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

A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning

The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement learning (RL) techniques have emerged as a popular approach for TSC and show promising results for highly adaptive control. However, existing RL-based methods suffer from notably poor real-world applicability and hardly have any successful deployments. The reasons for such failures are mostly due to the reliance on over-idealized traffic simulators for policy optimization, as well as using unrealistic fine-grained state observations and reward signals that are not directly obtainable from real-world sensors. In this paper, we propose a fully Data-Driven and simulator-free framework for realistic Traffic Signal Control (D2TSC). Specifically, we combine well-established traffic flow theory with machine learning to construct a reward inference model to infer the reward signals from coarse-grained traffic data. With the inferred rewards, we further propose a sample-efficient offline RL method to enable direct signal control policy learning from historical offline datasets of real-world intersections. To evaluate our approach, we collect historical traffic data from a real-world intersection, and develop a highly customized simulation environment that strictly follows real data characteristics. We demonstrate through extensive experiments that our approach achieves superior performance over conventional and offline RL baselines, and also enjoys much better real-world applicability.Comment: 15 pages, 6 figure

A Genome-Wide Analysis of StTGA Genes Reveals the Critical Role in Enhanced Bacterial Wilt Tolerance in Potato During Ralstonia solanacearum Infection

TGA is one of the members of TGACG sequence-specific binding protein family, which plays a crucial role in the regulated course of hormone synthesis as a stress-responsive transcription factor (TF). Little is known, however, about its implication in response to bacterial wilt disease in potato (Solanum tuberosum) caused by Ralstonia solanacearum. Here, we performed an in silico identification and analysis of the members of the TGA family based on the whole genome data of potato. In total, 42 StTGAs were predicted to be distributed on four chromosomes in potato genome. Phylogenetic analysis showed that the proteins of StTGAs could be divided into six sub-families. We found that many of these genes have more than one exon according to the conserved motif and gene structure analysis. The heat map inferred that StTGAs are generally expressed in different tissues which are at different stages of development. Genomic collinear analysis showed that there are homologous relationships among potato, tomato, pepper, Arabidopsis, and tobacco TGA genes. Cis-element in silico analysis predicted that there may be many cis-acting elements related to abiotic and biotic stress upstream of StTGA promoter including plant hormone response elements. A representative member StTGA39 was selected to investigate the potential function of the StTGA genes for further analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) assays indicated that the expression of the StTGAs was significantly induced by R. solanacearum infection and upregulated by exogenous salicylic acid (SA), abscisic acid (ABA), gibberellin 3 (GA3), and methyl jasmonate (MeJA). The results of yeast one-hybrid (Y1H) assay showed that StTGA39 regulates S. tuberosum BRI1-associated receptor kinase 1 (StBAK1) expression. Thus, our study provides a theoretical basis for further research of the molecular mechanism of the StTGA gene of potato tolerance to bacterial wilt

The Oncogene IARS2 Promotes Non-small Cell Lung Cancer Tumorigenesis by Activating the AKT/MTOR Pathway

A limited number of studies have indicated an association between isoleucyl-tRNA synthetase 2 (IARS2) and tumorigenesis. We evaluated IARS2 protein expression in lung tumor tissues and paired non-tumor tissues. We found higher IARS2 expression in the tumor tissues, which was associated with the late Tumor and Node stages of the Tumor, Node, Metastasis staging system. Silencing IARS2 inhibited the activity of A549 and H1299 cells, resulting in G0/G1 stasis of A549 cells and mitochondrial apoptosis. IARS2 silencing was also found to inhibit NSCLC tumor growth in nude mice. Complementary DNA microarray analysis revealed 742 differentially expressed genes (507 upregulated and 235 downregulated) in IARS2-silenced A549 cells compared to controls. Ingenuity Pathway Analysis of the differential expression data suggested that multiple pathways are associated with IARS2 silencing in NSCLC cells; upstream analysis predicted the activation or inhibition of transcriptional regulators. Correlation analysis revealed that AKT and MTOR activities were significantly inhibited in IARS2-silenced cells, but were partially restored by the AKT-stimulating agent SC79. IARS2 appears to regulate lung cancer cell proliferation via the AKT/MTOR pathway. Our results help clarify the complex roles of IARS2 in tumorigenesis and suggest that it may be a novel regulator of lung cancer development

