Iris cysts in children:Classification, incidence, and management. The 1998 Torrence A Makley Jr Lecture

Background - Iris cysts in children are uncommon and there is relatively little information on their classification, incidence, and management. Methods - The records of all children under age 20 years who were diagnosed with iris cyst were reviewed and the types and incidence of iris cysts of childhood determined. Based on these observations recommendations were made regarding management of iris cysts in children. Results - Of 57 iris cysts in children, 53 were primary and four were secondary. There were 44 primary cysts of the iris pigment epithelium, 34 of which were of the peripheral or iridociliary type, accounting for 59% of all childhood iris cysts. It was most commonly diagnosed in the teenage years, more common in girls (68%), was not recognised in infancy, remained stationary or regressed, and required no treatment. The five mid-zonal pigment epithelial cysts were diagnosed at a mean age of 14 years, were more common in boys (83%), remained stationary, and required no treatment. The pupillary type of pigment epithelial cyst was generally recognised in infancy and, despite involvement of the pupillary aperture, also required no treatment. There were nine cases of primary iris stromal cysts, accounting for 16% of all childhood iris cysts. This cyst was usually diagnosed in infancy, was generally progressive, and required treatment in eight of the nine cases, usually by aspiration and cryotherapy or surgical resection. Among the secondary iris cysts, two were post-traumatic epithelial ingrowth cysts and two were tumour induced cysts, one arising from an intraocular lacrimal gland choristoma and one adjacent to a peripheral iris naevus. Conclusions - Most iris cysts of childhood are primary pigment epithelial cysts and require no treatment. However, the iris stromal cyst, usually recognised in infancy, is generally an aggressive lesion that requires treatment by aspiration or surgical excision.

Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art

Erythrocytosis in children and adolescents-classification, characterization, and consensus recommendations for the diagnostic approach

During recent years, the increasing knowledge of genetic and physiological changes in polycythemia vera (PV) and of different types of congenital erythrocytosis has led to fundamental changes in recommendations for the diagnostic approach to patients with erythrocytosis. Although widely accepted for adult patients this approach may not be appropriate with regard to children and adolescents affected by erythrocytosis. The "congenital erythrocytosis" working group established within the framework of the MPN&MPNr-EuroNet (COST action BM0902) addressed this question in a consensus finding process and developed a specific algorithm for the diagnosis of erythrocytosis in childhood and adolescence which is presented here. Pediatr Blood Cancer 2013;9999:XX-XX. © 2013 Wiley Periodicals, Inc.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification

Aims/hypothesis: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD. Methods: We exploited a novel algorithm, ‘Bag of Naive Bayes’, whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK–Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US). Results: Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case–control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p < 0.05), and rs12137135 upstream of WNT4 was associated with ESRD in Steno. Conclusions/interpretation: This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.

Tunnel spoil classification and applicability of lime addition in weak formations for muck reuse

Tunnel construction planning requires careful consideration of the spoil management part, as this involves environmental, economic and legal requirements. In this paper a methodological approach that considers the interaction between technical and geological factors in determining the features of the resulting muck is proposed. This gives indications about the required treatments as well as laboratory and field characterisation tests to be performed to assess muck recovery alternatives. While this reuse is an opportunity for excavations in good quality homogeneous grounds (e.g. granitic mass), it is critical for complex formation. This approach has been validated, at present, for three different geo-materials resulting from a tunnel excavation carried out with a large diameter Earth Pressure Balance Shield (EPB) through a complex geological succession. Physical parameters and technological features of the three materials have been assessed, according to their valorisation potential, for defining re-utilisation patterns. The methodology proved to be effective and the laboratory tests carried out on the three materials allowed the suitability and treatment effectiveness for each muck recovery strategy to be defined. © 2014 Elsevier Ltd.

The distribution of COPD in UK general practice using the new GOLD classification

The new Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2011 document recommends a combined assessment of chronic obstructive pulmonary disease (COPD) based on current symptoms and future risk.

A large database of primary-care COPD patients across the UK was used to determine COPD distribution and characteristics according to the new GOLD classification. 80 general practices provided patients with a Read code diagnosis of COPD. Electronic and hand searches of patient medical records were undertaken, optimising data capture.

Data for 9219 COPD patients were collected. For the 6283 patients with both forced expiratory volume in 1 s (FEV1) and modified Medical Research Council scores (mean¡SD age 69.2¡10.6 years, body mass index 27.3¡6.2 kg?m-2), GOLD 2011 group distributions were: A (low risk and fewer symptoms) 36.1%, B (low risk and more symptoms) 19.1%, C (high risk and fewer symptoms) 19.6% and D (high risk and more symptoms) 25.3%. This is in contrast with GOLD 2007 stage classification: I (mild) 17.1%, II (moderate) 52.2%, III (severe) 25.5% and IV (very severe) 5.2%. 20% of patients with FEV1 o50% predicted had more than two exacerbations in the previous 12 months. 70% of patients with FEV1 ,50% pred had fewer than two exacerbations in the previous 12 months.

This database, representative of UK primary-care COPD patients, identified greater proportions of patients in the mildest and most severe categories upon comparing 2011 versus 2007 GOLD classifications. Discordance between airflow limitation severity and exacerbation risk was observed.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.