Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

"It's a can of worms": understanding primary care practitioners' behaviours in relation to HPV using the Theoretical Domains Framework

Background: The relationship between infection with high-risk human papillomavirus (HPV) and cervical cancer is transforming cervical cancer prevention. HPV tests and vaccinations have recently become available. In Ireland, as elsewhere, primary care practitioners play a key role in prevention. ATHENS (A Trial of HPV Education and Support) aims to develop a theorybased intervention to support primary care practitioners in their HPV-related practice. This study, the first step in the intervention development process, aimed to: identify HPV-related clinical behaviours that the intervention will target; clarify general practitioners’ (GPs’) and practice nurses’ roles and responsibilities; and determine factors that potentially influence clinical behaviour. A secondary objective was to informally assess the utility of the Theoretical Domains Framework (TDF) in understanding clinical behaviours in an area with an evolving evidence-base. Methods: In-depth semi-structured telephone interviews were conducted with GPs and practice nurses. The topic guide, which contained open questions and HPV-related clinical scenarios, was developed through literature review and clinical experience. Interview transcripts were content-analysed using the TDF as the coding framework. Results: 19 GPs and 14 practice nurses were interviewed. The major HPV-related clinical behaviours were: initiating a discussion about HPV infection with female patients; offering/recommending HPV vaccination to appropriate patients; and answering patients’ questions about HPV testing. While the responsibility for taking smears was considered a female role, both male and female practitioners dealt with HPV-related issues. All 12 theoretical domains arose in relation to HPV infection; the domains judged to be most important were: knowledge, emotion, social influences, beliefs about capabilities and beliefs about consequences. Eleven domains emerged in relation to HPV vaccination, with beliefs about consequences, social influences, knowledge and environmental context and resources judged to be the most important. Nine domains were relevant to HPV testing, with knowledge and beliefs about capabilities judged to be the most important. Conclusions: The findings confirm the need for an intervention to support primary care practitioners around HPV and suggest it should target a range of theoretical domains. The TDF proved valuable in analysing qualitative data collected using a topic guide not specifically designed to capture TDF domains and understanding clinical behaviours in an area with an evolving evidence-base

Gaze Stability for Liveness Detection

Spoofing attacks on biometric systems are one of the major impediments to their use for secure unattended applications. This paper explores features for face liveness detection based on tracking the gaze of the user. In the proposed approach, a visual stimulus is placed on the display screen, at apparently random locations, which the user is required to follow while their gaze is measured. This visual stimulus appears in such a way that it repeatedly directs the gaze of the user to specific positions on the screen. Features extracted from sets of collinear and colocated points are used to estimate the liveness of the user. Data is collected from genuine users tracking the stimulus with natural head/eye movements and impostors holding a photograph, looking through a 2D mask or replaying the video of a genuine user. The choice of stimulus and features are based on the assumption that natural head/eye coordination for directing gaze results in a greater accuracy and thus can be used to effectively differentiate between genuine and spoofing attempts. Tests are performed to assess the effectiveness of the system with these features in isolation as well as in combination with each other using score fusion techniques. The results from the experiments indicate the effectiveness of the proposed gaze-based features in detecting such presentation attacks

Characterisation of the bacterial and fungal communities associated with different lesion sizes of Dark Spot Syndrome occurring in the Coral Stephanocoenia intersepta

The number and prevalence of coral diseases/syndromes are increasing worldwide. Dark Spot Syndrome (DSS) afflicts numerous coral species and is widespread throughout the Caribbean, yet there are no known causal agents. In this study we aimed to characterise the microbial communities (bacteria and fungi) associated with DSS lesions affecting the coral Stephanocoenia intersepta using nonculture molecular techniques. Bacterial diversity of healthy tissues (H), those in advance of the lesion interface (apparently healthy AH), and three sizes of disease lesions (small, medium, and large) varied significantly (ANOSIM R = 0.052 p,0.001), apart from the medium and large lesions, which were similar in their community profile. Four bacteria fitted into the pattern expected from potential pathogens; namely absent from H, increasing in abundance within AH, and dominant in the lesions themselves. These included ribotypes related to Corynebacterium (KC190237), Acinetobacter (KC190251), Parvularculaceae (KC19027), and Oscillatoria (KC190271). Furthermore, two Vibrio species, a genus including many proposed coral pathogens, dominated the disease lesion and were absent from H and AH tissues, making them candidates as potential pathogens for DSS. In contrast, other members of bacteria from the same genus, such as V. harveyii were present throughout all sample types, supporting previous studies where potential coral pathogens exist in healthy tissues. Fungal diversity varied significantly as well, however the main difference between diseased and healthy tissues was the dominance of one ribotype, closely related to the plant pathogen, Rhytisma acerinum, a known causal agent of tar spot on tree leaves. As the corals’ symbiotic algae have been shown to turn to a darker pigmented state in DSS (giving rise to the syndromes name), the two most likely pathogens are R. acerinum and the bacterium Oscillatoria, which has been identified as the causal agent of the colouration in Black Band Disease, another widespread coral disease

