Immature rat seminal vesicles show histomorphological and ultrastructural alterations following treatment with kisspeptin-10

<p>Abstract</p> <p>Background</p> <p>Degenerative effects of critical regulators of reproduction, the kisspeptin peptides, on cellular aspects of sexually immature male gonads are known but similar information on accessory sex glands remain elusive.</p> <p>Methods</p> <p>Prepubertal laboratory rats were injected kisspeptin-10 at three different dosage concentrations (10 pg, 1 ng and 1 microgram) for a period of continuous 12 days at the rate of two doses per day. Control rats were maintained in parallel. The day following the end of the experimental period, seminal vesicles were removed and processed for light and electron microscopic examination using the standard methods. DNA damage was estimated by DNA ladder assay and DNA fragmentation assay.</p> <p>Results</p> <p>The results demonstrated cellular degeneration. Epithelial cell height of seminal vesicles decreased significantly at all doses (<it>P </it>< 0.05). Marked decrease in epithelial folds was readily noticeable, while the lumen was dilated. Ultrastructural changes were characterized by dilatation of endoplasmic reticulum and Golgi complex, heterochromatization of nuclei, invagination of nuclear membranes and a decreased number of secretory granules. Percent DNA damage to the seminal vesicle was 19.54 +/- 1.98, 38.06 +/- 2.09 and 58.18 +/- 2.59 at 10 pg, 1 ng and 1 microgram doses respectively.</p> <p>Conclusion</p> <p>The study reveals that continuous administration of kisspeptin does not lead to an early maturation but instead severe degeneration of sexually immature seminal vesicles.</p

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm