Characterization of dimethacrylate polymeric networks: A study of the crosslinked structure formed by monomers used in dental composites

The resin phase of dental composites is mainly composed of combinations of dimethacrylate comonomers, with final polymeric network structure defined by monomer type/reactivity and degree of conversion. This fundamental study evaluates how increasing concentrations of the flexible triethylene glycol dimethacrylate (TEGDMA) influences void formation in bisphenol A diglycidyl dimethacrylate (BisGMA) co-polymerizations and correlates this aspect of network structure with reaction kinetic parameters and macroscopic volumetric shrinkage. Photopolymerization kinetics was followed in real-time by a near-infrared (NIR) spectroscopic technique, viscosity was assessed with a viscometer, volumetric shrinkage was followed with a linometer, free volume formation was determined by positron annihilation lifetime spectroscopy (PALS) and the sol-gel composition was determined by extraction with dichloromethane followed by (1)H NMR analysis. Results show that, as expected, volumetric shrinkage increases with TEGDMA concentration and monomer conversion. Extraction/(1)H NMR studies show increasing participation of the more flexible TEGDMA towards the limiting stages of conversion/crosslinking development. As the conversion progresses, either based on longer irradiation times or greater TEGDMA concentrations, the network becomes more dense, which is evidenced by the decrease in free volume and weight loss after extraction in these situations. For the same composition (BisGMA/TEGDMA 60-40 mol%) light-cured for increasing periods of time (from 10 to 600 s), free volume decreased and volumetric shrinkage increased, in a linear relationship with conversion. However, the correlation between free volume and macroscopic volumetric shrinkage was shown to be rather complex for variable compositions exposed for the same time (600 s). The addition of TEGDMA decreases free-volume up to 40 mol% (due to increased conversion), but above that concentration, in spite of the increase in conversion/crosslinking, free volume pore size increases due to the high concentration of the more flexible monomer. In those cases, the increase in volumetric shrinkage was due to higher functional group concentration, in spite of the greater free volume. Therefore, through the application of the PALS model, this study elucidates the network formation in dimethacrylates commonly used in dental materials. (C) 2010 Elsevier Ltd. All rights reserved.

The evolution of controlled multitasked gene networks: The role of introns and other noncoding RNAs in the development of complex organisms

Eukaryotic phenotypic diversity arises from multitasking of a core proteome of limited size. Multitasking is routine in computers, as well as in other sophisticated information systems, and requires multiple inputs and outputs to control and integrate network activity. Higher eukaryotes have a mosaic gene structure with a dual output, mRNA (protein-coding) sequences and introns, which are released from the pre-mRNA by posttranscriptional processing. Introns have been enormously successful as a class of sequences and comprise up to 95% of the primary transcripts of protein-coding genes in mammals. In addition, many other transcripts (perhaps more than half) do not encode proteins at all, but appear both to be developmentally regulated and to have genetic function. We suggest that these RNAs (eRNAs) have evolved to function as endogenous network control molecules which enable direct gene-gene communication and multitasking of eukaryotic genomes. Analysis of a range of complex genetic phenomena in which RNA is involved or implicated, including co-suppression, transgene silencing, RNA interference, imprinting, methylation, and transvection, suggests that a higher-order regulatory system based on RNA signals operates in the higher eukaryotes and involves chromatin remodeling as well as other RNA-DNA, RNA-RNA, and RNA-protein interactions. The evolution of densely connected gene networks would be expected to result in a relatively stable core proteome due to the multiple reuse of components, implying,that cellular differentiation and phenotypic variation in the higher eukaryotes results primarily from variation in the control architecture. Thus, network integration and multitasking using trans-acting RNA molecules produced in parallel with protein-coding sequences may underpin both the evolution of developmentally sophisticated multicellular organisms and the rapid expansion of phenotypic complexity into uncontested environments such as those initiated in the Cambrian radiation and those seen after major extinction events.

Status, legitimacy and ingroup bias in the context of an organizational merger

Adopting an intergroup perspective, the research was designed to examine predictors of employee responses to an organizational merger. Data were collected from 120 employees of a newly merged scientific organization. As predicted from social identity theory, the most negative responses to the merger were apparent among the employees of the low status premerger organization. There was also evidence of ingroup bias among both groups of employees involved in the merger—as expected, the bias was most marked on the status-irrelevant dimensions for the employees of the lower status organization, but most marked on the status-relevant dimensions for the employees of the high status organization. Also, in support of social identity theory, the perceived legitimacy of the basis for the status differentiation between the groups was associated with more positive responses to the merger among employees of the low status premerger organization, but with poorer responses among employees of the high status premerger organization. There was consistent evidence that the status by legitimacy interaction was mediated through the extent to which employees of the newly merged organization perceived a common ingroup identity

Employee adjustment to an organizational merger: An intergroup perspective

Adopting an intergroup perspective, the research was designed to examine predictors of employee responses to an organizational merger Data were collected from 465 fleet staff employed in a newly merged airline company. As predicted from social identity theory, the negative effects of the merger were most marked for employees of the low-status premerger organization. Also, as predicted, the perception of permeable intergroup boundaries in the new organization was associated positively with identification with the new organization and both job-related and person-related outcomes among employees of the low-status premerger organization but negatively with person-related outcomes among employees of the high-status premerger organization. As predicted, there was some evidence that the main and interactive effects involving status, perceived permeability, and intergroup contact on employee adjustment were mediated through strength of identification with the new organization.

General strain theory, situational anger, and social networks: An assessment of conditioning influences

Using a random sample of university students to test general strain theory (GST), this study expanded on previous tests of strain theory in two ways. First, situational anger was measured, a construct that had not been used thus far in assessments of general strain. In addition, this research examined the role of social support networks as a conditioning influence on the effects of strain and anger on intentions to commit three types of criminal behavior (serious assault, shoplifting, and driving under the influence of alcohol [DUI]). The results provided mixed support for GST. While the link between anger and crime was confirmed, the nature of that relationship in some cases ran counter to the theory. Moreover, the evidence indicated that the role of social support networks was complex, and varied as a conditioning influence on intentions to engage in criminal activities. (C) 2001 Elsevier Science Ltd. All rights reserved.

Genetics of cognition: Outline of a collaborative twin study

Amultidisciplinary collaborative study examining cognition in a large sample of twins is outlined. A common experimental protocol and design is used in The Netherlands, Australia and Japan to measure cognitive ability using traditional IQ measures (i.e., psychometric IQ), processing speed (e.g., reaction time [RT] and inspection time [IT]), and working memory (e.g., spatial span, delayed response [DR] performance). The main aim is to investigate the genetic covariation among these cognitive phenotypes in order to use the correlated biological markers in future linkage and association analyses to detect quantitativetrait loci (QTLs). We outline the study and methodology, and report results from our preliminary analyses that examines the heritability of processing speed and working memory indices, and their phenotypic correlation with IQ. Heritability of Full Scale IQ was 87% in the Netherlands, 83% in Australia, and 71% in Japan. Heritability estimates for processing speed and working memory indices ranged from 33–64%. Associations of IQ with RT and IT (−0.28 to −0.36) replicated previous findings with those of higher cognitive ability showing faster speed of processing. Similarly, significant correlations were indicated between IQ and the spatial span working memory task (storage [0.31], executive processing [0.37]) and the DR working memory task (0.25), with those of higher cognitive ability showing better memory performance. These analyses establish the heritability of the processing speed and working memory measures to be used in our collaborative twin study of cognition, and support the findings that individual differences in processing speed and working memory may underlie individual differences in psychometric IQ.

The reliability of immunohistochemistry as a prescreening method for the diagnosis of hereditary nonpolyposis colorectal cancer (HNPCC) - Results of an international collaborative study

Hereditary nonpolyposis colorectal cancer syndrome (HNPCC) is an autosomal dominant condition accounting for 2–5% of all colorectal carcinomas as well as a small subset of endometrial, upper urinary tract and other gastrointestinal cancers. An assay to detect the underlying defect in HNPCC, inactivation of a DNA mismatch repair enzyme, would be useful in identifying HNPCC probands. Monoclonal antibodies against hMLH1 and hMSH2, two DNA mismatch repair proteins which account for most HNPCC cancers, are commercially available. This study sought to investigate the potential utility of these antibodies in determining the expression status of these proteins in paraffin-embedded formalin-fixed tissue and to identify key technical protocol components associated with successful staining. A set of 20 colorectal carcinoma cases of known hMLH1 and hMSH2 mutation and expression status underwent immunoperoxidase staining at multiple institutions, each of which used their own technical protocol. Staining for hMSH2 was successful in most laboratories while staining for hMLH1 proved problematic in multiple labs. However, a significant minority of laboratories demonstrated excellent results including high discriminatory power with both monoclonal antibodies. These laboratories appropriately identified hMLH1 or hMSH2 inactivation with high sensitivity and specificity. The key protocol point associated with successful staining was an antigen retrieval step involving heat treatment and either EDTA or citrate buffer. This study demonstrates the potential utility of immunohistochemistry in detecting HNPCC probands and identifies key technical components for successful staining.

Probabilistic modelling of CO2 corrosion laboratory data using neural networks

The present paper addresses two major concerns that were identified when developing neural network based prediction models and which can limit their wider applicability in the industry. The first problem is that it appears neural network models are not readily available to a corrosion engineer. Therefore the first part of this paper describes a neural network model of CO2 corrosion which was created using a standard commercial software package and simple modelling strategies. It was found that such a model was able to capture practically all of the trends noticed in the experimental data with acceptable accuracy. This exercise has proven that a corrosion engineer could readily develop a neural network model such as the one described below for any problem at hand, given that sufficient experimental data exist. This applies even in the cases when the understanding of the underlying processes is poor. The second problem arises from cases when all the required inputs for a model are not known or can be estimated with a limited degree of accuracy. It seems advantageous to have models that can take as input a range rather than a single value. One such model, based on the so-called Monte Carlo approach, is presented. A number of comparisons are shown which have illustrated how a corrosion engineer might use this approach to rapidly test the sensitivity of a model to the uncertainities associated with the input parameters. (C) 2001 Elsevier Science Ltd. All rights reserved.