Browse Articles

Correction: Optimised MobileNet for very lightweight and accurate plant leaf disease detection

3-D Numerical modeling and assessment of subsidence caused by underground copper ore mine

The comparison of two pendrin inhibitors, YS-01 and PDSinh-C01, in lipopolysaccharide-induced acute lung injury

A machine learning framework for multimodal temporal prediction of neurological outcome after out-of-hospital cardiac arrest

Abstract Neurological prognostication after out-of-hospital cardiac arrest (OHCA) remains challenging. Existing clinical scores rely on static, single-timepoint assessments and fail to capture the dynamic interplay among coagulation derangement, systemic inflammation, brain injury, and evolving neurological status. Whether integrating serial multimodal data through machine learning can meaningfully improve prediction over established approaches has not been systematically evaluated. We conducted a retrospective cohort study of 414 consecutive OHCA patients treated with targeted temperature management (TTM) at a tertiary cardiac arrest center in South Korea (2009–2021), where withdrawal of life-sustaining treatment is not practiced. Ninety-one features spanning five modalities—coagulation, inflammation, brain injury biomarkers, neurological examination, and static clinical variables—were extracted at admission, 24 h, and 48 h. We compared four machine learning algorithms against single-modality models and a clinical score approximation using five-fold stratified cross-validation. Dynamic prediction models evaluated discriminative performance evolution. SHapley Additive exPlanations (SHAP) analysis quantified feature- and modality-level contributions. Robustness was assessed through temporal validation, self-fulfilling prophecy sensitivity analyses, and exclusion of clinician-decision-dependent variables. Of 414 patients (mean age 55.6 years, 71.8% male), 131 (31.6%) achieved favorable neurological outcome (Cerebral Performance Category [CPC] 1–2) at six months. The full multimodal random forest model achieved an area under the receiver operating characteristic curve (AUROC) of 0.983 (95% CI 0.972–0.991), significantly outperforming the clinical score approximation (AUROC 0.847; ΔAUROC + 0.136, p  < 0.001) and every single-modality model (all p  < 0.001). At 100% specificity, sensitivity was 0.519. Dynamic prediction improved from AUROC 0.950 at admission to 0.977 at 24 h ( p  < 0.001) and 0.981 at 48 h. SHAP analysis revealed that neurological examination and brain injury biomarkers dominated overall prediction, while coagulation markers—particularly initial international normalized ratio (INR)—provided the strongest early discriminative signal. The model remained robust on temporal validation (AUROC 0.977), after neurological examination exclusion (0.965), and after excluding clinician-decision-dependent variables (0.979). A multimodal machine learning framework integrating serial thromboinflammation, brain injury, and neurological data substantially outperforms conventional approaches for neurological prognostication after OHCA. The dynamic prediction capability and modality-level explainability offer a pathway toward clinically actionable, time-evolving decision support in post-cardiac arrest care.

Performance modeling and optimization of a smart sensor-based Hydroponic system using PSO and Genetic algorithms

Learning-based agricultural management in partially observable environments subject to climate variability

Prediction of college student psychological state based on deep learning framework combining the improved Whale Optimization Algorithm and LSTM

Endothelial KLF4 depletion drives age-related neurovascular dysfunction and neuropsychiatric impairment

Deterioration of the blood–brain barrier (BBB), including impaired neurovascular uncoupling, contributes to cognitive decline in aging. The BBB is formed principally by brain microvascular endothelial cells (ECs), and ECs throughout the body are enriched for the transcription factor Krüppel-like factor 4 (KLF4). Because KLF4 levels in ECs decrease with age, we tested whether that decline contributes to aging-related BBB deterioration, neurovascular dysfunction, and cognitive impairment. Using EC-specific Klf4 knockout mice (EC-K4KO), we show that loss of EC KLF4 accelerates multiple age-related brain pathologies. Indeed, middle-aged EC-K4KO mice display pathological features that are not normally observed until advanced age, including marked BBB leakage, impaired neurovascular coupling, loss of microvessels, increased oxidative damage, neuroinflammation, neurodegeneration, anxiety-like behavior, and cognitive deficits. Single-cell RNA sequencing of brain vasculature reveals dysregulation of immune response and barrier-related genes in ECs lacking KLF4, indicating that KLF4 maintains brain endothelial homeostasis by constraining proinflammatory and senescence programs at the chromatin level. Together, these results identify loss of EC KLF4 as a key driver of neurovascular decline and age-associated cognitive dysfunction.

