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1. Web-based Interactive Polyhedral Graphics Statics Platform

   CHAI, Hua ; AKBARZADEH, Masoud ( October 2021 , International Association of Shell and Spatial Structures)

   This paper introduces a web-based interactive educational platform for 3D/polyhedral graphic statics (PGS) [1]. The Block Research Group (BRG) at ETH Zürich developed a dynamic learning and teaching platform for structural design. This tool is based on traditional graphic statics. It uses interactive 2D drawings to help designers and engineers with all skill levels to understand and utilize the methods [2]. However, polyhedral graphic statics is not easy to learn because of its characteristics in three-dimensional. All the existing computational design tools are heavily dependent on the modeling software such as Rhino or the Python-based computational framework like Compass [3]. In this research, we start with the procedural approach, developing libraries using JavaScript, Three.js, and WebGL to facilitate the construction by making it independent from any software. This framework is developed based on the mathematical and computational algorithms deriving the global equilibrium of the structure, optimizing the balanced relationship between the external magnitudes and the internal forces, visualizing the dynamic reciprocal polyhedral diagrams with corresponding topological data. This instant open-source application and the visualization interface provide a more operative platform for students, educators, practicers, and designers in an interactive manner, allowing them to learn not only the topological relationship but also to deepen their knowledge and understanding of structures in the steps for the construction of the form and force diagrams and analyze it. In the simplified single-node example, the multi-step geometric procedures intuitively illustrate 3D structural reciprocity concepts. With the intuitive control panel, the user can move the constraint point’s location through the inserted gumball function, the force direction of the form diagram will be dynamically changed from compression-only to tension and compression combined. Users can also explore and design innovative, efficient spatial structures with changeable boundary conditions and constraints through real-time manipulating both force distribution and geometric form, such as adding the number of supports or subdividing the global equilibrium in the force diagram. Eventually, there is an option to export the satisfying geometry as a suitable format to share with other fabrication tools. As the online educational environment with different types of geometric examples, it is valuable to use graphical approaches to teach the structural form in an exploratory manner. 

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2. RTExtract: Time-series NMR spectra quantification based on 3D surface ridge tracking

   https://doi.org/10.1093/bioinformatics/btaa631  

   Wu, Yue ; Judge, Michael T ; Arnold, Jonathan ; Bhandarkar, Suchendra M ; Edison, Arthur S ; Cowen, Lenore ( July 2020 , Bioinformatics)

   Abstract Motivation Time-series NMR has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra. Results We introduce RTExtract (Ridge Tracking based Extract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than two hours instead of ∼48 hours, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked. Availability Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. Toytree: A minimalist tree visualization and manipulation library for Python

   https://doi.org/10.1111/2041-210X.13313  

   Eaton, Deren A. R. ; Matschiner, ed., Michael ( November 2019 , Methods in Ecology and Evolution)

   Toytreeis a lightweight Python library for programmatically visualizing and manipulating tree‐based data structures. It implements a minimalist design aesthetic and modern plotting architecture suited for interactive coding in IPython/Jupyter.

   Tree drawings are generated in HTML using thetoyplotlibrary backend, and display natively in Jupyter notebooks with interactivity features. Tree drawings can be combined with other plotting functions from thetoyplotlibrary (e.g. scatterplots, histograms) to create composite figures on a shared coordinate grid, and can be exported to additional formats including PNG, PDF and SVG.

   To parse and store tree data,toytreeuses a modified fork of theete3TreeNode object, which includes functions for manipulating, annotating and comparing trees.Toytreeintegrates these functions with a plotting layout to allow node values to be extracted from trees in the correct order to style nodes for plotting. In addition,toytreeprovides functions for parsing additional tree formats, generating random trees, inferring consensus trees and drawing grids or clouds from multiple trees to visualize discordance.

   The goal oftoytreeis to provide a simple Python equivalent to commonly used tree manipulation and plotting libraries in R, and in doing so, to promote further development of phylogenetic and other tree‐based methods in Python.Toytreeis released under the GPLv3 license. Source code is available on GitHub and documentation is available athttps://toytree.readthedocs.io.

    

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4. The Temple University Digital Pathology Corpus: The Breast Tissue Subset

   https://doi.org/10.1109/SPMB52430.2021.9672275  

   Wevodau, Z. ; Doshna, B. ; Jhala, N. ; Akhtar, I. ; Obeid, I. ; Picone, J. ( December 2021 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, I. (Ed.)

