Rees 2019: nanoparticle uptake per cell

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Design:
- What is the unit of replication? (replication_unit): images or fields
- Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0.346
Ask the scientist: Plate image channel that shows the colonies (colony_channel), Are the colonies brighter or darker than the agar? (colony_polarity), Colony threshold (method name or a number) (colony_threshold), Smallest colony to count (pixels across) (colony_min_diameter), Largest colony to count (pixels across, 0 = no limit) (colony_max_diameter), Smallest roundness of a colony (0 to 1) (colony_min_roundness), Dish rim to leave out (fraction of the dish radius) (rim_margin), Background smoothing for plates (pixels, 0 = none) (colony_background_radius), Split touching colonies (split_touching_colonies), Control condition for the plating efficiency (control_condition), Colocalization threshold method (coloc_threshold_method), Costes randomizations for the p value (0 = no test) (costes_randomizations), Blur spot size for the Costes test (pixels) (psf_size), How the tool finds each cell (cell_mode), Nucleus threshold (method name or a number) (nucleus_threshold), Smallest nucleus (pixels across) (nucleus_min_diameter), Largest nucleus (pixels across, 0 = no limit) (nucleus_max_diameter), Blur before splitting touching nuclei (pixels, 0 = automatic) (nucleus_smoothing), Smallest distance between two nucleus centers (pixels, 0 = automatic) (nucleus_min_distance), Cell threshold (method name or a number) (cell_threshold), Cell growth from the nucleus in expand mode (pixels) (cell_expand), Largest cell growth in membrane mode (pixels, 0 = no limit) (cell_max_growth), Cargo threshold (method name or a number) (cargo_threshold), Blur of the cargo channel before the threshold (pixels) (cargo_smoothing), Smallest cargo object (pixels) (cargo_min_area), Rule that calls a cell positive (positivity_rule), Smallest value of the rule for a positive cell (positivity_min), Leave out cells that touch the image edge (exclude_border_cells).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

{
 "ok": true,
 "summary": "Folder with 3 image files in 1 pattern(s). *.lsm: 3 files, for example 1_BEAS_0.5nM_1h_1.lsm",
 "metrics": {
  "n_files": 3,
  "n_patterns": 1
 },
 "data": {
  "patterns": {
   "*.lsm": {
    "n_files": 3,
    "example": "1_BEAS_0.5nM_1h_1.lsm"
   }
  }
 },
 "outputs": []
}

{
 "ok": true,
 "summary": "2048 by 2048 pixels, 3 channel(s), uint16, pixel size 0.3459 micrometers",
 "metrics": {
  "width": 2048,
  "height": 2048,
  "planes": 1,
  "channels": 3,
  "bit_depth": 16,
  "pixel_size_um": 0.3459440920682663,
  "max_saturated_fraction": 0.000371
 },
 "data": {
  "dtype": "uint16",
  "axes": "CYX",
  "warnings": [],
  "channel_names": "0 is the first channel"
 },
 "table": {
  "columns": [
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  ],
  "n_rows": 3
 },
 "outputs": []
}

- 1_BEAS_0.5nM_1h_1.lsm  25367350
- 1_BEAS_0.5nM_1h_2.lsm  25367350
- 1_BEAS_0.5nM_1h_3.lsm  25367350

{
 "ok": true,
 "summary": "40 of 588 cells positive (6.8 percent, rule: spots 1); 42 cargo objects in cells, 0.071 for each cell; cargo threshold 1.19e+04; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 40,
  "fraction_positive": 0.06802721088435375,
  "n_cargo_objects": 125,
  "n_cargo_in_cells": 42,
  "mean_cargo_per_cell": 0.07142857142857142,
  "mean_cargo_per_positive_cell": 1.05,
  "phagocytic_index": 7.142857142857143,
  "mean_cargo_area_per_cell_px": 3.256802721088435,
  "cargo_threshold": 11903.818359375,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
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   "cargo_objects",
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  "n_rows": 588,
  "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "274 of 588 cells positive (46.6 percent, rule: spots 1); 1135 cargo objects in cells, 1.930 for each cell; cargo threshold 640; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 274,
  "fraction_positive": 0.46598639455782315,
  "n_cargo_objects": 2966,
  "n_cargo_in_cells": 1135,
  "mean_cargo_per_cell": 1.9302721088435375,
  "mean_cargo_per_positive_cell": 4.142335766423358,
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  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
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 "outputs": [
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   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
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   "cargo_area_px",
   "cargo_area_fraction",
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  "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "170 of 588 cells positive (28.9 percent, rule: spots 1); 318 cargo objects in cells, 0.541 for each cell; cargo threshold 1401; 599 nuclei",
 "metrics": {
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  "n_positive": 170,
  "fraction_positive": 0.2891156462585034,
  "n_cargo_objects": 858,
  "n_cargo_in_cells": 318,
  "mean_cargo_per_cell": 0.5408163265306123,
  "mean_cargo_per_positive_cell": 1.8705882352941177,
  "phagocytic_index": 54.08163265306123,
  "mean_cargo_area_per_cell_px": 20.904761904761905,
  "cargo_threshold": 1400.638951431569,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
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   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
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 }
}

Cargo threshold (method name or a number)  fraction_positive  mean_cargo_per_cell  Result
otsu                                       0.06803            0.07143              ok
triangle                                   0.466              1.93                 ok
li                                         0.2891             0.5408               ok

Cargo threshold (method name or a number)  fraction_positive  mean_cargo_per_cell  Result
otsu                                       0.06803            0.07143              ok
triangle                                   0.466              1.93                 ok
li                                         0.2891             0.5408               ok
fraction_positive depends on the choice: 0.06803 with otsu, 0.466 with triangle, 0.2891 with li
mean_cargo_per_cell depends on the choice: 0.07143 with otsu, 1.93 with triangle, 0.5408 with li

{
 "ok": true,
 "summary": "88 of 176 cells positive (50.0 percent, rule: spots 1); 140 cargo objects in cells, 0.795 for each cell; cargo threshold 5794; 220 nuclei",
 "metrics": {
  "n_cells": 176,
  "n_positive": 88,
  "fraction_positive": 0.5,
  "n_cargo_objects": 184,
  "n_cargo_in_cells": 140,
  "mean_cargo_per_cell": 0.7954545454545454,
  "mean_cargo_per_positive_cell": 1.5909090909090908,
  "phagocytic_index": 79.54545454545455,
  "mean_cargo_area_per_cell_px": 53.36931818181818,
  "cargo_threshold": 5794,
  "n_excluded_border": 44,
  "n_excluded_small": 0,
  "n_nuclei": 220,
  "nucleus_threshold": 6553.5,
  "n_nuclei_excluded_size": 59,
  "cell_threshold": 3276.75,
  "mean_cell_area_um2": 1742.2126637045453
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
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 ],
 "table": {
  "columns": [
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   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
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  "rows": [
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  "n_rows": 176,
  "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

- ._1_BEAS_0.5nM_1h_1_cargo_per_cell.png  4096
- ._1_BEAS_0.5nM_1h_1_cargo_per_cell.svg  4096
- ._1_BEAS_0.5nM_1h_1_cells.csv  4096
- ._1_BEAS_0.5nM_1h_1_uptake_overlay.png  4096
- 1_BEAS_0.5nM_1h_1_cargo_per_cell.png  33928
- 1_BEAS_0.5nM_1h_1_cargo_per_cell.svg  9161
- 1_BEAS_0.5nM_1h_1_cells.csv  6618
- 1_BEAS_0.5nM_1h_1_uptake_overlay.png  4936296

{
 "ok": true,
 "summary": "1600 by 1748 pixels, 3 channel(s), uint8, pixel size not in the file",
 "metrics": {
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  "planes": 1,
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  "pixel_size_um": 0,
  "max_saturated_fraction": 0.08488
 },
 "data": {
  "dtype": "uint8",
  "axes": "YXS",
  "warnings": [
   "Channel 0: 8.20 percent of the pixels are at the maximum value 255. Saturated pixels make intensity sums too low.",
   "Channel 1: 8.12 percent of the pixels are at the maximum value 255. Saturated pixels make intensity sums too low.",
   "Channel 2: 8.49 percent of the pixels are at the maximum value 255. Saturated pixels make intensity sums too low."
  ],
  "channel_names": "0 is the first channel"
 },
 "table": {
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 },
 "outputs": []
}

cell,area_px,cargo_objects,cargo_area_px,cargo_area_fraction,mean_cargo_intensity,call
1,13415,2,33,0.00246,157.386,positive
2,5389,0,0,0.0,52.343,negative
3,9381,0,0,0.0,44.385,negative
4,12123,2,143,0.0118,327.44,positive
5,16667,0,0,0.0,57.783,negative
6,16753,1,64,0.00382,148.386,positive
7,5026,1,27,0.00537,227.316,positive
8,14430,1,139,0.00963,242.035,positive
9,5351,0,0,0.0,122.144,negative
10,14710,1,132,0.00897,237.3,positive
11,7579,0,0,0.0,40.173,negative
12,8922,0,0,0.0,44.161,negative
13,8195,0,0,0.0,60.905,negative
14,36589,1,14,0.00038,55.188,positive
15,18451,0,0,0.0,58.455,negative
16,17315,1,41,0.00237,93.334,positive
17,10607,0,0,0.0,34.415,negative
18,17190,0,0,0.0,54.064,negative
19,15407,2,112,0.00727,206.571,positive
20,5140,0,0,0.0,64.665,negative
21,4902,0,0,0.0,66.462,negative
22,4320,1,32,0.00741,210.992,positive
23,13001,3,40,0.00308,154.988,positive
24,10268,0,0,0.0,42.474,negative
25,13165,0,0,0.0,41.139,negative
26,8687,1,14,0.00161,132.863,positive
27,11387,2,109,0.00957,275.66,positive
28,19843,0,1,5e-05,64.879,negative
29,5816,2,201,0.03456,875.049,positive
30,14422,0,0,0.0,63.749,negative
31,16009,1,54,0.00337,107.743,positive
32,7059,1,64,0.00907,225.155,positive
33,14730,1,184,0.01249,338.478,positive
34,29955,1,19,0.00063,82.537,positive
35,15081,0,0,0.0,44.082,negative
36,14164,1,70,0.00494,116.807,positive
37,33605,3,29,0.00086,163.811,positive
38,7341,0,0,0.0,37.207,negative
39,8042,0,0,0.0,60.828,negative
40,9837,0,0,0.0,46.218,negative
41,11699,0,0,0.0,67.506,negative
42,10028,1,26,0.00259,100.823,positive
43,8603,1,29,0.00337,162.261,positive
44,12494,0,0,0.0,65.598,negative
45,5697,0,0,0.0,46.987,negative
46,12723,1,107,0.00841,211.472,positive
47,14300,1,41,0.00287,96.845,positive
48,11180,3,146,0.01306,374.146,positive
49,8272,0,0,0.0,62.629,negative
50,8953,0,0,0.0,37.419,negative
51,27482,0,0,0.0,51.414,negative
52,9327,1,26,0.00279,113.843,positive
53,22389,2,420,0.01876,601.212,positive
54,7695,0,0,0.0,79.309,negative
55,16243,0,0,0.0,65.645,negative
56,21955,0,0,0.0,52.377,negative
57,11722,4,307,0.02619,694.408,positive
58,17115,1,98,0.00573,140.483,positive
59,11009,0,0,0.0,67.554,negative
60,10842,1,202,0.01863,453.166,positive
61,14296,0,0,0.0,89.31,negative
62,46522,2,187,0.00402,116.237,positive
63,26138,1,22,0.00084,78.917,positive
64,6832,0,0,0.0,73.356,negative
65,8373,0,0,0.0,103.887,negative
66,14272,0,0,0.0,48.316,negative
67,21851,1,120,0.00549,166.153,positive
68,14277,2,161,0.01128,296.184,positive
69,21941,0,0,0.0,35.852,negative
70,7372,0,0,0.0,51.118,negative
71,13154,0,0,0.0,56.372,negative
72,18960,2,118,0.00622,204.555,positive
73,14944,0,0,0.0,42.47,negative
74,11099,0,0,0.0,82.321,negative
75,16741,0,0,0.0,56.589,negative
76,7366,0,0,0.0,133.764,negative
77,12481,1,124,0.00994,266.722,positive
78,13973,2,180,0.01288,380.236,positive
79,25935,2,183,0.00706,234.58,positive
80,23987,3,248,0.01034
... (1000 more characters in the session record)

