Institution
Stony Brook University
Education•Stony Brook, New York, United States•
About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.
Papers published on a yearly basis
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University of Florida1, University of Minnesota2, University of Oklahoma3, United States Department of Agriculture4, Donald Danforth Plant Science Center5, Stony Brook University6, Eckerd College7, Purdue University8, Cold Spring Harbor Laboratory9, University of Rhode Island10, University of California, Berkeley11, Colorado State University12, University of California, Riverside13, University of California, Davis14, Boyce Thompson Institute for Plant Research15, Stanford University16, Iowa State University17, University of Missouri18, University of Tennessee19
TL;DR: The state of plant transformation is reviewed and innovations needed to enable genome editing in crops are pointed to, including a potential game-changer in crop genetics when plant transformation systems are optimized.
Abstract: Plant transformation has enabled fundamental insights into plant biology and revolutionized commercial agriculture. Unfortunately, for most crops, transformation and regeneration remain arduous even after more than thirty years of technological advances. Genome editing provides new opportunities to enhance crop productivity, but relies on genetic transformation and plant regeneration, which are bottlenecks in the process. Herein we review the state of plant transformation and point to innovations needed to enable genome editing in crops. Plant tissue culture methods need optimization and simplification for efficiency and minimize time in culture. Currently, specialized facilities exist for crop transformation. Single cell and robotic techniques should be developed for high throughput genomic screens. Utilization of plant genes involved in developmental reprogramming, wound response, and/or homologous recombination could boost recovery of transformed plants. Engineering universal Agrobacterium strains and recruitment of other microbes, such as Ensifer or Rhizobium, could facilitate delivery of DNA and proteins into plant cells. Synthetic biology should be employed for de novo design of transformation systems. Genome editing is a potential game-changer in crop genetics when plant transformation systems are optimized.
419 citations
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TL;DR: The weekend–weekday differences in the cortisol awakening response and their association with chronic stress clearly demonstrate that the day of cortisol assessment is crucial in psychoendocrinological stress studies.
Abstract: OBJECTIVE: The cortisol increase after awakening has been shown to be associated with work-related stress. Several studies demonstrated a moderate stability of cortisol awakening responses on subsequent days, suggesting situation-dependent variance. This study tests whether cortisol awakening responses are different on weekdays compared with weekend days and whether such differences may be explained by chronic work overload and worrying. METHODS: Two hundred nineteen participants took saliva samples immediately after awakening and 30, 45, and 60 minutes later on 6 consecutive days starting on Saturday. Perceived chronic work overload and worrying were assessed by a standardized questionnaire. RESULTS: There is a clear weekend–weekday difference in the cortisol response to awakening. This difference is associated with chronic work overload and worry. Independent of sex and weekend–weekday differences in time of awakening and sleep duration, participants who report higher levels of chronic work overload and worrying show a stronger increase and higher mean levels of cortisol after awakening on weekdays, but not on weekend days. CONCLUSIONS: The weekend–weekday differences in the cortisol awakening response and their association with chronic stress clearly demonstrate that the day of cortisol assessment is crucial in psychoendocrinological stress studies. Abbreviations: ANOVA = analysis of variance; CAR = cortisol awakening response; GLM = general linear model; HPA = hypothalamic–pituitary–adrenal axis.
419 citations
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TL;DR: In this paper, the authors presented experimental data and a computational model of the cold spray solid particle impact process on a polished stainless steel substrate and showed that the impact deformation exposes clean surfaces that, under high impact pressures, result in significant bond strengths between the particle and substrate.
Abstract: This article presents experimental data and a computational model of the cold spray solid particle impact process. Copper particles impacting onto a polished stainless steel substrate were examined in this study. The high velocity impact causes significant plastic deformation of both the particle and the substrate, but no melting was observed. The plastic deformation exposes clean surfaces that, under the high impact pressures, result in significant bond strengths between the particle and substrate. Experimental measurements of the splat and crater sizes compare well with the numerical calculations. It was shown that the crater depth is significant and increases with impact velocity. However, the splat diameter is much less sensitive to the impact velocity. It was also shown that the geometric lengths of the splat and crater scale linearly with the diameter of the impacting particle. The results presented will allow a better understanding of the bonding process during cold spray.
418 citations
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TL;DR: The sampling algorithm is generally sufficient for the binding pose prediction problem for up to 7 rotatable bonds and could be improved through more advanced modeling of the receptor prior to docking and through improvement of the force field parameters, particularly for structures containing metal-based cofactors.
Abstract: We report on the development and validation of a new version of DOCK. The algorithm has been rewritten in a modular format, which allows for easy implementation of new scoring functions, sampling methods and analysis tools. We validated the sampling algorithm with a test set of 114 protein–ligand complexes. Using an optimized parameter set, we are able to reproduce the crystal ligand pose to within 2 A of the crystal structure for 79% of the test cases using our rigid ligand docking algorithm with an average run time of 1 min per complex and for 72% of the test cases using our flexible ligand docking algorithm with an average run time of 5 min per complex. Finally, we perform an analysis of the docking failures in the test set and determine that the sampling algorithm is generally sufficient for the binding pose prediction problem for up to 7 rotatable bonds; i.e. 99% of the rigid ligand docking cases and 95% of the flexible ligand docking cases are sampled successfully. We point out that success rates could be improved through more advanced modeling of the receptor prior to docking and through improvement of the force field parameters, particularly for structures containing metal-based cofactors.
418 citations
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TL;DR: In this article, the authors reported the observation of the X(3872) in the J/psipi(+)pi(-) channel with decaying to mu(+)mu(-), in p (p) over bar collisions at roots=1.96 TeV.
Abstract: We report the observation of the X(3872) in the J/psipi(+)pi(-) channel, with J/psi decaying to mu(+)mu(-), in p (p) over bar collisions at roots=1.96 TeV. Using approximately 230 pb(-1) of data collected with the Run II D0 detector, we observe 522+/-100 X(3872) candidates. The mass difference between the X(3872) state and the J/psi is measured to be 774.9+/-3.1(stat)+/-3.0(syst) MeV/c(2). We have investigated the production and decay characteristics of the X(3872) and find them to be similar to those of the psi(2S) state.
418 citations
Authors
Showing all 32829 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Dennis W. Dickson | 191 | 1243 | 148488 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
David Baker | 173 | 1226 | 109377 |
J. N. Butler | 172 | 2525 | 175561 |
Roderick T. Bronson | 169 | 679 | 107702 |
Nora D. Volkow | 165 | 958 | 107463 |
Jovan Milosevic | 152 | 1433 | 106802 |
Thomas E. Starzl | 150 | 1625 | 91704 |
Paolo Boffetta | 148 | 1455 | 93876 |
Jacques Banchereau | 143 | 634 | 99261 |
Larry R. Squire | 143 | 472 | 85306 |
John D. E. Gabrieli | 142 | 480 | 68254 |
Alexander Milov | 142 | 1143 | 93374 |
Meenakshi Narain | 142 | 1805 | 147741 |