An investigation on natural radioactivity from mining industry#

The environmental problem of naturally occurring radioactive materials (NORM) is omnipresent on earth and their radioactivity may become concentrated as a result of human activities. Various industries produce concentrated radioactivity in their by-products. Mining originating industries such as the coal industries, petroleum extraction and processing and natural gas, mining enrichment waste, phosphate, etc have been well known and widely investigated. The Environmental Protection Agency (EPA) describes NORM wastes from the mining and processing of three categories of metals: Rare earth metals, special application metals and metals produced in bulk quantities by industrial extraction processes. Moreover, NORM has a lot of negative effects on the natural resources (water supplies, soils, air, etc.) and living organisms (human, animals, plants, microorganisms, etc.). In this study, we investigated NORM levels that originated from mining industry and the concentration of NORM in drinking water supplies. NORM parameter of gross alpha and gross beta were also in this study, seasonal changes in gross alpha and gross beta were investigated. The obtained results showed that, natural activity concentrations of α- and β-emitting radionuclides in all water samples did not exceed World Health Organisation (WHO) and Turkish Standards of Drinking Water (TS 266) recommended levels (Table 1). Concentrations ranging from 0.0062 Bq/l to 0.79 Bq/l and from 0.004 to 0.18 Bq/l were observed for the gross α and gross β activities, respectively. For all samples, the gross β activities were higher than the corresponding gross α activities.Key words: Natural radioactivity, mining industries, gross alpha, gross beta

Building collaboration in multi-agent systems using reinforcement learning

© Springer Nature Switzerland AG 2018. This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory, either. Particles are devised with Q learning for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced experimental results are supportive to the proposed idea suggesting that a substantive collaboration can be build via proposed learning algorithm

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

Diversifying search in bee algorithms for numerical optimisation

© Springer Nature Switzerland AG 2018. Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving well-known highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks

Impact of antimicrobial drug restrictions on doctors' behaviors

Background/aim: Broad-spectrum antibiotics have become available for use only with the approval of infectious disease specialists (IDSs) since 2003 in Turkey. This study aimed to analyze the tendencies of doctors who are not disease specialists (non-IDSs) towards the restriction of antibiotics.Materials and methods: A questionnaire form was prepared, which included a total of 22 questions about the impact of antibiotic restriction (AR) policy, the role of IDSs in the restriction, and the perception of this change in antibiotic consumption. The questionnaire was completed by each participating physician.Results: A total of 1906 specialists from 20 cities in Turkey participated in the study. Of those who participated, 1271 (67.5%) had 5 years of occupational experience in their branch expressed that they followed the antibiotic guidelines more strictly than the JSs (P < 0.05) and 755 of physicians (88%) and 720 of surgeons (84.6%) thought that the AR policy was necessary and useful (P < 0.05).Conclusion: This study indicated that the AR policy was supported by most of the specialists. Physicians supported this restriction policy more so than surgeons did

Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake

Low atmospheric carbon dioxide (CO2) concentration during the Little Ice Age has been used to derive the global carbon cycle sensitivity to temperature. Recent evidence confirms earlier indications that the low CO2 was caused by increased terrestrial carbon storage. It remains unknown whether the terrestrial biosphere responded to temperature variations, or there was vegetation re-growth on abandoned farmland. Here we present a global numerical simulation of atmospheric carbonyl sulfide concentrations in the pre-industrial period. Carbonyl sulfide concentration is linked to changes in gross primary production and shows a positive anomaly during the Little Ice Age. We show that a decrease in gross primary production and a larger decrease in ecosystem respiration is the most likely explanation for the decrease in atmospheric CO2 and increase in atmospheric carbonyl sulfide concentrations. Therefore, temperature change, not vegetation re-growth, was the main cause of the increased terrestrial carbon storage. We address the inconsistency between ice-core CO2 records from different sites measuring CO2 and δ13CO2 in ice from Dronning Maud Land (Antarctica). Our interpretation allows us to derive the temperature sensitivity of pre-industrial CO2 fluxes for the terrestrial biosphere (γL = -10 to -90 Pg C K-1), implying a positive climate feedback and providing a benchmark to reduce model uncertainties

Clinicopathologic features of incidental prostatic adenocarcinoma in radical cystoprostatectomy specimens

