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RHCR: a reinforced heterogeneous knowledge graph for course recommendation
Thermochemical Treatment of Wastewater Residual Solids for Global Mitigation of Emerging Contaminants
Abstract Emerging contaminants are ubiquitous across the environment, posing rising ecological and public-health threats. Through a global data compilation, we show wastewater residual solids, by-products of wastewater treatment, represent a concentrated reservoir of emerging contaminants, capturing an estimated 20%, 24%, and 13% of global releases of microplastics, pharmaceuticals, and antibiotic resistance genes, respectively. This concentration creates a strategic intervention point where eliminating emerging contaminants within wastewater residual solids can substantially reduce environmental loading. Leveraging this opportunity requires next-generation technologies such as thermochemical processing capable of near-complete emerging contaminant destruction. Simulation-based evaluation suggests thermochemical wastewater residual solids management exhibits overlapping cost and greenhouse gas emission ranges with conventional systems, with average costs typically higher and average greenhouse gas emission consistently lower. The Global North faces higher costs yet greater investment capacity, whereas the Global South benefits economically but faces infrastructure gaps. Collectively, this analysis provides insights into intercepting emerging contaminants, reframing wastewater systems as active planetary defenses.
Breeding phenology plasticity to climate revealed by automated monitoring of two Eudyptes penguin species across the sub-Antarctic
Cadonilimab plus chemotherapy as first-line treatment in PD-L1-negative advanced non-small cell lung cancer: a phase II clinical trial
Validation of the Shoebox PureTest audiometry technology for remote data collection in clinical trials
Closed-loop iron chelate recycling via molecularly imprinted hydrogels suppresses ferroptosis
Environmental DNA from pumped deep-sea water enables monitoring of deep-sea fish diversity
Abstract Monitoring deep-sea biodiversity is challenging because conventional methods, such as net-sampling and visual surveys, require substantial logistical effort. Environmental DNA (eDNA) offers an alternative for detecting marine organisms; however, its application in deep-sea ecosystems is limited by the sampling accessibility. Because the influence of long‑pipeline transportation on eDNA is unknown, we compared two deep-sea water sampling methods: pumped deep-sea water (pumped method) and water collected near the intake using a Niskin bottle (Niskin method). Samples were collected from pumping stations in Sagami and Suruga bays, Japan, and baited camera observations were conducted to verify eDNA-based detections. In Sagami Bay, both sampling methods detected similar numbers of fish species. In Suruga Bay, the Niskin method exhibited higher species diversity and steeper accumulation curves than the pumped method. The habitat depth ranges of identified species corresponded to the water intake depths in both bays, supporting the effectiveness of the pumped method. Fish community composition showed substantial overlap between methods, and baited cameras recorded 11 and 17 fish taxa in Sagami and Suruga bays, respectively, with 7 and 10 taxa matching eDNA detections. These findings indicate that eDNA metabarcoding of pumped deep-sea water is a feasible method for monitoring deep-sea fish biodiversity.
Omics-driven plant breeding through phenomics-enviromics crosstalk
Genome-wide pervasiveness and localized variation of $$k$$-mer-based genomic signatures in eukaryotes
Abstract Genomic signatures–taxon-specific patterns in nucleotide composition–are widely used for taxonomic assignment and comparative genomics, yet their genome-wide pervasiveness across Telomere-to-Telomere assemblies, particularly within functionally diverse and highly repetitive regions, remains undercharacterized. We address this gap with an alignment-free, $$k$$ -mer-based analysis using Frequency Chaos Game Representations (FCGRs) across the human genome and three additional eukaryotes from distinct kingdoms. First, by combining qualitative inspection of FCGR landscapes with quantitative distance benchmarking, we show that each species exhibits a stable genomic signature across most chromosomes, with localized departures concentrated in regions enriched for short and long tandem repeats. Then, we introduce two computational pipelines that automatically select a short, contiguous representative genomic segment (500 Kbp) per genome and use it as a proxy to quantify intragenomic variation. Using DSSIM on a [0,1] scale, 80% of 500 Kbp segments in the human genome lie within 0.24 of the representative; segments exceeding this threshold align with tandem-repeat-dense loci. Leveraging these representatives in downstream tasks yields practical gains–for example, one-nearest-neighbor taxonomic classification improves by 7% relative to choosing a random segment. Finally, we provide k CGR-Diff , a graphical tool that enables side-by-side visualization and quantitative comparison of FCGR-based genomic signatures for sample or user-provided sequences, facilitating exploratory analyses of intragenomic variation within and across species. Collectively, our results provide extensive qualitative and quantitative evidence that $$k$$ -mer-based genomic signatures are pervasive at genome scale while varying predictably in repeat-dense regions, and they introduce practical methods and software for proxy selection and comparative analysis.
Pair density modulation from nematic superconductivity in systems with intra-unit-cell symmetry breaking
Optimizing circular industrial integration for sustainable yarn production with remanufacturing: a carbon policy-based modelling approach
Quantum Zeno effect in the spatial evolution of a single atom
Abstract The quantum Zeno effect (QZE) reveals that frequent measurements can suppress quantum evolution; however, the impact of measurements on the real-space motion of a single atom remains insufficiently explored experimentally. In this work, we employ an optical trap as a measurement pulse and, by monitoring atomic loss, directly observe the QZE in the real-space motion of a single atom. We find that the action of measurement on the atom consists of a projective measurement followed by subsequent periodic unitary evolution, thereby providing an intuitive physical picture of measurement backaction across different timescales. We further investigate the effects of measurement frequency, strength, and spatial position, demonstrating that measurements pulse not only suppress the spatial spreading of the quantum state but also enable deterministic preparation of distinct motional states. Moreover, by dynamically controlling the trap position, we realize measurement-induced directional transport of a single atom, with a velocity exceeding the maximum allowed by the adiabatic condition. Overall, our results provide a direct experimental demonstration of the QZE in real space and establish a versatile framework for measurement-based control of atomic motion, opening new possibilities for motional-state engineering in cold-atom systems.
