Methods

Cotton plants were grown in a well-controlled greenhouse in the NC State Phytotron as described previously (Pierce et al, 2019). Flowers were tagged on the day of anthesis and harvested three days post anthesis (3 DPA). The distinct fiber shapes had already formed by 2 DPA (Stiff and Haigler, 2016; Graham and Haigler, 2021), and fibers were still relatively short at 3 DPA, which facilitated the visualization of multiple fiber tips in one image.

Cotton fiber sample preparation, digital image collection, and image analysis:

Ovules with attached fiber were fixed in the greenhouse. The fixative previously used (Histochoice) (Stiff and Haigler, 2016; Pierce et al., 2019; Graham and Haigler, 2021) is obsolete, which led to testing and validation of another low-toxicity, formalin-free fixative (#A5472; Sigma-Aldrich, St. Louis, MO; Fig. S1). The boll wall was removed without damaging the ovules. (Using a razor blade, cut away the top 3 mm of the boll. Make about 1 mm deep longitudinal incisions between the locule walls, and finally cut around the base of the boll.) All of the ovules with attached fiber were lifted out of the locules and fixed (1 h, RT, 1:10 tissue:fixative ratio) prior to optional storage at 4°C. Immediately before imaging, ovules were examined under a stereo microscope (incident light, black background, 31X) to select three vigorous ovules from each boll while avoiding drying. Ovules were rinsed (3 x 5 min) in buffer [0.05 M PIPES, 12 mM EGTA. 5 mM EDTA and 0.1% (w/v) Tween 80, pH 6.8], which had lower osmolarity than a microtubule-stabilizing buffer used previously for aldehyde-fixed fibers (Seagull, 1990; Graham and Haigler, 2021). While steadying an ovule with forceps, one to three small pieces of its chalazal end with attached fibers were dissected away using a small knife (#10055-12; Fine Science Tools, Foster City, CA). Each ovule piece was placed in a single well of a 24-well slide (#63430-04; Electron Microscopy Sciences, Hatfield, PA) containing a single drop of buffer prior to applying and sealing a 24 x 60 mm coverslip with vaseline.

Samples were imaged with brightfield optics and default settings for the 2.83 mega-pixel, color, CCD camera of the Keyence BZ-X810 imaging system (www.keyence.com; housed in the Cellular and Molecular Imaging Facility of NC State). The location of each sample in the 24-well slides was identified visually using a 2X objective and mapped using the navigation function of the integrated Keyence software. Using the 10X objective lens (plan-apochromatic; NA 0.45) and 60% closed condenser aperture setting, a region with many fiber apices was selected for imaging using the multi-point and z-stack capture functions. The precise location was recorded by the software prior to visual setting of the limits of the z-plane range (1.2 µm step size). Typically, three 24-sample slides (representing three accessions) were set up in parallel prior to automatic image capture. The captured z-stacks for each sample were processed into one two-dimensional image using the full-focus function of the software. (Occasional samples contained too much debris for computer vision to be effective, and these were reimaged.)

• Resource Title: Deltapine 90 - Manually Annotated Training Set.

  File Name: GH3 DP90 Keyence 1_45 JPEG.zip

  Resource Description: These images were manually annotated in Labelbox.




• Resource Title: Deltapine 90 - AI-Assisted Annotated Training Set.

  File Name: GH3 DP90 Keyence 46_101 JPEG.zip

  Resource Description: These images were AI-labeled in RoboFlow and then manually reviewed in RoboFlow.




• Resource Title: Deltapine 90 - Manually Annotated Training-Validation Set.

  File Name: GH3 DP90 Keyence 102_125 JPEG.zip

  Resource Description: These images were manually labeled in LabelBox, and then used for training-validation for the machine learning model.




• Resource Title: Phytogen 800 - Evaluation Test Images.

  File Name: Gb cv Phytogen 800.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Pima 3-79 - Evaluation Test Images.

  File Name: Gb cv Pima 379.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Pima S-7 - Evaluation Test Images.

  File Name: Gb cv Pima S7.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Coker 312 - Evaluation Test Images.

  File Name: Gh cv Coker 312.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Deltapine 90 - Evaluation Test Images.

  File Name: Gh cv Deltapine 90.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Half and Half - Evaluation Test Images.

  File Name: Gh cv Half and Half.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Fiber Tip Annotations - Manual.

  File Name: manual_annotations.coco_.json

  Resource Description: Annotations in COCO.json format for fibers. Manually annotated in Labelbox.




• Resource Title: Fiber Tip Annotations - AI-Assisted.

  File Name: ai_assisted_annotations.coco_.json

  Resource Description: Annotations in COCO.json format for fibers. AI annotated with human review in Roboflow.




• Resource Title: Model Weights (iteration 600).

  File Name: model_weights.zip

  Resource Description: The final model, provided as a zipped Pytorch .pth file. It was chosen at training iteration 600. The model weights can be imported for use of the fiber tip type detection neural network in Python.

  Resource Software Recommended: Google Colab,url: https://research.google.com/colaboratory/

Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.

Paper: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: https://doi.org/10.1101/454793.

Details: A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:

1. Development (Phases I-II). 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).
2. Hold-out (Phase III). 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.
3. CERAD-like hold-out. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.

Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.

Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.

Slide source and preparation: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (https://www.ucdmc.ucdavis.edu/alzheimers/). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.

Code: Please visit https://github.com/keiserlab/plaquebox-paper