415 research outputs found
New isoforms and assembly of glutamine synthetase in the leaf of wheat (Triticum aestivum L.).
Glutamine synthetase (GS; EC 6.3.1.2) plays a crucial role in the assimilation and re-assimilation of ammonia derived from a wide variety of metabolic processes during plant growth and development. Here, three developmentally regulated isoforms of GS holoenzyme in the leaf of wheat (Triticum aestivum L.) seedlings are described using native-PAGE with a transferase activity assay. The isoforms showed different mobilities in gels, with GSII>GSIII>GSI. The cytosolic GSI was composed of three subunits, GS1, GSr1, and GSr2, with the same molecular weight (39.2kDa), but different pI values. GSI appeared at leaf emergence and was active throughout the leaf lifespan. GSII and GSIII, both located in the chloroplast, were each composed of a single 42.1kDa subunit with different pI values. GSII was active mainly in green leaves, while GSIII showed brief but higher activity in green leaves grown under field conditions. LC-MS/MS experiments revealed that GSII and GSIII have the same amino acid sequence, but GSII has more modification sites. With a modified blue native electrophoresis (BNE) technique and in-gel catalytic activity analysis, only two GS isoforms were observed: one cytosolic and one chloroplastic. Mass calibrations on BNE gels showed that the cytosolic GS1 holoenzyme was ~490kDa and likely a dodecamer, and the chloroplastic GS2 holoenzyme was ~240kDa and likely a hexamer. Our experimental data suggest that the activity of GS isoforms in wheat is regulated by subcellular localization, assembly, and modification to achieve their roles during plant development
Event-triggered communication for passivity and synchronisation of multi-weighted coupled neural networks with and without parameter uncertainties
A multi-weighted coupled neural networks (MWCNNs) model with event-triggered communication is studied here. On the one hand, the passivity of the presented network model is studied by utilising Lyapunov stability theory and some inequality techniques, and a synchronisation criterion based on the obtained output-strict passivity condition of MWCNNs with eventtriggered communication is derived. On the other hand, some robust passivity and robust synchronisation criteria based on output-strict passivity of the proposed network with uncertain parameters are presented. At last, two numerical examples are provided to testify the effectiveness of the output-strict passivity and robust synchronisation results
A Study on the Damage and Economic Threshold of the Soybean Aphid at the Seedling Stage
Both plot inoculation experiments and field pest scouting at the seedling stage indicated that soybean yield losses were closely related to the number of soybean aphids and the proportion of plants colonized by soybean aphids. The main factors affecting the soybean yield were decrease in plant height and number of pods and seeds, owing to injury by soybean aphids at the seedling stage. Under existing production conditions, the economic injury level was 3.36%. The control threshold was 500 soybean aphids per 100 plants, with 35% of plants colonized by soybean aphids.Originating text in Chinese.Citation: Wang, Xibei, Fang, Yihao, Lin, Zhizhong, Zhang, Lirong, Wang, Huadi. (1994). A Study on the Damage and Economic Threshold of the Soybean Aphid at the Seedling Stage. Plant Protection (Institute of Plant Protection, CAAS, China), 20, 12-13
CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
Point cloud completion aims to predict complete shape from its partial
observation. Current approaches mainly consist of generation and refinement
stages in a coarse-to-fine style. However, the generation stage often lacks
robustness to tackle different incomplete variations, while the refinement
stage blindly recovers point clouds without the semantic awareness. To tackle
these challenges, we unify point cloud Completion by a generic
Pretrain-Prompt-Predict paradigm, namely CP3. Inspired by prompting approaches
from NLP, we creatively reinterpret point cloud generation and refinement as
the prompting and predicting stages, respectively. Then, we introduce a concise
self-supervised pretraining stage before prompting. It can effectively increase
robustness of point cloud generation, by an Incompletion-Of-Incompletion (IOI)
pretext task. Moreover, we develop a novel Semantic Conditional Refinement
(SCR) network at the predicting stage. It can discriminatively modulate
multi-scale refinement with the guidance of semantics. Finally, extensive
experiments demonstrate that our CP3 outperforms the state-of-the-art methods
with a large margin
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent
years, due to its ability to detect object motion out of sight. Most previous
works on NLOS tracking rely on active illumination, e.g., laser, and suffer
from high cost and elaborate experimental conditions. Besides, these techniques
are still far from practical application due to oversimplified settings. In
contrast, we propose a purely passive method to track a person walking in an
invisible room by only observing a relay wall, which is more in line with real
application scenarios, e.g., security. To excavate imperceptible changes in
videos of the relay wall, we introduce difference frames as an essential
carrier of temporal-local motion messages. In addition, we propose PAC-Net,
which consists of alternating propagation and calibration, making it capable of
leveraging both dynamic and static messages on a frame-level granularity. To
evaluate the proposed method, we build and publish the first dynamic passive
NLOS tracking dataset, NLOS-Track, which fills the vacuum of realistic NLOS
datasets. NLOS-Track contains thousands of NLOS video clips and corresponding
trajectories. Both real-shot and synthetic data are included. Our codes and
dataset are available at https://againstentropy.github.io/NLOS-Track/.Comment: CVPR 2023 camera-ready version. Codes and dataset are available at
https://againstentropy.github.io/NLOS-Track
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