14 results
Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study
- Christian A. Webb, Madhukar H. Trivedi, Zachary D. Cohen, Daniel G. Dillon, Jay C. Fournier, Franziska Goer, Maurizio Fava, Patrick J. McGrath, Myrna Weissman, Ramin Parsey, Phil Adams, Joseph M. Trombello, Crystal Cooper, Patricia Deldin, Maria A. Oquendo, Melvin G. McInnis, Quentin Huys, Gerard Bruder, Benji T. Kurian, Manish Jha, Robert J. DeRubeis, Diego A. Pizzagalli
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- Journal:
- Psychological Medicine / Volume 49 / Issue 7 / May 2019
- Published online by Cambridge University Press:
- 02 July 2018, pp. 1118-1127
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Background
Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.
MethodsWithin an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.
ResultsFive pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).
ConclusionsA subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.
Flumetsulam mobility in two Mississippi soils as influenced by irrigation timing
- Chris H. Tingle, David R. Shaw, Patrick D. Gerard
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- Journal:
- Weed Science / Volume 47 / Issue 3 / June 1999
- Published online by Cambridge University Press:
- 12 June 2017, pp. 349-352
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Laboratory studies were conducted to evaluate 14C-flumetsulam mobility in two Mississippi soils of varied texture and organic matter content following delays in irrigation. Mobility was evaluated using packed soil columns, 25 cm deep, under unsaturated–saturated flow conditions. Irrigation timings included 0, 3, and 5 d after flumetsulam application. Flumetsulam mobility (defined as the amount collected in leachate) decreased from 45% to no more than 20% of the applied in the Prentiss sandy loam soil when irrigation was delayed 3 or 5 d. With the Okolona soil, flumetsulam recovery in the leachate was 21, 14, and 6%, respectively when irrigation occurred 0, 3, and 5 d after application. Flumetsulam proved to be mobile when irrigation immediately followed application, with 6 to 45% recovered in the leachate from all soils evaluated. The Prentiss soil retained 6% of the applied flumetsulam in the upper 5 cm and the Okolona soil retained 22% when irrigation immediately followed flumetsulam application. When the irrigation interval was delayed at least 3 d, the Okolona soil retained 40% in the upper 5 cm, whereas the Prentiss soil retained 10%. Flumetsulam mobility was dependent on irrigation timing and soil type.
Mechanisms of Resistance to Diclofop of Two Wild Oat (Avena fatua) Biotypes from the Willamette Valley of Oregon
- Steven S. Seefeldt, E. Patrick Fuerst, David R. Gealy, Amit Shukla, Gerard P. Irzyk, Malcolm D. Devine
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- Journal:
- Weed Science / Volume 44 / Issue 4 / December 1996
- Published online by Cambridge University Press:
- 12 June 2017, pp. 776-781
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Laboratory experiments were conducted to determine the mechanism of resistance to diclofop in two wild oat biotypes (designated ‘B’ and ‘C’ biotypes) from the Willamette Valley of Oregon. Resistance could not be attributed to differential absorption, translocation, or metabolism of diclofop. Resistance was not correlated with membrane plasmalemma repolarization following diclofop acid treatment. Compared to a susceptible (’S') wild oat biotype, acetyl CoA carboxylase from the B and C biotypes showed a 10.3 and 4.5 fold increase in the level of resistance, respectively, to diclofop acid. Cross-resistance to fenoxaprop acid was 5.5 and 7.3 times higher in the B and C biotypes, respectively than the S biotype. Correlation between resistance at the whole plant level and at the ACCase level was good for diclofop and fenoxaprop in the B biotype. For the C biotype, this correlation was not as good. Possible reasons for the discrepancy are given.
Using remote sensing to detect weed infestations in Glycine max
- Case R. Medlin, David R. Shaw, Patrick D. Gerard, Falba E. LaMastus
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- Journal:
- Weed Science / Volume 48 / Issue 3 / June 2000
- Published online by Cambridge University Press:
- 20 January 2017, pp. 393-398
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The objective of this research was to evaluate the accuracy of remote sensing for detecting weed infestation levels during early-season Glycine max production. Weed population estimates were collected from two G. max fields approximately 8 wk after planting during summer 1998. Seedling weed populations were sampled using a regular grid coordinate system on a 10- by 10-m grid. Two days later, multispectral digital images of the fields were recorded. Generally, infestations of Senna obtusifolia, Ipomoea lacunosa, and Solanum carolinense could be detected with remote sensing with at least 75% accuracy. Threshold populations of 10 or more S. obtusifolia or I. lacunosa plants m−2 were generally classified with at least 85% accuracy. Discriminant analysis functions formed for detecting weed populations in one field were at least 73% accurate in identifying S. obtusifolia and I. lacunosa infestations in independently collected data from another field. Due to highly variable soil conditions and their effects on the reflectance properties of the surrounding soil and vegetation, accurate classification of weed-free areas was generally much lower. Current remote sensing technology has potential for in-season weed detection; however, further advancements of the technology are needed to insure its use in future prescription weed management systems.
