Browse Articles

Pangenomics of Limosilactobacillus fermentum reveals genomic diversity and bacteriocin activity against Staphylococcus aureus and Escherichia coli

Integrated HPLC-ESI-QTOF-MS based characterization and biological evaluation of Alkanna orientalis and Alkanna verecunda roots

Blockchain-assisted privacy-preserving data sharing protocol for V2G-enabled electric vehicle IoT networks

Microplastic fibres as active agents in mud deposition

Addendum: Assessing suitable habitat for freshwater mussel reintroductions using 3D-printed subadultreplicates

A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives

Persistent homology-based multiscale Hi-C analysis uncovers TADs-mediated gene regulation

Greenstone artifacts in pre-Columbian Costa Rica: from raw material to local and interregional exchanges

Abstract Understanding the distribution pattern and relative abundance of the different greenstones used by pre-Columbian societies in the current territory of Costa Rica provides valuable insights into ancient exchange networks. This study examines the role of local and interregional exchange systems involving both locally sourced and allochthonous greenstones in the Greater Nicoya and the Central Caribbean regions. Non-invasive infrared spectroscopy was applied to identify the main mineral phases in 926 objects from 54 archaeological sites, and chemometric analyses were used to distinguish compositional groups and to visualize their spatial distribution. X-ray diffraction was additionally performed on five representative samples, one from each of the most frequent compositional groups, to complement the spectroscopic analysis. The results reveal that each region displays distinct patterns of greenstone use, reflecting differences in access to materials and participation in exchange networks. Data suggests that locally available minerals, such as serpentine, tremolite-actinolite, quartz and other siliceous rocks, were obtained through short-range procurement systems whereas allochthonous allochthonous jadeitite and albitite were involved in long-distance interregional exchange. These findings highlight the coexistence of regional and long-distance networks and demonstrate the key role of jadeite in linking distinct lithic traditions within Costa Rica and beyond.

Sequential visual stimuli increase high frequency power in the visual cortex

Abstract Today, flickering full-field visual stimulation is used to increase neuronal oscillations for a variety of research or therapeutic purposes. We propose spatially organized sequential visual flickering stimulation as a newer tool to circumvent the intrinsic low-pass filter nature of the vertebrate visual system, in order to increase the power of high frequency oscillations in the visual system. We show that spatially organized visual flickering can increase power in high frequencies (100 to 190 Hz) in the visual cortex of mice. Consequently, spatially organized sequential sensory stimulation should be regarded as a putative new way leading to power increases in high frequency domains.

Tunability of third-order nonlinear response in nematic liquid crystal E7 via SnO2 nanoparticle doping: a structured temperature-concentration analysis

Digital archive of ephemerally preserved sedimentary structure in surf zone uplifted by the 2024 Noto Peninsula Earthquake

A laccase–mediator system based on caffeic acid for the synthesis of benzimidazoles

The importance of seed characteristics in distinguishing the rare and common plant species in Astragalus (Fabaceae)

Patterns of brown bear (Ursus arctos) visits to human settlements provide insights for human–wildlife coexistence

Abstract Negative interactions between humans and large carnivores represent one of the main challenges for the conservation of these species due to the social conflict they can generate. In the case of brown bears ( Ursus arctos ), these interactions include visits of bears to human settlements, a type of event increasingly reported in recent years and often associated with conflicts. In this study, we collected 73 events of bear visits to human settlements between 2009 and 2021 in the Cantabrian population. These events were mainly caused by young individuals, occurring mostly at night and during the summer, when bears were primarily attracted by fruit trees available in human settlements. The affected settlements were located in areas of high habitat quality, close to breeding cores, and with a high intensity of bear damage. Within these areas, human settlements with bear visits differed from nearby ones by having a larger perimeter, a shorter distance to forest patches, and greater surrounding terrain ruggedness. Our study provides a basis for understanding the patterns and drivers of these particular human-large carnivore interactions and offers valuable insights to update and improve management strategies aimed at preventing such events, mitigating associated conflicts and promoting successful coexistence.

