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.     

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than both the statistical and artificial neural-network-based methods.     

Optimal Control of the Primary Drying Stage of Freeze Drying of Solutions in Vials Using Variational Calculus

The problem of operating freeze drying of pharmaceutical products in vials placed in trays of a freeze dryer to remove free water (in frozen state) at a minimum time was formulated as an optimal control problem. Two different types of freeze dryer designs were considered. In type I freeze dryer design, upper and lower plate temperatures were controlled together, while in type II freeze dryer design, upper and lower plate temperatures were controlled independently. The heat input to the material being dried and the drying chamber pressure were considered as control variables. Constraints were placed on the system state variables by the melting and scorch temperatures during primary drying stage. Necessary conditions of optimality for the primary drying stage of freeze drying process in vials are derived and presented. Furthermore, an approach for constructing the optimal control policies that would minimize the drying time for the primary drying stage was given. In order to analyze optimal control policy for the primary drying stage of the freeze-drying process in vials, a rigorous multi-dimensional unsteady state mathematical model was used. The theoretical approach presented in this work was applied in the freeze drying of skim milk. Significant reductions in the drying times of primary drying stage of freeze drying process in vials were obtained, as compared to the drying times obtained from conventional operational policies.     

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.     

A combo technology of autotrophic and heterotrophic denitrification processes for groundwater treatment

In this study,a sequential process (heterotrophic up-flow column and completely mixed membrane bioreactors) was proposed combining advantages of the both processes.The system was operated for 249 days with simulated and real groundwater for nitrate removal at concentrations varying from 25 to 145 mg·L-1 NO3-N.The contribution of heterotrophic process to total nitrate removal in the system was controlled by dozing the ethanol considering the nitrate concentration.By this way,sulfur based autotrophic denitrification rate was decreased and the effluent sulfate concentrations were controlled.The alkalinity requirement in the autotrophic process was produced in the heterotrophic reactor,and the system was operated without alkalinity supplementation.Throughout the study,the chemical oxygen demand in the heterotrophic reactor effluent was (23.7 ± 22) mg·L-1 and it was further decreased to(7.5 ± 7.2) mg·L-1 in the system effluent,corresponding to a 70％ reduction.In the last period of the study,the real groundwater containing 145 mg·L-1 NO3-N was completely removed.Membrane was operated without chemical washing in the first 114 days.Between days 115-249 weekly chemical washing was required.     

Leaching of a copper flotation concentrate with ammonium persulfate in an autoclave system

The leaching behavior of a copper flotation concentrate was investigated using ammonium persulfate （APS） in an autoclave systee. The decomposition products of APS, active oxygen, and acidic medium were used to extract metals from the concentrate. Leaching experiments were performed to compare the availability of APS as an oxidizing agent for leaching of the concentrate under atmospheric conditions and in an autoclave system. Leaching temperature and APS concentration were found to be important parameters in both leaching systems. Atmospheric leaching studies showed that the metal extractions increased with the increase in APS concentration and temperature （up to 333 K）. A similar tendency was determined in the autoclave studies up to 423 K. It was also determined that the metal extractions decreased at temperatures above 423 K due to the passivation of the particle surface by molten elemental sulfur. The results showed that higher copper extractions could be achieved using an autoclave system.