More Like this

1. Strain Localization Patterns and Thrust Propagation in 3‐D Discrete Element Method (DEM) Models of Accretionary Wedges

   https://doi.org/10.1029/2022TC007707  

   del Castillo, Enrique M. ; Ferdowsi, Behrooz ; Rubin, Allan M. ; Schoene, Blair ( August 2023 , Tectonics)

   High‐resolution three‐dimensional discrete element method (DEM) simulations of sandbox‐scale models of accretionary wedges suggest thrusts follow a variety of propagation processes and orientations depending on a number of factors. These include the stage of development of the wedge (precritical vs. critical), basal friction, and type of thrust (forward vs. backward‐vergent). In terms of propagation processes, two clear mechanisms are identified. The first involves propagation from the décollement to the wedge top, similar to the standard model of thrust propagation seen in many kinematic models, and in the second, thrusts grow downward from an initial nucleation point just below the top surface of the wedge as well as upward from the décollement joining in the middle. In terms of orientation, forward‐vergent thrusts initially form at Roscoe (θ_R = 45° − Ψ/2) or Arthur orientations (θ_A = 45° − (ϕ + Ψ)/4), and over greater shortening, rotate into Coulomb orientations (θ_C = 45° − ϕ/2). To arrive at these results, a wide array of continuum parameters and fields were extracted from the DEM simulations, including stress, strain, strain rate, kinetic energy, Mohr‐Coulomb parameters, and proximity to yielding using the Drucker‐Prager criterion to visualize thrust nucleation and propagation. Lastly, the advantages and disadvantages of these continuum proxies for discerning failure in the granular assembly are considered, and the spatial and temporal relationship between proximity to yielding and strain localization (both pre‐peak and subsequent persistent shear banding) in the granular model of an accretionary wedge is explored.

    

   more » « less
2. 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. 

   more » « less
3. Cepstral analysis for baseline-insensitive absorption spectroscopy using light sources with pronounced intensity variations

   https://doi.org/10.1364/AO.399405  

   Goldenstein, Christopher S. ; Mathews, Garrett C. ; Cole, Ryan K. ; Makowiecki, Amanda S. ; Rieker, Gregory B. ( September 2020 , Applied Optics)

   This paper presents a data-processing technique that improves the accuracy and precision of absorption-spectroscopy measurements by isolating the molecular absorbance signal from errors in the baseline light intensity ($I_o$) using cepstral analysis. Recently, cepstral analysis has been used with traditional absorption spectrometers to create a modified form of the time-domain molecular free-induction decay (m-FID) signal, which can be analyzed independently from$I_o$. However, independent analysis of the molecular signature is not possible when the baseline intensity and molecular response do not separate well in the time domain, which is typical when using injection-current-tuned lasers [e.g., tunable diode and quantum cascade lasers (QCLs)] and other light sources with pronounced intensity tuning. In contrast, the method presented here is applicable to virtually all light sources since it determines gas properties by least-squares fitting a simulated m-FID signal (comprising an estimated$I_o$and simulated absorbance spectrum) to the measured m-FID signal in the time domain. This method is insensitive to errors in the estimated$I_o$, which vary slowly with optical frequency and, therefore, decay rapidly in the time domain. The benefits provided by this method are demonstrated via scanned-wavelength direct-absorption-spectroscopy measurements acquired with a distributed-feedback (DFB) QCL. The wavelength of a DFB QCL was scanned across the CO P(0,20) and P(1,14) absorption transitions at 1 kHz to measure the gas temperature and concentration of CO. Measurements were acquired in a gas cell and in a laminar ethylene–air diffusion flame at 1 atm. The measured spectra were processed using the new m-FID-based method and two traditional methods, which rely on inferring (instead of rejecting) the baseline error within the spectral-fitting routine. The m-FID-based method demonstrated superior accuracy in all cases and a measurement precision that was$\approx <\#comment/>1.5$to 10 times smaller than that provided using traditional methods.

