Search Results (1,588)

Accurate estimation of regional forest carbon storage is essential for assessing forest carbon sink capacity and supporting climate change mitigation. This study developed a UAV-LiDAR-assisted multi-source remote sensing approach to estimate forest AGB and carbon storage in Fuzhou City. UAV-LiDAR data acquired close to satellite overpasses were used to generate temporally matched plot-scale AGB samples through individual-tree segmentation, tree height–DBH estimation, and allometric equations. After screening, 204 samples were retained. Four predictor combinations (Landsat, SAR, Landsat + SAR, and Landsat + SAR + other factors) were combined with SWR and CatBoost to build eight AGB models after RF-RFE feature selection. The optimal model was applied to analyze forest carbon storage changes from 2015 to 2023. CatBoost generally outperformed SWR, with lower RMSE, MAE, and rRMSE. The CatBoost model integrating Landsat, SAR, and other factors performed best (RMSE = 19.83 t·hm⁻², MAE = 17.02 t·hm⁻², rRMSE = 21.73%). Forest carbon storage increased from 32.43 × 10⁶ t in 2015 to 36.76 × 10⁶ t in 2023, alongside increases in forest area and carbon density. These findings suggest the potential of UAV-LiDAR for temporally matched AGB sampling and regional forest carbon storage estimation in subtropical areas, contributing to relevant SDGs. Full article

Recurrent detection of overlapping tRNA-derived fragments (tRFs) across diverse disease conditions supports the emerging view that tRFs may act as regulatory molecules rather than random degradation products. While cases of identical tRFs have been described, their comparative analyses are lacking. tRF-Glu-CTC is one such fragment, repeatedly detected in various pathological conditions. We performed a comparative analysis of tRF-Glu-CTC isoforms, their targets and binding regions reported in 18 disease-associated studies. An 18-nucleotide sequence, TCCCTGGTGGTCTAGTGG, was identified in most (14 out of 18) of these studies despite differences in tRF naming, length and disease context. Several reported tRF targets showed consistent binding regions, with reverse complementarity to the tRF sequence. Comparison with databases of tRF targets, tatDB and tRFTar, identified matching target entries and sequence overlaps, often involving common regions rather than full-length matches. Exploratory analysis of target homologs further illustrated that related genes might share candidate target sites. Our findings indicate that tRF-Glu-CTC represents a recurrent candidate regulatory fragment potentially relevant in a broad range of human diseases. Its structural stability, extracellular vesicle association, detection in multiple species and a core sequence shared between related isoforms support further investigation of its biological and translational relevance. Our work illustrates how tRF target databases can be leveraged to advance smaller-scale tRF studies. Full article

The escalating demand for high-frequency acoustic devices in 5G/6G communications imposes stringent requirements on surface acoustic wave (SAW) resonators, including high operating frequency, large electromechanical coupling coefficient $K^2$, and high quality factor Q. However, conventional SAW devices suffer from severe trade-offs among these metrics. This work proposes and theoretically analyzes an embedded-electrode LiNbO₃/SiC heterostructure SAW resonator tailored for high-order modes, with its frequency response evaluated via finite-element modeling. A quasi-three-dimensional periodic model consisting of LiNbO₃/IDT/SiC structure is established, and the effects of LiNbO₃ crystallographic orientation, normalized LiNbO₃ thickness, and embedded-Al-electrode thickness on the resonator performance are then systematically investigated. For the selected design with Euler angle β = 30°, the optimal normalized LiNbO₃ thickness is found to be $h_{\mathrm{LN}}/\mathrm{\lambda }=0.2$. Under this crystal orientation, the optimized normalized embedded-electrode thickness is $h_{\mathrm{I}\mathrm{D}\mathrm{T}}/\mathrm{\lambda }$ = 0.06. The optimized resonator achieves a resonant frequency of f_r = 12.792 GHz, a phase velocity of V = 12,792 m/s, a $K^2$ of 9.16%, and a Q of 1004.4. These investigation results validate the proposed LiNbO₃/IDT/SiC heterostructure as a viable platform for pushing SAW technology into the 10 GHz regime, thereby bridging the gap between acoustic-wave devices and millimeter-wave RF systems for next-generation communications. Full article