I could not view the overlay. `inspect_data` failed with a permission error, and `inspect_image` gives only metadata: 1600 × 1748 px, 3-channel uint8 (8-bit), no pixel size, and up to 8.5% saturated pixels in a channel. So I can't answer your four questions from what the image looks like. The per-cell table next to it (`1_BEAS_0.5nM_1h_1_cells.csv`) gives indirect clues only:

1. **Merged or split cells:** I can't confirm this by eye. The table lists 176 cells, and cell areas run from 2,467 px (cell 168) to 52,263 px (cell 111). Several cells are above 30,000 px (cells 14, 37, 62, 111, 123, 135, 162). Several are below 5,000 px (cells 132, 168, 175, 176, among others). That wide spread fits with some merged outlines at the large end and some split or fragment outlines at the small end. The overlay would need to be checked by eye to confirm.
2. **Spots or blobs:** The table suggests mostly small spots. Each cell has 0–4 cargo objects, and total cargo area per cell is at most 420 px (cell 53). One object alone is up to 207 px (cells 93 and 166), so there are no very large blobs. Some cells have only 6–14 px of cargo (cells 99, 14, 26, 120, 129), and these are still called positive at the default rule (at least 1 spot). Those may be noise picked up as cargo.
3. **Cargo outside cells:** The table records only cargo assigned to cells, so I can't tell how much lies outside them. Cell 28 has 1 px of cargo area but 0 objects, which may be an object sitting on a cell edge.
4. **Other problems:** I can't assess these without seeing the image. The 8.5% saturation figure is for the 8-bit overlay PNG, not the raw data.

Someone should open the PNG and check it by eye, especially the large cells (111, 62, 162, 135) and the tiny ones (168, 132, 175, 176).

{
 "ok": true,
 "summary": "3 images (cargo threshold 5794, rule spots 1): 230 of 459 cells positive (pooled 50.1 percent; mean of images 50.8 percent); 353 cargo objects in cells, 0.769 for each cell; 448 cargo objects in all; 566 nuclei; positive of all cells in each image: 1_BEAS_0.5nM_1h_1.lsm 88 of 176, 1_BEAS_0.5nM_1h_2.lsm 80 of 172, 1_BEAS_0.5nM_1h_3.lsm 62 of 111",
 "metrics": {
  "n_images": 3,
  "total_cells": 459,
  "total_positive": 230,
  "pooled_fraction_positive": 0.5010893246187363,
  "mean_fraction_positive": 0.5078933333333333,
  "sd_fraction_positive": 0.04721744310457028,
  "total_cargo_in_cells": 353,
  "mean_cargo_per_cell": 0.7690631808278867,
  "total_cargo_objects": 448,
  "total_nuclei": 566
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-5/uptake_counts.csv",
   "kind": "table",
   "name": "counts for each image"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_2_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_3_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  }
 ],
 "table": {
  "columns": [
   "image",
   "cells",
   "positive",
   "fraction_positive",
   "cargo_in_cells",
   "cargo_per_cell",
   "nuclei"
  ],
  "rows": [
   [
    "1_BEAS_0.5nM_1h_1.lsm",
    176,
    88,
    0.5,
    140,
    0.79545,
    220
   ],
   [
    "1_BEAS_0.5nM_1h_2.lsm",
    172,
    80,
    0.46512,
    110,
    0.63953,
    211
   ],
   [
    "1_BEAS_0.5nM_1h_3.lsm",
    111,
    62,
    0.55856,
    103,
    0.92793,
    135
   ]
  ],
  "n_rows": 3,
  "path": "{work}/count_cell_uptake-5/uptake_counts.csv"
 }
}

image,cells,positive,fraction_positive,cargo_in_cells,cargo_per_cell,nuclei
1_BEAS_0.5nM_1h_1.lsm,176,88,0.5,140,0.79545,220
1_BEAS_0.5nM_1h_2.lsm,172,80,0.46512,110,0.63953,211
1_BEAS_0.5nM_1h_3.lsm,111,62,0.55856,103,0.92793,135

{"items":[{"name":"mean_cargo_per_cell_of_fields","expression":"mean([0.79545, 0.63953, 0.92793])"},{"name":"sd_cargo_per_cell_of_fields","expression":"sd([0.79545, 0.63953, 0.92793])"},{"name":"cargo_outside_cells","expression":"448 - 353"}],"purpose":"Get the field-level mean and spread of vesicles per cell, and the vesicles outside cells."}

mean_cargo_per_cell_of_fields = mean([0.79545, 0.63953, 0.92793]) = 0.7876366667
sd_cargo_per_cell_of_fields = sd([0.79545, 0.63953, 0.92793]) = 0.1443586718
cargo_outside_cells = 448 - 353 = 95

- The answer uses 175 from a comparison run of another option (cargo_threshold), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison.
- 9 numbers in the answer match no logged tool result: 24, 200, 10.64, 4.7, 2467, 52263, 14, 162, 168. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
Fix these problems. Then give the answer again.

import pandas as pd
d = pd.read_csv("{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_1_cells.csv")
print("min area", d.area_px.min(), "max area", d.area_px.max())
print("largest", d.nlargest(4,"area_px")[["cell","area_px"]].values.tolist())
print("smallest", d.nsmallest(4,"area_px")[["cell","area_px"]].values.tolist())
p = d[(d.cargo_objects>=1)&(d.cargo_area_px<15)]
print("positive with cargo area <15 px:", len(p), "cargo area range", p.cargo_area_px.min(), p.cargo_area_px.max())
print("calls", d.call.value_counts().to_dict())

min area 2467 max area 52263
largest [[111, 52263], [62, 46522], [162, 41185], [135, 39280]]
smallest [[168, 2467], [132, 3054], [139, 3595], [176, 3636]]
positive with cargo area <15 px: 5 cargo area range 6 14
calls {'positive': 88, 'negative': 88}

{"items":[{"name":"sd_fraction_positive_percent","expression":"0.04721744310457028 * 100"},{"name":"mean_fraction_positive_percent","expression":"0.5078933333333333 * 100"},{"name":"pooled_fraction_positive_percent","expression":"0.5010893246187363 * 100"}],"purpose":"Change the fraction of positive cells and its spread to percent."}

sd_fraction_positive_percent = 0.04721744310457028 * 100 = 4.72174431
mean_fraction_positive_percent = 0.5078933333333333 * 100 = 50.78933333
pooled_fraction_positive_percent = 0.5010893246187363 * 100 = 50.10893246

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Step results that the answer does not mention, from the record:
- n7 inspect_image: 1600 by 1748 pixels, 3 channel(s), uint8, pixel size not in the file
- n10 run_script: The script ran in {work} and wrote 0 new file(s) to {work}.

Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0.346 · How the tool finds each cell: membrane · Nucleus threshold (method name or a number): 6553.5 · Smallest nucleus (pixels across): 24 · Largest nucleus (pixels across, 0 = no limit): 200 · Blur before splitting touching nuclei (pixels, 0 = automatic): 10.64 · Smallest distance between two nucleus centers (pixels, 0 = automatic): 12 · Cell threshold (method name or a number): 3276.75 · Cell growth from the nucleus in expand mode (pixels): 10 · Largest cell growth in membrane mode (pixels, 0 = no limit): 0 · Cargo threshold (method name or a number): 5794 · Blur of the cargo channel before the threshold (pixels): 1 · Smallest cargo object (pixels): 0 · Rule that calls a cell positive: spots · Smallest value of the rule for a positive cell: 1 · Leave out cells that touch the image edge: true.

Values that are not scored

• A trap value is the result of a wrong method, for example an unpaired test on paired data. A trap value in a main step means that the run used the wrong method. A trap value in a comparison run is correct, because the record keeps the scientist's choice.
• An optional value is a second result of the same method. The run can compute it or not.
• A reference value comes from a different program version or a check run by us. We show it for comparison.

