Class A CpG oligodeoxynucleotide inhibits IFN-γ-induced signaling and apoptosis in lung cancer. [PDF]
Teranishi S+9 more
europepmc +1 more source
CpG-oligodeoxynucleotides inhibit airway remodeling in a murine model of chronic asthma [PDF]
Vipul V. Jain+6 more
openalex +1 more source
Huijie Zhang,1 Shini Feng,1 Ting Yan,1 Chunyi Zhi,2 Xiao-Dong Gao,1 Nobutaka Hanagata3,41The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, People’s Republic of
Zhang HJ+5 more
doaj
Role of Kupffer cells in liver injury induced by CpG oligodeoxynucleotide and flucloxacillin in mice. [PDF]
Gao Y, Song B, Aoki S, Ito K.
europepmc +1 more source
Chloroquine prevention of murine MHC-disparate acute graft-versus-host disease correlates with inhibition of splenic response to CpG oligodeoxynucleotides and alterations in T-cell cytokine production [PDF]
Kirk R. Schultz+7 more
openalex +1 more source
Nanomaterials have provided an emerging solution to improve the efficacy of cancer vaccines against malignant tumors. However, developing nanoparticles possessing both potent immunoadjuvant and co-delivery activities without tedious functionalization ...
M. Jambhrunkar+9 more
doaj
Combination of CpG Oligodeoxynucleotide and Anti-4-1BB Antibody in the Treatment of Multiple Hepatocellular Carcinoma in Mice. [PDF]
Ma S+5 more
europepmc +1 more source
Toll-Like Receptor 21 of Chicken and Duck Recognize a Broad Array of Immunostimulatory CpG-oligodeoxynucleotide Sequences. [PDF]
Chuang YC+9 more
europepmc +1 more source
Convolution-based Probability Gradient Loss for Semantic Segmentation [PDF]
In this paper, we introduce a novel Convolution-based Probability Gradient (CPG) loss for semantic segmentation. It employs convolution kernels similar to the Sobel operator, capable of computing the gradient of pixel intensity in an image. This enables the computation of gradients for both ground-truth and predicted category-wise probabilities.
arxiv
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators [PDF]
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible. However, applying SNNs to sequential tasks, such as text classification and time-series forecasting, has been hindered by the challenge of creating an effective and hardware-friendly spike ...
arxiv