The mechanisms of plant litter decay in drylands are poorly understood, limiting the accuracy of nutrient‐cycling models for these systems. We monitored the decay of 12 leaf litter types on the soil surface of the Sonoran Desert for 34 months and assessed what traits predicted mass loss and how exposure to different wavebands of sunlight influenced mass loss. Mass loss varied considerably among litter types, ranging from 42%–96% after 34 months in full sunlight. Traditional indices of litter quality (e.g., initial C:N or lignin:N ratios) failed to predict differences in mass loss among litter types. The strongest predictor of mass loss was the microbial respiration rate of initial litter, which explained 45%–54% of the variation in loss among litter types. Microbial respiration rates were not correlated with traditional indices of litter quality, but were positively correlated with the water‐soluble fraction in litter and concentrations of dissolved organic C in this fraction. Traditional indices of litter quality failed to predict decay likely because they did a poor job of predicting microbial degradability of litter, not because microbial degradation was a minor driver of decay. In all radiation‐exposure treatments, water‐soluble fractions and respiration rates increased through decay and were several times higher after 34 months than initially. Hence, labile pools and microbial degradability of litter increased through decay in contrast to traditional views that labile pools decline and constrain microbes. Litter exposed to UV or UV through blue radiation wavelengths, lost on average 1.3 times or 1.5 times more mass, respectively, than litter not exposed to these wavebands. The magnitude of this photodegradation was greater in litter types that had higher initial concentrations of hemicellulose and cellulose per unit surface area. Litter exposed to full sun had higher water‐soluble fractions and usually had higher respiration rates, illustrating that sunlight accelerated microbial degradation by increasing labile pools. The processes driving litter decay appeared to differ appreciably from mesic systems and involved strong couplings between abiotic and biotic drivers.
While many aromatic polyurethane systems suffer from poor hydrolytic stability, more recently proposed aliphatic systems are oxidatively labile. The use of the renewable monomer glycerol as a more oxidatively resistant moiety for inclusion in shape memory polymers (SMPs) is demonstrated here. Glycerol‐containing SMPs and the amino alcohol control compositions are compared, with accelerated degradation testing displaying increased stability (time to complete mass loss) as a result of the inclusion of glycerol without sacrificing the shape memory, thermal transitions, or the ultralow density achieved with the control compositions. Gravimetric analysis in accelerated oxidative solution indicates that the control will undergo complete mass loss by approximately 18 days, while lower concentrations of glycerol will degrade fully by 30 days and higher concentrations will possess approximately 40% mass at the same time. In real‐time degradation analysis, high concentrations of glycerol SMPs have 96% mass remaining at 8 months with 88% gel fraction remaining that that time, compared to less than 50% mass for the control samples with 5% gelation. Mechanically, low glycerol‐containing SMPs were not robust enough for testing at three months, while high glycerol concentrations displayed increased elastic moduli (133% of virgin materials) and 18% decreased strain to failure. The role of the secondary alcohol, as well as isocyanates, is presented as being a crucial component in controlling degradation; a free secondary alcohol can more rapidly undergo oxidation or dehydration to ultimately yield carboxylic acids, aldehydes, carbon dioxide, and alkenes. Understanding these pathways will improve the utility of medical devices through more precise control of property loss and patient risk management through reduced degradation. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci.
- NSF-PAR ID:
- 10371296
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Applied Polymer Science
- Volume:
- 136
- Issue:
- 35
- ISSN:
- 0021-8995
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Obeid, Iyad ; Selesnick, Ivan ; Picone, Joseph (Ed.)The Temple University Hospital Seizure Detection Corpus (TUSZ) [1] has been in distribution since April 2017. It is a subset of the TUH EEG Corpus (TUEG) [2] and the most frequently requested corpus from our 3,000+ subscribers. It was recently featured as the challenge task in the Neureka 2020 Epilepsy Challenge [3]. A summary of the development of the corpus is shown below in Table 1. The TUSZ Corpus is a fully annotated corpus, which means every seizure event that occurs within its files has been annotated. The data is selected from TUEG using a screening process that identifies files most likely to contain seizures [1]. Approximately 7% of the TUEG data contains a seizure event, so it is important we triage TUEG for high yield data. One hour of EEG data requires approximately one hour of human labor to complete annotation using the pipeline described below, so it is important from a financial standpoint that we accurately triage data. A summary of the labels being used to annotate the data is shown in Table 2. Certain standards are put into place to optimize the annotation process while not sacrificing consistency. Due to the nature of EEG recordings, some records start off with a segment of calibration. This portion of the EEG is instantly recognizable and transitions from what resembles lead artifact to a flat line on all the channels. For the sake of seizure annotation, the calibration is ignored, and no time is wasted on it. During the identification of seizure events, a hard “3 second rule” is used to determine whether two events should be combined into a single larger event. This greatly reduces the time that it takes to annotate a file with multiple events occurring in succession. In addition to the required minimum 3 second gap between seizures, part of our standard dictates that no seizure less than 3 seconds be annotated. Although there is no universally accepted definition for how long a