Combining ability × environment interaction and genetic analysis for agronomic traits in safflower (Carthamus tinctorius L.): biplot as a tool for diallel data

Combining ability × environment interaction is considerable to identify the effect of environment on the combining ability and gene action of the traits to select appropriate parents for safflower hybrid production. The 36 genotype (28 F2 progenies of eight-parent half-diallel crosses across 8 parental genotypes) of safflower were studied to investigate the mentioned parameters across different geographical regions of Iran. The results indicated significant differences among parents for general and specific combining ability, except for seeds per capitulum across three environments. The overall results indicated that K21 and Mex.22-191 were excellent parents with greater general combining ability for the improvement of seed yield in safflower. The K21 × Mex.22-191 hybrid could be, therefore, employed for the production of high seed yield in safflower breeding. The estimates of genetic variance components recommended the importance of additive- dominance genetic effects that contributed to variation in yield per plant. Such gene action expression for seed yield needs auxiliary methods based on hybridization and selection for seed yield advancement in safflower

GPU-based 3D iceball modeling for fast cryoablation simulation and planning

Purpose The elimination of abdominal tumors by percutaneous cryoablation has been shown to be an effective and less invasive alternative to open surgery. Cryoablation destroys malignant cells by freezing them with one or more cryoprobes inserted into the tumor through the skin. Alternating cycles of freezing and thawing produce an enveloping iceball that causes the tumor necrosis. Planning such a procedure is difficult and time-consuming, as it is necessary to plan the number and cryoprobe locations and predict the iceball shape which is also influenced by the presence of heating sources, e.g., major blood vessels and warm saline solution, injected to protect surrounding structures from the cold. Methods This paper describes a method for fast GPU-based iceball modeling based on the simulation of thermal propagation in the tissue. Our algorithm solves the heat equation within a cube around the cryoprobes tips and accounts for the presence of heating sources around the iceball. Results Experimental results of two studies have been obtained: an ex vivo warm gel setup and simulation on five retrospective patient cases of kidney tumors cryoablation with various levels of complexity of the vascular structure and warm saline solution around the tumor tissue. The experiments have been conducted in various conditions of cube size and algorithm implementations. Results show that it is possible to obtain an accurate result within seconds. Conclusion The promising results indicate that our method yields accurate iceball shape predictions in a short time and is suitable for surgical planning

Seroepidemiological study of Toxoplasma gondii infection in a population of Iranian epileptic patients

Epilepsy is one of the most common neurologic disorders. Underlying cause of epilepsy is unknown in 60 % of the patients. Toxoplasma gondii is an intracellular parasite which is capable of forming tissue cysts in brain of chronically infected hosts including humans. Some epidemiological studies suggested an association between tox- oplasmosis and acquisition of epilepsy. In this study we determined seroprevalence of latent Toxoplasma infection in a population of Iranian epileptic patients. Participants were classified in three groups as Iranian epileptic patients (IEP, n = 414), non-epileptic patients who had other neurologic disorders (NEP, n = 150), and healthy people without any neurologic disorders (HP, n = 63). The presence of anti-Toxoplasma IgG antibodies and IgG titer in the sera were determined by ELISA method. Anti-T. gondii IgG seroprevalence obtained 35.3 %, 34.7 % and 38.1 % in IEP, NEP and HP, respectively. The seroprevalence rate was not significantly different among the three groups (P = 0.88). Anti-T. gondii IgG titer was 55.7 ± 78, 52.4 ± 74 and 69.7 ± 92 IU/ml in IEP, NEP and HP, respectively. There was not any statistically significant difference in the antibody titer between the study groups (P = 0.32). The rate of T. gondii infection in epileptic patients was not higher than non-epileptic patients and healthy people in the Iranian population

Application of Fractal Analysis in Detecting Trabecular Bone Changes in Periapical Radiograph of Patients with Periodontitis

Evaluation of detailed features of the supporting bone is an important step in diagnosis and treatment planning for teeth with clinical attachment loss. Fractal analysis can be used as a method for evaluating the complexity of trabecular bone structures. The aim of this study was to evaluate the trabecular bone changes in periapical radiographs of patients with different stages of periodontitis using fractal analysis. Methods. This comparative cross-sectional study was performed on patients with and without clinical attachment loss in mandibular first molars. Teeth with clinical attachment loss were divided into mild, moderate, and severe periodontitis groups. Digital periapical radiographs were obtained from the mandibular first molars using the same exposure parameters. DICOM file of the radiographs was exported to ImageJ software for fractal analysis. Three regions of interest (ROIs) were considered in each radiograph: two proximal ROIs mesial and distal to the mandibular first molar and one apical ROI. Fractal dimension (FD) values were calculated using the fractal box counting approach. Statistical analysis was performed using the chi-square test, Mann–Whitney test, intraclass correlation coefficient, and ANOVA (α = 0.05). Results. FD values were significantly different between moderate and severe periodontitis and healthy periodontal bone (P0.05). Conclusion. Fractal analysis is a useful tool for evaluation of bone alterations in moderate and severe periodontitis, but was not able to detect the most initial radiographic bone signs of mild periodontitis

Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions

Dynamic joint stiffness is a dynamic, nonlinear relationship between the position of a joint and the torque acting about it, which can be used to describe the biomechanics of the joint and associated limb(s). This paper models and quantifies changes in ankle dynamic stiffness and its individual elements, intrinsic and reflex stiffness, in healthy human subjects during isometric, time-varying (TV) contractions of the ankle plantarflexor muscles. A subspace, linear parameter varying, parallel-cascade (LPV-PC) algorithm was used to identify the model from measured input position perturbations and output torque data using voluntary torque as the LPV scheduling variable (SV). Monte-Carlo simulations demonstrated that the algorithm is accurate, precise, and robust to colored measurement noise. The algorithm was then used to examine stiffness changes associated with TV isometric contractions. The SV was estimated from the Soleus EMG using a Hammerstein model of EMG-torque dynamics identified from unperturbed trials. The LPV-PC algorithm identified (i) a non-parametric LPV impulse response function (LPV IRF) for intrinsic stiffness and (ii) a LPV-Hammerstein model for reflex stiffness consisting of a LPV static nonlinearity followed by a time-invariant state-space model of reflex dynamics. The results demonstrated that: (a) intrinsic stiffness, in particular ankle elasticity, increased significantly and monotonically with activation level; (b) the gain of the reflex pathway increased from rest to around 10–20% of subject's MVC and then declined; and (c) the reflex dynamics were second order. These findings suggest that in healthy human ankle, reflex stiffness contributes most at low muscle contraction levels, whereas, intrinsic contributions monotonically increase with activation level

Non-Gaussian multivariate modeling of plug-in electric vehicles load demand

This paper proposes an organized stochastic methodology to model the power demand of plug-in electric vehicles (PEVs) which can be embedded into probabilistic distribution system planning. Time schedules as well as traveling and refueling information of a set of commuter vehicles in Tehran are utilized as the input dataset. In order to generate the required synthetic data, the correlation structure of the aforesaid random variables is taken into account using a multivariate student's t function. Afterwards, a Monte Carlo based stochastic simulation is provided to extract the initial state-of-charge of batteries. Further, a non-Gaussian probabilistic decision making algorithm is developed that accurately infers whether the PEVs charging should take place every day or not. Then, through presenting a state transition model to describe the charging profile of a PEV battery, hourly demand distributions of the PEVs are derived. The obtained distributions can be used to generate the random samples required in probabilistic planning problems. Eventually, the extracted distributions are employed to estimate demand profile of a fleet that can be efficiently utilized in various applications. © 2014 Elsevier Ltd. All rights reserved

Inter-subject registration-based segmentation of thoracic-abdominal organs in 4 dimensional magnetic resonance imaging

4 Dimensional Magnetic Resonance Imaging (4D MRI) is currently gaining attention as an imaging modality which is able to capture inter-cycle variability of respiratory motion. Such information is beneficial for example in radiotherapy planning and delivery. In the latter case, there may be a need for organ segmentation, however 4D MRI are of low contrast, which complicates automated organ segmentation. This paper proposes a multi-subject thoracic-abdominal organ segmentation propagation scheme for 4D MRI. The proposed scheme is registration based, hence different combinations of deformation and similarity measures are used. For deformation we used either just an affine transformation or additionally free form deformation on top of an affine transform. For similarity measure, either the sum of squared intensity differences or normalised mutual information is used. Segmentations from multiple subjects are registered to a target MRI and the average segmentation is found. The result of the method is compared with the ground truth which is generated from a semi-automated segmentation method. The results are quantified using the Jaccard index and Hausdorff distance. The results show that using free form deformation with a sum of squared intensity differences similarity measure produces an acceptable segmentation of the organs with an overall Jaccard index of over 0.5. Hence, the proposed scheme can be used as a basis for automated organ segmentation in 4D MRI

Automatic Detection of Microaneurysms in OCT Images Using Bag of Features

Diabetic Retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical Coherence Tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina and it has been used in recent years to diagnose many eye diseases. As a result, in this paper has attempted to identify areas with MA from normal areas of the retina using OCT images. This work is done using the dataset collected from FA and OCT images of 20 patients with DR. In this regard, firstly Fluorescein Angiography (FA) and OCT images were registered. Then the MA and normal areas were separated and the features of each of these areas were extracted using the Bag of Features (BOF) approach with Speeded-Up Robust Feature (SURF) descriptor. Finally, the classification process was performed using a multilayer perceptron network. For each of the criteria of accuracy, sensitivity, specificity, and precision, the obtained results were 96.33%, 97.33%, 95.4%, and 95.28%, respectively. Utilizing OCT images to detect MAsautomatically is a new idea and the results obtained as preliminary research in this field are promising

Real-Time Curvature Defect Detection on Outer Surfaces Using Best-Fit Polynomial Interpolation

This paper presents a novel, real-time defect detection system, based on a best-fit polynomial interpolation, that inspects the conditions of outer surfaces. The defect detection system is an enhanced feature extraction method that employs this technique to inspect the flatness, waviness, blob, and curvature faults of these surfaces. The proposed method has been performed, tested, and validated on numerous pipes and ceramic tiles. The results illustrate that the physical defects such as abnormal, popped-up blobs are recognized completely, and that flames, waviness, and curvature faults are detected simultaneously