In order to test the effectiveness of oxalate-based polymeric adsorbents in the recovery of uranium from seawater, diallyl oxalate (DAOx) was grafted onto nylon 6 fabrics by exposing the fabric, immersed in pure liquid DAOx or in a surfactant-stabilized dispersion of DAOx in water, to electron beam or gamma radiation. Following drying and weighing to determine the degree of grafting (DoG), the presence of oxalate in the fabrics was verified using XPS. Zeta potential measurements showed the fabric surfaces to be negatively charged. The fabrics were tested by rotating them for 7 days in a rotary agitator with actual seawater spiked with 0.2 or 1.0 mg∙L−1 uranium. The fraction of uranium in the solution which was removed due to uptake on the fabrics was found to rise with increasing DoG at both uranium concentrations. EDS measurements were used to map the distribution of adsorbed uranium on the polymeric fibers.
In recent years, silicon photonic platforms have undergone rapid maturation enabling not only optical communication but complex scientific experiments ranging from sensors applications to fundamental physics investigations. There is considerable interest in deploying photonics-based communication and science instruments in harsh environments such as outer space, where radiation damage is a significant concern. In this study, we have examined the impact of cobalt-60 γ-ray radiation up to 1 megagray (MGy) absorbed dose on silicon photonic devices. We do not find any systematic impact of radiation on passivated devices, indicating the durability of passivated silicon devices under harsh conditions.
This paper presents some results of data mining HIV genotypic and structural data. Our aim is to try to relate structural features of HIV enzymes essential to its reproductive abilities to the drug resistance phenomenon. This paper concentrates on the HIV protease enzyme and Indinavir which is one of the FDA approved protease inhibitors.Our starting point was the current list of HIV mutations related to drug resistance.15 We used the fact that some molecular structures determined through high resolution X-ray crystallography were available for the proteaseIndinavir complex. Starting with these structures and the known mutations, we modelled the mutant proteases and studied the pattern of atomic contacts between the protease and the drug. After suitable pre-processing, these patterns have been used as the input of our data mining process. We have used both supervised and unsupervised learning techniques with the aim of understanding the relationship between structural features at a molecular level and resistance to Indinavir. The supervised learning was aimed at predicting 1C90 values for arbitrary mutants. The SOFM was aimed at identifying those structural features that are important for drug resistance and discovering a classifier based on such features. We have used validation and cross validation to test the generalization abilities of the learning paradigm we have designed. The straightforward supervised learning was able to learn very successfully but validation results are less than satisfactory. This is due to the insufficient number of patterns in the training set which in turn is due to the scarcity of the available data. The data mining using SOFM was very successful. We have managed to distinguish between resistant and non-resistant mutants using structural features. We have been able to divide all reported HIV mutants into several categories based on their 3-dimensional molecular structures and the pattern of contacts between the mutant protease and Indinavir. Our classfier shows reasonably good prediction performance being able to predict the drug resistance of previously unseen mutants with an accuracy of between 60% and 70%. We believe that this performance can be greatly improved once more data becomes available. The results presented here support the hypothesis that structural features of the molecular structure can be used in antiviral drug treatment selection and drug design.
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