scispace - formally typeset

Institution

Rice University

EducationHouston, Texas, United States
About: Rice University is a(n) education organization based out in Houston, Texas, United States. It is known for research contribution in the topic(s): Population & Carbon nanotube. The organization has 28664 authors who have published 56387 publication(s) receiving 2663929 citation(s). The organization is also known as: William Marsh Rice University.
Papers
More filters

Journal ArticleDOI
Chongjun Sun1, Xiaoyu Liang1, Xiaomei Gong1, Huamei Chen1  +5 moreInstitutions (2)
Abstract: Silicon (Si) has been shown to alleviate Cd stress in rice. Here, we investigated the beneficial effects of foliar Si in an indica rice Huanghuazhan (HHZ). Our results showed that foliar Si increases the dry weight and decreases Cd translocation in Cd-exposed rice at the grain-filling stage only, implying that the filling stage is critical for foliar Si to reduce Cd accumulation. We also investigated the transcriptomics in flag leaves (FLs), spikelets (SPs), and node Is (NIs) of Cd-exposed HHZ after foliar Si application at the filling stage. Importantly, the gene expression profiles associated with the Si-mediated alleviation of Cd stress were tissue specific, while shared pathways were mediated by Si in Cd-exposed rice tissues. Furthermore, after the Si treatment of Cd-exposed rice, the ATP-binding cassette (ABC)-transporters were mostly upregulated in FL and SP, while the bivalent cation transporters were mostly downregulated in FL and NI, possibly helping to reduce Cd accumulation. The genes associated with essential nutrient transporters, carbohydrate and secondary metabolite biosynthesis, and cytochrome oxidase activity were mostly upregulated in Cd-exposed FL and SP, which may help to alleviate oxidative stress and improve plant growth under Cd exposure. Interestingly, genes responsible for signal transduction were negatively regulated in FL, but positively regulated in SP, by foliar Si. Our results provide transcriptomic evidence that foliar Si plays an active role in alleviating the effects of Cd exposure in rice. In particular, foliar Si may alter the expression pattern of genes associated with transport, biosynthesis and metabolism, and oxidation reduction.

1 citations


Journal ArticleDOI
Om Prakash Gupta1, Rupesh Deshmukh, Awadhesh Kumar2, Sanjay Singh1  +3 moreInstitutions (2)
Abstract: Although at the infancy stage, biomolecular network biology is a comprehensive approach to understand complex biological function in plants. Recent advancements in the accumulation of multi-omics data coupled with computational approach have accelerated our current understanding of the complexities of gene function at the system level. Biomolecular networks such as protein-protein interaction, co-expression and gene regulatory networks have extensively been used to decipher the intricacies of transcriptional reprogramming of hundreds of genes and their regulatory interaction in response to various environmental perturbations mainly in the model plant Arabidopsis. This review describes recent applications of network-based approaches to understand the biological functions in plants and focuses on the challenges and opportunities to harness the full potential of the approach.

Journal ArticleDOI
Abstract: We consider a space–time adaptive splitting scheme for polycrystallization processes described by a two-field phase field model. The phase field model consists of a coupled system of evolutionary processes for the local degree of crystallinity ϕ and the orientation angle Θ one of them being of first order total variation flow type. The splitting scheme is based on an implicit discretization in time which allows a decoupling of the system in the sense that at each time step minimization problems in ϕ and Θ have to be solved successively. The discretization in space is taken care of by a standard finite element approximation for the problem in ϕ and an Interior Penalty Discontinuous Galerkin (IPDG) approximation for the one in Θ . The adaptivity in space relies on equilibrated a posteriori error estimators for the discretization errors in ϕ and Θ in terms of primal and dual energy functionals associated with the respective minimization problems. The adaptive time stepping is dictated by the convergence of a semismooth Newton method for the numerical solution of the nonlinear problem in Θ . Numerical results illustrate the performance of the adaptive space–time splitting scheme for two representative polycrystallization processes.

