Fungal endometritis in broodmares: clinical presentation, prevalence in Ireland and diagnostic performance of cytology.

Vibrational microspectroscopy, including both Raman-based and infrared-based techniques, can map the chemical distribution of samples based on molecular vibrations without labeling. However, imaging fast dynamics in living organisms remains challenging. To address this, we propose a wide-field infrared microspectroscopy capable of single-shot imaging, where each image is captured with a single pair of laser pulses lasting approximately one picosecond. It minimizes motion blur and allows observing fast dynamic processes at frame rates up to the laser repetition rate. This approach is based on the infrared-resonant third-order sum-frequency process, which converts infrared light to visible signals. We demonstrate the capability through single-shot in vivo imaging of alive Caenorhabditis elegans worms in water, achieving a spatial resolution of approximately 400 nm. Additionally, 1,000 Hz single-shot videos of moving worms are shown by using a kHz laser system. This approach opens more possibilities for imaging chemicals involved in fast dynamic processes, offering diverse applications in both chemistry and biology.

To address the dilemma of homogeneous talent training and the efficiency bottleneck of human resource management in universities, this study proposes an innovative personalized training framework integrating artificial intelligence, big data, and deep learning. Based on the 18-dimensional full-cycle behavior dataset of 5,000 students and OULAD dataset, a multimodal heterogeneous data fusion pipeline is constructed. This study adopts Generative Adversarial Network (GAN) for data imputation and bias optimization, designs Hierarchical Attention Graph Neural Network (HA-GNN) to capture hierarchical correlations among features, and uses Long Short-Term Memory (LSTM) to model temporal behavior patterns. The experimental results demonstrate that, under 10 independent repeated runs with random seed variation, the Hierarchical Attention Graph Neural Network-Long Short-Term Memory (HA-GNN-LSTM) model achieves lower prediction error on the academic performance prediction task, with a Mean Absolute Error (MAE) of 4.2 ± 0.3. Compared with the Temporal Fusion Transformer (TFT) baseline model, MAE is reduced by 31.1%. Welch's two-tailed t-tests based on independent run results remain statistically significant after Holm-Bonferroni multiple comparison correction [Formula: see text]. The Normalized Discontinued Cumulative Gain at Top 5 (NDCG @ 5) index of personalized recommendation system reaches 0.90, which verifies the effectiveness of spatio-temporal feature modeling. At the management application level, the improvements in advisor allocation response time and resource idle rate are derived from simulation experiments based on historical data replay, rather than online deployment in real campus management systems. The simulation results demonstrate that, under established constraints and historical sample distributions, advisor allocation response time could be reduced by 60% and resource idle rate could be decreased by 63.4%. These findings indicate the framework's potential for optimizing educational resource allocation. However, its managerial benefits require further validation through subsequent real-world deployment and long-term follow-up studies.

Clinical phenotyping of chronic insomnia in early childhood: family-history-informed vulnerability profiles in a retrospective chart review.

Milk is a suspension with a multimodal size distribution of fat droplets and protein micelles, which most sizing methods do not distinguish. We demonstrate the use of differential dynamic microscopy (DDM) and cryo-FIB-SEM tomography to size both fat globules and casein micelles in homogenised milk without the need for prior physical separation. The two techniques are complimentary: cryo FIB-SEM tomography can directly identify the 2 distinct constituents and reveal their overlapping size distributions. DDM reliably detects a bi-modal size distribution for whole milk samples, providing a fast high-throughput method to estimate volume-averaged mean sizes. Our results highlight that different sizing techniques seldom, if ever, yield the same answer. Instead, they can provide complementary information and further insights not obtainable from using each technique in isolation.

TDAN: Triplet depth-aware network with multi-domain fusion for unknown attack detection in autonomous vehicles.

A survey of external quality assessment schemes (EQAS) for faecal immunochemical tests for haemoglobin (FIT).

