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A Synthetic Molybdenum Hydroxylase Compound That Cleaves C–H Bonds by Hydride Abstraction

Kinetic Scaling Rules Governing Phase Instability in Perovskite Halides

Uranyl Tris(benzoate) Photocatalysts for Site-Selective Hydrocarbon Functionalization

Chiral Helical Metal–Organic Frameworks with Intrinsic Spin Polarization Enable Enantioselective Electropolymerization

DNA Assembly Templated by Chiral Nanotube Lattices: From Helix to Rings

Asymmetric Glycol-Ether Molecule Design Enables Temperature-Adaptive Anion Coordination for Durable High-Temperature Sodium Batteries

Cation Sampling Enables Regiodivergent Distal Functionalization of Ketones

A Synthetic Iron Model of Carbon–Sulfur Bond Activation by the Nitrogenase-Family Enzyme Methylthio-Alkane Reductase

Conformationally Switchable Molecular Trefoil Knot Assembled From 2,6-Bis(1,2,3-triazol-4-yl)pyridine (btp) Building Blocks

Stimuli-Modulated Metal–Organic Framework (MOF) Reactivity toward a Three-Component Coupling Reaction

Role of Stereochemistry in Controlling Magnetic Behavior in Polymeric Materials

In silico characterization of bioactive phytochemicals as antivirals targeting the reovirus σ1 protein for inhibiting σ1-mediated host cell entry

Mammalian orthoreoviruses (MRVs), commonly known as reoviruses, are an emerging zoonotic threat that are known for their broad host tropism and potential for causing severe clinical pathology in both humans and animals. Despite this epidemic risk, currently, there are no approved therapeutic agents that are able to disrupt MRV transmission. The viral attachment protein sigma1 (σ1), mediating the entry of the virus into the host cells is a critical target for antiviral intervention. This study used an in silico structure-based drug design strategy to screen for bioactive phytochemicals that are capable of inhibiting the function of σ1. We screened a library of 376 bioactive phytochemicals with known antiviral potential against the σ1 receptor binding domain using molecular docking. Among the candidates, catechin gallate was the most potent inhibitor, possessing a superior binding affinity of −8.1 kcal/mol followed by bilobetin, which also showed a favorable binding affinity of −7.8 kcal/mol. Structural interaction analysis showed that catechin gallate and bilobetin occupies the active JAM-A binding pocket, forming stable interactions with some of the residues, including Gly381, Glu384, and Arg316, which are essential for the reovirus in the cellular attachment process. Subsequent pharmacokinetic and toxicity profiling proved that catechin gallate possessed favorable safety and drug-like characteristics, whereas bilobetin exhibited an unfavorable toxicity profile. In addition, molecular dynamics (MD) simulations supported the stability of σ1-catechin gallate complex relative to the σ1-bilobetin complex. Extensive post-trajectory analyses including RMSD, RMSF, Rg, SASA, and H-bond, showed that the binding of the catechin gallate significantly increases the rigidity and compactness of the protein. PCA indicated that the first three principal components (PC1-PC3) accounted for 74.1% and 76.2% of the total variance for catechin gallate and bilobetin, respectively, with the σ1-catechin gallate complex displaying a more compact conformational cluster consistent with greater stability. MM-GBSA analysis also showed favorable binding for both complexes, with estimated binding energies of −15.6097 ± 3.21 kcal/mol and −13.7327 ± 5.44 kcal/mol for the σ1-catechin gallate and σ1-bilobetin complexes, respectively, with catechin gallate showing comparatively stronger binding. Our results reveal a precise mechanism by which the lead compound catechin gallate sterically occludes the σ1 receptor-binding pocket, thereby likely abrogating viral attachment to the host cell. This comprehensive preclinical evaluation provides supporting evidence for the further development of catechin gallate using in vivo models and clinical trials as a promising antiviral candidate against reovirus infection.

In-Plane Electronic Metal–Support Interaction Enables Efficient Sulfur Catalysis on Ni Single-Atom Catalysts

Performance of doubled haploid maize (Zea mays L.) testcross hybrids under optimal and drought-stressed environments

