Browse Articles

AIASSE—<i>Ab</i> <i>initio</i> augmented structure solving engine

We have developed a hybrid methodology that self-consistently integrates empirical potential-based refinement of total scattering data with first-principles density functional theory-based molecular dynamics calculations. The aim is to provide a holistic and coherent description of disordered materials, spanning from the bulk, macroscopic scale probed in diffraction experiments down to the atomic and electronic levels. In this study, we present this new methodology as implemented within a software package, revisiting previous measurements on dense fluid krypton, SiO2 glass, low-density amorphous ice, and water–methanol liquid mixtures.

Ameliorative effects of coriander extract supplementation in mitigating nickel toxicity in grass carp (Ctenopharyngodon idella)

Persistence with daily growth hormone among children and adolescents with growth hormone deficiency in Japan

Background Pediatric growth hormone deficiency (pGHD) is treated with daily somatropin (recombinant human growth hormone) injections. High rates of discontinuation and poor adherence to treatment, which are associated with worse growth outcomes, have been documented previously, for example in the US and Europe. Discontinuation of somatropin has not yet been evaluated using real-world data in Japan. Objectives To describe discontinuation of, and persistence to, daily somatropin treatment among children with pGHD in Japan. Methods This was a retrospective cohort study of children (≥3 and &lt;16 years old) who were prescribed somatropin, using 2 Japan-based databases, Japan Medical Data Center (JMDC) and Medical Data Vision (MDV). Children were required to have ≥1 prescription for somatropin (first prescription = index date) within each study period (1 January 2002–30 September 2021 for JMDC and 1 January 2009–31 October 2021 for MDV) and ≥1 GHD diagnosis code without a somatropin prescription during the 6-months pre-index period. Children were required to be continuously enrolled in the database ≥6 months preceding and ≥3 months following index date. Children were followed for up to 48 months post-index. Early persistence was defined as the proportion of children with ≥1 refill of somatropin subsequent to the initial prescription. Discontinuation was defined as the first observation of a gap in therapy (using &gt;60, &gt; 90, and 120-day gap thresholds) between successive somatropin prescription fill dates. Persistence was defined as continuous refills of somatropin with no gaps in therapy. Time to discontinuation/non-persistence was evaluated using Kaplan-Meier methods, and Cox proportional hazards models identified predictors of time to discontinuation. This analysis utilized de-identified patient data from 2 large, Japanese-based retrospective databases; as such this study does not meet the requirements for institutional review board (IRB) review. Results Among the children included in this study (JMDC N = 452, MDV N = 573), most were male (JMDC 64.8%, MDV 60.0%). Mean age (standard deviation) was 8.8 (3.6) years in JMDC and 7.5 (3.6) years in MDV. Early persistence was high across both cohorts (JMDC 91.2%, MDV 83.4%). Using the 90-day gap definition for discontinuation, a sizable proportion of children discontinued over the follow-up period: JMDC 19% at 12 months, 35% at 48 months; and MDV 33% at 12 months, 54% at 48 months. Fewer discontinuations were observed with the 120-day gap definition (~16% at 48 months in JMDC, ~ 28% at 48 months in MDV) and more were observed with the 60-day gap definition (~67% at 48 months in JMDC, ~ 83% at 48 months in MDV). No meaningful predictors of discontinuation were identified. Conclusions Despite high early persistence with somatropin, many children with pGHD in Japan were increasingly non-persistent over time: at 48 months post-index, at least 16% of children discontinued therapy, using the JDMC database and the most conservative measure of gap allowance. These results suggest a need for new strategies to support somatropin medication use over time among children with pGHD in Japan.

Development of a deep learning algorithm for radiographic detection of syndesmotic instability in ankle fractures with intraoperative validation

Abstract Identifying syndesmotic instability in ankle fractures using conventional radiographs is still a major challenge. In this study we trained a convolutional neural network (CNN) to classify the fracture utilizing the AO-classification (AO-44 A/B/C) and to simultaneously detect syndesmosis instability in the conventional radiograph by leveraging the intraoperative stress testing as the gold standard. In this retrospective exploratory study we identified 700 patients with rotational ankle fractures at a university hospital from 2019 to 2024, from whom 1588 digital radiographs were extracted to train, validate, and test a CNN. Radiographs were classified based on the therapy-decisive gold standard of the intraoperative hook-test and the preoperatively determined AO-classification from the surgical report. To perform internal validation and quality control, the algorithm results were visualized using Guided Score Class activation maps (GSCAM).The AO44-classification sensitivity over all subclasses was 91%. Furthermore, the syndesmosis instability could be identified with a sensitivity of 0.84 (95% confidence interval (CI) 0.78, 0.92) and specificity 0.8 (95% CI 0.67, 0.9). Consistent visualization results were obtained from the GSCAMs. The integration of an explainable deep-learning algorithm, trained on an intraoperative gold standard showed a 0.84 sensitivity for syndesmotic stability testing. Thus, providing clinically interpretable outputs, suggesting potential for enhanced preoperative decision-making in complex ankle trauma.