Charge detection mass spectrometry (CDMS) with electrostatic ion traps has been used to characterize the mass distributions of a variety of nanoparticles that have masses ranging from ∼1 to 400 megadaltons (MDa). Here, samples of polystyrene nanospheres from three different manufacturers with diameters ∼200 nm and masses of several gigadaltons (GDa) were characterized by both CDMS and transmission electron microscopy (TEM). From the centroid masses of ∼2.320, ∼2.602, and ∼2.897 GDa, centroid diameters of 191.4, 199.1, and 204.8 nm were obtained, respectively. These diameters are within 1% of those obtained from TEM measurements. The size distributions of the individual components in a mixture of these three samples were readily resolved with CDMS but not with TEM. The greater measurement time required for a statistically meaningful measurement from TEM led to some particle shrinkage that was not uniform between particles. The width of the most monodisperse nanoparticle sample distribution indicates that CDMS could distinguish two nanoparticle samples that differed by just 2 nm in diameter at this particle size (1%). The high resolution, minimal sample preparation requirements, compatibility with automation, and now extended mass range make CDMS a powerful high-throughput nanoparticle characterization tool for nanoparticles in biomedicine, advanced electronics, and material science.

Credible intervals and credible sets, such as highest posterior density (HPD) intervals, form an integral statistical tool in Bayesian phylogenetics, both for phylogenetic analyses and for development. Readily available for continuous parameters such as base frequencies and clock rates, the vast and complex space of tree topologies poses significant challenges for defining analogous credible sets. Traditional frequency-based approaches are inadequate for diffuse posteriors where sampled trees are often unique. To address this, we introduce novel and efficient methods for estimating the credible level of individual tree topologies using tractable tree distributions, specifically Conditional Clade Distribution (CCD). Furthermore, we propose a new concept called α credible CCD, which encapsulates a CCD whose trees collectively make up α probability. We present algorithms to compute these credible CCDs efficiently and to determine credible levels of tree topologies as well as of subtrees. We evaluate the accuracy of these credible set methods leveraging simulated and real datasets. Furthermore, to demonstrate the utility of our methods, we use well-calibrated simulation studies to evaluate the performance of different CCD models. In particular, we show how the credible set methods can be used to conduct rank-uniformity validation and produce Empirical Cumulative Distribution Function (ECDF) plots, supplementing standard coverage analyses for continuous parameters.

Laser directed energy deposition (LDED) was employed to fabricate Ti6Al4V titanium alloy and TiN/Ti6Al4V composites through nitrogen-assisted in-situ synthesis. Two fabrication routes were investigated, including direct deposition under a mixed nitrogen-argon atmosphere (TS sample) and nitrogen-assisted remelting under a pure nitrogen atmosphere (TR sample). Dry sliding tribological tests were conducted at room temperature and 300°C. The results showed that nitrogen-assisted processing promoted the in-situ formation of TiN within the Ti6Al4V matrix. Compared with the Ti6Al4V sample, the TR sample exhibited a higher friction coefficient but significantly improved wear resistance, with the wear volume reduced by approximately 44.8% at room temperature and 78.8% at 300°C. In contrast, the TS sample containing relatively sparse and smaller TiN particles showed limited improvement in wear resistance. At elevated temperature, all samples exhibited reduced wear loss, which was likely associated with oxidation-assisted surface protection during sliding. Microstructural observations indicated that the remelting-assisted process promoted the formation of larger TiN particles with a denser distribution in the TR sample, significantly influencing the wear evolution behavior. However, the tribological response was likely affected by the combined effects of TiN reinforcement and processing-induced microstructural evolution associated with different thermal histories. This study demonstrates that nitrogen-assisted remelting is an effective approach for improving the tribological performance of Ti6Al4V alloys and provides insight into the influence of TiN distribution on wear behavior in LDED-fabricated titanium matrix composites.

