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1. Spatial, temporal and taxonomic scaling of richness in an eastern African large mammal community

   https://doi.org/10.1111/geb.12762  

   Du, Andrew ; Behrensmeyer, Anna K. ( October 2018 , Global Ecology and Biogeography)

   Ecological patterns and process change across spatial, temporal and taxonomic scales. This confounds comparisons between modern and fossil communities, which are sampled across very different scales, especially temporal ones. We use a recent bone dataset (i.e., “death assemblages”) from a modern ecosystem to explore spatial, temporal and taxonomic scaling in biodiversity assessments. Our ultimate goal is to create a model based on these scaling relationships to facilitate meaningful comparisons between modern and fossil communities.

   Amboseli National Park, southern Kenya.

   Mid‐1960 s to present day.

   Large mammals (>1 kg).

   We implemented a random placement null model and used model selection methods to investigate how species richness at Amboseli scales as a function of time and area [i.e., the species–time–area relationship (STAR) model]. We then analysed how the model coefficients change at different taxonomic scales (i.e., genus, family, order).

   In agreement with previous studies, we find species richness scales positively with time and area but with a negative interaction between the two. Rates of richness turnover decrease as taxonomic scale increases.

   We hypothesize that decreasing rates of turnover with increasing spatial and/or temporal scale are caused by taking progressively larger samples from a species pool that is changing at a slower rate relative to turnover at the scale of sampling. Because increasing area and time are simply alternative ways of uncovering the species pool, increased time‐averaging of communities results in a more spatially averaged ecological signal. Increasing taxonomic scale causes turnover rates to decrease because of how lower‐level taxa are aggregated into coarser, higher‐level ones. The STAR model presents a framework for extrapolating and comparing richness between small‐scale modern and large‐scale fossil communities, as well as a means to understand the general processes involved with changing scale.

    

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2. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” 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, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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3. From DNA sequences to microbial ecology: Wrangling NEON soil microbe data with the neonMicrobe R package

   https://doi.org/10.1002/ecs2.3842  

   Qin, Clara ; Bartelme, Ryan ; Chung, Y. Anny ; Fairbanks, Dawson ; Lin, Yang ; Liptzin, Daniel ; Muscarella, Chance ; Naithani, Kusum ; Peay, Kabir ; Pellitier, Peter ; et al ( November 2021 , Ecosphere)

   Soil microbial communities play critical roles in various ecosystem processes, but studies at a large spatial and temporal scale have been challenging due to the difficulty in finding the relevant samples in available data sets as well as the lack of standardization in sample collection and processing. The National Ecological Observatory Network (NEON) has been collecting soil microbial community data multiple times per year for 47 terrestrial sites in 20 eco‐climatic domains, producing one of the most extensive standardized sampling efforts for soil microbial biodiversity to date. Here, we introduce the neonMicrobe R package—a suite of downloading, preprocessing, data set assembly, and sensitivity analysis tools for NEON’s newly published 16S and ITS amplicon sequencing data products which characterize soil bacterial and fungal communities, respectively. neonMicrobe is designed to make these data more accessible to ecologists without assuming prior experience with bioinformatic pipelines. We describe quality control steps used to remove quality‐flagged samples, report on sensitivity analyses used to determine appropriate quality filtering parameters for the DADA2 workflow, and demonstrate the immediate usability of the output data by conducting standard analyses of soil microbial diversity. The sequence abundance tables produced byneonMicrobecan be linked to NEON’s other data products (e.g., soil physical and chemical properties, plant community composition) and soil subsamples archived in the NEON Biorepository. We provide recommendations for incorporatingneonMicrobeinto reproducible scientific workflows, discuss technical considerations for large‐scale amplicon sequence analysis, and outline future directions for NEON‐enabled microbial ecology. In particular, we believe that NEON marker gene sequence data will allow researchers to answer outstanding questions about the spatial and temporal dynamics of soil microbial communities while explicitly accounting for scale dependence. We expect that the data produced by NEON and theneonMicrobeR package will act as a valuable ecological baseline to inform and contextualize future experimental and modeling endeavors.

