Classifying complex human motion using point distribution models

The Point Distribution Model (PDM) has been successfully used in modelling shape variations in groups of static images. It has also been effectively adapted to temporal image sets and used to track moving bodies such as hands and walking persons. However standard models do not consider the temporal characteristics of the data and are purely models of shape. This research proposes an extension to the PDM which explicitly considers the temporal sequencing of the images in the motion. The modified model can then be built from temporal quantities such as linear velocity and acceleration which are derived from the images. The new model formulation also enables movements to be tracked and classified according to their distinguishing temporal characteristics. This has been tested against distinct sets of arm movements under varying sets of experimental conditions.

MicroRNA in human cancer and chronic inflammatory diseases

MicroRNAs (miRNAs) are the non-coding RNAs that act as post-translational regulators to their complimentary messenger RNAs (mRNA). Due to their specific gene silencing property, miRNAs have been implicated in a number of cellular and developmental processes. Also, it has been proposed that a particular set of miRNA spectrum is expressed only in a particular type of tissue. Many interesting findings related to the differential expression of miRNAs in various human diseases including several types of cancers, neurodegenerative diseases and metabolic diseases have been reported. Deregulation of miRNA expression in different types of human diseases and the roles various miRNAs play as tumour suppressors as well as oncogenes, suggest their contribution to cancer and/or in other disease development. These findings have possible implications in the development of diagnostics and/or therapeutics in human malignancies. In this review, we discuss various miRNAs that are differentially expressed in human chronic inflammatory diseases, neurodegenerative diseases, cancer and the further prospective development of miRNA based diagnostics and therapeutics.

An association rule analysis framework for complex physiological and genetic data

Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set u i (e.g. u 1 numerical data streams, u 2 images, u 3 videos, u 4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases. © 2012 Springer-Verlag.

A preliminary study on human resource management in international construction

As construction companies continue to explore foreign construction markets, various international construction projects are being undertaken in all corners of the world. In an international construction project with many unique and complicated characteristics, human resource management can play a significant role in promoting the efficient use of complex human resources. The aim of this paper is to establish a valid foundation for further research on measuring the impact of human resource management economically for international construction projects. The paper examines human resource management literature and identifies the application of the related management techniques to the construction industry. In addition, the paper uses the literature analysis to describe the nature of human resource management with particular reference to international construction projects. In particular, the research described in this paper identifies economic performance factors in the implementation human resource management in international construction projects. This paper also identifies the social effects of human resource management practices.

Hierarchical recognition of intentional human gestures for sports video annotation

We present a novel technique for the recognition of complex human gestures for video annotation using accelerometers and the hidden Markov model. Our extension to the standard hidden Markov model allows us to consider gestures at different levels of abstraction through a hierarchy of hidden states. Accelerometers in the form of wrist bands are attached to humans performing intentional gestures, such as umpires in sports. Video annotation is then performed by populating the video with time stamps indicating significant events, where a particular gesture occurs. The novelty of the technique lies in the development of a probabilistic hierarchical framework for complex gesture recognition and the use of accelerometers to extract gestures and significant events for video annotation.

Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using Dynamic Time Warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique. © 2008 IEEE.

Medicinal plants of Malaysia

This article presents information about agricultural news and studies. Pharmacologists know the medicinal properties of the tropical species of Malaysia. Areca catechu is known as a common masticatory drug. An overview of the various medicinal plants, including Andrographis paniculata, Aquilaria malaccenis, and Cassia alata is offered. Malaysian medicinal plants act as strong therapeutic agents for several human diseases.

Positioning, linguistic theory and Orwell's short nonfiction

It is argued, making reference to an Orwell text sample, that linguistic theory illuminates "positioning" (as term and concept), that positioning's flexibility contributes to critical textual analysis and to attempts to understand complex human processes, but how far language may constrain as well as facilitate such understanding must remain open.

Genetic variation in PARL influences mitochondrian content

Given their involvement in processes necessary for life, mitochondrial damage and subsequent dysfunction can lead to a wide range of human diseases. Previous studies of both animal models and humans have suggested that presenilins-associated rhomboid-like protein (PARL) is a key regulator of mitochondrial integrity and function, and plays a role in cellular apoptosis. As a surrogate measure of mitochondrial integrity, we previously measured mitochondrial content in a Caucasian population consisting of large extended pedigrees, with results highlighting a substantial genetic component to this trait. To assess the inXuence of variation in the PARL gene on mitochondrial content, we re-sequenced 6.5 kb of the gene, identifying 16 SNPs and genotyped these in 1,086 Caucasian individuals, distributed across 170 families. Statistical genetic analysis revealed that one promoter variant, T-191C, exhibited signiWcant eVects (after correction for multiple testing) on mitochondrial content levels. Comparison of the transcription factor binding characteristics of the T-191C promoter SNP by EMSA indicates preferential binding of nuclear factors to the T allele, suggesting functional variation in PARL expression. These results suggest that genetic variation within PARL inXuences mitochondrial abundance and integrity.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Learning people movement model from multiple cameras for behaviour recognition

In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.

