Self-assembling peptides are biomedical materials with unique structures that are formed in response to various environmental conditions. Governed by their physicochemical characteristics, the ...peptides can form a variety of structures with greater reactivity than conventional non-biological materials. The structural divergence of self-assembling peptides allows for various functional possibilities; when assembled, they can be used as scaffolds for cell and tissue regeneration, and vehicles for drug delivery, conferring controlled release, stability, and targeting, and avoiding side effects of drugs. These peptides can also be used as drugs themselves. In this review, we describe the basic structure and characteristics of self-assembling peptides and the various factors that affect the formation of peptide-based structures. We also summarize the applications of self-assembling peptides in the treatment of various diseases, including cancer. Furthermore, the in-cell self-assembly of peptides, termed reverse self-assembly, is discussed as a novel paradigm for self-assembling peptide-based nanovehicles and nanomedicines.
Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users' assets, threaten the network's reliability, and destabilize the blockchain network. ...Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users' assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC.
With the increasing sophistication of the medical industry, various advanced medical services such as medical artificial intelligence, telemedicine, and personalized health care services have ...emerged. The demand for medical data is also rapidly increasing today because advanced medical services use medical data such as user data and electronic medical records (EMRs) to provide services. As a result, health care institutions and medical practitioners are researching various mechanisms and tools to feed medical data into their systems seamlessly. However, medical data contain sensitive personal information of patients. Therefore, ensuring security while meeting the demand for medical data is a very important problem in the information age for which a solution is required.
Our goal is to design a blockchain-based decentralized patient information exchange (PIE) system that can safely and efficiently share EMRs. The proposed system preserves patients' privacy in the EMRs through a medical information exchange process that includes data encryption and access control.
We propose a blockchain-based EMR-sharing system that allows patients to manage their EMRs scattered across multiple hospitals and share them with other users. Our PIE system protects the patient's EMR from security threats such as counterfeiting and privacy attacks during data sharing. In addition, it provides scalability by using distributed data-sharing methods to quickly share an EMR, regardless of its size or type. We implemented simulation models using Hyperledger Fabric, an open source blockchain framework.
We performed a simulation of the EMR-sharing process and compared it with previous works on blockchain-based medical systems to check the proposed system's performance. During the simulation, we found that it takes an average of 0.01014 (SD 0.0028) seconds to download 1 MB of EMR in our proposed PIE system. Moreover, it has been confirmed that data can be freely shared with other users regardless of the size or format of the data to be transmitted through the distributed data-sharing technique using the InterPlanetary File System. We conducted a security analysis to check whether the proposed security mechanism can effectively protect users of the EMR-sharing system from security threats such as data forgery or unauthorized access, and we found that the distributed ledger structure and re-encryption-based data encryption method can effectively protect users' EMRs from forgery and privacy leak threats and provide data integrity.
Blockchain is a distributed ledger technology that provides data integrity to enable patient-centered health information exchange and access control. PIE systems integrate and manage fragmented patient EMRs through blockchain and protect users from security threats during the data exchange process among users. To increase safety and efficiency in the EMR-sharing process, we used access control using security levels, data encryption based on re-encryption, and a distributed data-sharing scheme.
A naphthalene imide (
) and a naphthalene (
) bearing two pyrrole units have been synthesized, respectively, as anion receptors. It was revealed by
H NMR spectral studies carried out in CD
CN that ...receptors
and
bind various anions via hydrogen bonds using both C-H and N-H donors. Compared with receptor
, receptor
shows higher affinity for the test anions because of the enhanced acidity of its pyrrole NH and naphthalene CH hydrogens by the electron-withdrawing imide substituent. Molecular mechanics computations demonstrate that the receptors contact the halide anions via only one of the two respective available N-H and C-H donors whereas they use all four donors for binding of the oxyanions such as dihydrogen phosphate and hydrogen pyrophosphate. Receptor
, a push-pull conjugated system, displays a strong fluorescence centered at 625 nm, while receptor
exhibits an emission with a maximum peak at 408 nm. In contrast, upon exposure of receptors
and
to the anions in question, their fluorescence was noticeably quenched particularly with relatively basic anions including F
, H
PO
, HP
O
, and HCO
.
We demonstrate a gel polymer electrolyte (GPE) featuring a crosslinked polymer matrix formed by poly(ethylene glycol) diacrylate (PEGDA) and dipentaerythritol hexaacrylate (DPHA) using the radical ...photo initiator via ultraviolet (UV) photopolymerization for lithium-ion batteries. The two monomers with acrylate functional groups undergo chemical crosslinking, resulting in a three-dimensional structure capable of absorbing liquid electrolytes to form a gel. The GPE system was strategically designed by varying the ratios between the main polymer backbone (PEGDA) and the crosslinker (DPHA) to achieve an optimal gel polymer electrolyte network. The resulting GPE exhibited enhanced thermal stability compared to conventional liquid electrolytes (LE) and demonstrated high ionic conductivity (1.40 mS/cm) with a high lithium transference number of 0.65. Moreover, the obtained GPE displayed exceptional cycle performance, maintaining a higher capacity retention (85.2%) comparable to the cell with LE (79.3%) after 200 cycles.
