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The mechanisms by which MGMT-deficient glioblastomas acquire resistance to temozolomide are unclear. Yi et al. show that retrograde nuclear transportation of the DNA damage repair proteins XPC and CBX5 confers temozolomide resistance in MGMT-deficient glioblastoma cells. Development of DHC2 inhibitors could be a promising strategy for overcoming acquired temozolomide resistance., The acquisition of temozolomide resistance is a major clinical challenge for glioblastoma treatment. Chemoresistance in glioblastoma is largely attributed to repair of temozolomide-induced DNA lesions by O6-methylguanine-DNA methyltransferase (MGMT). However, some MGMT-deficient glioblastomas are still resistant to temozolomide, and the underlying molecular mechanisms remain unclear. We found that DYNC2H1 (DHC2) was expressed more in MGMT-deficient recurrent glioblastoma specimens and its expression strongly correlated to poor progression-free survival in MGMT promotor methylated glioblastoma patients. Furthermore, silencing DHC2, both in vitro and in vivo, enhanced temozolomide-induced DNA damage and significantly improved the efficiency of temozolomide treatment in MGMT-deficient glioblastoma. Using a combination of subcellular proteomics and in vitro analyses, we showed that DHC2 was involved in nuclear localization of the DNA repair proteins, namely XPC and CBX5, and knockdown of either XPC or CBX5 resulted in increased temozolomide-induced DNA damage. In summary, we identified the nuclear transportation of DNA repair proteins by DHC2 as a critical regulator of acquired temozolomide resistance in MGMT-deficient glioblastoma. Our study offers novel insights for improving therapeutic management of MGMT-deficient glioblastoma.

Reactive power optimization is a special kind of optimal power flow for optimizing voltage profile and reactive power flow in the steady state based on deterministic sets of the demand load and generation values, thus minimizing the real power losses or improving the voltage quality of the power grid. However, the input data in power systems have a certain degree of uncertainty that requires the reactive power optimization be solved by means of uncertain nonlinear programming, as advocated in the literature. To address this problem, we represent the uncertain input data as intervals and establish a model of the reactive power optimization that incorporates the interval uncertainties to describe the problem. The linear approximation method is formulated using the interval Taylor extension to help solve this type of problem. To obtain more accurate intervals for the state variables, the affine arithmetic-based power flow calculation is used to solve the interval power flow equation instead of crude computation based on the interval arithmetic, and thus the modified linear approximation method is developed. The proposed methods are presented in detail and the numerical results are analyzed to demonstrate their effectiveness and applicability, especially in comparison to the previously proposed chance constrained programming method.

DC power flow analysis is based on a linear approximation model for AC power flow equations, and represents an efficient approach for acquiring the bus angles and active transmission power of a power grid. However, the input data and network parameters of the power grid are uncertain due to numerous internal and external factors. Therefore, the DC power flow model should be considered as an uncertain problem, which makes deterministic DC power flow analysis methods unworkable. The present work overcomes this problem by regarding uncertain input data and network parameters as interval values, respectively, and establishes the interval DC power flow (IDCPF) models considering interval input data only (denoted as IDCPF01), and IDCPF model including both interval input data and network parameters (denoted as IDCPF02). Analyses based on the IDCPF models seek to obtain conservative ranges of bus angles and active transmission power flow, which can be employed by dispatchers to develop strategies for ensuring the operational security of the power system. The optimizing-scenarios method (OSM) is employed here to solve the IDCPF models. In addition, it is proven that the lower (or upper) bounds of the bus angles of the IDCPF01 model are simultaneously obtained when the injected power is set as its lower (or upper) interval bounds. This fact greatly improves the efficiency of the OSM for solving the IDCPF models. The results obtained by the proposed method are compared with those obtained by Monte Carlo simulation, the AA-based method, and the interval AC power flow method. The overall simulation results demonstrate the effectiveness and robustness of the proposed method.

Cytochrome c oxidase subunit 7A2 (COX7A2) is a nuclear-encoded polypeptide involved in assembly and regulation of cytochrome c oxidase (COX). Changes in the respiratory chain as big complex are known to be associated with cancer, but little research has been performed to discover COX7A2 as a prognostic marker in glioma. In the present study, we investigated COX7A2 expression and its prognostic significance in glioma. Glioma surgical tissue samples were taken from 126 patients who had been followed up from 4 to 51 months. Immunohistochemistry were used to test COX7A2 expression in the 126 tumor samples. Eighty-six of 126 (68.3%) paraffin-embedded glioma biopsies showed high expression of COX7A2. Statistical analysis displayed that there was significant difference of COX7A2 expression level in patients categorized according to WHO classification. Kaplan-Meier survival analysis revealed that patients with higher COX7A2 expression had longer overall survival time and better prognosis. R2: microarray analysis based on Tumor Glioma French 284 database, Tumor Glioblastoma TCGA 540 database, and Tumor Glioma Kawaguchi 50 database testified that high expression of COX7A2 is associated with a good prognosis in patients with glioma. Multivariate analysis showed that COX7A2 high expression was an independent prognostic indicator for survival. Our results suggest that COX7A2 could be served as a valuable prognostic marker of glioma.

