Browse Articles

Urban groundwater supplies facing dual pressures of depletion and contamination in China

Groundwater is essential to urban water supplies throughout the world, but we do not understand how quantity and quality issues may jeopardize the ability of cities to meet their water needs. Here, we present a national analysis of both quantity and quality challenges facing China’s urban groundwater supply security, using high-resolution groundwater depletion modeling and a recently compiled dataset detailing quality violations in drinking groundwater sources. We estimate that 180 cities (about half of the prefecture-level-and-above cities), accounting for 311 million urban residents, face at least one groundwater pressure between 2016 and 2021. Cities that face dual quantity and quality pressures pinpoints hotspots of intense groundwater threats— Specifically, 40 cities, mainly situated in Northeast China and the middle to lower reaches of the Yellow River, are exposed to dual groundwater pressures. The results highlight the interconnected and possibly mutually reinforcing nature of groundwater depletion and quality issues. The logistic regression models indicate that groundwater depletion and quality violations are associated with natural water endowment and anthropogenic factors. Particularly, drinking groundwater quality issues are clustered in relation to socioeconomic gaps, with larger, wealthier cities being less prone to such problems. We argue that securing drinking water sources in small and poor cities through integrated groundwater management and economic assistance should be an important national priority for achieving groundwater supply sustainability.

Association of blood urea nitrogen to glucose ratio with 365-day mortality in critically ill patients with chronic kidney disease: a retrospective study

Transcriptomic insights into early mechanisms underlying post-chikungunya chronic inflammatory joint disease

Subfunctionalization and epigenetic regulation of a biosynthetic gene cluster in <i>Solanaceae</i>

Biosynthetic gene clusters (BGCs) are sets of often heterologous genes that are genetically and functionally linked. Among eukaryotes, BGCs are most common in plants and fungi and ensure the coexpression of the different enzymes coordinating the biosynthesis of specialized metabolites. Here, we report the identification of a withanolide BGC in Physalis grisea (ground-cherry), a member of the nightshade family ( Solanaceae ). A combination of transcriptomic, epigenomic, and metabolic analyses revealed that, following a duplication event, this BGC evolved two tissue-specifically expressed subclusters, containing several pairs of paralogs that contribute to related but distinct biochemical processes; this subfunctionalization is tightly associated with epigenetic features and the local chromatin environment. The two subclusters appear strictly isolated from each other at the structural chromatin level, each forming a highly self-interacting chromatin domain with tissue-dependent levels of condensation. This correlates with gene expression in either above- or below-ground tissue, thus spatially separating the production of different withanolide compounds. By comparative phylogenomics, we show that the withanolide BGC most likely evolved before the diversification of the Solanaceae family and underwent lineage-specific diversifications and losses. The tissue-specific subfunctionalization is common to species of the Physalideae tribe but distinct from other, independent duplication events outside of this clade. In sum, our study reports on an instance of an epigenetically modulated subfunctionalization within a BGC and sheds light on the biosynthesis of withanolides, a highly diverse group of steroidal triterpenoids important in plant defense and amenable to pharmaceutical applications due to their anti-inflammatory, antibiotic, and anticancer properties.

Clinical implications of early blood transfusion after kidney transplantation

Listen for a change? A longitudinal field experiment on listening’s potential to enhance persuasion

Scholars and practitioners widely posit that listening to other people enhances efforts to persuade them. Listening may enhance persuasion by promoting cognitive processing, reducing defensiveness, and improving perceptions of the persuader. However, empirical tests of this widely theorized hypothesis are surprisingly scarce. We review the case for and against this hypothesis, arguing previous research has not sufficiently attended to reasons why listening may not enhance persuasion. We test this hypothesis using a preregistered, well-powered field experiment in which trained professional canvassers, acting as confederates, had ∼10 min video conversations with U.S. participants ( N = 1,485) about unauthorized immigration, a salient topic of disagreement. We independently randomized whether confederates shared a persuasive narrative about an undocumented immigrant and whether they practiced high-quality nonjudgmental listening to participants’ opinions. We measured outcomes immediately after the conversation and again five weeks later. Sharing a persuasive narrative meaningfully and durably reduced prejudice and changed policy attitudes. The listening manipulation also successfully improved perceptions of the persuader and increased processing. Surprisingly, however, the listening manipulation did not enhance persuasion: Sharing a persuasive narrative was just as effective in the absence of high-quality listening. We discuss theoretical and practical implications.

