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Ligand-stabilized dilithium (C6F5)2Li2 featuring two planar tetracoordinate lithium and carbon centers
The design of planar hypercoordinate Li represents a significant challenge because the stabilization in such molecules arises exclusively from electrostatic interactions, while covalent glue, particularly delocalized π/σ bond, is needed to stabilize a planar conformer. Here, we report a computational study of a novel system, two pentafluorophenyl ligands stabilized dilithium, (C6F5)2Li2, featuring two planar tetracoordinate lithium (ptLi) atoms and two planar tetracoordinate carbon (ptC). The design strategy was inspired by the recent synthesis of tolyl–lithium complexes and refined through systematic structural modifications to achieve a fully planar geometry corresponding to a true minimum on the potential energy surface. Both thermodynamic and kinetic analyses demonstrate that the structure is stable under static and dynamic conditions. A thorough bonding analysis using different methods reveals that the stabilization of the ptLi atoms arises primarily from the electrostatic interactions, while the orbital contributions are comparatively weak. On the other hand, the stabilization of the ptCs is the interplay of both electrostatic and covalent interactions. Aromaticity analysis based on magnetically induced current densities indicates that aromatic character is confined to the benzenoid rings, whereas the Li-containing core is non-aromatic. These findings expand the conceptual framework for hypercoordinate species in s-block elements and highlight the role of selecting proper ligands that can lead to the realization of such planar hypercoordinate Li, not only in the cluster form but also in molecular materials.
Enhancing mortality prediction in septic ICU patients with malignancies through ensemble machine learning: a retrospective cohort study
Polarizable embedding cluster perturbation theory for a coupled cluster singles and doubles target state
This study presents the polarizable embedding cluster perturbation framework (PE-CP) and derives the working equations needed to perform calculations in a singles and perturbative doubles excitation space, denoted PE-CPS(D). The method is implemented in the Penguin program through interfaces to the external libraries PyFraMe and CPPE. To obtain the cluster amplitudes and multipliers, which constitute two coupled sets of equations in PE-CCSD, a standard scheme of outer and inner iterations is usually applied. In the PE-CPS(D) model, only the singles excitation space requires this iterative procedure to determine the wave function parameters; the perturbative doubles contributions can be evaluated directly. This feature highlights one of the principal strengths of the PE-CPS(D) approach. Finally, we present numerical tests comparing PE-CPS(D) with PE-CCSD, to which it formally converges. These results demonstrate that PE-CPS(D) provides an efficient route to high-level coupled-cluster accuracy for large quantum-mechanical systems embedded in a classical polarizable environment.
Neuromodulatory effects of berberine chloride against aluminum chloride/D-Galactose induced Alzheimer-like neurodegeneration model in rats
The story of YiJing Yan: A life of learning and connection
eMARL: entanglement-assisted multi-agent learning for zero-signaling coordination in 6G edge networks
Metal–bicarbonate ion pairing in alkaline aqueous solutions from multilevel embedded correlated wavefunction theory and molecular dynamics
In this work, we examine ion-pairing mechanisms of bicarbonates in alkaline aqueous solutions with the divalent metal ions most abundantly present in seawater, namely, Ca2+ and Mg2+. We employ a rare-event enhanced sampling approach within first-principles molecular dynamics to explore regions of phase space spanning solvent-shared to contact ion pairs. Second-order Møller–Plesset perturbation theory (MP2) corrections are subsequently applied in an embedding framework (EMB) to refine the electronic structure of stationary states and associated reaction barriers along the free-energy profiles while retaining the extended solvent effects at the density functional theory (DFT) level. Ca2+–HCO3− was previously hypothesized to exist in a solvent-shared ion pair (SSHIP) by DFT studies with an endergonic contact ion pair (CIP) formation; however, our EMB-MP2 refinement of the DFT ion-pairing pathways reveals that Ca2+ and HCO3− form a virtually barrier-free CIP in alkaline solutions, with even more energetic ease than the widely studied Ca–CO3 ion pair. We find qualitative agreement between DFT and EMB-MP2 for Mg2+—unlike Ca2+, Mg2+ refuses to shed its strong hydration shell, thereby preferring a SSHIP state with a significant activation barrier to crossover to the CIP forms—a trait reminiscent of ion pairing in Mg–CO3 and closely related to the kinetic limitations underlying the famous subject of the dolomite problem. Our study highlights the importance of improved electronic structure descriptions of liquids, modeled as a condensed phase of matter lacking in long-range crystalline order. It also strongly suggests that Ca2+–HCO3−CIPs are likely precursors involved in prenucleation of CaCO3mineral formation in seawater.
