Browse Articles

On the large-scale radiative cooling induced by tropical cyclone activity

Tropical cyclones (TCs) are among the most energetic phenomena in the climate system. Given their energetic nature, TCs induce upscale effects on the climate system, although these effects have not been extensively assessed. Understanding the influence of TCs on climate is important given the expected changes in TC activity with a warming climate. In this study, we provide evidence that TCs induce large-scale radiative cooling. We compare the climates of a coupled global climate model in two configurations: a control configuration with Earth-like TC activity and a perturbed configuration where TC activity is reduced by suppressing wind-induced surface heat exchange in areas with TC-like conditions. By comparing climate states, we find a reduction of incoming top-of-atmosphere radiation with more TC activity, largely in the subtropics. Globally, TC activity drives increases to longwave emission that outweigh increases to absorbed solar radiation. We show that decreases in sea surface temperature occur as an adjustment to TC-induced perturbations, coincident with increases in longwave emission caused by a drying of the free troposphere and a reduction in high cloud fraction. The results demonstrate the role of TCs in organizing convection, suggesting that TCs play a role in modulating climate sensitivity and large-scale energy transport.

Personalized prediction of student progress in moral education using multi modal learning

Correction for Scherrer et al., Characterizing sliding and rolling contacts between single particles

Retraction Note: Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework

Womens experiences of disrespectful maternity care and its consequences during childbirth in Spain

Capturing nuclear quantum effects in high-pressure superconducting hydrides and ice with nuclear–electronic orbital theory

Nuclear quantum effects are essential for correctly describing hydrogen-rich materials at high pressures. Superconducting hydrides and ice are prime examples of such systems, requiring the inclusion of lattice anharmonicity and nuclear quantum effects to correctly predict and describe the structures and phase transition pressures observed experimentally. Herein, we show that the nuclear–electronic orbital density functional theory (NEO-DFT) method, which treats specified nuclei quantum mechanically on the same level as the electrons, is capable of accurately describing nuclear quantum effects in superconducting hydrides and ice. NEO-DFT predicts the hydrogen-bond symmetrization pressure in H 3 S and D 3 S, benchmarking against the more expensive stochastic self-consistent harmonic approximation method, and predicts the correct symmetric Fm 3 ¯ m structure for LaH 10 at a wide range of pressures. NEO-DFT also predicts the ice VIII to ice X phase transition pressures for H 2 O and D 2 O in agreement with experimental measurements. The accuracy, computational efficiency, and broad applicability of the NEO method opens the door for expanded large-scale studies into these types of systems.

Comparing multiple definitions of obesity in a large nationwide health program

Abstract Obesity represents a major global health challenge, yet its prevalence and clinical significance may vary substantially depending on the definition applied. While the Body Mass Index (BMI) remains the most widely used indicator, alternative frameworks such as the European Association for the Study of Obesity (EASO) classification, the metabolically healthy obesity (MHO) concept, and the recent Lancet functional framework offer broader, clinically oriented perspectives. We conducted a cross-sectional, population-based analysis of 108,350 adults. BMI was calculated from measured height and weight, with overweight defined as 25.0–29.9 kg/m² and obesity as ≥ 30.0 kg/m². EASO obesity classification incorporated both BMI and the presence of obesity-related complications. Metabolic health was assessed using established cut-offs for hypertension, diabetes, and dyslipidaemia, and MHO was defined as BMI ≥ 30.0 kg/m² without metabolic abnormalities. By conventional BMI cut-offs 77,393 participants were overweight or obese, within which 47.2% were obese and 52.8% were overweight, respectively. In contrast, the EASO classification identified 29,044 respondents of the total population as obese in the overweight strata, leaving only 11,815 as non-obese. Therefore, among individuals with BMI 25.0–29.9, 71.1% were classified as obese by EASO criteria. Among participants with BMI-defined obesity, 12,151 (35.0%) were metabolically healthy obese and 22,585 (65.0%) were considered metabolically unhealthy. In terms of comorbidities, 82.8% ( n  = 24,521) of obese were clinically obese and 17.2% ( n  = 5,094) were pre-clinical obese. Obesity prevalence and the identification of metabolically healthy subgroups depend on the definition applied. While BMI alone provides a conservative estimate, other frameworks capture a much broader population. These findings highlight the need for unified definitions that may better reflect clinical risk and guide both public health surveillance and individual patient management.