    

   more » « less
4. Effects of selective breeding for high voluntary wheel‐running behavior on femoral nutrient canal size and abundance in house mice

   https://doi.org/10.1111/joa.12830  

   Schwartz, Nicole E. ; Patel, Biren A. ; Garland, Jr, Theodore ; Horner, Angela M. ( May 2018 , Journal of Anatomy)

   Bone modeling and remodeling are aerobic processes that entail relatively high oxygen demands. Long bones receive oxygenated blood from nutrient arteries, epiphyseal‐metaphyseal arteries, and periosteal arteries, with the nutrient artery supplying the bulk of total blood volume in mammals (~ 50–70%). Estimates of blood flow into these bones can be made from the dimensions of the nutrient canal, through which nutrient arteries pass. Unfortunately, measuring these canal dimensions non‐invasively (i.e. without physical sectioning) is difficult, and thus researchers have relied on more readily visible skeletal proxies. Specifically, the size of the nutrient artery has been estimated from dimensions (e.g. minimum diameters) of the periosteal (external) opening of the nutrient canal. This approach has also been utilized by some comparative morphologists and paleontologists, as the opening of a nutrient canal is present long after the vascular soft tissue has degenerated. The literature on nutrient arteries and canals is sparse, with most studies consisting of anatomical descriptions from surgical proceedings, and only a few investigating the links between nutrient canal morphology and physiology or behavior. The primary objective of this study was to evaluate femur nutrient canal morphology in mice with known physiological and behavioral differences; specifically, mice from an artificial selection experiment for high voluntary wheel‐running behavior. Mice from four replicate high runner (HR) lines are known to differ from four non‐selected control (C) lines in both locomotor and metabolic activity, withHRmice having increased voluntary wheel‐running behavior and maximal aerobic capacity (VO₂max) during forced treadmill exercise. Femora from adult mice (average age 7.5 months) of the 11th generation of this selection experiment were μCT‐scanned and three‐dimensional virtual reconstructions of nutrient canals were measured for minimum cross‐sectional area as a skeletal proxy of blood flow. Gross observations revealed that nutrient canals varied far more in number and shape than prior descriptions would indicate, regardless of sex or genetic background (i.e.HRvs. C lines). Canals adopted non‐linear shapes and paths as they traversed from the periosteal to endosteal borders through the cortex, occasionally even branching within the cortical bone. Additionally, mice from bothHRand C lines averaged more than four nutrient canals per femur, in contrast to the one to two nutrient canals described for femora from rats, pigs, and humans in prior literature. Mice fromHRlines had significantly larger total nutrient canal area than C lines, which was the result not of an increase in the number of nutrient canals, but rather an increase in their average cross‐section size. This study demonstrates that mice with an evolutionary history of increased locomotor activity and maximal aerobic metabolic rate have a concomitant increase in the size of their femoral nutrient canals. Although the primary determinant of nutrient canal size is currently not well understood, the present results bolster use of nutrient canal size as a skeletal indicator of aerobically supported levels of physical activity in comparative studies.

    

   more » « less
5. Two-dimensional Mapping of Bulk Residual Stress Using Cut Mouth Opening Displacement

   https://doi.org/10.1007/s11340-021-00745-2  

   Chighizola, C. R. ; Hill, M. R. ( August 2021 , Experimental Mechanics)

   Prior work described an approach for mapping the two-dimensional spatial distribution of biaxial residual stress in plate-like samples, the approach combining multiple slitting measurements with elastic stress analysis.

    This paper extends the prior work by applying a new variation of the slitting method that uses measurements of cut mouth opening displacement (CMOD) rather than back-face strain (BFS). 

   First, CMOD slitting is validated using an experiment where: BFS and CMOD are measured simultaneously on the same sample during incremental slitting; two residual stress profiles are computed, one from the BFS data and a second from the CMOD data; and the two residual stress profiles are compared. Following validation, multiple adjacent CMOD slitting measurements are used to construct two-dimensional maps of residual stress in plates cut from quenched aluminum.

   The two residual stress versus depth profiles, each computed separately from BFS or CMOD data, are in agreement, with compression near the plate boundaries (-150 MPa) and tension near the plate center (100 MPa); differences between the two stress profiles have a maximum of 25 MPa and a RMS of 7.2 MPa. Repeated biaxial residual stress mapping measurements show the CMOD technique is repeatable, and complementary contour method measurements show the mappings are valid. Aspects of CMOD and BFS deformations during slitting are also described and show they are generally complementary but that CMOD slitting is favorable in narrow samples.

    

   more » « less