Engineering topics
Publications and source records attributed to Oostrom, Marjolein.
The code takes expert-labeled segmented images of irradiated and unirradiated pellets and trains DeepConvolutional Neural Networks to segment these images into defects, backgrounds, and boundaries. The code calculated qualitative microstructural information from these segmented images to facilitate the comparison of unirradiated and irradiated pellets
Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.
Light sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. We fine-tuned an existing model, TrailMap, using expert labeled data from axonal structures in neocortex. Without changing the network architecture, we implemented nnU-Net framework modifications in data augmentation, data foreground sampling, window learning rate, and the inference overlap method. The resulting model from these combined approaches yielded an improved F1 score
The DEIMoS Graphical User Interface was created as an interface for DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry. DEIMoS is a Python package to process data from mass spectrometry instrument developed at PNNL.
Abstract To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine‐tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe‐2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub‐classifications of the [2Fe‐2S] cofactor: (1) a standard [2Fe‐2S] cofactor and (2) a Rieske [2Fe‐2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal‐binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal‐binding site identification in proteins.
The molecular graphics program, PyMol, was employed to produce all images in the training/validation/testing datasets. A PyMol script identified the clusters of interest from a list of all proteins containing 1) Fe (III) and Fe (II) ion and 2) FeS, respectively. A PyMol script and a Python webscraper were also used to classify [2Fe-2S] cofactors into three classes: standard, Rieske, and other and to identify iron atoms with four cysteine residues. We also used the pyMol script to rotate amino acids for training
A pre-trained convolutional neural network was fine-tuned for three separate classification tasks, distinguishing 2D images of: 1) single amino acids, 2) protein structural ball and stick images of metalloproteins, and 3) protein structural ball and stick images of metalloenzymes with the metal cofactors removed. Images used in the training, testing and validation are shared here.
TEMWizard is used to visualize results from Atomap, an open-source software tool for determining the position and other features of atomic columns in transmission electron microscopy images.
TEMWizard pyCHIP.gui is used to visualize intermediate and final results from Atomap, an open-source software tool for determining the position and other features of atomic columns in transmission electron microscopy images. Also known as pyCHIP.gui.