Search Results (3)

Canine parvovirus (CPV-2) is one of the most important pathogens of dogs of all ages, causing pandemic infections that are characterized by fatal hemorrhagic enteritis. The CPV-2 vaccine is recommended as a core vaccine for pet animals. Despite the intensive practice of active immunization, CPV-2 remains a global threat. In this study, a multi-epitope vaccine against CPV-2 was designed, targeting the highly conserved capsid protein (VP2) via in silico approaches. Several immunoinformatics methods, such as epitope screening, molecular docking, and simulation were used to design a potential vaccine construct. The partial protein sequences of the VP2 gene of CPV-2 and protein sequences retrieved from the NCBI were screened to predict highly antigenic proteins through antigenicity, trans-membrane-topology screening, an allergenicity assessment, and a toxicity analysis. Homologous VP2 protein sequences typically linked to the disease were identified using NCBI BLAST, in which four conserved regions were preferred. Overall, 10 epitopes, DPIGGKTGI, KEFDTDLKP, GTDPDDVQ, GGTNFGYIG, GTFYFDCKP, NRALGLPP, SGTPTN, LGLPPFLNSL, IGGKTG, and VPPVYPN, were selected from the conserved regions to design the vaccine construct. The molecular docking demonstrated the higher binding affinity of these epitopes with dog leukocyte antigen (DLA) molecules. The selected epitopes were linked with Salmonella enterica flagellin FliC adjuvants, along with the PADRE sequence, by GGS linkers to construct a vaccine candidate with 272 nucleotides. The codon adaptation and in silico cloning showed that the generated vaccine can be expressed by the E. coli strain, K12, and the sequence of the vaccine construct showed no similarities with dog protein. Our results suggest that the vaccine construct might be useful in preventing canine parvoviral enteritis (CPE) in dogs. Further in vitro and in vivo experiments are needed for the validation of the vaccine candidate. Full article

Polylactides (PLAs) are thermoplastic materials known for their wide range of applications. Moreover, the equimolar mixtures of poly(L-Lactide) (PLLA) and poly(D-Lactide) (PDLA) can form stereocomplexes (SCs), which leads to the formation of new non-covalent complex macromolecular architectures. In this work, we report the synthesis and characterization of non-covalent triblock terpolymers of polystyrene-b-stereocomplex PLA-b-poly(2-vinylpyridine) (PS-b-SC-b-P2VP). Well-defined ω-hydroxy-PS and P2VP were synthesized by “living” anionic polymerization high-vacuum techniques with sec-BuLi as initiator, followed by termination with ethylene oxide. The resulting PS-OH and P2VP-OH were used as macroinitiators for the ring-opening polymerization (ROP) of DLA and LLA with Sn(Oct)₂ as a catalyst to afford PS-b-PDLA and P2VP-b-PLLA, respectively. SC formation was achieved by mixing PS-b-PDLA and P2VP-b-PLLA chloroform solutions containing equimolar PLAs segments, followed by precipitation into n-hexane. The molecular characteristics of the resulting block copolymers (BCPs) were determined by ¹H NMR, size exclusion chromatography, and Fourier-transform infrared spectroscopy. The formation of PS-b-SC-b-P2VP and the effect of molecular weight variation of PLA blocks on the resulting polymers, were investigated by differential scanning calorimetry, X-ray powder diffraction, and circular dichroism spectroscopies. Full article

Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded edge devices still take a lot of loads in inference operations due to low computing resources, so proper customization of DNN with architectural exploration is required. However, there are few integrated frameworks to facilitate exploration and customization of various DNN models and their operations in embedded edge devices. In this paper, we propose an integrated framework that can explore and customize DNN inference operations of DNN models on embedded edge devices. The framework consists of the GUI interface part, the inference engine part, and the hardware Deep Learning Accelerator (DLA) Virtual Platform (VP) part. Specifically it focuses on Convolutional Neural Network (CNN), and provides integrated interoperability for convolutional neural network models and neural network customization techniques such as quantization and cross-inference functions. In addition, performance estimation is possible by providing hardware DLA VP for embedded edge devices. Those features are provided as web-based GUI interfaces, so users can easily utilize them. Full article