Mathematical modelling of human corneal oxygenation and cell kinetics in corneal epithelial wound healing

Healthy transparent cornea depends upon the regulation of fluid, nutrient and oxygen transport through the tissue to sustain cell metabolism and other critical processes for normal functioning. This research considers the corneal geometry and investigates oxygen distribution using a two-dimensional Monod kinetic model, showing that previous studies make assumptions that lead to predictions of near-anoxic levels of oxygen tension in the limbal regions of the cornea. It also considers the comparison of experimental spatial and temporal data with the predictions of novel mathematical models with respect to distributed mitotic rates during corneal epithelial wound healing.

"Hey world, take a look at me!": Appreciating the human body on social network sites

Social network sites (SNSs) such as Facebook have the potential to persuade people to adopt a lifestyle based on exercise and healthy nutrition. We report the findings of a qualitative study of an SNS for bodybuilders, looking at how bodybuilders present themselves online and how they orchestrate the SNS with their offline activities. Discussing the persuasive element of appreciation, we aim to extend previous work on persuasion in web 2.0 technologies.

Human error risk management methodology for rail crack incidents

The paper presents an innovative approach to modelling the causal relationships of human errors in rail crack incidents (RCI) from a managerial perspective. A Bayesian belief network is developed to model RCI by considering the human errors of designers, manufactures, operators and maintainers (DMOM) and the causal relationships involved. A set of dependent variables whose combinations express the relevant functions performed by each DMOM participant is used to model the causal relationships. A total of 14 RCI on Hong Kong’s mass transit railway (MTR) from 2008 to 2011 are used to illustrate the application of the model. Bayesian inference is used to conduct an importance analysis to assess the impact of the participants’ errors. Sensitivity analysis is then employed to gauge the effect the increased probability of occurrence of human errors on RCI. Finally, strategies for human error identification and mitigation of RCI are proposed. The identification of ability of maintainer in the case study as the most important factor influencing the probability of RCI implies the priority need to strengthen the maintenance management of the MTR system and that improving the inspection ability of the maintainer is likely to be an effective strategy for RCI risk mitigation.

Virtual Special Issue: “The impact of temperature on human health: new insights”

Ambient temperature is one of the basic parameters characterising human comfort: are we too hot, too cold, or just right? The impact of temperature goes beyond comfort: inadequate temperature and temperature variations have consequences on human health, as the increasing numbers of studies have demonstrated. The topic is of particular significance at the times when climate change shifts the traditional – as we know them- temperature zones, and brings much wider temperature variations. For these reasons the impact of temperature on health has been one of the most popular topics among the articles submitted and published in Science of the Total Environment over the last few years. This Virtual Special Issue compiles 18 articles published in our journal on this topic since 2012. It is worth briefly summarizing the rich scientific insights brought by these articles, as well as broader considerations, particularly those extending to management, discussed by the authors of the articles.

An engineering or behavioural approach? A study into employee’s perceptions regarding the effectiveness of occupational road safety initiatives

Background and Aims Considerable variation has been documented with fleet safety interventions’ abilities to create lasting behavioural change, and research has neglected to consider employees’ perceptions regarding the effectiveness of fleet interventions. This is a critical oversight as employees’ beliefs and acceptance levels (as well as the perceived organisational commitment to safety) can ultimately influence levels of effectiveness, and this study aimed to examine such perceptions in Australian fleet settings. Method 679 employees sourced from four Australian organisations completed a safety climate questionnaire as well as provided perspectives about the effectiveness of 35 different safety initiatives. Results Countermeasures that were perceived as most effective were a mix of human and engineering-based approaches: - (a) purchasing safer vehicles; - (b) investigating serious vehicle incidents, and; - (c) practical driver skills training. In contrast, least effective countermeasures were considered to be: - (a) signing a promise card; - (b) advertising a company’s phone number on the back of cars for complaints and compliments, and; - (c) communicating cost benefits of road safety to employees. No significant differences in employee perceptions were identified based on age, gender, employees’ self-reported crash involvement or employees’ self-reported traffic infringement history. Perceptions of safety climate were identified to be “moderate” but were not linked to self-reported crash or traffic infringement history. However, higher levels of safety climate were positively correlated with perceived effectiveness of some interventions. Conclusion Taken together, employees believed occupational road safety risks could best be managed by the employer by implementing a combination of engineering and human resource initiatives to enhance road safety. This paper will further outline the key findings in regards to practice as well as provide direction for future research.

