Tailored Nanoparticles as Vaccine Components

Nanoparticles are components of many vaccines, helping to make them more stable and immunogenic. They protect antigens—or the genetic material encoding them—from degradation, target them to particular tissues or cells, promote their uptake into antigen-presenting cells, and activate the immune response (in the form of adjuvants). Nanoparticles come in many different kinds, some with uniform composition and some with elaborate core-and-shell structures, including lipid membranes. The antigen is usually retained inside, and the surface can be functionalized by targeting or activating proteins and carbohydrates. This minireview provides a general introductory overview to vaccination and a survey of nanoparticles, their types, production, characteristics, and individual applications in vaccines, and finally, a brief look into the world of artificial antigen-presenting cells.

Survey of NISQ Era Hybrid Quantum-Classical Machine Learning Research

Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia. Several physical quantum computers are now freely available to use through cloud services, with some implementations supporting upwards of hundreds of qubits. These advances mark the beginning of the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, paving the way for hybrid quantum-classical systems. This work provides an introductory overview of gate-model quantum computing through the Visual IoT/Robotics Programming Language Environment and a survey of recent applications of NISQ era quantum computers to hybrid quantum-classical machine learning.

Energy-Aware Controllability of Complex Networks

Understanding the fundamental principles and limitations of controlling complex networks is of paramount importance across natural, social, and engineering sciences. The classic notion of controllability does not capture the effort needed to control dynamical networks, and quantitative measures of controllability have been proposed to remedy this problem. This article presents an introductory overview of the practical (i.e., energy-related) aspects of controlling networks governed by linear dynamics. First, we introduce a class of energy-aware controllability metrics and discuss their properties. Then, we establish bounds on these metrics, which allow us to understand how the structure of the network impacts the control energy. Finally, we examine the problem of optimally selecting a set of control nodes so as to minimize the control effort, and compare the performance of some simple strategies to approximately solve this problem. Throughout the article, we include examples of structured and random networks to illustrate our results. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

An Introductory Overview of Open-Source and Commercial Software Options for the Analysis of Forensic Sequencing Data

The top challenges of adopting new methods to forensic DNA analysis in routine laboratories are often the capital investment and the expertise required to implement and validate such methods locally. In the case of next-generation sequencing, in the last decade, several specifically forensic commercial options became available, offering reliable and validated solutions. Despite this, the readily available expertise to analyze, interpret and understand such data is still perceived to be lagging behind. This review gives an introductory overview for the forensic scientists who are at the beginning of their journey with implementing next-generation sequencing locally and because most in the field do not have a bioinformatics background may find it difficult to navigate the new terms and analysis options available. The currently available open-source and commercial software for forensic sequencing data analysis are summarized here to provide an accessible starting point for those fairly new to the forensic application of massively parallel sequencing.

Artificial Economics

This introductory overview explores the methods, models and interdisciplinary links of artificial economics, a new way of doing economics in which the interactions of artificial economic agents are computationally simulated to study their individual and group behavior patterns. Conceptually and intuitively, and with simple examples, Mercado addresses the differences between the basic assumptions and methods of artificial economics and those of mainstream economics. He goes on to explore various disciplines from which the concepts and methods of artificial economics originate; for example cognitive science, neuroscience, artificial intelligence, evolutionary science and complexity science. Introductory discussions on several controversial issues are offered, such as the application of the concepts of evolution and complexity in economics and the relationship between artificial intelligence and the philosophies of mind. This is one of the first books to fully address artificial economics, emphasizing its interdisciplinary links and presenting in a balanced way its occasionally controversial aspects.

Ontologies: a revamped cross-disciplinary buzzword or a truly promising interdisciplinary research topic?

The following article provides an introductory overview of the different research domains (computational linguistics, termino graphy, artificial intelligence (AI), philosophy and database semantics) for which ontologies and the emerging field of the Semantic Web have become a main point of interest. It will be pointed out that each of these domains uses a different definition for an ontology. A specific ontology engineering methodology (VUB STAR Lab DOGMA) will be presented and emphasis will be put on the specific role and contribution of (multilingual) terminography in this ontology. In addition, we will explain what ontologies might offer to advance the state of the art of linguistics and terminography.

A Brief Survey of Question Answering Systems

Question Answering (QA) is a subfield of Natural Language Processing (NLP) and computer science focused on building systems that automatically answer questions from humans in natural language. This survey summarizes the history and current state of the field and is intended as an introductory overview of QA systems. After discussing QA history, this paper summarizes the different approaches to the architecture of QA systems -- whether they are closed or open-domain and whether they are text-based, knowledge-based, or hybrid systems. Lastly, some common datasets in this field are introduced and different evaluation metrics are discussed.

MISATTRIBUTED PATERNITY Nel v Jonker (WCHC) unreported case number A653/2009 dated 2001-02-17

The purpose of this note is to set out the problem of misattributed paternity and to determine the rights of the parties to such a dispute in South Africa, in the light of the current legal view of paternity. Although the case touched on these questions, the court was unable to consider all the issues and make specific findings as a result of the poorly drafted pleadings and the lack of evidence before it. This note therefore seeks to provide an introductory overview of the concept of misattributed paternity, in particular focusing onthe financial aspects of this problem and, specifically, the possibility of reclaiming any maintenance amounts already paid towards the upkeep of the child. The question will further be addressed: If there is a claim, from whom can be claimed and what would the basis of such a claim be? Although this note does not engage in a detailed survey of relevant comparative authority, references to selected arguments used in foreign cases are included to assess the possible applicability of these arguments in similar disputes in South Africa.