More Like this

1. Effects of Atmospheric Circulation on Stream Chemistry in Forested Watersheds Across the Northeastern United States: Part 2. Interannual Weather Type Variability

   https://doi.org/10.1029/2021JD034546  

   Suriano, Z. J. ; Siegert, C. M. ; Leathers, D. J. ; Gold, A. J. ; Addy, K. ; Schroth, A. W. ; Seybold, E. ; Inamdar, S. ; Levia, D. F. ( June 2021 , Journal of Geophysical Research: Atmospheres)

   Using synoptic classification techniques, synoptic‐scale weather types associated with large exports of dissolved organic carbon and nitrate‐nitrogen in three forested watersheds in the northeastern United States are analyzed. In contrast to Siegert et al. (2021,https://doi.org/10.1029/2020jd033413; Part 1), which details the general synoptic conditions associated with stream chemistry variations, this study focuses on the long‐term frequency, trends, and global‐scale forcing mechanisms of individual, chemistry‐relevant, synoptic types. Nine individual types are identified as the most important to stream chemistry exports in northeastern watersheds during a 2.5‐year period of high‐frequency stream chemistry observation (2014–2016). Streamflow chemistry was the most influenced by Northwest Flow synoptic weather types in Vermont, cold front passages associated with Weak Westerly Flow types in Rhode Island, and Southwest Flow (SWF) types in Maryland. Each type provides unique atmospheric conditions that result in variations in precipitation, and thus chemical signals. From 1948 to 2017, the Arctic and North Atlantic Oscillations greatly influenced the interannual frequency of the analyzed synoptic types. When the indices were positively phased, SWF types were more frequent, while Northwest Flow and Weak Westerly Flow types were less frequent. Long‐term trends in synoptic type frequency are, inpart, due to documented changes in these teleconnection indices toward more positively phased configurations. As the climate continues to change into the 21st century, such associations may continue to drive large changes in synoptic type frequency, and thus watershed hydrology and biogeochemistry in the Northeast region.

    

   more » « less
2. Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City (2016-2019)

   https://doi.org/10.6073/pasta/5194fc22aaa4e0a5d58cfc6884498aa0  

   Tellman, Elizabeth ( January 2021 , Environmental Data Initiative)

   This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021). The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM), at the Institute of Geography via Dr. Armando Peralta Higuera, who provided support for two students, Juan Alberto Guerra Moreno and Kimberly Mendez Gomez for validating the Landsat urbanization algorithm. Fidel Serrano-Candela, at the UNAM Laboratory of the National Laboratory for Sustainability Sciences (LANCIS) also provided support for urbanization algorithm development and validation, and Rodrigo Garcia Herrera, who provided support for hosting data at LANCIS (at: http://patung.lancis.ecologia.unam.mx/tellman/). Additional collaborators include Enrique Castelán, who provided support for the informal urbanization data from SEDEMA (Ministry of the Environmental for Mexico City). Electoral, land titling, and land zoning data were digitized with support from Juana Martinez, Natalia Hernandez, Alexia Macario Sanchez, Enrique Ruiz Durazo, in collaboration with Felipe de Alba, at CESOP (Center of Social Studies and Public Opinion, at the Mexican Legislative Assembly). The data include geospatial time series data regarding changes in urban land cover, digitized electoral results, land titling, land zoning, and public housing. Additional funding for this work was provided by NSF under Grant No. 1414052, CNH: The Dynamics of Multiscalar Adaptation in Megacities (PI H. Eakin), and the NSF-CONACYT GROW fellowship NSF No. 026257-001 and CONACYT number 291303 (PI Bojórquez). References: Tellman, B., Eakin, H., Janssen, M.A., Alba, F. De, Ii, B.L.T., 2021. The Role of Institutional Entrepreneurs and Informal Land Transactions in Mexico City’s Urban Expansion. World Dev. 140, 1–44. https://doi.org/10.1016/j.worlddev.2020.105374 Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A.K., Cheng, W.-H., Balling, R.C., 2018. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205, 253–275. https://doi.org/10.1016/j.rse.2017.11.026 

   more » « less
3. Coastal Ocean Data Analysis Product in North America (CODAP-NA) – an internally consistent data product for discrete inorganic carbon, oxygen, and nutrients on the North American ocean margins

   https://doi.org/10.5194/essd-13-2777-2021  

   Jiang, Li-Qing ; Feely, Richard A. ; Wanninkhof, Rik ; Greeley, Dana ; Barbero, Leticia ; Alin, Simone ; Carter, Brendan R. ; Pierrot, Denis ; Featherstone, Charles ; Hooper, James ; et al ( January 2021 , Earth System Science Data)

   null (Ed.)