The reactive power optimisation (RPO) is generally used to design an optimal profile of the voltage and reactive power of power systems in the steady state for deterministic sets of demand loads and generation values, and it is a significant procedure in voltage control. However, the input data of a power system is actually uncertain in practise, which makes RPO an uncertain non-linear programme. To address this problem, the input data were considered as intervals, and the RPO incorporating interval uncertainties model was proposed. To solve this model, the genetic algorithm was employed as the solution algorithm, where the reliable power flow calculation was used to judge the constraints of the model. The Pareto front was established as the solution of the proposed model since it has two objective functions, i.e. the midpoint and radius of real power losses. The application of this technique to the uncertain RPO was explained in detail. Two numerical results were analysed to demonstrate the effectiveness of the proposed method, especially in comparison with the previously proposed chance-constrained programming method.

Heterogeneity is regarded as the major factor leading to the poor outcomes of glioblastoma (GBM) patients. However, conventional two-dimensional (2D) analysis methods, such as immunohistochemistry and immunofluorescence, have limited capacity to reveal GBM spatial heterogeneity. Thus, we sought to develop an effective analysis strategy to increase the understanding of GBM spatial heterogeneity. Here, 2D and three-dimensional (3D) analysis methods were compared for the examination of cell morphology, cell distribution and large intact structures, and both types of methods were employed to dissect GBM spatial heterogeneity. The results showed that 2D assays showed only cross-sections of specimens but provided a full view. To visualize intact GBM specimens in 3D without sectioning, the optical tissue clearing methods CUBIC and iDISCO+ were used to clear opaque specimens so that they would become more transparent, after which the specimens were imaged with a two-photon microscope. The 3D analysis methods showed specimens at a large spatial scale at cell-level resolution and had overwhelming advantages in comparison to the 2D methods. Furthermore, in 3D, heterogeneity in terms of cell stemness, the microvasculature, and immune cell infiltration within GBM was comprehensively observed and analysed. Overall, we propose that 2D and 3D analysis methods should be combined to provide much greater detail to increase the understanding of GBM spatial heterogeneity.

Glioblastoma (GBM) is the most malignant and aggressive glioma, which has a very poor prognosis. Temozolomide (TMZ) is still a first-line treatment, but resistance is inevitable even in MGMT-deficient glioblastoma cells. The aims of this study were to comprehend the effect of TMZ on nucleus and the underlying mechanism of acquired TMZ resistance in MGMT-deficient GBM. We show the changes of nuclear proteome in the MGMT-deficient GBM U87 cells treated with TMZ for 1 week. Label-free-based quantitative proteomics were used to investigate nuclear protein abundance change. Subsequently, gene ontology function annotation, KEGG pathway analysis, protein-protein interaction (PPI) network construction analysis of DAPs, and immunofluorescence were applied to validate the quality of proteomics. In total, 457 (455 gene products) significant DAPs were identified, of which 327 were up-regulated and 128 were down-regulated. Bioinformatics analysis uncovered RAD50, MRE11, UBR5, MSH2, MSH6, DDB1, DDB2, RPA1, RBX1, CUL4A, and CUL4B mainly enriched in DNA damage repair related pathway and constituted a protein-protein interaction network. Ribosomal proteins were down-regulated. Cells were in a stress-responsive state, while the entire metabolic level was lowered. SIGNIFICANCE OF THE STUDY: In U87 cell treated with TMZ for 1 week, which resulted in DNA damage, we found various proteins dysregulated in the nucleus. Some proteins related to the DNA damage repair pathway were up-regulated, and there was a strong interaction. We believe this is the potential clues of chemotherapy resistance in tumour cells. These proteins can be used as indicators of tumour resistance screening in the future.

TMZ resistance remains one of the main reasons why treatment of glioblastoma (GBM) fails. In order to investigate the underlying proteins and pathways associated with TMZ resistance, we conducted a cytoplasmic proteome research of U87 cells treated with TMZ for 1 week, followed by differentially expressed proteins (DEPs) screening, KEGG pathway analysis, protein–protein interaction (PPI) network construction, and validation of key candidate proteins in TCGA dataset. A total of 161 DEPs including 65 upregulated proteins and 96 downregulated proteins were identified. Upregulated DEPs were mainly related to regulation in actin cytoskeleton, focal adhesion, and phagosome and PI3K-AKT signaling pathways which were consistent with our previous studies. Further, the most significant module consisted of 28 downregulated proteins that were filtered from the PPI network, and 9 proteins (DHX9, HNRNPR, RPL3, HNRNPA3, SF1, DDX5, EIF5B, BTF3, and RPL8) among them were identified as the key candidate proteins, which were significantly associated with prognosis of GBM patients and mainly involved in ribosome and spliceosome pathway. Taking the above into consideration, we firstly identified candidate proteins and pathways associated with TMZ resistance in GBM using proteomics and bioinformatic analysis, and these proteins could be potential biomarkers for prevention or prediction of TMZ resistance in the future.