Lightweight dual-branch attention and dynamic resource-aware scheduling for edge-deployed visual communication in cultural creative industries
Abstract The deployment of deep learning-based visual communication systems in cultural creative scenarios demands both high perceptual quality and real-time responsiveness, yet edge hardware imposes strict constraints on computation, memory, and energy. This paper proposes a unified framework that jointly optimizes attention mechanism design and resource consumption for edge-deployed visual communication in the cultural and creative industries. First, a Lightweight Dual-Branch Attention (LDBA) module is introduced, which decouples channel recalibration and spatial modeling into parallel branches using depth-wise separable convolutions and grouped strip pooling, achieving over 565 × compression in floating-point operations relative to standard self-attention. Second, a dynamic resource-aware scheduling strategy based on a greedy heuristic is developed to assign tasks to edge nodes according to a composite urgency score that integrates task priority, deadline constraints, and real-time node utilization. Third, a device-edge collaborative inference framework with adaptive split-point selection is designed to partition model execution between terminal devices and edge servers in response to fluctuating network conditions. Experiments on the ArtBench-10 and a self-constructed Cultural Creative Product (CCP-5K) dataset demonstrate that LDBA achieves accuracy within 0.3 percentage points of full Transformer attention at roughly one-third the inference latency, while the scheduling strategy reduces latency by 35.9% and energy consumption by 33.7% compared with static allocation. The collaborative inference framework maintains robust performance across a wide bandwidth range, validating the practical viability of the proposed approach for real-time intelligent visual services in cultural creative venues.
Exact tunneling splittings of rotationally excited states from symmetrized path-integral molecular dynamics
We extend our previous symmetrized path-integral molecular dynamics approach to calculate tunneling splittings of molecules in rotationally excited states. In this new formalism, the system is rigorously projected onto selected rotational manifolds and states of a chosen symmetry through an Eckart spring, which connects the two end beads of the ring polymer via a permutation–inversion–rotation operation. In this way, the tunneling splittings for the lowest vibrational states in the projected subspace can be obtained by taking the low-temperature limit. This method is numerically exact within statistical uncertainty once convergence with respect to all simulation parameters has been achieved. Importantly, it enables the simultaneous extraction of tunneling splittings for multiple total angular-momentum quantum numbers J from a single set of simulations, without additional computational cost relative to the original approach. After validating the formalism by computing the rotational levels of water (beyond the rigid-rotor approximation), we apply it to ammonia and obtain rotationally resolved tunneling splittings in excellent agreement with exact variational benchmarks. Except for small errors due to the underlying potential energy surface, the results capture the experimentally observed trend that the tunneling splitting decreases with J.
SA-TransU²Net: a rock thin section grain segmentation network based on multi-scale RSU and global context enhancement
Fourth-order extension of multichromophoric Förster energy transfer
Excited state energy transfer (EET) is an essential process in many systems with optical functionality, such as photosynthetic systems, photovoltaic materials, and light-emitting diodes. Describing EET in large open quantum systems, such as molecular aggregates, is difficult, especially in intermediate parameter regimes. In the limit where the population transfer between molecular aggregates is incoherent, one can make use of the recently implemented time domain Förster resonant energy transfer method to determine the population transfer rates between the aggregates. In this study, we present a fourth-order perturbation theory that extends the Time Domain Multichromophoric Förster Resonance Energy Transfer (TD-MCFRET) method and allows for the more precise calculation of population transfer rates between molecular aggregates in intermediate regimes of coupling and system memory. The fourth-order correction can also be used to improve the accuracy of FRET models. We present a computationally tractable way of determining these higher-order corrections and demonstrate their relevance and validity for two bacterial photosynthetic systems: LH2 and chlorosomes. This new theoretical refinement may improve the interpretation of various experimental techniques such as absorption, fluorescence, and two-dimensional electronic spectroscopies and will deepen our understanding of EET in photosynthetic systems, synthetic devices, and beyond.
An interpretable machine learning model for early prediction of ESBL-producing bacteraemia in the emergency department
Bead-spring systems in spatially periodic potentials show non-monotonous diffusion behavior with spring stiffness
Bead-spring systems in spatially periodic potentials, such as the Frenkel–Kontorova model, exhibit complex behavior due to the interplay of elastic spring energies and external potentials. Here, we study the one-dimensional diffusion of a trimer, three beads connected by springs. In our system, all beads feel a sinusoidal external potential, but with different amplitudes. Using Langevin dynamics simulations and analytical expressions in limiting cases, we show that the diffusion constant of this system exhibits non-monotonic behavior, depending on the spring stiffness. The elastic coupling of the springs can slow down diffusion by anchoring faster beads to slower ones or accelerate it by annihilating potential barriers of different signs.