Using a Grower Survey to Assess the Benefits and Challenges of Glyphosate-Resistant Cropping Systems for Weed Management in U.S. Corn, Cotton, and Soybean
- David R. Shaw, Wade A. Givens, Luke A. Farno, Patrick D. Gerard, David Jordan, William G. Johnson, Stephen C. Weller, Bryan G. Young, Robert G. Wilson, Michael D. K. Owen
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- Journal:
- Weed Technology / Volume 23 / Issue 1 / March 2009
- Published online by Cambridge University Press:
- 20 January 2017, pp. 134-149
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Over 175 growers in each of six states (Illinois, Indiana, Iowa, Mississippi, Nebraska, and North Carolina) were surveyed by telephone to assess their perceptions of the benefits of utilizing the glyphosate-resistant (GR) crop trait in corn, cotton, and soybean. The survey was also used to determine the weed management challenges growers were facing after using this trait for a minimum of 4 yr. This survey allowed the development of baseline information on how weed management and crop production practices have changed since the introduction of the trait. It provided useful information on common weed management issues that should be addressed through applied research and extension efforts. The survey also allowed an assessment of the perceived levels of concern among growers about glyphosate resistance in weeds and whether they believed they had experienced glyphosate resistance on their farms. Across the six states surveyed, producers reported 38, 97, and 96% of their corn, cotton, and soybean hectarage planted in a GR cultivar. The most widely adopted GR cropping system was a GR soybean/non-GR crop rotation system; second most common was a GR soybean/GR corn crop rotation system. The non-GR crop component varied widely, with the most common crops being non-GR corn or rice. A large range in farm size for the respondents was observed, with North Carolina having the smallest farms in all three crops. A large majority of corn and soybean growers reported using some type of crop rotation system, whereas very few cotton growers rotated out of cotton. Overall, rotations were much more common in Midwestern states than in Southern states. This is important information as weed scientists assist growers in developing and using best management practices to minimize the development of glyphosate resistance.
Evaluating the Potential for Differential Susceptibility of Common Reed (Phragmites australis) Haplotypes I and M to Aquatic Herbicides
- Joshua C. Cheshier, John D. Madsen, Ryan M. Wersal, Patrick D. Gerard, Mark E. Welch
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- Journal:
- Invasive Plant Science and Management / Volume 5 / Issue 1 / March 2012
- Published online by Cambridge University Press:
- 20 January 2017, pp. 101-105
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Common reed (Phragmites australis) is an invasive perennial grass in aquatic and riparian environments across the United States, forming monotypic stands that displace native vegetation that provides food and cover for wildlife. Genetic variation in global populations of common reed has given rise to two invasive haplotypes, I and M, in the United States. Our objectives were to (1) determine if any differences in herbicide efficacy exist with respect to common reed haplotypes I and M and (2) screen for other labeled aquatic herbicides that may have activity on common reed haplotypes I and M, most notably imazamox and diquat. A replicated outdoor mesocosm study was conducted in 1,136-L (300-gal) tanks using haplotypes I and M of common reed. Restriction fragment length polymorphism methodologies were used to verify the identification of I and M haplotypes used in this study. Diquat at 2.2 (1.9) and 4.5 (4.0) kg ai ha−1 (lb ai ac−1), glyphosate at 2.1 (1.8) and 4.2 (3.7) kg ae ha−1 (lb ae ac−1), imazamox at 0.6 (0.5) and 1.1 (0.9) kg ai ha−1 (lb ai ac−1), imazapyr at 0.8 (0.7) and 1.7 (1.5) kg ai ha−1 (lb ai ac−1), and triclopyr at 3.4 (3.0) and 6.7 (5.9) kg ae ha−1 (lb ae ac−1) were applied to the foliage of common reed. After 12 wk, no difference (P = 0.28) in herbicide tolerance was seen between the two haplotypes with respect to biomass. The 4.2-kg ae ha−1 rate of glyphosate and the 0.8- and 1.7 kg ai ha−1 rates of imazapyr reduced common reed by > 90% at 12 wk after treatment (WAT). Imazamox at 0.6 and 1.1 kg ai ha−1, and triclopyr at 3.4 and 6.7 kg ae ha−1 reduced common reed biomass (62–86%) at 12 WAT, though regrowth occurred. Diquat did not significantly reduce biomass by 12 wk. Glyphosate and imazapyr were the only herbicides that resulted in > 90% biomass reduction and corroborate control from previous studies.