Purpose: To develop an exploratory prognostic model integrating multiparametric MRI radiomics and clinical features for recurrence risk stratification in young-age breast cancer (YABC). Materials and Methods: In this prospective single-institution cohort, women under 40 years with invasive breast cancer were enrolled between March 2017 and August 2019 and followed for at least 5 years. Tumor and contralateral fibroglandular tissue were segmented on multiparametric breast MRI using a deep learning-assisted model. Radiomic features were extracted and reduced via principal component analysis, and then integrated with clinical variables. Recurrence-free survival (RFS) was compared between risk groups using Kaplan–Meier analysis and the log-rank test. Internal assessment used 1000 bootstrap resamples to evaluate selection stability and optimism-corrected performance. Results: Fifty women (mean age, 34.8 ± 3.6 years) were included, and 11 patients (22%) had recurrence during a median follow-up of 64.5 months. Triple-negative subtype and lower T1_cancer3 showed strong exploratory associations, whereas lower ADC_FGT4 and larger pathologic tumor size showed weaker associations. Risk groups differed in RFS (log-rank p = 0.0022). Bootstrap analysis showed moderate selection stability, with preserved discrimination but limited calibration stability after optimism correction. Conclusions: MRI radiomics combined with clinical factors may provide complementary prognostic information for recurrence risk stratification in YABC, warranting validation in larger independent cohorts. Full article

Lipid metabolites in maize kernels determine grain quality by influencing nutritional value, oxidative stability, and post-harvest deterioration, making their profiling essential for quality improvement and breeding. This study applies hyperspectral imaging (HSI) with a spectral range of 900–1700 nm to detect maize lipid metabolites. Six lipid metabolites—9-Octadecynoic acid (stearolic acid), pinolenic acid (Δ5,9,12 18:3), N-Acylethanolamine (16:0), N-Acylethanolamine (18:0), N-Acylethanolamine (18:1), and propionic acid—were selected due to their strong relevance to maize kernel quality and favorable spectral response. First, two-trace two-dimensional (2T2D) correlation spectroscopy with heterogeneous preprocessing is employed to capture both synchronous and asynchronous correlations across different preprocessing spectra. A convolutional autoencoder (CAE) was subsequently used to extract low-dimensional latent features from heterogeneous 2T2D-COS representations, followed by regression modeling using random forest (RF), support vector regression (SVR), gradient boosting (GB), and partial least squares regression (PLSR). A total of 82 maize seed varieties were employed for experimental validation. Compared with one-dimensional spectral, homogeneous preprocessing, and PCA-based feature extraction, the proposed approach provided improved predictive performance across the six lipid metabolites, with the optimal CAE-based models achieving R_P² values of 0.629–0.887, RMSEP values of 0.241–0.565, and RPD values of 1.656–2.069. Overall, this approach provides a rough screening solution for metabolite prediction in maize crop. Full article

Background: Obesity is frequently accompanied by metabolic dysfunction and micronutrient deficiencies, yet data on individuals with severe obesity (BMI ≥ 50 kg/m²) remain limited. Understanding how vitamin and mineral status changes across the adiposity continuum may optimize personalized nutritional management in severe obesity. Methods: We analyzed anthropometric, metabolic, hepatic, and micronutrient parameters in a retrospective, single-center cohort of adults evaluated before bariatric surgery, with a BMI range of 30 to 91 kg/m² (mean 48.7 ± 8.7). Measurements included bioimpedance-derived body composition, liver elastography (CAP and stiffness), and fasting laboratory parameters, vitamins B1, B6, B12, D, and folate and minerals (iron, selenium, and magnesium). Correlation analysis, principal component analysis (PCA), and random forest (RF) classification were applied to identify the variables most strongly associated with BMI and metabolic phenotypes. Results: Several micronutrient concentrations, most notably vitamin D, folate and vitamin B6, declined progressively with increasing BMI. Vitamin D additionally correlated inversely with HbA1c. PCA revealed a separation of participants with extreme obesity driven by anthropometric and hepatic parameters, while micronutrients clustered inversely. RF classification identified HbA1c, hepatic enzymes (ALAT and γ-GT) and body-composition measures (waist circumference and visceral fat) as the strongest discriminators of T2DM, MASLD and high BMI, respectively. Combined MASLD and T2DM was associated with the most pronounced vitamin B6 depletion, whereas vitamin D was low across metabolic subgroups. Conclusions: Severe obesity is associated with distinct micronutrient depletion patterns that mirror metabolic deterioration. Integrating nutritional and metabolic profiling enables refined risk stratification and may, pending prospective validation, inform targeted supplementation strategies in individuals with severe obesity. Full article

Land disturbance and ecological degradation are widespread in abandoned mining areas, and their redevelopment is shaped by multiple factors. Identifying construction suitability patterns and key influencing factors can provide a quantitative basis for the reuse of these areas. Focusing on an abandoned mining area in Luhe District, Nanjing, this study selected 16 influencing factors. K-means clustering and a self-organizing map (SOM) were used to identify construction suitability types. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were used to visualize the cluster structure. Random forest (RF) and SHapley Additive exPlanations (SHAP) were then combined to analyze the overall contributions and interaction patterns of the individual factors. The results showed the following: (1) The study area was classified into five construction suitability types with no inherent ranking: the Soil-Location Suitability Type, Entrance Accessibility–Public Service Suitability Type, Vegetation Conservation Suitability Type, Terrain-Location Suitability Type, and Ecological Restoration Suitability Type. Among them, the Entrance Accessibility–Public Service Suitability Type and Vegetation Conservation Suitability Type were the most widely distributed. (2) The influence of the central urban area made the greatest overall contribution, followed by soil texture and vegetation coverage. Vegetation coverage had the strongest effect on the model’s ability to reproduce the cluster-derived classes under spatial validation. Vegetation diversity, entrance accessibility, soil texture, and pollution level also showed high permutation importance. (3) Factor contributions were type-specific, with soil, location, accessibility, vegetation, and pollution conditions showing distinct contribution and interaction patterns across the five types. Differentiated strategies should therefore be tailored to each type, including location and service optimization, vegetation protection and low-disturbance use, terrain management, and pollution control and ecological restoration. Full article

The time-dependent removal performance under irradiation of activated-carbon-supported nickel oxide (AC@NiO) samples containing nominal Cu-loadings of 0%, 3%, 5%, 7.5%, and 10% was examined using experimental concentration data and an explainable machine-learning (ML) framework. Reaction time and nominal Cu-loading were used as predictors. Preliminary screening with $C_t/C_0$ as the common target showed that Random Forest (RF) outperformed K-Nearest Neighbors, Multi-Layer Perceptron, and Support Vector Regression. The target-specific regularized RF models were fitted for $C_t/C_0$ and $\ln (C_0/C_t)$; normalized apparent removal efficiency was derived deterministically as $\eta /100=1-C_t/C_0$. Across 10 controlled random seeds, the mean leave-one-out cross-validation $R^2$ values were $0.9691\pm 0.0013$ for $C_t/C_0$ and the derived $\eta /100$, and $0.9152\pm 0.0090$ for $\ln (C_0/C_t)$. A fixed chronological evaluation, trained at $t\le 100$ min and evaluated at $100<t\le 120$ min, yielded mean $R^2$ values of $0.9668\pm 0.0073$ and $0.8890\pm 0.0163$, respectively. Because the composition-specific RF predictions were constant across this boundary interval, these scores are interpreted as later-time boundary diagnostics rather than evidence of temporal extrapolation. Point-estimate SHAP, impurity-based, and permutation-based analyses assigned greater predictive importance to reaction time, whereas moving-block bootstrap results showed that this ranking was sensitive to temporal resampling. Among the five tested compositions, the nominal 5% Cu-containing sample exhibited the most favorable apparent removal profile. This finding is specific to the investigated conditions and does not establish a universal optimum or a causal physicochemical mechanism. Full article

$T_{1\rho }$ mapping is emerging as a potential, contrast-free alternative to late gadolinium enhancement (LGE) for assessment of myocardial viability. However, strong signal contributions from the blood pool can impede quantitative evaluation at the (sub)endocardium. In this work, we study the effectiveness of dark-blood (DB) contrast in adiabatic $T_{1\rho }$ ($T_{1\rho ,adiab}$) mapping at 3T, using slice-selective and non-selective adiabatic spin-lock pulses. Adiabatic DB-$T_{1\rho ,adiab}$ preparations consisted of an odd number of slice-selective adiabatic full passage (AFP) pulses, followed by a final, non-selective AFP pulse. This preparation induces $T_{1\rho ,adiab}$ decay within the imaging slice while inverting the magnetization outside. A delay ($\delta$) between preparation and imaging allowed for relaxation and inflow of the inverted blood to achieve DB contrast. Bias and precision of DB- and bright-blood (BB)-$T_{1\rho ,adiab}$ were compared in phantom and in healthy subjects (n = 10). Blood suppression efficacy and apparent myocardial thickness in DB imaging were investigated in simulations, phantom, and in vivo. The clinical feasibility of DB-$T_{1\rho ,adiab}$ mapping was evaluated in a small cohort of patients (n = 7) with suspected cardiovascular diseases. DB-$T_{1\rho ,adiab}$ values were in agreement with reference BB-$T_{1\rho ,adiab}$ values in phantom (myocardium-like vial BB: 219.27 ± 4.80 ms, DB: 218.09 ± 8.22 ms) and in healthy subjects (BB: 182.32 ± 28.27 ms, DB: 183.49 ± 45.54 ms). A moderate increase in intra-($wC{V}_{i,r}$) and inter-scan variability ($\overline{wC{V}_i}$) was observed in the DB method, compared with conventional BB imaging, for phantom and healthy subjects (in vivo $wC{V}_{i,r}$ BB: 15.51 ± 2.65%, DB: 24.82 ± 4.18%; in vivo $\overline{wC{V}_i}$ BB: 3.38 ± 0.86%, DB: 7.24 ± 2.55%). Longer delay times improved blood suppression in vivo for DB-$T_{1\rho ,adiab}$, albeit at increased intra-scan variability in phantom and in vivo (DB $wC{V}_{i,r}$ for $\delta$ = 0 ms: 4.90 ± 0.83% in phantom, 13.44 ± 2.91% in vivo, for $\delta$ = 600 ms: 8.14 ± 1.88% in phantom, 26.25 ± 5.19% in vivo). Average apparent myocardial thickness was slightly higher when using DB-$T_{1\rho ,adiab}$ compared with BB-$T_{1\rho ,adiab}$ (BB: 7.33 ± 2.05 mm, DB: 7.99 ± 2.46 mm). DB-$T_{1\rho ,adiab}$ maps yielded comparable image quality to BB-$T_{1\rho ,adiab}$ maps in patients. DB-$T_{1\rho ,adiab}$ mapping represents an alternative to BB-$T_{1\rho ,adiab}$ for myocardial assessment with the potential for improved visualization of the (sub-)endocardium. Full article

Excessive body fat deposition during pregnancy is believed to have an adverse effect on the reproductive performance of sows. Feed intake regulation (REG) is an effective method to modulate energy intake and fat deposition. This study aimed to explore the impact of feed intake and body fat deposition on reproductive performance. A total of 24 Shaziling sows (parity 3–6) were divided into two groups based on their BF on day 30 of gestation, and subsequently randomly assigned to control (CON) and REG groups: (1) BF between 12 and 16 mm (LB-CON, n = 6), (2) BF between 12 and 16 mm (LB-IF, n = 6), (3) BF between 18 and 22 mm (HB-CON, n = 6), and (4) BF between 18 and 22 mm (HB-RF, n = 6). Sows in the LB-CON and HB-CON groups were fed a basic diet at a dosage of 1.88 kg/d. The LB-IF and HB-RF groups received REG and were fed a basic diet at dosages of 2.07 kg/d and 1.69 kg/d, respectively. Higher total litter weight and live litter weight were observed in HB-RF sows (p < 0.05). In addition, HB-CON sows displayed higher serum levels of IL-6, TG, and GLU when compared with LB-CON sows (p < 0.05), whereas HB-RF sows exhibited lower levels of these markers than HB-CON sows (p < 0.05). The lipid droplet area fraction was higher in HB-CON sows than in LB-CON sows (p < 0.05), and this fraction was reduced in HB-RF sows compared with HB-CON sows (p < 0.05). The placental level of TC increased in HB-CON sows when compared with LB-CON sows (p < 0.05). The placental levels of T-AOC and GPX increased in HB-RF sows when compared with HB-CON sows (p < 0.05). Moreover, the mRNA expression of FABP3 was higher in HB-CON sows when compared with LB-CON sows (p < 0.05). The mRNA expression of GLUT3 and PlGF increased in HB-RF sows when compared with HB-CON sows (p < 0.05). In addition, HB-RF sows exhibited increased Shannon, Simpson, and ACE indices when compared with HB-CON sows (p < 0.05). The characterization of the fecal microbiota revealed that HB-RF sows displayed a lower ratio of Firmicutes/Bacteroidota and an increased relative abundance of Limosilactobacillus when compared with HB-CON sows (p < 0.05). Collectively, these observations suggested that reduced feed intake in Shaziling sows with high backfat thickness improved reproductive indices, alleviated placental oxidative stress and inflammatory status, elevated placental nutrient transport and angiogenesis-related gene expression, and reshaped fecal microbiota. These results provide valuable reference for pig production. Full article

This work proposes a comparative framework for malware classification that evaluates the synergy between traditional feature engineering and modern deep learning architectures. Our methodology follows two primary paths: first, we integrate opcode n-grams with word-embedding techniques (Word2Vec, Doc2Vec, and FastText) to capture local execution patterns in dense vector spaces. Second, we evaluate end-to-end representations using transformer-based models (BERT and ViT) and a raw opcode-based 1D Convolutional Neural Network (1D-CNN) to determine if effective features can be learned without explicit n-gram engineering. Both pathways are rigorously tested across a suite of classifiers, including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (k-NN), and CNNs. Experimental results for multi-class classification demonstrate that while transformer-based models offer high automated feature extraction capabilities, the combination of opcode n-grams with word embeddings remains a highly effective and interpretable approach for detecting real-world malware. Full article

Efficient detection and quantification of GMO events is necessary due to international regulatory requirements. Digital PCR (dPCR) has been widely used for detection and quantification of GMOs. The objective of this study was to detect and quantify canola GMO events using pentaplex droplet digital PCR (ddPCR) and a six-colour droplet reader. The first pentaplex qualitative ddPCR assay included three element-specific (CaMV P35S, Tnos and tE9) and two event-specific (DP73496 and MON94100) targets, and the assay was used to detect 15 canola GMO events. The second pentaplex event-specific ddPCR assay was designed for the detection and quantification of five GMO events that can be found in commercially grown canola cultivars (DP73496, GT73, RF3, MS8 and MON88302). A total of 15 canola GMO events were detected at the 0.1% level using the first pentaplex qualitative ddPCR. Specific GMO events were also detected at 0.01 and 0.05% levels. Five major canola GMO events were detected and quantified using the second event-specific pentaplex ddPCR. DNA samples spiked at 0.1, 0.5 and 1% were successfully quantified using the event-specific pentaplex ddPCR. DNA concentrations of 0.01 and 0.05% were detected with the event-specific pentaplex ddPCR. The two developed pentaplex ddPCR assays will help facilitate efficient screening and quantification of canola GMO events. Full article

For high-intensity focused ultrasound (HIFU) thermal ablation to be safe and effective, real-time, high-precision monitoring of tissue coagulative necrosis is essential. However, decoding the ultra-long, non-stationary radio frequency (RF) echoes produced during tissue phase transitions usually results in severe feature aliasing and high computational costs for conventional deep learning models. This paper suggests a highly interpretable, asymmetric classification framework that combines a lightweight Vision Transformer (ViT) with multifractal detrended fluctuation analysis (MFDFA) in order to overcome this obstacle. In terms of methodology, ViT may independently capture cross-scale thermodynamic dependencies without local inductive biases by using MFDFA as a physical prior to compress 1D RF sequences into dense 2D fractal tensors. This method greatly improved the algorithmic recognition of the extremely elusive “partially degenerated” transient state, achieving a strong 96.5% classification accuracy when validated on an ex vivo pig liver dataset. Furthermore, by firmly attaching its classifications to the macroscopic statistical correlates of the acoustic scattering process, the model achieves great decision transparency instead of functioning as an opaque black box. Importantly, this MFDFA-ViT architecture provides an ideal accuracy–latency trade-off with only 3.45M parameters and an end-to-end inference latency of 20.6 ms. This offers a real-time, intelligent monitoring paradigm that is highly deployable and specifically designed for upcoming clinical HIFU applications. Full article

Accurate quantification of aboveground biomass (AGB) for sparse desert vegetation is fundamental for assessing regional carbon sink potential, yet large-scale data retrieval remains constrained by high spatial heterogeneity and severe background noise. Focusing on Haloxylon ammodendron communities in the Gurbantunggut Desert, this study integrated plot-level ground truth derived from Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) with multi-source satellite imagery (Sentinel-2 and Jilin-1) to evaluate AGB estimation accuracy and spatial distribution patterns across various feature combinations and employed four machine learning algorithms at a 10 m pixel scale. The Difference Vegetation Index (DVI) exhibited the strongest explanatory power for AGB spatial variance, whereas downsampled high-resolution textures induced feature redundancy. Among the evaluated algorithms, the Random Forest (RF) model driven solely by multispectral parameters achieved the optimal cross-scale mapping accuracy (R² = 0.72, RMSE = 1.32 t ha⁻¹). The total regional AGB storage was estimated to be approximately 6.99 × 10⁴ t, with low-density habitats (0.2–2.0 t ha⁻¹) occupying 90.39% of the area. This study confirms the feasibility of integrating UAV point clouds with multi-source satellite imagery for the large-scale retrieval of sparse shrub biomass, providing a quantitative basis for desert carbon management. Full article

Phytophthora root and crown rot causes irreversible canopy decline in citrus before ground-level symptoms appear, yet UAV-based detection still relies almost exclusively on RGB imagery and, in citrus, on leaf-level classification rather than field-scale localisation. This work addresses that gap with a crown-level object-detection pipeline for Phytophthora in orange orchards, in three contributions. First, a mean-initialised patch embedding expansion that adapts a pretrained detection transformer to N-channel input while preserving its DINOv2 representations and activation magnitude, applicable to any ViT-based detector. Second, a two-stage protocol that screens seven vegetation indices (GNDVI, SAVI, EVI, GRVI, ExG, CARI, MCARI) as fourth channels over three seeds; the screening does not resolve them, and GNDVI is retained because both bands of its ratio respond to root dysfunction-induced chlorophyll degradation and both come from a single sensor. Third, a matched-modality comparison isolating the contribution of the architecture from that of the spectral channel. On 1147 georeferenced RGB–multispectral pairs with 5560 expert-annotated instances, RF-DETR + GNDVI attains a test mAP_50:95 of $0.590\pm 0.008$ and mAP₅₀ of $0.873\pm 0.005$ over three seeds, exceeding a YOLO26n baseline on identical four-channel input by 9.6 and 8.4 percentage points at half the resolution, the architecture proving the decisive component and supporting georeferenced crown-level alerts. Full article