Journal ArticleDOI
Keren Zhou1, Xiaozhu Meng1, Ryuichi Sai1, Dejan Grubisic1  +1 moreInstitutions (1)
TL;DR: GPA, a performance advisor that suggests potential code optimizations at a hierarchy of levels, including individual lines, loops, and functions, is described and experiments show that GPA provides useful advice for tuning GPU code.
Abstract: The US Department of Energy’s fastest supercomputers and forthcoming exascale systems employ Graphics Processing Units (GPUs) to increase the computational performance of compute nodes. However, the complexity of GPU architectures makes tailoring sophisticated applications to achieve high performance on GPU-accelerated systems a major challenge. At best, prior performance tools for GPU code only provide coarse-grained tuning advice at the kernel level. In this article, we describe GPA, a performance advisor that suggests potential code optimizations at a hierarchy of levels, including individual lines, loops, and functions. To gather the fine-grained measurements needed to produce such insights, GPA uses instruction sampling and binary instrumentation to monitor execution of GPU code. At the time of this writing, GPU instruction sampling is only available on NVIDIA GPUs. To understand performance losses, GPA uses data flow analysis to approximately attribute measured instruction stalls back to their causes. GPA then analyzes patterns of stalls using information about a program’s structure and the GPU architecture to identify optimization strategies that address inefficiencies observed. GPA then employs detailed performance models to estimate the potential speedup that each optimization might provide. Experiments with benchmarks and applications show that GPA provides useful advice for tuning GPU code. We applied GPA to analyze and tune a collection of codes on NVIDIA V100 and A100 GPUs. GPA suggested optimizations that it estimates will accelerate performance across the set of codes by a geometric mean of 1.21×. Applying these optimizations suggested by GPA accelerated these codes by a geometric mean of 1.19×.

Journal ArticleDOI
Abstract: Rice cultivars are major conduit of arsenic (As) poisoning to human. We quantified transferability of fifteen rice cultivars representing three groups i.e., high yielding variety (HYV), local aromatic rice (LAR) and hybrid for As from soil to cooked rice and its ingestion led health risk, elucidating the processes of its unloading at five check points. Conducting a field experiment with those cultivars, we sampled roots and shoots at tillering, booting and maturity (with grains), separated the grains into husk, bran and polished rice, cooked it through different methods and analyzed for As. Of the tested groups, As restriction from root to grain followed the order: LARs (94%) > HYVs (88.3%) > hybrids (87.2%). The low As sequestration by LARs was attributed to their higher root biomass (10.20 g hill−1) and Fe-plaque formation (2421 mg kg−1), and lower As transfer coefficients (0.17), and higher As retention in husk and bran (84%). On average, based on calculated four major risk indices, LARs showed 4.7–6.8 folds less As toxicity than HYVs and hybrids. These insights are helpful in advocating some remedies for As toxicity of the tested rice cultivars.

Authors

Showing all 28664 results

NameH-indexPapersCitations
Hagop M. Kantarjian2043708210208
Hongjie Dai197570182579
Alan C. Evans183866134642
Pulickel M. Ajayan1761223136241
Wei Li1581855124748
David Tilman158340149473
Menachem Elimelech15754795285
Richard E. Smalley153494111117
Jay Roberts1521562120516
Ashok Kumar1515654164086
Yoshio Bando147123480883
Marco Zanetti1451439104610
Frank Jm Geurts1441484107855
James M. Tour14385991364
Daniela Bortoletto1431883108433
Network Information
Related Institutions (5)
University of Texas at Austin

206.2K papers, 9M citations

93% related

University of Illinois at Urbana–Champaign

225.1K papers, 10.1M citations

93% related

Massachusetts Institute of Technology

268K papers, 18.2M citations

92% related

Cornell University

235.5K papers, 12.2M citations

92% related

Stanford University

320.3K papers, 21.8M citations

92% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202271
20213,142
20203,335
20192,890
20182,697
20172,749