7 research outputs found

    Highly polymorphic in silico-derived microsatellite loci in the potato-infecting fungal pathogen Rhizoctonia solani anastomosis group 3 from the Colombian Andes

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    Fourteen polymorphic microsatellite DNA markers derived from the draft genome sequence of Rhizoctonia solani anastomosis group 3 (AG-3), strain Rhs 1AP, were designed and characterized from the potato-infecting soil fungus R. solani AG-3. All loci were polymorphic in two field populations collected from Solanum tuberosum and S. phureja in the Colombian Andes. The total number of alleles per locus ranged from two to seven, while gene diversity (expected heterozygosity) varied from 0.11 to 0.81. Considering the variable levels of genetic diversity observed, these markers should be useful for population genetic analyses of this important dikaryotic fungal pathogen on a global scale

    Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation

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    In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB)

    Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation

    No full text
    In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB)

    Delivering clinical trials at home: protocol, design and implementation of a direct-to-family paediatric lupus trial

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    Introduction Direct-to-family clinical trials efficiently provide data while reducing the participation burden for children and their families. Although these trials can offer significant advantages over traditional clinical trials, the process of designing and implementing direct-to-family studies is poorly defined, especially in children with rheumatic disease. This paper provides lessons learnt from the design and implementation of a self-controlled, direct-to-family pilot trial aimed to evaluate the effects of a medication management device on adherence to hydroxychloroquine in paediatric SLE.Methods Several design features accommodate a direct-to-family approach. Participants meeting eligibility criteria from across the USA were identified a priori through a disease registry, and all outcome data are collected remotely. The primary outcome (medication adherence) is evaluated using electronic medication event-monitoring, plasma drug levels, patient questionnaires and pill counts. Secondary and exploratory endpoints include (1) lupus disease activity measured by a remote SLE Disease Activity Index examination and the Systemic Lupus Activity Questionnaire; and (2) hydroxychloroquine pharmacokinetics and pharmacodynamics. Recruitment of the initial target of 20 participants was achieved within 10 days. Due to initial recruitment success, enrolment was increased to 26 participants. Additional participants who were interested were placed on a waiting list in case of dropouts during the study.Discussion and dissemination Direct-to-family trials offer several advantages but present unique challenges. Lessons learnt from the protocol development, design, and implementation of this trial will inform future direct-to-family trials for children and adults with rheumatic diseases. Additionally, the data collected remotely in this trial will provide critical information regarding the accuracy of teleresearch in lupus, the impact of adherence to hydroxychloroquine on disease activity and a pharmacokinetic analysis to inform paediatric-specific dosing of hydroxychloroquine.Trial registration number ClinicalTrials.gov Registry (NCT04358302)
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