Hand, foot and mouth disease in China: Evaluating an automated system for the detection of outbreaks

Objective To evaluate the performance of China’s infectious disease automated alert and response system in the detection of outbreaks of hand, foot and mouth (HFM) disease. Methods We estimated size, duration and delay in reporting HFM disease outbreaks from cases notified between 1 May 2008 and 30 April 2010 and between 1 May 2010 and 30 April 2012, before and after automatic alert and response included HFM disease. Sensitivity, specificity and timeliness of detection of aberrations in the incidence of HFM disease outbreaks were estimated by comparing automated detections to observations of public health staff. Findings The alert and response system recorded 106 005 aberrations in the incidence of HFM disease between 1 May 2010 and 30 April 2012 – a mean of 5.6 aberrations per 100 days in each county that reported HFM disease. The response system had a sensitivity of 92.7% and a specificity of 95.0%. The mean delay between the reporting of the first case of an outbreak and detection of that outbreak by the response system was 2.1 days. Between the first and second study periods, the mean size of an HFM disease outbreak decreased from 19.4 to 15.8 cases and the mean interval between the onset and initial reporting of such an outbreak to the public health emergency reporting system decreased from 10.0 to 9.1 days. Conclusion The automated alert and response system shows good sensitivity in the detection of HFM disease outbreaks and appears to be relatively rapid. Continued use of this system should allow more effective prevention and limitation of such outbreaks in China.

A nationwide web-based automated system for early outbreak detection and rapid response in China

Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS) was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS) started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and to transmit that information either in real-time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC) in China at the county, prefecture, provincial and national levels are involved in the system. It assists with early outbreak detection at the local level and prompts reporting of unusual disease occurrences or potential outbreaks to CDCs throughout the country.

Achieving intention-centric BPM through automated planning

As a result of the more distributed nature of organisations and the inherently increasing complexity of their business processes, a significant effort is required for the specification and verification of those processes. The composition of the activities into a business process that accomplishes a specific organisational goal has primarily been a manual task. Automated planning is a branch of artificial intelligence (AI) in which activities are selected and organised by anticipating their expected outcomes with the aim of achieving some goal. As such, automated planning would seem to be a natural fit to the BPM domain to automate the specification of control flow. A number of attempts have been made to apply automated planning to the business process and service composition domain in different stages of the BPM lifecycle. However, a unified adoption of these techniques throughout the BPM lifecycle is missing. As such, we propose a new intention-centric BPM paradigm, which aims on minimising the specification effort by exploiting automated planning techniques to achieve a pre-stated goal. This paper provides a vision on the future possibilities of enhancing BPM using automated planning. A research agenda is presented, which provides an overview of the opportunities and challenges for the exploitation of automated planning in BPM.

BPMN Miner: Automated Discovery of BPMN Process Models with Hierarchical Structure

Existing techniques for automated discovery of process models from event logs gen- erally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of hierarchical BPMN models con- taining interrupting and non-interrupting boundary events and activity markers. The technique employs functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery tech- niques, it is possible to filter out noise in the event log arising for example from data entry errors or missing events. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.

Application of higher-order spectra for automated grading of diabetic maculopathy

Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39 % for MESSIDOR dataset and 95.93 and 93.33 % for local dataset, respectively.

Semi-automated annotation of environmental acoustic recordings

Environmental sensors collect massive amounts of audio data. This thesis investigates computational methods to support human analysts in identifying faunal vocalisations from that audio. A series of experiments was conducted to trial the effectiveness of novel user interfaces. This research examines the rapid scanning of spectrograms, decision support tools for users, and cleaning methods for folksonomies. Together, these investigations demonstrate that providing computational support to human analysts increases their efficiency and accuracy; this allows bioacoustics projects to efficiently utilise their valuable human analysts.

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response. © 2007 Elsevier Inc. All rights reserved.

Automated 3D mapping & shape analysis of the lateral ventricles via fluid registration of multiple surface-based atlases

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.

A reproducible method for automated extraction of brain volumes from 3D human head MR images

An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required. The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.

A novel 15N tracer approach for the quantification of N2 and N2O emissions from soil incubations in a completely automated laboratory set up

