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Quantitative model for imaging single fiber reflectance spectroscopy
Abstract Single-fiber reflectance (SFR) spectroscopy enables quantitative retrieval of tissue optical properties from highly localized measurements through a single multimode fiber, but its diagnostic yield can be limited by small sampling volumes and probe-pressure artifacts. Imaging SFR (iSFR) mitigates these issues by enabling contact-free, point-scanning spectral imaging using optics to project the fiber’s illumination–collection cone onto the tissue surface. We present a quantitative model tailored to iSFR that supports inverse retrieval of tissue absorption and scattering properties from reflectance spectra. The model is based on large-scale Monte Carlo simulations using a generalizable reflectance computation that replaces explicit simulation of arbitrary finite source–detector configurations with a distance-based probability weighting, substantially reducing computational cost. Across broad optical and geometric ranges, the iSFR model predicts reflectance with a median error of 6.2% (SFR: 4.2%). In inverse mode, using wavelength parameterizations for optical properties, the approach recovered absorption and scattering coefficients to within approximately 10% over a wide range of added noise for simulated spectra from two simplified tissue models, serving as a proof-of-concept. These results establish a quantitative model and an efficient computational pipeline for (i)SFR spectroscopy.
Third-order exchange-induction-dispersion energy in symmetry-adapted perturbation theory without single-exchange approximation
In symmetry-adapted perturbation theory (SAPT), the exchange corrections are often calculated utilizing the so-called single-exchange or S2 approximation, that is, by approximating the antisymmetrizer of the whole system by permutations of a single pair of electrons between weakly interacting subsystems. The error introduced in this approximation is typically tolerable; however, it is the reason for qualitatively wrong results of selected S2 SAPT corrections for some ionic systems. This work presents the third-order exchange-induction-dispersion correction (Eexch-ind-disp(30)) of SAPT, describing the quenching of the mixed induction-dispersion attractive energy by intermolecular exchange tunneling, derived without the single-exchange approximation in the second-quantization formalism. To obtain the explicit orbital expression for the Eexch-ind-disp(30) energy, the approach that was developed earlier by two of us [B. Tyrcha, F. Brzęk, and P. S. Żuchowski, J. Chem. Phys. 160, 044118 (2024)] was extended to the third order of SAPT. The quality of the S2 approximation to all available second- and third-order exchange corrections (including the newly developed Eexch-ind-disp(30) energy) has been studied on a selection of benchmark noncovalent interaction databases, including some ionic datasets.
Moiré artifact reduction in grating interferometry using multiple harmonics and total variation regularization
Abstract X-ray interferometry is an emerging imaging modality with a wide variety of potential clinical applications, including lung imaging. A grating interferometer uses a diffraction grating to produce a periodic interference pattern and measures how a patient or sample perturbs the pattern, producing three unique images that highlight X-ray absorption, refraction, and small angle scattering, known as the attenuation, differential-phase, and dark-field images, respectively. Inaccuracies in grating position and multi-harmonic fringes produce Moiré artifacts when assuming the fringe pattern is perfectly sinusoidal and the phase steps are evenly spaced. We have developed an image recovery algorithm that estimates the true phase stepping positions using multiple harmonics and total variation regularization, removing the Moiré artifacts present in the attenuation, differential-phase, and dark-field images. We demonstrate the algorithm’s utility for the Talbot-Lau and Modulated Phase Grating Interferometers by imaging multiple samples, including PMMA microspheres and a euthanized mouse.
Robustness and reliability of different CASPT2 flavors for nonadiabatic molecular dynamics
Nonadiabatic molecular dynamics (NAMD) simulations are highly sensitive to the underlying electronic structure description. Within the complete active space second-order perturbation theory (CASPT2) framework, several multistate formulations, namely, multistate (MS)-, extended multistate (XMS)-, and rotated multistate (RMS)-CASPT2, are available, yet their relative robustness for on-the-fly NAMD remains an open question. In this work, we systematically assess the sensitivity of these three CASPT2 variants with respect to state averaging by performing NAMD simulations on two representative molecular test systems, fulvene and ethylene, using both trajectory surface hopping and ab initio multiple spawning approaches. For fulvene, pronounced differences are observed between the CASPT2 flavors. MS-CASPT2 shows a strong dependence on the number of averaged states, affecting not only excited-state lifetimes but also the nuclear evolution along key reaction coordinates. XMS-CASPT2 displays improved stability, with only minor variations in population dynamics and structural evolution upon increasing the number of states. RMS-CASPT2 proves to be the most robust formulation for this system, yielding consistent results across different state-averaging schemes. In contrast, ethylene represents a well-behaved case where all three CASPT2 formulations provide consistent dynamical results largely independent of state averaging. Overall, this work confirms the general reliability of CASPT2-based NAMD while highlighting that the choice of CASPT2 flavor and number of states can significantly influence both population dynamics and nuclear motion. Careful preliminary electronic-structure analysis and chemical intuition remain essential for meaningful CASPT2-based dynamics simulations. The benchmarking protocol presented here provides a foundation for future systematic assessments on more complex photochemical processes, aiming toward community-wide guidelines for the application of CASPT2 in nonadiabatic dynamics.
Severity and duration-dependent aortic stiffening in a rabbit coarctation model identifies constitutive material parameters as promising regional biomarkers for hypertension progression
First-principles self-interaction free GWΓ simulations for first ionization potentials and electron affinities
We demonstrated self-interaction-free GWΓ simulations in a one-shot framework by using a Hartree–Fock approximation (HFA) as a reference and incorporating self-interaction corrections to the GW and GWΓ terms. This method is applied to simulate the first ionization potentials (IPs) and electron affinities (EAs) of 24 middle-sized molecules, yielding good agreement with available experimental data. Compared with simulations using the local density approximation (LDA) reference, the computational accuracy of the present method improved by ∼0.1 eV for IPs and 0.97 eV for EAs. The HFA reference reduces the contributions of the GW and Γ terms by 15%–38% and 65%–81%, respectively, compared with the LDA reference. Correspondingly, the self-interaction contributions in the GW and GWΓ methods are also reduced in HFA-reference simulations. However, self-interaction corrections to the Γ term remain important for achieving accurate results, as in the LDA-reference simulations.
Computational and preliminary screening of anti-caries active compounds of Caesalpinia sappan
Crystallization and structural rearrangement in nearly hard-sphere model colloidal suspensions: A long-time study across concentration regimes
Colloidal crystallization provides a flexible framework for studying how order develops or disappears in response to thermodynamic and kinetic constraints. Despite some studies on the formation of colloidal crystals near the glass transition, their evolution over ultra-long timescales remains largely unknown. In this work, we follow the structural and dynamical aging of a nearly hard-sphere suspension for about four years. Slow solvent evaporation provides a continuous, gentle densification that gradually drives the system across the fluid–crystal coexistence region and into the glassy regime. Light scattering measurements indicate that total crystallinity decreases as the remaining crystallites grow and become denser. The random hexagonal close-packed structure slowly changes into face-centered cubic ordering, while smaller or flawed domains turn into amorphous regions. This slow restructuring reflects a balance between densification, kinetic frustration, and defect annealing, leading to partial crystal amorphization instead of standard ripening. Our findings reveal a previously unobserved route of structural aging in colloidal glasses, relevant for understanding long-term reorganization and disordering processes in dense soft materials.
Observer effect modulates classification in a quantum epistemic framework
Nernst–Planck laws, distilled from Onsager–Stefan–Maxwell theory
Here, we generalize Newman’s concentrated solution theory, establish a framework to underpin experimental parameterizations of multicomponent electrolytic solutions, and prove thermodynamic consistency of the Nernst–Planck–Poisson model of dilute-ion transport. Our proposed constitutive model, based on Onsager–Stefan–Maxwell flux laws from irreversible thermodynamics, describes coupled material and charge transfer in isobaric, isothermal, single-phase electrolytes containing any number of components, accounting for excluded-volume effects, thermodynamic nonideality, cross-diffusion, and local electroneutrality violations. Key results from the mass-transport and electrochemical literature are combined and extended. We contextualize prior electrolyte-transport theories within the general framework and establish minimal composition-dependent parameter sets for targeting by future characterization initiatives. A thermodynamically consistent version of Nernst–Planck theory emerges after applying several foundational idealizations and simplifications within the non-neutral Onsager–Stefan–Maxwell electrolyte-transport model.
Mechanical properties and chemical synergistic mechanism of lithium slag concrete under mechanical-chemical activation conditions
Abstract This study systematically examines the mechanical performance and microstructural evolution of concrete incorporating alkali-mechanically activated lithium slag (LS). Unconfined compressive strength (UCS) tests were conducted at 7 and 28 days of curing to assess the influence of LS content on early and later-age strength development. Multiscale characterization techniques, including scanning electron microscopy (SEM), X-ray Fluorescence (XRF), X-ray Diffraction (XRD) nuclear magnetic resonance (NMR), and Fourier-transform infrared spectroscopy (FTIR), were used to reveal the dual mechanism of chemical activation and physical pore refinement. The results show a non-monotonic trend in compressive strength with increasing LS content, with an optimum at 20% replacement, where the UCS reached 103% and 105% of that of the control mix at 7 and 28 days, respectively. NMR analyses reveal that activated LS contributes to refinement of the pore structure, resulting in reduced pore size and enhanced matrix densification. SEM, energy dispersive spectroscopy (EDS), and thermogravimetric (TG) analyses indicate that the improved strength is primarily attributed to the pozzolanic reaction between activated LS and cement hydration products, forming additional calcium silicate hydrate (C-S-H) and other binding phases. These findings highlight the dual role of alkali-mechanically activated LS in enhancing concrete performance through coupled microstructural refinement and chemical reactivity, offering a sustainable approach for valorizing lithium slag in cementitious materials.
