聊聊Sci
淼淼Elva聊聊Science和Nature！
| Publishes | Daily | Episodes | 350 | Founded | 6 months ago |
|---|---|---|---|---|---|
| Language |  Vietnamese | Number of Listeners | Category | Science |
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这项研究介绍了一种名为 PhenoProfiler 的创新深度学习框架，旨在通过端到端的方式从高通量多通道细胞图像中提取药物诱导的表型特征。该模型整合了梯度编码器、Transformer 以及融合分类、回归和对比学习的多目标学习模块，有效克服了传统方法流程复杂且计算量大的局限。通过对数百万张图像的验证，该框架在生物匹配精度和鲁棒性上均显著优于现有技术，并能精准识别药物的作用机制。此外，研究提出的表型校正策略显著消除了批次效应，使生物信号更加清晰可辨。作为一种可扩展且具有解释性的工具，该系统为 A... more
这项研究通过分析果蝇与小鼠的大规模电子显微镜连接组数据，揭示了神经元中线粒体的组织规律。研究发现，线粒体的形态特征具有极强的细胞类型特异性，其精确程度足以作为识别神经元及其神经递质类型的“生物指纹”。在空间分布上，线粒体以2-3微米的精度精确定位，其位置受突触分布和神经元分支结构的严格调控。此外，线粒体的密度和覆盖率与脑区活动强度以及特定的突触连接目标密切相关，尤其在负责学习记忆的蘑菇体中表现显著。这些发现表明，线粒体并非随机分布，而是作为电路嵌入式细胞器，通过其空间与形态的精准组织支撑大脑的功... more
文章主要探讨了机械靶点雷帕霉素（mTOR）激酶及其复合物mTORC2的底物识别机制。mTORC2在调节细胞生长、代谢和增殖中起着关键作用，其功能失调与衰老、糖尿病和癌症等多种疾病相关。研究人员通过使用半合成底物，如Akt-Torin偶联物，并结合低温电子显微镜（cryo-EM）和综合建模，成功解析了mTORC2与底物Akt的复合物结构。他们的发现揭示了mTORC2不像其互斥复合物mTORC1那样识别线性序列，而是通过长距离对接识别Akt的三维结构，涉及mSin1的CRIM结构域和N-moorin... more
这篇研究文章探讨了进化过程中生物组织层面之间不可避免的冲突，特别是基因、细胞和种群等层面的冲突。作者们使用质粒：细菌宿主中普遍存在的自复制遗传元件作为理想的模型系统，来研究跨尺度冲突的动态，因为质粒在宿主细胞内部和之间都会进行竞争。研究人员开发了一种基于合成二聚体分裂的新方法，并结合微流控“母机”设备，首次成功地隔离并定量测量了质粒在细胞内部的竞争动态。实验结果揭示了转录活性会降低质粒的细胞内适应度，且质粒的转录和翻译活动共同决定了细胞内和细胞间选择的权衡，突显了遗传优势在塑造质粒种群动态中的复... more
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- 我最喜欢的播客之一，谢谢淼淼Elva
One of my favorite podcasts, thanks, 淼淼Elva
Apple Podcasts5TylerZou
China2 months ago - AI生成播客
选题挺好，真人聊就好了
Apple Podcasts1Yuantai SunChina3 months ago
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Apple Podcasts | #55 | China/Science |
Apple Podcasts | #124 |  Taiwan/Science |
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| Listeners per Episode | Gender Skew | Location | |||
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