   Abstract. Internally consistent, quality-controlled (QC) data products play animportant role in promoting regional-to-global research efforts tounderstand societal vulnerabilities to ocean acidification (OA). However,there are currently no such data products for the coastal ocean, where mostof the OA-susceptible commercial and recreational fisheries and aquacultureindustries are located. In this collaborative effort, we compiled, quality-controlled, and synthesized 2 decades of discrete measurements ofinorganic carbon system parameters, oxygen, and nutrient chemistry data fromthe North American continental shelves to generate a data product calledthe Coastal Ocean Data Analysis Product in North America (CODAP-NA). Thereare few deep-water (> 1500 m) sampling locations in the currentdata product. As a result, crossover analyses, which rely on comparisonsbetween measurements on different cruises in the stable deep ocean, couldnot form the basis for cruise-to-cruise adjustments. For this reason, carewas taken in the selection of data sets to include in this initial releaseof CODAP-NA, and only data sets from laboratories with known qualityassurance practices were included. New consistency checks and outlierdetections were used to QC the data. Future releases of this CODAP-NAproduct will use this core data product as the basis for cruise-to-cruisecomparisons. We worked closely with the investigators who collected andmeasured these data during the QC process. This version (v2021) of theCODAP-NA is comprised of 3391 oceanographic profiles from 61 researchcruises covering all continental shelves of North America, from Alaska toMexico in the west and from Canada to the Caribbean in the east. Data for 14variables (temperature; salinity; dissolved oxygen content; dissolvedinorganic carbon content; total alkalinity; pH on total scale; carbonateion content; fugacity of carbon dioxide; and substance contents of silicate,phosphate, nitrate, nitrite, nitrate plus nitrite, and ammonium) have beensubjected to extensive QC. CODAP-NA is available as a merged data product(Excel, CSV, MATLAB, and NetCDF; https://doi.org/10.25921/531n-c230,https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0219960.html, last access: 15 May 2021)(Jiang et al., 2021a). The original cruise data have also been updated withdata providers' consent and summarized in a table with links to NOAA'sNational Centers for Environmental Information (NCEI) archives(https://www.ncei.noaa.gov/access/ocean-acidification-data-stewardship-oads/synthesis/NAcruises.html). 

   more » « less
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. 

   more » « less
5. COVID-19 and Housing Security in Remote Alaska Native Communities

   Nicewonger, Todd E. ; McNair, Lisa D. ; Fritz, Stacey ; Supple, Laura ; Criss-Carboy, Laura ( November 2023 , Virginia Tech Publishing)

   Nicewonger, Todd E. ; McNair, Lisa D. ; Fritz, Stacey (Ed.)