Checks

Review findings

The review recorded 11 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 91 numbers in the answer. 83 numbers match a logged result. 5 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

1 tool call failed. The model then tried again or used another tool. The session above shows each failure.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rees2019-uptake/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rees2019-uptake/bench.yaml.

cuvette bench papers --papers rees2019-uptake --models claude:claude-opus-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. inspect_image (step n1)

   Fiji: File>Open..., then Image>Show Info... and Analyze>Histogram for each channel

   • Image Lab, Imaris, NIS-Elements, ZEN, Harmony: the image properties panel shows the size, bit depth and pixel size

   • File to open

     {data}/rees2019-uptake/fields

   The manual route that the harness recorded

   assay_tools.inspect_image(path="{data}/rees2019-uptake/fields")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

2. inspect_image (step n2)

   Fiji: File>Open..., then Image>Show Info... and Analyze>Histogram for each channel

   • Image Lab, Imaris, NIS-Elements, ZEN, Harmony: the image properties panel shows the size, bit depth and pixel size

   • File to open

     {data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm

   The manual route that the harness recorded

   assay_tools.inspect_image(path="{data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

3. count_cell_uptake (step n6)

   Fiji: split channels; threshold the nuclei (Image>Adjust>Threshold...), Process>Binary>Watershed, Analyze Particles to ROI Manager; enlarge the ROIs (Edit>Selection>Enlarge) or draw cells; threshold the cargo channel; for each cell ROI, Analyze>Analyze Particles inside it to count cargo objects; a cell is positive by your rule

   • CellProfiler: IdentifyPrimaryObjects (nuclei), IdentifySecondaryObjects (cells: Distance-N for expand, Propagation for membrane), IdentifyPrimaryObjects (cargo), RelateObjects (cargo to cells), then FilterObjects or a spreadsheet for the positivity rule
   • Harmony or Columbus: Find Nuclei, Find Cytoplasm, Find Spots in the cargo channel, Calculate Properties (spots per cell), Select Population (positive cells by spot count)
   • Imaris: Cells with Spots in the cargo channel, then filter cells by the spot count
   • Hand count: count all macrophages and the macrophages with red cargo inside in each field
   • Nucleus threshold method or value = 6553.5
   • How cells are drawn = membrane
   • Enlarge (pixels) = 10
   • Largest growth in membrane mode (pixels) = 0
   • Cargo threshold method or value = 5794
   • Positive cell rule = spots
   • Smallest value for a positive cell = 1
   • Warning: If you keep the default Default, you get a different result.
   • Warning: If you keep the default Default, you get a different result.
   • Note: The membrane mode grows cells with a watershed on the membrane image, not the CellProfiler Propagation method, so cell outlines differ. The Fiji route is not tested.

   The manual route that the harness recorded

   assay_tools.count_cell_uptake(path="{data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm", cargo_channel="2", nucleus_channel="0", cell_channel="1", cell_mode="membrane", pattern="*", nucleus_threshold="6553.5", nucleus_min_diameter=24, nucleus_max_diameter=200, split_nuclei=True, nucleus_smoothing=10.64, nucleus_min_distance=12, cell_expand=10, cell_max_growth=0, cell_threshold="3276.75", cell_polarity="bright", cell_min_diameter=0, split_cells=True, cargo_threshold="5794", cargo_smoothing=1, cargo_min_area=0, positivity_rule="spots", positivity_min=1, exclude_border_cells=True, pixel_size=0.346)

   The manual route uses the same method. The note in the route gives the known difference.

4. inspect_image (step n7)

   Fiji: File>Open..., then Image>Show Info... and Analyze>Histogram for each channel

   • Image Lab, Imaris, NIS-Elements, ZEN, Harmony: the image properties panel shows the size, bit depth and pixel size

   • File to open

     {work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_uptake_overlay.png

   The manual route that the harness recorded

   assay_tools.inspect_image(path="{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_uptake_overlay.png")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

5. count_cell_uptake (step n8)

   Fiji: split channels; threshold the nuclei (Image>Adjust>Threshold...), Process>Binary>Watershed, Analyze Particles to ROI Manager; enlarge the ROIs (Edit>Selection>Enlarge) or draw cells; threshold the cargo channel; for each cell ROI, Analyze>Analyze Particles inside it to count cargo objects; a cell is positive by your rule

   • CellProfiler: IdentifyPrimaryObjects (nuclei), IdentifySecondaryObjects (cells: Distance-N for expand, Propagation for membrane), IdentifyPrimaryObjects (cargo), RelateObjects (cargo to cells), then FilterObjects or a spreadsheet for the positivity rule
   • Harmony or Columbus: Find Nuclei, Find Cytoplasm, Find Spots in the cargo channel, Calculate Properties (spots per cell), Select Population (positive cells by spot count)
   • Imaris: Cells with Spots in the cargo channel, then filter cells by the spot count
   • Hand count: count all macrophages and the macrophages with red cargo inside in each field
   • Nucleus threshold method or value = 6553.5
   • How cells are drawn = membrane
   • Enlarge (pixels) = 10
   • Largest growth in membrane mode (pixels) = 0
   • Cargo threshold method or value = 5794
   • Positive cell rule = spots
   • Smallest value for a positive cell = 1
   • Warning: If you keep the default Default, you get a different result.
   • Warning: If you keep the default Default, you get a different result.
   • Note: The membrane mode grows cells with a watershed on the membrane image, not the CellProfiler Propagation method, so cell outlines differ. The Fiji route is not tested.

   The manual route that the harness recorded

   assay_tools.count_cell_uptake(path="{data}/rees2019-uptake/fields", cargo_channel="2", nucleus_channel="0", cell_channel="1", cell_mode="membrane", pattern="*.lsm", nucleus_threshold="6553.5", nucleus_min_diameter=24, nucleus_max_diameter=200, split_nuclei=True, nucleus_smoothing=10.64, nucleus_min_distance=12, cell_expand=10, cell_max_growth=0, cell_threshold="3276.75", cell_polarity="bright", cell_min_diameter=0, split_cells=True, cargo_threshold="5794", cargo_smoothing=1, cargo_min_area=0, positivity_rule="spots", positivity_min=1, exclude_border_cells=True, pixel_size=0.346)

   The manual route uses the same method. The note in the route gives the known difference.

6. calculate (step n9)

   Run the tool "calculate" with these settings: {"items":[{"name":"mean_cargo_per_cell_of_fields","expression":"mean([0.79545, 0.63953, 0.92793])"},{"name":"sd_cargo_per_cell_of_fields","expression":"sd([0.79545, 0.63953, 0.92793])"},{"name":"cargo_outside_cells","expression":"448 - 353"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

7. run_script (step n10)

   Run the Python code in {work}/script-1/script.py

   • Code only: this step has no route in the program menus. Run it with the script or flow export.

   The program has no menu route for this step. To repeat it, run the code.

8. calculate (step n11)

   Run the tool "calculate" with these settings: {"items":[{"name":"sd_fraction_positive_percent","expression":"0.04721744310457028 * 100"},{"name":"mean_fraction_positive_percent","expression":"0.5078933333333333 * 100"},{"name":"pooled_fraction_positive_percent","expression":"0.5010893246187363 * 100"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

Figure

Run facts

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Design:
- What is the unit of replication? (replication_unit): images or fields
- Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0.346
Ask the scientist: Plate image channel that shows the colonies (colony_channel), Are the colonies brighter or darker than the agar? (colony_polarity), Colony threshold (method name or a number) (colony_threshold), Smallest colony to count (pixels across) (colony_min_diameter), Largest colony to count (pixels across, 0 = no limit) (colony_max_diameter), Smallest roundness of a colony (0 to 1) (colony_min_roundness), Dish rim to leave out (fraction of the dish radius) (rim_margin), Background smoothing for plates (pixels, 0 = none) (colony_background_radius), Split touching colonies (split_touching_colonies), Control condition for the plating efficiency (control_condition), Colocalization threshold method (coloc_threshold_method), Costes randomizations for the p value (0 = no test) (costes_randomizations), Blur spot size for the Costes test (pixels) (psf_size), How the tool finds each cell (cell_mode), Nucleus threshold (method name or a number) (nucleus_threshold), Smallest nucleus (pixels across) (nucleus_min_diameter), Largest nucleus (pixels across, 0 = no limit) (nucleus_max_diameter), Blur before splitting touching nuclei (pixels, 0 = automatic) (nucleus_smoothing), Smallest distance between two nucleus centers (pixels, 0 = automatic) (nucleus_min_distance), Cell threshold (method name or a number) (cell_threshold), Cell growth from the nucleus in expand mode (pixels) (cell_expand), Largest cell growth in membrane mode (pixels, 0 = no limit) (cell_max_growth), Cargo threshold (method name or a number) (cargo_threshold), Blur of the cargo channel before the threshold (pixels) (cargo_smoothing), Smallest cargo object (pixels) (cargo_min_area), Rule that calls a cell positive (positivity_rule), Smallest value of the rule for a positive cell (positivity_min), Leave out cells that touch the image edge (exclude_border_cells).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

{
 "ok": true,
 "summary": "Folder with 3 image files in 1 pattern(s). *.lsm: 3 files, for example 1_BEAS_0.5nM_1h_1.lsm",
 "metrics": {
  "n_files": 3,
  "n_patterns": 1
 },
 "data": {
  "patterns": {
   "*.lsm": {
    "n_files": 3,
    "example": "1_BEAS_0.5nM_1h_1.lsm"
   }
  }
 },
 "outputs": []
}

- 1_BEAS_0.5nM_1h_1.lsm  25367350
- 1_BEAS_0.5nM_1h_2.lsm  25367350
- 1_BEAS_0.5nM_1h_3.lsm  25367350

{
 "ok": true,
 "summary": "2048 by 2048 pixels, 3 channel(s), uint16, pixel size 0.3459 micrometers",
 "metrics": {
  "width": 2048,
  "height": 2048,
  "planes": 1,
  "channels": 3,
  "bit_depth": 16,
  "pixel_size_um": 0.3459440920682663,
  "max_saturated_fraction": 0.000371
 },
 "data": {
  "dtype": "uint16",
  "axes": "CYX",
  "warnings": [],
  "channel_names": "0 is the first channel"
 },
 "table": {
  "columns": [
   "channel",
   "min",
   "max",
   "mean",
   "saturated_fraction"
  ],
  "rows": [
   [
    0,
    0,
    65535,
    2375.2079,
    0.000175
   ],
   [
    1,
    0,
    65535,
    7008.5306,
    0.000371
   ],
   [
    2,
    0,
    65535,
    123.9054,
    0.000013
   ]
  ],
  "n_rows": 3
 },
 "outputs": []
}