<p>Abstract</p> <p>Background</p> <p>The aim of this study is to review all features of incidentally discovered prostate adenocarcinoma in patients undergoing radical cystoprostatectomy for bladder cancer.</p> <p>Methods</p> <p>The medical charts of 300 male patients who underwent radical cystoprostatectomy for bladder cancer between 1997 and 2005 were retrospectively reviewed. The mean age of the patients was 62 (range 51-75) years.</p> <p>Results</p> <p>Prostate adenocarcinoma was present in 60 (20%) of 300 specimens. All were acinar adenocarcinoma. Of these, 40 (66.7%) were located in peripheral zone, 20 (33.3%) had pT2a tumor, 12 (20%) had pT2b tumor, 22(36.7%) had pT2c and, 6 (10%) had pT3a tumor. Gleason score was 6 or less in 48 (80%) patients. Surgical margins were negative in 54 (90%) patients, and tumor volume was less than 0.5 cc in 23 (38.3%) patients. Of the 60 incidentally detected cases of prostate adenocarcinoma 40 (66.7%) were considered clinically significant.</p> <p>Conclusion</p> <p>Incidentally detected prostate adenocarcinoma is frequently observed in radical cystoprostatectomy specimens. The majority are clinically significant.</p

Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

BACKGROUND: Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. METHODS: This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. RESULTS: Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. CONCLUSION: These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest information gathered from the whole section images will guide the excision of tissue for constructing tissue microarrays and for high throughput profiling of global gene expression

Generation of subnanometric platinum with high stability during transformation of a 2D zeolite into 3D