Maize ( Zea mays L.) productivity in Sub-Saharan Africa is increasingly constrained by recurrent drought linked to climate change. Improving yield stability under contrasting moisture conditions remains challenging, especially when breeding materials are derived from parental lines within the same heterotic group (e.g., Group A × Group A), where genetic divergence is limited. We hypothesized that doubled haploid (DH) lines derived from biparental populations still harbor sufficient within-population genetic variation to generate exploitable phenotypic diversity and adaptive differentiation. Furthermore, crossing each DH line with a single-cross tester from the opposite heterotic group provides an effective framework to capture this variation through hybrid performance. In the present work therefore, 855 DH testcross hybrids and six commercial checks were evaluated under optimal and managed drought conditions using an alpha-lattice design. Drought stress was imposed two weeks before flowering until harvest. The mean grain yield under optimal conditions were between 3.66 to 10.36 t ha -1 , while 0.16 to 6.13 t ha -1 under drought condition. The mean of top 10 hybrids (≈1.2% selection intensity) outperformed the mean of commercial checks by 60% under optimal conditions and 161% under drought stress. Under optimum conditions, 629 and 286 hybrids exhibited higher heterosis over the mean of checks and the best check, respectively. Similarly, 536 and 235 hybrids surpassed the mean of checks and the best check, respectively, in terms of standard heterosis. The mean and best check yields under drought stress were 1.97 and 3.10 while, 5.91 and 7.09 t ha -1 under optimum condition, respectively. The multi-trait clustering grouped the hybrids into four distinct adaptive categories. Cluster 3, defined primarily by grain yield, integrated temperate introgression with elite tropical backgrounds consistently expressed superior yield performance along with desirable secondary traits. These results demonstrate that DH lines derived from within-group parental crosses can generate functional diversity and predictable adaptive clusters. Thus supports a cluster-based selection strategy to improve drought tolerance, yield potential, and adaptation in maize breeding programs.

Electroenzymatic Oxidative Desymmetrization by Engineered Thiamine-Dependent Enzymes for the Enantioselective Synthesis of Axially Chiral Biaryls

In silico pharmacological analysis of Tinospora cordifolia compounds targeting African swine fever virus B175L

African swine fever virus (ASFV) is a highly lethal DNA virus that suppresses the host’s immune response by establishing infection. B175L, one of its key immune-evasion proteins, directly inhibits STING-mediated type I interferon (IFN-I) signalling, thereby preventing the activation of antiviral defences. Thus, targeting B175L could be a promising strategy for antiviral drug development as effective ASFV inhibitors remain unidentified. In this study, we investigated the potential of Tinospora cordifolia ’s bioactive compounds to disrupt B175L’s function and restore immune signalling. Gas chromatography-mass spectrometry (GC-MS) analysis of the methanol extract from T. cordifolia stems identified 86 compounds. These were filtered using SwissADME, ProTox 3.0, and DataWarrior, yielding 15 compounds with favourable drug-likeness and safety profiles. We generated a highly accurate 3D model of ASFV B175L with strong confidence scores for the structural accuracy, using AlphaFold3. The filtered compounds were then subjected to virtual screening with PyRx 0.8, and the 3 compounds with binding affinities ≤ −6 kcal/mol were selected for subsequent analysis. Molecular dynamics (MD) simulations were used to assess binding stability, including root mean square deviation and fluctuation (RMSD, RMSF), protein-ligand contacts, radius of gyration (rGyr), and solvent accessible surface area (SASA) using Schrödinger Maestro. Taken together, these in silico results suggest that T. cordifolia- derived Benzaldehyde, 5-bromo-2-hydroxy-, (5-trifluoromethyl-2-pyridyl) hydrazone, Carbamic acid, N-(3-oxo-4-isoxazolidinyl)-, benzyl ester, and 1H-Indol-5-ol may act as potential inhibitors of B175L and represent preliminary antiviral hits against ASFV, warranting further in vitro and in vivo validation.

Resolving the Kinetics-Stability Trade-Off in Prussian Blue Analogues for Aqueous Ca-Ion Batteries: A High-Entropy Vacancy Caging Strategy

Retraction: Mechanistic modeling and numerical simulation of axial flow catalytic reactor for naphtha reforming unit

Deciphering Competitive Kinetics in Nitrate Reduction via Mechanistic Modeling: Impact of Ru and Pd Dopants on Reaction Selectivity

Research on anomaly detection and operational status evaluation methods for smart electricity meters based on hybrid deep learning

To address the limitations of single-image feature information and the insufficient recognition capability of traditional power quality disturbance (PQD) identification systems, this paper proposes a PQD recognition method based on feature-image combination and an improved ResNet-18, following the concept of feature fusion. First, the PQD signal is subjected to variational mode decomposition (VMD) to obtain a series of intrinsic mode functions (IMFs) and a residual component. Second, the IMFs, residual component, original disturbance signal, and Subtract component are vertically concatenated into a component matrix, from which a color feature-component image is generated via a signal-to-image transformation method. Third, the original disturbance signal is processed using continuous wavelet transform (CWT) to produce a time–frequency scalogram. Finally, the color feature-component image and the wavelet time–frequency image are combined and input into an improved six-channel ResNet-18 for training and disturbance classification. Simulation analyses of the proposed PQD identification method are conducted and compared with commonly used recognition systems. The results demonstrate that the proposed method exhibits strong noise robustness, effectively extracts PQD feature information, and achieves higher recognition accuracy.