{
 "ok": true,
 "summary": "40 of 588 cells positive (6.8 percent, rule: spots 1); 42 cargo objects in cells, 0.071 for each cell; cargo threshold 1.19e+04; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 40,
  "fraction_positive": 0.06802721088435375,
  "n_cargo_objects": 125,
  "n_cargo_in_cells": 42,
  "mean_cargo_per_cell": 0.07142857142857142,
  "mean_cargo_per_positive_cell": 1.05,
  "phagocytic_index": 7.142857142857143,
  "mean_cargo_area_per_cell_px": 3.256802721088435,
  "cargo_threshold": 11903.818359375,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
    1,
    1569,
    0,
    0,
    0,
    99.112,
    "negative"
   ],
   [
    2,
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    0,
    0,
    0,
    173.802,
    "negative"
   ],
   [
    3,
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    0,
    0,
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    "negative"
   ],
   [
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    2518,
    0,
    0,
    0,
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    "negative"
   ],
   [
    5,
    2974,
    1,
    7,
    0.00235,
    227.984,
    "positive"
   ],
   [
    6,
    1691,
    0,
    0,
    0,
    48.734,
    "negative"
   ],
   [
    7,
    1316,
    0,
    0,
    0,
    59.531,
    "negative"
   ],
   [
    8,
    653,
    0,
    0,
    0,
    59.904,
    "negative"
   ],
   [
    9,
    2247,
    0,
    0,
    0,
    72.08,
    "negative"
   ],
   [
    10,
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    0,
    0,
    0,
    50.165,
    "negative"
   ],
   [
    11,
    249,
    0,
    0,
    0,
    61,
    "negative"
   ],
   [
    12,
    94,
    0,
    0,
    0,
    42.404,
    "negative"
   ]
  ],
  "n_rows": 588,
  "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "274 of 588 cells positive (46.6 percent, rule: spots 1); 1135 cargo objects in cells, 1.930 for each cell; cargo threshold 640; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 274,
  "fraction_positive": 0.46598639455782315,
  "n_cargo_objects": 2966,
  "n_cargo_in_cells": 1135,
  "mean_cargo_per_cell": 1.9302721088435375,
  "mean_cargo_per_positive_cell": 4.142335766423358,
  "phagocytic_index": 193.02721088435374,
  "mean_cargo_area_per_cell_px": 41.726190476190474,
  "cargo_threshold": 639.990234375,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
    1,
    1569,
    4,
    29,
    0.01848,
    99.112,
    "positive"
   ],
   [
    2,
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    2,
    47,
    0.06519,
    173.802,
    "positive"
   ],
   [
    3,
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    0,
    0,
    0,
    36.553,
    "negative"
   ],
   [
    4,
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    "positive"
   ],
   [
    5,
    2974,
    10,
    138,
    0.0464,
    227.984,
    "positive"
   ],
   [
    6,
    1691,
    0,
    0,
    0,
    48.734,
    "negative"
   ],
   [
    7,
    1316,
    0,
    0,
    0,
    59.531,
    "negative"
   ],
   [
    8,
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    0,
    0,
    0,
    59.904,
    "negative"
   ],
   [
    9,
    2247,
    4,
    15,
    0.00668,
    72.08,
    "positive"
   ],
   [
    10,
    1465,
    1,
    2,
    0.00137,
    50.165,
    "positive"
   ],
   [
    11,
    249,
    0,
    0,
    0,
    61,
    "negative"
   ],
   [
    12,
    94,
    0,
    0,
    0,
    42.404,
    "negative"
   ]
  ],
  "n_rows": 588,
  "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "170 of 588 cells positive (28.9 percent, rule: spots 1); 318 cargo objects in cells, 0.541 for each cell; cargo threshold 1401; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 170,
  "fraction_positive": 0.2891156462585034,
  "n_cargo_objects": 858,
  "n_cargo_in_cells": 318,
  "mean_cargo_per_cell": 0.5408163265306123,
  "mean_cargo_per_positive_cell": 1.8705882352941177,
  "phagocytic_index": 54.08163265306123,
  "mean_cargo_area_per_cell_px": 20.904761904761905,
  "cargo_threshold": 1400.638951431569,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
    1,
    1569,
    0,
    0,
    0,
    99.112,
    "negative"
   ],
   [
    2,
    721,
    2,
    12,
    0.01664,
    173.802,
    "positive"
   ],
   [
    3,
    463,
    0,
    0,
    0,
    36.553,
    "negative"
   ],
   [
    4,
    2518,
    3,
    77,
    0.03058,
    212.174,
    "positive"
   ],
   [
    5,
    2974,
    2,
    73,
    0.02455,
    227.984,
    "positive"
   ],
   [
    6,
    1691,
    0,
    0,
    0,
    48.734,
    "negative"
   ],
   [
    7,
    1316,
    0,
    0,
    0,
    59.531,
    "negative"
   ],
   [
    8,
    653,
    0,
    0,
    0,
    59.904,
    "negative"
   ],
   [
    9,
    2247,
    0,
    0,
    0,
    72.08,
    "negative"
   ],
   [
    10,
    1465,
    0,
    0,
    0,
    50.165,
    "negative"
   ],
   [
    11,
    249,
    0,
    0,
    0,
    61,
    "negative"
   ],
   [
    12,
    94,
    0,
    0,
    0,
    42.404,
    "negative"
   ]
  ],
  "n_rows": 588,
  "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

Cargo threshold (method name or a number)  fraction_positive  mean_cargo_per_cell  Result
otsu                                       0.06803            0.07143              ok
triangle                                   0.466              1.93                 ok
li                                         0.2891             0.5408               ok

Cargo threshold (method name or a number)  fraction_positive  mean_cargo_per_cell  Result
otsu                                       0.06803            0.07143              ok
triangle                                   0.466              1.93                 ok
li                                         0.2891             0.5408               ok
fraction_positive depends on the choice: 0.06803 with otsu, 0.466 with triangle, 0.2891 with li
mean_cargo_per_cell depends on the choice: 0.07143 with otsu, 1.93 with triangle, 0.5408 with li

{
 "ok": true,
 "summary": "88 of 176 cells positive (50.0 percent, rule: spots 1); 140 cargo objects in cells, 0.795 for each cell; cargo threshold 5794; 220 nuclei",
 "metrics": {
  "n_cells": 176,
  "n_positive": 88,
  "fraction_positive": 0.5,
  "n_cargo_objects": 184,
  "n_cargo_in_cells": 140,
  "mean_cargo_per_cell": 0.7954545454545454,
  "mean_cargo_per_positive_cell": 1.5909090909090908,
  "phagocytic_index": 79.54545454545455,
  "mean_cargo_area_per_cell_px": 53.36931818181818,
  "cargo_threshold": 5794,
  "n_excluded_border": 44,
  "n_excluded_small": 0,
  "n_nuclei": 220,
  "nucleus_threshold": 6553.5,
  "n_nuclei_excluded_size": 59,
  "cell_threshold": 3276.75,
  "mean_cell_area_um2": 1742.2126637045453
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
    1,
    13415,
    2,
    33,
    0.00246,
    157.386,
    "positive"
   ],
   [
    2,
    5389,
    0,
    0,
    0,
    52.343,
    "negative"
   ],
   [
    3,
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    0,
    0,
    0,
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    "negative"
   ],
   [
    4,
    12123,
    2,
    143,
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    327.44,
    "positive"
   ],
   [
    5,
    16667,
    0,
    0,
    0,
    57.783,
    "negative"
   ],
   [
    6,
    16753,
    1,
    64,
    0.00382,
    148.386,
    "positive"
   ],
   [
    7,
    5026,
    1,
    27,
    0.00537,
    227.316,
    "positive"
   ],
   [
    8,
    14430,
    1,
    139,
    0.00963,
    242.035,
    "positive"
   ],
   [
    9,
    5351,
    0,
    0,
    0,
    122.144,
    "negative"
   ],
   [
    10,
    14710,
    1,
    132,
    0.00897,
    237.3,
    "positive"
   ],
   [
    11,
    7579,
    0,
    0,
    0,
    40.173,
    "negative"
   ],
   [
    12,
    8922,
    0,
    0,
    0,
    44.161,
    "negative"
   ]
  ],
  "n_rows": 176,
  "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "3 images (cargo threshold 5794, rule spots 1): 230 of 459 cells positive (pooled 50.1 percent; mean of images 50.8 percent); 353 cargo objects in cells, 0.769 for each cell; 448 cargo objects in all; 566 nuclei; positive of all cells in each image: 1_BEAS_0.5nM_1h_1.lsm 88 of 176, 1_BEAS_0.5nM_1h_2.lsm 80 of 172, 1_BEAS_0.5nM_1h_3.lsm 62 of 111",
 "metrics": {
  "n_images": 3,
  "total_cells": 459,
  "total_positive": 230,
  "pooled_fraction_positive": 0.5010893246187363,
  "mean_fraction_positive": 0.5078933333333333,
  "sd_fraction_positive": 0.04721744310457028,
  "total_cargo_in_cells": 353,
  "mean_cargo_per_cell": 0.7690631808278867,
  "total_cargo_objects": 448,
  "total_nuclei": 566
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-5/uptake_counts.csv",
   "kind": "table",
   "name": "counts for each image"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_2_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_3_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  }
 ],
 "table": {
  "columns": [
   "image",
   "cells",
   "positive",
   "fraction_positive",
   "cargo_in_cells",
   "cargo_per_cell",
   "nuclei"
  ],
  "rows": [
   [
    "1_BEAS_0.5nM_1h_1.lsm",
    176,
    88,
    0.5,
    140,
    0.79545,
    220
   ],
   [
    "1_BEAS_0.5nM_1h_2.lsm",
    172,
    80,
    0.46512,
    110,
    0.63953,
    211
   ],
   [
    "1_BEAS_0.5nM_1h_3.lsm",
    111,
    62,
    0.55856,
    103,
    0.92793,
    135
   ]
  ],
  "n_rows": 3,
  "path": "{work}/count_cell_uptake-5/uptake_counts.csv"
 }
}

image,cells,positive,fraction_positive,cargo_in_cells,cargo_per_cell,nuclei
1_BEAS_0.5nM_1h_1.lsm,176,88,0.5,140,0.79545,220
1_BEAS_0.5nM_1h_2.lsm,172,80,0.46512,110,0.63953,211
1_BEAS_0.5nM_1h_3.lsm,111,62,0.55856,103,0.92793,135

{"items":[{"name":"mean_of_field_cargo_per_cell","expression":"mean([0.79545,0.63953,0.92793])"},{"name":"sd_fields","expression":"sd([0.79545,0.63953,0.92793])"},{"name":"vesicles_outside_cells","expression":"448-353"}]}

mean_of_field_cargo_per_cell = mean([0.79545,0.63953,0.92793]) = 0.7876366667
sd_fields = sd([0.79545,0.63953,0.92793]) = 0.1443586718
vesicles_outside_cells = 448-353 = 95

- 3 numbers in the answer match no logged tool result: 24, 200, 10.64. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
- The final answer says the vesicle threshold 5794 is the tool's automatic Otsu value. The log shows the scientist gave 5794 as a human answer to q15. The answer states a method that no step produced.
Fix these problems. Then give the answer again.

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0.346 · How the tool finds each cell: membrane · Nucleus threshold (method name or a number): 6553.5 · Smallest nucleus (pixels across): 24 · Largest nucleus (pixels across, 0 = no limit): 200 · Blur before splitting touching nuclei (pixels, 0 = automatic): 10.64 · Smallest distance between two nucleus centers (pixels, 0 = automatic): 12 · Cell threshold (method name or a number): 3276.75 · Cell growth from the nucleus in expand mode (pixels): 10 · Largest cell growth in membrane mode (pixels, 0 = no limit): 0 · Cargo threshold (method name or a number): 5794 · Blur of the cargo channel before the threshold (pixels): 1 · Smallest cargo object (pixels): 0 · Rule that calls a cell positive: spots · Smallest value of the rule for a positive cell: 1 · Leave out cells that touch the image edge: true.