[EN] Single metal atoms and metal clusters have attracted much attention thanks to their advantageous capabilities as heterogeneous catalysts. However, the generation of stable single atoms and clusters on a solid support is still challenging. Herein, we report a new strategy for the generation of single Pt atoms and Pt clusters with exceptionally high thermal stability, formed within purely siliceous MCM-22 during the growth of a two-dimensional zeolite into three dimensions. These subnanometric Pt species are stabilized by MCM-22, even after treatment in air up to 540 degrees C. Furthermore, these stable Pt species confined within internal framework cavities show size-selective catalysis for the hydrogenation of alkenes. High-temperature oxidation-reduction treatments result in the growth of encapsulated Pt species to small nanoparticles in the approximate size range of 1 to 2 nm. The stability and catalytic activity of encapsulated Pt species is also reflected in the dehydrogenation of propane to propylene.This work was funded by the Spanish Government (Consolider Ingenio 2010-MULTICAT (CSD2009-00050) and MAT2014-52085-C2-1-P) and by the Generalitat Valenciana (Prometeo). The Severo Ochoa Program (SEV-2012-0267) is gratefully acknowledged. L.L. thanks ITQ for a contract. The authors also thank the Microscopy Service of UPV for the TEM and STEM measurements. The HAADF-HRSTEM works were conducted in the Laboratorio de Microscopias Avanzadas (LMA) at the Instituto de Nanociencia de Aragon (INA)-Universidad de Zaragoza (Spain), a Spanish ICTS National Facility. Some of the research leading to these results has received funding from the European Union Seventh Framework Program under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative-I3). R.A. also acknowledges funding from the Spanish Ministerio de Economia y Competitividad (FIS2013-46159-C3-3-P) and the European Union Horizon 2020 research and innovation programme under the Marie Sldodowska-Curie grant agreement No. 642742.Liu, L.; Díaz Morales, UM.; Arenal, R.; Agostini, G.; Concepción Heydorn, P.; Corma Canós, A. (2017). Generation of subnanometric platinum with high stability during transformation of a 2D zeolite into 3D. Nature Materials. 16(1):132-138. https://doi.org/10.1038/NMAT4757S132138161Boronat, M., Leyva-Perez, A. & Corma, A. Theoretical and experimental insights into the origin of the catalytic activity of subnanometric gold clusters: attempts to predict reactivity with clusters and nanoparticles of gold. Acc. Chem. Res. 47, 834–844 (2014).Flytzani-Stephanopoulos, M. & Gates, B. C. Atomically dispersed supported metal catalysts. Ann. Rev. Chem. Bio. Eng. 3, 545–574 (2012).Gates, B. C. Supported metal clusters: synthesis, structure, and catalysis. Chem. Rev. 95, 511–522 (1995).Corma, A. et al. Exceptional oxidation activity with size-controlled supported gold clusters of low atomicity. Nat. Chem. 5, 775–781 (2013).Yang, M. et al. Catalytically active Au-O(OH)x-species stabilized by alkali ions on zeolites and mesoporous oxides. Science 346, 1498–1501 (2014).Rivallan, M. et al. Platinum sintering on H-ZSM-5 followed by chemometrics of CO adsorption and 2D pressure-jump IR spectroscopy of adsorbed species. Angew. Chem. Int. Ed. 49, 785–789 (2010).Zecevic, J., van der Eerden, A. M., Friedrich, H., de Jongh, P. E. & de Jong, K. P. Heterogeneities of the nanostructure of platinum/zeolite Y catalysts revealed by electron tomography. ACS Nano 7, 3698–3705 (2013).Philippaerts, A. et al. Unprecedented shape selectivity in hydrogenation of triacylglycerol molecules with Pt/ZSM-5 zeolite. Angew. Chem. Int. Ed. 50, 3947–3949 (2011).Kim, J., Kim, W., Seo, Y., Kim, J.-C. & Ryoo, R. n-Heptane hydroisomerization over Pt/MFI zeolite nanosheets: effects of zeolite crystal thickness and platinum location. J. Catalys. 301, 187–197 (2013).Goel, S., Wu, Z., Zones, S. I. & Iglesia, E. Synthesis and catalytic properties of metal clusters encapsulated within small-pore (SOD, GIS, ANA) zeolites. J. Am. Chem. Soc. 134, 17688–17695 (2012).Choi, M., Wu, Z. & Iglesia, E. Mercaptosilane-assisted synthesis of metal clusters within zeolites and catalytic consequences of encapsulation. J. Am. Chem. Soc. 132, 9129–9137 (2010).Choi, M., Yook, S. & Kim, H. Hydrogen spillover in encapsulated metal catalysts: new opportunities for designing advanced hydroprocessing catalysts. ChemCatChem 7, 1048–1057 (2015).Kulkarni, A., Lobo-Lapidus, R. J. & Gates, B. C. Metal clusters on supports: synthesis, structure, reactivity, and catalytic properties. Chem. Commun. 46, 5997–6015 (2010).Guzman, J. & Gates, B. C. Supported molecular catalysts: metal complexes and clusters on oxides and zeolites. Dalton Trans. 1, 3303–3318 (2003).Leonowicz, M. E., Lawton, J. A., Lawton, S. L. & Rubin, M. K. MCM-22: a molecular sieve with two independent multidimensional channel systems. Science 264, 1910–1913 (1994).Camblor, M. A. et al. A new microporous polymorph of silica isomorphous to zeolite MCM-22. Chem. Mater. 8, 2415–2417 (1996).Hyotanishi, M., Isomura, Y., Yamamoto, H., Kawasaki, H. & Obora, Y. Surfactant-free synthesis of palladium nanoclusters for their use in catalytic cross-coupling reactions. Chem. Commun. 