Browse Articles

Design of an ultra-compact and high-performance antenna for private 5G applications

Initial leukemic epigenomic state determines hypomethylating agent response

Clinical outcomes associated with Korean medicine home-visit care for patients with cognitive impairment: a multi-center retrospective observational study

Chaperone-mediated autophagy is required for regulatory T cell function

Spicy food intake and dietary factors shape the gut microbiome and metabolism of mucin and short-chain fatty acids in healthy adults

Coulombic control of charge transfer in radicals with quartet recycling luminescence

Abstract Excitons in organic materials are emerging as an attractive platform for tunable quantum technologies. Structures with near-degenerate doublet and triplet excitations in linked trityl radical, acene and carbazole units can host quartet states. These high spin states can be coherently manipulated, and later decay radiatively via the radical doublet transition. However, this requires controlling the deexcitation pathways of all metastable states. Here we establish design rules for efficient quartet generation and recycling to luminescence, using different connection arrangements of the molecular units. We discover that electronic coupling strength between these units dictates quartet formation and delayed emission yields, particularly through a Coulombically tuned acene-radical charge transfer state. This state acts as a source of non-radiative decay when acene-radical separation is small, but facilitates reversible doublet-quartet interconversion when acene-radical separation is large. Using these rules we report a material with 55% luminescence yield, where 94% of emitting excitons are recycled from the quartet with a 1.0 μ s lifetime. This reveals the central role of molecular topology in luminescent quantum materials.

Single-layer compact dual-band dual-circularly polarized antenna based on proximity-coupling feed patches

Anomalous quantized nonlinear soliton pumping

Synergistic fusion of a multilevel visual transformer in CNN for variable-length volumetric radiographic data analysis and content-based retrieval

Abstract Volumetric radiographic data analysis poses significant challenges due to its 3D structure and variable input lengths. Moreover, the unpredictable distribution of diseased regions, often spanning multiple slices and interspersed with normal tissue within abnormal volumes, further complicates the analysis. Despite advancements, existing 3D volumetric analysis methods predominantly rely on 2D slice selection and expert intervention, limiting scalability and efficiency. Additionally, a prevailing challenge is harmonizing the analysis of volumetric radiographic data with variable length. To address these limitations, we introduce a novel deep learning framework that synergistically fuses a lightweight multilevel vision transformer with a convolutional neural network (CNN). The proposed approach independently extracts and aggregates spatial features from 2D slices while preserving multilevel contextual information. A second-stage recurrent module is further integrated to handle variable-length inputs by leveraging single annotations for complete 3D volumes and exploiting their structural features. Empirical validation of our method is conducted on a composite of three publicly accessible radiographic repositories, demonstrating superiority (p-value $$<$$ 0.01) over existing alternatives. The results achieved highlight remarkable metrics: 98.54% accuracy, 98.51% F1-score, 98.77% average precision, and 98.25% average recall. To facilitate further research and development, we will publicly release the proposed framework and associated resources, providing a robust foundation for future studies. The implementation and materials is available at our GitHub .

Operando X-ray scattering reveals ordering-mediated solidification in additive manufacturing

Heart rate optimizer: a novel bio-inspired metaheuristic algorithm

Neofunctionalization underlies the evolutionary origin of sclareol biosynthesis in the mint family

Abstract Plant specialized metabolites play essential ecological roles, yet the mechanisms underlying their diversification remain poorly understood. Here, we investigate the biosynthesis of sclareol, a potent antifungal diterpene produced by Salvia sclarea (clary sage). A complete telomere-to-telomere genome assembly of clary sage, compared with genomes of related Lamiaceae species that do not produce sclareol, reveals a recent tandem duplication of a class II diterpene synthase gene ( SsLPPS ). This duplicated enzyme acquires a specific catalytic activity, synthesizing labda-13-en-8-ol diphosphate (LPP), the direct precursor of sclareol. Structural modeling and site-directed mutagenesis identify key amino acid substitutions responsible for this neofunctionalization. Integrative genome, chromatin, and transcriptome analyses show that SsLPPS and additional diterpenoid biosynthetic genes are organized in a trichome-specific, co-regulated gene cluster. Together, our findings illustrate how enzyme innovation and regulatory rewiring can give rise to unique metabolic pathways and may inform future strategies for engineering valuable plant terpenoids.

Evaluation of hydrogen and oxygen mixture addition in internal combustion engines under real driving conditions

Strongly-coordinating organoborates with eccentric solvation structure enable secondary calcium metal battery

Impact of hybrid solar and wind distributed generators on transient stability of captive power systems

Abstract This paper investigates the impact of solar and wind distributed generators on the transient stability of a 39-bus industrial power system. The system is analyzed under different configurations, including wind-only, solar-only, and hybrid DG integration. Critical Clearing Time (CCT), active power, and reactive power are used as performance metrics. The results show that DG integration improves system stability compared to the baseline case. Specifically, the CCT increases from 0.018 s (without DG) to 0.0248 s with wind DG, and up to 0.3168 s with solar DG. The hybrid configuration results in a CCT of 0.0297 s. These results indicate that solar DG provides the highest improvement in transient stability due to better voltage support characteristics. The study highlights the importance of DG type and placement in improving stability and provides insights for industrial power system design.