The microbial mediated production of nitrous oxide (N2O) and its reduction to dinitrogen (N2) via denitrification represents a loss of nitrogen (N) from fertilised agro-ecosystems to the atmosphere. Although denitrification has received great interest by biogeochemists in the last decades, the magnitude of N2lossesand related N2:N2O ratios from soils still are largely unknown due to methodical constraints. We present a novel 15N tracer approach, based on a previous developed tracer method to study denitrification in pure bacterial cultures which was modified for the use on soil incubations in a completely automated laboratory set up. The method uses a background air in the incubation vessels that is replaced with a helium-oxygen gas mixture with a 50-fold reduced N2 background (2 % v/v). This method allows for a direct and sensitive quantification of the N2 and N2O emissions from the soil with isotope-ratio mass spectrometry after 15N labelling of denitrification N substrates and minimises the sensitivity to the intrusion of atmospheric N2 at the same time. The incubation set up was used to determine the influence of different soil moisture levels on N2 and N2O emissions from a sub-tropical pasture soil in Queensland/Australia. The soil was labelled with an equivalent of 50 μg-N per gram dry soil by broadcast application of KNO3solution (4 at.% 15N) and incubated for 3 days at 80% and 100% water filled pore space (WFPS), respectively. The headspace of the incubation vessel was sampled automatically over 12hrs each day and 3 samples (0, 6, and 12 hrs after incubation start) of headspace gas analysed for N2 and N2O with an isotope-ratio mass spectrometer (DELTA V Plus, Thermo Fisher Scientific, Bremen, Germany(. In addition, the soil was analysed for 15N NO3- and NH4+ using the 15N diffusion method, which enabled us to obtain a complete N balance. The method proved to be highly sensitive for N2 and N2O emissions detecting N2O emissions ranging from 20 to 627 μN kg-1soil-1hr-1and N2 emissions ranging from 4.2 to 43 μN kg-1soil-1hr-1for the different treatments. The main end-product of denitrification was N2O for both water contents with N2 accounting for 9% and 13% of the total denitrification losses at 80% and 100%WFPS, respectively. Between 95-100% of the added 15N fertiliser could be recovered. Gross nitrification over the 3 days amounted to 8.6 μN g-1 soil-1 and 4.7 μN g-1 soil-1, denitrification to 4.1 μN g-1 soil-1 and 11.8 μN g-1 soil-1at 80% and 100%WFPS, respectively. The results confirm that the tested method allows for a direct and highly sensitive detection of N2 and N2O fluxes from soils and hence offers a sensitive tool to study denitrification and N turnover in terrestrial agro-ecosystems.

Automated fit assessment of intramedullary nail designs during insertion

Modern intramedullary nails, which are utilised for the treatment of bone fractures, need to be designed to fit the anatomy of the patient population. Traditional and recent semi-automated approaches for quantifying the anatomical fit between bones and nail designs suffer from various drawbacks. This thesis proposed an automated comprehensive nail design validation method. The developed software tool was utilised to quantify the anatomical fit of four commercial nail designs. Furthermore, the thesis demonstrated the existence of a bone-nail specific nail entry point. The developed method is of great benefit for the implant manufacturing industry as a nail design validation tool.

Automated categorisation of patent claims that reference human genome sequences

Debates on gene patents have necessitated the analysis of patents that disclose and reference human sequences. In this study, we built an automated classifier that assigns sequences to one of nine predefined categories according to their functional roles in patent claims by applying natural language processing and supervised learning techniques. To improve its correctness, we experimented with various feature mappings, resulting in the maximal accuracy of 79%.

Improved linear cryptanalysis of reduced-round SIMON-32 and SIMON-48

In this paper we analyse two variants of SIMON family of light-weight block ciphers against variants of linear cryptanalysis and present the best linear cryptanalytic results on these variants of reduced-round SIMON to date. We propose a time-memory trade-off method that finds differential/linear trails for any permutation allowing low Hamming weight differential/linear trails. Our method combines low Hamming weight trails found by the correlation matrix representing the target permutation with heavy Hamming weight trails found using a Mixed Integer Programming model representing the target differential/linear trail. Our method enables us to find a 17-round linear approximation for SIMON-48 which is the best current linear approximation for SIMON-48. Using only the correlation matrix method, we are able to find a 14-round linear approximation for SIMON-32 which is also the current best linear approximation for SIMON-32. The presented linear approximations allow us to mount a 23-round key recovery attack on SIMON-32 and a 24-round Key recovery attack on SIMON-48/96 which are the current best results on SIMON-32 and SIMON-48. In addition we have an attack on 24 rounds of SIMON-32 with marginal complexity.

An automated emergency landing system for fixed-wing aircraft: Planning and control

A number of hurdles must be overcome in order to integrate unmanned aircraft into civilian airspace for routine operations. The ability of the aircraft to land safely in an emergency is essential to reduce the risk to people, infrastructure and aircraft. To date, few field-demonstrated systems have been presented that show online re-planning and repeatability from failure to touchdown. This paper presents the development of the Guidance, Navigation and Control (GNC) component of an Automated Emergency Landing System (AELS) intended to address this gap, suited to a variety of fixed-wing aircraft. Field-tested on both a fixed-wing UAV and Cessna 172R during repeated emergency landing experiments, a trochoid-based path planner computes feasible trajectories and a simplified control system executes the required manoeuvres to guide the aircraft towards touchdown on a predefined landing site. This is achieved in zero-thrust conditions with engine forced to idle to simulate failure. During an autonomous landing, the controller uses airspeed, inertial and GPS data to track motion and maintains essential flight parameters to guarantee flyability, while the planner monitors glide ratio and re-plans to ensure approach at correct altitude. Simulations show reliability of the system in a variety of wind conditions and its repeated ability to land within the boundary of a predefined landing site. Results from field-tests for the two aircraft demonstrate the effectiveness of the proposed GNC system in live operation. Results show that the system is capable of guiding the aircraft to close proximity of a predefined keyhole in nearly 100% of cases.