   The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples [1], as part of its National Science Foundation-funded Major Research Instrumentation grant titled “MRI: High Performance Digital Pathology Using Big Data and Machine Learning” [2]. The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue. It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do not have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools. The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. Not every pathological feature is annotated, meaning excluded areas can include focuses particular to these labels that are not used for training. A summary of the number of patches within each label is given in Table 2. To maintain a balanced training set, 1,000 patches of each label were used to train the machine learning model. Throughout all sets, only annotated patches were involved in model development. The performance of this model in identifying all the patches in the evaluation set can be seen in the confusion matrix of classification accuracy in Table 3. The highest performing labels were background, 97% correct identification, and artifact, 76% correct identification. A correlation exists between labels with more than 6,000 development patches and accurate performance on the evaluation set. Additionally, these results indicated a need to further refine the annotation of invasive ductal carcinoma (“indc”), inflammation (“infl”), nonneoplastic features (“nneo”), normal (“norm”) and suspicious (“susp”). This pilot experiment motivated changes to the corpus that will be discussed in detail in this poster presentation. To increase the accuracy of the machine learning model, we modified how we addressed underperforming labels. One common source of error arose with how non-background labels were converted into patches. Large areas of background within other labels were isolated within a patch resulting in connective tissue misrepresenting a non-background label. In response, the annotation overlay margins were revised to exclude benign connective tissue in non-background labels. Corresponding patient reports and supporting immunohistochemical stains further guided annotation reviews. The microscopic diagnoses given by the primary pathologist in these reports detail the pathological findings within each tissue site, but not within each specific slide. The microscopic diagnoses informed revisions specifically targeting annotated regions classified as cancerous, ensuring that the labels “indc” and “dcis” were used only in situations where a micropathologist diagnosed it as such. Further differentiation of cancerous and precancerous labels, as well as the location of their focus on a slide, could be accomplished with supplemental immunohistochemically (IHC) stained slides. When distinguishing whether a focus is a nonneoplastic feature versus a cancerous growth, pathologists employ antigen targeting stains to the tissue in question to confirm the diagnosis. For example, a nonneoplastic feature of usual ductal hyperplasia will display diffuse staining for cytokeratin 5 (CK5) and no diffuse staining for estrogen receptor (ER), while a cancerous growth of ductal carcinoma in situ will have negative or focally positive staining for CK5 and diffuse staining for ER [9]. Many tissue samples contain cancerous and non-cancerous features with morphological overlaps that cause variability between annotators. The informative fields IHC slides provide could play an integral role in machine model pathology diagnostics. Following the revisions made on all the annotations, a second experiment was run using ResNet18. Compared to the pilot study, an increase of model prediction accuracy was seen for the labels indc, infl, nneo, norm, and null. This increase is correlated with an increase in annotated area and annotation accuracy. Model performance in identifying the suspicious label decreased by 25% due to the decrease of 57% in the total annotated area described by this label. A summary of the model performance is given in Table 4, which shows the new prediction accuracy and the absolute change in error rate compared to Table 3. The breast tissue subset we are developing includes 3,505 annotated breast pathology slides from 296 patients. The average size of a scanned SVS file is 363 MB. The annotations are stored in an XML format. A CSV version of the annotation file is also available which provides a flat, or simple, annotation that is easy for machine learning researchers to access and interface to their systems. Each patient is identified by an anonymized medical reference number. Within each patient’s directory, one or more sessions are identified, also anonymized to the first of the month in which the sample was taken. These sessions are broken into groupings of tissue taken on that date (in this case, breast tissue). A deidentified patient report stored as a flat text file is also available. Within these slides there are a total of 16,971 total annotated regions with an average of 4.84 annotations per slide. Among those annotations, 8,035 are non-cancerous (normal, background, null, and artifact,) 6,222 are carcinogenic signs (inflammation, nonneoplastic and suspicious,) and 2,714 are cancerous labels (ductal carcinoma in situ and invasive ductal carcinoma in situ.) The individual patients are split up into three sets: train, development, and evaluation. Of the 74 cancerous patients, 20 were allotted for both the development and evaluation sets, while the remain 34 were allotted for train. The remaining 222 patients were split up to preserve the overall distribution of labels within the corpus. This was done in hope of creating control sets for comparable studies. Overall, the development and evaluation sets each have 80 patients, while the training set has 136 patients. In a related component of this project, slides from the Fox Chase Cancer Center (FCCC) Biosample Repository (https://www.foxchase.org/research/facilities/genetic-research-facilities/biosample-repository -facility) are being digitized in addition to slides provided by Temple University Hospital. This data includes 18 different types of tissue including approximately 38.5% urinary tissue and 16.5% gynecological tissue. These slides and the metadata provided with them are already anonymized and include diagnoses in a spreadsheet with sample and patient ID. We plan to release over 13,000 unannotated slides from the FCCC Corpus simultaneously with v1.0.0 of TUDP. Details of this release will also be discussed in this poster. Few digitally annotated databases of pathology samples like TUDP exist due to the extensive data collection and processing required. The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201. [5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021]. [6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/. [7] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/nsf/mri_dpath/. [8] I. Hunt, S. Husain, J. Simons, I. Obeid, and J. Picone, “Recent Advances in the Temple University Digital Pathology Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2019, pp. 1–4. https://ieeexplore.ieee.org/document/9037859. [9] A. P. Martinez, C. Cohen, K. Z. Hanley, and X. (Bill) Li, “Estrogen Receptor and Cytokeratin 5 Are Reliable Markers to Separate Usual Ductal Hyperplasia From Atypical Ductal Hyperplasia and Low-Grade Ductal Carcinoma In Situ,” Arch. Pathol. Lab. Med., vol. 140, no. 7, pp. 686–689, Apr. 2016. https://doi.org/10.5858/arpa.2015-0238-OA. 

