CHARACTERIZING THE ROLES OF PIPECOLIC ACID AND REACTIVE OXYGEN SPECIES METABOLIC ENZYMES IN PLANT SYSTEMIC IMMUNITY

Systemic acquired resistance (SAR), initiated by a plant upon recognition of microbial effectors, involves the generation of mobile signals at the primary infection site, which translocate to and activate defense responses in distal tissues. Among the signals contributing to SAR include salicylic acid (SA), nitric oxide (NO), reactive oxygen species (ROS), glycerol-3-phosphate (G3P), and pipecolic acid (Pip). Our previous studies show there are two branches of SAR signaling pathways in Arabidopsis: one regulated by NO/ROS-G3P and the other by SA. Both NO/ROS-G3P and SA-mediated signaling branches function in parallel during SAR. To better understand the role of Pip in SAR and the molecular mechanisms underlying Pip-mediated signaling, I investigated relationship between Pip and other SAR signals. My results suggest that Pip-mediated SAR is dependent on the NO/ROS-G3P branch of the SAR pathway. This is supported by the results that exogenous Pip increases NO, ROS, and G3P, but not SA. Detailed characterization of Pip metabolism showed that Pip acts upstream of several known and unknown derivatives. I also investigated involvement of ascorbic acid biosynthetic enzymes and several ROS scavenging enzymes in SAR. Together, my results suggest that Pip- and ROS-metabolic pathways regulate key steps of SAR signaling in plants

Availability Allocation of Networked Systems Using Markov Model and Heuristics Algorithm

It is a common practice to allocate the system availability goal to reliability and maintainability goals of components in the early design phase. However, the networked system availability is difficult to be allocated due to its complex topology and multiple down states. To solve these problems, a practical availability allocation method is proposed. Network reliability algebraic methods are used to derive the availability expression of the networked topology on the system level, and Markov model is introduced to determine that on the component level. A heuristic algorithm is proposed to obtain the reliability and maintainability allocation values of components. The principles applied in the AGREE reliability allocation method, proposed by the Advisory Group on Reliability of Electronic Equipment, and failure rate-based maintainability allocation method persist in our allocation method. A series system is used to verify the new algorithm, and the result shows that the allocation based on the heuristic algorithm is quite accurate compared to the traditional one. Moreover, our case study of a signaling system number 7 shows that the proposed allocation method is quite efficient for networked systems

Molecular Conformation Generation via Shifting Scores

Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule. Generating molecular conformation via diffusion requires learning to reverse a noising process. Diffusion on inter-atomic distances instead of conformation preserves SE(3)-equivalence and shows superior performance compared to alternative techniques, whereas related generative modelings are predominantly based upon heuristical assumptions. In response to this, we propose a novel molecular conformation generation approach driven by the observation that the disintegration of a molecule can be viewed as casting increasing force fields to its composing atoms, such that the distribution of the change of inter-atomic distance shifts from Gaussian to Maxwell-Boltzmann distribution. The corresponding generative modeling ensures a feasible inter-atomic distance geometry and exhibits time reversibility. Experimental results on molecular datasets demonstrate the advantages of the proposed shifting distribution compared to the state-of-the-art.Comment: 18 pages, 7 figure

PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification

Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap. While recent cross-modal attention-based studies have attempted to align such two representations and achieved impressive performance, they required carefully-designed alignment modules and extra complex operations in the attention computation. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost. Specifically, after feeding the images and textual labels into the modality-specific encoders, we view each image as a mixture of patch embeddings and a mixture of label embeddings, which capture the local region features and the class prototypes, respectively. CT is then employed to learn and align those two semantic sets by defining the forward and backward navigators. Importantly, the defined navigators in CT distance model the similarities between patches and labels, which provides an interpretable tool to visualize the learned prototypes. Extensive experiments on three public image benchmarks show that the proposed model consistently outperforms the previous methods. Our code is available at https://github.com/keepgoingjkg/PatchCT.Comment: accepted by ICCV2

Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation and limited generalizability, this paper focuses on building a unified framework for multiple classes. Under such a challenging setting, popular reconstruction-based networks with continuous latent representation assumption always suffer from the "identical shortcut" issue, where both normal and abnormal samples can be well recovered and difficult to distinguish. To address this pivotal issue, we propose a hierarchical vector quantized prototype-oriented Transformer under a probabilistic framework. First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut. The vector quantized iconic prototype is integrated into the Transformer for reconstruction, such that the abnormal data point is flipped to a normal data point.Second, we investigate an exquisite hierarchical framework to relieve the codebook collapse issue and replenish frail normal patterns. Third, a prototype-oriented optimal transport method is proposed to better regulate the prototypes and hierarchically evaluate the abnormal score. By evaluating on MVTec-AD and VisA datasets, our model surpasses the state-of-the-art alternatives and possesses good interpretability. The code is available at https://github.com/RuiyingLu/HVQ-Trans

Hsa-miR-125b suppresses bladder cancer development by down-regulating oncogene SIRT7 and oncogenic long non-coding RNA MALAT1

AbstractMicroRNAs mainly inhibit coding genes and long non-coding RNA expression. Here, we report that hsa-miR-125b and oncogene SIRT7/oncogenic long non-coding RNA MALAT1 were inversely expressed in bladder cancer. Hsa-miR-125b mimic down-regulated, whereas hsa-miR-125b inhibitor up-regulated the expression of SIRT7 and MALAT1. Binding sites were confirmed between hsa-miR-125b and SIRT7/MALAT1. Up-regulation of hsa-miR-125b or down-regulation of SIRT7 inhibited proliferation, motility and increased apoptosis. The effects of up-regulation of hsa-miR-125b were similar to that of silencing MALAT1 in bladder cancer as we had previously described. These data suggest that hsa-miR-125b suppresses bladder cancer development via inhibiting SIRT7 and MALAT1

A Connected Components Based Layout Analysis Approach for Educational Documents

Layout analysis, which aims to detect and categorize areas of interest on document images, is an increasingly important part in document image processing. Existing researches have conducted layout analysis on various documents, but none has been proposed for documents yielded from teaching, i.e. exam papers and workbooks, which are worth studying. In this paper, we propose a novel layout analysis system to achieve two tasks for workbook pages and exam papers respectively. On one hand, we segment text and non-text areas of workbook pages. On the other hand, we extract regions of interest on exam papers. Our system is based on connected component (CC) analysis, specifically, it extracts geometric features and spatial information of CCs to recognize page elements. We carried out experiments on images collected from real-world scenarios, and promising results confirmed the applicability and effectiveness of our system

Pipecolic Acid Confers Systemic Immunity by Regulating Free Radicals

Pipecolic acid (Pip), a non-proteinaceous product of lysine catabolism, is an important regulator of immunity in plants and humans alike. In plants, Pip accumulates upon pathogen infection and has been associated with systemic acquired resistance (SAR). However, the molecular mechanisms underlying Pip-mediated signaling and its relationship to other known SAR inducers remain unknown. We show that in plants, Pip confers SAR by increasing levels of the free radicals, nitric oxide (NO), and reactive oxygen species (ROS), which act upstream of glycerol-3-phosphate (G3P). Plants defective in NO, ROS, G3P, or salicylic acid (SA) biosynthesis accumulate reduced Pip in their distal uninfected tissues although they contain wild-type-like levels of Pip in their infected leaves. These data indicate that de novo synthesis of Pip in distal tissues is dependent on both SA and G3P and that distal levels of SA and G3P play an important role in SAR. These results also suggest a unique scenario whereby metabolites in a signaling cascade can stimulate each other\u27s biosynthesis depending on their relative levels and their site of action

Future directions in ventilator-induced lung injury associated cognitive impairment: a new sight

Mechanical ventilation is a widely used short-term life support technique, but an accompanying adverse consequence can be pulmonary damage which is called ventilator-induced lung injury (VILI). Mechanical ventilation can potentially affect the central nervous system and lead to long-term cognitive impairment. In recent years, many studies revealed that VILI, as a common lung injury, may be involved in the central pathogenesis of cognitive impairment by inducing hypoxia, inflammation, and changes in neural pathways. In addition, VILI has received attention in affecting the treatment of cognitive impairment and provides new insights into individualized therapy. The combination of lung protective ventilation and drug therapy can overcome the inevitable problems of poor prognosis from a new perspective. In this review, we summarized VILI and non-VILI factors as risk factors for cognitive impairment and concluded the latest mechanisms. Moreover, we retrospectively explored the role of improving VILI in cognitive impairment treatment. This work contributes to a better understanding of the pathogenesis of VILI-induced cognitive impairment and may provide future direction for the treatment and prognosis of cognitive impairment