Although overt thyroid dysfunction has been associated with changes in pulmonary function, the effects of thyroid hormone levels and thyroid autoimmunity on lung function in euthyroid individuals remain unclear. We investigated the associations between subtle changes in thyroid hormones and thyroid peroxidase antibodies (TPOAb) and pulmonary function in a nationally representative cohort of Korean adults. We analyzed data from 2,626 euthyroid participants aged ≥ 40 years from the Korea National Health and Nutrition Examination Survey (2013-2015). Pulmonary function was assessed using spirometry-derived forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and FEV1/FVC ratio. Multivariable linear regression analyses were conducted after adjusting for age, sex, body mass index (BMI), smoking status, alcohol intake, and physical activity. In the unadjusted models, higher free thyroxine levels were positively associated with FVC and FEV1, while higher TSH levels were inversely associated. In contrast, elevated TPOAb levels were independently associated with reduced FEV1 (β = -0.330, P = 0.021). These associations were more pronounced among adults aged ≥60 years and individuals with a BMI ≤ 23 kg/m². Thyroid autoimmunity, as reflected by elevated TPOAb levels, was associated with a decline in pulmonary function among euthyroid individuals, independent of thyroid hormone levels. Our results support the clinical utility of TPOAb status as an early marker for detecting subclinical pulmonary vulnerability, particularly in older adults and those with a lower BMI.

Meta-analyses of personality change from the preclinical to the clinical stages of dementia.

13C metabolic tracing in human SGBS cells provides a potential new approach methodology for assessing metabolism-disrupting properties of environmental chemicals.

Deep aging clocks: AI-powered strategies for biological age estimation.

Long-term cell storage of RAW 264.7 cells in a deep freezer dampens M2 marker expression.

Cellulose nanofiber-reinforced chitosan conductive hydrogel tailored by double-network strategy for stretchable strain sensor.

Linker Length and Composition within Disordered Binding Motifs Modulates the Avidity and Reversibility of a Multivalent Protein Interaction Switch.

REVS: A new open-source platform for high-resolution analysis of rodent wheel running behavior.

With the increase in work-related stress, the issue of psychological pressure in occupational environments has gained increasing attention. This paper proposes an enhanced Informer stress recognition and classification method based on deep learning, which guarantees performance by integrating tailored spatial and channel attention mechanisms (SAM/CAM) with the Informer backbone. Unlike existing attention-augmented models, the proposed SAM is designed to prioritize time-sensitive physiological signal segments, while CAM dynamically weights complementary stress-related features, enabling precise capture of subtle stress-related patterns. With this dual attention mechanism, the proposed model can capture subtle changes associated with stress states accurately. To evaluate the performance of the proposed method, the experiments on one publicly available dataset were conducted. Experimental results demonstrate that the proposed method has outperformed existing approaches in terms of accuracy, recall, and F1-score for stress recognition. Additionally, we performed ablation studies to verify the contributions of spatial attention module and channel attention module to the proposed model. In conclusion, this study not only provides an effective technical means for the automatic detection of psychological stress, but also lays a foundation for the application of deep learning model in a broader range of health monitoring applications.

Background and Purpose: Hepatic encephalopathy (HE) is a neuropsychiatric syndrome associated with liver cirrhosis (LC) that often results in cognitive impairment. Minimal HE (mHE), a subtle form of the condition, significantly affects patients’ quality of life. Advanced imaging techniques, such as quantitative susceptibility mapping (QSM), provide new insights into the brain changes associated with HE. Materials and Methods: The study included 28 patients (17 with mHE and 11 without) with alcohol-induced LC and 25 healthy controls. MR imaging, including QSM, was utilized to assess microstructural tissue changes and iron deposition in the brain. Cognitive function was assessed through a neuropsychological test battery. QSM quantified magnetic susceptibility in deep gray matter, while enlarged perivascular spaces (EPVS) were evaluated using T2-weighted images. Statistical analyses, including non-parametric tests and linear regression, assessed differences in susceptibility and their correlation with cognitive performance and EPVS. Results: Significant differences in cognitive performance and brain susceptibility were observed between patients and controls. Patients exhibited lower susceptibility in the caudate nucleus with the accumbens (CNA); mHE patients, in particular, had a significant reduction in CNA susceptibility. Additionally, EPVS grade correlated positively with cognitive decline, suggesting that EPVS may play an essential role in the pathophysiology of mHE. Conclusions: This study demonstrates that QSM can detect subtle brain changes in LC patients, with decreased susceptibility in the CN (caudate nucleus) linked to cognitive impairment in mHE. The role of EPVS in HE warrants further investigation, as it may affect the efficacy of current diagnostic and therapeutic approaches. These findings highlight the potential of QSM to improve HE assessment.

Optical skyrmions, as topologically protected quasiparticles, hold great promise for on-chip photonic technologies. However, achieving programmable control over their properties through subtle structural changes remains challenging. This study introduces a minimal perturbation engineering strategy on a plasmonic metasurface. By applying controlled geometric perturbations (either continuous shortening or discrete segmentation) to a single edge of a hexagonal groove structure, combined with incident phase perturbations, we systematically manipulate the evolution of the skyrmion texture. These minimal perturbations induce reproducible shifts in the skyrmions’ center intensity and peak position, yielding up to ~32% center suppression, while the global topological charge remains conserved. This “geometry × phase” dual-perturbation approach provides a straightforward and efficient approach for engineering programmable topological light fields on a chip, with promising applications in integrated photonic devices.

FAHD1 is a mitochondrial enzyme involved in oxaloacetate metabolism, with emerging links to cellular redox balance, Ca2+-metabolism, and structural features. Building on previous work (Heberle et al. Sci Rep 14:9231, 2024), we investigated how overexpression of human FAHD1 (hFAHD1) variants, including wild-type, the catalytically inactive hFAHD1-K123A, and the hyperactive hFAHD1-T192S, affects nuclear morphology in U2OS osteosarcoma cells. Using high-content microscopy and automated classification, we observed variant-specific shifts in nuclear shape distributions. Notably, expression of K123A was associated with a higher frequency of large, rounded nuclei and a reduction in elongated forms, while the T192S variant produced subtler changes. By aligning morphological clusters with available proteomic profiles, we identified suggestive correlations with differences in biosynthetic activity and chromatin organization. These findings indicate that altered FAHD1 activity is correlated with changes in nuclear morphology and may be associated with broader cellular organization. Our results are descriptive and hypothesis-generating, highlighting possible links between mitochondrial metabolic states and nuclear architecture that warrant further validation.

The wide electrochemical potential window of ionic liquids (ILs) makes them potential candidates to replace common electrolytes in electrochemical devices, such as battery applications.[1] Here, the direct deposition of lithium from three IL-based electrolytes onto a nickel current collector in an anode-free cell approach is reported. It is found that the deposition morphology is strongly influenced by the exact electrolyte composition at a constant current density.It is also well-known that ILs form a prominent layered structure at the interface to charged surfaces, which can be controlled by an externally applied electrode potential. This has been extensively studied both experimentally and theoretically under static conditions. However, variations in electrode potential are inherent in electrochemical systems, making the interfacial structure of ILs prone to reorganization processes. The relaxation time for re-establishing a stable interfacial regime is of high importance for many electrochemical systems. In an unconventional approach, this dynamic reorganization of the IL interfacial structure is probed by force-distance spectroscopy and friction force microscopy on a Au(111) surface as a model system.[2] Friction between an atomic force microscope tip and the IL interfacial structure is very sensitive to even subtle changes and is recorded as a function of time and during chronoamperometric experiments. A clear influence of switching the applied potential from a cation-dominated to an anion-dominated interface or vice versa is obtained. Moreover, various time scales of relaxation processes are identified by careful analysis of the simultaneously obtained current and friction response. Finally, the influence of trace amounts of water on the relaxation process is critically discussed.[1] D. Stepien, B. Wolff, T. Diemant, G.-T. Kim, F. Hausen, D. Bresser, S. Passerini, ACS Appl. Mater. Interfaces 15, 25462, 2023 [2] F. Hausen, Small Methods 11(7), 2300250, 2023

