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纤毛虫通过学习来诊断和纠正交配策略中的经典错误综合征。

Ciliates learn to diagnose and correct classical error syndromes in mating strategies.

机构信息

Research and Development Service, Veterans Affairs Greater Los Angeles Healthcare System Los Angeles, CA, USA.

出版信息

Front Microbiol. 2013 Aug 19;4:229. doi: 10.3389/fmicb.2013.00229. eCollection 2013.

Abstract

Preconjugal ciliates learn classical repetition error-correction codes to safeguard mating messages and replies from corruption by "rivals" and local ambient noise. Because individual cells behave as memory channels with Szilárd engine attributes, these coding schemes also might be used to limit, diagnose, and correct mating-signal errors due to noisy intracellular information processing. The present study, therefore, assessed whether heterotrich ciliates effect fault-tolerant signal planning and execution by modifying engine performance, and consequently entropy content of codes, during mock cell-cell communication. Socially meaningful serial vibrations emitted from an ambiguous artificial source initiated ciliate behavioral signaling performances known to advertise mating fitness with varying courtship strategies. Microbes, employing calcium-dependent Hebbian-like decision making, learned to diagnose then correct error syndromes by recursively matching Boltzmann entropies between signal planning and execution stages via "power" or "refrigeration" cycles. All eight serial contraction and reversal strategies incurred errors in entropy magnitude by the execution stage of processing. Absolute errors, however, subtended expected threshold values for single bit-flip errors in three-bit replies, indicating coding schemes protected information content throughout signal production. Ciliate preparedness for vibrations selectively and significantly affected the magnitude and valence of Szilárd engine performance during modal and non-modal strategy corrective cycles. But entropy fidelity for all replies mainly improved across learning trials as refinements in engine efficiency. Fidelity neared maximum levels for only modal signals coded in resilient three-bit repetition error-correction sequences. Together, these findings demonstrate microbes can elevate survival/reproductive success by learning to implement classical fault-tolerant information processing in social contexts.

摘要

预配偶纤毛虫学习经典重复纠错码,以保护交配信息和回复免受“竞争对手”和局部环境噪声的干扰。由于单个细胞表现为具有西拉德引擎属性的记忆通道,这些编码方案也可能用于限制、诊断和纠正由于细胞内信息处理噪声引起的交配信号错误。因此,本研究评估了异毛纤毛虫是否通过改变引擎性能,从而改变代码的熵含量,来实现容错信号规划和执行。在模拟细胞间通信过程中,来自模糊人工源的有意义的串行振动引发了纤毛虫的行为信号表现,这些表现被认为可以通过不同的求偶策略来展示交配适应性。微生物利用依赖钙的赫布式决策来学习诊断和纠正错误综合征,通过在信号规划和执行阶段之间递归匹配玻尔兹曼熵来实现,这种方法称为“功率”或“制冷”循环。所有 8 种串行收缩和反转策略在执行阶段都会产生熵值的误差。然而,绝对误差值都在三比特回复中单个比特翻转错误的预期阈值范围内,这表明编码方案在整个信号产生过程中保护了信息内容。纤毛虫对振动的准备情况选择性地显著影响了模态和非模态策略校正循环中西拉德引擎性能的幅度和极性。但随着引擎效率的不断提高,所有回复的熵保真度在学习过程中都得到了显著改善。只有在弹性三比特重复纠错序列中编码的模态信号才能达到最大的保真度水平。总之,这些发现表明,微生物可以通过学习在社会环境中实现经典的容错信息处理来提高生存/繁殖成功率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/548d/3746415/22c9155075d9/fmicb-04-00229-g0001.jpg

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