The genomic footprint of myna invasion in Oman showcases desert isolation and urban connectivity

Spatial and temporal prediction of <i>Aedes aegypti</i> populations with atmospheric and urban forms dependence

Accurately predicting mosquito population dynamics in cities requires models that couple climatic sensitivity with urban spatial heterogeneity. We developed a spatially explicit, climate-driven framework that integrates satellite imagery, field observations, and biology to simulate Aedes aegypti dynamics across heterogeneous urban landscapes. A decomposition technique was introduced to disentangle entomological observations from mixed urban sites into landscape-specific time series for houses, streets, and parks. We provide a robust parameter estimation through a constrained inverse problem, revealing distinct temperature responses and biological processes across environments. Model validation against both egg and adult mosquito data from five Brazilian cities yielded strong correlations with the majority falling between ρ = 0.4 and 0.8, confirming the model’s ability to reproduce observed spatiotemporal patterns. This integration of climate dependence, landscape quantification, and empirical validation provides a potential tool for anticipating mosquito abundance across space and time. By identifying periods and locations of elevated risk, the framework supports targeted, cost-effective interventions against dengue and other vector-borne diseases in a rapidly urbanizing and warming world.

Nonlinear driving mechanisms of system adaptability in China’s art and cultural tourism industry

Composite atherogenic indices reveal a superior lipid profile in a Chinese longevity population: a cross-sectional cohort study

Abstract Our previous work revealed differences in conventional lipid parameters between individuals from the Bama longevity hotspot and general population controls. However, their comprehensive atherogenic risk profile, as assessed by novel composite indices, remains uncharacterized. This study aimed to perform an in-depth evaluation of key composite atherogenic indices in this unique longevity cohort. A total of 2,767 participants were enrolled, including 1,007 individuals from the Bama longevity area in Guangxi and 1,760 controls from Shimen County, Hunan. This analysis presents a novel evaluation of the Atherogenic Index of Plasma (AIP), Atherogenic Index (AI), Lipoprotein Combine Index (LCI), Remnant Cholesterol (RC), and Castelli’s Risk indices (CRI-I, CRI-II). Statistical analyses included between-group comparisons (Student’s t -test, Chi-square test) and Spearman correlation analysis. The Bama cohort demonstrated a significantly less atherogenic profile across most composite indices. Specifically, AIP, AI, LCI, RC, and CRI-I were markedly lower in Bama cohort than in Controls (all P  &lt; 0.001; Cohen’s d ranging from − 0.23 to -1.04), whereas CRI-II did not differ significantly ( P  = 0.141). AIP, LCI and RC showed strong positive correlations with TG ( r  = 0.933, 0.882 and 0.700 respectively, all P  &lt; 0.001). In contrast, AI and CRI-I were strongly negatively correlated with HDL-C ( r = -0.728 for both; all P  &lt; 0.001). The Bama longevity population possesses an associated capacity to regulate lipid metabolism, which may contribute to superior control of atherogenic risk, as captured by integrative indices like AIP, AI, LCI, and RC. These composite indices represent promising sensitive biomarkers that could improve clinical cardiovascular risk assessment in longevity populations, underscoring the potential translational value of identifying favorable metabolic profile shaped by genetic and local environmental factors.

Evolutionarily conserved and divergent mechanisms of dual Ca ²⁺ sensors in synaptic vesicle exocytosis

Neurotransmitter release at the Caenorhabditis elegans neuromuscular junction is governed by a dual Ca 2+ sensor system composed of SNT-1 and SNT-3, which function analogously to the Ca 2+ sensor systems found in certain mammalian neurons, such as synaptotagmin-1 and -7 (Syt1/Syt7) in the hippocampus. In this study, we investigated how SNT-1 and SNT-3 mediate fast and slow neurotransmitter release through their potential interactions with the SNARE complex and their polybasic motifs. AlphaFold 3 models of SNT-1–SNARE and SNT-3–SNARE complexes predicted a C2B–SNARE arrangement consistent with the canonical Syt1–SNARE primary interface [Zhou et al. , Nature 525 , 62–67 (2015)] and precisely identified conserved binding residues within the C2B domains, as well as in SNAP-25 and Syntaxin, highlighting the evolutionary conservation of this interaction. Electrophysiological analyses using targeted mutagenesis demonstrated that both SNT-1 and SNT-3 require C2B–SNARE interactions and polybasic motifs within their C2 domains to drive evoked fast and slow neurotransmitter release. Notably, SNT-1 and SNT-3 exhibited differential dependence on distinct regions of the C2B–SNARE interface and their respective polybasic motifs, suggesting that Ca 2+ -triggered fast and slow release operate via distinct mechanistic strategies. Furthermore, we found that SNT-1 mediates spontaneous neurotransmitter release through multiple pathways, involving not only the primary C2B–SNARE interface but also additional putative SNARE-binding interactions. Together, our findings uncover both conserved and divergent mechanisms for synaptic exocytosis regulated by the dual Ca 2+ sensors in C. elegans .

Exceptional-point stability boundaries from quantum dissipation to cosmological acceleration

Koopman mode decomposition of thermodynamic dissipation in nonlinear Langevin dynamics

Nonlinear oscillations are commonly observed in complex systems far from equilibrium, such as living organisms. These oscillations are essential for sustaining vital processes, like neuronal firing, circadian rhythms, and heartbeats. In such systems, thermodynamic dissipation is necessary to maintain oscillations against noise. However, due to their nonlinear dynamics, it has been challenging to determine how the characteristics of oscillations, such as frequency, amplitude, and coherent patterns across elements, influence dissipation. To resolve this issue, we employ Koopman mode decomposition, which recasts nonlinear dynamics as a linear evolution in a function space. This linearization allows the dynamics to be decomposed into temporal oscillatory modes coherent across elements, with the Koopman eigenvalues determining their frequencies. Using this method, we decompose thermodynamic dissipation caused by nonconservative forces into contributions from oscillatory modes in overdamped nonlinear Langevin dynamics. We show that the dissipation from each mode is proportional to its frequency squared and its intensity, providing an interpretable, mode-by-mode picture. In the noisy FitzHugh–Nagumo model, we demonstrate the effectiveness of this framework in quantifying the impact of oscillatory modes on dissipation during nonlinear phenomena like coherent resonance and bifurcation. For instance, our analysis of coherent resonance reveals that the greatest dissipation at the optimal noise intensity is supported by a broad spectrum of frequencies, whereas at nonoptimal noise levels, dissipation is dominated by specific frequency modes. Our work offers a general approach to connecting oscillations to dissipation in noisy environments and improves our understanding of diverse oscillation phenomena from a nonequilibrium thermodynamic perspective.

