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

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