Browse Articles

Efficient catalytic degradation of crystal violet dye over FeVO4 by spark plasma discharge: an eco-friendly approach for wastewater treatment

Abstract In the present study, catalytic degradation of crystal violet (CV) dye over ferric vanadate (FeVO 4 ) nanocatalyst was evaluated in the presence of spark plasma discharge and without additive oxidants. Spark plasma process, as a green technique and without the need for feed gas, can generate the oxidizing species which are capable of dissociating dye molecules in wastewater. Herein, FeVO 4 was synthesized by a hydrothermal method and thereupon utilized to remove CV dye under spark plasma discharge. FeVO 4 was introduced into the spark plasma system to increase reactive species activation and promote synergistic plasma-catalytic degradation without the need for external oxidants. The structural and morphological properties of the prepared catalyst were examined by XRD, FTIR, FESEM, EDX, and UV-vis DRS. The results indicated that the spark plasma enhanced catalytic activity of FeVO 4 , and subsequently facilitated the decomposition of CV dye. It was determined that the highest dye degradation efficiency occurred at pH 7.7, where about 99.4% of CV with an initial concentration of 10 mg.L −1 was decomposed after 10 min of the spark plasma treatment. Moreover, the results of radical trapping revealed that holes (h + ) and singlet oxygen ( 1 O 2 ) generated by spark plasma are the dominant agents in the oxidation and dye degradation process. Plasma-generated reactive oxygen/nitrogen species interact with the surface of FeVO 4 and increase the oxidative degradation of CV. The kinetic studies of the dye degradation process indicated that the pseudo-first-order kinetic model could explain it well. These results were obtained in the absence of oxidants, which subsequently reduces the cost of dye degradation, enhances operational safety by removing hazardous materials, and simplifies wastewater treatment operations.

Genetic variants affect diurnal glucose levels throughout the day

Abstract Circadian rhythms not only coordinate the timing of wake and sleep but also regulate homeostasis within the body, including glucose metabolism. The genetic variants that contribute to the temporal control of glucose levels have not been previously examined. Using genome-wide data from ~420,000 individuals from the UK Biobank and replication in ~100,000 individuals from the Estonian Biobank, ~500,000 from FinnGen, ~160,000 from the VA Million Veteran Program, and ~52,000 from the MGB Biobank, we show that glucose levels are under diurnal genetic control. We discover a robust temporal association of glucose levels at the Melatonin receptor 1B ( MTNR1B , rs10830963, P  = 1×10 −22 ) and a canonical circadian pacemaker gene Cryptochrome 2 ( CRY2) loci (rs12419690, P  = 1×10 −16 ). Furthermore, we show that sleep modulates glucose levels, and the genetic variants have an independent role in diurnal glucose control. Finally, we show that these variants independently modulate risk of type 2 diabetes and that sleep medications including melatonin associate with type 2 diabetes. Our findings, together with earlier genetic and epidemiological evidence, show a clear connection between sleep and metabolism and highlight genetic variation at MTNR1B and CRY2 in the control of diurnal glucose levels.

Item recommendation and quantum correlation on multiple datasets

Abstract Recommender systems are crucial for customizing user experiences across diverse sectors such as e-commerce and entertainment. Traditional correlation methods have been employed to forecast user preferences; however, they frequently prove inadequate when addressing intricate, high-dimensional datasets. Quantum computing presents a new approach for enhancing correlation computations, potentially resulting in more precise recommendations. This study seeks to address and compare the efficiency of classical and quantum correlation techniques in recommender systems utilizing four distinct datasets: Supermarket Sales, IMDB Top 250 movies, MovieLens 10k, and BigBasket products. The Item Recommendation and Quantum Correlation (IRQC) method, makes use of parameterized quantum circuits with rotation gates and entanglement. The experimental methodology comprised the utilization of both classical and quantum correlation approaches, evaluating their efficacy through critical metrics including mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrated that quantum correlations consistently surpassed classical correlations across all datasets. The proposed Quantum Correlation approach obtains lower mean absolute errors of 0.99, 0.30, 0.90, and 0.92 in BigBasket, Supermarket Sales, IMDB Top 250 Movies, and MovieLens 10K datasets, respectively, than 1.20, 1.48, 1.10, and 1.00 with the classical methods. This study underlines the potential of quantum computing in machine learning applications, notably for boosting recommendation systems.

