Proteomics: Clinical and research applications in respiratory diseases

The proteome is the study of the protein content of a definable component of an organism in biology. However, the tissue‐specific expression of proteins and the varied post‐translational modifications, splice variants and protein–protein complexes that may form, make the study of protein a challenging yet vital tool in answering many of the unanswered questions in medicine and biology to date. Indeed, the spatial, temporal and functional composition of proteins in the human body has proven difficult to elucidate for many years. Given the effect of microRNA and epigenetic regulation on silencing and enhancing gene transcription, the study of protein arguably provides more accurate information on homeostasis and perturbation in health and disease. There have been significant advances in the field of proteomics in recent years, with new technologies and platforms available to the research community. In this review, we briefly discuss some of these new technologies and developments in the context of respiratory disease. We also discuss the types of data science approaches to analyses and interpretation of the large volumes of data generated in proteomic studies. We discuss the application of these technologies with regard to respiratory disease and highlight the potential for proteomics in generating major advances in the understanding of respiratory pathophysiology into the future.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146450/1/resp13383_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146450/2/resp13383.pd

Activity Recognition using a Hierarchical Framework

This paper describes an approach for modelling and detecting activities of daily life based on a hierarchy of plans that contain a range of precedence relationships, representations of concurrency and other temporal relationships. Identification of activities of daily life is achieved by episode recovery models supported by using relationships expressed in the plans. The motivation is to allow people with Alzheimer’s disease to have additional years of independent living before the Alzheimer’s disease reaches the moderate and severe stages

Goal Lifecycles and Ontological Models for Intention Based Assistive Living within Smart Environments

Current ambient assistive living solutions have adopted a traditional sensor-centric approach, involving data analysis and activity recognition to provide assistance to individuals. The reliance on sensors and activity recognition in this approach introduces issues with scalability and ability to model activity variations. This study introduces a novel approach to assistive living which intends to address these issues via a paradigm shift from a sensor centric approach to a goal-oriented one. The goal-oriented approach focuses on identification of user goals in order to pro-actively offer assistance by either pre-defined or dynamically constructed instructions. This paper introduces the architecture of this goal-oriented approach and describes an ontological goal model to serve as its basis. The use of this approach is illustrated in a case study which focuses on assisting a user with activities of daily living

Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home

The integration of RFID sensors into everyday products has become a widespread solution for increasing efficiency in supply chain management. This has also led to a way of being able to monitor everyday activities in the home based on when and how these products are used, which is less intrusive than other monitoring approaches such as visual based systems. Monitoring activities in a home environment can be seen as a good way of analyzing behavior and tracking functional decline among elderly people. This paper describes a hierarchal approach for activity recognition using object usage data generated by everyday products used around the home. The motivation of this work is to allow people with early Alzheimer’s disease to have additional years of independent living before the disease reaches a stage where the person is fully dependable on someone else

A Comparison of Two Hidden Markov Approaches to Task Identification in the Home Environment

In today’s working world the elderly are often classified as a set of dependent people and are sometimes neglected by society. One of the ways to determine whether an elderly person is safe in their home is to find out what activities an elderly person is carrying out and give appropriate assistance or institute safeguards. This paper describes the lower tier of a two tiered approach that is being adopted. The higher tier consists of hierarchical sets of plans that model common goals and sub-goals associated with activities in daily life. The lower tier deals with recognition of tasks from the stream of sensor events. Tasks are the lowest level component of a plan. The tasks are modelled using a form of hidden Markov modelling

A hierarchal framework for recognising activities of daily life

PhDIn today’s working world the elderly who are dependent can sometimes be neglected by society. Statistically, after toddlers it is the elderly who are observed to have higher accident rates while performing everyday activities. Alzheimer’s disease is one of the major impairments that elderly people suffer from, and leads to the elderly person not being able to live an independent life due to forgetfulness. One way to support elderly people who aspire to live an independent life and remain safe in their home is to find out what activities the elderly person is carrying out at a given time and provide appropriate assistance or institute safeguards. The aim of this research is to create improved methods to identify tasks related to activities of daily life and determine a person’s current intentions and so reason about that person’s future intentions. A novel hierarchal framework has been developed, which recognises sensor events and maps them to significant activities and intentions. As privacy is becoming a growing concern, the monitoring of an individual’s behaviour can be seen as intrusive. Hence, the monitoring is based around using simple non intrusive sensors and tags on everyday objects that are used to perform daily activities around the home. Specifically there is no use of any cameras or visual surveillance equipment, though the techniques developed are still relevant in such a situation. Models for task recognition and plan recognition have been developed and tested on scenarios where the plans can be interwoven. Potential targets are people in the first stages of Alzheimer’s disease and in the structuring of the library of kernel plan sequences, typical routines used to sustain meaningful activity have been used. Evaluations have been carried out using volunteers conducting activities of daily life in an experimental home environment. The results generated from the sensors have been interpreted and analysis of developed algorithms has been made. The outcomes and findings of these experiments demonstrate that the developed hierarchal framework is capable of carrying activity recognition as well as being able to carry out intention analysis, e.g. predicting what activity they are most likely to carry out next

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain