Introduction Soil microbial communities, including biological soil crust microbiomes, play key roles in water, carbon and nitrogen cycling, biological weathering, and other nutrient releasing processes of desert ecosystems. However, our knowledge of microbial distribution patterns and ecological drivers is still poor, especially so for the Chihuahuan Desert. Methods This project investigated the effects of trampling disturbance on surface soil microbiomes, explored community composition and structure, and related patterns to abiotic and biotic landscape characteristics within the Chihuahuan Desert biome. Composite soil samples were collected in disturbed and undisturbed areas of 15 long-term ecological research plots in the Jornada Basin, New Mexico. Microbial diversity of cross-domain microbial groups (total Bacteria, Cyanobacteria, Archaea, and Fungi) was obtained via DNA amplicon metabarcode sequencing. Sequence data were related to landscape characteristics including vegetation type, landforms, ecological site and state as well as soil properties including gravel content, soil texture, pH, and electrical conductivity. Results Filamentous Cyanobacteria dominated the photoautotrophic community while Proteobacteria and Actinobacteria dominated among the heterotrophic bacteria. Thaumarchaeota were the most abundant Archaea and drought adapted taxa in Dothideomycetes and Agaricomycetes were most abundant fungi in the soil surface microbiomes. Apart from richness within Archaea ( p = 0.0124), disturbed samples did not differ from undisturbed samples with respect to alpha diversity and community composition ( p ≥ 0.05), possibly due to a lack of frequent or impactful disturbance. Vegetation type and landform showed differences in richness of Bacteria, Archaea, and Cyanobacteria but not in Fungi. Richness lacked strong relationships with soil variables. Landscape features including parent material, vegetation type, landform type, and ecological sites and states, exhibited stronger influence on relative abundances and microbial community composition than on alpha diversity, especially for Cyanobacteria and Fungi. Soil texture, moisture, pH, electrical conductivity, lichen cover, and perennial plant biomass correlated strongly with microbial community gradients detected in NMDS ordinations. Discussion Our study provides first comprehensive insights into the relationships between landscape characteristics, associated soil properties, and cross-domain soil microbiomes in the Chihuahuan Desert. Our findings will inform land management and restoration efforts and aid in the understanding of processes such as desertification and state transitioning, which represent urgent ecological and economical challenges in drylands around the world.
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Global Forensic Geolocation with Deep Neural Networks
An important problem in modern forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, which is known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application specific, labour intensive and often subjective. Purely data-driven methods have yet to be fully realized in this domain, because in part of the lack of a sufficiently rich source of data. However, high throughput sequencing technologies can identify tens of thousands of fungi and bacteria taxa by using DNA recovered from a single swab collected from nearly any object or surface. This microbial community, or microbiome, may be highly informative of the provenance of the sample, but data on the spatial variation of microbiomes are sparse and high dimensional and have a complex dependence structure that render them difficult to model with standard statistical tools. Deep learning algorithms have generated a tremendous amount of interest within the machine learning community for their predictive performance in high dimensional problems. We present DeepSpace: a new algorithm for geolocation that aggregates over an ensemble of deep neural network classifiers trained on randomly generated Voronoi partitions of a spatial domain. The DeepSpace algorithm makes remarkably good point predictions; for example, when applied to the microbiomes of over 1300 dust samples collected across continental USA, more than half of geolocation predictions produced by this model fall less than 100 km from their true origin, which is a 60% reduction in error from competing geolocation methods. Moreover, we apply DeepSpace to a novel data set of global dust samples collected from nearly 30 countries, finding that dust-associated fungi alone predict a sample's country of origin with nearly 90% accuracy.
