Abiotic stress severely influences plant growth and development. MYB transcription factors (TFs), which compose one of the largest TF families, play an important role in abiotic stress responses.
We ...identified 139 soybean MYB-related genes; these genes were divided into six groups based on their conserved domain and were distributed among 20 chromosomes (Chrs). Quantitative real-time PCR (qRT-PCR) indicated that GmMYB118 highly responsive to drought, salt and high temperature stress; thus, this gene was selected for further analysis. Subcellular localization revealed that the GmMYB118 protein located in the nucleus. Ectopic expression (EX) of GmMYB118 increased tolerance to drought and salt stress and regulated the expression of several stress-associated genes in transgenic Arabidopsis plants. Similarly, GmMYB118-overexpressing (OE) soybean plants generated via Agrobacterium rhizogenes (A. rhizogenes)-mediated transformation of the hairy roots showed improved drought and salt tolerance. Furthermore, compared with the control (CK) plants, the clustered, regularly interspaced, short palindromic repeat (CRISPR)-transformed plants exhibited reduced drought and salt tolerance. The contents of proline and chlorophyll in the OE plants were significantly greater than those in the CK plants, whose contents were greater than those in the CRISPR plants under drought and salt stress conditions. In contrast, the reactive oxygen species (ROS) and malondialdehyde (MDA) contents were significantly lower in the OE plants than in the CK plants, whose contents were lower than those in the CRISPR plants under stress conditions.
These results indicated that GmMYB118 could improve tolerance to drought and salt stress by promoting expression of stress-associated genes and regulating osmotic and oxidizing substances to maintain cell homeostasis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. In this paper, we propose a part-based hierarchical ...bidirectional recurrent neural network (PHRNN) to analyze the facial expression information of temporal sequences. Our PHRNN models facial morphological variations and dynamical evolution of expressions, which is effective to extract "temporal features" based on facial landmarks (geometry information) from consecutive frames. Meanwhile, in order to complement the still appearance information, a multi-signal convolutional neural network (MSCNN) is proposed to extract "spatial features" from still frames. We use both recognition and verification signals as supervision to calculate different loss functions, which are helpful to increase the variations of different expressions and reduce the differences among identical expressions. This deep evolutional spatial-temporal network (composed of PHRNN and MSCNN) extracts the partial-whole, geometry-appearance, and dynamic-still information, effectively boosting the performance of facial expression recognition. Experimental results show that this method largely outperforms the state-of-the-art ones. On three widely used facial expression databases (CK+, Oulu-CASIA, and MMI), our method reduces the error rates of the previous best ones by 45.5%, 25.8%, and 24.4%, respectively.
Microwave image reconstruction based on a deep learning method is investigated in this article. The neural network is capable of converting measured microwave signals acquired from a 24 × 24 antenna ...array at 4 GHz into a 128 × 128 image. To reduce the training difficulty, we first developed an autoencoder by which high-resolution images (128 × 128) were represented with 256 × 1 vectors; then we developed the second neural network which aimed to map microwave signals to the compressed features (256 × 1 vector). Two neural networks can be combined to a full network to make reconstructions, when both are successfully developed. The present two-stage training method reduces the difficulty in training deep learning networks (DLNs) for inverse reconstruction. The developed neural network is validated by simulation examples and experimental data with objects in different shapes/sizes, placed in different locations, and with dielectric constant ranging from 2 to 6. Comparisons between the imaging results achieved by the present method and two conventional approaches: distorted Born iterative method (DBIM) and phase confocal method (PCM) are also provided.
Abstract The number of relativistic species, N eff, has been precisely calculated in the standard model, and would be measured to the percent level by CMB-S4 in future. Neutral-current non-standard ...interactions would affect neutrino decoupling in the early Universe, thus modifying N eff. We parameterize those operators up to dimension-7 in the effective field theory framework, and then provide a complete, generic and analytical dictionary for the collision term integrals. From precision measurements of N eff, the most stringent constraint is obtained for the dimension-6 vector-type neutrino-electron operator, whose scale is constrained to be above about 195 (331) GeV from Planck (CMB-S4). We find our results complementary to other experiments like neutrino coherent scattering, neutrino oscillation, collider, and neutrino deep inelastic scattering experiments.
WRKYs are important regulators in plant development and stress responses. However, knowledge of this superfamily in soybean is limited. In this study, we characterized the drought- and salt-induced ...gene
based on RNA-Seq and qRT-PCR.
, which is 714 bp in length, encoded 237 amino acids and grouped into WRKY II. The promoter region of
included ABER4, MYB, MYC, GT-1, W-box and DPBF
-elements, which possibly participate in abscisic acid (ABA), drought and salt stress responses.
was minimally expressed in different tissues under normal conditions but highly expressed under drought and salt treatments. As a nucleus protein,
was responsive to drought, salt, ABA and salicylic acid (SA) stresses. Using a transgenic hairy root assay, we further characterized the roles of
in abiotic stress tolerance. Compared with control (Williams 82), overexpression of
enhanced drought and salt tolerance, increased proline (Pro) content and decreased malondialdehyde (MDA) content under drought and salt treatment in transgenic soybean seedlings. These results may provide a basis to understand the functions of
in abiotic stress responses in soybean.
Melatonin (N-acetyl-5-methoxytryptamine) is involved in many developmental processes and responses to various abiotic stresses in plants. Most of the studies on melatonin focus on its functions and ...physiological responses in plants, while its regulation mechanism remains unknown. Caffeic acid 3-O-methyltransferase (COMT) functions at a key step of the biosynthesis process of melatonin. In this study, a COMT-like gene,
(Traes_1AL_D9035D5E0.1) was identified in common wheat (
L.). Transient transformation in wheat protoplasts determined that TaCOMT is localized in cytoplasm.
in wheat was induced by drought stress, gibberellin (GA)3 and 3-Indoleacetic acid (IAA), but not by ABA. In
transgenic
, melatonin contents were higher than that in wild type (WT) plants. Under D-Mannitol treatment, the fresh weight of the transgenic
was significantly higher than WT, and transgenic lines had a stronger root system compared to WT. Drought tolerance assays in pots showed that the survival rate of
-overexpression lines was significantly higher than that of WT lines. this phenotype was similar to that the WT lines treated with melatonin under drought condition. In addition, the
transgenic lines had higher proline content and lower malondialdehyde (MDA) content compared to WT lines after drought treatment. These results indicated that overexpression of the wheat
gene enhances drought tolerance and increases the content of melatonin in transgenic
. It could be one of the potential genes for agricultural applications.
Non‐small cell lung cancer (NSCLC) with wild‐type epidermal growth factor receptor (EGFR) is intrinsic resistance to EGFR‐tyrosine kinase inhibitors (TKIs), such as afatinib. Celastrol, a natural ...compound with antitumor activity, was reported to induce paraptosis in cancer cells. In this study, intrinsic EGFR‐TKI‐resistant NSCLC cell lines H23 (EGFR wild‐type and KRAS mutation) and H292 (EGFR wild‐type and overexpression) were used to test whether celastrol could overcome primary afatinib resistance through paraptosis induction. The synergistic effect of celastrol and afatinib on survival inhibition of the NSCLC cells was evaluated by CCK‐8 assay and isobologram analysis. The paraptosis and its modulation were assessed by light and electron microscopy, Western blot analysis, and immunofluorescence. Xenografts models were established to investigate the inhibitory effect of celastrol plus afatinib on the growth of the NSCLC tumors in vivo. Results showed that celastrol acted synergistically with afatinib to suppress the survival of H23 and H292 cells by inducing paraptosis characterized by extensive cytoplasmic vacuolation. This process was independent of apoptosis and not associated with autophagy induction. Afatinib plus celastrol‐induced cytoplasmic vacuolation was preceded by endoplasmic reticulum stress and unfolded protein response. Accumulation of intracellular reactive oxygen species and mitochondrial Ca2+ overload may be initiating factors of celastrol/afatinib‐induced paraptosis and subsequent cell death. Furthermore, Celastrol and afatinib synergistically suppressed the growth of H23 cell xenograft tumors in vivo. The data indicate that a combination of afatinib and celastrol may be a promising therapeutic strategy to surmount intrinsic afatinib resistance in NSCLC cells.
Graphical
Celastrol acted synergistically with afatinib to suppress the survival of non‐small cell lung cancer cells by inducing paraptosis.
Afatinib plus celastrol‐induced paraptosis was mediated by endoplasmic reticulum stress and unfolded protein response.
Accumulation of intracellular reactive oxygen species and mitochondrial Ca2+ overload induced by celastrol/afatinib may be an initiating factor of paraptosis and subsequent cell death.
Human actions can be represented by the trajectories of skeleton joints. Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features ...and recognize human actions by well-designed classifiers. In this paper, considering that recurrent neural network (RNN) can model the long-term contextual information of temporal sequences well, we propose an end-to-end hierarchical RNN for skeleton based action recognition. Instead of taking the whole skeleton as the input, we divide the human skeleton into five parts according to human physical structure, and then separately feed them to five subnets. As the number of layers increases, the representations extracted by the subnets are hierarchically fused to be the inputs of higher layers. The final representations of the skeleton sequences are fed into a single-layer perceptron, and the temporally accumulated output of the perceptron is the final decision. We compare with five other deep RNN architectures derived from our model to verify the effectiveness of the proposed network, and also compare with several other methods on three publicly available datasets. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.
Motion characteristics of human actions can be represented by the position variation of skeleton joints. Traditional approaches generally extract the spatial-temporal representation of the skeleton ...sequences with well-designed hand-crafted features. In this paper, in order to recognize actions according to the relative motion between the limbs and the trunk, we propose an end-to-end hierarchical RNN for skeleton-based action recognition. We divide human skeleton into five main parts in terms of the human physical structure, and then feed them to five independent subnets for local feature extraction. After the following hierarchical feature fusion and extraction from local to global, dimensions of the final temporal dynamics representations are reduced to the same number of action categories in the corresponding data set through a single-layer perceptron. In addition, the output of the perceptron is temporally accumulated as the input of a softmax layer for classification. Random scale and rotation transformations are employed to improve the robustness during training. We compare with five other deep RNN variants derived from our model in order to verify the effectiveness of the proposed network. In addition, we compare with several other methods on motion capture and Kinect data sets. Furthermore, we evaluate the robustness of our model trained with random scale and rotation transformations for a multiview problem. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.
In this paper we present a general solution for multi-target tracking with superpositional measurements. Measurements that are functions of the sum of the contributions of the targets present in the ...surveillance area are called superpositional measurements. We base our modelling on Labeled Random Finite Set (RFS) in order to jointly estimate the number of targets and their trajectories. This modelling leads to a labeled version of Mahler's multi-target Bayes filter. However, a straightforward implementation of this tracker using Sequential Monte Carlo (SMC) methods is not feasible due to the difficulties of sampling in high dimensional spaces. We propose an efficient multi-target sampling strategy based on Superpositional Approximate CPHD (SA-CPHD) filter and the recently introduced Labeled Multi-Bernoulli (LMB) and Vo-Vo densities. The applicability of the proposed approach is verified through simulation in a challenging radar application with closely spaced targets and low signal-to-noise ratio.
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