Genomic resource development for a diploid mint: Mentha longifolia

This research project aimed to develop genomic resources needed to enable construction of a genetic linkage map of the diploid mint species Mentha longifolia. Such a map would facilitate identification of plant genes involved in resistance to Verticillium fungal infection. For this purpose, a small genomic library was constructed from germplasm accession CMEN 585, 279 genomic inserts were sequenced and annotated and 19 PCR primer pairs were designed and tested on two resistant and two susceptible accessions. The Cleaved Modified Polymorphic Sequence (CAPS) method of molecular marker genotyping was found to detect little variation between crossing parents CMEN 585 (resistant) and CMEN 584 (susceptible). Comparative sequencing of PCR products from two European and two South African accessions revealed greater diversity between than within geographic locations. Future efforts should focus on assessing more sensitive genotyping methods, and developing a mapping population from a cross between European and South African accessions

Finite Element Analysis and Design Optimization of Deep Cold Rolling of Titanium Alloy at Room and Elevated Temperatures

High strength-to-density ratio, high corrosion resistance and superior biocompatibility are the main advantages of Ti-6Al-4V (Ti64), making it a long been favored titanium alloy for aerospace and biomedical applications. Designing titanium components to last longer and refurbishing of aged ones using surface treatments have become a desirable endeavor considering high environmental damage, difficulty in casting, scarcity and high cost associated with this metal. Among mechanical surface treatments, Deep Cold Rolling (DCR) has been shown to be a very promising process to improve fatigue life by introducing a deep compressive residual stress and work-hardening in the surface layer of components. This process has shown to be superior compared with other surface treatment methods as it yields a better surface quality and induces a deeper residual stress profile which can effectively be controlled through the process parameters (i.e. ball diameter, rolling pressure and feed). However, residual stresses induced through this process at room temperature are generally relaxed upon exposure of the components to elevated operating temperatures. In this work, high-fidelity Finite Element (FE) models have been developed to simulate the DCR process in order to predict the induced residual stresses at room temperature and their subsequent relaxation following exposure to temperature increase. Accuracy of the developed models has been validated using experimental measurements available in the literature. A design optimization strategy has also been proposed to identify the optimal process parameters to maximize the induced beneficial compressive residual stress on and under the surface layer and thus prolong the fatigue life. Conducting optimization directly on the developed high-fidelity FE model is not practical due to high computational cost associated with nonlinear dynamic models. Moreover, responses from the FE models are typically noisy and thus cannot be utilized in gradient based optimization algorithms. In this research study, well-established machine learning principles are employed to develop and validate surrogate analytical models based on the response variables obtained from FE simulations. The developed analytical functions are smooth and can efficiently approximate the residual stress profiles with respect to the process parameters. Moreover the developed surrogate models can be effectively and efficiently utilized as explicit functions for the optimization process. Using the developed surrogate models, conventional (one-sided) DCR process is optimized for a thin Ti64 plate considering the material fatigue properties, operating temperature and external load. It is shown that the DCR process can lead to a tensile balancing residual stress on the untreated side of the component which can have a detrimental effect on the fatigue life. Additionally, application of conventional DCR on thin geometries such as compressor blades can cause manufacturing defects due to unilateral application of the rolling force and can also lead to thermal distortion of the part due to asymmetric profile of the induced residual stresses. Double-sided deep rolling has been shown as a viable alternative to address those issues since both sides of the component are treated simultaneously. The process induces a symmetric residual stress which can be further optimized to achieve a compressive residual stress on both sides of the component. For this case, a design optimization problem is formulated to improve fatigue life in high stress locations on a generic compressor blade. All the optimization problems are formulated for multi-objective functions to achieve most optimal residual stress profiles both at room temperature as well as elevated temperature of 450℃. A hybrid optimization algorithm based on combination of sequential quadratic programming (SQP) technique with stochastic based genetic algorithm (GA) has been developed to accurately catch the global optimum solutions. It has been shown that the optimal solution depends on the stress distribution in the component due to the external load as well as the operating temperature

Relationship between Toxic Leadership and Job Related Affective Well-Being: The Mediating Role of Job Stress

The present study aimed to examine the relationship between toxic leadership and the job-related affective well-being of workers with the mediating role of job stress. Research population consisted of knowledge workers in knowledge-based organizations, 213 of whom were selected and filled in the Job-Related Affective Well-being Scale (JAWS) (Van Katwyk, Fox, Spector, & Kelloway, 2000), Measures of Job Stressors and Strains (MJSS) (Spector & Jex, 1998), and Toxic Leadership Scale (TLS) (Schmidt, 2014). Data were analyzed through correlation and path analyses. Results showed the significant direct and indirect effects of toxic leadership, quantitative workload, organizational constraints, and interpersonal conflicts on job-related affective well-being. Four variables (interpersonal conflicts, organizational constraints, quantitative workload, and toxic leadership) accounted for 13% of the variance of job-related affective well-being. Moreover, results of the fit of the model revealed a direct significant effect of toxic leadership on interpersonal conflicts where it accounted for 12% of the variance of interpersonal conflicts. In addition, accounting for 9% of the variance of quantitative workload, toxic leadership was demonstrated to have a direct significant effect on quantitative workload. Results also indicated a direct significant effect of toxic leadership on organizational constraints where it accounted for 11% of the variance of organizational constraints. Finally, the necessity of paying attention to organizational management styles was discussed

