Search for: All records

Foraminifera are unicellular organisms that inhabit the oceans. They play an important role in the global carbon cycle and record valuable paleoclimate information through the uptake of trace elements such as strontium into their calcitic shells. Understanding how foraminifera control their internal fluid composition to make calcite is important for predicting their response to ocean acidification and for reliably interpreting the chemical and isotopic compositions of their shells. Here, we model foraminiferal calcification and strontium partitioning in the benthic foraminiferaCibicides wuellerstorfiandCibicidoides mundulusbased on insights from inorganic calcite experiments. The numerical model reconciles inter-ocean and taxonomic differences in benthic foraminifer strontium partitioning relationships and enables us to reconstruct the composition of the calcifying fluid. We find that strontium partitioning and mineral growth rates of foraminiferal calcite are not strongly affected by changes in external seawater pH (within 7.8–8.1) and dissolved inorganic carbon (DIC, within 2100–2300 μmol/kg) due to a regulated calcite saturation state at the site of shell formation.

 

Over the past decade, the seismicity rate in the state of Oklahoma has increased significantly, which has been linked to industrial operations, such as saltwater injection and hydraulic fracturing. Taking advantage of induced earthquakes and recently deployed seismometers, we construct a 3‐D radially anisotropic seismic velocity model for the crust of Oklahoma by using full waveform inversion. To mitigate the well‐known cycle‐skipping problem, we use misfit functions based on phase and waveform differences in several frequency bands. Relative velocity perturbations in the inverted model allow us to delineate major geological provinces in Oklahoma, such as the Anadarko Basin and the Cherokee Platform/Shelf. In addition, radial anisotropy in the inverted model reflects deformation within the crust of Oklahoma, which might correlate with sedimentary layering, microcracks/fractures, as well as dominant orientation of anisotropic minerals. The crystalline basement beneath Oklahoma can be inferred from the new velocity model, which enables us to better classify induced seismicity in current earthquake catalogs. Furthermore, synthetic experiments suggest that the new velocity model enables us to better constrain earthquake locations in Oklahoma, especially for determining their depths, which are important for investigating induced seismicity.

 

Transposable elements (TEs) are mobile genetic parasites that frequently invade new host genomes through horizontal transfer. Invading TEs often exhibit a burst of transposition, followed by reduced transposition rates as repression evolves in the host. We recreated the horizontal transfer of P-element DNA transposons into a Drosophila melanogaster host and followed the expansion of TE copies and evolution of host repression in replicate laboratory populations reared at different temperatures. We observed that while populations maintained at high temperatures rapidly go extinct after TE invasion, those maintained at lower temperatures persist, allowing for TE spread and the evolution of host repression. We also surprisingly discovered that invaded populations experienced recurrent insertion of P-elements into a specific long non-coding RNA, lncRNA:CR43651, and that these insertion alleles are segregating at unusually high frequency in experimental populations, indicative of positive selection. We propose that, in addition to driving the evolution of repression, transpositional bursts of invading TEs can drive molecular adaptation.

 

Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain “dark”—i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space. 

A table is composed of data values that are organized in %a 2D matrix with rows and columns providing implicit structural information. A table is usually accompanied by secondary information such as the caption, page title, etc., that form the textual information. Understanding the connection between the textual and structural information is an important, yet neglected aspect in table retrieval, as previous methods treat each source of information independently. In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table. We introduce the concept of horizontal self-attention, which extends the idea of vertical self-attention introduced in TaBERT and allows us to treat both dimensions of a table equally. StruBERT features are integrated in a new end-to-end neural ranking model to solve three table-related downstream tasks: keyword- and content-based table retrieval, and table similarity. We evaluate our approach using three datasets, and we demonstrate substantial improvements in terms of retrieval and classification metrics over state-of-the-art methods. 

The Sagittarius B2 (Sgr B2) molecular cloud complex is an X-ray reflection nebula whose nonthermal X-ray emissions have continued to decrease since 2001 as it reprocesses one or more past energetic outbursts from the supermassive black hole Sagittarius A* at the Galactic Center. The X-ray reflection model explains the observed time variability of Sgr B2 and provides a window into the luminous evolutionary history of our nearest supermassive black hole. In light of evidence of elevated cosmic particle populations in the Galactic Center, X-rays from Sgr B2 are also of interest as a probe of low-energy (sub-GeV) cosmic rays, which may be responsible for an increasing relative fraction of the nonthermal emission as the contribution from X-ray reflection decreases. Here, we present the most recent NuSTAR and XMM-Newton observations of Sgr B2, from 2018, and we emphasize the Kαfluorescence line of neutral Fe. These 2018 observations reveal small-scale variations within lower-density portions of the complex, including brightening features, yet still enable upper limits on X-rays from low-energy cosmic-ray interactions in Sgr B2. We present Fe Kαline fluxes from cloud regions of different densities, facilitating comparison with models of ambient low-energy cosmic-ray interactions throughout the cloud.

 

One important feature of the Greenland Ice Sheet (GrIS) change is its strong seasonal fluctuation. Taking advantage of deployed seismographic stations in Greenland, we apply cross‐component auto‐correlation of seismic ambient noise to measure in‐situ near surface relative velocity change (dv/v) in different regions of Greenland. Our results demonstrate thatdv/vmeasurements for most stations have less than 3 months lag times in comparison to the surface mass change. These various lag times may provide us constraints for the thickness of the subglacial till layer over different regions in Greenland. Moreover, in southwest Greenland, we observe a change in the long‐term trend ofdv/vfor three stations, which might be consistent with the mass change rate (dM/dt) due to the “2012–2013 warm‐cold transition.” These observations suggest that seismic noise auto‐correlation technique may be used to monitor both seasonal and long‐term changes of the GrIS.

 

Significant imbalances in terrestrial water storage (TWS) and severe drought have been observed around the world as a consequence of climate changes. Improving our ability to monitor TWS and drought is critical for water‐resource management and water‐deficit estimation. We use continuous seismic ambient noise to monitor temporal evolution of near‐surface seismic velocity,dv/v, in central Oklahoma from 2013 to 2022. The deriveddv/vis found to be negatively correlated with gravitational measurements and groundwater depths, showing the impact of groundwater storage on seismic velocities. The hydrological effects involving droughts and recharge of groundwater occur on a multi‐year time scale and dominate the overall derived velocity changes. The thermoelastic response to atmospheric temperature variations occurs primarily on a yearly timescale and dominates the superposed seasonal velocity changes in this study. The occurrences of droughts appear simultaneously with local peaks ofdv/v, demonstrating the sensitivity of near‐surface seismic velocities to droughts.