Enhanced capacity of a leaf beetle to combat dual stress from entomopathogens and herbicides mediated by associated microbiota.

Herbicides have demonstrated their impact on insect fitness by affecting their associated microbiota or altering the virulence of entomopathogenic fungi toward insects. However, limited research has explored the implications of herbicide stress on the intricate tripartite interaction among insects, associated bacterial communities, and entomopathogens. In this study, we initially demonstrated that associated bacteria confer a leaf beetle, Plagiodera versicolora, with the capability to resist the entomopathogenic fungus Aspergillus nomius infection, a capability sustained even under herbicide glyphosate stress. Further analysis of the associated microbiota revealed a significant alteration in abundance and composition due to glyphosate treatment. The dominant bacterium, post A. nomius infection or following a combination of glyphosate treatments, exhibited strong suppressive effects on fungal growth. Additionally, glyphosate markedly inhibited the pathogenic associated bacterium Pseudomonas though it inhibited P. versicolora's immunity, ultimately enhancing the beetle's tolerance to A. nomius. In summary, our findings suggest that the leaf beetle's associated microbiota bestow an augmented resilience against the dual stressors of both the entomopathogen and glyphosate. These results provide insight into the effects of herbicide residues on interactions among insects, associated bacteria, and entomopathogenic fungi, holding significant implications for pest control and ecosystem assessment.

Enhanced toxicity of entomopathogenic fungi Beauveria bassiana with bacteria expressing immune suppressive dsRNA in a leaf beetle.

The entomopathogenic fungus is recognized as an ideal alternative to chemical pesticides, nonetheless, its efficacy is often limited by insect's innate immune system. The suppression of the host immunity may overcome the obstacle and promote the toxicity of the fungi. Here, by using an entomopathogenic fungus Beauveria bassiana and immune genes dsRNA-expressing bacteria, we explored the potentially synergistic toxicity of the two agents on a leaf beetle Plagiodera versicolora (Coleoptera: Chrysomelidae). We first determined the susceptibilities of P. versicolora to a B. bassiana 476 strain (hereafter referred to Bb476). And the immune genes were identified based on the transcriptome of Bb476 challenged beetles. Subsequently, five immune genes (PGRP1, Toll1, Domeless，SPN1，and Lysozyme) were targeted by feeding dsRNA-expressing bacteria, which produced a 71.4, 39.0, 72.0, 49.0, and 68.7% gene silencing effect, respectively. Furthermore, we found a significantly increased mortality of P. versicolora when combined the Bb476 and the immune suppressive dsRNAs. Taking together, this study highlights the importance of insect immunity in the defense of entomopathogens and also paves the way toward the development of a more efficient pest management strategy that integrates both entomopathogens and immune suppressive dsRNAs.

Learned Image Compression With Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules.

Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures. Various models have been proposed, such as autoregressive, softmax, logistic mixture, Gaussian mixture, and Laplacian. Existing schemes only use one of these models. However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image. In this paper, we propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations, which can adapt to different contents in different images and different regions of one image more accurately and efficiently, given the same complexity. Besides, in the encoding/decoding network design part, we propose a concatenated residual blocks (CRB), where multiple residual blocks are serially connected with additional shortcut connections. The CRB can improve the learning ability of the network, which can further improve the compression performance. Experimental results using the Kodak, Tecnick-100 and Tecnick-40 datasets show that the proposed scheme outperforms all the leading learning-based methods and existing compression standards including VVC intra coding (4:4:4 and 4:2:0) in terms of the PSNR and MS-SSIM. The source code is available at https://github.com/fengyurenpingsheng.

Comparative Analysis of Adelphocoris suturalis Jakovlev (Hemiptera: Miridae) Immune Responses to Fungal and Bacterial Pathogens.

The wide-spread culture of transgenic Bt cotton resisting the infamous cotton bollworms has reduced the adoption of broad-spectrum insecticides to a large extent. Consequently, the non-targeted insect Adelphocoris suturalis Jakovlev has become a major cotton pest in China. Entomopathogenic microbes show promising results for controlling this pest in the future, but A. suturalis innate immune responses to these pathogens are poorly understood. Here, we used the entomopathogenic fungus Beauveria bassiana and the Gram-negative pathogenic bacteria Enterobactor cloacae to infect A. suturalis nymphs, followed by high throughput RNA-seq to analyze the immune transcriptomes of A. suturalis in response to the two pathogens. A total of 150 immunity-related genes were identified, including pattern recognition receptors, extracellular signal modulators, signal pathways (Toll, IMD, JNK, and JAK/STAT), and response effectors. Further quantitative real-time PCR analysis demonstrated that B. bassiana and E. cloacae were recognized by different receptors (GNBP and PGRP, respectively); activated Toll pathway and IMD pathway respectively; and both induced expression of the effector gene Defensin. However, melanization is suppressed in B. bassiana-infected nymphs. Collectively, this study provides a transcriptomic snapshot of the A. suturalis immune system, and at the genetic level, gains multifaceted insights of the immune response to fungal and Gram-negative bacterial pathogens. Ultimately this work pioneers the study of molecular mechanisms underlying immune interactions between A. suturalis and its pathogens and assists in the development of novel mitigation strategies to control this pest.

Metabolic and immunological effects of gut microbiota in leaf beetles at the local and systemic levels.

