Closed loop deep brain stimulation: an evolving technology

Deep brain stimulation is an effective and safe medical treatment for a variety of neurological and psychiatric disorders including Parkinson's disease, essential tremor, dystonia, and treatment resistant obsessive compulsive disorder. A closed loop deep brain stimulation (CLDBS) system automatically adjusts stimulation parameters by the brain response in real time. The CLDBS continues to evolve due to the advancement in the brain stimulation technologies. This paper provides a study on the existing systems developed for CLDBS. It highlights the issues associated with CLDBS systems including feedback signal recording and processing, stimulation parameters setting, control algorithm, wireless telemetry, size, and power consumption. The benefits and limitations of the existing CLDBS systems are also presented. Whilst robust clinical proof of the benefits of the technology remains to be achieved, it has the potential to offer several advantages over open loop DBS. The CLDBS can improve efficiency and efficacy of therapy, eliminate lengthy start-up period for programming and adjustment, provide a personalized treatment, and make parameters setting automatic and adaptive.

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.

Regulation of HbPIP2;3, a latex-abundant water transporter, is associated with latex dilution and yield in the rubber tree (Hevea brasiliensis Muell. Arg.)

Rubber tree (Hevea brasiliensis) latex, the source of natural rubber, is synthesised in the cytoplasm of laticifers. Efficient water inflow into laticifers is crucial for latex flow and production since it is the determinant of the total solid content of latex and its fluidity after tapping. As the mature laticifer vessel rings are devoid of plasmodesmata, water exchange between laticifers and surrounding cells is believed to be governed by plasma membrane intrinsic proteins (PIPs). To identify the most important PIP aquaporin in the water balance of laticifers, the transcriptional profiles of ten-latex-expressed PIPs were analysed. One of the most abundant transcripts, designated HbPIP2;3, was characterised in this study. When tested in Xenopus laevis oocytes HbPIP2;3 showed a high efficiency in increasing plasmalemma water conductance. Expression analysis indicated that the HbPIP2;3 gene was preferentially expressed in latex, and the transcripts were up-regulated by both wounding and exogenously applied Ethrel (a commonly-used ethylene releaser). Although regular tapping up-regulated the expression of HbPIP2;3 during the first few tappings of the virginal rubber trees, the transcriptional kinetics of HbPIP2;3 to Ethrel stimulation in the regularly tapped tree exhibited a similar pattern to that of the previously reported HbPIP2;1 in the virginal rubber trees. Furthermore, the mRNA level of HbPIP2;3 was associated with clonal yield potential and the Ethrel stimulation response. Together, these results have revealed the central regulatory role of HbPIP2;3 in laticifer water balance and ethylene stimulation of latex production in Hevea.

Evolving epidemiology of injecting drug use-associated infective endocarditis: a regional centre experience

INTRODUCTION AND AIMS: Injecting drug use (IDU) is a major risk factor for infective endocarditis (IE). An understanding of the epidemiology of IE and IDU is vital for delivery of health care for this disease. Our aim was to examine the rates of IDU-associated IE (IDU-IE) in a single centre over the last 12 years. DESIGN AND METHODS: Retrospective analysis of two cohorts of consecutive patients (n = 226) admitted with IE from 2002 to 2013. Numbers of cases and rates of IE were compared between two cohorts (2002-2006 and 2009-2013). Rate ratios were calculated using Poisson distributions. Poisson regression was used to examine relationship over time. RESULTS: One hundred thirty cases of endocarditis were seen in the first observation period (6 IDU-IE) and 96 in the second observation period (15 IDU-IE). The estimated incidence rate of IE had fallen from 10.1 to 6.45 per 100, 000 person-years [rate ratio 0.64, 95% confidence interval (CI) 0.48, 0.85]. In contrast, the estimated incidence rate of IDU-E has risen from 0.48 to 0.79 per 100, 000 person-years (rate ratio 1.65, 95% CI 0.59, 4.57). Incidence rate regression suggests that the number of IDU-IE cases is expected to increase by a factor of 1.25 (95%CI 1.09-1.44) for each increase of 1 year. DISCUSSION AND CONCLUSIONS: Over the last decade, there has been a decrease in incidence rate and total number of cases of IE but a rise in rate and number of cases of IDU-IE. This may indicate increasing IDU or increased rates of endocarditis in intravenous drug users in this region. This finding may inform health-care planning in the area.