Transcriptome and glycome profiling of human hematopoietic CD133+ and CD34+ cells

New blood cells are continuously provided by self-renewing multipotent hematopoietic stem cells (HSC). The capacity of HSCs to regenerate the hematopoietic system is utilized in the treatment of patients with hematological malignancies. HSCs can be enriched using an antibody-based recognition of CD34 or CD133 glycoproteins on the cell surface. The CD133+ and CD34+ cells may have partly different roles in hematopoiesis. Furthermore, each cell has a glycome typical for that cell type. Knowledge of HSC glycobiology can be used to design therapeutic cells with improved cell proliferation or homing properties. The present studies characterize the global gene expression profile of human cord blood-derived CD133+ and CD34+ cells, and demonstrate the differences between CD133+ and CD34+ cell populations that may have an impact in transplantation when CD133+ and CD34+ selected cells are used. In addition, these studies unravel the glycome profile of primitive hematopoietic cells and reveal the transcriptional regulation of N-glycan biosynthesis in CD133+ and CD34+ cells. The gene expression profile of CD133+ cells represents 690 differentially expressed transcripts between CD133+ cells and CD133- cells. CD34+ cells have 620 transcripts differentially expressed when compared to CD34- cells. The integrated CD133+/CD34+ cell gene expression profiles proffer novel transcripts to specify HSCs. Furthermore, the differences between the gene expression profiles of CD133+ and CD34+ cells indicate differences in the transcriptional regulation of CD133+ and CD34+ cells. CD133+ cells express a lower number of hematopoietic lineage differentiation marker genes than CD34+ cells. The expression profiles suggest a more primitive nature of CD133+ cells. Moreover, CD133+ cells have characteristic glycome that differ from the glycome of CD133- cells. High mannose-type and biantennary complex-type N-glycans are enriched in CD133+ cells. N-glycosylation-related gene expression pattern of CD133+ cells identify the key genes regulating the CD133+ cell-specific glycosylation including the overexpression of MGAT2 and underexpression of MGAT4. The putative role of MAN1C1 in the increase of unprocessed high mannose-type N-glycans in CD133+ cells is also discussed. These studies provide new information on the characteristics of HSCs. Improved understanding of HSC biology can be used to design therapeutic cells with improved cell proliferation and homing properties. As a result, HSC engineering could further their clinical use.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Concise review: Humanized models of tumor immunology in the 21st century: Convergence of cancer research and tissue engineering

Despite positive testing in animal studies, more than 80% of novel drug candidates fail to proof their efficacy when tested in humans. This is primarily due to the use of preclinical models that are not able to recapitulate the physiological or pathological processes in humans. Hence, one of the key challenges in the field of translational medicine is to “make the model organism mouse more human.” To get answers to questions that would be prognostic of outcomes in human medicine, the mouse's genome can be altered in order to create a more permissive host that allows the engraftment of human cell systems. It has been shown in the past that these strategies can improve our understanding of tumor immunology. However, the translational benefits of these platforms have still to be proven. In the 21st century, several research groups and consortia around the world take up the challenge to improve our understanding of how to humanize the animal's genetic code, its cells and, based on tissue engineering principles, its extracellular microenvironment, its tissues, or entire organs with the ultimate goal to foster the translation of new therapeutic strategies from bench to bedside. This article provides an overview of the state of the art of humanized models of tumor immunology and highlights future developments in the field such as the application of tissue engineering and regenerative medicine strategies to further enhance humanized murine model systems.

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.

Human health risk assessment of heavy metals in urban stormwater

Toxic chemical pollutants such as heavy metals (HMs) are commonly present in urban stormwater. These pollutants can pose a significant risk to human health and hence a significant barrier for urban stormwater reuse. The primary aim of this study was to develop an approach for quantitatively assessing the risk to human health due to the presence of HMs in stormwater. This approach will lead to informed decision making in relation to risk management of urban stormwater reuse, enabling efficient implementation of appropriate treatment strategies. In this study, risks to human health from heavy metals were assessed as hazard index (HI) and quantified as a function of traffic and land use related parameters. Traffic and land use are the primary factors influencing heavy metal loads in the urban environment. The risks posed by heavy metals associated with total solids and fine solids (<150µm) were considered to represent the maximum and minimum risk levels, respectively. The study outcomes confirmed that Cr, Mn and Pb pose the highest risks, although these elements are generally present in low concentrations. The study also found that even though the presence of a single heavy metal does not pose a significant risk, the presence of multiple heavy metals could be detrimental to human health. These findings suggest that stormwater guidelines should consider the combined risk from multiple heavy metals rather than the threshold concentration of an individual species. Furthermore, it was found that risk to human health from heavy metals in stormwater is significantly influenced by traffic volume and the risk associated with stormwater from industrial areas is generally higher than that from commercial and residential areas.

Water quality: Indicators, human impact and environmental health

This book provides an overview of state of the art assessments of water quality; with an understanding how water quality is affected, and improving water quality for irrigation, drinking and recreation activities.

Detection of recently discovered human polyomaviruses in a longitudinal kidney transplant cohort

A large number of human polyomaviruses have been discovered in the last 7 years. However, little is known about the clinical impact on vulnerable immunosuppressed patient populations. Blood, urine, and respiratory swabs collected from a prospective, longitudinal adult kidney transplant cohort (n = 167) generally pre-operatively, at day 4, months 1, 3, and 6 posttransplant, and at BK viremic episodes within the first year were screened for 12 human polyomaviruses using real-time polymerase chain reaction. Newly discovered polyomaviruses were most commonly detected in the respiratory tract, with persistent shedding seen for up to 6 months posttransplant. Merkel cell polyomavirus was the most common detection, but was not associated with clinical symptoms or subsequent development of skin cancer or other skin abnormalities. In contrast, KI polyomavirus was associated with respiratory disease in a subset of patients. Human polyomavirus 9, Malawi polyomavirus, and human polyomavirus 12 were not detected in any patient samples.