Insects' intestinal microbes have profound effects on the host's physiological traits, which can impact their physiology at both the local (gut) and systemic (body) levels. Nevertheless, the molecular mechanisms underlying host-microbiota interactions, especially in non-model insects, remain elusive. Recently, tissue-specific transcriptomic analysis has been highlighted as a robust tool in studying host-microbe interactions. Plagiodera versicolora is a worldwide leaf-eating pest that primarily feeds on willows and poplar. The interplay between gut microflora and this host beetle has yet to be studied. Herein, we investigate the effects of the gut microbiota on the body mass of P. versicolora larvae, compare the nutrition status of larvae in absence and presence of gut microbiota, and profile gut bacterial loads throughout its developmental larval stages. We then perform comparative transcriptomic analysis of gut and body tissues in axenic and non-axenic larvae. Finally, we confirm the expression patterns of representative genes in nutritional metabolism and immunity. Results show that weight growth is retarded in conventional larvae, with a concomitant increase of total bacterial load by the 5th development day, and germ-free larvae have a higher glucose content than conventional-reared larvae. Both nutritional and immunological analyses indicate that gut bacteria are a burden in the beetle's larval development. These findings elucidate the impacts of gut microbiota on P. versicolora, and provide insight into tissue-specific responses to gut microflora in this pest at the genetic level, boosting our understanding of the molecular mechanisms underlying host-microbe interactions in leaf beetles and beyond.

Multiple description coding with prediction compensation.

A new multiple description coding paradigm is proposed by combining the time-domain lapped transform, block level source splitting, linear prediction, and prediction residual encoding. The method provides effective redundancy control and fully utilizes the source correlation. The joint optimization of all system components and the asymptotic performance analysis are presented. Image coding results demonstrate the superior performance of the proposed method, especially at low redundancies.

Undersampled boundary pre-/postfilters for low bit-rate DCT-based block coders.

It has been well established that critically sampled boundary pre-/postfiltering operators can improve the coding efficiency and mitigate blocking artifacts in traditional discrete cosine transform-based block coders at low bit rates. In these systems, both the prefilter and the postfilter are square matrices. This paper proposes to use undersampled boundary pre- and postfiltering modules, where the pre-/postfilters are rectangular matrices. Specifically, the prefilter is a "fat" matrix, while the postfilter is a "tall" one. In this way, the size of the prefiltered image is smaller than that of the original input image, which leads to improved compression performance and reduced computational complexities at low bit rates. The design and VLSI-friendly implementation of the undersampled pre-/postfilters are derived. Their relations to lapped transforms and filter banks are also presented. Two design examples are also included to demonstrate the validity of the theory. Furthermore, image coding results indicate that the proposed undersampled pre-/postfiltering systems yield excellent and stable performance in low bit-rate image coding.

Wiener filter-based error resilient time-domain lapped transform.

In this paper, the design of the error resilient time-domain lapped transform is formulated as a linear minimal mean-squared error problem. The optimal Wiener solution and several simplifications with different tradeoffs between complexity and performance are developed. We also prove the persymmetric structure of these Wiener filters. The existing mean reconstruction method is proven to be a special case of the proposed framework. Our method also includes as a special case the linear interpolation method used in DCT-based systems when there is no pre/postfiltering and when the quantization noise is ignored. The design criteria in our previous results are scrutinized and improved solutions are obtained. Various design examples and multiple description image coding experiments are reported to demonstrate the performance of the proposed method.

Error resilient pre/post-filtering for DCT-based block coding systems.

Block coding based on the discrete cosine transform (DCT) is very popular in image and video compression. Pre/post-filtering can be attached to a DCT-based block coding system to improve coding efficiency as well as to mitigate blocking artifacts. Previously designed pre/post-filters are optimized to maximize coding efficiency solely. For image and video communication over unreliable channels, those pre/post-filters are sensitive to transmission errors. This paper addresses the problem of designing pre/post-filters which are more error resilient. Reconstruction performance is measured by how low the average reconstruction error is, and how uniformly the reconstruction error is distributed. A family of pre/post-filters is designed to provide desired tradeoffs between coding efficiency and robustness to transmission errors. Experiments show that these filtering operators can achieve superior reconstruction performance without sacrificing much coding performance.

Optimal block boundary pre/postfiltering for wavelet-based image and video compression.

This paper presents a pre/postfiltering framework to reduce the reconstruction errors near block boundaries in wavelet-based image and video compression. Two algorithms are developed to obtain the optimal filter, based on boundary filter bank and polyphase structure, respectively. A low-complexity structure is employed to approximate the optimal solution. Performances of the proposed method in the removal of JPEG 2000 tiling artifact and the jittering artifact of three-dimensional wavelet video coding are reported. Comparisons with other methods demonstrate the advantages of our pre/postfiltering framework.

Context-based entropy coding of block transform coefficients for image compression.

It has been well established that state-of-the-art wavelet image coders outperform block transform image coders in the rate-distortion (R-D) sense by a wide margin. Wavelet-based JPEG2000 is emerging as the new high-performance international standard for still image compression. An often asked question is: how much of the coding improvement is due to the transform and how much is due to the encoding strategy? Current block transform coders such as JPEG suffer from poor context modeling and fail to take full advantage of correlation in both space and frequency sense. This paper presents a simple, fast, and efficient adaptive block transform image coding algorithm based on a combination of prefiltering, postfiltering, and high-order space-frequency context modeling of block transform coefficients. Despite the simplicity constraints, coding results show that the proposed coder achieves competitive R-D performance compared to the best wavelet coders in the literature.