Multitasking: multiple, domain-specific cognitive functions in a virtual environment

Multitasking among three or more different tasks is a ubiquitous requirement of everyday cognition, yet rarely is it addressed in research on healthy adults who have had no specific training in multitasking skills. Participants completed a set of diverse subtasks within a simulated shopping mall and office environment, the Edinburgh Virtual Errands Test (EVET). The aim was to investigate how different cognitive functions, such as planning, retrospective and prospective memory, and visuospatial and verbal working memory, contribute to everyday multitasking. Subtasks were chosen to be diverse, and predictions were derived from a statistical model of everyday multitasking impairments associated with frontal-lobe lesions (Burgess, Veitch, de Lacy Costello, & Shallice, 2000b). Multiple regression indicated significant independent contributions from measures of retrospective memory, visuospatial working memory, and online planning, but not from independent measures of prospective memory or verbal working memory. Structural equation modelling showed that the best fit to the data arose from three underlying constructs, with Memory and Planning having a weak link, but with both having a strong directional pathway to an Intent construct that reflected implementation of intentions. Participants who followed their preprepared plan achieved higher scores than those who altered their plan during multitask performance. This was true regardless of whether the plan was efficient or poor. These results substantially develop and extend the Burgess et al. (2000b) model to healthy adults and yield new insight into the poorly understood area of everyday multitasking. The findings also point to the utility of using virtual environments for investigating this form of complex human cognition.

Relationship between health-related quality of life, comorbidities and acute health care utilisation, in adults with chronic conditions

Background: There is increased interest in developing multidisciplinary ambulatory care models of service delivery to manage patients with complex chronic diseases. These programs are expensive and given limited resources it is important that care is targeted effectively. One potential screening strategy is to identify individuals who report the greatest decrement in health related quality of life (HRQoL) and thus greater need. The aim of this study was to explore the relationship between HRQoL, comorbid conditions and acute health care utilisation. Methods: A prospective, longitudinal cohort design was used to evaluate the impact of HRQoL on acute care utilisation rates over three-years of follow-up. Participants were enrolled in chronic disease management programs run by a metropolitan health service in Australia. Baseline data was collected from 2007-2009 and follow-up data until 2012. Administrative data was used to classify patients' primary reasons for enrolment, number of comorbidities (Charlson Score) and presentations to acute care. At enrolment, HRQoL was measured using the Assessment of Quality of Life (AQoL) instrument, for analysis AQoL scores were dichotomised at two standard deviations below the population norm. Results: There were 1999 participants (54% male) with a mean age of 63years (range 18-101), enrolled in the study. Participants' primary health conditions at enrolment were: diabetes 915 (46%), chronic respiratory disease 463 (23%), cardiac disease 260 (13%), peripheral vascular disease, and 181 (9%) and aged care 180 (9%). At 1-year multivariate logistic regression models demonstrated that AQOL utility score was not predictive of acute care presentations after adjusting for comorbidities. Over 3-years an AQoL utility score in the lowest quartile was predictive of both ED presentation (OR 1.58, 95% CI, 1.16-2.13, p=0.003) and admissions (OR 1.67, 95% CI.1.21 to 2.30, p=0.002) after adjusting for differences in age and comorbidities. Conclusion: This study found that both HRQoL and comorbidities were predictive of subsequent acute care attendance over 3-years of follow-up. At 1-year, comorbidities was a better predictor of acute care representation than HRQoL. To maximise benefits, programs should initially focus on medical disease management, but subsequently switch to strategies that enhance health independence and raise HRQoL.

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

Substrates and inhibitors of human multidrug resistance associated proteins and the implications in drug development

Human contains 49 ATP-binding cassette (ABC) transporter genes and the multidrug resistance associated proteins (MRP1/ABCC1, MRP2/ABCC2, MRP3/ABCC3, MRP4/ABCC4, MRP5/ABCC5, MRP6/ABCC6, MRP7/ABCC10, MRP8/ABCC11 and MRP9/ABCC12) belong to the ABCC family which contains 13 members. ABCC7 is cystic fibrosis transmembrane conductance regulator; ABCC8 and ABCC9 are the sulfonylurea receptors which constitute the ATP-sensing subunits of a complex potassium channel. MRP10/ABCC13 is clearly a pseudo-gene which encodes a truncated protein that is highly expressed in fetal human liver with the highest similarity to MRP2/ABCC2 but without transporting activity. These transporters are localized to the apical and/or basolateral membrane of the hepatocytes, enterocytes, renal proximal tubule cells and endothelial cells of the blood-brain barrier. MRP/ABCC members transport a structurally diverse array of important endogenous substances and xenobiotics and their metabolites (in particular conjugates) with different substrate specificity and transport kinetics. The human MRP/ABCC transporters except MRP9/ABCC12 are all able to transport organic anions, such as drugs conjugated to glutathione, sulphate or glucuronate. In addition, selected MRP/ABCC members may transport a variety of endogenous compounds, such as leukotriene C(4) (LTC(4) by MRP1/ABCC1), bilirubin glucuronides (MRP2/ABCC2, and MRP3/ABCC3), prostaglandins E1 and E2 (MRP4/ABCC4), cGMP (MRP4/ABCC4, MRP5/ABCC5, and MRP8/ABCC11), and several glucuronosyl-, or sulfatidyl steroids. In vitro, the MRP/ABCC transporters can collectively confer resistance to natural product anticancer drugs and their conjugated metabolites, platinum compounds, folate antimetabolites, nucleoside and nucleotide analogs, arsenical and antimonial oxyanions, peptide-based agents, and in concert with alterations in phase II conjugating or biosynthetic enzymes, classical alkylating agents, alkylating agents. Several MRP/ABCC members (MRPs 1-3) are associated with tumor resistance which is often caused by an increased efflux and decreased intracellular accumulation of natural product anticancer drugs and other anticancer agents. Drug targeting of these transporters to overcome MRP/ABCC-mediated multidrug resistance may play a role in cancer chemotherapy. Most MRP/ABCC transporters are subject to inhibition by a variety of compounds. Based on currently available preclinical and limited clinical data, it can be expected that modulation of MRP members may represent a useful approach in the management of anticancer and antimicrobial drug resistance and possibly of inflammatory diseases and other diseases. A better understanding of their substrates and inhibitors has important implications in development of drugs for treatment of cancer and inflammation.