Exploring transcriptional signalling mediated by OsWRKY13, a potential regulator of multiple physiological processes in rice

BACKGROUND Rice transcription regulator OsWRKY13 influences the functioning of more than 500 genes in multiple signalling pathways, with roles in disease resistance, redox homeostasis, abiotic stress responses, and development. RESULTS To determine the putative transcriptional regulation mechanism of OsWRKY13, the putative cis-acting elements of OsWRKY13-influenced genes were analyzed using the whole genome expression profiling of OsWRKY13-activated plants generated with the Affymetrix GeneChip Rice Genome Array. At least 39 transcription factor genes were influenced by OsWRKY13, and 30 of them were downregulated. The promoters of OsWRKY13-upregulated genes were overrepresented with W-boxes for WRKY protein binding, whereas the promoters of OsWRKY13-downregulated genes were enriched with cis-elements putatively for binding of MYB and AP2/EREBP types of transcription factors. Consistent with the distinctive distribution of these cis-elements in up- and downregulated genes, nine WRKY genes were influenced by OsWRKY13 and the promoters of five of them were bound by OsWRKY13 in vitro; all seven differentially expressed AP2/EREBP genes and six of the seven differentially expressed MYB genes were suppressed by in OsWRKY13-activated plants. A subset of OsWRKY13-influenced WRKY genes were involved in host-pathogen interactions. CONCLUSION These results suggest that OsWRKY13-mediated signalling pathways are partitioned by different transcription factors. WRKY proteins may play important roles in the monitoring of OsWRKY13-upregulated genes and genes involved in pathogen-induced defence responses, whereas MYB and AP2/EREBP proteins may contribute most to the control of OsWRKY13-downregulated genes.This work was supported by grants from the National Program of High Technology Development of China, the National Program on the Development of Basic Research in China, and the National Natural Science Foundation of China

Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive realworld trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher

One size does not fit all: the differential impact of online reviews

There has been plenty of research on the impact of online reviews on product sales in the last decade. However, prior studies don’t always reach the same conclusions. Literature review indicates that, because of data limitations, prior studies treat the consumers as homogeneous and ignore their individual characteristics. There has only been very limited research that delves into the characteristics of the products being reviewed. Do online reviews have the same impact on consumers who may have different shopping habits or demographic characteristics? Do online reviews also impact the sales of all products/services to the same extent? Using a unique dataset that includes individually identifiable consumer online review browsing data and purchase data, this paper analyzes the effect of online reviews from a more nuanced perspective by examining how individual consumer shopping characteristics and vendor characteristics moderate the effect of online reviews as well as vendors’ marketing activities

Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation

Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query. Existing methods mainly rely on sophisticated pipelines to tackle such cross-modal task, and do not explicitly model the object-level spatial context which plays an important role in locating the referred object. Therefore, we propose an end-to-end RVOS framework completely built upon transformers, termed \textit{Fully Transformer-Equipped Architecture} (FTEA), which treats the RVOS task as a mask sequence learning problem and regards all the objects in video as candidate objects. Given a video clip with a text query, the visual-textual features are yielded by encoder, while the corresponding pixel-level and word-level features are aligned in terms of semantic similarity. To capture the object-level spatial context, we have developed the Stacked Transformer, which individually characterizes the visual appearance of each candidate object, whose feature map is decoded to the binary mask sequence in order directly. Finally, the model finds the best matching between mask sequence and text query. In addition, to diversify the generated masks for candidate objects, we impose a diversity loss on the model for capturing more accurate mask of the referred object. Empirical studies have shown the superiority of the proposed method on three benchmarks, e.g., FETA achieves 45.1% and 38.7% in terms of mAP on A2D Sentences (3782 videos) and J-HMDB Sentences (928 videos), respectively; it achieves 56.6% in terms of $\mathcal{J\&F}$ on Ref-YouTube-VOS (3975 videos and 7451 objects). Particularly, compared to the best candidate method, it has a gain of 2.1% and 3.2% in terms of P$@$0.5 on the former two, respectively, while it has a gain of 2.9% in terms of $\mathcal{J}$ on the latter one

Fast Fourier Inception Networks for Occluded Video Prediction

Video prediction is a pixel-level task that generates future frames by employing the historical frames. There often exist continuous complex motions, such as object overlapping and scene occlusion in video, which poses great challenges to this task. Previous works either fail to well capture the long-term temporal dynamics or do not handle the occlusion masks. To address these issues, we develop the fully convolutional Fast Fourier Inception Networks for video prediction, termed \textit{FFINet}, which includes two primary components, \ie, the occlusion inpainter and the spatiotemporal translator. The former adopts the fast Fourier convolutions to enlarge the receptive field, such that the missing areas (occlusion) with complex geometric structures are filled by the inpainter. The latter employs the stacked Fourier transform inception module to learn the temporal evolution by group convolutions and the spatial movement by channel-wise Fourier convolutions, which captures both the local and the global spatiotemporal features. This encourages generating more realistic and high-quality future frames. To optimize the model, the recovery loss is imposed to the objective, \ie, minimizing the mean square error between the ground-truth frame and the recovery frame. Both quantitative and qualitative experimental results on five benchmarks, including Moving MNIST, TaxiBJ, Human3.6M, Caltech Pedestrian, and KTH, have demonstrated the superiority of the proposed approach. Our code is available at GitHub

Physiological roles of Drosophila ADAR and modifiers

ADAR (Adenosine Deaminases acting on RNA) family proteins are double-strand RNA binding proteins that deaminate specific adenosines into inosines. This A-to-I conversion is called A-to-I RNA editing and is well conserved in the animal kingdom from nematodes to humans. RNA editing is a pre-splicing event on nascent RNA that may affect alternative splicing when the editing occurs in the exon-intron junction or in the intron. Also, editing may change biological function of small RNAs by editing the premicroRNAs or other noncoding RNAs. Editing also alters protein amino acid sequences because inosine in the mRNA base pairs with cytosine and is therefore read as guanosine. In mammals, there are three ADAR family proteins, ADAR1, ADAR2, and ADAR3, encoded by three different genes. So far, no enzymatic activity of ADAR3 is detected. The most frequently edited targets of ADAR1 and ADAR2 are regions covering copies of Alu transposable elements in primates. In addition, loss of some specific editing events leads to profound phenotypes when the editing does not occur correctly. For example, some human neural disorders – such as epilepsy, forebrain ischemia, and Amyotrophic Lateral Sclerosis – are known to be associated with abnormally edited ion channel transcripts. Drosophila has a single ADAR protein (encoded by the Adar gene) that is highly conserved with human ADAR2 (encoded by the ADARB1 gene). To date, 972 editing sites have been identified in 597 transcripts in Drosophila, and approximately 20% of AGO2-associated esiRNAs are edited. Similar to mammals, many ion channel-encoding mRNA transcripts undergo ADAR-mediated A-to-I editing in Drosophila. While Adar1 null mice die at the embryonic stage and Adar2 null mice die shortly after birth due to seizures, Adar null flies are morphologically normal and have normal life span under ideal conditions. However, Adar null flies exhibit severe neurodegeneration and locomotion defects from eclosion, whilst Adar overexpression (OE) is lethal. To better understand the physiological role of RNA editing and ADAR, and to shed light on ADAR-related human disease, I used Drosophila Adar mutant flies as a model organism to investigate phenotypes, and to find chromosomal deletions and specific mutations that rescue the neural-behavioural phenotype of the Adar null mutant flies. Using the publicly available chromosomal deletions collectively covering more than 80% of the euchromatic genome of Chromsome III, I performed a genetic screen to find rescuers of the lethality caused by Adar overexpression. I confirmed that mutation in Rdl (Resistant to dieldrin, the gene encoding GABAA receptor main subunit) rescues. This rescue was not likely caused by effects on Adar expression level or activity. Driven by the hypothesis that the rescue may be due to reduction in GABAergic input to neurons, I recorded spontaneous firing activity of Drosophila larval aCC motor neurons using in vivo extracellular current recording technique. As expected, the neurons overexpressing Adar had much less activities compared with wild type neurons. Also, I found that Adar null fly neurons fired much more and showed epilepsy-like increased excitability. Although feeding PTX (Picrotoxin), a GABAA receptor antagonist, failed to rescue the lethality, reducing the expression of GAD1 to reduce synthesis of GABA was able to rescue the ADAR overexpression lethality. These results suggest that ADAR may finetune neuron activity synergistically with the GABAergic inhibitory signal pathway. I used MARCM (mosaic analysis using a repressible cell marker) to detect cellautonomous phenotypes in Adar null cells in otherwise wild type flies. Although neurodegeneration, observed as enlarged vacuoles formation in neurophils, was detected both in histological staining and EM images, the Adar null neurons marked with GFP from early developmental stages were not lost with age. Nevertheless, swelling in the axons or fragmentation of the axon branches of Adar null neurons was sometimes observed in the midbrain. By comparing the Poly-A RNA sequencing data from Adar null and wild type fly heads, we detected significant upregulation of innate immune genes. I confirmed this by qRT PCR and found that inactive ADAR reduces the innate immune gene transcript levels almost as much as active ADAR does. Further, using the locomotion assay, I confirmed that reintroducing inactive ADAR into Adar null flies can improve the flies’ climbing ability. Based on the Adar null flies having comparatively low viability, I performed a second deficiency screen to find rescuers of Adar null low viability using the same set of deficiencies as in the lethality rescue screen described above. I found seven deletions removing 1 to 37 genes that significantly increased the relative viability of the Adar null flies. However, not all the rescuing deficiencies also improved the Adar null locomotion. One rescuing gene, CG11357 was mapped from one of the rescuing deficiencies, and some mutant alleles of cry, JIL-1 and Gem3 also showed significant effects on the Adar null fly viability. The single gene viability rescuers were also not necessarily locomotion or neurodegeneration rescuers. Although the initial aim was to find neural-behavioural rescuing genes from the viability screen, the viability rescuers found in the screen are more likely to play a role in different aspects of stress response for survival

Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation

To alleviate the expensive human labeling, semi-supervised semantic segmentation employs a few labeled images and an abundant of unlabeled images to predict the pixel-level label map with the same size. Previous methods often adopt co-training using two convolutional networks with the same architecture but different initialization, which fails to capture the sufficiently diverse features. This motivates us to use tri-training and develop the triple-view encoder to utilize the encoders with different architectures to derive diverse features, and exploit the knowledge distillation skill to learn the complementary semantics among these encoders. Moreover, existing methods simply concatenate the features from both encoder and decoder, resulting in redundant features that require large memory cost. This inspires us to devise a dual-frequency decoder that selects those important features by projecting the features from the spatial domain to the frequency domain, where the dual-frequency channel attention mechanism is introduced to model the feature importance. Therefore, we propose a Triple-view Knowledge Distillation framework, termed TriKD, for semi-supervised semantic segmentation, including the triple-view encoder and the dual-frequency decoder. Extensive experiments were conducted on two benchmarks, \ie, Pascal VOC 2012 and Cityscapes, whose results verify the superiority of the proposed method with a good tradeoff between precision and inference speed

A polarization study of the supernova remnant CTB 80

We present a radio polarization study of the supernova remnant CTB 80 based on images at 1420 MHz from the Canadian Galactic plane survey, at 2695 MHz from the Effelsberg survey of the Galactic plane, and at 4800 MHz from the Sino-German 6cm polarization survey of the Galactic plane. We obtained a rotation measure (RM) map using polarization angles at 2695 MHz and 4800 MHz as the polarization percentages are similar at these two frequencies. RM exhibits a transition from positive values to negative values along one of the shells hosting the pulsar PSR B1951+32 and its pulsar wind nebula. The reason for the change of sign remains unclear. We identified a partial shell structure, which is bright in polarized intensity but weak in total intensity. This structure could be part of CTB 80 or part of a new supernova remnant unrelated to CTB 80.Comment: 12 pages, 6 figures, accepted for publication in RA

Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation

Semantic segmentation plays an important role in widespread applications such as autonomous driving and robotic sensing. Traditional methods mostly use RGB images which are heavily affected by lighting conditions, \eg, darkness. Recent studies show thermal images are robust to the night scenario as a compensating modality for segmentation. However, existing works either simply fuse RGB-Thermal (RGB-T) images or adopt the encoder with the same structure for both the RGB stream and the thermal stream, which neglects the modality difference in segmentation under varying lighting conditions. Therefore, this work proposes a Residual Spatial Fusion Network (RSFNet) for RGB-T semantic segmentation. Specifically, we employ an asymmetric encoder to learn the compensating features of the RGB and the thermal images. To effectively fuse the dual-modality features, we generate the pseudo-labels by saliency detection to supervise the feature learning, and develop the Residual Spatial Fusion (RSF) module with structural re-parameterization to learn more promising features by spatially fusing the cross-modality features. RSF employs a hierarchical feature fusion to aggregate multi-level features, and applies the spatial weights with the residual connection to adaptively control the multi-spectral feature fusion by the confidence gate. Extensive experiments were carried out on two benchmarks, \ie, MFNet database and PST900 database. The results have shown the state-of-the-art segmentation performance of our method, which achieves a good balance between accuracy and speed