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Fibrillar amyloid aggregates are the pathological hallmarks of multiple neurodegenerative diseases. The amyloid-β (1–42) protein, in particular, is a major component of senile plaques in the brains of patients with Alzheimer’s disease and a primary target for disease treatment. Determining the essential domains of amyloid-β (1–42) that facilitate its oligomerization is critical for the development of aggregation inhibitors as potential therapeutic agents. In this study, we identified three key hydrophobic sites (17LVF19, 32IGL34, and 41IA42) on amyloid-β (1–42) and investigated their involvement in the self-assembly process of the protein. Based on these findings, we designed candidate inhibitor peptides of amyloid-β (1–42) aggregation. Using the designed peptides, we characterized the roles of the three hydrophobic regions during amyloid-β (1–42) fibrillar aggregation and monitored the consequent effects on its aggregation property and structural conversion. Furthermore, we used an amyloid-β (1–42) double point mutant (I41N/A42N) to examine the interactions between the two C-terminal end residues with the two hydrophobic regions and their roles in amyloid self-assembly. Our results indicate that interchain interactions in the central hydrophobic region (17LVF19) of amyloid-β (1–42) are important for fibrillar aggregation, and its interaction with other domains is associated with the accessibility of the central hydrophobic region for initiating the oligomerization process. Our study provides mechanistic insights into the self-assembly of amyloid-β (1–42) and highlights key structural domains that facilitate this process. Our results can be further applied toward improving the rational design of candidate amyloid-β (1–42) aggregation inhibitors.

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Sensor clustering and trajectory optimization are a hot topic for last decade to improve energy efficiency of wireless sensor network (WSN). Most of existing studies assume that the sensor is uniformly deployed or all regions in the WSN coverage have the same level of interest. However, even in the same WSN, areas with high probability of disaster will have to form a “hotspot” with more sensors densely placed in order to be sensitive to environmental changes. The energy hole can be serious if sensor clustering and trajectory optimization are formulated without considering the hotspot. Therefore, we need to devise a sensor clustering and trajectory optimization algorithm considering the hotspots of WSN. In this paper, we propose an iterative algorithm to minimize the amount of energy consumed by components of WSN named ISCTO. The ISCTO algorithm consists of two phases. The first phase is a sensor clustering phase used to find the suitable number of clusters and cluster headers by considering the density of sensor and residual battery of sensors. The second phase is a trajectory optimization phase used to formulate suitable trajectory of multiple mobile sinks to minimize the amount of energy consumed by mobile sinks. The ISCTO algorithm performs two phases repeatedly until the amount of energy consumed by the WSN is not reduced. In addition, we show the performance of the proposed algorithm in terms of the total amount of energy consumed by sensors and mobile sinks.

With the development of blockchain technology, participants need to have huge storage volumes to deal with the growing blockchain ledger size over time. This requirement leads to the conditional participation and verification of participants, thus weakening the decentralization of a blockchain system. Several compression schemes have been proposed to mitigate this storage problem by compressing a blockchain ledger based on redundancy, modular functions, and hash functions. However, these schemes have the limitation of accumulating the compression results to validate the retained blocks. The accumulation gradually reduces the storage volume for the blockchain ledger within the storage volume of nodes with limited resources, thus reducing the verification capability of the nodes. In this paper, a selective compression scheme using a checkpoint-chain is proposed to prevent the accumulation of compression results. The checkpoint-chain is a second blockchain that stores the checkpoints compressing existing blocks through a block Merkle tree. An update process is also proposed to prevent the accumulation of checkpoints by combining them. As numerous blocks can be verified with only a few updated checkpoints, blockchain nodes with limited resources can reduce the storage volume for the blockchain ledger and achieve high verification capabilities. Finally, compared with the existing compression schemes, the proposed scheme can achieve an average reduction in the storage overhead and an average increase in the verification capability of 76.02% and 13.90%, respectively. Moreover, the corresponding performance improvements are 86.14% and 15.44% when the update process is performed, respectively.