A new cow identification method using near-infrared spectral measurements and main components of raw milk features

Recent advances in cow identification have been instrumental in enhancing understanding of disease progression, optimizing vaccination strategies, improving production management, ensuring animal traceability, and facilitating ownership assignment. Cow identification and tracking involve the precise recognition of individual cows and their products through unique identifiers or markers. Traditional methods like computer vision, ear tags, branding, tattooing, microchips, and other electrical methods have been widely employed for cow identification and tracking over an extended period of time. However, these methods are prone to reliability issues caused by external factors such as physical damage, tag loss, weather-induced fading or damage, and the need for a software-based management system with RFID, which may not always be satisfactory for identifying cows. Merging near-infrared spectroscopy and routinely collected main components of raw milk (fat, protein, lactose, urea, and somatic cell count) with artificial intelligence offers a non-invasive, data-driven approach for cow identification, potentially increasing applicability in farm environments where such milk data are already part of routine monitoring. In this study, we presented an alternative approach to cow identification utilizing near-infrared spectral measurements alongside laboratory reference values for the main components of raw milk. In order to test our proposed method, we used a publicly available and newly released dataset of 1224 different measurements collected from 41 cows over a period of 8 weeks. Depending on the considered measurements and number of cows, the Naïve Bayes, Decision Tree, and Support Vector Machines classifiers achieved classification accuracy rates of between 69.23%−98.63%, 61.87%−100%, and 58.53%−97.26%, respectively. We believe that the proposed method has great potential to be an alternative way for cow identification applications.

Injured plants use gaseous cues to initiate repair of their outer layers

DGS-Yolov7-Tiny: a lightweight pest and disease target detection model suitable for edge computing environments

Radiographic criteria in developmental dysplasia of the hip in late infancy, inter and intrareader agreement

Introduction Developmental Dysplasia of the hip (DDH) is a common pediatric disorder screened for by antero-posterior (AP) pelvic radiographs in infants aged between 4–9 months. We chose from the radiographic indicators commonly used in the diagnosis the acetabular index, the Shenton line and the ossification and symmetry of the femoral head to assess for their reliability and variability among readers. In addition, this study aimed to obtain the mean age of appearance of the ossification center of the femoral head in the Lebanese population. Methods 149 pelvic AP radiographs of children between 4 and 9 months of age were collected. The criteria were assessed by three experienced readers: one orthopedic surgery fellow resident, one first-year and one second-year orthopedic surgery residents twice separated by a three-month interval. Results The bivariate analyses found a difference in the right Acetabular angle and left Acetabular angle significantly in the first- and second-year resident with a p &lt; 0.01. No significant difference was found when comparing the readings of each reader independently for the other variables or with the fellow. We found a significant difference p = 0.047 when comparing the readings of the first-year resident and the fellow of the right AI. Whereas the left AI readings revealed significant differences between the fellow and the second-year resident (p = 0.008) and between the first and second-year residents (p &lt; 0.001). Inter and intrareader consistency was high for the Shenton line rupture and the appearance of the ossification center on the femoral head but none of the parameters proved sufficient to significantly be associated with an acetabular angle&gt; 30°. The average age of ossification center appearance in the Lebanese population was determined to be 5.57 months, aligning with global averages. Conclusion These findings call for a diagnostic approach that integrates multiple parameters and focuses on the importance of standardized training to enhance the consistency of radiographic assessments. Further investigations should aim to establish more precise protocols and evaluate the diagnostic strength of individual parameters.

Calving-driven fjord dynamics resolved by seafloor fibre sensing

Abstract Interactions between melting ice and a warming ocean drive the present-day retreat of tidewater glaciers of Greenland1–3, with consequences for both sea level rise4 and the global climate system5. Controlling glacier frontal ablation, these ice–ocean interactions involve chains of small-scale processes that link glacier calving—the detachment of icebergs6—and submarine melt to the broader fjord dynamics7,8. However, understanding these processes remains limited, in large part due to the challenge of making targeted observations in hazardous environments near calving fronts with sufficient temporal and spatial resolution9. Here we show that iceberg calving can act as a submarine melt amplifier through excitation of transient internal waves. Our observations are based on front-proximal submarine fibre sensing of the iceberg calving process chain. In this chain, calving initiates with persistent ice fracturing that coalesces into iceberg detachment, which in turn excites local tsunamis, internal gravity waves and transient currents at the ice front before the icebergs eventually decay into fragments. Our observations show previously unknown pathways in which tidewater glaciers interact with a warming ocean and help close the ice front ablation budget, which current models struggle to do10. These insights provide new process-scale understanding pertinent to retreating tidewater glaciers around the globe.

