Enhanced Practical Byzantine Fault Tolerance via Dynamic Hierarchy Management and Location-Based Clustering.

Blockchain is a distributed database technology that operates in a P2P network and is used in various domains. Depending on its structure, blockchain can be classified into types such as public and private. A consensus algorithm is essential in blockchain, and various consensus algorithms have been applied. In particular, a non-competitive consensus algorithm called PBFT is mainly used in private blockchains. However, there are limitations to scalability. This paper proposes an enhanced PBFT with dynamic hierarchy management and location-based clustering to overcome these problems. The proposed method clusters nodes based on location information and adjusts the dynamic hierarchy to optimize consensus latency. As a result of the experiment, the proposed PBFT showed significant performance improvement compared to the existing typical PBFT and Dynamic Layer Management PBFT (DLM-PBFT). The proposed PBFT method showed a processing performance improvement rate of approximately 107% to 128% compared to PBFT, and 11% to 99% compared to DLM-PBFT.

Investigation on the effect of nanoparticle size on the blood-brain tumour barrier permeability by in situ perfusion via internal carotid artery in mice.

The blood-brain barrier (BBB) is a limiting factor in nanoparticle drug delivery to the brain, and various attempts have been made to overcome it for efficient drug delivery. Nowadays, it was considered as further issue for brain-drug delivery that the nanoparticle delivered to brain through the BBB reach cancer cells in tumour tissue. In this study, we investigated the effect of nanoparticle size on blood-brain tumour barrier (BBTB) permeation of fluorescence-labelled gold nanoparticles (AuNPs) in a mouse model of orthotopic glioblastoma multiforme (GBM), established by intracranial implantation of luciferase-expressing human glioblastoma U87MG cells. AuNPs sized 10, 50, and 100 nm were perfused into the GBM mice via internal carotid artery (ICA) for 5 min. Immediately after perfusion, the brains were fixed and prepared for LSCM observation. The AuNPs distribution in the normal and tumorous brain tissues was analysed qualitatively and quantitatively. Higher distribution of AuNPs was observed in the tumorous tissue than in the normal tissue. Furthermore, the smallest nanoparticle, 10 nm AuNPs, was widely distributed in the brain tumour tissue, whereas the 50 and 100 nm AuNPs were located near the blood vessels. Therefore, nanoparticle size affected the permeation of nanoparticles from the blood into brain tumour tissue.

Triphenylphosphine-docetaxel conjugate-incorporated albumin nanoparticles for cancer treatment.

AIM: The objective of this study was to develop a mitochondria-targeted anticancer drug, docetaxel (DTX), for chemotherapy. MATERIALS & METHODS: The DTX was conjugated to 4-carboxybutyl triphenylphosphonium (TPP) to enhance mitochondrial targeting, and the TPP-DTX conjugate was further loaded into folate-cholesteryl albumin (FA-chol-BSA) nanoparticles (NPs) to improve its biocompatibility. RESULTS & CONCLUSION: In vitro studies showed that TPP-DTX and its NP primarily accumulated in the mitochondria; generated high reactive oxygen species, leading to mitochondrial disruption and cell apoptosis; and had a higher cytotoxicity against cancer cells. In vivo antitumor studies indicated that the NP significantly suppressed tumor growth compared with free drugs in xenograft tumor-bearing mice. Our results demonstrated that TPP-DTX@FA-chol-BSA NPs could be a promising mitochondria-targeted anticancer prodrug for chemotherapy.

An efficient words typing P300-BCI system using a modified T9 interface and random forest classifier.

The conventional P300-based character spelling BCI system consists of a character presentation paradigm and a classification system. In this paper, we propose modifications to both in order to increase the word typing speed and accuracy. In the paradigm part, we have modified the T9 (Text on Nine keys) interface which is similar to the keypad of mobile phones being used for text messaging. Then we have integrated a custom-built dictionary to give word suggestions to a user while typing. The user can select one out of the given suggestions to complete word typing. Our proposed paradigms significantly reduce the word typing time and make words typing more convenient by typing complete words with only few initial character spellings. In the classification part we have adopted a Random Forest (RF) classifier. The RF improves classification accuracy by combining multiple decision trees. We conducted experiments with five subjects using the proposed BCI system. Our results demonstrate that our system increases typing speed significantly: our proposed system took an average time of 1.83 minutes per word, while typing ten random words, whereas the conventional spelling required 3.35 minutes for the same words under the same conditions, decreasing the typing time by 45.37%.