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Background: Systemic lupus erythematosus (SLE) is a heterogonous autoimmune disease involving
most immune cells. Studies have revealed a number of cytokine pathways that play important roles in
the disease process. Among these are B- cell activating factor (BAFF), which regulates B-cell maturation,
survival, and function. Objective: To study the association between BAFF promoter polymorphism and
systemic lupus erythematosus (SLE). Methods: Single nucleotide polymorphisms in the BAFF promoter
region; -2841 (T>C), -2701 (T>A), -871 (C>T) were investigated by PCR-RFLP genotyping in fifty Egyptian
SLE patients and thirty normal controls. Results: The frequency of mutant alleles of both -871C>T
and -2701 T>A was higher among SLE patients than controls (p-value <0.001 and 0.000 respectively).
There was a highly significant relationship between -871 C>T polymorphism and SLE (P<0.001), with the
sensitivity and the specificity of the test being 100 %, and 70%, respectively. Patients expressing the -2701
T>A allele were seven times more prone to SLE than those with the T/T wild genotype (sensitivity of the
test = 78%, specificity = 66.7%, odds ratio = 7.09, C.I at 95% = 2.29-22.64). Conclusion: Polymorphisms
in the regulatory region of the BAFF gene do contribute to the susceptibility to SLE in Egyptian patients,
which indicates BAFF as a potential therapeutic target.

The partially linear model (PLM) is one of semiparametric regression models; since it has both parametric (more than one) and nonparametric (only one) components in the same model, so this model is more flexible than the linear regression models containing only parametric components. In the literature, there are several estimators are proposed for this model; where the main difference between these estimators is the estimation method used to estimate the nonparametric component, since the parametric component is estimated by least squares method mostly. The Speckman estimator is one of the commonly used for estimating the parameters of the PLM, this estimator based on kernel smoothing approach to estimate nonparametric component in the model. According to the papers in nonparametric regression, in general, the spline smoothing approach is more efficient than kernel smoothing approach. Therefore, we suggested, in this paper, using the basis spline (B-spline) smoothing approach to estimate nonparametric component in the model instead of the kernel smoothing approach. To study the performance of the new estimator and compare it with other estimators, we conducted a Monte Carlo simulation study. The results of our simulation study confirmed that the proposed estimator was the best, because it has the lowest mean squared error.

Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.