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5. Linking Biodiversity Data Using Evolutionary History

   https://doi.org/10.3897/biss.3.36207  

   McTavish, Emily Jane ( June 2019 , Biodiversity Information Science and Standards)

   All life on earth is linked by a shared evolutionary history. Even before Darwin developed the theory of evolution, Linnaeus categorized types of organisms based on their shared traits. We now know these traits derived from these species’ shared ancestry. This evolutionary history provides a natural framework to harness the enormous quantities of biological data being generated today. The Open Tree of Life project is a collaboration developing tools to curate and share evolutionary estimates (phylogenies) covering the entire tree of life (Hinchliff et al. 2015, McTavish et al. 2017). The tree is viewable at https://tree.opentreeoflife.org, and the data is all freely available online. The taxon identifiers used in the Open Tree unified taxonomy (Rees and Cranston 2017) are mapped to identifiers across biological informatics databases, including the Global Biodiversity Information Facility (GBIF), NCBI, and others. Linking these identifiers allows researchers to easily unify data from across these different resources (Fig. 1). Leveraging a unified evolutionary framework across the diversity of life provides new avenues for integrative wide scale research. Downstream tools, such as R packages developed by the R OpenSci foundation (rotl, rgbif) (Michonneau et al. 2016, Chamberlain 2017) and others tools (Revell 2012), make accessing and combining this information straightforward for students as well as researchers (e.g. https://mctavishlab.github.io/BIO144/labs/rotl-rgbif.html). Figure 1. Example linking phylogenetic relationships accessed from the Open Tree of Life with specimen location data from Global Biodiversity Information Facility. For example, a recent publication by Santorelli et al. 2018 linked evolutionary information from Open Tree with species locality data gathered from a local field study as well as GBIF species location records to test a river-barrier hypothesis in the Amazon. By combining these data, the authors were able test a widely held biogeographic hypothesis across 1952 species in 14 taxonomic groups, and found that a river that had been postulated to drive endemism, was in fact not a barrier to gene flow. However, data provenance and taxonomic name reconciliation remain key hurdles to applying data from these large digital biodiversity and evolution community resources to answering biological questions. In the Amazonian river analysis, while they leveraged use of GBIF records as a secondary check on their species records, they relied on their an intensive local field study for their major conclusions, and preferred taxon specific phylogenetic resources over Open Tree where they were available (Santorelli et al. 2018). When Li et al. 2018 assessed large scale phylogenetic approaches, including Open Tree, for measuring community diversity, they found that synthesis phylogenies were less resolved than purpose-built phylogenies, but also found that these synthetic phylogenies were sufficient for community level phylogenetic diversity analyses. Nonetheless, data quality concerns have limited adoption of analyses data from centralized resources (McTavish et al. 2017). Taxonomic name recognition and reconciliation across databases also remains a hurdle for large scale analyses, despite several ongoing efforts to improve taxonomic interoperability and unify taxonomies, such at Catalogue of Life + (Bánki et al. 2018). In order to support innovative science, large scale digital data resources need to facilitate data linkage between resources, and address researchers' data quality and provenance concerns. I will present the model that the Open Tree of Life is using to provide evolutionary data at the scale of the entire tree of life, while maintaining traceable provenance to the publications and taxonomies these evolutionary relationships are inferred from. I will discuss the hurdles to adoption of these large scale resources by researchers, as well as the opportunities for new research avenues provided by the connections between evolutionary inferences and biodiversity digital databases. 

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