more » « less- NSF-PAR ID:
- 10398883
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Volume:
- 69
- Issue:
- 4
- ISSN:
- 0035-9254
- Page Range / eLocation ID:
- p. 909-929
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The Amundsen Sea sector of Antarctica has long been considered the most vulnerable part of the West Antarctic Ice Sheet (WAIS) because of the great water depth at the grounding line and the absence of substantial ice shelves. Glaciers in this configuration are thought to be susceptible to rapid or runaway retreat. Ice flowing into the Amundsen Sea Embayment is undergoing the most rapid changes of any sector of the Antarctic Ice Sheet outside the Antarctic Peninsula, including changes caused by substantial grounding-line retreat over recent decades, as observed from satellite data. Recent models suggest that a threshold leading to the collapse of WAIS in this sector may have been already crossed and that much of the ice sheet could be lost even under relatively moderate greenhouse gas emission scenarios. Drill cores from the Amundsen Sea provide tests of several key questions about controls on ice sheet stability. The cores offer a direct record of glacial history offshore from a drainage basin that receives ice exclusively from the WAIS, which allows clear comparisons between the WAIS history and low-latitude climate records. Today, warm Circumpolar Deep Water (CDW) is impinging onto the Amundsen Sea shelf and causing melting of the underside of the WAIS in most places. Reconstructions of past CDW intrusions can assess the ties between warm water upwelling and large-scale changes in past grounding-line positions. Carrying out these reconstructions offshore from the drainage basin that currently has the most substantial negative mass balance of ice anywhere in Antarctica is thus of prime interest to future predictions. The scientific objectives for this expedition are built on hypotheses about WAIS dynamics and related paleoenvironmental and paleoclimatic conditions. The main objectives are 1. To test the hypothesis that WAIS collapses occurred during the Neogene and Quaternary and, if so, when and under which environmental conditions; 2. To obtain ice-proximal records of ice sheet dynamics in the Amundsen Sea that correlate with global records of ice-volume changes and proxy records for atmospheric and ocean temperatures; 3. To study the stability of a marine-based WAIS margin and how warm deep-water incursions control its position on the shelf; 4. To find evidence for earliest major grounded WAIS advances onto the middle and outer shelf; 5. To test the hypothesis that the first major WAIS growth was related to the uplift of the Marie Byrd Land dome. International Ocean Discovery Program (IODP) Expedition 379 completed two very successful drill sites on the continental rise of the Amundsen Sea. Site U1532 is located on a large sediment drift, now called Resolution Drift, and penetrated to 794 m with 90% recovery. 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The drill sites on the continental rise were in the path of numerous icebergs of various sizes that frequently forced us to pause drilling or leave the hole entirely as they approached the ship. The overall downtime caused by approaching icebergs was 50% of our time spent on site. 3. An unfortunate injury to a member of the ship's crew cut the expedition short by one week. Recovery of core on the continental rise at Sites U1532 and U1533 cannot be used to precisely indicate the position of ice or retreat of the ice sheet on the shelf. However, these sediments contained in the cores offer a range of clues about past WAIS extent and retreat. At Sites U1532 and U1533, coarse-grained sediments interpreted to be ice-rafted debris (IRD) were identified throughout all recovered time periods. 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The channel is likely the path of such sediments transported downslope by turbidity currents or other sediment-gravity flows. The association of lithologic facies at both sites predominantly reflects the interplay of downslope and contouritic sediment supply with occasional input of more pelagic sediment. Despite the lack of cores from the shelf, our records from the continental rise reveal the timing of glacial advances across the shelf and thus the existence of a continent-wide ice sheet in West Antarctica at least during longer time periods since the late Miocene. Cores from both sites contain abundant coarse-grained sediments and clasts of plutonic origin transported either by downslope processes or by ice rafting. If detailed provenance studies confirm our preliminary assessment that the origin of these samples is from the plutonic bedrock of Marie Byrd Land, their thermochronological record will potentially reveal timing and rates of denudation and erosion linked to crustal uplift. The chronostratigraphy of both sites enables the generation of a seismic sequence stratigraphy not only for the Amundsen Sea rise but also for the western Amundsen Sea along the Marie Byrd Land margin through a connecting network of seismic lines.more » « less
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The Amundsen Sea sector of Antarctica has long been considered the most vulnerable part of the West Antarctic Ice Sheet (WAIS) because of the great water depth at the grounding line, a subglacial bed seafloor deepening toward the interior of the continent, and the absence of substantial ice shelves. Glaciers in this configuration are thought to be susceptible to rapid or runaway retreat. Ice flowing into the Amundsen Sea Embayment is undergoing the most rapid changes of any sector of the Antarctic ice sheets outside the Antarctic Peninsula, including substantial grounding-line retreat over recent decades, as observed from satellite data. Recent models suggest that a threshold leading to the collapse of WAIS in this sector may have been already crossed and that much of the ice sheet could be lost even under relatively moderate greenhouse gas emission scenarios. Drill cores from the Amundsen Sea provide tests of several key questions about controls on ice sheet stability. The cores offer a direct offshore record of glacial history in a sector that is exclusively influenced by ice draining the WAIS, which allows clear comparisons between the WAIS history and low-latitude climate records. Today, relatively warm (modified) Circumpolar Deep Water (CDW) is impinging onto the Amundsen Sea shelf and causing melting under ice shelves and at the grounding line of the WAIS in most places. Reconstructions of past CDW intrusions can assess the ties between warm water upwelling and large-scale changes in past grounding-line positions. Carrying out these reconstructions offshore from the drainage basin that currently has the most substantial negative mass balance of ice anywhere in Antarctica is thus of prime interest to future predictions. The scientific objectives for this expedition are built on hypotheses about WAIS dynamics and related paleoenvironmental and paleoclimatic conditions. The main objectives are: 1. 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We collected almost-continuous cores from recent age through the Pleistocene and Pliocene and into the upper Miocene. At Site U1533, we drilled 383 m (70% recovery) into the more condensed sequence at the lower flank of the same sediment drift. The cores of both sites contain unique records that will enable study of the cyclicity of ice sheet advance and retreat processes as well as ocean-bottom water circulation and water mass changes. In particular, Site U1532 revealed a sequence of Pliocene sediments with an excellent paleomagnetic record for high-resolution climate change studies of the previously sparsely sampled Pacific sector of the West Antarctic margin. Despite the drilling success at these sites, the overall expedition experienced three unexpected difficulties that affected many of the scientific objectives: 1. The extensive sea ice on the continental shelf prevented us from drilling any of the proposed shelf sites. 2. The drill sites on the continental rise were in the path of numerous icebergs of various sizes that frequently forced us to pause drilling or leave the hole entirely as they approached the ship. The overall downtime caused by approaching icebergs was 50% of our time spent on site. 3. A medical evacuation cut the expedition short by 1 week. Recovery of core on the continental rise at Sites U1532 and U1533 cannot be used to indicate the extent of grounded ice on the shelf or, thus, of its retreat directly. However, the sediments contained in these cores offer a range of clues about past WAIS extent and retreat. At Sites U1532 and U1533, coarse-grained sediments interpreted to be ice-rafted debris (IRD) were identified throughout all recovered time periods. A dominant feature of the cores is recorded by lithofacies cyclicity, which is interpreted to represent relatively warmer periods variably characterized by sediments with higher microfossil abundance, greater bioturbation, and higher IRD concentrations alternating with colder periods characterized by dominantly gray laminated terrigenous muds. Initial comparison of these cycles to published late Quaternary records from the region suggests that the units interpreted to be records of warmer time intervals in the core tie to global interglacial periods and the units interpreted to be deposits of colder periods tie to global glacial periods. Cores from the two drill sites recovered sediments of dominantly terrigenous origin intercalated or mixed with pelagic or hemipelagic deposits. In particular, Site U1533, which is located near a deep-sea channel originating from the continental slope, contains graded silts, sands, and gravels transported downslope from the shelf to the rise. The channel is likely the pathway of these sediments transported by turbidity currents and other gravitational downslope processes. The association of lithologic facies at both sites predominantly reflects the interplay of downslope and contouritic sediment supply with occasional input of more pelagic sediment. Despite the lack of cores from the shelf, our records from the continental rise reveal the timing of glacial advances across the shelf and thus the existence of a continent-wide ice sheet in West Antarctica during longer time periods since at least the late Miocene. Cores from both sites contain abundant coarse-grained sediments and clasts of plutonic origin transported either by downslope processes or by ice rafting. If detailed provenance studies confirm our preliminary assessment that the origin of these samples is from the plutonic bedrock of Marie Byrd Land, their thermochronological record will potentially reveal timing and rates of denudation and erosion linked to crustal uplift. The chronostratigraphy of both sites enables the generation of a seismic sequence stratigraphy for the entire Amundsen Sea continental rise, spanning the area offshore from the Amundsen Sea Embayment westward along the Marie Byrd Land margin to the easternmost Ross Sea through a connecting network of seismic lines.more » « less
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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.more » « less
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Abstract Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g., 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without prespecified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multilocus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data. [Deep learning; gene tree discordance; metagenomics; microbiome analyses; neural networks; phylogenetic placement.]
