A Radiotracer study of the adsorption behaviour of aqueous Ba2+ ions on nonoparticles of zero-valent iron

Cataloged from PDF version of article.Recently, iron nanoparticles are increasingly being tested as adsorbents for various types of organic and inorganic pollutants. In this study, nanoparticles of zero-valent iron (NZVI) synthesized under atmospheric conditions were employed for the removal of Ba2+ ions in a concentration range 10-3 to 10-6 M. Throughout the study, 133Ba was used as a tracer to study the effects of time, concentration, and temperature. The obtained data was analyzed using various kinetic models and adsorption isotherms. Pseudo-second-order kinetics and Dubinin-Radushkevich isotherm model provided the best correlation with the obtained data. Observed thermodynamic parameters showed that the process is exothermic and hence enthalpy-driven. © 2007 Elsevier B.V. All rights reserved

Photorespiration in Eelgrass (\u3ci\u3eZostera marina\u3c/i\u3e L.): A Photoprotection Mechanism for Survival in a CO₂-Limited World

Photorespiration, commonly viewed as a loss in photosynthetic productivity of C3 plants, is expected to decline with increasing atmospheric CO2, even though photorespiration plays an important role in the oxidative stress responses. This study aimed to quantify the role of photorespiration and alternative photoprotection mechanisms in Zostera marina L. (eelgrass), a carbon-limited marine C3 plant, in response to ocean acidification. Plants were grown in controlled outdoor aquaria at different [CO2]aq ranging from ~55 (ambient) to ~2121 μM for 13 months and compared for differences in leaf photochemistry by simultaneous measurements of O2 flux and variable fluorescence. At ambient [CO2], photosynthesis was carbon limited and the excess photon absorption was diverted both to photorespiration and non-photochemical quenching (NPQ). The dynamic range of NPQ regulation in ambient grown plants, in response to instantaneous changes in [CO2]aq, suggested considerable tolerance for fluctuating environmental conditions. However, 60 to 80% of maximum photosynthetic capacity of ambient plants was diverted to photorespiration resulting in limited carbon fixation. The photosynthesis to respiration ratio (PE : RD) of ambient grown plants increased 6-fold when measured under high CO2 because photorespiration was virtually suppressed. Plants acclimated to high CO2 maintained 4-fold higher PE : RD than ambient grown plants as a result of a 60% reduction in photorespiration. The O2 production efficiency per unit chlorophyll was not affected by the CO2 environment in which the plants were grown. Yet, CO2 enrichment decreased the light level to initiate NPQ activity and downregulated the biomass specific pigment content by 50% and area specific pigment content by 30%. Thus, phenotypic acclimation to ocean carbonation in eelgrass, indicating the coupling between the regulation of photosynthetic structure and metabolic carbon demands, involved the downregulation of light harvesting by the photosynthetic apparatus, a reduction in the role of photorespiration and an increase in the role of NPQ in photoprotection. The quasi-mechanistic model developed in this study permits integration of photosynthetic and morphological acclimation to ocean carbonation into seagrass productivity models, by adjusting the limits of the photosynthetic parameters based on substrate availability and physiological capacity

Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis

Abstract. This paper gives an example of evolved features that improve image retrieval performance. A content-based image retrieval system for skin lesion images is presented. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination. Skin lesions of five common classes, including two non-melanoma cancer types, are used. Colour and texture features are extracted from lesions. Evolutionary algorithms are used to create composite features that optimise a similarity matching function. Experiments on our database of 533 images are performed and results are compared to those obtained using simple features. The use of the evolved composite features improves the precision by about 7%.

Test beam studies of the TRD prototype filled with different gas mixtures based on Xe, Kr, and Ar

Towards the end of LHC Run1, gas leaks were observed in some parts of the Transition Radiation Tracker (TRT) of ATLAS. Due to these leaks, primary Xenon based gas mixture was replaced with Argon based mixture in various parts. Test-beam studies with a dedicated Transition Radiation Detector (TRD) prototype were carried out in 2015 in order to understand transition radiation performance with mixtures based on Argon and Krypton. We present and discuss the results of these test-beam studies with different active gas compositions.Comment: 5 pages,12 figures, The 2nd International Conference on Particle Physics and Astrophysics (ICPPA-2016); Acknowledgments section correcte

Some results of test beam studies of Transition Radiation Detector prototypes at CERN

Operating conditions and challenging demands of present and future accelerator experiments result in new requirements on detector systems. There are many ongoing activities aimed to develop new technologies and to improve the properties of detectors based on existing technologies. Our work is dedicated to development of Transition Radiation Detectors (TRD) suitable for different applications. In this paper results obtained in beam tests at SPS accelerator at CERN with the TRD prototype based on straw technology are presented. TRD performance was studied as a function of thickness of the transition radiation radiator and working gas mixture pressure

Betatrophin levels are related to the early histological findings in nonalcoholic fatty liver disease

Betatrophin, a liver hormone, regulates glucose and lipid metabolism. We investigated the betatrophin levels in nonalcoholic fatty liver disease (NAFLD) and searched for any relationship with histological severity and metabolic parameters. Fifty males with NAFLD [Nonalcoholic Steatohepati-tis (NASH) (n = 32); non-NASH (n = 18)] and 30 healthy controls were included. Plasma betatrophin was measured by ELISA method. Insulin sensitivity was assessed by HOMA-IR index. Histological features were scored by the semi quantitative classification and combined as the NAFLD activity score (NAS). Betatrophin levels in the non-NASH group were significantly higher than the controls. Betatrophin was positively correlated to the age, waist circumference, total cholesterol, triglycerides, LDL cholesterol, glucose, insulin, HOMA-IR index and gamma glutamyl transpeptidase levels, and negatively correlated to the steatosis and NAS. In the stepwise linear regression analysis, the triglyceride (β = 0.457, p < 0.001), glucose (β = 0.281, p = 0.02) and NAS (β = −0.260, p = 0.03) were the independent determinants of betatrophin. Betatrophin levels are higher in the early stages of NAFLD and tend to decrease when the disease progresses. This could be an important preliminary mechanistic finding to explain the increased frequency of glucose intolerance during the course of NAFLD

Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images

<p>Abstract</p> <p>Background</p> <p>Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density –greater than certain number of points- around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.</p> <p>Results</p> <p>Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracy measures: border error, precision, and recall. </p> <p>Conclusion</p> <p>As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.</p

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196