Novel Application of Laboratory Instrumentation Characterizes Mass Settling Dynamics of Oil-Mineral Aggregates (OMAs) and Oil-Mineral-Microbial Interactions

AbstractIt is reasonable to assume that microbes played an important role in determining the eventual fate of oil spilled during the 2010 Deepwater Horizon disaster, given that microbial activities in the Gulf of Mexico are significant and diverse. However, critical gaps exist in our knowledge of how microbes influence the biodegradation and accumulation of petroleum in the water column and in marine sediments of the deep ocean and the shelf. Ultimately, this limited understanding impedes the ability to forecast the fate of future oil spills, specifically the capacity of numerical models to simulate the transport and fate of petroleum under a variety of conditions and regimes.By synthesizing recent model developments and results from field- and laboratory-based microbial studies, the Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO) investigates (a) how microbial biodegradation influences accumulation of petroleum in the water column and in marine sediments and (b) how biodegradation can be influenced by environmental conditions and impact forecasts of potential future oil spills.</jats:p

Mineral chemistry of igneous melanite garnets from analcite-bearing volcanic rocks, Alberta, Canada

The mineral chemistry of melanite garnets from the Crowsnest volcanic rocks of SW Alberta, Canada, has been investigated by using electron microprobe scans, quantitative analyses and multivariate statistical analysis. The garnets occur with aegirine-augite, sanidine, analcite and rare plagioclase as phenocrysts in trachyte and phonolite flows, agglomerates and tuffs. Wavelength dispersive microprobe scans reveal complex zonation patterns, both normal and oscillatory. The results of fifty quantitative analyses were subjected to R-mode factor analysis to delineate the chemical exchanges producing the zonation. The chemical zonation of the garnets may be attributed to four independent binary exchanges; Al-Fe3+, Si-Ti, Ca-Mn and Mg-Fe2+. The stoichiometry of these garnets, based on microprobe and wet chemical Fe analyses, combined with the strongly antithetic behavior of Si and Ti lead us to infer that the Ti in these garnets is dominantly tetravalent. It is clear from this study that quantitative modelling of the processes of crystal growth and zonation of melanite garnets in alkaline, undersaturated igneous rocks should be aimed at simulating the four chemical exchanges listed above

Modelling creativity: identifying key components through a corpus-based approach

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research

Nomograms of Iranian fetal middle cerebral artery Doppler waveforms and uniformity of their pattern with other populations' nomograms

<p>Abstract</p> <p>Background</p> <p>Doppler flow velocity waveform analysis of fetal vessels is one of the main methods for evaluating fetus health before labor. Doppler waves of middle cerebral artery (MCA) can predict most of the at risk fetuses in high risk pregnancies. In this study, we tried to obtain normal values and their nomograms during pregnancy for Doppler flow velocity indices of MCA in 20 – 40 weeks of normal pregnancies in Iranian population and compare their pattern with other countries' nomograms.</p> <p>Methods</p> <p>During present descriptive cross-sectional study, 1037 normal pregnant women with 20^th–40^thweek gestational age were underwent MCA Doppler study. All cases were studied by gray scale ultrasonography initially and Doppler of MCA afterward. Resistive Index (RI), Pulsative Index (PI), Systolic/Diastolic ratio (S/D ratio), and Peak Systolic Velocity (PSV) values of MCA were determined for all of the subjects.</p> <p>Results</p> <p>Results of present study showed that RI, PI, S/D ratio values of MCA decreased with parabolic pattern and PSV value increased with simple pattern, as gestational age progressed. These changes were statistically significant (P = 0.000 for all of indices) and more characteristic during late weeks of pregnancy.</p> <p>Conclusion</p> <p>Values of RI, PI and S/D ratio indices reduced toward the end of pregnancy, but PSV increased. Despite the trivial difference, nomograms of various Doppler indices in present study have similar pattern with other studies.</p

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Prediction of sinus rhythm maintenance following DC-cardioversion of persistent atrial fibrillation – the role of atrial cycle length

BACKGROUND: Atrial electrical remodeling has been shown to influence the outcome the outcome following cardioversion of atrial fibrillation (AF) in experimental studies. The aim of the present study was to find out whether a non-invasively measured atrial fibrillatory cycle length, alone or in combination with other non-invasive parameters, could predict sinus rhythm maintenance after cardioversion of AF. METHODS: Dominant atrial cycle length (DACL), a previously validated non-invasive index of atrial refractoriness, was measured from lead V1 and a unipolar oesophageal lead prior to cardioversion in 37 patients with persistent AF undergoing their first cardioversion. RESULTS: 32 patients were successfully cardioverted to sinus rhythm. The mean DACL in the 22 patients who suffered recurrence of AF within 6 weeks was 152 ± 15 ms (V1) and 147 ± 14 ms (oesophagus) compared to 155 ± 17 ms (V1) and 151 ± 18 ms (oesophagus) in those maintaining sinus rhythm (NS). Left atrial diameter was 48 ± 4 mm and 44 ± 7 mm respectively (NS). The optimal parameter predicting maintenance of sinus rhythm after 6 weeks appeared to be the ratio of the lowest dominant atrial cycle length (oesophageal lead or V1) to left atrial diameter. This ratio was significantly higher in patients remaining in sinus rhythm (3.4 ± 0.6 vs. 3.1 ± 0.4 ms/mm respectively, p = 0.04). CONCLUSION: In this study neither an index of atrial refractory period nor left atrial diameter alone were predictors of AF recurrence within the 6 weeks of follow-up. The ratio of the two (combining electrophysiological and anatomical measurements) only slightly improve the identification of patients at high risk of recurrence of persistent AF. Consequently, other ways to asses electrical remodeling and / or other variables besides electrical remodeling are involved in determining the outcome following cardioversion

Regression and stabilization of advanced murine atherosclerotic lesions: a comparison of LDL lowering and HDL raising gene transfer strategies

Both reductions in atherogenic lipoproteins and increases in high-density lipoprotein (HDL) levels may affect atherosclerosis regression. Here, the relative potential of low-density lipoprotein (LDL) lowering and HDL raising gene transfer strategies to induce regression of complex murine atherosclerotic lesions was directly compared. Male C57BL/6 LDL receptor (LDLr)−/− mice were fed an atherogenic diet (1.25% cholesterol and 10% coconut oil) to induce advanced atherosclerotic lesions. A baseline group was killed after 6 months and remaining mice were randomized into a control progression (Adnull or saline), an apolipoprotein (apo) A-I (AdA-I), an LDLr (AdLDLr), or a combined apo A-I/LDLr (AdA-I/AdLDLr) adenoviral gene transfer group and followed-up for another 12 weeks with continuation of the atherogenic diet. Gene transfer with AdLDLr decreased non-HDL cholesterol levels persistently by 95% (p < 0.001) compared with baseline. This drastic reduction of non-HDL cholesterol levels induced lesion regression by 28% (p < 0.001) in the aortic root and by 25% (p < 0.05) in the brachiocephalic artery at 12 weeks after transfer. Change in lesion size was accompanied by enhanced plaque stability, as evidenced by increased collagen content, reduced lesional macrophage content, a drastic reduction of necrotic core area, and decreased expression of inflammatory genes. Elevated HDL cholesterol following AdA-I transfer increased collagen content in lesions, but did not induce regression. Apo A-I gene transfer on top of AdLDLr transfer resulted in additive effects, particularly on inflammatory gene expression. In conclusion, drastic lipid lowering induced by a powerful gene transfer strategy leads to pronounced regression and stabilization of advanced murine atherosclerosis