Values that are not scored

• A trap value is the result of a wrong method, for example an unpaired test on paired data. A trap value in a main step means that the run used the wrong method. A trap value in a comparison run is correct, because the record keeps the scientist's choice.
• An optional value is a second result of the same method. The run can compute it or not.
• A reference value comes from a different program version or a check run by us. We show it for comparison.

Checks

Review findings

The review recorded 8 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 75 numbers in the answer. 69 numbers match a logged result. 6 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rees2019-uptake/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rees2019-uptake/bench.yaml.

cuvette bench papers --papers rees2019-uptake --models claude:claude-sonnet-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. inspect_image (step n1)

   Fiji: File>Open..., then Image>Show Info... and Analyze>Histogram for each channel

   • Image Lab, Imaris, NIS-Elements, ZEN, Harmony: the image properties panel shows the size, bit depth and pixel size

   • File to open

     {data}/rees2019-uptake/fields

   The manual route that the harness recorded

   assay_tools.inspect_image(path="{data}/rees2019-uptake/fields")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

2. inspect_image (step n2)

   Fiji: File>Open..., then Image>Show Info... and Analyze>Histogram for each channel

   • Image Lab, Imaris, NIS-Elements, ZEN, Harmony: the image properties panel shows the size, bit depth and pixel size

   • File to open

     {data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm

   The manual route that the harness recorded

   assay_tools.inspect_image(path="{data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

3. count_cell_uptake (step n6)

   Fiji: split channels; threshold the nuclei (Image>Adjust>Threshold...), Process>Binary>Watershed, Analyze Particles to ROI Manager; enlarge the ROIs (Edit>Selection>Enlarge) or draw cells; threshold the cargo channel; for each cell ROI, Analyze>Analyze Particles inside it to count cargo objects; a cell is positive by your rule

   • CellProfiler: IdentifyPrimaryObjects (nuclei), IdentifySecondaryObjects (cells: Distance-N for expand, Propagation for membrane), IdentifyPrimaryObjects (cargo), RelateObjects (cargo to cells), then FilterObjects or a spreadsheet for the positivity rule
   • Harmony or Columbus: Find Nuclei, Find Cytoplasm, Find Spots in the cargo channel, Calculate Properties (spots per cell), Select Population (positive cells by spot count)
   • Imaris: Cells with Spots in the cargo channel, then filter cells by the spot count
   • Hand count: count all macrophages and the macrophages with red cargo inside in each field
   • Nucleus threshold method or value = 6553.5
   • How cells are drawn = membrane
   • Enlarge (pixels) = 10
   • Largest growth in membrane mode (pixels) = 0
   • Cargo threshold method or value = 5794
   • Positive cell rule = spots
   • Smallest value for a positive cell = 1
   • Warning: If you keep the default Default, you get a different result.
   • Warning: If you keep the default Default, you get a different result.
   • Note: The membrane mode grows cells with a watershed on the membrane image, not the CellProfiler Propagation method, so cell outlines differ. The Fiji route is not tested.

   The manual route that the harness recorded

   assay_tools.count_cell_uptake(path="{data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm", cargo_channel="2", nucleus_channel="0", cell_channel="1", cell_mode="membrane", pattern="*", nucleus_threshold="6553.5", nucleus_min_diameter=24, nucleus_max_diameter=200, split_nuclei=True, nucleus_smoothing=10.64, nucleus_min_distance=12, cell_expand=10, cell_max_growth=0, cell_threshold="3276.75", cell_polarity="bright", cell_min_diameter=0, split_cells=True, cargo_threshold="5794", cargo_smoothing=1, cargo_min_area=0, positivity_rule="spots", positivity_min=1, exclude_border_cells=True, pixel_size=0.346)

   The manual route uses the same method. The note in the route gives the known difference.

4. count_cell_uptake (step n7)

   Fiji: split channels; threshold the nuclei (Image>Adjust>Threshold...), Process>Binary>Watershed, Analyze Particles to ROI Manager; enlarge the ROIs (Edit>Selection>Enlarge) or draw cells; threshold the cargo channel; for each cell ROI, Analyze>Analyze Particles inside it to count cargo objects; a cell is positive by your rule

   • CellProfiler: IdentifyPrimaryObjects (nuclei), IdentifySecondaryObjects (cells: Distance-N for expand, Propagation for membrane), IdentifyPrimaryObjects (cargo), RelateObjects (cargo to cells), then FilterObjects or a spreadsheet for the positivity rule
   • Harmony or Columbus: Find Nuclei, Find Cytoplasm, Find Spots in the cargo channel, Calculate Properties (spots per cell), Select Population (positive cells by spot count)
   • Imaris: Cells with Spots in the cargo channel, then filter cells by the spot count
   • Hand count: count all macrophages and the macrophages with red cargo inside in each field
   • Nucleus threshold method or value = 6553.5
   • How cells are drawn = membrane
   • Enlarge (pixels) = 10
   • Largest growth in membrane mode (pixels) = 0
   • Cargo threshold method or value = 5794
   • Positive cell rule = spots
   • Smallest value for a positive cell = 1
   • Warning: If you keep the default Default, you get a different result.
   • Warning: If you keep the default Default, you get a different result.
   • Note: The membrane mode grows cells with a watershed on the membrane image, not the CellProfiler Propagation method, so cell outlines differ. The Fiji route is not tested.

   The manual route that the harness recorded

   assay_tools.count_cell_uptake(path="{data}/rees2019-uptake/fields", cargo_channel="2", nucleus_channel="0", cell_channel="1", cell_mode="membrane", pattern="*.lsm", nucleus_threshold="6553.5", nucleus_min_diameter=24, nucleus_max_diameter=200, split_nuclei=True, nucleus_smoothing=10.64, nucleus_min_distance=12, cell_expand=10, cell_max_growth=0, cell_threshold="3276.75", cell_polarity="bright", cell_min_diameter=0, split_cells=True, cargo_threshold="5794", cargo_smoothing=1, cargo_min_area=0, positivity_rule="spots", positivity_min=1, exclude_border_cells=True, pixel_size=0.346)

   The manual route uses the same method. The note in the route gives the known difference.

5. calculate (step n8)

   Run the tool "calculate" with these settings: {"items":[{"name":"mean_of_field_cargo_per_cell","expression":"mean([0.79545,0.63953,0.92793])"},{"name":"sd_fields","expression":"sd([0.79545,0.63953,0.92793])"},{"name":"vesicles_outside_cells","expression":"448-353"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

Figure

Run facts

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Design:
- What is the unit of replication? (replication_unit): images or fields
- Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0.346
Ask the scientist: Plate image channel that shows the colonies (colony_channel), Are the colonies brighter or darker than the agar? (colony_polarity), Colony threshold (method name or a number) (colony_threshold), Smallest colony to count (pixels across) (colony_min_diameter), Largest colony to count (pixels across, 0 = no limit) (colony_max_diameter), Smallest roundness of a colony (0 to 1) (colony_min_roundness), Dish rim to leave out (fraction of the dish radius) (rim_margin), Background smoothing for plates (pixels, 0 = none) (colony_background_radius), Split touching colonies (split_touching_colonies), Control condition for the plating efficiency (control_condition), Colocalization threshold method (coloc_threshold_method), Costes randomizations for the p value (0 = no test) (costes_randomizations), Blur spot size for the Costes test (pixels) (psf_size), How the tool finds each cell (cell_mode), Nucleus threshold (method name or a number) (nucleus_threshold), Smallest nucleus (pixels across) (nucleus_min_diameter), Largest nucleus (pixels across, 0 = no limit) (nucleus_max_diameter), Blur before splitting touching nuclei (pixels, 0 = automatic) (nucleus_smoothing), Smallest distance between two nucleus centers (pixels, 0 = automatic) (nucleus_min_distance), Cell threshold (method name or a number) (cell_threshold), Cell growth from the nucleus in expand mode (pixels) (cell_expand), Largest cell growth in membrane mode (pixels, 0 = no limit) (cell_max_growth), Cargo threshold (method name or a number) (cargo_threshold), Blur of the cargo channel before the threshold (pixels) (cargo_smoothing), Smallest cargo object (pixels) (cargo_min_area), Rule that calls a cell positive (positivity_rule), Smallest value of the rule for a positive cell (positivity_min), Leave out cells that touch the image edge (exclude_border_cells).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

{
 "ok": true,
 "summary": "Folder with 3 image files in 1 pattern(s). *.lsm: 3 files, for example 1_BEAS_0.5nM_1h_1.lsm",
 "metrics": {
  "n_files": 3,
  "n_patterns": 1
 },
 "data": {
  "patterns": {
   "*.lsm": {
    "n_files": 3,
    "example": "1_BEAS_0.5nM_1h_1.lsm"
   }
  }
 },
 "outputs": []
}

{
 "ok": true,
 "summary": "2048 by 2048 pixels, 3 channel(s), uint16, pixel size 0.3459 micrometers",
 "metrics": {
  "width": 2048,
  "height": 2048,
  "planes": 1,
  "channels": 3,
  "bit_depth": 16,
  "pixel_size_um": 0.3459440920682663,
  "max_saturated_fraction": 0.000371
 },
 "data": {
  "dtype": "uint16",
  "axes": "CYX",
  "warnings": [],
  "channel_names": "0 is the first channel"
 },
 "table": {
  "columns": [
   "channel",
   "min",
   "max",
   "mean",
   "saturated_fraction"
  ],
  "rows": [
   [
    0,
    0,
    65535,
    2375.2079,
    0.000175
   ],
   [
    1,
    0,
    65535,
    7008.5306,
    0.000371
   ],
   [
    2,
    0,
    65535,
    123.9054,
    0.000013
   ]
  ],
  "n_rows": 3
 },
 "outputs": []
}