Machine learning-based analysis of oral rinse samples to identify candidate proteomic signatures for severe periodontitis: a pilot study
Formation and thermodynamic behavior of THF-water hydrates in confined mesoporous media
Tetrahydrofuran (THF) is a benchmark guest for probing clathrate hydrate thermodynamics because a stoichiometric aqueous solution (THF·17H2O) forms structure-II (sII) hydrate at ambient pressure with a well-defined dissociation temperature. Here, we combine differential scanning calorimetry (DSC) and wide-angle x-ray scattering (WAXS) in bulk and confined media to resolve how composition, pore filling, and cooling rate govern hydrate formation in SBA-15 mesoporous silica. Bulk DSC establishes mass-balanced enthalpies for ice and sII hydrate and confirms reversible dissociation/melting temperatures. In confinement, the heating traces separate into a Gibbs–Thomson depressed ice melt (=−14.7 ± 0.2 °C), an in-pore hydrate dissociation (=−13.2 ± 0.2 °C). Confined hydrate appears only when two criteria are met: near-percolating filling (φ = 1.0–1.1 cm3/g) and sufficient THF (≥1:16 mol:mol). Cooling-rate experiments (1.0 vs 0.5 °C/min) demonstrate that slower precooling increases the confined-hydrate fraction and reduces confined ice without shifting equilibrium temperatures: at φ = 1.1, the hydrate enthalpy rises by ∼60% at 1:11 and ∼54% at 1:14, but by ≤17% at 1:16. Temperature-cycling tests show invariant reheating peak positions, indicating that capillarity and composition, rather than kinetic history, fix the liquidus and dissociation temperatures. WAXS indicates that the phase formed in pores is crystallographically identical to bulk sII. Finally, the variation of melting points (ΔTm) plotted against inverse pore radius follows the Gibbs–Thomson law for both ice melting and hydrate dissociation, quantitatively linking the observed shifts to crystalline size and clarifying how confinement, cooling rate, and composition govern the competition between hydrate formation and water crystallization.
Prediction and field application of water-conducting fracture zone height using a PSO-BP neural network optimised by dynamic mutation
Embedded cluster density approximation for scalable high-level exchange–correlation calculations in periodic systems
Accurate prediction of electronic properties generally requires high-level electronic-structure methods, whose computational cost often scales steeply with system sizes. One way to extend high-level methods to large systems is to calculate electronic structures in local regions using high-level methods and then assemble these results across the system. Recently, we developed a method, termed embedded cluster density approximation (ECDA), to scale up high-level exchange–correlation (XC) calculations in finite systems. In this work, we extend ECDA to periodic systems. With ECDA, a cluster’s electron density is defined based on the density functional embedding theory, which ensures that the cluster’s density is seamlessly embedded in the system. The cluster’s XC energy density is then calculated using a high-level XC functional and projected to its central atom. The system’s XC energy is constructed by patching these atom-centered XC energies over the entire system. Using a hybrid XC functional as the high-level method, we demonstrate that ECDA is a nearly black-box method that can be applied to systems with various bond types. In general, good accuracy can be achieved with modest cluster sizes. It is also straightforward for ECDA to calculate energy differences and produce smooth energy surfaces. All these appealing features are due to the use of density partitioning for defining local active spaces. However, we also note that, for covalent systems, density partitioning produces unsaturated bonds at cluster boundaries, which may make the convergence of ECDA against cluster size zigzag.
Downregulation of cytokine responses and STAT signaling in children with sickle cell disease
Abstract Sickle cell disease (SCD) is characterized by chronic inflammation and immune dysfunction, yet the underlying signaling defects and their relationship with cellular and systemic immune profiles remain incompletely understood, particularly across different genotypes (HbSS vs HbSC) in pediatric populations. This study characterized the immune landscape in children with SCD. We conducted a case-control study with 53 children with SCD (HbSS and HbSC genotypes) and 27 healthy children with the HbAA genotype. The phenotypes of T helper cells and the phosphorylation status of their STAT proteins were characterised using flow cytometry. Cytokine levels in systemic circulation were quantified using Sandwich ELISA. Children with the HbSC genotype showed significant reductions in CD4 + Tbet + IFN-γ + (Th1) and CD4 + Foxp3 + IL-10 + (Treg) cell populations compared to healthy controls. SCD patients exhibited low activation of STAT1, STAT2, STAT4, STAT5, and STAT6 pathways, along with decreased circulating levels of IL-1β, IL-6, TNF-α, IFN-γ, and IL-10. In contrast, IL-17A (Th17) and IL-5 (Th2) levels did not differ between groups. In untreated pediatric SCD, we observed STAT downregulation, Th1/Treg deficits, and decreased IL-1β, TNF-α, IL-6, IFN-γ, and IL-10, with preserved Th2/Th17 responses. HbSS and HbSC showed minimal differences, suggesting a shared impairment of the STAT-Th1-IFN-γ axis.
Optimized curvilinear coordinates in vibration correlation methods: Quasi-degenerate perturbation theory
We employ curvilinear molecular vibrational coordinates constructed using recently developed normalizing flow techniques and optimize them within the vibrational self-consistent field framework. This is done by minimizing either the ground-state vibrational energy or a configuration-averaged energy over a selected set of vibrational states. The quality of the optimized coordinates for correlated calculations is assessed using second-order quasi-degenerate vibrational perturbation theory. Applications to H2CO, trans-HCOOH, and CH3F demonstrate significant improvements in vibrational energies compared to standard valence coordinates, across fundamental, combination, and overtone bands, including states exhibiting strong Fermi resonances. The improvements arise from a reduction in vibration correlation, produced by redistributing dominant low-order couplings in the Hamiltonian into weaker and more uniformly distributed higher-order contributions.