Human papillomavirus (HPV) is a highly prevalent sexually transmitted infection in the world, particularly among young adults shortly after they become sexually active. Beyond its commonality, HPV is a significant public health concern due to its carcinogenic potential. Globally, HPV is responsible for a substantial number of cancer cases annually, including the vast majority of cervical cancers, as well as a notable proportion of other anogenital cancers. To convey the urgent need for comprehensive prevention strategies, this study aimed to assess HPV test positivity and genotype distribution among individuals referred for HPV testing in Tehran, Iran, over a 14-year period (2011-2024), providing the foundational data required to guide future national vaccination and screening programs. This study examined the demographic characteristics of 6418 individuals (5988 women and 430 men) tested for HPV using data and samples collected from patients visiting a Saeed pathology and genetics laboratory between 2011 and 2024. HPV DNA was extracted from genital samples, (cervical/vaginal specimens for women, and penile/urethral swabs and urine for men) representative of both female and male individuals in Tehran, and analyzed for the presence of HPV. Genotyping was performed by real-time PCR (Cobas 4800 and Sansure G26) and Reverse Hybridization (INNO-LiPA and HPV Direct Flow Chip) methods for the identification of high-risk (HR) and low-risk (LR) HPV genotypes. Over the 14-year study period, HPV prevalence was assessed using four diagnostic methods (INNO-LIPA, HybriSpot, COBAS X480, and Sansure Kit), reflecting the evolution of available laboratory assays over time yielding positive detection rates of 55.7%, 61.5%, 17.2%, and 52.1%, respectively. The proportion of positive HPV test results varied across age groups, with the highest prevalence observed in the 20-30 year age group. Within the studied cohort, the HPV positivity rate was 40.8% among female patients and 63.3% among the smaller cohort of male patients. The most frequently detected genotypes overall were 6 (12.3%), 16 (7.1%), 52 (3.6%), and 31 (3.3%), in descending order. Notably, 74.4% (517) of patients reported were asymptomic and were referred for routine screening. Among HPV-positive individuals, 69.5% (163) were asymptomatic. Conversely, 23.0% (106) of HPV- negative individuals reported at least one clinical symptom. Positive HPV rates were 56.3% and 24.5% in single and married subjects, respectively. This study contributes valuable insights into HPV prevalence and genotype distribution in samples collected over a 14-year period in Tehran. The high prevalence of HPV infection, particularly with high-risk Genotypes coupled with the highest prevalence observed in the 20-30 year age group and HPV-16 being common in younger ages, without clinical sign of infection, underscores the necessity for sustained vigilance in monitoring HPV prevalence, early detection through screening, and targeted prevention programs, and comprehensive cervical cancer prevention strategies.

Anomaly detection plays a crucial role in various applications such as industrial defect inspection and safety perception. Existing mainstream approaches typically rely on modeling the distribution of normal samples to build anomaly discrimination models. However, these methods often face challenges in practical scenarios due to ambiguous decision boundaries and limited capability to capture complex semantic and structural variations in defects. To overcome these limitations, we propose a novel industrial defect detection framework based on hyperbolic space. This framework exploits the negative curvature property of hyperbolic geometry to dynamically extract semantic prototypes and embed them into the hyperbolic space, enhancing the model's ability to represent intricate semantic and structural changes. Furthermore, a semantic prototype-guided attention mechanism is integrated to assist the reconstruction of images, enabling accurate localization of anomalies through reconstruction error. Extensive experiments demonstrate that our method achieves state-of-the-art results on multiple industrial anomaly detection datasets. Notably, on the MVTec AD benchmark, our approach attains 99.8% and 99.1% AUROC scores for image-level and pixel-level tasks, respectively, significantly surpassing current leading methods.

Transition interface sampling (TIS) and replica exchange TIS (RETIS) are powerful methods for computing rates of rare events inaccessible to straightforward molecular dynamics simulations. Path reweighting extends their output, enabling the evaluation of diverse thermodynamic and kinetic quantities, including reaction prediction metrics, activation barriers, committor functions, and free energies. The recently developed ∞RETIS algorithm boosts parallel efficiency through asynchronous replica exchanges in the infinite-swap limit, thereby eliminating the wall-time bottlenecks of conventional RETIS. This approach introduces fractional samples and biased sampling distributions, requiring a generalized path reweighting framework, for which we derive expressions demonstrating how exact dynamic and thermodynamic variables can be computed. We then focus on a special class of free energy surfaces defined by history-dependent conditions, whose values are influenced by kinetic factors such as particle mass and friction, unlike standard unconditional free energy surfaces. Even with suboptimal reaction coordinates, these conditional free energies can reveal kinetically relevant barriers that may be misrepresented by standard unconditional free energies, thereby providing a rigorous and versatile tool for characterizing complex molecular transitions.

