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| 1 | Review article: restorative proctocolectomy, indications, management of complications and follow‐up – a guide for gastroenterologists显示文摘 | S. D.MCLAUGHLIN S. K.CLARK P. P.TEKKIS P. J.CICLITIRA R. J.NICHOLLS | 2008 | Alimentary Pharmacology & Therapeutics2008，，10： | 1 |
| 2 | Between the Country and the Concrete: Rediscovering the Rural‐Urban Fringe显示文摘 | Jeff S.Sharp Jill K.Clark | 2008 | City & Community2008，，1： | 1 |
| 3 | Lipid Emulsion?as Rescue for Local Anesthetic- Related Cardiotoxicity显示文摘 | Mary K.Clark | | 0，，02： | 1 |
| 4 | Dietary supplementation with turmeric polyherbal formulation decreases facial redness: a randomized double-blind controlled pilot study显示文摘Background: Facial redness is multifactorial in nature and may be a sign of many different conditions,including rosacea, photo damage and flushing. Herbal medicines have been used for thousands of years to treat a variety of dermatological conditions. Turmeric(Curcuma longa) and its constituents have been shown to mediate dilation and constriction of peripheral arterioles and have demonstrated anti-oxidant,anti-inflammatory and wound-healing properties.Objective: To investigate the effects of turmeric and turmeric-containing polyherbal combination tablets versus placebo on facial redness.Design, setting, participants, and interventions: This was a prospective, double-blind, randomized pilot study. Thirty-three healthy participants were recruited from the dermatology clinic at the University of California, Davis and nearby community from 2016 to 2017. Thirty participants were enrolled, and28 participants completed the study. The enrolled participants were randomized to receive one of three interventions(placebo, turmeric or polyherbal combination tablets) and were told to take the intervention tablets by mouth twice daily for 4 weeks. Facial redness was assessed at baseline and 4 weeks after intervention by clinical grading and by image-based analysis.Main outcome measures: The primary outcome measure was image-based facial quantification of redness using a research camera and software analysis system. The investigators performed an intention-to-treat analysis by including all subjects who were enrolled in the trial and received any study intervention.Differences were considered statistically significant after accounting for multiple comparisons. Effect sizes for clinical grading were calculated with a Hedges' g where indicated.Results: Twenty-eight participants completed the study and there were no reported adverse events.Based on clinical grading, facial redness intensity and distribution down trended in the polyherbal combination group after 4 weeks(P = 0.1). Under photographic image analysis, the polyherbal combination group had a significant decrease in redness of 40% compared to baseline(P = 0.03). The placebo and turmeric groups had no statistically significant changes in image analysis-based facial redness.Conclusion: Polyherbal combination tablet supplementation improved facial redness compared to the turmeric or placebo. Overall, our findings suggested further investigations into the effects of turmeric and polyherbal formulations in skin conditions associated with facial redness would be warranted.Trial registration: ClinicalTrials.gov identifier: NCT03065504. | Alexandra R.Vaughn Aunna Pourang Ashley K.Clark Waqas Burney Raja K.Sivamani | 2019 | Journal of Integrative Medicine2019，17，1： | 0 |
| 5 | Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images显示文摘The rise of automation and machine learning(ML)in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing.A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions.We address this by employing a cycle generative adversarial network(CycleGAN)with a reciprocal space discriminator,which augments simulated data with realistic spatial frequency information.This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications.We showcase our approach by training a fully convolutional network(FCN)to identify single atom defects in a 4.5 million atom data set,collected using automated acquisition in an aberration-corrected scanning transmission electron microscope(STEM).Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention,marking a crucial step towards fully autonomous harnessing of microscopy big data. | Abid Khan Chia-Hao Lee Pinshane Y.Huang Bryan K.Clark | 2023 | npj Computational Materials2023，，1： | 0 |
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