Survey of Soybean Weeds in Mississippi
- Alfred Rankins, Jr., John D. Byrd, Jr., Donald B. Mask, Jimmy W. Barnett, Patrick D. Gerard
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- Journal:
- Weed Technology / Volume 19 / Issue 2 / June 2005
- Published online by Cambridge University Press:
- 20 January 2017, pp. 492-498
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A survey was conducted in 2000 across 38 counties in Mississippi on 192 randomly selected soybean fields to assess the most common occurring weeds. Statewide, prickly sida, which was present in 40% of the fields sampled, was the most common. Pitted and entireleaf morningglory were present in 34 and 29% of the soybean fields, respectively. Broadleaf signalgrass and barnyardgrass were the most common annual grasses, and yellow nutsedge was the most common sedge observed. Trumpetcreeper and redvine were the most common perennial vines. In the Mississippi Delta region of Mississippi, prickly sida was present in 45% of the fields sampled. The trend of occurrence of other species in the Delta mirrored statewide results. In eastern Mississippi, prickly sida and broadleaf signalgrass were found in 43% of soybean fields. Sicklepod, common cocklebur, and balloonvine were more prevalent in eastern Mississippi, when compared with the Mississippi Delta. Since 1982, there has been a sevenfold decline in the occurrence of common cocklebur and a fourfold decline in the occurrence of johnsongrass in Mississippi soybean. Also, the occurrences of redroot pigweed, common ragweed, and fall panicum have declined. Conversely, the occurrences of yellow nutsedge and broadleaf signalgrass have increased. The occurrences of barnyardgrass, prickly sida, redvine and trumpetcreeper have been relatively static over the past two decades.
Assessing the Aquatic Plant Community within the Ross Barnett Reservoir, Mississippi
- Michael C. Cox, Ryan M. Wersal, John D. Madsen, Patrick D. Gerard, Mary L. Tagert
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- Journal:
- Invasive Plant Science and Management / Volume 7 / Issue 2 / June 2014
- Published online by Cambridge University Press:
- 20 January 2017, pp. 375-383
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Alligatorweed, waterhyacinth, and hydrilla are three nonnative aquatic species of concern in the Ross Barnett Reservoir near Jackson, MS. Point-intercept surveys were conducted on the reservoir from 2005 to 2010 to monitor native and nonnative species' distributions and assess herbicide treatment efficacy across the reservoir. Foliar applications of 2,4-D, glyphosate, imazapyr, and diquat were made during summer months for emergent and free-floating vegetation, whereas submersed applications of liquid copper and granular fluridone were applied in spring and late summer for subsurface hydrilla populations. American lotus is the native species that has been observed the most throughout the survey years, with occurrence frequencies averaging between 17 and 27%. Alligatorweed populations significantly decreased from 21% in 2005 to 4% in 2006; however, they consistently increased in the next 4 yr to 12% occurrence in 2010. Waterhyacinth occurrence has remained relatively constant over the study period, averaging below 10% occurrence. Hydrilla was discovered in the reservoir in late 2005 and has remained below 2% in frequency of occurrence since 2006. Suppression of these nonnative species has been attributed to rigorous monitoring and herbicide applications conducted on the reservoir since 2005. A logistic regression model indicated that as native species richness increased, the likelihood of a nonnative species occurring also increased.