Searching Baxter's URDF robot joint and link tree for active serial chains

Teleoperation is integral to society's uptake of modern robotic systems. Given the wide array of readily available robots, ranging from simple mobile platforms and UAVs to advanced humanoid robots such as ASIMO and PR2, teleoperation is required in many different forms. The recent advances in virtual reality systems, interactive input controls and even haptic devices facilitate a wide range of new approaches to teleoperation control. This paper considers a dynamic user interface for improving the operator's ability to teleoperate heterogeneous robotic systems in dynamic and challenging environments. In order to achieve the proposed dynamic user interface the robot(s) comprising the heterogeneous robotic system and their active components need to be categorized. The recent uptake of ROS means that many robots are now represented within the standardized Unified Robot Descriptive Format (URDF), and this paper proposes a method for searching the URDF for active serial chains in individual robot systems. Results demonstrate the ability of the approach to determine active serial chains and associated kinematic information for the Baxter torso robot.

A predictive framework for modeling healthcare data with evolving clinical interventions

Medical interventions critically determine clinical outcomes. But prediction models either ignore interventions or dilute impact by building a single prediction rule by amalgamating interventions with other features. One rule across all interventions may not capture differential effects. Also, interventions change with time as innovations are made, requiring prediction models to evolve over time. To address these gaps, we propose a prediction framework that explicitly models interventions by extracting a set of latent intervention groups through a Hierarchical Dirichlet Process (HDP) mixture. Data are split in temporal windows and for each window, a separate distribution over the intervention groups is learnt. This ensures that the model evolves with changing interventions. The outcome is modeled as conditional, on both the latent grouping and the patients' condition, through a Bayesian logistic regression. Learning distributions for each time-window result in an over-complex model when interventions do not change in every time-window. We show that by replacing HDP with a dynamic HDP prior, a more compact set of distributions can be learnt. Experiments performed on two hospital datasets demonstrate the superiority of our framework over many existing clinical and traditional prediction frameworks.

Keeping up with innovation: a predictive framework for modeling healthcare data with evolving clinical interventions

Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture variations. Interventions also evolve over time as more advanced interventions replace older ones. To this end, we propose a Bayesian nonparametric, supervised framework that models a set of intervention groups through a mixture distribution building a separate prediction rule for each group, and allows the mixture distribution to change with time. This is achieved by using a hierarchical Dirichlet process mixture model over the interventions. The outcome is then modeled as conditional on both the latent grouping and the disease information through a Bayesian logistic regression. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines.

LC–MS and GC–MS metabolite profiling of nickel(II) complexes in the latex of the nickel-hyperaccumulating tree Sebertia acuminata and identification of methylated aldaric acid as a new nickel(II) ligand

Targeted liquid chromatography–mass spectrometry (LC–MS) technology using size exclusion chromatography and metabolite profiling based on gas chromatography–mass spectrometry (GC–MS) were used to study the nickel-rich latex of the hyperaccumulating tree Sebertia acuminata. More than 120 compounds were detected, 57 of these were subsequently identified. A methylated aldaric acid (2,4,5-trihydroxy-3-methoxy-1,6-hexan-dioic acid) was identified for the first time in biological extracts and its structure was confirmed by 1D and 2D nuclear magnetic resonance (NMR) spectroscopy. After citric acid, it appears to be one of the most abundant small organic molecules present in the latex studied. Nickel(II) complexes of stoichiometry NiII:acid = 1:2 were detected for these two acids as well as for malic, itaconic, erythronic, galacturonic, tartaric, aconitic and saccharic acids. These results provide further evidence that organic acids may play an important role in the transport and possibly in the storage of metal ions in hyperaccumulating plants.