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As seismic data availability increases, the necessity for automated processing techniques has become increasingly evident. Expanded geophysical datasets collected over the past several decades across Antarctica provide excellent resources to evaluate different event detection approaches. We have used the traditional Short-Term Average/Long-Term Average (STA/LTA) algorithm to catalogue seismic data recorded by 19 stations in East Antarctica between 2012 and 2015. However, the complexities of the East Antarctic dataset, including low magnitude earthquakes and other types of seismic events such as icequakes or firnquakes, warrant more advanced automated detection techniques. Therefore, we have also applied template matching as well as several deep learning algorithms, including Generalized Phase Detection (GPD), PhaseNet, BasicPhaseAE, and EQTransformer (EQT), to identify seismic phases within our dataset. Our goal is not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different automated approaches. Our assessment evaluates the completeness of the newly generated catalogs, the precision of identified event locations, and the quality of the picks. The performance of these different event detection techniques applied to continuous seismic data from a polar environment will be highlighted. We will also identify potential limitations and necessary adjustments for deep learning algorithm training, which is essential for their reliable application to specific datasets. 

We compile and make publicly available a global digital database of body wave observations of seismic anisotropy in the D′′ layer, grouped using the method used to analyze deep mantle anisotropy. Using this database, we examine the global distribution of seismic anisotropy in the D′′ layer, evaluating the question of whether seismic anisotropy is more likely to be located at the edges of the two large-low velocity provinces (LLVPs) in Earth's mantle than elsewhere. We show that this hypothesis lacks statistical justification if we consider previously observed lowermost mantle anisotropy, although there are multiple factors that are difficult to account for quantitatively. One such factor is the global lowermost mantle ray coverage for different phases that are commonly used to detect deep mantle anisotropy in shear wave splitting studies. We find that the global ray coverage of the relevant seismic phases is highly uneven, with LLVP edges and their interiors less well-sampled than the global average. 

Inhibition of overexpressed enzymes is among the most promising approaches for targeted cancer treatment. However, many cancer-expressed enzymes are “nonlethal,” in that the inhibition of the enzymes’ activity is insufficient to kill cancer cells. Conventional antibody-based therapeutics can mediate efficient treatment by targeting extracellular nonlethal targets but can hardly target intracellular enzymes. Herein, we report a cancer targeting and treatment strategy to utilize intracellular nonlethal enzymes through a combination of selective cancer stem-like cell (CSC) labeling and Click chemistry-mediated drug delivery. A de novo designed compound, AAMCHO [N-(3,4,6-triacetyl- N-azidoacetylmannosamine)-cis-2-ethyl-3-formylacrylamideglycoside], selectively labeled cancer CSCs in vitro and in vivo through enzymatic oxidation by intracellular aldehyde dehydrogenase 1A1. Notably, azide labeling is more efficient in identifying tumorigenic cell populations than endogenous markers such as CD44. A dibenzocyclooctyne (DBCO)-toxin conjugate, DBCO-MMAE (Monomethylauristatin E), could next target the labeled CSCs in vivo via bioorthogonal Click reaction to achieve excellent anticancer efficacy against a series of tumor models, including orthotopic xenograft, drug-resistant tumor, and lung metastasis with low toxicity. A 5/7 complete remission was observed after single-cycle treatment of an advanced triple-negative breast cancer xenograft (~500 mm³).

 

Seismic anisotropy has been detected at many depths of the Earth, including its upper layers, the lowermost mantle and the inner core. While upper mantle seismic anisotropy is relatively straightforward to resolve, lowermost mantle anisotropy has proven to be more complicated to measure. Due to their long, horizontal ray paths along the core–mantle boundary (CMB), S waves diffracted along the CMB (Sdiff) are potentially strongly influenced by lowermost mantle anisotropy. Sdiff waves can be recorded over a large epicentral distance range and thus sample the lowermost mantle everywhere around the globe. Sdiff therefore represents a promising phase for studying lowermost mantle anisotropy; however, previous studies have pointed out some difficulties with the interpretation of differential SHdiff–SVdiff traveltimes in terms of seismic anisotropy. Here, we provide a new, comprehensive assessment of the usability of Sdiff waves to infer lowermost mantle anisotropy. Using both axisymmetric and fully 3-D global wavefield simulations, we show that there are cases in which Sdiff can reliably detect and characterize deep mantle anisotropy when measuring traditional splitting parameters (as opposed to differential traveltimes). First, we analyze isotropic effects on Sdiff polarizations, including the influence of realistic velocity structure (such as 3-D velocity heterogeneity and ultra-low velocity zones), the character of the lowermost mantle velocity gradient, mantle attenuation structure, and Earth’s Coriolis force. Secondly, we evaluate effects of seismic anisotropy in both the upper and the lowermost mantle on SHdiff waves. In particular, we investigate how SHdiff waves are split by seismic anisotropy in the upper mantle near the source and how this anisotropic signature propagates to the receiver for a variety of lowermost mantle models. We demonstrate that, in particular and predictable cases, anisotropy leads to Sdiff splitting that can be clearly distinguished from other waveform effects. These results enable us to lay out a strategy for the analysis of Sdiff splitting due to anisotropy at the base of the mantle, which includes steps to help avoid potential pitfalls, with attention paid to the initial polarization of Sdiff and the influence of source-side anisotropy. We demonstrate our Sdiff splitting method using three earthquakes that occurred beneath the Celebes Sea, measured at many transportable array stations at a suitable epicentral distance. We resolve consistent and well-constrained Sdiff splitting parameters due to lowermost mantle anisotropy beneath the northeastern Pacific Ocean. 

Shear-wave splitting measurements are commonly used to resolve seismic anisotropy in both the upper and lowermost mantle. Typically, such techniques are applied to SmKS phases that have reflected (m-1) times off the underside of the core-mantle boundary before being recorded. Practical constraints for shear-wave splitting studies include the limited number of suitable phases as well as the large fraction of available data discarded because of poor signal-to-noise ratios (SNRs) or large measurement uncertainties. Array techniques such as beamforming are commonly used in observational seismology to enhance SNRs, but have not been applied before to improve SmKS signal strength and coherency for shear wave splitting studies. Here, we investigate how a beamforming methodology, based on slowness and backazimuth vespagrams to determine the most coherent incoming wave direction, can improve shear-wave splitting measurement confidence intervals. Through the analysis of real and synthetic seismograms, we show that (a) the splitting measurements obtained from the beamformed seismograms (beams) reflect an average of the single-station splitting parameters that contribute to the beam; (b) the beams have (on average) more than twice as large SNRs than the single-station seismograms that contribute to the beam; (c) the increased SNRs allow the reliable measurement of shear wave splitting parameters from beams down to average single-station SNRs of 1.3. Beamforming may thus be helpful to more reliably measure splitting due to upper mantle anisotropy. Moreover, we show that beamforming holds potential to greatly improve detection of lowermost mantle anisotropy by demonstrating differential SKS– SKKS splitting analysis using beamformed USArray data. 

Climate impacts are not always easily discerned in wild populations as detecting climate change signals in populations is challenged by stochastic noise associated with natural climate variability, variability in biotic and abiotic processes, and observation error in demographic rates. Detection of the impact of climate change on populations requires making a formal distinction between signals in the population associated with long-term climate trends from those generated by stochastic noise. The time of emergence (ToE) identifies when the signal of anthropogenic climate change can be quantitatively distinguished from natural climate variability. This concept has been applied extensively in the climate sciences, but has not been explored in the context of population dynamics. Here, we outline an approach to detecting climate-driven signals in populations based on an assessment of when climate change drives population dynamics beyond the envelope characteristic of stochastic variations in an unperturbed state. Specifically, we present a theoretical assessment of the time of emergence of climate-driven signals in population dynamics (urn:x-wiley:13541013:media:gcb16041:gcb16041-math-0001). We identify the dependence of urn:x-wiley:13541013:media:gcb16041:gcb16041-math-0002 on the magnitude of both trends and variability in climate and also explore the effect of intrinsic demographic controls on urn:x-wiley:13541013:media:gcb16041:gcb16041-math-0003. We demonstrate that different life histories (fast species vs. slow species), demographic processes (survival, reproduction), and the relationships between climate and demographic rates yield population dynamics that filter climate trends and variability differently. We illustrate empirically how to detect the point in time when anthropogenic signals in populations emerge from stochastic noise for a species threatened by climate change: the emperor penguin. Finally, we propose six testable hypotheses and a road map for future research. 

The Great Calcite Belt (GCB) is a band of high concentrations of suspended particulate inorganic carbon (PIC) spanning the subantarctic Southern Ocean and plays an important role in the global carbon cycle. The key limiting factors controlling coccolithophore growth supporting this high PIC have not yet been well‐characterized in the remote Pacific sector, the lowest PIC but largest area of the GCB. Here, we present in situ physical and biogeochemical measurements along 150°W from January to February 2021, where a coccolithophore bloom occurred. In both months, PIC was elevated in the Subantarctic Zone (SAZ), where nitrate was >1 μM and temperatures were ∼13°C in January and ∼14°C in February, consistent with conditions previously associated with optimal coccolithophore growth potential. The highest PIC was associated with a relatively narrow temperature range that increased about 1°C between occupations. A fresher water mass had been transported to the 150°W meridian between occupations, and altimetry‐informed Lagrangian backtracking estimates show that most of this water was likely transported from the southeast within the SAZ. Applying the observations in a coccolithophore growth model for both January and February, we show that the ∼1.7°C increase in temperature can explain the rise in PIC between occupations.

 

Large‐scale loss of oxygen under global warming is termed “ocean deoxygenation” and is caused by the imbalance between physical supply and biological consumption of oxygen in the ocean interior. Significant progress has been made in the theoretical understanding of ocean deoxygenation; however, many questions remain unresolved. The oxygen change in the tropical thermocline is poorly understood, with diverging projections among different models. Physical oxygen supply is controlled by a suite of processes that transport oxygen‐rich surface waters into the interior ocean, which is expected to weaken due to increasing stratification under global warming. Using a numerical model and a series of sensitivity experiments, the role of ocean mixing is examined in terms of effects on the mean state and the response to a transient warming. Both vertical and horizontal (isopycnal) mixing coefficients are systematically varied over a wide range, and the resulting oxygen distributions in equilibrated and transient simulations are examined. The spatial patterns of oxygen loss are sensitive to both vertical and isopycnal mixing, and the sign of tropical oxygen trend under climate warming can reverse depending on the choice of mixing parameters. An elevated level of isopycnal mixing disrupts the vertical advective‐diffusive balance of the tropical thermocline, increasing the mean state oxygen as well as the magnitude of the transient oxygen decline. These results provide first‐order explanations for the diverging behaviors of simulated tropical oxygen with respect to ocean mixing parameters.