ABSTRACT Change detection (CD) in remote sensing (RS) images is essential for earth observation, monitoring dynamic environmental and urban changes. Existing deep learning (DL)-based methods often struggle with pseudochanges and subtle small-scale changes. To address this, we propose a spatial and wavelet interactive learning (SWIL) framework, which jointly optimizes spatial and spectral feature learning. By leveraging multidomain feature complementarity, SWIL suppresses pseudochanges while enhancing fine-scale detection, improving noise robustness. Specifically, we integrate the wavelet transform into feature filtering, exploiting its multiscale geometric analysis for fine-grained frequency-domain representation. Our spatial and wavelet feature enhancement (SWE) module decomposes and aggregates multiscale features with local convolutions, effectively reducing pseudo-change noise. Additionally, the Channel Selective Multiscale Filtering (CSF) module refines spatial-frequency features, improving small-change detection in complex scenes. Experiments on three benchmarks confirm SWIL's superiority over state-of-the-art methods, achieving higher accuracy and noise resilience under complex conditions.

The microstructure of porous electrodes of solid oxide cells (SOCs) significantly impacts their electrochemical performance. Therefore, various attempts have been made to analyze their complex porous microstructure in three dimensions. The obtained microstructures have been quantitatively evaluated using structural metrics, such as volume fraction, surface area density, and tortuosity factor of the constituent phases, as well as double- and triple-phase boundaries. These structural characteristics are useful to correlate the electrode performance with their microstructure, thereby determining optimal electrode design. However, these intuitive metrics often fails to explain the electrode performance. In fact, structural changes during long term operation of SOCs are subtle so that the performance degradation of the electrodes cannot be fully detected by these intuitive characteristics. Therefore, it is necessary to find a way to extract hidden structural metrics that characterize the electrode microstructure and to clarify their correlation with electrode performance.One of the promising candidates for such structural metrics is topological information. Topology is a structural property that is invariant to successive deformation operations, such as connected structures and the number of holes in the structures. Topology-based structural analysis has many applications in material science, where macroscopic properties of materials are correlated with topological information about their internal structures (e.g., crystal structures). For example, Wang et al. [1] performed a topological analysis on the crystal structure of double-phase steel to characterize its structure and to predict its macroscopic properties, such as stress-strain curves. Application of topological data analysis is also found in the analysis of SOFC electrodes; Pawlowski et al. [2] attempted to capture the structural changes in SOFC anodes during long-term operation from a topological perspective.In addition, topological analysis is regarded as a tool for dimensional reduction of complex structural information. Therefore, the information extracted from the topological analysis can be used to evaluate conventional metrics of porous microstructures, such as surface area and triple-phase boundary. If these metrics can be accurately evaluated from a limited number of information from the topological analysis, the numerical cost is expected to be significantly reduced.Therefore, this study investigates the applicability of the topological analysis to the electrode microstructure analysis. Fig. 1 shows the schematic diagram of the structural analysis in this study. First, persistent homology analysis [3] is employed to extract the topological information of the electrodes, where the birth and death of topological features are detected during the filtration process within the structure datasets to produce the persistent diagram (PD). The persistent diagram is then discretized and concatenated to obtain the persistent image (PI). Subsequently, principal component analysis (PCA) is conducted to further reduce the dimensionality of the structural information extracted in the persistent image. The obtained values of the principal components are correlated with the conventional structural metrics of the porous electrodes, such as volume fraction, surface area density, and triple-phase boundary density.Moreover, neural networks (NNs) consisting of fully-connected layers are constructed to quantify the structural metrics from the principal components. The constructed neural networks are trained using the real electrode microstructure datasets obtained using the focused ion beam and scanning electron microstructure [4]. To validate the developed neural networks, they are compared with the convolutional neural network (CNN), which directly quantifies the microstructural metrics from the three-dimensional structures, in terms of quantification accuracy, the number of training datasets, and required training time.The values obtained from the topological analysis followed by the principal component analysis are found to contain essential structural information in the porous electrodes. This is implied by the fact that the principal component values obtained from the real structure datasets are significantly different from those from the artificial sphere-packing structures, even though the conventional structural metrics, i.e., volume fraction and surface density, are identical between the real and artificial structures. In addition, the principal component values have sensitivity to the conventional statistical metrics, because the structures with different solid compositions form clusters in different locations in a principal component space. These suggest that the topological information will be useful not only to detect subtle structural changes in the porous electrodes undetectable in conventional structural metrics, but also to reduce the dimensionality of the complex porous electrodes without losing the information quality.Since the information extracted using the persistent homology and principal component analysis possess essential information about the electrode structures, conventional microstructural metrics are accurately quantified by the constructed neural network. It should be emphasized that the number of parameters in the neural network that need to be adjusted during the training process is significantly reduced compared with the convolutional neural network. As a result, the number of required datasets for the training of the network is significantly reduced. These clearly indicate the effectiveness of the dimensional reduction in the quantification of the electrode microstructures.

