This paper evaluated anti-gastric ulcer and anti-secretory effects of a popular spice Piper cubeba L, (Family: Piperaceae) in rats. The gastric ulcer protective potential of an aqueous suspension of Piper cubeba (PCS) was evaluated against different acute gastric ulcer models in rats induced by pyloric ligation (Shay), hypothermic restraint stress, indomethacin and by necrotizing agents (80% ethanol, 0.2 M NaOH and 25% NaCl) induced gastric mucosal injury. Piper cubeba aqueous suspension (PCS) at the doses 250 and 500 mg/kg body weight administered orally (intraperitoneally in Shay rat model) showed a dose-dependent ulcer protective effects in all the above models. Besides, the PCS offered protection against ethanol-induced depletion of gastric wall mucus (GWM); replenished the reduced non-protein sulfhydryls (NP-SH) concentration and significantly replenished malondialdehyde (MDA) contents in the gastric tissue. Ethanol induced histopathological lesions of the stomach wall characterized by mucosal hemorrhages and edema was reversed by Piper cubeba aqueous suspension treatment. Pretreatment of rats with Piper cubeba provided significant protection of gastric mucosa through its antioxidant capacity and/or by attenuating the offensive and by enhancing the defensive factor.
Problem statement: With the rapid advancement in information technology and communications, computer systems increasingly offer the users the opportunity to interact with information through speech. The interest in speech synthesis and in building voices is increasing. Worldwide, speech synthesizers have been developed for many popular languages English, Spanish and French and many researches and developments have been applied to those languages. Arabic on the other hand, has been given little attention compared to other languages of similar importance and the research in Arabic is still in its infancy. Based on these ideas, we introduced a system to transform Arabic text that was retrieved from a search engine into spoken words. Approach: We designed a textto-speech system in which we used concatenative speech synthesis approach to synthesize Arabic text. The synthesizer was based on artificial neural networks, specifically the unsupervised learning paradigm. Different sizes of speech units had been used to produce spoken utterances, which are words, diphones and triphones. We also built a dictionary of 500 common words of Arabic. The smaller speech units (diphones and triphones) used for synthesis were chosen to achieve unlimited vocabulary of speech, while the word units were used for synthesizing limited set of sentences. Results: The system showed very high accuracy in synthesizing the Arabic text and the output speech was highly intelligible. For the word and diphone unit experiments, we could reach an accuracy of 99% while for the triphone units we reached an accuracy of 86.5%. Conclusion: An Arabic text-tospeech synthesizer was built with the ability to produce unlimited number of words with high quality voice.
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