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1. Spatial habitat heterogeneity influences host–pathogen dynamics in a patchy population of R anchman's tiger moth

   https://doi.org/10.1002/ecy.4144  

   Pepi, Adam ; Pan, Vincent ; Grof‐Tisza, Patrick ; Holyoak, Marcel ; Ballman, Alexis ; Laws‐McNeil, Aiyanna ; Mase, Vinay ; Moseley, Cameron ; Karban, Richard ( August 2023 , Ecology)

   Host–pathogen dynamics are influenced by many factors that vary locally, but models of disease rarely consider dynamics across spatially heterogeneous environments. In addition, theory predicts that dispersal will influence host–pathogen dynamics of populations that are linked, although this has not been examined empirically in natural systems. We examined the spatial dynamics of a patchy population of tiger moths and its baculovirus pathogen, in which habitat type and weather influence dynamics. Theoretical models of host–baculovirus dynamics predict that such variation in dynamics between habitat types could be driven by a range of factors, of which we predict two are likely to be operating in this system: (1) differences in the environmental persistence of pathogens or (2) differences in host intrinsic rates of increase. We used time series models and monitored infection rates of hosts to characterize population and disease dynamics and distinguish between these possibilities. We also examined the role of host dispersal (connectivity) and weather as important contributors to dynamics, using time series models and experiments. We found that the population growth rate was higher, delayed density dependence was weaker, and long‐period oscillations had lower amplitudes in high‐quality habitat patches. The infection rate was higher on average in high‐quality habitat, and this was likely to have been driven by higher mean population densities and no differences in pathogen persistence in different habitats (delayed density dependence). Time series modeling and experiments also showed an interactive effect of temperature and precipitation on moth population growth rates (likely caused by variation in host plant quality and quantity), and an effect of connectivity. Our results showed that spatial heterogeneity, connectivity, climate, and their interactions were important in driving host–baculovirus dynamics. In particular, our study found that connected patches and spatial heterogeneity generated differences in dynamics that only partially aligned with theoretical predictions.

    

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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. 

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3. Evaluating conceptual models of landscape change

   https://doi.org/10.1111/ecog.02543  

   Brudvig, Lars A. ; Leroux, Shawn J. ; Albert, Cécile H. ; Bruna, Emilio M. ; Davies, Kendi F. ; Ewers, Robert M. ; Levey, Douglas J. ; Pardini, Renata ; Resasco, Julian ( November 2016 , Ecography)

   A variety of landscape models are used to conceptualize and interpret human impacts on ecosystems and their biodiversity. The simplest, a ‘patch‐matrix’ model, is rooted in island biogeography theory and assumes a dichotomy between generic, easily‐defined habitat patches and a surrounding matrix that is completely inhospitable. This dichotomy between patch and matrix habitats has been recently relaxed, with the ‘continuum’ model taking this relaxation to its extreme and logical endpoint – a species‐based model with no a priori definition of habitat or matrix, but rather focusing on ecological gradients. Yet, because few empirical comparisons of these bookending models exist, we lack understanding of their relative utility or the merits of hybrid approaches that combine attributes of patch‐matrix and continuum models. To guide such considerations, we first develop a decision‐making framework for the application of patch‐matrix, continuum, and hybrid models. The framework takes into account study objectives, attributes of the landscape, and species traits. We then evaluate this framework by empirically comparing how continuum, patch‐matrix, and hybrid models explain beetle distributions across two contrasting fragmented landscapes, for species differing in trophic level and habitat specificity. Within the Hope River Forest Fragmentation Project, a system with strong landscape contrast and distinct (‘hard’) structural edges between forest fragments and grassland, we find broad support for hybrid models, particularly those incorporating surrounding landscape structure. Conversely, within the Wog Wog Habitat Fragmentation Experiment, a system with weak landscape contrast and ‘soft’ structural edges between natural and plantation forest, we find co‐support for continuum and hybrid models. We find no support in either system for patch‐matrix, relative to continuum and hybrid models. We conclude by considering key questions and areas of research for advancing the application of models to understand species responses and biodiversity patterns associated with land‐use change.

