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eScholarship Publishing
Scholarly Works (5 results)
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Book
Peer Reviewed
Managing Glyphosate-Resistant Weeds in Glyphosate-Resistant Crops
Al-Khatib, Kassim
;
Hanson, Brad
;
Miller, Tim
;
Peachey, Ed
;
Boydston, Rick
Farm
(2013)
Glyphosate-resistant crops made farming a lot easier when they first came out, but then the weeds started to catch on, too. Nowadays, herbicide-tolerant crops will only work as part of a more comprehensive, Integrated Pest Management plan.
Book
Peer Reviewed
Glyphosate Stewardship: Maintaining the Effectiveness of a Widely Used Herbicide
Miller, Tim
;
Hanson, Brad
;
Peachey, Ed
;
Boydston, Rick
;
Al-Khatib, Kassim
Farm
(2013)
Glyphosate (Roundup) is very effective against weeds, but you can't use it just any old way or you'll end up encouraging the development of resistant weeds. Learn to find the best timing, mix, and application method, and complementary control methods.
Book
Peer Reviewed
Preventing and Managing Glyphosate-Resistant Weeds in Orchards and Vineyards
Peachey, Ed
;
Boydston, Rick
;
Hanson, Brad
;
Miller, Tim
;
Al-Khatib, Kassim
Farm
(2013)
Rely too much on any one herbicide and you end up with weeds that will resist its effects—and that's just what is happening now with glyphosate (Roundup). See how you can increase effectiveness by diversifying your weed-management strategies.
Book
Peer Reviewed
Selection Pressure, Shifting Populations, and Herbicide Resistance and Tolerance
Hanson, Brad
;
Fischer, Albert
;
Shrestha, Anil
;
Jasieniuk, Marie
;
Peachey, Ed
;
Boydston, Rick
;
Miller, Tim
;
Al-Khatib, Kassim
Farm
(2013)
A good herbicide will control the current population of weeds, but also create a powerful “selection pressure”: any weeds that survive its effects will go on to thrive, immune to any further applications. Learn how to keep selection pressure in check.
Article
Peer Reviewed
Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines
Baxi, Emily G
;
Thompson, Terri
;
Li, Jonathan
;
Kaye, Julia A
;
Lim, Ryan G
;
Wu, Jie
;
Ramamoorthy, Divya
;
Lima, Leandro
;
Vaibhav, Vineet
;
Matlock, Andrea
;
Frank, Aaron
;
Coyne, Alyssa N
;
Landin, Barry
;
Ornelas, Loren
;
Mosmiller, Elizabeth
;
Thrower, Sara
;
Farr, S Michelle
;
Panther, Lindsey
;
Gomez, Emilda
;
Galvez, Erick
;
Perez, Daniel
;
Meepe, Imara
;
Lei, Susan
;
Mandefro, Berhan
;
Trost, Hannah
;
Pinedo, Louis
;
Banuelos, Maria G
;
Liu, Chunyan
;
Moran, Ruby
;
Garcia, Veronica
;
Workman, Michael
;
Ho, Richie
;
Wyman, Stacia
;
Roggenbuck, Jennifer
;
Harms, Matthew B
;
Stocksdale, Jennifer
;
Miramontes, Ricardo
;
Wang, Keona
;
Venkatraman, Vidya
;
Holewenski, Ronald
;
Sundararaman, Niveda
;
Pandey, Rakhi
;
Manalo, Danica-Mae
;
Donde, Aneesh
;
Huynh, Nhan
;
Adam, Miriam
;
Wassie, Brook T
;
Vertudes, Edward
;
Amirani, Naufa
;
Raja, Krishna
;
Thomas, Reuben
;
Hayes, Lindsey
;
Lenail, Alex
;
Cerezo, Aianna
;
Luppino, Sarah
;
Farrar, Alanna
;
Pothier, Lindsay
;
Prina, Carolyn
;
Morgan, Todd
;
Jamil, Arish
;
Heintzman, Sarah
;
Jockel-Balsarotti, Jennifer
;
Karanja, Elizabeth
;
Markway, Jesse
;
McCallum, Molly
;
Joslin, Ben
;
Alibazoglu, Deniz
;
Kolb, Stephen
;
Ajroud-Driss, Senda
;
Baloh, Robert
;
Heitzman, Daragh
;
Miller, Tim
;
Glass, Jonathan D
;
Patel-Murray, Natasha Leanna
;
Yu, Hong
;
Sinani, Ervin
;
Vigneswaran, Prasha
;
Sherman, Alexander V
;
Ahmad, Omar
;
Roy, Promit
;
Beavers, Jay C
;
Zeiler, Steven
;
Krakauer, John W
;
Agurto, Carla
;
Cecchi, Guillermo
;
Bellard, Mary
;
Raghav, Yogindra
;
Sachs, Karen
;
Ehrenberger, Tobias
;
Bruce, Elizabeth
;
Cudkowicz, Merit E
;
Maragakis, Nicholas
;
Norel, Raquel
;
Van Eyk, Jennifer E
;
Finkbeiner, Steven
;
Berry, James
;
Sareen, Dhruv
;
Thompson, Leslie M
;
Fraenkel, Ernest
;
Svendsen, Clive N
;
Rothstein, Jeffrey D
UC Irvine Previously Published Works
(2022)
Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.
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