Using soil parameters to predict weed infestations in soybean
- Case R. Medlin, David R. Shaw, Michael S. Cox, Patrick D. Gerard, Melinda J. Abshire, Milton C. Wardlaw III
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- Journal:
- Weed Science / Volume 49 / Issue 3 / June 2001
- Published online by Cambridge University Press:
- 20 January 2017, pp. 367-374
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An understanding of environmental factors governing patchy weed distribution in fields could prove to be a valuable tool in weed management. The objectives of this research were to investigate the relationships between weed distribution patterns and environmental properties in two Mississippi soybean fields and to construct models based on those relationships to predict weed distribution. Two months before planting, fields were soil sampled on a 60- by 60-m coordinate grid, and samples were analyzed for calcium, magnesium, potassium, sodium, phosphorus, zinc, cation exchange capacity, percent organic matter, and soil pH. The relative elevation of each sample location was also recorded. Approximately 8 wk after planting, weed populations were estimated on a 30- by 30-m grid over the soil sample grid. Punctual kriging was used to estimate environmental values at each weed sample location. Discriminant analysis techniques were used to evaluate the associations between environmental characteristics on weed population densities of sample areas within each field. Generally, as sicklepod and pitted morningglory infestations increased, the prediction accuracy of the discriminant functions also increased; however, horsenettle infestations were not closely correlated to the environmental properties. Discriminant functions reasonably predicted presence or absence of sicklepod and pitted morningglory within the field. However, validation of the functions across years within the same field and with data collected from the other field resulted in poor classification of all species infestations. Prediction of weed infestations with environmental properties was specific for each field, year, and species.
Utility of Multispectral Imagery for Soybean and Weed Species Differentiation
- Cody J. Gray, David R. Shaw, Patrick D. Gerard, Lori M. Bruce
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- Journal:
- Weed Technology / Volume 22 / Issue 4 / December 2008
- Published online by Cambridge University Press:
- 20 January 2017, pp. 713-718
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An experiment was conducted to determine the utility of multispectral imagery for identifying soybean, bare soil, and six weed species commonly found in Mississippi. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Multispectral imagery was analyzed using supervised classification techniques based upon 2-class, 3-class, and 8-class systems. The 2-class system was designed to differentiate bare soil and vegetation. The 3-class system was used to differentiate bare soil, soybean, and weed species. Finally, the 8-class system was designed to differentiate bare soil, soybean, and all weed species independently. Soybean classification accuracies classified as vegetation for the 2-class system were greater than 95%, and bare soil classification accuracies were greater than 90%. In the 3-class system, soybean classification accuracies were 70% or greater. Classification of soybean decreased slightly in the 3-class system when compared to the 2-class system because of the 3-class system separating soybean plots from the weed plots, which was not done in the 2-class system. Weed classification accuracies increased as weed density or weeks after emergence (WAE) increased. The greatest weed classification accuracies were obtained once weed species were allowed to grow for 10 wk. Palmleaf morningglory and pitted morningglory classification accuracies were greater than 90% for 10 WAE using the 3-class system. Palmleaf morningglory and pitted morningglory at the highest densities of 6 plants/m2 produced the highest classification accuracies for the 8-class system once allowed to grow for 10 wk. All other weed species generally produced classification accuracies less than 50%, regardless of planting density. Thus, multispectral imagery has the potential for weed detection, especially when being used in a management system when individual weed species differentiation is not essential, as in the 2-class or 3-class system. However, weed detection was not obtained until 8 to 10 WAE, which is unacceptable in production agriculture. Therefore, more refined imagery acquisition with higher spatial and/or spectral resolution and more sophisticated analyses need to be further explored for this technology to be used early-season when it would be most valuable.
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. Thomasson, Katherine Thomson-Jones, Joshua C. Thurow, Vzalerie Tiberius, Terrence N. Tice, Paul Tidman, Mark C. Timmons, William Tolhurst, James E. Tomberlin, Rosemarie Tong, Lawrence Torcello, Kelly Trogdon, J. D. Trout, Robert E. Tully, Raimo Tuomela, John Turri, Martin M. Tweedale, Thomas Uebel, Jennifer Uleman, James Van Cleve, Harry van der Linden, Peter van Inwagen, Bryan W. Van Norden, René van Woudenberg, Donald Phillip Verene, Samantha Vice, Thomas Vinci, Donald Wayne Viney, Barbara Von Eckardt, Peter B. M. Vranas, Steven J. Wagner, William J. Wainwright, Paul E. Walker, Robert E. Wall, Craig Walton, Douglas Walton, Eric Watkins, Richard A. Watson, Michael V. Wedin, Rudolph H. Weingartner, Paul Weirich, Paul J. Weithman, Carl Wellman, Howard Wettstein, Samuel C. Wheeler, Stephen A. White, Jennifer Whiting, Edward R. Wierenga, Michael Williams, Fred Wilson, W. Kent Wilson, Kenneth P. Winkler, John F. Wippel, Jan Woleński, Allan B. Wolter, Nicholas P. Wolterstorff, Rega Wood, W. Jay Wood, Paul Woodruff, Alison Wylie, Gideon Yaffe, Takashi Yagisawa, Yutaka Yamamoto, Keith E. Yandell, Xiaomei Yang, Dean Zimmerman, Günter Zoller, Catherine Zuckert, Michael Zuckert, Jack A. Zupko (J.A.Z.)