Optimized exposer region-based modified adaptive histogram equalization method for contrast enhancement in CXR imaging

Resolved—Editorial Expression of Concern for Duckworth et al., Role of test motivation in intelligence testing

LIGHt-based rapid detection of starch in tobacco leaves by smartphone sensing

Structural requirements of KAI2 ligands for activation of signal transduction

Karrikin Insensitive 2 (KAI2), identified as the receptor protein for karrikins (KARs), which are smoke-derived seed germination stimulants, belongs to the same α/β-hydrolase family as D14, the receptor for strigolactones (SLs). KAI2 is believed to recognize an endogenous butenolide (KAI2 ligand; KL), but the identity of this compound remains unknown. Recent studies have suggested that ligand hydrolysis by KAI2 is a prerequisite for receptor activation to induce interaction with the target proteins, similar to the situation with D14. However, direct experimental evidence has been lacking. Here, we designed KAI2 ligands (carba-dMGers) whose butenolide rings were modified so that they cannot be hydrolyzed or dissociated from the original ligand molecule by KAI2, by structurally modifying dMGer, a potent and selective KAI2 agonist. Using these dMGer analogs, we found that the strongly bioactive ligand, (+)-dMGer, was hydrolyzed by KAI2 at a lower enzymatic rate compared with the weakly bioactive ligand, (+)-1′-carba-dMGer, and the hydrolyzed butenolide ring of (+)-dMGer was transiently trapped in the catalytic pocket of KAI2. Additionally, structural analysis revealed that (+)-6′-carba-dMGer bound to the catalytic pocket of KAI2 in the unhydrolyzed state. However, this binding did not induce the interaction between KAI2 and SMAX1, indicating that ligand binding to the receptor alone was not sufficient for KAI2 signaling. This study showed experimental data from a ligand structure–activity study that ligand hydrolysis and subsequent covalent adduct formation with the catalytic triad plays a key role in KAI2 activation, providing insight into the chemical structure of the Arabidopsis KL.

Mathematical modeling and optimal control of depression dynamics influenced by saboteurs

Abstract Depression disorder affects millions globally, characterized by symptoms such as profound sadness, loss of interest in activities, and disruptions in eating and sleeping patterns. Understanding depression within the context of chronic pain is essential for developing effective management and intervention strategies. This study utilizes mathematical modeling to analyze depression trends using empirical data from Spain spanning from 2011 to 2022. Our depression model incorporates distinct compartments for primary and secondary depressed populations, along with a category for individuals categorized as saboteurs, who may actively influence the depression prevalence. We calculated the basic reproduction number $$(R_0)$$ and identified four equilibrium points and evaluated their stability. Additionally, sensitivity analysis was conducted to assess the impact of $$(R_0)$$ on depression prevalence. Furthermore, optimal control strategies were explored for the model. These strategies aim to improve treatment adherence, encourage doctor consultations, promote self-medication practices, and enhance recovery rates, ultimately aiming to reduce spread of depressive disorders and associated mortality. Data fitting was conducted using Python, and simulations were carried out in MATLAB to ensure rigorous validation of the model.

Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of the influenza A virus

Viral proteins frequently mutate to evade host innate immune responses, yet the impact of these mutations on the molecular energy landscape remains unclear. Epistasis, the intramolecular communications between mutations, often renders the combined mutational effects unpredictable. Nonstructural protein 1 (NS1) is a major virulence factor of the influenza A virus (IAV) that activates host PI3K by binding to its p85β subunit. Here, we present a deep analysis of the impact of evolutionary mutations in NS1 that emerged between the 1918 pandemic IAV strain and its descendant PR8 strain. Our analysis reveals how the mutations rewired interresidue communications, which underlie long-range allosteric and epistatic networks in NS1. Our findings show that PR8 NS1 binds to p85β with approximately 10-fold greater affinity than 1918 NS1 due to allosteric mutational effects, which are further tuned by epistasis. NMR chemical shift perturbation and methyl-axis order parameter analyses revealed that the mutations induced long-range structural and dynamic changes in PR8 NS1, relative to 1918 NS1, enhancing its affinity to p85β. Complementary molecular dynamics simulations and graph theory-based network analysis for conformational dynamics on the submicrosecond timescales uncover how these mutations rewire the dynamic network, which underlies the allosteric epistasis. Significantly, we find that conformational dynamics of residues with high betweenness centrality play a crucial role in communications between network communities and are highly conserved across influenza A virus evolution. These findings advance our mechanistic understanding of the allosteric and epistatic communications between distant residues and provide insight into their role in the molecular evolution of NS1.