Thermo-CR: real-time physics-based cloud shadow removal via thermodynamic atmospheric modelling and multi-source fusion

Relaxor behavior in rocksalt cation-ordered material induced by (anti)ferroelectric phase competition

Abstract Relaxor ferroelectrics are characterized by dispersion of the temperature-dependent dielectric constant with frequency and enhanced electromechanical coupling. These properties arise from the dynamic polar response of correlated nanodomains that are strongly associated with atomic-scale compositional disorder, which disrupts long-range ferroelectric ordering and enables nanodomain formation. Here, we report relaxor properties originating from spontaneous low temperature phase competition in fully cation-ordered antiferroelectric PbMg 0.5 W 0.5 O 3 epitaxial films, including the identification of a new low-energy polar phase. Unlike prototypical relaxors, the B -site cations in coherently strained PbMg 0.5 W 0.5 O 3 films exhibit long-range rocksalt chemical ordering. Temperature-dependent polarization studies reveal the switching behaviors associated with the phase transitions from paraelectric to antiferroelectric to ferroelectric, and the characteristic dielectric relaxation is ascribed instead to phase competition between the polar and antipolar phases mediated by temperature and substrate clamping. This phase competition breaks long-range dipole correlation and leads to dielectric dispersion and relaxor behavior. These findings demonstrate a new paradigm for designing relaxor material properties through engineered phase competition.

Structural basis for lysophosphatidic acid recognition and atypical Gα _q coupling by LPAR5

Lysophosphatidic acid receptor 5 (LPAR5) is a non-endothelial differentiation gene class A G protein–coupled receptor that regulates neuropathic pain, itch, and cancer progression through coupling to G proteins. Here, we report the cryo-EM structure of LPAR5 bound to 1-oleoyl-lysophosphatidic acid (LPA) in complex with G q at 2.96 Å resolution, revealing a distinct mode of receptor activation and G protein coupling. The phosphate headgroup of LPA forms extensive polar interactions with residues from extracellular loop 2 and transmembrane helices TM5–TM7, while the lipid tail inserts into a deep hydrophobic cavity formed by TM3–TM5. Site-directed mutagenesis confirms the functional importance of these interactions. Remarkably, LPAR5 exhibits a noncanonical G protein coupling mode. Unlike previously reported GPCR–G protein structures in which the Gα C-terminal α5 helix (“wavy hook”) primarily engages TM6, the wavy hook in LPAR5 is positioned toward the intracellular loop 1–helix 8 interface. This configuration is associated with limited TM6 outward displacement and modest rearrangement at the toggle-switch position (6.48). The resulting interface is stabilized by receptor-specific interactions and supported by functional data. Together, these findings reveal an alternative mode of GPCR–G protein coupling and highlight the structural plasticity underlying signaling specificity in LPA receptors.

Microwave-accelerated heating in preparation of pectin nanoparticles cross-linked with Mg2+: process optimization and lyophilized product characterization

Nanometric mineral inclusions from a fluid-rich diamond: identification, structure, and implications for deep Earth

Abstract Fluid-rich (cloudy/fibrous) diamonds host millions of micrometric fluid inclusions that can reveal the nature of diamond-forming media. Mineral inclusions in fluid-rich diamonds are nano to micrometric making structural and chemical characterization of the different phases difficult. Consequently, limited work has been done determining the pressures, temperatures and depths at which such inclusions form. The relationship between such conditions and those of fluid-rich diamond formation remains unclear. We report the use of micro-electron diffractometry to identify and anisotropically refine the structure of a nanometric åkermanite inclusion in a fluid-rich diamond from South Africa. Additional nanometric Ba/Sr-carbonate inclusions were detected. FTIR analyses revealed a highly aggregated, gem-quality core surrounded by a rim rich in high-density fluid (HDF) inclusions from which åkermanite crystallized. Åkermanite formed during HDF depressurization due to kimberlite eruption or exhumation. In the latter scenario, åkermanite constrained diamond formation to a minimum temperature of 1000 °C at ∼140 km depth (~4.6 GPa). Analysis of the HDFs reveal low-Mg carbonatitic to silicic compositions. The high BaO and Cl and the δ 13 C values of the diamond (−5.72 to −7.84 ‰) are attributed to formation via penetration of saline fluid into an eclogite, related to subduction of carbonated altered oceanic crust.