Light-controlled disruption of cancer cell dormancy via photoswitchable stress hormone receptor degraders

Cancer cell dormancy is a key contributor to therapy resistance and disease relapse. The glucocorticoid receptor (GR), a major mediator of stress hormone signaling, has emerged as a central regulator of dormancy in non-lymphoid solid tumors, particularly lung cancer. However, systemic GR inhibition or degradation using conventional Proteolysis Targeting Chimeras (PROTACs) risks widespread on-target toxicity due to their constitutive activity. We hypothesized that integrating photoswitchable elements into PROTACs, termed photoPROTACs, would enable wavelength-specific, spatiotemporally precise modulation of GR degradation and dormancy-associated signaling pathways. Here, we synthesized a diverse series of photoPROTACs incorporating photoswitchable arylazotriazole or arylazopyrazole scaffolds, including previously unreported (OEt) 2 - and (NMe 2 ) 2 -substituted photoswitches. Arylazopyrazole-based GR photoPROTACs bearing Me 2 - and (OEt) 2 substituents exhibited near-quantitative photoisomerization (95% Z- isomer; 89 to 92% E- isomer), no photobleaching, and thermal half-lives in the range of 3 to 12.2 d in dimethyl sulfoxide (DMSO). Among them, KH-5-306 and KH-5-309 induced potent, specific, and reversible GR degradation in their thermodynamically stable E- isomeric form at low nanomolar concentrations, with markedly reduced activity in the Z- isomeric state. Transcriptomic profiling showed that E- KH-5-309 disrupts GR-driven dormancy-associated gene expression programs in a non–small cell lung cancer (NSCLC) model, while the Z- isomer remains functionally inert. Our findings establish a framework for the rational design of photoswitchable PROTACs beyond GR and demonstrate their potential to achieve spatiotemporal control of stress hormone receptor signaling, enabling mechanistic insights into GR function and the targeted disruption of cancer cell dormancy.

Integrative multimodal graph convolutional models for predictive short-form video recommendations

The simplicity of the Hodge bundle

This paper establishes the simplicity of the Hodge bundle, a theorem in the branch of modern mathematics known as algebraic geometry. Notably, the essential mathematical content was autonomously generated by Aletheia, a custom AI agent powered by Gemini Deep Think.

A cross-sectional analysis of research waste in randomized controlled trials on postoperative cognitive dysfunction

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine

Autonomous physical learning systems modify their internal parameters and solve computational tasks without relying on external computation. Compared to traditional computers, they enjoy distributed and energy-efficient learning due to their physical dynamics. In this paper, we introduce a self-learning resistor network, the Restricted Kirchhoff Machine, capable of solving unsupervised learning tasks akin to the Restricted Boltzmann Machine algorithm. The circuit relies on existing technology based on Contrastive Local Learning Networks, in which two identical networks compare different physical states to implement a contrastive local learning rule. We simulate the training of the machine on a dataset of handwritten digits, providing a proof of concept of its learning capabilities. Finally, we compare the scaling behavior of time, power, and energy per operation as the number of nodes increases to that of a Restricted Boltzmann Machine implemented on central processing unit (CPU) and graphics processing unit (GPU) platforms.

Energy-aware priority-based task scheduling in cloud data centers using bacterial foraging optimization

The effector NlOBP1b from the brown planthopper suppresses rice immunity by manipulating the OsCK2 complex

Odorant-binding proteins (OBPs) are crucial mediators in the peripheral olfactory perception of insects, functioning as a link between the external environment and odor receptors. Recent research has revealed their noncanonical role as salivary proteins that mediate plant–herbivore interactions, though the underlying mechanisms remain poorly understood. This study investigates the molecular basis of NlOBP1b, a salivary effector protein in the brown planthopper ( Nilaparvata lugens , BPH), which suppresses plant immune responses and enhances insect fitness. NlOBP1b -RNAi BPH results in reduced adaptability to host plants. Furthermore, NlOBP1b specifically interacts with the regulatory subunits of casein kinase II, OsCK2β3 and OsCK2β4. On one hand, NlOBP1b disrupts the assembly of the OsCK2β3–OsCK2α2 holoenzyme complex; on the other hand, it competes with the bZIP superfamily transcription factor OsTGA5 for binding to the OsCK2β4–OsCK2α2 complex, results suppressing holoenzyme-mediated phosphorylation and transcriptional activity of OsTGA5, ultimately reducing lignin accumulation. This disruption undermines the defense mechanisms of rice and significantly enhances the adaptability of BPH to its host.

Modified hybrid DC-DC boost converter with high voltage gain for renewable energy integration using GWO-DE parameter optimization

Abstract This paper presents a modified hybrid DC-DC boost converter topology with enhanced voltage gain capability for renewable energy applications, particularly suited for photovoltaic and micro-hydro power integration with high-voltage direct current (HVDC) transmission systems. The proposed topology incorporates a coupled inductor, switched capacitor, and voltage multiplier cell to achieve a significantly higher voltage conversion ratio compared to conventional boost converters while maintaining reduced voltage stress across the power semiconductor devices. A comprehensive steady-state analysis is performed under continuous conduction mode (CCM), and the voltage gain, current ripple, and efficiency expressions are derived analytically. Furthermore, a hybrid grey wolf optimizer–differential evolution (GWO-DE) algorithm is developed for the optimization of converter parameters, and a gradient-boosting regression surrogate is trained for rapid design-space exploration. The benefit of the GWO-DE optimization is quantified against both a classical analytical design and an exhaustive grid search, yielding a 2.7 percentage-point efficiency advantage over the analytical design and a 22-fold reduction in design wall-clock time relative to the grid search. The proposed converter achieves a voltage gain of 12.5 at a duty cycle of 0.65, with a peak efficiency of 96.8%. Extensive simulation results obtained in MATLAB/Simulink validate the theoretical analysis and confirm the superior performance of the proposed topology in comparison with existing high-gain DC-DC converter configurations reported in the literature.