{
 "ok": true,
 "summary": "40 of 588 cells positive (6.8 percent, rule: spots 1); 42 cargo objects in cells, 0.071 for each cell; cargo threshold 1.19e+04; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 40,
  "fraction_positive": 0.06802721088435375,
  "n_cargo_objects": 125,
  "n_cargo_in_cells": 42,
  "mean_cargo_per_cell": 0.07142857142857142,
  "mean_cargo_per_positive_cell": 1.05,
  "phagocytic_index": 7.142857142857143,
  "mean_cargo_area_per_cell_px": 3.256802721088435,
  "cargo_threshold": 11903.818359375,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
    1,
    1569,
    0,
    0,
    0,
    99.112,
    "negative"
   ],
   [
    2,
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    0,
    0,
    0,
    173.802,
    "negative"
   ],
   [
    3,
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    "negative"
   ],
   [
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    0,
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    "negative"
   ],
   [
    5,
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    1,
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    227.984,
    "positive"
   ],
   [
    6,
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    0,
    0,
    0,
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    "negative"
   ],
   [
    7,
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    59.531,
    "negative"
   ],
   [
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    "negative"
   ],
   [
    9,
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    0,
    0,
    0,
    72.08,
    "negative"
   ],
   [
    10,
    1465,
    0,
    0,
    0,
    50.165,
    "negative"
   ],
   [
    11,
    249,
    0,
    0,
    0,
    61,
    "negative"
   ],
   [
    12,
    94,
    0,
    0,
    0,
    42.404,
    "negative"
   ]
  ],
  "n_rows": 588,
  "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "274 of 588 cells positive (46.6 percent, rule: spots 1); 1135 cargo objects in cells, 1.930 for each cell; cargo threshold 640; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 274,
  "fraction_positive": 0.46598639455782315,
  "n_cargo_objects": 2966,
  "n_cargo_in_cells": 1135,
  "mean_cargo_per_cell": 1.9302721088435375,
  "mean_cargo_per_positive_cell": 4.142335766423358,
  "phagocytic_index": 193.02721088435374,
  "mean_cargo_area_per_cell_px": 41.726190476190474,
  "cargo_threshold": 639.990234375,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
    1,
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    4,
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    "positive"
   ],
   [
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   ],
   [
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    "positive"
   ],
   [
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    "positive"
   ],
   [
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    "negative"
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   [
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    "negative"
   ],
   [
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    "negative"
   ],
   [
    9,
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    4,
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    0.00668,
    72.08,
    "positive"
   ],
   [
    10,
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    1,
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    50.165,
    "positive"
   ],
   [
    11,
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    0,
    0,
    61,
    "negative"
   ],
   [
    12,
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    0,
    0,
    0,
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    "negative"
   ]
  ],
  "n_rows": 588,
  "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "170 of 588 cells positive (28.9 percent, rule: spots 1); 318 cargo objects in cells, 0.541 for each cell; cargo threshold 1401; 599 nuclei",
 "metrics": {
  "n_cells": 588,
  "n_positive": 170,
  "fraction_positive": 0.2891156462585034,
  "n_cargo_objects": 858,
  "n_cargo_in_cells": 318,
  "mean_cargo_per_cell": 0.5408163265306123,
  "mean_cargo_per_positive_cell": 1.8705882352941177,
  "phagocytic_index": 54.08163265306123,
  "mean_cargo_area_per_cell_px": 20.904761904761905,
  "cargo_threshold": 1400.638951431569,
  "n_excluded_border": 11,
  "n_excluded_small": 0,
  "n_nuclei": 599,
  "nucleus_threshold": 8579.603347171487,
  "n_nuclei_excluded_size": 0,
  "mean_cell_area_um2": 174.69944137414964
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
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    0,
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   [
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   ],
   [
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   [
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    "negative"
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   [
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   ],
   [
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    0,
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    "negative"
   ]
  ],
  "n_rows": 588,
  "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

Cargo threshold (method name or a number)  fraction_positive  mean_cargo_per_cell  Result
otsu                                       0.06803            0.07143              ok
triangle                                   0.466              1.93                 ok
li                                         0.2891             0.5408               ok

Cargo threshold (method name or a number)  fraction_positive  mean_cargo_per_cell  Result
otsu                                       0.06803            0.07143              ok
triangle                                   0.466              1.93                 ok
li                                         0.2891             0.5408               ok
fraction_positive depends on the choice: 0.06803 with otsu, 0.466 with triangle, 0.2891 with li
mean_cargo_per_cell depends on the choice: 0.07143 with otsu, 1.93 with triangle, 0.5408 with li

{
 "ok": true,
 "summary": "88 of 176 cells positive (50.0 percent, rule: spots 1); 140 cargo objects in cells, 0.795 for each cell; cargo threshold 5794; 220 nuclei",
 "metrics": {
  "n_cells": 176,
  "n_positive": 88,
  "fraction_positive": 0.5,
  "n_cargo_objects": 184,
  "n_cargo_in_cells": 140,
  "mean_cargo_per_cell": 0.7954545454545454,
  "mean_cargo_per_positive_cell": 1.5909090909090908,
  "phagocytic_index": 79.54545454545455,
  "mean_cargo_area_per_cell_px": 53.36931818181818,
  "cargo_threshold": 5794,
  "n_excluded_border": 44,
  "n_excluded_small": 0,
  "n_nuclei": 220,
  "nucleus_threshold": 6553.5,
  "n_nuclei_excluded_size": 59,
  "cell_threshold": 3276.75,
  "mean_cell_area_um2": 1742.2126637045453
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cargo_per_cell.png",
   "kind": "image",
   "name": "cargo per cell histogram (PNG)"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cargo_per_cell.svg",
   "kind": "file",
   "name": "cargo per cell histogram (SVG)"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cells.csv",
   "kind": "table",
   "name": "cells"
  }
 ],
 "table": {
  "columns": [
   "cell",
   "area_px",
   "cargo_objects",
   "cargo_area_px",
   "cargo_area_fraction",
   "mean_cargo_intensity",
   "call"
  ],
  "rows": [
   [
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   [
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   [
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   ],
   [
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    "negative"
   ],
   [
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    "positive"
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   [
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    "positive"
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   [
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    242.035,
    "positive"
   ],
   [
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    0,
    0,
    122.144,
    "negative"
   ],
   [
    10,
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    132,
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    237.3,
    "positive"
   ],
   [
    11,
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    0,
    0,
    0,
    40.173,
    "negative"
   ],
   [
    12,
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    0,
    0,
    0,
    44.161,
    "negative"
   ]
  ],
  "n_rows": 176,
  "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_cells.csv"
 }
}

{
 "ok": true,
 "summary": "3 images (cargo threshold 5794, rule spots 1): 230 of 459 cells positive (pooled 50.1 percent; mean of images 50.8 percent); 353 cargo objects in cells, 0.769 for each cell; 448 cargo objects in all; 566 nuclei; positive of all cells in each image: 1_BEAS_0.5nM_1h_1.lsm 88 of 176, 1_BEAS_0.5nM_1h_2.lsm 80 of 172, 1_BEAS_0.5nM_1h_3.lsm 62 of 111",
 "metrics": {
  "n_images": 3,
  "total_cells": 459,
  "total_positive": 230,
  "pooled_fraction_positive": 0.5010893246187363,
  "mean_fraction_positive": 0.5078933333333333,
  "sd_fraction_positive": 0.04721744310457028,
  "total_cargo_in_cells": 353,
  "mean_cargo_per_cell": 0.7690631808278867,
  "total_cargo_objects": 448,
  "total_nuclei": 566
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-5/uptake_counts.csv",
   "kind": "table",
   "name": "counts for each image"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_2_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-5/1_BEAS_0.5nM_1h_3_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  }
 ],
 "table": {
  "columns": [
   "image",
   "cells",
   "positive",
   "fraction_positive",
   "cargo_in_cells",
   "cargo_per_cell",
   "nuclei"
  ],
  "rows": [
   [
    "1_BEAS_0.5nM_1h_1.lsm",
    176,
    88,
    0.5,
    140,
    0.79545,
    220
   ],
   [
    "1_BEAS_0.5nM_1h_2.lsm",
    172,
    80,
    0.46512,
    110,
    0.63953,
    211
   ],
   [
    "1_BEAS_0.5nM_1h_3.lsm",
    111,
    62,
    0.55856,
    103,
    0.92793,
    135
   ]
  ],
  "n_rows": 3,
  "path": "{work}/count_cell_uptake-5/uptake_counts.csv"
 }
}

image,cells,positive,fraction_positive,cargo_in_cells,cargo_per_cell,nuclei
1_BEAS_0.5nM_1h_1.lsm,176,88,0.5,140,0.79545,220
1_BEAS_0.5nM_1h_2.lsm,172,80,0.46512,110,0.63953,211
1_BEAS_0.5nM_1h_3.lsm,111,62,0.55856,103,0.92793,135

{"items":[{"name":"total_nuclei","expression":"sum([220,211,135])"},{"name":"total_cells","expression":"sum([176,172,111])"},{"name":"total_cells_positive","expression":"sum([88,80,62])"},{"name":"vesicles_in_cells","expression":"sum([140,110,103])"},{"name":"mean_vesicles_per_cell_pooled","expression":"sum([140,110,103])/sum([176,172,111])"},{"name":"fraction_cells_with_vesicle_pooled","expression":"sum([88,80,62])/sum([176,172,111])"},{"name":"mean_of_field_fractions","expression":"mean([0.5,0.46512,0.55856])"},{"name":"sd_of_field_fractions","expression":"sd([0.5,0.46512,0.55856])"}],"purpose":"Compute the totals, the pooled ratios and the spread over the three fields."}

total_nuclei = sum([220,211,135]) = 566
total_cells = sum([176,172,111]) = 459
total_cells_positive = sum([88,80,62]) = 230
vesicles_in_cells = sum([140,110,103]) = 353
mean_vesicles_per_cell_pooled = sum([140,110,103])/sum([176,172,111]) = 0.7690631808
fraction_cells_with_vesicle_pooled = sum([88,80,62])/sum([176,172,111]) = 0.5010893246
mean_of_field_fractions = mean([0.5,0.46512,0.55856]) = 0.5078933333
sd_of_field_fractions = sd([0.5,0.46512,0.55856]) = 0.0472174431

- 1 number in the answer match no logged tool result: 95. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
Fix these problems. Then give the answer again.

{"items":[{"name":"vesicles_outside_cells","expression":"448 - 353"}],"purpose":"Compute the number of detected vesicles outside the cell outlines, so the value has a logged calculation."}

vesicles_outside_cells = 448 - 353 = 95

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0.346 · How the tool finds each cell: membrane · Nucleus threshold (method name or a number): 6553.5 · Smallest nucleus (pixels across): 24 · Largest nucleus (pixels across, 0 = no limit): 200 · Blur before splitting touching nuclei (pixels, 0 = automatic): 10.64 · Smallest distance between two nucleus centers (pixels, 0 = automatic): 12 · Cell threshold (method name or a number): 3276.75 · Cell growth from the nucleus in expand mode (pixels): 10 · Largest cell growth in membrane mode (pixels, 0 = no limit): 0 · Cargo threshold (method name or a number): 5794 · Blur of the cargo channel before the threshold (pixels): 1 · Smallest cargo object (pixels): 0 · Rule that calls a cell positive: spots · Smallest value of the rule for a positive cell: 1 · Leave out cells that touch the image edge: true.

