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Male infertility is often associated with a decreased sperm count. Pygo2 gene is expressed in the elongating spermatid when chromatin remodeling occurs, thus it is possible that impairment of Pygo2 function could lead to spermatogenic arrest, reduction of sperm count and subsequent infertility. The aim of this study was to detect mutations in Pygo2 that lead to idiopathic oligospermia and azoospermia in human. DNA was isolated from venous blood from 77 fertile and 195 idiopathic oligospermic or azoospermic men. PCR-sequencing analysis was performed for the 3 coding regions of Pygo2. Non-synonymous single nucleotide polymorphisms (SNPs) were detected and analyzed using SIFT, Polyphen-2 and Mutation Taster software to determine possible changes in protein structure that could affect phenotype. Of the 195 patients analyzed, sufficient gene sequencing was accomplished for 178 men (30 mild or moderate oligospermic, 57 severe oligospermic and 91 azoospermic men). Three previously reported non-synonymous SNPs were identified in azoospermic and severe oligospermic patients and not in mild and moderate oligozoopermic or normozoospermic men. SNP rs61758740 (M141I) causes the replacement of a hydrophobic amino acid with another hydrophobic amino acid, rs61758741 (K261E) causes the replacement of a basic amino acid with an acidic amino acid and rs141722381 (N261I) causes the replacement of a hydrophilic amino acid with another hydrophobic amino acid. The data predicted by three different software programs showed that SNP rs141722381 results in the damage of tertiary protein structure and thus could be involved in relevant diseases. The study demonstrates that SNPs in the coding region of Pygo2 gene may be one of the causative factors in idiopathic oligospermia and azoospermia, resulting in male infertility.

With differential games and classical pursuit-evasion problems as the main focus, this article aims to trace the historical development of group pursuit-evasion differential games. By addressing large-scale group pursuit-evasion issues from the point of mean-field games, the prospects of applying reinforcement learning techniques are elucidated. It proposes exploring solutions to inverse pursuit-evasion differential games, suitable for scenarios such as underwater autonomous vessels, terrestrial robots, and swarms of unmanned aerial vehicles. Diverging from other review papers, it devotes significant attention to the distinctive academic schools of thought in Russia and the former Soviet Union, highlighting their influence in the evolution of this field. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the important branches in the field of machine learning and artificial intelligence, fully cooperative multi- agent deep reinforcement learning effectively combines the expression and decision-making ability of deep reinforcement learning with the distributed cooperation ability of multi-agent system in a general way, which provides an endto- end solution to the model-free sequential decision-making problem in fully cooperative multi-agent system. Firstly, the basic principles of deep reinforcement learning are described, and the development of single agent deep reinforcement learning is summarized from three main directions: value function based, policy gradient based and actor-critic based. Secondly, the main challenges and training framework of multi-agent deep reinforcement learning are analyzed. Then, according to the different ways of realizing the maximum team joint reward, the fully cooperative multi-agent deep reinforcement learning is divided into four categories: independent learning, communication learning, collaborative learning and reward function shaping. Finally, from the perspective of solving practical problems, the future development direction of fully cooperative multi-agent deep reinforcement learning algorithm is prospected. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guilin University of Technology is the property of Journal of Guilin University of Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Hydrobiology is the property of Editorial Department of Journal of Hydrobiology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)