Cortical spectral dynamics of vibrotactile frequency processing

Hypogonadism and its associated factors among adult male type 2 diabetes mellitus patients at the university of gondar comprehensive specialized hospital, Northwest Ethiopia 2024: A comparative cross-sectional study

Introduction Hypogonadism is an established complication in male patients with type 2 diabetes mellitus, potentially exacerbating metabolic dysregulation and impairing quality of life. Global studies have documented an elevated prevalence of hypogonadism among diabetic individuals. However, there remains a scarcity of data regarding the prevalence and contributing factors of hypogonadism in patients with type 2 diabetes mellitus in Ethiopia, particularly in the Gondar region. Methods and Materials This comparative cross-sectional study included 330 participants (165 with type 2 diabetes mellitus and 165 age-matched controls), selected via systematic random sampling from June 27 to August 20, 2024. Data were collected through structured questionnaires and 5 mL blood samples were obtained by laboratory technologist. Analysis using SPSS version 25 involved descriptive statistics, independent t-tests, and binary logistic regression to identify factors associated with hypogonadism, with significance set at p &lt; 0.05. Result The mean age of participants was 57 ± 8.9 years in the type 2 diabetes mellitus (T2DM) group and 55 ± 8.6 years among controls. Hypogonadism was identified in 30.3% of individuals with T2DM, compared to 7.2% in the control group. Multivariable logistic regression revealed that T2DM, elevated fasting blood glucose, advancing age, dys-regulated high-density lipoprotein levels, and obesity were independently associated with increased odds of hypogonadism. Conclusion and Recommendation The prevalence of hypogonadism was markedly higher in individuals with type 2 diabetes mellitus compared to the control group, concomitant with a significantly lower mean testosterone level. Multivariate analyses identified fasting blood glucose, high-density lipoprotein levels, advanced age, the presence of diabetes, and obesity as independent risk factors for hypogonadism. Notably, modifiable factors such as obesity, in conjunction with key metabolic indicators, underscore potential targets for intervention to alleviate the burden of hypogonadism in diabetic patients.

Electromagnetic induction heating of polymer nanocomposites: a computational study on design parameters

Wear state identification of reciprocating sliding friction Pairs with frictional vibration

Real-time monitoring of the wear state of reciprocating sliding friction pairs has long been a challenging issue. To address this problem, this paper innovatively proposes a new method of constructing feature vectors based on the fractal parameters of frictional vibration signals and employing a nonlinear support vector machine to identify different wear states. Three typical wear states, namely running-in wear, normal wear, and severe wear, were designed by adjusting the amount of lubricating oil and distinguished by variations in the friction coefficient. Unlike conventional time-frequency or statistical features, our approach uniquely employs multifractal spectrum parameters to characterize wear states. The research results demonstrate that this method achieves recognition accuracies exceeding 90% for all three wear states in 10-fold cross-validation, indicating the effectiveness of the nonlinear support vector machine in realizing the recognition of different wear states of reciprocating sliding friction pairs. This achievement not only provides a new technical approach for online monitoring of wear states but also offers a valuable reference for the application of nonlinear signal analysis in other fields.

Network intrusion detection based on improved KNN algorithm

Filling gaps in PM2.5 time series: A broad evaluation from statistical to advanced neural network models

This study addressed the critical challenge of filling gaps in PM2.5 time series data from Pavlodar, Kazakhstan. We developed and evaluated a comprehensive hierarchy of 46 gap-filling methods across five representative gap lengths (5–72 hours), introducing dynamic models capable of adapting to gaps of variable duration. Tree-based models with bidirectional sequence-to-sequence architectures delivered superior performance, with XGB Seq2Seq achieving a mean absolute error of 5.231 ± 0.292 μg/m3 for 12-hour gaps, representing a 63% improvement over basic statistical methods. The advantage of multivariate models incorporating meteorological variables increased substantially with gap length, from modest improvements of 2–3% for 5-hour gaps to significant enhancements of 16–18% for 48–72 hour gaps. Dynamic multivariate models demonstrated remarkable operational flexibility by successfully processing real-world gaps ranging from 1 to 191 hours despite being trained on maximum lengths of 72 hours. Analysis of the reconstructed complete time series revealed that 61.2% of monitored hours exceeded the WHO daily threshold of 15 μg/m3, with strong seasonal patterns and pronounced diurnal cycles. This research advances environmental monitoring capabilities by providing robust methodological tools for addressing data continuity challenges that currently limit the utility of PM2.5 measurements for public health applications and scientific analysis.