IL-HS: a deep inception-LSTM architecture for enhanced lithological mapping using EnMAP hyperspectral remote sensing data

RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images

Abstract Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work proposes a Robust Deep Ensemble for Diabetic Retinopathy detection (RDE-DR) by analyzing ensemble fusion strategies. Four pre-trained convolutional neural networks (ResNet50, VGG16, VGG19, and DenseNet121) are trained using CLAHE-enhanced APTOS 2019 fundus images and integrated through seven heterogeneous fusion mechanisms, including voting-based, rank-based, and fuzzy-integral-inspired strategies. A consistent evaluation protocol is adopted, incorporating threshold optimization and probabilistic calibration analysis to validate robustness, decision margins, and accuracy–precision trade-offs. Experimental results show that multiple fusion techniques achieve comparable high performance and stable behavior on the APTOS 2019 benchmark, with the best configuration reaching 98.64% accuracy, 98.40% precision, 98.92% recall, 98.66% F1-score, and 99.78% Area-Under-Curve (AUC). Beyond peak accuracy, the study provides insights into ensemble reliability, calibration characteristics, and practical design choices for medical image classification systems. These results show that integrating transfer learning with CLAHE preprocessing and ensemble fusion yields stable experimental performance on the APTOS 2019 benchmark, suggesting potential for future medical decision support.

Tau T205 phosphorylation modulates engram cell recruitment and remote memory in mice

Pharmabiotics, Phocaeicola dorei, ameliorates cholestatic liver fibrosis by alleviating macrophage efferocytosis of neutrophils

A hyperspectral imaging framework integrating band selection and deep learning for beverage stain classification in forensic analysis

Abstract The technique of Hyperspectral Imaging (HSI) is significant in the field of non-destructive forensic crime scene investigation, as it allows for the identification of minor spectral changes over a broad range of wavelengths. In the present study, the spectral characteristics of nine beverage stains, such as Papaya, Coffee, Pomegranate, Orange, Tea, Wine, Whisky, Rum, and Brandy, were studied by simulating a controlled environment for a mock crime scene. The hyperspectral images were collected by an HSI system with 204 spectral bands in the visible and near-infrared (VNIR) range. To eliminate spectral redundancy, the ANOVA-based feature selection technique was implemented, which selected 162 spectral bands. These spectral characteristics were employed to train four architectures of deep learning for the classification of the beverage stains, which were implemented as Multi-Layer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and CNN-LSTM. The training process was carried out using standardized spectral data with adaptive learning rates and early stopping for stable convergence. The strength of the models was evaluated through five-fold cross-validation on stratified data. The experimental results demonstrate that the MLP model attained the greatest classification accuracy of 95.58%. The results show that there is great potential for combining hyperspectral imaging with deep learning for non-destructive stain identification in forensics.

Adaptive fuzzy deep learning with multimodal sensor fusion for enhanced plant disease detection

Abstract Timely and accurate plant disease detection is important for enhancing agricultural productivity and promoting sustainability. The study introduces Multimodal Adaptive Fuzzy-based Deep Neural Network (MAF-DNN) for classification of plant diseases. The proposed method combines fuzzy logic with multimodal data fusion to effectively address the complex interactions and uncertainties in agricultural datasets. The MAF-DNN employs a robust adaptive fuzzy framework with dynamic rule optimization and integrates Hyperspectral Imaging Data (HID) with RGB imaging data to acquire detailed spectral information and high-resolution visual cues for disease classification. The multimodal fusion enhances the model’s ability to capture intricate patterns that relate to plant health, improving the accuracy of disease classification. The experimental results showed that the MAF-DNN outperforms traditional models by achieving an accuracy of 97.8%, precision of 96.5%, recall of 98.2%, and F1-score of 97.3%. Additionally, the adaptive design reduces computational overhead, increases efficiency, and improves scalability for large-scale agricultural applications. The MAF-DNN represents a significant advancement in plant disease classification and provides a robust and efficient solution for precision agriculture.