Values that are not scored

• A trap value is the result of a wrong method, for example an unpaired test on paired data. A trap value in a main step means that the run used the wrong method. A trap value in a comparison run is correct, because the record keeps the scientist's choice.
• An optional value is a second result of the same method. The run can compute it or not.
• A reference value comes from a different program version or a check run by us. We show it for comparison.

Checks

Review findings

The review recorded 9 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 70 numbers in the answer. 70 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

1 tool call failed. The model then tried again or used another tool. The session above shows each failure.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rees2019-uptake/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rees2019-uptake/bench.yaml.

cuvette bench papers --papers rees2019-uptake --models claude:claude-haiku-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. inspect_image (step n1)

   Fiji: File>Open..., then Image>Show Info... and Analyze>Histogram for each channel

   • Image Lab, Imaris, NIS-Elements, ZEN, Harmony: the image properties panel shows the size, bit depth and pixel size

   • File to open

     {data}/rees2019-uptake/fields

   The manual route that the harness recorded

   assay_tools.inspect_image(path="{data}/rees2019-uptake/fields")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

2. inspect_image (step n2)

   Fiji: File>Open..., then Image>Show Info... and Analyze>Histogram for each channel

   • Image Lab, Imaris, NIS-Elements, ZEN, Harmony: the image properties panel shows the size, bit depth and pixel size

   • File to open

     {data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm

   The manual route that the harness recorded

   assay_tools.inspect_image(path="{data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

3. count_cell_uptake (step n6)

   Fiji: split channels; threshold the nuclei (Image>Adjust>Threshold...), Process>Binary>Watershed, Analyze Particles to ROI Manager; enlarge the ROIs (Edit>Selection>Enlarge) or draw cells; threshold the cargo channel; for each cell ROI, Analyze>Analyze Particles inside it to count cargo objects; a cell is positive by your rule

   • CellProfiler: IdentifyPrimaryObjects (nuclei), IdentifySecondaryObjects (cells: Distance-N for expand, Propagation for membrane), IdentifyPrimaryObjects (cargo), RelateObjects (cargo to cells), then FilterObjects or a spreadsheet for the positivity rule
   • Harmony or Columbus: Find Nuclei, Find Cytoplasm, Find Spots in the cargo channel, Calculate Properties (spots per cell), Select Population (positive cells by spot count)
   • Imaris: Cells with Spots in the cargo channel, then filter cells by the spot count
   • Hand count: count all macrophages and the macrophages with red cargo inside in each field
   • Nucleus threshold method or value = 6553.5
   • How cells are drawn = membrane
   • Enlarge (pixels) = 10
   • Largest growth in membrane mode (pixels) = 0
   • Cargo threshold method or value = 5794
   • Positive cell rule = spots
   • Smallest value for a positive cell = 1
   • Warning: If you keep the default Default, you get a different result.
   • Warning: If you keep the default Default, you get a different result.
   • Note: The membrane mode grows cells with a watershed on the membrane image, not the CellProfiler Propagation method, so cell outlines differ. The Fiji route is not tested.

   The manual route that the harness recorded

   assay_tools.count_cell_uptake(path="{data}/rees2019-uptake/fields/1_BEAS_0.5nM_1h_1.lsm", cargo_channel="2", nucleus_channel="0", cell_channel="1", cell_mode="membrane", pattern="*", nucleus_threshold="6553.5", nucleus_min_diameter=24, nucleus_max_diameter=200, split_nuclei=True, nucleus_smoothing=10.64, nucleus_min_distance=12, cell_expand=10, cell_max_growth=0, cell_threshold="3276.75", cell_polarity="bright", cell_min_diameter=0, split_cells=True, cargo_threshold="5794", cargo_smoothing=1, cargo_min_area=0, positivity_rule="spots", positivity_min=1, exclude_border_cells=True, pixel_size=0.346)

   The manual route uses the same method. The note in the route gives the known difference.

4. count_cell_uptake (step n7)

   Fiji: split channels; threshold the nuclei (Image>Adjust>Threshold...), Process>Binary>Watershed, Analyze Particles to ROI Manager; enlarge the ROIs (Edit>Selection>Enlarge) or draw cells; threshold the cargo channel; for each cell ROI, Analyze>Analyze Particles inside it to count cargo objects; a cell is positive by your rule

   • CellProfiler: IdentifyPrimaryObjects (nuclei), IdentifySecondaryObjects (cells: Distance-N for expand, Propagation for membrane), IdentifyPrimaryObjects (cargo), RelateObjects (cargo to cells), then FilterObjects or a spreadsheet for the positivity rule
   • Harmony or Columbus: Find Nuclei, Find Cytoplasm, Find Spots in the cargo channel, Calculate Properties (spots per cell), Select Population (positive cells by spot count)
   • Imaris: Cells with Spots in the cargo channel, then filter cells by the spot count
   • Hand count: count all macrophages and the macrophages with red cargo inside in each field
   • Nucleus threshold method or value = 6553.5
   • How cells are drawn = membrane
   • Enlarge (pixels) = 10
   • Largest growth in membrane mode (pixels) = 0
   • Cargo threshold method or value = 5794
   • Positive cell rule = spots
   • Smallest value for a positive cell = 1
   • Warning: If you keep the default Default, you get a different result.
   • Warning: If you keep the default Default, you get a different result.
   • Note: The membrane mode grows cells with a watershed on the membrane image, not the CellProfiler Propagation method, so cell outlines differ. The Fiji route is not tested.

   The manual route that the harness recorded

   assay_tools.count_cell_uptake(path="{data}/rees2019-uptake/fields", cargo_channel="2", nucleus_channel="0", cell_channel="1", cell_mode="membrane", pattern="*.lsm", nucleus_threshold=6553.5, nucleus_min_diameter=24, nucleus_max_diameter=200, split_nuclei=True, nucleus_smoothing=10.64, nucleus_min_distance=12, cell_expand=10, cell_max_growth=0, cell_threshold=3276.75, cell_polarity="bright", cell_min_diameter=0, split_cells=True, cargo_threshold="5794", cargo_smoothing=1, cargo_min_area=0, positivity_rule="spots", positivity_min=1, exclude_border_cells=True, pixel_size=0.346)

   The manual route uses the same method. The note in the route gives the known difference.

5. calculate (step n8)

   Run the tool "calculate" with these settings: {"items":[{"name":"total_nuclei","expression":"sum([220,211,135])"},{"name":"total_cells","expression":"sum([176,172,111])"},{"name":"total_cells_positive","expression":"sum([88,80,62])"},{"name":"vesicles_in_cells","expression":"sum([140,110,103])"},{"name":"mean_vesicles_per_cell_pooled","expression":"sum([140,110,103])/sum([176,172,111])"},{"name":"fraction_cells_with_vesicle_pooled","expression":"sum([88,80,62])/sum([176,172,111])"},{"name":"mean_of_field_fractions","expression":"mean([0.5,0.46512,0.55856])"},{"name":"sd_of_field_fractions","expression":"sd([0.5,0.46512,0.55856])"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

6. calculate (step n9)

   Run the tool "calculate" with these settings: {"items":[{"name":"vesicles_outside_cells","expression":"448 - 353"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

Figure

Run facts

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Design:
- What is the unit of replication? (replication_unit): images or fields
- Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0.346
Ask the scientist: Plate image channel that shows the colonies (colony_channel), Are the colonies brighter or darker than the agar? (colony_polarity), Colony threshold (method name or a number) (colony_threshold), Smallest colony to count (pixels across) (colony_min_diameter), Largest colony to count (pixels across, 0 = no limit) (colony_max_diameter), Smallest roundness of a colony (0 to 1) (colony_min_roundness), Dish rim to leave out (fraction of the dish radius) (rim_margin), Background smoothing for plates (pixels, 0 = none) (colony_background_radius), Split touching colonies (split_touching_colonies), Control condition for the plating efficiency (control_condition), Colocalization threshold method (coloc_threshold_method), Costes randomizations for the p value (0 = no test) (costes_randomizations), Blur spot size for the Costes test (pixels) (psf_size), How the tool finds each cell (cell_mode), Nucleus threshold (method name or a number) (nucleus_threshold), Smallest nucleus (pixels across) (nucleus_min_diameter), Largest nucleus (pixels across, 0 = no limit) (nucleus_max_diameter), Blur before splitting touching nuclei (pixels, 0 = automatic) (nucleus_smoothing), Smallest distance between two nucleus centers (pixels, 0 = automatic) (nucleus_min_distance), Cell threshold (method name or a number) (cell_threshold), Cell growth from the nucleus in expand mode (pixels) (cell_expand), Largest cell growth in membrane mode (pixels, 0 = no limit) (cell_max_growth), Cargo threshold (method name or a number) (cargo_threshold), Blur of the cargo channel before the threshold (pixels) (cargo_smoothing), Smallest cargo object (pixels) (cargo_min_area), Rule that calls a cell positive (positivity_rule), Smallest value of the rule for a positive cell (positivity_min), Leave out cells that touch the image edge (exclude_border_cells).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

- 1_BEAS_0.5nM_1h_1.lsm  25367350
- 1_BEAS_0.5nM_1h_2.lsm  25367350
- 1_BEAS_0.5nM_1h_3.lsm  25367350

{
 "ok": true,
 "summary": "3 images (cargo threshold otsu, rule spots 1): 101 of 1514 cells positive (pooled 6.7 percent; mean of images 6.6 percent); 107 cargo objects in cells, 0.071 for each cell; 299 cargo objects in all; 1536 nuclei; positive of all cells in each image: 1_BEAS_0.5nM_1h_1.lsm 40 of 588, 1_BEAS_0.5nM_1h_2.lsm 39 of 561, 1_BEAS_0.5nM_1h_3.lsm 22 of 365",
 "metrics": {
  "n_images": 3,
  "total_cells": 1514,
  "total_positive": 101,
  "pooled_fraction_positive": 0.0667107001321004,
  "mean_fraction_positive": 0.06594,
  "sd_fraction_positive": 0.004966558164362922,
  "total_cargo_in_cells": 107,
  "mean_cargo_per_cell": 0.07067371202113606,
  "total_cargo_objects": 299,
  "total_nuclei": 1536
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-1/uptake_counts.csv",
   "kind": "table",
   "name": "counts for each image"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_2_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-1/1_BEAS_0.5nM_1h_3_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  }
 ],
 "table": {
  "columns": [
   "image",
   "cells",
   "positive",
   "fraction_positive",
   "cargo_in_cells",
   "cargo_per_cell",
   "nuclei"
  ],
  "rows": [
   [
    "1_BEAS_0.5nM_1h_1.lsm",
    588,
    40,
    0.06803,
    42,
    0.07143,
    599
   ],
   [
    "1_BEAS_0.5nM_1h_2.lsm",
    561,
    39,
    0.06952,
    40,
    0.0713,
    567
   ],
   [
    "1_BEAS_0.5nM_1h_3.lsm",
    365,
    22,
    0.06027,
    25,
    0.06849,
    370
   ]
  ],
  "n_rows": 3,
  "path": "{work}/count_cell_uptake-1/uptake_counts.csv"
 }
}