Atom-specific vibrational analysis reveals labile bonds in linear and branched PFOA molecules
We have performed atom-specific vibrational analyses of a large number of perfluorooctanoic acid isomers as well as their anions and show that differences in the vibrational features of corresponding anion and neutral molecules clearly identify the C–F bonds that are most strongly activated in the anions. We discuss two analysis tools for associating vibrational modes with individual atoms, both based on density functional theory calculations. The first involves computing the Einstein frequencies for a given atom and the second projects the full vibrational spectrum onto a given atom using participation factors derived from the normal mode eigenvectors. We show that the two methods give results that are qualitatively the same and that either can be used to identify the most labile C–F bonds in the anions. We also show that the results of the vibrational analyses are consistent with systematically computed F atom removal energies. The vibrational analysis tools are shown to be related to the local mode analysis of Cremer and co-workers. Finally, we compare and contrast branched and linear PFAS molecules and analogous hydrogenated carbon chains.
Intelligent diagnosis of ovarian cancer in PET/CT imaging based on KiteNet-MobileNetv3 fusion and CarveMix augmentation
Abstract Ovarian cancer refers to a malignant tumor that grows in the ovary, which has the highest mortality rate of gynecological cancers. Positron emission tomography and computer tomography (PET/CT) imaging is widely used for the localization and characterization of ovarian tumors, but its analysis is susceptible to the subjectivity of clinicians. A deep learning-based PET/CT image diagnosis approach was investigated to achieve the segmentation and classification of ovarian tumors. We proposed several traditional convolutional neural networks (CNNs) for automatic ovarian tumors segmentation and classification. For segmentation, we design a hybrid network that integrates U-Net-MobileNetv3 with KiteNet to jointly capture global structure and lesion edge details, further enhanced by lesion-aware CarveMix augmentation and Dice-CE loss. For classification, we adopt ConvNeXt as the backbone and improve its robustness via Mixup data augmentation. Our method achieves a Dice coefficient of 0.826 and an accuracy of 0.912, outperforming all baseline models including U-Net, Deeplabv3, DenseNet, and Swin-Transformer. 1228 PET/CT images were employed to train and evaluate the CNN approach. Our segmentation model obtained a Dice of 0.826 compared to 0.773 obtained by U-Net-MobileNetv3, 0.444 by KiteNet, 0.634 by FCN, 0.744 by Deeplabv3, 0.667 by U-Net, and 0.747 by U-Net-VGG. Our classification model achieved the ACC of 0.912 compared to 0.907 achieved by ConvNeXt, 0.898 by DenseNet, 0.895 by EfficientNet and 0.848 by Swin-Transformer. In this study, we developed a novel deep learning framework for the simultaneous segmentation and classification of ovarian tumors in PET/CT imaging. Our key findings are: (1) A hybrid segmentation architecture that integrates U-Net-MobileNetv3 with KiteNet, enhanced by lesion-aware CarveMix augmentation and Dice-CE loss, achieved a Dice coefficient of 0.826, significantly outperforming standard models such as U-Net (0.667) and Deeplabv3 (0.744); (2) A classification pipeline based on ConvNeXt with Mixup data augmentation attained an accuracy of 0.912, surpassing DenseNet (0.898) and Swin-Transformer (0.848). These results demonstrate that our method can effectively support clinicians in both precise tumor delineation and reliable benign/malignant differentiation, offering a promising tool for intelligent ovarian cancer diagnosis.
Excitonic-coupling enhancement in double π-helical dimers for highly efficient and robust circularly polarized luminescence
Developing circularly polarized luminescence (CPL) materials with high brightness (BCPL) remains a central challenge, as it demands the synergistic optimization of both chiral response and luminescence efficiency. Through a comparative study of an N-annulated double π-helical perylene diimide dimer and its unmodified counterpart, this work demonstrates that strong excitonic coupling serves as a key strategy for achieving bright and robust CPL. The enhanced excitonic coupling promotes BCPL through a dual pathway: statically, it elevates the luminescence dissymmetry factor (glum) by markedly suppressing the electric transition dipole moment under H-aggregation while keeping the rotational strength essentially unchanged; dynamically, it suppresses excited-state symmetry-breaking charge transfer in polar dielectric environments, thereby sustaining a high photoluminescence quantum yield (ΦPL). Consequently, this work establishes the targeted enhancement of excitonic coupling as an effective design paradigm for developing high-brightness CPL materials with concurrently high glum and ΦPL.