Reentrant Landau levels in a Dirac topological insulator

Ecological risk assessment of rare earth elements in agricultural soils adjacent to an abandoned coal gangue pile in Chongqing, Southwest China

Light- and chemical-induced ciliary signaling governs dorsal/ventral regionalization of human telencephalic organoids

Abstract Neural stem/progenitor cells (NPCs) have primary cilia, which are critical organelles for Sonic hedgehog signaling. However, little is known about the components of primary cilia in NPCs and whether manipulating signaling in the cilia is sufficient to alter dorsal/ventral regional identity. Using a human telencephalic organoid model, we perform comprehensive proteomic profiling of NPC cilia and find enrichment in GTPase signaling. Deletion of the ciliary GTPase ARL13B reduces ciliary localization of GPR161, an orphan G protein-coupled receptor 161 that negatively regulates Sonic hedgehog, resulting in ventralization of NPCs. GPR161 deletion also induces ventralization. To investigate whether manipulation of ciliary signaling is sufficient to restore dorsal identity in this context, we optogenetically elevate ciliary cAMP, rescuing dorsal fate in GPR161 KO organoids. Furthermore, chemogenetic induction of GPR161 removal from cilia is sufficient to increase ventral NPCs. These data indicate that ciliary signaling functions as a critical switch regulating dorsal/ventral fate decisions.

Influence of temperature and storage time on microplastic release from packaged sausages

Hydrazine-mediated carbonyl–alkyne/allene reductive olefination

PIFC-SABiLSTM: physics-inspired feature constraint with self-attention bidirectional LSTM for interpretable fault diagnosis of rolling bearings

Neighbouring group participation hindered by force as a molecular design for covalent catch bonds

Abstract Catch bonds—dynamic molecular interactions whose lifetimes increase under mechanical load—are central to biological mechanotransduction but remain challenging to replicate synthetically. Here, we report a covalent catch-bonding mechanism in a low-molecular-weight motif based on hydroxyethyl phosphate (HEP) triesters. Our design uses force-mediated inhibition of a neighboring group participation (NGP) pathway: mechanical tension suppresses intramolecular assistance, thereby increasing the reaction barrier and prolonging bond lifetimes. Density Functional Theory calculations confirm that tensile force hinders the geometric contraction required for NGP, providing a mechanistic basis for catch-bond behaviour. Single-molecule force spectroscopy reveals that HEP triester lifetimes increase over threefold at 400 pN. This work establishes a molecular mechanism for engineering covalent catch bonds, offering opportunities to design force-responsive polymer networks. By translating a biological concept into a synthetic framework, our findings open new avenues for adaptive materials and mechanochemical sensing.

SGCM-Net: structure-guided mural inpainting with state space model

Oxygen-induced multimodal ultramicroporous structure in 10-nm-thick carbon membranes for enhanced hydrogen separation

Abstract Carbon membranes yielding high selectivity as well as high permeance are attractive to advance the membrane-based gas separation. Herein, we report ultrathin carbon membranes (UCMs) which deliver enhanced gas separation performance through oxygen-modulated pyrolysis of poly(4-vinylpyridine) precursor. We show that O 2 in pyrolysis environment, transforms the otherwise uniform carbon network featuring a ~ 3.9 Å characteristic interlayer spacing into disrupted UCMs (d-UCMs). These d-UCMs possess a multimodal ultramicroporous structure characterized by distinct d-spacings of ~3.4 Å, 3.9 Å, and 5.5 Å. This optimized distribution of free volume in a 10-nm-thick membrane enables a record combination of H 2 permeance exceeding 10,000 gas permeation units (GPUs) and H 2 /N 2 mixture selectivity surpassing 200. Meanwhile, d-UCM exhibits physical and thermal stability, showing no aging over 7 days of elevated temperature permeance testing, which overcomes the common issue of rapid aging in carbon membranes. Mechanistic investigations reveal that O 2 pyrolysis environment selectively removes relatively weakly-bound carbon species, altering pyrolysis intermediates, resulting in a nitrogen-rich framework with disordered nanodomains and heterogeneous ultramicroporosity. This work advances the material chemistry of ultrathin carbon membranes, attractive for ultrafast and high-precision molecular-sieving for molecular separation.