Optimal Design of Magnetorheological Dampers Constrained in a Specific Volume Using Response Surface Method

In recent years, semi-active magnetorheological (MR) and electrorheological (ER) fluid technology based devices and systems have been developed and successfully utilized in many applications as valves, shock absorbers, dampers and clutch/brake systems. These promising devices have the adaptivity of the fully active systems to accommodate varying external excitations while maintaining the reliability and fail-safe features of the passive systems. Compared with ER based devices or systems, MR based devices have recently received special attention due to their high performance with minimal power requirements. Moreover MR fluids have significantly higher yield strength and are less sensitive to contaminants and temperature compared with the ER fluids. The geometric optimal design of MR valves/dampers is an important issue to improve the damper performance, such as damping force, valve ratio and inductive time constant. Considering this, the primary purpose of this study is to establish a general design optimization methodology to optimally design single–coil annular MR valves constrained in a specific volume in MR damper. To accomplish this, first the damping force of MR damper has been modeled using Bingham plastic model. The magnetic circuit of MR damper has been analyzed using finite element method in ANSYS environment to obtain magnetic field intensity which can be subsequently used to obtain the yield stress of the MR fluid in the active volume where the magnetic flux crosses. Then the developed finite element model of the MR valve is effectively used to construct an approximate response function relating the magnetic field intensity to the identified design parameters in the selected design space using response surface method and design of experiment methodology. Using the derived approximate relation for the magnetic field intensity in the MR damper model, the design optimization problem has been formulated using gradient based nonlinear mathematical programming technique based on the Sequential Quadratic Programming (SQP) technique and also stochastic optimization technique based on the Genetic Algorithm (GA) to find optimal geometrical parameters of the MR valve in order to maximize the damping performance under given constrained volume. Finally a PID controller has been designed to evaluate the close-loop performance of the optimally designed MR damper in a quarter-car suspension model

SVNN:An efficient PacBio-specific pipeline for structural variations calling using neural networks

Abstract Background Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads that takes raw reads as the input and detects structural variants of size larger than 50 bp. Our pipeline utilizes state-of-the-art long-read aligners, namely NGMLR and Minimap2, and structural variation callers, videlicet Sniffle and SVIM. We found that by using a neural network, we can extract features from Minimap2 output to detect a subset of reads that provide useful information for structural variation detection. By only mapping this subset with NGMLR, which is far slower than Minimap2 but better serves downstream structural variation detection, we can increase the sensitivity in an efficient way. As a result of using multiple tools intelligently, SVNN achieves up to 20 percentage points of sensitivity improvement in comparison with state-of-the-art methods and is three times faster than a naive combination of state-of-the-art tools to achieve almost the same accuracy. Conclusion Since prohibitive costs of using high-coverage data have impeded long-read applications, with SVNN, we provide the users with a much faster structural variation detection platform for PacBio reads with high precision and sensitivity in low-coverage scenarios

Adaptive On-the-Fly Changes in Distributed Processing Pipelines

Distributed data processing systems have become the standard means for big data analytics. These systems are based on processing pipelines where operations on data are performed in a chain of consecutive steps. Normally, the operations performed by these pipelines are set at design time, and any changes to their functionality require the applications to be restarted. This is not always acceptable, for example, when we cannot afford downtime or when a long-running calculation would lose significant progress. The introduction of variation points to distributed processing pipelines allows for on-the-fly updating of individual analysis steps. In this paper, we extend such basic variation point functionality to provide fully automated reconfiguration of the processing steps within a running pipeline through an automated planner. We have enabled pipeline modeling through constraints. Based on these constraints, we not only ensure that configurations are compatible with type but also verify that expected pipeline functionality is achieved. Furthermore, automating the reconfiguration process simplifies its use, in turn allowing users with less development experience to make changes. The system can automatically generate and validate pipeline configurations that achieve a specified goal, selecting from operation definitions available at planning time. It then automatically integrates these configurations into the running pipeline. We verify the system through the testing of a proof-of-concept implementation. The proof of concept also shows promising results when reconfiguration is performed frequently

Biomimetic phantom with anatomical accuracy for evaluating brain volumetric measurements with magnetic resonance imaging

Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM. Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation–maximization (EM) method, to estimate its accuracy in BVM. Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 (±0.03 uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation. Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods

A Comparative Study of the Detection of cAMP response element binding protein (CREB) in the Peripheral Blood of Alzheimer's Patients and the Healthy subjects as a Biomarker for the diagnosis of Alzheimer

Introduction: Alzheimer's disease is a neurodegenerative disease, which usually helps some biomarkers, such as amyloid proteins, to diagnose the disease. Therefore, the purpose of this study was to compare the expression of a protein binding protein to the adjuvant responder to circular adenosine monophosphate (CREB) in peripheral blood of patients to Alzheimer's and healthy elderly people as a biomarker for diagnosing Alzheimer. Materials and Methods: In this case-control study, 32 patients with Alzheimer's disease and 32 normal blood samples were taken. Using real time PCR, CREB expression was evaluated. Results: The mean CREB level in the case group was 0.89 ± 0.30 and in the control group was 1.01 ± 0.03. The mean of BDNF level in the case group was significantly higher than the control group (P <0.001). There was no significant relationship between the level of CREB with age, sex, MMSE score and Cornell scale for depression in dementia (P> 0.05). Conclusion: Reducing CREB levels in people with Alzheimer's disease can be a factor in diagnosis in comparison to healthy people