Prion diseases are neurodegenerative disorders in humans and animals for which no therapies are currently available. Here, we report that
Valeton (Zingiberaceae) (
) extract was partly effective in ...decreasing prion aggregation and propagation in both in vitro and in vivo models.
extract inhibited self-aggregation of recombinant prion protein (PrP) in a test tube assay and decreased the accumulation of scrapie PrP (PrP
) in ScN2a cells, a cultured neuroblastoma cell line with chronic prion infection, in a concentration-dependent manner.
extract also modified the course of the disease in mice inoculated with mouse-adapted scrapie prions, completely preventing the onset of prion disease in three of eight mice. Biochemical and neuropathological analyses revealed a statistically significant reduction in PrP
accumulation, spongiosis, astrogliosis, and microglia activation in the brains of mice that avoided disease onset. Furthermore, PrP
accumulation in the spleen of mice was also reduced.
extract precluded prion infection in cultured cells as demonstrated by the modified standard scrapie cell assay. This study suggests that
extract could contribute to investigating the modulation of prion propagation.
Prions are infectious protein particles known to cause prion diseases. The biochemical entity of the pathogen is the misfolded prion protein (PrP
Sc
) that forms insoluble amyloids to impair brain ...function. PrP
Sc
interacts with the non-pathogenic, cellular prion protein (PrP
C
) and facilitates conversion into a nascent misfolded isoform. Several small molecules have been reported to inhibit the aggregation of PrP
Sc
but no pharmacological intervention was well established thus far. We, here, report that acylthiosemicarbazides inhibit the prion aggregation. Compounds 7x and 7y showed almost perfect inhibition (EC
50
= 5 µM) in prion aggregation formation assay. The activity was further confirmed by atomic force microscopy, semi-denaturing detergent agarose gel electrophoresis and real-time quaking induced conversion assay (EC
50
= 0.9 and 2.8 µM, respectively). These compounds also disaggregated pre-existing aggregates in vitro and one of them decreased the level of PrP
Sc
in cultured cells with permanent prion infection, suggesting their potential as a treatment platform. In conclusion, hydroxy-2-naphthoylthiosemicarbazides can be an excellent scaffold for the discovery of anti-prion therapeutics.
With the recently increasing prevalence of deep learning, both academia and industry exhibit substantial interest in neuromorphic computing, which mimics the functional and structural features of the ...human brain. To realize neuromorphic computing, an energy‐efficient and reliable artificial synapse must be developed. In this study, the synaptic ferroelectric field‐effect‐transistor (FeFET) array is fabricated as a component of a neuromorphic convolutional neural network. Beyond the single transistor level, the long‐term potentiation and depression of synaptic weights are achieved at the array level, and a successful program‐inhibiting operation is demonstrated in the synaptic array, achieving a learning accuracy of 79.84% on the Canadian Institute for Advanced Research (CIFAR)‐10 dataset. Furthermore, an efficient self‐curing method is proposed to improve the endurance of the FeFET array by tenfold, utilizing the punch‐through current inherent to the device. Low‐frequency noise spectroscopy is employed to quantitatively evaluate the curing efficiency of the proposed self‐curing method. The results of this study provide a method to fabricate and operate reliable synaptic FeFET arrays, thereby paving the way for further development of ferroelectric‐based neuromorphic computing.
The primary challenge that ferroelectric field‐effect transistors face is their vulnerability to the repeated program/erase cycle. To solve this issue, an efficient self‐curing method is presented. The proposed method successfully recovers synaptic fatigue damage, enhancing learning accuracy in the convolutional neural network.
Languages and earnings management Kim, Jaehyeon; Kim, Yongtae; Zhou, Jian
Journal of accounting & economics,
04/2017, Letnik:
63, Številka:
2-3
Journal Article
Recenzirano
Odprti dostop
We predict that managers of firms in countries where languages do not require speakers to grammatically mark future events perceive future consequences of earnings management to be more imminent, and ...therefore they are less likely to engage in earnings management. Using data from 38 countries, we find that accrual-based earnings management and real earnings management are less prevalent where there is weaker time disassociation in the language. Our study is the first to examine the relation between the grammatical structure of languages and financial reporting characteristics, and it extends the literature on the effect of informal institutions on corporate actions.
In recent years, neuromorphic computing has been rapidly developed to overcome the limitations of von Neumann architecture. In this regard, the demand for high‐performance synaptic devices with high ...switching speeds, low power consumption, and multilevel conductance is increasing. Among the various synaptic devices, ferroelectric tunnel junctions (FTJs) are promising candidates. While previous studies have focused on improving reliability of FTJs to enhance the synaptic behavior, low‐frequency noise (LFN) of FTJs has not been characterized and its impact on the learning accuracy in neuromorphic computing remains unknown. Herein, the LFN characteristics of FTJs fabricated on n‐ and p‐type Si along with the impact of 1/f noise on the learning accuracy of convolutional neural networks (CNNs) are investigated. The results indicate that the FTJ on p‐type Si exhibits a far lower 1/f noise than that on n‐type Si. The FTJ on p‐type Si exhibits a significantly higher learning accuracy (86.26%) than that on n‐type Si (78.70%) owing to its low‐noise properties. This study provides valuable insights into the LFN characteristics of FTJs and a solution to improve the performance of synaptic devices by significantly reducing the 1/f noise.
Comprehensive investigation of 1/f noise in synaptic ferroelectric tunnel junctions with high reliability is provided.
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