Browse Articles
Discover research articles across all indexed journals
The effects of investment cost and cues of reward and punishment on cognitive task performance
Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance
Abstract Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by IME insertions contribute to increased neuroinflammation and reduced neural recording performance. Here, we evaluated dexamethasone sodium phosphate-loaded platelet-inspired nanoparticles (DEXSPPIN) to simultaneously augment local hemostasis and serve as an implant-site targeted drug-delivery vehicle. Weekly systemic treatment or control therapy was provided to rats for 8 weeks following IME implantation, while evaluating extracellular single-unit recording performance. End-point immunohistochemistry was performed to further assess the local tissue response to the IMEs. Treatment with DEXSPPIN significantly increased the recording capabilities of IMEs compared to controls over the 8-week observation period. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggested that the improved neural recording performance may be attributed to reduced neuron degeneration and neuroinflammation. Overall, we found that DEXSPPIN treatment promoted an anti-inflammatory environment that improved neuronal density and enhanced IME recording performance.
An environmentally friendly bio-based approach to control invasive sun corals (Tubastrea spp.)
Interleukin-12 anchored drug conjugate (tolododekin alfa) in patients with advanced solid tumors: first-in-human Phase 1 trial
Semantic locality-aware biclustering for brain functional network connectivity
Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions
Abstract Predicting the binding affinity of ligands to protein pockets is key in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a range of attractive and repulsive electronic interactions during binding. Accurately accounting for all interactions requires robust quantum-mechanical (QM) benchmarks, which are scarce for ligand-pocket systems. Additionally, disagreement between “gold standard” Coupled Cluster (CC) and Quantum Monte Carlo (QMC) methods casts doubt on many benchmarks for larger non-covalent systems. We introduce the “QUantum Interacting Dimer” (QUID) benchmark framework containing 170 non-covalent (non-)equilibrium systems modeling chemically and structurally diverse ligand-pocket motifs. Symmetry-adapted perturbation theory shows that QUID broadly covers non-covalent binding motifs and energetic contributions. Robust binding energies are obtained using complementary CC and QMC methods, achieving agreement of 0.5 kcal/mol. The benchmark data analysis reveals that several dispersion-inclusive density functional approximations provide accurate energy predictions, though their atomic van der Waals forces differ in magnitude and orientation. Contrarily, semiempirical methods and empirical force fields require improvements in capturing non-covalent interactions (NCIs) for out-of-equilibrium geometries. The wide span of NCIs, highly accurate interaction energies, and analysis of molecular properties take QUID beyond the “gold standard” for QM benchmarks of ligand-protein systems.
Comparison of glucose/potassium ratio and revised trauma score in predicting mortality in patients with isolated blunt head trauma
A newborn derived monoclonal IgM antibody selectively modulates microbial metabolism in the gut
RETRACTED ARTICLE: An attention-based multi-residual and BiLSTM architecture for early diagnosis of autism spectrum disorder
Atomic-resolution imaging reveals nucleus-free crystallization in two-dimensional amorphous ice on graphite
Influence of sudden expansion geometries on cavitation behavior of control valves
Pulsed electromagnetic fields mediate sensory nerve regulation for bone formation in aging models
Advanced deep feature engineering with crayfish optimization for diabetes detection using tongue images
Guided phase transition for mitigating voltage hysteresis of iron fluoride positive electrodes in lithium-ion batteries
Abstract Despite the high capacity attained by conversion-reaction-based metal-fluoride positive materials in lithium-ion batteries through multiple electron storage, the large voltage hysteresis and low structural reversibility constrain their use. Herein, we propose guided phase transitions for designing conversion-type positive materials that undergo minimal structural changes upon lithium-ion storage. This approach reduces the compositional inhomogeneity, a culprit of the voltage hysteresis, while providing high structural reversibility. The thermodynamically stable rhombohedral FeF 3 involves irreversible phase transitions accompanied by significant structural rearrangement during lithiation. In contrast, the metastable tetragonal FeF 3 , electrochemically derived from a LiF-FeF 2 composite, undergoes facile and reversible phase transitions by maintaining structural integrity, enabled by conversion reactions between structurally analogous phases. Our study provides valuable insights into the importance of avoiding irreversible reaction pathways and deliberately guiding them to minimize structural changes in the crystal lattice, which is critical for designing positive materials with high structural reversibility.
Mass spectrometry-based serum peptidomic profiling reveals potential biomarker for canine hepatozoonosis
Disentangling climate and policy uncertainties for the Colorado River post-2026 operations
Abstract Lakes Mead and Powell in the Colorado River Basin underpin water and hydropower supply for the western United States. While the policies currently regulating the basin will expire by 2026, planning remains challenging due to intertwined climate variability and policy uncertainties. Based on streamflow projections from 10 dynamically downscaled CMIP6 global climate models and unique methods that add and remove internal variability, we evaluate future conditions at Powell and Mead under existing and alternative policies. Due to projected streamflow declines, under existing policy, both reservoirs will face substantial risks (>80% likelihood) of reaching dead pool before 2060. Adopting recently proposed alternative policies reduces but doesn’t eliminate such risks. All policies also exhibit tipping points where reservoir levels can change rapidly with a slight change in streamflow. A sustainable policy may require larger reductions to further reduce the reservoirs’ dead pool risks and provide better buffers from sudden changes.
Using large language models to suggest informative prior distributions in Bayesian regression analysis
Abstract Selecting prior distributions in Bayesian regression analysis is a challenging task. Even if knowledge already exists, gathering this information and translating it into informative prior distributions is both resource-demanding and difficult to perform objectively. In this paper, we analyze the idea of using large-language models (LLMs) to suggest suitable prior distributions. The substantial amount of information absorbed by LLMs gives them a potential for suggesting knowledge-based and more objective informative priors. We have developed an extensive prompt to not only ask LLMs to suggest suitable prior distributions based on their knowledge but also to verify and reflect on their choices. We evaluated the three popular LLMs Claude Opus, Gemini 2.5 pro, and ChatGPT 4o-mini for two different real datasets: an analysis of heart disease risk and an analysis of variables affecting the strength of concrete. For all the variables, the LLMs were capable of suggesting the correct direction for different associations, e.g., that the risk of heart disease is higher for males than females or that the strength of concrete is reduced with the amount of water added. The LLMs suggested both moderately and weakly informative priors, and the moderate priors were in many cases too confident, resulting in prior distributions with little agreement with the data. The quality of the suggested prior distributions was measured by computing the distance to the distribution of the maximum likelihood estimator (“data distribution”) using the Kullback-Leibler divergence. In both experiments, Claude and Gemini provided better prior distributions than ChatGPT. For weakly informative priors, ChatGPT and Gemini defaulted to a mean of 0, which was “unnecessarily vague” given their demonstrated knowledge. In contrast, Claude did not. This is a significant performance difference and a key advantage for Claude’s approach. The ability of LLMs to suggest the correct direction for different associations demonstrates a great potential for LLMs as an efficient and objective method to develop informative prior distributions. However, a significant challenge remains in calibrating the width of these priors, as the LLMs demonstrated a tendency towards both overconfidence and underconfidence. Our code is available at https://github.com/hugohammer/LLM-priors .
Electron shuttling promotes denitrification and mitigates nitrous oxide emissions in lakes
Abstract Eutrophication is an emerging global issue that is becoming increasingly severe due to the rising nutrient inputs and limited availability of electron donors for nitrogen removal. In sediments where redox conditions fluctuate dramatically, extracellular electron transfer (EET) critically supports microbial metabolism. However, the biogeochemical significance of EET-coupled denitrification and its EET mechanisms remain unclear. Here, through field investigations and laboratory 15N isotope experiments, we reveal that humic substance (HS)-mediated electron shuttling significantly enhances denitrification primarily by stimulating bacterial outer membrane c-type cytochrome. Specifically, EET mitigates the emission of greenhouse gas nitrous oxide by enriching nosZII-type reducers. Notably, the efficacy of exogenous HS amendment attenuates in sediment with high native HS concentration. Metagenomic binning further reveals multiple cytochromes forming a complete EET-coupled denitrification electron transport chain. Our findings elucidate the microbial mechanisms underlying electron shuttling-driven denitrification in lakes, thereby expanding the understanding of biogeochemical cycles.