   https://pressbooks.lib.vt.edu/alaskanative/ At the start of the pandemic, the editors of this annotated bibliography initiated a remote (i.e., largely virtual) ethnographic research project that investigated how COVID-19 was impacting off-site modular construction practices in Alaska Native communities. Many of these communities are located off the road system and thus face not only dramatically higher costs but multiple logistical challenges in securing licensed tradesmen and construction crews and in shipping building supplies and equipment to their communities. These barriers, as well as the region’s long winters and short building seasons, complicate the construction of homes and related infrastructure projects. Historically, these communities have also grappled with inadequate housing, including severe overcrowding and poor-quality building stock that is rarely designed for northern Alaska’s climate (Marino 2015). Moreover, state and federal bureaucracies and their associated funding opportunities often further complicate home building by failing to accommodate the digital divide in rural Alaska and the cultural values and practices of Native communities.[1] It is not surprising, then, that as we were conducting fieldwork for this project, we began hearing stories about these issues and about how the restrictions caused by the pandemic were further exacerbating them. Amidst these stories, we learned about how modular home construction was being imagined as a possible means for addressing both the complications caused by the pandemic and the need for housing in the region (McKinstry 2021). As a result, we began to investigate how modular construction practices were figuring into emergent responses to housing needs in Alaska communities. We soon realized that we needed to broaden our focus to capture a variety of prefabricated building methods that are often colloquially or idiomatically referred to as “modular.” This included a range of prefabricated building systems (e.g., manufactured, volumetric modular, system-built, and Quonset huts and other reused military buildings[2]). Our further questions about prefabricated housing in the region became the basis for this annotated bibliography. Thus, while this bibliography is one of multiple methods used to investigate these issues, it played a significant role in guiding our research and helped us bring together the diverse perspectives we were hearing from our interviews with building experts in the region and the wider debates that were circulating in the media and, to a lesser degree, in academia. The actual research for each of three sections was carried out by graduate students Lauren Criss-Carboy and Laura Supple.[3] They worked with us to identify source materials and their hard work led to the team identifying three themes that cover intersecting topics related to housing security in Alaska during the pandemic. The source materials collected in these sections can be used in a variety of ways depending on what readers are interested in exploring, including insights into debates on housing security in the region as the pandemic was unfolding (2021-2022). The bibliography can also be used as a tool for thinking about the relational aspects of these themes or the diversity of ways in which information on housing was circulating during the pandemic (and the implications that may have had on community well-being and preparedness). That said, this bibliography is not a comprehensive analysis. Instead, by bringing these three sections together with one another to provide a snapshot of what was happening at that time, it provides a critical jumping off point for scholars working on these issues. The first section focuses on how modular housing figured into pandemic responses to housing needs. In exploring this issue, author Laura Supple attends to both state and national perspectives as part of a broader effort to situate Alaska issues with modular housing in relation to wider national trends. This led to the identification of multiple kinds of literature, ranging from published articles to publicly circulated memos, blog posts, and presentations. These materials are important source materials that will likely fade in the vastness of the Internet and thus may help provide researchers with specific insights into how off-site modular construction was used – and perhaps hyped – to address pandemic concerns over housing, which in turn may raise wider questions about how networks, institutions, and historical experiences with modular construction are organized and positioned to respond to major societal disruptions like the pandemic. As Supple pointed out, most of the material identified in this review speaks to national issues and only a scattering of examples was identified that reflect on the Alaskan context. The second section gathers a diverse set of communications exploring housing security and homelessness in the region. The lack of adequate, healthy housing in remote Alaska communities, often referred to as Alaska’s housing crisis, is well-documented and preceded the pandemic (Guy 2020). As the pandemic unfolded, journalists and other writers reported on the immense stress that was placed on already taxed housing resources in these communities (Smith 2020; Lerner 2021). The resulting picture led the editors to describe in their work how housing security in the region exists along a spectrum that includes poor quality housing as well as various forms of houselessness including, particularly relevant for the context, “hidden homelessness” (Hope 2020; Rogers 2020). The term houseless is a revised notion of homelessness because it captures a richer array of both permanent and temporary forms of housing precarity that people may experience in a region (Christensen et al. 2107). By identifying sources that reflect on the multiple forms of housing insecurity that people were facing, this section highlights the forms of disparity that complicated pandemic responses. Moreover, this section underscores ingenuity (Graham 2019; Smith 2020; Jason and Fashant 2021) that people on the ground used to address the needs of their communities. The third section provides a snapshot from the first year of the pandemic into how CARES Act funds were allocated to Native Alaska communities and used to address housing security. This subject was extremely complicated in Alaska due to the existence of for-profit Alaska Native Corporations and disputes over eligibility for the funds impacted disbursements nationwide. The resources in this section cover that dispute, impacts of the pandemic on housing security, and efforts to use the funds for housing as well as barriers Alaska communities faced trying to secure and use the funds. In summary, this annotated bibliography provides an overview of what was happening, in real time, during the pandemic around a specific topic: housing security in largely remote Alaska Native communities. The media used by housing specialists to communicate the issues discussed here are diverse, ranging from news reports to podcasts and from blogs to journal articles. This diversity speaks to the multiple ways in which information was circulating on housing at a time when the nightly news and radio broadcasts focused heavily on national and state health updates and policy developments. Finding these materials took time, and we share them here because they illustrate why attention to housing security issues is critical for addressing crises like the pandemic. For instance, one theme that emerged out of a recent National Science Foundation workshop on COVID research in the North NSF Conference[4] was that Indigenous communities are not only recovering from the pandemic but also evaluating lessons learned to better prepare for the next one, and resilience will depend significantly on more—and more adaptable—infrastructure and greater housing security. 

   more » « less