How Green is Your Plasticizer?

Plasticizers are additives that are used to impart flexibility to polymer blends and improve their processability. Plasticizers are typically not covalently bound to the polymers, allowing them to leach out over time, which results in human exposure and environmental contamination. Phthalates, in particular, have been the subject of increasing concern due to their established ubiquity in the environment and their suspected negative health effects, including endocrine disrupting and anti-androgenic effects. As there is mounting pressure to find safe replacement compounds, this review addresses the design and experimental elements that should be considered in order for a new or existing plasticizer to be considered green. Specifically, a multi-disciplinary and holistic approach should be taken which includes toxicity testing (both in vitro and in vivo), biodegradation testing (with attention to metabolites), as well as leaching studies. Special consideration should also be given to the design stages of producing a new molecule and the synthetic and scale-up processes should also be optimized. Only by taking a multi-faceted approach can a plasticizer be considered truly green

Intelligent System for Breast Cancer Prognosis using Multiwavelet Packets and Neural Network

Abstract—This paper presents an approach for early breast cancer diagnostic by employing combination of artificial neural networks (ANN) and multiwaveletpacket based subband image decomposition. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands,, reconstructing the mammograms from the subbands containing only high frequencies. For this approach we employed different types of multiwaveletpacket. We used the result as an input of neural network for classification. The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases and images collected from local hospitals. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve. Keywords—Breast cancer, neural networks, diagnosis, multiwavelet packet, microcalcification. I

The glycocalyx is present as soon as blood flow is initiated and is required for normal vascular development

AbstractThe glycocalyx, and the thicker endothelial surface layer (ESL), are necessary both for endothelial barrier function and for sensing mechanical forces in the adult. The goal of this study is to use a combination of imaging techniques to establish when the glycocalyx and endothelial surface layer form during embryonic development and to determine the biological significance of the glycocalyx layer during vascular development in quail embryos. Using transmission electron microscopy, we show that the glycocalyx layer is present as soon as blood flow starts (14 somites). The early endothelial glycocalyx (14 somites) lacks the distinct hair-like morphology that is present later in development (17 and 25 somites). The average thickness does not change significantly (14 somites, 182nm±33nm; 17 somites, 218±30nm; 25 somites, 212±32nm). The trapping of circulating fluorescent albumin was used to evaluate the development of the ESL. Trapped fluorescent albumin was first observed at 25 somites. In order to assess a functional role for the glycocalyx during development, we selectively degraded luminal glycosaminoglycans. Degradation of hyaluronan compromised endothelial barrier function and prevented vascular remodeling. Degradation of heparan sulfate down regulated the expression of shear-sensitive genes but does not inhibit vascular remodeling. Our findings show that the glycocalyx layer is present as soon as blood flow starts (14 somites). Selective degradations of major glycocalyx components were shown to inhibit normal vascular development, examined through morphology, vascular barrier function, and gene expression

Rheology of Green Plasticizer/Poly(vinyl chloride) Blends via Time–Temperature Superposition

Plasticizers are commonly added to poly(vinyl chloride) (PVC) and other brittle polymers to improve their flexibility and processing properties. Phthalate plasticizers such as di(2-ethylhexyl phthalate) (DEHP) are the most common PVC plasticizers and have recently been linked to a wide range of developmental and reproductive toxicities in mammals. Our group has developed several replacement compounds that have good biodegradation kinetics, low toxicity profiles, and comparable plasticizer properties to DEHP. Knowledge of the rheology of PVC–plasticizer blends at elevated temperatures is crucial for understanding and predicting the behavior of the compounds during processing. In this work, the time–temperature profiles of PVC blended with our replacement green plasticizers—succinates, maleates, and dibenzoates, of varying alkyl chain length—are compared to blends prepared with DEHP and a commercially available non-phthalate plasticizer, di(isononyl cyclohexane-1,2-dicarboxylate) (Hexamoll® DINCH®). The relationship between the plasticizer molecular structure and viscoelastic response was examined by applying time–temperature superposition. All compounds except the diethyl esters showed a comparable viscoelastic response to DEHP and Hexamoll® DINCH®, and dihexyl succinate exhibited the most effective reduction of the storage modulus G′. All of the dibenzoate blends exhibited a lower stiffness than the DEHP blends. These experiments help to show that the green plasticizers described herein are viable replacements for DEHP, providing a less toxic alternative with comparable processing and rheological performance

A statistical framework for evaluating neural networks to predict recurrent events in breast cancer

Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier