Chirality-induced spin selectivity (CISS) has emerged as a striking phenomenon in which electron transport through chiral systems generates highly spin-polarized currents, even at room temperature and in the absence of a magnetic field or strong atomic spin-orbit coupling. While CISS has been intensively studied in molecular systems, its microscopic origin remains controversial, partly due to experimental limitations inherent to nanoscale molecules. In this review, the author focuses on CISS in chiral solids and introduces a classification into two categories based on time-reversal symmetry : CISS(I), a -even response associated with collinear current-induced spin polarization, and CISS(II), a -broken response involving antiparallel spin-pair formation under non-equilibrium conditions. The author further proposes an operational definition based on transport measurements, allowing direct comparison with experiments, including magnetoconductance studies in molecular systems. Recent experiments on chiral metals and superconductors are discussed in this framework, highlighting enhanced spin polarization, nonlocal spin transport, and symmetry conversion between structural and magnetic chirality. These results suggest that chiral solids provide a platform to bridge molecular and condensed-matter perspectives and to explore non-equilibrium spin-chirality coupling.

Phantom brain models are essential for overcoming the limitations of animal experiments in developing medical devices, such as ECoG multichannel electrodes, which are crucial for diagnosing severe brain disorders and advancing brain-computer interface (BCI) technology. However, conventional phantom brains still use bulky electrodes, resulting in low spatial resolution and volume conduction effects. These limitations lead to aliasing between adjacent electrodes and signal interference, making them insufficient for accurately evaluating high-density ECoG electrodes. Here, we present a phantom model that mimics the real cerebral cortex by replicating multiple ECoG signals simultaneously. The phantom brain model, which consists of graphene electrodes to mimic small-scale ECoG and perforated structure filled with Sodium chloride (NaCl) gel chosen for its electrical properties similar to the cerebral cortex, was designed using multiple arrays to ensure no signal interference. This model mimicked different epileptic seizure signals originating from distinct regions of the cerebral cortex. Using multiple ECoG electrodes, it was confirmed that the ECoG signals caused by seizures in the cortex could be successfully monitored. This demonstrated the excellent mimicry performance of the phantom brain and proved that it can also be used to test the performance of ECoG electrodes. This approach can serve as an alternative to preclinical testing and offer great potential to examine the performance of different ECoG electrodes through a model that mimics accurately ECoG signals.

Soil salinization and alkalization severely constrain crop production worldwide, yet the combined effects of mixed saline-alkaline stress on minor cereal germination have received limited investigation. This study sought to elucidate the effects of mixed saline-alkaline stress on seed germination and seedling growth of proso millet (Panicum miliaceum L.) under controlled laboratory conditions, and to comparatively assess the relative contributions of osmotic stress, ionic toxicity, and high-pH damage through analysis of different neutral-to-alkaline salt ratios at equivalent Na⁺ concentrations. Seeds were subjected to nine treatment combinations comprising three salinity levels (80, 160, and 240 mM Na⁺) and three neutral-to-alkaline salt ratios (3:1, 1:1, and 1:3), employing NaCl, Na₂SO₄, NaHCO₃, and Na₂CO₃. Germination characteristics, seedling morphology, and key physiological parameters were assessed over seven days. Mixed saline-alkaline stress markedly suppressed germination in a concentration-dependent manner, with germination percentage decreasing from 94.5% (control) to 23.8% at 240 mM Na⁺ under high-alkali conditions (P < 0.05). At equivalent Na⁺ concentrations, high-alkali treatments (pH > 9.5) diminished germination index by 35.6-52.3% relative to low-alkali treatments. The IC₅₀ values were 187.3 mM Na⁺ (95% CI: 171.2-205.8 mM; R² = 0.982), 142.6 mM Na⁺ (95% CI: 131.4-155.1 mM; R² = 0.976), and 98.4 mM Na⁺ (95% CI: 89.7-108.3 mM; R² = 0.991) for low-, medium-, and high-alkali ratios, respectively. High-alkali stress was associated with 67.4% greater electrolyte leakage and 2.3-fold higher MDA accumulation than neutral salt treatments at equivalent Na⁺ concentrations, indicating greater membrane disruption under high-pH conditions. Two-way ANOVA revealed significant main effects of both Na⁺ concentration and alkaline proportion, as well as a significant salinity × alkalinity interaction (P < 0.001; Supplementary Table S1). These results demonstrate that alkaline stress exerts stronger inhibitory effects on proso millet germination compared to neutral salt stress, with the magnitude of salinity effects being dependent on the pH level. While complete mechanistic separation of osmotic, ionic, and pH components was not achieved due to experimental design limitations (absence of iso-osmotic non-ionic controls and direct ion measurements), comparative analysis across salt ratio treatments suggests that high pH is associated with additional inhibitory effects beyond those observed in neutral salt treatments. These controlled-condition findings establish preliminary germination -stage thresholds that may inform screening protocols for saline-alkaline tolerance breeding in proso millet, pending validation under field conditions.

An interference fit is employed for cementless tibial total knee arthroplasty components to ensure stability post-operatively. Relative motion between implant and underlying bone should be minimised, facilitating osseointegration. Previous experimental limitations have prevented quantification of this motion across the entire bone-implant interface. Furthermore, the intended (nominal) interference fit differs to that actually achieved. This study aimed to 1) experimentally quantify the relative motion across the entire interface of a commercially available cementless tibial component and underlying bone, for cadaveric tibiae through micro-computed tomography (micro-CT) imaging and digital volume correlation (DVC); and 2) assess the relationship between relative motion, actual interference fit, post-impaction strain and bone volume fraction (BV/TV).Seven cadaveric tibiae were micro-CT scanned when intact, following resection and, once implanted with a cementless titanium tibial component (Attune, DePuy Synthes), during two time-elapsed mechanical loading sequences that replicated stair descent (SD, 0.0 - 2.5 bodyweight) and deep knee bend (DKB, 0.0-3.5 bodyweight). Actual interference fit was quantified from micro-CT datasets. DVC was employed to extract the post-impaction strain field and, for each load case, the relative motion at the bone-implant interface.A sagittal rocking motion of the tray was detected in both SD and DKB, that increased with increased load applied. Tray lift-off was enhanced in the central- and lateral-anterior regions during DKB. Reduced relative motion was significantly related to higher interference in most cases, whilst it was not related to post-impaction strain or BV/TV. This study provides a comprehensive insight into the mechanical environment surrounding a cementless tibial tray in the immediate post-implantation period.

Escalating global protein insecurity, combined with the environmental and economic limitations of conventional plant and animal protein systems, underscores the need for sustainable alternatives. Fruit processing residues, generated at >500 million tons annually worldwide, represent underutilized, carbon-rich bio-resources with potential for microbial protein production. In this PRISMA-compliant meta-analysis, peer-reviewed studies on fruit-derived single-cell protein (SCP) were systematically evaluated across substrate types and microbial platforms. Meta-analysis revealed mean biomass yields of 0.24-37.40 g/L substrate and protein contents ranging from 20.58 to 54.74% (dry weight). Evidence from engineered strains suggests possible improvements in essential amino acid composition, particularly lysine and methionine, although direct validation on fruit-waste substrates remains limited. Synthetic biology interventions may enhance nutrient enrichment and digestibility, yet human clinical or in vivo data are currently lacking. Life-cycle analyses indicate potential reductions in greenhouse gas emissions and land use relative to animal proteins, although energy-intensive pretreatment and substrate heterogeneity may influence these outcomes. Overall, fruit-waste-derived SCP presents a promising avenue for circular bio-economy strategies, offering a framework for programmable nutrition systems that integrate synthetic biology, precision fermentation, and regional waste valorization, while acknowledging the current experimental and translational limitations.

ABSTRACT Reliable detection of free‐air electric arc faults in high‐voltage overhead transmission lines, together with accurate discrimination between transient and permanent faults, is essential for secure single‐phase auto‐reclosing and for maintaining system stability. However, the nonlinear, time‐varying, and environmentally dependent nature of secondary arcs, as well as the influence of shunt reactors, line configuration, and measurement constraints, has led to a fragmented body of literature with no unified analytical map of available detection strategies. This paper presents a systematic and analytical review of the reported methods for free‐air arc‐fault detection in overhead transmission lines. The reviewed studies are classified into three main categories: circuit‐equation‐based methods, signal‐processing‐based methods, and pattern‐recognition/intelligent‐learning‐based methods. For each category, the conceptual basis, required measurements, processing sequence, decision indices, implementation steps, and numerical or experimental limitations are extracted and synthesized. In addition, representative methods are comparatively evaluated using an integrated set of technical and operational criteria, including detection accuracy, response speed, robustness to noise and operating‐condition variation, information requirements, computational burden, and real‐time implementation capability. The results show a clear methodological evolution from physics‐based analytical formulations toward data‐driven and adaptive detection frameworks. Circuit‐equation‐based approaches provide high physical interpretability and direct estimation of arc‐related parameters, but their performance is often constrained by model assumptions, threshold dependence, and multi‐terminal measurement requirements. Signal‐processing‐based methods offer faster and more localized detection by exploiting transient, harmonic, and time–frequency signatures of the arc, although their stability and generalizability may be affected by noise level, parameter tuning, and signal decomposition settings. Intelligent‐learning‐based methods achieve the highest adaptability and classification capability in complex conditions, but they remain strongly dependent on the quality and diversity of training datasets and impose greater computational and implementation burdens. Overall, the review indicates that no single methodological family simultaneously satisfies the full set of protection requirements for modern transmission systems. The main conclusion is that the most promising direction for next‐generation arc‐fault detection lies in hybrid and multi‐layer architectures that combine physical modeling, transient signal analysis, and adaptive intelligent decision‐making within relay‐deployable protection frameworks.

Three-dimensional electron diffraction (3D ED) has become increasingly popular over the last two decades, challenging the limits of established single-crystal X-ray diffraction experiments. In particular, continuous-rotation and precession-assisted protocols have been established as important tools in the collection of electron diffraction data, and the data for most of the crystal structures solved by 3D ED nowadays are acquired with one of these two methods. This is particularly true for ceramic materials, where 3D ED allows deep insights into complex structure-property relationships. As ceramic syntheses tend to yield thick and highly crystalline particles, one issue in the refinement against electron diffraction data is the effect of coherent inelastic scattering. While dynamical refinement procedures allow the simulation of multiple scattering events, in general they ignore the inelastic contribution. This work aims to evaluate the impact of dynamical inelastic effects on the diffraction data of ceramics and how their overall quality depends on the measurement strategy used. This was done by comparing the structure models derived from the same crystals under similar experimental conditions of three ceramic compounds, namely NATP [Na1+xAlxTi2-x(PO4)3], LATP [Li2-xAlxTi2-x(PO3)4] and (Fe3Al2[SiO4]3), based on precession, continuous-rotation and stepwise static tilt data. The different methods allowed the detection of low-occupancy sites in the difference electrostatic potential maps corresponding to mobile sodium and lithium ions, paving the way for future studies on their migration behaviour in solid-state electrolytes.

Incorporating dynamic mode decomposition and domain adversarial training for cross-domain state of health estimation of lithium-ion batteries

Shear behavior of concrete beams reinforced with new-generation GFRP bars and stirrups: Experimental and theoretical strain limit analysis

