Identification of stress-responsive genes in Ammopiptanthus mongolicus using ESTs generated from cold- and drought-stressed seedlings

BACKGROUND: Ammopiptanthus mongolicus is the only evergreen broadleaf shrub in the northwest desert of China, which can survive long-term aridity and extremely cold environments. In order to understand the genetic mechanisms underlying stress tolerance and adaptation to unfavorable environments of woody plants, an EST approach was used to investigate expression patterns of A. mongolicus in response to abiotic stresses. RESULTS: ESTs were generated from a cDNA library constructed from A. mongolicus seedlings subjected to cold and drought stresses. Analysis of 5,637 cDNA sequences led to the identification of 5,282 ESTs and 1,594 unigenes, which were denoted as the AmCDUnigene set. Of these, 70% of unigenes were annotated and classified into 12 functional categories according to Gene Ontology, and 30% of unigenes encoded unknown function proteins, suggesting some of them were novel or A. mongolicus specific genes. Using comparative analysis with the reported genes from other plants, 528 (33%) unigenes were identified as stress-responsive genes. The functional classification of the 528 genes showed that a majority of them are associated with scavenging reactive oxygen species, stress response, cellular transport, signal transduction and transcription. To further identify candidate abiotic stress-tolerance genes, the 528 stress-responsive genes were compared with reported abiotic stress genes in the Comparative Stress Genes Catalog of GCP. This comparative analysis identified 120 abiotic stress-responsive genes, and their expression in A. mongolicus seedlings under cold or drought stress were characterized by qRT-PCR. Significantly, 82 genes responded to cold and/or drought stress. These cold- and/or drought-inducible genes confirmed that the ROS network, signal transduction and osmolyte accumulation undergo transcriptional reorganization when exposed to cold or drought stress treatments. Additionally, among the 1,594 unigenes sequences, 155 simple sequence repeats (SSRs) were identified. CONCLUSION: This study represents a comprehensive analysis of cold and/or drought stress-responsive transcriptiome of A. mongolicus. The newly characterized genes and gene-derived markers from the AmCDUnigene set are valuable resources for a better understanding of the mechanisms that govern stress tolerance in A. mongolicus and other related species. Certain up-regulated genes characterizing these processes are potential targets for breeding for cold and/or drought tolerance of woody plants

Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression

Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial information with confidence intervals. However, it is difficult to handle cooperative online mapping tasks because of its high computation and communication costs. This letter proposes a resource-efficient cooperative online field mapping method via distributed sparse Gaussian process regression. A novel distributed online Gaussian process evaluation method is developed such that robots can cooperatively evaluate and find observations of sufficient global utility to reduce computation. The bounded errors of distributed aggregation results are guaranteed theoretically, and the performances of the proposed algorithms are validated by real online light field mapping experiments

Interaction of Mammalian Mitochondrial Ribosomes with the Inner Membrane

All of the products of mitochondrial protein biosynthesis in animals are hydrophobic proteins that are localized in the inner membrane. Hence, it is possible that the synthesis of these proteins could occur on ribosomes associated with the inner membrane. To examine this possibility, inner membrane and matrix fractions of bovine mitochondria were examined for the presence of ribosomes using probes for the rRNAs. Between 40 and 50% of the ribosomes were found to fractionate with the inner membrane. About half of the ribosomes associated with the inner membrane could be released by high salt treatment, indicating that they interact with the membrane largely through electrostatic forces. No release of the ribosome was observed upon treatment with puromycin, suggesting that the association observed is not due to insertion of a nascent polypeptide chain into the membrane. A fraction of the ribosomes remained with residual portions of the membranes that cannot be solubilized in the presence of Triton X-100. These ribosomes may be associated with large oligomeric complexes in the membrane

IBVC: Interpolation-driven B-frame Video Compression

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.Comment: Submitted to IEEE TCSV

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation

Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling. However, they insufficiently consider the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. Specifically, the motion trajectory between the target frame and the multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM is adopted to construct this joint distribution of complete motions in temporal dimension. Moreover, the feature learning network is designed to optimize for the joint regression modeling. A coarse-to-fine synthesis enhancement module is also conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Experimental results on VFI show that the effectiveness and significant improvement of joint motion regression compared with the state-of-the-art methods. The code is available at https://github.com/ruhig6/JNMR.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Temporal Consistency Learning of inter-frames for Video Super-Resolution

Video super-resolution (VSR) is a task that aims to reconstruct high-resolution (HR) frames from the low-resolution (LR) reference frame and multiple neighboring frames. The vital operation is to utilize the relative misaligned frames for the current frame reconstruction and preserve the consistency of the results. Existing methods generally explore information propagation and frame alignment to improve the performance of VSR. However, few studies focus on the temporal consistency of inter-frames. In this paper, we propose a Temporal Consistency learning Network (TCNet) for VSR in an end-to-end manner, to enhance the consistency of the reconstructed videos. A spatio-temporal stability module is designed to learn the self-alignment from inter-frames. Especially, the correlative matching is employed to exploit the spatial dependency from each frame to maintain structural stability. Moreover, a self-attention mechanism is utilized to learn the temporal correspondence to implement an adaptive warping operation for temporal consistency among multi-frames. Besides, a hybrid recurrent architecture is designed to leverage short-term and long-term information. We further present a progressive fusion module to perform a multistage fusion of spatio-temporal features. And the final reconstructed frames are refined by these fused features. Objective and subjective results of various experiments demonstrate that TCNet has superior performance on different benchmark datasets, compared to several state-of-the-art methods.Comment: Accepted by IEEE Trans. Circuits Syst. Video Techno