Adenovirus-mediated delivery of bFGF small interfering RNA reduces STAT3 phosphorylation and induces the depolarization of mitochondria and apoptosis in glioma cells U251

Glioblastoma multiforme (GBM) carries a dismal prognosis primarily due to its aggressive proliferation in the brain regulated by complex molecular mechanisms. One promising molecular target in GBM is over-expressed basic fibroblast growth factor (bFGF), which has been correlated with growth, progression, and vascularity of human malignant gliomas. Previously, we reported significant antitumor effects of an adenovirus-vector carrying bFGF small interfering RNA (Ad-bFGF-siRNA) in glioma in vivo and in vitro. However, its mechanisms are unknown. Signal transducer and activator of transcription 3 (STAT3) is constitutively active in GBM and correlates positively with the glioma grades. In addition, as a specific transcription factor, STAT3 serves as the convergent point of various signaling pathways activated by multiple growth factors and/or cytokines. Therefore, we hypothesized that the proliferation inhibition and apoptosis induction by Ad-bFGF-siRNA may result from the interruption of STAT3 phosphorylation. In the current study, we found that in glioma cells U251, Ad-bFGF-siRNA impedes the activation of ERK1/2 and JAK2, but not Src, decreases IL-6 secretion, reduces STAT3 phosphorylation, decreases the levels of downstream molecules CyclinD1 and Bcl-xl, and ultimately results in the collapse of mitochondrial membrane potentials as well as the induction of mitochondrial-related apoptosis. Our results offer a potential mechanism for using Ad-bFGF-siRNA as a gene therapy for glioma. To our knowledge, it is the first time that the bFGF knockdown using adenovirus-mediated delivery of bFGF siRNA and its potential underlying mechanisms are reported. Therefore, this finding may open new avenues for developing novel treatments against GBM

Adenovirus-mediated delivery of bFGF small interfering RNA increases levels of connexin 43 in the glioma cell line, U251

BACKGROUND: bFGF is an important growth factor for glioma cell proliferation and invasion, while connexin 43 is implicated in the suppression of glioma growth. Correspondingly, gliomas have been shown to have reduced, or compromised, connexin 43 expression. METHODS: In this study, a bFGF-targeted siRNA was delivered to the glioma cell line, U251, using adenovirus (Ad-bFGF-siRNA) and the expression of connexin 43 and its phosphorylation state were evaluated. U251 cells were infected with Ad-bFGF-siRNA (100, 50, or 25 MOI), and infection with adenovirus expressing green fluorescent protein (Ad-GFP) at 100 MOI served as a control. Western blotting and immunofluorescence were used to detect the expression levels, phosphorylation, and localization of connexin 43 in U251 cells infected, and not infected, with Ad-bFGF-siRNA. RESULTS: Significantly higher levels of connexin 43 were detected in U251 cells infected with Ad-bFGF-siRNA at 100 and 50 MOI than in cells infected with Ad-GFP, and the same amount of connexin 43 was detected in Ad-GFP-infected and uninfected U251 cells. Connexin 43 phosphorylation did not differ between Ad-bFGF-siRNA-infected and uninfected U251 cells. However, the ratio of phosphorylated to unphosphorylated connexin 43 in Ad-bFGF-siRNA cells was lower, and connexin 43 was predominantly localized to the cytoplasm. Using a scrape loading dye transfer assay, more Lucifer Yellow was transferred to neighboring cells in the Ad-bFGF-siRNA treated group than in the control group. CONCLUSION: To our knowledge, this is the first description of a role for connexin 43 in the inhibition of U251 growth using Ad-bFGF-siRNA

Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Many adversarial-based UDA methods involve high-instability training and have to carefully tune the optimization procedure. Some non-adversarial UDA methods employ a consistency regularization on the target predictions of a student model and a teacher model under different perturbations, where the teacher shares the same architecture with the student and is updated by the exponential moving average of the student. However, these methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model. In this paper, we propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation. By exploiting the latent uncertainty information of the target samples, more meaningful and reliable knowledge from the teacher model can be transferred to the student model. In addition, we further reveal the reason why the current consistency regularization is often unstable in minimizing the distribution discrepancy. We also show that our method can effectively ease this issue by mining the most reliable and meaningful samples with a dynamic weighting scheme of consistency loss. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods on two domain adaptation benchmarks, $i.e.,$ GTAV $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes

