The study of extracellular vesicles (EVs) has the potential to identify unknown cellular and molecular mechanisms in intercellular communication and in organ homeostasis and disease. Exosomes, with an average diameter of ~100 nanometers, are a subset of EVs. The biogenesis of exosomes involves their origin in endosomes, and subsequent interactions with other intracellular vesicles and organelles generate the final content of the exosomes. Their diverse constituents include nucleic acids, proteins, lipids, amino acids, and metabolites, which can reflect their cell of origin. In various diseases, exosomes offer a window into altered cellular or tissue states, and their detection in biological fluids potentially offers a multicomponent diagnostic readout. The efficient exchange of cellular components through exosomes can inform their applied use in designing exosome-based therapeutics.

The biology, function, and biomedical applications of exosomes

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

This chapter discusses leading problems linked to energy that the world is now confronting and to propose some ideas concerning possible solutions. Oil deserves special attention among all energy sources. Since the beginning of 1981, it has merely been continuing and enhancing the downward movement in consumption and prices caused by excessive rises, especially for light crudes such as those from Africa, and the slowing down of worldwide economic growth. Densely-populated oil-producing countries need to produce to live, to pay for their food and their equipment. If the economic growth of the industrialized countries were to be 4%, even if investment in the rational use of energy were pushed to the limit and the development of nonpetroleum energy sources were also pursued actively, it would be extremely difficult to prevent a sharp rise in prices. It is evident that it is absolutely necessary to pursue actively the development of coal, natural gas, and nuclear power if a physical shortage of energy is not to block economic growth.

World energy outlook

The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/.

https://par.nsf.gov/servlets/purl/10021656

Expansion of the Gene Ontology knowledgebase and resources

Reassessment of Exosome Composition

ExoCarta: A Web-Based Compendium of Exosomal Cargo

Distributed attack detection scheme using deep learning approach for Internet of Things

Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.

Survey on SDN based network intrusion detection system using machine learning approaches

With the fast development of wireless communication technologies and semiconductor technologies, the wireless sensor network (WSN) has been widely used in many applications As an application of the WSN, the wireless medical sensor network (WMSN) could improve health-care quality and has become important in the modern medical system In the WMSN, physiological data are collected by sensors deployed in the patient's body and sent to health professionals' mobile devices through wireless communication Then health professionals could get the status of the patient anywhere and anytime The data collected by sensors are very sensitive and important The leakage of them could compromise the patient's privacy and their malicious modification could harm the patient's health Therefore, both security and privacy are two important issues in WMSNs Recently, Kumar et al proposed an efficient authentication protocol for health-care applications using WMSNs and claimed that it could withstand various attacks However, we find that their protocol is vulnerable to the off-line password guessing attack and the privileged insider attack We also point out that their protocol cannot provide user anonymity In this paper, we will propose a robust anonymous authentication protocol for health-care applications using WMSNs Compared with Kumar et al's protocol, the proposed protocol has strong security and computational efficiency Therefore, it is more suitable for health-care applications using WMSNs

Robust anonymous authentication protocol for health-care applications using wireless medical sensor networks

The increase in the number and diversity of smart objects has raised substantial cybersecurity challenges due to the recent exponential rise in the occurrence and sophistication of attacks Although cloud computing has transformed the world of business in a dramatic way, its centralization hammers the application of distributed services such as security mechanisms for IoT applications The new and emerging IoT applications require novel cybersecurity controls, models, and decisions distributed at the edge of the network Despite the success of the existing cryptographic solutions in the traditional Internet, factors such as system development flaws, increased attack surfaces, and hacking skills have proven the inevitability of detection mechanisms The traditional approaches such as classical machine-learning-based attack detection mechanisms have been successful in the last decades, but it has already been proven that they have low accuracy and less scalability for cyber-attack detection in massively distributed nodes such as IoT The proliferation of deep learning and hardware technology advancement could pave a way to detecting the current level of sophistication of cyber-attacks in edge networks The application of deep networks has already been successful in big data areas, and this indicates that fog-tothings computing can be the ultimate beneficiary of the approach for attack detection because a massive amount of data produced by IoT devices enable deep models to learn better than shallow algorithms In this article, we propose a novel distributed deep learning scheme of cyber-attack detection in fog-to-things computing Our experiments show that deep models are superior to shallow models in detection accuracy, false alarm rate, and scalability

Naveen Chilamkurti

Papers

ExoCarta: A Web-Based Compendium of Exosomal Cargo

Distributed attack detection scheme using deep learning approach for Internet of Things

Survey on SDN based network intrusion detection system using machine learning approaches

Robust anonymous authentication protocol for health-care applications using wireless medical sensor networks

Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing