Population-Based Trachoma Mapping in Six Evaluation Units of Papua New Guinea.

PURPOSE: We sought to determine the prevalence of trachomatous inflammation - follicular (TF) in children aged 1-9 years, and trachomatous trichiasis (TT) in those aged ≥15 years, in suspected trachoma-endemic areas of Papua New Guinea (PNG). METHODS: We carried out six population-based prevalence surveys using the protocol developed as part of the Global Trachoma Mapping Project. RESULTS: A total of 19,013 individuals were sampled for inclusion, with 15,641 (82.3%) consenting to participate. Four evaluation units had prevalences of TF in children ≥10%, above which threshold the World Health Organization (WHO) recommends mass drug administration (MDA) of azithromycin for at least three years; Western Province (South Fly/Daru) 11.2% (95% confidence interval, CI, 6.9-17.0%), Southern Highlands (East) 12.2% (95% CI 9.6-15.0%), Southern Highlands (West) 11.7% (95% CI 8.5-15.3%), and West New Britain 11.4% (95% CI 8.7-13.9%). TF prevalence was 5.0-9.9% in Madang (9.4%, 95% CI 6.1-13.0%) and National Capital District (6.0%. 95% CI 3.2-9.1%) where consideration of a single round of MDA is warranted. Cases of TT were not found outside West New Britain, in which four cases were seen, generating an estimated population-level prevalence of TT in adults of 0.10% (95% CI 0.00-0.40%) for West New Britain, below the WHO elimination threshold of 0.2% of those aged ≥15 years. CONCLUSION: Trachoma is a public health issue in PNG. However, other than in West New Britain, there are few data to support the idea that trachoma is a cause of blindness in PNG. Further research is needed to understand the stimulus for the active trachoma phenotype in these populations

Population-based patient care study for breast cancer

Background: Different approaches for an effective quality management are funded by the Ministry of Health to verify, to assess and, if necessary to optimize the quality of health care using the tracer diagnoses of breast, rectal, and lung cancer in eight regions in Germany. The conception of these observational studies and initial findings are shown here, using breast cancer in the region of Munich (population 2.4 million) as an example. Patients and Methods: The study started on April 1, 1996. The recruitment phase for all primary boast cancer patients in this region is planned for 2 years with a 3-5-year follow-up. Established documentation sheets are used to document basic medical information of each patient, along with the original reports (pathology: radiotherapy, doctors' reports, etc.), follow-up reports and quality of life questionnaires (QLQ, including the EORTC QLQ C30). Results: In 1996, the Munich region has a crude incidence of 125/100,000 women (world standard 71.5). After almost complete documentation the incidence is 10-15% higher. In the period from April 1 1996 to June 30, 1997 1,360 patients have been recruited into the study. 79% of the patients were 50 years of age or older. pT stages are distributed as follows: pTIS 5%, pT1 54%, pT2 32%, pT3 4%, pT4 6%. 4.5% had primary metastases. Breast-conserving therapy (BCT) was performed in 57% of patients. Five of the 46 departments involved recruited more than 50 patients each within these 14 months. These larger departments treat 59% of all patients. The proportion of older patients and pT4 stages is significantly higher in the smaller departments. BCT is performed significantly more often in the larger departments. First results of quality of life show dependencies on age, but no differences between mastectomy and BCT 3 months after operation. Not only the addressed patients (response rate to QLQ over 80%) but also almost all hospitals and many physicians are milling to support and to partake in quality assurance. 35 hospitals, 46 surgical departments. 80 heads of department and surgically: active general practioners, 330 general practioners. 7 radiotherapy departments, and 13 pathology departments have so far documented for this study. Conclusions: An effective quality management in oncology needs a modern cancer registry which uses documentation sheets as well as original reports and organizes the complicated infrastructure for an interdisciplinary cooperation. To be able to evaluate the health care reality it is necessary to carry out a data analysis and assess each individual case. A feedback of the results have to be available for each physician and each department. The cost of this information management is approximately 0.3% of the health care cost for this group of patients

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Population-based incremental learning with memory scheme for changing environments

