Implication of Antimicrobial Peptides in Atopic Dermatitis: Role in Regulation of Skin Barrier | Chapter 1 | Recent Research Advances in Biology Vol. 8

 Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease in which the skin barrier is disrupted and the immune system is dysregulated. Patients with Alzheimer's disease are vulnerable to cutaneous infections, which can lead to consequences such as staphylococcal septicemia. Although the majority of research has focused on filaggrin mutations, the physical barrier and antimicrobial barrier are equally important in the aetiology of Alzheimer's disease. The stratum corneum and tight connections are the most significant components of the physical barrier. Tight connections not only compromise the physical barrier, but they also cause immunological problems. Antimicrobial peptides (AMPs) are the innate immune system's initial line of defence against microbial infections. Antimicrobial peptides such LL-37, human -defensins, and S100A7 also help to improve tight junction barrier function. Recent research into the pathophysiology of Alzheimer's disease has led to the creation of a skin barrier repair therapy for people with the disease. This chapter investigates the link between skin barrier disruption and antimicrobial peptides in Alzheimer's disease patients in order to determine the effect of these peptides on skin barrier repair and to consider using antimicrobial peptides in barrier repair strategies as a supplement to standard AD treatment.

Author (s) Details

Hai Le Thanh Nguyen
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan and Department of Dermatology and Allergology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

Juan Valentin Trujillo-Paez
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

Yoshie Umehara
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

Hainan Yue
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan and Department of Dermatology and Allergology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

Ge Peng
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan and Department of Dermatology and Allergology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

Chanisa Kiatsurayanon
Institute of Dermatology, Department of Medical Services, Ministry of Public Health, Bangkok 10400, Thailand.

Panjit Chieosilapatham
Department of Microbiology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.

Pu Song
Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, China.

Ko Okumura
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

Hideoki Ogawa
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

Shigaku Ikeda
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan and Department of Dermatology and Allergology, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan.

François Niyonsaba
Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan and Faculty of International Liberal Arts, Juntendo University, Tokyo 113-8421, Japan.

View Book :- https://stm.bookpi.org/RRAB-V8/article/view/1196

Research on Error Analysis of Data throughput and BER for the Wireless OFDM PSK Network | Chapter 1 | New Ideas Concerning Science and Technology Vol. 12

 Wireless communication is one of humanity's greatest achievements. The key aim of wireless communication is to provide high-data-rate communication. The sidebands from various carriers can overlap and cause interference during this transmission over a wireless connection. OFDM counteracts these interfering signals. OFDM is a multicarrier technique that sends a high-data single stream over a series of low-data-rate parallel sub-carriers. The symbol length appears to increase as a result, reducing the effects of multipath spread delay. In addition, the cognitive radio is built to use the best channels in the periphery for spectral efficiency. It automatically identifies the channels in a wireless spectrum that are able to be drawn and then changes the reception and transmission parameters accordingly. The error analysis is presented in this paper in OFDM system error correction with PSK modulation using forward codes (Reed-Solomon codes and Bose–Chaudhuri–Hocquenghem codes) in order to obtain low BER, high data rate, and also tolerance to interferences in AWGN, RICIAN, and RAYLEIGH channels, and the result is stimulated using LABVIEW. Wireless networking, which uses free space as a communication medium, has become essential, and cognitive radio is a technology that has proven to be the best in developing wireless systems.

Author (s) Details

M. Meena
Department of ECE, Vels Institute of Science, Technology & Advanced Studies, India.

V. Rajendran
Department of ECE, Vels Institute of Science, Technology & Advanced Studies, India.

View Book :- https://stm.bookpi.org/NICST-V12/article/view/771

A Statistical Analysis and Artificial Neural Network Behavior on Wind Speed Prediction: Case Study | Chapter 4 | Theory and Practices of Mathematics and Computer Science Vol. 6

The quest for renewable and emission-free energy sources has been encouraged by the increased usage of energy and the decline of fossil fuel supplies, along with an increase in environmental pollution. Wind energy is one of these. In the last few years, the wind power industry has seen exponential growth. The increase in wind turbine orders has resulted in a manufacturer's market. This disparity in the market, the relative immaturity of the wind industry and the rapid evolution of data processing technologies have provided opportunities to enhance the efficiency of wind farms and to change the myths surrounding their operations. This study provides data-driven modeling, a new paradigm for the wind power industry. For several parameters, each wind mast produces extensive data, recorded as frequently as every minute. Since the predictive performance approach is new to the wind industry, it is important to build a viable road map for study. This paper proposes a long-term wind forecasting (ANN) predictive analysis and data-mining approach, which is ideal for dealing with broad real-world datasets. The paper provides a case study focused on a real database of five years of wind speed data for a location and addresses wind power density results calculated using the probability density functions of Weibull and Rayleigh. Wind speed predicted using wind speed data using intelligent technologies such as Artificial Neural Networks with Datamining methodology (ANN). The MATLAB R2008a Neural Network Toolbox is designed to measure the monthly and annual mean wind speed for the training of the ANN back propagation algorithm and the PROLOG software. The statistical analysis of wind speed prediction shows that the distribution of Weibull is more acceptable than the distribution of Rayleigh and we can infer that higher values of k mean a sharper limit in the frequency distribution curve and thus a lower density of wind power by seeing the values of k.

Aurthor(s) Details:

K. Mahesh
Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology, Bengaluru, India.

View Book :- https://stm.bookpi.org/TPMCS-V6/issue/view/6