Mahmoud Abd El Hafez M. Adam

Introduction: Varicoceles are present in about 30-50% of infertile men. However, the histological changes in the testes
are not well-defined. Also, Resveratrol (RES) is a powerful antioxidant used recently in the treatment of infertility with
other causes.
Aim of the Study: So, this study aimed to elucidate the histological and immunohistochemical changes of both testes
after left experimental varicocele and to evaluate the possible protective role of RES.
Materials and Methods: This study included forty adult male albino rats divided into four equal groups: Control group
(I); sham-operated group (II); left varicocele group (III) and left varicolcele+RES group (IV). Varicocele was induced
by partial ligation of the left renal vein. RES was orally administered to group IV in a dose of 20 mg/kg body weight
every day for five weeks. At the end, all animals were sacrificed and testes were excised. Paraffin sections were prepared
and stained with H&E, PAS reaction and immunohistochemical staining for proliferating cell nuclear antigen (PCNA).
PCNA-labeling index was calculated to assess spermatogenesis.
Results: Left experimental varicocele was shown to affect both ipsi- and contra-lateral testes. The changes in the
ipsilateral testes were in the form of irregular degenerated seminefirous tubules with numerous blood vessels and
thickened basement membranes. These changes were present in some tubules in the contralateral testes. PCNA-LI
values were significantly lower than the control and sham-operated groups. Treatment with RES proved to improve
these changes.
Conclusion: Left experimental varicocele has deleterious effects on the structure of both testes and supplementation
with RES in cases of infertility with varicocele may have a protective effect.

This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.

Hypercalcemia is common in patients with advanced head and neck squamous cell carcinoma (HNSCC). In this study, hypercalcemia was detected in 46 (51%) of 90 eligible patients with HNSCC over 4 years. Compared with nonhypercalcemics, hypercalcemics were more likely to be referred to palliative care, while they were inpatients (P = 0.004). During the last 3 months of follow-up, hypercalcemic patients were more likely than nonhypercalcemics to be hospitalized for ≥ 14 days (P = 0.01) and to visit the emergency room more than once (P = 0.04). The median survival from the first hypercalcemic episode was 74 days (95% CI, 0.234). With data calculated from the date of referral to palliative care, hypercalcemics had a shorter survival than did nonhypercalcemics (43 vs 128 days, respectively; P = 0.046). Early detection and management of hypercalcemia in patients with HNSCC may improve the chance of preventing distressing symptoms and reducing unnecessary frequent emergency room visits and lengthy hospitalization.

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The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic
Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune
systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this re-
search, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algo-
rithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which
is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so
that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the well-
known Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance,
with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable.
The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two
other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid
algorithm overcomes also GAs and AIS and reached the convergence point before them.

Keywords: genetic algorithms, artificial immune system, fuzzy logic, breast cancer diagnosis, memory acquisition.

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic
Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune
systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this re-
search, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algo-
rithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which
is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so
that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the well-
known Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance,
with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable.
The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two
other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid
algorithm overcomes also GAs and AIS and reached the convergence point before them.

Keywords: genetic algorithms, artificial immune system, fuzzy logic, breast cancer diagnosis, memory acquisition.

The nature of playing video streams over a network in a real time requires that the transmitted frames are sent in a bounded delay. Also, video frames need to be displayed at a certain rate; therefore, sending and receiving encrypted packets must be sent in a certain amount of time in order to utilize the admissible delay. There are two main components for securing multimedia transmissions. Key management protocols such as transport layer security (TLS) protocol is used as cryptographic protocol that provides security and data integrity for communications over IP networks. Advanced encryption standard (AES) is used as a symmetric block cipher, that can encrypt (encipher) and decrypt (decipher) video frames. The real-time transport protocol (RTP) protocol is used as a standardized packet format for delivering video over the IP Networks. This makes secure video encryption feasible for real-time applications without any extra dedicated hard-ware. In this paper; we studied the impact of frame rate on a secured real time transmission of video over IP networks. Through this paper, we will try to find the most suitable frame rate in order to achieve better data rate and fewer frame and packet loss. This is done via java as a programming language for implementation.