The discovery of silicon (Si) accumulation inSynechococcus has drawn increasing attention to the physiological and mechanistic basis in marine environments. Although growth rates of Synechococcus are generally insensitive to silicate availability, the influence of silicate on Si accumulation and its regulatory mechanisms remains poorly understood. To investigate the response mechanisms of Synechococcus to silicate availability, we integrated physiological, biochemical, and transcriptomic analyses of a coastal strain and a marine strain. Cultures were grown under silicate-depleted (<1 μmol·L-1) and silicate-repleted (100 μmol·L-1) conditions. Silicate addition enhanced photosynthetic parameters in Synechococcus sp. XM24, reduced cellular carotenoid content in Synechococcus sp. PCC7002, and increased biogenic silica (BSi) in both strains. Transcriptomics revealed that silicate modulated photosynthesis and oxidative phosphorylation pathways in both strains. Under Si-replete conditions, ribosomal genes and protein synthesis were upregulated in Synechococcus sp. XM24, while membrane transport increased and sphingolipid metabolism decreased in Synechococcus sp. PCC7002. Collectively, our results reveal that Synechococcus exhibits a previously unrecognized physiological and molecular sensitivity to changes in silicate availability at an environmentally relevant concentration (100 μmol·L-1), with strain-specific acclimation strategies linked to their ecological origins. While these findings provide initial insights into the potential mechanisms underlying silicon accumulation in Synechococcus, the binary experimental design limits conclusions about uptake kinetics, which require validation with a full concentration gradient in future studies. Nonetheless, this study still advances our understanding of the mechanisms underlying silicon accumulation in Synechococcus.

Abstract The long-term performance of pavement structures is strongly governed by temperature-dependent distress mechanisms, including rutting and thermally induced stresses. As heat transfer properties regulate internal temperature gradients within asphalt layers, the accurate determination of thermal conductivity is essential for reliable service-life prediction. Yet, accurately assessing the thermal properties of such a complex and heterogeneous material remains challenging due to variations in mixture composition, inconsistent heat transfer mechanisms, and experimental limitations. To overcome these challenges, this work employs both steady-state and transient-state testing methodologies to determine the thermal conductivity of asphalt concrete samples. Following this, the study conducts a detailed comparative evaluation of the two approaches to better understand their strengths and limitations. Accordingly, a new, well-insulated apparatus equipped with a heat flux sensor was designed for the steady-state testing approach, significantly enhancing the thermal insulation process necessary for achieving one-dimensional heat transfer, a capability lacking in current studies. In addition, transient heat transfer experiments were performed using the Hot Disk TPS 2500S instrument. In this study, twelve different mixtures were prepared with two aggregate sources, three distinct air void levels, and two different maximum aggregate sizes, and their thermal conductivities were compared under two heat transfer conditions. The TPS method, which is quick and easy, consistently showed higher values due to aggregate particles in the sensor’s contact zone. Conversely, the improved steady-state approach yielded relatively lower but more reliable values, effectively reflecting the sample's thermal conductivity by considering the distribution of mixture components.

Lymph Node Stromal Cells (LNSCs) are a diverse population of cells responsible for maintaining the lymph node environment and regulating the immune response. Given these roles, they have the potential to help replicate lymph node functions invitro. However, LNSCs are challenging to work with due to their high heterogeneity. Here, we demonstrate the challenges of working with heterogeneous cell populations, where ratios between populations can change over time. We show how similar marker expression profiles between populations, along with non-optimized controls due to experimental limitations, can make flow cytometry analysis difficult. To better assess this heterogeneous population, we demonstrate how to use machine learning algorithms to identify changing populations while overcoming the limitations of any single algorithm. This approach reduces the effects of user bias when placing gates while also increasing confidence in population identification. This analysis method is robust, utilizes existing tools, and provides information that can inform various directions of future studies.

Abstract Spin mixtures of degenerate fermions are a cornerstone of quantum many-body physics, enabling superfluidity, polarons, and rich spin dynamics through s -wave scattering resonances. Combining them with strong, long-range dipolar interactions provides highly flexible control schemes promising even more exotic quantum phases. Recently, microwave shielding gave access to spin-polarized degenerate samples of dipolar fermionic molecules, where tunable p -wave interactions were enabled by field-linked resonances available only by compromising the shielding (due to experimental limitations). Here, we study the scattering properties of a fermionic dipolar spin mixture and show that a universal s -wave resonance is readily accessible without compromising the shielding. We develop a universal description of the tunable s -wave interaction and weakly bound tetratomic states based on the microwave-field parameters. The s -wave resonance paves the way to stable, controllable and strongly-interacting dipolar spin mixtures of deeply degenerate fermions and supports favorable conditions to reach this regime via evaporative cooling.

Single-cell multi-omics technologies offer unprecedented opportunities to decipher complex cellular mechanisms. To overcome experimental limitations in scale, cost, and coverage, powerful computational methods are essential for integrating diverse data modalities and generating high-fidelity in-silico data. In this paper, we present scDiffusion-X, a latent diffusion model for multi-omics data integration, generation, and translation. The core innovation is a Dual-Cross-Attention (DCA) module that adaptively captures intricate, hidden relationships between molecular modalities, offering a more flexible and interpretable approach than existing integration strategies. Extensive benchmarking experiments demonstrate that scDiffusion-X excels at generating realistic multi-omics data, preserving cellular heterogeneity and global data structures with excellent scalability. Beyond simulation, scDiffusion-X uniquely enables accurate modality translation, predicting one molecular modality from another with robust uncertainty quantification. Furthermore, we designed a gradient-based interpretation framework to transform theDCA module into a discovery tool, enabling inference of comprehensive cell-type-specific heterogeneous gene regulatory networks (GRNs). By integrating state-of-the-art generative modeling with biological interpretability, scDiffusion-X serves as a powerful tool for dissecting regulatory relationships, predicting perturbation responses, and accelerating discovery in single-cell multi-omics research.

Reliable prediction of swelling and degradation behavior of hydrogels used in biomedical applications due to the simultaneous and nonlinear effects of multiple parameters is a critical requirement in hydrogel design. However, conventional experimental approaches present significant limitations in multiparameter systems due to high time consumption and experimental costs. In this study, the structural, swelling, and degradation characteristics of poly-(vinyl alcohol)/chitosan (PVA/CS) composite hydrogels synthesized using a physical cross-linking method based on freeze-thaw (F-T) cycles with varying chitosan contents and F-T cycle numbers were systematically investigated. To prioritize biocompatibility, the hydrogels were produced without the use of chemical cross-linking agents, and the effects of pH-sensitive behavior, network density, and structural stability were examined in detail. FT-IR analyses confirmed the formation of hydrogen bonding interactions and an IPN-like network structure between PVA and chitosan chains, while SEM observations revealed significant changes in pore morphology depending on chitosan content and the number of F-T cycles. The swelling capacities of the hydrogels were found to vary between 4.02 and 26.28 g water/g polymer, whereas degradation ratios ranged from 3.38% to 37.36%, depending on the environmental pH, chitosan concentration, and F-T cycle number. The experimentally obtained nonlinear swelling and degradation data were further modeled using an artificial neural network (ANN) approach, and the behaviors were predicted with high accuracy (R > 0.99). The results demonstrate that ANN-based modeling provides a reliable and efficient design and optimization tool for multiparameter hydrogel systems by significantly reducing experimental workload.

Abstract Despite the widespread industrial and environmental relevance of clinoptilolite, a natural zeolite, its surface-level adsorption behavior remains largely underexplored , especially in the context of large pharmaceutical molecules and periodic density functional theory (DFT) slab models. Previous investigations have mainly focused on bulk properties, routinely reporting steric hindrance within micropores without taking the critical step of analyzing adsorption at the external surfaces. This gap persists due to experimental limitations in resolving surface terminations and the positions of extra-framework cations. In this work, we present a computationally elegant framework to explore molecular adsorption on cation-exchanged clinoptilolite surfaces. Using a hybrid multiscale approach that combines classical force-field-based molecular dynamics (MD) sampling with dispersion-corrected DFT (DFT-D3) refinement, we systematically evaluate the adsorption of 5-fluorouracil, an anticancer drug, across four representative systems: Na–, Ca–, Na–Ca–, and Na–Ca–K–clinoptilolite. Our results reveal a clear and reproducible trend in adsorption strength: Na–Ca–K &gt; Na &gt; Ca &gt; Na–Ca, with the most favorable configuration reaching an adsorption energy of − 430.6 kJ/mol. The strongest binding occurs when the molecule coordinates with only one surface cation (typically Ca 2+ or closely spaced Na + ); the presence of additional, non-coordinating cations on the surface (e.g., K + ) nevertheless enhances adsorption by modulating the local electrostatic field. In contrast, direct mixed coordination to different cation types reduces adsorption strength. We also show that asymmetric Al distributions and accessible cation positions near the outer regions of the slab further amplify adsorption via favorable hydrogen bonding and spatial confinement. This study closes a crucial gap in the literature by providing quantitative insights and a mechanistic understanding of how surface cation composition and arrangement influence adsorption. Our findings offer valuable design principles for tailoring clinoptilolite surfaces and lay the groundwork for cation engineering in pharmaceutical loading, ion-exchange, and selective adsorption applications.