The digitization of paper documents enables rapid sharing and long-term preservation of information, making it a widely adopted approach for efficient document storage and management across various domains. However, the recent advances in image editing software pose an increasing threat to the integrity of document images. Comparing the input with the corresponding reference document image is a direct and effective approach to verification. Nevertheless, this task is challenging due to two key factors, namely, the need for efficient retrieval of the reference document images and the difficulty of detecting subtle content changes under the print–scan (PS) distortions. To address these challenges, this work proposes a document image verification scheme based on paragraph alignment and subtle change detection. It first extracts paragraph structural features from both input and reference document images to achieve efficient image retrieval and accurate paragraph alignment. Based on the alignment results, the proposed scheme employs contrastive learning to reduce the effect of PS distortions in extracting features from the input and reference document images. Finally, an additional verification step is introduced that significantly reduces the false positive detection by addressing the feature misalignment within the extracted paragraphs. To evaluate the proposed scheme, extensive experiments were conducted on databases constructed from public datasets, and various benchmark methods were compared. Experimental results show that the proposed scheme outperforms benchmark methods, achieving an accuracy score of 0.963.

Quantum materials with novel spin textures from strong spin-orbit coupling (SOC) are essential components for a wide array of proposed spintronic devices. Topological insulators have a necessary strong SOC that imposes a unique spin texture on topological states and Rashba states that arise on the boundary, but there is no established methodology to control the spin texture reversibly. Here, it is demonstrated that functionalizing Bi2Se3 films by altering the step-edge termination directly changes the strength of SOC and thereby modifies the Rashba strength of 1D edge states. Scanning tunneling microscopy/spectroscopy shows that these Rashba edge states arise and subsequently vanish through the Se functionalization and reduction process of the step edges. The observations are corroborated by density functional theory calculations, which show that a subtle chemical change of edge termination fundamentally alters the underlying electronic structure. Importantly, fully reversible and repeatable switching of Rashba edge states across multiple cycles at room temperature is experimentally demonstrated. The results imply Se functionalization as a practical method to control SOC and spin texture of quantum states in topological insulators.