Dual-channel optogenetics in yeast for multiplexed light-based control of cellular processes and pathways

Abstract Optogenetics which involves the use of light to control cell functions on a genetic level has found utility in studying cell physiology, biomaterials and metabolic engineering. S. cerevisiae is an industrially relevant model organism that is used in many applications, but due to the large number of genes required and issues relating to cross-activation between different colours, optogenetics for different wavelengths of light have not been multiplexed in S. cerevisiae . In this paper, we develop a compact red light responsive optogenetic system for S. cerevisiae that requires only a single gene and no exogenous cofactors. Through engineering modular protein domains, we reduce the cross-activation of our system by blue light. We integrate our red light optogenetic system with EL222 blue light optogenetics to establish dual channel optogenetics in S. cerevisiae and demonstrate its utility for engineering biology through the light-based control of flavonoid luteolin synthesis and flocculation for ease of product extraction. We also demonstrate our system’s potential for the development of living materials by producing dual-coloured optogenetic patterns using S. cerevisiae . This work expands optogenetic applications in S. cerevisiae from single-light to multi-light systems, introducing the potential to multiplex different colours of light for dynamic, orthogonal control of separate cell processes.

Association between body roundness index and frailty among middle aged and older adults in China

DepoCatalog: mapping diversity of 129 recombinantly produced Klebsiella phage depolymerases

Abstract Our understanding of how depolymerase sequence and structure determine substrate specificity is fragmentary due to the limited number of experimentally characterized enzymes. Here we show DepoCatalog - an experimentally validated collection of 129 recombinantly prepared Klebsiella phage depolymerases (90 enzymes produced in this study and 39 homologs from the literature), with specificity spanning 75 KL-types. Enzymes originated from podo-, sipho-, myo-, jumbo phages, and prophages. Using activity profiling, structural modeling, and domain dissection, we propose a five‑class framework that captures the architectural and functional diversity of these enzymes. DepoCatalog uncovers cross-reactivity and taxa‑specific enzymes. Structural comparisons indicate that specificity switching or extension is associated with modifications to the C‑terminal domain. We further hypothesize that podoviruses encoding up to two RBPs show greater receptor adaptability than jumbo phages with multiple specialized RBPs. Finally, we develop a publicly accessible, DepoCat dataset ( https://depocat.uwr.edu.pl ) for specificity, structural classification and comparison of newly identified depolymerases.

MEET-eaters: An agent-based model of food consumption practices at the household level

The shift towards less meat and more plant-based consumption is vital for environmental quality, public health, animal welfare, and food security. So far, no studies have examined both the two-way interaction between supermarket supply and household demand, and the role of decision-making about meals within households. This study introduces the agent-based model MEET-eaters, a computational model representing supermarkets, households, and consumers, that was developed to explore these two-way interactions in the Dutch socio-cultural context. The model simulates the effect of supply that is unlimited, fixed, or responsive to the meal choices of households. The approaches to choosing meals are based on the Dutch context. Model development and assumptions were reviewed with experts to enhance model acceptance and understanding. Two interventions were tested, that aimed at either consumer meal preference or supermarket supply. Results show that supermarket supply and household demand both strongly influence the meal choice. Moreover, patterns in meal choices of households are similar under unlimited and responsive supply of supermarkets. The intervention aiming to change supply was most effective when animal-based options were restricted. Results from the intervention changing dietary preference shows that targeting either a large group at random or a smaller high-status group resulted in a similar reduction of animal-based and increase in plant-based meals. For shorter intervention durations, meal choice reverted to pre-intervention patterns once the interventions were discontinued. The results imply that the persistent high availability of animal-based (meat, fish, dairy) options under responsive supply weakens incentives for consumers to purchase plant-based options. In addition, targeting population groups based on status or size could result in more plant-based consumption. Overall, the findings suggest that long-term policies are required to achieve and sustain a shift towards more plant-based diets.

Dynamic isolation forest for anomaly detection in post-PCI myocardial infarction patients

Abstract Patients with myocardial infarction require continuous monitoring of multiple laboratory parameters after percutaneous coronary intervention (PCI), but assessment of their dynamic changes and of whether they deviate from the typical recovery trajectory still relies largely on clinical experience, and objective methods remain lacking. This study included 183 patients with myocardial infarction who underwent PCI, constructed fixed three-time-point windows, used blinded expert review as the reference, compared dynamic isolation forest (DIF) with other unsupervised methods, and conducted an external supportive prognostic analysis in MIMIC-IV. The results showed that DIF had the best agreement with expert ratings (Spearman’s ρ = 0.585; Kendall’s τ = 0.452), with an AUC of 0.859, and overall outperformed the other methods; the MIMIC-IV analysis showed that higher DIF anomaly scores were associated with an increased risk of death. DIF can be used to identify abnormal recovery windows after PCI that deviate from the typical recovery trajectory and may have potential for risk stratification, although its clinical utility still requires further validation.