Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Expression of muscarinic receptor subtypes in tree shrew ocular tissues and their regulation during the development of myopia

Muscarinic receptors are known to regulate several important physiologic processes in the eye. Antagonists to these receptors such as atropine and pirenzepine are effective at stopping the excessive ocular growth that results in myopia. However, their site of action is unknown. This study details ocular muscarinic subtype expression within a well documented model of eye growth and investigates their expression during early stages of myopia induction. Total RNA was isolated from tree shrew corneal, iris/ciliary body, retinal, choroidal, and scleral tissue samples and was reverse transcribed. Using tree shrew-specific primers to the five muscarinic acetylcholine receptor subtypes (CHRM1-CHRM5), products were amplified using polymerase chain reaction (PCR) and their identity confirmed using automated sequencing. The expression of the receptor proteins (M1-M5) were also explored in the retina, choroid, and sclera using immunohistochemistry. Myopia was induced in the tree shrew for one or five days using monocular deprivation of pattern vision, and the expression of the receptor subtypes was assessed in the retina, choroid, and sclera using real-time PCR. All five muscarinic receptor subtypes were expressed in the iris/ciliary body, retina, choroid, and sclera while gene products corresponding to CHRM1, CHRM3, CHRM4, and CHRM5 were present in the corneal samples. The gene expression data were confirmed by immunohistochemistry with the M1-M5 proteins detected in the retina, choroid, and sclera. After one or five days of myopia development, muscarinic receptor gene expression remained unaltered in the retinal, choroidal, and scleral tissue samples. This study provides a comprehensive profile of muscarinic receptor gene and protein expression in tree shrew ocular tissues with all receptor subtypes found in tissues implicated in the control of eye growth. Despite the efficacy of muscarinic antagonists at inhibiting myopia development, the genes of the muscarinic receptor subtypes are neither regulated early in myopia (before measurable axial elongation) nor after significant structural change.

The A622 gene in Nicotiana glauca (tree tobacco): evidence for a functional role in pyridine alkaloid synthesis

Nicotiana glauca (Argentinean tree tobacco) is atypical within the genus Nicotiana, accumulating predominantly anabasine rather than nicotine and/or nornicotine as the main component of its leaf pyridine alkaloid fraction. The current study examines the role of the A622 gene from N. glauca (NgA622) in alkaloid production and utilises an RNAi approach to down-regulate gene expression and diminish levels of A622 protein in transgenic tissues. Results indicate that RNAi-mediated reduction in A622 transcript levels markedly reduces the capacity of N. glauca to produce anabasine resulting in plants with scarcely any pyridine alkaloids in leaf tissues, even after damage to apical tissues. In addition, analysis of hairy roots containing the NgA622-RNAi construct shows a substantial reduction in both anabasine and nicotine levels within these tissues, even if stimulated with methyl jasmonate, indicating a role for the A622 enzyme in the synthesis of both alkaloids in roots of N. glauca. Feeding of Nicotinic Acid (NA) to hairy roots of N. glauca containing the NgA622-RNAi construct did not restore capacity for synthesis of anabasine or nicotine. Moreover, treatment of these hairy root lines with NA did not lead to an increase in anatabine levels, unlike controls. Together, these results strongly suggest that A622 is an integral component of the final enzyme complex responsible for biosynthesis of all three pyridine alkaloids in Nicotiana.

A new application of an evolving tree to failure mode and effect analysis methodology

Failure Mode and Effect Analysis (FMEA) is a popular safety and reliability analysis methodology for examining potential failure modes of products, process, designs, or services, in a wide range of industries. Despite its popularity, there are a number of limitations of FMEA, and two highlighted issues are the bulky FMEA form and its intricacy of use. To overcome these shortcomings, we introduce the idea of visualisation pertaining to the failure modes or control actions in FMEA. A visualisation model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes or control actions in FMEA to be clustered and visualized. The failure modes or control actions are grouped and visualized with consideration of their Severity, Occurrence, and Detection scores. Our proposed approach allows the failure modes or control actions to be mapped into a tree structure for visualisation. The devised approach is evaluated with a benchmark problem. The experiments show that the control actions of FMEA can be visualised through the tree structure, which provides a quick and easily understandable platform of the FMEA spreadsheet to facilitate decision making tasks.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

An assessment of riparian restoration outcomes in two rural catchments in south-western Victoria: Focusing on tree and shrub species richness, structure and recruitment characteristics

Riparian ecosystems are among the most degraded systems in the landscape,and there has been substantial investment in their restoration. Consequently, monitoring restoration interventions offers opportunities to further develop the science of riparian restoration, particularly how to move from small-scale implementation to a broader landscape scale. Here, we report on a broad range of riparian revegetation projects in two regions of south-western Victoria, the Corangamite and Glenelg-Hopkins Catchment Management Areas. The objectives of restoration interventions in these regions have been stated quite broadly, for example, to reinstate terrestrial habitat and biodiversity, control erosion and improve water quality. This study reports on tree and shrub composition, structure and recruitment after restoration works compared with remnant vegetation found regionally. Within each catchment, a total of 57 sites from six subcatchments were identified, representing three age-classes: <4, 4–8 and >8–12 years after treatment, as well as untreated (control) sites. Treatments comprised fencing to exclude stock, spraying or slashing to reduce weed cover, followed by planting with tube stock. Across the six subcatchments, 12 reference (remnant) sites were used to provide a benchmark for species richness, structural and recruitment characteristics and to aid interpretation of the effects of the restoration intervention. Vegetation structure was well developed in the treated sites by 4–8 years after treatment. However, structural complexity was higher at remnant sites than at treated or untreated sites due to a higher richness of small shrubs. Tree and shrub recruitment occurred in all remnant sites and at 64% of sites treated >4 years ago. Most seedling recruitment at treatment sites was by Acacia spp. This assessment provides data on species richness, structure and recruitment characteristics following restoration interventions. Data from this study will contribute to longitudinal studies of vegetation processes in riparian landscapes of south-western Victoria.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.