    

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4. Welcome to the Atta world: A framework for understanding the effects of leaf‐cutter ants on ecosystem functions

   https://doi.org/10.1111/1365-2435.13319  

   Swanson, Amanda C. ; Schwendenmann, Luitgard ; Allen, Michael F. ; Aronson, Emma L. ; Artavia‐León, Allan ; Dierick, Diego ; Fernandez‐Bou, Angel S. ; Harmon, Thomas C. ; Murillo‐Cruz, Catalina ; Oberbauer, Steven F. ; et al ( March 2019 , Functional Ecology)

   Leaf‐cutter ants are a prominent feature in Neotropical ecosystems, but a comprehensive assessment of their effects on ecosystem functions is lacking. We reviewed the literature and used our own recent findings to identify knowledge gaps and develop a framework to quantify the effects of leaf‐cutter ants on ecosystem processes.

   Leaf‐cutter ants disturb the soil structure during nest excavation changing soil aeration and temperature. They mix relatively nutrient‐poor soil from deeper layers with the upper organic‐rich layers increasing the heterogeneity of carbon and nutrients within nest soils.

   Leaf‐cutter ants account for about 25% of all herbivory in Neotropical forest ecosystems, moving 10%–15% of leaves in their foraging range to their nests. Fungal symbionts transform the fresh, nutrient‐rich vegetative material to produce hyphal nodules to feed the ants. Organic material from roots and arbuscular mycorrhizal fungi enhances carbon and nutrient turnover in nest soils and creates biogeochemical hot spots. Breakdown of organic matter, microbial and ant respiration, and nest waste material decomposition result in increased CO₂, CH_4,and N₂O production, but the build‐up of gases and heat within the nest is mitigated by the tunnel network ventilation system. Nest ventilation dynamics are challenging to measure without bias, and improved sensor systems would likely solve this problem.

   Canopy gaps above leaf‐cutter ant nests change the light, wind and temperature regimes, which affects ecosystem processes. Nests differ in density and size depending on colony age, forest type and disturbance level and change over time resulting in spatial and temporal changes of ecosystem processes. These characteristics remain a challenge to evaluate rapidly and non‐destructively.

   Addressing the knowledge gaps identified in this synthesis will bring insights into physical and biological processes driving biogeochemical cycles at the nest and ecosystem scale and will improve our understanding of ecosystem biogeochemical heterogeneity and larger scale ecological phenomena.

   Aplain language summaryis available for this article.

    

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5. Modern approaches for leveraging biodiversity collections to understand change in plant-insect interactions

   https://doi.org/10.3389/fevo.2022.924941  

   Balmaki, Behnaz ; Rostami, Masoud A. ; Christensen, Tara ; Leger, Elizabeth A. ; Allen, Julie M. ; Feldman, Chris R. ; Forister, Matthew L. ; Dyer, Lee A. ( August 2022 , Frontiers in Ecology and Evolution)

   Research on plant-pollinator interactions requires a diversity of perspectives and approaches, and documenting changing pollinator-plant interactions due to declining insect diversity and climate change is especially challenging. Natural history collections are increasingly important for such research and can provide ecological information across broad spatial and temporal scales. Here, we describe novel approaches that integrate museum specimens from insect and plant collections with field observations to quantify pollen networks over large spatial and temporal gradients. We present methodological strategies for evaluating insect-pollen network parameters based on pollen collected from museum insect specimens. These methods provide insight into spatial and temporal variation in pollen-insect interactions and complement other approaches to studying pollination, such as pollinator observation networks and flower enclosure experiments. We present example data from butterfly pollen networks over the past century in the Great Basin Desert and Sierra Nevada Mountains, United States. Complementary to these approaches, we describe rapid pollen identification methods that can increase speed and accuracy of taxonomic determinations, using pollen grains collected from herbarium specimens. As an example, we describe a convolutional neural network (CNN) to automate identification of pollen. We extracted images of pollen grains from 21 common species from herbarium specimens at the University of Nevada Reno (RENO). The CNN model achieved exceptional accuracy of identification, with a correct classification rate of 98.8%. These and similar approaches can transform the way we estimate pollination network parameters and greatly change inferences from existing networks, which have exploded over the past few decades. These techniques also allow us to address critical ecological questions related to mutualistic networks, community ecology, and conservation biology. Museum collections remain a bountiful source of data for biodiversity science and understanding global change. 

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