were used as enrichment analysis. Infiltration levels of specific types of immune cells were estimated at TIMER database. Clinical data of patients were obtained from TCGA and were employed for survival analyses. Results Here we revealed that EGFR mutation leads to an up-regulation of immune response related pathways and dismal prognosis in lower-grade glioma. Infiltration of CD4+ T cells, neutrophils, macrophages, and dendritic cells were significantly increased in EGFR-mutant cases. Infiltration of specific types of immune cells were correlated with shorter survival time. PD-L1 was elevated in EGFR-mutant cases and correlated with infiltration level of CD4+ T cells, neutrophils and dendritic cells. Conclusion EGFR mutation indicates increasing infiltration of specific types of immune cells and poor prognosis in lower-grade glioma. Alteration of immune microenvironment since the EGFR mutation might influence the survival of glioma. We also provided a novel evidence and indicator of PD-1 inhibitor application in glioma.

The classification of urban points of interest (POI) reflects the development of various industries in a city, making their distribution analysis significant. Traditional mapping methods often face inefficiency and high costs, leading to limited data quality and inaccuracies in classification. To address this, a low-cost, high-quality method is essential. Mobile Crowd Sensing (MCS) technology offers an innovative solution for identifying urban POIs. This paper introduces a hybrid MCS perception framework (MCS-APF) that includes a data collection module and a clustering module. The data collection module combines traditional participatory and opportunistic methods, incorporating a new recruitment criterion considering workers’ abilities, reputations, and POI popularity to enhance data quality. The clustering module employs an improved version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN-H) algorithm using Haversine distance, which effectively analyzes the combined data for accurate POI classification. Experimental results show that POI classifications derived from DBSCAN-H feature significant intra-cluster tightness and inter-cluster separation, outperforming traditional techniques. Overall, MCS-APF provides more accurate, efficient, and cost-effective POI sensing outcomes.

Background Alternative splicing (AS), one of the main post-transcriptional biological regulation mechanisms, plays a key role in the progression of glioblastoma (GBM). Systematic AS profiling in GBM is limited and urgently needed. Methods TCGA SpliceSeq data and the corresponding clinical data were downloaded from the TCGA data portal. Survival-related AS events were identified through Kaplan–Meier survival analysis and univariate Cox analysis. Then, splicing correlation network was constructed based on these AS events and associated splicing factors. LASSO regression followed by multivariate Cox analysis was performed to validate independent AS biomarkers and to construct a risk prediction model. Enrichment analysis was subsequently conducted to explore potential signaling pathways of these AS events. Results A total of 132 TCGA GBM samples and 45,610 AS events were included in our study, among which 416 survival-related AS events were identified. An AS correlation network, including 54 AS events and 94 splicing factors, was constructed, and further functional enrichment was performed. Moreover, the novel risk prediction model we constructed displayed moderate performance (the area under the curves were > 0.7) at both one, two and three years. Conclusions Survival-related AS events may be vital factors of both biological function and prognosis. Our findings in this study can deepen the understanding of the complicated mechanisms of AS in GBM and provide novel insights for further study. Moreover, our risk prediction model is ready for preliminary clinical applications. Further verification is required.

Leucine-rich repeats and immunoglobulin-like domains 3 (LRIG3) functions as a tumor suppressor in glioma. Although our previous study demonstrated that LRIG3 inhibited angiogenesis via the PI3K/AKT/VEGFA pathway under normoxia, its impact on glioma vascularization under hypoxia remains elusive. Vasculogenic mimicry (VM), an alternative form of neovascularization, plays a pivotal role in glioma progression, particularly within hypoxic tumor microenvironments. This study aimed to investigate the effects of LRIG3 on hypoxia-induced VM in glioma and to elucidate the underlying molecular mechanisms. The effects of LRIG3 on VM were evaluated in vitro using tube formation and 3D spheroid invasion assays. Histological analysis of intracranial xenografts and glioblastoma specimens was performed to assess LRIG3's impact on glioma vascularization in vivo. The underlying mechanisms were investigated using western blot, quantitative real-time PCR (qRT-PCR), and ubiquitination assays. LRIG3 expression was inversely correlated with VM density in the central hypoxic regions of both xenografts and glioblastoma specimens. Under hypoxia, LRIG3 overexpression inhibited the invasion and tube formation capacities of glioma cells, whereas its knockdown promoted these activities. Mechanistically, LRIG3 suppressed VM phenotypes by downregulating Snail2 at the post-translational level, rather than affecting VEGFA. LRIG3 promoted the ubiquitination of Snail2, leading to its proteasomal degradation and destabilization under hypoxia. LRIG3 inhibits hypoxia-induced VM in glioma by facilitating the proteasomal degradation of Snail2 via ubiquitination.

