Marine organisms provide an abundant source of potential medicines. Many of the marine-derived biomaterials have been shown to act as different mechanisms in immune responses, and in each case they can significantly control the immune system to produce effective reactions. Marine-derived proteins, peptides, and protein hydrolysates exhibit various physiologic functions, such as antimicrobial, anticancer, antioxidant, antihypertensive, and anti-inflammatory activities. Recently, the immunomodulatory properties of several antimicrobial peptides have been demonstrated. Some of these peptides directly kill bacteria and exhibit a variety of immunomodulatory activities that improve the host innate immune response and effectively eliminate infection. The properties of immunomodulatory proteins and peptides correlate with their amino acid composition, sequence, and length. Proteins and peptides with immunomodulatory properties have been tested in vitro and in vivo, and some of them have undergone different clinical and preclinical trials. This review provides a comprehensive overview of marine immunomodulatory proteins, peptides, and protein hydrolysates as well as their production, mechanisms of action, and applications in human therapy.

Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

Marine bioresources are a valuable source of bioactive compounds with industrial and nutraceutical potential. Numerous clinical trials evaluating novel chemotherapeutic agents derived from marine sources have revealed novel mechanisms of action. Recently, marine-derived bioactive peptides have attracted attention owing to their numerous beneficial effects. Moreover, several studies have reported that marine peptides exhibit various anti-infective activities, such as antimicrobial, antifungal, antimalarial, antiprotozoal, anti-tuberculosis, and antiviral activities. In the last several decades, studies of marine plants, animals, and microbes have revealed tremendous number of structurally diverse and bioactive secondary metabolites. However, the treatments available for many infectious diseases caused by bacteria, fungi, and viruses are limited. Thus, the identification of novel antimicrobial peptides should be continued, and all possible strategies should be explored. In this review, we will present the structures and anti-infective activity of peptides isolated from marine sources (sponges, algae, bacteria, fungi and fish) from 2006 to the present.

Staphylococcus aureus (S. aureus) is a widespread cutaneous pathogen responsible for the great majority of bacterial skin infections in humans. The incidence of skin infections by S. aureus reflects in part the competition between host cutaneous immune defenses and S. aureus virulence factors. As part of the innate immune system in the skin, cationic antimicrobial peptides (CAMPs) such as the β-defensins and cathelicidin contribute to host cutaneous defense, which prevents harmful microorganisms, like S. aureus, from crossing epithelial barriers. Conversely, S. aureus utilizes evasive mechanisms against host defenses to promote its colonization and infection of the skin. In this review, we focus on host-pathogen interactions during colonization and infection of the skin by S. aureus and methicillin-resistant Staphylococcus aureus (MRSA). We will discuss the peptides (defensins, cathelicidins, RNase7, dermcidin) and other mediators (toll-like receptor, IL-1 and IL-17) that comprise the host defense against S. aureus skin infection, as well as the various mechanisms by which S. aureus evades host defenses. It is anticipated that greater understanding of these mechanisms will enable development of more sustainable antimicrobial compounds and new therapeutic approaches to the treatment of S. aureus skin infection and colonization.

A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many applications such as in banking, finance, insurance, healthcare and business. Recently, many communities in blockchain networks want to deploy machine learning models to get meaningful knowledge from geographically distributed large-scale data owned by each participant. To run a learning model without data centralization, distributed machine learning (DML) for blockchain networks has been studied. While several works have been proposed, privacy and security have not been sufficiently addressed, and as we show later, there are vulnerabilities in the architecture and limitations in terms of efficiency. In this paper, we propose a privacy-preserving DML model for a permissioned blockchain to resolve the privacy, security, and performance issues in a systematic way. We develop a differentially private stochastic gradient descent method and an error-based aggregation rule as core primitives. Our model can treat any type of differentially private learning algorithm where non-deterministic functions should be defined. The proposed error-based aggregation rule is effective to prevent attacks by an adversarial node that tries to deteriorate the accuracy of DML models. Our experiment results show that our proposed model provides stronger resilience against adversarial attacks than other aggregation rules under a differentially private scenario. Finally, we show that our proposed model has high usability because it has low computational complexity and low transaction latency.

Circular dichroism (CD) is a useful technique for monitoring changes in the conformation of antimicrobial peptides or gelatin. In this study, interactions between cationic peptides and gelatin were observed without affecting the triple helical content of the gelatin, which was more strongly affected by anionic surfactant. The peptides did not adopt a secondary structure in the presence of aqueous solution or Tween 80, but a peptide secondary structure formed upon the addition of sodium dodecyl sulfate (SDS). The peptides bound to the phosphate group of lipopolysaccharide (LPS) and displayed an alpha-helical conformation while (KW)₄ adopted a folded conformation. Further, the peptides did not specifically interact with the fungal cell wall components of mannan or laminarin. Tryptophan blue shift assay indicated that these peptides interacted with SDS, LPS, and gelatin but not with Tween 80, mannan, or laminarin. The peptides also displayed antibacterial activity against P. aeruginosa without cytotoxicity against HaCaT cells at MIC, except for HPA3NT3-analog peptide. In this study, we used a CD spectroscopic method to demonstrate the feasibility of peptide characterization in numerous environments. The CD method can thus be used as a screening method of gelatin-peptide interactions for use in wound healing applications.