- Edited by Robert Audi, University of Notre Dame, Indiana
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- The Cambridge Dictionary of Philosophy
- Published online:
- 05 August 2015
- Print publication:
- 27 April 2015, pp ix-xxx
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- By Jimmy N. Avari, Joshua Berman, David A. Brent, Benjamin D. Brody, Carolyn Broudy, Gerard E. Bruder, Deborah L. Cabaniss, Megan S. Chesin, Melissa P. DelBello, Davangere P. Devanand, Jordan W. Eipper, Jean Endicott, Eric A. Fertuck, Michael B. First, Benicio N. Frey, Emily Gastelum, Lucas Giner, Barbara L. Gracious, David J. Hellerstein, Aerin M. Hyun, David A. Kahn, Jürgen Kayser, S. Aiden Kelly, James H. Kocsis, Robert A. Kowatch, Gonzalo Laje, Martin J. Lan, Kyle A. B. Lapidus, Frances R. Levin, Sarah H. Lisanby, J. John Mann, Sanjay J. Mathew, Patrick J. McGrath, Francis J. McMahon, Barnett S. Meyers, Luciano Minuzzi, Diana E. Moga, Philip R. Muskin, Edward V. Nunes, Maria A. Oquendo, Ramin V. Parsey, Joan Prudic, Annie E. Rabinovitch, Drew Ramsey, Steven P. Roose, Moacyr A. Rosa, Bret R. Rutherford, Roberto Sassi, Peter A. Shapiro, Margaret G. Spinelli, Barbara H. Stanley, Meir Steiner, Jonathan W. Stewart, M. Elizabeth Sublette, Craig E. Tenke, Jiuan Su Terman, Michael Terman, Michael E. Thase, Helen Verdeli, Myrna M. Weissman
- Edited by J. John Mann, Columbia University, New York
- Edited in association with Patrick J. McGrath, Columbia University, New York, Steven P. Roose, Columbia University, New York
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- Book:
- Clinical Handbook for the Management of Mood Disorders
- Published online:
- 05 May 2013
- Print publication:
- 09 May 2013, pp vii-x
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Solution chemistry effects on cracking and damage evolution during chemical-mechanical planarization
- Markus D. Ong, Patrick Leduc, Daniel W. McKenzie, Thierry Farjot, Gerard Passemard, Sylvain Maitrejean, Reinhold H. Dauskardt
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- Journal:
- Journal of Materials Research / Volume 25 / Issue 10 / October 2010
- Published online by Cambridge University Press:
- 31 January 2011, pp. 1904-1909
- Print publication:
- October 2010
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We describe progress in understanding the effect of simulated chemical-mechanical planarization (CMP) slurry chemistry on the evolution of defects and formation of damage that occurs during CMP processing. Specifically, we demonstrate the significant effect of aqueous solution chemistry on accelerating crack growth in porous methylsilsesquioxane (MSSQ) films. In addition, we show that the same aqueous solutions can diffuse rapidly into the highly hydrophobic nanoporous MSSQ films containing interconnected porosity. Such diffusion has deleterious effects on both dielectric properties and the acceleration of defect growth rates. Crack propagation rates were measured in several CMP solutions, and the resulting crack growth behavior was used to qualitatively predict the extent of damage during CMP. These predictions are compared with damage formed during actual CMP processes in identical chemistries. We discuss the effects of both the high and low crack growth rate regimes, including the presence of a crack growth threshold, on the predicted CMP damage. Finally, implications for improved CMP processing were considered.
The Genealogy of the Intellectual since the French Enlightenment
- Tzvetan Todorov, Alina Clej, Lawrence D. Kritzman, Frances Ferguson, Howard Young, Patrick Saveau, Gerard Genette
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- Journal:
- PMLA / Publications of the Modern Language Association of America / Volume 112 / Issue 5 / October 1997
- Published online by Cambridge University Press:
- 23 October 2020, pp. 1121-1128
- Print publication:
- October 1997
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