Effects of the Anti-Tumorigenic Agent AT101 on Human Glioblastoma Cells in the Microenvironmental Glioma Stem Cell Niche

Glioblastoma (GBM) is a barely treatable disease due to its profound chemoresistance. A distinct inter- and intratumoral heterogeneity reflected by specialized microenvironmental niches and different tumor cell subpopulations allows GBMs to evade therapy regimens. Thus, there is an urgent need to develop alternative treatment strategies. A promising candidate for the treatment of GBMs is AT101, the R(-) enantiomer of gossypol. The present study evaluates the effects of AT101, alone or in combination with temozolomide (TMZ), in a microenvironmental glioma stem cell niche model of two GBM cell lines (U251MG and U87MG). AT101 was found to induce strong cytotoxic effects on U251MG and U87MG stem-like cells in comparison to the respective native cells. Moreover, a higher sensitivity against treatment with AT101 was observed upon incubation of native cells with a stem-like cell-conditioned medium. This higher sensitivity was reflected by a specific inhibitory influence on the p-p42/44 signaling pathway. Further, the expression of CXCR7 and the interleukin-6 receptor was significantly regulated upon these stimulatory conditions. Since tumor stem-like cells are known to mediate the development of tumor recurrences and were observed to strongly respond to the AT101 treatment, this might represent a promising approach to prevent the development of GBM recurrences.     

The design of multiple feedback topology Chebyshev low-pass active filter with average differential evolution algorithm

Neural Computing and Applications - This study presents the design of a tenth-order multiple feedback Chebyshev low-pass filter (MF-C-LPF). Component selection and gain calculation of filters are...     

Tungsten(VI) oxide supported rhodium nanoparticles: Highly active catalysts in hydrogen generation from ammonia borane

Herein, we report the use of tungsten(VI) oxide (WO₃) as support for Rh⁰ nanoparticles. The resulting Rh⁰/WO₃ nanoparticles are highly active and stable catalysts in H₂ generation from the hydrolysis of ammonia borane (AB). We present the results of our investigation on the particle size distribution, catalytic activity and stability of Rh⁰/WO₃ catalysts with 0.5%, 1.0%, 2.0% wt. Rh loadings in the hydrolysis reaction. The results reveal that Rh⁰/WO₃ (0.5% wt. Rh) is very promising catalyst providing a turnover frequency of 749 min^?1 in releasing 3.0 equivalent H₂ per mole of AB from the hydrolysis at 25.0 °C. The high catalytic activity of Rh⁰/WO₃ catalyst is attributed to the reducible nature of support. The report covers the results of kinetics study as well as comparative investigation of activity, recyclability, and reusability of colloidal(0) nanoparticles and Rh⁰/WO₃ (0.5 % wt. Rh) catalyst in the hydrolysis reaction.     

Comprehensive meta-analysis of integrity test validities: Findings and implications for personnel selection and theories of job performance.

Conducted a comprehensive meta-analysis based on 665 validity coefficients across 576,460 data points to investigate whether integrity test validities are generalizable and to estimate differences in validity due to potential moderating influences. Results indicate that integrity test validities are substantial for predicting job performance and counterproductive behaviors on the job, such as theft, disciplinary problems, and absenteeism. The estimated mean operational predictive validity of integrity test for predicting supervisory ratings of job performance is .41. Results from predictive validity studies conducted on applicants and using external criterion measures (i.e., excluding self-reports) indicate that integrity tests predict the broad criterion of organizationally disruptive behaviors better than they predict employee theft alone. Despite the influence of moderators, integrity test validities are positive across situations and settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

Language learners' digital literacies: Focus on students' information literacy and reading practices online

The widespread use of technology and the Internet have changed many of language learners' everyday practices, including literacies. While there have been many studies with the focus on language learners' digital literacies, few, however, have explored language learners' digital information literacy and online reading practices with the use of social bookmarking tools, especially in a community college setting. We address this gap by investigating community-college language learners' digital literacies when social bookmarking with the focus on digital information and online reading practices from an ecological perspective. In this qualitative multiple-case study, the focal participants were five English learners, students in an English as a Second Language writing course in a community college in the northeastern United States. Data collection included interviews, observations, and researchers' e-journals. Thorough within- and cross-case analysis of data shows that language learners searched for digital texts and evaluated them based on relevance, reliability, interest, language, and importance for them and their learning community in the social bookmarking tool. The participants struggled with the number of results in search engines, keywords, and evaluation of digital texts for relevance and reliability. We show the need for more instruction, support, and guidance of language learners' digital information literacy practices as well as the benefits of providing students with opportunities to read digital texts. Our suggestions for future research include investigating the role of multimodality and other factors that influence language learners' evaluation practices when they look for and read information online.     

Retrospective adaptive prefetching for interactive Web GIS applications

A major task of a Web GIS (Geographic Information Systems) system is to transfer map data to client applications over the Internet, which may be too costly. To improve this inefficient process, various solutions are available. Caching the responses of the requests on the client side is the most commonly implemented solution. However, this method may not be adequate by itself. Besides caching the responses, predicting the next possible requests from a client and updating the cache with responses for those requests together provide a remarkable performance improvement. This procedure is called “prefetching” and makes caching mechanisms more effective and efficient. This paper proposes an efficient prefetching algorithm called Retrospective Adaptive Prefetch (RAP), which is constructed over a heuristic method that considers the former actions of a given user. The algorithm reduces the user-perceived response time and improves user navigation efficiency. Additionally, it adjusts the cache size automatically, based on the memory size of the client’s machine. RAP is compared with four other prefetching algorithms. The experiments show that RAP provides better performance enhancements than the other methods.     

Optimum topology design of various geometrically nonlinear latticed domes using improved harmony search method

Domes are elegant and economical structures used in covering large areas. They are built in various forms. According to their form, they are given special names such as lamella, network, and geodesic domes. In this paper, optimum topological design algorithm is presented that determines the optimum number of rings, the optimum height of crown and tubular section designations for the member groups of these domes. The design algorithm developed has a routine that generates the data required for the geometry of these domes automatically. The minimum weight of each dome is taken as the objective function. The design constraints are implemented according to the provision of LRFD-AISC (Load and Resistance Factor Design–American Institute of Steel Constitution). The optimum topological design problem that considers these constraints turns out to be discrete programming problem. Improved harmony search algorithm is suggested to determine its optimum solution. The design algorithm also considers the geometric nonlinearity of these dome structures. Design examples are presented to demonstrate the effectiveness and robustness of the design optimization algorithm developed.