Biodiesel effects on particulate radiocarbon (14C) emissions from a diesel engine

Author Posting. © Elsevier B.V., 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Aerosol Science 39 (2008): 667-678, doi:10.1016/j.jaerosci.2008.04.001.The relative amount of 14C in a sample of atmospheric particulate matter (PM), defined as percent modern carbon (pMC), allows EPA to infer the fraction of PM derived from anthropogenic pollution sources. With increased use of biofuels that contain 14C, the main assumption of the two-source model, that 14C is solely derived from biogenic sources, may become invalid. The goal of this study was to determine the 14C content of PM emitted from an off-highway diesel engine running on commercial grade biodiesel. Tests were conducted with an off-highway diesel engine running at 80% load fueled by various blends of soy-based biodiesel. A dilution tunnel was used to collect PM10 emissions on quartz filters that were analyzed for their 14C content using accelerator mass spectrometry. A mobility particle sizer and 5-gas analyzer provided supporting information on the particle size distribution and gas-phase emissions. The pMC of PM10 aerosol increased linearly with the percentage of biodiesel present in the fuel. Therefore, PM emissions resulting from increased combustion of biodiesel fuels will likely affect contemporary 14C apportionment efforts that attempt to split biogenic vs. anthropogenic emissions based on aerosol-14C content. Increasing the biodiesel fuel content also reduced emissions of total hydrocarbons (THC), PM10 mass, and particulate elemental carbon. Biodiesel had variable results on oxides of nitrogen (NOx) emissions

Image-Based Classification of Double-Barred Beach States Using a Convolutional Neural Network and Transfer Learning

Nearshore sandbars characterize many sandy coasts, and unravelling their dynamics is crucial to understanding nearshore sediment pathways. Sandbar morphologies exhibit complex patterns that can be classified into distinct states. The tremendous progress in data-driven learning in image recognition has recently led to the first automated classification of single-barred beach states from Argus imagery using a Convolutional Neural Network (CNN). Herein, we extend this method for the classification of beach states in a double-barred system. We used transfer learning to fine-tune the pre-trained network of ResNet50. Our data consisted of labelled single-bar time-averaged images from the beaches of Narrabeen (Australia) and Duck (US), complemented by 9+ years of daily averaged low-tide images of the double-barred beach of the Gold Coast (Australia). We assessed seven different CNNs, of which each model was tested on the test data from the location where its training data came from, the self-tests, and on the test data of alternate, unseen locations, the transfer-tests. When the model trained on the single-barred data of both Duck and Narrabeen was tested on unseen data of the double-barred Gold Coast, we achieved relatively low performances as measured by F1 scores. In contrast, models trained with only the double-barred beach data showed comparable skill in the self-tests with that of the single-barred models. We incrementally added data with labels from the inner or outer bar of the Gold Coast to the training data from both single-barred beaches, and trained models with both single- and double-barred data. The tests with these models showed that which bar the labels used for training the model mattered. The training with the outer bar labels led to overall higher performances, except at the inner bar. Furthermore, only 10% of additional data with the outer bar labels was needed for reasonable transferability, compared to the 20% of additional data needed with the inner bar labels. Additionally, when trained with data from multiple locations, more data from a new location did not always positively affect the model’s performance on other locations. However, the larger diversity of images coming from more locations allowed the transferability of the model to the locations from where new training data were added

Scanning Tunneling Microscopy in TTF-TCNQ :direct proof of phase and amplitude modulated charge density waves

Charge density waves (CDW) have been studied at the surface of a cleaved TTF-TCNQ single crystal using a low temperature scanning tunneling microscope (STM) under ultra high vacuum (UHV) conditions. All CDW phase transitions of TTF-TCNQ have been identified. The measurement of the modulation wave vector along the a direction provides the first evidence for the existence of domains comprising single plane wave modulated structures in the temperature regime where the transverse wave vector of the CDW is temperature dependent, as hinted by the theory more than 20 years ago.Comment: To appear in Phys.Rev.Rapid. Com

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

Somatic LKB1 Mutations Promote Cervical Cancer Progression

Human Papilloma Virus (HPV) is the etiologic agent for cervical cancer. Yet, infection with HPV is not sufficient to cause cervical cancer, because most infected women develop transient epithelial dysplasias that spontaneously regress. Progression to invasive cancer has been attributed to diverse host factors such as immune or hormonal status, as no recurrent genetic alterations have been identified in cervical cancers. Thus, the pressing question as to the biological basis of cervical cancer progression has remained unresolved, hampering the development of novel therapies and prognostic tests. Here we show that at least 20% of cervical cancers harbor somatically-acquired mutations in the LKB1 tumor suppressor. Approximately one-half of tumors with mutations harbored single nucleotide substitutions or microdeletions identifiable by exon sequencing, while the other half harbored larger monoallelic or biallelic deletions detectable by multiplex ligation probe amplification (MLPA). Biallelic mutations were identified in most cervical cancer cell lines; HeLa, the first human cell line, harbors a homozygous 25 kb deletion that occurred in vivo. LKB1 inactivation in primary tumors was associated with accelerated disease progression. Median survival was only 13 months for patients with LKB1-deficient tumors, but >100 months for patients with LKB1-wild type tumors (P = 0.015, log rank test; hazard ratio = 0.25, 95% CI = 0.083 to 0.77). LKB1 is thus a major cervical tumor suppressor, demonstrating that acquired genetic alterations drive progression of HPV-induced dysplasias to invasive, lethal cancers. Furthermore, LKB1 status can be exploited clinically to predict disease recurrence

‘There is a Time to be Born and a Time to Die’ (Ecclesiastes 3:2a): Jewish Perspectives on Euthanasia

Reviewing the publications of prominent American rabbis who have (extensively) published on Jewish biomedical ethics, this article highlights Orthodox, Conservative and Reform opinions on a most pressing contemporary bioethical issue: euthanasia. Reviewing their opinions against the background of the halachic character of Jewish (biomedical) ethics, this article shows how from one traditional Jewish textual source diverse, even contradictory, opinions emerge through different interpretations. In this way, in the Jewish debate on euthanasia the specific methodology of Jewish (bio)ethical reasoning comes forward as well as a diversity of opinion within Judaism and its branches