47, 5750–5752 (2011).Duchesne, P. N. & Zhang, P. Local structure of fluorescent platinum nanoclusters. Nanoscale 4, 4199–4205 (2012).Lu, J., Aydin, C., Browning, N. D. & Gates, B. C. Imaging isolated gold atom catalytic sites in zeolite NaY. Angew. Chem. Int. Ed. 51, 5842–5846 (2012).Yacamán, M. J., Santiago, U. & Mejía-Rosales, S. in Advanced Transmission Electron Microscopy: Applications to Nanomaterials (eds Francis, L., Mayoral, A. & Arenal, R.) 1–29 (Springer, 2015).Jena, P., Khanna, S. N. & Rao, B. K. Physics and Chemistry of Finite Systems: From Clusters to Crystals (Springer, 1992).Yamasaki, J. et al. Ultramicroscopy 151, 224–231 (2015).Sohlberg, K., Pennycook, T. J., Zhoud, W. & Pennycook, S. J. Insights into the physical chemistry of materials from advances in HAADF-STEM. Phys. Chem. Chem. Phys. 17, 3982–4006 (2015).Aydin, C., Lu, J., Browning, N. D. & Gates, B. C. A ‘smart’ catalyst: sinter-resistant supported iridium clusters visualized with electron microscopy. Angew. Chem. Int. Ed. 51, 5929–5934 (2012).Wei, H. et al. FeOx-supported platinum single-atom and pseudo-single-atom catalysts for chemoselective hydrogenation of functionalized nitroarenes. Nat. Commun. 5, 5634 (2014).Addou, R. et al. Influence of hydroxyls on Pd atom mobility and clustering on rutile TiO2(011)-2 × 1. ACS Nano 8, 6321–6333 (2014).Jung, U. et al. Comparative in operando studies in heterogeneous catalysis: atomic and electronic structural features in the hydrogenation of ethylene over supported Pd and Pt catalysts. ACS Catal. 5, 1539–1551 (2015).Agostini, G. et al. Effect of different face centered cubic nanoparticle distributions on particle size and surface area determination: a theoretical study. J. Phys. Chem. C 118, 4085–4094 (2014).Alexeev, O. & Gates, B. C. EXAFS characterization of supported metal-complex and metal-cluster catalysts made from organometallic precursors. Top. Catal. 10, 273–293 (2000).Chakraborty, I., Bhuin, R. G., Bhat, S. & Pradeep, T. Blue emitting undecaplatinum clusters. Nanoscale 6, 8561–8564 (2014).Zheng, J., Nicovich, P. R. & Dickson, R. M. Highly fluorescent noble-metal quantum dots. Ann. Rev. Phys. Chem. 58, 409–431 (2007).Okrut, A. et al. Selective molecular recognition by nanoscale environments in a supported iridium cluster catalyst. Nat. Nanotech. 9, 459–465 (2014).Zhou, C. et al. On the sequential hydrogen dissociative chemisorption on small platinum clusters: a density functional theory study. J. Phys. Chem. C 111, 12773–12778 (2007).De La Cruz, C. & Sheppard, N. An exploration of the surfaces of some Pt/SiO2 catalysts using CO as an infrared spectroscopic probe. Spectrochim. Acta A 50, 271–285 (1994).Klünker, C., Balden, M., Lehwald, S. & Daum, W. CO stretching vibrations on Pt(111) and Pt(110) studied by sum frequency generation. Surf. Sci. 360, 104–111 (1996).Stakheev, A. Y., Shpiro, E. S., Jaeger, N. I. & Schulz-Ekloff, G. Electronic state and location of Pt metal clusters in KL zeolite: FTIR study of CO chemisorption. Catal. Lett. 32, 147–158 (1995).Heiz, U., Sanchez, A., Abbet, S. & Schneider, W. D. Catalytic oxidation of carbon monoxide on monodispersed platinum clusters: each atom counts. J. Am. Chem. Soc. 121, 3214–3217 (1999).Levitas, V. I. & Samani, K. Size and mechanics effects in surface-induced melting of nanoparticles. Nat. Commun. 2, 284 (2011).Jiang, H., Moon, K.-s., Dong, H., Hua, F. & Wong, C. P. Size-dependent melting properties of tin nanoparticles. Chem. Phys. Lett. 429, 492–496 (2006).Nanda, K. K., Kruis, F. E. & Fissan, H. Evaporation of free PbS nanoparticles: evidence of the Kelvin effect. Phys. Rev. Lett. 89, 256103 (2002).Vajda, S. et al. Subnanometre platinum clusters as highly active and selective catalysts for the oxidative dehydrogenation of propane. Nat. Mater. 8, 213–216 (2009).Ortalan, V., Uzun, A., Gates, B. C. & Browning, N. D. Direct imaging of single metal atoms and clusters in the pores of dealuminated HY zeolite. Nat. Nanotech. 5, 506–510 (2010).Koch, C. Determination of Core Structure Periodicity and Point Defect Density along Dislocations PhD thesis, Univ. Arizona (2002).Mathon, O. et al. The time-resolved and extreme conditions XAS (TEXAS) facility at the European Synchrotron Radiation Facility: the general-purpose EXAFS bending-magnet beamline BM23. J. Synchrotron Radiat. 22, 1548–1554 (2015).Newville, M. IFEFFIT: interactive XAFS analysis and FEFF fitting. J. Synchrotron Radiat. 8, 322–324 (2001)

Diabetes-related molecular signatures in infrared spectra of human saliva

WOS: 000290261500001PubMed ID: 20630088Background: There is an ongoing need for improvements in non-invasive, point-of-care tools for the diagnosis and prognosis of diabetes mellitus. Ideally, such technologies would allow for community screening. Methods: In this study, we employed infrared spectroscopy as a novel diagnostic tool in the prediction of diabetic status by analyzing the molecular and sub-molecular spectral signatures of saliva collected from subjects with diabetes (n = 39) and healthy controls (n = 22). Results: Spectral analysis revealed differences in several major metabolic components - lipid, proteins, glucose, thiocyanate and carboxylate - that clearly demarcate healthy and diseased saliva. The overall accuracy for the diagnosis of diabetes based on infrared spectroscopy was 100% on the training set and 88.2% on the validation set. Therefore, we have established that infrared spectroscopy can be used to generate complex biochemical profiles in saliva and identify several potential diabetes-associated spectral features. Conclusions: Infrared spectroscopy may represent an appropriate tool with which to identify novel diseases mechanisms, risk factors for diabetic complications and markers of therapeutic efficacy. Further study into the potential utility of infrared spectroscopy as diagnostic and prognostic tool for diabetes is warranted