Hyperspectral imaging has emerged as a powerful tool for food quality assessment, yet most existing methods rely on supervised classification and require prior knowledge of adulterant categories. This study applies a non-targeted screening approach based on a deep spectral autoencoder to detect adulterants in Fritillaria. While autoencoder-based anomaly detection has been established in other hyperspectral domains, its application to congeneric species discrimination and exogenous adulterant screening in Fritillaria has not been systematically explored. A deep spectral autoencoder was constructed and trained exclusively on pure samples to learn the intrinsic spectral distribution of authentic materials. During inference, reconstruction error was used as an anomaly score, and samples deviating from the learned spectral manifold were identified as suspicious. Spectral data augmentation and band trimming were applied to enhance model robustness, while the anomaly threshold was determined solely from the distribution of pure samples. The proposed method achieved strong discrimination performance, with an area under the receiver operating characteristic curve (AUC) of 0.9903 and high detection rates across multiple adulterant types. Typical exogenous adulterants such as starch and talc powder were completely detected, while congeneric species also showed high detection sensitivity despite their spectral similarity to authentic samples. Latent space visualization and residual spectral analysis further revealed clear separation patterns and interpretable spectral deviations. These results demonstrate the proof-of-concept viability of the proposed non-targeted framework for open-set screening of adulteration risks. However, the authentic samples used for training originated from a single source, and only a limited set of anomaly types was tested. Therefore, the current model should be regarded as an early proof-of-concept only, not as a ready-to-deploy screening tool. Further validation with diverse authentic samples and a wider range of adulterants under realistic variability is necessary before the method can be considered a practical strategy for quality control.

Estimating the Effects of MTEER in U.S. Practice: A Transportability Analysis of the COAPT Trial.

In the social sciences, it is often necessary to debias studies and surveys before valid conclusions can be drawn. Debiasing algorithms enable the computational removal of bias using sample weights. However, an issue arises when only a subset of features is highly biased, while the rest are already representative. Algorithms need to substantially alter the sample distribution to handle a few highly biased features, which can, in turn, introduce bias into otherwise representative variables. To address this issue, we developed a method that uses feature weights to minimize the impact of highly biased features on the computation of sample weights. Our algorithm is based on Maximum Representative Subsampling (MRS), which debiases datasets by iteratively removing elements from a non-representative sample to align it with a representative one. The new algorithm, named feature-weighted MRS (FW-MRS), decreases the emphasis on highly biased features, allowing it to retain more instances for downstream tasks. The feature weights are derived from the feature importance of a domain classifier trained to differentiate between the representative and non-representative datasets. We validated FW-MRS using eight tabular datasets, each of which we artificially biased. Biased features can be important for downstream tasks, and focusing less on them could reduce generalization. For this reason, we assessed the generalization performance of FW-MRS on downstream tasks and found no statistically significant differences. Additionally, FW-MRS was applied to a real-world dataset from the social sciences. The source code is available at https://github.com/kramerlab/FeatureWeightDebiasing.

Evaluating the relationship between crystal structures of V and hydrogen diffusion to extract key features is important for understanding the relationship between hydrogen diffusion behavior and crystal structure in metals. We applied an image fusion method to surface image datasets of the vanadium alloy (a single-phase bcc V-10mol% Fe alloy) sample, obtained through different measurement methods, such as optical microscopy, scanning electron microscopy/energy-dispersive X-rays, and electron backscatter diffraction, to construct a multimodal dataset comprising hydrogen distribution and crystallographic orientation image data. The analysis of fused multimodal data by two unsupervised learning methods, such as principal component analysis and multivariate curve resolution, revealed that the hydrogen diffusion behavior differed depending on the crystallographic orientation. For example, grains oriented along the [111] and [101] directions exhibit greater hydrogen diffusion behavior than those oriented along the [001] direction. This trend was consistently observed across various analysis methods, but not when analyzed individually. In addition, it was also suggested that the shift from a pure orientation or the presence of other orientations changes the hydrogen diffusion behavior. Through multimodal data analysis of vanadium alloys, key characteristics of crystal orientation that affect hydrogen diffusion rate were extracted.

Tissue biobanking is essential for biomedical research. Well-defined interfaces and standardised procedures are required to ensure sample quality and the subsequent reproducibility of research results. This paper provides an overview of the key interfaces involved in tissue biobanking workflows, including sample collection, processing, storage, distribution and data management. It outlines the minimum standards required to maintain high-quality samples and associated data, and references relevant national and international guidelines. The paper also addresses the current challenges faced by biobanks, such as harmonisation across institutions, evolving regulatory landscapes, and the integration of digital infrastructure. The primary aim of this work is to present recommendations for the effective implementation and documentation of minimum standards. These recommendations are intended to help biobanks to align with regulatory expectations, optimise operational procedures, and facilitate high-quality, ethically sound biomedical research.

Background Early adolescence (10–14 years) is a foundational stage for positive sexual development, when affective bonds, relational meanings and orientations toward intimacy begin to emerge. In this study, “affective–sexual development” refers to the integrated domain of emotional bonds, relational meanings, and sexual subjectivities through which young people make sense of intimacy, affection, and desire—with affective and sexual dimensions treated as conceptually inseparable. In Amazonian Peru, adolescents construct these meanings within sociocultural ecologies shaped by family functioning, religiosity, peer dynamics, and limited sexuality education. Evidence on affective-sexual development in this region remains scarce. Objectives To identify factors associated with three a priori outcomes of early affective-sexual development—high openness to early intimacy, high affective commitment, and the reporting of socially sensitive intimate experiences—and to characterize sociocultural profiles of meaning-making, among early adolescents in a midsized town of the Peruvian Amazon. Methods A descriptive cross-sectional study was conducted in Requena, a midsized town in the Peruvian Amazon, with 121 adolescents aged 10 to 14 years (92 boys [76.0%] and 29 girls [24.0%]) recruited through local educational institutions. A culturally adapted 31-item questionnaire assessed openness to early intimacy, affective commitment, family functioning, religiosity, information pathways, and socially sensitive intimate experiences. Three a priori binary outcomes were examined using binary logistic regression: high openness to early intimacy and high affective commitment (defined as values at or above the 75th percentile of the sample distribution), and the reporting of at least one socially sensitive intimate experience. Candidate explanatory variables included age, sex, religiosity, family functioning and source of information (family-based vs peer/social), with sex and age retained in all models a priori. Sociocultural profiles were explored through k-means cluster analysis. Ethical approval, parental consent, and adolescent assent were obtained. Results Adolescents endorsed cautious orientations toward early intimacy and moderate affective commitment. Older age (aOR = 1.52, 95% CI: 1.05–2.20, p = 0.026) and reliance on peer/social sources of information (aOR = 2.48, 95% CI: 1.08–5.68, p = 0.031) were associated with greater likelihood of high openness to early intimacy, whereas higher religiosity (aOR = 0.71, 95% CI: 0.52–0.97, p = 0.034) and family-based information (aOR = 0.35, 95% CI: 0.14–0.88, p = 0.027) were associated with lower openness. High affective commitment was associated with female sex (aOR = 1.98, 95% CI: 1.01–3.94, p = 0.047), higher religiosity (aOR = 1.28, 95% CI: 1.02–1.61, p = 0.031) and reliance on family-based sources of information (aOR = 1.90, 95% CI: 1.02–3.55, p = 0.043). Higher openness to early intimacy was associated with greater likelihood of reporting socially sensitive intimate experiences (aOR = 2.51, 95% CI: 1.23–5.11, p = 0.012). Cluster analysis identified three sociocultural profiles –relational-family oriented, morally regulated and exploratory-peer oriented– with the exploratory profile reporting the highest frequency of socially sensitive experiences. Conclusions Affective-sexual development in early adolescence reflects interlinked relational, emotional, and sociocultural processes. Family functioning and religiosity support affective bonding and emotional well-being, whereas peer influence shapes openness to intimacy. Findings underscore the importance of sexual health promotion approaches that strengthen caregiver communication, support culturally grounded meaning-making and address peer-based pathways of information. Such interventions may foster positive sexual development and healthier relational trajectories in early adolescence.

Functional connectivity signatures of trauma-specific reflective functioning: The interplay between childhood trauma and maternal competence.