Temozolomide (TMZ) is the preferred chemotherapeutic drug approved for the Glioblastoma multiforme (GBM) treatment. However, resistance to TMZ is the most intractable challenge for treatment of GBM. Screening of miRNAs is becoming a novel strategy to reveal underlying mechanism of drug-resistance of human tumors. We conducted RNA sequencing (RNA-seq) for GBM cells treated continuously with TMZ 1 or 2 week or not. Bioinformatic analysis was used to predict targets of these altered miRNAs. Subsequently, we studied the potential role of miR-1268a in TMZ-resistance of GBM cells. Expression levels of 55 miRNAs were identified altering both after 1 and 2 weeks TMZ treatment. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to illuminate the biological implication and related pathways of predicted target genes. We showed that miR-1268a was downregulated after TMZ treatment and targeted ABCC1/MRP1, a membrane transporter contributing to drug resistance, using dual-luciferase assay. Furthermore, we confirmed overexpression of miR-1268a inhibited protein translation of ABCC1 and restored upregulated expression of ABCC1 due to TMZ. Inversely, knockdown of miR-1268a increased ABCC1 at protein level and enhanced upregulation of ABCC1 with TMZ treatment. In addition, our data indicated that miR-1268a enhanced TMZ sensitivity in GBM cells. Through RNA-seq analysis, we discovered miR-1268a and elucidated its role in modulating TMZ-resistance of GBM cells by targeting ABCC1.

A rectangular interval power flow (RIPF) method is proposed to evaluate solutions' intervals of power flow including uncertain loads and output of generators. It is based on affine arithmetic which is a more accurate method to compute intervals, and it obtains intervals through optimization methods to enhance efficiency of interval calculation. Because no approximation is used, RIPF can acquire narrower intervals of voltage phase results than polar interval power flow (PIPF), which is an excellent method raised before. However, RIPF is no better than PIPF in estimating intervals of voltage amplitude, since it could not satisfy the voltage equations of interval power flow. Therefore, a mixed interval power flow (MIPF) is established to incorporate merits of RIPF in computing intervals of voltage phase and PIPF's advantages in evaluating intervals of voltage magnitude. The IEEE118 system is analyzed to conclude that MIPF can acquire more accurate power flow intervals than PIPF.

Under the opening environment of the electricity market, it's very important to study bidding strategy to balance the benefit between suppliers and their retailers. At present, machine game is the most popular approach to deal with this problem. However, it's unable to present the practical trade process, for it ignores the humanity of the traders. To overcome this problem, behavior game is used to reflect and trade off the benefit between suppliers and retailers, and experimental economics platform z-Tree is employed to realize this procedure. In this paper, the algorithm based on historical trading data is designed by using z-Tree and simulate the bidding strategies of suppliers and their retailers in different rebate factor. Aforementioned research result shows simulation in z-Tree can reflect the actual electricity market situation and get optimal bidding spread in different rebate factor. It has some significance for suppliers and their retailers for bidding strategies.

With the widely use of wide area measurement system (WAMS), data measured by PMU is becoming more and more important for real-time operation in power system. At present, error warnings and neglect of real alarms problems are produced in WAMS when considering the injection of wrong data. However, state estimation performs poorly in dealing with the wrong data in WAMS causing tremendous computation resources and time. In this paper, a rapid identification and recovery method based on the concept of pattern recognition is put forward. Without repeated iterations and the injection of parameters of power grid, all the wrong data can be easily and quickly identified by changing from feature space to pattern space. To support our conclusion, 90 samples in practical application of WAMS are processed, demonstrating the effectiveness of the proposed method.

Because the dynamics of synchronous generator relates to the stability of power system closely, there is growing concern about estimating the status of synchronous generator when disturbances occur in power system. This paper employs Set Membership Filter based on DC Programming (SMFDC) for estimating the states of synchronous generator, by using local measurements of each generation unit. The estimator only assumes the process and measurement noises are bounded without any other statistics assumptions. By transforming the estimation model to Deference of Convex (DC) function form, the problem of bounding the linearization errors can be convert to solve DC programming, which is different from the interval analysis method in ESMF. To compare with ESMF [20], the estimation results of SMFDC are better and the estimator makes a great improvement in real-time property.