Process optimization of centrifugal dehydration–hydrocolloid pretreatments for quality preservation of frozen kimchi
Abstract The growing global demand for kimchi has led to the adoption of frozen distribution as a strategy to extend shelf-life. However, freezing can cause quality deterioration through ice crystal-related structural damage, resulting in texture softening and reduced microbial viability. We evaluated pretreatment strategies to improve the quality stability of frozen kimchi, focusing on the combined use of centrifugal dehydration and formulation-based cryoprotectant/hydrocolloid additions. Five pretreatment approaches were tested, including centrifugal dehydration, glucose addition, and hydrocolloid incorporation using sodium carboxymethyl cellulose and xanthan gum. Centrifugal dehydration was the primary factor reducing moisture content and thawing loss and thereby improving texture retention during frozen storage. Glucose and hydrocolloid treatments exhibited limited, index-dependent additional effects, with modest differences observed in selected parameters such as reducing sugar (RS) levels, lactic acid bacteria (LAB) counts, and antioxidant-related indices (total phenolic content (TPC) and DPPH radical-scavenging activity (DPPH)). Principal component analysis (PCA) suggested that the RS levels, LAB counts, and texture variables were associated with overall quality variation among treatments during frozen storage. Overall, centrifugal dehydration serves as an effective baseline pretreatment for frozen kimchi, while glucose/hydrocolloid additions may offer incremental stabilization in specific quality indices, warranting further validation under longer storage durations and diverse distribution conditions.
Snooping helices: The elastic path finding algorithm of growing hyphae
How living organisms utilize physical mechanisms to sense their environments and make informed decisions is an open question at the interface of biology and physics. In filamentous organisms like fungal hyphae, the decisions are taken by their growing tip cells and later imprinted onto the rest of the multicellular filament. Here we report on the growth and pathfinding of hyphae from the opportunistic fungal pathogen Candida albicans , whose ability to cross intestinal epithelial layers is associated to severe systemic infections in humans. It has been sporadically reported that C. albicans ’s hyphae display helical growth inside or on top of agar gels, helicity turning in the latter case into two-dimensional oscillatory shapes. We provide an extended description of oscillatory C. albicans hyphal growth modalities, revealed under various physical confinements thanks to the use of dedicated microfluidic devices and quantitative time-lapse imaging-based analysis. These include sudden sliding events accompanied by curvature switching of the tip portion, resulting in a final oscillatory morphology of the entire filament, and stable curved tips moving against vertical microfluidic channel’s walls. These behaviors are unified under the formalism of growing squeezed helices, in which the final hyphal curved shapes result from an elastic energy minimization of a spatially confined helical portion at the tip followed by a continuous solidification front. Ultimately, the combination of our experimental results and theoretical framework provides an insight into the penetration strategy of C. albicans hyphae, which is essential for the virulence of this fungal microorganism.
Effects of Lacticaseibacillus rhamnosus L156.4 and Lactococcus lactis NCDO 2118 strains on reducing alcohol intake and preference in an animal model of high alcohol consumption and preference
Abstract Alcohol use disorder (AUD) involves a progressive series of neuroadaptations influenced by interactions along the gut-brain axis. Disruption of the intestinal barrier enables bacterial-derived metabolites and inflammatory mediators to translocate into the systemic circulation, amplifying neuroimmune activation within mesocorticolimbic reward circuits. Probiotic strains have emerged as promising adjuvant therapies for AUD due to their ability to regulate the gut microbiota and thereby influence neurotransmission. In this study, we investigated the potential of a probiotic blend containing the strains Lacticaseibacillus rhamnosus L156.4 and Lactococcus lactis NCDO 2118 to reduce alcohol intake and preference behaviors in a free-access drinking paradigm (H₂O vs. 10% v/v ethanol). The blend significantly decreased ethanol intake and preference and was accompanied by striatum upregulation of genes related to dopaminergic inhibitory control and downregulation of inflammation-related genes. Serum biochemical parameters remained unchanged and comparable between vehicle- and probiotic-treated groups, supporting the safety of the intervention. Additionally, we observed improved tissue integrity and structural restoration in both the colon and liver. Behavioral assessments using the open field test and marble-burying tests revealed reduced anxiety- and compulsive-like behaviors in the treated groups. These results suggest that this probiotic blend modulates distinct pathways, thereby attenuating alcohol`s rewarding effects and associated compulsive behaviors.