Copyright @ 2005 ACMIn recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several approaches have been developed, such as the memory scheme. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic optimization problems. A PBIL-specific memory scheme is proposed to improve its adaptability in dynamic environments. In this memory scheme the working probability vector is stored together with the best sample it creates in the memory and is used to reactivate old environments when change occurs. Experimental study based on a series of dynamic environments shows the efficiency of the memory scheme for PBILs in dynamic environments. In this paper, the relationship between the memory scheme and the multipopulation scheme for PBILs in dynamic environments is also investigated. The experimental results indicate a negative interaction of the multi-population scheme on the memory scheme for PBILs in the dynamic test environments

A population-based approach to background discrimination in particle physics

Background properties in experimental particle physics are typically estimated using control samples corresponding to large numbers of events. This can provide precise knowledge of average background distributions, but typically does not consider the effect of fluctuations in a data set of interest. A novel approach based on mixture model decomposition is presented as a way to estimate the effect of fluctuations on the shapes of probability distributions in a given data set, with a view to improving on the knowledge of background distributions obtained from control samples. Events are treated as heterogeneous populations comprising particles originating from different processes, and individual particles are mapped to a process of interest on a probabilistic basis. The proposed approach makes it possible to extract from the data information about the effect of fluctuations that would otherwise be lost using traditional methods based on high-statistics control samples. A feasibility study on Monte Carlo is presented, together with a comparison with existing techniques. Finally, the prospects for the development of tools for intensive offline analysis of individual events at the Large Hadron Collider are discussed.Comment: Updated according to the version published in J. Phys.: Conf. Ser. Minor changes have been made to the text with respect to the published article with a view to improving readabilit

Maternal and fetal risk factors for stillbirth : population based study

Objective: To assess the main risk factors associated with stillbirth in a multiethnic English maternity population. Design: Cohort study. Setting: National Health Service region in England. Population: 92 218 normally formed singletons including 389 stillbirths from 24 weeks of gestation, delivered during 2009-11. Main outcome measure: Risk of stillbirth. Results: Multivariable analysis identified a significant risk of stillbirth for parity (para 0 and para ≥3), ethnicity (African, African-Caribbean, Indian, and Pakistani), maternal obesity (body mass index ≥30), smoking, pre-existing diabetes, and history of mental health problems, antepartum haemorrhage, and fetal growth restriction (birth weight below 10th customised birthweight centile). As potentially modifiable risk factors, maternal obesity, smoking in pregnancy, and fetal growth restriction together accounted for 56.1% of the stillbirths. Presence of fetal growth restriction constituted the highest risk, and this applied to pregnancies where mothers did not smoke (adjusted relative risk 7.8, 95% confidence interval 6.6 to 10.9), did smoke (5.7, 3.6 to 10.9), and were exposed to passive smoke only (10.0, 6.6 to 15.8). Fetal growth restriction also had the largest population attributable risk for stillbirth and was fivefold greater if it was not detected antenatally than when it was (32.0% v 6.2%). In total, 195 of the 389 stillbirths in this cohort had fetal growth restriction, but in 160 (82%) it had not been detected antenatally. Antenatal recognition of fetal growth restriction resulted in delivery 10 days earlier than when it was not detected: median 270 (interquartile range 261-279) days v 280 (interquartile range 273-287) days. The overall stillbirth rate (per 1000 births) was 4.2, but only 2.4 in pregnancies without fetal growth restriction, increasing to 9.7 with antenatally detected fetal growth restriction and 19.8 when it was not detected. Conclusion: Most normally formed singleton stillbirths are potentially avoidable. The single largest risk factor is unrecognised fetal growth restriction, and preventive strategies need to focus on improving antenatal detection

A population-based microbial oscillator

Genetic oscillators are a major theme of interest in the emerging field of synthetic biology. Until recently, most work has been carried out using intra-cellular oscillators, but this approach restricts the broader applicability of such systems. Motivated by a desire to develop large-scale, spatially-distributed cell-based computational systems, we present an initial design for a population-level oscillator which uses three different bacterial strains. Our system is based on the client-server model familiar to computer science, and uses quorum sensing for communication between nodes. We present the results of extensive in silico simulation tests, which confirm that our design is both feasible and robust.Comment: Submitte

Dynamic railway junction rescheduling using population based ant colony optimisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency