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Multi-omics prediction for yellow rust in bread and durum wheat through conventional and Ai-based frameworks
Combined effect of cauliflower intercropping and Pseudomonas fluorescens seed biopriming on the suppression of fusarium wilt in okra
Comparative efficacy of methylated and WPC-microencapsulated OligoDNAs for controlling Verticillium dahliae through gene silencing
Potential effect of early snowmelt on nestling growth and first-year apparent survival in an alpine bird
Abstract Climate change is shifting the environmental conditions during key life cycle events such as during reproduction. The shift in the environmental conditions can change for example the food availability and therewith influence growth and survival of offspring. We used nine years of ringing data of white-winged snowfinches Montifringilla nivalis nivalis , a high-elevation bird species, to assess how variation in temperature and snow conditions affected nestling growth and first-year apparent survival (probability that an individual survives and returns to the study area). We used linear mixed models and a path analysis to estimate the effect of environmental conditions on nestling growth and a multi-state mark-recapture model to estimate apparent survival. Nestlings raised during snowmelt tended to have slightly higher body mass, longer tarsi and longer wings than nestlings raised after snowmelt and apparent first-year survival was lower in years when snowfinches breed late relative to the timing of snowmelt. These findings suggest that advancing snowmelt may be associated with reduced offspring fitness and potentially contributed to observed population declines in this high‑elevation species.
Dynamic changes in amygdala and insula responses to the onset and offset of conditioned stimuli during threat learning
Abstract The amygdala and insula are central to learning how to anticipate and respond to threats, yet their precise roles during different phases of fear conditioning remain incompletely understood. In this study, we used functional magnetic resonance imaging (fMRI) to examine amygdala and insula responses in a large sample ( N = 286) of adult twins during fear conditioning. Participants were exposed to two virtual characters: one (CS +) was paired with a mild electric shock (US) on 50% of acquisition trials (partial reinforcement), while the other (CS −) was never paired with the US. During habituation, neither the amygdala nor the insula discriminated between the cues. During acquisition, amygdala responses were strongest and most consistent at CS + offset on non-reinforced CS + trials (i.e., when the US was expected but omitted), whereas insula responses differentiated CS + from CS − at both CS onset and CS offset. Whole-brain analyses showed that CS differentiation at CS onset expanded from early engagement of anterior insula and temporal regions to broader networks as learning progressed. CS differentiation at CS offset (non-reinforced CS + > CS −) emerged in posterior insula and extended to broader predominantly contralateral networks in later acquisition blocks. Voxel-wise pattern similarity analyses indicated high within-event similarity across acquisition blocks (onset-with-onset; offset-with-offset), but lower similarity between event types (onset vs offset) and between either CS-related event and the US. Together, these findings are consistent with partially distinct large-scale activation patterns across cue onset, omission-related cue offset, and US delivery during threat learning.
A method for recognizing and detecting the physiological state of silkworms based on an improved YOLOv8 approach
Cross-cultural adaptation and psychometric evaluation of the Japanese version of the scale for the assessment of non-experts’ AI literacy among medical trainees in a multicenter cross-sectional study
Abstract This cross-sectional study aimed to develop the Japanese version of the “Scale for the assessment of non-experts’ AI literacy” (J-SNAIL) and provide its initial psychometric evidence. In 2025, after translating the SNAIL into Japanese according to an international cross-cultural adaptation guideline, we distributed the translated scale as an anonymous online self-administered questionnaire to medical students in five universities and residents in nine hospitals across Japan. We tested the questionnaire’s structural and convergent validity and internal consistency reliability. 326 participants were included in the analysis. Exploratory factor analysis led to a modified 25-item J-SNAIL comprising 3 factors, including “technical understanding,” “critical and ethical appraisal,” and “risk awareness.” Notably, the original SNAIL’s “practical application” factor was not identified; instead, “risk awareness” factor emerged, which may reflect linguistic, cultural, or educational contextual differences. The J-SNAIL total score had a significantly positive correlation with the global rating scale for measuring AI literacy, which indicated good convergent validity. Cronbach’s alpha value for the total of 25 items was 0.92, indicating good internal consistency reliability of the J-SNAIL. Future investigation of the scale may strengthen factor construction. J-SNAIL may contribute to future educational initiatives and research on AI literacy in Japan.
CD25 genetic ablation on lymphoid cell lines to obtain models of stimulation through the Interleukin-2 beta/gamma receptor
Comprehensive modelling of coastal landuse and land cover (LULC) dynamics and future changes using random forest, GEE, and MLP–Markov chain integration: a geo-computational approach
Abstract Coastal landuse / land cover (LULC) faces dynamic transformations driven by natural and human activities. This study investigates decadal LULC dynamics and their future projections (2034, 2044, and 2054) along the Ernakulam–Alappuzha coastal stretch of Kerala, India, using Google Earth Engine (GEE)- based time-series Landsat ETM + and OLI images from 2004, 2015, and 2024. GIS-based machine learning (ML) techniques, including Random Forests (RF), Support Vector Machines (SVM), and Classification and Regression Trees (CART), were used to extract LULC features. The spectral indices, such as NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), MNDWI (Modified Normalized Difference Water Index), NDBI (Normalized Difference Built-up Index), NDBaI (Normalized Difference Bareness Index), BSI (Bare Soil Index), and UI (Urban Index), were incorporated to enhance the future LULC trends. Among all ML algorithms, the RF achieved the highest classification accuracy with 95.46%, 92.38%, and 93.94% for 2004, 2015, and 2024, respectively. The LULC patterns indicate pronounced urban expansion, with built-up areas increasing from 109.93 km 2 (6.47%) in 2004 to 373.49 km 2 (21.98%) in 2024, largely replacing plantations, which declined from 648.94 km 2 (38.17%) to 397.03 km 2 (23.36%). The results reflect an accelerating urbanization trend and a gradual decline of natural and semi-natural coastal land covers. Future LULC projections for 2034, 2044, and 2054, simulated using the Multi-Layer Perceptron–Markov Chain (MLP–MC) model in the Land Change Modeller (LCM) of TerrSet v.20, reveal significant transformations in the Ernakulam–Alappuzha coastal stretch. Validation of the 2024 predicted map against the actual LULC map produced a Kappa value of 0.71, confirming the model’s reliability. The projections indicate a dominant expansion of built-up areas, from 373.49 km 2 (21.98%) in 2024 to 747.52 km 2 (43.99%) by 2054, with the most rapid increase occurring between 2024 and 2034 (147.58 km 2 , 39.51%, 14.76 km 2 /year). The changes of LULC reflect the trend of future patterns which accelerate urban growth, primarily through the conversion of plantations, fallow land, vegetation, and wetlands. In contrast, natural and semi-natural land covers are expected to decline markedly, with the steepest changes in the first decade (2024–2034) and slower, continued transformations thereafter. These patterns suggest ongoing urban dominance and degradation of coastal ecosystems, underscoring the need for strategic land-use planning and sustainable coastal management practices.
Hierarchical bandwidth-adaptive variational mode decomposition: algorithms and applications
Mix network implementation using ECIES encryption and XOR shuffling
Influence of curing pressure and surface treatment on mechanical properties of hybrid fiber metal laminates
Abstract This study investigates the effect of key manufacturing parameters and graphene nanoparticle additions on the tensile behavior of fiber metal laminates (FMLs) using a Taguchi-based experimental design. Several manufacturing parameters were considered: laminate configuration (glass fiber and hybrid glass-carbon fiber reinforcement), aluminum surface treatment (chemical treatment and laser surface texturing with scanning spacings of 1 mm and 2 mm), aluminum thickness (0.5, 0.7, and 1.0 mm), graphene nanoparticle content (0, 0.1, and 0.25 wt%), and curing pressure (2, 5, and 7 bar). Eighteen FMLs specimens were fabricated according to the Taguchi orthogonal array and tested under tensile loading. Ultimate tensile strength ( $$\:{\upsigma\:}$$ ult ), tensile modulus ( E ), toughness modulus (U T ), and failure strain ( $$\:\epsilon\:$$ f ) were evaluated as performance responses. The findings show laminate configuration significantly affects $$\:{\upsigma\:}$$ ult , U T , and $$\:\epsilon\:$$ f , with the all-glass fiber configuration exhibiting superior performance responses. Graphene content and curing pressure had a minimal effect on tensile properties. The optimal parameter combination for $$\:{\upsigma\:}$$ ult and U T involved a glass fiber laminate configuration with chemically treated aluminum, an aluminum thickness of 0.5 mm, 0% graphene, and a curing pressure of 2 bar. Optimal parameters for E include a laser 1 mm scanning texture, glass fiber laminate configuration, aluminum thickness of 0.5 mm, 0% graphene nanoparticles, and a curing pressure of 5 bar. Additionally, optimal parameters for $$\:\epsilon\:$$ f are glass fiber configuration, chemical surface treatment, aluminum thickness of 1 mm, 0% graphene, and curing pressure of 2 bar. Validation tests indicated the model’s predictions were accurate, with prediction errors under 5%, highlighting its statistical reliability.
A quantitative study on user interaction and brand perception of visual elements in social media advertising driven by deep learning
Diltiazem versus metoprolol for atrial fibrillation with rapid ventricular rate in ICU patients using target trial emulation
Air transport in Africa: a statistical physics approach
Abstract Within the last few decades, concepts from statistical physics have been leveraged to shed light on different aspects of air transport, from its connectivity structure to the dynamics of delay propagation. While countless studies have been published for large markets like the US, Europe and China, much less attention has been devoted to Africa, in spite of the relevance of this transportation mode in this continent. We here analyse the African air transport system from three complementary viewpoints: the structure of the airport network; the structure created by delay propagation; and the micro-scale dynamics of delays at individual airports. These three aspects are analysed through time, from 2020 to 2024; and further compared with what is observed in the US system. The resulting scenario is highly heterogeneous, with the US and African systems having important macro-scale differences, but also similarities at a local level. We further discuss the importance of these findings, and how similar scientific endeavours could benefit from higher-quality data.
Breast cancer detection and classification via a robust deep learning approach
Abstract This work presents a leakage-controlled deep-learning framework for breast cancer classification using the CBIS-DDSM mammography archive. The proposed pipeline combines patient-level data partitioning before augmentation, a two-stage transfer-learning strategy based on ResNet50, and an Inter-View Attention Fusion (IVAF) module for adaptive fusion of paired craniocaudal (CC) and mediolateral oblique (MLO) feature maps. IVAF was modeled as a light-weighted convolutional gating strategy added after the last ResNet50 convolutional layer in order to create a weighted spatial-channel representation from the paired mammography images. In terms of the performance of the model under CBIS-DDSM held-out testing protocol, the entire model scored an accuracy of 97.12%, sensitivity of 96.44%, specificity of 97.68%, and AUC-ROC of 0.9876 based on the test results obtained on 6,117 images of 222 different patients. The average accuracy obtained using 100 random seeds was found to be 97.11% ± 0.18%.