    

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4. Multiscale analysis of canopy arthropod diversity in a volcanically fragmented landscape

   https://doi.org/10.1002/ecs2.2653  

   Tielens, Elske K. ; Neel, Maile N. ; Leopold, Devin R. ; Giardina, Christian P. ; Gruner, Daniel S. ( April 2019 , Ecosphere)

   Habitat fragmentation resulting in habitat loss and increased isolation is a dominant driver of global species declines. Habitat isolation and connectivity vary across scales, and understanding how connectivity affects biodiversity can be challenging because the relevant scale depends on the taxa involved. A multiscale analysis can provide insight in biodiversity patterns across spatial scale when information on dispersal ability is not available, in particular for community‐level studies focusing on multiple taxa. In this study, we examine the relationship between arthropod diversity, patch area, and connectivity using a multiscale approach. We make use of a natural experiment on Hawai‘i Island, where historic volcanic activity has transformed contiguous native forests to lava matrix and discrete forest patches. This landscape of patches has persisted for 150 yr, and we selected 10,000 ha consisting of 863 patches to analyze landscape connectivity using a graph theory approach. We collected arthropod samples fromMetrosideros polymorpha tree canopies in 34 forest patches during multiple years. We analyzed the relationship of arthropod diversity with area, as well as with connectivity across increasing scales, or dispersal threshold distances. In contrast to well‐established ecological theory as well as prior work on birds and fungi in this system, we did not find support for a canonical species–area relationship. Next, we calculated connectivity across spatial scales and found lower Shannon diversity with higher connectivity at small scales, but no effect at increased dispersal threshold distances. We examined the landscape structure and found all habitat patches connected into three subnetworks at a 350 m threshold distance. All patches were connected at 700 m threshold distance, indicating structural dispersal limitation only at small scales. Our findings suggest that canopy arthropods are not dispersal limited at scales shown to impact both soil fungi and birds in this system. Instead, Hawaiian canopy arthropods may perceive the landscape as a connected area where discrete forest patches and the early‐successional matrix contribute resources that vary spatially with regard to habitat quality. We argue for the utility of multiscale approaches, and the importance of examining maintenance of biodiversity in fragmented landscapes that persist for hundreds of years.

    

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5. Experimental evidence does not support the Habitat Amount Hypothesis

   https://doi.org/10.1111/ecog.02535  

   Haddad, Nick M. ; Gonzalez, Andrew ; Brudvig, Lars A. ; Burt, Melissa A. ; Levey, Douglas J. ; Damschen, Ellen I. ( November 2016 , Ecography)

   For a half century, habitat configuration – the arrangement of habitat patches within a landscape – has been central to theories of landscape ecology, population dynamics, and community assembly, in addition to conservation strategies. A recent hypothesis advanced by Fahrig (2013) would, if supported, greatly diminish the relevance of habitat configuration as a predictor of diversity. The Habitat Amount Hypothesis posits that the sample area effect overrides patch size and patch isolation effects of habitat fragmentation on species richness. It predicts that the amount of habitat in a local landscape, regardless of configuration, is the main landscape‐level determinant of species richness. If habitat amount is indeed the major, landscape‐level driver of species richness, the slopes of the species–area relationship (SAR) for otherwise similar fragmented and unfragmented landscapes should be indistinguishable. We tested the Habitat Amount Hypothesis with data from two replicated and controlled habitat fragmentation experiments that disentangle the effects of habitat amount and configuration. One experiment provided time‐series data on plant species richness and the other on micro‐arthropod species richness. We found that, relative to less fragmented habitats, the SARs for fragmented habitats have significantly higher slopes and that the magnitude of the difference in slopes increased over time. Relatively more species were lost in smaller areas when fragments were more isolated. In both experiments, the proportion of species lost due to increased habitat fragmentation was nearly identical to the proportion lost due to reduced habitat amount. Our results provide a direct and experimentally derived refutation of the Habitat Amount Hypothesis, supporting the long‐held view that in addition to area, patch isolation and configuration are important determinants of species richness. Differences in species richness between fragmented and non‐fragmented habitats increase over time, demonstrating that long‐term studies are needed to understand the effects of fragmentation, above and beyond the amount of habitat lost.

    

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