Bryostatin inhibits proliferation of ependymoma cells by suppressing expressions of cyclooxygenase-2 and interleukin-8

Purpose: To investigate the effect of bryostatin on the proliferation of ependymoma cells, and the underlying mechanism(s).Methods: Ependymoma cell lines (SC-EPN1 and SC-EPN2) were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10 % fetal bovine serum (FBS) and streptomycin (10 mg/ml) in a humidified incubator at 37 °C and 5 % CO2 atmosphere. Rhe cells were randomly assigned to six groups: control group and five bryostatin groups treated with increasing concentrations of bryostatin (10 - 50 μM). Cell proliferation was determined by MTT assay, while real-time quantitative polymerase chain reaction (qRT-PCR) was used to determine the levels of expressions of apoptosisrelated genes. Expressions of cyclooxygenase-2 (COX-2), interleukin-8 (IL-8), Bcl-2, Bax and Pglycoprotein were determined using Western blotting.Results: Treatment with bryostatin significantly and concentration-dependently down-regulated COX-2 and IL-8 mRNAs expressions (p < 0.05). On the other hand, proliferation of SC-EPN1 and SC-EPN2 cells were significantly and concentration-dependently inhibited by bryostatin, relative to control group (p < 0.05). After 72 h of treatment with bryostatin (50 μM), the extent of apoptosis was significantly higher in SC-EPN1 (57.43 %) and SC-EPN2 cells (52.29 %) than in control group (2.37 %, p < 0.05). The results of Western blotting showed that treatment with bryostatin significantly reduced the expressions of Bcl-2 in ependymoma cells, relative to the control group (p < 0.05). However, there were no significant differences in the expression of Bax among the groups (p > 0.05). P-glycoprotein expression was significantly higher in bryostatin groups than in control group (p < 0.05). The results of flow cytometric analysis of rhodamine-123 (rh123) fluorescence showed that after 72 h of treatment with bryostatin (50 μM), rhl23 fluorescence significantly decreased in SC-EPN1 (8.10 %) and SC-EPN2 cells (10.11 %), relative to control group (20.83 %, p < 0.05).Conclusion: Bryostatin exerts anti-proliferative and apoptotic effects on ependymoma cells by suppressing COX-2 and IL-8 expressions. Thus, the inhibition of COX-2 expression may constitute an effective chemotherapeutic strategy for ependymoma treatment.Keywords: Bryostatin, Ependymoma cells, Proliferation, Apoptosis, Expressio

Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets

The problem of quickest detection of a change in the distribution of a sequence of independent observations is considered. It is assumed that the pre-change distribution is known (accurately estimated), while the only information about the post-change distribution is through a (small) set of labeled data. This post-change data is used in a data-driven minimax robust framework, where an uncertainty set for the post-change distribution is constructed using the Wasserstein distance from the empirical distribution of the data. The robust change detection problem is studied in an asymptotic setting where the mean time to false alarm goes to infinity, for which the least favorable post-change distribution within the uncertainty set is the one that minimizes the Kullback-Leibler divergence between the post- and the pre-change distributions. It is shown that the density corresponding to the least favorable distribution is an exponentially tilted version of the pre-change density and can be calculated efficiently. A Cumulative Sum (CuSum) test based on the least favorable distribution, which is referred to as the distributionally robust (DR) CuSum test, is then shown to be asymptotically robust. The results are extended to the case where the post-change uncertainty set is a finite union of multiple Wasserstein uncertainty sets, corresponding to multiple post-change scenarios, each with its own labeled data. The proposed method is validated using synthetic and real data examples

Hierarchical Quantification of Utilization Rate and Related Indicators of Mixed-Use High-Rise Buildings

Mixed-use high-rise buildings are vertical superpositions of various business types in the category of mixed-use development. It has become a highly intensive organizational form in the urban high-density environment. Under China’s “height limit” policy, the simple superposition of business types does not meet the government requirements for planning, construction, and management, and does not fully utilize the advantages of the mixed development mode. The single utilization rate index used in the past could not accurately describe such buildings’ usage value and spatial variation characteristics. In this study, a quantitative analysis of data from eight construction projects was carried out, and a utilization rate index system was established at three levels, namely, the typical floor utilization rate k1, business utilization rate k2, and building utilization rate k3. In terms of k1, the emphasis was placed on the design elements of the mixed-use development and the variation of relative indicators. In k2 and k3, it was found that the business type, floor area, and utilization rate were negatively correlated. In conclusion, by establishing a hierarchical utilization rate calculation method, the efficiency values and design characteristics of mixed-use high-rise (MUHR) buildings were explored, which provide references for the future design of such building

Continual Learning on Dynamic Graphs via Parameter Isolation

Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines

Fine Tuning the Pore Surface in Zirconium Metal−Organic Frameworks for Selective Ethane/Ethylene Separation

Ethylene is an important chemical feedstock for production of polymers and high-value organic chemicals, and yet its conventional purification process is plagued with high consumption of energy. Metal−organic frameworks (MOFs) provide a suitable adsorption platform for selective ethane/ ethylene separation thanks to their structural diversity, tunable pore characteristics, designable pore sizes, and high pore volumes. Although there are empirical design rules like avoiding open metal sites and creating nonpolar pore surfaces for development of adsorptive MOFs, it is still challenging to design robust MOFs that can realize direct ethane-selective separation. Herein, we systematically designed and synthesized three Zr-MOFs based on the assembly of angular ligands and 12-connected Zr6 clusters that feature the pcu network structure. By changing the size and flexibility of the substituent on the angular ligand, we were able to prevent interpenetration and identified NPF-802, which exhibits good C2H6/C2H4 separation performance that is attributed to the bulky and inert tert-butyl groups of its carbazole ligand. This work provides insights for design of ligands of MOFs with suitable pore environments to address important and challenging gas separations