The mechanistic target of rapamycin (mTOR) is a master regulator of cell growth, proliferation, and metabolism by integrating various environmental inputs including growth factors, nutrients, and energy, among others. mTOR signaling has been demonstrated to control almost all fundamental cellular processes, such as nucleotide, protein and lipid synthesis, autophagy, and apoptosis. Over the past fifteen years, mapping the network of the mTOR pathway has dramatically advanced our understanding of its upstream and downstream signaling. Dysregulation of the mTOR pathway is frequently associated with a variety of human diseases, such as cancers, metabolic diseases, and cardiovascular and neurodegenerative disorders. Besides genetic alterations, aberrancies in post-translational modifications (PTMs) of the mTOR components are the major causes of the aberrant mTOR signaling in a number of pathologies. In this review, we summarize current understanding of PTMs-mediated regulation of mTOR signaling, and also update the progress on targeting the mTOR pathway and PTM-related enzymes for treatment of human diseases.Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.The gene coding for the telomerase reverse transcriptase (TERT) is essential for the maintenance of telomeres. Previously we described the presence of three TERT paralogs in the allotetraploid plant Nicotiana tabacum, while a single TERT copy was identified in the paleopolyploid model plant Arabidopsis thaliana. Here we examine the presence, origin and functional status of TERT variants in allotetraploid Nicotiana species of diverse evolutionary ages and their parental genome donors, as well as in other diploid and polyploid plant species. A combination of experimental and in silico bottom-up analyses of TERT gene copies in Nicotiana polyploids revealed various patterns of retention or loss of parental TERT variants and divergence in their functions. RT-qPCR results confirmed the expression of all the identified TERT variants. In representative plant and green algal genomes, our synteny analyses show that their TERT genes were located in a conserved locus that became advantageous after the divergence of eudicots, and the gene was later translocated in several plant groups. In various diploid and polyploid species, translocation of TERT became fixed in target loci that show ancient synapomorphy.Consumer interest in foods with enhanced nutritional quality has increased in recent years. The nutritional and bioactive characterization of fruits and their byproducts, as well as their use in the formulation of new food products, is advisable, contributing to decrease the global concerns related to food waste and food security. Moreover, the compounds present in these raw materials and the study of their biological properties can promote health and help to prevent some chronic diseases. Opuntia ficus-indica (L.) Mill.  https://www.selleckchem.com/products/Carboplatin.html  (prickly pear) is a plant that grows wild in the arid and semi-arid regions of the world, being a food source for ones and a potential for others, but not properly valued. This paper carries out an exhaustive review of the scientific literature on the nutritional composition and bioactive compounds of prickly pear and its constituents, as well as its main biological activities and applications. It is a good source of dietary fiber, vitamins and bioactive compounds. Many of its natural compounds have interesting biological activities such as anti-inflammatory, hypoglycemic and antimicrobial. The antioxidant power of prickly pear makes it a good candidate as an ingredient of new food products with fascinating properties for health promotion and/or to be used as natural extracts for food, pharmaceutic or cosmetic applications. In addition, it could be a key player in food security in many arid and semi-arid regions of the world, where there are often no more plants.Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.