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| 1 | Use and perceived effectiveness of non‐analgesic medical therapies for chronic pancreatitis in the United States显示文摘 | F.Burton S.Alkaade D.Collins V.Muddana A.Slivka R. E.Brand A.Gelrud P. A.Banks S.Sherman M. A.Anderson J.Romagnuolo C.Lawrence J.Baillie T. B.Gardner M. D.Lewis S. T.Amann J. G.Lieb M.O’Connell E. D.Kennard D.Yadav D. C.Whitcomb C. E.Forsmark | 2010 | Alimentary Pharmacology & Therapeutics2010，，1： | 1 |
| 2 | Mathematical model of the corneo-scleral shell as applied to intraocular pressure-volume relations and applanation tonometry显示文摘 | S.L.Woo A.S.Kobayashi C.Lawrence | | 0，，01： | 1 |
| 3 | Mathematical model of the corneo-scleral shell as applied to intraocular pressure-volume relations and applanation tonometry显示文摘 | S.L.Woo A.S.Kobayashi C.Lawrence e t al | | 0，，01： | 1 |
| 4 | Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: Part II显示文摘 | Reva C.Lawrence David T.Felson Charles G.Helmick Lesley M.Arnold HyonChoi Richard A.Deyo SherineGabriel RosemarieHirsch Marc C.Hochberg Gene G.Hunder Joanne M.Jordan Jeffrey N.Katz Hilal MaraditKremers FrederickWolfe | 2007 | Arthritis & Rheumatism2007，，1： | 1 |
| 5 | Use and perceived effectiveness of non‐analgesic medical therapies for chronic pancreatitis in the United States显示文摘 | F.Burton S.Alkaade D.Collins V.Muddana A.Slivka R. E.Brand A.Gelrud P. A.Banks S.Sherman M. A.Anderson J.Romagnuolo C.Lawrence J.Baillie T. B.Gardner M. D.Lewis S. T.Amann J. G.Lieb M.O’Connell E. D.Kennard D.Yadav D. C.Whitcomb C. E.Forsmark | 2010 | Alimentary Pharmacology & Therapeutics2010，，1： | 1 |
| 6 | Evaluation of growth characteristics of Aspergillus parasiticus inoculated in different culture media by shortwave infrared(SWIR) hyperspectral imaging显示文摘The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media. | Xuan Chu Wei Wang Xinzhi Ni Haitao Zheng Xin Zhao Hong Zhuang Kurt C.Lawrence Chunyang Li Yufeng Li Chengjun Lu | 2018 | Journal of Innovative Optical Health Sciences2018，，5： | 0 |
| 7 | A Unique Opportunity显示文摘By stepping up efforts to restructure the economy to boost domestic demand,adopt a green growth model,and make growth more inclusive,the current | C.Lawrence Greenwood | 2011 | China's Foreign Trade2011，，1： | 0 |
| 8 | AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials显示文摘Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications.A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest.Traditionally,the identification of low-energy adsorbate-surface configurations relies on heuristic methods and researcher intuition.As the desire to perform high-throughput screening increases,it becomes challenging to use heuristics and intuition alone.In this paper,we demonstrate machine learning potentials can be leveraged to identify low-energy adsorbate-surface configurations more accurately and efficiently.Our algorithm provides a spectrum of trade-offs between accuracy and efficiency,with one balanced option finding the lowest energy configuration 87.36%of the time,while achieving a~2000×speedup in computation.To standardize benchmarking,we introduce the Open Catalyst Dense dataset containing nearly 1000 diverse surfaces and~100,000 unique configurations. | Janice Lan Aini Palizhati Muhammed Shuaibi Brandon M.Wood Brook Wander Abhishek Das Matt Uyttendaele C.Lawrence Zitnick Zachary W.Ulissi | 2023 | npj Computational Materials2023，，1： | 0 |
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