The global control of tuberculosis (TB) urgently demands diagnostic tools that are rapid, specific, and amenable to resource-limited settings. Here, we report a novel, label-free aptasensor for Mycobacterium tuberculosis (MTB) H37Rv, which operates on an innovative two-step "pre-incubation & hybridization-capture" strategy, fundamentally departing from conventional competitive displacement designs. First, a high-affinity, in-house selected aptamer specifically complexes with target bacteria in solution. This pre-formed complex is then efficiently captured on a gold interdigitated electrode via hybridization with a short, dithiol-anchored DNA probe, which is pre-assembled with conductive gold nanoparticles (AuNPs) to form a robust sensing interface. This architecture decouples target recognition from signal transduction, enhancing assay robustness. The captured bulky and negatively charged bacterial complex synergistically creates pronounced steric and electrostatic barriers at the interface, drastically impeding charge transfer and generating a measurable frequency shift in a multichannel piezoelectric quartz crystal (MSPQC) system. The sensor demonstrates exceptional specificity, clearly distinguishing H37Rv (ΔF = 116Hz) from non-target bacteria, including Bacillus Calmette-Guérin (BCG) (ΔF < 30Hz). It exhibits a wide linear response from 103 to 106CFU/mL (R2 = 0.9666) and a low experimental detection limit of 100CFU/mL within a total assay time of 65min. With excellent reproducibility (RSD 2.1-4.6%) and stability, this work establishes a new paradigm for rapid, label-free and equipment-simplified whole-cell detection, holding significant promise for point-of-need TB diagnosis.

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are critical regulators in health and disease but remain underexploited as drug targets. Unlike folded proteins, they populate dynamic ensembles where interactions can be transient or multivalent, and both enthalpic and entropic contributions shape binding, complicating ligand discovery. Here, we analyze three key barriers hindering progress: (1) nontraditional binding mechanisms that challenge classical drug design, (2) experimental and computational limitations for studying disorder, and (3) a lack of systematic datasets. Our analysis of the Biological Magnetic Resonance Data Bank (BMRB) and BindingDB highlights the extreme underrepresentation of IDPs and IDRs, underscoring the need for community-driven data resources. By integrating new binding paradigms, tailored methodologies, and standardized datasets, drug discovery can begin to harness IDPs as a new therapeutic frontier.

Animal cumulative culture through changing environments.

Adsorption-electrochemistry coupling strategies have attracted considerable interest due to their high efficiency for uranium extraction, in which solid electrocatalysts are widely adopted in diverse uranium extraction systems. Experimental limitations in resolving atomic-scale details hinder the elucidation of the uranium adsorption-electrochemical reduction mechanisms at the solid-liquid interface. Moreover, the complex uranyl coordination chemistry and strong coupling between the electronic structure and interfacial electrostatics have rendered relevant theoretical studies relatively scarce. To address this gap, we take the typical uranium extraction system over Fe-N-C-based electrocatalysts as the model and employ the density functional theory (DFT) to investigate the adsorption-electrochemical reduction mechanism of uranyl species. Our calculations reveal a more favorable end-on adsorption mode via stronger Fe-O bonding to uranyl axial oxygen than to the coordinated water in the side-on configuration. The critical role of proton binding to uranyl axial oxygen in U(VI) electroreduction is further demonstrated. Among the three proposed electron-transfer pathways, the bridgewater-assisted internal proton transfer (B-IPT) is identified as the most advantageous for promoting the reduction, outperforming both the direct-internal (D-IPT) and external (EPT) pathways. These results provide important insights into adsorption-electrochemical uranium extraction, advancing the fundamental understanding of the coordination chemistry of uranium.