Application of a multiple transmitter spacing gradient array TDIP survey in the Huaniushan mining area, Gansu province, China

SpaVGN: A hybrid deep learning framework for high-resolution spatial transcriptomics data reconstruction and spatial domain identification

Spatial transcriptomics has revolutionized the analysis of gene expression while preserving tissue spatial information, which provides novel insights into the cellular composition and function of complex biological tissues. However, current technologies are constrained by limited resolution and data sparsity, compromising the accuracy of downstream analyses. To address these challenges, we developed SpaVGN, a deep learning framework integrating convolutional neural networks, vision transformer, and graph neural networks for high-fidelity gene expression imputation and spatial domain identification. By combining local feature extraction, global attention mechanisms, and spatial graph-based modeling, SpaVGN effectively reconstructs missing transcriptomic data while preserving spatial tissue architecture. Evaluated on melanoma and sagittal posterior mouse brain datasets, SpaVGN outperformed existing methods in gene expression prediction, achieving Pearson correlation coefficients of 0.609 (melanoma) and 0.682 (mouse brain). It clearly delineated tumor regions and lymphoid niches in melanoma tissue, achieving fine-grained resolution of hippocampal subfields, including Cornu Ammonis and Dentate Gyrus, with a Silhouette Score of 0.43 and a Davies-Bouldin Index of 0.86. Validation through UMAP dimensionality reduction and PAGA network analysis demonstrated that SpaVGN significantly mitigates the negative impact of data sparsity in spatial transcriptomics, improving data completeness and spatial continuity. This study presents an innovative solution that enhances the resolution of spatial transcriptomics data, offering cross-tissue applicability and providing a valuable tool for research in biological development, disease, and tumor heterogeneity.

Craniological differentiation amongst Southeast Asian small cats

Nano-polymeric curing agents for enhancing water stability in sandy soils: A sustainable approach for ecological slope protection

The susceptibility of sandy soil slopes to erosion, particularly during rainfall events, poses significant challenges for soil conservation and ecological slope protection. This study explores the potential of nano-polymeric curing agents (NPCA) as a sustainable solution to enhance water stability and slope integrity. Reinforcement depth experiments were conducted to determine the optimal application depth of NPCA, while permeability and erosion tests assessed its impact on water retention and soil stability. Advanced analytical techniques, including scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR), were employed to examine the interactions between NPCA and soil particles. Results show that a 3% NPCA content (mass ratio) achieves the maximum reinforcement depth of 23 mm. Within the optimal reinforcement range (mass ratio &lt; 3%, concentration &lt; 17%), increasing NPCA content enhances soil permeability, reduces the disintegration coefficient, and improves erosion resistance. NPCA encapsulates soil particles, filling pore spaces and binding them through van der Waals forces and hydrogen bonds, forming a durable, elastic membrane that enhances surface stability and water resistance. These findings suggest that NPCA treatment creates a stable, permeable, and breathable environment, crucial for promoting vegetation growth on sandy slopes and offering an effective, sustainable approach to ecological slope protection.

An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier

Abstract Stroke is a leading cause of death and long-term disability worldwide, and early detection remains a significant clinical challenge. This study proposes an Effective Brain Stroke Diagnosis Strategy (EBDS). The hybrid deep learning framework integrates Vision Transformer (ViT) and VGG16 to enable accurate and interpretable stroke detection from CT images. The model was trained and evaluated using a publicly available dataset from Kaggle, achieving impressive results: a test accuracy of 99.6%, a precision of 1.00 for normal cases and 0.98 for stroke cases, a recall of 0.99 for normal cases and 1.00 for stroke cases, and an overall F1-score of 0.99. These results demonstrate the robustness and reliability of the EBDS model, which outperforms several recent state-of-the-art methods. To enhance clinical trust, the model incorporates explainability techniques, such as Grad-CAM and LIME, which provide visual insights into its decision-making process. The EBDS framework is designed for real-time application in emergency settings, offering both high diagnostic performance and interpretability. This work addresses a critical research gap in early brain stroke diagnosis and contributes a scalable, explainable, and clinically relevant solution for medical imaging diagnostics.