Assessing the effects of conductivity on egg development and survival of Eastern Hellbenders (Cryptobranchus a. alleganiensis)

Abstract Increasing concentrations of dissolved ions in freshwater ecosystems often stem from anthropogenic sources and have been implicated in the decline of sensitive aquatic organisms. Eastern hellbenders (Cryptobranchus a. alleganiensis) are fully aquatic salamanders that are experiencing range-wide declines and increased conductivity has been suggested as a cause. Declining populations are skewed towards older age classes, indicating a lack of successful reproduction (i.e., population recruitment). Therefore, insights into mechanisms that may cause mortality in early life stages are of great value to hellbender conservation. We conducted an experimental study to evaluate the effects of increased conductivity on the survival and development of hellbender eggs and newly hatched larvae. Wild-collected eggs were incubated across a range of conductivities (100, 300, 600, 1,000 µS/cm). We used two types of salt, aquarium salt and rock salt, to manipulate conductivity during and after hatching. Overall mortality rates were low (&lt; 0.07) across all treatments, but highest in the 1,000 µS/cm treatments (0.14). There was no difference in mortality between aquarium salt and rock salt treatments, however the rock salt treatment stimulated earlier hatching times. Larvae reared in the 1,000 µS/cm treatments had shorter snout-vent length (SVLs) (mean = 25.44 mm) than those reared at 300 µS/cm (mean = 26.95 mm) and marginally smaller head and mid-section width than individuals reared in the 100 µS/cm treatments. Our study suggests that higher conductivity has limited effects on egg and larval survival and development, and that water conductivity alone may be a suboptimal metric relating the persistence of hellbender populations to water quality. In addition, the type of salt affected hatching rates and timing, thus evaluating the composition and concentration of various ions leading to elevated conductivity is essential to understanding how degraded water quality may affect early life stages of this imperiled amphibian.

Planar device–enabled speckle illumination for dark-field label-free imaging beyond the diffraction limit

Dark-field microscopy is a technique used in optical microscopy to increase the contrast in unstained samples, making it possible to observe details that would otherwise be difficult to see under bright-field microscopy; thus, it has been widely employed in biological research, material science, and medical diagnostics. However, most dark-field microscopy methods cannot overcome the optical diffraction limit and require a bulky dark-field condenser and precise alignment of each optical element. In this study, we introduce a planar photonic device that can produce random speckles for dark-field illumination and improve the optical resolution. This planar device is made of random distribution fibers for injection of a laser beam, a scattering layer to produce random speckles, a one-dimensional photonic crystal (1DPC) to produce a hollow cone of light, and a metallic film to increase the energy efficiency. This planar device can work as a substrate for conventional microscopy. Taking advantage of the hollow cone of light with random speckles generated by the proposed planar device, we achieve a high-contrast, label-free image with a 1.55-fold improvement in spatial resolution. Furthermore, random evanescent speckles can be generated on the 1DPC just through tuning the incident wavelength, which demonstrates the ability for optical surface imaging beyond the diffraction limit. The advantage of this technique is that it does not require complex optical system or precise knowledge of the illumination pattern. This study will expand the potential applications of dark-field microscopy and provide insights into samples that might otherwise be invisible under traditional dark-field microscopy.

Deep learning-based debris flow hazard detection and recognition system: a case study

Abstract Debris flows are characterized by their suddenness, rapidity, large scale and destructive power, causing serious threat to the population in mountainous areas. Surveillance cameras are widely used in geological hazard monitoring and early warning projects. So far, video cameras are used as a passive tool for post inspection and not as an active role for debris flow monitoring and early warning. Inspired by recent developments of anomaly detection in the field of computer vision, in this paper, we propose a novel automatic debris flow detection and recognition system based on deep learning. It consists of a video feature extraction network using a 3D convolutional neural network (CNN), a debris flow hazard detection network using a multi-layer perceptron (MLP), and a debris flow hazard recognition network for verification employing another CNN. The proposed system takes the video sequences captured by the cameras as inputs and enables the detection and recognition of debris flow hazards. All the networks are optimized and evaluated on a newly annotated image dataset called Debrisflow23. Extensive experimental evaluations with a detection accuracy of 86.3 % AUC, a recognition accuracy of 83.7 % AUC, and an overall identification accuracy of 88.1 % AUC on the test dataset demonstrate that the proposed method possesses accurate and reliable debris flow warning capability. Thus, further precautions can be taken in advance to reduce the damage to human settlements and infrastructure caused by debris flows.