IntroductionIn swine disease surveillance, obtaining labeled data for supervised learning models can be challenging because many farms lack standardized diagnostic routines and consistent health monitoring systems. Unsupervised learning is particularly suitable in such scenarios because it does not require labeled data, allowing for detecting anomalies without predefined labels. This study evaluates the effectiveness of unsupervised machine learning models in detecting anomalies in productivity indicators in swine breeding herds.MethodsAnomalies, defined as deviations from expected patterns, were identified in indicators such as abortions per 1000 sows, prenatal losses, preweaning mortality, total born, liveborn, culled sows per 1000 sows, and dead sows per 1000 sows. Three unsupervised models - Isolation Forest, Autoencoder, and K-Nearest Neighbors (KNN) - were applied to data from two swine production systems. The herd-week was used as the unit of analysis, and anomaly scores above the 75th percentile were used to flag anomalous weeks. A permutation test assessed differences between anomalous and non-anomalous weeks. Performance was evaluated using F1-score, precision, and recall, with true anomalous weeks defined as those coinciding with reported health challenges, including porcine reproductive and respiratory syndrome (PRRS) and Seneca Valley virus outbreaks. A total of 8,044 weeks were analyzed.ResultsThe models identified 336 anomalous weeks and 1,008 non-anomalous weeks in Production System 1, and 1,675 anomalous weeks and 5,025 non-anomalous weeks in Production System 2. The results from the permutation test revealed significant differences in productivity indicators between anomalous and non-anomalous weeks, especially during PRRS outbreaks, with more subtle changes observed during Seneca Valley virus outbreaks. The models performed well in detecting the PRRSV anomaly, achieving perfect precision (100%) across all models for both production systems. For anomalies like SVV the models showed lower performance compared to PRRSV.DiscussionThese findings suggest that unsupervised machine learning models are promising tools for early disease detection in swine herds, as they can identify anomalies in productivity data that may signal health challenges.

This study presents a comprehensive investigation of the high-pressure behavior of barium azide Ba(N3)2 through synchrotron X-ray diffraction, revealing critical insights into its anisotropic compressibility and phase transitions under pressures up to 28 GPa. At ambient conditions, Ba(N3)2 crystallizes in a monoclinic structure (space group P21/m), exhibiting pronounced anisotropic compression with axial compressibility following the order b > a > c. The distinct compressibility arises from the arrangement of azide ions, where interlayer interactions along the b-axis dominate the response to pressure. A reversible phase transition (Phase I → Phase II) initiates at 2.6 GPa, characterized by a monoclinic-to-monoclinic transformation involving subtle symmetry changes driven by azide ion rotation and lattice plane slippage. Above 11.8 GPa, emergent diffraction peaks suggest a potential secondary transition, though the structure remains stable up to 28 GPa. These findings underscore the unique role of azide ion dynamics in governing structural stability and phase evolution in divalent azides, offering implications for their utility as precursors in polymeric nitrogen synthesis.

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique with established clinical relevance in neuro-oncology. While CEST contrast differences between gray matter (GM) and white matter (WM) are documented, brain region-specific contrast variations remain underexplored. This study investigates the regional variability of CEST contrasts in healthy brains to provide a baseline reference, which could enhance the detection of subtle pathological changes in clinical settings. Ten healthy volunteers (five female, mean age 25 ± 3.1 years) underwent 3D CEST imaging on a 3-T Siemens Prisma scanner. Using a custom segmentation tool, GM and WM regions of interest (ROIs) were automatically selected in the frontal, parietotemporal, and occipital regions and the calcarine sulcus to analyze regional contrast changes for the relaxation-compensated MTRRex and asymmetry-based APTw CEST contrasts. Individual and grouped analyses showed significant regional differences in GM and WM for all CEST contrasts. Globally, significant GM-WM differences were also detected for the APTw, MTRRex AMIDE, and MTRRex ssMT, which demonstrated higher GM contrast values for APTw and MTRRex AMIDE and lower GM contrast values for the MTRRex ssMT. Regionally, all contrasts showed reduced GM signals in the frontal lobe and increased signals in the calcarine sulcus when compared to the occipital and parietotemporal lobe; however, these differences were less pronounced for MTRRex rNOE and MTRRex ssMT. Relaxation-compensated CEST and APTw CEST contrast values exhibit significant regional variation in the healthy brain, highlighting the importance of consistent ROI placement in clinical studies. At the same time, low intersubject variability was observed, providing robust normative values for future comparisons. These regional reference values can aid in the detection of subtle pathological changes in CEST MRI by offering a reliable baseline for interpreting deviations in patient data.