Development of morphologically restored and deproteinized Juglans regia pollen-derived sporopollenin exine capsules: A multi-analytical verification study

Abstract Sporopollenin exine capsules (SECs) are attractive natural microcarriers, but allergenic protein residues remain a critical limitation for biomedical applications. In this study, the development of morphologically restored and deproteinized JSECs from Juglans regia pollen was verified. High-purity capsules were isolated via sequential chemical treatments, followed by a PEG-4000-assisted stabilization process to resolve structural collapse observed during acidolysis. Protein elimination and morphological recovery were validated using SDS-PAGE, solid-state ¹³C CP/MAS NMR, and BET analysis. The absence of detectable protein bands in SDS-PAGE, coupled with attenuation of peptide-associated signals in NMR and FT-IR, indicates that allergenic protein content was reduced below the detection limit of these methods. Morphological analyses revealed that PEG treatment effectively restored the spherical capsule structure, increasing surface area to 13.56 m 2 /g with a ~ 25% yield. Statistical analysis ( n  = 150) confirmed an 83.3% morphological recovery rate, demonstrating that PEG-4000 effectively prevents structural collapse while maintaining intact, spherical capsules. These results demonstrate that Juglans -derived capsules can be rendered with high purity while preserving structural integrity, highlighting their potential as sustainable natural carriers for future biomedical applications.

Ultrahigh charge utilization of C−C bridge-dependent MOF@COFs empowering sustainable removal of trace pharmaceuticals

Prevalence and its associated factors of cancer-related fatigue among liver cancer patients: a cross-sectional study

Long-term temporal stability of circulating proteins in older adults

Robust machine learning modeling for multi-parameter prediction in friction stir welding of naval brass: a case study towards industry 4.0

Abstract This study establishes a comprehensive machine learning (ML) framework for accurately predicting essential friction stir welding (FSW) attributes in naval brass, focusing on weld temperature, strength, and hardness. To address insufficient experimental data, a customized variational autoencoder (VAE) was used to generate high-fidelity augmented datasets. The fidelity of the augmented datasets was verified through distributional similarity analysis and feature-correlation divergence measures, ensuring the synthetic samples strictly adhered to the underlying physical-statistical signatures of the experimental data. A comparative assessment of four ML algorithms, support vector regression (SVR), AdaBoost, XGBoost, and decision tree (DT), demonstrated that SVR consistently attained greater predictive accuracy. The SVR model produced mean absolute percentage errors (MAPEs) of 1.0% for weld temperature, 3.2% for weld strength, and 1.6% for weld hardness. The model’s generalization was further validated with an independent experimental dataset, affirming its reliability for practical industrial applications. The results demonstrate that ML models can effectively elucidate intricate process-property correlations in friction stir welding of naval brass, reducing reliance on experiments and enabling data-driven optimization. The suggested framework facilitates intelligent process monitoring, quality control, and the incorporation of Industry 4.0 methods in advanced welding applications.

Developmental molecular signatures define de novo cortico-brainstem circuit for skilled forelimb movement

Assessing the stability and adaptability of wheat genotypes in well-watered and rainfed conditions using AMMI, BLUP, GGE biplot and MTSI approaches

A Rahman Syndrome mutation in histone H1.4 disrupts chromatin compaction and phase separation

Abstract Rahman syndrome is a rare developmental disorder caused by frameshift mutations in linker histone H1.4 that produce a truncated carboxy-terminal domain with reduced positive charge. We investigated the effects of a disease-associated mutation on chromatin structure and dynamics, focusing on H1.4-bound nucleosomes and hexanucleosomal arrays. We report that this mutation induces a more extended and flexible array conformation, characterized by enhanced linker DNA accessibility and an inability to form compact, regularly stacked nucleosome structures. Notably, mutant H1.4-bound arrays show a reduced capacity to undergo liquid-liquid and liquid-solid phase separation, closely resembling linker histone-free arrays. Molecular dynamics simulations corroborated by fluorescence resonance energy transfer measurements indicate that the mutated carboxy-terminal domain interacts with a shorter linker DNA segment, resulting in a more open nucleosome conformation. Consistent with these structural changes, the mutation significantly enhances H1.4 mobility within cell nuclei, reflecting a weaker chromatin association. The combined data suggest that Rahman syndrome-associated mutations promote an aberrantly relaxed chromatin state, potentially leading to the dysregulation of gene expression that may drive disease pathology. These findings underscore the essential role of the carboxy-terminal domain in chromatin compaction and provide mechanistic insights into the molecular etiology of Rahman syndrome.