Study on diversity, nitrogen-fixing capacity, and heavy metal tolerance of culturable Pongamia pinnata rhizobia in the vanadium-titanium magnetite tailings

IntroductionThe diversity, nitrogen-fixing capacity and heavy metal tolerance of culturable rhizobia in symbiotic relationship with Pongamia pinnata surviving in vanadium (V) - titanium (Ti) magnetite (VTM) tailings is still unknown, and the rhizobia isolates from the extreme barren VTM tailings contaminated with a variety of metals would provide available rhizobia resources for bioremediation.MethodsP. pinnata plants were cultivated in pots containing the VTM tailings until root nodules formed, and then culturable rhizobia were isolated from root nodules. The diversity, nitrogen-fixing capacity and heavy metal tolerance of rhizobia were performed.ResultsAmong 57 rhizobia isolated from these nodules, only twenty strains showed different levels of tolerance to copper (Cu), nickel (Ni), manganese (Mn) and zinc (Zn), especially strains PP1 and PP76 showing high tolerance against these four heavy metals. Based on the phylogenetic analysis of 16S rRNA and four house-keeping genes (atpD, recA, rpoB, glnII), twelve isolates were identified as Bradyrhizobium pachyrhizi, four as Ochrobactrum anthropic, three as Rhizobium selenitireducens and one as Rhizobium pisi. Some rhizobia isolates showed a high nitrogen-fixing capacity and promoted P. pinnata growth by increasing nitrogen content by 10%-145% in aboveground plant part and 13%-79% in the root. R. pachyrhizi PP1 showed the strongest capacity of nitrogen fixation, plant growth promotion and resistance to heavy metals, which provided effective rhizobia strains for bioremediation of VTM tailings or other contaminated soils. This study demonstrated that there are at least three genera of culturable rhizobia in symbiosis with P. pinnata in VTM tailings.DiscussionAbundant culturable rhizobia with the capacity of nitrogen fixation, plant growth promotion and resistance to heavy metals survived in VTM tailings, indicating more valuable functional microbes could be isolated from extreme soil environments such as VTM tailings

TAMeBS: A sensitive bisulfite-sequencing read mapping tool for DNA methylation analysis

Cytosine methylation plays an important role in many biological regulation processes. The current gold-standard method for analyzing cytosine methylation is based on sodium bisulfite treatment and high-throughput sequencing technologies. In this paper we introduce a new tool called TAMeBS for cytosine methylation analysis using bisulfite sequencing data. It aims to align long bisulfite-treated DNA reads onto a reference genome sequence with high mapping efficiency and estimate the methylation status of each cytosine very accurately. Our approach builds on recent advances in alignment techniques, including bidirectional FM-index, approximate seeds, and the likelihood-ratio scoring matrix which was designed particularly for aligning bisulfite-treated DNA reads. We compared TAMeBS with several popular bisulfite-treated read mapping tools on both simulation and real data. Experimental results showed that TAMeBS could detect many more uniquely best mapped reads than other tested tools while achieving a good balance between sensitivity and precision. The source code of TAMeBS is freely available at https://sourceforge.net/projects/tamebs/.MOE (Min. of Education, S’pore)NMRC (Natl Medical Research Council, S’pore)Accepted versio

Effect of astragaloside IV on indoxyl sulfate-induced kidney injury in mice via attenuation of oxidative stress

Abstract Background Astragalus membranaceus, a traditional Chinese medicine (TCM), has been widely used in the treatment of chronic kidney disease (CKD) in China. Astragaloside IV is one of the major compounds of Astragalus membranaceus. Recent research has shown that astragaloside IV demonstrates pharmacological effects, such as anti-inflammatory, anti-fibrotic and anti-oxidative stress activities. Our aim was to investigate the effects of astragaloside IV on indoxyl sulfate (IS)-induced kidney injury in vivo, and to study the underlying mechanism. Methods Forty C57BL/6 mice with ½ nephrectomy were divided into four groups: control group (n = 10), IS group (n = 10), IS plus 10 mg/kg of astragaloside IV group (n = 10) and IS plus 20 mg/kg of astragaloside IV group (n = 10). IS intraperitoneal injection and astragaloside IV treatment were administered continuously for 1 month. Next, the blood urea nitrogen (BUN) level, serum IS level, tubulointerstitial injury, renal oxidative stress and inflammatory injury were assessed. Results The IS intraperitoneal injection mouse group showed increasing levels of serum IS, BUN, tubulointerstitial injury, renal oxidative stress and inflammatory injury. Astragaloside IV treatment couldn’t reduce the serum IS level or renal nuclear factor-κB and interleukin-1β levels. However, 20 mg/kg astragaloside IV treatment reduced the BUN level and significantly attenuated IS-induced tubulointerstitial injury. Renal oxidative stress was decreased by the administration of astragaloside IV. Conclusions These results suggest that astragaloside IV prevents IS-induced tubulointerstitial injury by ameliorating oxidative stress and may be a promising agent for the treatment of uremia toxin-induced injury

Study on the Characteristics of Coherent Supersonic Jet with Superheated Steam

By establishing a mathematical model to simulate a mixed jet of oxygen and superheated steam from a coherent supersonic jet oxygen lance, we studied the effect of superheated steam on the fluid characteristics of the mixed jet. The model was initially verified through laboratory experiments prior to analyzing the fluid characteristics of the mixed jet in detail. These characteristics included the jet velocity, the temperature, the turbulent kinetic energy (TKE), and the mass distribution. The results showed that, at an ambient temperature of 1700 K, the jet velocity measured in the laboratory experiment was consistent with the fluid velocity obtained by numerical simulations, with an error of only 2.7%. In a high-temperature environment, the jet velocity of the mixed oxygen and superheated steam jet was increased, the TKE around the center jet was enhanced, the superheated steam exhibited an inhibitory effect on the combustion reaction of annular methane, and the potential core length of the coherent supersonic jet was reduced, which was conducive to methane combustion and delayed the reduction in the central jet velocity

Rural sustainable development: A case study of the Zaozhuang Innovation Demonstration Zone in China

Sustainable development is the central theme of modern global development. With the arrival of the urban era, the vulnerability and instability of rural areas have significantly increased, and rural sustainable development faces serious challenges. To address these issues, the study took the Zaozhuang Innovation Demonstration Zone in China under the National Sustainable Development Agenda as a case, combined with 2016–2020 economic and social and land use data, and applied Granger causality test method to explore the theoretical and practical pathways of “innovation-driven rural sustainable development”. The results showed that rural sustainable development and economic sustainability displayed a trend of synergistic change, with “explosive” growth from 2018 to 2020. The social sustainability steadily increased from 2016 to 2020. Ecological and spatial sustainability continuously declined during the study period. Moreover, the rural innovation capacity of the Zaozhuang Innovation Demonstration Zone displayed rapid growth during 2016–2020. Although the rural innovation capacity of the Zaozhuang Innovation Demonstration Zone has rapidly improved, it has a weak driving effect on rural sustainable development and economic sustainability. There are two primary challenges that must be overcome to ensure the rural sustainable development of the Zaozhuang Innovation Demonstration Zone. The first challenge is the imbalance among the multi-dimensional relationships in the process of rural sustainable development, and the second challenge is the weakening of rural innovation capacity to drive rural sustainable development. To overcome these challenges, this study proposed a systematic pathway for rural sustainable development in the Zaozhuang Innovation Demonstration Zone from multi-dimensions, such as policy actions, technologies, projects, and institutional guarantees, and formed a universal and representative “Zaozhuang model”. This study expands the theoretical foundation of rural sustainable development and provides theoretical and practical support for innovation-driven rural sustainable development

Fabric defect detection via saliency model based on adjacent context coordination and transformer

Fabric defect detection is a pivotal step in quality control in the textile manufacturing industry. Due to the diversity and complexity of defects, manual visual inspection and traditional fabric defect detection methods suffer from low efficiency and accuracy. To address the issues, a saliency model capable of mining local and global information from CNN and vision Transformer is proposed for fabric defect detection in this paper, named ACCTNet. Specifically, to enhance the feature interaction of different scales, an adjacent context coordination module composed of one local branch and two adjacent branches is proposed. Meanwhile, a contrast-aggregation module is proposed to highlight the defects from low contrast background using pooling and subtraction operations. In addition, vision Transformer is adopted to capture global contextual information with long-range dependencies, which can guide local information to further refines the defect detection results. Experimental results demonstrate that the proposed method can accurately inspect the defects from plain and patterned fabric surfaces, achieving E m values of 78.49% and 97.19% respectively, which significantly surpasses the existing state-of-the-art fabric defect detection methods