Browse Articles

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 (< 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

A structural atlas of death domain fold proteins reveals their versatile roles in biology and function

Death domain fold (DDF) superfamily proteins are critically important players in pathways of cell death and inflammation. DDFs are often essential scaffolding domains in receptors, adaptors, or effectors of these pathways by mediating homo- and hetero-oligomerization including helical filament assembly. At the downstream ends of these pathways, effector oligomerization by DDFs brings the enzyme domains into proximity for their dimerization and activation. Hundreds of structures of these domains have been solved. However, a comprehensive understanding of DDFs is lacking. In this article, we report the curation of a DDF structural atlas as a public website (deathdomain.org) and deduce the common and distinct principles of DDF-mediated oligomerization among the four families (death domain or DD, death effector domain or DED, caspase recruitment domain or CARD, and pyrin domain or PYD). We further annotate DDFs genome-wide based on AlphaFold-predicted models and protein sequences. These studies reveal mechanistic rules for this widely distributed domain superfamily.

Neuroleptics used in critical COVID associated with moderate-severe dyspnea after hospital discharge

Characterization of jackfruit Artocarpus heterophyllus Lam. for economic traits in dry zones of Karnataka, India

<i>Enterobacter hormaechei</i> replaces virulence with carbapenem resistance via porin loss

Pathogenic Enterobacter species are of increasing clinical concern due to the multidrug-resistant nature of these bacteria, including resistance to carbapenem antibiotics. Our understanding of Enterobacter virulence is limited, hindering the development of new prophylactics and therapeutics targeting infections caused by Enterobacter species. In this study, we assessed the virulence of contemporary clinical Enterobacter hormaechei isolates in a mouse model of intraperitoneal infection and used comparative genomics to identify genes promoting virulence. Through mutagenesis and complementation studies, we found two porin-encoding genes, ompC and ompD , to be required for E. hormaechei virulence. These porins imported clinically relevant carbapenems into the bacteria, and thus loss of OmpC and OmpD desensitized E. hormaechei to the antibiotics. Our genomic analyses suggest porin-related genes are frequently mutated in E. hormaechei , perhaps due to the selective pressure of antibiotic therapy during infection. Despite the importance of OmpC and OmpD during infection of immunocompetent hosts, we found the two porins to be dispensable for virulence in a neutropenic mouse model. Moreover, porin loss provided a fitness advantage during carbapenem treatment in an ex vivo human whole blood model of bacteremia. Our data provide experimental evidence of pathogenic Enterobacter species gaining antibiotic resistance via loss of porins and argue antibiotic therapy during infection of immunocompromised patients is a conducive environment for the selection of porin mutations enhancing the multidrug-resistant profile of these pathogens.

Efficacy of Naringenin against aging and degeneration of nucleus pulposus cells through IGFBP3 inhibition

Temporal autocorrelation is predictive of age—An extensive MEG time-series analysis

Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography (MEG) in 350 adults (18 to 88 y). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred time-series features proved superior. Notably, temporal autocorrelation (AC) emerged as the most robust predictor of age. Distinct patterns of AC within the visual and temporal cortex were most informative, offering a versatile measure of age-related signal changes for comprehensive health assessments based on brain activity.

Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning

A novel quinone biosynthetic pathway illuminates the evolution of aerobic metabolism

The dominant organisms in modern oxic ecosystems rely on respiratory quinones with high redox potential (HPQs) for electron transport in aerobic respiration and photosynthesis. The diversification of quinones, from low redox potential (LPQ) in anaerobes to HPQs in aerobes, is assumed to have followed Earth’s surface oxygenation ~2.3 billion years ago. However, the evolutionary origins of HPQs remain unresolved. Here, we characterize the structure and biosynthetic pathway of an ancestral HPQ, methyl-plastoquinone (mPQ), that is unique to bacteria of the phylum Nitrospirota . mPQ is structurally related to the two previously known HPQs, plastoquinone from Cyanobacteriota /chloroplasts and ubiquinone from Pseudomonadota /mitochondria, respectively. We demonstrate a common origin of the three HPQ biosynthetic pathways that predates the emergence of Nitrospirota , Cyanobacteriota , and Pseudomonadota . An ancestral HPQ biosynthetic pathway evolved ≥ 3.4 billion years ago in an extinct lineage and was laterally transferred to these three phyla ~2.5 to 3.2 billion years ago. We show that Cyanobacteriota and Pseudomonadota were ancestrally aerobic and thus propose that aerobic metabolism using HPQs significantly predates Earth’s surface oxygenation. Two of the three HPQ pathways were later obtained by eukaryotes through endosymbiosis forming chloroplasts and mitochondria, enabling their rise to dominance in modern oxic ecosystems.

Posterior approach unilateral laminectomy, debridement, and preshaped titanium mesh bone grafting with internal fixation for treatment of lumbar tuberculosis