Saccharopolyspora spinosa is a well-known actinomycete for producing the secondary metabolites, spinosad, which is a potent insecticides possessing both efficiency and safety. In the previous researches, great efforts, including physical mutagenesis, fermentation optimization, genetic manipulation and other methods, have been employed to increase the yield of spinosad to hundreds of folds from the low-yield strain. However, the metabolic network in S. spinosa still remained un-revealed. In this study, two S. spinosa strains with different spinosad production capability were fermented and sampled at three fermentation periods. Then the total RNA of these samples was isolated and sequenced to construct the transcriptome libraries. Through transcriptomic analysis, large numbers of differentially expressed genes were identified and classified according to their different functions. According to the results, spnI and spnP were suggested as the bottleneck during spinosad biosynthesis. Primary metabolic pathways such as carbon metabolic pathways exhibited close relationship with spinosad formation, as pyruvate and phosphoenolpyruvic acid were suggested to accumulate in spinosad high-yield strain during fermentation. The addition of soybean oil in the fermentation medium activated the lipid metabolism pathway, enhancing spinosad production. Glutamic acid and aspartic acid were suggested to be the most important amino acids and might participate in spinosad biosynthesis.

Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis (OSTIA) data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.

As we know, harmonic noises widely exist in industrial fields and have crucial impact on the computational accuracy of the zeroing neural network (ZNN) model. For tackling this issue, by combining the dynamics of harmonic signals, two harmonic noise-tolerant ZNN (HNTZNN) models are designed for the dynamic matrix pseudoinversion. In the design of HNTZNN models, an adaptive compensation term is adopted to eliminate the influence of harmonic noises, and a Li activation function is introduced to further improve the convergence rate. The convergence and robustness to harmonic noises of the proposed HNTZNN models are proved through theoretical analyses. Besides, compared with the ZNN model without adaptive compensation term, the HNTZNN models are more effective for tacking the problem of dynamic matrix pseudoinverse under harmonic noises environments. Moreover, HNTZNN models are further applied to the kinematic control of a four-link planar robot manipulator under harmonic noises. In general, the experimental results verify the effectiveness, superiority and broad application prospect of the models.

Glioblastoma Multiforme (GBM) is the most common and aggressive form of intracranial tumors with poor prognosis. In recent years, tumor immunotherapy has been an attractive strategy for a variety of tumors. Currently, most immunotherapies take advantage of the adaptive anti-tumor immunity, such as cytotoxic T cells. However, the predominant accumulation of tumor-associated microglia/macrophages (TAMs) results in limited success of these strategies in the glioblastoma. To improve the immunotherapeutic efficacy for GBM, it is detrimental to understand the role of TAM in glioblastoma immunosuppressive microenvironment. In this review, we will discuss the roles of CD47-SIRPα axis in TAMs infiltration and activities and the promising effects of targeting this axis on the activation of both innate and adaptive antitumor immunity in glioblastoma.

A typical class of recurrent neural networks called zeroing neural network (ZNN) has been considered as a powerful alternative for time-varying problems solving. In this paper, a new ZNN model is proposed and studied to solve the bound-constrained time-varying nonlinear equation (BCTVNE). Specifically, by introducing a time-varying nonnegative vector, the BCTVNE is reformulated as a combined system of nonlinear equations. On the basis of two indefinite error functions and the exponential decay formula, the new ZNN model is thus developed, which can zero in on the combined system. Theoretical analysis and simulation results are provided to verify the effectiveness of the proposed ZNN model. The applicability is further indicated under the simulations on an omnidirectional mobile robot manipulator via the proposed ZNN model.