Development and characterization of ivermectin nanoformulations for topical acaricidal activity against Rhipicephalus sanguineus ticks
Abstract Ticks are among the most significant ectoparasites of livestock and humans, posing serious health and economic risks. The growing resistance to conventional acaricides highlights the need for safer and more effective alternatives. This study aimed to evaluate the acaricidal efficacy of different ivermectin (IVM) nanoformulations against different developmental stages of Rhipicephalus sanguineus tick. Different ivermectin nanoformulations were fabricated and evaluated for their physicochemical properties. An in vitro study was performed using larval, nymphal, and adult immersion tests, followed by an in vivo trial against unfed adults using the most effective formulation. All nanoformulations showed particle sizes from 450 to 650 nm and a polydispersity index range from 0.34 to 0.65. IVM spanlastics exhibited the smallest particle size, highest encapsulation efficiency (92%), and a sustained release profile. Based on LC 50 values, IVM spanlastics showed superior acaricidal activity against larvae (LC 50 : 0.05%), nymphs (LC 50 : 0.09%), and unfed adults (LC 50 :0.16%); followed by IVM-SeNPs, where the LC 50 values were 0.10, 0.23, and 0.38% for larvae, nymphs, and unfed adults, respectively. The in vivo study of IVM spanlastics demonstrated 100% mortality of adult ticks within four days after application. IVM spanlastics could be topically applied as an alternative to conventional injectable IVM to control R. sanguineus ticks. Further toxicological studies are necessary to ensure the safety of these formulations for environmental and veterinary use.
K+-free mica-assisted epitaxy of Bi-based chalcogenide and oxychalcogenide single-crystals
Genome-wide analysis reveals structured ecological and functional divergence within Geobacillus stearothermophilus
Abstract Geobacillus stearothermophilus is a thermophilic bacterium widely used in food sterilization and industrial processes. Although it has long been treated as a single, well-defined species, its internal genomic diversity has not been systematically evaluated. Here, we analyzed 36 strains using comparative genomics to clarify the structure of diversity within this species. Phylogenetic analyses consistently revealed two major genomic groups. Genome similarity measurements showed that most strains met current species-level criteria, yet clear internal differentiation was present. The two groups differed in ecological origin and genome composition. Strains associated with food-related environments tended to have smaller genomes and fewer metabolic genes, whereas strains from natural thermal habitats possessed larger genomes and broader metabolic capabilities, including genes for carbohydrate and fatty acid utilization. A small number of strains displayed intermediate positions, suggesting gradual diversification rather than sharp separation. Despite pronounced internal structuring, the strains remain within accepted species boundaries. These findings demonstrate that substantial ecological and functional divergence can accumulate within a single bacterial species. Our results provide a genomic framework for understanding intraspecific diversity in thermophilic bacteria and illustrate the importance of interpreting genome similarity thresholds in the context of population structure.
A highly stretchable tri-channel fiber for composite motion decoupling
Abstract Fiber electronics have shown considerable potential in various applications, including electronic skin, human-machine interfaces, and intelligent sensing systems. However, stretchable fiber-based strain sensors confront fundamental challenges in concurrently achieving robust mechanical endurance, wide linear response range, and effective composite motions decoupling under complex deformation conditions. Here we present a highly stretchable tri-channel fiber featuring concentric and double-helical microchannels integrated with gallium-based liquid metal, constructing a dual-strain fiber sensor capable of decoupling composite motions involving both elongation and torsional deformations. The helical architecture promotes a three-dimensional orientation of polymer chains, thereby effectively enhancing both the stretchability and cyclic durability of the sensor. Owing to the specific configuration within the fiber, the sensor exhibits a highly linear response to tensile strain, along with bidirectional torsional strain sensing across a wide operational range. Furthermore, by synergistically integrating geometric deformation with hybrid resistive-capacitive sensing mechanisms, the sensor demonstrates the ability to simultaneously monitor and decouple stretching and twisting composite motion behaviors. This strategy enables the precise characterization of object motion and deformation states, offering valuable prospects for real-time health monitoring and motion tracking applications.
Hybrid deep learning model for brain age prediction using time-distributed convolutional and bidirectional LSTM networks
Abstract Brain age prediction has gained significant attention due to its strong correlation with neurological and cognitive disorders. The discrepancy between an individual’s chronological age and their predicted brain age–known as the Brain Age Gap–has been linked to conditions such as schizophrenia, Alzheimer’s disease, cognitive decline, and lifestyle factors like stress and poor health. A positive Brain Age Gap is often associated with accelerated aging and neurodegeneration, highlighting the need for precise and reliable estimation methods. In this study, we propose a novel deep learning model that incorporates time-distributed, convolutional and bidirectional LSTM layers for brain age estimation. Using MRI data from the OpenBHB dataset, processed through Voxel-Based Morphometry (VBM), our model undergoes rigorous preprocessing, including outlier detection, data augmentation, and MRI slice selection, to enhance learning efficiency. The model is optimized with the Adam optimizer with a scheduled learning-rate decay and evaluated using Mean Absolute Error (MAE) and $$R^{2}$$ Score. Experimental results demonstrate that our model achieves an MAE of 3.1573 years, outperforming previous methods and improving brain age prediction accuracy. These findings underscore the importance of advances in deep learning and data preprocessing in enhancing brain age estimation.