Effect of arsenic-phosphorus interaction on arsenic-induced oxidative stress in chickpea plants

Arsenic-induced oxidative stress in chickpea was investigated under glasshouse conditions in response to application of arsenic and phosphorus. Three levels of arsenic (0, 30 and 60 mg kg−1) and four levels of P (50, 100, 200, and 400 mg kg−1) were applied to soil-grown plants. Increasing levels of both arsenic and P significantly increased arsenic concentrations in the plants. Shoot growth was reduced with increased arsenic supply regardless of applied P levels. Applied arsenic induced oxidative stress in the plants, and the concentrations of H2O2 and lipid peroxidation were increased. Activity of superoxide dismutase (SOD) and concentrations of non-enzymatic antioxidants decreased in these plants, but activities of catalase (CAT) and ascorbate peroxidase (APX) were significantly increased under arsenic phytotoxicity. Increased supply of P decreased activities of CAT and APX, and decreased concentrations of non-enzymatic antioxidants, but the high-P plants had lowered lipid peroxidation. It can be concluded that P increased uptake of arsenic from the soil, probably by making it more available, but although plant growth was inhibited by arsenic the P may have partially protected the membranes from arsenic-induced oxidative stress

Male predominance of pneumonia and hospitalization in pandemic influenza A (H1N1) 2009 infection

<p>Abstract</p> <p>Background</p> <p>Pandemic influenza A (H1N1) disproportionately affects different age groups. The purpose of the current study was to describe the age and gender difference of pandemic influenza A (H1N1) cases that lead to pneumonia, hospitalization or ICU admission.</p> <p>Methods</p> <p>Data were collected retrospectively between May 2009 and December 2009. All of the diagnoses of H1N1 were confirmed by real-time reverse-transcription polymerase chain reaction (RT-PCR).</p> <p>Results</p> <p>During the study period there were 3402 cases of RT-PCR positive H1N1, among which 1812 were males and 1626 were adults (> 15 years of age). 6% (206/3402) of patients required hospitalization, 3.6% (122/3402) had infiltrates on chest radiographs, and 0.70% (24/3402) were admitted to intensive care unit (ICU). The overall fatality rate was 0.1% (4/3402). The rate of hospitalization was sharply increased in patients ≥ 50 years of age especially in male. Out of 122 pneumonia patients, 68.8% (84 patients) were male. Among the patients admitted to the ICU, 70.8% (17 patients) were male. Approximately 1 of 10 H1N1-infected patients admitted to the ICU were ≥ 70 years of age.</p> <p>Conclusions</p> <p>Among the confirmed cases of H1N1, the ICU admission rate was < 1% and the case fatality rate was 0.1%. Male had a significantly higher rate of pneumonia and hospital admission. These findings should be taken into consideration when developing vaccination and treatment strategies.</p

Developing Crisis Training Software for Local Governments – From User Needs to Generic Requirements

In this paper we analyze and present the generic requirements identified for a software aiming at supporting crisis management training in local governments. The generic requirements are divided into overall requirements, requirements connected to the trainer’s role and requirements connected to the trainee’s role. Moreover, the requirements are mapped to problems as well as opportunities. Finally, we present examples of elaborations of the addressed requirements based on software design considerations. In our work we applied a design science approach and the artifact presented in this paper is a list of generic requirement. The presented requirements and the systems development process used, provide guidelines for systems analysts and developers in future systems development projects aiming at constructing new software for crisis management training

Language Model Co-occurrence Linking for Interleaved Activity Discovery

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from previous work in that it explicitly aims to deal with interleaving (switching back and forth between between activities) in a principled manner, by utilising the long-term memory capabilities of a recurrent neural network cell. We present our approach and test it on a realistic dataset to evaluate its performance. Our results show the viability of the approach and that it shows promise for further investigation. We believe this is a useful direction to consider in accounting for the continually changing nature of behaviours

Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos

Automatically recognizing activities in video is a classic problem in vision and helps to understand behaviors, describe scenes and detect anomalies. We propose an unsupervised method for such purposes. Given video data, we discover recurring activity patterns that appear, peak, wane and disappear over time. By using non-parametric Bayesian methods, we learn coupled spatial and temporal patterns with minimum prior knowledge. To model the temporal changes of patterns, previous works compute Markovian progressions or locally continuous motifs whereas we model time in a globally continuous and non-Markovian way. Visually, the patterns depict flows of major activities. Temporally, each pattern has its own unique appearance-disappearance cycles. To compute compact pattern representations, we also propose a hybrid sampling method. By combining these patterns with detailed environment information, we interpret the semantics of activities and report anomalies. Also, our method fits data better and detects anomalies that were difficult to detect previously