Evolution of genome-wide barriers to gene flow during complex speciation in rattlesnakes

Speciation with gene flow poses a central paradox: how do genome-wide barriers to gene exchange accumulate as recombination continually breaks down associations among selected loci? Although theory predicts that together recombination, selection, and genome structure shape reproductive isolation, empirical studies often report conflicting patterns, suggesting that these determinants change across the speciation continuum. Here we compare genomic landscapes of introgression across rattlesnake lineages spanning a range of divergence. We generated a chromosome-level reference genome for the Southwestern Speckled Rattlesnake ( Crotalus pyrrhus ) and analyzed whole genome data from 181 individuals across two species complexes with a history of gene flow upon secondary contact. We show that reproductive isolation is highly polygenic and dynamically structured. At early divergence, introgression is most reduced in high recombination regions, consistent with increased efficacy of selection against gene flow at few large-effect loci. As divergence progresses, linked selection against gene flow dominates, generating a positive relationship between recombination and introgression expected to occur through the genome-wide coupling of polygenic barrier effects. Introgression landscapes also become increasingly correlated across species pairs as divergence increases due to repeated evolution of barriers in the same genomic regions. Here, we infer that the Z chromosome plays a prominent role in reproductive isolation, harboring a disproportionate number of barrier loci and showing reduced introgression even at early divergence. Together, these results reveal how recombination, selection, and genome organization interact to shape speciation with gene flow upon secondary contact, reconciling empirical patterns with predictions of speciation theory.

Information overload and pregnancy outcomes in IVF: psychological distress as an explanatory pathway

Illuminating proinflammatory myeloid cells with PET tracers targeting GPR84

Innate immunity mediated by myeloid cells defends against infection and injury, but when chronically activated, it drives tissue damage and neurodegeneration. Molecular imaging with positron emission tomography (PET) enables noninvasive, real-time monitoring of such processes in vivo. However, most current neuroinflammation PET tracers lack specificity for activated myeloid cells. G protein–coupled receptor 84 (GPR84) is a promising biomarker that is selectively upregulated on activated microglia and macrophages. Here, we report the development and validation of two fluorine-18-labeled GPR84 tracers, [ 18 F]MGX-110S and [ 18 F]MGX-111S. Both exhibit specific binding to human GPR84-expressing cells, with [ 18 F]MGX-110S demonstrating superior affinity, selectivity, and signal-to-background ratio. [ 18 F]MGX-110S enables sensitive detection of systemic- and neuro-inflammation in LPS-treated mice and outperforms PET images obtained using a radiotracer specific for translocator protein 18 kDa in 5xFAD mice—revealing pathology-correlated activation across cortical, hippocampal, and thalamic regions. Taken together, our data indicate that [ 18 F]MGX-110S is a highly sensitive and specific tool for visualizing maladaptive myeloid cell activation; its clinical translation could enable more precise detection and staging of inflammation in addition to improved therapeutic monitoring in neurodegenerative disorders and more broadly in inflammatory diseases.

Controlling the structural, optical, electrical, and compositional properties of silica-graphite core-shell for supercapacitor applications

Cultural evolution of beauty standards

Beauty standards shape self-perception and health through social comparison and objectification, while exposure to idealized imagery exacerbates body-image concerns. Media and fashion are central arbiters of these ideals, yet long-term, quantitative, intersectional studies on how representation has changed remain scarce. We assembled a dataset of 793,199 records spanning 25 y of advertising, magazine covers, runway shows, and editorials to quantify changes in anthropometric and demographic representation. We find a paradox in the evolution of beauty ideals: While representational diversity has increased, the median model physique remains stable. This is driven by selective plus-size inclusion at the upper tail, while the typical physique continues to diverge from the US population. Intersectionally, non-White models are 4.5 times more likely to be plus-size, suggesting that the industry consolidates multiple markers of diversity onto already underrepresented individuals rather than broadening inclusion structurally. Stratifying the industry via a data-driven prestige hierarchy, we find that thinness is overrepresented at the top tier. Finally, descriptive comparisons of two regulatory interventions suggest that numeric thresholds may be more effective than flexible guidelines at reducing underweight appearances. Our results quantify the cultural evolution in media and fashion, revealing that inclusion has increased; however, gains are uneven and intersectionally concentrated on size and ethnicity, whereas the prevailing thin ideal remains largely unchanged.