Expression of Chitinase and shRNA Gene Exhibits Resistance to Fungi and Virus.

With the increasing global population, saving crops from diseases caused by different kinds of bacteria, fungi, viruses, and nematodes is essential. Potato is affected by various diseases, destroying many crops in the field and storage. In this study, we developed potato lines resistant to fungi and viruses, Potato Virus X (PVX) and Potato Virus Y (PVY), by inoculating chitinase for fungi and shRNA designed against the mRNA of the coat protein of PVX and PVY, respectively. The construct was developed using the pCAMBIA2301 vector and transformed into AGB-R (red skin) potato cultivar using Agrobacterium tumefaciens. The crude protein extract of the transgenic potato plant inhibited the growth of Fusarium oxysporum from ~13 to 63%. The detached leaf assay of the transgenic line (SP-21) showed decreased necrotic spots compared to the non-transgenic control when challenged with Fusarium oxysporum. The transgenic line, SP-21, showed maximum knockdown when challenged with PVX and PVY, i.e., 89 and 86%, while transgenic line SP-148 showed 68 and 70% knockdown in the PVX- and PVY-challenged conditions, respectively. It is concluded from this study that the developed transgenic potato cultivar AGB-R showed resistance against fungi and viruses (PVX and PVY).

Neural minimization methods (NMM) for solving variable order fractional delay differential equations (FDDEs) with simulated annealing (SA).

To enrich any model and its dynamics introduction of delay is useful, that models a precise description of real-life phenomena. Differential equations in which current time derivatives count on the solution and its derivatives at a prior time are known as delay differential equations (DDEs). In this study, we are introducing new techniques for finding the numerical solution of fractional delay differential equations (FDDEs) based on the application of neural minimization (NM) by utilizing Chebyshev simulated annealing neural network (ChSANN) and Legendre simulated annealing neural network (LSANN). The main purpose of using Chebyshev and Legendre polynomials, along with simulated annealing (SA), is to reduce mean square error (MSE) that leads to more accurate numerical approximations. This study provides the application of ChSANN and LSANN for solving DDEs and FDDEs. Proposed schemes can be effortlessly executed by using Mathematica or MATLAB software to get explicit solutions. Computational outcomes are depicted, for various numerical experiments, numerically and graphically with error analysis to demonstrate the accuracy and efficiency of the methods.

Pattern of gynaecological malignancies in south western region of Pakistan: An overview of 12 years.

Gynaecological malignancies contribute significantly to cancer burden and have a higher rate of mortality and morbidity. The aim of this retrospective study was to determine the pattern of gynaecological malignancies identified between January, 2000 and December, 2011, at the Centre for Nuclear Medicine and Radiotherapy (CENAR). At CENAR 5,072 female patients were registered with different malignancies, of which 632 cases were gynaecological malignancies. Ovarian cancer (47%) was the most common gynaecological malignancy, followed by cervical cancer (29%), uterine cancer (14%), vulvar and vaginal cancer (6%), and gestational trophoblastic neoplasm (4%). Of the ovarian cancer cases, 72.5% had epithelial while 26.5% had non-epithelial cancer. Squamous cell carcinoma was 75.9% in cervix and 87.8% in vulva and vagina while endometrial carcinoma (75.9%) was more frequent in uterus. For gestational trophoblastic neoplasm, 69.2% of patients had choriocarcinoma. Ovarian cancer was the most common type for the age range of 50-59 years. In the case of cervical and gestational trophoblastic neoplasm the majority of patients presented at the ages of 40-49 and 30-39 years while uterus, vulvar and vaginal tumor presented in the elderly (>60 years). Thus, ovarian cancer is the leading gynecological malignancy in Pakistan.

Detection of BCR/ABL Fusion Gene by Hematological and Cytogenetical Analysis in Chronic Myeloid Leukemia Patients in Quetta, Pakistan.

BACKGROUND: Chronic myeloid leukemia (CML) is a myeloproliferative disorder of pluripotent stem cells, caused by reciprocal translocation between the long arms of chromosomes 9 and 22, t(9;22)(q34;q11), known as the Philadelphia chromosome. MATERIALS AND METHODS: A total of 51 CML patients were recruited in this study. Complete blood counts of all CML patients were performed to find out their total leukocytes, hemoglobin and platelets. FISH was performed for the detection of BCR-ABL fusion and cryptogenic tests using bone marrow samples were performed for the conformation of Ph (9;22)(q34;q11) and variant translocation mechanisms. RESULTS: In cytogenetic analysis we observed that out of 51 CML patients 40 (88.9%) were Ph positive and 4 (8.88%) had Ph negative chromosomes. Mean values of WBC 134.5 103/µl, hemoglobin 10.44 mg/dl, and platelets 288.6 103/µl were observed in this study. CONCLUSIONS: In this study, Ph positive translocation between chromosome (9:22)(q34;q11) were observed in 40 (88.9%) CML patients.

A streamlined artificial variable free version of simplex method.

This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method. For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP. This method follows the same pivoting sequence as of simplex phase 1 without showing any explicit description of artificial variables which also makes it space efficient. Later in this paper, a dual version of the new method has also been presented which provides a way to easily implement the phase 1 of traditional dual simplex method. For a problem having an initial basis which is both primal and dual infeasible, our methods provide full freedom to the user, that whether to start with primal artificial free version or dual artificial free version without making any reformulation to the LP structure. Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.

A space efficient flexible pivot selection approach to evaluate determinant and inverse of a matrix.

This paper presents new simple approaches for evaluating determinant and inverse of a matrix. The choice of pivot selection has been kept arbitrary thus they reduce the error while solving an ill conditioned system. Computation of determinant of a matrix has been made more efficient by saving unnecessary data storage and also by reducing the order of the matrix at each iteration, while dictionary notation [1] has been incorporated for computing the matrix inverse thereby saving unnecessary calculations. These algorithms are highly class room oriented, easy to use and implemented by students. By taking the advantage of flexibility in pivot selection, one may easily avoid development of the fractions by most. Unlike the matrix inversion method [2] and [3], the presented algorithms obviate the use of permutations and inverse permutations.