DMT: Dynamic Mutual Training for Semi-Supervised Learning

Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Self-training methods usually rely on single model prediction confidence to filter low-confidence pseudo labels, thus remaining high-confidence errors and wasting many low-confidence correct labels. In this paper, we point out it is difficult for a model to counter its own errors. Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors. With this new viewpoint, we propose mutual training between two different models by a dynamically re-weighted loss function, called Dynamic Mutual Training (DMT). We quantify inter-model disagreement by comparing predictions from two different models to dynamically re-weight loss in training, where a larger disagreement indicates a possible error and corresponds to a lower loss value. Extensive experiments show that DMT achieves state-of-the-art performance in both image classification and semantic segmentation. Our codes are released at https://github.com/voldemortX/DST-CBC .Comment: Reformatte

Context-Aware Mixup for Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce the domain shifts in pixel level, feature level, and output level. However, almost all of them largely neglect the contextual dependency, which is generally shared across different domains, leading to less-desired performance. In this paper, we propose a novel Context-Aware Mixup (CAMix) framework for domain adaptive semantic segmentation, which exploits this important clue of context-dependency as explicit prior knowledge in a fully end-to-end trainable manner for enhancing the adaptability toward the target domain. Firstly, we present a contextual mask generation strategy by leveraging the accumulated spatial distributions and prior contextual relationships. The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels. Besides, provided the context knowledge, we introduce a significance-reweighted consistency loss to penalize the inconsistency between the mixed student prediction and the mixed teacher prediction, which alleviates the negative transfer of the adaptation, e.g., early performance degradation. Extensive experiments and analysis demonstrate the effectiveness of our method against the state-of-the-art approaches on widely-used UDA benchmarks.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Hierarchical porous carbon with ordered straight micro-channels templated by continuous filament glass fiber arrays for high performance supercapacitors

Hierarchical porous carbon with highly ordered straight micro-channels was prepared though a facile melt vacuum infiltration method using continuous filament glass fiber arrays as the template and glucose as the precursor. The as-prepared carbon material shows high specific surface areas up to 1880 m2 g�1 profited from the unique structure of straight micro-channels. A fine pore structure is formed in the channel wall through KOH activation after the removal of the glass fiber array. electrochemical evaluation of the carbon material indicates that the hierarchical porous carbon exhibits a high specific capacitance of 283 F g�1 at a current density of 0.25 A g�1 with an alkaline electrolyte (6 M KOH) in a threeelectrode system. It also demonstrates excellent cycling stability with a capacity retention of 88.5% over 10 000 cycles at a high current density of 6 A g�1. These exciting results demonstrate a very simple and low-cost method for large-scale preparation of electrode materials for supercapacitors

Posterior inferior cerebellar artery reimplantation: Buffer lengths, perforator anatomy, and technical limitations

Objective: Reimplantation of the posterior inferior cerebellar artery (PICA) to the vertebral artery (VA) is a safe and effective bypass option after deliberate PICA sacrifice during the treatment of nonsaccular and dissecting aneurysms at this location. However, the anatomy and limitations of this technique have not been studied. The goal of this study was to define the surgical anatomy and buffer lengths specific to the proximal segment of the PICA related to 2 variations of PICA reimplantation: 1) reimplantation along-VA (simulating a dissecting VA aneurysm), and 2) reimplantation across- VA (simulating a nonclippable, proximal PICA aneurysm). Methods: Ten cadaver heads (20 sides) were prepared for surgical simulation. Twenty far-lateral approaches were performed. The PICA was mobilized and reimplanted onto the VA according to 2 different paradigms: 1) transposition along the axis of the VA (along-VA) to simulate a dissecting VA, and 2) transposition perpendicular to the axis of the VA (across-VA) to simulate a nonclippable, proximal PICA aneurysm. The buffer lengths provided by mobilization of the artery in each paradigm were measured and the anatomy of perforator branching on the proximal PICAs was analyzed. Results; The PICA was reimplanted in all surgical simulations. The most common perforating artery on the P1 and P2 segments was the short circumflex type. No direct perforator was found on the P1 segment. The mean buffer length with reimplantation along the VA axis was 13.43 ± 4.61 mm, and it was 6.97 ± 4.04 mm with reimplantation across the VA. The PICA was less maneuverable when it was reimplanted across the VA, due to perforator branches of the PICA (P3 segment). Conclusions: The buffer lengths measured in this study describe the limitations of PICA reimplantation as a revascularization procedure for nonsaccular aneurysms in this location. PICA reimplantation is a revascularization option for dissecting VA aneurysms incorporating the PICA origin that are \u3c 13 mm in length, and for nonsaccular proximal PICA aneurysms that are \u3c 6 mm in diameter. The final decision to reimplant the PICA depends on careful inspection of perforator anatomy that is not visible preoperatively on angiography, as well as an assessment of technical difficulty intraoperatively