Insight into the Interaction Between Carbopol® 940 and Ionic/Nonionic Surfactant

Mixtures of a cross‐linked polyacrylic acid (Carbopol^® 940) and two types of surfactants, namely anionic sodium dodecylsulfate (SDS) and nonionic Tween^® 80, were investigated by viscometry, conductometry, tensiometry, spectrophotometry, fluorimetry and scanning electron microscopy (SEM). The addition of nonionic surfactant decreased the reduced viscosity and the transmittance of the Carbopol^® polymer aqueous solutions. Furthermore, the interaction between Carbopol^® 940 and SDS was characterized by two significant concentration values: the critical aggregation concentration of SDS was particularly independent of Carbopol^® polymer concentration while the polymer saturation point of both surfactants increased with the increase in polymer content. The values of critical aggregation concentration and polymer saturation point obtained using various techniques confirmed the occurrence of Carbopol^® polymer–surfactant associations. The effect of different SDS and Tween^® 80 concentrations on the conformation of Carbopol^® 940 in aqueous solution could be explained through hydrophobic association between surfactant micelles and Carbopol^® polymer tails and through hydrogen bonding in the case of Tween^® 80. Additionally, the surfactant‐induced structural changes were confirmed in Carbopol^® 940–SDS and Carbopol^® 940–Tween^® 80 aqueous solutions by SEM measurements.     

Gene Mutation Profiles in Primary Diffuse Large B Cell Lymphoma of Central Nervous System: Next Generation Sequencing Analyses

The existence of a potential primary central nervous system lymphoma-specific genomic signature that differs from the systemic form of diffuse large B cell lymphoma (DLBCL) has been suggested, but is still controversial. We investigated 19 patients with primary DLBCL of central nervous system (DLBCL CNS) using the TruSeq Amplicon Cancer Panel (TSACP) for 48 cancer-related genes. Next generation sequencing (NGS) analyses have revealed that over 80% of potentially protein-changing mutations were located in eight genes (CTNNB1, PIK3CA, PTEN, ATM, KRAS, PTPN11, TP53 and JAK3), pointing to the potential role of these genes in lymphomagenesis. TP53 was the only gene harboring mutations in all 19 patients. In addition, the presence of mutated TP53 and ATM genes correlated with a higher total number of mutations in other analyzed genes. Furthermore, the presence of mutated ATM correlated with poorer event-free survival (EFS) (p = 0.036). The presence of the mutated SMO gene correlated with earlier disease relapse (p = 0.023), inferior event-free survival (p = 0.011) and overall survival (OS) (p = 0.017), while mutations in the PTEN gene were associated with inferior OS (p = 0.048). Our findings suggest that the TP53 and ATM genes could be involved in the molecular pathophysiology of primary DLBCL CNS, whereas mutations in the PTEN and SMO genes could affect survival regardless of the initial treatment approach.     

Writing as thinking.

Writing is analyzed as thinking that uses paper or other media to externalize and manipulate symbolic expressions. Mental operations of natural language can occur somewhat independently, and they communicate well with language that has been written, but for skilled writing these operations need elaborate installation in the mind. We explore four methods to see how expert writers externalize thoughts and interact with them: laboratory comparisons of novices and experts, interviews with accomplished writers (mostly of prose fiction), biographical analysis of Jane Austen's development as a writer, and consideration of Gustave Flaubert's notes and drafts. Writers can use paper to extend their thinking, and to create frameworks of cues that enable readers of a story to construct mental models that they may enter empathetically. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

RF plasma modified W5O14 and MoS2 hybrid nanostructures and photovoltaic properties

Modification of W₅O₁₄ and MoS₂ nanostructures was carried out using 3,4-ethylenedioxythiophene monomer in a capacitively coupled, RF rotating plasma reactor. Raman spectroscopy and X-ray diffraction (XRD) measurements were used for structural characterization. The surface morphologies of nanomaterials were investigated using transmission electron microscopy. Polymer coated (W₅O₁₄/PEDOT, MoS₂/PEDOT) and untreated (W₅O₁₄, MoS₂) nanostructures were used as the counter electrode for dye-sensitized solar cells. Photovoltaic performances of W₅O₁₄/PEDOT and MoS₂/PEDOT hybrid nanostructures were higher than those of W₅O₁₄ and MoS₂ inorganic nanostructures. Our results indicate that plasma polymer coated W₅O₁₄ and MoS₂ nanostructures of the device for cathode increase both its fill factor and its energy conversion efficiency.     

Intelligent modeling with agent‐based fuzzy cognitive map

This article presents an agent‐based fuzzy cognitive map (ABFCM) developed injecting the concept of multi‐agent system (MAS) into the fuzzy cognitive map (FCM). Fuzzy cognitive map is used for qualitative modeling and simulation. Compared to the FCM, the ABFCM enables different inference algorithms in each node enabling simulation of systems with diverse behavior concepts. Each map node can exhibit individual, more or less intelligent, behavior and still can interact with other nodes to conclude on system behavior. Resulting method also enables automatic results interpretation adding additional intelligence to a classic FCM. Explanation of the obtained system architecture with FCM and MAS integration is presented in the article. The experimental results in the article are obtained with the ABFCM prototype, developed on the basis of ABFCM structure given in the article. Multi‐agent technology can bring new properties into existing fields and methods, like in the ABFCM case. © 2010 Wiley Periodicals, Inc.     

Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions

Learning in the mobile robot domain is a very challenging task, especially in non-stationary conditions. The behavior-based approach has proven to be useful in making mobile robots work in real-world situations. Since the behaviors are responsible for managing the interactions between the robots and its environment, observing their use can be exploited to model these interactions. In our approach, the robot is initially given a set of behavior-producing modules to choose from, and the algorithm provides a memory-based approach to dynamically adapt the selection of these behaviors according to the history of their use. The approach is validated using a vision- and sonar-based Pioneer I robot in non-stationary conditions, in the context of a multi-robot foraging task. Results show the effectiveness of the approach in taking advantage of any regularities experienced in the world, leading to fas t and adaptable specialization for the learning robot.     

Proteomic approaches to the study of malignant lymphoma: Analyses on patient samples

We describe the application of proteomic techniques for protein profiling and biomarker discovery in malignant lymphoma. Hematologic malignancies are primarily characterized by their clinical, morphological, immunophenotypical, and molecular-genetic features. However, when based on these parameters, apparently identical lymphomas may show distinct clinical courses, suggesting underlying biological heterogeneity. Recent proteomic analyses have identified differences in protein expression both with regard to subclassification of the malignant lymphoma entities, as well as in correlation with clinical outcome. In this review, studies on quantification of differential protein expression in and between malignant lymphoma entities are included. Studies are included that are based on patient samples, that is, serum/plasma or cytological specimens, as well as intact tumor tissues, together with studies that focus on tumor cells alone, or in conjunction with the tumor microenvironment. For biomarker discovery in malignant lymphoma, these approaches are used to uncover the underlying biological mechanisms and identify proteins with potential diagnostic and prognostic utility, either as predictive biomarkers or as novel future treatment targets.