Molecular diagnosis of peste des petits ruminants virus (PPR) in goats and sheep populations

Peste des petits ruminants (PPR) is an economically important viral disease of goats and sheep. The disease is confused clinically with other infections such as the mild strain of rinderpest in small ruminants. Effective control measures for PPR need that a proper and rapid diagnostic technique of disease. Therefore, the use of reverse transcriptase-polymerase chain reaction (RT-PCR) to detect suspected field samples collected from diseased goats and sheep in Dammam city, Kingdom of Saudia Arabia (KSA) has helped to give an effective diagnosis that was needed to control measure of the spread of the disease. This assay is based on the rapid purification of RNA on glass beads followed by the reverse transcription-polymerase chain reaction (RT-PCR). The primers (NP3/NP4) were used to amplify specifically a fragment of about 350 bp, that technique has a more specific and sensitive method for rapid diagnosis of disease.Key words: Peste des petits ruminants virus; reverse transcriptase polymerase chain reaction; diagnosis; goats; shee

Coronary Subclavian Steal Syndrome

INTRODUCTION Coronary subclavian steal (CSS) syndrome is a rare complication of coronary artery bypass graft surgery (CABG) involving the left internal mammary artery (LIMA) graft to the left anterior descending (LAD) artery. It results from stenosis of the left subclavian artery proximal to the LIMA, which compromises myocardial blood flow. The incidence of CSS syndrome is between 0.1-3.4% in the United States.1 Most cases occur as a result of long-standing subclavian stenosis due to progres-sion of the stenosis following CABG. We report a case of CSS syndrome, which presented as a non-ST elevation myocardial infarction (NSTEMI)

A case of parasitic leiomyoma with serpentine omental blood vessels: An unusual variant of uterine leiomyoma

AbstractLeiomyoma is considered as the commonest benign tumor of the genital tract. This case represents a multiparous woman who presented with a history of progressive abdominal distension. On examination, a mobile ill-defined centrally located intra-abdominal mass was noticed. At laparotomy a parasitic fibroid attached to the greater omentum was seen. Resection of the mass and partial omentectomy was performed which was reported as leiomyoma by the histological examination. The patient had an uneventful post-operative recovery. She has been followed up for twelve months with no evidence of recurrence or residual disease

Application of Hyperspectral Imaging and Acoustic Emission Techniques for Apple Quality Prediction

There is a growing demand for developing effective non-destructive quality assessment methods with quick response, high accuracy, and low cost for fresh fruits. In this study, hyperspectral reflectance imaging (400 to 1000 nm) and acoustic emission (AE) tests were applied to ‘GoldRush‘ apples (total number, n = 180) to predict fruit firmness, total soluble solids (TSS), and surface color parameters (L*, a*, b*) during an eight-week storage period. Partial least squares (PLS) regression, least squares support vector machine (LS-SVM), and multivariate linear regression (MLR) methods were used to establish models to predict the quality attributes of the apples. The results showed that hyperspectral imaging (HSI) could accurately predict all the attributes except TSS, while the AE method was capable of predicting fruit firmness, b* color index, and TSS. Overall, HSI regression using PLS had better comprehensive ability for predicting firmness, TSS, and color parameters (L*, a*, b*) than AE, with correlation coefficients of prediction (rp) of 0.92, 0.41, 0.83, 0.87, and 0.94 and root mean square errors of prediction (RMSEP) of 4.32 (N), 1.78 (°Brix), 3.41, 2.28, and 4.29, respectively, while AE regression using LS-SVM gave rp values of 0.88, 0.74, 0.34, 0.37, and 0.81 and RMSEP values of 4.26 (N), 0.64 (°Brix), 4.69, 1.8, and 5.17 for firmness, TSS, and color parameters (L*, a*, b*), respectively. The results show the potential of these two non-destructive methods for predicting some of the quality attributes of apples

Application of Acoustic Emission and Machine Learning to Detect Codling Moth Infested Apples

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. \u27GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect the data emitted by 80 larvae-infested and intact apples in total. Eleven AE features that changed with signaling time were obtained with the AE system. For each feature, the area under the curve along the signaling time was calculated and used as an independent input variable for the machine learning algorithms, which included linear discriminant analysis (LDA) and ensemble method adaptive boosting. With signaling times ranging from 0.5 to 120 s, classification rates for infested versus intact apples ranged from 91% to 100% for the training set and from 83% to 100% for the test set. The quick signal collection and high classification accuracy obtained in this study show the potential of AE for detecting and classifying CM-infested apples

A new approach for solving systems of fractional differential equations via natural transform

In this paper, A new method proposed and coined by the authors as the natural variational iteration  transform method(NVITM) is utilized to solve linear and nonlinear systems of fractional differential equations. The new method is a combination of natural transform method and variational iteration method. The solutions of our modeled systems are calculated in the form of convergent power series with easily computable components. The numerical results shows that the approach is easy to implement and accurate when applied to various linear and nonlinear systems of fractional differential equations

On Sampling Distribution of Improved Estimators for Coefficients in Seemingly Unrelated Regression SUR Models

The main objective of this study is to estimate the parameters of the SUR models. A new family of biased estimators, called k-class that has the same asymptotic normal distribution as the Aitken generalized least squares (GLS) with the assumption that the covariance matrix is known. The exact bias have been studied and derived