13,161 research outputs found

    Evaporative Deposition Patterns Revisited: Spatial Dimensions of the Deposit

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    A model accounting for finite spatial dimensions of the deposit patterns in the evaporating sessile drops of colloidal solution on a plane substrate is proposed. The model is based on the assumption that the solute particles occupy finite volume and hence these dimensions are of the steric origin. Within this model, the geometrical characteristics of the deposition patterns are found as functions of the initial concentration of the solute, the initial geometry of the drop, and the time elapsed from the beginning of the drying process. The model is solved analytically for small initial concentrations of the solute and numerically for arbitrary initial concentrations of the solute. The agreement between our theoretical results and the experimental data is demonstrated, and it is shown that the observed dependence of the deposit dimensions on the experimental parameters can indeed be attributed to the finite dimensions of the solute particles. These results are universal and do not depend on any free or fitting parameters; they are important for understanding the evaporative deposition and may be useful for creating controlled deposition patterns.Comment: 34 pages, 14 figures, LaTeX; submitted to Physical Review

    Careers Service Work with Adults: A Survey

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    This report summarises the findings from a postal survey of all Careers Service Companies in England about their adult guidance services and services to employers. The survey was conducted by the Centre for Guidance Studies (University of Derby) and the National Institute for Careers Education and Counselling (NICEC) for the Department for Education and Employment (DfEE)

    The prevalence of metabolic syndrome and its related factors among adults in Palestine: a meta-analysis

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    Background: Metabolic syndrome (MetS)is increasingly becoming a challenging public health issue in Palestine. The current burden of MetS in the country is unknown. There has been limited research on the prevalence of MetS. This meta-analysis is the first to estimate the population prevalence of MetS and its related factors among adults in Palestine.Methods: A PRISMA systematic search appraisal and meta-analysis were conducted. A systematic literature search of PubMed, Scopus and Google Scholar was conducted in December 2014 up to February 2015. Generic, methodological and statistical data was extracted from the eligible studies which reported MetS prevalence. A random effect meta-analysis was conducted on crude MetS prevalence rates. Heterogeneity was assessed by Cochran’s Q and I2 tests. Subgroup analyses were also performed according to the predefined criteria.Results: The literature search yielded a total of 49 studies. Eight papers were included in the final analysis with sample size ranging 163 to 992. In addition, 2937 cases with MetS among people aged 15 years or more were estimated in Palestine between 2001 and 2014. There was high heterogeneity among studies (I2 = 95.8% p<0.001). The prevalence of MetS was 37.0% among adult Palestinians population ranging from 17 to 59.5%. Subgroup analysis did not show source of heterogeneity based on subject's health status and MetS criteria.Conclusion: Our meta-analysis clearly demonstrates that MetS is highly prevalent (37.0%) among Palestinian adults. The high prevalence of MetS in Palestine should be seriously considered and planners should take steps to reduce it.Keywords: Prevalence, Metabolic syndrome, Meta-analysis, Palestin

    Guidance and Individual Learning Accounts

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    This report presents the results of a CeGS/NICEC project on the role of formal and informal guidance in support of Individual Learning Accounts. It draws on five casestudies of good/interesting practice in implementing this role, including learner profiles, and supported by a literature review. It concludes that guidance can play a significant role in engaging non-traditional learners; that it needs to be marketed more effectively; that a strategy is needed for funding for in-depth guidance; and that the use of ILAs themselves as a route for such funding should be encouraged

    Guidance and Individual Learning Accounts

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    This report presents the results of a CeGS/NICEC project on the role of formal and informal guidance in support of Individual Learning Accounts. It draws on five casestudies of good/interesting practice in implementing this role, including learner profiles, and supported by a literature review. It concludes that guidance can play a significant role in engaging non-traditional learners; that it needs to be marketed more effectively; that a strategy is needed for funding for in-depth guidance; and that the use of ILAs themselves as a route for such funding should be encouraged

    The Development of Novel Nanodiamond Based MALDI Matrices for the Analysis of Small Organic Pharmaceuticals

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    The utility of novel functionalized nanodiamonds (NDs) as matrices for matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) is described herein. MALDI-MS analysis of small organic compounds (<1000 Da) is typically complex due to interferences from numerous cluster ions formed when using conventional matrices. To expand the use of MALDI for the analysis of small molecules, , novel matrices were designed by covalently linking conventional matrices (or a lysine moiety) to detonated NDs. Four new functionalized NDs were evaluated for their ionization capabilities using five pharmaceuticals with varying molecular structures. Two ND matrices were able to ionize all tested pharmaceuticals in the negative ion mode, producing the deprotonated ions [M-H]-. Ion intensity for target analytes was generally strong with enhanced signal-to-noise ratios compared with conventional matrices. The negative ion mode is of great importance for biological samples as interference from endogenous compounds is inherently minimized in the negative ion mode. Since the molecular structures of the tested pharmaceuticals did not suggest that negative ion mode would be preferable, this result magnifies the importance of these findings. On the other hand, conventional matrices primarily facilitated the ionization as expected in the positive ion mode, producing either the protonated molecules [M+H]+ or cationic adducts (typically producing complex spectra with numerous adduct peaks). The data presented in this study suggests that these matrices may offer advantages for the analysis of low molecular weight pharmaceuticals/metabolites

    Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

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    Objective: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. Methods: Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. Results: Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. Significance: Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization

    The diastereoselective Meth-Cohn epoxidation of camphor-derived vinyl sulfones

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    Some camphor-derived vinyl sulfones bearing oxygen functionality at the allylic position have been synthesized and their nucleophilic epoxidation reactions under Meth-Cohn conditions have been explored. The γ-oxygenated camphor-derived vinyl sulfones underwent mildly diastereoselective nucleophilic epoxidation reactions, affording the derived sulfonyloxiranes in up to 5.8:1 dr. The observed diastereoselectivities were sensitive to the reaction conditions employed. In contrast, no stereoselectivity was observed in the nucleophilic epoxidation of the corresponding γ-oxygenated isobornyl vinyl sulfone. A tentative mechanism has been proposed to explain the origins of the diastereoselectivit
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