{
 "ok": true,
 "summary": "3 images (cargo threshold triangle, rule spots 1): 783 of 1514 cells positive (pooled 51.7 percent; mean of images 52.4 percent); 3060 cargo objects in cells, 2.021 for each cell; 8085 cargo objects in all; 1536 nuclei; positive of all cells in each image: 1_BEAS_0.5nM_1h_1.lsm 274 of 588, 1_BEAS_0.5nM_1h_2.lsm 300 of 561, 1_BEAS_0.5nM_1h_3.lsm 209 of 365",
 "metrics": {
  "n_images": 3,
  "total_cells": 1514,
  "total_positive": 783,
  "pooled_fraction_positive": 0.5171730515191546,
  "mean_fraction_positive": 0.52445,
  "sd_fraction_positive": 0.0540476188189637,
  "total_cargo_in_cells": 3060,
  "mean_cargo_per_cell": 2.02113606340819,
  "total_cargo_objects": 8085,
  "total_nuclei": 1536
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-2/uptake_counts.csv",
   "kind": "table",
   "name": "counts for each image"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_2_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-2/1_BEAS_0.5nM_1h_3_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  }
 ],
 "table": {
  "columns": [
   "image",
   "cells",
   "positive",
   "fraction_positive",
   "cargo_in_cells",
   "cargo_per_cell",
   "nuclei"
  ],
  "rows": [
   [
    "1_BEAS_0.5nM_1h_1.lsm",
    588,
    274,
    0.46599,
    1135,
    1.93027,
    599
   ],
   [
    "1_BEAS_0.5nM_1h_2.lsm",
    561,
    300,
    0.53476,
    1091,
    1.94474,
    567
   ],
   [
    "1_BEAS_0.5nM_1h_3.lsm",
    365,
    209,
    0.5726,
    834,
    2.28493,
    370
   ]
  ],
  "n_rows": 3,
  "path": "{work}/count_cell_uptake-2/uptake_counts.csv"
 }
}

{
 "ok": true,
 "summary": "3 images (cargo threshold li, rule spots 1): 467 of 1514 cells positive (pooled 30.8 percent; mean of images 31.2 percent); 854 cargo objects in cells, 0.564 for each cell; 2312 cargo objects in all; 1536 nuclei; positive of all cells in each image: 1_BEAS_0.5nM_1h_1.lsm 170 of 588, 1_BEAS_0.5nM_1h_2.lsm 173 of 561, 1_BEAS_0.5nM_1h_3.lsm 124 of 365",
 "metrics": {
  "n_images": 3,
  "total_cells": 1514,
  "total_positive": 467,
  "pooled_fraction_positive": 0.3084544253632761,
  "mean_fraction_positive": 0.31240999999999997,
  "sd_fraction_positive": 0.025544543448650626,
  "total_cargo_in_cells": 854,
  "mean_cargo_per_cell": 0.5640686922060766,
  "total_cargo_objects": 2312,
  "total_nuclei": 1536
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-3/uptake_counts.csv",
   "kind": "table",
   "name": "counts for each image"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_2_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-3/1_BEAS_0.5nM_1h_3_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  }
 ],
 "table": {
  "columns": [
   "image",
   "cells",
   "positive",
   "fraction_positive",
   "cargo_in_cells",
   "cargo_per_cell",
   "nuclei"
  ],
  "rows": [
   [
    "1_BEAS_0.5nM_1h_1.lsm",
    588,
    170,
    0.28912,
    318,
    0.54082,
    599
   ],
   [
    "1_BEAS_0.5nM_1h_2.lsm",
    561,
    173,
    0.30838,
    295,
    0.52585,
    567
   ],
   [
    "1_BEAS_0.5nM_1h_3.lsm",
    365,
    124,
    0.33973,
    241,
    0.66027,
    370
   ]
  ],
  "n_rows": 3,
  "path": "{work}/count_cell_uptake-3/uptake_counts.csv"
 }
}

Cargo threshold (method name or a number)  mean_cargo_per_cell  Result
otsu                                       0.07067              ok
triangle                                   2.021                ok
li                                         0.5641               ok

Cargo threshold (method name or a number)  mean_cargo_per_cell  Result
otsu                                       0.07067              ok
triangle                                   2.021                ok
li                                         0.5641               ok
mean_cargo_per_cell depends on the choice: 0.07067 with otsu, 2.021 with triangle, 0.5641 with li

{
 "ok": true,
 "summary": "3 images (cargo threshold 5794, rule spots 1): 230 of 459 cells positive (pooled 50.1 percent; mean of images 50.8 percent); 353 cargo objects in cells, 0.769 for each cell; 448 cargo objects in all; 566 nuclei; positive of all cells in each image: 1_BEAS_0.5nM_1h_1.lsm 88 of 176, 1_BEAS_0.5nM_1h_2.lsm 80 of 172, 1_BEAS_0.5nM_1h_3.lsm 62 of 111",
 "metrics": {
  "n_images": 3,
  "total_cells": 459,
  "total_positive": 230,
  "pooled_fraction_positive": 0.5010893246187363,
  "mean_fraction_positive": 0.5078933333333333,
  "sd_fraction_positive": 0.04721744310457028,
  "total_cargo_in_cells": 353,
  "mean_cargo_per_cell": 0.7690631808278867,
  "total_cargo_objects": 448,
  "total_nuclei": 566
 },
 "outputs": [
  {
   "path": "{work}/count_cell_uptake-4/uptake_counts.csv",
   "kind": "table",
   "name": "counts for each image"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_1_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_2_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  },
  {
   "path": "{work}/count_cell_uptake-4/1_BEAS_0.5nM_1h_3_uptake_overlay.png",
   "kind": "image",
   "name": "QC overlay"
  }
 ],
 "table": {
  "columns": [
   "image",
   "cells",
   "positive",
   "fraction_positive",
   "cargo_in_cells",
   "cargo_per_cell",
   "nuclei"
  ],
  "rows": [
   [
    "1_BEAS_0.5nM_1h_1.lsm",
    176,
    88,
    0.5,
    140,
    0.79545,
    220
   ],
   [
    "1_BEAS_0.5nM_1h_2.lsm",
    172,
    80,
    0.46512,
    110,
    0.63953,
    211
   ],
   [
    "1_BEAS_0.5nM_1h_3.lsm",
    111,
    62,
    0.55856,
    103,
    0.92793,
    135
   ]
  ],
  "n_rows": 3,
  "path": "{work}/count_cell_uptake-4/uptake_counts.csv"
 }
}

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0.346 · How the tool finds each cell: membrane · Nucleus threshold (method name or a number): 6553.5 · Smallest nucleus (pixels across): 24 · Largest nucleus (pixels across, 0 = no limit): 200 · Blur before splitting touching nuclei (pixels, 0 = automatic): 10.64 · Smallest distance between two nucleus centers (pixels, 0 = automatic): 12 · Cell threshold (method name or a number): 3276.75 · Cell growth from the nucleus in expand mode (pixels): 10 · Largest cell growth in membrane mode (pixels, 0 = no limit): 0 · Cargo threshold (method name or a number): 5794 · Blur of the cargo channel before the threshold (pixels): 1 · Smallest cargo object (pixels): 0 · Rule that calls a cell positive: spots · Smallest value of the rule for a positive cell: 1 · Leave out cells that touch the image edge: true.

Values that are not scored

• A trap value is the result of a wrong method, for example an unpaired test on paired data. A trap value in a main step means that the run used the wrong method. A trap value in a comparison run is correct, because the record keeps the scientist's choice.
• An optional value is a second result of the same method. The run can compute it or not.
• A reference value comes from a different program version or a check run by us. We show it for comparison.

Checks

Review findings

The review recorded 3 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 8 numbers in the answer. 8 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

1 tool call failed. The model then tried again or used another tool. The session above shows each failure.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rees2019-uptake/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rees2019-uptake/bench.yaml.

cuvette bench papers --papers rees2019-uptake --models ollama:qwen3:8b

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. count_cell_uptake (step n4)

   Fiji: split channels; threshold the nuclei (Image>Adjust>Threshold...), Process>Binary>Watershed, Analyze Particles to ROI Manager; enlarge the ROIs (Edit>Selection>Enlarge) or draw cells; threshold the cargo channel; for each cell ROI, Analyze>Analyze Particles inside it to count cargo objects; a cell is positive by your rule

   • CellProfiler: IdentifyPrimaryObjects (nuclei), IdentifySecondaryObjects (cells: Distance-N for expand, Propagation for membrane), IdentifyPrimaryObjects (cargo), RelateObjects (cargo to cells), then FilterObjects or a spreadsheet for the positivity rule
   • Harmony or Columbus: Find Nuclei, Find Cytoplasm, Find Spots in the cargo channel, Calculate Properties (spots per cell), Select Population (positive cells by spot count)
   • Imaris: Cells with Spots in the cargo channel, then filter cells by the spot count
   • Hand count: count all macrophages and the macrophages with red cargo inside in each field
   • Nucleus threshold method or value = 6553.5
   • How cells are drawn = membrane
   • Enlarge (pixels) = 10
   • Largest growth in membrane mode (pixels) = 0
   • Cargo threshold method or value = 5794
   • Positive cell rule = spots
   • Smallest value for a positive cell = 1
   • Warning: If you keep the default Default, you get a different result.
   • Warning: If you keep the default Default, you get a different result.
   • Note: The membrane mode grows cells with a watershed on the membrane image, not the CellProfiler Propagation method, so cell outlines differ. The Fiji route is not tested.

   The manual route that the harness recorded

   assay_tools.count_cell_uptake(path="{data}/rees2019-uptake/fields", cargo_channel="2", nucleus_channel="0", cell_channel="1", cell_mode="membrane", pattern="*.lsm", nucleus_threshold="6553.5", nucleus_min_diameter=24, nucleus_max_diameter=200, split_nuclei=True, nucleus_smoothing=10.64, nucleus_min_distance=12, cell_expand=10, cell_max_growth=0, cell_threshold="3276.75", cell_polarity="bright", cell_min_diameter=0, split_cells=True, cargo_threshold="5794", cargo_smoothing=1, cargo_min_area=0, positivity_rule="spots", positivity_min=1, exclude_border_cells=True, pixel_size=0.346)

   The manual route uses the same method. The note in the route gives the known difference.

Figure

Run facts