Jev 细胞图谱：Opus 5.5 标注细胞器，Jev 做类型标签层
一个细胞图谱流程用 unet3d 分割体素、Opus 5.5 标注细胞器、Jev 作为带类型的标签层，量化 8 个区室的体积占比并实时记录膜电位等指标。
WHOEVER LEAKED THIS JEV CELL ATLAS SHOULD BE RUNNING THE LAB I thought quantifying a cell meant waiting three months for a grad student to count mitochondria under a microscope. Apparently not. This atlas's setup is much more interesting: → SEGMENT runs unet3d over cell voxels at k=1 and asserts dice above 0.94, no organelle counted that didn't pass the segment threshold first → ANNOTATE calls Opus 5.5 against the segmented volume with Jev as the typed label layer, each organelle gets a size, membrane class and typical dimension → QUANTIFY computes volume fractions across 8 compartments (Cytosol 55.5, Mitochondria 22.9, Rough ER 9.1, Nucleus 5.9, Golgi 2.9, Lyso and Peroxi and Endo 1.0 each), no averaging across cells → MEASURE logs membrane potential (-72.4 mV), ATP synthesis (35.3 mol/s) and protein synthesis (11,655 aa/s) in real time, 1,423 samples per second, no snapshot bias → EVENT LOG tracks every vesicle, lysosome and mitochondrion detected with its diameter, flags 7 organelles whose dimension drifted past the known baseline this scan so you get: Voxels segmented. Organelles typed. Fractions quantified. Rates measured. the interesting part isn't annotating faster. it's giving every organelle a type the atlas can trust the next cell on. Instead of a grad student counting Rough ER on 40 cells with ImageJ, you point Opus 5.5 at a mammalian cell, assert dice above 0.94, and get volume fractions across 8 compartments in one scan. Segment the voxels. Annotate the organelle. Quantify the fraction.
这条是引用着另一条写的：「长文：如何搭好 Jev 驱动的 agent 技术栈」