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A Semiautomated Multilayer Picking Algorithm for Ice-sheet Radar Echograms Applied to Ground-Based Near-Surface Data
Snow accumulation over an ice sheet is the sole mass input, making it a primary measurement for understanding the past, present, and future mass balance. Near-surface frequency-modulated continuous-wave (FMCW) radars image isochronous firn layers recording accumulation histories. The Semiautomated Multilayer Picking Algorithm (SAMPA) was designed and developed to trace annual accumulation layers in polar firn from both airborne and ground-based radars. The SAMPA algorithm is based on the Radon transform (RT) computed by blocks and angular orientations over a radar echogram. For each echogram's block, the RT maps firn segmented-layer features into peaks, which are picked using amplitude and width threshold parameters of peaks. A backward RT is then computed for each corresponding block, mapping the peaks back into picked segmented-layers. The segmented layers are then connected and smoothed to achieve a final layer pick across the echogram. Once input parameters are trained, SAMPA operates autonomously and can process hundreds of kilometers of radar data picking more than 40 layers. SAMPA final pick results and layer numbering still require a cursory manual adjustment to correct noncontinuous picks, which are likely not annual, and to correct for inconsistency in layer numbering. Despite the manual effort to train and check SAMPA results, it is an efficient tool for picking multiple accumulation layers in polar firn, reducing time over manual digitizing efforts. The trackability of good detected layers is greater than 90%.
A self-supervised workflow for particle picking in cryo-EM
High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion , cryoSPARC , cisTEM , SPHIRE and EMAN2 .
CryoTransformer: a transformer model for picking protein particles from Cryo-EM micrographs
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise-ratio (SNR).
CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net
Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet
A large expert-curated cryo-EM image dataset for machine learning protein particle picking
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges
Abstract Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.
Automatic microseismic event picking via unsupervised machine learning
SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.
Divide and conquer: separating the two probabilities in seismic phase picking
There are two fundamental probabilities in the seismic phase picking process—the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labelled phase arrival time and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks), and the height of the peaks are taken as ‘probability’ of the picks. Here, we show that this ‘probability’ represents neither the detection nor the timing probability because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multiclass classifier to obtain a probability mass function to address the timing probability. This new approach can make the deep learning-based phase picking process more interpretable and provide options to logically control seismic monitoring workflows.
Development of sensitized pick coal interface detector system
One approach for detection of the coal interface is measurement of the pick cutting hoads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telementry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder.
Development of sensitized pick coal interface detector system
One approach for detection of the coal interface is measurement of pick cutting loads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telemetry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder. The design of components in the test data system were finalized, the required instruments were assembled, the instrument system was evaluated in an above-ground simulation test, and an underground test series to obtain tape recorded sensor data was conducted.
Instrumented Pick Detects Coal/Rock Interface
Instrumented pick installed on cutting drum of coal shearer for longwall mining measures cutting force with strain-gage-bridge load cell. Force signal transmitted to remote recorder. Transmitter located in base of pick assembly. Antenna located in shadow of rotating pick. Changes in characteristics of force signals from pick used to determine whether pick is cutting coal or rock.
Pick-Up Ion Instabilities at Planetary Magnetospheres
This effort involved the analysis of low frequency waves as observed by the Galileo spacecraft near the Galilean moon, Io. Io is a significant source of material, especially SO2, and various products of dissociation, and further these atoms and molecules are readily ionized. The initial velocity of the ions is essentially that of the neutral species, i.e., the Keplerian velocity. The plasma, on the other hand is co-rotating, and there is a differential flow of the order 57 km/s between the plasma and the neutral particles. Thus pick-up ion instabilities are Rely to occur within the Jovian magnetosphere. Indeed, magnetometer observations from the Galileo spacecraft clearly show ion cyclotron waves that have been identified with a large variety of plasma species, such as O+, S++ (which has the same gyro-frequency as O+), S+, and SO2+. Typically, however, the dominant frequency is near the SO2+ gyro-frequency. The research effort was originally planned to be a team effort between Robert J. Strangeway as the Principal Investigator, and Debbie Huddleston, who was an Assistant Research Geophysicist at UCLA. Unfortunately, Dr. Huddleston took a position within Industry. The effort was therefore descoped, and Dr. Strangeway instead pursued a collaboration with Dr. Xochitl Blanco-Cano, of the Instituto de Geofisica, Universidad Nacional Autonoma de Mexico. This has proved to be a productive collaboration, with several papers and publications arising out of the effort. The magnetic field oscillations near lo generally fall into two types: ion cyclotron waves, with frequencies near an ion gyro-frequency, and lower frequency mirror-mode waves. The ion cyclotron waves are mainly transverse, and frequently propagate along the ambient magnetic field. The mirror-mode waves are compressional waves, and they have essentially zero frequency in the plasma rest frame. One of the purposes of our investigation is to understand what controls the types of wave modes that occur, since both wave modes can be drive unstable by the pressure anisotropy associated with the pick up ions. The pick ion velocity is perpendicular to the ambient magnetic field, and is generally much larger than the thermal velocity, at least initially. At its simplest, we found that the ion cyclotron waves are controlled by the parameters of the species in gyro-resonance with the wave. Thus, while the growth rates for the lower mass (higher gyro-frequency) pick-up ions are generally larger, we found that the heavier SO2+ ion cyclotron waves are generally preferred. This is because one of the effects of the wave instability is to diffuse the ions in pitch angle and energy. The lower mass ions therefore consist of both a ring of recently created pick-up ions, and a thermal background. This thermal background quenches the ion cyclotron instability. SO2+ is different, however. Being a molecule, the species can also dissociate. Our analysis suggests that the dissociation acts on a time scale comparable to or faster than the velocity space diffusion time scale. There are consequently no thermal SO2+ ions to quench the instability. We have also investigated the mirror-mode. This mode can at times grow more rapidly than the individual ion cyclotron waves. This is mainly because the mirror-mode can grow off the pressure anisotropy of the individual species with which the waves are in resonance. Lastly, as part of this effort we have begun to investigate the instability for obliquely propagating modes. Galileo observations show that at times the ion cyclotron waves are significantly elliptically polarized, and further the wave vector is at a large angle to the field, significant growth can occur for oblique propagation. Depending on the group velocity of the waves, it is possible that obliquely propagating modes have higher advective growth rate, but this has yet to be determined.
Making Phase-Picking Neural Networks More Consistent and Interpretable
Improving the interpretability of phase-picking neural networks remains an important task to facilitate their deployment to routine, real-time seismic monitoring. The popular phase-picking neural networks published in the literature lack interpretability because their output prediction scores do not necessarily correspond with the reliability of phase picks and can even be highly inconsistent depending on how we window the waveform data. Here, we show that systematically shifting the waveforms during training and using an antialiasing filter within the neural network architecture can substantially improve the consistency of the output prediction scores and can even make them scale with the signal-to-noise ratios of the waveforms. We demonstrate the improvements by applying these approaches to a commonly used phase-picking neural network architecture and using waveform data from the 2019 Ridgecrest earthquake sequence.
The Value of Hyperparameter Optimization in Phase-Picking Neural Networks
The effectiveness of neural networks for picking seismic phase arrival times has been demonstrated through several case studies, and seismic monitoring programs are starting to adopt the technology into their workflows. However, published models were designed and trained using rather arbitrary choices of hyperparameters, limiting their performance. In this study, we use phase picks from both routine and template-matching analyses from multiple regions (Ridgecrest, California; Kilauea, Hawaii; Yellowstone, Wyoming–Montana–Idaho) to test a hyperparameter optimization scheme for phase-picking neural networks and to evaluate their performance. We show that a published model, namely PhaseNet (Zhu and Beroza, 2019), can be simplified and improved with reasonable effort and there are preferred choices of hyperparameters that increase the performance. We also show that models optimized based on the arrival times reported in routine event catalogs consistently perform well when picking arrival times of smaller events, which is crucial for many tasks from microseismicity to explosion monitoring.
Coal/rock interface detection by sensitized pick, part A
In order to increase the operating margins of the detector for safe, reliable operation under difficult in-mine conditions the transmitted signal strength was increased to provide additional signal margin for in-mine conditions and the transmitter section was redesigned to reduce frequency pulling of the transmitter frequency with variations in antenna load. The linearity of the pick load SCO signal with true pick load was increased, and hysteresis effects were minimized. The sensitized pick hardware was ruggedized for rough inmine use. The sensitized pick and telemetry system provided excellent, high quality signals proportional to cutting load under all conditions experienced during testing.
Pick-up ions at Pluto
Methane molecules escaping from Pluto's atmosphere are ionized, and the resulting ions are picked up by the solar wind. The mass loading associated with this ion pick-up can produce a cometlike interaction of the solar wind with Pluto. Heavy ion gyroradii are as large as a half million km in the weak interplanetary magnetic field that exists at 30 AU, which is about an order of magnitude larger than the size of the 'interaction region'. We have calculated velocity space distributions of pick-up ions using numerically determined ion trajectories. The predicted pick-up ion fluxes are high enough to be detectable by standard charged particle detectors as far upstream of Pluto as 10 exp 6 km.
Pick-up ion pressure gradients modulating the solar wind dynamics
Neutral interstellar atoms penetrate deeply into the inner heliosphere before they become ionized by various processes. As ions they are picked-up by the frozen-in magnetic fields and are convected outwards with the solar wind plasma. Thereby the primary plasma flow is mass, momentum, and energy-loaded. The dynamics of the distant multi-constituent solar wind is, however, not solely determined by these loading processes, but is also affected by the wave-mediated pick-up ion pressure gradients derivable from the pick-up ion distribution function. The action of the radial components of these pressures essentially counter balances the decelerating effect of the solar wind momentum loading, diminishing strongly the deceleration of the distant solar wind. Furthermore the latitudinal components of the pick-up ion pressures induce latitudinal forces acting on the multiconstituent solar plasma outflow and inducing nonradial bulk flow components. The enforced nonradial outflow geometry on the upwind hemisphere may partly be responsible for the magnetic flux deficit which was claimed since several years in the PIONEER-10 magnetic flux data.