Discovery of a new mitophagy-related gene signature for predicting the outlook and immunotherapy in triple-negative breast cancer

Explicitly unbiased large language models still form biased associations

Large language models (LLMs) can pass explicit social bias tests but still harbor implicit biases, similar to humans who endorse egalitarian beliefs yet exhibit subtle biases. Measuring such implicit biases can be a challenge: As LLMs become increasingly proprietary, it may not be possible to access their embeddings and apply existing bias measures; furthermore, implicit biases are primarily a concern if they affect the actual decisions that these systems make. We address both challenges by introducing two measures: LLM Word Association Test, a prompt-based method for revealing implicit bias; and LLM Relative Decision Test, a strategy to detect subtle discrimination in contextual decisions. Both measures are based on psychological research: LLM Word Association Test adapts the Implicit Association Test, widely used to study the automatic associations between concepts held in human minds; and LLM Relative Decision Test operationalizes psychological results indicating that relative evaluations between two candidates, not absolute evaluations assessing each independently, are more diagnostic of implicit biases. Using these measures, we found pervasive stereotype biases mirroring those in society in 8 value-aligned models across 4 social categories (race, gender, religion, health) in 21 stereotypes (such as race and criminality, race and weapons, gender and science, age and negativity). These prompt-based measures draw from psychology’s long history of research into measuring stereotypes based on purely observable behavior; they expose nuanced biases in proprietary value-aligned LLMs that appear unbiased according to standard benchmarks.

Developing a dynamic combined power quality index for assessing the performance of a nuclear facility

Abstract Studying and evaluating the power quality (PQ) of an electrical network for nuclear installation is an important issue and a hot research topic for guaranteeing reliable and safe operation of sensitive electrical loads during this type of installation. As several PQ phenomena determine the overall PQ performance, analyzing PQ signals for evaluating the overall PQ is one of the major challenges for researchers in this field. Technically, voltage imbalance, current imbalance, voltage harmonic distortion, current harmonic distortion, and the power factor are five important PQ phenomena that judge the overall PQ performance of an electrical system. Multicriteria decision-making (MCDM) is used here as a methodology to identify a weighting for each PQ phenomenon. This paper proposes a power quality evaluation method for a nuclear research reactor (NRR) electrical network based on two MCDM. Methods the analytic hierarchy process (AHP) and criterion importance through inter-criteria correlation (CRITIC). A MATLAB/Simulink model for the NRR electrical system is presented, and then its validity and credibility are verified via measurements. In this study, different abnormal conditions are simulated in the NRR network to generate power quality disturbances, including a three-phase nonlinear load to simulate harmonics, an unbalanced load to simulate unbalance, and an inductive load to simulate the change in the power factor. The effectiveness and robustness of the proposed methodology are demonstrated through these different case studies. The results show that the obtained CPQI based on the dynamic weight approach allows for more accurate evaluations by adjusting the importance of various PQ phenomena depending on operational conditions and priorities. The main contribution of this paper is that a single compound power quality index (CPQI) was developed based on both the dynamic weights obtained from AHP-CRITIC methods and the results of the five PQ phenomena obtained under different abnormal conditions, considering the threshold level for each of these PQ phenomena. The analysis of the obtained results shows that this method accurately evaluates the overall PQ performance.

Manipulating hydrogenation pathways enables economically viable electrocatalytic aldehyde-to-alcohol valorization

Electrocatalytic reduction (ECR) of furfural represents a sustainable route for biomass valorization. Unfortunately, traditional Cu-catalyzed ECR suffers from diversified product distribution and industrial-incompatible production rates, mainly caused by the intricate mechanism−performance relationship. Here, we manipulate hydrogenation pathways on Cu by introducing ceria as an auxiliary component, which enables the mechanism switching from proton-coupled electron transfer to electrochemical hydrogen-atom transfer (HAT) and thus high-speed furfural-to-furfuryl alcohol electroconversion. Theoretical and kinetic analyses show that oxygen-vacancy-rich ceria delivers an efficient formation−diffusion−hydrogenation chain of H* by diminishing H* adsorption. Spectroscopic characterizations indicate that Cu/ceria interfacial perimeter enriches the local furfural, synergistically lowering the barrier of the rate-determining HAT step across the perimeter. Our Cu/ceria catalyst realizes high-rate HAT-dominated ECR for electrosynthesis of single-product furfuryl alcohol, achieving a high production rate of 19.1 ± 0.4 mol h −1 m −2 and a Faradaic efficiency of 97 ± 1% at an economically viable partial current density of over 0.1 A cm −2 . Our results demonstrate a highly efficient route for biofeedstock valorization with enhanced techno-economic feasibility.

The investigation of nonlinear vibration on metaconcrete single aggregate system and aggregate optimal design