A proposed new classification system for whiplash associated disorders - implications for assessment and management

The development of chronic symptoms following whiplash injury is common and contributes substantially to costs associated with this condition. The currently used Quebec Task Force classification system of whiplash associated disorders is primarily based on the severity of signs and symptoms following injury and its usefulness has been questioned. Recent evidence is emerging that demonstrates differences in physical and psychological impairments between individuals who recover from the injury and those who develop persistent pain and disability. Motor dysfunction, local cervical mechanical hyperalgesia and psychological distress are present soon after injury in all whiplash injured persons irrespective of recovery. In contrast those individuals who develop persistent moderate/severe pain and disability show a more complex picture, characterized by additional impairments of widespread sensory hypersensitivity indicative of underlying disturbances in central pain processing as well as acute posttraumatic stress reaction, with these changes present from soon after injury. Based on this heterogeneity a new classification system is proposed that takes into account measurable disturbances in motor, sensory and psychological dysfunction. The implications for the management of this condition are discussed. (C) 2004 Elsevier Ltd. All rights reserved.

Papillary urothelial neoplasm of low malignant potential: reliability of diagnosis and outcome

OBJECTIVE To determine the ability of pathologists to reproducibly diagnose a newly defined lesion, i.e. the papillary urothelial neoplasm of low malignant potential (PUNLMP) using the published criteria, defined by the 1998 World Health Organisation/International Society of Urological Pathology (WHO/ISUP) classification system; in addition, debate remains about the clinical behaviour of these lesions, thus the rates of recurrence and progression of PUNLMP lesions were assessed and compared with low-grade papillary urothelial carcinomas (LG-PUC) and high-grade (HG-PUC) over a 10-year follow-up. PATIENTS AND METHODS Forty-nine cases of superficial bladder cancer (G1-3 pTa) representing an initial diagnosis of transitional cell carcinoma made in 1990 were identified and re-graded using the 1998 WHO/ISUP classification by two pathologists. Inter-observer agreement was assessed using Cohen weighted kappa statistics. After reclassification the clinical follow-up was reviewed retrospectively, and episodes of recurrence and progression recorded. RESULTS The inter-observer agreement was moderate, regardless of whether one (kappa 0.45) or two (kappa 0.60) pathologists were used to grade these lesions. Re-classification identified 12 PUNLMP, 28 LG-PUC and nine HG-PUC. PUNLMP lesions recurred in 25% (3/12) of cases; no progression was documented. Recurrence rates were 75% (21/28) and 67% (6/9) for LG- and HG-PUC, respectively, and progression rates were 4% (1/28) and 22% (2/9). CONCLUSION The 1998 WHO/ISUP classification of urothelial neoplasms can be reproducibly applied by pathologists, with a moderate level of agreement. There is evidence that PUNLMP lesions have a more indolent clinical behaviour than urothelial carcinomas. However, the risk of recurrence and progression remains, and clinical monitoring of these patients is important.

Columnar cell lesions of the breast: The missing link in breast cancer progression? A morphological and molecular analysis

Columnar cell lesions (CCLs) of the breast are a spectrum of lesions that have posed difficulties to pathologists for many years, prompting discussion concerning their biologic and clinical significance. We present a study of CCL in context with hyperplasia of usual type (HUT) and the more advanced lesions ductal carcinoma in situ (DCIS) and invasive ductal carcinoma. A total of 81 lesions from 18 patients were subjected to a comprehensive morphologic review based upon a modified version of Schnitt's classification system for CCL, immunophenotypic analysis (estrogen receptor [ER], progesterone receptor [PgR], Her2/neu, cytokeratin 5/6 [CK5/6], cytokeratin 14 [CK14], E-cadherin, p53) and for the first time, a whole genome molecular analysis by comparative genomic hybridization. Multiple CCLs from 3 patients were studied in particular detail, with topographic information and/or showing a morphologic spectrum of CCL within individual terminal duct lobular units. CCLs were ER an PgR positive, CK5/6 and CK14 negative, exhibit low numbers of genetic alterations and recurrent 16q loss, features that are similar to those of low grade in situ and invasive carcinoma. The molecular genetic profiles closely reflect the degree of proliferation and atypia in CCL, indicating some of these lesions represent both a morphologic and molecular continuum. In addition, overlapping chromosomal alterations between CCL and more advanced lesions within individual terminal duct lobular units suggest a commonality in molecular evolution. These data further support the hypothesis that CCLs are a nonobligate, intermediary step in the development of some forms of low grade in situ and invasive carcinoma. Copyright: © 2005 Lippincott Williams & Wilkins, Inc.

Fitness assessment of document model

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Classifying an opponent's behaviour in robot soccer

This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observableenvironment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Using machine vision classification to control access of animals to water

Invasive vertebrate pests together with overabundant native species cause significant economic and environmental damage in the Australian rangelands. Access to artificial watering points, created for the pastoral industry, has been a major factor in the spread and survival of these pests. Existing methods of controlling watering points are mechanical and cannot discriminate between target species. This paper describes an intelligent system of controlling watering points based on machine vision technology. Initial test results clearly demonstrate proof of concept for machine vision in this application. These initial experiments were carried out as part of a 3-year project using machine vision software to manage all large vertebrates in the Australian rangelands. Concurrent work is testing the use of automated gates and innovative laneway and enclosure design. The system will have application in any habitat throughout the world where a resource is limited and can be enclosed for the management of livestock or wildlife.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.