25,846 research outputs found
A pivotal role for starch in the reconfiguration of 14C-partitioning and allocation in Arabidopsis thaliana under short-term abiotic stress.
Plant carbon status is optimized for normal growth but is affected by abiotic stress. Here, we used 14C-labeling to provide the first holistic picture of carbon use changes during short-term osmotic, salinity, and cold stress in Arabidopsis thaliana. This could inform on the early mechanisms plants use to survive adverse environment, which is important for efficient agricultural production. We found that carbon allocation from source to sinks, and partitioning into major metabolite pools in the source leaf, sink leaves and roots showed both conserved and divergent responses to the stresses examined. Carbohydrates changed under all abiotic stresses applied; plants re-partitioned 14C to maintain sugar levels under stress, primarily by reducing 14C into the storage compounds in the source leaf, and decreasing 14C into the pools used for growth processes in the roots. Salinity and cold increased 14C-flux into protein, but as the stress progressed, protein degradation increased to produce amino acids, presumably for osmoprotection. Our work also emphasized that stress regulated the carbon channeled into starch, and its metabolic turnover. These stress-induced changes in starch metabolism and sugar export in the source were partly accompanied by transcriptional alteration in the T6P/SnRK1 regulatory pathway that are normally activated by carbon starvation
Pregelix: Big(ger) Graph Analytics on A Dataflow Engine
There is a growing need for distributed graph processing systems that are
capable of gracefully scaling to very large graph datasets. Unfortunately, this
challenge has not been easily met due to the intense memory pressure imposed by
process-centric, message passing designs that many graph processing systems
follow. Pregelix is a new open source distributed graph processing system that
is based on an iterative dataflow design that is better tuned to handle both
in-memory and out-of-core workloads. As such, Pregelix offers improved
performance characteristics and scaling properties over current open source
systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up
to 35x speedup compared to distributed GraphLab), and makes more effective use
of available machine resources to support Big(ger) Graph Analytics
Motion estimation for H.264/AVC on multiple GPUs using NVIDIA CUDA
To achieve the high coding efficiency the H.264/AVC standard offers, the encoding process quickly becomes computationally demanding. One of the most intensive encoding phases is motion estimation. Even modern CPUs struggle to process high-definition video sequences in real-time. While personal computers are typically equipped with powerful Graphics Processing Units (GPUs) to accelerate graphics operations, these GPUs lie dormant when encoding a video sequence. Furthermore, recent developments show more and more computer configurations come with multiple GPUs. However, no existing GPU-enabled motion estimation architectures target multiple GPUs. In addition, these architectures provide no early-out behavior nor can they enforce a specific processing order. We developed a motion search architecture, capable of executing motion estimation and partitioning for an H.264/AVC sequence entirely on the GPU using the NVIDIA CUDA (Compute Unified Device Architecture) platform. This paper describes our architecture and presents a novel job scheduling system we designed, making it possible to control the GPU in a flexible way. This job scheduling system can enforce real-time demands of the video encoder by prioritizing calculations and providing an early-out mode. Furthermore, the job scheduling system allows the use of multiple GPUs in one computer system and efficient load balancing of the motion search over these GPUs. This paper focuses on the execution speed of the novel job scheduling system on both single and multi-GPU systems. Initial results show that real-time full motion search of 720p high-definition content is possible with a 32 by 32 search window running on a system with four GPUs
Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism
Dangerous damage to mitochondrial DNA (mtDNA) can be ameliorated during
mammalian development through a highly debated mechanism called the mtDNA
bottleneck. Uncertainty surrounding this process limits our ability to address
inherited mtDNA diseases. We produce a new, physically motivated, generalisable
theoretical model for mtDNA populations during development, allowing the first
statistical comparison of proposed bottleneck mechanisms. Using approximate
Bayesian computation and mouse data, we find most statistical support for a
combination of binomial partitioning of mtDNAs at cell divisions and random
mtDNA turnover, meaning that the debated exact magnitude of mtDNA copy number
depletion is flexible. New experimental measurements from a wild-derived mtDNA
pairing in mice confirm the theoretical predictions of this model. We
analytically solve a mathematical description of this mechanism, computing
probabilities of mtDNA disease onset, efficacy of clinical sampling strategies,
and effects of potential dynamic interventions, thus developing a quantitative
and experimentally-supported stochastic theory of the bottleneck.Comment: Main text: 14 pages, 5 figures; Supplement: 17 pages, 4 figures;
Total: 31 pages, 9 figure
Complexity Analysis Of Next-Generation VVC Encoding and Decoding
While the next generation video compression standard, Versatile Video Coding
(VVC), provides a superior compression efficiency, its computational complexity
dramatically increases. This paper thoroughly analyzes this complexity for both
encoder and decoder of VVC Test Model 6, by quantifying the complexity
break-down for each coding tool and measuring the complexity and memory
requirements for VVC encoding/decoding. These extensive analyses are performed
for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD),
Random-Access (RA), and All-Intra (AI) conditions (a total of 320
encoding/decoding). Results indicate that the VVC encoder and decoder are 5x
and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI,
respectively. Detailed analysis of coding tools reveals that in LD on average,
motion estimation tools with 53%, transformation and quantization with 22%, and
entropy coding with 7% dominate the encoding complexity. In decoding, loop
filters with 30%, motion compensation with 20%, and entropy decoding with 16%,
are the most complex modules. Moreover, the required memory bandwidth for VVC
encoding/decoding are measured through memory profiling, which are 30x and 3x
of HEVC. The reported results and insights are a guide for future research and
implementations of energy-efficient VVC encoder/decoder.Comment: IEEE ICIP 202
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