Osteoarthritis, which typically arises from aging, traumatic injury, or obesity, is the most common form of arthritis, which usually leads to malfunction of the joints and requires medical interventions due to the poor self-healing capacity of articular cartilage. However, currently used medical treatment modalities have reported, at least in part, disappointing and frustrating results for patients with osteoarthritis. Recent progress in the design and fabrication of tissue-engineered microscale/nanoscale platforms, which arises from the convergence of stem cell research and nanotechnology methods, has shown promising results in the administration of new and efficient options for treating osteochondral lesions. This paper presents an overview of the recent advances in osteochondral tissue engineering resulting from the application of micro- and nanotechnology approaches in the structure of biomaterials, including biological and microscale/nanoscale topographical cues, microspheres, nanoparticles, nanofibers, and nanotubes.

Background: Protein pharmaceuticals routinely display a series of intrinsic physicochemical instability problems during their production and administration that can unfavorably affect their therapeutic effectiveness. Glycoengineering is one of the most desirable techniques to improve the attributes of therapeutic proteins. One aspect of glycoengineering is the rational manipulation of the peptide backbone to introduce new N-glycosylation consensus sequences (Asn-X-Ser/Thr, where X is any amino acid except proline). Methods: In this work, the amino acid sequence of human chorionic gonadotropin (hCG) was analyzed to identify the suitable positions in order to create new N-glycosylation sites. This survey led to the detection of 46 potential N-glycosylation sites. The N-glycosylation probability of the all potential positions was measured with the NetNGlyc 1.0 server. After theoretical reviews and the removal of unsuitable positions, the five acceptable ones were selected for more analyses. Then, three-dimensional (3D) structures of the selected analogs were generated and evaluated by SPDBV software. The molecular stability and flexibility profile of five designed analogs were examined using molecular dynamics (MD) simulations. Results: Finally, three analogs with one additional N-glycosylation site (V68T, V79N and R67N) were proposed as the qualified analogs that could be glycosylated at the new sites. Conclusion: According to the results of this study, further experimental investigations could be guided on the three analogs. Therefore, our computational strategy can be a valuable method due to the reduction in the number of the expensive, tiresome and time-consuming experimental studies of hCG analogs.

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Semantic segmentation is a process of classifying each pixel in the image. Due to its advantages, semantic segmentation is used in many tasks such as cancer detection, robot-assisted surgery, satellite image analysis, self-driving car control, etc. In this process, accuracy and efficiency are the two crucial goals for this purpose, and there are several state-of-theart neural networks. In each method, by employing different techniques, new solutions have been presented for increasing efficiency, accuracy, and reducing the costs. The diversity of the implemented approaches for semantic segmentation makes it difficult for researches to achieve a comprehensive view of the field. In this paper, an abstraction model for the task of semantic segmentation is offered to offer a comprehensive view. The proposed framework consists of four general blocks that cover the majority of methods that have been proposed for semantic segmentation. We also compare different approaches and consider the importance of each part in the overall performance of a technique.

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Saliency detection is one of the most challenging problems in the fields of image analysis and computer vision. Many approaches propose different architectures based on the psychological and biological properties of the human visual attention system. However, there is not still an abstract framework, which summarized the existed methods. In this paper, we offered a general framework for saliency models, which consists of five main steps: pre-processing, feature extraction, saliency map generation, saliency map combination, and post-processing. Also, we study different saliency models containing each level and compare their performance together. This framework helps researchers to have a comprehensive view of studying new methods.