139 research outputs found
Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection
Weakly Supervised Object Detection (WSOD) enables the training of object
detection models using only image-level annotations. State-of-the-art WSOD
detectors commonly rely on multi-instance learning (MIL) as the backbone of
their detectors and assume that the bounding box proposals of an image are
independent of each other. However, since such approaches only utilize the
highest score proposal and discard the potentially useful information from
other proposals, their independent MIL backbone often limits models to salient
parts of an object or causes them to detect only one object per class. To solve
the above problems, we propose a novel backbone for WSOD based on our tailored
Vision Transformer named Weakly Supervised Transformer Detection Network
(WSTDN). Our algorithm is not only the first to demonstrate that self-attention
modules that consider inter-instance relationships are effective backbones for
WSOD, but also we introduce a novel bounding box mining method (BBM) integrated
with a memory transfer refinement (MTR) procedure to utilize the instance
dependencies for facilitating instance refinements. Experimental results on
PASCAL VOC2007 and VOC2012 benchmarks demonstrate the effectiveness of our
proposed WSTDN and modified instance refinement modules
SLPW: A Virulent Bacteriophage Targeting Methicillin-Resistant Staphylococcus aureus In vitro and In vivo
Development and Control of Soybean Aphid, Aphis glycines, in Heilongjiang Province
Originating text in Chinese.Citation: Wang, Chunrong, Chen, Jiguang, Guo, Yuren, Gong, Xiangyu, Xu, Zhaofei, Lin, Chao. (1998). Development and Control of Soybean Aphid, Aphis glycines, in Heilongjiang Province. Soybean Bulletin, 6, 15
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Noise injection-based method has been shown to be able to improve the
robustness of artificial neural networks in previous work. In this work, we
propose a novel noise injection-based training scheme for better model
robustness. Specifically, we first develop a likelihood ratio method to
estimate the gradient with respect to both synaptic weights and noise levels
for stochastic gradient descent training. Then, we design an approximation for
the vanilla noise injection-based training method to reduce memory and improve
computational efficiency. Next, we apply our proposed scheme to spiking neural
networks and evaluate the performance of classification accuracy and robustness
on MNIST and Fashion-MNIST datasets. Experiment results show that our proposed
method achieves a much better performance on adversarial robustness and
slightly better performance on original accuracy, compared with the
conventional gradient-based training method
Potential biomarkers of Parkinson’s disease revealed by plasma metabolic profiling
The plasma of Parkinson's disease (PD) patients may contain various altered metabolites associated with the risk or progression of the disease. Characterization of the abnormal metabolic pattern in PD plasma is therefore critical for the search for potential PD biomarkers. We collected blood plasma samples from PD patients and used an LC-MS based metabolomics approach to identify 17 metabolites with significantly altered levels. Metabolic network analysis was performed to place the metabolites linked to different pathways. The metabolic pathways involved were associated with tyrosine biosynthesis, glycerol phospholipid metabolism, carnitine metabolism and bile acid biosynthesis, within which carnitine and bile acid metabolites as potential biomarkers are first time reported. These abnormal metabolic changes in the plasma of patients with PD were mainly related to lipid metabolism and mitochondrial function
Integration of the Vegetation Phenology Module Improves Ecohydrological Simulation by the SWAT-Carbon Model
Vegetation phenology and hydrological cycles are closely interacted from leaf and species levels to watershed and global scales. As one of the most sensitive biological indicators of climate change, plant phenology is essential to be simulated accurately in hydrological models. Despite the Soil and Water Assessment Tool (SWAT) has been widely used for estimating hydrological cycles, its lack of integration with the phenology module has led to substantial uncertainties. In this study, we developed a process-based vegetation phenology module and coupled it with the SWAT-Carbon model to investigate the effects of vegetation dynamics on runoff in the upper reaches of Jinsha River watershed in China. The modified SWAT-Carbon model showed reasonable performance in phenology simulation, with root mean square error (RMSE) of 9.89 days for the start-of-season (SOS) and 7.51 days for the end-of-season (EOS). Simulations of both vegetation dynamics and runoff were also substantially improved compared to the original model. Specifically, the simulation of leaf area index significantly improved with the coefficient of determination (R2) increased by 0.62, the Nash–Sutcliffe efficiency (NSE) increased by 2.45, and the absolute percent bias (PBIAS) decreased by 69.0 % on average. Additionally, daily runoff simulation also showed notably improvement, particularly noticeable in June and October, with R2 rising by 0.22 and NSE rising by 0.43 on average. Our findings highlight the importance of integrating vegetation phenology into hydrological models to enhance modeling performance
Goose STING mediates IFN signaling activation against RNA viruses
Stimulator of the interferon gene (STING) is involved in mammalian antiviral innate immunity as an interferon (IFN) activator. However, there is still a lack of clarity regarding the molecular characterization of goose STING (GoSTING) and its role in the innate immune response. In the present study, we cloned GoSTING and performed a series of bioinformatics analyses. GoSTING was grouped into avian clades and showed the highest sequence similarity to duck STING. The in vitro experiments showed that the mRNA levels of GoSTING, IFNs, IFN-stimulated genes (ISGs), and proinflammatory cytokines were significantly upregulated in goose embryo fibroblast cells (GEFs) infected with Newcastle disease virus (NDV). Overexpression of GoSTING in DF-1 cells and GEFs strongly activated the IFN-β promoter as detected by a dual-luciferase reporter assay. Furthermore, overexpression of GoSTING induced the expression of other types of IFN, ISGs, and proinflammatory cytokines and inhibited green fluorescent protein (GFP)-tagged NDV (NDV-GFP) and GFP-tagged vesicular stomatitis virus (VSV) (VSV-GFP) replication in vitro. In conclusion, these data suggest that GoSTING is an important regulator of the type I IFN pathway and is critical in geese’s innate immune host defense against RNA viruses
Phage vB_PaeS-PAJD-1 Rescues Murine Mastitis Infected With Multidrug-Resistant Pseudomonas aeruginosa
Pseudomonas aeruginosa is a Gram-negative pathogen that causes a variety of infections in humans and animals. Due to the inappropriate use of antibiotics, multi-drug resistant (MDR) P. aeruginosa strains have emerged and are prevailing. In recent years, cow mastitis caused by MDR P. aeruginosa has attracted attention. In this study, a microbial community analysis revealed that P. aeruginosa could be a cause of pathogen-induced cow mastitis. Five MDR P. aeruginosa strains were isolated from milk diagnosed as mastitis positive. To seek an alternative antibacterial agent against MDR, P. aeruginosa, a lytic phage, designated vB_PaeS_PAJD-1 (PAJD-1), was isolated from dairy farm sewage. PAJD-1 was morphologically classified as Siphoviridae and was estimated to be about 57.9 kb. Phage PAJD-1 showed broad host ranges and a strong lytic ability. A one-step growth curve analysis showed a relatively short latency period (20 min) and a relatively high burst size (223 PFU per infected cell). Phage PAJD-1 remained stable over wide temperature and pH ranges. Intramammary-administered PAJD-1 reduced bacterial concentrations and repaired mammary glands in mice with mastitis induced by MDR P. aeruginosa. Furthermore, the cell wall hydrolase (termed endolysin) from phage PAJD-1 exhibited a strong bacteriolytic and a wide antibacterial spectrum against MDR P. aeruginosa. These findings present phage PAJD-1 as a candidate for phagotherapy against MDR P. aeruginosa infection
In situ crystal growth of zeolitic imidazolate frameworks (ZIF) on electrospun polyurethane nanofibers
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