Functional mapping of reaction norms to multiple environmental signals through nonparametric covariance estimation

<p>Abstract</p> <p>Background</p> <p>The identification of genes or quantitative trait loci that are expressed in response to different environmental factors such as temperature and light, through functional mapping, critically relies on precise modeling of the covariance structure. Previous work used separable parametric covariance structures, such as a Kronecker product of autoregressive one [AR(1)] matrices, that do not account for interaction effects of different environmental factors.</p> <p>Results</p> <p>We implement a more robust nonparametric covariance estimator to model these interactions within the framework of functional mapping of reaction norms to two signals. Our results from Monte Carlo simulations show that this estimator can be useful in modeling interactions that exist between two environmental signals. The interactions are simulated using nonseparable covariance models with spatio-temporal structural forms that mimic interaction effects.</p> <p>Conclusions</p> <p>The nonparametric covariance estimator has an advantage over separable parametric covariance estimators in the detection of QTL location, thus extending the breadth of use of functional mapping in practical settings.</p

Human activity recognition for physical rehabilitation

The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance; we undertook an incremental increase in the dataset size.We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set. © 2013 IEEE

Three Dimensional Measurements of Asphaltene Deposition in a Transparent Micro-Channel

This study describes a novel experimental approach to directly measure the thicknesses of asphaltene deposits in micro-channels. The thickness of the asphaltene deposit is estimated using a visualization technique based on 3D digital microscopy. The working fluid is a mixture of n-heptane and dead oil. Induced by the addition of n-heptane, the asphaltenes present in crude oil phase separate at ambient temperature to form aggregates of asphaltene-rich phase. Part of the asphaltene aggregates deposit on the walls of the transparent micro-channel. A two-dimensional profile of the deposit across the channel at selected axial sections is measured. The influences of injection mixture volume on the growth of the thickness of deposited asphaltenes is investigated using two experimental conditions, (i) varying elapsed time at constant flow rate and (ii) increasing the flow rate at a constant elapsed time. In both cases the deposit thickness of asphaltene (δ) increases with the total injection volume (V). The experimental results obtained in this work provide new insights into the deposition process at the micro-scale level, which can be used to facilitate the development of more accurate numerical model for this applicatio

Conjugate Heat Transfer in Stratified Two-Fluid Flows with a Growing Deposit Layer

The article presents a numerical model for moving boundary conjugate heat transfer in stratified two-fluid flows with a growing deposit layer. The model is applicable to other general moving boundary conjugate heat transfer problem in a two-fluid flow environment with deposition occurring simultaneously. The level-set method is adopted to capture the fluid-fluid interface and fluid-deposit interface. The governing equations are solved using a finite volume method. Upon verification of the model, the effects of inlet velocity ratio, Damköhler number and thermal conductivity ratio on the flow, deposition as well as heat transfer are investigated. Generally, Nusselt number on the lower wall (with a growing deposit layer), Nulx and upper wall, Nuux show distinct features with the change of these parameters. Nuux increases with the increase of lower fluid layer (fluid 1) inlet velocity and the thermal conductivity of deposit layer while it decreases with the increase of Damkholer number. Nulx varies differently in the upstream and the downstream of the channel. A higher lower fluid layer (fluid 1) velocity and a higher thermal conductivity of deposit layer result in a higher Nulx upstream but a lower Nulx downstream. However, a higher Damkholer number results in a lower Nulx upstream and a higher Nulx downstream

Examining Asphaltene Solubility on Deposition in Model Porous Media

Asphaltenes are known to cause severe flow assurance problems in the near-wellbore region of oil reservoirs. Understanding the mechanism of asphaltene deposition in porous media is of great significance for the development of accurate numerical simulators and effective chemical remediation treatments. Here, we present a study of the dynamics of asphaltene deposition in porous media using microfluidic devices. A model oil containing 5 wt % dissolved asphaltenes was mixed with n-heptane, a known asphaltene precipitant, and flowed through a representative porous media microfluidic chip. Asphaltene deposition was recorded and analyzed as a function of solubility, which was directly correlated to particle size and Péclet number. In particular, pore-scale visualization and velocity profiles, as well as three stages of deposition, were identified and examined to determine the important convection−diffusion effects on deposition

Minimally interrupted novel oral anticoagulant versus uninterrupted vitamin K antagonist during atrial fibrillation ablation

The safety and efficacy of a minimally interrupted novel oral anticoagulant (NOAC) strategy at the time of atrial fibrillation (AF) ablation is uncertain. The purpose of this study was to compare rates of bleeding and thromboembolic events between minimally interrupted NOAC and uninterrupted vitamin K antagonist (VKA) in patients undergoing AF ablation.This was a retrospective single-center cohort study of consecutive patients who underwent AF catheter ablation between January 2013 and April 2017. Endpoints included major bleeding, clinically relevant non-major bleeding and systemic thromboembolic event from the time of ablation through 30 days. Bleeding events were defined by the Bleeding Academic Research Consortium (BARC) and International Society on Thrombosis and Haemostasis (ISTH).A total of 637 patients were included in the analysis, 520 patients used uninterrupted VKA and 117 patients minimally interrupted NOAC (dabigatran: n = 68; apixaban: n = 30; rivaroxaban, n = 14; edoxaban, n = 5). The rate of clinically relevant non-major bleeding was lower in the NOAC group in comparison to the VKA group (BARC type 2: 2.6% versus 8.3%, P = 0.03; ISTH: 0% versus 3.8%, P = 0.03). Rates of major bleeding were similar between groups (BARC type 3 to 5: 3.4% versus 4.2%, P = NS; ISTH: 6.0% versus 8.7%, P = NS; for NOAC and VKA groups, respectively). Rates of systemic embolism were 0% with minimally interrupted NOAC, and 0.6% with uninterrupted VKA (P = NS).In patients undergoing AF ablation, anticoagulation with minimally interrupted NOAC was associated with fewer clinically relevant non-major bleeding events in comparison with uninterrupted VKA without compromising thromboembolic safety

Undescended testis: 513 patients’ characteristics, age at orchidopexy and patterns of referral

10.1136/archdischild-2013-305225Archives of Disease in Childhood995401-40

Human Activity Recognition for Physical Rehabilitation

The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance; we undertook an incremental increase in the dataset size.We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set. © 2013 IEEE

Immunofluorescence Assay for Serologic Diagnosis of SARS

We evaluated a virus-infected cell-based indirect immunofluorescence assay for detecting anti–severe acute respiratory syndrome-associated coronavirus (SARS-CoV) immunoglobulin (Ig) G antibody. All confirmed SARS cases demonstrated seroconversion or fourfold rise in IgG antibody titer; no control was positive. Sensitivity and specificity of this assay were both 100%. Immunofluorescence assay can ascertain the status of SARS-CoV infection