DIFFERENTIAL EXPRESSION OF NITRIC OXIDE SYNTHASES IN THE SKIN TISSUE OF P-PHENYLENEDIAMINE-TREATED MICE

Objective: p-Phenylenediamine (PPD) is a possible contact sensitizer in skin tissues; however, data are lacking regarding its specific effects on nitric oxide synthase (NOS) expression during the sensitization phase. The purpose of this study was to investigate nNOS and iNOS expression in the skin tissue of PPD-treated mice.Methods: BALB/c mice were dermally exposed to PPD, at a dose of 10 or 50 mg/kg, on three occasions. The ear and dorsal skin tissues were then isolated from PPD-treated mice and vehicle-treated controls. Western blot analyses were performed on samples derived from the skin tissues.Results: The dorsal skin tissues of PPD-treated BALB/c mice showed significantly increased levels of iNOS. However, nNOS expression in dorsal skin, and nNOS and iNOS expression in the ear, was not significantly altered in PPD-treated skin tissues compared to controls.Conclusion: Because enhanced expression of iNOS may contribute to inflammation in allergic contact dermatitis, our data suggest that increased levels of iNOS may be involved in early immunological responses to PPD-induced pathogenesis in dorsal skin.Â

Role of extracellular matrix and microenvironment in regulation of tumor growth and LAR-mediated invasion in glioblastoma

The cellular dispersion and therapeutic control of glioblastoma, the most aggressive type of primary brain cancer, depends critically on the migration patterns after surgery and intracellular responses of the individual cancer cells in response to external biochemical cues in the microenvironment. Recent studies have shown that miR-451 regulates downstream molecules including AMPK/CAB39/MARK and mTOR to determine the balance between rapid proliferation and invasion in response to metabolic stress in the harsh tumor microenvironment. Surgical removal of the main tumor is inevitably followed by recurrence of the tumor due to inaccessibility of dispersed tumor cells in normal brain tissue. In order to address this complex process of cell proliferation and invasion and its response to conventional treatment, we propose a mathematical model that analyzes the intracellular dynamics of the miR-451-AMPK- mTOR-cell cycle signaling pathway within a cell. The model identifies a key mechanism underlying the molecular switches between proliferative phase and migratory phase in response to metabolic stress in response to fluctuating glucose levels. We show how up- or down-regulation of components in these pathways affects the key cellular decision to infiltrate or proliferate in a complex microenvironment in the absence and presence of time delays and stochastic noise. Glycosylated chondroitin sulfate proteoglycans (CSPGs), a major component of the extracellular matrix (ECM) in the brain, contribute to the physical structure of the local brain microenvironment but also induce or inhibit glioma invasion by regulating the dynamics of the CSPG receptor LAR as well as the spatiotemporal activation status of resident astrocytes and tumor-associated microglia. Using a multi-scale mathematical model, we investigate a CSPG-induced switch between invasive and non-invasive tumors through the coordination of ECM-cell adhesion and dynamic changes in stromal cells. We show that the CSPG-rich microenvironment is associated with non-invasive tumor lesions through LAR-CSGAG binding while the absence of glycosylated CSPGs induce the critical glioma invasion. We illustrate how high molecular weight CSPGs can regulate the exodus of local reactive astrocytes from the main tumor lesion, leading to encapsulation of non-invasive tumor and inhibition of tumor invasion. These different CSPG conditions also change the spatial profiles of ramified and activated microglia. The complex distribution of CSPGs in the tumor microenvironment can determine the nonlinear invasion behaviors of glioma cells, which suggests the need for careful therapeutic strategies.<br/

Deep Learning based Cryptanalysis of Lightweight Block Ciphers, Revisited

Cryptanalysis is to infer the secret key of cryptography algorithm. There are brute-force attack, differential attack, linear attack, and chosen plaintext attack. With the development of artificial intelligence, deep learning-based cryptanalysis has been actively studied. There are works in which known-plaintext attacks against lightweight block ciphers, such as S-DES, have been performed. In this paper, we propose a cryptanalysis method based on the-state-of-art deep learning technologies (e.g. residual connections and gated linear units) for lightweight block ciphers (e.g. S-DES and S-AES). The number of parameters required for training is significantly reduced by 93.16~\% and the average of bit accuracy probability increased by about 5.3~\%, compared with previous work

Transformer encoder-based Crypto-Ransomware Detection for Low-Power Embedded Processors

Crypto-ransomware has a process to encrypt the victim\u27s files, and crypto-ransomware requests the victim for money for a key to decrypt the encrypted file. In this paper, we present new approaches to prevent crypto-ransomware by detecting block cipher algorithms for Internet of Things (IoT) platforms. The generic software of the AVR package and the lightweight block cipher library (FELICS) written in C language was trained through the neural network, and then we evaluated the result. Unlike the previous technique, the proposed method does not extract sequence and frequency characteristics, but considers opcodes and opcode sequences as words and sentences, performs word embedding, and then inputs them to the neural network based on the encoder structure of the transformer model. Through this approach, the file size was reduced by 0.5 times while maintaining a similar level of classification performance compared to the previous method. The detection success rate for the proposed method was evaluated with the F-measured value, which is the harmonic mean of precision and recall. In addition to achieving 98% crypto-ransomware detection success rates, classification by benign firmware and lightweight cryptography algorithm, Substitution-Permutation-Network (SPN) structure, Addition-Rotation-eXclusive-or structure (ARX) and normal firmware classification are also possible