seizure must be, we find that it is difficult to discern with confidence between burst suppression or other morphologically similar impressions when the event is only a couple seconds long. This is due to several reasons, the most notable being the lack of evolution which is oftentimes crucial for the determination of a seizure. After the EEG files have been triaged, a team of annotators at NEDC is provided with the files to begin data annotation. An example of an annotation is shown in Figure 1. A summary of the workflow for our annotation process is shown in Figure 2. Several passes are performed over the data to ensure the annotations are accurate. Each file undergoes three passes to ensure that no seizures were missed or misidentified. The first pass of TUSZ involves identifying which files contain seizures and annotating them using our annotation tool. The time it takes to fully annotate a file can vary drastically depending on the specific characteristics of each file; however, on average a file containing multiple seizures takes 7 minutes to fully annotate. This includes the time that it takes to read the patient report as well as traverse through the entire file. Once an event has been identified, the start and stop time for the seizure is stored in our annotation tool. This is done on a channel by channel basis resulting in an accurate representation of the seizure spreading across different parts of the brain. Files that do not contain any seizures take approximately 3 minutes to complete. Even though there is no annotation being made, the file is still carefully examined to make sure that nothing was overlooked. In addition to solely scrolling through a file from start to finish, a file is often examined through different lenses. Depending on the situation, low pass filters are used, as well as increasing the amplitude of certain channels. These techniques are never used in isolation and are meant to further increase our confidence that nothing was missed. Once each file in a given set has been looked at once, the annotators start the review process. The reviewer checks a file and comments any changes that they recommend. This takes about 3 minutes per seizure containing file, which is significantly less time than the first pass. After each file has been commented on, the third pass commences. This step takes about 5 minutes per seizure file and requires the reviewer to accept or reject the changes that the second reviewer suggested. Since tangible changes are made to the annotation using the annotation tool, this step takes a bit longer than the previous one. Assuming 18% of the files contain seizures, a set of 1,000 files takes roughly 127 work hours to annotate. Before an annotator contributes to the data interpretation pipeline, they are trained for several weeks on previous datasets. A new annotator is able to be trained using data that resembles what they would see under normal circumstances. An additional benefit of using released data to train is that it serves as a means of constantly checking our work. If a trainee stumbles across an event that was not previously annotated, it is promptly added, and the data release is updated. It takes about three months to train an annotator to a point where their annotations can be trusted. Even though we carefully screen potential annotators during the hiring process, only about 25% of the annotators we hire survive more than one year doing this work. To ensure that the annotators are consistent in their annotations, the team conducts an interrater agreement evaluation periodically to ensure that there is a consensus within the team. The annotation standards are discussed in Ochal et al. [4]. An extended discussion of interrater agreement can be found in Shah et al. [5]. The most recent release of TUSZ, v1.5.2, represents our efforts to review the quality of the annotations for two upcoming challenges we hosted: an internal deep learning challenge at IBM [6] and the Neureka 2020 Epilepsy Challenge [3]. One of the biggest changes that was made to the annotations was the imposition of a stricter standard for determining the start and stop time of a seizure. Although evolution is still included in the annotations, the start times were altered to start when the spike-wave pattern becomes distinct as opposed to merely when the signal starts to shift from background. This cuts down on background that was mislabeled as a seizure. For seizure end times, all post ictal slowing that was included was removed. The recent release of v1.5.2 did not include any additional data files. Two EEG files had been added because, originally, they were corrupted in v1.5.1 but were able to be retrieved and added for the latest release. The progression from v1.5.0 to v1.5.1 and later to v1.5.2, included the re-annotation of all of the EEG files in order to develop a confident dataset regarding seizure identification. Starting with v1.4.0, we have also developed a blind evaluation set that is withheld for use in competitions. The annotation team is currently working on the next release for TUSZ, v1.6.0, which is expected to occur in August 2020. It will include new data from 2016 to mid-2019. This release will contain 2,296 files from 2016 as well as several thousand files representing the remaining data through mid-2019. In addition to files that were obtained with our standard triaging process, a part of this release consists of EEG files that do not have associated patient reports. Since actual seizure events are in short supply, we are mining a large chunk of data for which we have EEG recordings but no reports. Some of this data contains interesting seizure events collected during long-term EEG sessions or data collected from patients with a history of frequent seizures. It is being mined to increase the number of files in the corpus that have at least one seizure event. We expect v1.6.0 to be released before IEEE SPMB 2020. The TUAR Corpus is an open-source database that is currently available for use by any registered member of our consortium. To register and receive access, please follow the instructions provided at this web page: https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml. 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