Quantum Neural Network based Distinguisher for Differential Cryptanalysis on Simplified Block Ciphers

Differential cryptanalysis is a block cipher analysis technology that infers a key by using the difference characteristics. Input differences can be distinguished using a good difference characteristic, and this distinguishing task can lead to key recovery. Artificial neural networks are a good solution for distinguishing tasks. For this reason, recently, neural distinguishers have been actively studied. We propose a distinguisher based on a quantum-classical hybrid neural network by utilizing the recently developed quantum neural network. To our knowledge, we are the first attempt to apply quantum neural networks for neural distinguisher. The target ciphers are simplified ciphers (S-DES, S-AES, S-PRESENT-[4]), and a quantum neural distinguisher that classifies the input difference from random data was constructed using the Pennylane library. Finally, we obtained quantum advantages in this work: improved accuracy and reduced number of parameters. Therefore, our work can be used as a quantum neural distinguisher with high reliability for simplified ciphers

Disruption of Microtubules Sensitizes the DNA Damage-induced Apoptosis Through Inhibiting Nuclear Factor κB (NF-κB) DNA-binding Activity

The massive reorganization of microtubule network involves in transcriptional regulation of several genes by controlling transcriptional factor, nuclear factor-kappa B (NF-κB) activity. The exact molecular mechanism by which microtubule rearrangement leads to NF-κB activation largely remains to be identified. However microtubule disrupting agents may possibly act in synergy or antagonism against apoptotic cell death in response to conventional chemotherapy targeting DNA damage such as adriamycin or comptothecin in cancer cells. Interestingly pretreatment of microtubule disrupting agents (colchicine, vinblastine and nocodazole) was observed to lead to paradoxical suppression of DNA damage-induced NF-κB binding activity, even though these could enhance NF-κB signaling in the absence of other stimuli. Moreover this suppressed NF-κB binding activity subsequently resulted in synergic apoptotic response, as evident by the combination with Adr and low doses of microtubule disrupting agents was able to potentiate the cytotoxic action through caspase-dependent pathway. Taken together, these results suggested that inhibition of microtubule network chemosensitizes the cancer cells to die by apoptosis through suppressing NF-κB DNA binding activity. Therefore, our study provided a possible anti-cancer mechanism of microtubule disrupting agent to overcome resistance against to chemotherapy such as DNA damaging agent

Strategies of Eradicating Glioma Cells: A Multi-Scale Mathematical Model with MiR-451-AMPK-mTOR Control

The cellular dispersion and therapeutic control of glioblastoma, the most aggressive type of primary brain cancer, depends critically on the migration patterns after surgery and intracellular responses of the individual cancer cells in response to external biochemical and biomechanical cues in the microenvironment. Recent studies have shown that a particular microRNA, miR-451, regulates downstream molecules including AMPK and mTOR to determine the balance between rapid proliferation and invasion in response to metabolic stress in the harsh tumor microenvironment. Surgical removal of main tumor is inevitably followed by recurrence of the tumor due to inaccessibility of dispersed tumor cells in normal brain tissue. In order to address this multi-scale nature of glioblastoma proliferation and invasion and its response to conventional treatment, we propose a hybrid model of glioblastoma that analyses spatio-temporal dynamics at the cellular level, linking individual tumor cells with the macroscopic behaviour of cell organization and the microenvironment, and with the intracellular dynamics of miR-451-AMPK-mTOR signaling within a tumour cell. The model identifies a key mechanism underlying the molecular switches between proliferative phase and migratory phase in response to metabolic stress and biophysical interaction between cells in response to fluctuating glucose levels in the presence of blood vessels (BVs). The model predicts that cell migration, therefore efficacy of the treatment, not only depends on oxygen and glucose availability but also on the relative balance between random motility and strength of chemoattractants. Effective control of growing cells near BV sites in addition to relocalization of invisible migratory cells back to the resection site was suggested as a way of eradicating these migratory cells.Publisher PDFPeer reviewe

Simulation of SVPWM Based Multivariable Control Method for a DFIG Wind Energy System

This paper deals with a variable speed device toproduce electrical energy on a power network based on adoubly-fed induction machine used in generating mode(DFIG) in wind energy system by using SVPWM powertransfer matrix. This paper presents a modeling and controlapproach which uses instantaneous real and reactive powerinstead of dq components of currents in a vector controlscheme. The main features of the proposed model comparedto conventional models in the dq frame of reference arerobustness and simplicity of realization. The sequential loopclosing technique is adopted to design a multivariable controlsystem including six compensators for a DFIG wind energysystem to capture the maximum wind power and to inject therequired reactive power to the generator. In this paperSVPWM method is used for better controlling of converters.It also provides fault ride through method to protect theconverter during a fault. The time-domain simulation of thestudy system is presented by using MATLAB Simulink to testthe system robustness, to validate the proposed model and toshow the enhanced tracking capability